remote sensing

Article Objective Estimation of Tropical Cyclone Intensity from Active and Passive Microwave Remote Sensing Observations in the Northwestern Pacific Ocean

Kunsheng Xiang 1,2,3, Xiaofeng Yang 1,3,* , Miao Zhang 4, Ziwei Li 1 and Fanping Kong 1,2

1 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; [email protected] (K.X.); [email protected] (Z.L.); [email protected] (F.K.) 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 The Hainan Key Laboratory of Earth Observation, Sanya 572029, China 4 Key Lab of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration (LRCVES/CMA) and National Satellite Meteorological Center, Beijing 100081, China; [email protected] * Correspondence: [email protected]; Tel.: +86-010-64806215



Received: 18 February 2019; Accepted: 11 March 2019; Published: 14 March 2019


Abstract: A method of estimating tropical cyclone (TC) intensity based on Haiyang-2A (HY-2A) scatterometer, and Special Sensor Microwave Imager and Sounder (SSMIS) observations over the northwestern Pacific Ocean is presented in this paper. Totally, 119 TCs from the 2012 to 2017 typhoon seasons were selected, based on satellite-observed data and China Meteorological Administration (CMA) TC best track data. We investigated the relationship among the TC maximum-sustained wind (MSW), the microwave brightness temperature (TB), and the sea surface wind speed (SSW). Then, a TC intensity estimation model was developed, based on a multivariate linear regression using the training data of 96 TCs. Finally, the proposed method was validated using testing data from 23 other TCs, and its root mean square error (RMSE), mean absolute error (MAE), and bias were 5.94 m/s, 4.62 m/s, and −0.43 m/s, respectively.

Keywords: tropical cyclone; intensity; SSMIS; HY-2A; Northwestern Pacific

1. Introduction Tropical cyclones (TCs) are strong cyclonic vortices that occur over tropical seas. TCs are one of the most destructive natural disasters, causing considerable damage and economic loss [1]. The analysis and determination of TC intensity is of great importance for disaster prevention. Because field measurement of TCs are very difficult, in situ observation data are very rare under TC conditions. Satellite remote sensing has become an effective means of TC monitoring, based on its high temporal and spatial resolution, and large coverage. It is possible to estimate TC intensity using these satellite measurements when direct measurements are not available. The Dvorak technique (DT) [2,3] and its subsequent versions, the Objective DT [4], the Advanced Objective DT [5], and the Advanced DT [6], are well-known examples of methodologies that are used to estimate the intensity of TCs from visible and thermal infrared satellite remote sensing imagery. In addition to Dvorak technology, there are some other methods used to estimate TC intensity, based on infrared satellite image data. The deviation angle variance (DAV) technique has been used based on infrared TB data [7–11]. Thirty-year (1980–2009) TC images from geostationary satellite infrared sensors covering the northwestern Pacific have also been used to build a TC size dataset based on objective models [12].

Remote Sens. 2019, 11, 627; doi:10.3390/rs11060627 www.mdpi.com/journal/remotesensing Remote Sens. 2019, 11, 627 2 of 18

There have been significant advances in the estimation of TC intensity using satellite visible-infrared (VIS-IR) measurements over the last decade. In some special situations, such as the eye appearing ragged or the TC center being densely overcast, a skilled analyst can produce accurate intensity estimates, and some automated VIS-IR algorithms also perform well. However, there is reduced skill for these types of convective structures versus imagery where the eye is well-defined. Under these circumstances, microwave observations from polar-orbiting satellites can play a crucial role in revealing convective organization and eyewall structure that would otherwise be obscured by cloud tops [13,14]. An automated method to estimate TC intensity using the Special Sensor Microwave Imager (SSM/I) microwave imagery was introduced by Bankert and Tag [15]. Bankert et al. [16] updated TC intensity estimation via passive microwave data features. Hoshino et al. [17] also put forward a method to estimate the intensity of TC, using Tropical Rainfall Measuring Mission Microwave Imager (TRMM/TMI) brightness temperature (TB) data. Yang et al. [18] improved on a near-real-time TC monitoring system based on multiple satellite passive microwave (PMW) sensors. In addition, Zhou et al. [19] estimated tropical cyclone intensity (the maximum wind speed and the central pressure) and wind fields, from synthetic aperture radar (SAR) imagery. Zhang et al. developed a hurricane wind speed retrieval model for c-band RADARSAT-2 cross-polarization ScanSAR images [20]. Jiang et al. [21] developed a statistical passive microwave intensity estimation (PMW-IE) algorithm based on TRMM/TMI TB data and near-surface rain rate retrievals. Active microwave remote sensing instruments, such as scatterometer and SAR, are suitable alternatives for overcoming VIS-IR shortcomings, and they have an advantage over the aforementioned traditional remote sensing methods [22–24]. However, the sea surface winds derived from the scatterometer are not accurate enough under TC conditions. The co-polarization backscattering signal will saturate when the wind speed is larger than 24 m/s [25]. Heavy rainfalls inside TCs will also cause large errors to retrieved wind product. As mentioned above, both the radiometer and scatterometer have their own advantages for estimating the intensity of TCs. The scatterometer can provide TC intensity at the sea surface level, while the multi-channel polarized microwave radiometer can provide additional vertical information of TC structure. Therefore, we try to develop a TC intensity estimation method that synergistically uses active and passive microwave observations. In this study, satellite observations and best-track data of TCs in the Northwestern Pacific Ocean are collocated. Several predictors of TC intensity are proposed, based on statistical analyses, and a multivariate linear model is built, based on these predictors. Then, the proposed method is tested, with satellite and aircraft observations. The remainder of this paper is organized as follows. The study area and data are described in Section2. Then, we describe the methodology in Section3. In Section4, the experiments are described. Lastly, Sections5 and6 include discussion and conclusions.

2. Study Area and Data

2.1. Study Area The frequency of TCs occur more frequently in the ocean with higher sea surface temperature from 5◦ to 20◦ in the North and South latitudes. TCs occur in eight basins around the world (Figure1), and the Northwest Pacific Basin is the highest frequency region [26]. On average, approximately 33 TCs have developed in Northwest Pacific Basin each year from 1949 to 2016. Therefore, the Northwest Pacific was chosen as the study area in this paper. Remote Sens. 11 Remote Sens. 20192019,, 11,, 627x FOR PEER REVIEW 3 of 1819 Remote Sens. 2019, 11, x FOR PEER REVIEW 3 of 19

Figure 1. Locations of tropical cyclones (TCs) and their basins. Figure 1. Locations of tropical cyclon cycloneses (TCs) and their basins.

2.2. Data Description 2.2. Data Description 2.2.1. HY-2A MicrowaveMicrowave ScatterometerScatterometer 2.2.1. HY-2A Microwave Scatterometer Microwave scatterometers can offer wind data of the seasea surfacesurface (wind(wind directiondirection andand windwind Microwave scatterometers can offer wind data of the sea surface (wind direction and wind speed). QuickQuick ScatterometerScatterometer (QuikSCAT) (QuikSCAT) This This has has proven proven to to be be a very a very effective effective scatterometer scatterometer that that can speed). Quick Scatterometer (QuikSCAT) This has proven to be a very effective scatterometer that obtaincan obtain sea surfacesea surface wind wind speeds speeds with with high high accuracy accuracy [27,28 [27,28],], but this but hasthis not has been not been available available since since 2009. can obtain sea surface wind speeds with high accuracy [27,28], but this has not been available since Haiyang-2A2009. Haiyang-2A (HY-2A) (HY-2A) is the firstis the altimetry first altimetry and scatterometry and scatterometry satellite projectsatellite of project the China of the National China 2009. Haiyang-2A (HY-2A) is the first altimetry and scatterometry satellite project of the China SpaceNational Administration Space Administration (CNSA), and (CNSA), it is dedicated and it is to de thedicated studies to ofthe physical studies oceanography of physical oceanography and geodesy. National Space Administration (CNSA), and it is dedicated to the studies of physical oceanography Theand Ku-bandgeodesy. scatterometer The Ku-band is carriedscatterometer on HY-2A. is carrie Quantitatived on HY-2A. analysis Quantitative shows the effectiveness analysis shows of HY-2A the and geodesy. The Ku-band scatterometer is carried on HY-2A. Quantitative analysis shows the seaeffectiveness surface wind of fieldHY-2A observations, sea surface and wind its advantages field observations, for typhoon and monitoring its advantages [29]. Wind for speeds typhoon and effectiveness of HY-2A sea surface wind field observations, and its advantages for typhoon directionsmonitoring that [29]. are Wind derived speeds from HY-2Aand dire arections accurate that under are mediumderived from and low HY-2A wind are speeds accurate [30], thoughunder monitoring [29]. Wind speeds and directions that are derived from HY-2A are accurate under itmedium is easily and saturated low wind at highspeeds wind [30], speeds though (>30 it is m/s). easily HY-2A saturated satellite at high microwave wind speeds scatterometer (>30 m/s). dataHY- medium and low wind speeds [30], though it is easily saturated at high wind speeds (>30 m/s). HY- can2A satellite be obtained microwave through scatterometer the application data of thecan National be obtained Satellite through Ocean the Application application Service of the (NSOAS).National 2A satellite microwave scatterometer data can be obtained through the application of the National FigureSatellite2a Ocean is a HY-2A Application observation Service of Typhoon(NSOAS). Fitow. Figure 2a is a HY-2A observation of Typhoon Fitow. Satellite Ocean Application Service (NSOAS). Figure 2a is a HY-2A observation of Typhoon Fitow.

