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Quality Control of the National RAWS Database for FPA

Quality Control of the National RAWS Database for FPA. Beth L Hall Timothy J Brown Desert Research Institute Program for Climate, Ecosystem and Fire Applications February 2006. Project Objectives. Run coarse QC on all requested RAWS sites Remove any erroneous or questionable values

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Quality Control of the National RAWS Database for FPA

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  1. Quality Control of the National RAWS Database for FPA Beth L Hall Timothy J Brown Desert Research Institute Program for Climate, Ecosystem and Fire Applications February 2006

  2. Project Objectives • Run coarse QC on all requested RAWS sites • Remove any erroneous or questionable values • Create a ‘complete’ dataset • Replace all missing, erroneous and questionable values • Assess level of confidence in estimated values • Run validation tests

  3. The Station List • 1373 Total stations for over 120 FPUs • 1199 RAWS • 174 non-RAWS matches • Manual station • METAR, AWOS, etc.

  4. Issues with station lists • Could not find matching WRCC RAWS data • No NIFMID data to correlate to hourly RAWS • Periods of record between NIFMID and RAWS do not overlap

  5. Coarse QC • RAWS parameters • Precipitation • Temperature • Relative Humidity • Wind Speed • Wind Direction • Flag all values • Reasonable? • Missing? • Impossible? • Negative wind speed, humidity, direction, etc. • Questionable? • Wind speed > 120 mph; temperature < -40F • Unchanged values for too many consecutive hours

  6. Create ‘Complete’ Datasets • Correlate RAWS with North American Regional Reanalysis • 32 km spatial resolution • 3-hourly • Upper-air variables • Surface variables • Radiation, cloud cover • Include 9 surrounding grid cells centered on RAWS site • Persistence Variables • Last hour • Yesterday • Climatology Values • When persistence observation is unavailable • Use multiple regression output to estimate all missing, questionable, or erroneous data

  7. NARR Coverage and spatial resolution

  8. North American Regional Reanalysis (NARR) 180 km Global Reanalysis 32 km N.A. Regional Reanalysis

  9. Processing statistics • 942 potential predictor variables • 101 reanalysis variables at… • 9 surrounding grid points • Surface and pressure level variables • RAWS and NARR persistence surface variables for… • last hour • yesterday, same hour • Grid location with RAWS station • Reduced list unique for each variables • 112 equations per stations • 7 variables, 8 time periods/day, 2 situations (persistence using RAWS vs. NARR) • 153,776 equations (112 eqs * 1373 stations)

  10. Products • Data sets from original station start date through 2004 (if possible) • Once-a-Day -- old 1972 NIFMID format (*.fwx) • Hourly -- new 1998 NIFMID format (*.fw9) • Comma delimited complete dataset with flags indicating value status (*.dat) • Summary and Statistics per FPU • File that lists station status (could it be processed? Why or why not) • File that lists percentage of data that was estimated or had to be removed

  11. Validation of Estimations • Compare known observations with estimated values • Good estimations • Temperature • Relative Humidity • Poor estimations • Precipitation • Wind speed and Direction • Unknown quality of estimation (but likely needs improvement) • State of the weather

  12. Precipitation and Winds • Wind speed • Though low correlations, actual differences were small • Wind direction • Not a critical variable • Precipitation • RAWS issues • NARR does not include RAWS data • Measurement accuracy (winter)

  13. Improving Precipitation Estimates • Divide observations by ranges (bins) of precipitation amounts • <0.05”, 0.05”-0.1”, 0.1”-0.2”, etc • Develop separate equations for each range • Assign yes/no if threshold of probability is met for each bin • Report worst-case scenario bin • Apply worst-case scenario to duration • Report shortest durations

  14. Optimizing State of the Weather (SOW) • Low confidence in SOW from NIFMID • High confidence in present weather conditions from 1st order stations • Calibrate algorithm for SOW based upon weather reports from 1st order stations (4 stations) • Denver, Seattle, St. Louis, Fresno • Apply algorithm to RAWS SOW estimations

  15. Data Delivery • WRCC web site • Choose data format • Summary, statistics files • Select by approximate GACC region

  16. Future Plans • Include 2005 NARR/RAWS • Provide data since 1980 • Integrate pseudo real-time QC’d/estimated RAWS • National RAWS status reports • Regions of consistently poor data • Regions of data gaps • Modify web delivery to be by FPU

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