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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, , South

HWA-JEONG SEO National Meteorological Satellite Center, Korea Meteorological Administration, Gwanghyewon-myeon,

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 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 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 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

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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 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:

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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 ; 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). For example, atmospheric attenuations at R,P 6.9 GHz are less than 0.2 K for V polarization and less l u than 0.8 K for H polarization (Wentz 2002). where is the wavelength (cm) and is the incidence angle (8). Under rainy conditions, satellite-observed TB,P re- ceives contributions from the cosmic background tem- The specular reflectance RS,P of the sea surface is perature, both the atmospheric and surface brightness governed by the Fresnel formula for a given complex temperatures in relation to rainfall, and from the surface refractive index at a local incident angle as follows: in relation to the wind speed. However, G is reduced pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi _ 2 under rainy conditions because rain increases atmo- cosu 2 n2 2 sin2u R 5 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi S,V _ and spheric attenuation, and this effect occurs particularly at cosu 1 n2 2 sin2u higher frequencies. It thus becomes more difficult to pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi _ _ 2 make an accurate estimation of rainfall because of the n2 cosu 2 n2 2 sin2u R 5 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi , (3) S,H _ _ high variability of certain parameters such as rain size n2 cosu 1 n2 2 sin2u distribution and form. Meissner and Wentz (2009) found _ pffiffiffiffiffiffiffi no saturation in the wind-induced emissivity signal at WS where n 5 h 1 ik(i 5 21 ) is the complex refractive 21 values up to 35 m s under rainy conditions using C index of a medium, and h and k are the real and imag- band. In addition, numerical modeling has shown that inary parts of the refractive indices, respectively. sea surface WS can be retrieved in hurricanes under an However, in general, complex refractive indices are 21 amount of rain of up to 20–30 mm h for C- and X-band unknown for complex and heterogeneous surfaces. channels, because the brightness temperature at these Hong (2009b) developed and validated an approximate channels is not saturated (Kummerow and Ferraro relationship between RS,V and RS,H (the Hong approxi- 2006). The term WS can also be retrieved because 6- and mation) that is irrespective of a priori information on the 10-GHz data are not saturated (Shibata 2002), but surface refractive index, and uses the generalized Fres- 10 GHz is slightly more sensitive to wind speed than nel equation (Tousey 1939) and the first term in the 6 GHz (Shibata 2007). It is known that inside deep Taylor series of the natural logarithm ratio lnRS,V/lnRS,H convective cells, and inside the TC’s eyewall, wind as follows: 2 speeds up to 60 m s 1 have been estimated using air- borne passive microwave radiometers at frequencies 5 sec2u RS,H (RS,V) . (4) between 4.5 and 7.2 GHz (Uhlhorn and Black 2003). The Hong approximation [Eq. (4)] has been applied b. Surface reflectivity and roughness successfully to surface roughness studies (Hong 2009a, Estimating wind speed using passive microwave ra- 2010a,b,c,d; Hong et al. 2010; Hong and Shin 2010, 2011; diometers depends on the relationship between sea Hong et al. 2014, 2015). It is of note that the Azzam– surface reflectivity and changing sea state. In particular, Sohn–Hong (ASH) approximation, which is similar to the generation of small ocean waves of centimeter the Hong approximation, has been derived (Hong 2013), length (capillary waves) is driven by instantaneous WS but it is not appropriate for use in this study because the (English and Hewison 1998) and can be generally ex- ASH approximation is effective under a small value of pressed by the relationship between the rough and the imaginary part of the complex refractive index specular surface reflectivities (Choudhury et al. 1979; (Hong 2013).

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TABLE 1. Characteristics of the AMSR-2 instrument. NEdT stands for noise equivalent differential temperature.

Center frequency (GHz) Spatial resolution (km) Bandwidth (MHz) Polarization NEdT (K) 6.925 35 3 62 350 0.34 7.3 34 3 58 350 0.43 10.65 24 3 42 100 ,0.70 18.7 14 3 22 200 V and H ,0.70 23.8 15 3 26 400 ,0.60 36.5 7 3 12 1000 ,0.70 89.0 3 3 5 3000 ,1.20/1.40

3. Methods removing the radio frequency interference (RFI)- contaminated ASMR-2 observations over oceans. The a. Data and procedure observed data at V polarization and the simulated data The AMSR 2 (AMSR-2) is operated and well cali- were used to estimate surface reflectivity. The compari- brated at several frequencies from 6.925 to 89.0 GHz at son results for estimating TC intensity from WS,MAX are the constant incidence angle of 55.08 (Kawanishi et al. primarily presented using the Hong WS algorithm at a 2003). The AMSR-2 instrument and channel charac- frequency of 6.925 GHz with AMSR-2 TB observations. teristics are summarized in Table 1 (Kramer 2014). In In this study, the selected spatial range encompassed an this study, we present an inversion algorithm to retrieve ocean area between 6508 latitude with the exception of WS at a frequency of 6.925 GHz with AMSR-2 TB ob- sea ice areas. Land-covered areas were excluded using the servations. Both 6.925 and 10.65 GHz were used for European Centre for Medium-Range Weather Forecasts

FIG. 1. Diagram of the presented WS retrieval algorithm under rainy and rain-free conditions. RFI-contaminated observations were eliminated using AMSR-2 TB observations at 6.925 and 10.65 GHz. The boxes in boldface refer to key steps as outlined within the text.

