Diagnosis of inconsistencies in multi-year gridded precipitation data over mountainous Diagnosis of inconsistencies in multi-year gridded precipitation data over mountainous areas and related impacts on hydrologic simulations areas and related impacts on hydrologic simulations 1. Introduction 1. Introduction Multi-year gridded Quantitative Precipitation Estimates (QPE) were derived for 1988-2002 for the western basin experiments of the Distributed Model Intercomparison Project (DMIP 2). This data set was subsequently extended to 2006 as more recent data became available. However, we uncovered consistency errors in the extended data set, requiring diagnostic analysis of the underlying gauge data. In this poster we: • Present a method to diagnose consistency errors in high spatial-temporal resolution QPE grids. • Illustrate the importance of temporally consistent QPE for hydrologic modeling. Naoki Mizukami 1,2 , Michael Smith 1 1 NWS/OHD, 2 Len Tech Office of Hydrologic Development, NOAA National Weather Service 1325 East-West Highway, Silver Spring, MD 20910, U.S.A. e-mail [email protected] 2. Background 2. Background AGU 2010 Fall Meeting Dec 11- 17, San Francisco, CA 3. Consistency Check - Gridded Double Mass 3. Consistency Check - Gridded Double Mass Analysis Analysis 4. Diagnosis of QPE Inconsistency 4. Diagnosis of QPE Inconsistency 6. Summary 6. Summary Plot of ‘reference’ cumulative time series R against deviation of cumulative P at target cell from cumulative time series R • DMIP2 Sierra Nevada basins - North Folk of American (NFAR) - East Folk of Carson (EFCR) • Generation of gridded precipitation and air temperature for hydrologic model forcing (Moreda et al. 2006) -Resolution: 4km and hourly -Period: WY1988 - WY 2006 -Spatial interpolation of hourly gauge data 1) Generating historical temporally-consistent QPE grid can be challenging given temporally varying gage networks as well as inconsistent data at each gauge. 2) Developed a method for consistency detection for high spatial and temporal resolution QPE. 3) More consistent QPE leads to more consistent error trends within the simulation, making it easier to improve the simulation with further model calibration H23A-1172 -150 -50 50 150 250 350 450 550 650 750 Oct- 88 Oct- 89 Oct- 90 Oct- 91 Oct- 92 Oct- 93 Oct- 94 Oct- 95 Oct- 96 Oct- 97 Oct- 98 Oct- 99 Oct- 00 Oct- 01 Oct- 02 Oct- 03 Oct- 04 Oct- 05 Date Accum ulated Error,m m O riginal D M IP2 M AP Adjusted D M IP2 M AP Accum ulated error= Σ(Q sim -Q obs ) 5. Hydrologic Simulations with Corrected Data 5. Hydrologic Simulations with Corrected Data Each point corresponds a specific measurement time (e.g. year, month etc.) Time when inconsistency occurs Reference time series: R i n i G G P R k j j m j ij j j i ,..... 1 1 2 1 2 n: the number of time steps ρ j : correlation coefficient between two time series at a target gage and at a neighbor gauge j G ij : time series of a neighbor gauge j, G j : mean of the series at the target gage. P: mean of the time series at the neighbor gauge j. Inconsistency found at groups 4 through 8 in Mar 2003 (upper portion of basin) S S S S S S S S S S S S S S S S S S S S S S S S S S S S S 9 7 6 5 4 3 2 1 82 81 79 77 76 75 73 72 71 70 69 68 67 66 65 64 61 60 58 57 56 54 52 50 48 47 46 45 44 43 42 40 39 38 37 34 33 31 29 28 27 26 25 24 23 22 21 20 19 18 16 15 13 11 10 H igh :4274 Low :-84.8521 N FA R EFCR • Need to diagnose the error trend change for NFAR. • Started investigation of consistency of gridded QPE time series. (consistency: error magnitude and sign is consistent over the period) Target -> time series at pixel groups 1 thru 8 (average P over 4 pixels) Reference -> time series based on 4 reference gages Target -> time series at lower and upper zones (average P over each zone) Reference -> time series based on 4 reference gages DMIP2 Sierra Nevada basins, CA DMIP2 Sierra Nevada basins, CA Streamflow Simulation Results Using Gridded SAC-SMA and SNOW-17 Streamflow Simulation Results Using Gridded SAC-SMA and SNOW-17 Inconsistency found at upper elev. zone The streamflow simulations made with corrected QPE illustrate improvement of the error trend. The simulations below were generated by running the SAC-SMA and SNOW-17 models in two elevation zones for North Folk of American Deviation of Acc. precip. of target cell from