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Using NOAA's Gridpoint Statistical Interpolation (GSI) dataassimilation system and NCAR's Advanced Research WRF (Weather Research and Forecasting) (ARW-WRF) regionalmodel, six experiments are designed by (1) a control experiment (CTRL) andfive 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) a combination of microwave andinfrared data (MWIR); and (6) a combination of conventional, microwave andinfrared observation data (ALL). One-month experiments in July 2012 and theimpacts of the DA on temperature and moisture forecasts at the surface andfour vertical layers over the western United States have been investigated.The four layers include lower troposphere (LT) from 800 to 1000 hPa, middletroposphere (MT) from 400 to 800 hPa, upper troposphere (UT) from 200 to400 hPa, and lower stratosphere (LS) from 50 to 200 hPa. The results show thatthe regional GSI–WRF system is underestimating the observed temperature inthe LT and overestimating in the UT and LS. The MW DA reduced the forecastbias from the MT to the LS within 30 h forecasts, and the CON DA kept asmaller forecast bias in the LT for 2-day forecasts. The largest root mean squareerror (RMSE) is observed in the LT and at the surface (SFC). Compared to the CTRL, the MWDA produced the most positive contribution in the UT and LS, and the CON DAmainly improved the temperature forecasts at the SFC. However, the IR DAgave a negative contribution in the LT.Most of the observed humidity in the different vertical layers isoverestimated in the humidity forecasts except in the UT. The smallest biasin the humidity forecast occurred at the SFC and in the UT. The DA experimentsapparently reduced the bias from the LT to UT, especially for the IR DAexperiment, but the RMSEs are not reduced in the humidity forecasts.Compared to the CTRL, the IR DA experiment has a larger RMSE in themoisture forecast, although the smallest bias is found in the LT and MT

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