Editorial Type:
Article
Article Type:
Research Article

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

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Hwa-Jeong Seo National Meteorological Satellite Center, Korea Meteorological Administration, Gwanghyewon-myeon, South Korea

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Young-Joo Kwon Department of Environment, Energy, and Geoinfomatics, Sejong University, Seoul, South Korea

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Print Publication:
01 Jul 2016
DOI:
https://doi.org/10.1175/JTECH-D-15-0128.1
Page(s):
1363–1375
Full access

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 temperatures, 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 temperature 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 between the proposed algorithm and the Tropical Atmosphere Ocean (TAO) data show that the estimated bias and RMSE for AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 1.13 m s−1, and −0.52 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.

Corresponding author address: Dr. Sungwook Hong, Department of Environment, Energy, and Geoinfomatics, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul, South Korea. E-mail: sesttiya@gmail.com

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 temperatures, 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 temperature 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 between the proposed algorithm and the Tropical Atmosphere Ocean (TAO) data show that the estimated bias and RMSE for AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 1.13 m s−1, and −0.52 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.

Corresponding author address: Dr. Sungwook Hong, Department of Environment, Energy, and Geoinfomatics, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul, South Korea. E-mail: sesttiya@gmail.com
Keywords: Observational techniques and algorithms; Algorithms; Microwave observations; Remote sensing; Satellite observations; Surface observations; Applications; Wind effects

1. Introduction

Tropical cyclones (TCs), particularly typhoons, are major natural disasters on the Korean Peninsula, and they inflict huge damage within a period of a few days to weeks. Trends in intensities of TCs across the western North Pacific basin have increased recently (Webster et al. 2005), and peak wind speeds have also increased by over 50% in this region since 1949 (Emanuel 2005).

Information related to the eye and the center of a TC together with its intensity and wind field (radius of maximum wind) are important factors used in the analysis of such phenomena. The maritime nature of 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 between their position and motion, in addition to monitoring the advent, maturation, and dissipation stages of a TC’s lifetime sequentially.

Remote sensing developed quickly after the advent of Earth-orbiting satellites, and it has since been used to analyze TCs. The Dvorak TC intensity estimation method (Dvorak 1975), based on infrared (IR) and visible (VIS) satellite imagery (Dvorak 1984), is most notable for its operational use and its TC best-track archives (Velden et al. 2012). However, previous research has evaluated the shortcomings and accuracy of the Dvorak technique (Guard 1988; Mayfield et al. 1988; Brown and Franklin 2004; Kossin and Velden 2004; Velden et al. 2006; Knaff et al. 2010). The main disadvantage appears to be the inevitable subjectivity of the individual analysts (Lu and Yu 2013). Misapplications and a number of regional modifications have taken place over a period of many years by various national tropical cyclone analysis centers (Velden et al. 2012).

When using the Dvorak technique, the TC center location is determined first. Second, after an estimation of pattern recognition and two quasi-independent TC intensities relying on cloud systems (eye, curved band, shear, and covered center), the best TC intensity is chosen and is finally determined through selected rules. For the northwest Pacific Ocean, including the South China Sea, the Regional Specialized Meteorological Center (RSMC) Tokyo at the Japan Meteorological Agency (JMA) has the responsibility of issuing TC track and intensity forecasts. RSMC Tokyo produces forecasts of the center’s position, with an associated 70% probability of the TC direction and speed through the following 120 h. In addition, the minimum sea level pressure () and the maximum surface wind () are forecast through 72 h.

The majority of TC values reported by operational centers are derived from application of the Dvorak technique by converting a Dvorak current intensity (CI) number directly to a (Velden et al. 2012). Thus, differences in the CI values between agencies are commonly expected within a ±0.5 CI number between different analysts performing the calculations, because different conversion tables are used to obtain from CI numbers (Velden et al. 2012). For example, the JMA-verified Dvorak CI number uses the conversion table of Koba et al. (1991) for TCs passing through the Japanese islands, or those observed using experimental aircraft observations for 1995–2009. JTWC (1974) found 74% and 91% within ±0.5 and ±1.0 of a CI number (Δ), respectively, when comparing CI numbers with CI derived from JTWC’s best-track data. In addition, the JMA found 65.7%, 89.1%, and 97.6% within ±0.5, ±1.0, and ±1.5 of CI differences (Δ), respectively, between CI numbers and CI derived from JMA’s best-track data (Kruk et al. 2010).

Therefore, obtaining an accurate measurement of sea surface wind speed () is very important for monitoring typhoon intensities; it also affects the ability to provide an accurate TC warning by eliminating the subjectivity of individual analysts. Microwave remote sensors have an advantage in estimating because the increase of sea surface emissivity, due to roughness (English and Hewison 1998; Liu et al. 2011) and foam effects (Tang 1974) driven by , is related physically to the observed brightness temperature (). Many well-calibrated ocean emissivity models have been developed for passive microwave radiometers (Stogryn 1967; Wilheit 1979; Wentz et al. 1986; Wentz and Meissner 2000) and applied to a number of passive satellite sensors, including the TRMM Microwave Imager (TMI), the Special Sensor Microwave Imager (SSM/I), Special Sensor Microwave Imager/Sounder (SSMIS), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sensor on board the Aqua satellite. A method for estimating the TC intensity utilizing TMI data, based on a multiple regression technique, has also been developed (Hoshino and Nakazawa 2007). In addition, Saitoh and Shibata (2010) described a method for estimating the using horizontal at 6.925- and 10.65-GHz channels of AMSR-E on board the Aqua satellite. Currently, active and passive microwave remote sensing have become established as critical operational tools for TC analysis. In particular, passive microwave imagery (36–37 and 85–91 GHz), using an Advanced Microwave Sounding Unit (AMSU), provides direct diagnosis of the inner structure of TCs (Brueske and Velden 2003; Herndon et al. 2004; Demuth et al. 2004, 2006).

In this study, we present a physical algorithm for estimating surface wind speed using passive microwave remote sensing, the 6.925- and 10.65-GHz bands of AMSR-2 on board the Global Change Observation Mission–Water (GCOM-W1) satellite because of their radiative properties in relation to rain. We also present a retrieval scheme for estimating TC intensity (CI number) and from using the Hong algorithm.

2. Theoretical background

The energy emissions measured by satellite radiometers are often expressed in terms of , which can be calculated for polarizations (Randa et al. 2008). The polarized brightness temperature () is influenced by the cosmic background temperature, the atmosphere, and Earth’s surface at a given incidence angle in the microwave range:
e1
where is the rough sea surface reflectivity; the subscript indicates vertical (V) or horizontal (H) polarization; is the sea surface temperature; is the atmospheric transmittance; and and are the upward and downward atmospheric brightness temperatures, respectively.

a. Atmospheric transmittance

Under the no-rainy conditions at low microwave frequencies (<10 GHz), atmospheric contributions to the brightness temperature in satellite observations are negligible (Yan and Weng 2008; Uhlhorn and Black 2003). For example, atmospheric attenuations at 6.9 GHz are less than 0.2 K for V polarization and less than 0.8 K for H polarization (Wentz 2002).

