data assimilation with wrf-hydro & the national water model · data assimilation improvements...
TRANSCRIPT
J. McCreight
National Center for Atmospheric Research
Data Assimilation with
WRF-Hydro & The National Water Model
MPE
NLDAS
0.0
0.5
1.0
0.0
0.5
1.0
May 15 Jun 01 Jun 15 Jul 01 Jul 15 Aug 01
2012S
trea
mflow
(cm
s)
Model ensemble
Obs 95% uncert
Fourmile Creek at Orodell
MPE
NLDAS
0.0
0.5
1.0
1.5
0.0
0.5
1.0
1.5
May 15 Jun 01 Jun 15 Jul 01 Jul 15 Aug 01
2012
Str
eam
flow
(cm
s)
Observed
Model state
MPE
NLDAS
0.0
0.5
1.0
0.0
0.5
1.0
May 15 Jun 01 Jun 15 Jul 01 Jul 15 Aug 01
2012
Str
ea
mflow
(cm
s)
Model ensemble
Obs 95% uncert
Fourmile Creek at OrodellMPE
NLDAS
0.0
0.5
1.0
0.0
0.5
1.0
May 15 Jun 01 Jun 15 Jul 01 Jul 15 Aug 01
2012
Str
ea
mflow
(cm
s)
Model ensemble
Obs 95% uncert
Fourmile Creek at OrodellMPE
NLDAS
0.0
0.5
1.0
0.0
0.5
1.0
May 15 Jun 01 Jun 15 Jul 01 Jul 15 Aug 01
2012S
trea
mflow
(cm
s)
Model ensemble
Obs 95% uncert
Fourmile Creek at Orodell
DA without parameter estimation
DA with parameter estimation
http://www.image.ucar.edu/DAReS/DART/
• Ensemble DA with NCAR’s DART
• Ensemble error generation package
• State variables: – discharge, head, soil moisture, ground
water level
• Uncalibrated model =>join state-parameter estimation– Parameter multipliers: precipitation,
saturated conductivity, porosity, surface roughness, bucket exponent, soil column discharge
• Various research questions and directions
Once (before “IOC”) & Future Data Assimilation
• Ensemble DA with NCAR’s DART
• NWM Channel-only configuration
• (Buckets and reservoir are optional in the updating)
• Streamflow• Overland+subsurface -> channel fluxes• Bucket -> channel fluxes
• Channel flux diagnostics• Observation localization• Path towards “full” model updating
Future Data Assimilation: Ensemble DA with NWM
Cycling ForecastConfigurations &
Outputs
Hourly
Daily x 16
ensembles
Daily
0 – 18 hrs
to 10 days
to 30 days
1-km spatial fluxes
(water & energy);
250-m routed fluxes
(water);
NHDPlus channel routing
1-km spatial fluxes
(water & energy);
250-m routed fluxes
(water);
NHDPlus channel routing
1-km spatial fluxes
(water & energy); NHDPlus
channel routing
Downscaled
HRRR/RAP Blend
Downscaled GFS
Downscaled & NLDAS2
Bias Corrected CFS
Meteorological
Forcing
Hourly -3 – 0 hrs
1-km spatial fluxes
(water & energy);
250-m routed fluxes
(water);
NHDPlus channel routing
MRMS QPE
National Water Model (CONUS) Forecast Configurations:
3. Terrain Routing Module(250 m grid)
2-w
ay
cou
plin
g
5. Channel & ReservoirRouting Modules
4. NHDPlus Catchment Aggregation
2. NoahMP LSM(1 km grid)
1. WRF-Hydro Forcing Engine (1 km grid) 6. USGS stream gages
7. GOES satellites
8. USGS National Water Information System (NWIS)
NWM Chain with Nudging Data Assimilation
• Lots of available observations from USGS NWIS
– 2015: • 6,000 – 8,000 available stations
(.2-.3% of NHD reaches)
• 15,000,000 – 25,000,000 observations monthly
• State Agencies...
• Why nudging?
