Idioma:

0-10 KM TEMPERATURE AND HUMIDITY PROFILES RETRIEVAL FROM GROUND-BASED MICROWAVE RADIOMETER

Bao, Yan-song ; Cai, Xi ; Qian, Cheng ; Min, Jin-zhong ; Lu, Qi-feng ; Zuo, Quan

Journal of Tropical Meteorology, 2018-06, Vol.24 (2), p.243-252 [Periódico revisado por pares]

Guangzhou: Guangzhou Institute of Tropical & Marine Meteorology

Sem texto completo

Citações Citado por

Enviar para

Título:
0-10 KM TEMPERATURE AND HUMIDITY PROFILES RETRIEVAL FROM GROUND-BASED MICROWAVE RADIOMETER
Autor: Bao, Yan-song ; Cai, Xi ; Qian, Cheng ; Min, Jin-zhong ; Lu, Qi-feng ; Zuo, Quan
Assuntos: Accuracy ; Artificial neural networks ; Atmospheric models ; Atmospheric temperature ; Back propagation ; Back propagation networks ; Bias ; Channels ; Computer simulation ; Data acquisition ; Density ; Downwelling ; Errors ; Humidity ; Humidity profiles ; Microwave radiometers ; Neural networks ; Profiles ; Propagation ; Radiance ; Radiative transfer ; Radiometers ; Regression analysis ; Regression models ; Relative humidity ; Remote sensing ; Retrieval ; Sounding ; Soundings ; Temperature ; Temperature and humidity profiles ; Temperature data ; Temperature effects ; Vapor density ; Water vapor ; Water vapour
É parte de: Journal of Tropical Meteorology, 2018-06, Vol.24 (2), p.243-252
Descrição: 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 were 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%.
Editor: Guangzhou: Guangzhou Institute of Tropical & Marine Meteorology
Idioma: Inglês

Voltar para lista de resultados

Realização: Logos de Redes Sociais:

0-10 KM TEMPERATURE AND HUMIDITY PROFILES RETRIEVAL FROM GROUND-BASED MICROWAVE RADIOMETER

Bao, Yan-song ; Cai, Xi ; Qian, Cheng ; Min, Jin-zhong ; Lu, Qi-feng ; Zuo, Quan

Guangzhou: Guangzhou Institute of Tropical & Marine Meteorology

Buscando em bases de dados remotas. Favor aguardar.