Figure 2.2. Satellite observation of Typhoon Fitow (1323) in 2 October 2013, (a) HY-2A, andand ((bb)) SSMISSSMIS 19Figure19 GHzGHz 2. H-polarization.H-polarization. Satellite observation of Typhoon Fitow (1323) in 2 October 2013, (a) HY-2A, and (b) SSMIS 19 GHz H-polarization. 2.2.2. SSMIS Microwave Radiometer 2.2.2. SSMIS Microwave Radiometer 2.2.2.A SSMIS microwave Microwave radiometer Radiometer sensor is mounted onto HY-2A, but the radiometer data quality accuracy A microwave radiometer sensor is mounted onto HY-2A, but the radiometer data quality is lower than that of the microwave scatterometer. Therefore, a microwave radiometer with the closest accuracyA microwave is lower than radiometer that of the sensor microwave is mounted scatterometer. onto HY-2A, Therefore, but athe mi crowaveradiometer radiometer data quality with possible matching time to the HY-2A transit time is needed. The HY-2A microwave scatterometer accuracythe closest is lowerpossible than matching that of the time microwave to the HY-2A scatterometer. transit timeTherefore, is needed. a microwave The HY-2A radiometer microwave with crosses the equator time onto its descending node at 6:00 local time, and on its ascending node at thescatterometer closest possible crosses matching the equator time timeto the onto HY-2A its de transitscending time node is needed. at 6:00 The local HY-2A time, microwaveand on its 18:00 local time. Therefore, a sensor that crosses the equator time at around 6:00, or 18:00 local time, scatterometerascending node crosses at 18:00 the local equator time. timeTherefore, onto aits sensor descending that crosses node the at equator6:00 local time time, at around and on 6:00, its is a priority when choosing a microwave radiometer. Therefore, the F17 SSMIS, carried onboard the ascendingor 18:00 local node time, at 18:00 is a priority local time. when Therefore, choosing a asensor microwave that crosses radiometer. the equator Therefore, time atthe around F17 SSMIS, 6:00, Defense Meteorological Satellite Program (DMSP) satellites, was selected. orcarried 18:00 onboard local time, the is Defense a priority Meteorological when choosing Satel a litemicrowave Program radiometer. (DMSP) satellites, Therefore, was theselected. F17 SSMIS, carried onboard the Defense Meteorological Satellite Program (DMSP) satellites, was selected.

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SSMIS is a seven-channel passive microwave radiometer operating at four frequencies (19.35, 22.235, 37.0, and 91 GHz) and dual-polarization (except at 22.235 GHz which is V-polarization only) [31,32]. SSMIS data are available through the NOAA Data Center’s Comprehensive Large Array-data Stewardship System (CLASS). Figure2b is an SSMIS 19 GHz H-polarization observation of Typhoon Fitow.

2.2.3. Best-Track Data Aircraft reconnaissance obviously provides the most accurate observations [33]. However, the global aircraft reconnaissance program has been inactivate since 1987, except for in the Atlantic Basin to the west of 55◦W[34]. Although, a few dropsondes have been launched from aircraft reconnaissance flights during Pacific Asian Regional special observation campaigns, there is an insufficient number of TC cases for a statistically significant validation of the method using dropsondes alone. The best track intensities are initially calculated by using DT methods, and then modified by other observations such as aircraft reconnaissance, station, and ship weather reports, and Doppler radar. Thus, the best track intensity data are the only available references at present in the Northwest Pacific regions. The China Meteorological Administration (CMA) best-track datasets [35] were ultimately chosen as the validation data for this study. This best-track data can provide the location and intensity of TCs every six hours. Among the CMA best-track data, there are six types of TC intensity grades: Tropical depression (TD, 10.8–17.1 m/s), tropical Storm (TS, 17.2–24.4 m/s), severe tropical storm (STS, 24.5–32.6 m/s), typhoon (TY, 32.7–41.4 m/s), severe typhoon (STY, 41.5–50.9 m/s), and super typhoon (Super TY, ≥51.0 m/s). The data from 2012–2017 were selected for model training and validation. Above all, HY-2A microwave scatterometer and SSMIS microwave radiometer data were matched according to the satellite overpass time difference. When collocating the satellite observations with the best track data, we used a time-varying criterion based on the TC intensity variation. The basic idea of this scheme is that we relax the restrictions for TCs with stable intensity and set more strict requirements for TCs with rapid intensity changes. There are three thresholds of overpass time difference: 10 min, 30 min, and 60 min. If the variation in the maximum-sustained wind (MSW) is greater than 5 m/s during the 6-hour interval of the nearest best track record time, the threshold of the overpass time difference between HY-2A and SSMIS is 10 min. If the variation in the MSW is less than 5 m/s, the threshold of the overpass time difference is 30 min. If there is no change in the MSW, the threshold is set to 60 min. This strategy brings us more matchup data, and reduces the impact of the overpass time difference on the estimated MWS. Later, the referenced MWS is linearly interpolated from the best track data, according to overpass time of SSMIS and HY-2A. For example, the overpass time of SSMIS and HY-2A are 6:10 (UTC), 6:20 (UTC), respectively. The intensity of TC corresponding to 6:15 (UTC) serves as our reference intensity. The intensity of 6:15 (UTC) is linearly interpolated according to the data values corresponding to the best track 6:00 (UTC) and 12:00 (UTC). Lastly, 406 valid satellite-observed data contained in 119 TCs were selected. Among them, 261 satellite-observed data in 96 TCs are used as training sample data, and the other 145 data are used as test data (as shown in Table1).

Table 1. Information for the training data and test data.

Training Data Test Data Year TCs Sample TCs Sample 2012 18 55 5 31 2013 23 63 5 29 2014 15 43 3 10 2015 16 47 7 57 2016 9 15 1 4 2017 15 38 2 14 Total 96 261 23 145 RemoteRemote Sens.Sens.2019 2019, ,11 11,, 627 x FOR PEER REVIEW 55 ofof 1819