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TABLE 2. Linear regression coefficients, bias, and RMSE be- TABLE 3. Typhoon data used to derive regression coefficients tween TB,Sim and TB,Obs for AMSR-2 6.925V- and 10.65V-GHz under rainy conditions. channels under rain-free and rain conditions. Name of No. of Channel Year typhoon Date data (GHz) Slope Offset Bias (K) RMSE (K) R 2012 Khanun 17 Jul 1 6.925 a1,V 5 1.06 a2,V 5 134.46 3.91 4.07 0.97 Damrey 30 and 31 Jul, 1 Aug 3 a3,V 5 0.26 a4,V 5 126.42 9.07 10.88 0.79 Saola 1 Aug 2 10.65 a1,V 5 0.99 a2,V 5 143.19 6.98 7.09 0.95 Haikui 2, 5, and 6 Aug 3 a3,V 5 0.13 a4,V 5 153.38 19.70 23.71 0.69 Kai-Tak 16 Aug 1 Tembin 22, 27, 28, and 29 Aug 6 Bolaven 22, 23, 25, 26, 27, and 28 Aug 7 Sanba 13, 15, and 16 Sep 4 (ECMWF) land–sea mask. Rain-contaminated observa- Jelawat 24–27, and 29 Sep 7 tions were determined using AMSR-2 TS data. The RFI- Ewiniar 25 and 27 Sep 3 contaminated observations over oceans were excluded Prapiroon 10, 11, 13, and 15 Oct 5 using the following conditions (Wu and Weng 2011): Maria 14 Oct 1 Son-Tinh 26 and 28 Oct 2 5 : RFIP TB,P,Obs(6 925 GHz) 2 T (10:65 GHz) . 5 K, (5) between TB,Obs and TB,Sim for V polarization is estimated B,P,Obs under rain-free and rainy conditions as follows: 5 1 where the subscript Obs denotes observation. TB,V,Sim a1,VTB,V,Obs a2,V under rain-free conditions, To find a conversion relationship between the AMSR-2 (6) TB observation and the AMSR-2 TB simulation, the T 5 a T 1 a under rainy conditions, AMSR-2 TB simulation was performed using a radiative B,V,Sim 3,V B,V,Obs 4,V transfer calculation such as RTTOV, version 9 (RTTOV-9), (7) with temperature and humidity profiles, TS,surface pressure, and wind speed information. AMSR-2 TS is used where the subscript Sim denotes the simulation without under rain-free conditions, while the NWP model TS is rain effects and with surface wind effects; and a1,V and used under rainy conditions, such as occurs inside TCs. In a2,V, and a3,V and a4,V are the regression coefficients of this study, the NWP model data used are the ECMWF the slope and offset, respectively. In this study, ‘‘under data, which provide surface- and height-dependent profiles rainy conditions’’ refers to the use of the retrieval al- of temperature, pressure, and humidity every 6 h on a gorithm with observations obtained under rainy con- global 0.25830.258 latitude–longitude grid (Uppala et al. ditions and simulations generated under rain-free 2005). The ECMWF surface and atmospheric profile data, assumption due to the lack of information on the rain and AMSR-2 TS, are used for estimating the polarized structure. AMSR-2 TB,V simulations generated under surface reflectivities. In this study, we use the NWP model rain-free assumptions inside TCs showed a linear rela- with RTTOV-9, which includes the Fast Emissivity Model, tionship with the AMSR-2 TB,V observations with rain version 3 (FASTEM-3) (Saunders 2006), which was de- and wind effects (see Fig. 4). Table 2 summarizes the signed for microwave sensors such as SSM/I, SSMIS, regression coefficients a1,V and a2,V under rain-free AMSU, TMI, AMSR-E, AMSR-2, and WindSat (Liu and conditions, as derived using the matchup data between Weng 2003; Saunders 2006). FASTEM-3 includes specular AMSR-2 TB observations and AMSR-2 TB simulations reflection, small-scale roughness, the foam effect, large- for 1 month (1–31 October 2013), for ocean areas within 6 8 scale roughness, and wind direction effects (English and latitude 50 . The regression coefficients a3,V and a4,V Hewison 1998; Saunders 2006). are derived from temporally and spatially collocated data for 13 typhoons (in 2012) using AMSR-2 TB obser- b. WS retrieval for use in rainy conditions vations and AMSR-2 TB simulations for 45 days from

The Hong WS algorithm for rainy and rain-free con- July to October 2012, obtained from the northwestern ditions consists of five steps, as outlined in Fig. 1. Pacific Ocean. In addition, Table 3 summarizes the name, First, a regression relationship between the AMSR-2 dates, and number of data relating to the 13 typhoons. TB observations and the AMSR-2 TB simulation for the Second, for V polarization, a regression relationship AMSR-2 6.925- and 10.65-GHz channels is computed, between AMSR-2 TB simulation TB,V,Sim and rough using collocation data between the AMSR-2 TB obser- surface reflectivity RR,V is estimated as follows: vation data and that of the RTTOV simulation with R 5 b T 1 b , (8) ECMWF and AMSR-2 TS data. A regression relationship R,V 1,V B,V,Sim 2,V

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TABLE 4. Linear regression coefficients and correlations be- tween TB,Sim and RR for AMSR-2 6.925 and 10.65 GHz for V polarization.

