87th AMS Annual Meeting

Tuesday, 16 January 2007: 1:45 PM
Utilizing Geographic Information Systems (GIS) to assess gridded NWS forecasts of Probability of Precipitation (PoP) and Quantitative Precipitation Forecast (QPF)
217A (Henry B. Gonzalez Convention Center)
John B. Settelmaier, NOAA/NWS, Fort Worth, TX
Poster PDF (540.6 kB)
The National Weather Service (NWS) issues weather warning and forecast information for the nation. This information is contained in a myriad of data and products continuously disseminated to partners and users. The intent of NWS forecast information is to support our core mission--to protect against life and property losses and promote commerce.

In recent years, the NWS has begun issuing its forecast information in gridded form and making it available via the National Digital Forecast Database (NDFD). These gridded forecast datasets, in contrast to the traditional NWS point- or zone-based forecasts, invite more relevant verification methods and techniques.

The first portion of this paper will describe automated steps employed to gather, convert, geoprocess, and display gridded hydrometeorological datasets to assess their accuracy and value. GIS' involvement in this overall process provides atmospheric scientists an enabling technology to assist in better understanding the relationships between absolutely accurate weather forecasts and ones that, although they may have some degree of inaccuracy, still contain useful or valuable information for users.

The second portion of this paper will explore and summarize verification statistics gathered through GIS geoprocessing of gridded NWS forecasts of probability of precipitation (PoP) and quantitative precipitation forecasts (QPF) from 2005 and 2006. Comparing and contrasting the GIS-based statistics calculated on gridded forecast data with statistics based on official NWS forecast verification will illustrate the added utility of incorporating GIS in our overarching verification paradigm. GIS-based assessment tools provide users with useful analysis, query, and display options to better understand the temporal and geospatial influences on forecast verification data.

Employing GIS-based geoprocessing as part of NWS verification acknowledges the inherent geospatial nature of these gridded datasets. Statistics grounded in GIS can assist forecasters in better understanding the geospatial nature of their forecast performance. Recognizing where improvements in their forecasting efforts can be achieved can lead to a clearer delivery of the NWS mission.

Supplementary URL: http://www.srh.noaa.gov/srh/ssd/assess/assessment.html