remote sensing

Letter Retrieval of Sea Surface Wind Fields Using Multi-Source Remote Sensing Data

Tangao Hu 1,* , Yue Li 2 , Yao Li 1, Yiyue Wu 1 and Dengrong Zhang 1

1 Provincial Key Laboratory of Urban Wetlands and Regional Change, Hangzhou Normal University, Hangzhou 311121, ; [email protected] (Y.L.); [email protected] (Y.W.); [email protected] (D.Z.) 2 Department of Geography and Resource Management, The Chinese University of , Shatin, New Territories, Hong Kong; [email protected] * Correspondence: [email protected]; Tel.: +86-0571-2886-6138

 Received: 10 April 2020; Accepted: 5 May 2020; Published: 7 May 2020 

Abstract: Timely and accurate sea surface wind field (SSWF) information plays an important role in marine environmental monitoring, weather forecasting, and other atmospheric science studies. In this study, a piecewise linear model is proposed to retrieve SSWF information based on the combination of two different satellite sensors (a microwave scatterometer and an infrared scanning radiometer). First, the time series wind speed dataset, extracted from the HY-2A satellite, and the brightness temperature dataset, extracted from the FY-2E satellite, were matched. The piecewise linear regression model with the highest R2 was then selected as the best model to retrieve SSWF information. Finally, experiments were conducted with the Usagi, Fitow, and Nari in 2013 to evaluate accuracy. The results show that: (1) the piecewise linear model is successfully established for all typhoons with high R2 (greater than 0.61); (2) for all three cases, the root mean square error () and mean bias error (MBE) are smaller than 2.2 m/s and 1.82 m/s, which indicates that it is suitable and reliable for SSWF information retrieval; and (3) it solves the problem of the low temporal resolution of HY-2A data (12 h), and inherits the high temporal resolution of the FY-2E data (0.5 h). It can provide reliable and high temporal SSWF products.

Keywords: sea surface wind fields; piecewise linear model; tropical cyclones; Northwestern Pacific; sensor interoperability

1. Introduction Tropical cyclones (TCs) are one of the most intense weather hazards out of all meteorological phenomena that form over tropical oceans [1]. In order to better prevent the disasters caused by TCs, it is necessary to continuously monitor sea surface wind field (SSWF) information, such as wind speed and direction, so as to protect life and property as much as possible [2,3]. These data contribute to improved warnings for ships at sea, and to improved global weather forecasts [4]. Accurate and high temporal resolution SSWF data are significantly important for meteorological and oceanographic applications [5,6]. Traditionally, the SSWF measurements are mainly detected or acquired from ships, buoys, and coastal monitoring stations. These measures are highly restricted, owing to the large ocean area as well as the coarsely and unevenly distributed monitoring stations. Microwave scatterometers (MSs) are often used to monitor global SSWFs due to their wide coverage and have the abilities of day-and-night operation and all-weather detection. They can be adopted to continuously and simultaneously detect marine dynamic environmental elements with high precision [7,8]. However, they are mainly sun-synchronous orbit satellites with low temporal resolution; one single satellite can only obtain

Remote Sens. 2020, 12, 1482; doi:10.3390/rs12091482 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 1482 2 of 13

1–3 views per day. As a huge and complex weather system, TCs change rapidly in one day, and daily wind speed products struggle to meet the needs of monitoring and forecasting. Geostationary orbit satellites can observe a wide area frequently at a high temporal resolution every 10–30 min under all-weather conditions and are effective at observing TCs over the ocean [9]. However, due to the cover and occlusion of clouds, both visible and infrared bands cannot penetrate the cloud, so it is impossible to directly observe the SSWFs [10]. Therefore, any single satellite source cannot meet the needs of continuous SSWF observation during periods of TCs. Considering the advantages of multi-source data, the method of multi-source data combination has been widely applied to observe SSWFs. An inversion method based on multi-source data fusion has been shown to improve the accuracy of meteorological, marine, and other products in SSWFs [11]. Some researchers have inverted the sea surface wind speed by studying the correlation between brightness temperature and wind speed. The previous results showed that brightness temperature has a significant correlation with wind speed [12,13]. In this study, the relationship between infrared brightness temperature data (obtained from an infrared scanning radiometer) and wind speed data (obtained from a microwave scatterometer) was analyzed to establish an SSWF inversion model. Taking the typhoons Usagi, Fitow, and Nari as examples, the inversion model is used to retrieve SSWFs with high temporal resolution and large coverage. All relevant materials are presented in Section2. A description of the multi-source remote sensing inversion model is presented in Section3, while Section4 introduces the inversion results and accuracy assessment. A discussion and conclusions are presented in Sections5 and6.

2. Materials

2.1. Study Area In this study, we selected the Northwestern Pacific and coastal areas as a study area (Figure1). There are, on average, 26–28 tropical storms and typhoons each year, two or three of which can reach intensity. Typhoons here are seasonal and usually occur from July to September [14]. We chose three representative typhoons (Usagi, Fitow, and Nari) that occurred in 2013 to validate the effectiveness of the proposed model. Detailed information about the TCs is described as follows.

