Analysis, Prediction and Observing Systems Experiments for the CASA Dallas/Ft. Worth Testbed

The desire to prototype a network of low-cost X-band radars as a result of research performed by the Collaborative Adaptive Sensing of the Atmosphere (CASA) center, and to fulfill the regional testbed vision of the National Research Council's “Observing Weather and Climate from the Ground Up. A Nationwide Network of Networks” report (NoN) has motivated the creation of an observational/numerical testbed in the Dallas/Ft. Worth (DFW) metropolitan area. The testbed area contains many ground-based conventional observations (e.g. - ASOS, AWOS, radiosondes, WSR-88D radars) and even more numerous non-conventional observational systems (e.g. - several X-band radars, 2 C-band TDWR radars, radiometers, Sodars, CWOP, GST MoPED, Earth Networks Weatherbug, and TAMDAR data). The Center for Analysis and Prediction of Storms (CAPS) group at the University of Oklahoma has been using these data to perform both real-time and research mode high-resolution urban-scale analyses and forecasts for the benefit of local communities, the National Weather Service Forecast Office in Fort Worth, and other organizations. Thus it is an academic-public-private sector effort to prototype a candidate model to greatly increase observational capacity and their subsequent benefits to heavily populated or observationally deficient areas. In this presentation, we will show examples of high-resolution analyses (400 m) and forecasts (1 km) over the DFW domain for different weather scenarios. In particular, we will show the results of observing system experiments (OSE) that are permitted by the large number of instrumentation systems in the Testbed. This is done in two modes: the first is a long-term assessment (30 days) of the value of selected components of the non-conventional observations. This is necessary to increase the reliability of the results, but is somewhat computationally expensive. The second mode is to perform OSE for particularly challenging severe weather case studies to examine the sensitivity of the storm forecasts to different data configurations. Although one can't say that this sensitivity will be duplicated in other storms, the results, if consistent with physical reasoning, and other case study results, can be very suggestive of what type of observations will be most beneficial for scale-scale prediction. For example, we will show that a tornadic supercell forecast is positively benefited by additional surface moisture observations (provided by Weatherbug in this case), provided we have the correct error characteristics of these non-conventional data. We will also provide an up-to-date status of the DFW Testbed at the time of the conference, and invite other individuals and organizations who can contribute observing systems, expertise or user applications to participate in future Testbed activities.