Under rainy conditions, satellite-observed receives contributions from the cosmic background temperature, both the atmospheric and surface brightness temperatures in relation to rainfall, and from the surface in relation to the wind speed. However, is reduced under rainy conditions because rain increases atmospheric attenuation, and this effect occurs particularly at higher frequencies. It thus becomes more difficult to make an accurate estimation of rainfall because of the high variability of certain parameters such as rain size distribution and form. Meissner and Wentz (2009) found no saturation in the wind-induced emissivity signal at values up to 35 m s−1 under rainy conditions using C band. In addition, numerical modeling has shown that sea surface can be retrieved in hurricanes under an amount of rain of up to 20–30 mm h−1 for C- and X-band channels, because the brightness temperature at these channels is not saturated (Kummerow and Ferraro 2006). The term can also be retrieved because 6- and 10-GHz data are not saturated (Shibata 2002), but 10 GHz is slightly more sensitive to wind speed than 6 GHz (Shibata 2007). It is known that inside deep convective cells, and inside the TC’s eyewall, wind speeds up to 60 m s−1 have been estimated using airborne passive microwave radiometers at frequencies between 4.5 and 7.2 GHz (Uhlhorn and Black 2003).

b. Surface reflectivity and roughness

Estimating wind speed using passive microwave radiometers depends on the relationship between sea surface reflectivity and changing sea state. In particular, the generation of small ocean waves of centimeter length (capillary waves) is driven by instantaneous (English and Hewison 1998) and can be generally expressed by the relationship between the rough and specular surface reflectivities (Choudhury et al. 1979; English and Hewison 1998; Hong 2010b,c,d; Hong and Shin 2013; Hong 2013).

Rough surface reflectivities and specular surface reflectivities for each polarization are related to each other by small-scale roughness , which corresponds to the height probability density function with a Gaussian distribution when using a semiempirical model based on the incoherent approach depending on amplitudes only (Ulaby et al. 1981) instead of depending on both the amplitude and phases within the medium, and is shown as follows (Choudhury et al. 1979):
e2
where is the wavelength (cm) and θ is the incidence angle (°).
The specular reflectance of the sea surface is governed by the Fresnel formula for a given complex refractive index at a local incident angle as follows:
e3
where is the complex refractive index of a medium, and and are the real and imaginary parts of the refractive indices, respectively.
However, in general, complex refractive indices are unknown for complex and heterogeneous surfaces. Hong (2009b) developed and validated an approximate relationship between and (the Hong approximation) that is irrespective of a priori information on the surface refractive index, and uses the generalized Fresnel equation (Tousey 1939) and the first term in the Taylor series of the natural logarithm ratio as follows:
e4

The Hong approximation [Eq. (4)] has been applied successfully to surface roughness studies (Hong 2009a, 2010a,b,c,d; Hong et al. 2010; Hong and Shin 2010, 2011; Hong et al. 2014, 2015). It is of note that the Azzam–Sohn–Hong (ASH) approximation, which is similar to the Hong approximation, has been derived (Hong 2013), but it is not appropriate for use in this study because the ASH approximation is effective under a small value of the imaginary part of the complex refractive index (Hong 2013).

3. Methods

a. Data and procedure

The AMSR 2 (AMSR-2) is operated and well calibrated at several frequencies from 6.925 to 89.0 GHz at the constant incidence angle of 55.0° (Kawanishi et al. 2003). The AMSR-2 instrument and channel characteristics are summarized in Table 1 (Kramer 2014). In this study, we present an inversion algorithm to retrieve at a frequency of 6.925 GHz with AMSR-2 observations. Both 6.925 and 10.65 GHz were used for removing the radio frequency interference (RFI)-contaminated ASMR-2 observations over oceans. The observed data at V polarization and the simulated data were used to estimate surface reflectivity. The comparison results for estimating TC intensity from are primarily presented using the Hong algorithm at a frequency of 6.925 GHz with AMSR-2 observations.

Table 1.

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

Table 1.

In this study, the selected spatial range encompassed an ocean area between ±50° latitude with the exception of sea ice areas. Land-covered areas were excluded using the European Centre for Medium-Range Weather Forecasts (ECMWF) land–sea mask. Rain-contaminated observations were determined using AMSR-2 data. The RFI-contaminated observations over oceans were excluded using the following conditions (Wu and Weng 2011):
e5
where the subscript Obs denotes observation.

To find a conversion relationship between the AMSR-2 observation and the AMSR-2 simulation, the AMSR-2 simulation was performed using a radiative transfer calculation such as RTTOV, version 9 (RTTOV-9), with temperature and humidity profiles, , surface pressure, and wind speed information. AMSR-2 is used under rain-free conditions, while the NWP model is used under rainy conditions, such as occurs inside TCs. In this study, the NWP model data used are the ECMWF data, which provide surface- and height-dependent profiles of temperature, pressure, and humidity every 6 h on a global 0.25° × 0.25° latitude–longitude grid (Uppala et al. 2005). The ECMWF surface and atmospheric profile data, and AMSR-2 , are used for estimating the polarized surface reflectivities. In this study, we use the NWP model with RTTOV-9, which includes the Fast Emissivity Model, version 3 (FASTEM-3) (Saunders 2006), which was designed for microwave sensors such as SSM/I, SSMIS, AMSU, TMI, AMSR-E, AMSR-2, and WindSat (Liu and Weng 2003; Saunders 2006). FASTEM-3 includes specular reflection, small-scale roughness, the foam effect, large-scale roughness, and wind direction effects (English and Hewison 1998; Saunders 2006).

b. retrieval for use in rainy conditions

The Hong algorithm for rainy and rain-free conditions consists of five steps, as outlined in Fig. 1.

Fig. 1.
Fig. 1.

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

Citation: Journal of Atmospheric and Oceanic Technology 33, 7; 10.1175/JTECH-D-15-0128.1

First, a regression relationship between the AMSR-2 observations and the AMSR-2 simulation for the AMSR-2 6.925- and 10.65-GHz channels is computed, using collocation data between the AMSR-2 observation data and that of the RTTOV simulation with ECMWF and AMSR-2 data. A regression relationship between and for V polarization is estimated under rain-free and rainy conditions as follows:
e6
e7
where the subscript Sim denotes the simulation without rain effects and with surface wind effects; and and , and and are the regression coefficients of the slope and offset, respectively. In this study, “under rainy conditions” refers to the use of the retrieval algorithm with observations obtained under rainy conditions and simulations generated under rain-free assumption due to the lack of information on the rain structure. AMSR-2 simulations generated under rain-free assumptions inside TCs showed a linear relationship with the AMSR-2 observations with rain and wind effects (see Fig. 4). Table 2 summarizes the regression coefficients and under rain-free conditions, as derived using the matchup data between AMSR-2 observations and AMSR-2 simulations for 1 month (1–31 October 2013), for ocean areas within ±latitude 50°. The regression coefficients and are derived from temporally and spatially collocated data for 13 typhoons (in 2012) using AMSR-2 observations and AMSR-2 simulations for 45 days from July to October 2012, obtained from the northwestern Pacific Ocean. In addition, Table 3 summarizes the name, dates, and number of data relating to the 13 typhoons.
Table 2.

Linear regression coefficients, bias, and RMSE between and for AMSR-2 6.925V- and 10.65V-GHz channels under rain-free and rain conditions.

Table 2.
Table 3.

Typhoon data used to derive regression coefficients under rainy conditions.

Table 3.
Second, for V polarization, a regression relationship between AMSR-2 simulation and rough surface reflectivity is estimated as follows:
e8
where and are the regression coefficients of the slope and offset, respectively. The terms and are summarized in Table 4.
Table 4.

Linear regression coefficients and correlations between and for AMSR-2 6.925 and 10.65 GHz for V polarization.

Table 4.

