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Article The Structure of the Mediterranean Tropical-Like Numa: Analysis of GPM Observations and Numerical Prediction Model Simulations

Anna Cinzia Marra 1, Stefano Federico 1 , Mario Montopoli 1 , Elenio Avolio 2 , Luca Baldini 1 , Daniele Casella 1, Leo Pio D’Adderio 1, Stefano Dietrich 1 , Paolo Sanò 1 , Rosa Claudia Torcasio 1 and Giulia Panegrossi 1,*

1 Institute of Atmospheric Sciences and , National Research Council (ISAC/CNR), 00133 Rome, 2 Institute of Atmospheric Sciences and Climate, National Research Council (ISAC/CNR), 88046 Lamezia Terme, Italy * Correspondence: [email protected]; Tel.: +39-06-4993-4274

 Received: 30 May 2019; Accepted: 11 July 2019; Published: 17 July 2019 

Abstract: This study shows how satellite-based passive and active microwave (MW) sensors can be used in conjunction with high-resolution Numerical Weather Prediction (NWP) simulations to provide insights of the precipitation structure of the tropical-like cyclone (TLC) Numa, which occurred on 15–19 November 2017. The goal of the paper is to characterize and monitor the precipitation at the different stages of its evolution from development to TLC phase, throughout the transition over the Mediterranean Sea. Observations by the NASA/JAXA Global Precipitation Measurement Core Observatory (GPM-CO) and by the GPM constellation of MW radiometers are used, in conjunction with the Regional Atmospheric Modeling System (RAMS) simulations. The GPM-CO measurements are used to analyze the passive MW radiometric response to the microphysical structure of the storm, while the comparison between successive MW radiometer overpasses shows the evolution of Numa precipitation structure from its early development stage on the into its TLC phase, as it persists over southern coast of Italy (Apulia region) for several hours. Measurements evidence stronger convective activity at the development phase compared to the TLC phase, when strengthening or weakening phases in the development, and the occurrence of warm processes in the areas surrounding the eye, are identified. The weak scattering and polarization signal at and above 89 GHz, the lack of scattering signal at 37 GHz, and the absence of electrical activity in correspondence of the during the TLC phase, indicate weak and the presence of supercooled droplets at high levels. RAMS high-resolution simulations support what inferred from the observations, evidencing Numa TLC characteristics (closed circulation around a warm core, low vertical shear, intense surface , heavy precipitation), persisting for more than 24 h. Moreover, the implementation of DPR 3D reflectivity field in the RAMS data assimilation system shows a small (but non negligible) impact on the precipitation forecast over the sea up to a few hours after the DPR overpass.

Keywords: Medicanes; GPM mission; NWP models; data assimilation

1. Introduction The Mediterranean Sea is recognized as a climatic hotspot [1], often affected by events that are becoming more and more frequent in the last decades. Among these events, increasing attention has been recently devoted to the so-called Mediterranean hurricanes (Medicanes) or

Remote Sens. 2019, 11, 1690; doi:10.3390/rs11141690 www.mdpi.com/journal/remotesensing Remote Sens. 2019, 11, 1690 2 of 26 tropical-like (TLCs). During the TLC phase, these share some features with the well-known tropical cyclones, even if smaller in size. TLCs have a typical diameter of 100–300 km, 1 while the associated surface can occasionally reach 22–28 ms− . They are characterized by the presence of a quasi-cloud-free calm eye, strong winds close to the center, spiral-like cloud bands elongated from the center, vertical alignment of pressure minima, weak vertical , and a warm anomaly [2–12]. These cyclones may last for several days, although the presence of tropical characteristics is generally limited to a few hours. In the last years many studies tried to investigate the origin of these events and to link their development to specific meteorological conditions, by using both observations and modeling [6,7,13,14]. Several studies have discussed the crucial role of the latent heating and the sea surface fluxes of heat and moisture, in conjunction with other factors such as the low-level baroclinicity, the orography, , and an upper tropospheric potential vorticity (PV) anomaly, for the occurrence, intensity, and movement of Medicanes [2,6,8–11,13–17]. The accepted conceptual model for the intensification and maintenance of Medicanes is similar to that of tropical cyclones, i.e., governed by surface energy fluxes within pre-existing organized cyclonic environments. An upper-level cold is required to contribute to cool and moisten the low and mid-tropospheric environment, thus increasing the air–sea gradient of saturation moist static energy [18]. Therefore, Medicanes can be described as nonfrontal synoptic-scale cyclones, originating in baroclinic environments, associated with a cold trough (or a cut-off low) extending in the Mediterranean and developing, in the mature stage, a warm core due to the release of latent heat associated with convection developing around the pressure minimum. Carrio et al. [19] analyzing tropicalization process of the 7 November 2014 Mediterranean cyclone, show the importance of the upper-level dynamics to generate a baroclinic environment prone to surface and in supporting the posterior tropicalization of the system. Some baroclinic cyclones evolve into symmetrical structures during their occlusion phase, with intense latent heat release around their core. The intensity of sea surface heat fluxes, enhanced by the contrast between cold air and a relatively warmer sea surface, is very important for the intensification and maintenance of TLCs. Therefore, differently from tropical hurricanes, cold air aloft is required in order to enhance thermodynamic disequilibrium and trigger the formation of Mediterranean TLCs [20]. Lower and upper level potential vorticity anomalies associated with TLCs have been identified and characterized by Miglietta et al. [11] as dry PV, associated with the intrusion of dry stratospheric air, and wet PV generated by latent heat release due to . Such latent heat release at low levels would extend to upper levels only in case of long lasting Medicanes. Therefore, previous studies (e.g. [2,21,22]) have shown that the vertical extension of the warm core varies from the lower to the upper and is linked to duration of the TLC phase (as also evidenced by Emanuel [18]). Medicanes are generally considered as quite rare events. Recently, for the first time, Cavicchia et al. [23], exploiting high-resolution dynamical downscaling of atmospheric fields obtained from the NCEP/NCAR reanalysis, found systematic and homogeneous statistics of Medicanes, over a six-decades period. Their study estimated an average number of about 1.5 Medicanes per year. The advancements in satellite imagery, along with a comprehensive exploitation of all the available observations, have favored the clear identification of such peculiar events.As highlighted by Cioni et al. [10], since extratropical and tropical-like cyclone characteristics can alternate or even coexist in the same cyclonic system, satellite data and numerical modeling can be very useful to identify and characterize the development and mature phases of these systems. In this study we benefit from the unprecedented availability of microwave (MW) satellite observations in the NASA/JAXA Global Precipitation Measurement (GPM) mission era [24,25] to characterize and monitor the different development phases of a meteorological event, named Numa by the Free University of Berlin’s Meteorological Institute, that occurred in November 2017 in Southern Italy. The GPM international satellite mission was specifically conceived to unify and advance precipitation measurements from research and operational MW sensors for delivering next-generation Remote Sens. 2019, 11, 1690 3 of 26 global precipitation data products. The GPM constellation consists of a variable and continuously updating number of Low Earth Orbit (LEO) satellites equipped with conical and cross-track scanning radiometers. The main satellite of the constellation, the GPM Core Observatory (GPM-CO), a joint effort by NASA and JAXA, carries an advanced combined active/passive sensor package—the GPM Microwave Imager (GMI), acting also as a calibrator for the other MW constellation radiometers, and the Dual-frequency Precipitation Radar (DPR). The GPM-CO is currently considered the reference satellite-borne platform for the quantitative estimation of precipitation and precipitation microphysics characterization from space. The combined active/passive MW sensor measurements are used to derive consistent precipitation estimates from a constellation of satellites carrying MW radiometers provided by a consortium of international partners. The GPM constellation of satellites currently ensures 1-hourly coverage (on average) over the Mediterranean area. Panegrossi et al. [26] illustrated the potential of the GPM constellation for monitoring heavy precipitation events in the Mediterranean region. In their work, the authors also showed the improved capabilities of the GMI with respect to other radiometers to depict the structure of Medicane Qendresa, which occurred on 7–8 November 2014. The study focused on the use of GPM MW radiometers for surface precipitation monitoring, but no DPR overpasses were available for that case. In another study, Marra et al. [27] carried out an observational analysis of an extremely severe hailstorm, developed over the Mediterranean Sea, and they showed the great capabilities of both GMI and DPR to depict the microphysical structure of the storm and to evidence its unique and rare features compared to global GPM observations. More recently, a preliminary study of GPM capabilities in monitoring the development of Medicane Numa and associated surface precipitation has been carried out by Panegrossi et al. [28]. The goal of the present paper is to illustrate the potential of the GPM-CO and of the GPM constellation to provide unprecedented insight of Medicane Numa, especially during its offshore development, where no ground-based observations are available. In this respect, since Numa is the first Medicane observed by the DPR, to the best of our knowledge, both passive and active MW measurements are used to infer the structure of the precipitation and to identify the main dynamical and microphysical processes responsible for its changes during the transition from the development to the mature stage. Moreover, the present study illustrates the potential of using GPM precipitation products as a tool to verify and improve Numerical Weather Prediction (NWP) model forecast, particularly useful over the sea. In the first part of this study we describe the evolution of Numa, from its formation to its dissipation, by using a “traditional” approach suitable for Near Real Time (NRT) applications, i.e., based on visible (VIS) observations, environmental conditions provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), and activity associated with the storm, as measured by the LIghtning NETwork (LINET) [29]. In the second part of the paper we analyze in detail the added value of the GPM in characterizing the precipitation structure associated with the storm, also thanks to an overpass of the GPM-CO precipitation radar available during its development phase. MW observations and LINET strokes are used to identify specific features during the different stages of the storm evolution. We also compare the satellite-based precipitation rates and pattern to the ones provided by a ground-based radar and NWP simulations (specifically RAMS-ISAC [30]) for this case. The NWP is also used to support what derived from observations and to highlight the TLC characteristics of Numa. Finally, a sensitivity analysis of the RAMS-ISAC short-term forecast of Numa on the assimilation of DPR observations is also shown. The paper is organized as follows: Section2 describes the sensors and products used in this study, while Section3 briefly reviews the RAMS-ISAC model setup. In Section4, ECMWF forecast and lightning observations are used to analyze the evolution of Numa. Section5 is dedicated to the analysis of GPM observations, while Sections6 and7 investigate the performances of RAMS-ISAC Remote Sens. 2019, 11, 1690 4 of 26 model and the impact of DPR assimilation. Section8 discusses the main results of the paper. Finally some conclusions are drawn in the last Section.

2. Observational Dataset Description The observational analysis of Numa has been carried out by using both spaceborne and ground-based data. Two overpasses by the GPM-CO available for Numa are analyzed in this study. The GPM-CO carries the most advanced spaceborne precipitation MW radiometer (GMI) and the first spaceborne dual-frequency precipitation radar (DPR). The GMI is equipped with 13 different channels: 10 GHz, 19 GHz, 37 GHz, 89 GHz, and 166 GHz (in dual polarization V and H), and 23 GHz, 183.31 3 GHz, ± and 183.31 7 GHz (V polarization only). The DPR allows for a 3D insight of precipitation structure. ± Simultaneous observations by the DPR and GMI are only available for the overpass during Numa development phase, because of the narrower radar swath size (245 km for Ku-band, and 125 km for Ka-band) compared to GMI (885 km). Measurements by the other GPM constellation radiometers are also considered. At the time of NUMA, ten satellites, equipped with five different radiometers, were available, providing overpasses at 1-h temporal resolution over the Mediterranean area (on average). In the present paper, besides the GMI, the two other conically scanning radiometers in the constellation are considered: the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board JAXA’s GCOM W1 satellite, providing measurements at spatial resolution comparable to GMI (roughly three times higher than the other radiometers) and the Special Sensor Microwave Imager and Sounder (SSMIS) on board the U.S. DMSP satellites F16, F17, F18. The official NASA/JAXA GPM products [31] are used in the study, specifically 1C-R and 1C intercalibrated brightness (TBs), for GMI and the other radiometers respectively, the 2A-CLIM GPROF product for GMI, AMSR2, and SSMIS [32], and the 2A-DPR product for DPR [33]. Moreover MODIS Corrected Reflectance (true color) images [34] are used to describe the upper-level cloud structure during Numa TLC phase. Optimized Coastal Ocean wind product [35], retrieved from ASCAT scatterometer measurements, is used to analyze the RAMS-ISAC surface wind forecast. Besides space-based observations, ground-based data are also used in the analysis. In particular, the operational dual-polarization (hereinafter GR) located in Pettinascura (39.37◦N, 16.62◦E, ~1700 m asl), Calabria region, Italy, and managed by the Civil Protection Department of Italy, provided measurements during Numa’s mature phase, when it was approaching land. In addition, lightning data from the ground-based low-frequency (VLF/LF, between 1 and 200 KHz) LIghtning NETwork (LINET) are used in this study. The LINET network counts over 550 sensors in several countries around the world, with very good coverage over a wide area in central Europe and central and western Mediterranean (from 10◦ W to 35◦ E in longitude and from 30◦ N to 65◦ N in latitude). The system measures the time (temporal resolution is about 512 ms), the horizontal and vertical location of VLF sources, their amplitude, and it is also able to discriminate between cloud-to-ground (CG) and intracloud (IC) strokes, and their polarity [29,36]. The 3D discrimination method, based on time of arrival (TOA) difference triangulation technique, is reliable (with a position accuracy of 125 m) when the sensor baseline does not exceed ~ 200 km. Moreover LINET allows for estimation of IC emission height, although at least four sensors are needed for a reliable determination of the IC stroke height [36,37].

3. NWP Model: Configuration of the RAMS-ISAC Model for the Numa Case Study Numerical experiments are performed using the RAMS-ISAC model. This model is developed at the Institute of Atmospheric Sciences and Climate of the National Research Council starting from the version 6.0 of RAMS (Regional Atmospheric Modeling System [38]), and implements some new features. Among them, the most important are (a) the use of a 3D-Var assimilation scheme, hereafter RAMS-3DVar [39] to assimilate the following observations; wind, , relative vertical profiles, Global Positioning System-Zenith Total Delay (GPS-ZTD), ground-radar reflectivity Remote Sens. 2019, 11, 1690 5 of 26 volumes, and lightning (LINET strokes); (b) implementation of new microphysics schemes [40]; and (c) a diagnostic scheme to simulate electrical activity [41,42]. The grid configuration used in this study consists of one grid at 3 km horizontal resolution, covering the Central Mediterranean Basin. The main parameters of the model grid are shown in Table1, while the physics set up is shown in Table2. No cumulus parameterization scheme is used and precipitation is assumed explicitly resolved at the model resolution.

