Development of a GOES-based Verification and Forecaster Guidance System for the High-Resolution Rapid Refresh Model

Infrared sensors onboard geostationary satellites provide detailed information about cloud top properties and the water vapor distribution with high spatial and temporal resolutions that makes them very useful as a numerical weather prediction model validation tool. To promote the routine use of these observations for this purpose, we are developing a near real-time GOES-based verification system for the U.S. High Resolution Rapid Refresh (HRRR) model that will provide operational forecasters objective tools to determine the accuracy of current and prior HRRR model forecasts when they are creating or updating short-range forecasts. This capability has become increasingly more important in recent years due to the implementation of rapidly updating, high-resolution numerical models with many overlapping forecast cycles. The HRRR model covers the contiguous U.S. with 3-km horizontal resolution and produces 15-hour forecasts at hourly intervals on a continuous basis.

For this presentation, we will describe the capabilities of the analysis system and present results from demonstration activities with researchers and operational forecasters at the 2015 NOAA Hazardous Weather Testbed Spring Experiment and the Aviation Weather Testbed. Synthetic GOES infrared brightness temperatures are generated during each HRRR model forecast cycle using the Community Radiative Transfer Model (CRTM) and are then compared to real GOES observations using various statistical methods in order to assess the accuracy of the cloud and moisture fields at each model forecast time. Currently, pixel-based verification metrics are being employed, but work is ongoing to devise applicable neighborhood and object-based verifications metrics. Because forecast skill often varies with space and time, these statistics are computed for pre-defined regions across the contiguous U.S. Forecasters can easily access the satellite-based verification results via an interactive webpage and sort the model forecast accuracy based on a given statistical measure. GOES, and eventually GOES-R, observations are critical for this verification system because they provide unique information about the spatial distribution of water vapor and clouds associated with various hazardous weather phenomena such as severe thunderstorms, winter storms, and turbulence.