Evaluation of brightness temperature from a forward model of ground-based microwave radiometer

Abstract

Ground-based microwave radiometers are getting great attention in recent years due to their capability to profile the temperature and humidity at high temporal and vertical resolution in the lower troposphere. The process of retrieving these parameters from the measurements of radiometric brightness temperature (T B) includes the inversion algorithm, which uses the background information from a forward model. In the present study, an algorithm development and evaluation of this forward model for a ground-based microwave radiometer, being developed by Society for Applied Microwave Electronics Engineering and Research (SAMEER) of India, is presented. Initially, the analysis of absorption coefficient and weighting function at different frequencies was made to select the channels. Further the range of variation of T B for these selected channels for the year 2011, over the two stations Mumbai and Delhi is discussed. Finally the comparison between forward-model simulated T B s and radiometer measured T B s at Mahabaleshwar (73.66 • E and 17.93 • N) is done to evaluate the model. There is good agreement between model simulations and radiometer observations, which suggests that these forward model simulations can be used as background for inversion models for retrieving the temperature and humidity profiles.

Seasonal and Diurnal Changes of Air Temperature and Water Vapor Observed with a Microwave Radiometer in Wuhan, China

“…In this study, the backward propagation neural network algorithm was trained by using a 5-year historical RS data set from the same location in the period of 2015-2019. The MWR measures the intensity and shape of the emission spectrum from pressure-broadened water vapor lines near 22 GHz sampled by 21 channels in the K-band (22)(23)(24)(25)(26)(27)(28)(29)(30). The intensity of the emission spectrum is proportional to the water vapor density (VD) and temperature; thus, the water vapor profile can be obtained by scanning the spectral profile and then mathematically inverting the observed data.…”

“…The MWR measures the brightness temperature at multiple microwave channels to retrieve the profiles of the air temperature, water vapor, and liquid water. The retrieval techniques generally include the linear and nonlinear regression algorithms and artificial neural networks [21][22][23]. The artificial neural network algorithm is extensively applied to derive temperature and humidity measurements due to its high accuracy [24,25].…”

Study on the north-east monsoon onset features using a ground-based microwave radiometer over SHAR

“…In addition, with the advantage of monitoring stability and turbulence, the MWRs have been used together with lidar to measure wind and temperature for wind energy applications (Friedrich et al 2012). Rambabu et al (2014) studied forward model-based brightness temperature variations over different regions in India using radiosonde and MWR measurements. G€ uldner and Sp€ ankuch (1999) showed increases in the parameter such as the liquid water content and precipitable water (PW) vapour 2 h before the rain.…”