(1) Usagi was the 19th Northwestern Pacific typhoon in 2013. The storm began at 2:00 on September 17 (UTC +8) to the east of the , and then moved to the northwest. At 5:00 the next day, it became a tropical storm. At 11:00 on September 19, it grew into a severe typhoon and soon became a super typhoon, with a maximum wind speed of 60 m/s. At 19:40 on September 22, it made landfall at Shanwei City in Province, China [1]. (2) Fitow was the 23rd Northwestern Pacific typhoon in 2013. It formed on September 30 over the Eastern Philippines and strengthened to a typhoon around 21:00 on October 2 (UTC +8). At 17:15 on October 6, it made landfall with a minimum pressure of 955 hPa and a maximum wind speed of 42 m/s at Fuding City in Province, China [15]. (3) Nari was the 25th Northwestern Pacific typhoon in 2013. The storm began at 2:00 on October 8 (UTC + 8) to the east of Philippines. At 21:00 on October 9, it became a tropical storm. At 21:00 on October 11, it made landfall for the first time at D’Aran City in Oruna Province, Philippines. It then continued to move westward, and at 7:00 on October 15 it made landfall again at Sanchi City, Quang Nan Province, . Remote Sens. 2020, 12, 1482 3 of 13 Remote Sens. 2020, 12, x FOR PEER REVIEW 3 of 13

FigureFigure 1.1. TheThe movingmoving trackstracks andand intensityintensity of of typhoons typhoons Usagi, Usagi, Fitow, Fitow, and Nari. 2.2. Data 2.2. Data 2.2.1. Microwave Scatterometer Data 2.2.1. Microwave Scatterometer Data The HY-2A satellite (HaiYang-2A is a Chinese sun-synchronous polar orbiting marine satellite, whichThe was HY-2A launched satellite in August (HaiYang-2A 2011) microwave is a Chinese scatterometer sun-synchronous is dedicated polar toorbiti determiningng marine the satellite, wind vectorwhich fieldwas launched (including in wind August speed 2011) and microwave direction) of scat theterometer sea surface. is dedicated Its swath istoabout determining 1750 km the and wind can covervector more field than(including 90% of wind global speed open and sea direction) area within of onethe daysea surface. [16]. The Its accuracy swath is of about the scatterometer 1750 km and datacan cover has been more confirmed, than 90% which of global can characterize open sea ar theea SSWFwithin [17 one]. Therefore, day [16]. weThe chose accuracy the level of the 2B (L2B)scatterometer products data of HY-2A has been as sea confirmed, surface wind which speed can data characterize from the Nationalthe SSWF Satellite [17]. Therefore, Ocean Application we chose Servicethe level (NSOAS) 2B (L2B) [products18]. of HY-2A as sea surface wind speed data from the National Satellite Ocean Application Service (NSOAS) [18]. 2.2.2. Infrared Scanning Radiometer Data 2.2.2. Infrared Scanning Radiometer Data The FY-2E satellite (FengYun-2E is a Chinese operational geostationary meteorological satellite whichThe was FY-2E launched satellite in June (FengYun-2E 2008) payload is a includesChinese theoperational Visible and geostationary Infrared Spin meteorological Scanning Radiometer satellite (VISSR),which was which launched can directly in June measure 2008) the payload thermal includes radiation the intensity Visible of and surface Infrared features. Spin It Scanning provides eRadiometerffective brightness (VISSR), temperature which can datadirectly through measure an infrared the thermal channel radiation (the spectrum intensity range of surface is 10.3–11.3 features.µm), andIt provides observations effective of the brightness brightness temperature temperature data radiation through are an conducted infrared channel twice every (the hour, spectrum with arange spatial is resolution10.3–11.3 μ ofm), 5 km and [19 observations]. The brightness of the temperature brightness datasetstemperature used radiation in this study are wereconducted downloaded twice every from thehour, National with a Satellitespatial resolution Meteorological of 5 km Centre [19]. (NSMC)The brightness [20]. temperature datasets used in this study wereAccording downloaded to from the life the cycle National of the Satellite three Meteorological cyclones mentioned Centre in (NSMC) Section [20].2.2 and the temporal resolutionAccording of the to HY-2Athe life and cycle FY-2E of the satellites, three cyclones we downloaded mentioned 2 in HY-2A Section files 2.2 and and 48 the FY-2E temporal files, respectively,resolution of for the SSWF HY-2A inversion and FY-2E and verification.satellites, we Specific downloaded information 2 HY-2A is shown files inand Table 481 FY-2E. files, respectively, for SSWF inversion and verification. Specific information is shown in Table 1.

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TableTable 1. 1.Specific Specific information information aboutabout thethe HY-2AHY-2A MSMS and FY-2E Visible and and Infrared Infrared Spin Spin Scanning Scanning RadiometerRadiometer (VISSR) (VISSR) data. data.