3. Methodology 3. Methodology 3.1. Relationship between the Satellite-Observed Parameters and the Maximum Wind Speed 3.1. Relationship between the Satellite-Observed Parameters and the Maximum Wind Speed In reference to Cecil and Zipser [36], the TB parameters of the SSMIS microwave radiometer and sea surfaceIn reference wind to speed Cecil (SSW) and Zipser parameters [36], the of TB HY-2 parametersA microwave of the SSMISscatterometer microwave in concentric radiometer circles and seaand surface annuli wind from speedthe TC (SSW) center parameters were calculated, of HY-2A and microwave then compared scatterometer with the in MSW concentric using circles the CMA and annuliTC best from track the data. TC centerTB parameters were calculated, are computed and then for comparedeach TB of with 19, 22, the 37, MSW and using91 GHz the over CMA the TC ocean. best trackSimilar data. TB to parameters Hoshino et are al. computed [17], we forused each two TB ofwa 19,ys 22,for 37,the and selection 91 GHz of over coverage the ocean. area when calculatingSimilar each to Hoshino parameter. et al. [One17], weway used is a twoconcentric ways for circle, the selection with different of coverage radii areaevery when 0.25° calculating from 0.5° ◦ ◦ ◦ eachto 2.5°. parameter. Another Oneway wayis an isannuli a concentric enclosed circle, by circles with every different 0.25°, radii such every as annulus 0.25 from with 0.5 a 1°to inner 2.5 . ◦ ◦ Anotherradius and way 1.25° is an outer annuli radius enclosed (Figure by circles3). The every center 0.25 of the, such TC asis set annulus as the with center a 1 ofinner the circle, radius and and it ◦ 1.25can beouter obtained radius by (Figure interpolation3). The center of the of 6-hourly the TC is best set astrack the data. center It of should the circle, be noted and it that can bethe obtained different bysizes interpolation of TCs and of thethe 6-hourlyeyewall bestreplacement track data. cycles It should may be cause noted some that the intensity different estimation sizes of TCs errors. and theTherefore, eyewall the replacement sizes of tropical cycles maycyclones cause and some eyew intensityall replacement estimation cycles errors. should Therefore, be considered the sizes ofin tropicalfuture work. cyclones Then, and the eyewall maximum, replacement minimum, cycles mean, should and standard be considered deviation in future (STD) work. of TB Then,and SSW, the maximum,and the ratio minimum, of pixels mean, over andthe threshold standard deviation(RAPT) of (STD) TB are of TBcomputed and SSW, in andthe thecomputation ratio of pixels coverage over thearea. threshold For RAPT, (RAPT) the minimum of TB are computedand maximum in the threshol computationd temperatures coverage for area. each For frequency RAPT, the channel minimum are and180 maximumK and 270 K, threshold respectively. temperatures There are, for in each total, frequency 1785 parameters channel that are 180are Kcalculated and 270 K,in respectively.this process, Thereso we are,formulated in total, unified 1785 parameters rules to name that them. are calculated For the TB inparameters this process, obtained so we by formulated SSMIS microwave unified rulesradiometer, to name frequency, them. For and the TBpolarization parameters are obtained first displayed by SSMIS (for microwave instance, radiometer,TB19H for frequency,horizontal andpolarized polarization TB of 19 are GHz). first Then, displayed the parameter (for instance, type (MAX, TB19H MIN, for horizontal MEAN, STD, polarized MAX-MIN, TB of and 19 GHz).MAX- Then,MEAN) the was parameter displayed type (TB19H_MEAN). (MAX, MIN, MEAN, Lastly, STD, a type MAX-MIN, of computation and MAX-MEAN) coverage (A or was C) and displayed radius (TB19H_MEAN).follows. Computational Lastly, aregions type of are computation written as coverage‘A’ to represent (A or C) annuli, and radius followed follows. by inner Computational radius and regionsouter radius are written for annuli as ‘A’ (Axxxxxx, to represent the annuli,first three followed x (xxx) byare inner used radiusfor the and position outer of radius the inner for annuli circle, (Axxxxxx,the next three the first x (xxx) three are x (xxx)for the are position used for of the the position outer circle., of the i.e., inner TB19 circle,H_MEAN_A100125 the next three x (xxx) represents are for thean positionannulus ofwhose the outer inner circle., radius i.e., is TB19H_MEAN_A100125 1.0° and outer radius is represents1.25°), and an ‘C’ annulus to represent whose innera concentric radius ◦ ◦ iscircle, 1.0 andfollowed outer radiusby the is radius 1.25 ), andof the ‘C’ tocircle represent (Cxxx, a concentric3 x (xxx) circle,is used followed for circle by the radius, radius i.e., of theTB19H_MEAN_C100 circle (Cxxx, 3 x (xxx) represents is used fora circle circle whose radius, radius i.e., TB19H_MEAN_C100 is 1.0°). For the SSW represents parameters a circle obtained whose by ◦ radiusthe HY-2A is 1.0 microwave). For the SSW scatterometer, parameters obtainedbecause bythey the do HY-2A not microwavehave as much scatterometer, frequency becausepolarization they doinformation, not have as muchthey frequencydo not polarizationhave a first information, step in theynaming do not rules have alike first TB step inparameters naming rules do like(SSW_MEAN_A100125 TB parameters do (SSW_MEAN_A100125 or SSW_MEAN_C100). or SSW_MEAN_C100).

FigureFigure 3.3.An An example example of howof how the calculationthe calculation regions regions for parameters for parameters are named. are Thenamed. circles The are concentriccircles are ◦ ◦ ◦ circlesconcentric whose circles radii whose represent radii every represent 0.25 everylatitude 0.25° from lati 0.5tudeto from 2.5 0.5°latitude. to 2.5° The latitude. cross isThe the cross center is the of ◦ thecenter TC. of C100 the representsTC. C100 arepresents concentric a circle;concentric the radius circle; is the 1 . radius A100125 is represents1°. A100125 the represents annuli whose the annuli inner ◦ ◦ radiuswhose is inner 1.0 andradius the is outer 1.0° radiusand the is 1.25outer (adaptedradius is from1.25° Hoshino (adapted & from Nakazawa, Hoshino 2007 & [Nakazawa,17]). 2007 3.1.1.[17]). TB Parameters

3.1.1.In TB all, Parameters 1680 TB parameters were computed. In this section, the correlations between the TB parameters and TC intensity will be analyzed. The relationship between the single parameter of TB

Remote Sens. 2019, 11, 627 6 of 18 and the MSW of TC was analyzed by regression analysis, and then the Root Mean Square Error (RMSE) between the estimated and the best track MSW was calculated. The correlation coefficients between all of the parameters and the MSW of TC was calculated, and a significant horizontal t-test was conducted for the correlation coefficients. The 10 highest correlated TB parameters with the maximum wind speeds for the best CMA tracks are shown in Table2. The correlation coefficients and RMSEs are also given in the table. The highest correlated parameters were for TB19H_MIN_C100. The highest correlation coefficient was 0.84, and the RMSE was 6.67 m/s.

Table 2. The highest correlated brightness temperature (TB) parameters.

Correlation Root Mean TB Parameter Coefficient Square Error (m/s) TB19H_MIN_C100 0.84 6.67 TB37H_MIN_C125 0.84 6.72 TB19H_MIN_C125 0.83 6.98 TB37H_MIN_C100 0.83 7.00 TB19H_MIN_C075 0.82 7.01 TB22V_RAPT270_C100 0.82 7.04 TB22V_RAPT270_C125 0.82 7.14 TB19H_MIN_A075100 0.82 7.17 TB22V_RAPT270_C075 0.82 7.19 TB19V_MIN_C100 0.81 7.19

The following conclusions can be inferred from Table2: There is a tendency for the parameters of 19, 22, and 37 GHz channels to give higher correlations with the TC maximum wind speed. The high frequency band reaches saturation when the cloud liquid water path is greater than 0.3 mm [37], while the cloud liquid water path in the eyewall of TC is between 0 and 0.5 mm [38]. Lower frequencies are less sensitive to the path of liquid water in clouds within this range. With the decreased sensitivity to the atmosphere, these channels will provide more information about the surface, and therefore lower frequencies could have higher correlations with the maximum wind speed. The best correlated parameters for all frequencies are MIN and a few RAPT, but not MAX. MAX brightness temperature will be very high when there are more precipitation particles in a small area of TC, but this does not represent the overall intensity of TC [39]. Also, with the increase of precipitation intensity, different frequencies will have different degrees of saturation. MIN informs that all other points are larger than this number, and RAPT indicates the ratio of an active area, and hence it may be a more appropriate indicator to represent the TC intensity. The parameters in the surrounding region (0.75–1.25◦ away from the TC center, especially 1◦) tend to give higher correlations with the TC intensity. It is inferred that the intensity of the TC would be close to the radius of maximum wind speed. TC intensity estimates using a fixed annulus or circle could be impacted by TC eye size [39], so we should consider the sizes of the eyes, or the TCs in the future work. The test dataset in Table1 was used to verify the performance of only using TB19H_MIN_C100 parameter. The results of the validation are shown in Figure4. The horizontal axis is the maximum wind speed that is estimated by the TB parameter model, and the vertical axis is the CMA TC best track intensity data. The R2 of the verification result for the test dataset was 0.76, the RMSE was 6.97 m/s, the MAE was 5.50 m/s, and the bias was −1.78 m/s. Figure4b,c show the differences between the TB parameter model-estimated TC intensity and the CMA best-track data in two subgroups. Figure4b relates to lower-intensity TCs, i.e., tropical depressions, tropical storms, and severe tropical storms. Figure4c relates to typhoons, severe typhoons, and super typhoons. Remote Sens. 2019, 11, 627 7 of 18 Remote Sens. 2019, 11, x FOR PEER REVIEW 7 of 19

