[1] This study demonstrates the added benefits of assimilating the Advanced Technology Microwave Sounder (ATMS) radiances in the Hurricane Weather Research and Forecasting (HWRF) system to forecasts of four Atlantic hurricane cases that made landfall in 2012. In the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation data assimilation system, the HWRF model top is raised to~0.5 hPa and the cold start embedded in the HWRF system is changed to a warm start. The ATMS data quality control (QC) procedure is examined and illustrated for its effectiveness in removing cloudy radiances of all the 22 ATMS channels using primarily the information from ATMS channels 1 and 2. For each hurricane case, two pairs of data assimilation and forecasting experiments are carried out and compared with and without including ATMS data. The only difference between the two pairs of experiments is that the second pair also includes data from several other polar-orbiting satellite instruments. It is shown that ATMS data assimilation in HWRF results in a consistent positive impact on the track and intensity forecasts of the four landfall hurricanes.

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[1] The differences between brightness temperature observations and simulated observations based on numerical weather predictions, i.e., O-B, for Advanced Technology Microwave Sounder (ATMS) temperature sounding channels exhibit a clear striping pattern [Bormann et al., 2013]. We propose to first use the principal component analysis to isolate scan-dependent features such as the cross-track striping from the atmospheric signal and then to use an Ensemble Empirical Mode Decomposition (EEMD) to extract the striping noise in ATMS Earth scene brightness temperature observations for both temperature and water vapor sounding channels. It is shown that the Principal Component (PC) coefficient of the first PC mode, which mainly describes a scan-dependent feature of cross-track radiometer measurements, captures the striping noise. The EEMD is then applied to the PC coefficient to extract the first three high-frequency intrinsic mode functions (IMFs), which are denoted as the PC1/IMF3 noise. When the PC1/IMF3 noise is removed from the data, the striping noise is imperceptible in the global distribution of O-B for ATMS temperature sounding channels 1-16. Using the same method, it is demonstrated that the striping noise is also present in ATMS water vapor sounding channels 17-22. The magnitude of the ATMS striping noise is about ±0.3 K for the temperature sounding channels and ±1.0 K for the moisture sounding channels. The same technique is also applied to Advanced Microwave Sounding Unit-A (AMSU-A), AMSU-B, and Microwave Humidity Sounder (MHS). The striping noise is undetectable for AMSU-A but present in AMSU-B and MHS data.

The Geostationary Operational Environmental Satellite (GOES) imagers provide high temporal- and spatial-resolution data for many applications, such as monitoring severe weather events. In this study, radiance observations of four infrared channels from GOES-13 and GOES-15 imagers are directly assimilated using the National Centers for Environmental Prediction (NCEP) gridpoint statistical interpolation (GSI) analysis system to produce the initial conditions for the Hurricane Weather Research and Forecasting Model (HWRF). Impacts of GOES imager data assimilation on track and intensity forecasts are demonstrated for a landfalling tropical storm that moved across the Gulf of Mexico—Debby (2012). With a higher model top and a warm start, an asymmetric component is also added to the original HWRF symmetric vortex initialization. Two pairs of data assimilation and forecasting experiments are carried out for assessing the impacts of the GOES imager data assimilation on tropical storm forecasts. The first pair employs a symmetric vortex initialization and the second pair includes an asymmetric vortex initialization. Numerical forecast results from these experiments are compared against each other. It is shown that a direct assimilation of GOES-13 and GOES-15 imager radiance observations, which are available at all analysis times, in HWRF results in a consistently positive impact on the track and intensity forecasts of Tropical Storm Debby in the Gulf of Mexico. The largest positive impact on the track and intensity forecasts comes from a combined effect of GOES imager radiance assimilation and an asymmetric vortex initialization.