From a previous study (Hong et al. 2015) using Global Data Assimilation System (GDAS) data (NOAA ARL 2014) for rain-free conditions, shows a relatively good correlation with , while exhibits low correlations. In this study, the correlation between and for H polarization using ECMWF data and AMSR-2 observations is approximately of approximately −0.4, in relation to the ocean wind (Shibata 2003; Hong et al. 2015). Thus, a regression relationship between and for H polarization was not applied in this study.

Third, small-scale roughness is estimated using Hong’s roughness equation [Eq. (9)] with the rough surface reflectivities and . The term is computed using the rain-free RTTOV simulation with ECMWF and AMSR-2 data for rain-free conditions, and ECMWF surface and atmospheric profile data for rainy conditions. In addition, Hong’s roughness equation is derived using Eq. (2) using the Hong approximation [Eq. (4)], and the characteristics of the polarized surface reflectivities that are near Brewster’s angle (e.g., 52.75° with a view angle of 55° at AMSR-2 6.925 GHz) for specular surfaces (namely, and ) are as follows (Hong 2010d):
e9
Hong’s roughness estimation [Eq. (9)] was validated using buoys and model data (Hong and Shin 2013; Hong et al. 2015). In this study, surface reflectivities are estimated using the FASTEM-3 considering small-scale roughness driven by the instantaneous surface wind.
Fourth, is then calculated using the following relationship (Hong and Shin 2013; Hong et al. 2015) between Hong’s roughness estimation and ECMWF , which was fitted using the least squares regression for the data of low (<5 m s−1) and high (>5 m s−1) during a 1-month period (October 2013):
e10
where and are the regression coefficients of the slope and offset, respectively. Figure 2 shows the relationship for AMSR-2 6.925 to 89.0 GHz. The terms and for AMSR-2 6.925 and 10.65 GHz are summarized in Table 5. Table 6 summarizes the error of the retrieved as the estimated varied within ±20% uncertainty for AMSR-2 6.925 GHz. In this case, was 10.01 m s−1. The terms and were 0.475 and 0.475, respectively. As a result, varies from −49.474% to 32.632%; the uncertainty of was between −74.236% and 48.172%. Accordingly, the error of the retrieved wind speed depended on the accuracy of to a large extent.
Fig. 2.
Fig. 2.

The relationship for AMSR-2 6.925 to 89.0 GHz. The gray area around the colored lines indicates the retrieved using Hong’s roughness estimation [Eq. (9)] at given and .

Citation: Journal of Atmospheric and Oceanic Technology 33, 7; 10.1175/JTECH-D-15-0128.1

Table 5.

Linear regression coefficients of the relationship between and for 6.925 and 10.65 GHz obtained using the Hong algorithm.

Table 5.
Table 6.

Relationship between the errors of estimated and retrieved .

Table 6.

Finally, the obtained from the relationship was validated using obtained from the Tropical Atmosphere Ocean (TAO) buoys over a period of 1 month (October 2013) for rain-free conditions, in a similar way to the study of Hong et al. (2015), and then indirectly validated with the maximum values of 13 TCs that were derived using the Dvorak method in the northwestern Pacific Ocean during 2012 for rainy conditions (inside TCs).

c. and CI index retrieval

The central pressure of TCs is currently mainly determined from satellite IR imagery using the Dvorak method. The CI number also gives the and in the vicinity of the center.

In this study, we present a method for retrieving sea surface wind speed using passive microwave satellite observations. The term can then be estimated from the Hong algorithm, and the CI number and can be determined using a conversion table among the , CI number, and . We use the conversion table provided by Koba et al. (1991) in Table 7. The estimated , CI number, and are compared with best-track data from 2012 and 2013 in the satellite report (SAREP), which is issued annually by JMA’s typhoon center (RSMC Tokyo) (JMA 2013). Most meteorological organizations, including JMA, the China Meteorological Administration (CMA), and the Korea Meteorological Administration (KMA), use the Dvorak method for analyzing TCs. JMA performs TC analysis eight times per day at 0000, 0300, 0600, 0900, 1200, 1500, 1800, and 2100 UTC. Generally, forecasters first determine the center position of the TC using IR satellite imagery. The CI number is then determined by the Dvorak method, and finally, and of TCs are estimated from the CI number. The general procedures used in the Dvorak technique, and the presented method using the Hong algorithm (the Hong technique) are described in Fig. 3. It is of note that the Dvorak technique depends on the CI number, while the Hong technique depends on . The Hong technique has advantages over the Dvorak technique because of its simple procedure, the derivation of without the CI number using microwave satellite observation instead of VIS or IR satellite images, and its independence from a forecaster’s subjective experiences.

Table 7.

Conversion table among CI number, , and (Koba et al. 1991).

Table 7.
Fig. 3.
Fig. 3.

(a) Dvorak technique and (b) Hong technique used in estimations (including , , and CI number).

Citation: Journal of Atmospheric and Oceanic Technology 33, 7; 10.1175/JTECH-D-15-0128.1

4. Results

Figures 4a and 4b show the correlation of observations and simulations under rain-free and rainy conditions at AMSR-2 6.925V GHz, respectively. The bias, RMSE, and correlation between AMSR-2 observations and AMSR-2 simulations are 3.91 K, 4.07 K, and 0.97 under rain-free conditions, and 9.07 K, 10.88 K, and 0.79 under rainy conditions, respectively. The results for AMSR-2 10.65V GHz are depicted in Figs. 4c and 4d. The bias, RMSE, and correlation between AMSR-2 observations and AMSR-2 simulations are 6.98 K, 7.09 K, and 0.95 under rain-free conditions, and 19.7 K, 23.71 K, and 0.69 under rainy conditions.

Fig. 4.
Fig. 4.

(a) Relationship between and under rain-free conditions. (b) Relationship between and 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.

Citation: Journal of Atmospheric and Oceanic Technology 33, 7; 10.1175/JTECH-D-15-0128.1

For the rainy conditions, AMSR-2 observations contain both atmospheric effects due to rainfall and surface effects due to surface wind speeds. AMSR-2 simulations generated under rain-free assumptions inside TCs showed a linear relationship with the AMSR-2 observations with rain and wind effects, even when the correlation under rainy conditions was lower than that under the rain-free conditions. The linear relationship between AMSR-2 observations and AMSR-2 simulations was significant for estimation inside TCs because it make a connection between AMSR-2 observations and without relying on the presence of highly variable rainfall. These results were consistent with the previous work performed by Meissner and Wentz (2009). Validation needs to be performed indirectly using intensity, maximum wind speed, and minimum central pressure of TCs because there is a lack of ground observation data for comparison.

Figures 5a and 5b show the relationship for ECMWF wind speeds on 7 October 2013, calculated using the inversion method for AMSR-2 6.925 and 10.65 GHz. The results were similar to those of a previous study in terms of bias, RMSE, and correlation of the retrieved values (Hong and Shin 2013). Figure 5c shows the validation results of a comparison between TAO buoy data and the retrieved from the current AMSR-2 observations at 6.925 GHz for one entire month (1–30 October 2013). For the presented algorithm (the Hong algorithm), the estimation of TAO under rain-free conditions using AMSR-2 data and ECMWF data had a relatively low bias and RMSE of the outputs: 0.09 and 1.13 m s−1, respectively. Figure 5d shows the validation results at AMSR-2 10.65 GHz, and the bias and RMSE of the Hong algorithm were −0.52 and 1.21 m s−1, respectively.

Fig. 5.
Fig. 5.