FigureFigure 4.4. ScatterScatter plot of the maximum wind speedspeed betweenbetween thethe estimatedestimated results,results, usingusing thethe TB19H_MIN_C100TB19H_MIN_C100 parameter-only parameter-only model model and and the the Chin Chinaa Meteorological Meteorological Administration Administration (CMA) (CMA) best- best-tracktrack data. data. (a) All (a of) All the oftesting the testing data. ( data.b) Subgroup (b) Subgroup testing testingdata of datatropical of tropicaldepression depression (TD), tropical (TD), c tropicalstorm (TS), storm and (TS), severe and tropical severe tropicalstorm (STS). storm (c (STS).) Subgroup ( ) Subgroup testing data testing of typhoon data of typhoon(TY), severe (TY), typhoon severe typhoon (STY), and super typhoon (Super TY). (STY), and super typhoon (Super TY). 3.1.2. SSW Parameters 3.1.2. SSW Parameters In total, 105 SSW parameters were computed. In this section, the correlation between the SSW parametersIn total, and 105 TC SSW intensity parameters will bewere analyzed. computed. The In relationship this section, between the correlation the single between parameter the SSW of SSWparameters and the and MSW TC intensity of TC was will analyzed be analyzed. by regression The relationship analysis, between and then the single the RMSE parameter between of SSW the estimatedand the MSW and of the TC best-track was analyzed MSW by was regression calculated. analysis, The correlationand then the coefficients RMSE between between the estimated all of the parametersand the best-track and the MSW ofwas TC calculated. were calculated, The correlati and a significanton coefficients horizontal betweent-test all wasof the conducted parameters for theand correlation the MSW coefficient.of TC were calculated, and a significant horizontal t-test was conducted for the correlationThe highest coefficient. 15 correlated SSW parameters, correlation coefficients, and RMSEs with the CMA best tracksThe are highest shown in15 Table correlated3. The highestSSW pa correlatedrameters, correlation parameter wascoefficients, for SSW_MEAN_C100. and RMSEs with The the highest CMA correlationbest tracks coefficientare shown wasin Table 0.83, and3. The the highest RMSE wascorrelated 7.09 m/s. parameter was for SSW_MEAN_C100. The highest correlation coefficient was 0.83, and the RMSE was 7.09 m/s. Table 3. The highest correlated sea surface wind (SSW) field parameters. Table 3. The highest correlated sea surface wind (SSW) field parameters. SSW Parameter Correlation Coefficient RMSE (m/s) SSW Parameter Correlation Coefficient RMSE(m/s) SSW_MEAN_C100 0.83 7.09 SSW_MEAN_C100SSW_MEAN_C125 0.83 0.82 7.10 7.09 SSW_MEAN_C125SSW_MEAN_C150 0.82 0.81 7.20 7.10 SSW_MEAN_C150SSW_MIN_C100 0.81 0.81 7.29 7.20 SSW_MIN_C100SSW_MEAN_C075 0.81 0.81 7.24 7.29 SSW_MEAN_C175 0.81 7.38 SSW_MEAN_C075 0.81 7.24 SSW_MIN_A075100 0.80 7.20 SSW_MEAN_C175SSW_MEAN_C200 0.81 0.79 7.57 7.38 SSW_MIN_A075100SSW_MEAN_A075100 0.80 0.79 7.36 7.20 SSW_MEAN_C200SSW_MEAN_C225 0.79 0.78 7.78 7.57 SSW_MEAN_A075100SSW_MEAN_A100125 0.79 0.78 7.69 7.36 SSW_MIN_C075 0.78 7.70 SSW_MEAN_C225SSW_MIN_C125 0.78 0.78 7.87 7.78 SSW_MEAN_A100125SSW_MAX_C100 0.78 0.77 7.90 7.69 SSW_MIN_C075SSW_MAX_C125 0.78 0.77 7.91 7.70 SSW_MIN_C125 0.78 7.87 FromSSW_MAX_C100 Table3, the following conclusions can0.77 be inferred: 7.90 TheSSW_MAX_C125 most closely correlated parameter for all0.77 radii is MEAN (and a few MIN). 7.91 MEAN is computed over the entire corresponding region, and MIN describes that all other points are larger than this number;From hence, Table it 3, may the befollowing a more appropriateconclusions indicatorcan be inferred: to represent the TC intensity. TheThe parametersmost closely in thecorrelated surrounding parameter regions for (0.75–1.5 all radii◦ awayis MEAN from the(and TC a center)few MIN). tended MEAN to give is highercomputed correlations over the with entire the corresponding CMA best-track region, data. Weand speculate MIN describes that this that reason all other is related points to theare radiuslarger ofthan the this wind number; circle. Forhence, most it may TCs, be the a radiusmore appr of theopriate Beaufort indicator wind to force represent 10 cyclone the TC (24.5–28.4 intensity. m/s) is 50–100The km, parameters and that of in the the wind surrounding force 12 (32.7–36.9regions (0.75–1.5° m/s) cyclone away isfrom 100–150 the TC km. center) tended to give higher correlations with the CMA best-track data. We speculate that this reason is related to the

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Remote Sens. 2019, 11, 627 8 of 18 radius of the wind circle. For most TCs, the radius of the Beaufort wind force 10 cyclone (24.5–28.4 m/s) is 50–100 km, and that of the wind force 12 (32.7–36.9 m/s) cyclone is 100–150 km. TheThe test test dataset dataset in in Table Table 1 1 werewere usedused toto verifyverify thethe performanceperformance fromfrom usingusing onlyonly thethe SSW_MEAN_C100SSW_MEAN_C100 parameter.parameter. TheThe resultsresults of of the the validation validation are are shown shown in Figurein Figure5. The 5. The horizontal horizontal axis axisis the is maximumthe maximum wind wind speed, speed, as estimated as estimated by the by SSWthe SSW parameter parameter model, model, and and the the vertical vertical axis axis is the is theCMA CMA TC best-trackTC best-track intensity intensity data. data. The RThe2 of R the2 of verification the verification result result for the for test the dataset test dataset is 0.70, is the 0.70, RMSE the RMSEis 7.84 m/s,is 7.84 the m/s, MAE the is MAE 6.11 m/s,is 6.11 and m/s, the and BIAS the is BIAS−1.49 is m/s. −1.49 Figure m/s.5 Figureb,c shows 5b,c the shows differences the differences between betweenthe SSW the parameter SSW parameter model-estimated model-estimated TC intensity, TC intensity, and the CMA and best-trackthe CMA databest-track in two data subgroups. in two subgroups.Figure5b relates Figure to lower-intensity5b relates to lower-intensity TCs, i.e., tropical TCs, depressions, i.e., tropical tropical depressions, storms tropical and severe storms tropical and severestorms. tropical Figure 5storms.c relates Figure to typhoons, 5c relates severe to typhoons, typhoons, severe and supertyphoons, typhoons. and super typhoons.

FigureFigure 5. ScatterScatter plot plot of of the the maximum maximum wind wind speed speed between between the the estimated estimated results, results, using using the the SSW_MEAN_C100SSW_MEAN_C100 parameter-only parameter-only model, model, and and the the CMA CMA best-track best-track data. data. (a (a) )All All of of the the testing testing data. data. ((bb)) Subgroup Subgroup testing testing data data of of the the TD, TD, TS, TS, and and STS. STS. ( (cc)) Subgroup Subgroup testing testing data data of of TY, TY, STY, STY, and and super super TY. TY. 3.2. Estimation of the Maximum Wind Speed using Selected Parameters 3.2. Estimation of the Maximum Wind Speed using Selected Parameters As described in the above section, it is shown that we can estimate the maximum wind speed As described in the above section, it is shown that we can estimate the maximum wind speed by by using a single parameter, with a certain accuracy (with 6.7–7.8 m/s RMSEs). However, including more using a single parameter, with a certain accuracy (with 6.7–7.8 m/s RMSEs). However, including more predictors and using linear regression techniques could improve the estimation of TC intensity [40,41]. predictors and using linear regression techniques could improve the estimation of TC intensity A stepwise regression method was used to select the most useful predictors from all of the above- [40,41]. A stepwise regression method was used to select the most useful predictors from all of the mentioned 1785 parameters. Finally, 15 parameters were selected as follows. TB19H_MIN_C100, above-mentioned 1785 parameters. Finally, 15 parameters were selected as follows. TB19H_MIN_C100, SSW_ MIN_C100, TB19H_RAPT250_C075, SSW_MAX_C250, TB37H_RAPT210_C075, TB19H_RAPT_ 270_ SSW_MIN_C100, TB19H_RAPT250_C075, SSW_MAX_C250, TB37H_RAPT210_C075, A225250, TB22V_RAPT270_A125150, TB37H_MAX_A125150, PCT92_MAX_C125, PCT92_MAX_C100, TB19H_RAPT_270_A225250, TB22V_RAPT270_A125150, TB37H_MAX_A125150, TB37V_RAPT260_C075, TB37H_RAPT180_A125150, TB37H_MIN_C100, PCT92_MAX_A150175, and PCT92_MAX_C125, PCT92_MAX_C100, TB37V_RAPT260_C075, TB37H_RAPT180_A125150, SSW_MEAN_C100. TB37H_MIN_C100, PCT92_MAX_A150175, and SSW_MEAN_C100. PCT (polarization-corrected temperature) is the parameter that is proposed by Spencer et al. [42], PCT (polarization-corrected temperature) is the parameter that is proposed by Spencer et al. [42], which represents the radiation eliminating the radiation from ocean using polarization diversity, and which represents the radiation eliminating the radiation from ocean using polarization diversity, and is calculated as: is calculated as: PCT = 1.818TBv − 0.818TBh (1) PCT = 1.818TB − 0.818TB (1) where TBv is the TB for the vertically polarized channel, and TBh is that of the horizontally polarized wherechannel TB at 85 is GHzthe TB of thefor SSM/I.the vertically In this paper,polarized we usedchannel, PCT and at 92 TB GHz is of that the SSMISof the ashorizontally candidate polarizedparameters. channel at 85 GHz of the SSM/I. In this paper, we used PCT at 92 GHz of the SSMIS as candidateHowever, parameters. the number of variables does not correspond to “the more the better” in linear regression. FigureHowever,6 shows thethe dependencenumber of ofvariables R 2 and RMSEdoes not with co anrrespond increase to in “the variable more numbers. the better” From in Figure linear6, regression.we can conclude Figure that 6 shows when the numberdependence of parameters of R2 and was RMSE six, with the minimum an increase RMSE in variable and maximum numbers. R2 Fromvalues Figure occurred. 6, we Therefore, can conclude these that six parameters when the number were chosen of parameters as the final was regression six, the variables,minimum and RMSE the andproposed maximum TC intensity R2 values estimation occurred. modelTherefore, is as these follows: six parameters were chosen as the final regression variables, and the proposed TC intensity estimation model is as follows:

