Value-added Impact from Future Geostationary Hyperspectral Infrared Sounder Observations on Hurricane Forecasts

Abstract:

Future geostationary (Geo) advanced InfraRed (IR) sounders have finer spatial, spectral, and temporal resolutions compared with the existing GOES sounders, providing much improved resolving power of atmospheric thermodynamic information. When quantitatively assessing the value-added impact from such instruments over the current sounding systems onboard the Low Earth Orbit (Leo) satellites, the real question is what is the optimal impact using the current assimilation/forecast systems. More specifically, will assimilation of more observations from Geo IR sounders with the current assimilation/forecast systems yield improved forecast as expected? And if so, how to assimilate the high temporal resolution Geo sounding information and what is the impact on forecasts? Taken tropical cyclone (TC) forecasting as an example, this study tries to address these questions through a quick regional Observing System Simulation Experiments (r-OSSE) study. The synthetic observations are simulated from the sample ECMWF T1279 nature run (NR) for Hurricane Sandy (2012), including RAOB, the Leo AIRS, and Geo AIRS. Various experiments were carried out using WRF 3.6.1 and GSI 3.3 to study the impact on Sandy track forecast. And the study shows that a) it is critical to assign an appropriate observational error (observation error covariance matrix – O matrix) in order to show improved positive impacts from Geo AIRS over Leo AIRS; b) cycling of 3/6-hourly shows improved positive impacts over none cycling, but hourly cycling does not show further improvement on forecasts among all experiments, and c) with thinning (120 ~ 240 km), the impacts have the following order: hourly > 3-hourly > 6-hourly > none cycling. These experiments indicate that while more observations may improve forecasts, much more observations are difficult to show further improvement with the current assimilation/forecast system configurations. There exists a tradeoff between the number of observations to be assimilated and the impact of single observation in order to maintain the value-added impact. The ultimate objective of this study is to find the optimal point where the advantage of observations is maximized with the existing assimilation/forecast systems. This will be done by adjusting the relative weighting between the background and the observations.