A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection During 2018 January MJO

remote sensing

Article A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO

Xuanli Li 1,* , John R. Mecikalski 1 and Timothy J. Lang 2 1 University of Alabama in Huntsville, Huntsville, AL 35805, USA; [email protected] 2 NASA Marshall Space Flight Center, Huntsville, AL 35812, USA; [email protected] * Correspondence: [email protected]

Received: 27 February 2020; Accepted: 10 April 2020; Published: 14 April 2020

Abstract: The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016. CYGNSS provides ocean surface wind speed retrieval along specular reflection tracks at an interval resolution of approximately 25 km. With a median revisit time of 2.8 h covering a 35 latitude, CYGNSS can provide more frequent ± ◦ and accurate measurements of surface wind over the tropical oceans under heavy precipitation, especially within tropical cyclone cores and deep convection regions, than traditional scatterometers. In this study, CYGNSS v2.1 Level 2 wind speed data were assimilated into the Weather Research and Forecasting (WRF) model using the WRF Data Assimilation (WRFDA) system with hybrid 3- and 4-dimensional variational ensemble technology. Case studies were conducted to examine the impact of the CYGNSS data on forecasts of tropical cyclone (TC) Irving and a westerly wind burst (WWB) during the Madden–Julian oscillation (MJO) event over the Indian Ocean in early January 2018. The results indicate a positive impact of the CYGNSS data on the wind field. However, the impact from the CYGNSS data decreases rapidly within 4 h after data assimilation. Also, the influence of CYGNSS data only on precipitation forecast is found to be limited. The assimilation of CYGNSS data was further explored with an additional experiment in which CYGNSS data was combined with Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) hourly precipitation and Advanced Scatterometer (ASCAT) wind vector and were assimilated into the WRF model. A significant positive impact was found on the tropical cyclone intensity and track forecasts. The short-term forecast of wind and precipitation fields were also improved for both TC Irving and the WWB event when the combined satellite data was assimilated.

Keywords: CYGNSS; data assimilation; tropical cyclone; tropical convection; MJO

1. Introduction Owing to better global analyses and advances in numerical models and physics parameterizations, tropical cyclone (TC) forecasts have improved steadily in the past two decades (See, e.g., [1,2]). Despite the substantial amount of effort, however, accurate predictions of the structure and intensity of TCs and tropical convections remain challenging. The structure and intensity of TCs are not only influenced by the large-scale environment; the air–sea exchange also plays a vital role. The variations in structure and intensity also depend largely on the meso- and micro- scale in-storm dynamic, physics, and radiative processes, as well as the interaction of environmental flow and storm-scale processes [3,4]. An accurate description of the atmospheric state and the environment is essential for simulations of tropical cyclones and tropical convections. Since tropical cyclones and maritime tropical convection spend most of their lifetime over oceans where conventional data and aircraft measurements are limited in space and time, the need for a more

Remote Sens. 2020, 12, 1243; doi:10.3390/rs12081243 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 1243 2 of 20 accurate representation of storms and their environment motivates the application of satellite-retrieved products. Satellite wind products, especially the surface wind from scatterometers, can provide information about the location and intensity of tropical cyclones and tropical convections. However, these observation systems [e.g., Quick Scatterometer—QuikSCAT, Advanced Scatterometer—ASCAT, and OceanSat-2 Scatterometer—OSCAT] suffer from severe limitations under extreme conditions (e.g., 1 heavy precipitation, strong winds > 35 m s− , etc.), and long revisit times (~12+ h) also limit their usefulness in detecting rapid changes in intensity and location [5,6]. To obtain ocean surface wind data with high-spatial and temporal resolutions, and in adverse surface weather conditions of thick clouds and precipitation, the Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016 [7]. The CYGNSS constellation is composed of eight bistatic microsatellites in a slightly elliptical orbit plane, at a 35◦ inclination angle, and between roughly 510 and 540 km altitude. As low Earth orbiting (LEO) satellites, CYGNSS provides a relatively frequent revisit time, with a median value of 2.8 h and a mean of 7.2 h [7,8]. Each CYGNSS microsatellite is equipped with a Delay Doppler Mapping Instrument (DDMI) that consists of a multichannel Global Navigation Satellite System reflectometry (GNSS-R) receiver, a low-gain zenith antenna to receive the direct global positioning system (GPS) signals, and two high-gain downward pointing antennas to receive the scattered GPS signals from the ocean surface [9,10]. CYGNSS measures the pattern and intensity of the scattered and reflected GPS signals at specular reflection points within the 35 latitude coverage ± ◦ zone. CYGNSS uses the GPS “L1” channel, which suffers no appreciable attenuation, even under heavy rainfall conditions. This allows CYGNSS to retrieve ocean surface winds for a variety of tropical phenomena, e.g., the tropical cyclone core region and deep convection associated with the Madden–Julian Oscillation (MJO; [11,12]), with a level of accuracy that was previously impossible in practice. Each CYGNSS observatory is capable of sampling 4 different specular reflection tracks at a frequency of 1 Hz, and ocean surface wind speed is retrieved along the specular tracks at a resolution of approximately 25 km. It is expected that the CYGNSS will be able to accurately monitor the rapidly changing wind of precipitating regions over the tropics (e.g., [13]), and to improve forecasting operations (e.g., [14]). Numerical models commonly struggle in predicting the accurate intensity of TCs. It is especially challenging to capture the rapid intensity change of TCs during the intensification or weakening stages [15]. Another great challenge in forecasts of TCs and tropical convections is the wind speed distribution, especially over large variability regions [16,17]. Many studies have shown that the assimilation of wind observations is an effective method to improve the initial condition of numerical weather prediction (NWP) models, and hence, to improve intensity and wind predictions of TCs and tropical convection. A number of studies (e.g., [18–23]) have shown that the assimilation of in situ wind data from dropwindsondes and flight level airborne surveillance provided significant improvements in TC track and intensity forecasts. The assimilation of satellite-derived motion vector winds has improved forecast skills of global and regional NWP models at operation centers, such as the National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), the United Kingdom Met Office, and the Korea Meteorological Agency [24–27]. Ocean surface winds, including retrievals from QuikSCAT, ASCAT, and OSCAT, are especially useful for improving TC position, intensity, and structure by providing additional measurements of enhancements of the air-sea exchange and the cyclonic circulation [28–34]. Prior to the use of real CYGNSS data, a number of previous studies evaluated the assimilation of the CYGNSS data derived from an end-to-end simulator into observing system simulation experiments (OSSEs). Their results suggested that the simulated CYGNSS data would positively impact the forecasts of TCs in both track and intensity [35–39]. The assimilation of real CYGNSS observation was recently investigated by Cui et al. [14]; the authors indicated the potential of CYGNSS data in improving the track, intensity, and structure of TCs. Unfortunately, due to data quality issues, Cui et al. [14] were only able to assimilate CYGNSS v2.0 fully developed seas (FDS) data, which greatly underestimates the wind field within deep convection regions related to TCs. A recent study (Ruf et al. [40]) indicated that, with a Remote Sens. 2020, 12, 1243 3 of 20 number of improvements to the data calibration, v2.1 is of much better quality than the previous version; this is especially true for the young seas/limited fetch (YSLF) product. Ruf et al. [41] calculated the root-mean-square (RMS) difference between CYGNSS v2.0 YSLF data and Stepped Frequency Microwave Radiometers (SFMRs) data onboard the NOAA P-3 aircraft in and near hurricanes. For wind 1 1 retrievals above 20 m s− , the RMS was 5.01 m s− when the uncertainty of the SFMR measurements was removed. When CYGNSS v2.1 YSLF data were evaluated, Ruf et al. [40] indicated that this RMS 1 value had been reduced to 3.2 m s− . In the current study, we examined the assimilation of both FDS and YSLF v2.1 wind speed products. Through the present study, our objectives are: (1) to evaluate the impact and reveal the challenge of utilizing the CYGNSS ocean surface wind speed data in a numerical model, and (2) to explore how to better utilize the CYGNSS data and improve short-term forecast skill.

2. Data and Methods

2.1. Data The main datasets used in this study include the Level 2 CYGNSS version 2.1 data, Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) data, and Level 2 ASCAT data. The CYGNSS Level 2 data is obtainable from the Physical Oceanography Distributed Active Archive Center (PODAAC) (https://podaac-tools.jpl.nasa.gov/ drive/ﬁles/OceanWinds/cygnss/L2/v2.1). The GPM IMERG is a Level 3 gridded product for global rainfall estimates. Its spatial resolution is 0.1◦ and temporal resolution is 0.5 h. The IMERG product incorporates precipitation estimates from GPM and partner satellite microwave and infrared (IR) sensors. Rain gauge data was used in calibration of IMERG [42]. GPM IMERG rainfall data is available at https://pmm.nasa.gov/data-access/downloads/gpm. ASCAT is one of several instruments on the Meteorological Operational (MetOp) polar-orbiting satellite. It is an active microwave sensor designed to provide surface wind observations over the global oceans. The ASCAT ocean surface wind vector data has a resolution of 25 km and is available twice a day over the model domain. ASCAT data can be accessed from the PODAAC (https://podaac-tools.jpl.nasa.gov/drive/ﬁles/OceanWinds/ascat).

