# Presentation: 319645

## 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
- Create a ‘complete’ dataset
- Replace all missing, erroneous and questionable values
- Assess level of confidence in estimated values
- Run validation tests

## The Station List

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

## 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

## 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

## 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

## NARR

*Coverage and spatial resolution*

## North American Regional Reanalysis (NARR)

- 180 km
- Global Reanalysis

- 32 km
- N.A. Regional Reanalysis

## 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)

## 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

## 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

## Slide 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)

## 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

## 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

## Data Delivery

- WRCC web site
- Choose data format
- Summary, statistics files
- Select by approximate GACC region

## 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