TyphoonTyphoon TimeTime NumberNumber of of SpatialSpatial TemporalTemporal DateDate SatelliteSatellite/Sensor/Sensor NameName RangeRange FilesFiles ResolutionResolution ResolutionResolution 2 HY-2A / MS 25 km 12 h Usagi 1818 September September 2013 00:00–23:30 2 HY-2A/MS 25 km 12 h Usagi 00:00–23:30 2013 4848 FY-2E/ /VISSR VISSR 5 5 km km 0.5 0.5 h h 22 HY-2A/ /MS MS 25 25 km km 12 12 hh FitowFitow 3 03October October 2013 2013 00:00–23:30 00:00–23:30 4848 FY-2E/ /VISSR VISSR 5 5 km km 0.5 0.5 h h 22 HY-2A/ /MS MS 25 25 km km 12 12 hh NariNari 12 12 October October 2013 2013 00:00–23:30 00:00–23:30 4848 FY-2E/ /VISSR VISSR 5 5 km km 0.5 0.5 h h

2.2.3.2.2.3. Reference Reference Datasets Datasets HY-2AHY-2A MS MS L2B L2B wind wind products products provide provide high-precisionhigh-precision global SSWFs SSWFs (compared (compared with with the the in in situ situ data,data, the the error error is is less less than than 2 2 m m/s)/s) and and playplay anan importantimportant role in in marine marine and and meteorological meteorological research. research. Therefore,Therefore, we we use use independent independent wind wind speed speed products products as as reference reference datasets datasets to to evaluate evaluate the the accuracy accuracy of of the model. For each typhoon, we selected two wind speed files with different acquire times: one the model. For each typhoon, we selected two wind speed files with different acquire times: one for for model inversion and the other for accuracy assessment. model inversion and the other for accuracy assessment.

3.3. Methods Methods

3.1.3.1. Technical Technical Process Process TheThe process process detailed detailed in in Figure Figure2 includes2 includes the the following following steps: steps: (1)1) wind wind speed speed data data extraction extraction from from HY-2A;HY-2A; (2) 2) brightness brightness temperature temperature datadata extractionextraction from FY-2E; (3)3) temporal temporal and and spatial spatial matching matching of of wind speed and brightness temperature; 4) sea surface wind speed inversion model development; wind speed and brightness temperature; (4) sea surface wind speed inversion model development; and 5) model validation. and (5) model validation.

FY-2E infrared scanning HY-2A microwave Linear Logarithmic radiometer product scatterometer product Model Model Exponential Power Model Model

Best regression model seletcion

Brightness temperature data Wind speed data Piecewise regression

No

First Model validation Temporal matching Grayscale Wind speed segment Yes Grayscale Second Wind speed segment Third Spatial matching Grayscale Wind speed segment Sea surface wind fields FigureFigure 2. 2.Flowchart Flowchart ofof the technical process. process.

3.2. Wind Speed Data Extraction from HY-2A

The MS L2B product of the HY-2A satellite is composed of header files and product data. The product data includes longitude, latitude, wind speed, wind direction, and other related information, which is stored in a hierarchical data format (HDF). First, we downloaded the HY-2A MS L2B products

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3.2. Wind Speed Data Extraction from HY-2A

RemoteThe Sens. MS2020 ,L2B12, 1482 product of the HY-2A satellite is composed of header files and product data.5 ofThe 13 product data includes longitude, latitude, wind speed, wind direction, and other related information, which is stored in a hierarchical data format (HDF). First, we downloaded the HY-2A MS L2B fromproducts NSOAS. from We NSOAS. then transformed We then transformed the wind direction the wind to direction u- and v-wind to u- componentsand v-wind andcomponents interpolated and theinterpolated components. the components. Finally, we generated Finally, we wind generate speedd and wind direction speed and maps. direction We overlaid maps. theWe windoverlaid speed the mapwind and speed wind map direction and wind map todirect produceion map a wind to fieldproduce map. a Accordingwind field to map. the above-mentioned According to the method, above- wementioned acquire interpolatedmethod, we acquire maps (with interpolated a spatial maps resolution (with ofa spatial 25 km resolution and a temporal of 25 km resolution and a temporal of 12 h) forresolution wind speed, of 12 windh) for direction,wind speed, and wind wind direction, field (Figure and3 ).wind field (Figure 3).

25 25

20 23 20 23 23

15 15 18 18 18

10 10 Latitude Latitude Latitude 13 13 13 5 5

8 8 8 0 0 122 127 132 137 122 127 132 137 Wind speed 122 127 132 137 Wind speed Longitude m/s Longitude Longitude m/s (a) (b) (c)

FigureFigure 3.3. SchematicSchematic diagramdiagram ofof thethe windwind fieldfield process:process: ((aa)) windwind speedspeed map;map; ((bb)) windwind directiondirection map;map; ((cc)) windwind fieldfield map. map.