2.2. The 2018 January MJO and CYGNSS Observation The MJO is the dominant mode of intraseasonal variability (30–90 days), characterized by eastward 1 propagation of enhanced deep convection at an average speed of 5 m s− during boreal winter [43]. The MJO convective signal typically initiates in the Indian Ocean and propagates to (and often across) the Maritime Continent to the western Pacific. The Real-time Multivariate MJO (RMM) index [44] is a commonly used index in MJO studies and operation forecasts as a measure of the phase and intensity of an MJO event. Figure1 illustrates the RMM index provided by the Australian Bureau of Meteorology (data available at http://www.bom.gov.au/climate/mjo/graphics/rmm.74toRealtime. txt) from late December 2017 to early February 2018. RMM1 and RMM2 represent the first and second principal components of the combined empirical orthogonal functions (EOFs) of outgoing longwave radiation (OLR), as well as zonal wind at 200 and 850 hPa averaged between 15◦N and 15◦S. Points outside the center circle indicate a significant MJO event; the farther from the origin, the stronger the MJO amplitude. The phase triangles indicate the primary location of Indian Ocean, Maritime Continent, western Pacific, or western hemisphere and Africa. The 2018 MJO was one of the strongest events in the last few decades [45]. Barrett [45] showed that the MJO progressed eastward in January from the Indian Ocean to the Pacific Ocean in February, and then back to the Indian Ocean in March. The apparent counterclockwise motion shown in Figure1a indicates the eastward propagation of the event. As in Figure1a, the MJO was in phases 2 and 3 over the Indian Ocean from January 1 to 16, with maximum RMM of 2.1. The MJO intensified as it passed through Maritime Continent (Phases 4 and 5) from January 17 to 26. The MJO was strong in the western Pacific in phases 6 and 7 from January 27 to February 4, with a peak RMM close to 4. Hoover et al. [46] found that synthetic CYGNSS observations were able to measure outflow from convective storms in simulated MJO events Remote Sens. 2020,, 12,, 1243x FOR PEER REVIEW 44 of of 21 20 were able to measure outflow from convective storms in simulated MJO events from October and fromDecember October 2011. and Figure December 1b shows 2011. the Figure CYGNSS1b shows surf theace CYGNSSwind speed surface averaged wind over speed the averaged domain over70°– the80°E domain and 0°–10°S 70◦–80 from◦E and 1 to 0 31◦–10 January◦S from 2018. 1 to This 31 January is within 2018. the Thiscore isinitiation within theregion core of initiation the MJO region in the 1 ofIndian the MJOOcean. in theThe Indianaverage Ocean. CYGNSS The wind average speed CYGNSS showed wind a peak speed of 8 showedm s-1 on 7–8 a peak January, of 8 mwhich s− on is 7–8in agreement January, which with the is in maximum agreement RMM with thevalue maximum at the sa RMMme time value in atphases the same 2 and time 3. A in trough phases in 2 andthe 1 3.average A trough CYGNSS in the wind average speed CYGNSS with a magnitude wind speed lower with than a magnitude 4 m s-1 occurred lower thanin 12–16 4 m January. s− occurred This inis 12–16also in January. agreement This with is also the in low agreement RMM index with in the 10–15 low RMMJanuary index shown in 10–15 in Figure January 1a. shownAnother in Figureperiod1 ofa. 1 Anotherlarge CYGNSS period wind of large over CYGNSS 7 m s-1 wind occurred over 7during m s− 24–29occurred January during 2018 24–29, which January also 2018, coincides which with also coincidesstrong convection with strong and increasing convection RMM and increasing values during RMM that values time period during as that the timeMJO periodsignal transitioned as the MJO signalto the transitionedMaritime Continent. to the Maritime This indicates Continent. that, This in general, indicates CYGNSS that, in general, data were CYGNSS able to data detect were strong able towinds detect related strong to winds this MJO related event. to this However, MJO event. it is However,noteworthy itis that noteworthy the average that CYGNSS the average wind CYGNSS speed windcalculated speed here calculated is sensitive here to is sensitiveWWBs during to WWBs the MJO, during and the also MJO, to andany alsoweather to any system weather enhancing system enhancingwind speeds wind within speeds the domain; within the thus domain; this metric thus should this metric not be should viewed not as be a viewed definitive as ameasure definitive of measureMJO strength. of MJO The strength. late January The late enhancement January enhancement in CYGNSS in wind CYGNSS speed wind appears speed to appears be similar to beto similarthat of toearly that January, of early January,with an withenhanced an enhanced near-Equator near-Equator westerly westerly flow associated flow associated with withthe presence the presence of TC of TCCebile. Cebile.

Figure 1. (a) RMM index phase diagram from 27 December 2017 to 4 February 2018. Different color linesFigure represent 1. (a) RMM the di indexfferent phase months diagram (red for from December, 27 December green for2017 January, to 4 February and blue 2018. for February); Different datescolor arelines annotated represent to the the different left of the months values. (red Phases for Dece are labelledmber, green on the for diagram. January, Pointsand blue outside for February); the circle indicatedates are a annotated significant to MJO the event, left of while the values. phase trianglesPhases are indicate labelled its primaryon the diagram. location. Points (b) CYGNSS outside daily the circle indicate a significant MJO event, while phase triangles indicate its primary location. (b) wind speed averaged within 70◦–80◦E, 10◦S–0◦. CYGNSS daily wind speed averaged within 70°–80°E, 10°S–0°. Previous studies found that CYGNSS measurements during TC overpasses can help in finding the correctPrevious center studies location, found estimating that CYG theNSS intensity, measurements and constructing during TC the ov winderpasses distribution can help withinin finding the TCthe [correct40,47,48 center]. Hoover location, et al. estimating [46] also the showed intensity, that syntheticand constructing CYGNSS the data wind may distribution be usedto within monitor the westerlyTC [40,47,48]. wind Hoover burst (WWB) et al. [46] events also during showed MJO that events. synthetic In this CYGNSS study, wedata will may examine be used the to CYGNSS monitor datawesterly for TCwind Irving burst and (WWB) a WWB events event during during MJO the even 2018ts. January In this MJO,study, and we evaluatewill examine the impactthe CYGNSS of the CYGNSSdata for TC data Irving on the and short-term a WWB event numerical during prediction the 2018 ofJanuary these events. MJO, and evaluate the impact of the CYGNSSTC Irving data on initiated the short-term from a low-pressure numerical prediction system in of the these southern events. Indian Ocean at ~1800 UTC on 4 JanuaryTC Irving 2018 initiatedand developed from a into low-pressure a tropical stormsystem after in the 18 h.southern It continued Indian to Ocean strengthen at ~1800 and UTC became on 4 a tropicalJanuary cyclone2018 and by developed 0600 UTC into on 7 a January, tropical with storm the afte centerr 18 h. minimum It continued sea level to strengthen pressure (MSLP)and became falling a 1 totropical 981 hPa cyclone and the by maximum 0600 UTC surface on 7 January, winds reaching with the 36 mcenter s− . TCminimum Irving gained sea level further pressure intensity (MSLP) as it movedfalling to southwestward, 981 hPa and the and maximum reached peak surface intensity winds with reaching a MSLP 36 of m 962 s-1. hPa TC andIrving maximum gained surfacefurther 1 windintensity of49 as m it s−movedat 0000 southwestward, UTC on 8 January. and reached Thereafter, peak it startedintensity to with weaken a MSLP before of it dissipated962 hPa and at aroundmaximum 0600 surface UTC onwind 11 Januaryof 49 m 2018.s-1 at 0000 In this UTC study, on 8 we January. focused Thereafter, on the intensification it started to fromweaken 0000 before UTC onit dissipated 6–8 January, at around and the 0600 weakening UTC on afterwards 11 January until 2018. 0000 In this UTC study, on 9 Januarywe focused 2018. on the intensification fromIn 0000 this UTC study, on a 6–8 few January, quality control and the steps weakenin were appliedg afterwards to the until Level 0000 2 CYGNSS UTC on data 9 January v2.1. Data 2018. with the rangeIn this corrected study, a gain few below quality 3 and control the zenith steps sunwere angle applied lower to than the zeroLevel were 2 CYGNSS removed data to filter v2.1. out Data the with the range corrected gain below 3 and the zenith sun angle lower than zero were removed to

