Development of Gridded QPE Datasets for Mountainous Area Distributed Hydrologic Modeling

1. Development of Gridded QPE Datasets for Mountainous Area Distributed Hydrologic Modeling Mike Smith1, Feng Ding1, 2, Zhengtao Cui1, 3, Victor Koren1, Naoki Mizukami1, 3, Ziya Zhang1, 4, Brian Cosgrove1, David Kitzmiller1, and John Schaake1,5 1Office of Hydrologic Development, National Weather Service National Oceanic and Atmospheric Administration 2Wiley Information Systems Group 3MHW 4University Corporation for Atmospheric Research 5Riverside Technology, Inc. 2010 EWRI Conference Providence Rhode Island May 17-21

2. Overview • Purpose • Methodology • Data QC Issues • Results • Conclusions

3. Purpose • Develop and test a method to generate gridded gauge-only quantitative precipitation estimates (QPE) to support NWS R&D and operational river forecasting • Leverage RFC tools and data • Multi-year duration • Hourly time step • 4km scale • Data QC

4. NCDC Hourly Daily Methodology for Gauge-Only Gridded QPE • Data Analysis • -Check data consistency – double mass analysis • - Generate monthly station means • - Estimate missing data using station means • - Disaggregate all daily data to hourly values • - Non-disaggregated daily obs put into one hour • Manual QC: Fix ‘non-disaggregated’ values • - Uniformly distribute remaining daily values SNOTEL Daily • Generate QPE Grids • - Use NWS Multi-Sensor Precip. Estimator (MPE) • ‘Gauge-only’ option • Uses PRISM monthly climatology grids • Uses single optimal estimation (Seo et al., 1998, J. Hydrology) Hourly Point Time Series

5. Comptonville Methodology 2 North Fork American River Bowman Dam 67.5” N. Bloomfield 54.6” Ind. Cr. 33.8 Deer Cr. Forebay 72.6” Ind. Lake 47” Ind. Camp 34.67 Lake Spaulding 75.6” Blue Canyon 64 Grass Valley Sagehen Cr. 32.5 CSS Lab 70.7” Donner 38.9” Gold Run 55.3” Colfax 48.3” Truckee 33.1” Soda Springs 60.7” Truckee # 2 34.8” Iowa Hill 59.5” Squaw Valley 69.4” Forest Hill 55.6” Hell Hole 47” Ward Cr. 70.7” Georgetown 54.5” Auburn 37” Blodget Ex. Forest 64” Rob’s Peak 56.3” Legend NCDC Hourly NCDC Daily CSS Lab SNOTEL Donner Soda Springs 20K30 48332 42467

6. Methodology 3 QPE Derivation North Fork American River • Generate hourly 4km QPE grids 1980 – 2006 • Use PRISM 1961-1990 gridded monthly climatology • Based on 36 NCDC and SNOTEL stations • Three cases (227,760 grids each case!) • No correction of non-distributed daily observations (312 cases > 0.5 in) • Correction of non-distributed daily observations and other errors • Repeat No. 2 with 1971-2000 PRISM climatology • Hydrologic analysis • Run distributed model for 1988 to 2006 • Generate hourly streamflow simulation for each case • Compute statistics compared to observed streamflow • Water balance analysis

7. Example of Data Errors Data QC Issues 1 Missing Flags: Foresthill changed from zero to -998 to agree with Georgetown *= Missing accumulation; wrongly coded as -999 in data file: should be -998

8. Data QC Issues 2 Impact of Data Errors on Hourly Gridded QPE Non-disaggregated daily value at Lake Spaulding station Max grid value 4.59 in 00Z 1/22/2000 = Snotel D = Daily H = Hourly

9. Results 1 Distributed Model Hourly Streamflow Simulation Statistics Compared to Observed Flow 10/1988 – 9/2006

10. 1. No Data QC ‘61-’90 PRISM 2. Data QC ‘61-’90 PRISM 3. Data QC ‘71- ‘00 PRISM Results 2 Accumulated Streamflow Simulation Error, mm Monthly Cumulative Error, mm