Table 1. Basic parameters of the RAMS-ISAC grid (see Figure 2a for a representation of the domain). NNXP is the number of grid points in the WE direction, NNYP is the number of grid-points in the SN direction, NNZP is the number of vertical levels, DX is the size of the grid spacing in the WE direction, and DY is the grid-spacing in the SN direction. Lx, Ly, and Lz are the domain extensions in the NS, WE, and vertical directions. CENTLON and CENTLAT are the coordinates of the grid center.

Lx Lx Lz DX DY CENTLAT CENTLON NNXP NNYP NNZP (km) (km) (m) (km) (km) (◦N) (◦E) 631 631 40 1890 1890 ~20000 3 3 41.0 12.5

Table 2. List of physical parameterizations used for RAMS-ISAC in this paper.

Physical Parameterization Selected Scheme WSM6 bulk microphysics with six hydrometeors (cloud, rain, Explicit precipitation parameterization , , ice, and ) described in Hong and Lim [43], implemented in RAMS-ISAC (Federico [40]). LEAF3 [44]. LEAF includes prognostic equations for soil Exchange between the surface, the temperature and moisture for multiple layers, vegetation biosphere and . temperature and surface water, and temperature and water vapor mixing ratio of canopy air. The turbulent mixing in the horizontal directions is parameterized following Smagorinsky [45], vertical diffusion is Sub-grid mixing parameterized according to the Mellor and Yamada [46] scheme, which employs a prognostic turbulent kinetic energy. Chen-Cotton [47]. The scheme accounts for condensate in the Radiation scheme atmosphere.

4. Numa Track and Evolution Numa has been classified in slightly different ways by many international meteorological organizations. According to NOAA/NESDIS it is a hybrid storm, with both tropical and subtropical characteristics. In the analysis of CIMMS, that traces its origin back to the remnants of Tropical Storm Rina, cyclone Numa acquired subtropical characteristics, making it a relatively rare Medicane. In the EUMETSAT webpage dedicated to NUMA ([48]) the storm is clearly classified as a Medicane. The storm starts developing on 15 November over the Strait of Sicily and on 16 November, as it moves toward East, it shows hybrid characteristics. Then, during its transition over the Ionian Sea, it reaches its mature phase, and, on 17 November at 12:00 UTC, it hits Apulia Region in Southern Italy, where it persists for 24 h, until 18 November at 12:00 UTC. Here the storm shows typical TLC features, with a quasi-cloud-free calm eye, a warm core, and strong winds close to the vortex center. Then, it moves eastward and, on 19 November, it completely dissipates over . Figure1 shows two MODIS Corrected Reflectance (true color) images capturing Numa as it hits Apulia region and evidences how its upper-level cloud structure changes as subtropical and tropical-like cyclone characteristics alternate. On 17 November at 12:00 UTC MODIS Aqua (Figure1a) shows a spiral-like cloud band elongated from the center (39.1◦N, 18.1◦E), with the main rainband in the north hitting the coast of Apulia region, without evidencing an axisymmetric structure or a well identifiable cloud-free eye. On 18 November 2017 at 10:30 UTC, while the storm persists over Southern Apulia region, MODIS Terra (Figure1b) Remote Sens. 2019, 11, 1690 6 of 26

reveals a well-defined center (39.4◦N, 18.7◦E) with a cloud-free eye structure, surrounded by a whirl of cloudsRemote Sens. that 2019 appear, 11, x FOR deeper PEER in REVIEW the northeastern sector. At both times the storm is characterized6 byof 26 a closed surface wind circulation and a warm core. Remote Sens. 2019, 11, x FOR PEER REVIEW 6 of 26

(a) (b)

(a) (b) Figure 1. (a) MODIS/Aqua on 17 November at 12:00 UTC; (b) MODIS/Terra on 18 November at Figure 1. (a) MODIS/Aqua on 17 November at 12:00 UTC; (b) MODIS/Terra on 18 November at 10:30 10:30UTC. UTC. Figure 1. (a) MODIS/Aqua on 17 November at 12:00 UTC; (b) MODIS/Terra on 18 November at 10:30 The synoptic environment of Numa both at the surface and 500 hPa is shown by ECMWF TheUTC synoptic. environment of Numa both at the surface and 500 hPa is shown by ECMWF operational analyses (Figure2). On 16 November at 12:00 UTC (Figure2a,b), a wide low pressure operational analyses (Figure 2). On 16 November at 12:00 UTC (Figures 2a,b), a wide low pressure area (minimumThe synoptic 1004 hPa) environment is present ofover Numa the Strait both of at Sicily the surface and over and the 500 Ionian hPa Sea, is shownwith two by distinct ECMWF area (minimum 1004 hPa) is present over the Strait of Sicily and over the Ionian Sea, with two minima.operational The mean analyses sea level (Figure pressure 2). On (mslp) 16 November minimum a overt 12:00 the UTC Ionian (Figures Sea evolves 2a,b), in a Numa,wide low while pressure the distinct minima. The mean sea level pressure (mslp) minimum over the Ionian Sea evolves in Numa, minimumarea (minimum over the Strait1004 hPa) of Sicily is present is filled over in the the following Strait of hours. Sicily The and wind over at the 900 Ionian hPa (red Sea, arrows) with two while the minimum over the Strait of Sicily is filled in the following hours. The wind at 900 hPa (red showsdistinct a cyclonic minima. circulation The mean over sea level the Strait pressure of Sicily (mslp) and minimum Ionian Sea, over which the Ionian is not Sea completely evolves in closed Numa, arrows) shows a cyclonic circulation over the Strait of Sicily and Ionian Sea, which is not completely aroundwhile the the Numaminimum mslp over minimum the Strait (Figure of Sicily2a). is Atfilled 500 in hPa, the following the synoptic hours. situation The wind shows at 900 a low hPa of (red closed around the Numa mslp minimum (Figure 2a). At 500 hPa, the synoptic situation shows a low geopotentialarrows) shows height a cyclonic over Southern circulation Italy withover the two Strait separate of Sicily cut-o andffs. OneIonian of themSea, which is over is the not two completely mslp of geopotential height over Southern Italy with two separate cut-offs. One of them is over the two minimaclosed at around the surface. the Numa Interestingly, mslp minimum the cyclone (Figure over the2a). IonianAt 500Sea, hPa, evolving the synoptic in Numa, situation does shows not show a low mslp minima at the surface. Interestingly, the cyclone over the Ionian Sea, evolving in Numa, does a warmof geopotential core surrounded height byover a close Southern circulation Italy with at this two stage separate of development cut-offs. One (Figure of them3a). is over the two not show a warm core surrounded by a close circulation at this stage of development (Figure 3a). mslp minima at the surface. Interestingly, the cyclone over the Ionian Sea, evolving in Numa, does not show a warm core surrounded by a close circulation at this stage of development (Figure 3a).

(a) (b)

Figure 2. (a) Sea level pressure(a) (hPa) and wind vectors (900 hPa) and (b) geopotential(b) height (m) and windFigure vectors 2. (a) S atea 500 level hPa pressure on 16 November (hPa) and atwind 12:00 vector UTC.s (900 hPa) and (b) geopotential height (m) and wind vectors at 500 hPa on 16 November at 12:00 UTC. Figure 2. (a) Sea level pressure (hPa) and wind vectors (900 hPa) and (b) geopotential height (m) and wind vectors at 500 hPa on 16 November at 12:00 UTC.

RemoteRemote Sens.Sens.2019 2019,,11 11,, 1690x FOR PEER REVIEW 77 ofof 2626

a) b)

FigureFigure 3.3. ((aa)) TemperatureTemperature andand windwind vectorsvectors atat 850850 hPahPa atat 12:0012:00 UTCUTC onon 1616 NovemberNovember 2017.2017. ((bb)) AsAs inin ((aa)) forfor 00:0000:00 UTCUTC onon 1717 NovemberNovember 2017.2017. OnlyOnly thethe zoomzoom overover thethe IonianIonian SeaSea isis shownshown forfor clarity.clarity. The well-defined mslp minimum, a warm core, as well as the closed cyclonic circulation at 850 hPa, start becoming evident between 16 November 18:00 UTC and 17 November 00:00 UTC (Figure3b). a) The mslp on 18 November at 00:00 UTC is shownb) in Figure4a. The cyclone center is located over the Sea, just offshore the southernmost tip of Apulia region. At 500 hPa (Figure4b), the geopotential height shows a cut-off and a well-defined cyclonic circulation around its center, which is over the mslp minimum. At this stage of development, Numa clearly shows a warm core at several vertical levels (up to 300 hPa). In particular, at 700 hPa (not shown), the temperature gradient around the Numa center is ~4 ◦C/120 km.

Figure 4. (a) Sea level pressure (hPa) and wind vectors (900 hPa) and (b) geopotential height (m) and wind vectors at 500 hPa on 18 November at 00:00 UTC.

The well-defined mslp minimum, a warm core, as well as the closed cyclonic circulation at 850 hPa, start becoming evident between 16 November 18:00 UTC and 17 November 00:00 UTC (Figure 3b). The mslp on 18 November(a) at 00:00 UTC is shown in Figure 4a. The ( bcyclone) center is located overFigure theFigure Sea, 4. 4.( a ( just)a Sea) Sea offshore level level pressure pressure the (hPa) s(hPa)outhernmost and and windwind vectorsvectors tip of (900 Apulia hPa) and andregion ( (bb)) .geopotential geopotential At 500 hPa height height (Figure (m) (m) and and4b ), the geopotentialwindwind vectors vectors height atat 500 shows hPa hPa on ona 18 cut 18 November November-off and at 00:00a atwell 00:00 UTC.-defined UTC. cyclonic circulation around its center, which is over the mslp minimum. At this stage of development, Numa clearly shows a warm core at several During vertical the levels following (up to 12–18 300 hPa). h, the In mslp particular, and 500 at 700 hPa hPa geopotential (not shown) height, the temperature minima were gradi almostent stationaryaround the in Numa both center position is ~ and4 °C/120 value, km. causing rainfall and intense winds over Southern Apulia.

Thereafter,During the the storm following moved 12 towards–18 h, the Greece mslp while and 500 the hPa mslp geopotential started to increase. height minima were almost stationaryFigure 5in shows both theposition Numa and track value, constructed causing from rainfall the mslp and field intense provided winds by over ECMWF Southern operational Apulia. analysisThereafter,/forecast the storm at 0.125 mov◦.ed It towards illustrates Greece the storm while movement the mslp started traced to using increase. the position of the mslp minimaFigure every 5 threeshows hours the throughout Numa track 15 constructed November to from 19 November. the mslp fieldDuring provided its evolution, by ECMWF Numa showsoperational mslp minimumanalysis/forecast values rangingat 0.125° from. It illustrates 1006 hPa tothe 1003 storm hPa, movement higher than traced other using Medicanes. the position Figure of5 the mslp minima every three hours throughout 15 November to 19 November. During its evolution,

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Numa shows mslp minimum values ranging from 1006 hPa to 1003 hPa, higher than other shows alsoMedicanes the lightning. Figure activity 5 shows associated also the withlightning the storm, activity as associated monitored with by the the LINET storm network., as monitored The by the total (bothLINET CG and network IC) strokes. The total detected (both within CG and 200 IC) km strokes radial detected distance within and within 200 km 1 h radial around distance the and 3-hourlywithin mslp minimum1 hour around are shown. the 3-hourly The persistence mslp minimum of the arestorm shown over. SouthernThe persistence Apulia o regionf the storm is over highlightedSouthern by the Apulia fact that region the same is highlighted position of mslpby the minima fact that is foundthe same three position times for of two mslp consecutive minima is found 3-hour timethree intervals times for between two consecutive 17 November 3-hour at 12:00 time UTC intervals and 18 between November 17 Nov at 12:00ember UTC. at 12:00 UTC and 18 November at 12:00 UTC.

Figure 5.FigureNuma 5 track. Numa based track on ECMWFbased on mslpECMWF minima mslp every minima three every hours. three Locations hours. of Locations both CG of and both IC CG and strokes detectedIC strokes within detected 200 km within radial 200distance km radialand within distance 1 h around and within the 3-hourly 1 hour mslp around minimum the 3-hourly are mslp shown asminimum time-colored are crosses.shown as The time values-colored of mslpcrosses. at 00:00 The UTCvalues and of 12:00mslp UTCat 00:00 are UTC printed and next 12:00 to UTC are their solidprinted circle time-colorednext to their solid markers. circle time-colored markers.

The temporal evolution of the storm in terms of lightning activity and mslp can be seen more in detail in Figure6, showing the temporal evolution of mslp and the corresponding number of total strokes detected within 200 km radial distance and within 1 h around the location and time of the mslp minima. The figure evidences a more intense lightning activity at the initial stage of the storm when strokes are distributed around the cyclone center. This activity reaches its peak on 17 November at 12:00 UTC after the transition of the storm to the TLC phase. It is worth noting, however, as shown in the next section, that the peak is due to strokes registered in the outer regions of the cyclone, while the lightning activity is absent in the main rainbands in proximity of the cyclone center. As Numa persists over Southern Apulia regions, maintaining its TLC structure, and with the minimum mslp persisting for 24 h over the same area, the lightning activity strongly decreases (Figure6) because it is confined to the outer regions of the cyclone (at distances > 200 km from the cyclone center). It is worthwhile to note that the LINET detection efficiency is high over the whole domain crossed by the storm throughout its evolution and thus the analysis of lightning activity carried out in this study is reliable.

Figure 6. Temporal evolution of hourly total lightning (magenta line) and mslp minimum (black line) as a function of time.

