Investigating the Impact of High-Resolution Land–Sea Masks on Hurricane Forecasts in HWRF

atmosphere

Article Investigating the Impact of High-Resolution Land–Sea Masks on Hurricane Forecasts in HWRF

Zaizhong Ma 1,2,*, Bin Liu 1,2, Avichal Mehra 1, Ali Abdolali 1,2 , Andre van der Westhuysen 1,2, Saeed Moghimi 3,4 , Sergey Vinogradov 3, Zhan Zhang 1,2, Lin Zhu 1,2, Keqin Wu 1,2, Roshan Shrestha 1,2, Anil Kumar 1,2, Vijay Tallapragada 1 and Nicole Kurkowski 5

1 NWS/NCEP/Environmental Modeling Center, National Oceanic and Atmospheric Administration (NOAA), College Park, MD 20740, USA; [email protected] (B.L.); [email protected] (A.M.); [email protected] (A.A.); [email protected] (A.v.d.W.); [email protected] (Z.Z.); [email protected] (L.Z.); [email protected] (K.W.); [email protected] (R.S.); [email protected] (A.K.); [email protected] (V.T.) 2 I. M. Systems Group, Inc. (IMSG), Rockville, MD 20852, USA 3 NOAA Coast Survey Development Laboratory, National Ocean Service, Silver Spring, MD 20910, USA; [email protected] (S.M.); [email protected] (S.V.) 4 University Corporation for Atmospheric Research, Boulder, CO 80305, USA 5 NOAA National Weather Service (NWS) Oﬃce of Science and Technology Integration, Silver Spring, MD 20910, USA; [email protected] * Correspondence: [email protected]

Received: 8 June 2020; Accepted: 20 August 2020; Published: 22 August 2020

Abstract: Realistic wind information is critical for accurate forecasts of landfalling hurricanes. In order to provide more realistic near-surface wind forecasts of hurricanes over coastal regions, high-resolution land–sea masks are considered. As a leading hurricane modeling system, the National Centers for Environmental Prediction (NCEP) Hurricane Weather Research Forecast (HWRF) system has been widely used in both operational and research environments for studying hurricanes in different basins. In this study, high-resolution land–sea mask datasets are introduced to the nested domain of HWRF, for the first time, as an attempt to improve hurricane wind forecasts. Four destructive North Atlantic hurricanes (Harvey and Irma in 2017; and Florence and Michael in 2018), which brought historic wind damage and storm surge along the Eastern Seaboard of the United States and Northeastern Gulf Coast, were selected to demonstrate the methodology of extending the capability to HWRF, due to the introduction of the high-resolution land–sea masks into the nested domains for the first time. A preliminary assessment of the numerical experiments with HWRF shows that the introduction of high-resolution land–sea masks into the nested domains produce significantly more accurate definitions of coastlines, land-use, and soil types. Furthermore, the high-resolution land–sea mask not only improves the quality of simulated wind information along the coast, but also improves the hurricane track, intensity, and storm-size predictions.

Keywords: hurricane; tropical cyclone; HWRF; high-resolution land–sea masks; numerical experiment; COASTAL Act

1. Introduction Hurricanes are characterized by maximum sustained winds exceeding 74 mph (64 knots). The eye of a hurricane and the strong wind associated with the eyewall can vary considerably [1,2]. Damaging winds, devastating storm surge, ﬂooding rains, and tornadoes are the greatest threats to coastal areas associated with landfalling hurricanes. Therefore, an accurate description of wind and water forecasts

Atmosphere 2020, 11, 888; doi:10.3390/atmos11090888 www.mdpi.com/journal/atmosphere Atmosphere 2020, 11, 888 2 of 17 Atmosphere 2020, 11, x FOR PEER REVIEW 2 of 17 bywater numerical forecasts simulation by numerical models simulation is needed models to prepare is needed for theto prepare potential for threats the potential and impacts threats from and landfallingimpacts from hurricanes landfalling and hurricanes for post-storm and for assessments. post-storm assessments. The Named Storm Event Model (NSEM) is a high-resolutionhigh-resolution hindcast system, which is being developed to meet the requirements requirements of of the the National National Oceanic Oceanic and and Atmospheric Atmospheric Administration’s Administration’s (NOAA)(NOAA) Consumer OptionOption for an Alternative System to Allocate Losses (COASTAL) (COASTAL) Act of 2012 2012 [3]. [3]. The assessments generated by the NSEM will indicate the timing and strength of damaging winds and water at a given location in the areaarea impactedimpacted byby thethe hurricane.hurricane. The NSEM is a coupledcoupled system of atmosphere, inland flooding,flooding, wave, and stormstorm surgesurge withinwithin aa flexibleflexible framework.framework. The detailed workflowworkflow of the the NSEM NSEM is is shown shown in in Figure Figure1 . 1. The The Hurricane Hurricane Weather Weather Research Research and and Forecasting Forecasting (HWRF;(HWRF; [[4,54,5]) model model has has been been selected selected as as the the atmospheric atmospheric componentcomponent of the NSEM to provide atmospheric forcing forcing fields fields for for the the wave wave/surge/surge simulation simulation in NSEM. in NSEM. Therefore, Therefore, the quality the quality of the HWRF of the atmosphericHWRF atmospheric forcing fieldsforcing in thefields entire in the simulation entire simulation domain, especially domain, especially in coastal areasin coastal where areas hurricanes where makehurricanes landfall, make will landfall, be one ofwill the be key one components of the key tocomponents the success to of the the success NSEM systemof the NSEM in the COASTALsystem in Actthe COASTAL program. Act program.

Figure 1. 1. TheThe design design of the of Named the Named Storm StormEvent Model Event (NSEM Model) framework (NSEM) framework (pre-decisional). (pre-decisional). The NSEM Theincludes NSEM the includes ADvanced the ADvanced CIRCulation CIRCulation (ADCIRC; (ADCIRC; [6]) model [6]) model as the as hydrodynamic the hydrodynamic component, component, the theWAVEWATCH WAVEWATCH III (WW3; III (WW3; [7,8]) [as7, 8the]) aswave the model, wave model,the Hurricane the Hurricane Weather Weather Research Research Forecast Forecast(HWRF) (HWRF)model as model the atmospheric as the atmospheric component, component, and, in and, the infuture, the future, the National the National Water Water Model Model (NWM) (NWM) as the as theinland inland hydrologi hydrologicalcal component. component. “Atmospheric “Atmospheric Data” Data” indicates indicates that that the theatmospheric atmospheric forcing forcing fields ﬁelds are areprovided provided by the by thehurricane hurricane simulation simulation with with the theHWRF HWRF model model [3]. [3].

