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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.

2010 EWRI Conference Providence Rhode Island May 17-21

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Overview

• Purpose• Methodology• Data QC Issues • Results• Conclusions

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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

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NCDCHourly Daily

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

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

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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

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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

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* = 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

8

00Z1/22/2000

= Snotel

D

H

= Daily

= Hourly

Non-disaggregated daily valueat Lake Spaulding station

Max grid value4.59 in

Data QC Issues 2

Impact of Data Errors on Hourly Gridded QPE

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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

10-200

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

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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

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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

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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

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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.

Next Steps

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Thank you!

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Extra slides

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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

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North Fork American River

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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

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00Z1/22/2000

= Snotel

D

H

= Daily

= Hourly

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MAP3 Computational Sequence1. Read in data and corrections2. Applies consistency corrections to observed data3. Estimates missing hourly data using only other hourly stations.

n

iix,

ii

n

i i

x

x

w

wPP

P

P

1

12

ix,

ix,d

w1

i estimator to x station from distanced

weightstationw

i station for ionprecipitat monthly meanP

x station for ionprecipitat monthly meanP

estimator an as used being station i

stations estimating of number n

station estimator the at ionprecipitatP

estimated being station at ionprecipitatP

ix,

ix,

x

x

i

x

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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

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Spa

tial E

xten

t of

DM

IP2

Am

eric

an P

reci

pita

tion

Grid

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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

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• HMT experiments 2005-2006 data

• Freezing level, precipitation type

• Value of ‘gap’ filling radar QPE.

“DMIP 2” Western Basin Experiments