Remote Sens. 2019, 11, x FOR PEER REVIEW 9 of 19 Remote Sens. 2019, 11, 627 9 of 18 =× Vmax 0.7582 SSW_MIN_C100

V max +0.1645= 0.7582×× TB19H_RAPT250_C075SSW_MIN_C100 +0.1645×× TB19H_RAPT250_C075 +0.3410 SSW_MAX_C250 +0.3410 × SSW_MAX_C250 (2) -0.0722× TB37H_RAPT210_C075 (2) −0.0722 × TB37H_RAPT210_C075 +0.0806× TB22V_RAPT270_A125150 +0.0806 × TB22V_RAPT270_A125150 × +0.2861+0.2861 × TB37H_MIN_C100-46TB37H_MIN_C100−46.884.884 where, Vmax is the maximum wind speed of TC, SSW_MIN_C100 is the minimum wind speed within where, Vmax is the maximum wind speed of TC, SSW_MIN_C100 is the minimum wind speed within 1 degree of distance from the TC center, TB19H_RAPT250_C075TB19H_RAPT250_C075 is the percentagepercentage of pixels with TB19GHz, H-pol larger than 250 K within 0.75 degrees of distance from the TC center, SSW_MAX_C250 is TB19GHz, H-pol larger than 250 K within 0.75 degrees of distance from the TC center, SSW_MAX_C250 is the maximum windwind speedspeed withinwithin 2.52.5 degreesdegrees of of distance distance from from the the TC TC center, center, TB37H_RAPT210_C075 TB37H_RAPT210_C075 is is the percentage of pixels, with TB37GHz, H-pol being larger than 210 K within 0.75 degrees from the TC the percentage of pixels, with TB37GHz, H-pol being larger than 210 K within 0.75 degrees from the TC center, TB22V_RAPT270_A125150 is the percentage of pixels, with TB22GHz, V-pol being larger than 270 center, TB22V_RAPT270_A125150 is the percentage of pixels, with TB22GHz, V-pol being larger than 270 K between 1.25 degrees and 1.5 degrees from the TC center, and TB37H_MIN_C100 is the minimum TB of 37 GHz horizontal polarization within 1 degree ofof distancedistance fromfrom thethe TCTC center.center.

Figure 6. Parameters of different numbers used to determ determineine the best estimation model. The X-axis is the number of parameters, the left Y-axisY-axis isis RMSE,RMSE, andand thethe rightright Y-axisY-axis isis RR22..

4. Experimental Results 4.1. Model Verification 4.1. Model Verification 4.1.1. Comparison with the TC Best-Track Wind Speed Estimates 4.1.1. Comparison with the TC Best-Track Wind Speed Estimates The training dataset and test dataset in Table1 were used to verify the performance of our estimationThe training method. dataset The results and test of thedataset validation in Table are 1 shownwere used in Figure to verify7. The the horizontal performance axis of is theour estimationmaximum windmethod. speed The estimated results of by the proposedvalidation model, are shown and the in verticalFigure axis7. The is thehorizontal CMA TC axis best-track is the maximumintensity data. wind Unexpectedly, speed estimated the accuracy by the proposed of the training model, dataset and isthe higher, vertical but axis there is wasthe CMA little difference TC best- trackbetween intensity the training data. Unexpectedly, dataset and test the dataset accuracy when of the the intensity trainingof dataset TC is TY,is higher, STY, and but super there TY. was The little R2 differenceof our method between verification the training result dataset for the trainingand test datasetdataset was when 0.89, the the intensity RMSE wasof TC 4.19 is m/s,TY, STY, the MAE and 2 wassuper 3.20 TY. m/s, The andR of the our BIAS method was −verification0.008 m/s. result The R 2forof th oure training method verificationdataset was result0.89, the for testRMSE dataset was 2 was4.19 0.83,m/s, the RMSEMAE was was 3.20 5.94 m/s, and the MAEthe BIAS was 4.62was m/s,−0.008 and m/s. the The BIAS R wasof our−0.43 method m/s. verification Figure7b–f resultshows for the test differences dataset was between 0.83, model-estimatedthe RMSE was 5.94 TC m/s, intensity the MAE and was the CMA4.62 m/s, best and track the data BIAS in fourwas −subgroups.0.43 m/s. Figure Figure 7b–f7b,e shows relate tothe lower-intensity differences between TCs, i.e., model-estimated tropical depressions, TC intensity tropical and storms, the CMA and bestsevere track tropical data storms.in four subgroups. Figure7c,f relate Figure to typhoons,7b,e relate severeto lower-intensity typhoons, and TCs, super i.e., typhoons.tropical depressions, Compared withtropical Figures storms,4 and and5, severe we can tropical see from storms. Figure Figure7 that using7c,f relate combined to typhoons, HY-2A severe and SSMIS typhoons, is better and thansuper typhoons.the use of eitherCompared HY-2A with or SSMISFigure separately.4 and Figure 5, we can see from Figure 7 that using combined HY- 2A and SSMIS is better than the use of either HY-2A or SSMIS separately.

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FigureFigure 7.7. ScatterScatter plot ofof thethe comparisoncomparison betweenbetween thethe estimatedestimated maximum wind speed and the CMACMA best-trackbest-track data. The solid line is y = x. ( (aa)) all all data for training dataset, ( b) TD, TS and STSSTS forfor thethe trainingtraining dataset,dataset, ((c)) TY,TY, STYSTY andand thethe supersuper TYTY forfor trainingtraining dataset,dataset, (d)) all data for test dataset, ( e) TD, TS,TS, andand STSSTS forfor thethe testtest dataset,dataset, ((ff)) TY,TY, STY, STY, and and super super TY TY for for test test dataset. dataset. 4.1.2. Comparison with the Passive-Only Model 4.1.2. Comparison with the Passive-Only Model In order to test the usefulness of the include active observations into the TC intensity estimation In order to test the usefulness of the include active observations into the TC intensity estimation model, the Hoshino et al. [17] method was chosen to compare with the proposed model. The selected model, the Hoshino et al. [17] method was chosen to compare with the proposed model. The selected parameters, the coefficients and RMSEs of the 10 candidates of regression equations are shown in parameters, the coefficients and RMSEs of the 10 candidates of regression equations are shown in Equation (3) and Table4. Equation (3) and Table 4. Vmax = a × Pi + b × Pj + c × Pk + d (3) =× +× +× + As shown in Table4, the averageVaPbPcPdmax RMSE ofijk the Hoshino model was 6.57 m/s. This RMSE is larger(3) than the estimation of the proposed method, comparing with both the training dataset or the test dataset As shown in Table 4, the average RMSE of the Hoshino model was 6.57 m/s. This RMSE is larger (see Figure7a,d). It is also indicated that synergy by using active and passive microwave observations than the estimation of the proposed method, comparing with both the training dataset or the test can obtain better estimation results than by only using passive observation as model predictors. dataset (see Figure 7a,d). It is also indicated that synergy by using active and passive microwave 4.1.3.observations Comparison can obtain with Other better Existing estimation Models results than by only using passive observation as model predictors. Following Jiang et al. [21], a comparison with several existing algorithms for estimating TC intensities was made. Their RMSE and MAE are shown in Table5. It is worth pointing out that we did not apply these existing methods to the same dataset used in this study, to calculate their errors. The statistical numbers listed in this table are all calculated from different data in different regions. It is worth mentioning that the smaller RMSE of the proposed method shown in Table5 does not mean that this method is superior to others. It is only compared with the best track data, but not with an independent observation, like most of the other methods listed in the table.

