Impacts of AMSU-A/MHS and IASI data assimilation on temperature and humidity forecasts with GSI/WRF over the Western United States

Abstract

Abstract. Using NOAA's Gridpoint Statistical Interpolation (GSI) data assimilation system and NCAR's Advanced Research WRF (ARW-WRF) regional model, six experiments are designed by (1) control experiment (CTRL) and five data assimilation (DA) experiments with different data sets including (2) conventional data only (CON), (3) microwave data (AMSU-A + MHS) only (MW), (4) infrared data (IASI) only (IR), (5) combination of microwave and infrared data (MWIR), (6) combination of conventional, microwave and infrared observation data (ALL). One month experiments in July 2012 and impacts of the DA on temperature and moisture forecasts at the surface and four vertical layers, which over the western United States have been investigated. The four layers include lower troposphere (LT) from 800 to 1000 hPa}, middle troposphere (MT) from 400 to 800 hPa, upper troposphere (UT) from 200 to 400 hPa and lower stratosphere (LS) from 50 to 200 hPa. The results show that the regional GSI/WRF system is underestimating the observed temperature in the LT and overestimating in the UT and LS. The MW DA reduced the forecast bias from the MT to the LS within 30 h forecasts, and the CON DA kept a smaller forecast bias in the LT for 2-day forecasts. The largest RMS error is observed in the LT and at the surface (SFC). Compared to the CTRL, the MW DA made the most positive contribution in the UT and LS, and the CON DA mainly improved the temperature forecasts at the SFC. However, the IR DA made a negative contribution in the LT. Most of the observed humidity in the different vertical layers is overestimated in the humidity forecasts except in the UT. The smallest bias in the humidity forecast occurred at the SFC and UT. The DA experiments apparently reduced the bias from the LT to UT, especially for the IR DA experiment, but the RMS errors are not reduced in the humidity forecasts. Compared to the CTRL, the IR DA experiment has a larger RMS error in the moisture forecast although the smallest bias is found in the LT and MT.