11 B . 5 PREDICTION AND MITIGATION OF ANOMALOUS PROPAGATION

Anomalous propagation (AP) impacts the quality of precipitation estimates and many decisions, public safety resource management, and economic, that rely on accurate geolocated precipitation measurements. Techniques to automatically identify and mitigate the effects of AP in the radar data have been developed and implemented in systems such as the Hydrological Decision Support System (HDSS) and the Radar Echo Classifier (REC). These techniques detect AP through its statistical properties in the radar data and remove contaminated data. Stagliano (2008) described a process to forcast and mitigate anomalous propagation through modeling the propagation environment. Sounding data is ingested into WRF which produces a three dimensional refractivity field. The refractivity field is ingested into propagation modeling software which modeled the propagation environment. The forecast element developed naturally from WRF forecasting the future conditions. The process performed reasonably well provided the soundings were of sufficient resolution to capture the phenomena producing the AP. As discussed in Stagliano (2008), there are cases where the first data point of a sounding is above the inversion layer causing the AP. In these cases, WRF fails to capture the conditions resulting in AP. The process described herein extends that earlier work by preparing a propagation module for WRF to directly model the propagation environment. The propagation model is being developed with consideration of adjoint development allowing WRF to assimilate radar data to discipline the model with respect to the measured propagation environment. This paper briefly reviews anomalous propagation, briefly describes the model under development, and applies the model to a significant AP event around Wilmington, NC on June 7, 2008.