A change of microwave radiation from the ocean surface induced by air‐sea temperature difference

Shibata, Akira

doi:10.1029/2002rs002670

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…The KS model used by SMOS results in smaller biases at many temperatures but lead to very large and temperature dependent biases in cold waters. Our results with the KS model are consistent with assessments of microwave radiometers at higher frequencies which found that corrections to TB at the cold end (0 • C) needed to be larger than at the warm end (30 • C) by 0.5 K and 3.7 K at 6 GHz [55] and 37 GHz [56] respectively. It is therefore critical to improve the current dielectric constant models.…”

Section: Discussionsupporting

confidence: 88%

“…In waters colder than 5 • C, the KS model exhibits very problematic performances. Considering the large errors it produces (>1 psu), and the fast change in the bias with SST (1 psu over a 5 • C range), it appears that the accuracy of the KS model is questionable below 5 • C. This result is consistent with the assessment of microwave radiometers at higher frequencies which found an increase in the KS model bias in cold waters [55,56]. It is doubtful that all the SST-dependent error can be attributed to the dielectric constant model, as other factors can commingle their error and its dependence on SST (e.g., roughness model).…”

Section: Discussionsupporting

confidence: 73%

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Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

et al. 2019

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Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency’s (ESA’s) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration’s (NASA’s) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from the Argo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS. We also identify the impact of the corrections for atmospheric effects recently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionsupporting

confidence: 73%

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

et al. 2019

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“…Unstable atmospheric conditions enhance the generation and the thickness and coverage of foam and whitecaps. Although the phenomenon of increases in observed radiances under unstable conditions has been studied (Monahan and O'Muircheartaigh, 1986;Reul and Chapron, 2003;Shibata, 2003Shibata, , 2007Wei, 2013), no emissivity model is yet available that includes the effect of atmospheric stability on foam coverage.…”

Section: Biases In Cold Sectorsmentioning

confidence: 99%

Effects of all‐sky assimilation of GCOM‐W/AMSR2 radiances in the ECMWF numerical weather prediction system

Kazumori

Geer

English

2015

Quart J Royal Meteoro Soc

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This article assesses the impact of Advanced Microwave Scanning Radiometer 2 (AMSR2) radiances in the all‐sky assimilation of the European Centre for Medium‐Range Weather Forecasts (ECMWF). Individual impacts of three microwave imagers including AMSR2 were examined by using a baseline experiment that had no microwave imager data. The three microwave imagers brought similar improvements in humidity, temperature and wind first‐guess (FG) fields in the troposphere. Improvements were found in fits to both analyses and other observations. Moreover, significant improvements in wind and geopotential height fields in the troposphere were found in day 3–6 forecasts. AMSR2 made larger improvements than other microwave imagers to geopotential height forecasts in the Northern Hemisphere. The use of AMSR2 radiance data in addition to the other existing microwave imagers gave mixed results. Consistent improvements in the FG fit to humidity observations were confirmed. However, the FG fit for several channels of the microwave temperature sounding instruments was degraded. The mean FG departure of AMSR2 showed biases that varied according to the time of day and meteorological conditions. The causes of the biases were identified as insufficient representation of the cloud liquid water path (LWP) in the forecast model under unstable conditions and insufficient amplitude of LWP diurnal variation in stratocumulus areas in the Tropics. The assimilation of too much biased data might start to bring negative effects for the analyses and forecasts, which for some parameters could outweigh the improvements in the assimilation. However, despite this, AMSR2 brought significant improvements of the geopotential height field in the Southern Hemisphere lower troposphere for day 5–7 forecasts.

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“…Previous studies have shown that the sensitivity to the ocean wind at horizontal polarization is larger than that at vertical polarization [7,40]. The study of [15] only used 6.9 and 10.7 GHz horizontal polarization (hereafter, 6H and 10H) data from AMSR-E to retrieve wind speed inside hurricanes.…”

Section: Wind Speed Retrieval Modelmentioning

confidence: 99%

Hurricane Wind Speed Estimation Using WindSat 6 and 10 GHz Brightness Temperatures

et al. 2016

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Abstract:The realistic and accurate estimation of hurricane intensity is highly desired in many scientific and operational applications. With the advance of passive microwave polarimetry, an alternative opportunity for retrieving wind speed in hurricanes has become available. A wind speed retrieval algorithm for wind speeds above 20 m/s in hurricanes has been developed by using the 6.8 and 10.7 GHz vertically and horizontally polarized brightness temperatures of WindSat. The WindSat measurements for 15 category 4 and category 5 hurricanes from 2003 to 2010 and the corresponding H*wind analysis data are used to develop and validate the retrieval model. In addition, the retrieved wind speeds are also compared to the Remote Sensing Systems (RSS) global all-weather product and stepped-frequency microwave radiometer (SFMR) measurements. The statistical results show that the mean bias and the overall root-mean-square (RMS) difference of the retrieved wind speeds with respect to the H*wind analysis data are 0.04 and 2.75 m/s, respectively, which provides an encouraging result for retrieving hurricane wind speeds over the ocean surface. The retrieved wind speeds show good agreement with the SFMR measurements. Two case studies demonstrate that the mean bias and RMS difference are 0.79 m/s and 1.79 m/s for hurricane Rita-1 and 0.63 m/s and 2.38 m/s for hurricane Rita-2, respectively. In general, the wind speed retrieval accuracy of the new model in hurricanes ranges from 2.0 m/s in light rain to 3.9 m/s in heavy rain.

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A change of microwave radiation from the ocean surface induced by air‐sea temperature difference

Cited by 11 publications

References 17 publications

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

Effects of all‐sky assimilation of GCOM‐W/AMSR2 radiances in the ECMWF numerical weather prediction system

Hurricane Wind Speed Estimation Using WindSat 6 and 10 GHz Brightness Temperatures

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