The temporal evolution of the storm in terms of lightning activity and mslp can be seen more in detail in Figure 6, showing the temporal evolution of mslp and the corresponding number of total strokes detected within 200 km radial distance and within 1 hour around the location and time of the

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Numa shows mslp minimum values ranging from 1006 hPa to 1003 hPa, higher than other Medicanes. Figure 5 shows also the lightning activity associated with the storm, as monitored by the LINET network. The total (both CG and IC) strokes detected within 200 km radial distance and within 1 hour around the 3-hourly mslp minimum are shown. The persistence of the storm over Southern Apulia region is highlighted by the fact that the same position of mslp minima is found three times for two consecutive 3-hour time intervals between 17 November at 12:00 UTC and 18 November at 12:00 UTC.

Figure 5. Numa track based on ECMWF mslp minima every three hours. Locations of both CG and IC strokes detected within 200 km radial distance and within 1 hour around the 3-hourly mslp Remote Sens.minimum2019, 11, are 1690 shown as time-colored crosses. The values of mslp at 00:00 UTC and 12:00 UTC 9are of 26 printed next to their solid circle time-colored markers.

FigureFigure 6. Temporal 6. Temporal evolution evolution of hourlyof hourly total total lightning lightning (magenta (magenta line) line) and and mslp mslp minimum minimum (black (black line) line) as aas function a function of time.of time.

5. NumaThe Observations temporal evolution by GPM of the storm in terms of lightning activity and mslp can be seen more in detailSeveral in Figure overpasses 6, showing of GPM the constellationtemporal evolution radiometers of mslp are and available the corresponding for Numa. number Specifically, of total 53strokes overpasses detected are available within 200 between km radial 15 Novemberdistance and at within 12:00 UTC 1 hour and around 19 November the location at 12:00 and UTC,time of as the the storm evolves and moves over the Mediterranean Sea. Fifteen of them capture the storm between

17 November at 12:00 UTC and 18 November at 12:00 UTC, while rainfall persists over the coast of Apulia. It is worthwhile to note that most of these observations occurred during the offshore development of the storm, when ground-based observations were not available.

5.1. AMSR2 and GMI Brightness Temperature Analysis In Figures7 and8, the maps of 37 GHz and 89 GHz TBs for four overpasses by AMSR2 and GMI radiometers over Numa are shown. These two radiometers provide measurements at the highest spatial resolution available in the GPM constellation (~5 km at frequencies > 85 GHz) and are able to depict the precipitation structure with much more detail compared to other MW radiometers [26]. Moreover, the sequence of GMI and AMSR2 measurements and the comparable spatial resolution at each frequency between the two radiometers allow analyzing the evolution of the precipitation at different stages of the storm development. The 37 GHz channel is particularly suitable for the detection of precipitation because TBs are affected by the combined effect of the emission from the rain layers and the scattering from solid-phase hydrometeors, usually associated with intense updraft (e.g., Panegrossi et al. [49]). Over the sea, in most cases (except in presence of deep convection), precipitation areas appear as regions of high TBs compared to the radiatively cold background surface because the emission by the rain layers dominates the scattering effect at this frequency. Over the land, the emission signal by the surface tends to mask the emission signal by the rain layers. Therefore only heavy precipitation is visible as areas of larger or smaller TB depression. Such depression, which depends on the effect of the scattering on the upwelling radiation, is mostly due to large and dense ice particles found in the convective cores. In Figure7, it is evident how the 37 GHz channel, at spatial resolution around 7 12 km2 and × 8.6 14 km2 for AMSR2 and GMI respectively, depicts quite well the location and structure of the × rain bands, visible as areas of warmer TBs with respect to the background. The variability of the TB values across the different rainbands originates from the combined contribution of the liquid and frozen hydrometeors within the cloud. Comparing the different panels, it is possible to see that the precipitation structure has changed throughout the storm evolution. The first GPM-CO overpass on 16 November at 13:49 UTC occurs when the storm is at its development stage, more than 200 km off the Remote Sens. 2019, 11, 1690 10 of 26 coast of Sicily and Calabria regions (Figure7a). At the initial phase the cyclone center is hardly visible, surrounded by a whirling rainband with higher TBs at 37 GHz (up to 260 K) in north/northeastern sector and lower TBs (around 230 K) in the southern sector. The outer rainband extends from the north to the south, on the east side of the cyclone center, while other areas of scattered precipitation extend to the north reaching Southern Italian coastal areas. A wide well-defined eye is visible in both AMSR2 overpasses, on 17 November at 00:51 UTC and at 11:55 UTC, and it becomes narrower and well defined in the GMI overpass on 18 November at 03:59 UTC (Figure7b–d). The di fference of the AMSR2 image at 11:55 UTC (Figure7c) with respect to the MODIS image shown in Figure1a is worth noting. AMSR2 shows that the rainfall structure is organized and axisymmetric around the cyclone center, as opposed to the upper level which do not show evidence of a well-defined cloud-free eye. On 17 November, the AMSR2 maps at 37 GHz show that the cyclone center moves from 38.5◦N 18.5◦E to 39◦N 18◦E (~70 km) in 12 h, while areas of higher TBs with values around 260 K are found mostly in the northern sector of the storm, over the Ionian Sea. Specifically, at 00:51 UTC an area of high TBs is found in correspondence of the outer rainfall structures in the northern sector of the cyclone, extending to the Calabria region (visible as an area of slight TB depression over land compared to the background), while at 11:55 UTC, this area is part of the main rainband around the cycloneRemote Sens. center, 2019, hitting11, x FOR the PEER coast REVIEW of Salento in Southern Apulia region. 10 of 26

(a) (b)

(c) (d)

FigureFigure 7. 7.Brightness Brightness temperaturetemperature measured measured at at 37 37 GHz GHz V-pol V-pol channel channel for for four four overpasses overpasses by by GMI GMI and and AMSR2AMSR2 radiometers radiometers over over Numa. Numa. ( a(a)) GMI GMI on on 16 16 November November at at 13:49 13:49 UTC, UTC, ( b(b)) AMSR2 AMSR2 on on 17 17 November November atat 00:51 00:51 UTC, UTC, ( c()c) AMSR2 AMSR2 on on 17 17 November November at at 11:55 11:55 UTC, UTC and, and ( d(d)) GMI GMI on on 18 18 November November at at 03:59 03:59 UTC. UTC.

In Figure 7, it is evident how the 37 GHz channel, at spatial resolution around 7×12 km2 and 8.6×14 km2 for AMSR2 and GMI respectively, depicts quite well the location and structure of the rain bands, visible as areas of warmer TBs with respect to the background. The variability of the TB values across the different rainbands originates from the combined contribution of the liquid and frozen hydrometeors within the cloud. Comparing the different panels, it is possible to see that the precipitation structure has changed throughout the storm evolution. The first GPM-CO overpass on 16 November at 13:49 UTC occurs when the storm is at its development stage, more than 200 km off the coast of Sicily and Calabria regions (Figure 7a). At the initial phase the cyclone center is hardly visible, surrounded by a whirling rainband with higher TBs at 37 GHz (up to 260 K) in north/northeastern sector and lower TBs (around 230 K) in the southern sector. The outer rainband extends from the north to the south, on the east side of the cyclone center, while other areas of scattered precipitation extend to the north reaching Southern Italian coastal areas. A wide well-defined eye is visible in both AMSR2 overpasses, on 17 November at 00:51 UTC and at 11:55 UTC, and it becomes narrower and well defined in the GMI overpass on 18 November at 03:59 UTC (Figures 7b–d). The difference of the AMSR2 image at 11:55 UTC (Figure 7c) with respect to the MODIS image shown in Figure 1a is worth noting. AMSR2 shows that the rainfall structure is organized and axisymmetric around the cyclone center, as opposed to the upper level clouds which do not show evidence of a well-defined cloud-free eye. On 17 November, the AMSR2 maps at 37 GHz show that the cyclone center moves from 38.5°N 18.5°E to 39°N 18°E (~70 km) in 12 hours, while areas of higher TBs with values around 260 K are found mostly in the northern sector of the

Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 26 storm, over the Ionian Sea. Specifically, at 00:51 UTC an area of high TBs is found in correspondence of the outer rainfall structures in the northern sector of the cyclone, extending to the Calabria region (visible as an area of slight TB depression over land compared to the background), while at 11:55 UTC, this area is part of the main rainband around the cyclone center, hitting the coast of Salento in Southern Apulia region. The absence of areas of significant TB depression at 37 GHz might be due to one or a combination of the two following factors: 1) the absence of high amounts of large high density ice particles, that significantly scatter the upwelling radiation at 37 GHz; 2) the low spatial resolution of the 37 GHz channels, that cannot distinguish small convective cores. An ambiguity at this frequency comes between the regions of precipitation with no significant contribution of the ice to the scattering (optically thin clouds), and the regions of strong precipitation and significant ice scattering (optically thick clouds). The ambiguity is solved looking at the other channels. For example, maps of TBs at 10 and 19 GHz (not shown), mostly affected by the emission from the rain layers, can be more directly related to rainfall intensity (especially at 10 GHz in Rayleigh regime) with higher values of TBs generally associated with heavier rainfall. However, since they are Remoteavailable Sens. at2019 lower, 11, 1690 spatial resolution (24 × 42 km2 and 14 × 22 km2 for AMSR2, at 10 GHz and 1911 GHz of 26 respectively), they do not allow to depict clearly the rainband structure.

(a) (b)

(c) (d)

FigureFigure 8.8. BrightnessBrightness temperaturetemperature measuredmeasured atat 8989 GHzGHz V-polV-pol channelchannel forfor fourfour overpassesoverpasses ofof NumaNuma byby GMIGMI andand AMSR2AMSR2 radiometers. ( (aa)) GMI GMI on on 16 16 November November at at 13:49 13:49 UTC; UTC; (b (b) )AMSR2 AMSR2 on on 17 17 November November at at00:51 00:51 UTC; UTC; (c ()c AMSR2) AMSR2 on on 17 17 November November at at 11:55 11:55 UTC; UTC; ( (dd)) GMI GMI on 18 November at 03:5903:59 UTC.UTC. TheThe strokesstrokes registeredregistered byby LINETLINET inin oneone hourhour intervalinterval aroundaround thethe timetime ofof thethe overpass,overpass, withinwithin aa 200200 kmkm radius circle from the position of the eye (indicated by a black ), are also shown as black crosses. radius circle from the position of the eye (indicated by a black× ×), are also shown as black crosses. The absence of areas of significant TB depression at 37 GHz might be due to one or a combination of the two following factors: (1) the absence of high amounts of large high density ice particles, that significantly scatter the upwelling radiation at 37 GHz; (2) the low spatial resolution of the 37 GHz channels, that cannot distinguish small convective cores. An ambiguity at this frequency comes between the regions of precipitation with no significant contribution of the ice to the scattering (optically thin clouds), and the regions of strong precipitation and significant ice scattering (optically thick clouds). The ambiguity is solved looking at the other channels. For example, maps of TBs at 10 and 19 GHz (not shown), mostly affected by the emission from the rain layers, can be more directly related to rainfall intensity (especially at 10 GHz in Rayleigh regime) with higher values of TBs generally associated with heavier rainfall. However, since they are available at lower spatial resolution (24 42 km2 and 14 22 km2 for AMSR2, at 10 GHz and 19 GHz respectively), they do not allow to × × depict clearly the rainband structure. The TBs measured by GMI and AMSR2 at 89 GHz (V polarization) are shown in Figure8, along with the LINET strokes registered within 1 h around the time of the overpass. At this frequency water vapor emission increases the TB background values. In correspondence of the clouds, the upwelling Remote Sens. 2019, 11, 1690 12 of 26 radiation is affected by the emission of cloud liquid water, which tends to further increase the TBs, and by the scattering by the ice hydrometeors (mostly those found in the convective regions) visible as areas of higher or lower TB depression (e.g., [50]). In the development stage, captured by the GMI overpass on 16 November (Figure8a), most of the clouds in the outer rainbands show significant TB depression compared to the background. The TBs as low as 158 K in the southern portion of the rainband (35.23◦N, 19.07◦E) suggest the presence of strong convective activity [51–53], associated with significant amounts of ice, presumably either heavily rimed ice or high density graupel. On the other hand, in the two AMSR2 images on 17 November (Figure8b,c), the area surrounding the cyclone center, exhibiting a symmetric rainfall structure at 37 GHz, does not show any scattering signal (a ring of higher TBs in correspondence of the eyewall is evident in Figure6c), indicating that rainfall mostly originates from warm rain processes. Scattering signal is evident in some regions of the outer rainbands in the AMSR2 overpasses, including the significant TB depression area over the Ionian Sea and over Calabria region at 00:51 UTC, and over the coast of Salento region at 11:55 UTC. The areas of TB depression are closer to the cyclone center as the storm persists in its mature stage (GMI overpass on 18 November – Figure8d), while the eye becomes smaller and well defined (MODIS image in Figure1b), clear indications of the intensification of the storm. In this phase, only a small portion of the rainbands on the west side of the cyclone eye shows significant TB depression at 89 GHz. The lack of very low TBs at 89 GHz (minimum 191 K at 39.38◦N 17.93◦E) compared to typical convective systems (as categorized in [51–53]), and the absence of scattering signal at 37 GHz, are a clear indication of a weak convective activity associated with Numa during its TLC stage. Coherently with what observed in Figures5,6 and8, the electrical activity is more intense during the development stage of the Medicane. More specifically, 177 strokes (IC 10 - CG 167) in one hour are detected by LINET for the first overpass, while only 8 strokes (IC 2 - CG 6) occur during the mature stage, within 200 km from the position of the eye. Overall, the number of strokes registered is low compared to typical convective systems observed in the Mediterranean (see also [26] and [27]). Figure8 shows that the electrical activity is mostly located in correspondence with the areas of lower 89 GHz TBs during the development phase, while it is absent in the cyclone center and in correspondence of the TB minima at 89 GHz during the TLC phase. During this phase, the lightning activity is mostly found in correspondence with the convective clouds at the edge of the cyclone. It is clear that the peak shown in Figure6, found on 17 November at 15:00 UTC, is associated exclusively with the convection in the outer regions of the storm. The spatial distribution of the regions of lower TBs at 89 GHz changes throughout the storm evolution, while the rainfall pattern shown at 37 GHz maintains a symmetric structure around the eye (with variable intensity). GMI channels above 89 GHz (not shown), sensitive to the scattering of frozen hydrometeors found in the upper cloud layers in the regions of the outflow, show slight TB depression, mostly in correspondence with 89 GHz rainband pattern, and more evident during the development phase. The lack of scattering signal at higher frequency (>150 GHz) in correspondence of the cloudy areas of the MODIS VIS image shown in Figure1, associated with the strong depolarization signal at 89 GHz (not shown), is an indication that the upper cloud layers in the Medicane during its TLC phase may be characterized by the presence of supercooled cloud droplets.