Channel (GHz) Slope (b1,V) Offset (b2,V) R 6.925 20.000 24 0.457 018 20.60 10.65 20.000 38 0.375 621 0.60

where b1,V and b2,V are the regression coefficients of the slope and offset, respectively. The terms b1,V and b2,V are summarized in Table 4. From a previous study (Hong et al. 2015) using Global Data Assimilation System (GDAS) data (NOAA ARL 2014) for rain-free conditions, RR,V shows a relatively good correlation with TB,V,Sim, while RR,H exhibits low correlations. In this study, the correlation between RR,H FIG. 2. The s 2 WS relationship for AMSR-2 6.925 to 89.0 GHz. s and TB,H,Sim for H polarization using ECMWF data and The gray area around the colored lines indicates the retrieved AMSR-2 observations is approximately R of approxi- using Hong’s roughness estimation [Eq. (9)] at given RR,V and RR,H. mately 20.4, in relation to the ocean wind (Shibata 2003; Hong et al. 2015). Thus, a regression relationship be- where c1 and c2 are the regression coefficients of the slope and offset, respectively. Figure 2 shows the s 2 W tween TB,H,Sim and RR,H for H polarization was not S applied in this study. relationship for AMSR-2 6.925 to 89.0 GHz. The terms Third, small-scale roughness s is estimated using Hong’s c1 and c2 for AMSR-2 6.925 and 10.65 GHz are sum- roughness equation [Eq. (9)] with the rough surface re- marized in Table 5. Table 6 summarizes the error of the retrieved W as the estimated R varied within 620% flectivities RR,V and RR,H.ThetermRR,H is computed us- S R,V ing the rain-free RTTOV simulation with ECMWF and uncertainty for AMSR-2 6.925 GHz. In this case, WS was 21 AMSR-2 T data for rain-free conditions, and ECMWF 10.01 m s .ThetermsRR,V and RR,H were 0.475 and S s 2 surface and atmospheric profile data for rainy conditions. 0.475, respectively. As a result, varies from 49.474% to 2 In addition, Hong’s roughness equation is derived using Eq. 32.632%; the uncertainty of WS was between 74.236% (2) using the Hong approximation [Eq. (4)], and the char- and 48.172%. Accordingly, the error of the retrieved wind acteristics of the polarized surface reflectivities that are speed depended on the accuracy of RR,V to a large extent. s 2 near Brewster’s angle (e.g., 52.758 with a view angle of 558 Finally, the WS obtained from the WS relationship at AMSR-2 6.925 GHz) for specular surfaces (namely, was validated using WS obtained from the Tropical Atmo- sphere Ocean (TAO) buoys over a period of 1 month RS,V . RR,V and RS,H ’ RR,H)areasfollows(Hong 2010d): vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (October 2013) for rain-free conditions, in a similar way to u 2 3 u the study of Hong et al. (2015), and then indirectly validated u cos2u l u 6(R ) 7 with the maximum WS values of 13 TCs that were derived s ’ t 4 R,H 5 p u ln . (9) using the Dvorak method in the northwestern Pacific Ocean 4 cos RR,V during 2012 for rainy conditions (inside TCs).

Hong’s roughness estimation [Eq. (9)] was validated c. PMIN and CI index retrieval using buoys and model data (Hong and Shin 2013; Hong The central pressure of TCs is currently mainly de- et al. 2015). In this study, surface reflectivities are esti- termined from satellite IR imagery using the Dvorak mated using the FASTEM-3 considering small-scale roughness driven by the instantaneous surface wind. TABLE 5. Linear regression coefficients of the relationship be- Fourth, WS is then calculated using the following tween s and W for 6.925 and 10.65 GHz obtained using the Hong s 2 W relationship (Hong and Shin 2013; Hong et al. S S algorithm. 2015) between Hong’s roughness estimation and 21 ECMWF WS, which was fitted using the least squares Channel (GHz) WS (m s ) Slope (c1) Offset (c2) 21 regression for the data of low (,5ms ) and high 6.925 ,5 152.662 24.804 21 . 2 (.5ms ) WS during a 1-month period (October 2013): 5 156.521 4.847 10.65 ,5 230.737 24.865 5 s 1 .5 227.217 24.436 WS c1 c2 , (10)

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TABLE 6. Relationship between the errors of estimated RR,V and TABLE 7. Conversion table among CI number, WS,MAX, and PMIN retrieved WS. (Koba et al. 1991).