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filter out the unreliable observations. Also, the uncertainties in the Block II-F GPS transmitter power unreliable observations. Also, the uncertainties in the Block II-F GPS transmitter power and antenna and antenna gain patterns can significantly degrade the quality of wind speed products [49]. gain patterns can significantly degrade the quality of wind speed products [49]. Therefore, all Level 2 Therefore, all Level 2 retrievals from Block II-F GPS satellites were excluded from the Level 2 product. retrievals from Block II-F GPS satellites were excluded from the Level 2 product. Further quality control Further quality control (e.g., gross check, vertical consistency, dry convective adjustment, etc.) was (e.g., gross check, vertical consistency, dry convective adjustment, etc.) was conducted during WRFDA conducted during WRFDA data process before the CYGNSS wind speed observation was data process before the CYGNSS wind speed observation was assimilated. Two different wind speed assimilated. Two different wind speed products retrieved by different geophysical model functions products retrieved by different geophysical model functions (GMFs) are provided in v2.1, i.e., the FDS (GMFs) are provided in v2.1, i.e., the FDS and the YSLF. The FDS is appropriate for most conditions, and the YSLF. The FDS is appropriate for most conditions, while the YSLF data can be used typically while the YSLF data can be used typically for the inner core of TCs. YSLF GMF was developed for for the inner core of TCs. YSLF GMF was developed for conditions when the long-wave portion of the conditions when the long-wave portion of the sea state has not responded fully to the local surface sea state has not responded fully to the local surface winds [40,41]. Figure2 displays the sample data of winds [40,41]. Figure 2 displays the sample data of the CYGNSS v2.1 YSLF (Figure 2a) product the CYGNSS v2.1 YSLF (Figure2a) product around 1500 UTC, and FDS product over-plotted on GPM around 1500 UTC, and FDS product over-plotted on GPM IMERG rainfall (Figure 2b) at around 1500 IMERG rainfall (Figure2b) at around 1500 UTC, and the ASCAT wind speed (Figure2c) at 1448 UTC UTC, and the ASCAT wind speed (Figure 2c) at 1448 UTC on 7 January 2018. In Figure 2, CYGNSS on 7 January 2018. In Figure2, CYGNSS detected TC Irving with a maximum wind of 24 m s 1 in the detected TC Irving with a maximum wind of 24 m s-1 in the FDS product and 44 m s-1 in− the YSLF FDS product and 44 m s 1 in the YSLF product over the heavily-precipitating eyewall region. ASCAT product over the heavily-precipitating− eyewall region. ASCAT views could not penetrate the core views could not penetrate the core region of TC Irving, but a maximum wind speed of 26 m s 1 was region of TC Irving, but a maximum wind speed of 26 m s-1 was observed in the surrounding− area. observed in the surrounding area. For the WWB event, IMERG data indicates that convection was For the WWB event, IMERG data indicates that convection was observed with a maximum observed with a maximum precipitation rate of around 10 mm h 1. ASCAT observed a portion of the precipitation rate of around 10 mm h-1. ASCAT observed a portion− of the WWB event with maximum WWB event with maximum wind speed of 14 m s 1. Strong winds related to this WWB event were wind speed of 14 m s-1. Strong winds related to− this WWB event were detected by CYGNSS with a detected by CYGNSS with a maximum wind speed of 17 m s 1 in the FDS product. maximum wind speed of 17 m s-1 in the FDS product. −

Figure 2. (a) WRF model 9-km and 3-km domains (d01 and d02) and CYGNSS v2.1 YSLF data within 1.5 hFigure from 15002. (a) UTCWRF onmodel 7 January 9-km and 2018. 3-km The domains black circle (d01 shows and d02) the and high CYGNSS winds in v2.1 CYGNSS YSLF data related within to the1.5 WWB h from and 1500 the UTC red circleon 7 January shows high2018. winds The black related circ tole TCshows Irving. the high (b) CYGNSS winds in v2.1 CYGNSS FDS data related in to 1400–1500the WWB UTC and over-plotted the red circle on IMERG shows 1-hhigh rainfall winds atrelated 1400 UTC to TC on Irving. 7 January (b) 2018.CYGNSS The v2.1 magenta FDS boxdata in shows1400–1500 the approximate UTC over-plotted location on of theIMERG WWB 1-h event. rainfall (c) at ASCAT 1400 UTC wind on speed 7 January at around 2018. The 1448 magenta UTC on box 7 Januaryshows 2018.the approximate location of the WWB event. (c) ASCAT wind speed at around 1448 UTC on 7 January 2018.

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2.3. Data Assimilation Experiments The advanced research Weather Research and Forecasting (WRF-ARW; [50]) model and the WRF Data Assimilation (WRFDA) system ([51]) were used in this study. The WRF model and WRFDA system are open-source and can be downloaded from https://www2.mmm.ucar.edu/wrf/users/downloads.html. This study used a double nested domain with resolutions of 9- and 3-km to simulate TC Irving and the WWB event (Figure2a). The following model physics schemes were used: the rapid radiative transfer model (RRTM; [52]) long-wave radiation, Dudhia shortwave radiation [53], Betts-Miller-Janjic cumulus parameterization [54], Mellor-Yamada_Janjic (MYJ) planetary boundary layer (PBL) schemes [54], Morrison 2-moment microphysical scheme [55], and the Unified Noah land-surface model [56]. The cumulus parameterization was only used for the outer domain. The WRFDA system was used to conduct data assimilation with the hybrid ensemble variational technique. WRFDA is a unified community data assimilation system that has 3-dimensional variational (3DVAR) and 4-dimensional ensemble variational (4DVAR) capabilities, a hybrid variational ensemble algorithm (EnVar) and ensemble transform Kalman Filter technique. Being built in model-space, WRFDA is a flexible, state-of-the-art atmospheric data assimilation system that is portable and efficient for various platforms and for multiple models. WRFDA is suitable for the assimilation of a wide variety of observations, including conventional surface and upper-air soundings, ship, buoy, aircraft, satellite retrieved products, satellite radiance, radar, surface precipitation, etc. In this research, data assimilation is conducted with “cv option 5” which defines streamfunction, unbalanced velocity potential, unbalanced temperature, pseudo relative humidity, and unbalanced surface pressure as the control variables. The wind observations were assimilated using the wind speed and direction assimilation package based on Huang et al. [57]. Four different experiments were conducted. Table1 lists the observation data assimilated in each experiment and the data assimilation analysis times. All experiments began at 0000 UTC on 6 January and ended at 0000 UTC on 9 January 2018. The control experiment (CTRL) was a WRF regional simulation. The initial and boundary conditions were interpolated from the 0.25◦ resolution global NCEP Final Analysis (FNL). The impact of the CYGNSS data was firstly examined with two experiments—DA_cyg_fd and DA_cyg_lf, in which the 3-dimensional ensemble variational (3DEnVar) technique was adopted in the assimilation of only CYGNSS FDS and YSLF data, respectively. Ensemble WRF forecasts with 12 members were generated with randomly perturbed initial conditions created using the ensemble transform Kalman filter (ETKF) technique [58]. The ensemble forecast was used to produce a flow-dependent background error matrix. Cycled assimilation of the CYGNSS data was conducted at 0000, 1200, 1500, and 2100 UTC from 6 to 8 January, based on the data availability. There are more CYGNSS data available for TC Irving and the WWB event at 0000 and 1500 UTC 1 1 than at other times. The observational error was set as 2 m s− for winds below 20 m s− and 10% 1 for winds above 20 m s− based on the general guidance from Ruf et al. [40]. If there were multiple observations within a model grid box, a distance-weighted average of all observations was calculated and assimilated. The last experiment (DA_com) assimilated the combined CYGNSS wind speed, IMERG rainrate, and ASCAT vector wind data using the 4-dimensional ensemble variational (4DEnVar) technique. The 4DEnVar also used 12-member ensemble WRF forecasts to generate the flow dependent background error matrix. In this experiment, the CYGNSS data include YSLF data within 800 km from the center of TC Irving and FDS data elsewhere. Figure3 illustrates the time windows of the 4DEnVAR process in DA_com. Remote Sens. 2020, 12, 1243 7 of 20

Table 1. Numerical Experiments setup. Remote Sens. 2020, 12, x FOR PEER REVIEW 7 of 21 Experiment Data Data Assimilation Analysis Times 6–8 January 2018 CTRL N/AN/A 0000, 0230, 1100, 1500, 2000, and 2300 UTC on 6 DA_cyg_fd CombinedCYGNSS IMERG FDS wind rainfall, speed 0000, 1200, 1500, and 2100 UTC on 6–8 January 2018 January; 0300, 1100, 1530, 2000, and 2300 UTC on 7 DA_com ASCAT vector wind, DA_cyg_lf CYGNSS YSLF wind speed 0000, 1200, 1500, and 2100January; UTC on 6–8 January 2018 and CYGNSS wind speed Combined IMERG rainfall, 0000,0300, 0230, 1100, 1100, 1500, 1500, and 2000, 2000 and UTC 2300 on UTC 8 January on 6 January; 2018 DA_com ASCAT vector wind, and CYGNSS 0300, 1100, 1530, 2000, and 2300 UTC on 7 January; 0300, wind speed 1100, 1500, and 2000 UTC on 8 January 2018