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

Citation: Journal of Atmospheric and Oceanic Technology 33, 7; 10.1175/JTECH-D-15-0128.1

In this study, we applied the Hong algorithm at 6.925 GHz to the typhoon analysis. Figures 6a and 6b show the observed and retrieved , respectively, at 6.925-GHz V polarization of AMSR-2 for Typhoon Bolaven on 26 August 2012. Figure 6c shows the ECMWF , and Fig. 6d shows the difference between the retrieved and that of ECMWF . The retrieved was larger than that of ECMWF for rainy conditions, such as inside Typhoon Bolaven, with increases in .

Fig. 6.
Fig. 6.

(a) The term at AMSR-2 6.925-GHz V polarization with rainfall effects and surface wind effects, (b) Hong retrieved using the Hong algorithm at AMSR-2 6.925 GHz, (c) ECMWF , and (d) Hong minus ECMWF . This is representative of the case of Typhoon Bolaven, which occurred on 26 Aug 2012.

Citation: Journal of Atmospheric and Oceanic Technology 33, 7; 10.1175/JTECH-D-15-0128.1

Figure 7a and 7b show and , respectively, which were obtained from JMA (RSMC Tokyo), JTWC, Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) analysis, and the Hong technique for 13 typhoons in the northwestern Pacific Ocean in 2012. The term derived by the Hong technique varies between approximately 20 and 60 m s−1, and by CIMSS was estimated as being larger than by JTWC, RSMC, and the Hong technique. The term showed the opposite characteristics of those exhibited in the analysis. Figure 7c shows the comparison results of the CI number by the Hong technique and that of SATREP for a period of two years for typhoons in the northwestern Pacific area. The bias, RMSE, and correlations were −0.559, 1.386, and 0.574, respectively. In terms of , the bias and RMSE between the Hong technique and that of SATREP were −4.141 and 9.481 m s−1, respectively. Table 8 summarizes the bias and RMSE in and between the Hong technique and the JMA (RSMC), JTWC, and CIMMS analyses for the typhoons in the northwestern Pacific Ocean during 2012.

Fig. 7.
Fig. 7.

Hong techniques at AMSR-2 6.925 GHz vs Dvorak technique for (a) and (b) 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 using the Hong technique for Typhoon Kong-Rey, which occurred at 0300 UTC 27 Aug 2013.

Citation: Journal of Atmospheric and Oceanic Technology 33, 7; 10.1175/JTECH-D-15-0128.1

Table 8.

Bias and RMSE among and between the Hong technique and the JMA (RSMC), JTWC, and CIMMS analyses for the typhoons in the northwestern Pacific Ocean from 2012 to 2013.

Table 8.

Both results show good agreement for low values of CI number, but the difference between two CI numbers increases as the CI number increases, and the SAREP CI number is larger than that of the Hong technique. Figure 7d shows the derived using the Hong technique for at AMSR-2 6.925 GHz in relation to Typhoon Kong-Rey, which occurred at 0300 UTC 27 August 2013. In this case, the Hong technique produces the CI number 2.4, whereas the SAREP reports the CI number 2.5. This result is consistent with the results shown in Fig. 7c.

5. Summary and conclusions

This study presented a unique algorithm for retrieving sea surface wind speed (the Hong algorithm) using the characteristics of V and H polarization at 6.925- and 10.65-GHz bands from spaceborne passive microwave radiometers with an AMSR-2 sensor under both rainy and rain-free conditions. The Hong algorithm shows good agreement with TAO buoy observations under rain-free conditions. The estimated bias and RMSE for AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 1.13 m s−1, and −0.52 and 1.21 m s−1, respectively. This result is also in agreement with previous work using AMSR-E 6.925- and 10.65-GHz bands (Hong et al. 2015). In that case, the bias and RMSE were −0.13 and 1.19 m s−1, and −0.09 and 1.15 m s−1, respectively, for all 12 months of 2010 (Hong et al. 2015). The error of the retrieved depended on the accuracy of to a large extent. However, a comparison of these results with the TAO buoy data under rain-free conditions showed that the proposed algorithm retrieves with high accuracy. The validation results indicated that is estimated approximately within 5% uncertainty for AMSR-2 6.925 GHz.

This study also proposed a TC intensity estimation technique from using the AMSR-2 6.925-GHz band, known as the Hong technique, which is applied to the estimation of TC intensity using values of the CI number, , and . TC intensities estimated from the Hong technique are compared with the best-track data for typhoons in the northwestern Pacific area reported by SATREP over two years (2012–13). The validation result for TC CI numbers shows a bias and RMSE of −0.559 and 1.386, respectively. The Hong technique using the AMSR-2 6.925-GHz band exhibits good agreement with the best-track data, but the discrepancy between the Hong technique and SAREP becomes relatively large as the CI number increases. In our future works, we will analyze the effectiveness of TC intensity estimation using the Hong technique at a frequency of 10.65 GHz with AMSR-2, TMI, and Global Precipitation Measurement (GPM) Microwave Imager (GMI) observations.

Acknowledgments

The authors thank the anonymous reviewers for their constructive comments on the manuscript. This study is supported by the Meteorological Satellite Center (Project 153-3100-3137-302-210-13) and the ISABU project of the Korea Institute of Ocean Science and Technology (PE99361).

REFERENCES

  • Brown, D. B., and Franklin J. L. , 2004: Dvorak TC wind speed biases determined from reconnaissance-based “best track” data (1997–2003). 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 3D.5. [Available online at https://ams.confex.com/ams/26HURR/techprogram/paper_75193.htm.]

  • Brueske, K. F., and Velden C. S. , 2003: Satellite-based tropical cyclone intensity estimation using the NOAA-KLM series Advanced Microwave Sounding Unit (AMSU). Mon. Wea. Rev., 131, 687–697, doi:10.1175/1520-0493(2003)131<0687:SBTCIE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Choudhury, B. J., Schmugge T. J. , Chang A. , and Newton R. W. , 1979: Effect of surface roughness on the microwave emission from soils. J. Geophys. Res., 84, 5699–5706, doi:10.1029/JC084iC09p05699.

    • Search Google Scholar
    • Export Citation

  • Demuth, J. L., DeMaria M. , Knaff J. A. , and Vonder Haar T. H. , 2004: Evaluation of Advanced Microwave Sounder Unit (AMSU) tropical-cyclone intensity and size estimation algorithms. J. App. Meteor., 43, 282–296, doi:10.1175/1520-0450(2004)043<0282:EOAMSU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Demuth, J. L., DeMaria M. , and Knaff J. A. , 2006: Improvement of Advanced Microwave Sounding Unit tropical cyclone intensity and size estimation algorithms. J. Appl. Meteor. Climatol., 45, 1573–1581, doi:10.1175/JAM2429.1.

    • Search Google Scholar
    • Export Citation

  • Dvorak, V. F., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, 420–430, doi:10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Dvorak, V. F., 1984: Tropical cyclone intensity analysis using satellite data. NOAA Tech. Rep. NESDIS 11, 47 pp.

  • Emanuel, K., 2005: Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, 686–688, doi:10.1038/nature03906.

    • Search Google Scholar
    • Export Citation

  • English, S. J., and Hewison T. J. , 1998: Fast generic millimeter-wave emissivity model. Microwave Remote Sensing of the Atmosphere and Environment, T Hayasaka et al., Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 3503), 288–300, doi:10.1117/12.319490.

  • Guard, C. P., 1988: Tropical cyclone studies: Part 3—Results of a tropical cyclone accuracy study using polar orbiting satellite data. Federal Coordinator for Meteorological Services and Supporting Research FCM-R11-1988, 3-1–3-36.

