Real-time high resolution analyses and forecasts are being performed at the University of Oklahoma by the Center for Analysis and Prediction of Storms. Analysis are being done at 400-m horizontal grid spacing using the Advanced Regional Prediction System (ARPS) 3-D Variational (3DVAR) with cloud and hydrometeor analysis and real-time nowcasts are produced at 1-km horizontal resolution initialized with 3DVAR and cloud analysis using Incremental Analysis Updating in the ARPS forecast model. In this way 2-hour forecasts are produced every 15-minutes with just 20 minutes latency on fewer than 200 CPU cores. Among the successes of the CAPS forecasts in the testbed was the successful forecast of the Garland-Rowlett tornadic storms in December, 2015.
Important cases from the real-time system are being used for Observing System Experiments (OSEs) using the operational configuration, the Weather Research and Forecasting (WRF) model, and the GSI-EnKF system, with recent results reported in other conferences at this AMS Annual Meeting.
In addition to the above, Network-of-Networks projects are being conducted by researchers at the University of Massachusetts (UMass). These include the deployment of high sample rate, high-resolution barometric pressure instruments (Paroscientific 6000-16B) to study the infrasound emissions from the severe weather detected by its radars and the use of precipitablewater measurements (derived using the GPS-Met technique from the regional CORS and ASOS networks) as a way to improve the quality of CASA’s radar-based precipitation nowcasts.
The impact of the CASA radar network on local government and public response to weather threats in the D-FW area is also being evaluated at UMass. Working with the North Central Texas Council of Governments, who provide part of the funding for operation of the radars, they are studying weather warnings for flash floods and tornadoes derived from the testbed observations, quantitative precipitation estimates and nowcasts.
Much of this work demonstrates how a network of observing systems with complementary components can be stronger than individual components.