Figure 3. Data assimilation process for the experiment DA_com. Orange boxes show the time windows Figure 3. Data assimilation process for the experiment DA_com. Orange boxes show the time for CYGNSS data assimilation, and the blue boxes show the time windows when the ASCAT and windows for CYGNSS data assimilation, and the blue boxes show the time windows when the IMERG data were assimilated. In the time window near 1500 UTC of each day, all three types of data ASCAT and IMERG data were assimilated. In the time window near 1500 UTC of each day, all three were assimilated. types of data were assimilated. 3. Results 3. Results 3.1. CYGNSS Data Assimilation 3.1. CYGNSS Data Assimilation To examine the impact of the CYGNSS wind speed data, the analysis fields and short-term forecasts fromTo experiments examine the with impact and without of the theCYGNSS assimilation wind ofspee CYGNSSd data, datathe analysis are compared fields andand analyzed short-term in thisforecasts section. from Figure experiments4 plots the with CYGNSS and without wind, background the assimilation wind of field, CYGNSS and data data assimilation are compared analysis and incrementanalyzed in (‘analysis this section. – background’, Figure 4 plots or the the di ffCYGNSSerence between wind, databackground assimilation wind analysis field, and and data the background)assimilation analysis in the surface increment wind (‘analysis field at 1500– backgrou UTC onnd’, 6 or January the difference 2018. At be thistween time, data CYGNSS assimilation FDS 1 (Figureanalysis4a) and observed the background) a maximum in wind the surface speed ofwind 18 m field s − , at with 1500 most UTC of on the 6 observed January wind2018. beingAt this slower time, 1 -1 1 thanCYGNSS 15 m FDS s− , while(Figure CYGNSS 4a) observed YSLF a (Figure maximum4b) detected wind speed wind of as 18 strong m s ,as with 47 mmost s − ofnear theTC observed Irving -1 1 - andwind many being points slower with than wind 15 m speeds , while above CYGNSS 15 m sYSLF− . In (Figure the background 4b) detected field wind (Figure as strong4e), an as area 47 m of s 1 1 -1 wind nearabove TC Irving 15 m and s− manywas predicted points with over wind the eyewallspeed above region 15 of m TC s . IrvingIn the withbackground the maximum field (Figure wind 1 -1 speed4e), an of area 20 m of s −wind. Therefore, above 15 the m wind s was speed predicted observed over by the CYGNSS eyewall FDS region is generally of TC Irving lower with than thethe background.maximum wind When speed the FDSof 20 datam s-1 were. Therefore, assimilated, the wind the analysis speed obse incrementsrved by wereCYGNSS mostly FDS negative is generally near TClower Irving than (Figure the background.4c). With the When assimilation the FDS of highdata winds were fromassimilated, CYGNSS the YSLF anal data,ysis theincrements surface windwere 1 1 increasedmostly negative by up near to 6 mTC s Irving− near (Figure TC Irving 4c). With and 2the m assimilation s− for the WWB of high event winds (Figure from4 CYGNSSd). However, YSLF thedata, limitation the surface of assimilatingwind increased the by CYGNSS up to 6 m data s-1 near was alsoTC Irving apparent. and 2 Due m s-1 to for the the track-based WWB event nature (Figure of CYGNSS4d). However, observation, the limitation the analysis of assimilating increment th ine DA_cyg_lf CYGNSS data shows was elongated also apparent. shapes Due at several to the places.track- Thisbased very nature likely of does CYGNSS not represent observation, the correct the analysis characteristics increment of the in DA_cyg_lf wind field, andshows therefore, elongated may shapes cause inaccurateat several places. asymmetry This very in wind likely and, does hence, not represen mass fields.t the correct characteristics of the wind field, and therefore,The RMS may di causefference inaccurate in surface asymmetr wind speedy in iswind calculated and, hence, between mass the fields. data assimilation experiments (DA_cyg_lf and DA_cyg_fd) and the background field, and averaged over the model domain. Figure5 shows the domain averaged RMS difference of surface wind speed within 9 h after the assimilation of CYGNSS data was complete at 1500 UTC on 6 January 2018. At the analysis time, the averaged RMS 1 1 was 1.2 m s− for DA_cyg_lf and 0.86 m s− for DA_cyg_fd. It rapidly declined for both experiments 1 within the first hour after data assimilation. After 4 h, the averaged RMS were only 0.41 m s− and 1 0.32 m s− for DA_cyg_lf and DA_cyg_fd, which were 30% and 37% of the original values, respectively. This indicates that the strongest impact of the CYGNSS data will be within the first ~4 h of the forecast.

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Figure 4. CYGNSS wind speed observation (a) FDS and (b) YSLF. The analysis increments from Figure 4. CYGNSS wind speed observation (a) FDS and (b) YSLF. The analysis increments from (c) (c) DA_cyg_fd and (d) DA_cyg_lf. The blue contours represent negative values and red contours DA_cyg_fd and (d) DA_cyg_lf. The blue contours represent negative values and red contours represent positive values. The contour interval is 1 m s 1.(e) wind ﬁeld in background WRF simulation. represent positive values. The contour interval −is 1 m s-1. (e) wind field in background WRF All plotssimulation. are at 1500 All UTC plots on are 6 at January 1500 UTC 2018. on 6 January 2018.

With theThe assimilation RMS difference of CYGNSS in surface data wind in the speed initial is conditions, calculated positivebetween impactsthe data have assimilation been made in the simulationsexperiments (DA_cyg_lf of TC Irving and and DA_cyg_fd) the WWB and event. the background Figure6 displays field, and the averaged surface over wind the ﬁeld model from DA_cyg_fd,domain. DA_cyg_lf, Figure 5 shows and CTRL the domain compared averaged with RMS ASCAT difference observation of surface around wind 0239speed UTC within on 9 8h Januaryafter 1 2018. ASCATthe assimilation (Figure of6a) CYGNSS observed data an was area complete of wind at 1500 speed UTC over on 186 January m s − 2018.around At the the analysis vortex time, with a -1 1 -1 maximumthe averaged surface windRMS was speed 1.2 overm s for 30 mDA_cyg_lf s− . There and is 0.86 no m apparent s for DA_cyg_fd. quadrant It asymmetry rapidly declined in ASCAT for data. Inboth comparison, experiments a within strong the quadrant first hour asymmetryafter data assimilation. was found After in 4 all h, numericalthe averaged experiments RMS were only with -1 -1 the strongest0.41 m winds and appearing 0.32 m s infor the DA_cyg_lf front-left and quadrant. DA_cyg_fd, In addition,which were all 30% three and numerical 37% of the experiments original values, respectively. This indicates that the strong1 est impact of the CYGNSS data will be within the produced a larger area of wind speed > 18 m s− . The best track data shows the maximum surface first ~4 h of the forecast. 1 wind of 49 (0000 UTC) to 46 m s− (0600 UTC) at this time. Therefore, all numerical experiments produced an underestimate of the storm intensity. With assimilation of the YSLF data, DA_cyg_lf (Figure6d) generated a stronger TC than CTRL (Figure6b) and DA_cyg_fd (Figure4c). A smaller eye

1 with stronger maximum surface wind speed (28 m s− ) was found in DA_cyg_lf when comparing with 1 1 CTRL (25 m s− ) and DA_cyg_fd (22 m s− ). Also note that the center location in the three experiments Remote Sens. 2020, 12, 1243 9 of 20 are quite diﬀerent. At this time, the observed storm center derived from the Joint Typhoon Warning Center (JTWC) best track data was near 79.9◦E, 17.05◦S. The 51-h forecast in CTRL predicted the storm center at 81.15◦E, 18.5◦S, which is 208-km away from the observed one. After seven cycles of CYGNSS data assimilation, the predicted center location was at 80.5◦E, 18.5◦S and 80.5◦E, 17.8◦S in DA_cyg_lf and DA_cyg_fd, respectively. Both forecasts were better than CTRL, with track errors of 174-km and Remote Sens. 2020, 12, x FOR PEER REVIEW 9 of 21 105-km for DA_cyg_lf and DA_cyg_fd, respectively.

Figure 5. Time series of domain averaged RMS diﬀerence of surface wind speed between data assimilationFigure 5. Time experiments series of and domain the background averaged RMS ﬁeld fromdiffer 1500ence UTC of surface on 6 January wind 2018speed to between 0000 UTC data on Remote Sens. 2020, 12, x FOR PEER REVIEW 10 of 21 7assimilation January 2018. experiments and the background field from 1500 UTC on 6 January 2018 to 0000 UTC on 7 January 2018.

With the assimilation of CYGNSS data in the initial conditions, positive impacts have been made in the simulations of TC Irving and the WWB event. Figure 6 displays the surface wind field from DA_cyg_fd, DA_cyg_lf, and CTRL compared with ASCAT observation around 0239 UTC on 8 January 2018. ASCAT (Figure 6a) observed an area of wind speed over 18 m s-1 around the vortex with a maximum surface wind speed over 30 m s-1. There is no apparent quadrant asymmetry in ASCAT data. In comparison, a strong quadrant asymmetry was found in all numerical experiments with the strongest wind appearing in the front-left quadrant. In addition, all three numerical experiments produced a larger area of wind speed > 18 m s-1. The best track data shows the maximum surface wind of 49 (0000 UTC) to 46 m s-1 (0600 UTC) at this time. Therefore, all numerical experiments produced an underestimate of the storm intensity. With assimilation of the YSLF data, DA_cyg_lf (Figure 6d) generated a stronger TC than CTRL (Figure 6b) and DA_cyg_fd (Figure 4c). A smaller eye with stronger maximum surface wind speed (28 m s-1) was found in DA_cyg_lf when comparing with CTRL (25 m s-1) and DA_cyg_fd (22 m s-1). Also note that the center location in the three experiments are quite different. At this time, the observed storm center derived from the Joint Typhoon Warning Center (JTWC) best track data was near 79.9°E, 17.05°S. The 51-h forecast in CTRL predicted the storm center at 81.15°E, 18.5°S, which is 208-km away from the observed one. After seven cycles of CYGNSS data assimilation, the predicted center location was at 80.5°E, 18.5°S and 80.5°E, 17.8°S in DA_cyg_lf and DA_cyg_fd, respectively. Both forecasts were better than CTRL, with track errors of 174-km and 105-km for DA_cyg_lf and DA_cyg_fd, respectively.