Acc. Reference precip. Σ(P i -R i ) 0 Acc. precip. of reference ΣR i Consistent time series Σ(P-R) Σ(P-R) Σ(P-R) ΣR ΣR ΣR History for the individual gauges used to generate the gridded QPE indicates temporally varying gauge network. Discontinuation of the Lake Spaulding (ID-58) caused gridded QPE inconsistency Jan- 89 Jan- 90 Jan- 91 Jan- 92 Jan- 93 Jan- 94 Jan- 95 Jan- 96 Jan- 97 Jan- 98 Jan- 99 Jan- 00 Jan- 01 Jan- 02 Jan- 03 Jan- 04 Jan- 05 Jan- 06 Jan- 07 1 2 3 7 10 11 13 16 19 20 23 27 28 37 38 42 43 44 46 51 52 54 56 57 58 66 67 68 69 73 79 Application to QPE over NFAR and Results Application to QPE over NFAR and Results Overview - Double Mass Analysis (DMA) Overview - Double Mass Analysis (DMA) Gauge numeric ID Lake Sp aulding Square symbols indicate months with more than 80% of hourly or daily data in one month available as valid data for that month. Mar 2003 Mar 2003 Evaluation of average QPE over 4- pixel groups Mar 2003 Mar 2003 Mar 2003 Mar 2003 Mar 2003 -700 -600 -500 -400 -300 -200 -100 0 Oct- 88 Oct- 89 Oct- 90 Oct- 91 Oct- 92 Oct- 93 Oct- 94 Oct- 95 Oct- 96 Oct- 97 Oct- 98 Oct- 99 Oct- 00 Oct- 01 Oct- 02 Oct- 03 Oct- 04 Oct- 05 Date Accum ulated Error,m m EFCR NFAR Accum ulated error= Σ(Q sim -Q obs ) E ast Folk of Carson ast Folk of Carson North F olk of American Error trend changes from under- to overestimation Consistent error trend Simulation with corrected QPE Simulation with original QPE Evaluation of average QPE over elevation zones Inconsistency correction: Use correction factor based on two slopes. (NWS/HL On-line documentation of Interactive Double Mass Analysis (IDMA) User's Guide . [http://www.nws.noaa.gov/oh/hrl/idma/html/dma_home_frame.htm] ΣR Moreda, F., Cong, S., Schaake, J., and Smith, M., 2006. Gridded Rainfall Estimation for Distributed Modeling in Western Mountainous Areas. Poster H23A, Spring Meeting of the AGU, May 23-27, Baltimore, MD. Reference Reference
1. Introduction
Jan 15, 2016
Deviation of Acc. precip. of target cell from Acc. Reference precip. Σ ( P i - R i ). 0. Acc. precip. of reference Σ R i. Diagnosis of inconsistencies in multi-year gridded precipitation data over mountainous areas and related impacts on hydrologic simulations. - PowerPoint PPT Presentation
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Diagnosis of inconsistencies in multi-year gridded precipitation data over mountainous Diagnosis of inconsistencies in multi-year gridded precipitation data over mountainous areas and related impacts on hydrologic simulationsareas and related impacts on hydrologic simulations
1. Introduction1. Introduction
Multi-year gridded Quantitative Precipitation Estimates (QPE) were derived for 1988-2002 for the western basin experiments of the Distributed Model Intercomparison Project (DMIP 2). This data set was subsequently extended to 2006 as more recent data became available. However, we uncovered consistency errors in the extended data set, requiring diagnostic analysis of the underlying gauge data. In this poster we:
• Present a method to diagnose consistency errors in high spatial-temporal resolution QPE grids.
• Illustrate the importance of temporally consistent QPE for hydrologic modeling.
Naoki Mizukami1,2, Michael Smith1
1NWS/OHD, 2 Len TechOffice of Hydrologic Development, NOAA National Weather Service 1325 East-West Highway, Silver Spring, MD 20910, U.S.A.
e-mail [email protected]
2. Background2. Background
AGU 2010 Fall Meeting Dec 11- 17, San Francisco, CA
3. Consistency Check - Gridded Double Mass Analysis3. Consistency Check - Gridded Double Mass Analysis 4. Diagnosis of QPE Inconsistency 4. Diagnosis of QPE Inconsistency
6. Summary6. Summary
Plot of ‘reference’ cumulative time series R against deviation of cumulative P at target cell from cumulative time series R
• DMIP2 Sierra Nevada basins
- North Folk of American (NFAR)
- East Folk of Carson (EFCR)
• Generation of gridded precipitation and air temperature for hydrologic model forcing (Moreda et al. 2006)
-Resolution: 4km and hourly
-Period: WY1988 - WY 2006
-Spatial interpolation of hourly gauge data
1) Generating historical temporally-consistent QPE grid can be challenging given temporally varying gage networks as well as inconsistent data at each gauge.
2) Developed a method for consistency detection for high spatial and temporal resolution QPE.