3.3.3.3. BrightnessBrightness TemperatureTemperature DataData ExtractionExtraction fromfrom FY-2E FY-2E TheThe cloudcloud imageimage productproduct ofof thethe FY-2EFY-2E satellitesatellite includesincludes twotwo parts:parts: headerheader filesfiles andand datadata segments,segments, whichwhich areare storedstored inin anan advancedadvanced weather-satelliteweather-satellite exchangeexchange formatformat (AWX).(AWX). HeaderHeader filesfiles consistconsist ofof aa first-levelfirst-level headerheader file,file, aa second-levelsecond-level headerheader file,file, aa paddingpadding segment,segment, andand anan extendedextended segment.segment. They They mainly mainly contain contain the the acquire acquire time, time, channe channel,l, projection projection type, type, and data and datainformation. information. First, First,we downloaded we downloaded the FY-2E the FY-2E VISSR VISSR data data from from the the NSMC. NSMC. We We then then gridded gridded the data informationinformation segment.segment. Finally,Finally, we we generated generated geocoded geocoded brightness brightness temperature temperature images using imag thees kriging using interpolationthe kriging methodinterpolation (with method a spatial (with resolution a spatial of 5 resolution km and a temporalof 5 km and resolution a temporal of 0.5 resolution h). of 0.5 h). 3.4. Temporal and Spatial Matching of Wind Speed and Brightness Temperature 3.4. Temporal and Spatial Matching of Wind Speed and Brightness Temperature 3.4.1. Temporal Matching 3.4.1. Temporal Matching As weather conditions differ over time, before spatial matching, temporal matching is first needed As weather conditions differ over time, before spatial matching, temporal matching is first to ensure that the acquire time of the HY-2A data is close to that of the FY-2E data. Because the temporal needed to ensure that the acquire time of the HY-2A data is close to that of the FY-2E data. Because resolution of the FY-2E data (0.5 h) is higher than that of the HY-2A data (12 h), we used the HY-2A data the temporal resolution of the FY-2E data (0.5 h) is higher than that of the HY-2A data (12 h), we used as the basis to match the corresponding FY-2E data. The schematic diagram of the temporal matching the HY-2A data as the basis to match the corresponding FY-2E data. The schematic diagram of the process is shown in Figure4a. temporal matching process is shown in Figure 4a.

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TIME FY-2A HY-2A TIME 00:00matching 00:00 LA . 00:30 . 4 ...... HA 2 11:30 . 1 12:00 matching 12:00 6 . 12:30 . . 3 . . . . OA . 23:30 . 5

24:00 matching 24:00

(a) (b)

FigureFigure 4. 4.SchematicSchematic diagram diagram of the of thetemporal temporal and and spatial spatial matching matching process: process: (a) temporal (a) temporal matching; matching; (b) spatial(b) spatial matching. matching. HA represents HA represents high wind high windspeed speedareas, areas,LA repres LA representsents low wind low speed wind speedareas, areas,and OAand represents OA represents other otherareas; areas;1, 2, 3, 1, 4, 2, 5, 3, and 4, 5, 6 andrepresent 6 represent the specific the specific locations locations of the of sample the sample points. points.

3.4.2.3.4.2. Spatial Spatial Matching Matching First,First, based based on on the the wind wind speed speed data data extracted extracted from from HY-2A, HY-2A, we we used used the the kriging kriging interpolation interpolation methodmethod to to ensure ensure that that the the interpolated interpolated values values of of gridded gridded points points were were the the same same as as the the original original data data values.values. Second, Second, based based on onthe the brightness brightness temperatur temperaturee data data extracted extracted from from FY-2E, FY-2E, we wegenerated generated a geocodeda geocoded brightness brightness temperature temperature image. image. We We then then compared compared the the longitude longitude and latitudelatitude coordinatescoordinates of ofthe the wind wind speed speed map map and and the brightnessthe brightness temperature temperature image, image, and established and established a whole a spatialwhole matching spatial matchinglist. The list. specific The locationsspecific locations of six sample of six points sample are points shown are in shown Figure in4b. Figure 4b. 3.4.3. Region Segmentation 3.4.3. Region Segmentation According to the following principles, the image was divided into three segments based on According to the following principles, the image was divided into three segments based on wind wind speed. Among the parameters announced by the HY-2A satellite, the wind speed measurement speed. Among the parameters announced by the HY-2A satellite, the wind speed measurement range range is 2–24 m/s. More noise and errors are introduced when the wind speed is greater than is 2–24 m/s. More noise and errors are introduced when the wind speed is greater than 30 m/s [21]. 30 m/s [21]. Therefore, the image is divided into three segments: (1) high wind speed areas Therefore, the image is divided into three segments: 1) high wind speed areas (HA, between 17 and (HA, between 17 and 30 m/s); (2) low wind speed areas (LA, between 2 and 17 m/s); and (3) other areas 30 m/s); 2) low wind speed areas (LA, between 2 and 17 m/s); and 3) other areas (OA, areas without (OA, areas without wind speed gridded points, with speeds less than 2 m/s or greater than 30 m/s). wind speed gridded points, with speeds less than 2 m/s or greater than 30 m/s). The results of region The results of region segmentation are shown in Figure4b. segmentation are shown in Figure 4b 3.5. Sea Surface Wind Speed Inversion Model 3.5. Sea Surface Wind Speed Inversion Model Best regression model selection: In this study, we built four commonly used regression models to predictBest windregression speed, model including selection: a linear In model, this study, an exponential we built four model, commonly a logarithmic used regression model, and models a power tomodel. predictF (windx) represents speed, including wind speed, a linear and x representsmodel, an brightnessexponential temperature model, a logarithmic (grayscale value).model, We and then a powerused themodel. determination F(x) represents coeffi windcient (speed,R2) to selectand x therepresents best regression brightness model. temperature The higher (grayscale the R2, the value). better 2 2 Wethe then fit. used the determination coefficient (R ) to select the best regression model. The higher the R , the betterPiecewise the fit. regression based on the best model: First, based on a spatial matching list of wind speed (F(xPiecewise)) and brightness regression temperature based on (thex), fourbest regressionmodel: First, models based were on a used spatial to buildmatching inversion list of models, wind speedand the(F(x) model) and withbrightness the highest temperatureR2 was selected(x), four asregression the best model.models Second,were used according to build to inversion the region 2 models,segmentation and the results, model thewith best the modelhighest was R usedwas selected to fit the as three the best areas model. (HA, LA,Second, and OA,according respectively). to the regionTherefore, segmentation a piecewise results, regression the best model model was wa established.s used to fit Finally, the three based areas on the(HA, time-series LA, and FY-2EOA, respectively).infrared scanning Therefore, radiometer a piecewise data and regression piecewise mo regressiondel was established. model, the Finally, time-series based SSWF on the data time- with serieshigh temporalFY-2E infrared resolution scanning (0.5 h) radiometer were successfully data and generated. piecewise regression model, the time-series SSWF data with high temporal resolution (0.5 h) were successfully generated.

