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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2016-286
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-2016-286
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Submitted as: research article 22 Sep 2016

Submitted as: research article | 22 Sep 2016

Status
This preprint has been retracted.

Research on Retrieval of Atmospheric Temperature and Humidity Profiles from combined Ground-based Microwave Radiometer and Cloud Radar Observations

Yunfei Che1, Shuqing Ma2, Fenghua Xing3, Siteng Li4, and Yaru Dai5 Yunfei Che et al.
  • 1Key Laboratory for Cloud Physic, Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • 2Meteorological Observation Centre of China Meteorological Administration, Beijing 100081, China
  • 3Hainan Institute of Meteorological Sciences, Haikou, Hainan 570203, China
  • 4Beijing Municipal Meteorological Observation Center, Beijing 100089, China
  • 5National Space Science Center, Chinese Academy of Sciences, Beijing 100029, China

Abstract. This paper focuses on the retrieval of temperature and relative humidity profiles through combining ground-based microwave radiometer observations with those of millimeter-wavelength cloud radar. The cloud-base height and cloud thickness from the cloud radar were added into the atmospheric profile retrieval process, and a back propagation neural network method was used as the retrieval tool.

Because substantial data are required to train a neural network, and microwave radiometer data are insufficient for this purpose, eight years of radiosonde data from Beijing were used as a database. The model MonoRTM was used to calculate the brightness temperature of the same channel as the microwave radiometer. Part of the cloud-base height and cloud thickness in the training dataset was also estimated using the radiosonde data.

The accuracy of the results was analyzed by comparing with L-band sounding radar data, and quantified using the mean bias, root-mean-square error and correlation coefficient. The statistical results showed that inversion with cloud information was the optimal method. Compared with the inversion profiles without cloud information, the RMSE values after adding the cloud information were to a varying degree reduced for the vast majority of height layers. These reductions were particularly clear in layers with cloud present. The maximum reduction of RMSE for temperature was 2.2 K, and for the humidity profile was 16 %.

This preprint has been retracted.
Yunfei Che et al.
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Yunfei Che et al.
Yunfei Che et al.
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Short summary
The way of measuring atmospheric profiles by the microwave radiometer is relatively mature. However, it is existing great uncertainty in cloudy condition, especially humidity profiles. This research achieved a significant improvement on the retrieval atmospheric humidity profiles in the cloud weather through combined microwave radiometer and millimeter-wavelength cloud radar.
The way of measuring atmospheric profiles by the microwave radiometer is relatively mature....
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