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Home > iSGTW 27 February 2008 > iSGTW Feature - Stormy weather: grid computing powers fine-scale climate modeling

 

Feature - Stormy weather: grid computing powers fine-scale climate modeling


Scientists are using Open Science Grid to run an ensemble of 16 different climate models in an effort to more accurately predict stormy “convective precipitation” in the Carolinas (top). The results are compared with those based on precipitation radars and gauges (bottom).
Image courtesy of UNC Charlotte

During spring, summer and early fall, sea breezes can combine with mountainous terrain to trigger heavy precipitation and stormy conditions.

This phenomenon, known as “convective precipitation,” is inherently difficult to predict and requires fine-scale atmospheric modeling.

Thanks to guidance from the Renaissance Computing Institute and access to computing power from the Open Science Grid, a team from the University of North Carolina Charlotte in the U.S. is now increasing the accuracy of these storm predictions.

Multiple models increase accuracy

Brian Etherton and his colleagues in the Department of Meteorology at UNC Charlotte are using the Weather Research and Forecasting (WRF) system—a next-generation mesoscale numerical weather prediction system—to model volumes of space over the Carolinas at a fine resolution of around four kilometers.

In modeling these spaces, many quantities, such as wind and temperature, can be assumed to be constant throughout a particular volume. Others, like rain drops and the reflection of sunlight off clouds, vary within it. Different physics packages can be used within WRF to represent different physical phenomena and conditions.

Assembling ensembles for forecasting

Etherton’s team analyzes individual WRF model runs as well as “ensembles” of multiple runs, comparing generated results to real-world observations and varying parameter values for each run. The accuracy of results produced by ensembles is established as unbeatable compared to individual models alone.

The ensembles run thus far consist of 16 forecasts per day, differentiated by start time and a variety of physical parameterizations such as air/surface exchanges of heat and moisture.

The Weather Research and Forecasting Model facilitates real-time predictions of precipitation, surface temperature, reflectivity, wind speed, hurricane tracks, storm potential, humidity and more. Combinations or “ensembles” of these models can produce even more accurate results.
Images courtesy of WRF

And the result? Probabilistic forecasts determined using the 16-member ensemble are far more accurate than those based on any one model run.

Introducing cumulus clouds

Etherton’s team is now using additional OSG resources to study the effect of another parameter: the impact of cumulus clouds transporting heat and moisture.  

“These cumulus schemes were originally designed for use on larger grids of around 30 kilometers,” says Etherton. “Their validity for such small grid sizes [4km] has always been in question. By running the ensemble both with and without a cumulus scheme, we can quantify the benefits or drawbacks of using a convective scheme at a four kilometer resolution.”

Etherton’s team continues to make groundbreaking progress in understanding the impact of convective schemes. Their work will not only improve the forecasting of sudden stormy  conditions, but will also influence longer-term climate modeling, both areas of increasing importance.

- Leesa Brieger, Renaissance Computing Institute, and Anne Heavey, Open Science Grid

The Weather Research and Forecasting Model is in the public domain and is freely available for community use. 

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