– Calibration challenges =>model biases =>improper error covariances
– (No error covariances => treat symptom not cause)
– Computationally tractable atnational scale
(6% vs 6000% overhead)
– Future: hybrid with other DA methods
Discharge: Monthly Observations Reported
Discharge: Monthly Stations Repor ting
15,000,000
20,000,000
25,000,000
6,000
7,000
2008 2009 2010 2011 2012 2013 2014 2015 2016
co
un
t
allperfect QC
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25
30
35
40
45
50
−120 −100 −80
Gages Reporting Discharge to NWIS 2013−2015
Nudging DA: Motivation
• Muskingum-Cunge (v1.0 – v1.?)– No “backwater” effects (innundation)
– Various parameters (incl. channel geom)
• Channel Network– NHD+ v1.2 (lightly edited)
– NCAR OCONUS contributing domains
• Operational Output Variables (v1.1)– time & reference_time
– feature_id
– Streamflow (prognostic)
– velocity
– q_lateral
– nudge
NWM Channel Routing
• NLDAS Forcing
• 2011-2015
• NHD+ v1.2 + OCONUSstream network
• 3 runs
– Open Loop
– “Reanalysis”: all gages assimilated
– “Validation”:959 gages witheld
NWM V1 Retrospective Reanalysis
Lumber River near Maxton, NC (validation)belowDrowning Creek near Hoffman, NC (assimilated upstream)
Inner 86% of distribution shown.
Skill Distribution Improvements
30
40
50
−120 −100 −80
lon
lat
% Improvementin Open LoopAbsolute Bias
[0,20](20,40](40,60](60,80](80,100]
Open LoopAbsolute Bias
02550
75
100
Percent Improvement in Open Loop Absolute Bias
• Noisy but clear relationship between skill and % ungaged area.• Why negative improvements: topology issues with NHD, constructive gage errors, etc?• Bigger drainage areas are likely to have larger gaged % area and better results from DA.• Gages-II tend to fit better than (conditonal) average, but still negative improvements.
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Absolute Bias Correlation RMSE KGE NSE Log NSE
−50
0
50
100
0 .25 .5 .75 1 0 .25 .5 .75 1 0 .25 .5 .75 1 0 .25 .5 .75 1 0 .25 .5 .75 1 0 .25 .5 .75 1
% Gaged Drainage Area Above Validation Gage
% I
mpro
ve
me
nt
inO
pe
n L
oop
Sta
tistics
54.598151096.6331622026.46579442413.39201
Drainage Area(sq km)
Gages−IIReference
FALSETRUE
100m terrain
NLCD 2011 Land Cover
• Estimate the model bias over some period before forecast
•Remove that bias during the forecast (blend from persistence to bias correction)
• Improved correlation in forecast (though more bias than full persistence)
Data Assimilation Improvements for v1.1-v1.2: Fix bias in Forecast
V1.1 NWM Reforecast Archive
• Sept 15 – Oct 16, 2016 (32 days)
•Hurricane Matthew
• Flooding in Carolinas
•Drought in CA
• Flooding in Carolinas
NWM Channel-only model
• The channel + lake and bucket models are 1-way FORCED by the rest of the model: run separately at less expense.
V1.2 NWM Science Evaluation Period
• June 13– July 13, 2016 (30 days)
•North East flooding
Continuous “bulk” short-range forecast statistics better for V1.2
Improvements in “bulk” short-range forecast statistics depend on RFC
Continuous “cycle-wise” short-range forecast statistics better for V1.2
V1.1
V1.2
Short-range reforecastlead-time statistics
NOTE: COLORS ARE REVERSED
Lead-time Improvements in short-range forecast statistics
Improved innovations V1.2 Changed in nudging method
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25
30
35
40
45
50
−120 −100 −80
Gages Reporting Discharge to NWIS 2013−2015
But all was not well in CONUS
V1.1
V1.2
Phase issues highlighted by bias correction were not well revealed in continuous variable analysis
Improvements in ‘bulk’ short-range contingency statistics
Select RFCS have worse False Alarm Rates
• Probabilistic / Ensemble DA
– Exists with HydroDART
– Scheduled to appear in a future version of NWM
• Channel-only EnKF
• NWM Streamflow nudging
– USGS gages (~7000 every 15 mins)
– Computationally cheap
– Analysis: Improvements proportional to % upstream gaged area
– Forecast: Drift to bias
– Forecast Bias Correction:
• Lots of pros
• A few cons: Improved evaluation strategy in conjunction with RFCs
Summary