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3.6. Model Validation First, three representative typhoons (Usagi, Fitow, and Nari) that occurred in 2013 were selected to evaluate the effectiveness of the sea surface wind speed inversion model. Next, two statistical metrics were used to estimate the inversion accuracy: the root mean square error (RMSE) and the mean bias error (MBE) of the wind speed estimates. The MBE is the average of the error, indicating over- or underestimation. The RMSE is a combined measure of the bias and variance associated with an estimator. The RMSE and MBE values are given as s Pn 2 = (xi xˆi) RMSE = i 1 − , (1) n

Xn (xˆi xi) MBE = − , (2) i=1 n where n is the number of reference points, xi is the reference wind speed of the ith point, and xˆi is the inversion wind speed of the ith point. The lower the RMSE and MBE, the better the prediction.

4. Results

4.1. Sea Surface Wind Speed Inversion Model Establishment Best regression model selection results: According to the temporal and spatial matching process, we successfully obtained two matching lists of the on September 18th. The first group of the matching list was at 9:00, with a total of 390 matching points. The second group of the matching list was at 22:00, with a total of 544 matching points. The first group was used for accuracy assessment, and the second group was used for modeling. Four regression models (a linear model, an exponential model, a logarithmic model, and a power model) were fitted separately. The SSWF inversion maps by four regression models are shown in Figure5. The R2 and accuracy assessment results are shown in Table2. The R2 of these four models were all around 0.6, which proves that the brightness temperature and the wind speed are positively correlated, and can be adopted for further inversion. We tested the significance of four models respectively. All groups demonstrated the p value less than 0.001, which confirmed that the regression models were statistically significant. Among the four models, the linear regression model had the maximum R2 of 0.61 and the lowest RMSE of 2.55, indicating the best fitting effect between the brightness temperature and wind speed. Therefore, the linear model was selected as the best regression model in this paper.

Table 2. Four regression models and their parameters.

Models Equation R2 RMSE (m/s) Linear Model F(x) = 0.2245x 24.7755 0.61 2.55 − Exponential Model F(x) = 0.3449e0.0166x 0.60 2.57 Logarithmic Model F(x) = 37.556 ln x 188.28 0.58 4.46 − Power Model F(x) = x3.4036 10 7 0.59 3.98 × − Piecewise regression model establishment results: Based on the best regression model selection and region segmentation results, a piecewise regression model was established. A three-stage regression model was fitted, using different inversion equations for each segment, and further integrated to obtain the final inversion model. For the typhoon Usagi, a total of 544 points were matched at 22:00 on September 18th, for piecewise regression modeling. For the , a total of 615 points were matched at 22:00 on October 3rd, for piecewise regression modeling. For the , a total of 580 points were matched at 22:00 on October 12th, for piecewise regression modeling. Remote Sens. 2020, 12, 1482 8 of 13 Remote Sens. 2020, 12, x FOR PEER REVIEW 8 of 13

20 20

23 23

15 15

18 18

Latitude 10 10 Latitude

13 13 5 5

0 8 0 8 120 125 130 135 140 Wind speed 120 125 130 135 140 Wind speed Longitude m/s Longitude m/s (a) (b) 20 20

23 15 23 15

18 18 10 10 Latitude Latitude

13 13 5 5

8 0 8 0 120 125 130 135 140 Wind speed 120 125 130 135 140 Wind speed Longitude m/s Longitude m/s (c) (d)