11. Jan 22, 2000 4.59 in Results 3 Hydrographs for 3 Cases 1. No Data QC ’61-’90 PRISM 2. Data QC ’61-’90 PRISM 3. Data QC ’71-’00 PRISM Observed Flow Time January 16-30, 2000

12. Results 4 Water Balance Analysis

13. Conclusions • Methodology is sound • Hourly time step simulations require intensive data QC • Data errors not readily seen in streamflow simulation statistics • Automated procedure to correct wrong data flags would streamline the process

14. Next Steps 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 HMT QPE Data Processing for Use in DMIP 2 ‘Advanced’ DMIP 2 Data: Multi-year time series of gridded data comprised of 1) ‘Basic’ data and 2) Processed and gridded HMT data for each IOP Step 2: Extend ‘Basic’ Data: gridded precip. and temp. from NCDC, Snotel sites Step 1: ‘Basic’ DMIP 2 Data: Time series of gridded precipitation and temperature from NCDC, Snotel sites to Dec. 2002; -Represent what the RFC uses for current Forecast operations. -Used for the initial lumped and distributed DMIP 2 simulations in the western basins. Gridded Precipitation for each IOP replaces Basic Data Analysis of Data ESRL, NSSL, OHD Step 3 Note: the time scale describes the attributes of the time series, not the schedule for processing the HMT data. The HMT observations will be processed after each campaign and inserted into the Basic Data time series. HMT-West Observations Gathered 1 2 3 Year

15. Thank you!

16. Extra slides

17. DMIP 2 Western Basin Experiments • NCEP/EMC: J. Dong • HRC: K. Georgakakos • U. Washington: J. Lundquist with DHSVM • CEMAGREF: V. Andreassian • UCI: Sorooshian • U. Illinois: Sivapalan • U. Bologna: E. Todini

19. North Fork American River

20. NCDC Hourly Daily Methodology for Gauge-Only Gridded QPE Precipitation Preprocessor -Data QC: -Double mass analysis -Suspect values -Generate monthly station means Mean Areal Precip. Processor - Generate mean areal precip time series - Check data consistency – double mass analysis - Estimate missing data using station means - Disaggregate all daily data to hourly values - Non-disaggregated daily obs put into one hour - Write out hourly time series for all stations SNOTEL Daily -Manual QC: Fix ‘non-disaggregated’ daily precipitation values -Script to uniformly distribute remaining daily values Hourly Point Time Series • Multi-Sensor Precip. Estimator (MPE) • Uses PRISM monthly climatology grids • Uses single optimal estimation in interpolation • Generate gauge-only 4km gridded QPE

21. 00Z 1/22/2000 = Snotel D = Daily H = Hourly

22. MAP3 Computational Sequence • Read in data and corrections • Applies consistency corrections to observed data • Estimates missing hourly data using only other hourly stations.

23. MAP3 Computational Sequencecontinued • Time distribute observed daily amounts into hourly values based on surrounding hourly stations. • Procedure uses 1/d2 weighting for surrounding hourly stations. • If all hourly stations = 0, then all precipitation is put in last hour of the daily station. Hour of the observation time. NFAR example • Estimate missing daily amounts using both hourly and daily gages; time distribute these amounts -If all estimators are missing, then uses 0.0 • Generates file of station and group accumulated precipitation for IDMA • IDMA • -Compute correction factors • -Preliminary check of correction factors • -Insert correction factors into input file • -Re-run MAP3 for final check of consistency • Applies weights to station for each area • Computes hourly MAP time series • Sums to selected time interval, e.g., 3hr, 6hr.

24. Spatial Extent of DMIP2 American Precipitation Grid

25. Observed Schaake old Schaake New OHD no data QC OHD Data QC Jan 22, 2000 Corrected 116.58 mm in one hour at Lake Spaulding. Corrected Foresthill: changed zero to -998 Jan 18 to agree with Georgetown. Corrected Georgetown data to agree with NCDC paper records (-998 not -999 on Jan 15-17)

26. “DMIP 2” Western Basin Experiments • HMT experiments 2005-2006 data • Freezing level, precipitation type • Value of ‘gap’ filling radar QPE.