Characteristics Analysis of the Multi-Channel Ground-Based Microwave Radiometer Observations during Various Weather Conditions

Abstract

Ground-based multi-channel microwave radiometers (MWRs) can continuously detect atmospheric profiles in the tropospheric atmosphere. This makes MWR an ideal tool to supplement radiosonde and satellite observations in monitoring the thermodynamic evolution of the atmosphere and improving numerical weather prediction (NWP) through data assimilation. The analysis of product characteristics of MWR is the basis for applying its data to real-time monitoring and assimilation. In this paper, observations from the latest generation of ground-based multi-channel MWR RPG-HATPRO-G5 installed in Shanghai, China, are compared with the radiosonde observations (RAOB) observed in the same location. The detection performance, characteristics of various channels, and the accuracy of the retrieval profile products of the MWR RPG are comprehensively evaluated during various weather conditions. The results show that the brightness temperatures (BTs) observed by the ground-based MWR RPG during precipitation conditions were high, which affected its detection performance. The bias and the standard deviation (SD) between the BT observed by MWR RPG and the simulated BT during clear and cloudy sky conditions were slight and large, respectively, and the coefficient of determination (R2) was high and low, respectively. However, when the cloud liquid water (CLW) information was added when simulating BT, the bias and the SD of the observed BT and the simulated BT during cloudy days were reduced and the R2 value improved, which indicated that CLW information should be taken into account when simulating BT during cloudy conditions. The temperature profiles of the MWR retrieval had the same accuracy of RMSEs (root-mean-square error) with heights during both clear-sky and cloudy sky conditions, where the RMSEs were below 2 K when the heights were below 4 km. In addition, the MWR RPG has the potential ability to retrieve the temperature inversion in the boundary layer, which has important application value for fog and air pollution monitoring.

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

“…Because of the height-and time-dependent coefficients trained in the neural network algorithm, the statistical results of temperature and water vapor show that the differences between the MWR and RS observations vary with height and season, which is consistent with results obtained in previous reports [64,65]. Besides the different sensing methods and retrieval algorithms, other factors, such as the two-station distance, balloon drift, and the lake, terrain, and high building near the MWR site, may bring about the discrepancy of air temperature and water vapor between the two sites.…”

“…The results are roughly consistent with those in different sites from previous studies where the temperature is generally 0-3 K lower in the MWR data than in the RS data but the humidity is about 0-20% higher [31,32,67]. In addition, differences between the MWR and RS observations vary with height and season, which is similar to the differences recorded in in previous studies [64,65,68]. The discrepancy between the two measurements may be related to the sensing methods, retrieval algorithms, two-site distance, and environment around the MWR site.…”

Wind Profile Retrieval Based on LSTM Algorithm and Mobile Observation of Brightness Temperature over the Tibetan Plateau

“…Under clear-sky conditions, there are negative deviations at most of the oxygen channels, except for the 54.94 GHz and 56.02 GHz channels, whereas those at the water vapor channels under clear-sky and cloudy conditions are positive. With the exception of individual oxygen channels, most discrepancies under clear-sky conditions are smaller than on cloudy days, mainly because cloud water content, cloud thickness, and cloud height lead to great uncertainty [46][47][48][49][50][51]. Figure 5b,c show that there is a strong correlation between MOTB and STB under two weather conditions, with correlation coefficients nearing unity.…”

Evaluation of an Automatic Meteorological Drone Based on a 6-Month Measurement Campaign

“…In this study, the temperature measurements from a microwave radiometer HAT-PRO-G5 [39][40][41] manufactured by RPG were used. The radiometer measured the temperature profiles every 5 min by performing a scan with 11 elevation angles.…”