Figure 5. The sea surface wind field (SSWF) inversion maps by four regression models; the rectangular boxFigure represents 5. The the sea place surface where wind the inversionfield (SSWF) accuracy inversion is low: maps (a) the by linear four model; regression (b) the models; exponential the model;rectangular (c) the box logarithmic represents model; the place (d) wh theere power the inversion model. accuracy is low: (a) the linear model; (b) the exponential model; (c) the logarithmic model; (d) the power model. In addition, for the first segment (OA), most areas had no observation wind speed points, or had largeThe noises R2 and and accuracy errors. In assessment order to ensure results the are accuracy shown in of Table the inversion2. The R2 results,of these wefour masked models them. were Therefore,all around the0.6, ranges which of proves the three that segments the brightne basedss temperature on the grayscale and valuethe wind were speed defined are as positively follows: (1)correlated, Usagi: 0–146, and 146–205,can be adopted and 205–255; for further (2) Fitow: inversion. 0–150, 150–205,We tested and the 205–255; significance and (3)of Nari:four models 0–160, 160–190,respectively. and All 190–250. groups Thedemonstrated piecewise the regression p value less model than 0.001, establishment which confirmed results arethatshown the regression in Figure models6. were statistically significant. Among the four models, the linear regression model had the maximum R2 of 0.61 and the lowest RMSE of 2.55, indicating the best fitting effect between the brightness temperature and wind speed. Therefore, the linear model was selected as the best regression model in this paper.

Table 2. Four regression models and their parameters.

Models Equation R2 RMSE (m/s) Linear Model () = 0.2245 − 24.7755 0.61 2.55 Exponential Model () = 0.3449. 0.60 2.57 Logarithmic Model () = 37.556 ln − 188.28 0.58 4.46 Power Model () =. ×10 0.59 3.98

Piecewise regression model establishment results: Based on the best regression model selection and region segmentation results, a piecewise regression model was established. A three-stage regression model was fitted, using different inversion equations for each segment, and further integrated to obtain the final inversion model. For the typhoon Usagi, a total of 544 points were matched at 22:00 on September 18th, for piecewise regression modeling. For the typhoon Fitow, a total of 615 points were matched at 22:00 on October 3rd, for piecewise regression modeling. For the

Remote Sens. 2020, 12, x FOR PEER REVIEW 9 of 13 typhoon Nari, a total of 580 points were matched at 22:00 on October 12th, for piecewise regression modeling. In addition, for the first segment (OA), most areas had no observation wind speed points, or had large noises and errors. In order to ensure the accuracy of the inversion results, we masked them. Therefore, the ranges of the three segments based on the grayscale value were defined as follows: 1) Usagi:Remote Sens. 0–146,2020 146–205,, 12, 1482 and 205–255; 2) Fitow: 0–150, 150–205, and 205–255; and 3) Nari: 0–160, 9160– of 13 190, and 190–250. The piecewise regression model establishment results are shown in Figure 6.

F(x) = 0.1577x – 15.804 F(x) = 0.3057x – 47.958 R² = 0.63 R² = 0.61 Wind speed (m/s) speed Wind (m/s) speed Wind

Grayscale value Grayscale value (a) (b)

F(x) = 0.1273x – 10.091 F(x) = 0.3843x – 61.9213 R² = 0.63 R² = 0.63 Wind speed (m/s) speed Wind (m/s) speed Wind

Grayscale value Grayscale value (c) (d)

F(x) = 0.3344x – 43.7273 F(x) = 0.2341x – 24.3659 R² = 0.63 R² = 0.64 Wind speed (m/s) speed Wind (m/s) speed Wind

Grayscale value Grayscale value (e) (f) Figure 6. Piecewise regression model results: (a) the second segment result of Usagi; (b) the third Figure 6. Piecewise regression model results: (a) the second segment result of Usagi; (b) the third segment result of Usagi; (c) the second segment result of Fitow; (d) the third segment result of Fitow; segment result of Usagi; (c) the second segment result of Fitow; (d) the third segment result of Fitow; (e) the second segment result of Nari; (f) the third segment result of Nari. (e) the second segment result of Nari; (f) the third segment result of Nari. 4.2. Model Validation Results 4.2. Model validation results First, we took Usagi on September 18th as an example. Because the temporal resolution of FY-2E satelliteFirst, data we wastook 0.5Usagi h, thereon September are 48 images 18th as in an a example. day (00:00–23:30). Because the Therefore, temporal usingresolution the specific of FY- 2Epiecewise satellite regression data was 0.5 model, h, there we successfullyare 48 images inverted in a day 48 (00:00–23:30). scenes of corresponding Therefore, windusing speedthe specific maps. piecewiseHere, we regression selected nine model, scenes we ofsuccessfully inversion resultsinverted (1:00, 48 scenes 3:00, of 5:00, corresponding 7:00, 11:00, 13:00,wind speed 15:00, maps. 17:00, Here,and 20:00) we selected to display. nine According scenes of in toversion Figure 7results, we can (1:00, accurately 3:00, 5:00, extract 7:00, the 11:00, sea 13:00, surface 15:00, wind 17:00, speed and at 20:00)each moment to display. (the According redder the to color Figure is, the7, we greater can a theccurately wind speed extract is), the and sea we surface can further wind determinespeed at each the momentrange of (the the strongredder wind the color area is, (green the greater area) and the the wind possible speed location is), and ofwe the can typhoon further eyedetermine (the center the rangeof the of red the area). strong In wind addition, area (green combined area) withand the the possible results oflocation sea surface of the typhoon wind field at (the nine center scenes, of thewe canred area). accurately In addition, determine combined the moving with the direction results of of Usagi sea surface on September wind field 18th at (movingnine scenes, westward). we can accuratelySimilar results determine were inverted the moving for Fitow direction and of Nari. Usagi on September 18th (moving westward). Similar resultsWe were then inverted calculated for two Fitow statistical and Nari. metrics (RMSE and MBE) to estimate the inversion accuracy for all three typhoons. (1) Usagi: 390 points were successfully matched between the inversion result and the HY-2A wind speed product at 9:00 on September 18th. The wind speed accuracy was calculated, and the RMSE and MBE are 1.99 and 1.82 m/s. (2) Fitow: 430 points were successfully matched between the inversion result and the HY-2A wind speed product at 9:00 on October 3rd. The wind speed accuracy was calculated, and the RMSE and MBE are 2.10 and 1.35 m/s. (3) Nari: 360 points were successfully matched between the inversion result and the HY-2A wind speed product at 9:00 on Remote Sens. 2020, 12, 1482 10 of 13

