Improving the Retrieval of Cloudy Atmospheric Profiles from Brightness Temperatures Observed with a Ground-Based Microwave Radiometer

Abstract

Atmospheric temperature and humidity retrievals from ground-based microwave remote sensing are useful in a variety of meteorological and environmental applications. Though the influence of clouds is usually considered in current retrieval algorithms, the resulting temperature and humidity estimates are still biased high in overcast conditions compared to radiosonde observations. Therefore, there is a need to improve the quality of retrievals in cloudy conditions. This paper presents an approach to make brightness temperature (TB) correction for cloud influence before the data can be used in the inversion of vertical profiles of atmospheric temperature and humidity. A three-channel method is proposed to make cloud parameter estimation, i.e., of the total 22 channels of the ground-based radiometer, three are adopted to set up a relationship between cloud parameters and brightness temperatures, so that the observations from the three channels can be used to estimate cloud thickness and water content and complete the cloud correction for the rest of the channels used in the retrieval. Based on two years of data from the atmosphere in Beijing, a comparison of the retrievals with radiosonde observations (RAOB) shows: (1) the temperature retrievals from this study have a higher correlation with RAOB and are notably better than in the vendor-provided LV2. The bias of the temperature retrievals from this study is close to zero at all heights, and the RMSE is greatly reduced from >5 °C to <2 °C in the layer, from about 1.5 km up to 5 km. The temperature retrievals from this study have higher correlation with RAOB data compared to the vendor-provided LV2, especially at and above a 2 km height. (2) The bias of the water vapor density profile from this study is near to zero, while the LV2 has a positive bias as large as 4 g/m3. The RMSE of the water vapor density profile from this study is <2 g/m3, while the RMSE for LV2 is as large as 10 g/m3. That is, both the bias and RMSE from this study are evidently less than the LV2, with a greater improvement in the lower troposphere below 5 km. Correlation with RAOB is improved even more for the water vapor density. The correlation of the retrievals from this study increases to one within the boundary layer, but the correlation of LV2 with RAOB is only 0.8 at 0.5 km height, 0.7 at 1 km, and even less than 0.5 at 2 km. (3) A parameter named the Cloud Impact index, determined by cloud water concentration and cloud thickness, together with the cloud base height, has been defined to show that both BIAS and RMSE of “high-CI subsample” are larger than those of the “low-CI subsample”, indicating that high-CI cloud has a higher impact on the retrievals and the correction for cloud influence is more necessary.

Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer

“…The lack of cloud information led to larger errors in RH in the middle layer from 700 hPa to 750 hPa. As shown in Li et al [17] and Bao et al [24], the correlation between the RH derived from the MWR and radiosonde data is much smaller than the correlation of temperature. This also proves our conclusion that the temperature inversion results are better than the RH inversion results.…”

“…Meanwhile, when the sun is in the observation direction of the MWR, the BT data will be abnormally increased due to the influence of solar radiation, especially those used in low latitudes [14]. Therefore, it is essential to control the quality of the MWR observation data for a better forecast [15][16][17].…”

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.…”

“…The RMSE of WS reaches 1.75 m/s during rainy conditions, while there is a large discrepancy in In-WD. The differences of WS and WD inversion errors under three weather conditions mainly depend on the measurement errors of TB, and the inversion of WD under non-clear sky conditions needs further improvement in terms of the inversion accuracy by adding information about cloud height and cloud thickness [47]. The main reason for the inversion error differences between WS and WD under the three weather conditions is that the atmospheric composition is relatively stable under clear-sky conditions in the radiative transfer process, and the existence of clouds and raindrops on cloudy and rainy days affects the scattering and absorption of radiation, which increases the uncertainty of the radiative transfer process.…”