The HWRF modeling system, which has been operational at the NOAA’s National Centers for Environmental Prediction (NCEP), has undergone significant signiﬁcant advances over the past several years, and it hashas evolvedevolved asas aa uniqueunique high-resolutionhigh-resolution atmosphere–ocean-waveatmosphere–ocean-wave coupledcoupled system providingproviding real-timereal-time forecast forecast guidance guidance for for all all global global tropical tropical cyclones cyclones [4[4,9,9].]. Hurricane track and intensity intensity forecasts from thethe HWRFHWRF modelmodel outperformoutperform manymany otherother operationaloperational forecastforecast guidanceguidance available to the forecastersforecasters [[4,104,10].]. Advancements in in current forecast capabilities have resulted in a demand for generating more more accurate accurate hurricane hurricane reanalysis reanalysis and reforecast and reforecast datasets datasets with a wide with range a wide of applications. range of Experimentsapplications. haveExperiments been conducted have been with conducted the NSEM andwith include the NSEM reruns and of historicallyinclude reruns signiﬁcant of historically tropical cyclones,significan usingt tropical the operational cyclones, using HWRF the model. operational These experiments HWRF model. provide These valuable experiments information provide on hurricanevaluable information analysis for landfallingon hurricane storms analysis which for islandfalling needed for storms downstream which marineis needed/coastal for downstream applications undermarine/coastal the COASTAL applications Act. under the COASTAL Act. High-resolution wind information is critical for accurate forecasting of landfalling hurricanes. The operational HWRF system has a triple-nest capability with horizontal grid resolutions of 13.5, 4.5, and 1.5 km. The same resolutions were originally used for the land–sea mask, to avoid dealing

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High-resolution wind information is critical for accurate forecasting of landfalling hurricanes. The operational HWRF system has a triple-nest capability with horizontal grid resolutions of 13.5, 4.5, and 1.5 km. The same resolutions were originally used for the land–sea mask, to avoid dealing with inconsistent land–sea masks between the parent and nested domains [11]. As a result, details of the land–sea features within the nested domains (4.5 and 1.5 km) are lost due to the use of the coarser resolution (13.5 km) land–sea mask dataset. In this study, we introduce the high-resolution land–sea masks for the inner HWRF nests (4.5 and 1.5 km) into the operational HWRF system for a more accurate definition of coastlines, land-use, and soil categories. Four destructive North Atlantic hurricanes (Harvey and Irma in 2017; and Florence and Michael in 2018), which brought historic wind damage and storm surge along the Eastern Seaboard of the United States and Northeastern Gulf Coast, were selected to demonstrate the methodology of introducing the high-resolution land–sea masks in HWRF. A set of hurricane simulations was carried out, using the HWRF model, with and without the high-resolution land–sea masks. The accurate definition of coastlines, land use, and the quality of simulated wind along the coastline were investigated, as well as a detailed assessment of the hurricane track and intensity forecasts. This paper is organized as follows: In Section2, an overview of the operational HWRF system upgrades performed by the NOAA Environmental Modeling Center (EMC) during the fiscal year 2018 is provided, as well as a detailed description of the HWRF land–sea mask issue; Section3 summarizes the storms selected from the 2017–2018 Atlantic hurricane seasons for this study; Section4 presents detailed results of structural verifications with and without high-resolution land–sea masks; and a summary and conclusions are provided in Section5.

2. HWRF System and Land–Sea Mask Issue

2.1. HWRF System The HWRF system, which is used as the atmospheric component in NSEM in this study, is an atmosphere–ocean coupled system dedicated to tropical cyclone applications [11]. Since 2007, the HWRF system has been widely applied in both operational and research environments as a leading storm simulation model. It includes the Weather Research and Forecasting (WRF) model as the software infrastructure, the Non-Hydrostatic Mesoscale Model on the E Grid (NMM-E) as the dynamic core, the Message Passing Interface Princeton Ocean Model for Tropical Cyclone (MPIPOM-TC) as the ocean model component, and the NCEP coupler. HWRF employs a suite of advanced physical parameterizations developed for tropical cyclone applications [12]. These parameterizations include the NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL) surface-layer parameterization, the Ferrier–Aligo microphysics, the Noah land surface model (LSM; [13]), the Rapid Radiative Transfer Model (RRTMG; [14]) for general circulation model radiation schemes, the Global Forecast System (GFS) Hybrid Eddy Diﬀusivity Mass-Flux (Hybrid-ESMF) planetary boundary layer (PBL) scheme [15], and the scale-aware GFS simpliﬁed Arakawa–Schubert (SAS) deep and shallow convection scheme [16]. The HWRF model has three nested grids, at 13.5, 4.5, and 1.5 km, for the parent domain, intermediate nest, and innermost domains, respectively (Figure2). The parent domain is approximately 77.2 77.2 , with its center determined by the initial location of the storm’s position and the 72-h ◦ × ◦ forecast from National Hurricane Center (NHC) or the Joint Typhoon Warning Center (JTWC), while the intermediate domain covers ~17.8 ~17.8 and the innermost domain covers ~5.9 ~5.9 . Both of ◦ × ◦ ◦ × ◦ the nested domains move and are centered on the storm. They are also two-way interactive, (i.e., model information is exchanged between the nested domains and their parent domain). Additionally, the HWRF system has two ghost domains, which are generated for storm (ghost-d02) and inner-core environment (ghost-d03) data assimilations [17]. They have the same spatial resolutions as the nested domains. However, the ghost domains have a larger spatial coverage (20 20 and 11 11 ). ◦ × ◦ ◦ × ◦ Atmosphere 2020, 11, 888 4 of 17

The ghost-d02 domain suﬃciently covers the entire storm and the near-storm environment, while the ghost-d03Atmosphere 20 domain20, 11, x FOR is used PEER primarily REVIEW to assimilate aircraft reconnaissance data. 4 of 17

Figure 2. The HWRF model forecast domains are indicated by the 13.5 km parent domain (d01, blue region) and twotwo nestednested vortex-followingvortex-following domains (d02 and d03, 4.5 and 1.5 km, andand purplepurple solid boxes), while the HWRF data assimilation domains are depicted by the ghost domains (ghost-d02(ghost-d02 and ghost-d03,ghost-d03, black black dashed dashed lines). lines). The The uniﬁed unified Atlantic Atlantic Message Message Passing Passing Interface Interface Princeton Princeton Ocean ModelOcean forModel Tropical for Tropical Cyclone Cyclone (MPIPOM-TC) (MPIPOM domain-TC) domain is denoted is denoted by the red by box.the red box.

The HWRF HWRF system system is triggered, is triggered, operationally, operationally, when whenNHC or NHC JTWC or identifies JTWC identifiesan area or disturbance an area or thatdisturbance has the potentialthat has the of becomingpotential of a becoming tropical depression. a tropical depression. Once a disturbance Once a disturbance is identified, is theidentified, HWRF systemthe HWRF is executed system is four executed times dailyfour times (0000, daily 0600, (0000, 1200, 0600, and 18001200 UTC), and 1800 to generate UTC) to five-day generate hurricane five-day forecastshurricane (hurricane forecasts track, (hurricane intensity, track, structure, intensity, and structure, rainfall). Theand HWRFrainfall). is no The longer HWRF executed is no when longer a tropicalexecuted cyclone when a either tropical dissipates cyclone after either making dissipates landfall, after becomes making extratropical,landfall, becomes or degenerates extratropical, into or a remnantdegenerates low when into a convection remnant becomeslow when disorganized convection around becomes the center disorganized of circulation. around A morethe center detailed of descriptioncirculation. ofA more this state-of-the-art detailed description tropical of cyclone this state forecast-of-the system-art tropical may becyclone found forecast in the HWRF system users’ may guidebe found [11 ].in the HWRF users’ guide [11].

