Fine-scale variations in atmospheric moisture near the earth's surface are thought to be pivotal to skillful forecasts of a variety of weather phenomena, most notably the initiation and subsequent evolution of severe thunderstorms. While networks of relatively dense direct surface observations by automated weather stations are of considerable value, cost and other practical considerations prevent their implementation on a sufficiently fine mesh so as to capture key moisture variations required to accurately anticipate the timing and location of thunderstorm development. Likewise, while emerging means to remotely sense atmospheric water vapor via satellite borne GPS-based technology hold promise for thermodynamic profiling at larger scales, they lack the resolution required for such detailed applications. Hence ground-based remote sensing techniques are viewed as a the most promising approach for mapping moisture variations on horizontal scales <10 km, even in quiescent pre-storm environments.
Recent work has suggested that operational Doppler radars may have the ability to reliably measure the near-surface refractivity field (which relates to the propagation speed of radar signals through the atmospheric medium) that is in turn strongly dependent on atmospheric moisture content. These signals can be meaningfully interpreted only when they result from reflection (or more accurately, backscatter) by stationary ground targets immediately surrounding any given radar. The work supported here centers on a systematic exploration and evaluation of this refractivity technique as applied to a variety of operational ground-based radar platforms--both those presently in use (such as the WSR-88D/NEXRAD system) and on the drawing board (such as the NSF-supported CASA celltower-mounted prototype network). This study seeks to develop a flexible yet efficient "refractivity engine" that can be used to reliably map refractivity changes from a variety of radar platforms likely to be deployed over any given region. Experiments are planned for the 2008-2010 severe storm seasons using seven strategically located Doppler radars in Oklahoma. Advanced radar simulation techniques will also be developed to conduct statistical error analyses and determine the effective resolution (i.e. fineness of the resulting measurement mesh) effectively represented by actual radar refractivity measurements. In order to assess the meteorological impact of this information, the investigators will develop the framework necessary for optimal inclusion (aka assimilation) of refractivity data as input to numerical forecast models. Using both simulated and real observations, a systematic study will be conducted to assess the impact of refractivity measurements from various/selected platforms on forecasts of thunderstorm initiation, as well as the evolution of pre-existing storms and ultimate accuracy of quantitative precipitation forecasts.
Broader impacts emerging from this work will include development of software/hardware tools potentially applicable to Doppler radar networks operating across the U.S., and could ultimately contribute to improved forecasting of severe storms and precipitation. This work will also support training a new generation of students in subjects such as advanced radar signal processing, data assimilation, and numerical modeling of storms.
The propagation speed of a weather radar signal is very near the speed of light, but can be slowed by the presence of water vapor, which is closely related to a characteristic of the atmosphere called "refractivity". This effect on propagation speed can be exploited using radars to measure near-surface refractivity using time delay (i.e., phase) measurements between ground clutter targets aligned along the radar beams. Due to fine-scale structures of the boundary layer, the near-surface moisture field has high spatial and temporal variability. The analysis and prediction of convective-scale weather is known to be very sensitive to these fine-scale structures in low-level moisture, which can affect the exact timing and location of convective initiation within a numerical weather prediction (NWP) model. This project had the major goal of developing a robust refractivity algorithm and code, which could be used on a network of weather radars in Oklahoma. Over three years of continuous refractivity data were collected providing a wealth of information about the structure of the water vapor field in the boundary layer. Statistical studies were carried out using the Oklahoma Mesonet to understand uncertainties in these unique radar measurements. Example issues include the uncertainty in the clutter target exact range and height, finite beamwidth effects, and moisture field gradients. In addition, initial studies of assimilation of radar refractivity into NWP models have been conducted, and are beginning to show the importance of such measurements for the improvement of weather forecasting. As expected, small-scale spatial variations in wave vapor do in fact have a major impact on NWP forecasts. Future work will include use of actual radar data for these studies, including all the imperfections of noise-corrupted data.