Channel Max wind speed WS,MAX Min sea level 21 21 (GHz) DRR,V (%) Ds (cm) DWS (m s ) Ds (%) DWS (%) 210 min winds (m s ) CI No. pressure PMIN 20 20.047 27.431 249.474 274.236 11.0 1.0 1005 15 20.032 24.964 233.684 249.590 14.5 1.5 1002 10 20.020 23.083 221.053 230.799 18.0 2.0 998 6.925 5 20.009 21.456 29.474 214.545 21.5 2.5 993 1 20.002 20.280 22.105 22.797 25.0 3.0 987 21 0.002 0.275 2.105 2.797 28.5 3.5 981 25 0.009 1.331 9.474 14.545 32.0 4.0 973 210 0.016 2.567 16.842 25.644 35.5 4.5 965 215 0.024 3.726 25.263 37.223 39.0 5.0 956 220 0.031 4.822 32.632 48.172 42.5 5.5 947 46.5 6.0 937 50.0 6.5 926 53.5 7.0 914 method. The CI number also gives the WS,MAX and PMIN 57.5 7.5 901 in the vicinity of the center. 61.0 8.0 888 In this study, we present a method for retrieving sea surface wind speed using passive microwave satellite observations. The term WS,MAX can then be estimated Meteorological Administration (CMA), and the Korea from the Hong WS algorithm, and the CI number and Meteorological Administration (KMA), use the Dvorak PMIN can be determined using a conversion table among method for analyzing TCs. JMA performs TC analysis the WS,MAX, CI number, and PMIN. We use the conver- eight times per day at 0000, 0300, 0600, 0900, 1200, 1500, sion table provided by Koba et al. (1991) in Table 7. The 1800, and 2100 UTC. Generally, forecasters first de- estimated WS,MAX, CI number, and PMIN are compared termine the center position of the TC using IR satellite with best-track data from 2012 and 2013 in the satellite imagery. The CI number is then determined by the report (SAREP), which is issued annually by JMA’s Dvorak method, and finally, WS,MAX and PMIN of TCs typhoon center (RSMC Tokyo) (JMA 2013). Most me- are estimated from the CI number. The general teorological organizations, including JMA, the China procedures used in the Dvorak technique, and the

FIG. 3. (a) Dvorak technique and (b) Hong technique used in estimations (including WS,MAX, PMIN, and CI number).

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FIG. 4. (a) Relationship between TB,V,Obs and TB,V,Sim under rain-free conditions. (b) Relationship between TB,V,Obs and TB,V,Sim under rainy conditions for AMSR-2 6.925 GHz. (c),(d) As in (a) and (b), but for with AMSR-2 10.65-GHz data.

presented method using the Hong WS algorithm (the The results for AMSR-2 10.65V GHz are depicted in Hong technique) are described in Fig. 3. It is of note that Figs. 4c and 4d. The bias, RMSE, and correlation be- the Dvorak technique depends on the CI number, while tween AMSR-2 TB observations and AMSR-2 TB sim- the Hong technique depends on WS,MAX. The Hong ulations are 6.98 K, 7.09 K, and 0.95 under rain-free technique has advantages over the Dvorak technique conditions, and 19.7 K, 23.71 K, and 0.69 under rainy because of its simple procedure, the derivation of conditions. WS,MAX without the CI number using microwave satel- For the rainy conditions, AMSR-2 TB observations lite observation instead of VIS or IR satellite images, contain both atmospheric effects due to rainfall and and its independence from a forecaster’s subjective surface effects due to surface wind speeds. AMSR-2 TB experiences. simulations generated under rain-free assumptions in- side TCs showed a linear relationship with the AMSR-2 T observations with rain and wind effects, even when 4. Results B the correlation under rainy conditions was lower than Figures 4a and 4b show the correlation of TB obser- that under the rain-free conditions. The linear re- vations and TB simulations under rain-free and rainy lationship between AMSR-2 TB observations and conditions at AMSR-2 6.925V GHz, respectively. The AMSR-2 TB simulations was significant for WS estima- bias, RMSE, and correlation between AMSR-2 TB ob- tion inside TCs because it make a connection between servations and AMSR-2 TB simulations are 3.91 K, AMSR-2 TB observations and WS without relying on the 4.07 K, and 0.97 under rain-free conditions, and 9.07 K, presence of highly variable rainfall. These results were 10.88 K, and 0.79 under rainy conditions, respectively. consistent with the previous work performed by Meissner

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FIG. 5. The s 2 WS relationships for AMSR-2 (a) 6.925 and (b) 10.65 GHz; scatterplots of TAO WS against retrieved WS for AMSR-2 (c) 6.925- and (d) 10.65-GHz data.