2.2. Issues with Land Land–Sea–Sea Masks in HWRF Static geographical conditions, such as terrain and land–sealand–sea masks, provide important external forcings on on the the dynamics dynamics andand thermodynamics thermodynamics ofof the the HWRF HWRF system.system. Therefore, a vital aspect in numerical weather prediction is in properlyproperly representingrepresenting geographicalgeographical conditionsconditions (topography(topography and land-useland-use type) on thethe modelmodel gridgrid andand withwith respectrespect toto resolution.resolution. In this study, thethe 22 arcarc minutesminutes (approximately(approximately 44 km)km) horizontalhorizontal gridgrid spacingspacing topographytopography isis thethe only static high-resolutionhigh-resolution dataset in the HWRF system. Before the HWRF model forecast forecast is executed, executed, the WRF Preprocessing SystemSystem (WPS)(WPS) is used to interpolate topographic information from prescribed high-resolution-terrainhigh-resolution-terrain datasets to the required grid resolutions over the nested domains to ensure the movablemovable nestsnests alwaysalways havehave accessaccess toto high-resolutionhigh-resolution topography.topography. However, due toto pragmaticpragmatic considerations, the the land–sea land–sea masks masks for for the the two two inner inner nest nestdomains domains within within the FY18 the operational FY18 operational HWRF systemHWRF system were designed were designed to be identical to be identical to the coarser-resolutionto the coarser-resolution parent parent domain domain to avoid to avoid dealing dealing with diwithﬀerent differen deﬁnitionst definitions of land of/sea land/sea points forpoints moving for moving nests [11 nests]. This [11]. means This the means resolution the resolution of the land–sea of the maskland–sea in the mask innermost in the innermost domain (d03) domain of the (d03) FY18 of operational the FY18 operational HWRF system HWRF is not system 1.5 km, is butnot 13.51.5 km, km, retainingbut 13.5 km, the sameretaining resolution the same as theresolution parent as domain. the parent Note domain that the. Note land–sea that maskthe land in HWRF–sea mask is the in proportionHWRF is the of land, proportion as opposed of land, to ocean as opposed or inland towaters ocean (coastal or inland waters, waters lakes, (coastal reservoirs, waters, and rivers), lakes, inreservoirs a numerical, and modelrivers), grid in a box. numerical model grid box.

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In order to provide more realistic wind information in areas near the coastline during hurricane landfall, high-resolution land–sea masks for the nested domains were introduced in the NCEP HWRF system. The high-resolution land–sea masks were developed to meet the requirements of NOAA COASTAL Act of 2012. Rather than using the same land–sea masks as the outermost coarse parent domain, the two storm-following moving nests use the high-resolution land–sea masks together with the land-use and soil types compatible with their own grid resolutions. Meanwhile, for the parent–nest interaction, a nearest point search algorithm is utilized to search for the nearest point with the same land-use type when the parent and nested domains have inconsistent land–sea masks at collocated grids. When the parent and nest domains interact with each other, especially when the storm-following nests move within their parent domains, special treatment is needed to provide proper values for those land-use type related variables (e.g., land/water surface temperature, soil temperature, and soil moisture) when the collocated parent/nest grid boxes have inconsistent land–sea masks. A nearest point search method is utilized to provide proper values for those land-use type related variables from a nearest point with the same land–sea mask. For example, considering the situation when the nested domain moves within its parent domain, the leading edge of the nested grid will need get values from its coarse parent grid. For a water point in the nested grid, if its collocated or nearest parent grid point is a water point, it will take the value from that parent grid point. If the collocated or nearest parent grid point is a land point, then it will search for the next nearest water point among the four surrounding parent grid points and take that value instead. If all the four surrounding coarse grid points are land points, which means this is a one-cell lake/ocean point, it will then take the value from its nearest parent point and ignore the land–sea mask diﬀerence. A similar method is used when the nested grid point is a land point. Of course, this will introduce some additional input/output and memory considerations, as well as communications during the model integration. However, the overall overhead added for the model forecast wall clock time is negligible (less than 2%).

3. Storms Selection from the 2017–2018 Atlantic Hurricane Seasons The COASTAL Act (2012) requires NOAA to produce detailed “post-storm assessments” in the aftermath of a damaging tropical cyclone that strikes the US or its territories. Therefore, high-resolution wind information is needed in the areas close to the coastline during landfall. To meet this requirement, four destructive landfalling Atlantic hurricanes (Harvey and Irma in 2017; and Florence and Michael in 2018) were selected to investigate the successful implementation of the high-resolution land–sea masks for the moving nested domains in the HWRF system. The NHC best tracks for these four Atlantic hurricanesAtmosphere 2020 are, 11 shown, x FOR PEER in Figure REVIEW3. 5 of 17

Figure 3. The National Hurricane Center (NHC) best tracks for the four Atlantic hurricanes used in Figure 3. The National Hurricane Center (NHC) best tracks for the four Atlantic hurricanes used in this study. this study.

In order to provide more realistic wind information in areas near the coastline during hurricane landfall, high-resolution land–sea masks for the nested domains were introduced in the NCEP HWRF system. The high-resolution land–sea masks were developed to meet the requirements of NOAA COASTAL Act of 2012. Rather than using the same land–sea masks as the outermost coarse parent domain, the two storm-following moving nests use the high-resolution land–sea masks together with the land-use and soil types compatible with their own grid resolutions. Meanwhile, for the parent– nest interaction, a nearest point search algorithm is utilized to search for the nearest point with the same land-use type when the parent and nested domains have inconsistent land–sea masks at collocated grids. When the parent and nest domains interact with each other, especially when the storm-following nests move within their parent domains, special treatment is needed to provide proper values for those land-use type related variables (e.g., land/water surface temperature, soil temperature, and soil moisture) when the collocated parent/nest grid boxes have inconsistent land– sea masks. A nearest point search method is utilized to provide proper values for those land-use type related variables from a nearest point with the same land–sea mask. For example, considering the situation when the nested domain moves within its parent domain, the leading edge of the nested grid will need get values from its coarse parent grid. For a water point in the nested grid, if its collocated or nearest parent grid point is a water point, it will take the value from that parent grid point. If the collocated or nearest parent grid point is a land point, then it will search for the next nearest water point among the four surrounding parent grid points and take that value instead. If all the four surrounding coarse grid points are land points, which means this is a one-cell lake/ocean point, it will then take the value from its nearest parent point and ignore the land–sea mask difference. A similar method is used when the nested grid point is a land point. Of course, this will introduce some additional input/output and memory considerations, as well as communications during the model integration. However, the overall overhead added for the model forecast wall clock time is negligible (less than 2%).