3) More consistent QPE leads to more consistent error trends within the simulation, making it easier to improve the simulation with further model calibration
H23A-1172
-150
-50
50
150
250
350
450
550
650
750
Oct-88
Oct-89
Oct-90
Oct-91
Oct-92
Oct-93
Oct-94
Oct-95
Oct-96
Oct-97
Oct-98
Oct-99
Oct-00
Oct-01
Oct-02
Oct-03
Oct-04
Oct-05
Date
Acc
um
ulat
ed E
rror
, m
m
Original DMIP2 MAP
Adjusted DMIP2 MAP
Accumulated error = Σ(Qsim-Qobs)
5. Hydrologic Simulations with Corrected Data5. Hydrologic Simulations with Corrected Data
Each point corresponds a specific measurement time (e.g. year, month etc.)
Time when inconsistency occurs
Reference time series: Ri
niG
GP
R k
jj
m
jij
j
j
i ,........,1
1
2
1
2
n: the number of time stepsρj: correlation coefficient between two time series at a target gage
and at a neighbor gauge jGij: time series of a neighbor gauge j, Gj: mean of the series at the target gage.P: mean of the time series at the neighbor gauge j.
Inconsistency found at groups 4 through 8 in Mar 2003 (upper portion of basin)
S
SS
S
S
S
S
S
S
S
S
SS
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
SS
9
7
6
5
4
3
2
1
82
81
7977
76
75
73
72
71
70
69
68
676665
64
61
60
58
57
56
54
52
5048
47
46
45
44
43
42
40
39
38
37
35
34
33
31
29
2827
26
25
24
23
22
21
20
19
18
16
15
13
11
10
High : 4274
Low : -84.8521
NFAR
EFCR
• Need to diagnose the error trend change for NFAR.
• Started investigation of consistency of gridded QPE time series. (consistency: error magnitude and sign is consistent over the period)
Target -> time series at pixel groups 1 thru 8 (average P over 4 pixels)Reference -> time series based on 4 reference gages
Target -> time series at lower and upper zones (average P over each zone)Reference -> time series based on 4 reference gages
DMIP2 Sierra Nevada basins, CADMIP2 Sierra Nevada basins, CA
Streamflow Simulation Results Using Gridded SAC-SMA and SNOW-17Streamflow Simulation Results Using Gridded SAC-SMA and SNOW-17
Inconsistency found at upper elev. zone
The streamflow simulations made with corrected QPE illustrate improvement of the error trend. The simulations below were generated by running the SAC-SMA and SNOW-17 models in two elevation zones for North Folk of American
Devia
tion
of
Acc.
pre
cip
. of
targ
et
cell f
rom
Acc.
Refe
ren
ce p
recip
. Σ(P
i-R
i)
0Acc. precip. of reference
ΣRi
Consistent time series
Σ(P
-R)
Σ(P
-R)
Σ(P
-R)
ΣR
ΣR ΣR
History for the individual gauges used to generate the gridded QPE indicates temporally varying gauge network. Discontinuation of the Lake Spaulding (ID-58) caused gridded QPE inconsistency
Jan-89
Jan-90
Jan-91
Jan-92
Jan-93
Jan-94
Jan-95
Jan-96
Jan-97
Jan-98
Jan-99
Jan-00
Jan-01
Jan-02
Jan-03
Jan-04
Jan-05
Jan-06
Jan-07
1237101113161920232728373842434446515254565758666768697379
Application to QPE over NFAR and ResultsApplication to QPE over NFAR and Results
Overview - Double Mass Analysis (DMA)Overview - Double Mass Analysis (DMA)
Gau
ge n
um
eri
c I
D
Lake Spaulding
Square symbols indicate months with more than 80% of hourly or daily data in one month available as valid data for that month.
Mar 2003
Mar 2003
Evaluation of average QPE over 4-pixel groups
Mar 2003
Mar 2003
Mar 2003
Mar 2003
Mar 2003
-700
-600
-500
-400
-300
-200
-100
0
Oct-88
Oct-89
Oct-90
Oct-91
Oct-92
Oct-93
Oct-94
Oct-95
Oct-96
Oct-97
Oct-98
Oct-99
Oct-00
Oct-01
Oct-02
Oct-03
Oct-04
Oct-05
Date
Acc
um
ulat
ed E
rror
, m
m
EFCR
NFAR
Accumulated error = Σ(Qsim-Qobs)
East Folk of Carson
ast Folk of CarsonNorth Folk of American
Error trend changes from under- to overestimation
Consistent error trend
Simulation with corrected QPE
Simulation with original QPE
Evaluation of average QPE over elevation zones
Inconsistency correction:Use correction factor based on two slopes. (NWS/HL On-line documentation of Interactive Double Mass Analysis (IDMA) User's Guide . [http://www.nws.noaa.gov/oh/hrl/idma/html/dma_home_frame.htm]
ΣR
Moreda, F., Cong, S., Schaake, J., and Smith, M., 2006. Gridded Rainfall Estimation for Distributed Modeling in Western Mountainous Areas. Poster H23A, Spring Meeting of the AGU, May 23-27, Baltimore, MD.
ReferenceReference
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