Improving Assimilation of AMSU Radiances in Cloudy Situations with Collocated MODIS Cloud Mask

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Wednesday, 7 January 2015
Hyojin Han, CIMSS/Univ. of Wisconsin, Madison, WI; and J. Li, M. Goldberg, P. Wang, J. Li, and Z. Li

Tropical cyclones (TCs) accompanied with heavy rainfall and strong wind are high impact weather systems, often causing extensive property damage and even fatalities when landed. Better prediction of TCs can lead to substantial reduction of social and economic damage; there are growing interests in the enhanced satellite data assimilation for improving TC forecasts. Accurate cloud detection is one of the most important factors in satellite data assimilation due to the uncertainties of cloud properties and their impacts on satellite observed radiances. To enhance the accuracy of cloud detection and improve the TC forecasting, AMSU-A/Aqua measurements are collocated with high spatial resolution cloud mask from MODIS/Aqua. The collocated measurements are assimilated for Hurricane Sandy (2012) forecasting using the Weather Research and Forecasting (WRF) model and the 3DVAR-based Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments will be carried out to determine a cloud cover threshold to distinguish between cloud affected and cloud unaffected AMSU-A FOVs. The results indicate that the use of the high spatial resolution cloud mask from MODIS can improve the accuracy of hurricane forecasts by eliminating cloud contaminated AMSU pixels. The methodology used in this study is applicable to advanced microwave sounders such as ATMS and high spatial resolution imagers such as VIIRS onboard NPP and JPSS series, for the improved TC track and intensity forecasts.