  • Herndon, D., Velden C. S. , Brueske K. , Wacker R. , and Kabat B. , 2004: Upgrades to the UW-CIMSS AMSU-based tropical cyclone intensity estimation algorithm. 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 4D.1. [Available online at https://ams.confex.com/ams/26HURR/techprogram/paper_75933.htm.]

  • Hong, S., 2009a: Detection of Asian dust (Hwangsa) over the Yellow Sea by decomposition of unpolarized infrared reflectivity. Atmos. Environ., 43, 5887–5893, doi:10.1016/j.atmosenv.2009.08.024.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2009b: Retrieval of refractive index over specular surfaces for remote sensing applications. J. Appl. Remote Sens., 3, 033560, doi:10.1117/1.3265997.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2010a: Decomposition of unpolarized emissivity. Int. J. Remote Sens., 31, 2109–2114, doi:10.1080/01431160903329349.

  • Hong, S., 2010b: Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing. Remote Sens. Environ., 114, 1136–1140, doi:10.1016/j.rse.2009.12.015.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2010c: Global retrieval of small-scale roughness over land surfaces at microwave frequency. J. Hydrol., 389, 121–126, doi:10.1016/j.jhydrol.2010.05.036.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2010d: Surface roughness and polarization ratio in microwave remote sensing. Int. J. Remote Sens., 31, 2709–2716, doi:10.1080/01431161003627855.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2013: Polarization conversion for specular components of surface reflection. IEEE Geosci. Remote Sens. Lett., 10, 1469–1472, doi:10.1109/LGRS.2013.2260524.

    • Search Google Scholar
    • Export Citation

  • Hong, S., and Shin I. , 2010: Global trends of sea ice: Small-scale roughness and refractive index. J. Climate, 23, 4669–4676, doi:10.1175/2010JCLI3697.1.

    • Search Google Scholar
    • Export Citation

  • Hong, S., and Shin I. , 2011: A physically-based inversion algorithm for retrieving soil moisture in passive microwave remote sensing. J. Hydrol., 405, 24–30, doi:10.1016/j.jhydrol.2011.05.005.

    • Search Google Scholar
    • Export Citation

  • Hong, S., and Shin I. , 2013: Wind speed retrieval based on sea surface roughness measurements from spaceborne microwave radiometers. J. Appl. Meteor. Climatol., 52, 507–516, doi:10.1175/JAMC-D-11-0209.1.

    • Search Google Scholar
    • Export Citation

  • Hong, S., Shin I. , and Ou M. , 2010: Comparison of the Infrared Surface Emissivity Model (ISEM) with a physical emissivity model. J. Atmos. Oceanic Technol., 27, 345–352, doi:10.1175/2009JTECHA1311.1.

    • Search Google Scholar
    • Export Citation

  • Hong, S., Shin I. , Byun Y. , Seo H.-J. , and Kim Y. , 2014: Analysis of sea ice surface properties using ASH and Hong approximations in satellite remote sensing. Remote Sens. Lett., 5, 139–147, doi:10.1080/2150704X.2014.888106.

    • Search Google Scholar
    • Export Citation

  • Hong, S., Seo H.-J. , Kim N. , and Shin I. , 2015: Physical retrieval of tropical ocean surface wind speed under rain-free conditions using spaceborne microwave radiometers. Remote Sens. Lett., 6, 380–389, doi:10.1080/2150704X.2015.1037466.

    • Search Google Scholar
    • Export Citation

  • Hoshino, S., and Nakazawa T. , 2007: Estimation of tropical cyclone’s intensity using TRMM/TMI brightness temperature data. J. Meteor. Soc. Japan, 85, 437–454. http://www.jstage.jst.go.jp/article/jmsj/85/4/437/_pdf/-char/ja/, doi:10.2151/jmsj.85.437.

    • Search Google Scholar
    • Export Citation

  • JMA, 2013: Annual report on the activities of the RSMC Tokyo–Typhoon Center 2012. 92 pp. [Available online at http://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/AnnualReport/2012/Text/Text2012.pdf.]

  • JTWC, 1974: Annual tropical cyclone report: 1974. U.S. Fleet Weather Central, JTWC, 126 pp. [Available online at http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc/atcr/1974atcr.pdf.]

  • Kawanishi, T., and Coauthors, 2003: The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA’s contribution to the EOS for global energy and water cycle studies. IEEE Trans. Geosci. Remote Sens., 41, 184–194, doi:10.1109/TGRS.2002.808331.

    • Search Google Scholar
    • Export Citation

  • Knaff, J. A., Brown D. P. , Courtney J. , Gallina G. M. , and Beven J. L. II, 2010: An evaluation of Dvorak technique–based tropical cyclone intensity estimates. Wea. Forecasting, 25, 1362–1379, doi:10.1175/2010WAF2222375.1.

    • Search Google Scholar
    • Export Citation

  • Koba, H., Osano S. , Hagiwara T. , Akashi S. , and Kikuchi T. , 1991: Relationships between the CI number and central pressure and maximum wind speed in typhoons (in Japanese). Geophys. Mag., 44, 15–25.

    • Search Google Scholar
    • Export Citation

  • Kossin, J. P., and Velden C. S. , 2004: A pronounced bias in tropical cyclone minimum sea level pressure estimation based on the Dvorak technique. Mon. Wea. Rev., 132, 165–173, doi:10.1175/1520-0493(2004)132<0165:APBITC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kramer, H. J., 2014: GCOM (Global Change Observation Mission-Water). European Space Agency, accessed 31 October 2014. [Available online at https://directory.eoportal.org/web/eoportal/satellite-missions/g/gcom.]

  • Kruk, M. C., Knapp K. R. , and Levinson D. H. , 2010: A technique for combining global tropical cyclone best track data. J. Atmos. Oceanic Technol., 27, 680–692, doi:10.1175/2009JTECHA1267.1.

    • Search Google Scholar
    • Export Citation

  • Kummerow, C., and Ferraro R. , 2006: EOS/AMSR-E level-2 rainfall. Algorithm Theoretical Basis Doc., 10 pp. [Available online at http://nsidc.org/sites/nsidc.org/files/files/amsr_atbd_supp06_L2_rain.pdf.]

  • Liu, Q., and Weng F. , 2003: Retrieval of sea surface wind vectors from simulated satellite microwave polarimetric measurements. Radio Sci., 38, 8078, doi:10.1029/2002RS002729.

    • Search Google Scholar
    • Export Citation

  • Liu, Q., Weng F. , and English S. J. , 2011: An improved fast microwave water emissivity model. IEEE Trans. Geosci. Remote Sens., 49, 1238–1250, doi:10.1109/TGRS.2010.2064779.

    • Search Google Scholar
    • Export Citation

  • Lu, X., and Yu H. , 2013: An objective tropical cyclone intensity estimation model based on digital IR satellite images. Trop. Cyclone Res. Rev., 2, 233–241, doi:10.6057/2013TCRR04.05.

    • Search Google Scholar
    • Export Citation

  • Mayfield, M., McAdie C. J. , and Pike A. C. , 1988: Tropical cyclone studies: Part 2—A preliminary evaluation of the dispersion of tropical cyclone position and intensity estimates determined from satellite imagery. Federal Coordinator for Meteorological Services and Supporting Research FCM-R11-1988, 2-1–2-17.

  • Meissner, T., and Wentz F. , 2009: Wind vector retrievals under rain with passive satellite microwave radiometers. IEEE Trans. Geosci. Electron., 47, 3065–3083.

    • Search Google Scholar
    • Export Citation

  • NOAA ARL, 2014: Global Data Assimilation System (GDAS1) archive information. Accessed 3 February 2014. [Available online at http://ready.arl.noaa.gov/gdas1.php.]

