Wednesday, 10 January 2018: 3:00 PM
Salon K (Hilton) (Austin, Texas)
The accuracy of numerical weather prediction (NWP) largely depends on the initial conditions of the atmospheric state. Assimilation of satellite infrared radiances into convection allowing (~3 km) NWP models have the potential to improve the model analysis where conventional observations are sparse. However, many challenges remain on how best to accomplish this task. The proposed work aims to improve short-term (0-3 hour) forecasts of high impact weather by assimilating the clear-sky satellite hyperspectral radiances. One dataset is from Atmospheric Infrared Sounder (AIRS) instruments on board the Earth Observing System (EOS) Aqua satellite. The other dataset is from the Cross-track Infrared Sounder (CrIS) on board the Suomi-NPP (National Polar-orbiting Partnership) spacecraft. AIRS is a grating spectrometer with 2378 channels covering the thermal infrared spectrum between 3.7 and 15.4 μm with a spatial resolution of 13.5 km at nadir. A total of 120 channels are selected for assimilation (out of 2378 channels) according to the peak weighting function and meteorological importance. In addition, out of 1305 CrIS channels, a set of 134 channels are selected for assimilation in this study. These datasets are assimilated into the Gridpoint Statistical Interpolation (GSI) based Ensemble Kalman Filter (EnKF) assimilation system connected to the WRF-ARW model core which is one of several potential configurations being tested for an operational Warn-on-Forecast (WoF) system. Several convective events are considered in this study during the year 2016 to assess the potential of assimilating satellite hyperspectral radiances into a WoF system to improving forecasts of high impact weather events. The impact to the overall model environment will be verified using available upper-air observations from radiosondes while high impact weather forecasts will be verified against parameters such as radar reflectivity and rotation tracks.
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