5.2. GPM-CO Precipitation Product Analysis Panegrossi et al. [28] have carried out a comparison of the precipitation retrieved from the AMSR2 and GMI overpasses shown in the previous section. Here we focus on the precipitation products available from the GPM-CO overpasses. For the GPM overpass over Numa on 16 November at 13:49 UTC, both GMI and DPR observations are available. The availability of the radar observation allows for a 3D insight of the storm. The reflectivity factor measured by the DPR is a proxy of the storm top height (Figure9a). It is worth noting that the storm top height could be underestimated, because of the low sensitivity of the DPR (both Ku and Ka band) unable to detect the small ice particles in the upper cloud layers. Remote Sens. 2019, 11, x FOR PEER REVIEW 13 of 26

Remoteworth Sens. noting2019 ,that11, 1690 the storm top height could be underestimated, because of the low sensitivity13 of of the 26 DPR (both Ku and Ka band) unable to detect the small ice particles in the upper cloud layers.

(a) (b)

FigureFigure 9.9. ((aa)) StormStorm toptop heightheight (km)(km) asas reconstructedreconstructed fromfrom thethe reflectivityreflectivity factorfactor measuredmeasured byby DPRDPR atat Ku-band.Ku-band. The The selected selected 5047 5047 DPR DPR scan scanis also is shown also byshown the black by thedashed black line. dashed (b) Vertical line. cross-section (b) Vertical ofcross DPR-section Ku corrected of DPR Ku reflectivity corrected (Zc) reflectivity for across-track (Zc) for scanacross 5047.-track The scan black 5047. dashed The black box dashed highlights box thehighlights bright-band. the bright-band.

AtAt this this stage stage of of the the storm storm development, development, the 3D the structure 3D structure provided provided by the DPR by the shows DPR the shows presence the ofpresence a quasi-cloud of a quasi free-cloud region free around region the around storm centerthe storm (as identified center (as by identified the mslp by minimum the mslp at minimum the time ofat thethe overpass)time of the surrounded overpass) surrounded by clouds (precipitation) by clouds (precipitation) reaching a maximum reaching heighta maximum of about height 6 km. of Along about the6 km. northwest Along the to southeast northwest direction, to southeast on the direction, eastern side on the of theeastern storm side center, of the there storm is a rainbandcenter, there as high is a asrainband ~10 km. as The high limited as ~10 Ku-bandkm. The limited swath width Ku-band does swath not allow width analysis does not of allow the system analysis on of the the western system edgeon the of western the storm, edge but of the the GMI storm, measurements but the GMI (Figuresmeasurem7 andents8) (Figure evidence 7 and that 8 there) evidence are no that rainbands there are developingno rainbands in developing that region. in that region. ThanksThanks to to the the availability availability of of co-located co-located radar radar and and radiometer radiometer observations, observations, the the DPR DPR Ku Ku measurements can be related to the GMI TB behavior and to the 3D structure of the storm. To measurements can be related to the GMI TB behavior and to the 3D structure of the storm. To this thisend, end, we weconsider consider the the vertical vertical cross cross-section-section of ofthe the Ku Ku-band-band reflectivity reflectivity,, corrected corrected from attenuationattenuation effects (Zc), along the across-track DPR Ku/Ka scan 5047 (indicated by the black dashed line in Figure9a), effects (Zc), along the across-track DPR Ku/Ka scan 5047 (indicated by the black dashed line in Figure and9a), shownand shown in Figure in Figure9b. 9b. ItIt isis evidentevident thethe coexistencecoexistence ofof convectiveconvective corescores (e.g.,(e.g., 37.137.1°N,◦N, 18.818.8°E)◦E) embeddedembedded inin stratiformstratiform regionsregions (e.g.,(e.g., 36.736.7°N,◦N, 18.018.0°E)◦E) inin thethe rainband.rainband. TheThe shallowshallow convectionconvection activityactivity cancan bebe easilyeasily inferredinferred from the high value of Zc, that is about 50 dBZ up to 4 km. These convective cores seem coincident with from the high value of Zc, that is about 50 dBZ up to 4 km. These convective cores seem coincident thewith areas the ofareas lower of TBslower at 89TB GHzs at 89 and GHz intense and lightning intense lightning activity, shown activity, in Figureshown8a. in On Figure the other 8a. On hand, the theother presence hand, the of stratiform presence cloudsof stratiform is evidenced clouds by is aevidenced signature by of bright-banda signature of (BB) bright in some-band portions (BB) in ofsome the rainbands,portions of i.e., the a rainbands, peak of Ku-band i.e., a reflectivity peak of Ku factor-band in reflectivity the vertical factor profile in due the to vertical melting profile ice particles, due to moremelting evident ice particles, in other more along-track evident cross-sections in other along (as-track shown cross in-sections Panegrossi (as shown et al. [ 28in]). Panegrossi The height et ofal. the[28] BB). The signal height is coherent of the BB with sign theal is freezing coherent level with height the freezing (~2400 m)level inferred height from(~2400 the m) corresponding inferred from ECMWFthe corresponding reanalysis ECMWF temperature reanalysis profile. temperature profile. TheThe precipitation precipitation rate rate estimated estimated by by GMI, GMI, obtained obtained from from the the combination combination of of the the information information providedprovided by by all all the the channels channels available, available, is shown is shown in Figure in 10 Figurea. It can 10a be. compared It can bewith compared the precipitation with the rateprecipitation estimated rate from estimated the Ku-band from DPR the measurements, Ku-band DPR shown measurements, in Figure shown10b (the in precipitation Figure 10b rate(the estimateprecipitation from Ka-bandrate estimate is not from shown Ka because-band is ofnot its shown narrower because swath, of compared its narrower to DPR swath, Ku-band). compared In the to regionDPR Ku of- theband). storm In seen the by region both instruments, of the storm there seen is by a relatively both instruments good agreement, there between is a relatively the patterns good ofagreement precipitation, between especially the pattern considerings of precipitation the nonuniform, especially beam-filling considering effect the related nonuniform to the lower beam spatial-filling resolutioneffect related of the to radiometerthe lower spatial (in particular resolution for theof the low radiometer frequency channels(in particular< 37 GHz)for the compared low frequency to the DPR,channels the di< ff37GHzerent viewing) compared angle, to the and DPR the di, thefferent different approaches viewing used angle, for DPRand the and different GMI to retrieveapproaches the surfaceused for precipitation. DPR and GMI DPR to shows retrieve very the small surface areas precipitation of high rainfall. DPR rate shows which very appear small smoothed areas of out high in therainfall GMI retrieval.rate which As appear a matter smoothed of fact, the out highest in the precipitation GMI retrieval. rate estimated As a matter from of GMI fact, is found the highest in the southernprecipitation portion rate of estimated the main from rainband GMI aroundis found 35 in◦ N,the where southern TBs portion at 89 GHz of the show main an rainband extended around area of minimum values in Figure8, and it is 19 mm /h, while the maximum DPR estimate is ~80 mm/h in that region.

Remote Sens. 2019, 11, x FOR PEER REVIEW 14 of 26

35°N, where TBs at 89 GHz show an extended area of minimum values in Figure 8, and it is 19 mm/h, while the maximum DPR estimate is ~80 mm/h in that region. Remote Sens. 2019, 11, x FOR PEER REVIEW 14 of 26 (a) (b) Remote Sens.35°N,2019, 11 where, 1690 TBs at 89 GHz show an extended area of minimum values in Figure 8, and14 ofit 26is 19 mm/h, while the maximum DPR estimate is ~80 mm/h in that region.

(a) (b)

Figure 10. GPM CO observation of Numa on 16 November 16 at 13:49 UTC. (a) Precipitation rate (mm h–1), estimated by the NASA GPROF CLIM algorithm from GMI observation. (b) Precipitation rate (mm h-1), estimated from Ku-band DPR observation.

FigureComparing 10.FigureGPM the CO 10 GMI.observation GPM precipitation CO observati of Numa rateon on of 16map Numa November in Figureon 16 16 November at10 13:49 with UTC. the 16 TBsat (a )13:49 Precipitation at 37 UTC. GHz (a rate(Figures) Precipitation (mm 7a,d), rate –1 it ish evident), estimated (mmthat byhlower–1), the estimated NASA TBs, found GPROF by the in NASA CLIM the development algorithmGPROF CLIM from algorithmphase GMI observation. compared from GMI (tob observation.) the Precipitation mature (b phase,) rate Precipitation are -1 due(mm to the h ),scattering estimatedrate (mm hby from-1), the estimated Ku-band ice hydrometeors from DPR Ku observation.-band in DPR the convectiveobservation. cores (shown by DPR cross-section), which tends to mask the emission signal by the rain layers. Comparing the GMI precipitation rate map in Figure 10 with the TBs at 37 GHz (Figure7a,d), it is For the ComparingGMI overpass the onGMI 18 precipitation November at rate 03:59 map UTC, in Figure when 10Numa with hitsthe TBsthe coastat 37 GHzof Southern (Figures 7a,d), evident that lower TBs, found in the development phase compared to the mature phase, are due to the Apulia itregion, is evident the pattern that lower of the TBs precipitation, found in (Figurethe development 11a) follows phase the patterncompared of theto theTBs mature at 37 GHz phase, are scattering by the ice hydrometeors in the convective cores (shown by DPR cross-section), which tends (over thedue sea), to thewith scattering peaks around by the 14ice mm/hhydrometeors in the region in the of convective minimum cores TBs (shownat 89 GHz by DPRin the cross main-section), to mask the emission signal by the rain layers. rainband.which DPR tends observations to mask the are emission not available signal forby the this rain overpass, layers. because the storm is outside the For the GMI overpass on 18 November at 03:59 UTC, when Numa hits the coast of Southern Ku-band swath.For theHowever, GMI overpass a portion on of18 theNo vember storm at at this 03:59 time UTC, lies when within Numa the field hits ofthe view coast of of the Southern Apulia region, the pattern of the precipitation (Figure 11a) follows the pattern of the TBs at 37 GHz C-band Apulia Doppler region, dual the polarization pattern of groundthe precipitation radar (GR) (Figure located 11a) in follows Pettinascura, the pattern Calabria of the region,TBs at 37 GHz (over the sea), with peaks around 14 mm/h in the region of minimum TBs at 89 GHz in the main managed(over by the Departmentsea), with peaks of Civil around Protection 14 mm/h of Italy.in the Unfortunately,region of minimum for this TB events at 89 only GHz single in the main rainband. DPR observations are not available for this overpass, because the storm is outside the polarizationrainband. and Doppler DPR observations radar volumes are at not 1 km available radial forresolution this overpass, were available. because the storm is outside the Ku-band swath. However, a portion of the storm at this time lies within the field of view of the C-band TheKu GPROF-band swath.GMI rainfall However, rate, shown a portion in Figure of the 11a storm, is compared at this time with lies the within rainfall the rate field estimated of view of the Doppler dual polarization ground radar (GR) located in Pettinascura, Calabria region, managed by from theC -GRband observations Doppler dual available polarization at the time ground of the radar GMI (GR) overpass located (Figure in Pet 11btinascura,). At this Calabriatime the region, the Department of Civil Protection of Italy. Unfortunately, for this event only single polarization and cyclone managedeye is 150 by km the from Department the radar of site, Civil and Protection the radar of shows Italy. portionsUnfortunately, of two forrainbands, this event on eonly of single Doppler radar volumes at 1 km radial resolution were available. them closerpolarization to the radar and site,Doppler which radar are fullyvolumes observed at 1 km by radial the GMI. resolution were available. The GPROF GMI rainfall rate, shown in Figure 11a, is compared with the rainfall rate estimated (a) from the GR observations available at the(b) time of the GMI overpass (Figure 11b). At this time the cyclone eye is 150 km from the radar site, and the radar shows portions of two rainbands, one of them closer to the radar site, which are fully observed by the GMI.

(a) (b)

FigureFigure 11. 11Numa. Numa observation observation on on 18 18 November. November (.a ()a Precipitation) Precipitation rate rate map map estimated estimated by by the the NASA NASA GPROFGPROF CLIM CLIM algorithm algorithm from from GMI overpassGMI overpass at 03:59 at UTC. 03:59 (b ) UTC Near-surface. (b) Near precipitation-surface precipitation rate estimated rate fromestimated the GR from scan the in PettinascuraGR scan in Pettinascura at 04:00 UTC at 04:00 using UTC algorithms using algorithms adopted by adopted the radar by the rain radar mosaic rain

product.mosaic product The circle. The indicates circle indicates the radar the field-of-view radar field-of (radius-view (radius ~200 km), ~200 while km), thewhile black the dashedblack dashed line indicates the 80 azimuth direction. line indicatesFigure ◦the 11 80°. Numa azimuth observation direction .on 18 November. (a) Precipitation rate map estimated by the NASA GPROF CLIM algorithm from GMI overpass at 03:59 UTC. (b) Near-surface precipitation rate The GPROF GMI rainfall rate, shown in Figure 11a, is compared with the rainfall rate estimated from the GR observationsestimated from available the GR scan at thein Pettinascura time of the at GMI04:00 overpassUTC using (Figure algorithms 11b). adopted At this by timethe radar the rain mosaic product. The circle indicates the radar field-of-view (radius ~200 km), while the black dashed cyclone eye is 150 km from the radar site, and the radar shows portions of two rainbands, one of them line indicates the 80° azimuth direction. closer to the radar site, which are fully observed by the GMI.

