Atmospheric Radar Research Center (ARRC)

The Phased Array Radar represents a paradigm shift for weather observations and is an ideal testbed for the proposed work. It can adaptively scan multiple regions of interest and provide high-quality, rapidly-updated weather observations. Moreover, an independent tracking channel sponsored by the FAA has recently been installed and tested. Therefore, the PAR at the National Weather Radar Testbed (NWRT) has the potential to perform multi-function/multi-mission experiments, for example, simultaneous weather observations and aircraft tracking.

Weather radars typically only observe the radial component of the wind and the reflectivity. The cross-beam wind components as well as other state variables of the atmosphere, including temperature, pressure, water vapor, cloud and hydrometeor species, have to be retrieved from observed parameters, ideally in combination with all available observational data and a prior estimate of the state. An accurate estimate of all the state variables is extremely important for military operations for assessing and characterizing the on-scene tactical environment, and for initializing high-resolution cloud-resolving numerical weather prediction (NWP) models.

The process of combining all available information, often those available over a period of time, is the process of data assimilation. Fritsch and Carbone (2004) have reported that the data assimilation may be the most critical element in the overall process of improving the quantitative precipitation forecast. An advanced data assimilation technique is even more important for assimilating radar observations for precipitation systems, due to the additional difficulties cited above. The Ensemble Kalman Filter (EnKF) technique ( Evensen, 1994; Burgers et al., 1998; Evensen, 2003) has emerged in recent years to be a very promising technique for data assimilation (e.g., Snyder and Zhang, 2003; Dowell et al., 2004; Tong and Xue, 2005; Xue et al., 2005a). It also appears to be as effective as the four-dimensional variational method (4DVAR) but is much more flexible for implementation (Caya et al., 2005). In addition, the EnKF system can provide uncertainty information on both analysis and forecast, which is important for decision making as well as for dynamically steering the phased array radar for adaptive sensing.

In this project, we propose to integrate two state-of-the-art technologies, the phased array weather radar and the emerging EnKF data assimilation method, with the goal of improving environmental characterization and forecast to optimize naval operation. This project will further enhance the existing collaboration among U.S. Navy’s Office of Naval Research (ONR), NOAA’s National Severe Storms Laboratory (NSSL), and the University of Oklahoma (OU) to achieve the following specific research objectives.

After developing an EnKF-based framework to optimally assimilate the high-rate PAR data into numerical models, we will investigate and evaluate various PAR scanning strategies/waveforms that would allow for optimal estimation and prediction of the state of the atmosphere through EnKF data assimilation.