Assimilation of radiance tendency of sounding bands from geostationary satellites using GFS

The sounding bands from advanced imagers onboard the domestic and international constellation of geostationary satellites provide observations with near global coverage. These observations provide thermodynamic information at high temporal and spatial resolutions. Their information is critical for high impact weather forecasting. Despite the use of these observations in Numerical Weather Prediction (NWP) models, Cardinali (2009) showed that both Geostationary Operational Environmental Satellite (GOES) Imager and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instruments have smaller forecast error reductions than all other polar orbiting satellite observations. The high temporal resolutions from geostationary observations offer a unique advantage that is absent from Low Earth Orbiting satellites. (Schmit et al. 2005).

Time series of innovations (observation – background) revealed that satellite radiances are biased. Bias correction schemes are applied within data assimilation systems to remove these biases. The process of applying a bias correction can potentially reduce or compromise the useful information in the observations. Radiance observation biases do not change dramatically within a few hours. The temporal variations of radiances, or tendencies, can be considered bias free and can provide information on how the atmosphere varies with time. The radiance tendencies can be assimilated into NWP models without being bias corrected. The absolute calibration of sensors is less important when radiance tendencies are used.

Technique to assimilate the radiance tendency observations derived from the GOES-16 and 18 Advanced Baseline Imager and Himawri-8 Advanced Himawri Imager clear sky radiance (CSR) data was implemented into a version of the National Centers for Environmental Prediction’s Gridpoint Statistical Interpolation (GSI) software. Assimilation statistics and forecast impact from radiance tendency assimilation will be presented.

References

Cardinali, C. (2009), Monitoring the observation impact on the short‐range forecast. Q.J.R. Meteorol. Soc., 135: 239-250. doi:10.1002/qj.366

Schmit, T.J., M.M. Gunshor, W.P. Menzel, J.J. Gurka, J. Li, and A.S. Bachmeier, 2005: INTRODUCING THE NEXT-GENERATION ADVANCED BASELINE IMAGER ON GOES-R. Bull. Amer. Meteor. Soc., 86, 1079–1096, https://doi.org/10.1175/BAMS-86-8-1079