Remote Sens. 2019, 11, 627 11 of 18

Table 4. The parameters, coefficients, R2, and RMSE of the candidates for the regression equation.

2 Equation a Pi b Pj c Pk d R RMSE 1 0.24 TBH19_RAPT250_C125 0.35 TBH37_MIN_C100 0.070 TBV22_RAPT270_C125 −50.16 0.79 6.51 2 1.40 TBH19_MIN_C100 0.23 TBH19_RAPT250_C125 −1.78 TBV19_MIN_C100 167.71 0.79 6.55 3 0.39 TB19H_MIN_C100 0.21 TBH19_RAPT250_C125 0.059 TBV22_RAPT270_C125 −51.12 0.79 6.55 4 0.44 TBH19_MIN_C100 0.22 TBH19_RAPT250_C125 −0.07 TBPCT91_RAPT230_C075 −52.95 0.79 6.55 5 0.33 TBH19_MIN_C100 −0.15 TBPCT91_RAPT230_C075 0.15 TBV22_RAPT270_C125 −27.28 0.79 6.55 6 0.13 TBH19_MIN_C100 0.27 TBH19_RAPT250_C125 0.30 TBH37_MIN_C100 −64.20 0.79 6.56 7 0.43 TBH19_MIN_C100 0.24 TBH19_RAPT250_C125 0.36 TBV22_MAX_C150 −154.64 0.79 6.59 8 0.28 TBH19_RAPT250_C125 0.38 TBH37_MIN_C100 0.47 TBV22_MAX_C150 −182.53 0.79 6.60 9 1.41 TBH19_MIN_C100 −1.87 TBV19_MIN_C100 0.13 TBV22_RAPT270_C125 184.14 0.79 6.60 10 −0.16 TBPCT91_RAPT230_C075 0.29 TBH37_MIN_C100 0.17 TBV22_RAPT270_C125 −24.55 0.79 6.61

Table 5. Comparison of the model-estimated TC intensities with previously developed techniques using the statistical values of RMSE and MAE.

Technique Sensors Verification Against RMSE (m/s) Mean Absolute Error (m/s) References Feature-based K-nearest SSM/I Best Track 9.31–10.19 7.20–8.23 Bankert and Tag [15] Warm Core Anomaly AMSU Best Track 7.20 5.40 Demuth et al. [43,44] Within 3 h aircraft Multivariate Regression IR 8.59 6.79 Kossin et al. [45] reconnaissance-based best track Advanced Within 1 h aircraft IR 7.67 5.61 Olander and Velden [6] Dvorak Technique reconnaissance-based best track Within 2 h aircraft Dvorak Technique (DT) Visible/IR 3.09–7.20 (avg. ~5.14) 2.57–5.66 (avg. ~4.12) Knaff et al. [46] reconnaissance-based best track Deviation Angle IR Best Track 6.17–7.72 - Ritchie et al. [9,10] Variance (DAV) Feature Analogs in Within 12 haircraft IR 6.53 5.61 Fetanat et al. [47] Satellite Imagery (FASI) reconnaissance-based best track Deep Convolutional IR Aircraft reconnaissance dataset 4.63–8.23 (avg. ~6.02) - Pradhan et al. [48] Neutral Network PMW-IE Combined Best Track/Within 3 h aircraft TMI 6.17/6.48 4.63/4.94 Jiang et al. [21] Model for t = 6 h reconnaissance-based best track The proposed model SSMIS/HY-2A Best Track 5.94 4.62 This study Remote Sens. 2019, 11, 627 12 of 18

4.1.4. The Impact of Overpass Time Difference on Model Estimation Since the HY-2A scatterometer and SSMIS radiometer have different overpass times when collecting the dataset used in this study, it needs to further analyze its impact on the estimated TC intensity. We calculated the RMSEs between the MSW that was estimated by the proposed model andRemote the Sens. CMA 2019 best, 11, x track FOR PEER data REVIEW at 5 min intervals for the overpass time difference (see Figure8). 13 of 19

FigureFigure 8.8. RMSERMSE valuesvalues ofof thethe proposedproposed modelmodel calculatedcalculated atat 55 minmin intervalsintervals ofof overpassoverpass timetime differencedifference (upper(upper panel panel);); and and the the percentage percentage of of the the sample sample number number at at each each bin bin ( lower(lower panel panel).).

AsAs shownshown inin FigureFigure8 ,8, the the RMSEs RMSEs did did not not increase increase as as the the time time difference difference became became larger. larger. Most Most of of thethe RMSEs RMSEs were were in in the the range range of of 3.5 3.5 to to 4 4 m/s. m/s. The smallest RMSE occurredoccurred atat thethe 5555 minmin timetime differencedifference bin,bin, and and itit waswas probablyprobably due due toto thethe smallsmall samplesample sizesize ofof thethe highhigh windwind conditions.conditions. ThisThis resultsresults indicateindicate thatthat thethe datadata matchingmatching schemescheme usedused inin thisthis studystudy waswas effective;effective; thethe errorserrors introducedintroduced byby thesethese timetime differencesdifferences had had no no significant significant effect effect on on the the estimated estimated MSW. MSW. 4.2. Case Studies 4.2. Case Studies 4.2.1. Typhoon Tembin (1214) 4.2.1. Typhoon Tembin (1214) The time-series plot of the maximum wind speed for Typhoon Tembin (1214) is shown in Figure9a. The time-series plot of the maximum wind speed for Typhoon Tembin (1214) is shown in Figure In this figure, the green dot indicates the estimated maximum wind speed by the proposed regression 9a. In this figure, the green dot indicates the estimated maximum wind speed by the proposed model, and the red line showed the maximum wind speed by the CMA best-track data. This figure regression model, and the red line showed the maximum wind speed by the CMA best-track data. shows that the variations of the maximum wind speed estimated by the regression model has good This figure shows that the variations of the maximum wind speed estimated by the regression model consistency with the maximum wind speed by the TC best-track data. However, in individual cases, has good consistency with the maximum wind speed by the TC best-track data. However, in the estimated intensity of TC will sometimes be underestimated or overestimated. Figure9b is the individual cases, the estimated intensity of TC will sometimes be underestimated or overestimated. time-series plot of the maximum wind speed for Super Typhoon Noru (1705). We can see that the Figure 9b is the time-series plot of the maximum wind speed for Super Typhoon Noru (1705). We can tendency of intensity change corresponded reasonably well with the maximum wind speed by the see that the tendency of intensity change corresponded reasonably well with the maximum wind TC best-track data, but there were two clearly underestimated values in the initial stage and in the speed by the TC best-track data, but there were two clearly underestimated values in the initial stage maximum wind speed stage. and in the maximum wind speed stage. We can observe that in the weak TC stages (<15 m/s), the outputs of the proposed method tended to underestimate the maximum wind speeds (Figures7e and9). The possible reason for this was the difficulty in positioning the TC center, due to an unorganized cloud pattern. Figure 10 shows the SSMIS and HY-2A images of Super Typhoon Noru (1705) at 20 July 2017 (the early stage). The maximum wind speed estimated by the regression model was 8.1 m/s, but the maximum wind speed in the CMA best-track data was 13.6 m/s. This may be related to the accuracy in the positioning of the center, and the unorganized patterns.

Remote Sens. 2019, 11, x FOR PEER REVIEW 13 of 19

Figure 8. RMSE values of the proposed model calculated at 5 min intervals of overpass time difference (upper panel); and the percentage of the sample number at each bin (lower panel).

As shown in Figure 8, the RMSEs did not increase as the time difference became larger. Most of the RMSEs were in the range of 3.5 to 4 m/s. The smallest RMSE occurred at the 55 min time difference bin, and it was probably due to the small sample size of the high wind conditions. This results indicate that the data matching scheme used in this study was effective; the errors introduced by these time differences had no significant effect on the estimated MSW.

4.2. Case Studies

4.2.1. Typhoon Tembin (1214) The time-series plot of the maximum wind speed for Typhoon Tembin (1214) is shown in Figure 9a. In this figure, the green dot indicates the estimated maximum wind speed by the proposed regression model, and the red line showed the maximum wind speed by the CMA best-track data. This figure shows that the variations of the maximum wind speed estimated by the regression model has good consistency with the maximum wind speed by the TC best-track data. However, in individual cases, the estimated intensity of TC will sometimes be underestimated or overestimated. Figure 9b is the time-series plot of the maximum wind speed for Super Typhoon Noru (1705). We can see that the tendency of intensity change corresponded reasonably well with the maximum wind

Remotespeed Sens. by the2019 TC, 11 ,best-track 627 data, but there were two clearly underestimated values in the initial13 stage of 18 and in the maximum wind speed stage.