October 12th. The wind speed accuracy was calculated, and the RMSE and MBE are 2.20 and 0.94 m/s. Therefore, the results of RMSE and MBE are in the ranges of 1.99–2.20 m/s and 0.94–1.82 m/s, indicating Remotethat theSens. accuracy 2020, 12, x FOR of the PEER proposed REVIEW piecewise linear model is relatively high. The metrics results10 of are13 shown in Table3.

26 26 26

23 23 23 20 20 20

18 18 14 14 18 14 Latitude Latitude Latitude

13 8 13 8 13 8

8 2 8 2 8 2 120 125 130 135 140 Wind speed 120 125 130 135 140 Wind speed 120 125 130 135 140 Wind speed Longitude Longitude m/s m/s Longitude m/s

(a) (b) (c)

26 26 26

23 23 23 20 20 20

18 14 18 14 18 14 Latitude Latitude Latitude

13 8 13 8 13 8

8 2 8 2 8 2 120 125 130 135 140 Wind speed 120 125 130 135 140 Wind speed 120 125 130 135 140 Wind speed Longitude m/s Longitude m/s Longitude m/s

(d) (e) (f)

26 26 26

23 23 23 20 20 20

18 18 18 14 14 14 Latitude Latitude Latitude

13 13 13 8 8 8

8 2 8 2 8 2 120 125 130 135 140 Wind speed 120 125 130 135 140 Wind speed 120 125 130 135 140 Wind speed Longitude m/s Longitude m/s Longitude m/s

(g) (h) (i)

FigureFigure 7. 7. TheThe sea sea surface surface wind wind field field of ofUsagi Usagi at different at different times: times: (a) 1:00; (a) 1:00; (b) 3:00; (b) 3:00; (c) 5:00; (c) 5:00; (d) 7:00; (d) 7:00;(e) 11:00;(e) 11:00; (f) 13:00; (f) 13:00; (g) 15:00; (g) 15:00; (h) 17:00; (h) 17:00; (i) 20:00. (i) 20:00.

We then calculatedTable two 3. statisticalThe metrics metrics results for(RMSE typhoons and Usagi,MBE) Fitow,to estimate and Nari. the inversion accuracy for all three typhoons. (1) Usagi: 390 points were successfully matched between the inversion result Number of RMSE Typhoon Reference Data Time MBE (m/s) and the HY-2A wind speed product at 9:00 on VerificationSeptember Points 18th. The(m wind/s) speed accuracy was calculated, and the RMSE and MBE are 1.99 and 1.82 m/s. (2) Fitow: 430 points were successfully Usagi At 9:00, on September 18th 390 1.99 1.82 matched between the inversion result and the HY-2A wind speed product at 9:00 on October 3rd. Fitow At 9:00, on October 3rd 430 2.10 1.35 The wind speedNari accuracy At was 9:00, calculated, on October 12thand the RMSE and 360 MBE are 2.10 2.20 and 1.35 m/s. 0.94 (3) Nari: 360 points were successfully matched between the inversion result and the HY-2A wind speed product at5. 9:00 Discussion on October 12th. The wind speed accuracy was calculated, and the RMSE and MBE are 2.20 and 0.94 m/s. Therefore, the results of RMSE and MBE are in the ranges of 1.99–2.20 m/s and 0.94– 1.825.1. m/s, Comparison indicating between that the Piecewiseaccuracy Linearof the Modelpropos anded thepiecewise Whole Linear linear Model model is relatively high. The metrics results are shown in Table 3. Based on the same matching datasets, we used the piecewise linear model and the whole linear model to compare theTable fitting 3. The eff metricsects of results the three for typhoons typhoons. Usagi, There Fitow, were and no Nari. observation wind speed points and no large errors, so the first segment (OA) did not participate in the modeling. The piecewise Typhoon Reference Data Time Number of Verification Points RMSE (m/s) MBE (m/s) Usagi At 9:00, on September 18th 390 1.99 1.82 Fitow At 9:00, on October 3rd 430 2.10 1.35 Nari At 9:00, on October 12th 360 2.20 0.94