  • Randa, J., and Coauthors, 2008: Recommended terminology for microwave radiometry. NIST Tech. Note 1551, 32 pp.

  • Saitoh, S., and Shibata A. , 2010: AMSR-E all weather sea surface wind speed (in Japanese). Tenki, 57 (1), 5–18. [Available online at http://www.metsoc.jp/tenki/pdf/2010/2010_01_0005.pdf.]

    • Search Google Scholar
    • Export Citation

  • Saunders, R., 2006: RTTOV-8—Science and validation report. Version 1.6, NWP SAF Doc. NWPSAF-MO-TV-007, Met Office Doc. R8REP2006, 46 pp.

  • Shibata, A., 2002: AMSR/AMSR-E sea surface wind speed algorithm. EORC Bull./Tech. Rep. 9, 45–46.

  • Shibata, A., 2003: A change of microwave radiation from the ocean surface induced by air-sea temperature difference. Radio Sci., 38, 8063, doi:10.1029/2002RS002670.

    • Search Google Scholar
    • Export Citation

  • Shibata, A., 2007: Effect of air-sea temperature difference on ocean microwave brightness temperature estimated from AMSR, SeaWinds, and buoys. J. Oceanogr., 63, 863–872, doi:10.1007/s10872-007-0073-y.

    • Search Google Scholar
    • Export Citation

  • Stogryn, A., 1967: The apparent temperature of the sea at microwave frequencies. IEEE Trans. Antennas Propag., 15, 278–286, doi:10.1109/TAP.1967.1138900.

    • Search Google Scholar
    • Export Citation

  • Tang, C., 1974: The effect of droplets in the air–sea transition zone on the sea brightness temperature. J. Phys. Oceanogr., 4, 579–593, doi:10.1175/1520-0485(1974)004<0579:TEODIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Tousey, R., 1939: On calculating the optical constants from reflection coefficients. J. Opt. Soc. Amer., 29, 235–238, doi:10.1364/JOSA.29.000235.

    • Search Google Scholar
    • Export Citation

  • Uhlhorn, E. W., and Black P. G. , 2003: Verification of remotely sensed sea surface winds in hurricanes. J. Atmos. Oceanic Technol., 20, 99–116, doi:10.1175/1520-0426(2003)020<0099:VORSSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Ulaby, F. T., Moore R. K. , and Fung A. E. , 1981: Microwave Remote Sensing Fundamentals and Radiometry. Vol. 1, Microwave Remote Sensing: Active and Passive, Artech House, 470 pp.

  • Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131, 2961–3012, doi:10.1256/qj.04.176.

  • Velden, C. S., and Coauthors, 2006: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years. Bull. Amer. Meteor. Soc., 87, 1195–1210, doi:10.1175/BAMS-87-9-1195.

    • Search Google Scholar
    • Export Citation

  • Velden, C. S., Burton A. , and Kuroiwa K. , 2012: The First International Workshop on Satellite Analysis of Tropical Cyclones: Summary of current operational methods to estimate intensity. Trop. Cyclone Res. Rev., 1, 469–481, doi:10.6057/2012TCRR04.05.

    • Search Google Scholar
    • Export Citation

  • Webster, P. J., Holland G. J. , Curry J. A. , and Chang H.-R. , 2005: Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309, 1844–1846, doi:10.1126/science.1116448.

    • Search Google Scholar
    • Export Citation

  • Wentz, F. J., 2002: AMSR ocean algorithm. EORC Bull./Tech. Rep. 9, 8–28.

  • Wentz, F. J., and Meissner T. , 2000: AMSR ocean algorithm. Version 2, Algorithm Theoretical Basis Doc., Remote Sensing Systems Tech. Proposal 121599A-1, 74 pp.

  • Wentz, F. J., Mattox L. A. , and Peteherych S. , 1986: New algorithms for microwave measurements of ocean winds: Applications to Seasat and the special sensor microwave imager. J. Geophys. Res., 91, 2289–2307, doi:10.1029/JC091iC02p02289.

    • Search Google Scholar
    • Export Citation

  • Wilheit, T. T., 1979: A model for the microwave emissivity of the ocean’s surface as a function of wind speed. IEEE Trans. Geosci. Electron., 17, 244–249, doi:10.1109/TGE.1979.294653.

    • Search Google Scholar
    • Export Citation

  • Wu, Y., and Weng F. , 2011: Detection and correction of AMSR-E radio-frequency interference (RFI). Acta Meteor. Sin., 25, 669–681, doi:10.1007/s13351-011-0510-0.

    • Search Google Scholar
    • Export Citation

  • Yan, B., and Weng F. , 2008: Applications of AMSR-E measurements for tropical cyclone predictions Part I: Retrieval of sea surface temperature and wind speed. Adv. Atmos. Sci., 25, 227–245, doi:10.1007/s00376-008-0227-x.

    • Search Google Scholar
    • Export Citation
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  • Brown, D. B., and Franklin J. L. , 2004: Dvorak TC wind speed biases determined from reconnaissance-based “best track” data (1997–2003). 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 3D.5. [Available online at https://ams.confex.com/ams/26HURR/techprogram/paper_75193.htm.]

  • Brueske, K. F., and Velden C. S. , 2003: Satellite-based tropical cyclone intensity estimation using the NOAA-KLM series Advanced Microwave Sounding Unit (AMSU). Mon. Wea. Rev., 131, 687–697, doi:10.1175/1520-0493(2003)131<0687:SBTCIE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Choudhury, B. J., Schmugge T. J. , Chang A. , and Newton R. W. , 1979: Effect of surface roughness on the microwave emission from soils. J. Geophys. Res., 84, 5699–5706, doi:10.1029/JC084iC09p05699.

    • Search Google Scholar
    • Export Citation

  • Demuth, J. L., DeMaria M. , Knaff J. A. , and Vonder Haar T. H. , 2004: Evaluation of Advanced Microwave Sounder Unit (AMSU) tropical-cyclone intensity and size estimation algorithms. J. App. Meteor., 43, 282–296, doi:10.1175/1520-0450(2004)043<0282:EOAMSU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Demuth, J. L., DeMaria M. , and Knaff J. A. , 2006: Improvement of Advanced Microwave Sounding Unit tropical cyclone intensity and size estimation algorithms. J. Appl. Meteor. Climatol., 45, 1573–1581, doi:10.1175/JAM2429.1.

    • Search Google Scholar
    • Export Citation

  • Dvorak, V. F., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, 420–430, doi:10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Dvorak, V. F., 1984: Tropical cyclone intensity analysis using satellite data. NOAA Tech. Rep. NESDIS 11, 47 pp.

  • Emanuel, K., 2005: Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, 686–688, doi:10.1038/nature03906.

    • Search Google Scholar
    • Export Citation

  • English, S. J., and Hewison T. J. , 1998: Fast generic millimeter-wave emissivity model. Microwave Remote Sensing of the Atmosphere and Environment, T Hayasaka et al., Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 3503), 288–300, doi:10.1117/12.319490.

  • Guard, C. P., 1988: Tropical cyclone studies: Part 3—Results of a tropical cyclone accuracy study using polar orbiting satellite data. Federal Coordinator for Meteorological Services and Supporting Research FCM-R11-1988, 3-1–3-36.

  • Herndon, D., Velden C. S. , Brueske K. , Wacker R. , and Kabat B. , 2004: Upgrades to the UW-CIMSS AMSU-based tropical cyclone intensity estimation algorithm. 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 4D.1. [Available online at https://ams.confex.com/ams/26HURR/techprogram/paper_75933.htm.]

