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. 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