3. Storms Selection from the 2017–2018 Atlantic Hurricane Seasons The COASTAL Act (2012) requires NOAA to produce detailed “post-storm assessments” in the aftermath of a damaging tropical cyclone that strikes the US or its territories. Therefore, high- resolution wind information is needed in the areas close to the coastline during landfall. To meet this requirement, four destructive landfalling Atlantic hurricanes (Harvey and Irma in 2017; and Florence and Michael in 2018) were selected to investigate the successful implementation of the high- resolution land–sea masks for the moving nested domains in the HWRF system. The NHC best tracks for these four Atlantic hurricanes are shown in Figure 3.

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In addition, Table1 provides a short summary of these four selected hurricanes, based on the NOAA NHC’s tropical cyclone reports.

Table 1. Summary of the selected hurricane cases in this study. More detailed information is available in the National Oceanic and Atmospheric Administration (NOAA) NHC’s Tropical Cyclone Reports. Intensity categories for hurricanes are listed according to the Saﬃr–Simpson scale. Cat indicates category. The kt indicates knot, which is a unit of speed equal to one nautical mile per hour (approximately 1.15 mph or 1.85 km/h).

Number of Storm Peak Lowest Highest Landfalling Period Veriﬁable Name Intensity Pressure Wind in US Forecasts 17 August–01 Texas and Harvey Cat 4 937 hPa 115 kt 40 September 2017 Louisiana 30 August–12 Irma Cat 5 914 hPa 155 kt Florida 40 September 2017 31 August–September Florence Cat 4 950 hPa 115 kt Carolinas 66 17 2018 October 07–October Michael Cat 5 920 hPa 135 kt Florida 20 12 2018

The 2017 Atlantic hurricane season featured 17 named storms, 10 hurricanes, and 6 major hurricanes. Harvey and Irma (2017), two of the costliest hurricanes in US history, brought widespread death and destruction to Texas and Florida, respectively. Hurricane Irma developed into one of the most intense storms of the season in the Atlantic, as it moved over the open warm ocean for a long distance, without encountering land. Hurricane Irma soared to Category 5 strength. The storm caused damage from high winds, prolonged rainfall and flooding, and severe convective storms that were recorded well inland from the initial landfall location. Hurricanes Florence and Michael (2018) were selected in this high-resolution land–sea mask case study due to the highly significant impacts of these two very different hurricanes. Hurricane Florence was a destructive hurricane that produced historic flooding in parts of North Carolina and South Carolina. Hurricane Florence made landfall near Wrightsville Beach, North Carolina as a Category 1 storm on 14 September 2018, after it had weakened over the Atlantic Ocean from Category 4 intensity four days earlier. Over the course of a seven-day period, widespread catastrophic flooding occurred across the impacted region after many areas received 15 to 35 inches of rainfall. Unlike Florence, Michael developed much closer to the continental United States and underwent rapid intensification. Michael was a catastrophic hurricane that produced historic storm surge and wind damage across parts of Florida, Alabama, and Georgia. Hurricane Michael made landfall on 10 October 2018, near Mexico Beach, Florida, as a Category 5 storm, with maximum sustained winds of 135 knots.

4. Numerical Experiments and Results Two experiments were carried out to assess the impact of the high-resolution land–sea masks in the HWRF system. The first experiment (hereafter CTRL) was comprised of the hurricane analyses and forecasts, using the operational FY18 HWRF system, where the 13.5 km land–sea mask was used for all three domains. The second experiment (hereafter HMSK) was configured identical to CTRL, but with the high-resolution land–sea masks in the inner nested domains. Therefore, the resolution of the land–sea mask in HMSK is consistent with the model grid resolution of each domain (13.5, 4.5, or 1.5 km). A case study conducted for Hurricane Irma (2017) and the verification of all four selected hurricane forecasts generated by CTRL and HMSK are discussed in this section. In the Hurricane Irma case study, the timeseries of HWRF simulated wind speeds are validated with NOAA National Data Buoy Center Atmosphere 2020, 11, x FOR PEER REVIEW 7 of 17

AtmosphereIrma case2020 study,, 11, 888the timeseries of HWRF simulated wind speeds are validated with NOAA National7 of 17 Data Buoy Center (NDBC) buoys [18] along the coast. The meteorological information collected at (NDBC)NDBC locati buoysons [18 are] along widely the used, coast. due The to meteorological their high quality, information long duration, collected and at availability NDBC locations of wave are widelymeasurements used, due for tohydrodynamic their high quality, model long evaluations. duration, The and NHC availability best-track of wavedata [19], measurements which include for hydrodynamican official “best model track” evaluations. chronology Theof storm NHC center best-track locations, data [ 1910], m which maximum include wind an o speeds,fficial “best and track”wind chronologyradii, are used of as storm a reference center locations, for verification 10 m maximum. wind speeds, and wind radii, are used as a reference for verification. 4.1. Hurricane Irma Case Study 4.1. Hurricane Irma Case Study 4.1.1. The Impact of High-Resolution Land–Sea Masks 4.1.1. The Impact of High-Resolution Land–Sea Masks One cycle simulation of Hurricane Irma was selected as the case study to investigate the impact of highOne-resolution cycle simulation land–sea of masks Hurricane within Irma the was HWRF selected system. as the Hurricane case study Irma to investigate (2017) was the the impact costliest of high-resolutionstorm in the history land–sea of the masks US within state of the Florida. HWRF To system. clearly Hurricane demonstrate Irma the (2017) impact was of the the costliest high- stormresolution in the land history–sea ofmask the USon multiple state of Florida. atmospheric To clearly field demonstrate configuration thes, impacta cycle valid of the at high-resolution 0000 UTC, 11 land–seaSeptember mask 2017 on was multiple selected atmospheric for a more fielddetailed configurations, analysis because a cycle Irma valid made at 0000 landfall UTC, near 11 September this time. 2017The was accuracy selected forof the a more land detailed–sea mask analysis distribution because along Irma the made coastline landfall was near assessed this time. first. Compared with The Figure accuracy 4a, it of i thes clear land–sea that maskthe high distribution-resolution along mask the coastline in Figure was 4b assessed provides first. an Compared improved withrepresentation Figure4a,it for is clearFlorida that in the the high-resolution United States, mask especially in Figure in 4theb provides regions anclose improved to the ocean representation or inland forwaters, Florida which in the is United better represented States, especially after in using the regions the high close-resolution to the ocean land– orsea inland masks waters, in the which HWRF is betterinnermost represented domain after (d03). using the high-resolution land–sea masks in the HWRF innermost domain (d03).