  • Hong, S., 2009a: Detection of Asian dust (Hwangsa) over the Yellow Sea by decomposition of unpolarized infrared reflectivity. Atmos. Environ., 43, 5887–5893, doi:10.1016/j.atmosenv.2009.08.024.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2009b: Retrieval of refractive index over specular surfaces for remote sensing applications. J. Appl. Remote Sens., 3, 033560, doi:10.1117/1.3265997.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2010a: Decomposition of unpolarized emissivity. Int. J. Remote Sens., 31, 2109–2114, doi:10.1080/01431160903329349.

  • Hong, S., 2010b: Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing. Remote Sens. Environ., 114, 1136–1140, doi:10.1016/j.rse.2009.12.015.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2010c: Global retrieval of small-scale roughness over land surfaces at microwave frequency. J. Hydrol., 389, 121–126, doi:10.1016/j.jhydrol.2010.05.036.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2010d: Surface roughness and polarization ratio in microwave remote sensing. Int. J. Remote Sens., 31, 2709–2716, doi:10.1080/01431161003627855.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2013: Polarization conversion for specular components of surface reflection. IEEE Geosci. Remote Sens. Lett., 10, 1469–1472, doi:10.1109/LGRS.2013.2260524.

    • Search Google Scholar
    • Export Citation

  • Hong, S., and Shin I. , 2010: Global trends of sea ice: Small-scale roughness and refractive index. J. Climate, 23, 4669–4676, doi:10.1175/2010JCLI3697.1.

    • Search Google Scholar
    • Export Citation

  • Hong, S., and Shin I. , 2011: A physically-based inversion algorithm for retrieving soil moisture in passive microwave remote sensing. J. Hydrol., 405, 24–30, doi:10.1016/j.jhydrol.2011.05.005.

    • Search Google Scholar
    • Export Citation

  • Hong, S., and Shin I. , 2013: Wind speed retrieval based on sea surface roughness measurements from spaceborne microwave radiometers. J. Appl. Meteor. Climatol., 52, 507–516, doi:10.1175/JAMC-D-11-0209.1.

    • Search Google Scholar
    • Export Citation

  • Hong, S., Shin I. , and Ou M. , 2010: Comparison of the Infrared Surface Emissivity Model (ISEM) with a physical emissivity model. J. Atmos. Oceanic Technol., 27, 345–352, doi:10.1175/2009JTECHA1311.1.

    • Search Google Scholar
    • Export Citation

  • Hong, S., Shin I. , Byun Y. , Seo H.-J. , and Kim Y. , 2014: Analysis of sea ice surface properties using ASH and Hong approximations in satellite remote sensing. Remote Sens. Lett., 5, 139–147, doi:10.1080/2150704X.2014.888106.

    • Search Google Scholar
    • Export Citation

  • Hong, S., Seo H.-J. , Kim N. , and Shin I. , 2015: Physical retrieval of tropical ocean surface wind speed under rain-free conditions using spaceborne microwave radiometers. Remote Sens. Lett., 6, 380–389, doi:10.1080/2150704X.2015.1037466.

    • Search Google Scholar
    • Export Citation

  • Hoshino, S., and Nakazawa T. , 2007: Estimation of tropical cyclone’s intensity using TRMM/TMI brightness temperature data. J. Meteor. Soc. Japan, 85, 437–454. http://www.jstage.jst.go.jp/article/jmsj/85/4/437/_pdf/-char/ja/, doi:10.2151/jmsj.85.437.

    • Search Google Scholar
    • Export Citation

  • JMA, 2013: Annual report on the activities of the RSMC Tokyo–Typhoon Center 2012. 92 pp. [Available online at http://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/AnnualReport/2012/Text/Text2012.pdf.]

  • JTWC, 1974: Annual tropical cyclone report: 1974. U.S. Fleet Weather Central, JTWC, 126 pp. [Available online at http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc/atcr/1974atcr.pdf.]

  • Kawanishi, T., and Coauthors, 2003: The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA’s contribution to the EOS for global energy and water cycle studies. IEEE Trans. Geosci. Remote Sens., 41, 184–194, doi:10.1109/TGRS.2002.808331.

    • Search Google Scholar
    • Export Citation

  • Knaff, J. A., Brown D. P. , Courtney J. , Gallina G. M. , and Beven J. L. II, 2010: An evaluation of Dvorak technique–based tropical cyclone intensity estimates. Wea. Forecasting, 25, 1362–1379, doi:10.1175/2010WAF2222375.1.

    • Search Google Scholar
    • Export Citation

  • Koba, H., Osano S. , Hagiwara T. , Akashi S. , and Kikuchi T. , 1991: Relationships between the CI number and central pressure and maximum wind speed in typhoons (in Japanese). Geophys. Mag., 44, 15–25.

    • Search Google Scholar
    • Export Citation

  • Kossin, J. P., and Velden C. S. , 2004: A pronounced bias in tropical cyclone minimum sea level pressure estimation based on the Dvorak technique. Mon. Wea. Rev., 132, 165–173, doi:10.1175/1520-0493(2004)132<0165:APBITC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kramer, H. J., 2014: GCOM (Global Change Observation Mission-Water). European Space Agency, accessed 31 October 2014. [Available online at https://directory.eoportal.org/web/eoportal/satellite-missions/g/gcom.]

  • Kruk, M. C., Knapp K. R. , and Levinson D. H. , 2010: A technique for combining global tropical cyclone best track data. J. Atmos. Oceanic Technol., 27, 680–692, doi:10.1175/2009JTECHA1267.1.

    • Search Google Scholar
    • Export Citation

  • Kummerow, C., and Ferraro R. , 2006: EOS/AMSR-E level-2 rainfall. Algorithm Theoretical Basis Doc., 10 pp. [Available online at http://nsidc.org/sites/nsidc.org/files/files/amsr_atbd_supp06_L2_rain.pdf.]

  • Liu, Q., and Weng F. , 2003: Retrieval of sea surface wind vectors from simulated satellite microwave polarimetric measurements. Radio Sci., 38, 8078, doi:10.1029/2002RS002729.

    • Search Google Scholar
    • Export Citation

  • Liu, Q., Weng F. , and English S. J. , 2011: An improved fast microwave water emissivity model. IEEE Trans. Geosci. Remote Sens., 49, 1238–1250, doi:10.1109/TGRS.2010.2064779.

    • Search Google Scholar
    • Export Citation

  • Lu, X., and Yu H. , 2013: An objective tropical cyclone intensity estimation model based on digital IR satellite images. Trop. Cyclone Res. Rev., 2, 233–241, doi:10.6057/2013TCRR04.05.

    • Search Google Scholar
    • Export Citation

  • Mayfield, M., McAdie C. J. , and Pike A. C. , 1988: Tropical cyclone studies: Part 2—A preliminary evaluation of the dispersion of tropical cyclone position and intensity estimates determined from satellite imagery. Federal Coordinator for Meteorological Services and Supporting Research FCM-R11-1988, 2-1–2-17.

  • Meissner, T., and Wentz F. , 2009: Wind vector retrievals under rain with passive satellite microwave radiometers. IEEE Trans. Geosci. Electron., 47, 3065–3083.

    • Search Google Scholar
    • Export Citation

  • NOAA ARL, 2014: Global Data Assimilation System (GDAS1) archive information. Accessed 3 February 2014. [Available online at http://ready.arl.noaa.gov/gdas1.php.]

