Retrieval of sea surface temperature from the scanning microwave radiometer aboard HY-2B

Liu, Shubo; Xin-dong, Cui; Li, Yinan; Xu, Jisheng; Zhou, Wu; Dang, Hongxing; Li, Hao

doi:10.1080/01431161.2021.1899330

Cited by 8 publications

(2 citation statements)

References 48 publications

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“…As shown in Figure 1, over seventy percent of the global coverage could be achieved every 6 h by the HY-2B SMR jointed with MWRIs onboard the FY-3C/D satellites. Furthermore, some efforts have been made on radiometric calibration [13], the initial evaluation of early in-orbit performance [14,15], and the assessments of retrieval products [16,17], demonstrating that the high-quality SMR radiance data are made available for weather monitoring. The assimilation of the SMR radiance data has the potential capability to provide a better analysis and forecast of atmospheric variables.…”

Section: Introductionmentioning

confidence: 99%

Biases’ Characteristics Assessment of the HY-2B Scanning Microwave Radiometer (SMR)’s Observations

Han

et al. 2023

Remote Sensing

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The second Chinese ocean dynamic environment satellite Haiyang-2B (HY-2B), carrying a scanning microwave radiometer (SMR) to provide information on the ocean and atmosphere, was successfully launched on 25 October 2018. Before the data assimilation, it is necessary to characterize and evaluate the biases of the HY-2B SMR observations. This study is the first to conduct a systematic assessment of the SMR radiance data based on observation minus background simulation (O-B). Three types of numerical weather prediction (NWP) datasets, including ECMWF Reanalysis v5 (ERA5), the analysis fields from the NCEP Global Forecast System (NCEP-GFS), and the analysis fields from the Global Regional Assimilation and Prediction System-Global Forecast System (GRAPES-GFS), were used as input information for RTTOV v12.3 to simulate the SMR’s observed brightness temperature (TB) under clear-sky conditions. Study results showed that the O-B biases and IQR of the SMR for most channels were within −2.5–0.4 K and smaller than 4 K, respectively. The SMR observations were generally consistent with the RTTOV simulations, even based on the different NWP fields. These results indicate a good prospect for the assimilated application of HY-2B SMR radiance data. However, due to the impact of RFI, the SMR’s descending data for two 10.7 GHz channels showed some significant positive biases larger than 50 K over the seas of the European region. In addition, it seems that the bias characteristics of the SMR’s ascending data were obviously different from those of the descending data. It was also found that the variation trend of scan-position-dependent bias was generally stable for the SMR’s ascending data but fluctuates significantly for the descending data, with a maximum amplitude greater than 0.7 K for some channels.

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Section: Introductionmentioning

confidence: 99%

Biases’ Characteristics Assessment of the HY-2B Scanning Microwave Radiometer (SMR)’s Observations

Han

et al. 2023

Remote Sensing

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“…Under rain-free conditions, the measured backscattering signal strongly depends on surface wind speed, wind direction, azimuth angle, and incidence angle [5]. On the other hand, the microwave radiometer is a passive sensor measuring surface winds by observing the surface emission in terms of brightness temperature (TB), which is related to surface roughness, and thus to the wind speed [6,7]. Validations with buoy data and numerical weather prediction products have suggested that the rain-free retrieval algorithm can obtain the sea surface wind vector with an accuracy of about 1 m/s for passive radiometers [8], and less than 1.5 m/s and 16 • for scatterometers [9].…”

Section: Introductionmentioning

confidence: 99%

Sea Surface Wind Retrieval under Rainy Conditions from Active and Passive Microwave Measurements

Liu

Yang

et al. 2022

Remote Sensing

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The space-borne microwave radiometers and scatterometers can effectively measure global sea surface winds under non-precipitation. However, the measurements in rainy conditions significantly degrade, which are usually flagged as poor quality or invalidated for some scientific purposes. This paper develops a combined active–passive wind vector retrieval model for rainy conditions based on the HY-2B radiometer and scatterometer measurements. In our model, the polarization ratio of brightness temperatures at 6.925 GHz (PR06) is used as an indicator to implicitly represent the rain effect. For wind speed retrieval, a statistical regression model is trained as a function of PR06 and brightness temperatures of the radiometer. Moreover, two new geophysical model functions, including rain effect, are developed for wind direction inversion. Comparisons between HY-2B retrieval results and ERA5 wind products indicate that the retrieval model performs well under all rainy conditions. The overall root mean squared errors (RMSEs) of wind speed and direction retrievals are 1.60 m/s and 20.60°, respectively. With an increase in the rain rate, the wind retrieval performance degrades slightly and still provides a reliable retrieval result.

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Stability of the HY-2B Scanning Microwave Radiometer (SMR) Brightness Temperature Using a Modified Vicarious Cold Reference

Wang

Zhou

Yin

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Retrieval of sea surface temperature from the scanning microwave radiometer aboard HY-2B

Cited by 8 publications

References 48 publications

Biases’ Characteristics Assessment of the HY-2B Scanning Microwave Radiometer (SMR)’s Observations

Biases’ Characteristics Assessment of the HY-2B Scanning Microwave Radiometer (SMR)’s Observations

Sea Surface Wind Retrieval under Rainy Conditions from Active and Passive Microwave Measurements

Stability of the HY-2B Scanning Microwave Radiometer (SMR) Brightness Temperature Using a Modified Vicarious Cold Reference

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