Added-value of GEO-hyperspectral Infrared Radiances for Local Severe Storm Forecasts Using the Hybrid OSSE Method

Abstract

AbstractHigh spectral resolution (or hyperspectral) infrared (IR) sounders onboard low earth orbiting satellites provide high vertical resolution atmospheric information for numerical weather prediction (NWP) models. In contrast, imagers on geostationary (GEO) satellites provide high temporal and spatial resolution which are important for monitoring the moisture associated with severe weather systems, such as rapidly developing local severe storms (LSS). A hyperspectral IR sounder onboard a geostationary satellite would provide four-dimensional atmospheric temperature, moisture, and wind profiles that have both high vertical resolution and high temporal/spatial resolutions. In this work, the added-value from a GEO-hyperspectral IR sounder is studied and discussed using a hybrid Observing System Simulation Experiment (OSSE) method. A hybrid OSSE is distinctively different from the traditional OSSE in that, (a) only future sensors are simulated from the nature run and (b) the forecasts can be evaluated using real observations. This avoids simulating the complicated observation characteristics of the current systems (but not the new proposed system) and allows the impact to be assessed against real observations. The Cross-track Infrared Sounder (CrIS) full spectral resolution (FSR) is assumed to be onboard a GEO for the impact studies, and the GEO CrIS radiances are simulated from the ECMWF Reanalysis v5 (ERA5) with the hyperspectral IR all-sky radiative transfer model (HIRTM). The simulated GEO CrIS radiances are validated and the hybrid OSSE system is verified before the impact assessment. Two LSS cases from 2018 and 2019 are selected to evaluate the value-added impacts from the GEO CrIS-FSR data. The impact studies show improved atmospheric temperature, moisture, and precipitation forecasts, along with some improvements in the wind forecasts. An added-value, consisting of an overall 5% Root Mean Square Error (RMSE) reduction, was found when a GEO CrIS-FSR is used in replacement of LEO ones indicating the potential for applications of data from a GEO hyperspectral IR sounder to improve local severe storm forecasts.