and Wentz (2009). Validation needs to be performed In this study, we applied the Hong WS algorithm at indirectly using intensity, maximum wind speed, and 6.925 GHz to the typhoon analysis. Figures 6a and 6b minimum central pressure of TCs because there is a show the observed TB and retrieved WS, respectively, at lack of ground observation data for comparison. 6.925-GHz V polarization of AMSR-2 for Typhoon Figures 5a and 5b show the s 2 WS relationship for Bolaven on 26 August 2012. Figure 6c shows the ECMWF wind speeds on 7 October 2013, calculated ECMWF WS, and Fig. 6d shows the difference between using the inversion method for AMSR-2 6.925 and the retrieved WS and that of ECMWF WS. The retrieved 10.65 GHz. The results were similar to those of a pre- WS was larger than that of ECMWF WS for rainy con- vious study in terms of bias, RMSE, and correlation of ditions, such as inside Typhoon Bolaven, with increases the retrieved WS values (Hong and Shin 2013). Figure 5c in WS. shows the validation results of a comparison between Figure 7a and 7b show WS,MAX and PMIN, respectively, TAO buoy WS data and the retrieved WS from the which were obtained from JMA (RSMC Tokyo), JTWC, current AMSR-2 TB observations at 6.925 GHz for one Cooperative Institute for Mesoscale Meteorological entire month (1–30 October 2013). For the presented Studies (CIMMS) analysis, and the Hong technique for algorithm (the Hong WS algorithm), the estimation of 13 typhoons in the northwestern Pacific Ocean in 2012. TAO WS under rain-free conditions using AMSR-2 data The term WS,MAX derived by the Hong technique varies 21 and ECMWF data had a relatively low bias and RMSE between approximately 20 and 60 m s , and WS,MAX by 21 of the WS outputs: 0.09 and 1.13 m s , respectively. CIMSS was estimated as being larger than WS,MAX by Figure 5d shows the validation results at AMSR-2 JTWC, RSMC, and the Hong technique. The term PMIN 10.65 GHz, and the bias and RMSE of the Hong WS showed the opposite characteristics of those exhibited in 21 algorithm were 20.52 and 1.21 m s , respectively. the WS,MAX analysis. Figure 7c shows the comparison

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FIG. 6. (a) The term TB,V,Obs at AMSR-2 6.925-GHz V polarization with rainfall effects and surface wind effects, (b) Hong WS retrieved using the Hong WS algorithm at AMSR-2 6.925 GHz, (c) ECMWF WS, and (d) Hong WS minus ECMWF WS. This is representative of the case of Typhoon Bolaven, which occurred on 26 Aug 2012. results of the CI number by the Hong technique and that Both results show good agreement for low values of of SATREP for a period of two years for typhoons in the CI number, but the difference between two CI numbers northwestern Pacific area. The bias, RMSE, and corre- increases as the CI number increases, and the SAREP lations were 20.559, 1.386, and 0.574, respectively. In CI number is larger than that of the Hong technique. terms of WS, the bias and RMSE between the Hong Figure 7d shows the WS derived using the Hong tech- technique and that of SATREP were 24.141 and nique for TB at AMSR-2 6.925 GHz in relation to Ty- 2 9.481 m s 1, respectively. Table 8 summarizes the bias phoon Kong-Rey, which occurred at 0300 UTC 27 and RMSE in WS,MAX and PMIN between the Hong August 2013. In this case, the Hong technique produces technique and the JMA (RSMC), JTWC, and CIMMS the CI number 2.4, whereas the SAREP reports the CI analyses for the typhoons in the northwestern Pacific number 2.5. This result is consistent with the results Ocean during 2012. shown in Fig. 7c.

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FIG. 7. Hong techniques at AMSR-2 6.925 GHz vs Dvorak technique for (a) WS,MAX and (b) PMIN in JMA (RSMC), JTWC, CIMMS analyses, and the Hong technique. (c) Scatterplot of CI numbers reported from SAREP and those derived from the Hong technique (typhoons in the northwestern Pacific Ocean from 2012 are used in this case). (d) Map of WS using the Hong technique for Typhoon Kong-Rey, which occurred at 0300 UTC 27 Aug 2013.

5. Summary and conclusions 12 months of 2010 (Hong et al. 2015). The error of the retrieved W depended on the accuracy of R to a This study presented a unique algorithm for retrieving S R,V large extent. However, a comparison of these results sea surface wind speed (the Hong W algorithm) using S with the TAO buoy data under rain-free conditions the characteristics of V and H polarization at 6.925- and showed that the proposed algorithm retrieves W with 10.65-GHz bands from spaceborne passive microwave S radiometers with an AMSR-2 sensor under both rainy TABLE 8. Bias and RMSE among W and P between the W S,MAX MIN and rain-free conditions. The Hong S algorithm shows Hong technique and the JMA (RSMC), JTWC, and CIMMS an- good agreement with TAO buoy observations under alyses for the typhoons in the northwestern Pacific Ocean from rain-free conditions. The estimated bias and RMSE for 2012 to 2013.

AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 21 2 2 W (m s ) P (hPa) 1.13 m s 1, and 20.52 and 1.21 m s 1, respectively. This S,MAX MIN result is also in agreement with previous work using Bias RMSE Bias RMSE AMSR-E 6.925- and 10.65-GHz bands (Hong et al. JMA 24.141 9.481 13.272 25.332 2015). In that case, the bias and RMSE were 20.13 and JTWC 210.447 16.566 13.655 23.987 2 2 2 1.19 m s 1, and 20.09 and 1.15 m s 1, respectively, for all CIMSS 11.235 15.170 14.505 23.537