FigureFigure 6. Surface 6. Surface wind wind speed speed from from (a ()a) ASCAT ASCAT aroundaround 0239 0239 UTC UTC and and (b ()b CTRL,) CTRL, (c) DA_cyg_fd, (c) DA_cyg_fd, and and (d) DA_cyg_lf(d) DA_cyg_lf at 0300 at 0300 UTC UTC on on 8 January8 January 2018. 2018.

The impact of CYGNSS data assimilation on the intensity and track forecasts of TC Irving was compared with the JTWC best track data in Figures 7 and 8. From 00 UTC on 6 January to 00 UTC on 8 January, TC Irving experienced an intensification, from a tropical storm to a category-2 tropical cyclone, with a peak intensity of 962 hPa in MSLP of and 49 m s-1 in maximum surface wind speed, followed by a weakening stage until 0000 UTC on 9 January with 973 hPa in MSLP of and 41 m s-1 in maximum surface wind speed. From Figure 7, CTRL greatly underestimated the intensity of TC Irving. The forecast in CTRL shows a slow strengthening of Irving from 999 hPa in MSLP and 17 m s-1 in maximum surface wind speed at 0000 UTC on 6 to 990 hPa and 25 m s-1 at 0000 UTC on 9 January 2018. Improvement in intensity forecast by the assimilation of the CYGNSS data was found from 0000 UTC on 7 January to 0000 UTC on 8 January 2018. During this time period, both DA_cyg_lf and DA_cyg_fd outperformed CTRL with the strongest storm generated by DA_cyg_lf. CTRL missed the weakening of the storm from 0000 UTC on 8 to 0000 UTC on 9 January 2018. The intensity of the storms in DA_cyg_lf and DA_cyg_fd showed some fluctuations, but no apparent weakening was predicted in either experiment. It is noteworthy that the intensity forecast in data assimilation experiments was not always better than CTRL (e.g., from 1200 UTC on 8 January to 0000 UTC on 9 January 2018 in DA_cyg_fd). The track forecasts from CTRL and data assimilation experiments were compared with the JTWC best track in Figure 8. As shown in Figure 8a, all experiments captured the southwestward path of TC Irving. CTRL produced the track to the south of the observed storm with a track error ranging from 35 km to 265 km. The assimilation of CYGNSS FDS data provided help with the tracking forecast. The track error in DA_cyg_fd was generally smaller than those of either CTRL or DA_cyg_lf,

Remote Sens. 2020, 12, 1243 10 of 20

The impact of CYGNSS data assimilation on the intensity and track forecasts of TC Irving was compared with the JTWC best track data in Figures7 and8. From 00 UTC on 6 January to 00 UTC on 8 January, TC Irving experienced an intensiﬁcation, from a tropical storm to a category-2 tropical 1 cyclone, with a peak intensity of 962 hPa in MSLP of and 49 m s− in maximum surface wind speed, 1 followed by a weakening stage until 0000 UTC on 9 January with 973 hPa in MSLP of and 41 m s− in maximum surface wind speed. From Figure7, CTRL greatly underestimated the intensity of TC Irving. 1 The forecast in CTRL shows a slow strengthening of Irving from 999 hPa in MSLP and 17 m s− in 1 maximum surface wind speed at 0000 UTC on 6 to 990 hPa and 25 m s− at 0000 UTC on 9 January 2018. Improvement in intensity forecast by the assimilation of the CYGNSS data was found from 0000 UTCRemote on Sens. 7 January2020, 12, x FOR to 0000 PEER UTCREVIEW on 8 January 2018. During this time period, both DA_cyg_lf 11 of 21and DA_cyg_fd outperformed CTRL with the strongest storm generated by DA_cyg_lf. CTRL missed the Remoteexcept Sens. at 18002020, 12UTC, x FOR on PEER 7 January REVIEW and in the last 6 h of the forecast period. For example, at 0000 11 ofUTC 21 weakening of the storm from 0000 UTC on 8 to 0000 UTC on 9 January 2018. The intensity of the storms on 8 January, the center location of TC Irving in DA_cyg_fd (the track error of 100 km) was much except at 1800 UTC on 7 January and in the last 6 h of the forecast period. For example, at 0000 UTC in DA_cyg_lfcloser to and the observed DA_cyg_fd location showed when some compared ﬂuctuations, with the but one no in apparentCTRL (the weakeningtrack error of was 175 predictedkm) and in on 8 January, the center location of TC Irving in DA_cyg_fd (the track error of 100 km) was much eitherthe experiment. one in DA_cyg_lf It is noteworthy (the track error that of the 137 intensity km). forecast in data assimilation experiments was not alwayscloser to better the observed than CTRL location (e.g., when from compared 1200 UTC with on the 8 one January in CTRL to 0000(the track UTC error on 9of January175 km) and 2018 in DA_cyg_fd).the one in DA_cyg_lf (the track error of 137 km). (a) (b) 1005 100 (a) (b) OBS 1000 90 CTRL 1005995 10080 OBSDA_com 1000990 9070 CTRLDA_cyg_fd DA_cyg_lf 995985 8060 DA_com DA_cyg_fd 990980 7050 OBS DA_cyg_lf

MSLP (hPa) MSLP 985 60 975 CTRL 40 980 DA_com 50 970 OBS 30

MSLP (hPa) MSLP 975 DA_cyg_fd 40 965 CTRL (kts) WInd Surface Maximum 20 DA_cyg_lf DA_com 970960 3010 DA_cyg_fd

965 0 6 12 18 24 30 36 42 48 54 60 66 72 (kts) WInd Surface Maximum 20 0 6 12 18 24 30 36 42 48 54 60 66 72 DA_cyg_lf 960 Hours since 00 UTC 6 January 2018 10 Hours since 00 UTC 6 January 2018 0 6 12 18 24 30 36 42 48 54 60 66 72 0 6 12 18 24 30 36 42 48 54 60 66 72 Figure 7. TimeHours sinceseries 00 of UTC TC 6 IrJanuaryving intensity2018 for (a) MSLP and (b) maximumHours since surface00 UTC 6 windJanuary speed 2018 from JTWC best track data, CTRL, DA_cyg_fd, DA_cyg_lf, and DA_com at 0000 UTC on 6–9 January 2018. FigureFigure 7. Time 7. Time series series of TC of TC Irving Irving intensity intensity for for ( a(a)) MSLP MSLP and ( (bb)) maximum maximum surface surface wind wind speed speed from from JTWC best track data, CTRL, DA_cyg_fd, DA_cyg_lf, and DA_com at 0000 UTC on 6–9 January 2018. JTWC best track data, CTRL, DA_cyg_fd, DA_cyg_ lf, and DA_com at 0000 UTC on 6–9 January 2018.

300 b) CTRL 250 DA_com b) 300 DA_cyg_fd 200 CTRL 250 DA_comDA_cyg_lf 150 DA_cyg_fd 200 100 DA_cyg_lf

Track Error (km) Error Track 150 50 100 0 Track Error (km) Error Track 50 0 6 12 18 24 30 36 42 48 54 60 66 72 0 Hours since 00 UTC 6 January 2018 0 6 12 18 24 30 36 42 48 54 60 66 72

Hours since 00 UTC 6 January 2018 FigureFigure 8. (a) TC8. (a Irving) TC Irving track track from from JTWC JTWC best best track track data, data, CTRL, CTRL, DA_cyg_fd, DA_cyg_lf, DA_cyg_lf, and and DA_com DA_com