(a) (b)

Figure 4. The distribution of the land land–sea–sea masks for the innermost domain (d03) covering the state of Florida is shown in the Hurricane Irma (2017) case study before/after before/after introducing the high high-resolution-resolution land–sealand–sea maskmask intointo HWRF.HWRF. The The date date for for this this simulation simulation cycle cycle is atis 0000at 0000 UTC UTC 11 SeptemberSeptember 2017.11, 2017. (a) The (a) land–seaThe land– mask’ssea mask’s resolution resolution in d03 in is d03 the is same the assame the as parent the parent domain domain (d01, 13.5 (d01, km) 13.5 in km) the FY18in the HWRF. FY18 (HWRFb) The. land–sea(b) The land mask’s–sea resolution mask’s resolution in d03 is in 1.5 d03 km is after 1.5 km applying after applying the high-resolution the high-resolution land–sea land mask– method.sea mask Themethod. land–sea The maskland–sea is dimensionless mask is dimensionless (0 = water; (0 1== water;land). 1 = land).

Furthermore, the the 13.5 13.5 km km coarse coarse resolution resolution of the of land–sea the land masks–sea masks in the HWRFin the HWRFinnermost innermost domain (d03)domain reduces (d03) thereduces capability the capability to accurately to accurately analyze the anal spatialyze the distribution spatial distribution of atmospheric of atmospheric ﬁelds at veryfields high at very spatial high (1.5 spatial km) ( resolutions.1.5 km) resolutions. Figure5 Figurea,c illustrates 5a,c illustrate the impacts the toimpact U10 andto U10 V10 and (U V10 and (U V componentsand V components of wind of speed wind at speed 10 m aboveat 10 m the above land /thesea land/sea surface) fromsurface) the low-resolutionfrom the low-resolution land–sea masksland– insea the masks current in the operational current operational HWRF. The HWRF jagged. The pattern jagged ofpattern the U10 of the and U10 V10 and wind V10 distribution wind distribution along thealong coastline the coastline is due tois due the coarseto the resolutioncoarse resolution (13.5 km) (13.5 of thekm) land–sea of the land mask–sea from mask the from parent the domain. parent Afterdomain implementing. After implementing the high-resolution the high-resolution land–sea masks, land–sea high ma resolutionsks, high windresolution information wind information is produced inis produced the area along in the the area coastline along the during coastline hurricane during landfall hurricane (shown landfall in Figure (shown5b,d). in Figure 5b,d).

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(a) (b)

FigureFigure 5. 5. AA comparison comparison of of 10 10 m m U U and and V V components components of of wind wind speed speed with/without with/without the the high high-resolution-resolution landland–sea–sea masks masks within within the the innermost innermost domain domain (d03) (d03) for for Hurricane Hurricane Irma Irma (2017) (2017) at at 0000 0000 UTC UTC,, September September 1111,, 2017. 2017. ( (aa,c,c)) U10 U10 and and V10 V10 with with the the coarse coarse resolution resolution (13.5 (13.5 km) km) of of land land–sea–sea masks masks in in the the F FY18Y18 HWRF; HWRF; ((bb,,dd)) U10 U10 and and V10 V10 with with the the high high resolution resolution (1.5 (1.5 km) of land land–sea–sea masks.

4.1.2.4.1.2. Ten Ten-Meter-Meter Wind Wind Forecast Forecast Verification Verification against NDBC Buoys VerificationVerificationof of windwind forecasts forecasts by by HWRF HWRF were were conducted conducted by usingby using NDBC NDBC buoy buoy measurements. measurements. Buoy Buoydata were data obtained were directly obtained from NOAAdirectly NDBC’s from online NOAA data archives NDBC’s (http: online//www.ndbc.noaa.gov data archives/). (Notehttp://www.ndbc.noaa.gov/ that the velocity profile). is Note logarithmic that the where velocity one profile variable is islogarithmic the surface where roughness. one variable Land surfaces is the surfacehave di ffroughness.erent issues Land than surfaces water surfaces. have different For water issues surfaces, than the water roughness surfaces. changes For water as a function surfaces, of the the roughnessurface stress,s changes which as is a infunction turn modified of the surface by stratification. stress, which The is estimation in turn modified of Wind by Speed stratification. measurement The estimationat NDBC, whichof Wind is Speed not at 10measurement m, raised or at lowered NDBC, towhich a height is not of at 10 10 m m, following raised or a lowered logarithmic to a height profile ofcalculation 10 m following method a logarithmic [20]. Figure 6profile shows calculation the locations method of the [20 NDBC]. Figure buoy 6 measurementsshows the locations selected of the for NDBCthe performance buoy measurements assessment ofselected 10 m wind for the forecasts performance from CTRL assessment and HMSK of 10 for m Hurricane wind forecasts Irma. A from total CTRLof 43 stationsand HMSK were for available Hurricane along Irma. the A hurricane total of 43 track stations and withinwere available close vicinity along of the hurricane hurricane landfall, track andon both within sides close of thevicinity Florida of hurricane Peninsula, landfall, and were on usedboth forsides the of case the studyFlorida wind Peninsula, forecast and assessment. were used for the case study wind forecast assessment.

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Figure 6. National Data Buoy Center (NDBC) observations near Hurricane Irma’s landfall, used for Figure 6. National Data Buoy Center (NDBC) observations near Hurricane Irma’s landfall, used for HWRF validation, are indicated by black dots. Magenta depicts Hurricane Irma’s best track. HWRF validation, are indicated by black dots. Magenta depicts Hurricane Irma’s best track. Figure7 shows the qualitative assessment of the 10 m wind speed simulations for two experiments Figure 7 shows the qualitative assessment of the 10 m wind speed simulations for two (CTRL and HMSK). The results were calculated on the basis of computing bulk statistics of modeled experiments (CTRL and HMSK). The results were calculated on the basis of computing bulk statistics wind speed at 10 m above mean sea level relative to available NDBC buoy measurements. The time of modeled wind speed at 10 m above mean sea level relative to available NDBC buoy measurements. period is from 7 to 11 September 2017, when Hurricane Irma had the most destructive wind speeds The time period is from 7 to 11 September 2017, when Hurricane Irma had the most destructive wind around landfall. The bias error and root-mean-square errors (RMSEs) of the 10 m wind speed speeds around landfall. The bias error and root-mean-square errors (RMSEs) of the 10 m wind speed simulations, as a function of forecast lead time, are shown in Figure7a,b. A noticeable improvement simulations, as a function of forecast lead time, are shown in Figure 7a,b. A noticeable improvement for the point source observations is revealed from the HMSK experiment with the high-resolution for the point source observations is revealed from the HMSK experiment with the high-resolution land–sea mask in the moving nested domains. In addition, an improvement is shown in Figure7c land–sea mask in the moving nested domains. In addition, an improvement is shown in Figure 7c for for the skill score of 10 m wind speed forecast error, which was calculated from the 120-h forecast by the skill score of 10 m wind speed forecast error, which was calculated from the 120-hour forecast by comparing the 10 m wind speed forecast error of HMSK to CTRL, using the following equation: comparing the 10 m wind speed forecast error of HMSK to CTRL, using the following equation: Skill score = 1 (errorHMSK/errorCTRL) (1) Skill score = 1− − (errorHMSK/errorCTRL) (1) TheThe modelmodel skill statistics in in Figure Figure 7c7c also also show showss the the accuracy accuracy improvement improvement from from HMSK HMSK for for the Atmospheretheentire 20 entire20 ,forecast 11, forecast x FOR time PEER time period, REVIEW period, with with the theexception exception of the of first the ﬁrst couple couple of hours, of hours, due dueto model to model spin spin-up,-up, and 10 of 17 andthe 72 the–84 72–84 h time h time period. period.