5. Discussion

Remote Sens. 2020, 12, 1482 11 of 13 linear models for Usagi, Fitow, and Nari are shown in Figure6, and the whole linear model results are shown in Table4. Comparing the results between Table4 and Figure6, we find that the R2 of the piecewise linear model is higher than that of the whole linear model for all three typhoons, which shows that the piecewise model is better than the whole model.

Table 4. The whole linear model for Usagi, Fitow, and Nari.

Typhoon Segment Equation Grayscale Range R2 Usagi Whole (HA and LA) F(x) = 0.2245x 24.7755 146 x 255 0.57 − ≤ ≤ Fitow Whole (HA and LA) F(x) = 0.2476x 28.1428 150 x 255 0.58 − ≤ ≤ Nari Whole (HA and LA) F(x) = 0.2778x 34.6794 160 x 250 0.59 − ≤ ≤ In addition, taking Usagi as an example, we produced wind speed maps by the piecewise linear model and the whole linear model, respectively. We found (1) that the extent of HA in the piecewise map was smaller than that in the whole map, which is closer to real situations, and (2) that the inversion value range of the piecewise model is 2–28 m/s, and the inversion range of the whole model is 0–22 m/s. The former was closer to the real wind speed range. Therefore, through piecewise fitting of the brightness temperature data, the interference between different data was reduced, and more accurate wind speed results were retrieved.

5.2. Limitations of the Proposed Piecewise Model According to the results from 144 brightness temperature images of the three typhoons, the limitations of the proposed piecewise model include the following.

(1) The quality of the microwave scatterometer products. This model is a wind-based method, and highly depends on the quality of the wind speed products, which impacts the accuracy of inversion results the most. First, rain is one of the main sources of error in scatterometer winds. Extremely high rain rates exist near the core of a typhoon eye, and backscatter is dependent more on rain than on wind. Second, the inability to resolve the maximum winds in the inner cores of most typhoon eyes is due to insufficient retrieval resolution and instrument signal saturation (which limits the maximum retrievable wind speed, even under rain-free conditions) [22]. Therefore, the proposed model is not suitable for wind speeds greater than 30 m/s. Finally, the swath of the HY-2A microwave scatterometer is about 1750 km and can cover more than 90% of global open sea areas within one day. However, 10% of the sea areas are still not covered. In addition, it is ineffective in the land area. Therefore, the proposed model is not suitable for those areas. (2) The parameters of the regression model change case by case. Because a typhoon is a complex weather system with drastic changes, there is no fixed piecewise linear model to retrieve SSWF information. Even for different days of the same typhoon, it is suggested that a new piecewise linear model is established to retrieve the wind speed.

6. Conclusions In this paper, we proposed a piecewise linear model to retrieve SSWF information using two different satellite sensors (a microwave scatterometer and an infrared scanning radiometer). The model was applied to the Usagi, Fitow, and Nari typhoons, based on 144 brightness temperature images. The results show that (1) for all three typhoons, compared to the original MBE of the satellite scatterometer (2 m/s) [21], the results of RMSE and MBE are in the ranges of 1.99–2.20 m/s and 0.94–1.82 m/s, which indicate that it is suitable and reliable for SSWF inversion; and (2) it solves the problem of the low temporal resolution of HY-2A (12 h), and inherits the high temporal resolution of FY-2E data (0.5 h). Although this new SSWF inversion model can obtain a relatively high precision wind speed (the error is about 2 m/s) and a high temporal resolution (0.5 h), it can also be studied from the following two aspects: (1) the inversion accuracy needs to be further improved so that it can be Remote Sens. 2020, 12, 1482 12 of 13 compared with land/ship/other observational data; and (2) the temporal resolution needs to be further improved so that it can be used in near real time.

Author Contributions: Conceptualization: T.H. and Y.W.; methodology: T.H.; validation: Y.W. and Y.L. (Yao Li); result analysis: T.H.; investigation: Y.L. (Yue Li) and Y.W.; writing—original draft preparation: T.H. and Y.W.; writing—review and editing: Y.L. (Yao Li); visualization: Y.L. (Yue Li); supervision: D.Z.; funding acquisition: T.H. and D.Z. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Key R&D Program of China under contract #2017YB0502800 and #2017YFB0502805, the Zhejiang Provincial Natural Science Foundation of China (Grant No. LY19D010004), and the Science and Technology Program of Hangzhou (Grant No. 20191203B19). Acknowledgments: The HY-2A and FY-2E datasets were provided by the National Satellite Oceanic Application Center and the National Satellite Meteorological Centre, respectively. Conflicts of Interest: The authors declare that there are no conflicts of interest.

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