and (b)and track (b) errortrack fromerror CTRL,from CTRL, DA_cyg_fd, DA_cyg_fd, DA_cyg_lf, DA_cyg_ andlf, and DA_com DA_com at 0000at 0000 UTC UTC on on 6–9 6–9 January January 2018. Figure2018. 8. (a) TC Irving track from JTWC best track data, CTRL, DA_cyg_fd, DA_cyg_lf, and DA_com The trackand (b forecasts) track error from from CTRL CTRL, and DA_cyg_fd, data assimilation DA_cyg_lf, and experiments DA_com at were0000 UTC compared on 6–9 January with the JTWC 2018. best track inFigure Figure 9 displays8. As shown IMERG in precipitation, Figure8a, all CYGNSS experiments FDS and captured YSLF thewind southwestward speed, ASCAT pathwind of speed and model simulated wind from DA_cyg_lf and CTRL over the region of the WWB event when TC Irving.Figure CTRL 9 produceddisplays IMERG the track precipitation, to the south CYGNSS of the FDS observed and YSLF storm wind with speed, a track ASCAT error wind ranging ASCAT captured a portion of the event near 1500 UTC on 8 January 2018. IMERG (Figure 9a) shows from 35speed km and to 265 model km. simulated The assimilation wind from of DA_cyg_lf CYGNSS and FDS CTRL data over provided the region help of with the WWB the tracking event when forecast. the convection related to the WWB event produced a maximum precipitation over 10 mm h-1. ASCAT The trackASCAT error captured in DA_cyg_fd a portion was of the generally event near smaller 1500 UTC than on those 8 January of either 2018. CTRL IMERG or DA_cyg_lf,(Figure 9a) shows except at (Figure 9b) indicates a large area with wind speed over 12 m s-1 and a maximum of 20 m s-1. CYGNSS the convection related to the WWB event produced a maximum precipitation over 10 mm h-1. ASCAT 1800 UTCFDS on(Figure 7 January 9c) detected and in weaker the last winds 6 h of for the the forecast WWB with period. < 10 For m s example,-1 at most points, at 0000 and UTC a maximum on 8 January, (Figure 9b) indicates a large area with wind speed over 12 m s-1 and a maximum of 20 m s-1. CYGNSS value of 12 m s-1, while in YSLF data (Figure 9d), many points in the three tracks across the observed FDS (Figure 9c) detected weaker winds for the WWB with < 10 m s-1 at most points, and a maximum WWB region had wind speed > 12 m s-1 with a maximum of 27 m s-1. The assimilation of high winds value of 12 m s-1, while in YSLF data (Figure 9d), many points in the three tracks across the observed in YSLF data explains the apparent increase in the analysis wind field. Surface wind speed in WWB region had wind speed > 12 m s-1 with a maximum of 27 m s-1. The assimilation of high winds DA_cyg_lf (Figure 9f) exceeded 14 m s-1 over the storm area, while the surface wind speed in CTRL in YSLF data explains the apparent increase in the analysis wind field. Surface wind speed in (Figure 9e) was generally below12 m s-1. DA_cyg_lf (Figure 9f) exceeded 14 m s-1 over the storm area, while the surface wind speed in CTRL

(Figure 9e) was generally below12 m s-1.

Remote Sens. 2020, 12, 1243 11 of 20 the center location of TC Irving in DA_cyg_fd (the track error of 100 km) was much closer to the observed location when compared with the one in CTRL (the track error of 175 km) and the one in DA_cyg_lf (the track error of 137 km). Figure9 displays IMERG precipitation, CYGNSS FDS and YSLF wind speed, ASCAT wind speed and model simulated wind from DA_cyg_lf and CTRL over the region of the WWB event when ASCAT captured a portion of the event near 1500 UTC on 8 January 2018. IMERG (Figure9a) 1 shows the convection related to the WWB event produced a maximum precipitation over 10 mm h− . 1 1 ASCAT (Figure9b) indicates a large area with wind speed over 12 m s − and a maximum of 20 m s− . 1 CYGNSS FDS (Figure9c) detected weaker winds for the WWB with < 10 m s− at most points, and a 1 maximum value of 12 m s− , while in YSLF data (Figure9d), many points in the three tracks across the 1 1 observed WWB region had wind speed > 12 m s− with a maximum of 27 m s− . The assimilation of high winds in YSLF data explains the apparent increase in the analysis wind ﬁeld. Surface wind speed 1 in DA_cyg_lf (Figure9f) exceeded 14 m s − over the storm area, while the surface wind speed in CTRL 1 (FigureRemote9e) Sens. was 2020 generally, 12, x FOR belowPEER REVIEW 12 m s − . 12 of 21

FigureFigure 9. ( a9.) IMERG(a) IMERG precipitation precipitation for for thethe WWBWWB event event at at 1500 1500 UTC UTC on on8 January 8 January 2018, 2018, (b) ASCAT (b) ASCAT windwind speed speed around around 1436 1436 UTC UTC on on 8 8 January January 2018,2018, ( c)) CYGNSS CYGNSS FDS FDS and and (d) ( dYSLF) YSLF wind wind speed speed around around 15001500 UTC UTC on 8 on January 8 January 2018, 2018, and and surface surface wind wind speed speed from from ((ee)) CTRLCTRL and ( (ff)) DA_cyg_lf DA_cyg_lf at at 1500 1500 UTC UTC on 8 Januaryon 8 January 2018. 2018.

The overall impacts of the CYGNSS observation on surface wind and precipitation fields are shown in Tables 2 and 3. Table 2 lists the domain averaged RMS of surface wind speed difference between different experiments and ASCAT wind speed within the model domain available around 0302 and 1448 UTC on 7 January and 0239 and 1436 UTC on 8 January 2018. As shown in the table, the assimilation of both CYGNSS FDS and YSLF data led to smaller RMS values at all times. At 1500 UTC on 8 January, the RMS reduced by up to 0.7 m s-1 in DA_cyg_lf when comparing with CTRL. Table 3 lists the threat scores (TS) of precipitation forecast with a threshold value of 2 mm h-1 from different experiments. The IMERG data were used as the reference dataset. The TS was calculated using the equation from Xiao et al. [59]: 𝐶 𝑇𝑆 (1) 𝐹𝑅𝐶

Remote Sens. 2020, 12, 1243 12 of 20

The overall impacts of the CYGNSS observation on surface wind and precipitation fields are shown in Tables2 and3. Table2 lists the domain averaged RMS of surface wind speed di fference between different experiments and ASCAT wind speed within the model domain available around 0302 and 1448 UTC on 7 January and 0239 and 1436 UTC on 8 January 2018. As shown in the table, the assimilation of both CYGNSS FDS and YSLF data led to smaller RMS values at all times. 1 At 1500 UTC on 8 January, the RMS reduced by up to 0.7 m s− in DA_cyg_lf when comparing with 1 CTRL. Table3 lists the threat scores (TS) of precipitation forecast with a threshold value of 2 mm h − from different experiments. The IMERG data were used as the reference dataset. The TS was calculated using the equation from Xiao et al. [59]:

C TS = (1) F + R + C where C is the number of correct forecast events (points with precipitation exceeding the threshold value), R is the number of events in observation, and F is the number of events in forecast. As indicated in Table3, the di fferences in TSs between CTRL and the data assimilation experiments DA_cyg_fd and DA_cyg_lf were as small as 0.01–0.02, which indicates that the influence from assimilation of CYGNSS data only was not significant. This could be attributed to the continuous correction of model dynamics, which commonly occurs when only surface data are assimilated. Another possible reason is the lack of dynamic support from the convective and thermodynamic fields for convection to grow. This was the motivation for the DA_com data assimilation experiment, as described next.

Table 2. Domain averaged RMS diﬀerence of surface wind speed between ASCAT observation and diﬀerent numerical experiments.

0300 UTC on 7 1500 UTC on 7 0300 UTC on 8 1500 UTC on 8 Experiment January 2018 January 2018 January 2018 January 2018 CTRL 2.63 2.92 3.12 3.67 DA_cyg_fd 2.55 2.43 2.85 3.08 DA_cyg_lf 2.45 2.37 2.70 2.97 DA_com 1.54 1.47 1.70 2.08

1 Table 3. Threat Score of precipitation forecast with threshold value of 2 mm h− 0000 UTC on 8–9 January 2018 for diﬀerent experiments with IMERG rainfall as the reference dataset.

0000 UTC on 8 0600 UTC on 8 1200 UTC on 8 1800 UTC on 8 0000 UTC on 9 Experiment January 2018 January 2018 January 2018 January 2018 January 2018 CTRL 0.18 0.19 0.17 0.14 0.15 DA_cyg_fd 0.19 0.18 0.17 0.15 0.16 DA_cyg_lf 0.19 0.19 0.18 0.16 0.15 DA_com 0.38 0.35 0.34 0.32 0.33