Unauthenticated | Downloaded 09/28/21 09:34 PM UTC 1374 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 33 high accuracy. The validation results indicated that RR,V ——, 1984: Tropical cyclone intensity analysis using satellite data. is estimated approximately within 5% uncertainty for NOAA Tech. Rep. NESDIS 11, 47 pp. AMSR-2 6.925 GHz. Emanuel, K., 2005: Increasing destructiveness of tropical cyclones over thepast30years.Nature, 436, 686–688, doi:10.1038/nature03906. This study also proposed a TC intensity estimation English, S. J., and T. J. Hewison, 1998: Fast generic millimeter- technique from WS using the AMSR-2 6.925-GHz band, wave emissivity model. Microwave Remote Sensing of the known as the Hong technique, which is applied to the Atmosphere and Environment, T Hayasaka et al., Eds., In- estimation of TC intensity using values of the CI num- ternational Society for Optical Engineering (SPIE Pro- ber, W , and P . TC intensities estimated from ceedings, Vol. 3503), 288–300, doi:10.1117/12.319490. S,MAX MIN Guard, C. P., 1988: Tropical cyclone studies: Part 3—Results of a the Hong technique are compared with the best-track tropical cyclone accuracy study using polar orbiting satellite data for typhoons in the northwestern Pacific area re- data. Federal Coordinator for Meteorological Services and ported by SATREP over two years (2012–13). The val- Supporting Research FCM-R11-1988, 3-1–3-36. idation result for TC CI numbers shows a bias and Herndon, D., C. S. Velden, K. Brueske, R. Wacker, and B. Kabat, RMSE of 20.559 and 1.386, respectively. The Hong 2004: Upgrades to the UW-CIMSS AMSU-based tropical cyclone intensity estimation algorithm. 26th Conf. on Hurri- technique using the AMSR-2 6.925-GHz band exhibits canes and Tropical Meteorology, Miami, FL, Amer. Meteor. good agreement with the best-track data, but the dis- Soc., 4D.1. [Available online at https://ams.confex.com/ams/ crepancy between the Hong technique and SAREP 26HURR/techprogram/paper_75933.htm.] becomes relatively large as the CI number increases. In Hong, S., 2009a: Detection of Asian dust (Hwangsa)overtheYellow our future works, we will analyze the effectiveness of TC Sea by decomposition of unpolarized infrared reflectivity. Atmos. Environ., , 5887–5893, doi:10.1016/j.atmosenv.2009.08.024. intensity estimation using the Hong technique at a fre- 43 ——, 2009b: Retrieval of refractive index over specular surfaces for quency of 10.65 GHz with AMSR-2, TMI, and Global remote sensing applications. J. Appl. Remote Sens., 3, 033560, Precipitation Measurement (GPM) Microwave Imager doi:10.1117/1.3265997. (GMI) TB observations. ——, 2010a: Decomposition of unpolarized emissivity. Int. J. Remote Sens., 31, 2109–2114, doi:10.1080/01431160903329349. Acknowledgments. The authors thank the anonymous ——, 2010b: Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing. Remote reviewers for their constructive comments on the man- Sens. Environ., 114, 1136–1140, doi:10.1016/j.rse.2009.12.015. uscript. This study is supported by the Meteorological ——, 2010c: Global retrieval of small-scale roughness over land Satellite Center (Project 153-3100-3137-302-210-13) and surfaces at microwave frequency. J. Hydrol., 389, 121–126, the ISABU project of the Korea Institute of Ocean doi:10.1016/j.jhydrol.2010.05.036. Science and Technology (PE99361). ——, 2010d: Surface roughness and polarization ratio in micro- wave remote sensing. Int. J. Remote Sens., 31, 2709–2716, doi:10.1080/01431161003627855. REFERENCES ——, 2013: Polarization conversion for specular components of surface reflection. IEEE Geosci. Remote Sens. Lett., 10, 1469– Brown, D. B., and J. L. Franklin, 2004: Dvorak TC wind speed 1472, doi:10.1109/LGRS.2013.2260524. biases determined from reconnaissance-based ‘‘best track’’ ——, and I. Shin, 2010: Global trends of sea ice: Small-scale data (1997–2003). 26th Conf. on Hurricanes and Tropical roughness and refractive index. J. Climate, 23, 4669–4676, Meteorology,Miami,FL,Amer.Meteor.Soc.,3D.5. doi:10.1175/2010JCLI3697.1. [Available online at https://ams.confex.com/ams/26HURR/ ——, and ——, 2011: A physically-based inversion algorithm for techprogram/paper_75193.htm.] retrieving soil moisture in passive microwave remote sensing. Brueske, K. F., and C. S. Velden, 2003: Satellite-based tropical cyclone J. Hydrol., 405, 24–30, doi:10.1016/j.jhydrol.2011.05.005. intensity estimation using the NOAA-KLM series Advanced ——, and ——, 2013: Wind speed retrieval based on sea surface Microwave Sounding Unit (AMSU). Mon. Wea. Rev., 131,687– roughness measurements from spaceborne microwave radi- 697, doi:10.1175/1520-0493(2003)131,0687:SBTCIE.2.0.CO;2. ometers. J. Appl. Meteor. Climatol., 52, 507–516, doi:10.1175/ Choudhury, B. J., T. J. Schmugge, A. Chang, and R. W. Newton, 1979: JAMC-D-11-0209.1. Effect of surface roughness on the microwave emission from soils. ——, ——, and M. Ou, 2010: Comparison of the Infrared Surface J. Geophys. Res., 84, 5699–5706, doi:10.1029/JC084iC09p05699. Emissivity Model (ISEM) with a physical emissivity model. Demuth, J. L., M. DeMaria, J. A. Knaff, and T. H. Vonder Haar, J. Atmos. Oceanic Technol., 27, 345–352, doi:10.1175/ 2004: Evaluation of Advanced Microwave Sounder Unit 2009JTECHA1311.1. (AMSU) tropical-cyclone intensity and size estimation ——, ——, Y. Byun, H.-J. Seo, and Y. Kim, 2014: Analysis of sea algorithms. J. App. Meteor., 43, 282–296, doi:10.1175/ ice surface properties using ASH and Hong approximations in 1520-0450(2004)043,0282:EOAMSU.2.0.CO;2. satellite remote sensing. Remote Sens. Lett., 5, 139–147, ——, ——, and ——, 2006: Improvement of Advanced Microwave doi:10.1080/2150704X.2014.888106. Sounding Unit tropical cyclone intensity and size estima- ——, H.-J. Seo, N. Kim, and I. Shin, 2015: Physical retrieval of tion algorithms. J. Appl. Meteor. Climatol., 45, 1573–1581, tropical ocean surface wind speed under rain-free conditions doi:10.1175/JAM2429.1. using spaceborne microwave radiometers. Remote Sens. Lett., Dvorak, V. F., 1975: Tropical cyclone intensity analysis and fore- 6, 380–389, doi:10.1080/2150704X.2015.1037466. casting from satellite imagery. Mon. Wea. Rev., 103, 420–430, Hoshino, S., and T. Nakazawa, 2007: Estimation of tropical cy- doi:10.1175/1520-0493(1975)103,0420:TCIAAF.2.0.CO;2. clone’s intensity using TRMM/TMI brightness temperature

