Polarization signature from the FengYun-3 Microwave Humidity Sounder

Zou, Xiaolei; Xu, Chengpei; Weng, Fuzhong

doi:10.1007/s11707-014-0479-y

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…These channels can give information near the Earth's surface and lower atmosphere, and can also be used to measure atmospheric cloud parameters. For the 150 GHz channels, Zou et al (2014) investigated the polarization information and concluded that the polarization signal is related to the scanning angle and also to information such as surface wind speed, wind direction and salinity, especially in the clear-sky condition. The three channels around the 183.31GHz absorption line aim to get profiles of atmospheric humidity.…”

Section: Fy-3b/mwhsmentioning

confidence: 99%

Retrieval of ice water path from the FY-3B MWHS polarimetric measurements based on deep neural network

Wang

et al. 2022

Preprint

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Abstract. Ice water path (IWP) is an important cloud parameter in atmospheric radiation, and there are still great difficulties in retrieval. The artificial neural network is a popular method in atmospheric remote sensing in recent years. This study presents a global IWP retrieval based on deep neural networks using the measurements from Microwave Humidity Sounder (MWHS) onboard the FengYun-3B (FY-3B) satellite. Since FY-3B/MWHS has quasi-polarization channels at 150 GHz, the effect of polarimetric radiance difference (PD) is also investigated. A retrieval database is established using collocations between MWHS and CloudSat 2C-ICE. Then two types of networks are trained for cloud scene filtering and IWP retrieval, respectively. For the cloud filtering network, using IWP of 10 g/m2 and 100 g/m2 as the threshold show the filtering accuracy of 86.48 % and 94.22 % respectively. For the IWP retrieval network, different training input combinations of auxiliary information and channels are compared. The results show that the MWHS IWP retrieval performs well at IWP > 100 g/m2. The mean and median relative errors are 72.02 % and 46.29 % compared to the 2C-ICE IWP. PD shows an important impact when IWP is larger than 1000 g/m2. At last, two tropical cyclone cases are chosen to test the performance of the networks, the results show a good agreement with the characteristics of the brightness temperature observed by the satellite. The monthly MWHS IWP shows a good consistency compared to the ERA5 and 2C-ICE while it is lower than MODIS IWP.

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Section: Fy-3b/mwhsmentioning

confidence: 99%

Retrieval of ice water path from the FY-3B MWHS polarimetric measurements based on deep neural network

Wang

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…These channels can provide information near the Earth's surface and lower atmosphere and can also be used to measure atmospheric cloud parameters. For the 150 GHz channels, Zou et al (2014) investigated the polarization information and concluded that the polarization signal is related to the scan angle and information such as surface wind speed and wind direction and can also be strongly scattered by non-spherical ice cloud particles. Under all weather conditions, except heavy precipitation, all five channels of MWHS can observe water vapour and ice clouds in the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of ice water path from the Microwave Humidity Sounder (MWHS) aboard FengYun-3B (FY-3B) satellite polarimetric measurements based on a deep neural network

Wang

et al. 2022

Atmos. Meas. Tech.

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Abstract. The ice water path (IWP) is an important cloud parameter in atmospheric radiation, and there are still great difficulties in its retrieval. Artificial neural networks have become a popular method in atmospheric remote sensing in recent years. This study presents a global IWP retrieval based on deep neural networks using the measurements from the Microwave Humidity Sounder (MWHS) aboard the FengYun-3B (FY-3B) satellite. Since FY-3B/MWHS has quasi-polarization channels at 150 GHz, the effect of the polarimetric radiance difference (PD) was also studied. A retrieval database was established using collocations between MWHS and CloudSat 2C-ICE (CloudSat and CALIPSO Ice Cloud Property Product). Then, two types of networks were trained for cloud scene filtering and IWP retrieval. For the cloud filtering network, the microwave channels show a capacity with a false alarm ratio (FAR) of 0.31 and a probability of detection (POD) of 0.61. For the IWP retrieval network, different combination inputs of auxiliaries and channels were compared. The results show that the five MWHS channels combined with scan angle, latitude, and the ocean/land mask of inputs of auxiliary variables perform best. Applying the cloud filtering network and IWP retrieval network, the final root mean squared error (RMSE) is 916.76 g m−2, the mean absolute percentage error (MAPE) is 92 %, and the correlation coefficient (CC) is 0.65. Then, a tropical cyclone case measured simultaneously by MWHS and CloudSat was chosen to test the performance of the networks, and the result shows a good correlation (0.73) with 2C-ICE. Finally, the global annual mean IWP of MWHS is very close to that of 2C-ICE, and the 150 GHz channels give a significant improvement in the midlatitudes compared to using only 183 GHz channels.

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2020

Atmospheric Satellite Observations

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Polarization signature from the FengYun-3 Microwave Humidity Sounder

Cited by 6 publications

References 27 publications

Retrieval of ice water path from the FY-3B MWHS polarimetric measurements based on deep neural network

Retrieval of ice water path from the FY-3B MWHS polarimetric measurements based on deep neural network

Retrieval of ice water path from the Microwave Humidity Sounder (MWHS) aboard FengYun-3B (FY-3B) satellite polarimetric measurements based on a deep neural network

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