Post on 19-Jan-2016
description
Development of Gridded QPE Datasets for Mountainous Area
Distributed Hydrologic ModelingMike 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 ServiceNational Oceanic and Atmospheric Administration
2Wiley Information Systems Group3MHW
4University Corporation for Atmospheric Research5Riverside Technology, Inc.
Overview
• Purpose• Methodology• Data QC Issues • Results• Conclusions
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
NCDCHourly Daily
Data Analysis
1. Check data consistency – double mass analysis2. Generate monthly station means3. Estimate missing data using station means4. Disaggregate all daily data to hourly values
a. Use surrounding hourly stationsb. Identify values that can’t be disaggregatedc. Manual QC: Fix ‘non-disaggregated’ valuesd. Uniformly distribute remaining daily values
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 PointTime Series
Methodology for Gauge-Only Gridded QPE
SNOTELDaily
Rob’s Peak 56.3”
Georgetown 54.5”
Blodget Ex. Forest 64”
Bowman Dam 67.5”
Truckee 33.1”
Blue Canyon 64
N. Bloomfield 54.6”
Comptonville
Grass Valley
Soda Springs 60.7”
Hell Hole 47”
CSS Lab 70.7”
Ind. Cr. 33.8
Ind. Camp 34.67Ind. Lake 47”
Squaw Valley 69.4”
Truckee # 2 34.8”
Ward Cr. 70.7”
Auburn 37”
Colfax 48.3”
Deer Cr. Forebay 72.6”
Donner 38.9”
Forest Hill 55.6”
Gold Run 55.3”
Iowa Hill 59.5”
Lake Spaulding 75.6”
Sagehen Cr. 32.5
North Fork American RiverMethodology 2
Soda SpringsCSS Lab DonnerNCDC
HourlyNCDCDaily
SNOTEL
Legend
48332 4246720K30
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!)
1. No correction of non-distributed daily observations (312 cases > 0.5 in)
2. Correction of non-distributed daily observations and other errors
3. 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
Methodology 3
* = Missing accumulation;
wrongly coded as -999 in data file: should be -998
Missing Flags: Foresthill changed from zero to -998 to agree with Georgetown
Example of Data ErrorsData QC Issues 1
00Z1/22/2000
= Snotel
D
H
= Daily
= Hourly
Non-disaggregated daily valueat Lake Spaulding station
Max grid value4.59 in
Impact of Data Errors on Hourly Gridded QPE
Case % Bias
Hourly
RMS Error
(cms)
Hourly Modified Correlation Coefficient
1. No data QC
’61-’90 PRISM 8.2 17.3 0.90
2. Data QC
’61-’90 PRISM 6.2 16.9 0.88
3. Data QC ’71-’00 PRISM 3.1 16.0 0.89
Distributed ModelHourly Streamflow Simulation Statistics
Compared to Observed Flow10/1988 – 9/2006
Results 1
-200
0
200
400
600
800
1000
1200
Oct-8
8
Oct-8
9
Oct-9
0
Oct-9
1
Sep
-92
Sep
-93
Sep
-94
Sep
-95
Sep
-96
Sep
-97
Sep
-98
Sep
-99
Sep
-00
Sep
-01
Sep
-02
Sep
-03
Sep
-04
Sep
-05
Date
Mo
nth
ly C
um
ula
tive
err
or,
mm
MPE+1971-00PRISM+gage QC
MPE+1961-90PRISM+gage QC
MPE+1961-90PRISM+no gage QC
1. No Data QC ‘61-’90 PRISM
2. Data QC ‘61-’90 PRISM
3. Data QC ‘71- ‘00 PRISM
Results 2Accumulated Streamflow Simulation Error, mm
Mon
thly
Cum
ulat
ive
Err
or,
mm
1. No Data QC ’61-’90 PRISM
2. Data QC ’61-’90 PRISM
3. Data QC ’71-’00 PRISM
Observed Flow
TimeJanuary 16-30, 2000
Results 3
Hydrographs for 3 CasesJan 22, 2000
4.59 in
0
0.2
0.4
0.6
0.8
0.25 0.5 0.75 1 1.25 1.5 1.75
P/PET
Q/P
ET
ABRFC NFprzPclbPEDCARSnew PprioPED NFnew PclbPEDDMIP2 MARCnew PprioPEDWalker CARSoldPprioPEDMARColdPprioPED NFprzmPprioPEDNFnew PprioPED HLEC1
Results 4 Water Balance Analysis
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
Thank you!
Extra slides
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
19902003
20042005
20062007
20081997
19981999
20002001
20021994
19951996
19911992
1993
Step 1: ‘Basic’ DMIP 2 Data: Time series of gridded precipitation and temperature from NCDC, Snotel sites to Dec. 2002;
Step 2:Extend ‘Basic’ Data: gridded precip. and temp. from NCDC, Snotel sites
HMT-West Observations
Gathered
Analysis of DataESRL, NSSL, OHD
Gridded Precipitationfor each IOP
replaces Basic Data
1 2 3
Step 3
‘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
Year
Note: the time scale describes the attributes of the time series, not the schedule for processing the HMT data. The HMT observationswill be processed after each campaign and inserted intothe Basic Data time series.
HMT QPE Data Processing for Use in DMIP 2
-Represent what the RFC uses for current Forecast operations. -Used for the initial lumped and distributed DMIP 2 simulations in the western basins.
North Fork American River
NCDCHourly Daily
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
Multi-Sensor Precip. Estimator (MPE)-Uses PRISM monthly climatology grids-Uses single optimal estimation in interpolation-Generate gauge-only 4km gridded QPE
-Manual QC: Fix ‘non-disaggregated’ daily precipitation values-Script to uniformly distribute remaining daily values
Hourly PointTime Series
Methodology for Gauge-Only Gridded QPE
SNOTELDaily
00Z1/22/2000
= Snotel
D
H
= Daily
= Hourly
MAP3 Computational Sequence1. Read in data and corrections2. Applies consistency corrections to observed data3. Estimates missing hourly data using only other hourly stations.
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4. Time distribute observed daily amounts into hourly values based on surrounding hourly stations.
1. Procedure uses 1/d2 weighting for surrounding hourly stations.2. If all hourly stations = 0, then all precipitation is put in last hour of the
daily station. Hour of the observation time. NFAR example5. Estimate missing daily amounts using both hourly and daily gages; time
distribute these amounts-If all estimators are missing, then uses 0.0
6. Generates file of station and group accumulated precipitation for IDMA7. IDMA
1. -Compute correction factors2. -Preliminary check of correction factors3. -Insert correction factors into input file4. -Re-run MAP3 for final check of consistency
8. Applies weights to station for each area9. Computes hourly MAP time series10. Sums to selected time interval, e.g., 3hr, 6hr.
MAP3 Computational Sequencecontinued
Spa
tial E
xten
t of
DM
IP2
Am
eric
an P
reci
pita
tion
Grid
Jan 22, 2000Corrected 116.58 mm in one hour at Lake Spaulding.
Corrected Foresthill:changed zero to -998 Jan 18to agree with Georgetown.Corrected Georgetown datato agree with NCDC paper records (-998 not -999 on Jan15-17)
Observed Schaake oldSchaake NewOHD no data QCOHD Data QC
• HMT experiments 2005-2006 data
• Freezing level, precipitation type
• Value of ‘gap’ filling radar QPE.
“DMIP 2” Western Basin Experiments