Unauthenticated | Downloaded 09/28/21 09:34 PM UTC JULY 2016 H O N G E T A L . 1375

data. J. Meteor. Soc. Japan, 85, 437–454. http://www.jstage.jst.go.jp/ Saunders, R., 2006: RTTOV-8—Science and validation report. article/jmsj/85/4/437/_pdf/-char/ja/,doi:10.2151/jmsj.85.437. Version 1.6, NWP SAF Doc. NWPSAF-MO-TV-007, Met JMA, 2013: Annual report on the activities of the RSMC Tokyo– Office Doc. R8REP2006, 46 pp. Typhoon Center 2012. 92 pp. [Available online at http://www. Shibata, A., 2002: AMSR/AMSR-E sea surface wind speed algo- jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/AnnualReport/ rithm. EORC Bull./Tech. Rep. 9, 45–46. 2012/Text/Text2012.pdf.] ——, 2003: A change of microwave radiation from the ocean sur- JTWC, 1974: Annual tropical cyclone report: 1974. U.S. Fleet face induced by air-sea temperature difference. Radio Sci., 38, Weather Central, JTWC, 126 pp. [Available online at http://www. 8063, doi:10.1029/2002RS002670. usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc/atcr/1974atcr.pdf.] ——, 2007: Effect of air-sea temperature difference on ocean mi- Kawanishi, T., and Coauthors, 2003: The Advanced Microwave crowave brightness temperature estimated from AMSR, Scanning Radiometer for the Earth Observing System SeaWinds, and buoys. J. Oceanogr., 63, 863–872, doi:10.1007/ (AMSR-E), NASDA’s contribution to the EOS for global s10872-007-0073-y. energy and water cycle studies. IEEE Trans. Geosci. Remote Stogryn, A., 1967: The apparent temperature of the sea at micro- Sens., 41, 184–194, doi:10.1109/TGRS.2002.808331. wave frequencies. IEEE Trans. Antennas Propag., 15, 278– Knaff, J. A., D. P. Brown, J. Courtney, G. M. Gallina, and J. L. 286, doi:10.1109/TAP.1967.1138900. Beven II, 2010: An evaluation of Dvorak technique–based Tang, C., 1974: The effect of droplets in the air–sea transition zone on tropical cyclone intensity estimates. Wea. Forecasting, 25, the sea brightness temperature. J. Phys. Oceanogr., 4, 579–593, 1362–1379, doi:10.1175/2010WAF2222375.1. doi:10.1175/1520-0485(1974)004,0579:TEODIT.2.0.CO;2. Koba, H., S. Osano, T. Hagiwara, S. Akashi, and T. Kikuchi, 1991: Tousey, R., 1939: On calculating the optical constants from Relationships between the CI number and central pressure reflection coefficients. J. Opt. Soc. Amer., 29, 235–238, and maximum wind speed in typhoons (in Japanese). Geo- doi:10.1364/JOSA.29.000235. phys. Mag., 44, 15–25. Uhlhorn, E. W., and P. G. Black, 2003: Verification of remotely Kossin, J. P., and C. S. Velden, 2004: A pronounced bias in tropical sensed sea surface winds in hurricanes. J. Atmos. Oceanic cyclone minimum sea level pressure estimation based on the Technol., 20, 99–116, doi:10.1175/1520-0426(2003)020,0099: Mon. Wea. Rev. Dvorak technique. , 132, 165–173, doi:10.1175/ VORSSS.2.0.CO;2. , . 1520-0493(2004)132 0165:APBITC 2.0.CO;2. Ulaby, F. T., R. K. Moore, and A. E. Fung, 1981: Microwave Remote Kramer, H. J., 2014: GCOM (Global Change Observation Mission- Sensing Fundamentals and Radiometry.Vol.1,Microwave Water). European Space Agency, accessed 31 October 2014. Remote Sensing: Active and Passive, Artech House, 470 pp. [Available online at https://directory.eoportal.org/web/eoportal/ Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis. Quart. satellite-missions/g/gcom.] J. Roy. Meteor. Soc., 131, 2961–3012, doi:10.1256/qj.04.176. Kruk, M. C., K. R. Knapp, and D. H. Levinson, 2010: A technique for Velden, C. S., and Coauthors, 2006: The Dvorak