Home   About the Journal   中文界面
  Revised:January 30, 2018
KeyWords:ground-based microwave radiometer  BP neural network  atmospheric profiles  regression accuracy
Author NameAffiliationE-mail
BAO Yan-song 1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Key Laboratory of the China Meteorological Administration Aerosol and Cloud Precipitation, Nanjing University of Information Science and Technology, Nanjing 210044 China
2. School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044 China 
CAI Xi   
QIAN Cheng   
MIN Jin-zhong   
LU Qi-feng   
ZUO Quan   
Hits: 143
Download times: 80
      Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural network models. This paper evaluated a retrieving atmospheric temperature and humidity profiles method by adopting an input data adjustment-based Back Propagation artificial neural networks model. First, the sounding data acquired at a Nanjing meteorological site in June 2014 are inputted into the MonoRTM Radiative transfer model to simulate atmospheric downwelling radiance at the 22 spectral channels from 22.234GHz to 58.8GHz, and we performed a comparison and analysis of the real observed data; an adjustment model for the measured microwave radiometer sounding data was built. Second, we simulated the sounding data of the 22 channels using the sounding data acquired at the site from 2011 to 2013. Based on the simulated rightness temperature data and the sounding data, BP neural network-based models were trained for the retrieval of atmospheric temperature, water vapor density and relative humidity profiles. Finally, we applied the adjustment model to the microwave radiometer sounding data collected in July 2014, generating the corrected data. After that, we inputted the corrected data into the BP neural network regression model to predict the atmospheric temperature, vapor density and relative humidity profile at 58 high levels from 0 to 10 km. We evaluated our model’s effect by comparing its output with the real measured data and the microwave radiometer’s own second-level product. The experiments showed that the inversion model improves atmospheric temperature and humidity profile retrieval accuracy; the atmospheric temperature RMS error is between 1K and 2.0K; the water vapor density’s RMS error is between 0.2 g/m3 and 1.93g/m3; and the relative humidity’s RMS error is between 2.5% and 18.6%.
View Full Text  View/Add Comment  Download reader
      Copyright:Journal of Tropical Meteorology Editorial Office
Address:6 Fu Jin Road Guangzhou   Postcode:510080   Tel:020-87675987   Fax:020-87675987
Technical support: Beijing E-Tiller Co.,Ltd.