13.6 Improving assimilation of CrIS radiances under cloudy skies using collocated VIIRS data

Thursday, 26 January 2017: 11:45 AM
607 (Washington State Convention Center )
Pei Wang, CIMSS/Univ. of Wisconsin, Madison, WI; and J. Li, Z. Li, J. Li, A. Lim, T. J. Schmit, and M. D. Goldberg

Measurements from hyperspectral infrared (IR) sounders such as AIRS, IASI and CrIS have been widely assimilated into numerical weather prediction (NWP) models for improving weather forecasting. However, there is still room to improve hyperspectral IR sounder radiance assimilation, especially under cloudy skies, where current strategy is to throw away all cloud-affected radiances. These observations contain critical information of cloudy regions where most of the weather events reside. It is therefore important to study how to assimilate the cloud-affected IR sounder radiances in NWP. Although direct assimilation of IR sounder cloudy radiances is desired, it is quite challenge due to larger uncertainties of radiative transfer model (RTM) in cloudy skies. An alternative and effective way is to use the collocated high spatial resolution imagers such as Visible Infrared Imaging Radiometer Suite (VIIRS) to detect clouds within each pixel of IR sounders such as Cross-track Infrared Sounder (CrIS). This ensures no radiances from cloud contaminated FOVs assimilated. In order to assimilate IR sounder radiances in cloudy skies, a cloud-clearing technique which uses collocated high resolution VIIRS IR band radiances within two adjacent CrIS field-of-view (FOV) to remove the cloud effect and obtain the clear equivalent CrIS radiances. The cloud cleared radiances (CCRs) can be assimilated as clear radiances in NWP models. With only 3 usable IR bands from VIIRS, a stricter quality control scheme is applied to remove CrIS CCRs with low quality. To test the impact of the CrIS/VIIRS CCRs, the WRF-ARW (Advanced Research Version of the Weather Research and Forecasting) is used as the forecasting model, and GSI (Gridpoint Statistical Interpolation) 3DVAR as the data assimilation system. Hurricane Joaquin (2015) is used for impact study on VIIRS-based CrIS CCRs assimilation, two experiments are conducted: a) assimilation of CrIS clear pixel radiances using collocated VIIRS cloud mask for CrIS clear detection, and b) assimilation of CrIS clear radiances and VIIRS-based CrIS CCRs. The results indicate that the assimilation of CrIS CCRs provides additional very important thermodynamic information under partially cloudy skies, and therefore improves the 120-hour forecasts of both Hurricane Joaquin track and intensity.

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