(a)

Figure 7. Cont.

(b)

(c)

Figure 7. Performance comparison of 10 m winds relative to NDBC buoy measurements (shown in Figure 6) during Hurricane Irma landfall in Florida. (a) The absolute error, (b) root-mean-square error (RMSE), and (c) model skill as a function of the forecast lead time between control run and high- resolution land–sea mask.

4.2. Verification of HWRF Hurricane Predictions

4.2.1. Track Forecast Verification Hurricane track verifications for these selected Atlantic hurricanes were examined. Track forecast errors, which are defined as the distance between the predicted storm center and the best track location, were conducted for all hurricane cases. The NHC post-processed best-track data (bdeck) in the Automated Tropical Cyclone Forecasting (ATCF) format were utilized, along with HWRF model-generated tracker outputs (adeck) [21]. The cases selected for track forecast verification in Figure 8 include four Atlantic hurricanes in 2017–2018 which made landfall along the US East Coast (shown in Table 1). When comparing model performance between the two experiments (CTRL and HMSK), all forecast cycles (except the early invest cycles, when the storm’s initial intensity is weaker than 35 kt or the late forecast cycles when

Atmosphere 2020, 11, x FOR PEER REVIEW 10 of 17

Atmosphere 2020, 11, 888 10 of 17 (a)

(b)

(c)

Figure Figure7. Performance 7. Performance comparison comparison of of 10 10 m m winds relative relative to NDBCto NDBC buoy buoy measurements measurements (shown in (shown in Figure 6)Figure during6) during Hurricane Hurricane Irma Irma landfall landfall in in Florida. Florida. (a ()a The) The absolute absolute error, ( b(b)) root-mean-square root-mean-square error error (RMSE), and (c) model skill as a function of the forecast lead time between control run and (RMSE), and (c) model skill as a function of the forecast lead time between control run and high- high-resolution land–sea mask. resolution land–sea mask. 4.2. Veriﬁcation of HWRF Hurricane Predictions

4.2. Verification4.2.1. Track of ForecastHWRF VerificationHurricane Predictions Hurricane track verifications for these selected Atlantic hurricanes were examined. Track forecast 4.2.1. Track Forecast Verification errors, which are defined as the distance between the predicted storm center and the best track Hurricanelocation, were track conducted verification for alls hurricanefor these cases. selected The NHC Atlantic post-processed hurricanes best-track were data examined. (bdeck) Track forecastin errors, the Automated which are Tropical defined Cyclone as the Forecasting distance (ATCF) between format the were predicted utilized, storm along withcenter HWRF and the best model-generated tracker outputs (adeck) [21]. track location,The cases were selected conducted for track for forecast all hurricane verification cases. in Figure The8 include NHC post four- Atlanticprocessed hurricanes best-track data (bdeck)in in 2017–2018 the Automated which made Tropical landfall Cyclone along the Forecasting US East Coast (ATCF) (shown in format Table1). were When utilized, comparing along with HWRF model performance-generated between tracker the outputs two experiments (adeck) [21]. (CTRL and HMSK), all forecast cycles (except the Theearly cases invest selected cycles, for when track the storm’sforecast initial verification intensity is in weaker Figure than 8 35include kt or the four late Atlantic forecast cycles hurricanes in 2017–20when18 which the storm made has landfall already transitionedalong the US into East an extra-tropical Coast (shown cyclone) in Table were included1). When as comparing verifiable model forecast cycles against the best track data. Therefore, there are 166 verification forecast cycles available performancefrom all between of the selected the two storms exp ineriments this study. (CTRL The average and HMSK), track forecast all forecast error of cycles HMSK, (except with the the early invest cycleshigh-resolution, when the land–sea storm’s mask, initial is close intensity to or lower is than weaker the CTRL than experiment 35 kt or forthe almost late forecast all of the leadcycles when times throughout 120 h. In the first 48 h, the HMSK experiment has a relatively neutral impact on the track forecast, but the track forecast error of HMSK is substantially smaller than CTRL from 48 to Atmosphere 2020, 11, x FOR PEER REVIEW 11 of 17 the storm has already transitioned into an extra-tropical cyclone) were included as verifiable forecast cycles against the best track data. Therefore, there are 166 verification forecast cycles available from all of the selected storms in this study. The average track forecast error of HMSK, with the high- resolution land–sea mask, is close to or lower than the CTRL experiment for almost all of the lead Atmosphere 2020, 11, 888 11 of 17 times throughout 120 h. In the first 48 h, the HMSK experiment has a relatively neutral impact on the track forecast, but the track forecast error of HMSK is substantially smaller than CTRL from 48 to 120 h120. The h. The95% 95%confidence confidence intervals, intervals, which which represent represent statistically statistically significant significant pattern pattern correlati correlations,ons, were were calculatedcalculated to to quantify quantify the the uncertainty uncertainty in in the the average average track track forecast forecast error error and and are are shown shown in in Figure Figure 88aa forfor each each lead lead time. time. This This analysis analysis is is also also used used for for intensity intensity and and storm storm size size forecast forecast verifications verifications in in the the followingfollowing discussion. discussion.

(a) (b)

FigureFigure 8. 8. PerformancePerformance comparison comparison of oftrack track forecasts forecasts between between CTRL CTRL (the (thefirst firstexperiment, experiment, red) and red) HMSKand HMSK (the second (the second experiment, experiment, blue) blue)from fromthe four the selected four selected Atlantic Atlantic hurricanes hurricanes in 2017 in– 2017–2018.2018. The numberThe number of cases of casesfor each for lead each time lead is time provided is provided at the atbottom the bottom of each of column. each column. (a) Verification (a) Verification of track of forecasttrack forecast errors errors from from experiments experiments with/without with/without high high-resolution-resolution land land–sea–sea mask. mask. Absolute Absolute average average errorerror (lines) (lines) and and standard standard deviation deviation (vertical (vertical bars) bars) are are a a function function of of forecast forecast time time and and number number of of cases. cases. TheThe error error bar bar re representspresents the the 95% 95% confidence confidence interval. interval. ( (bb)) Track Track improvements improvements (vs (vs.. CTRL) CTRL) are are shown shown forfor hurricane hurricane forecasts forecasts from from HMSK. HMSK.

