Estimating missing station weather data using North American Regional Reanalysis

Station weather data from Remote Automated Weather Stations (RAWS) is useful for the fire management and planning community in addition to the research community since these are stations located in fire prone and often remote areas. Having a complete historical record of data from these stations is important from a research perspective. Unfortunately, data from these stations can often be missing or consisting of a significant amount of error. A national quality control and clean up of historical RAWS data was initiated from the Fire Planning Analysis (FPA) group so that an understanding of the weather associated with fire prone areas can be analyzed and used for future planning and budgetary purposes. As a result, all missing data had to be replaced with a best guess, estimated value. The North American Regional Reanalysis (NARR) from the National Center for Atmospheric Research (NCAR) and the National Center for Environmental Prediction (NCEP) is a high-resolution (32 km; 3-hourly) dataset that offers a data source for filling in the data gaps from RAWS. Results of the quality control of national RAWS data in addition to the effectiveness of using the NARR data to estimate missing RAWS data will be presented.