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Development of Gridded QPE Datasets for Mountainous Area Distributed Hydrologic Modeling Mike Smith 1 , Feng Ding 1, 2 , Zhengtao Cui 1, 3 , Victor Koren 1 , Naoki Mizukami 1, 3 , Ziya Zhang 1, 4 , Brian Cosgrove 1 , David Kitzmiller 1 , and John Schaake 1,5

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Presentation Transcript
slide1

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

overview
Overview
  • Purpose
  • Methodology
  • Data QC Issues
  • Results
  • Conclusions
purpose
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
slide4

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

slide5

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

qpe derivation north fork american river

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
slide7

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

slide8

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

slide9

Results 1

Distributed Model

Hourly Streamflow Simulation Statistics

Compared to Observed Flow

10/1988 – 9/2006

slide10

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

slide11

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

slide12

Results 4

Water Balance Analysis

conclusions
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
slide14

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

dmip 2 western basin experiments
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
slide19

North Fork

American River

slide20

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
slide21

00Z

1/22/2000

= Snotel

D

= Daily

H

= Hourly

map3 computational sequence
MAP3 Computational Sequence
  • Read in data and corrections
  • Applies consistency corrections to observed data
  • Estimates missing hourly data using only other hourly stations.
map3 computational sequence continued
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.
slide25

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)

dmip 2 western basin experiments1
“DMIP 2” Western Basin Experiments
  • HMT experiments 2005-2006 data
  • Freezing level, precipitation type
  • Value of ‘gap’ filling radar QPE.
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