Inchul Shin

Comparison of the Infrared Surface Emissivity Model (ISEM) with a Physical Emissivity Model

Abstract Accurate models of surface emissivity are important for interpreting satellite radiance observations. Surface emissivity depends on the satellite zenith angle, roughness, polarization, and refractive index of the surface. The effects of the roughness parameter on emissivity have not been studied thoroughly. By using an infrared geostationary satellite observation and a physical model based on the radiative transfer equation, the forward emissivity model and physical emissivity model are validated in the northwestern Pacific Ocean. First, the unpolarized emissivity at a given view angle and a wavelength of 10.8 μm in the thermal infrared spectral region is decomposed for the clear sky. The refractive index of the sea surface is quantitatively retrieved using the inversion of Fresnel equations. The refractive index of the sea surface derived from the physical emissivity exhibits a reasonable range within the infrared wavelength. The result of this investigation can be applied to the land emissivity...

Global Trends of Sea Ice: Small-Scale Roughness and Refractive Index

Abstract Sea ice is one of the most important parameters in the global climate system, specifically the exchange of energy and momentum between the ocean and the atmosphere. In previous studies, a steady decline in Arctic sea ice has been observed over recent decades. The aim of this study is to estimate and analyze the spatial and temporal characteristics of the averaged sea ice extent, surface roughness, and refractive index from March 2003 to September 2009. A unique inversion algorithm is used for deriving the surface roughness and refractive index from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) daily observations. Surface roughness significantly affects the microwave emission of the sea ice/snow surface. The sea ice, snow, and water show the dielectric contrast in the microwave frequencies. Consequently, the averaged roughness as well as the sea ice extent shows a downward trend, while the averaged refractive index shows the opposite signature. The increased trend of t...

Wind Speed Retrieval Based on Sea Surface Roughness Measurements from Spaceborne Microwave Radiometers

AbstractWind speed is the main factor responsible for the increase in ocean thermal emission because sea surface emissivity strongly depends on surface roughness. An alternative approach to estimate the surface wind speed (SWS) as a function of surface roughness is developed in this study. For the sea surface emissivity, the state-of-the-art forward Fast Microwave Emissivity Model, version 3 (FASTEM-3), which is applicable for a wide range of microwave frequencies at incidence angles of less than 60°, is used. Special Sensor Microwave Imager and Advanced Microwave Scanning Radiometer (AMSR-E) observations are simulated using FASTEM-3 and the Global Data Assimilation and Prediction System operated by the Korea Meteorological Administration. The performance of the SWS retrieval algorithm is assessed by comparing its SWS output to that of the Global Data Assimilation System operated by the National Centers for Environmental Prediction. The surface roughness is computed using the Hong approximation and charac...

Physical retrieval of tropical ocean surface wind speed under rain-free conditions using spaceborne microwave radiometers

This research proposes a physical algorithm for retrieving sea surface wind speed using a combination of passive microwave satellite observations and simulations, namely Advanced Microwave Scanning Radiometer (AMSR-E) observations for vertical polarization, and a simulation based on the fast Radiative Transfer for TOVS with Global Data Assimilation System data for horizontal polarizations. A regression relationship between sea surface roughness and sea surface wind speed was derived using the Hong approximation and polarization ratio near Brewster’s angle. The sea surface wind speed algorithm was assessed using the AMSR-E data. Comparing sea surface wind speed from the proposed algorithm using AMSR-E 6.925, 10.65, and 18.7 GHz data with Tropical Atmospheric Ocean (TAO) data from 1 January 2010 to 31 December 2010 shows that the estimated bias and root mean square error (RMSE) were −0.126 and 1.191 ms−1, −0.094 and 1.152 ms−1, and −0.085 and 1.338 ms−1, respectively. The current official AMSR-E sea surface wind speed shows the bias and RMSE were −0.614 and 1.330 ms−1, respectively, when compared with TAO buoy observations, which was similar to those of the proposed algorithm using AMSR-E 18.7 GHz. This study provides a useful sea surface wind speed retrieval algorithm with high accuracy on the sea surface, which can be applied to a variety of spaceborne passive microwave radiometers.

Analysis of sea ice surface properties using ASH and Hong approximations in satellite remote sensing

The Hong approximation, an approximation between the vertically and horizontally polarized reflectivities of specular surfaces has been useful for estimating the changes and characteristics produced in sea ice surfaces by climate change. Recently, the direct relationship (Azzam relationship) and the analytical (Azzam–Sohn–Hong (ASH)) approximation were derived and validated between two polarized reflectivities. In this study, the small-scale roughness and refractive index over sea ice are estimated using the ASH and Hong approximations with observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). The ASH approximation is useful to monitor the properties of ice or snow, because those materials have a small value of the imaginary part of the refractive index ( ). Meanwhile, the Hong approximation is effective for estimating the melting of sea ice, because the melting of ice or snow indicates the increase in the imaginary part of the refractive index. Consequently, this study suggests that combining ASH and Hong approximations provides an effective procedure for analysing and understanding the melting sea ice surfaces in terms of the small-scale roughness and refractive index.

Improved estimation of insolation by using calibrated COMS MI images over South Korea

In this study, a correction function based on a comprehensive vicarious calibration was applied to Communication, Ocean and Meteorological Satellite (COMS) and Meteorological Imager (MI) visible channel for improving the estimate of insolation. The COMS MI visible channel was calibrated by using natural targets such as ocean, desert, water cloud and deep convective cloud, which are carefully selected due to their theoretical reference reflectance. Based on calibration with four targets, the corrected COMS MI visible channel is used as an input parameter of an insolation physical model, especially for interpreting cloud effects. Results showed that corrected insolation has better accuracy than previous methods for cloudy conditions. Seasonal bias of insolation, which generally has the worst accuracy in a cloudy summer period, is improved by using calibrated COMS MI visible data.

Sea ice remote sensing using AMSR-E data: surface roughness and refractive index

Sea ice is a good indicator to monitor the global climate change. Many of previous studies using the satellite observations show a steady decline in Arctic sea ice. The study investigates the characteristics of the averaged surface roughness, and refractive index from March 2003 to July 2011 using the AMSR-E daily data. The surface roughness and refractive index of the sea ice is retrieved using a unique inversion algorithm based on the characteristics of the polarized reflectivities, the Hong approximation, and the incidence angles of the many current passive microwave satellite sensors. The averaged roughness and refractive index show the downward trend and opposite signature with an acceleration, respectively. From the seasonal variations, the averaged roughness and refractive index show the minimum and maximum values in the summer period, respectively. In addition, the annual peaks of two physical parameters exhibit the phase difference of a month. In conclusion, this research provides a physical explanation that the sea ice is melting increasingly using the satellite observation.

Detection of cirrus based on refractive index using MODIS data

Cirrus is important in the energy balance of the earth-atmosphere system. Previous studies are based on the forward models using the single scattering in the cirrus. In this study, we provide a unique method to detect the cirrus using the polarized reflectivities, single-scattering, and optical properties of water and ice with Moderate Resolution Imaging Spectrometer (MODIS) observations. Consequently, the polarized reflectivities at MODIS 1.375 μm channel show the reasonable characteristics of the cirrus. The effective refractive indexes are retrieved approximately from 1.1 to 1.4. This investigation provides an effective inversion procedure for detecting the cirrus using the physical characteristics in the cirrus on the basis of the polarization and refractive index.