Ground-Based Microwave Radiometer Variational Analysis during No-Rain and Rain Conditions

Abstract

Ground-based microwave radiometer (MWR) has been used for high-frequency retrievals of thermodynamic environments. However, raindrops on the radome of MWR and in the air cause errors in retrievals during precipitation events. Although a recent study has noted that off-zenith observations with neural networks (NN) reduce the retrieval errors, the effect of off-zenith observations with one-dimensional variational (1DVAR) technique, which is known to be more accurate than other methods, has not been studied. We developed a new 1DVAR technique that considers the effect of cloud liquid water. We statistically investigated the accuracy of vertical profiles of atmospheric temperature and water vapor retrieved by NN and 1DVAR techniques by using zenith and off-zenith observation at 15° elevation angle under no-rain and rainy conditions and compared them with results of radiosonde observations. The results showed that the 1DVAR technique outperforms NN and numerical model simulation in the estimation of thermodynamic profiles under no-rain conditions. The results also indicated that the error in retrieved profiles in the low-level troposphere can be reduced by the 1DVAR technique by using off-zenith observations even under rainy conditions with rainfall rate less than 1.0 mm h −1 , especially when the environment cannot be accurately reproduced by a numerical model.(Citation: Araki, K., M. Murakami, H. Ishimoto, and T. Tajiri, 2015: Ground-based microwave radiometer variational analysis during no-rain and rain conditions. SOLA, 11, 108−112,

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

“…Notably, the discrepancy between observations and simulations during rainy conditions is large, with the maximum difference in the water vapor channel and the TB exceeding 200 K. Furthermore, the difference in the water vapor channel is obviously larger than in the oxygen channel. This is primarily due to the attachment of water droplets to the antenna radome during rainy conditions, which predominantly detects atmospheric water vapor near the Earth's surface, resulting in a significant decrease in the performance of the MR under rainy conditions [44,45]. Figure 5 presents a comparison between MOTB and STB under different weather conditions.…”

Retrieval of Atmospheric Profiles in the New York State Mesonet Using One‐Dimensional Variational Algorithm

“…The weighting function, which represents the sensitivity of a component at different heights for each wavelength (Araki et al, ), can be computed in the PAM‐VRT model for temperature sensitive channels and water vapor absorption bands, respectively. By setting a slight change in the temperature (Δ T ) of each layer, the perturbation variance of brightness temperature (Δ F ) can be simulated using the PAM‐VRT model.…”

Review of Advances in Precipitation Enhancement Research

“…Here, also complementary laboratory studies are essential to identify the underlying processes of ice nucleation and multiplication.Precipitation enhancement often involves the transformation of SLW to ice, and so detailed measurement of SLW is needed for research as well as for any operational cloud seeding. Total liquid water path is effectively measured by microwave radiometry(Osburn et al 2016), except when rain affects the radiometer radome(Araki et al 2015).Aircraft-based (Geerts et al 2010; French et al 2018) and ground-based (Delanoë et al 2016) radars and lidars can readily identify changes in cloud microphysics from unseeded to seeded conditions. However, in situ measurements are needed to obtain detailed information on hydrometeors.…”