Remote Sens. 2019, 11, x FOR PEER REVIEW 14 of 19

Figure 9. Time series plot of the maximum wind speed estimated by the regression model, and the best track data for (a) Typhoon Tembin (1214) and (b) Super Typhoon Noru (1705).

We can observe that in the weak TC stages (<15 m/s), the outputs of the proposed method tended to underestimate the maximum wind speeds (Figures 7e and 9). The possible reason for this was the difficulty in positioning the TC center, due to an unorganized cloud pattern. Figure 10 shows the SSMIS and HY-2A images of Super Typhoon Noru (1705) at 20 July 2017 (the early stage). The maximum wind speed estimated by the regression model was 8.1 m/s, but the maximum wind speed in the CMA best-track data was 13.6 m/s. This may be related to the accuracy in the positioning of the center, Figureand the 9. Timeunorganized series plot patterns. of the maximum wind speed estimated by the regression model, and the best track data for (a) Typhoon Tembin (1214) and (b) Super Typhoon Noru (1705).

Figure 10. Images of Super Typhoon Noru (1705) at 20 July 2017, during the early stages of Figure 10. Images of Super Typhoon Noru (1705) at 20 July 2017, during the early stages of development,(a) SSMIS, (b) HY-2A. The estimated maximum wind speed was 8.1 m/s, but the CMA development,(a) SSMIS, (b) HY-2A. The estimated maximum wind speed was 8.1 m/s, but the CMA best-track data was 13.6 m/s. The cross symbol is the center of Noru. The radii of the circles from best-track data was 13.6 m/s. The cross symbol is the center of Noru. The radii of the circles from inside to outside are 0.5◦, 0.75◦, 1◦, and 1.25◦, respectively. inside to outside are 0.5°, 0.75°, 1°, and 1.25°, respectively. 4.2.2. Typhoon Noru (1705) 4.2.2. Typhoon Noru (1705) In Figure9, we also noted that our method tended to underestimate the maximum wind speeds whenIn Figure the best 9, trackwe also data noted was aboutthat our 40 m/s.method Figure tended 11 shows to underestimate the SSMIS and the HY-2A maximum images wind of Super speeds whenTyphoon the best Noru track (1705) data at was 30 Julyabout 2017 40 (them/s. matureFigure stage).11 shows The the maximum SSMIS and wind HY-2A speed images estimated of Super by Typhoonthe regression Noru (1705) model at was 30 45.98July 2017 m/s, (the but mature the maximum stage). windThe maximum speed in the wind CMA speed best-track estimated data wasby the regression52 m/s. Inmodel the regression was 45.98 model, m/s, but SSW_MAX_C250 the maximum waswind 28.54 speed m/s. in Atthe the CMA same best-track intensity, data we found was 52 m/s.that In in the the regression previous image,model, SSW_MAX_C250 SSW_MAX_C250 value was was 28.54 38.5 m/s. m/s. At Meanwhile,the same intensity, we analyzed we found that the that in valuethe previous of the Advanced image, SSW_MAX_C250 Scatterometer (ASCAT) value was wind 38 field.5 m/s. data Meanwhile, on this day we was analyzed 28.23 m/s. that Also, the value the precipitation rate obtained by the GPM was above 450 mm/day. Therefore, we inferred that this error of the Advanced Scatterometer (ASCAT) wind field data on this day was 28.23 m/s. Also, the was probably due to the wind speed retrieval error caused by heavy rainfall. precipitation rate obtained by the GPM was above 450 mm/day. Therefore, we inferred that this error was probably due to the wind speed retrieval error caused by heavy rainfall.

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Figure 11. Images of Super Typhoon Noru (1705) at 30 July 2017, during the mature stage of Figure 11. Images of Super Typhoon Noru (1705) at 30 July 2017, during the mature stage of development, (a) SSMIS, (b) HY-2A. The estimated maximum wind speed was 45.98 m/s, but the CMA development, (a) SSMIS, (b) HY-2A. The estimated maximum wind speed was 45.98 m/s, but the CMA best-track data was 52 m/s. The cross symbol is the center of Noru. The radii of the circles from inside best-track data was 52 m/s. The cross symbol is the center of Noru. The radii of the circles from inside to outside are 0.5◦, 0.75◦, 1◦, and 1.25◦, respectively. to outside are 0.5°, 0.75°, 1°, and 1.25°, respectively. 5. Discussion 5. Discussion The sea surface wind field can be derived directly from the microwave scatterometer. However, thereThe was sea a largesurface error wind under field highcan be wind derived speed direct conditions,ly from the which microwave makes scatterometer. it impossible toHowever, directly theredetermine was a the large typhoon error intensityunder high from wind scatterometer speed conditions, observations. which However, makes it theimpossible scatterometer to directly data determinecan still provide the typhoon useful informationintensity from when scatteromete estimatingr observations. the TC intensity. However, In our the study, scatterometer when choosing data canthe modelstill provide predictors, useful we information consider different when estimating radius ranges, the TC and intensity. we take In into our account study, when the maximum, choosing theminimum, model predictors, and average we values consider of thedifferent wind speedradius obtainedranges, and from we HY-2A take into scatterometer. account the Themaximum, results (asminimum, shown inand Table average3) show values that of parameters, the wind speed such ob astained the mean from windHY-2A speed scatterometer. near the inner The results center (as of TCshown are muchin Table correlated 3) show withthat parameters, TC intensity. such Therefore, as the mean when wind the wind speed speed near isthe higher inner thancenter 30 of m/s, TC arethe much active correlated microwave with observations TC intensity. will Therefore, still add some when useful the wind information. speed is Thishigher is whythan we30 m/s, want the to activeintroduce microwave the active observations microwave observationswill still add into some the TCuseful intensity information. estimation This model. is why we want to introduceThe overall the active accuracy microwave of our observations estimated model into hasthe TC been intensity improved, estimation compared model. with the method of usingThe the overall microwave accuracy radiometer of our estimated alone [18 model]. But therehas been are improved, still large errors compared in some with cases. the method Here are of usingseveral the possible microwave reasons. radiometer (1) Errors alone in the [18]. determination But there ofare the still cyclone large centers,errors in because some cases. of unorganized Here are severalclouds withpossible the TC reasons. in its earlier (1) orErrors dissipation in the stage. determination In the future, of we the will cyclone need to improvecenters, thebecause accuracy of unorganizedof TC center determination,clouds with the especially TC in its in theearlier early or or di dissipationssipation stage. stage. In Automated the future, techniques we will need such asto improvethe Automated the accuracy Rotational of CenterTC center Hurricane determination, Eye Retrieval especially [15,49] method in the mightearly beor ofdissipation benefit for centerstage. Automateddetermination. techniques (2) The timesuch differenceas the Automated between theRota overpasstional Center time ofHurricane HY-2A and Eye F17 Retrieval will introduce [15,49] methoderrors into might the modelbe of benefit estimates. for Adoptingcenter determinatio more restrictiven. (2) criteriaThe time when difference matching between the training the overpass dataset timecould of reduce HY-2A this and error. F17 (3) will The introduce asymmetry errors of cyclone into the shape model has anestimates. influence Adopting on our circular more hypothesis.restrictive Also,criteria to when solve thematching problem the of traini asymmetry,ng dataset we needcould to reduce introduce this asymmetricerror. (3) The characteristic asymmetry factorsof cyclone and shapeother parametershas an influence to increase on our the circular stability hypothesis. of the model. Also, (4) to Wesolve used the theproblem radius of of asymmetry, the TC center we in need the torange introduce of 2.5 degrees, asymmetric so that characteristic the size of thefactors TC also and had other a certainparameters impact to onincrease our model. the stability Therefore, of the we model.may consider (4) We theused size the of radius the TC of as the a parameterTC center in for the future range research. of 2.5 degrees, (5) One so other that possiblethe size of source the TC of alsoerror had are a the certain potential impact for lagon our in the model. intensity, Therefore, especially we may during consider rapid intensification,the size of the TC where as a the parameter satellite presentationfor future research. leads the (5) intensity One other by severalpossible hours. source In of follow-up error are work, the potential we should fortake lag in into the account intensity, the especiallyoccurrence during of this rapid rapid intensification, intensification. where the satellite presentation leads the intensity by several hours.In In this follow-up paper, firstly, work, we we selected should 15take parameters into account from the many occurrence parameters of this by rapid the stepwise intensification. regression method.In this Three paper, out offirstly, the 15 we selected selected parameters 15 parame wereters related from to many 92 GHz. parameters In the proposed by the model, stepwise the regressionoptimal combination method. Three of six out parameters of the 15 selected was chosen, parameters but there were were related no 92to 92 GHz GHz. parameters. In the proposed In the model,future, morethe optimal feature combination parameters willof six be parameters introduced, was and chosen, we will but try there to use were more no complicated 92 GHz parameters. statistical In the future, more feature parameters will be introduced, and we will try to use more complicated statistical methods to select the optimal combination of the feature parameters. Our feature parameters are selected for all stages of TC. Some parameters may be better for certain TC stages;