AnAn improvement ofof approximatelyapproximately 5% 5% is is shown shown in in Figure Figure8b for8b thefor hurricanethe hurricane track track forecast forecast error errorskill score,skill score, which which was calculated was calculated from the from 12-h the forecast, 12-h forecast by comparing, by comparing the track the forecast track errorforecast of HMSK error ofto HMSK CTRL, usingto CTRL Equation, using (1).Equation Note that(1). theNote skill that score the skill commonly score commonly used in hurricane used in forecast hurricane veriﬁcation forecast verificationis useful for is evaluating useful for predictionsevaluating predictions of hurricane of track, hurricane intensity, track, and intensity, other parameters. and other parameters. HurricaneHurricane cross-cross- andand along-trackalong-track errors errors were were also also investigated, investigated, as shownas shown in Figure in Figure9a,c. 9a Total,c. Total track trackforecast forecast errors errors may be may decomposed be decomposed into two into components two components along and along perpendicular and perpendicular to its direction. to its direction.The errors The in termserrors of in the terms cross-track of the cross forecast-track error forecast (C) and error the (C) along-track and the along forecast-track error forecast (A) may error be (A)deﬁned may asbe follows:defined as follows: E = (C2 + A2)1/2, (2) E = (C2 + A2)1/2, (2) where E represents hurricane total track error. The cross-track forecast error (Figure9c) from two where E represents hurricane total track error. The cross-track forecast error (Figure 9c) from two experiments are similar, but the along-track forecast error from HMSK is much smaller than that of experiments are similar, but the along-track forecast error from HMSK is much smaller than that of CTRL, in Figure9a. CTRL, in Figure 9a.

Atmosphere 2020, 11, 888 12 of 17 Atmosphere 2020, 11, x FOR PEER REVIEW 12 of 17

(a) (b)

Figure 9. PerformancePerformance comparison comparison of along along-- and and cross cross-track-track forecasts between CTRL (red) and HMSK (blue) fromfrom fourfour selected selected Atlantic Atlantic hurricanes hurricanes in in 2017–2018. 2017–2018. (a) ( Along-tracka) Along-track forecast forecast error, error (b), along-track (b) along- trackbias; (bias;c) cross-track (c) cross- forecasttrack forecast error, error and (,d and) cross-track (d) cross- bias.track bias.

The along-along- andand cross-track cross-track biases biases shown shown in in Figure Figure9b,d 9b, wered were further further examined examined for hurricane for hurricane track trackforecasts, forecasts as well., as Thewell. along-track The along forecast-track forecast bias is abias component is a component of the total-track of the total forecast-track forecast bias and bias can andbe used can asbe a used measure as a ofmeasure whether of the whether forecast the is fastforecast or slow. is fast Along-track or slow. Along biases-track from twobiases experiments from two experimentshave a similar have pattern: a similar negative pattern: between negative the 24 between and 72 hthe forecast 24 and period, 72 h forecast suggesting period, slower sugges movingting slowerspeeds along moving the speeds best track along direction, the best and track positive direction, after the and 72-h positive forecast, after suggesting the 72-h fasterour forecast, moving suggestingspeeds along faster the moving best track speeds direction. along However,the best track the direction. results show However, the along-track the results forecast show the from along the- trackHMSK forecast is closer from to the the best HMSK track is than closer that to of the CTRL, best especiallytrack thanafter that 48of h.CTRL, The cross-trackespecially after forecast 48 h. bias The is crossanother-track component forecast ofbias the is total-track another component forecast bias of and the can total be-track used asforecast a measure bias ofand how can far be the used forecast as a measureis to the leftof how or right far the of the forecast verifying is to position.the left or Figure right9 ofd showsthe verifying that HMSK, position. with Figure the high-resolution 9d shows that HMSK,land–sea with mask, the ishigh close-resolution to the best land track–sea moving mask, is direction, close to the especially best track in moving the forecast direction, period especially between in48 the and forecast 96 h. period between 48 and 96 h. Based on the above track forecast performance comparison for all four landfalling storms, it is evident thethe HMSKHMSK experiment experiment produced produced better better track track forecasts forecasts for nearlyfor nearly all forecast all forecast lead times, lead times, in which in whicha major a part major of the part improvement of the improvement is reﬂected is in reflected smaller along-trackin smaller along errors.-track The HMSKerrors. experiment,The HMSK withexperiment, more realistic with more high-resolution realistic high land–sea-resolution masks, land is–sea a better masks, representation is a better represent of the land-useation of the type land and- usesurface-layer type and processes, surface-layer which processes, may have which ledto may better have steering led to ﬂows better and steering smaller flows translation and smaller speed translationbiases for both speed the biases along-track for both and the cross-track along-track biases and (Figurecross-track9b,d). biases (Figure 9b,d).

4.2.2. Intensity Forecast Verification Verification The intensity, in terms of 10 mm maximummaximum wind, wind, which definesdefines the strength and destructive capability of of a a hurricane, hurricane, is isexamined examined and and compared compared in the in two the twoexperiments experiments with/without with/without the high the- resolutionhigh-resolution land–sea land–sea mask in mask HWRF. in HWRF. The hurricane The hurricane intensity intensity forecasts forecasts were verified were against verified the against NHC bestthe NHC-track best-trackdata, as mentioned data, as mentioned earlier. Figure earlier. 10a Figureshows 10thea showsperformance the performance comparison comparison of CTRL and of HMSK intensity forecast errors, which are the absolute value of the difference between the forecast and the best-track intensity during the forecast verifying time. The error from HMSK, with the high-

Atmosphere 2020, 11, 888 13 of 17

AtmosphereCTRL and 2020 HMSK, 11, x FOR intensity PEER REVIEW forecast errors, which are the absolute value of the difference between13 of the17 forecast and the best-track intensity during the forecast verifying time. The error from HMSK, with the resolutionhigh-resolution land–sea land–sea mask, mask,is lower is lowerthan that than of that CTRL of CTRLfor nearly for nearly the entire the entireforecast forecast period period for the for four the Atlanticfour Atlantic landfalling landfalling hurricanes hurricanes that are that verified are verified in this in thisstudy. study. Overall, Overall, the theaverage average improvement improvement is approximatelyis approximately 4%, 4%, as asreflected reflected in inthe the intensity intensity forecast forecast skill skill comparison comparison in in Figure Figure 10b. 10b. Wind Wind bias bias reflectsreflects whether whether the the model model predicts predicts a a stronger stronger (positive) (positive) or or weaker weaker (negative) (negative) storm. storm. Figure Figure 10c 10c shows shows thatthat both both the the CTRL CTRL and and HMSK HMSK under-predict under-predict intensity, intensit withy, with negative negative wind wind biases biases during during most forecast most forecasthours. However,hours. However, the bias the of HMSKbias of isHMSK less than is less CTRL than after CTRL 24 after h. 24 h.

(a) (b)

(c)

FigureFigure 10. 10. PerformancePerformance comparison comparison of of intensity intensity forecasts forecasts between between CTRL CTRL (red) (red) and and HMSK HMSK (blue) (blue) from from fourfour selected selected Atlantic Atlantic hurricanes hurricanes in in 2017 2017–2018.–2018. (a ()a )Intensity Intensity forecast forecast error error,, (b (b) )intensity intensity forecast forecast skill skill relativerelative to to CTRL CTRL,, and and (c (c) )intensity intensity forecast forecast bias. bias.

