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JULY 2016 H O N G E T A L . 1363 A Unique Satellite-Based Sea Surface Wind Speed Algorithm and Its Application in Tropical Cyclone Intensity Analysis SUNGWOOK HONG Department of Environment, Energy, and Geoinfomatics, Sejong University, Seoul, South Korea HWA-JEONG SEO National Meteorological Satellite Center, Korea Meteorological Administration, Gwanghyewon-myeon, South Korea YOUNG-JOO KWON Department of Environment, Energy, and Geoinfomatics, Sejong University, Seoul, South Korea (Manuscript received 2 June 2015, in final form 29 December 2015) ABSTRACT This study proposes a sea surface wind speed retrieval algorithm (the Hong wind speed algorithm) for use in rainy and rain-free conditions. It uses a combination of satellite-observed microwave brightness tempera- tures, sea surface temperatures, and horizontally polarized surface reflectivities from the fast Radiative Transfer for TOVS (RTTOV), and surface and atmospheric profiles from the European Centre for Medium- Range Weather Forecasts (ECMWF). Regression relationships between satellite-observed brightness tem- perature and satellite-simulated brightness temperatures, satellite-simulated brightness temperatures, rough surface reflectivities, and between sea surface roughness and sea surface wind speed are derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2). Validation results of sea surface wind speed be- tween the proposed algorithm and the Tropical Atmosphere Ocean (TAO) data show that the estimated bias 2 2 and RMSE for AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 1.13 m s 1, and 20.52 and 1.21 m s 1, respectively. Typhoon intensities such as the current intensity (CI) number, maximum wind speed, and minimum pressure level based on the proposed technique (the Hong technique) are compared with best-track data from the Japan Meteorological Agency (JMA), the Joint Typhoon Warning Center (JTWC), and the Cooperative Institute for Mesoscale Meteorological Studies (CIMSS) for 13 typhoons that occurred in the northeastern Pacific Ocean throughout 2012. Although the results show good agreement for low- and medium-range typhoon intensities, the discrepancy increases with typhoon intensity. Consequently, this study provides a useful retrieval algorithm for estimating sea surface wind speed, even during rainy conditions, and for analyzing characteristics of tropical cyclones. 1. Introduction et al. 2005), and peak wind speeds have also increased by over 50% in this region since 1949 (Emanuel 2005). Tropical cyclones (TCs), particularly typhoons, are Information related to the eye and the center of a TC major natural disasters on the Korean Peninsula, and together with its intensity and wind field (radius of they inflict huge damage within a period of a few days to maximum wind) are important factors used in the weeks. Trends in intensities of TCs across the western analysis of such phenomena. The maritime nature of North Pacific basin have increased recently (Webster TCs and the lack of extensive in situ observations over oceans result in a strong dependence on satellite remote sensing. This has led the forecasters to analyze factors such as TC cloud type, intensity, and the relationships Corresponding author address: Dr. Sungwook Hong, De- partment of Environment, Energy, and Geoinfomatics, Sejong between their position and motion, in addition to mon- University, 209 Neungdong-ro, Gwangjin-gu, Seoul, South Korea. itoring the advent, maturation, and dissipation stages E-mail: [email protected] of a TC’s lifetime sequentially. DOI: 10.1175/JTECH-D-15-0128.1 Ó 2016 American Meteorological Society Unauthenticated | Downloaded 09/28/21 09:34 PM UTC 1364 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 33 Remote sensing developed quickly after the advent Therefore, obtaining an accurate measurement of of Earth-orbiting satellites, and it has since been used sea surface wind speed (WS)isveryimportantfor to analyze TCs. The Dvorak TC intensity estimation monitoring typhoon intensities; it also affects the method (Dvorak 1975), based on infrared (IR) and ability to provide an accurate TC warning by elimi- visible (VIS) satellite imagery (Dvorak 1984), is most nating the subjectivity of individual analysts. Micro- notable for its operational use and its TC best-track wave remote sensors have an advantage in estimating archives (Velden et al. 2012). However, previous WS because the increase of sea surface emissivity, due research has evaluated the shortcomings and accu- to roughness (English and Hewison 1998; Liu et al. racy of the Dvorak technique (Guard 1988; Mayfield 2011) and foam effects (Tang 1974) driven by WS,is et al. 1988; Brown and Franklin 2004; Kossin and related physically to the observed brightness temper- Velden 2004; Velden et al. 2006; Knaffetal.2010). ature (TB). Many well-calibrated ocean emissivity The main disadvantage appears to be the inevitable models have been developed for passive microwave subjectivity of the individual analysts (Lu and Yu radiometers (Stogryn 1967; Wilheit 1979; Wentz et al. 2013). Misapplications and a number of