Remote Sens. 2019, 11, 627 15 of 18

Remote Sens. 2019, 11, x FOR PEER REVIEW 16 of 19 methods to select the optimal combination of the feature parameters. Our feature parameters are selectedtherefore, for one all stages may consider of TC. Some different parameters feature may parame be betterter combinations for certain TC in stages; different therefore, stages one of TC may in considertheir new different models. feature parameter combinations in different stages of TC in their new models. TheThe proposed proposed model model is is developed developed based based on on observations observations of of typhoons typhoons in in the the Northwestern Northwestern PacificPacific Ocean. Ocean. To To further further test test whether whether it it will will still still be be useful useful to to hurricanes hurricanes in in the the Atlantic Atlantic and and East East PacificPacific Oceans, Oceans, we we have have collected collected more more test test data data in in these these regions. regions. Unlike Unlike the the Northwestern Northwestern Pacific, Pacific, therethere are are airborne airborne observations observations for for hurricanes. hurricanes. Aircraft Aircraft reconnaissance reconnaissance datadata of of the the Atlantic Atlantic basin, basin, NortheastNortheast and and NorthNorth Central Pacific Pacific basin basin can can be be obtained obtained from from the theNational National Hurricane Hurricane Center Center (NHC) (NHC)at its at itswebsite website (http://www.aoml.noaa.gov/hrd/ (http://www.aoml.noaa.gov/hrd/data_sub/hurr.htmldata_sub/hurr.html). With). With the the operationaloperational deploymentdeployment of of the the Stepped Stepped Frequency Frequency Microwave Microwave Radiometer Radiometer (SFMR), (SFMR), hurricane hurricane reconnaissance reconnaissance and and researchresearch aircraft aircraft provide provide near near real-time real-time observations observations of of the the 10 10 ocean-surface ocean-surface wind-speed, wind-speed, both both within within andand around around tropical tropical cyclones cyclones [ 50[50].]. In In total, total, 13 13 matchups matchups were were collected collected in in 2014–2015 2014–2015 (see (see Figure Figure 12 12 below).below). Since Since the the matchups matchups were were too too few few to to recalculate recalculate the the model model coefficients, coefficients, we we directly directly used used the the coefficientscoefficients obtained obtained from from Northwest Northwest Pacific Pacific region. region. In Figure In Figure12, we can12, seewe thatcan thesee model-estimated that the model- TCestimated intensities TC did intensities not perform did not well perform compared well to comp the aircraftared to reconnaissance the aircraft reconnaissance data. The differences data. The amongdifferences the model-estimated among the model-estimated MWS, best-track, MWS, and best-tra aircraftck, measurements and aircraft measurements were very large, were with very MAE large, > 12with m/s MAE and RMSE > 12 ~8m/s m/s. and Both RMSE were ~8 much m/s. worseBoth thanwere the much performance worse than in the the Northwestern performance Pacific in the OceanNorthwestern (as shown Pacific in Figure Ocean7). Hurricanes(as shown in and Figure typhoons 7). Hurricanes have some and differences typhoons in have their some structure differences and formationin their structure mechanisms. and Itformation is indicated mechanisms. that the coefficients It is indicated of the that proposed the coefficients model were of more the proposed suitable formodel typhoons were thanmore forsuitable hurricanes. for typhoons Thus, thethan coefficients for hurricanes. of the Thus, proposed the coefficients model need of the to be proposed tuned withmodel enough need matchupto be tuned samples with enough before itsmatchup application sample tos TCs before in basinsits application other than to theTCs Northwestern in basins other Pacificthan the Ocean. Northwestern In addition, Pacific the current Ocean. comparison In addition among, the current SSMIS-only, comparison HY2A-only, among and SSMIS-only, their combined HY2A- methodsonly, and is their not verycombined rigorous. methods Only is one not parametervery rigorous. was Only tested one for parameter the SSMIS-only was tested and for HY2A-only the SSMIS- methods,only and and HY2A-only the one-parameter methods, approachand the one-parameter is not the best approach option for is estimating not the best the option TC intensity. for estimating In the future,the TC we intensity. will need In tothe consider future, we more will comprehensive need to consider and more solid comprehensive comparisons between and solid the comparisons proposed modelbetween and the other proposed estimation model models, and other using estimation more sufficient models, ground using true more data. sufficient ground true data.

Figure 12. Scatter plot of the comparison between the estimated maximum wind speed and the Figure 12. Scatter plot of the comparison between the estimated maximum wind speed and the National Hurricane Center (NHC) best-track data and aircraft reconnaissance data in the Atlantic basin, National Hurricane Center (NHC) best-track data and aircraft reconnaissance data in the Atlantic and the Northeast and North Central Pacific basin. The solid line is the fitting line. (a) Proposed model basin, and the Northeast and North Central Pacific basin. The solid line is the fitting line. (a) Proposed vs best-track data (b) proposed model vs aircraft observations. model vs best-track data (b) proposed model vs aircraft observations. 6. Conclusions 6. Conclusions In this paper, a method to estimate TC intensity was developed, using SSMIS radiometer and HY-2AIn scatterometer this paper, a method data from to theestimate Northwest TC inte Pacific,nsity was from developed, 2012 to 2017.using First,SSMIS we radiometer studied theand correlationHY-2A scatterometer between the TBdata parameters, from the Northwest SSW parameters, Pacific, and from the MSW,2012 to to 2017. form theFirst, best-track we studied data ofthe thecorrelation TCs. We found,between for the the TB SSMIS parameters, radiometer, SSW that paramete (1) thers, parameters and the MSW, of 19 GHz to form and the 37 GHzbest-track channels data giveof the higher TCs. correlations We found, withfor the the SSMIS TC MSW; radiometer, (2) the horizontal that (1) the polarization parameters has of a19 higher GHz and correlation 37 GHz channels give higher correlations with the TC MSW; (2) the horizontal polarization has a higher correlation than the vertical polarization at the same frequency; (3) the most well-correlated parameter for all frequencies is MIN; (4) the parameters in the surrounding regions (0.75–1.25° away from the TC center, especially 1°) tend to give higher correlations with the TC intensity. The highest

Remote Sens. 2019, 11, 627 16 of 18 than the vertical polarization at the same frequency; (3) the most well-correlated parameter for all frequencies is MIN; (4) the parameters in the surrounding regions (0.75–1.25◦ away from the TC center, especially 1◦) tend to give higher correlations with the TC intensity. The highest correlation coefficient is 0.84, and the highest RMSE is 6.67 m/s. In addition, for HY-2A scatterometer, (1) the most well-correlated parameter for all radii is MEAN (and a few MIN); (2) the parameters for the surrounding regions (0.75–1.5◦ away from the TC center) tend to give higher correlations with the CMA best-track data. The highest correlation coefficient is 0.83, and the highest RMSE is 7.09 m/s. Then, the TC intensity-estimated model was developed based on multiple linear regression, using selected parameters. We evaluated our estimated method by using independent verification data. The R2 of our verification is 0.83, the RMSE is 5.94 m/s, the MAE is 4.62 m/s, and the BIAS is −0.43 m/s. The estimated results of our model are therefore comparable to those of other algorithms. On 25 October 2018, the HY-2B satellite was launched, as well as a subsequent series of HY-2 satellites, which formed an ocean dynamics satellite constellation to provide additional TC observation data. In the next five years, the Chinese HY-2C and FY-3E, which also carry radiometer and scatterometer equipment, will be launched. There will be more satellite data for both typhoons and hurricanes available to improve and implement the proposed method at that time.

Author Contributions: All authors have made great contributions to this article. Conceptualization, X.Y.; Formal analysis, K.X.; Methodology, X.Y., K.X.; Software, K.X., S.W.; Validation, K.X.; Writing—original draft, K.X., X.Y.; Writing—review & editing, K.X., X.Y. Funding: This research was supported by the National Key R&D Program of China under Grant 2017YFB0502800, and in part, by the National Natural Science Foundation of China under Grant 41871268, and in part, by the Key Research and Development Program of Hainan Province under Grant ZDYF2017167. Acknowledgments: The HY-2A microwave scatterometer data are provided by the National Satellite Ocean Application Service (NSOAS). The SSMIS microwave radiometer data are produced by Remote Sensing Systems, with support from NASA. Data are available at www.remss.com. The best-track data were obtained through the China Meteorological Administration (CMA). We are grateful to NSOAS, NOAA, and CMA for access to these data. Conflicts of Interest: The authors declare no conflicts of interest.

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