3.2. Assimilation of the Combined Satellite Datasets The above results indicate the positive impacts of CYGNSS data, as well as the limitations of the CYGNSS data when being assimilated into the initial conditions of NWP simulations. Since the IMERG data can measure the precipitation of tropical convection, the ASCAT data can describe the cyclonic circulation of TCs, and the CYGNSS data can provide wind speed observations under heavy precipitation, it would be of great interest to examine how the combined datasets would help with TC and tropical convection intensity and structure. The overall performance of DA_com is compared with other experiments in Tables2 and3. As shown in Table2, DA_com produced the lowest RMS values at all times among all experiments. 1 Specifically, the domain averaged RMS values in DA_com ranged from 1.47 to 2.08 m s− . 1 When comparing to CTRL, the RMS values reduced by 1.09 to 1.59 m s− , indicating a 41–49% Remote Sens. 2020, 12, 1243 13 of 20 relative reduction. From Table3, it is shown that the precipitation forecast skill in DA_com was apparently higher than the rest of the experiments. For example, the TS in CTRL was only 0.15 at 0000 UTC on 9 January 2018, while the value in DA_com was 0.33, which is a relative improvement of 120%. These results indicate large positive impacts with the assimilation of the combined datasets. RemoteTo illustrate Sens. 2020, the12, x FOR prediction PEER REVIEW of intensity and structure of TC Irving, Figure 10 shows 14 of a 21 comparison of thebetween sea level 986 pressurehPa (0000 (SLP)UTC) and over-plotted 981 hPa (0600 on UTC) surface and winda maximum from DA_comsurface wind and speed CTRL between near TC Irving from31 around m s-1 (0000 0300 UTC) UTC and on 36 7 m January s-1 (0600and UTC). 1500 ASCAT UTC (Figure on 8 January10a) missed 2018, the whenstrong wind ASCAT in the observation core was available.region Atdueboth to contamination times, DA_com by heavy produced precipitation. better Therefore, intensity the estimateswind asymmetry than CTRLof TC Irving compared is to the bestunclear. track data. CTRL At (Figure 0300 10b) UTC produced on 7 January, a weak stor them best with tracka MSLP data of 995 showed hPa and that maximum TC Irving wind hadof a MSLP 20 m s-1, while DA_com (Figure 10c) was able to generate a stronger cyclone with a MSLP of 992 hPa between 986 hPa (0000 UTC) and 981 hPa (0600 UTC) and a maximum surface wind speed between and maximum wind of 30 m s-1. The strongest wind appeared at the front left quadrant of TC Irving 1 1 31 min s− DA_com(0000 UTC)and at andthe front 36 m in s− CTRL,(0600 respectively. UTC). ASCAT At 1500 (Figure UTC on10 a)8 January, missed the best strong track wind data in the core regionshows due that to contaminationthe intensity of the by storm heavy was precipitation. between 967 hPa Therefore, (1200 UTC) the and wind 970 asymmetryhPa (1800 UTC) of in TC Irving is unclear.MSLP, CTRL and 47 (Figure m s-1 (1200 10b) UTC) produced to 44 m a weaks-1 (1800 storm UTC) with in maximum a MSLP surface of 995 wind hPa andspeed. maximum ASCAT wind of (Figure1 10d) observed the maximum wind speed of 28 m s-1 with a small eye. CTRL (Figure 10e) 20 m s− , while DA_com (Figure 10c) was able to generate a stronger cyclone with a MSLP of 992 hPa underestimated TC Irving with1 a MSLP of 991 hPa and a maximum wind speed of 27 m s-1. A better and maximum wind of 30 m s− . The strongest wind appeared at the front left quadrant of TC Irving intensity and wind field was produced in DA_com (Figure 10f) with a MSLP of 982 hPa and a in DA_com and at the front in CTRL, respectively. At 1500 UTC on 8 January, the best track data shows maximum surface wind speed of 32 m s-1 and a smaller eye. From ASCAT, it seems that the strongest that thewind intensity at this time of appeared the storm at the was front between right quadrant 967 hPa of (1200TC Irving, UTC) and and that 970there hPa was (1800no apparent UTC) in MSLP, 1 1 and 47wind m sasymmetry− (1200 UTC) around to the 44 eyewall. m s− (1800 In CTRL, UTC) the in strongest maximum wind surface was at the wind front speed. to front ASCAT left of TC (Figure 10d) 1 observedIrvingthe with maximum a strong wind wind asymmetry. speed of DA_com 28 m s− showwiths the a small strong eye. wind CTRL occurring (Figure in both 10 e)front underestimated left and front right quadrants. The asymmetry was not as apparent as the wind field in 1CTRL. TC Irving with a MSLP of 991 hPa and a maximum wind speed of 27 m s− . A better intensity and wind fieldFigure was 11 produced shows the insurface DA_com wind field (Figure for the 10 f)WWB with event a MSLP around of 1500 982 UTC hPa on and 7 January a maximum and surface 1600 UTC on 8 January1 2018 when ASCAT captured a portion of the event. At both times, DA_com windproduced speed of stronger 32 m s wind− and field a smallerthan CTRL. eye. ASCAT From (Figure ASCAT, 11a,d) it seems shows thatwind thespeed strongest > 8 m s-1 over wind the at this time appearedarea 65°–85°E at the frontand 10°S–0° right quadrantwith a maximum of TC wind Irving, speed and of that 16 m there s-1. CTRL was (Figure no apparent 11b,e) predicted wind asymmetry aroundmuch the smaller eyewall. areas Inthat CTRL, have wind the strongestspeed > 8 m wind s-1 with was the at maximum the front of to 13 frontm s-1. leftIn comparison, of TC Irving with a strongDA_com wind (Figure asymmetry. 11c,f) produced DA_com large shows areas the with strong wind windspeed occurringover 8 m s-1 inand both a maximum front left wind and of front right -1 quadrants.18 m s . The The asymmetrylocation of the was WWB not was as apparentalso better aspredicted the wind in DA_com field in at CTRL. 1600 UTC on 8 January 2018.

FigureFigure 10. 10.Surface Surface wind wind speedspeed near near TC TC Irving Irving from from (a) ASCAT (a) ASCAT observation observation at 0302 UTC, at 0302 and UTC, surface and surface windwind speed speed prediction prediction overplotted overplotted withwith sea level pressure pressure from from (b) ( bCTRL,) CTRL, and and(c) DA_com (c) DA_com at 0300 at 0300 UTC UTC on 7 January 2018; and (d) ASCAT at 1436 UTC, and surface wind speed with sea level pressure on 7 January 2018; and (d) ASCAT at 1436 UTC, and surface wind speed with sea level pressure from from (e) CTRL, and (f) DA_com at 1500 UTC on 8 January 2018. (e) CTRL, and (f) DA_com at 1500 UTC on 8 January 2018.

Remote Sens. 2020, 12, 1243 14 of 20

Figure 11 shows the surface wind ﬁeld for the WWB event around 1500 UTC on 7 January and 1600 UTC on 8 January 2018 when ASCAT captured a portion of the event. At both times, DA_com 1 produced stronger wind ﬁeld than CTRL. ASCAT (Figure 11a,d) shows wind speed > 8 m s− over the 1 area 65◦–85◦E and 10◦S–0◦ with a maximum wind speed of 16 m s− . CTRL (Figure 11b,e) predicted 1 1 much smaller areas that have wind speed > 8 m s− with the maximum of 13 m s− . In comparison, 1 RemoteDA_com Sens. 2020 (Figure, 12, x FOR 11 PEERc,f) produced REVIEW large areas with wind speed over 8 m s− and a maximum 15 wind of 21 of 1 18 m s− . The location of the WWB was also better predicted in DA_com at 1600 UTC on 8 January 2018.

FigureFigure 11. Surface 11. Surface wind wind speed speed near near the WWB the WWB event event from from (a) ASCAT (a) ASCAT observation observation around around 1448 1448 UTC, UTC, and andprediction prediction from from (b) ( bCTRL,) CTRL, and and (c ()c )DA_com DA_com at at 1500 UTCUTC onon 7 7 January January 2018; 2018; and and (d) ( ASCATd) ASCAT around around1524 1524 UTC, UTC, and surfaceand surface wind predictionwind prediction from ( efrom) CTRL, (e) andCTRL, (f) DA_comand (f) DA_com at 1600 UTC at 1600 on 8 UTC January on 8 2018. January 2018. Figure 12 compares the precipitation predictions from CTRL and DA_com with IMERG data atFigure 1500 UTC12 compares on 7 January the precipitation and 2100 UTCpredictions on 8 January from CTRL 2018. and At DA_com both times, with DA_com IMERG data generated at 1500a UTC much on stronger 7 January and and better 2100 organized UTC on 8 precipitationJanuary 2018. overAt both the times, core region DA_com of TCgenerated Irving thana much CTRL. 1 strongerAt 1500 and UTC better on 7organized January, DA_com precipitation (Figure over 12c) th producede core region maximum of TC rainfall Irving ofthan 36 mmCTRL. h− Ataround 1500 the 1 1 UTCeyewall, on 7 January, which was DA_com pretty close(Figure to the12c) observed produced 34 maximum mm h− by rainfall IMERG of (Figure 36 mm 12 ha),-1 andaround 12 mm the h− eyewall,higher which than thewas one pretty predicted close to in the CTRL observed (Figure 34 12 mmb). Ath-1 2100by IMERG UTC on (Figure 8 January 12a), 2018, and both12 mm DA_com h-1 higherand than CTRL the underestimatedone predicted in the CTRL precipitation (Figure 12b) for. At TC 2100 Irving. UTC on IMERG 8 January (Figure 2018, 12 bothd) estimated DA_com the 1 1 andmaximum CTRL underestimated rainfall rate of the 56 mmprecipitation h− . CTRL for (Figure TC Irving. 12e) predicted IMERG maximum(Figure 12d) rainfall estimated of 20 mm the h− . maximumDA_com rainfall (Figure rate 12f) of outperformed 56 mm h-1. CTRL CTRL (Figure with a much12e) predicted stronger precipitation maximum rainfall ﬁeld with of 20 a maximum mm h-1. of 1 DA_com42 mm (Figure h− . Precipitation 12f) outperformed related toCTRL the WWBwith a event much was stronger also better precipitation represented field in with DA_com a maximum (Figure 13). of 42At mm 1200 h-1 UTC. Precipitation on 7 January related 2018, to IMERG the WWB observed event was strong also convection better represented with the maximumin DA_com precipitation (Figure 1 13).rate At of1200 32 mmUTC h− onat 757 ◦January–80◦E and 2018, 5◦–7 IMERG◦S. In CTRL, observed this storm strong was convection not predicted. with With the the maximum assimilation precipitationof the combined rate of 32 datasets, mm h-1DA_com at 75°–80°E produced and 5°–7°S. heavy In precipitationCTRL, this storm near was the observednot predicted. location With with 1 the theassimilation maximum of precipitationthe combined> datasets,40 mm DA_com h− . A similar produced result heavy was precipitation found at 2100 near UTC the onobserved 8 January 1 location2018. with IMERG the observedmaximum convection precipitation with > maximum40 mm h-1 precipitation. A similar result of 23 was mm found h− at at 67 2100◦–78◦ UTCE and on 4◦ 8–8 ◦S. JanuaryCTRL 2018. predicted IMERG multiple observed strong convection convective with storms, maximum but precipitation the locations of were 23 mm far from h-1 at the67°–78°E observed and one. 4°–8°S.DA_com CTRL was predicted able to generate multiple the strong precipitation convecti atve 70 storms,◦–78◦E andbut 4◦the–8 ◦locationsS with maximum were far precipitation from the of 1 observed26 mm one. h− .DA_com was able to generate the precipitation at 70°–78°E and 4°–8°S with maximum precipitation of 26 mm h-1.

