Four independently developed high-resolution precipitation products [HRPPs; the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction CenterMorphing Method (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the National Research Laboratory (NRL) blended precipitation dataset (NRL-blended)], with a spatial resolution of 0.258 and a temporal resolution of 3 h, were compared with surface rain measurements for the four summer seasons (June, July, and August) from 2003 to 2006. Surface measurements are 1-min rain gauge data from the Automated Weather Station (AWS) network operated by the Korean Meteorological Administration (KMA) over South Korea, which consists of about 520 sites. The summer mean rainfall and diurnal cycles of TMPA are comparable to those of the AWS, but with larger magnitudes. The closer agreement of TMPA with surface observations is due to the adjustment of the realtime version of TMPA products to monthly gauge measurements. However, the adjustment seems to result in significant overestimates for light or moderate rain events and thus increased RMS error. In the other three products (CMORPH, PERSIANN, and NRL-blended), significant underestimates are evident in the summer mean distribution and in scatterplots for the grid-by-grid comparison. The magnitudes of the diurnal cycles of the three products appear to be much smaller than those suggested by AWS, although CMORPH shows nearly the same diurnal phase as in AWS. Such underestimates by three methods are likely due to the deficiency of the passive microwave (PMW)-based rainfall retrievals over the South Korean region. More accurate PMW measurements (in particular by the improved land algorithm) seem to be a prerequisite for better estimates of the rain rate by HRPP algorithms. This paper further demonstrates the capability of the Korean AWS network data for validating satellite-based rain products.

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Satellite measurements are an important source of global observations in support of numerical weather prediction (NWP). The assimilation of satellite radiances under clear skies has greatly improved NWP forecast scores. However, the application of radiances in cloudy skies remains a significant challenge. In order to better assimilate radiances in cloudy skies, it is very important to detect any clear field-of-view (FOV) accurately and assimilate cloudy radiances appropriately. Research progress on both clear FOV detection methodologies and cloudy radiance assimilation techniques are reviewed in this paper. Overview on approaches being implemented in the operational centers and studied by the satellite data assimilation research community is presented. Challenges and future directions for satellite sounder radiance assimilation in cloudy skies in NWP models are also discussed.Citation: Li Jun, Wang Pei, Han Hyojin, et al., 2016: On the assimilation of satellite sounder data in cloudy skies in numerical weather prediction models.

Generally, only clear-infrared spectral radiances (not affected by clouds) are assimilated in weather analysis systems. This is due to difficulties in modeling cloudy radiances as well as in observing their vertical structure from space. To take full advantage of the thermodynamic information in advanced infrared (IR) sounder observations requires assimilating radiances from cloud-contaminated regions. An optimal imager/sounder cloud-clearing technique has been developed by the Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin-Madison. This technique can be used to retrieve clear column radiances through combining collocated multiband imager IR clear radiances and the sounder cloudy radiances; no background information is needed in this method. The imager/sounder cloud-clearing technique is similar to that of the microwave/IR cloud clearing in the derivation of the clear-sky equivalent radiances. However, it retains the original IR sounder resolution, which is critical for regional numerical weather prediction applications. In this study, we have investigated the assimilation of cloud-cleared IR sounder radiances using Atmospheric Infrared Sounder (AIRS)/Moderate Resolution Imaging Spectroradiometer for three hurricanes, Sandy (2012), Irene (2011), and Ike (2008). Results show that assimilating additional cloud-cleared AIRS radiances reduces the 48 and 72 h temperature forecast root-mean-square error by 0.1-0.3 K between 300 and 850 hPa. Substantial improvement in reducing track forecasts errors in the range of 10 km to 50 km was achieved.