4.2.3.4.2.3. Storm Storm-Size-Size Forecast Forecast Verification Verification FigureFigure 11a 11a–h–h show showss the the verification verification of of storm storm size size in in terms terms of of wind wind radii radii at at 34, 34, 50, 50, and and 64 64 kt kt thresholdsthresholds in in different different quadrants, quadrants, as as well well as as for for the the radius radius of of maximum maximum wind wind (RMW). (RMW). Here, Here, the the 34 34 kt kt windwind radii radii errors errors in in each each quadrant quadrant are are defined defined as as the the radius radius in in which which the the mean mean t tangentialangential wind wind is is equalequal to to 34 34 kt kt (and (and similarly similarly for for 50 50 and and 64 64 kt). kt). The The mean mean radii radii are are obtained obtained by bytaking taking an average an average of theof theradii radii in four in four different different quadrants quadrants [22,23 [22,].23 Except]. Except for for the the 34 34kt ktwind wind radii radii errors errors in in Figure Figure 11a, 11a, improvementsimprovements with with the the high high-resolution-resolution land land–sea–sea mask mask in in HMSK HMSK are are shown shown in in the the forecast forecast error error comparisoncomparison after after 48 48 h h forecast forecast times, times, particularly particularly for for the the 50 50 and and 64 64 kt kt wind wind radii radii errors errors (Figure (Figure 11c, 11c,e).e). SomeSome neutral neutral-to-positive-to-positive improvements, improvements, especially especially for for forecast forecast lead lead times times after after 84 84 h h,, may may also also be be seen seen inin the the RMW RMW forecast forecast errors errors and and biases biases (Figure (Figure 11g,11g,h).h).

Atmosphere 2020, 11, 888 14 of 17 Atmosphere 2020, 11, x FOR PEER REVIEW 14 of 17

(a) (b)

(e) (f)

(g) (h)

Figure 11. StormStorm size size statistics statistics between between CTRL CTRL (red) (red) and and HMSK HMSK (blue) (blue) from four selected Atlantic hurricanes in in 2017 2017–2018.–2018. (a) ( a34) 34kt, kt,(c), (50c), kt 50 (e kt), and (e), and64 kt 64 wind kt windradii error radiis errors;; (b) 34 (ktb,) ( 34d), kt,50 kt, (d), and 50 ( kt,f) 64and kt ( f) wind 64 kt radii wind bias radiies biases.. (g) Radius (g) Radius of maximum of maximum wind wind (RMW) (RMW) error error and and (h ()h bias.) bias. The The error error bar bar representsrepresents 95% confidence conﬁdence interval.

Atmosphere 2020, 11, 888 15 of 17

The reduction of strong wind radii errors indicates that HMSK, with the high-resolution land–sea mask, is able to more realistically capture the inner-core region of storms. This is expected, as storm structure and fine-scale processes are believed to be better resolved with a higher resolution land–sea mask in the moving nested domains. More specifically, the better represented air–sea–land–water interactions and surface-layer processes (including the surface friction effects), with more realistic land-use categories consistent with the domain’s own resolution, should result in more realistic surface wind distributions and thus improved wind radii forecasts. It should also be noted that, for the 34 kt wind radii, the HMSK experiment shows overall neutral results without noticeable improvements. One possible reason for this may be the domain size of the innermost nest (~5.9 ~5.9 ) is not large ◦ × ◦ enough, and the 34 kt wind radii of a storm can sometimes be close to the D03 domain boundaries or even extend outside of the innermost domain. Thus, the benefits from using the highest land–sea masks in the innermost domain will have less or no impact for those situations.

5. Summary and Conclusions In this study, high-resolution land–sea masks were successfully introduced into the moving nested domains in the HWRF system to meet the requirement of the COASTAL Act of 2012. The land–sea mask issue in HWRF was addressed upon review of the 10 m wind analysis and forecast in coastal regions and along islands during hurricane landfall. Non-realistic wind information along the coastline was produced by the HWRF system due to the coarse resolution of the land–sea mask in the innermost domain (d03), which was designed by model developers with the same coarse resolution as the parent domain to avoid dealing with different definitions of land/sea points for moving nests. The resolutions of the land–sea mask and other atmospheric variables that are likely to be influenced by the use of the high-resolution land–sea mask dataset, such as 10 m wind, skin temperature, and accumulated precipitation along coastlines, were investigated carefully after the implementation of the high-resolution land–sea masks with a case study of hurricane Irma in 2017. The high-resolution (1.5 km) spatial distribution of atmospheric fields related to the land–sea masks may be represented more accurately along coastlines in the innermost domain (d03). To further understand why the high-resolution land–sea mask produced more skillful wind forecasts than the operational system, a detailed 10 m wind forecast was evaluated by using NOAA NDBC observations in the Hurricane Irma case study. Comparisons of simulated wind information with NDBC observations show that the forecasts from HMSK captured strong maximum wind speeds more accurately along coastlines. Two sets (CTRL and HMSK) of HWRF analyses and forecasts for several destructive North Atlantic hurricanes (Harvey and Irma in 2017; Florence and Michael in 2018) were selected to validate the COASTAL Act NSEM, with the same configuration except for the high-resolution land–sea masks. The results show the higher-resolution land–sea masks in the moving nested domains improve HWRF model performance in terms of track and intensity forecasts for a majority of forecast lead times in this set of destructive North Atlantic hurricanes. The most significant improvement in the forecasts with the high-resolution land–sea masks is the reduction of intensity errors compared with operational FY18 HWRF system forecasts. In addition to highlighting the introduction of the high-resolution land–sea masks, which lead to an improvement of hurricane track and intensity forecasts, this study shows this approach may be beneficial to the quality of the wind forecasts along the coastlines during hurricane landfall. In the future, additional hurricane cases will be examined. This high-resolution land–sea mask technique will be transitioned to the next operational HWRF system implementation, along with the other updates. Atmosphere 2020, 11, 888 16 of 17

Author Contributions: Conceptualization, Z.M., B.L., and A.M.; methodology, Z.M. and B.L.; software, B.L. and Z.M.; validation, Z.M., A.A., L.Z., and K.W.; formal analysis, A.A., A.v.d.W., S.M., and Z.Z.; investigation, Z.M.; resources, Z.M., B.L., and A.A.; writing—original draft preparation, Z.M.; writing—review and editing, B.L., A.A., S.V., R.S., and A.K.; supervision, A.M. and V.T.; project administration and funding acquisition, N.K. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by The Consumer Option for an Alternative System to Allocate Losses (COASTAL) Act Program and Water Initiative project within the National Oceanic and Atmospheric Administration (NOAA). Acknowledgments: This work has been supported by the COASTAL Act program within NOAA. The authors thank Weiguo Wang and Jili Dong at NOAA/NCEP/Environmental Modeling Center (EMC) for very detailed and thorough reviews as EMC internal reviewers. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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