Remote Sens. 2019, 11, 1690 15 of 26 Remote Sens. 2019, 11, x FOR PEER REVIEW 15 of 26

From theFrom visual the comparisonvisual comparison of GPROF of GPROF GMI and GMI GR and rain GR rate rain products rate products shown shown in Figure in Figure11, we 11, we note thatnote the that GMI the rainfall GMI rainfall rate estimate rate estimate for the for western the western rainband rainband is in is very in very good good agreement agreement with with the the GRGR rainfall rainfall rate rate estimate, estimate, showing showing values values as highas high as 5 as mm 5 mm/h/h. However,. However we, notewe note a disagreement a disagreement in in termsterms of rainfall of rainfall rate intensity rate intensity for the for eastern the eastern rainband rainband (approximately (approximately centered centered at 39.5 ◦ atN, 39.5°N, 18◦E), 18°E), which appearswhich appears to be more to be intense more intense in the GPROF in the GPROF GMI products GMI products than that than shown that show in then GR in the map. GR This map. This discrepancydiscrepancy in the retrievedin the retri raineved rate rain intensity rate intensity could becould explained be explained by considering by considering the slanted the slanted GR GR viewingviewing geometry geometry that constrains that constrains the GR the near-surface GR near-surface bin height bin height (NSBH) (NSBH) to progressively to progressively increase increase with thewith distance the distance from the from radar the site.radar Thus, site. Thus, at longer at longer distance distance the near-surface the near-surface rain raterain estimationrate estimation is is expectedexpected to be to of be lower of lower quality. quality. Figure Figure 12 shows 12 shows the GR the perspective GR perspective of the of western the western and eastern and eastern rainbandsrainbands positioned positioned at ~50 km at and~50 ~120km and km ~120 from thekm radar,from the with radar reflectivity, with factorreflectivity peaks factor of the peaks order of the of 30 dBZorder and of 20 30 dBZ, dBZ respectively.and 20 dBZ, Torespectivel comparey. more To compare in detail more GPROF in detail GMI vs.GPROF GR, inGMI Figure vs. GR,12 the in Figure GMI TBs12 arethe superimposed GMI TBs are superimposedto GR reflectivity. to GR This reflectivity. comparison This confirms comparison that the confirms eastern rainband,that the eastern with largerrainband, TB depression with larger at 166 TB GHz depression and 89 atGHz 166 (150 GHz K and and 210 89 K, GHz respectively), (150K and corresponds 210 K, respectively) to , the radarcorresponds raincell having to the larger radar vertical raincell extension, having larger but highervertical NSBH extension and, lower but higher GR rain NSBH rate. and On thelower GR other hand,rain rate the. western On the other rainband, hand with, the smallerwestern 166 rainband GHz and, with 89 GHzsmaller TB 166 depression GHz and (210 89 GHz K and TB 240 depression K, respectively)(210K showsand 240K, a radar respectively) raincell with shows a smaller a radar vertical raincell extension with a smaller but with vertical lower extension NSBH and but higher with lower GR rainNSBH rate. Pathand attenuationhigher GR rain produced rate. Path by the attenuation western radar produced raincell by could the western be an additional radar raincell element could to be an explainadditional the radar rain element rate underestimation to explain the radarof the easternrain rate raincell. underestimation This is supported of the byeastern the consistency raincell. This is betweensupported the freezing by the level consistency height (which betw iseen expected the freezing to be level at 2400 height m as (which previously is expected reported) to be and at the2400 m as core ofpreviously the western reported) raincell (whichand the extends core of fromthe western 1700 m raincell to 2500 ( m).which In thisextends situation, from 1700 the core m to of 2500 the m). In westernthis raincell situation, may likelythe core partially of the attenuate western theraincell lower may end likely of the partially eastern cell attenuate thus compromising the lower end its of the near-surfaceeastern precipitation cell thus compromising retrieval. its near-surface precipitation retrieval.

Figure 12.FigureVertical 12. cross-sectionVertical cross of- radarsection reflectivity of radar observed reflectivity at 80 ◦ observedazimuth by at the 80° GR azimuth in Pettinascura by the GR in on 18 NovemberPettinascura at 04:00 on 18 UTC. November GMI TBs at at 04:00 37 GHz UTC. V-pol, GMI 89 TBs GHz at V-pol, 37 GHz 166 V GHz-pol, V-pol 89 GHz and V 183.31-pol, +1663 GHz GHz V-pol are superimposed.and 183.31+3 GHz are superimposed.

6. Analysis6. Analysis of RAMS-ISAC of RAMS- SimulationsISAC Simulations The forecastThe forecast of Medicane of Medicane Numa was Numa characterized was characterized by a significant by a significant change of change the prediction of the prediction of its of positionits depending position depending on the model on the initialization model initialization time. In an time. unstable In an flow unstable regime, flow such regime as Numa,, such theas Numa, sensitivitythe sensitivity of the predicted of the storm predict features,ed storm for features, example for the example position the of theposition cyclone of eye,the tocyclone the initial eye, to the and boundaryinitial and conditions boundary is well conditions known [is5 ,13 well]. Nevertheless, known [5,13] in. Nevertheless this section we, in highlight this section important we highlight aspectsimportant related to anaspects operational related NWP to an modeloperational setup NWP [13]. Here model we setup analyze [13] the. Here operational we analyze RAMS-ISAC the operational forecastsRAMS issued-ISAC on 16 forecasts and on issued 17 November. on 16 and The on first 17 operational November. forecast The first uses operational the Global forecast Forecast uses the Global Forecast System (GFS) analysis/forecast cycle issued at 12:00 UTC on 16 November as initial and boundary conditions, while the second forecast uses the GFS analysis/forecast cycle issued 24

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Remote Sens. 2019, 11, x FOR PEER REVIEW 16 of 26 System (GFS) analysis/forecast cycle issued at 12:00 UTC on 16 November as initial and boundary conditions, while the second forecast uses the GFS analysis/forecast cycle issued 24 h later at 12:00 UTC hours later at 12:00 UTC on 17 November. GFS initial and dynamic boundary conditions are on 17 November. GFS initial and dynamic boundary conditions are downloaded at 0.25 horizontal downloaded at 0.25° horizontal resolution. The boundary conditions are updated every◦ 6 h and the resolution. The boundary conditions are updated every 6 h and the finalization date is +60 h after the finalization date is +60 h after the initialization time. The (SST) is initialization time. The sea surface temperature (SST) is interpolated onto the RAMS-ISAC grid from interpolated onto the RAMS-ISAC grid from GFS analysis at 0.083° horizontal resolution. SST is GFS analysis at 0.083 horizontal resolution. SST is constant during the simulation and it is that of the constant during the◦ simulation and it is that of the initial time of the simulation (12:00 UTC on 16 and initial time of the simulation (12:00 UTC on 16 and 17 November, depending on the simulation). 17 November, depending on the simulation). It is important to note that we do not consider the impact of rapidly generated sea waves on the It is important to note that we do not consider the impact of rapidly generated sea waves on the sea surface roughness, which plays an important role for the exchange of energy and momentum sea surface roughness, which plays an important role for the exchange of energy and momentum between the sea surface and the atmosphere. The sea surface roughness is estimated by the Charnock between the sea surface and the atmosphere. The sea surface roughness is estimated by the relation [54], which increases with the square of the friction velocity. So, the sea surface roughness is Charnock relation [54], which increases with the square of the friction velocity. So, the sea surface higher for more intense surface winds. We used rain gauges to independently verify the accuracy of the roughness is higher for more intense surface winds. We used rain gauges to independently verify forecasts. Based on rain gauges, the most abundant precipitation registered over land during the event the accuracy of the forecasts. Based on rain gauges, the most abundant precipitation registered over was in the southern part of the Apulia region (Salento), between 12:00 UTC on 17 November and 12:00 land during the event was in the southern part of the Apulia region (Salento), between 12:00 UTC on UTC on 18 November. The Italian rain gauge network (Figure 13a,b) recorded 24 h precipitation varying 17 November and 12:00 UTC on 18 November. The Italian rain gauge network (Figure 13a,b) from 64 mm/24 h of Corigliano d’Otranto (40.1 N, 18.3 E) to 172 mm/24 h of Presicce (39.9 N, 18.3 E), recorded 24 hour precipitation varying from◦ 64 mm/24h◦ of Corigliano d’Otranto (40.1°N,◦ 18.3°E)◦ to with the second largest precipitation (147 mm/24 h) in Ruffano (40.0 N, 18.3 E). It is worth noting that 172 mm/24h of Presicce (39.9°N, 18.3°E), with the second largest◦ precipitation◦ (147 mm/24h) in the mean annual (monthly) precipitation in Salento is ~810 mm/year (~125 mm in November) and that Ruffano (40.0°N, 18.3°E). It is worth noting that the mean annual (monthly) precipitation in Salento the 17–18 November 2017 event was the most intense in terms of 24h cumulated precipitation since is ~810 mm/year (~125 mm in November) and that the 17–18 November 2017 event was the most 1970 [55]. intense in terms of 24h cumulated precipitation since 1970 [55].

(a) (b)

(c) (d)

FigureFigure 13. 13(a.) ( Twenty-foura) Twenty-four hour hour rainfall rainfall (mm) (mm) measured measured by the by Italian the Italian rain gaugerain gauge network network starting starting on 17 Novemberon 17 November at 12:00 at UTC.12:00 OnlyUTC. rain Only gauges rain gauge withs rainfall with rainfall larger thanlarger 0.2 than mm 0.2/2h mm/2h are shown. are shown The first. The numberfirst number insidethe inside parentheses the parentheses in the title in shows the title the shows total availablethe total available rain gauges rain for gauge the times for period, the time whileperiod, the second while numberthe second shows number the rain shows gauges the withrain precipitationgauges with largerprecipitation than 0.2 larger mm/24 than h; ( b0.2) same mm/24h; as in ((ab),) same but with as in a zoom(a), but over with Salento a zoom region over Salento (black box region in (a)); (black (c) rainfallbox in (a)) forecast; (c) rainfall for the forecast 24 h ending for the at 24 12:00h ending UTC on at 18 12 November:00 UTC on for 18 the November simulation for initialized the simulation on 16 initialized November on at 12:0016 November UTC; (d) at same 12:00 as UTC (c) ; but(d for) same the simulations as (c) but for initialized the simulations on 17 November initialized aton 12:00 17 November UTC. at 12:00 UTC.

Figure 13c shows the 24 h precipitation predicted between on 17 November at 12:00 UTC and on 18 November at 12:00 UTC for the simulation starting on 16 November at 12:00 UTC. The amount

Remote Sens. 2019, 11, 1690 17 of 26

Figure 13c shows the 24 h precipitation predicted between on 17 November at 12:00 UTC and on 18 November at 12:00 UTC for the simulation starting on 16 November at 12:00 UTC. The amount of 100 mm/24 h is predicted over the Ionian Sea, with the maximum found 50 km south of the Apulia coast. Precipitation is predicted also over Calabria (39.7◦N, 16.8◦E) with maximum amounts of 90 mm/24 h. While the forecast shows that Numa could be a potentially dangerous meteorological system because, in addition to the high winds, it brings considerable precipitation, the forecast on 16 November shows small precipitation amounts over Apulia, and northeastern Calabria. The situation is different for the forecast issued on 17 November (Figure 13d); in this forecast, the impact of Numa in Southern Apulia is evident, because RAMS-ISAC shows a maximum of 140 mm/h and 120 mm/24 h in Presicce and Ruffano, respectively, in good agreement with the observations. Interestingly, the simulation starting at 12:00 on 16 November was able to forecast the TLC features of Numa, similarly to that on 17 November (see Figures 15 and 16) and, considering the characteristics of the storm (low vertical wind shear, quasi-neutral moist environment, sea level pressure minima, Remote Sens. 2019, 11, x FOR PEER REVIEW 17 of 26 amount of precipitation at the surface, etc.), the two forecasts issued on 16 and 17 November are similar.of 100 Nevertheless,mm/24h is predicted an important over the di Ionianfference Sea, arises with comparing the maximum the stormfound tracks50 km ofsouth the twoof the simulations Apulia (Figurecoast. 14Precipitation). The storm is predicted track of thealso forecast over Calabria issued (39.7 on 16°N, November16.8°E) with (R16) maximum is shifted amounts to the of south 90 comparedmm/24h. with While that the issued forecast on 17 shows November that Numa (R17). could Also, while be a potentially the R17 storm dangerous track is almostmeteorological stationary forsystem the first because, 12 h, the in addition R16 center to the of the high storm winds, moves it brings more considerable rapidly towards precipitation, Greece. the The forecast R17 track on 16 is in betterNovember agreement shows with small that precipitation of Figure5 amountswhich, being over Apulia, derived and from northeastern ECMWF analysisCalabria. andThe short-termsituation forecast,is different can befor consideredthe forecast asissued the reference.on 17 November The mslp (Figure minima 13d); have in this similar forecast values, the impact between of Numa R16 and R17in andSouthern vary fromApulia 1003 is evi hPadent, to 1006because hPa, RAMS in good-ISAC agreement shows a maximum with values of in140 Figure mm/h5 .and 120 mm/24 h in Presicce and Ruffano, respectively, in good agreement with the observations.

FigureFigure 14. 14.Numa Numa track track between between 12:00 12 UTC:00 UTC on 17 on November 17 November and 00:00 and UTC 00:00 on UTC 19 November on 19 November simulated bysimulated RAMS-ISAC by RAMS starting-ISAC at 12:00 starting UTC at on12:00 16 UTC November on 16 November (asterisks) (asterisks) and at 12:00 and UTC at 12 on:00 17UTC November on 17 (filledNovember circle). (filled Specific circle). times Specific are shown times in are colors. shown The in centercolors. ofThe the center storm of onthe each storm time on iseach given time by is the mslpgiven minimum by the mslp simulated minimum by RAMS-ISAC.simulated by RAMS-ISAC.

