Atmospheric Radar Research Center (ARRC)

One of the main goals of this project is to investigate the impact of phased array data sampled and processed in various ways on data assimilation and NWP. Therefore, a comprehensive simulation system will be designed to integrate the processes of designing radar scanning strategies, making observations, assimilating data, and producing forecasts with the goal of improving short-term weather prediction and better understanding the relationships among all involved processes.

The simulation process is illustrated in Figure 5. The raw time series data output from the emulator will be processed by a signal processor with functions of data QC, spectral moment estimation, and cross-beam wind measurement. The synthetic radar products together with a proper characterizations of their errors will be assimilated using the EnKF method and to produce short-term forecasts. Both retrieved and prediction results will be compared with the high-resolution model outputs and the impact of radar sampling can be determined. The SPY-1 waveform with 1-pulse (reflectivity only) in clear mode, 3- or 4-pulse in Moving Target Indicator (MTI) mode, 16-pulse, and 32-pulse (Robinson, 2002) will be simulated and their impact on data assimilation and weather forecast will be evaluated and quantified.

Moreover, a framework for developing an optimal scanning strategy will be established based on a feedback design in the simulation. In other words, the information of the difference between the forecast and high-resolution model outputs can be used to adjust scanning pattern until optimal results are achieved. Possible criteria for the optimal pattern can be the one with minimum use of radar resource and the most accurate forecast.

In general, experiments that examine the impact of simulated data from the given observational platform(s) are termed Observing System Simulation Experiments (OSSEs; e.g. Lord et al., 1997). The advantages include easy control of the experiments, precise knowledge of the data properties and errors, and knowledge of the truth. For studies on different data collection strategies, OSSEs are essential, because the real weather can not be sampled many different ways at the same time.
On the other hand, collection and testing with real data are also essential as OSSEs have the tendency of producing overly optimistic results, especially when various sources of error are not properly accounted for. For this reason, real data collection and assimilation will be examined.

One important component of our OSSE framework is the sophisticated PAR emulator. To simulate the real radar observations as closely as possible, realistic simulations of atmospheric phenomena at resolution up to few tens of meters will be produced, with data output at sub-second intervals. These “large-eddy” simulations will be able to directly resolve a significant portion of the turbulence spectrum of the atmosphere, which is important for radar sampling. Due to the high spatial resolution, the numerical solutions are likely to be realistic too.

In addition to assessing the optimality of the scanning strategies, through the simulation and assimilation of the standard radial velocity and reflectivity data, we will also examine the impact of potentially available cross-beam wind estimates (Zhang et al., 2003; Doviak et al., 2004). For OSSEs, the quality of analysis and forecasts can be easily evaluated against the truth simulation data. Such evaluations will be performed systematically. Special attention will be given to parameters that have high impact on Navy operations, including wind speed, precipitation types and amount.