tropical cyclone combining global tropical cyclone best track data. J. Atmos. intensity estimation technique: A satellite-based method that Oceanic Technol., 27, 680–692, doi:10.1175/2009JTECHA1267.1. has endured for over 30 years. Bull. Amer. Meteor. Soc., 87, Kummerow, C., and R. Ferraro, 2006: EOS/AMSR-E level-2 1195–1210, doi:10.1175/BAMS-87-9-1195. rainfall. Algorithm Theoretical Basis Doc., 10 pp. [Available ——, A. Burton, and K. Kuroiwa, 2012: The First International online at http://nsidc.org/sites/nsidc.org/files/files/amsr_atbd_ Workshop on Satellite Analysis of Tropical Cyclones: supp06_L2_rain.pdf.] Summary of current operational methods to estimate in- Liu, Q., and F. Weng, 2003: Retrieval of sea surface wind vectors from simulated satellite microwave polarimetric measurements. tensity. Trop. Cyclone Res. Rev., 1, 469–481, doi:10.6057/ Radio Sci., 38, 8078, doi:10.1029/2002RS002729. 2012TCRR04.05. ——, ——, and S. J. English, 2011: An improved fast microwave Webster, P. J., G. J. Holland, J. A. Curry, and H.-R. Chang, 2005: water emissivity model. IEEE Trans. Geosci. Remote Sens., Changes in tropical cyclone number, duration, and intensity 49, 1238–1250, doi:10.1109/TGRS.2010.2064779. in a warming environment. Science, 309, 1844–1846, doi:10.1126/ Lu, X., and H. Yu, 2013: An objective tropical cyclone intensity science.1116448. estimation model based on digital IR satellite images. Trop. Wentz, F. J., 2002: AMSR ocean algorithm. EORC Bull./Tech. Cyclone Res. Rev., 2, 233–241, doi:10.6057/2013TCRR04.05. Rep. 9, 8–28. Mayfield, M., C. J. McAdie, and A. C. Pike, 1988: Tropical cyclone ——, and T. Meissner, 2000: AMSR ocean algorithm. Version 2, studies: Part 2—A preliminary evaluation of the dispersion Algorithm Theoretical Basis Doc., Remote Sensing Systems of tropical cyclone position and intensity estimates de- Tech. Proposal 121599A-1, 74 pp. termined from satellite imagery. Federal Coordinator for ——, L. A. Mattox, and S. Peteherych, 1986: New algorithms for Meteorological Services and Supporting Research FCM- microwave measurements of ocean winds: Applications to R11-1988, 2-1–2-17. Seasat and the special sensor microwave imager. J. Geophys. Meissner, T., and F. Wentz, 2009: Wind vector retrievals under rain Res., 91, 2289–2307, doi:10.1029/JC091iC02p02289. with passive satellite microwave radiometers. IEEE Trans. Wilheit, T. T., 1979: A model for the microwave emissivity of the Geosci. Electron., 47, 3065–3083. ocean’s surface as a function of wind speed. IEEE Trans. NOAA ARL, 2014: Global Data Assimilation System (GDAS1) Geosci. Electron., 17, 244–249, doi:10.1109/TGE.1979.294653. archive information. Accessed 3 February 2014. [Available Wu, Y., and F. Weng, 2011: Detection and correction of AMSR-E online at http://ready.arl.noaa.gov/gdas1.php.] radio-frequency interference (RFI). Acta Meteor. Sin., 25, Randa, J., and Coauthors, 2008: Recommended terminology for 669–681, doi:10.1007/s13351-011-0510-0. microwave radiometry. NIST Tech. Note 1551, 32 pp. Yan, B., and F. Weng, 2008: Applications of AMSR-E measure- Saitoh, S., and A. Shibata, 2010: AMSR-E all weather sea surface ments for tropical cyclone predictions Part I: Retrieval of sea wind speed (in Japanese). Tenki, 57 (1), 5–18. [Available online surface temperature and wind speed. Adv. Atmos. Sci., 25, at http://www.metsoc.jp/tenki/pdf/2010/2010_01_0005.pdf.] 227–245, doi:10.1007/s00376-008-0227-x.

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