FromInterestingly, the above the analysis, simulation it follows starting that at the 12:00 di ffonerences 16 Nove betweenmber was R16 able and toR17 forecast are mainly the TLC in the tracksfeatures followed of Numa, by the similarly TLCs of to the that two on simulations, 17 November showing (see Figures the important 15 and role16) and, of initial considering and boundary the conditionscharacteristics for the of forecast the storm of (low this kindvertical of wind events, shear, similarly quasi- toneutral other moist studies environment, ([5,13] among sea others).level Therefore,pressure theminima, consequences amount of on precipitation the precipitation at the fieldsurface forecast, etc.), the over two the forecast land ares issued significant. on 16 and 17 NovemberSince the are simulation similar. Nevertheless, starting on 17 an November important at difference 12:00 UTC arises provides comparing a good the rainfall storm field tracks forecast of overthe Apulia two simulati region,ons in the(Figure following 14). The the output of this of simulation the forecast is used issued to onpresent 16 November some characteristics (R16) is ofshifted Numa TLCto the phase. south Ancompared important with feature that issued of the Medicaneson 17 November is the surface(R17). Also, wind, while which the can R17 be storm intense, causingtrack is damages almost stationary and being for a threat the first for 12 the h, populations. the R16 center Satellite of the storm observations moves more are an rapidly important towards source ofGreece. data in thisThe contextR17 track because is in better they can agreement be used towith verify that the of wind Figure field 5 whic forecasth, being over derived the sea, from where ECMWF analysis and short-term forecast, can be considered as the reference. The mslp minima have similar values between R16 and R17 and vary from 1003 hPa to 1006 hPa, in good agreement with values in Figure 5. From the above analysis, it follows that the differences between R16 and R17 are mainly in the tracks followed by the TLCs of the two simulations, showing the important role of initial and boundary conditions for the forecast of this kind of events, similarly to other studies ([5,13] among others). Therefore, the consequences on the precipitation field forecast over the land are significant.

Remote Sens. 2019, 11, 1690 18 of 26 conventional observations are sparse. Figure 15 shows the comparison between RAMS-ISAC forecast on 17 November at 19:00 UTC, and the ASCAT observations at 19:09 UTC on the same day. The model is able to predict several features of the storm, such as the eye position (shifted few kilometers to the southwest of the real position) and the strong wind, up to 20 m/s, on the western side of the eye. The main areas of moderate-intense wind over the Adriatic and Ionian Seas are also well predicted even if the model underestimates observed winds. There are also local scale features that are not forecast by the model, as the local wind patterns south of 37◦N and east of 18 ◦E. As already stated, Numa shows TLC features at this time of development. To highlight these features, Figure 16a shows the cross-section of relative humidity and temperature anomaly fields along 39.2◦ N latitude on 17 November at 19:00 UTC. The temperature anomaly is computed with respect to the mean value at each vertical level. The warm core caused by the release of the latent heat of the evolving TLC extends from 1 km to 10 km height (with 1.5 K anomaly between 2 and 5 km height), showing a deep vertical extension of Numa. Relative humidity is lower in the warm core region. It is higher below 2 km height and in the outer regions of the TLC, indicating (a) low-level moisture convergenceRemote Sens. 2019 and, 11 (b), x moistureFOR PEER REVIEW redistribution by the convection. 18 of 26 Remote Sens. 2019, 11, x FOR PEER REVIEW 18 of 26

(a) (b) (a) (b)

FigureFigure 15. 1(5a.) ( RAMS-ISACa) RAMS-ISAC winds winds on 17 on November 17 November at 19:00 at UTC19:00 at UTC z=24 at m z=24 above m the above surface. the (surfaceb) Winds. (b) retrievedFigureWinds 15 fromretrieved. (a) RAMS ASCAT from-ISAC onboard ASCAT winds MetOpA onboard on 17 Nov (orbit MetOpAember 57497) (orbitat overpass19:00 57497) UTC on overpassat 17 z=24 November m on above 17 at Nov 19:09theember surface UTC. at. The (19:09b) productWindsUTC. retrievedThe optimized product from for optimized coastal ASCAT regions onboardfor coastal is shown MetOpA regions here. is (orbit shown 57497) here. overpass on 17 November at 19:09 UTC. The product optimized for coastal regions is shown here.

(a) (b) (a) (b) Figure 16. (a) Vertical cross-section of temperature anomaly (filled contour) and relative humidity Figure 16. (a) Vertical cross-section of temperature anomaly (filled contour) and relative humidity (contours from 30% to 100% every 10%) on 17 November at 19:00 UTC. The cross-section along 39.2 ◦N Figure 16. (a) Vertical cross-section of temperature anomaly (filled contour) and relative humidity latitude(contours is shown from 30% between to 100% 15◦ Eevery and 2010%)◦E foron 17 clarity. November (b) As at in 19:00 (a) for UTC. the meridionalThe cross-section wind velocityalong 39.2 (contours from 30% to 100% every 10%) on 17 November at 19:00 UTC. The cross-section along 39.2 (contours°N latitude from is –20shown m/s between to 14 m/ s15°E every and 2 20°E m/s; blackfor clarity. contours (b) As are in positive (a) for the values, meridional blue contours wind velocit are y °N latitude is shown between 15°E and 20°E for clarity. (b) As in (a) for the meridional wind velocity negative(contours values, from magenta –20 m/s contoursto 14 m/s is every 0) and 2 equivalentm/s; black potentialcontours temperatureare positive (filledvalues, contours). blue contours are (contoursnegative from values, –20 magenta m/s to 14 contours m/s every is 0 2) andm/s ; equivalentblack contours potential are positive temperature values, (filled blue contours). contours are negative values, magenta contours is 0) and equivalent potential temperature (filled contours). Since the simulation starting on 17 November at 12:00 UTC provides a good rainfall field forecastSince overthe simulationApulia region starting, in the on following 17 November the output at 12:00 of this UTC simulation provides isa usedgood to rainfall present field some forecastcharacteristics over Apulia of Numa region TLC, in the phase following. An important the output feature of this of simulation the Medicanes is used is to the present surface some wind, characteristicswhich can be ofintense, Numa causing TLC phase damages. An and important being a feature threat for of the populations. Medicanes is Satellite the surface observations wind, whichare an can important be intense, source causing of ddataamages in this and context being abecause threat for they the can populations. be used to Satellite verify observationsthe wind field areforecast an important over the source sea, where of data conventional in this context observations because theyare sparse can be. Figure used to 15 verify shows the the wind comparison field forecastbetween over RAMS the sea,-ISAC where forecast conventional on 17 November observations at 19 :00are UTC sparse, and. Figure the ASCAT 15 shows observations the comparison at 19:09 betweenUTC on RAMS the same-ISAC day. forecast The modelon 17 November is able to predictat 19:00 several UTC, and features the ASCAT of the observationsstorm, such as at the19:09 eye UTCposition on the (shifted same day.few kilometersThe model to is the able southwest to predict of several the real features position) of and the thestorm, strong such wind, as the up eye to 20 positionm/s, on (shifted the western few kilometers side of the to eye. the The southwest main areas of the of realmoderate position)-intense and thewind strong over wind,the Adriatic up to 20and m/s,Ionian on the Seas western are also side well of predictedthe eye. The even main if the areas model of moderate underestimates-intense observed wind over winds. the Adriatic There are and also Ionianlocal Seasscale are features also well that predicted are not forecast even if bythe the model model, underestimates as the local windobserved patterns winds. south There of are37°N also and localeast scale of 18 features °E. that are not forecast by the model, as the local wind patterns south of 37°N and east of As18 °E.already stated, Numa shows TLC features at this time of development. To highlight these features,As already Figure stated, 16a shows Numa the shows cross TLC-section features of relative at this timehum idityof development. and temperature To highlight anomaly these fields features,along 39.2° Figure N latitude16a shows on the17 Novembercross-section at 19:00of relative UTC. humThe iditytemperature and temperature anomaly isanomaly computed fields with along 39.2° N latitude on 17 November at 19:00 UTC. The temperature anomaly is computed with

Remote Sens. 2019, 11, 1690 19 of 26

Figure 16b shows the vertical cross-section, at the same latitude of Figure 16a of the equivalent potentialRemote temperature Sens. 2019, 11 and, x FOR of the PEER meridional REVIEW wind speed. Typical features of TLC are apparent as the 19 of 26 state of nearly-moist neutrality of ascending air parcels, the limited horizontal extension of the TLC, the axisymmetricrespect to the shape, mean the value windless at each region vertical in proximity level. The of warm cyclone core center caused (the by TLC the calm release eye), of and the latent the lowheat vertical of the wind evolving shear upTLC 6 kmextends height from [5,7 ,113 km]. to 10 km height (with 1.5 K anomaly between 2 and 5 Thekm results height), of showing Figure 16 a a,bdeep indicate vertical that extension the so-called of Numa. Wind Relative Induced humidity Surface is lower Heat Exchangein the warm core (WHISHEregion. [56,57 It ])is may higher play below an important 2 km height role and in Numa in the TLC outer phase regions maintenance. of the TLC, indicating (a) low-level moisture convergence and (b) moisture redistribution by the convection. 7. The Implementation of DPR (Dual Frequency Precipitation Radar) Reflectivity Observation in Figure 16b shows the vertical cross-section, at the same latitude of Figure 16a of the equivalent RAMS-3Dvar potential temperature and of the meridional wind speed. Typical features of TLC are apparent as the Instate this sectionof nearly we-moist show neutrality the implementation of ascending of air DPR parcels 3D, reflectivity the limited field horizontal as an observationextension of inthe TLC, the RAMS-ISACthe axisymmetric data assimilation shape, the scheme.windless The region RAMS-3DVar in proximity assimilation of cyclone schemecenter (the [39] TLC modified calm eye), to and assimilatethe reflectivitylow vertical factor wind observed shear up by 6 km ground height radars [5,7,13] [58.] is used for this purpose. The background is given by aThe short-term results of forecast Figure of 16a the,b RAMS-ISAC. indicate that More the so specifically,-called Wind since Induced the DPR Surface observation Heat Exchange is available(WHISHE at 13:49 [56,57] UTC on) may 16November, play an important we use role the backgroundin Numa TLC at phase 14:00 maintenance. UTC neglecting the time difference between the DPR observation and the RAMS-ISAC background. The DPR reflectivity is sampled7. The at seven Implementation vertical levels of fromDPR (Dual 2000 m Frequency to 8000 m Precipitation to match the Radar) model R verticaleflectivity bins, Observation and is in assimilatedRAMS following-3Dvar Caumont et al. [59]. Hereafter, the simulation with no DPR assimilation is referred to as CTRL, while the simulation with DPR assimilation is referred to as ANL. In this section we show the implementation of DPR 3D reflectivity field as an observation in the Figure 17a shows the difference between ANL and CTRL forecast of the relative humidity (RH) RAMS-ISAC data assimilation scheme. The RAMS-3DVar assimilation scheme [39] modified to field at 14:00 UTC, at 3172 m in the terrain following coordinate system used by RAMS-ISAC. There assimilate reflectivity factor observed by ground radars [58] is used for this purpose. The are several areas of the domain where the assimilation increases the relative humidity of the model background is given by a short-term forecast of the RAMS-ISAC. More specifically, since the DPR (RH difference up to 30%). For comparison, Figure 17b shows the DPR reflectivity at 3000 m at 13:49 observation is available at 13:49 UTC on 16 November, we use the background at 14:00 UTC UTC, quantifying the impact of the DPR reflectivity assimilation on the RH field. The largest impact neglecting the time difference between the DPR observation and the RAMS-ISAC background. The on the forecast is introduced south of 36 N, in correspondence of the main “comma”-shaped rain band DPR reflectivity is sampled at ◦seven vertical levels from 2000 m to 8000 m to match the model observed by DPR (see Figure 10b). The RH perturbation is significant and, once assimilated into the vertical bins, and is assimilated following Caumont et al. [59]. Hereafter, the simulation with no DPR model, it may determine saturation and precipitation where/when introduced. Other perturbations to assimilation is referred to as CTRL, while the simulation with DPR assimilation is referred to as the RH field are given in Northeastern Sicily and in Central-Eastern Italy. ANL.

(a) (b)

Figure 17.Figure(a) Relative 17. (a) humidityRelative humidity difference difference at the level at 3171 the m level in the 3171 terrain m followingin the terrain coordinate following system coordinate of the ANLsystem and of CTRL the onANL 16 Novemberand CTRL aton 14:00 16 November UTC. (b) DPR at 14:00 reflectivity UTC. ( atb) 3000DPR m reflectivity on 16 November at 3000 at m on 16 13:49 UTC.November at 13:49 UTC. As stated above, the DPR data are assimilated at 14:00 UTC on 16 November. As shown in the Figure 17a shows the difference between ANL and CTRL forecast of the relative humidity (RH) previous section, the forecast starting on 16 November at 12:00 UTC missed the precipitation over field at 14:00 UTC, at 3172 m in the terrain following coordinate system used by RAMS-ISAC. There Southern Apulia 24–48 h later (between 17 November at 12:00 UTC and 18 November at 12:00 UTC). are several areas of the domain where the assimilation increases the relative humidity of the model The DPR reflectivity assimilation is not able to correct the precipitation forecast in Southern Apulia, (RH difference up to 30%). For comparison, Figure 17b shows the DPR reflectivity at 3000 m at 13:49 likely because the (only) DPR overpass is available 24 h before rainfall hits Southern Apulia. This UTC, quantifying the impact of the DPR reflectivity assimilation on the RH field. The largest impact on the forecast is introduced south of 36°N, in correspondence of the main “comma”-shaped rain band observed by DPR (see Figure 10b). The RH perturbation is significant and, once assimilated into the model, it may determine saturation and precipitation where/when introduced. Other perturbations to the RH field are given in Northeastern Sicily and in Central-Eastern Italy.

