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.

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.

Accurate cloud detection is very important for infrared (IR) radiance assimilation; improved cloud detection could reduce cloud contamination and hence improve the assimilation. Although operational numerical weather prediction (NWP) centers are using IR sounder radiance data for cloud detection, collocated high spatial resolution imager data could help sounder subpixel cloud detection and characterization. IR sounder radiances with improved cloud detection using Atmospheric Infrared Sounder (AIRS)/Moderate Resolution Imaging Spectroradiometer (MODIS) were assimilated for Hurricane Sandy (2012). Forecast experiments were run with Weather Research and Forecasting (WRF) as the forecast model and the Three‐Dimensional Variational Assimilation (3DVAR)‐based Gridpoint Statistical Interpolation (GSI) as the analysis system. Results indicate that forecasts of both hurricane track and intensity are substantially improved when the collocated high spatial resolution MODIS cloud mask is used for AIRS subpixel cloud detection for assimilating radiances. This methodology can be applied to process Crosstrack Infrared Sounder (CRIS)/Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi‐NPOESS Preparatory Project (NPP)/Joint Polar Satellite System (JPSS) and Infrared Atmospheric Sounding Interferometer (IASI)/Advanced Very High Resolution Radiometer (AVHRR) onboard the Metop series for improved radiance assimilation in NWP.

Observations of dynamical information in the upper levels of tropical cyclones at high spatiotemporal resolutions are rare but very important to the analysis and prediction of the storm evolution and landfall impacts. These observations are now becoming routinely available from the new generation of geostationary weather satellites. Understanding and optimizing the utilization of that information in numerical weather prediction models is a vital step toward simulating tropical cyclone behavior and improving forecasts. The Advanced Baseline Imager (ABI) onboard GOES‐16 is providing high spatial and temporal resolution images that can be targeted on North Atlantic tropical cyclones. In addition to a full‐disk scan every 10 min and a CONUS scan every 5 min, the ABI also has a flexible “mesoscale scan” mode featuring limited moving domains at 1‐min intervals. The mesosector can focus on a targeted storm center with a 10°×10° domain coverage that follows the storm movement. Using this 1‐min ABI imagery to track cloud motions, automated algorithms have been developed to produce enhanced, high‐resolution atmospheric motion vectors (AMVs) during a targeted tropical cyclone event. These high spatiotemporal AMVs represent estimates of the wind field around the storm and can provide critical dynamical information on the targeted storm and its near environment. This information can help improve the representation of the initialized vortex in numerical model analyses. To study the impact of the enhanced AMV observations on numerical weather prediction, the Hurricane Weather Research Forecast (HWRF) model is used in a series of assimilation and forecast experiments. Three destructive Atlantic hurricane cases from 2017, Harvey, Irma, and Maria, are chosen as case studies. The results show that the assimilation of the enhanced AMVs from GOES‐16 consistently improves the HWRF hurricane track and size forecasts, and have mixed impacts on intensity forecasts. These results augment previously published studies on optimizing the quantitative use of new generation geostationary satellite rapid‐scan observations for improving high impact weather forecasts.