Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system

Abstract

An adjoint-based procedure for assessing the impact of observations on the short-range forecast error in numerical weather prediction is described. The method is computationally inexpensive and allows observation impact to be partitioned for any set or subset of observations, by instrument type, observed variable, geographic region, vertical level or other category. The cost function is the difference between measures of 24-h and 30-h global forecast error in the Navy Operational Global Atmospheric Prediction System (NOGAPS) during June and December 2002. Observations are assimilated at 00UTC in the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS). The largest error reductions in the Northern Hemisphere are produced by rawinsondes, satellite wind data, and aircraft observations. In the Southern Hemisphere, the largest error reductions are produced by Advanced TIROS Operational Vertical Sounder (ATOVS) temperature retrievals, satellite wind data and rawinsondes. Approximately 60% (40%) of global observation impact is attributed to observations below (above) 500 hPa. A significant correlation is found between observation impact and cloud cover at the observation location. Currently, without consideration of moisture observations and moist processes in the forecast model adjoint, the observation impact procedure accounts for about 75% of the actual reduction in 24-h forecast error.