Remote Sens. 2019, 11, x FOR PEER REVIEW 20 of 26

As stated above, the DPR data are assimilated at 14:00 UTC on 16 November. As shown in the previous section, the forecast starting on 16 November at 12:00 UTC missed the precipitation over Remote Sens.Southern2019, 11, 1690 Apulia 24–48 hours later (between 17 November at 12:00 UTC and 18 November20 of 26 at 12:00 UTC). The DPR reflectivity assimilation is not able to correct the precipitation forecast in Southern Apulia, likely because the (only) DPR overpass is available 24 h before rainfall hits Southern Apulia. result is similar to other studies with radar data assimilation (see Hu et al. [60] and Jones et al. [61]), This result is similar to other studies with radar data assimilation (see Hu et al. [60] and Jones et al. showing that the resilience of the impact of the assimilation is about 6 h. [61]), showing that the resilience of the impact of the assimilation is about 6h. Nevertheless, weak effects of assimilating DPR can be seen in the comparison of the CTRL and Nevertheless, weak effects of assimilating DPR can be seen in the comparison of the CTRL and ANL precipitation rate at 16:20 UTC (Figure 18), i.e., 2h and 20 minutes after the assimilation time, ANL precipitation rate at 16:20 UTC (Figure 18), i.e., 2h and 20 minutes after the assimilation time, when an SSMIS overpass is available and captures the storm during its transition over the Ionian Sea. when an SSMIS overpass is available and captures the storm during its transition over the Ionian The precipitation rate field is compared to that obtained from the NASA GPROF-CLIM-SSMIS V05 Sea. The precipitation rate field is compared to that obtained from the NASA GPROF-CLIM-SSMIS product. The comparison shows important differences between SSMIS precipitation rates and both V05 product. The comparison shows important differences between SSMIS precipitation rates and ANL and CTRL. This difference is caused by both forecast errors, and the low spatial resolution of both ANL and CTRL. This difference is caused by both forecast errors, and the low spatial resolution the GPROF precipitation rate (SSMIS has spatial resolution three times lower than GMI and AMSR2). of the GPROF precipitation rate (SSMIS has spatial resolution three times lower than GMI and Nevertheless, the GPROF-CLIM-SSMIS rainfall pattern shows precipitation south of 36 N and west of AMSR2). Nevertheless, the GPROF-CLIM-SSMIS rainfall pattern shows precipitation◦ south of 36°N 18 E, which is not simulated by CTRL forecast. ◦ and west of 18°E, which is not simulated by CTRL forecast.

(a) (b)

(c) (d)

Figure 18. Figure(a) Rain 1 rate8. (a simulated) Rain rate at simulated 16:20 UTC at by 16:20 the CTRLUTC by forecast the CTRL of RAMS-ISAC; forecast of RAMS (b) as in-ISAC; (a) for (b the) as in (a) for simulationthe assimilating simulation DPR assimilating reflectivity DPR (ANL); reflectivity (c) NASA (ANL) GPROF-CLIM-SSMIS; (c) NASA GPROF rainfall-CLIM rate-SSMIS observed rainfall rate on 16 Novemberobserved at 16:20on 16 UTC; November and (d )at di 16:20fference UTC between; and (d the) difference rainfall accumulatedbetween the onrainfall 16 November accumulated on 16 between 14:00November UTC and between 18:00 UTC 14:00 by UTC ANL and and 18:00 CTRL. UTC by ANL and CTRL .

ANL precipitation, however, extends more to the south compared to CTRL and, in particular, it ANL precipitation, however, extends more to the south compared to CTRL and, in particular, it extends to 35 N latitude and east of 18 E, in better agreement with GPROF-CLIM-SSMIS precipitation extends◦ to 35°N latitude ◦ and east of 18°E, in better agreement with GPROF-CLIM-SSMIS pattern. The southward shift of the ANL precipitation is confirmed by the comparison of the CRTL precipitation pattern. The southward shift of the ANL precipitation is confirmed by the comparison and ANL accumulated precipitation between 14:00 UTC and 18:00 UTC (Figure 18d). In this case differences up to 7 mm/4h are predicted south of 37◦N. The southward extension of the precipitation forecast of ANL compared to CTRL is a consequence of DPR data assimilation, as shown in Figure 17a. Remote Sens. 2019, 11, 1690 21 of 26

As shown above, the assimilation of the DPR reflectivity did not have a significant impact on the simulation of TLC Numa. However, the above results show the possibility to implement DPR reflectivity as an observation in data assimilation systems, leaving the question of its impact on the forecast open.

8. Discussion The analysis carried out for Medicane Numa highlights how powerful MW radiometers are in identifying the details of its precipitation structure, not discernible in conventional (VIS or IR) satellite imagery, as it evolves over the Mediterranean Sea. This is particularly evident for AMSR2 and GMI, thanks to their high spatial resolution compared to the other radiometers. The behavior of the measured TBs (at 37 and 89 GHz for example) is a clear indication that the main precipitation formation mechanism in the storm during its TLC stage, and in particular in the rainbands around the eye, is likely slantwise convection characterized by a weak updraft. Ice hydrometeors show different scattering properties from those found in the high convective cores (high TB minimum at 89 GHz, absence of scattering at 37 GHz, and no electrical activity). As the eye becomes more defined and the cyclone strengthens, the main rainband shows signatures of weak convection at 89 GHz (and at higher frequencies), due to scattering by precipitation-size frozen hydrometeors. However, even in this case, the absence of strong vertical motions inhibits the electrical activity. The analysis of GMI TBs and surface rainfall rate at the development phase evidences stronger convection activity. The minimum Polarization Corrected Temperature (PCT) at 89 GHz, computed according to [62], is 160.65 K and 195.35 K, in the development phase and in the mature phase of Numa, respectively. The 30 K difference of the 89 GHz PCT minimum values confirms that the convective activity is much weaker in the mature phase than in the development phase, in agreement with what found in previous studies (e.g. [3]). Comparing these values with TMI climatology of convective systems (e.g., Cecil et al. [51] and Liu and Zipser [52]), it is evident that the convection identified during the mature phase can be characterized as “weak”, whereas during its development phase Numa shows features typical of “strong” convection. The lack of scattering signal at higher frequency (> 150 GHz) in correspondence of the MODIS VIS cloud image during the TLC phase, and the depolarization at 89 GHz, indicate the presence of supercooled cloud droplets. The GMI and AMSR2 TB behavior finds correspondence to what found in other studies about in situ observations in tropical cyclones [63–65]. They confirm that tropical oceanic convection is usually not known for the strength of its updraft (exceptions occasionally occur), which is required to loft substantial quantities of rain drops and dense graupel to high altitude. In situ observations evidence, instead, the presence of supercooled cloud droplets at high levels and that the ice, mostly rimed particles and low density graupel, forms in the weak updraft regions and is redistributed throughout the storm by the upper and midlevel outflows. In the development phase, we have analyzed for the first time the 3D structure of this kind of storms, as observed by a spaceborne radar. DPR is particularly valuable because it can provide information on the storm features, from both the microphysical and meteorological perspective, thus supporting what already evidenced by the GMI TB analysis. On the other hand, during the TLC phase, GMI allows highlighting precipitation underestimation by the ground radar of Pettinascura, mainly due to the blind zone caused by its viewing geometry. In fact, the area of Numa, although frequently hit by Medicanes or intense precipitation, is not yet adequately covered by operational weather radars. These results confirm that spaceborne MW observations can provide useful insights about rainfall structure, intensity, and pattern, especially over the sea where no ground-based data are available, and when GR rainfall rate estimates might be affected by large uncertainties (as shown also in Panegrossi et al. [26]). The investigation of numerical modeling performance has shown that the RAMS-ISAC forecast for this event is sensitive to the initial and boundary conditions. In particular the forecast starting on 16 November at 12:00 UTC was unable to correctly predict the over Apulia, while the forecast starting on 17 November at 12:00 UTC correctly predicted the rainfall amount and landfall over Remote Sens. 2019, 11, 1690 22 of 26

Apulia. The storm track simulated on 16 November is too far south from the real path, and rainfall was forecast mainly over the sea. The simulation starting on 17 November shows a good prediction of the precipitation field and was used to gain insight into the TLC phase of Numa. Wind speeds up to 20 m/s were simulated on 17 November at 19:00 UTC, in good agreement with ASCAT observations. The warm core extended up to 10 km showing a deep vertical extension of Numa, favored by the long-lasting TLC phase [18]. Other TLC characteristics as the small horizontal extension of Numa, the low vertical wind shear, the axisymmetric shape, and the calm eye of the Medicane, were well captured by the model. These results, in agreement with previous model-based studies on Medicanes (e.g., Lagouvardos et al. [2], Miglietta and Rotunno [5], Davolio et al. [13], Picornell et al. [21]), support the passive MW observation analysis, showing that during the TLC phase warm rain processes occur in the area surrounding the eye, while weak convective activity is observed in small portions of the eyewall. We have analyzed how DPR measurements, available over the sea, can be also used in NWP data assimilation. However, because only one overpass of the DPR is available, the impact of the data assimilation on the Numa forecast was limited. Nevertheless, DPR reflectivity data assimilation caused an extension of the rainfall forecast toward the South, in better agreement with NASA GPROF-CLIM-SSMIS V05 product.

9. Conclusions In this study a Medicane event, named Numa, which occurred in November 2017, has been investigated by using both observations (mainly satellite-based) and numerical modeling. We have stressed how MODIS VIS images, ECMWF forecast and LINET lightning data are very useful for a NRT monitoring of the track of a Medicane like Numa. The main focus of the study, however, is on the added value by the GPM constellation of MW radiometers, that, together with 3D DPR measurements, provides a valuable tool for monitoring and characterizing precipitation features of TLCs, especially during their offshore development, when ground-based observations (rain gauges and radars) cannot be used. The ability of the different MW frequencies to penetrate the cloud at different heights, and their sensitivity to the horizontal and vertical distribution of liquid and frozen hydrometeors within the cloud, allow not only to observe rainbands and eyewall structure, but also to infer the of rainfall formation processes. From the comparison of different MW radiometer overpasses it is possible to identify trends in the TLC and eye development (strengthening or weakening phases), to depict the evolution of precipitation structure and intensity from its development throughout its mature phase, and to localize and characterize the convective activity (as confirmed by ground-based lightning network data). For these reasons, MW radiometers are also a unique tool to verify the ability of cloud resolving models to reproduce the observed structure of the storm. This is particularly effective over the sea where the low frequency channels allow clearly depicting the structure and intensity of the precipitation. RAMS-ISAC high-resolution simulations support what inferred from the observations, evidencing Numa TLC characteristics (closed circulation around a warm core, low vertical wind shear, intense surface winds, heavy precipitation), persisting for more than 24 h. The first attempt to assimilate the DPR observation in RAMS-ISAC has shown an impact, although weak, on the simulated rainfall rates and amounts. This is because the availability of just one DPR overpass throughout the lifetime of the storm does not allow long-lasting impact on the forecast outcomes. Standing the actual scarce availability of space radar observation, a future development of this study will include the assimilation of precipitation retrieved by the GPM constellation of MW radiometers.

Author Contributions: Conceptualization, A.C.M., S.F., and G.P.; methodology, A.C.M., S.F., and G.P.; software A.C.M. and S.F.; ground radar data processing and critical analysis, M.M.; writing—original draft preparation, A.C.M.; writing—review and editing, A.C.M., S.F., and G.P.; G.P., E.A., L.B., D.C., L.P.D., S.D., P.S., and R.C.T. contributed deeply to discussions and corrections and revisions, providing important feedback and suggestions. Remote Sens. 2019, 11, 1690 23 of 26

Funding: This research was carried out in the framework of the project ‘OT4CLIMA’ which was funded by the Italian Ministry of Education, University and Research (D.D. 2261 del 6.9.2018, PON R&I 2014-2020 and FSC). The financial support by the EUMETSAT “Satellite Application Facility on Support to Operational Hydrology and Water Management” (H SAF), by the Italian Research Project of National Interest 2015 (PRIN 2015) 4WX5NA, and by the agreement between CNR-ISAC and the Italian Department of Civil Protection (DPC) is also acknowledged. Acknowledgments: The authors would like to thank NASA PPS for providing the GPM data and products (ftp: //arthurhou.pps.eosdis..gov/) and the Italian Department for Civil Protection for providing the ground-based radar and rain gauge data. ASCAT data are available at https://podaac-tools.jpl.nasa.gov/drive/files/OceanWinds/ ascat/, MODIS images are available at https://worldview.earthdata.nasa.gov. LINET data are provided by Nowcast GmhB (https://www.nowcast.de/) within a scientific agreement with CNR ISAC-Rome. Part of the computational time used for this paper was granted by the ECMWF (European Centre for Medium Weather range Forecast) throughout the special project SPITFEDE. The authors are very grateful to the three reviewers for their comments and suggestions that helped improving the manuscript. Conflicts of Interest: The authors declare no conflicts of interest.

References

1. Giorgi, F. Climate change hot-spots. Geophys. Res. Lett. 2006, 33.[CrossRef] 2. Lagouvardos, K.; Kotroni, V.; Nickovic, S.; Jovic, D.; Kallos, G.; Tremback, C.J. Observations and model simulations of a sub-synoptic vortex over the central Mediterranean. Meteorol. Appl. 1999, 6, 371–383. [CrossRef] 3. Miglietta, M.M.; Laviola, S.; Malvaldi, A.; Conte, D.; Levizzani, V.; Price, C. Analysis of tropical-like cyclones over the Mediterranean Sea through a combined modeling and satellite approach. Geophys. Res. Lett. 2013, 40, 2400–2405. [CrossRef] 4. Miglietta, M.M.; Mastrangelo, D.; Conte, D. Influence of physics parameterization schemes on the simulation of a tropical-like cyclone in the Mediterranean Sea. Atmospheric Res. 2015, 153, 360–375. [CrossRef] 5. Miglietta, M.M.; Rotunno, R. Development mechanisms for Mediterranean tropical-like cyclones (medicanes). Q. J. R. Meteorol. Soc. 2019, 145.[CrossRef] 6. Pytharoulis, I.; Craig, G.; Ballard, S. The hurricane-like Mediterranean cyclone of January 1995. Meteorl. Appl. 2000, 7, 261–279. [CrossRef] 7. Reale, O.; Atlas, R. Like Vortices in the Extratropics: Observational Evidence and Synoptic Analysis. Weather Forecast. 2001, 16, 7–34. [CrossRef] 8. Moscatello, A.; Miglietta, M.M.; Rotunno, R. Numerical Analysis of a Mediterranean “Hurricane” over Southeastern Italy. Mon. Weather Rev. 2008, 136, 4373–4397. [CrossRef] 9. Tous, M.; Romero, R.; Ramis, C. Surface heat fluxes influence on medicane trajectories and intensification. Atmos. Res. 2013, 123, 400–411. [CrossRef] 10. Cioni, G.; Malguzzi, P.; Buzzi, A. Thermal structure and dynamical precursor of a Mediterranean tropical-like cyclone: Thermal Structure and Precursor of a MTLC. Q. J. R. Meteorol. Soc. 2016, 142, 1757–1766. [CrossRef] 11. Miglietta, M.M.; Cerrai, D.; Laviola, S.; Cattani, E.; Levizzani, V. Potential vorticity patterns in Mediterranean “hurricanes. ” Geophys. Res. Lett. 2017, 44, 2537–2545. [CrossRef] 12. Fita, L.; Romero, R.; Luque, A.; Emanuel, K.; Ramis, C. Analysis of the environments of seven Mediterranean tropical-like storms using an axisymmetric, nonhydrostatic, cloud resolving model. Nat. Hazards Earth Syst. Sci. 2007, 7, 41–56. [CrossRef] 13. Davolio, S.; Miglietta, M.M.; Moscatello, A.; Pacifico, F.; Buzzi, A.; Rotunno, R. Numerical forecast and analysis of a tropical-like cyclone in the Ionian Sea. Nat. Hazards Earth Syst. Sci. 2009, 9, 551–562. [CrossRef] 14. Miglietta, M.M.; Moscatello, A.; Conte, D.; Mannarini, G.; Lacorata, G.; Rotunno, R. Numerical analysis of a Mediterranean ‘hurricane’ over south-eastern Italy: Sensitivity experiments to sea surface temperature. Atmos. Res. 2011, 101, 412–426. [CrossRef] 15. Pytharoulis, I. Analysis of a Mediterranean tropical-like cyclone and its sensitivity to the sea surface temperatures. Atmos. Res. 2018, 208, 167–179. [CrossRef] 16. Homar, V.; Romero, R.; Stensrud, D.J.; Ramis, C.; Alonso, S. Numerical diagnosis of a small, quasi-tropical cyclone over the western Mediterranean: Dynamical vs. boundary factors. Q. J. R. Meteorol. Soc. 2003, 129, 1469–1490. [CrossRef] 17. Chaboureau, J.-P.; Pantillon, F.; Lambert, D.; Richard, E.; Claud, C. Tropical transition of a Mediterranean storm by jet crossing. Q. J. R. Meteorol. Soc. 2012, 138, 596–611. [CrossRef] Remote Sens. 2019, 11, 1690 24 of 26