  • Randa, J., and Coauthors, 2008: Recommended terminology for microwave radiometry. NIST Tech. Note 1551, 32 pp.

  • Saitoh, S., and Shibata A. , 2010: AMSR-E all weather sea surface wind speed (in Japanese). Tenki, 57 (1), 5–18. [Available online at http://www.metsoc.jp/tenki/pdf/2010/2010_01_0005.pdf.]

    • Search Google Scholar
    • Export Citation

  • Saunders, R., 2006: RTTOV-8—Science and validation report. Version 1.6, NWP SAF Doc. NWPSAF-MO-TV-007, Met Office Doc. R8REP2006, 46 pp.

  • Shibata, A., 2002: AMSR/AMSR-E sea surface wind speed algorithm. EORC Bull./Tech. Rep. 9, 45–46.

  • Shibata, A., 2003: A change of microwave radiation from the ocean surface induced by air-sea temperature difference. Radio Sci., 38, 8063, doi:10.1029/2002RS002670.

    • Search Google Scholar
    • Export Citation

  • Shibata, A., 2007: Effect of air-sea temperature difference on ocean microwave brightness temperature estimated from AMSR, SeaWinds, and buoys. J. Oceanogr., 63, 863–872, doi:10.1007/s10872-007-0073-y.

    • Search Google Scholar
    • Export Citation

  • Stogryn, A., 1967: The apparent temperature of the sea at microwave frequencies. IEEE Trans. Antennas Propag., 15, 278–286, doi:10.1109/TAP.1967.1138900.

    • Search Google Scholar
    • Export Citation

  • Tang, C., 1974: The effect of droplets in the air–sea transition zone on the sea brightness temperature. J. Phys. Oceanogr., 4, 579–593, doi:10.1175/1520-0485(1974)004<0579:TEODIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Tousey, R., 1939: On calculating the optical constants from reflection coefficients. J. Opt. Soc. Amer., 29, 235–238, doi:10.1364/JOSA.29.000235.

    • Search Google Scholar
    • Export Citation

  • Uhlhorn, E. W., and Black P. G. , 2003: Verification of remotely sensed sea surface winds in hurricanes. J. Atmos. Oceanic Technol., 20, 99–116, doi:10.1175/1520-0426(2003)020<0099:VORSSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Ulaby, F. T., Moore R. K. , and Fung A. E. , 1981: Microwave Remote Sensing Fundamentals and Radiometry. Vol. 1, Microwave Remote Sensing: Active and Passive, Artech House, 470 pp.

  • Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131, 2961–3012, doi:10.1256/qj.04.176.

  • Velden, C. S., and Coauthors, 2006: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years. Bull. Amer. Meteor. Soc., 87, 1195–1210, doi:10.1175/BAMS-87-9-1195.

    • Search Google Scholar
    • Export Citation

  • Velden, C. S., Burton A. , and Kuroiwa K. , 2012: The First International Workshop on Satellite Analysis of Tropical Cyclones: Summary of current operational methods to estimate intensity. Trop. Cyclone Res. Rev., 1, 469–481, doi:10.6057/2012TCRR04.05.

    • Search Google Scholar
    • Export Citation

  • Webster, P. J., Holland G. J. , Curry J. A. , and Chang H.-R. , 2005: Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309, 1844–1846, doi:10.1126/science.1116448.

    • Search Google Scholar
    • Export Citation

  • Wentz, F. J., 2002: AMSR ocean algorithm. EORC Bull./Tech. Rep. 9, 8–28.

  • Wentz, F. J., and Meissner T. , 2000: AMSR ocean algorithm. Version 2, Algorithm Theoretical Basis Doc., Remote Sensing Systems Tech. Proposal 121599A-1, 74 pp.

  • Wentz, F. J., Mattox L. A. , and Peteherych S. , 1986: New algorithms for microwave measurements of ocean winds: Applications to Seasat and the special sensor microwave imager. J. Geophys. Res., 91, 2289–2307, doi:10.1029/JC091iC02p02289.

    • Search Google Scholar
    • Export Citation

  • Wilheit, T. T., 1979: A model for the microwave emissivity of the ocean’s surface as a function of wind speed. IEEE Trans. Geosci. Electron., 17, 244–249, doi:10.1109/TGE.1979.294653.

    • Search Google Scholar
    • Export Citation

  • Wu, Y., and Weng F. , 2011: Detection and correction of AMSR-E radio-frequency interference (RFI). Acta Meteor. Sin., 25, 669–681, doi:10.1007/s13351-011-0510-0.

    • Search Google Scholar
    • Export Citation

  • Yan, B., and Weng F. , 2008: Applications of AMSR-E measurements for tropical cyclone predictions Part I: Retrieval of sea surface temperature and wind speed. Adv. Atmos. Sci., 25, 227–245, doi:10.1007/s00376-008-0227-x.

    • Search Google Scholar
    • Export Citation
  • <sc>Fig</sc>. 1.
    View in gallery
    Fig. 1.

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

  • <sc>Fig</sc>. 2.
    View in gallery
    Fig. 2.

    The relationship for AMSR-2 6.925 to 89.0 GHz. The gray area around the colored lines indicates the retrieved using Hong’s roughness estimation [Eq. (9)] at given and .

  • <sc>Fig</sc>. 3.
    View in gallery
    Fig. 3.

    (a) Dvorak technique and (b) Hong technique used in estimations (including , , and CI number).

  • <sc>Fig</sc>. 4.
    View in gallery
    Fig. 4.

    (a) Relationship between and under rain-free conditions. (b) Relationship between and 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.

  • <sc>Fig</sc>. 5.
    View in gallery
    Fig. 5.

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

  • <sc>Fig</sc>. 6.
    View in gallery
    Fig. 6.

    (a) The term at AMSR-2 6.925-GHz V polarization with rainfall effects and surface wind effects, (b) Hong retrieved using the Hong algorithm at AMSR-2 6.925 GHz, (c) ECMWF , and (d) Hong minus ECMWF . This is representative of the case of Typhoon Bolaven, which occurred on 26 Aug 2012.

  • <sc>Fig</sc>. 7.
    View in gallery
    Fig. 7.

    Hong techniques at AMSR-2 6.925 GHz vs Dvorak technique for (a) and (b) 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 using the Hong technique for Typhoon Kong-Rey, which occurred at 0300 UTC 27 Aug 2013.

Fig. 1.

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

The relationship for AMSR-2 6.925 to 89.0 GHz. The gray area around the colored lines indicates the retrieved using Hong’s roughness estimation [Eq. (9)] at given and .
Fig. 3.

(a) Dvorak technique and (b) Hong technique used in estimations (including , , and CI number).
Fig. 4.

(a) Relationship between and under rain-free conditions. (b) Relationship between and 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.
Fig. 5.

The relationships for AMSR-2 (a) 6.925 and (b) 10.65 GHz; scatterplots of TAO against retrieved for AMSR-2 (c) 6.925- and (d) 10.65-GHz data.
Fig. 6.

(a) The term at AMSR-2 6.925-GHz V polarization with rainfall effects and surface wind effects, (b) Hong retrieved using the Hong algorithm at AMSR-2 6.925 GHz, (c) ECMWF , and (d) Hong minus ECMWF . This is representative of the case of Typhoon Bolaven, which occurred on 26 Aug 2012.
Fig. 7.

Hong techniques at AMSR-2 6.925 GHz vs Dvorak technique for (a) and (b) 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 using the Hong technique for Typhoon Kong-Rey, which occurred at 0300 UTC 27 Aug 2013.
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