climate diagnostics and prediction workshop lincoln, nw october 22, 2008
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Drought Monitoring and Prediction Systems at the University of Washington and Princeton University . Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington. Climate Diagnostics and Prediction Workshop Lincoln, NW October 22, 2008. - PowerPoint PPT PresentationTRANSCRIPT
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Drought Monitoring and Prediction Systems at the University of Washington and
Princeton University
Climate Diagnostics and Prediction WorkshopLincoln, NW
October 22, 2008
Dennis P. LettenmaierDepartment of Civil and Environmental Engineering
University of Washington
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Outline of this presentation
• Motivation for experimental hydrological prediction systems
• Evolution of the UW and Princeton systems• Current components
– UW west-wide prediction system– UW surface water monitor– Princeton eastern U.S. and CONUS systems– Integration
• Outstanding issues
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Motivation for experimental hydrological prediction systems: Traditional “bottom up” hydrologic modeling approach (subbasin by subbasin)
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Flood of record
• Principal calibration locations were the Skykomish at Gold Bar and the Snoqualmie at Carnation
Snoqualmie River at Carnation, WA
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How important is calibration for seasonal hydrologic prediction?
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uncalibrated
uncalibrated bias corrected
calibrated
How important is calibration: ensemble mean (from ESP) vs obs for April-July forecasts on six forecast dates, Gunnison River, CO
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From Wood and Lettenmaier (BAMS, 2006):
•Despite the potential benefits of improved hydrologic forecasts, most operational hydrologic prediction at seasonal lead times … are based on methods and data sources that have been in place for almost half a century.
•The skill of western U.S. seasonal streamflow forecasts has generally not improved since the 1960s.
•While forecast accuracy improvements would likely result from observing system densification, the need for long data records in regression-based methods would take decades to realize, and would be complicated by a changing climate.
•We believe that a more promising pathway lies in the development of methods … for assimilating new sources of observational data into land surface energy and water balance models, which can then be forced with modern climate and weather forecasts.
Why do we need an experimental hydrological prediction system?
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One reason for the slow progress in hydrologic prediction has been the lack of real-time testing of new prediction models and methods …
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The need for a national perspective on hydrologic prediction
• Will help to address emerging water resources operation and planning issues (e.g., nonstationarity)
• Better exploit predictability in weather and climate (which is inherently at progressively larger scales with lead time)
• Make better use of methods, like data assimilation, that can use large scale data sources to improve hydrologic initial conditions
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Evolution of the UW and Princeton (near) real-time hydrologic forecast systems
From Wood et al (2002) – development of a hydrologically based statistical downscaling method
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GSM Regional Bias:a spatial exampleBias is removed at the monthly GSM-scale from the meteorological forecasts
(so 3rd column ~= 1st column)
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Downscaling Test1. Start with GSM-scale
monthly observed met data for 21 years
2. Downscale into a daily VIC-scale time series
3. Force hydrology model to produce streamflow
4. Is observed streamflow reproduced?
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Simulations
Forecast Productsstreamflow soil moisture
runoffsnowpack
VIC model spin-upVIC forecast ensemble
climate forecast
information (from GSM)
VIC climatology ensemble
1-2 years back start of month 0 end of month 6
NCDC met. station obs. up to
2-4 months from
current
LDAS/other met.
forcings for remaining
spin-up
data sources
A B C
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Model forecasting domain
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East Coast hindcast
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Approach: 1/8 - 1/4 degree implementation
Pilot scale implementationPacific Northwest
UpdatesDec 28, 2002 ESPJan 15, 2003 ESPFeb 1 ESP, GSM, NSIPPFeb 15 ESPMar 1 ESP, GSM, NSIPPMar 16 ESPApr 1 ESP, GSM, NSIPP<disk crash>
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Pilot Forecasts: Initial Conditions
Jan 15, 2003Dec 28, 2002
Feb 1, 2003 Mar 1, 2003 Apr 1, 2003
This past winter, alarmingly low PNW December snowpacks mostly recovered by April, although some locations are still well off their long term averages
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Winter 2002/03 forecasts: UW/NRCS comparison
Apr-Sep Streamflow Forecasts Columbia River at the Dalles, OR
50
60
70
80
90
1-Jan 1-Feb 1-Mar 1-Apr 1-Mayforecast date
perc
ent o
f nor
mal
UWNRCSBest Estimate
UW pilot results were comparable to the official streamflow forecasts of the National Resources Conservation Service (NRCS) streamflow forecast group (one location shown).
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UW West-wide forecast system – current domain and streamflow forecast points
•~250 forecast points, including ~15 in Mexico
•Forecast models/methods include CPC “official” forecasts, ESP, and stratified ESP
•Forecasts for 6-12 month lead issued twice monthly (winter), monthly otherwise
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UW West-wide forecast system soil moisture nowcast (8/6/08)
•Daily updates, 24 hour lag effective ~2 pm Pacific
•Based on ~2000 index stations, adjusted to long-term (1915 – present) climatology
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Princeton University drought monitoring and prediction system~weekly nowcast update, eastern U.S. domain
Uses NLDAS forcings
Focus on (soil moisture) drought nowcast and forecast
Forecasts based on Bayesian MME merging of GFS and ESP
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UW National Surface Water Monitor•½ degree spatial resolution
•Updates daily (same lag as west-wide system)
•Same index station approach as west-wide system
•Climatology 1915-present
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UW Multi-model monitor
•Same approach as VIC-based SWM
•Models include VIC, Noah, CLM, Sac
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0
100
Multi-ModelCumulative Probability,
1916-2004
50 800Soil Moisture (mm)
%
Multi-model Ensemble
100
0
Model iCumulative Probability,
1916-2004
50 800Soil Moisture (mm)
%
For each model, re-express current soil moisture as percentile of climatology for this day of year
Model isoil moisture
Model ipercentile
Average all models’ percentiles = 1/N Σ (i=1 to N) percentile i
Multi-ModelpercentileMulti-model ensemble result is
the percentile of the average of model percentiles
This procedure occurs separately for each grid cell
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Soil Moisture Percentiles w.r.t. 1920-20032008-07-01
CLM
SAC NOAH
ENSEMBLE
VIC
US Drought Monitor
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US Drought Monitor UW Surface Water MonitorMultimodel Ensemble
Jul 1
Aug 5
Sep 2
Agreement: WI drying trend
Agreement: Gulf wetting trend
Disagreement: Dry conditions in N.,S. Carolina?
Agreement: Dry west coast
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Soil Moisture Percentiles w.r.t. 1916-20042008-07-01
CLM
SAC NOAH
ENSEMBLE
VIC
US Drought Monitor
Multimodel results with drought monitor color scheme (truncated at 30th percentile)
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US Drought Monitor UW multimodel SWM Summer 2008
Jul 1
Aug 5
Sep 2
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Ongoing unification of UW and Princeton systems
a) unified nowcast (completed, in testing)b) expansion of multimodel SWM domain
into Mexico (in progress)c) merger of forecast methods (esp.
multimodel Bayesian MME) – plannedd) improved data assimilation – plannede) multiple (land) model forecasts – plannedf) reservoir storage forecasts -- planned
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Conclusions and challenges
• Need for national scale hydrological prediction (including streamflow)
• Need for better ways of including a historical perspective (what historical period?) post-data assimilation
• Need for site-specific calibration (MOS-type approaches?) and verification
• Mechanisms for inclusion of local information?