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B. J. Sohn, Geun-Hyeok Ryu, Hwan-Jin Song, and Mi-Lim Ou


In contrast to the view that deep convection causes heavy rainfall, Tropical Rainfall Measuring Mission (TRMM) measurements demonstrate that heavy rainfall (ranging from moderate to extreme rain rate) over the Korean peninsula is associated more with low-level clouds (referred to as warm-type clouds in this study) than with conventional deep convective clouds (cold-type clouds). Moreover, it is noted that the low-level warm-type clouds producing heavy rainfall over Korea appear to be closely linked to the atmospheric river, which can form a channel that transports water vapor across the Korean peninsula along the northwestern periphery of the North Pacific high. Much water vapor is transported through the channel and converges on the Korean peninsula when warm-type heavy rain occurs there. It may be possible to produce abundant liquid water owing to the excess of water vapor; this could increase the rate and extent of raindrop growth, primarily below the melting layer, causing heavy rain when these drops fall to the surface. The occurrence of heavy rainfall (also exhibited as medium-depth convection in radar observations over Okinawa, Japan) due to such liquid-water-rich lower warm clouds should induce difficulties in retrieving rainfall from space owing to the lack of scattering-inducing ice crystals over land and the warmer cloud tops. An understanding of the microphysical processes involved in the production of warm-type rain appears to be a prerequisite for better rain retrieval from space and rain forecasting in this wet region.

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Hyojin Han, Jun Li, Mitch Goldberg, Pei Wang, Jinlong Li, Zhenglong Li, B.-J. Sohn, and Juan Li


Accurate cloud detection is one of the most important factors in satellite data assimilation due to the uncertainties associated with cloud properties and their impacts on satellite-simulated radiances. To enhance the accuracy of cloud detection and improve radiance assimilation for tropical cyclone (TC) forecasts, measurements from the Advanced Microwave Sounding Unit-A (AMSU-A) on board the Aqua satellite and the Advanced Technology Microwave Sounder (ATMS) are collocated with high spatial resolution cloud products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua and the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) satellite. The cloud-screened microwave radiance measurements are assimilated for Hurricane Sandy (2012) and Typhoon Haiyan (2013) forecasts using the Weather Research and Forecasting (WRF) Model and the three-dimensional variational (3DVAR)-based Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments are carried out to determine the optimal thresholds of cloud fraction (CF) for minimizing track and intensity forecast errors. The results indicate that the use of high spatial resolution cloud products can improve the accuracy of TC forecasts by better eliminating cloud-contaminated microwave sounder field-of-views (FOVs). In conclusion, the combination of advanced microwave sounders and collocated high spatial resolution imagers is able to improve the radiance assimilation and TC forecasts. The methodology used in this study can be applied to process data from other pairs of microwave sounders and imagers on board the same platform.

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