Remote Sens. 2020, 12, 1243 15 of 20 Remote Sens. 2020, 12, x FOR PEER REVIEW 16 of 21 Remote Sens. 2020, 12, x FOR PEER REVIEW 16 of 21

Figure 12. 1-h precipitation around TC Irving from (a) IMERG, (b) CTRL, (c) DA_com at 1500 UTC FigureFigure 12. 12.1-h1-h precipitation precipitation around around TC TC Irving Irving from from (a ()a )IMERG, IMERG, (b (b) )CTRL, CTRL, ( (cc)) DA_com DA_com at at 1500 UTC on on 7 January 2018 and (d) IMERG, (e) CTRL, and (f) DA_com at 2100 UTC on 8 January 2018. on 77 JanuaryJanuary 20182018 andand ((dd)) IMERG,IMERG, ((ee)) CTRL,CTRL, andand ((ff)) DA_comDA_com atat 21002100 UTCUTC onon 88 JanuaryJanuary 2018.2018.

Figure 13. 1-h precipitation for convection related to the WWB event from (a) IMERG, (b) CTRL, (c) FigureFigure 13. 13.1-h 1-hprecipitation precipitation for convection for convection related related to the to WWB the WWB event eventfrom ( froma) IMERG, (a) IMERG, (b) CTRL, (b) CTRL, (c) DA_com at 1200 UTC on 7 January 2018 and (d) IMERG, (e) CTRL, and (f) DA_com at 2100 UTC on DA_com(c) DA_com at 1200 at UTC 1200 UTCon 7 January on 7 January 2018 2018and ( andd) IMERG, (d) IMERG, (e) CTRL, (e) CTRL, and and(f) DA_com (f) DA_com at 2100 at 2100 UTC UTC on on 8 January 2018. 8 January8 January 2018. 2018.

TheThe impacts impacts of ofthe the combined combined satellite satellite datasets datasets on onthe the intensity intensity and and track track of ofTC TC Irving Irving are are displayeddisplayed in Figures in Figures 7 and7 and 8. As8. Asshown shown in Figure in Figure 7, DA_com7, DA_com also also underestimated underestimated the intensity the intensity of TC of Irving,TC Irving, but apparently but apparently performed performed better betterwhen comp whenaring comparing to CTRL, to CTRL,DA_cyg_fd, DA_cyg_fd, and DA_cyg_lf. and DA_cyg_lf. An intensification was predicted from 999 hPa in MSLP and 17 m s-1 in maximum1 surface wind speed at intensificationAn intensiﬁcation was predicted was predicted from from999 hPa 999 in hPa MSLP in MSLP and 17 and m 17s-1 min smaximum− in maximum surface surface wind windspeed speed at 0000 UTC on 6 January to 985 hPa and 35 m s-1 at 18001 UTC on 7 January 2018. The weakening after 0000at 0000UTC UTCon 6 onJanuary 6 January to 985 to 985hPa hPaand and35 m 35 s m-1 at s− 1800at 1800 UTC UTC on 7 on January 7 January 2018. 2018. The The weakening weakening after after 06000600 UTC UTC on on8 January 8 January was was also also captured. captured. With With the the assimilation assimilation of the of the combined combined satellite satellite datasets, datasets, thethe track track forecast forecast of TC of TC Irving Irving in inDA_com DA_com was was also also improved. improved. The The center center locations locations in DA_com in DA_com were were veryvery close close to the to the observed observed ones ones with with the the track track erro errorr ranges ranges from from 11 11km km at 1200 at 1200 UTC UTC on on6 January 6 January 2018 2018 to 88 km at 0000 UTC on 9 January 2018. In comparison, the track errors were 125, 93, and 94 km at

Remote Sens. 2020, 12, 1243 16 of 20 to 88 km at 0000 UTC on 9 January 2018. In comparison, the track errors were 125, 93, and 94 km at 1200 UTC on 6 January 2018, and 266, 259, 198 km at 0000 UTC on 9 January 2018 in CTRL, DA_cyg_fd, and DA_cyg_lf, respectively.

4. Discussion In this study, the application of CYGNSS v2.1 Level 2 data was examined in a numerical simulation of TC Irving and a WWB event that occurred during the 2018 January MJO event. WRF model simulations and WRFDA 3DEnVar and 4DEnVar data assimilation experiments were conducted to investigate the impact of the CYGNSS data when it was assimilated individually, as well as combined with the IMERG rainfall and ASCAT ocean surface wind vector observations. The goal of this study was to report on the benefits and challenge in the assimilation of CYGNSS data. The results indicate that CYGNSS data can capture the high winds and sharp wind gradient under heavily precipitating conditions within TC Irving and the WWB event. When CYGNSS data were assimilated individually, a positive impact was found on the wind field for both TC Irving and the WWB event. The intensity and track forecast of TC Irving were also positively affected. However, the main impact of the data appears to only last about 4 h. In addition, the influence of the CYGNSS winds on the precipitation field was modest at best. When CYGNSS winds were assimilated together with IMERG hourly rainfall and ASCAT ocean surface vector winds, significant impacts were found in the wind, pressure, and precipitation fields. The intensity and track forecast of TC Irving were largely improved. Specifically, the structure in wind and precipitation fields in the vicinity of the TC core region and across the WWB event was better represented with the assimilation of the combined satellite data. Unfortunately, one caveat of the current research is the lack of an experiment parallel to DA_com that assimilates IMERG and ASCAT data using 4DEnVar. Due to the expensive computational cost of 4DEnVar, we did not have the time or computational resource to conduct this experiment. Therefore, the sole impact of CYGNSS data is unclear when IMERG and ASCAT data are also assimilated. This is quite important for a thorough understanding on the overall benefit and limitation of CYGNSS observation. In future studies, we will conduct these experiments with more case studies to further explore the impact of CYGNSS data when it is combined with other datasets.

5. Conclusions The results shown here are quite encouraging. However, the conclusions in this study are based on only one case study. More experiments with additional cases for a variety of tropical weather systems are needed to demonstrate a thorough evaluation of the consistent impacts of the CYGNSS observations to NWP forecasts. New versions of the CYGNSS data (v3.0 and Climate Data Record—CDR v1.0) are expected to be available soon; v3.0 includes a near-real-time corrections for variations in the transmit power of the GPS satellites, and is expected to improve the temporal and spatial sampling properties, as well as the accuracy. CDR data include track-wise debiasing that is expected to improve wind speed calibration. In both new data versions, a reduction is expected in the uncertainty of the wind speed products for both high and low wind speed [40]. Therefore, future research should be conducted with WRF simulations and data assimilation experiments for new versions of CYGNSS data to further explore the application of the latest products. In addition, the frequent revisit time of CYGNSS is one of the great advantages of this instrument. Hence, it would be of great interest to conduct continuous assimilation of CYGNSS data and to investigate the beneﬁt it may bring to midrange to subseasonal forecasts of tropical convection and MJO events.

Author Contributions: Conceptualization, X.L., J.R.M., and T.J.L.; Methodology, X.L., J.R.M., and T.J.L.; Software, X.L. and T.J.L.; Validation, X.L., J.R.M., and T.J.L.; Formal analysis, X.L., J.R.M., and T.J.L.; Investigation, X.L., J.R.M., and T.J.L.; Resources, X.L., J.R.M., and T.J.L.; Data curation, X.L. and T.J.L.; Writing—original draft preparation, X.L., J.R.M., and T.J.L.; Writing—review and editing, X.L., J.R.M., and T.J.L.; Visualization, X.L., J.R.M., and T.J.L.; Supervision, J.R.M. and T.J.L.; Project administration, J.R.M. and T.J.L.; Funding acquisition, X.L., J.R.M., and T.J.L. All authors have read and agreed to the published version of the manuscript. Remote Sens. 2020, 12, 1243 17 of 20

Funding: This research was funded by NASA, grant number NNM11AA01A. Acknowledgments: The authors also acknowledge the NASA CYGNSS science and operations team for providing the observational data. The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to make the paper having a much improved quality. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

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