regional 1986; Wentz and Meissner 2000) and applied to a modifications have taken place over a period of many number of passive satellite sensors, including the years by various national tropical cyclone analysis TRMM Microwave Imager (TMI), the Special Sensor centers (Velden et al. 2012). Microwave Imager (SSM/I), Special Sensor Micro- When using the Dvorak technique, the TC center lo- wave Imager/Sounder (SSMIS), and the Advanced cation is determined first. Second, after an estimation of Microwave Scanning Radiometer for Earth Observing pattern recognition and two quasi-independent TC in- System (AMSR-E) sensor on board the Aqua satellite. tensities relying on cloud systems (eye, curved band, A method for estimating the TC intensity utilizing shear, and covered center), the best TC intensity is TMI data, based on a multiple regression technique, chosen and is finally determined through selected rules. has also been developed (Hoshino and Nakazawa For the northwest Pacific Ocean, including the South 2007). In addition, Saitoh and Shibata (2010) described China Sea, the Regional Specialized Meteorological a method for estimating the WS using horizontal TB at Center (RSMC) Tokyo at the Japan Meteorological 6.925- and 10.65-GHz channels of AMSR-E on board the Agency (JMA) has the responsibility of issuing TC track Aqua satellite. Currently, active and passive microwave and intensity forecasts. RSMC Tokyo produces fore- remote sensing have become established as critical op- casts of the center’s position, with an associated 70% erational tools for TC analysis. In particular, passive mi- probability of the TC direction and speed through the crowave imagery (36–37 and 85–91 GHz), using an following 120 h. In addition, the minimum sea level Advanced Microwave Sounding Unit (AMSU), provides pressure (PMIN) and the maximum surface wind (WS,MAX) direct diagnosis of the inner structure of TCs (Brueske are forecast through 72 h. and Velden 2003; Herndon et al. 2004; Demuth et al. The majority of TC WS,MAX values reported by oper- 2004, 2006). ational centers are derived from application of the In this study, we present a physical algorithm for es- Dvorak technique by converting a Dvorak current in- timating surface wind speed using passive microwave tensity (CI) number directly to a WS,MAX (Velden et al. remote sensing, the 6.925- and 10.65-GHz bands of 2012). Thus, differences in the CI values between AMSR-2 on board the Global Change Observation agencies are commonly expected within a 60.5 CI Mission–Water (GCOM-W1) satellite because of their number between different analysts performing the cal- radiative properties in relation to rain. We also pres- culations, because different conversion tables are used ent a retrieval scheme for estimating TC intensity (CI to obtain WS,MAX from CI numbers (Velden et al. 2012). number) and PMIN from WS,MAX using the Hong WS For example, the JMA-verified Dvorak CI number uses algorithm. the conversion table of Koba et al. (1991) for TCs passing through the Japanese islands, or those observed 2. Theoretical background using experimental aircraft observations for 1995–2009. JTWC (1974) found 74% and 91% within 60.5 and 61.0 The energy emissions measured by satellite radiom- of a CI number (DWS,MAX), respectively, when com- eters are often expressed in terms of TB, which can be paring CI numbers with CI derived from JTWC’s best- calculated for polarizations (Randa et al. 2008). The track data. In addition, the JMA found 65.7%, 89.1%, polarized brightness temperature (TB,P) is influenced by and 97.6% within 60.5, 61.0, and 61.5 of CI differences the cosmic background temperature, the atmosphere, (DWS,MAX), respectively, between CI numbers and CI and Earth’s surface at a given incidence angle in the derived from JMA’s best-track data (Kruk et al. 2010). microwave range: Unauthenticated | Downloaded 09/28/21 09:34 PM UTC JULY 2016 H O N G E T A L . 1365 5 1G 2 1 TB,P T[ [(1 RR,P)TS RR,PTY], (1) English and Hewison 1998; Hong 2010b,c,d; Hong and Shin 2013; Hong 2013). where RR,P is the rough sea surface reflectivity; the Rough surface reflectivities RR,P and specular surface subscript P indicates vertical (V) or horizontal (H) po- reflectivities RS,P for each polarization are related to larization; TS is the sea surface temperature; G is the each other by small-scale roughness s, which corre- atmospheric transmittance; and T[ and TY are the up- sponds to the height probability density function with a ward and downward atmospheric brightness tempera- Gaussian distribution when using a semiempirical model tures, respectively. based on the incoherent approach depending on am- plitudes only (Ulaby et al. 1981) instead of depending on a. Atmospheric transmittance both the amplitude and phases within the medium, and Under the no-rainy conditions at low microwave fre- is shown as follows (Choudhury et al. 1979): quencies (,10 GHz), atmospheric contributions to the vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u ! brightness temperature in satellite observations are l u t RS,P negligible (Yan and Weng 2008; Uhlhorn and Black s 5 ln , (2) 4p cosu R 2003).