18. Emanuel, K. Genesis and maintenance of “Mediterranean hurricanes. ” Adv. Geosci. 2005, 2, 217–220. [CrossRef] 19. Carrió, D.S.; Homar, V.; Jansa, A.; Romero, R.; Picornell, M.A. Tropicalization process of the 7 November 2014 Mediterranean cyclone: Numerical sensitivity study. Atmos. Res. 2017, 197, 300–312. [CrossRef] 20. Cavicchia, L.; von Storch, H.; Gualdi, S. Mediterranean Tropical-Like Cyclones in Present and Future Climate. J. Clim. 2014, 27, 7493–7501. [CrossRef] 21. Picornell, M.A.; Campins, J.; Jansà, A. Detection and thermal description of medicanes from numerical simulation. Nat. Hazards Earth Syst. Sci. 2014, 14, 1059–1070. [CrossRef] 22. Ragone, F.; Mariotti, M.; Parodi, A.; von Hardenberg, J.; Pasquero, C. A Climatological Study of Western Mediterranean Medicanes in Numerical Simulations with Explicit and Parameterized Convection. Atmosphere 2018, 9, 397. [CrossRef] 23. Cavicchia, L.; von Storch, H.; Gualdi, S. A long-term climatology of medicanes. Clim. Dyn. 2014, 43, 1183–1195. [CrossRef] 24. Hou, A.Y.; Kakar, R.K.; Neeck, S.; Azarbarzin, A.A.; Kummerow, C.D.; Kojima, M.; Oki, R.; Nakamura, K.; Iguchi, T. The Global Precipitation Measurement Mission. Bull. Am. Meteorol. Soc. 2014, 95, 701–722. [CrossRef] 25. Skofronick-Jackson, G.; Petersen, W.A.; Berg, W.; Kidd, C.; Stocker, E.F.; Kirschbaum, D.B.; Kakar, R.; Braun, S.A.; Huffman, G.J.; Iguchi, T.; et al. The Global Precipitation Measurement (GPM) Mission for Science and Society. Bull. Am. Meteorol. Soc. 2017, 98, 1679–1695. [CrossRef] 26. Panegrossi, G.; Casella, D.; Dietrich, S.; Marra, A.C.; Sano, P.; Mugnai, A.; Baldini, L.; Roberto, N.; Adirosi, E.; Cremonini, R.; et al. Use of the GPM Constellation for Monitoring Heavy Precipitation Events Over the Mediterranean Region. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 2733–2753. [CrossRef] 27. Marra, A.C.; Porcù, F.; Baldini, L.; Petracca, M.; Casella, D.; Dietrich, S.; Mugnai, A.; Sanò, P.; Vulpiani, G.; Panegrossi, G. Observational analysis of an exceptionally intense hailstorm over the Mediterranean area: Role of the GPM Core Observatory. Atmos. Res. 2017, 192, 72–90. [CrossRef] 28. Panegrossi, G.; Marra, A.C.; Sanò, P.; Baldini, L.; Casella, D.; Porcù, F. Heavy precipitation systems in the Mediterranean area: The role of GPM. In Satellite Precipitation Measurement; Levizzani, V., Kidd, C., Kirschbaum, D.B., Kummerow, C.D., Nakamura, K., Turk, F.J., Eds.; Springer: Dordrecht, The Netherlands, 2019. 29. Betz, H.D.; Schmidt, K.; Laroche, P.; Blanchet, P.; Oettinger, W.P.; Defer, E.; Dziewit, Z.; Konarski, J. LINET—An international lightning detection network in Europe. Atmos. Res. 2009, 91, 564–573. [CrossRef] 30. Federico, S.; Petracca, M.; Panegrossi, G.; Dietrich, S. Improvement of RAMS precipitation forecast at the short-range through lightning data assimilation. Nat. Hazards Earth Syst. Sci. 2017, 17, 61–76. [CrossRef] 31. NASA/JAXA GPM products. Available online: ftp://arthurhou.pps.eosdis.nasa.gov/ (accessed on 15 July 2019). 32. Kummerow, C.D.; Randel, D.L.; Kulie, M.; Wang, N.-Y.; Ferraro, R.; Joseph Munchak, S.; Petkovic, V. The Evolution of the Goddard Profiling Algorithm to a Fully Parametric Scheme. J. Atmos. Ocean. Technol. 2015, 32, 2265–2280. [CrossRef] 33. Iguchi, T.; Seto, S.; Meneghini, R.; Yoshida, N.; Awaka, J.; Chandrasekar, V.; Kubota, T. GPM/DPR Level-2. Algorithm Theoretical Basis Document (ATBD). 2017. Available online: https://pps.gsfc.nasa.gov/Documents/ ATBD_DPR_201708_whole_1.pdf (accessed on 15 July 2019). 34. MODIS Corrected Reflectance (true color) images. Available online: https://worldview.earthdata.nasa.gov (accessed on 15 July 2019). 35. ASCAT Optimized Coastal Ocean wind product. Available online: https://podaac-tools.jpl.nasa.gov/drive/ files/OceanWinds/ascat/preview/L2/ (accessed on 15 July 2019). 36. Betz, H.-D.; Schmidt, K.; Oettinger, P.; Wirz, M. Lightning detection with 3-D discrimination of intracloud and cloud-to-ground discharges. Geophys. Res. Lett. 2004, 31, L11108. [CrossRef] 37. Höller, H.; Betz, H.-D.; Schmidt, K.; Calheiros, R.V.; May, P.; Houngninou, E.; Scialom, G. Lightning characteristics observed by a VLF/LF lightning detection network (LINET) in Brazil, , Africa and Germany. Atmos. Chem. Phys. 2009, 9, 7795–7824. [CrossRef] 38. Regional Atmospheric Modeling System (RAMS). Available online: www.atmet.com (accessed on 15 July 2019). Remote Sens. 2019, 11, 1690 25 of 26

39. Federico, S. Implementation of a 3D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results. Atmos. Meas. Tech. 2013, 6, 3563–3576. [CrossRef] 40. Federico, S. Implementation of the WSM5 and WSM6 Single Moment Microphysics Scheme into the RAMS Model: Verification for the HyMeX-SOP1. Adv. Meteorol. 2016, 2016, 1–17. [CrossRef] 41. Dahl, J.M.L.; Höller, H.; Schumann, U. Modeling the Flash Rate of . Part II: Implementation. Mon. Weather Rev. 2011, 139, 3112–3124. [CrossRef] 42. Federico, S.; Avolio, E.; Petracca, M.; Panegrossi, G.; Sanò, P.; Casella, D.; Dietrich, S. Simulating lightning into the RAMS model: implementation and preliminary results. Nat. Hazards Earth Syst. Sci. 2014, 14, 2933–2950. [CrossRef] 43. Hong, S.-Y.J.; Lim, J.-O. The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteor. Soc. 2006, 42, 129–151. 44. Walko, R.L.; Band, L.E.; Baron, J.; Kittel, T.G.F.; Lammers, R.; Lee, T.J.; Ojima, D.; Pielke, R.A.; Taylor, C.; Tague, C.; et al. Coupled Atmosphere–Biophysics–Hydrology Models for Environmental Modeling. J. Appl. Meteorol. 2000, 39, 931–944. [CrossRef] 45. Smagorinsky, J. General circulation experiments with the primitive equations. Mon. Weather Rev. 1963, 91, 99–164. [CrossRef] 46. Mellor, G.L.; Yamada, T. Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. 1982, 20, 851. [CrossRef] 47. Chen, C.; Cotton, W.R. A one-dimensional simulation of the stratocumulus-capped mixed layer. Bound. Layer Meteorol. 1983, 25, 289–321. [CrossRef] 48. EUMETSAT Numa webpage. Available online: https://www.eumetsat.int/website/home/Images/ ImageLibrary/DAT_3746043.html (accessed on 15 July 2019). 49. Panegrossi, G.; Dietrich, S.; Marzano, F.S.; Mugnai, A.; Smith, E.A.; Xiang, X.; Tripoli, G.J.; Wang, P.K.; Poiares Baptista, J.P.V. Use of Cloud Model Microphysics for Passive Microwave-Based Precipitation Retrieval: Significance of Consistency between Model and Measurement Manifolds. J. Atmos. Sci. 1998, 55, 1644–1673. [CrossRef] 50. Petty, G.W. Physical retrievals of over-ocean rain rate from multichannel microwave imagery. Part I: Theoretical characteristics of normalized polarization and scattering indices. Meteorol. Atmos. Phys. 1994, 54, 79–99. [CrossRef] 51. Cecil, D.J.; Goodman, S.J.; Boccippio, D.J.; Zipser, E.J.; Nesbitt, S.W. Three Years of TRMM Precipitation Features. Part I: Radar, Radiometric, and Lightning Characteristics. Mon. Weather Rev. 2005, 133, 543–566. [CrossRef] 52. Liu, C.; Zipser, E.J. Global distribution of convection penetrating the tropical tropopause. J. Geophys. Res. 2005, 110.[CrossRef] 53. Zipser, E.J.; Cecil, D.J.; Liu, C.; Nesbitt, S.W.; Yorty, D.P. Where are the most intense thunderstorms on Earth? Bull. Am. Meteorol. Soc. 2006, 87, 1057–1072. [CrossRef] 54. Charnock, H. Wind stress on a water surface. Q. J. R. Meteorol. Soc. 1955, 81, 639–640. [CrossRef] 55. Protezione Civile Puglia. Centro Funzionale Decentrato. Annali Idrologici – Parte I – Dati storici aggiornati al 2013. Available online: http://www.protezionecivile.puglia.it/centro-funzionale/analisielaborazione-dati/ annali-idrologici-parte-i-dati-storici-2 (accessed on 15 July 2019). 56. Emanuel, K.A. An Air-Sea Interaction Theory for Tropical Cyclones. Part I: Steady-State Maintenance. J. Atmos. Sci. 1986, 43, 585–605. [CrossRef] 57. Rotunno, R.; Emanuel, K.A. An Air–Sea Interaction Theory for Tropical Cyclones. Part II: Evolutionary Study Using a Nonhydrostatic Axisymmetric Numerical Model. J. Atmos. Sci. 1987, 44, 542–561. [CrossRef] 58. Federico, S.; Torcasio, R.C.; Avolio, E.; Caumont, O.; Montopoli, M.; Baldini, L.; Vulpiani, G.; Dietrich, S. The impact of lightning and radar data assimilation on the performance of very short term rainfall forecast for two case studies in Italy. Nat. Hazards Earth Syst. Sci. Discuss. 2018, 1–45. [CrossRef] 59. Caumont, O.; Ducrocq, V.; Wattrelot, É.; Jaubert, G.; Pradier-Vabre, S. 1D+3DVar assimilation of radar reflectivity data: a proof of concept. Tellus Dyn. Meteorol. Oceanogr. 2010, 62, 173–187. [CrossRef] 60. Hu, M.; Xue, M.; Brewster, K. 3DVAR and Cloud Analysis with WSR-88D Level-II Data for the Prediction of the Fort Worth, Texas, Tornadic Thunderstorms. Part I: Cloud Analysis and Its Impact. Mon. Weather Rev. 2006, 134, 675–698. [CrossRef] Remote Sens. 2019, 11, 1690 26 of 26

61. Jones, T.A.; Otkin, J.A.; Stensrud, D.J.; Knopfmeier, K. Forecast Evaluation of an Observing System Simulation Experiment Assimilating Both Radar and Satellite Data. Mon. Weather Rev. 2014, 142, 107–124. [CrossRef] 62. Cecil, D.J.; Chronis, T. Polarization-Corrected Temperatures for 10-, 19-, 37-, and 89-GHz Passive Microwave Frequencies. J. Appl. Meteorol. Climatol. 2018, 57, 2249–2265. [CrossRef] 63. Black, R.A. Extra large particle images at 12 km in a hurricane eyewall: Evidence of high-altitude supercooled water? Geophys. Res. Lett. 2003, 30.[CrossRef] 64. Black, R.A.; Hallett, J. Observations of the Distribution of Ice in Hurricanes. J. Atmos. Sci. 1986, 43, 802–822. [CrossRef] 65. Heymsfield, G.M.; Halverson, J.B.; Simpson, J.; Tian, L.; Bui, T.P. ER-2 Doppler Radar Investigations of the Eyewall of during the Convection and Moisture Experiment-3. J. Appl. Meteorol. 2001, 40, 1310–1330. [CrossRef]

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