Development of an Ensemble
Gridded Hydrometeorological
Forcing Dataset over the
Contiguous United States
Andrew J. Newman1, Martyn P. Clark1, Jason Craig1, Bart Nijssen2,
Andrew Wood1, Ethan Gutmann1, Naoki Mizukami1, Levi Brekke3, and
Jeff R. Arnold4
National Center for Atmospheric Research, Boulder CO, USA
2 University of Washington, Seattle WA, USA
3U.S. Department of Interior, Bureau of Reclamation, Denver CO, USA
4 U. S. Army Corps of Engineers, Institute for Water Resources, Seattle
WA, USA
1
Outline
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Motivation
Methodology
Example output
Validation
▫ Comparisons to deterministic
▫ Statistical properties
• Summary
Motivation
Methodology
Input Data
Example Output
Validation
Summary
Opportunities for hydrologic prediction
hydrological predictability
meteorological predictability
Meteorological predictability:
How well can we forecast the
weather and climate?
Hydrological Prediction: How
well can we estimate the
amount of water stored?
Accuracy in precipitation
Opportunity:
estimates
Characterize
historical
Fidelity of hydro model
forcing
uncertainty ->
simulations
improve
waterofstored
Effectiveness
hydrologic data
estimates
assimilation methods
• Current uncertainties
for land-surface
modeling are generally
order of magnitude
• Can we begin to quantify
space-time uncertainty?
Water Cycle (from NASA)
Motivation
Methodology
Input Data
Example Output
Validation
Summary
Precipitation Data Uncertainty
• Point to grid uncertainties:
▫ Measurement errors
 Gauge placement issues (e.g. roof)
 Is a gauge working properly?
▫ Measurement representativeness
▫ Missing Data
 Interpolate available obs?
 Fill missing data?
1
adjusted
Correlation coefficient
 Sub-pixel variability
 Take closest measurement?
 Interpolate nearby gauges?
0.8
0.6
0.4
light rain
0.2
heavy rain
0
0
1
2
3
4
5
6
Gauge separation distance (km)
7
Motivation
Methodology
Input Data
Ensemble Generation
Step 1:
Locally weighted
regression at each grid cell:
Probability of Precipitation
(PoP) via logistic
regression, then amount
and uncertainty (least
squares mean & residuals)
Example Output
Validation
Summary
Example over the Colorado Headwaters
observations
Example over the Colorado Headwaters
Clark & Slater (2006), Newman et al. (2014, in prep)
Motivation
Methodology
Input Data
Example Output
Validation
Summary
Ensemble Generation
Step 2:
Synthesize ensembles
from PoP, amount &
uncertainty using spatially
correlated random fields
(SCRFs)
observations
•Other Methodological choices:
•Topographic lapse rates derived at each grid cell for each
day vs. climatology (e.g. PRISM)
•Used serially complete (filled) station data rather
than only available obs vs. using only available
observations
•Included SNOTEL observations vs. excluding
•Final Product: 12 km, daily 1980-2012, 100
members, precipitation & temperature (1.5 TB)
Example over the Colorado Headwaters
Clark & Slater (2006), Newman et al. (2014, in prep)
Motivation
Methodology
Input Data
Example Output
Validation
Summary
Development of Serially Complete Station Dataset
• 12,000+ stations with serially complete data (interpolation & CDF
matching)
• Precipitation, temperature or both
Motivation
Methodology
Input Data
Example Output
Validation
Summary
Development of Serially Complete Station Dataset
• GHCN-Daily(Cooperative networks, Automated (ASOS/AWOS)) in US,
Canada and Mexico (thanks to Kyoko Ikeda)
• Use QC from National Climatic Data Center (NCDC)
• SNOwpack TELemetry (SNOTEL) observations from National Resource
Conservation Service (NRCS) have no QC
• Applied QC from Serreze et al. (1999)
• Removes gross temperature and precipitation errors
• Also corrects for gauge under-catch using large accumulation
events -> Precip to SWE ratio
Motivation
Methodology
Input Data
Example Output
Validation
Summary
Development of Serially Complete Station Dataset
• Variable station data availability
• Stations come and go, have missing data
• Use all stations with > 10 years of data and > 10 years of overlap with ≥
30 stations within 500 km (generally following Eischeid et al. 2000)
• Interpolate temperature gaps of 1-2 days
• Use CDF matching from best available rank correlated station to fill larger
temperature gaps and all precipitation missing data
• Assumes stationary CDF in time
Motivation
Methodology
Input Data
Example Output
Example Output
• Central US Flood of 1993
• June 1993 total precipitation
Validation
Summary
Motivation
Methodology
Input Data
Example Output
Validation
Summary
Validation: Comparisons to other datasets (temperature)
• Minimal differences in mean
temperature
• Except in intermountain
west
• Maurer uses constant
elevation lapse rate (-6.5
K km-1)
• Ensemble lapse rate
derived from station data
(including Snotel)
• Maurer shown to be too
cold in higher elevations
(e.g. Mizukami et al. 2014)
• Ensemble warmer than
Maurer in high terrain
Motivation
Methodology
Input Data
Example Output
Validation
Summary
Validation: Comparisons to other datasets (PoP)
• Maurer et al. (2002)
• Interpolation between
observations increases
precip occurrence
• Ensemble:
• SCRFs applied to PoP
field more realistic PoP
• Generally reduces PoP
• Data differences may be
responsible for PoP
increases
Motivation
Methodology
Input Data
Example Output
Validation
Validation: Comparisons to other datasets (amount)
• Ensemble - Maurer
Maurer et al. (2002)
•
NLDAS - Maurer
Xia et al. (2012)
•
Daymet - Maurer
Thornton et al. (2014)
Version 2 (regridded to
12km)
Summary
Motivation
Methodology
Input Data
Example Output
Validation
Summary
Validation: Comparisons to other datasets (amount)
• CONUS precip difference PDFs
• NLDAS & Maurer agree very closely (both use PRISM
correction, similar input gauges)
• Daymet wettest
• Slightly larger spread in ensemble – most distinctly different
Motivation
Methodology
Input Data
Example Output
Validation
Summary
Validation: Discrimination & Reliability
• Reliability (blue & black lines)
• Want to be close to 1-1 line (predicted = observed
probabilities)
• Generally good reliability
>0 mm
>13 mm
>25 mm
>50 mm
>0 mm
>25 mm
>13 mm
>50 mm
Motivation
Methodology
Input Data
Example Output
Validation
Summary
Validation: Discrimination & Reliability
• Discrimination (red & black lines)
• Want to maximize separation of PDFs
• Generally good separation
>0 mm
>13 mm
>25 mm
>50 mm
>0 mm
>25 mm
>13 mm
>50 mm
Motivation
Methodology
Input Data
Example Output
Validation
Summary
Example Application
• Snowmelt dominated basin in Colorado Rockies
• Example water year daily temperature (a)
• Snow water equivalent accumulation (b)
• Simple temperature index model (optimized for Daymet
(green))
Motivation
Methodology
Input Data
Example Output
Validation
Summary
Summary
• Developed ensemble hydrometeorolgical dataset
• Required development of serially complete station dataset
• Both will be available for download at:
http://ral.ucar.edu/projects/hap/flowpredict/subpages/pqpe.php
• Methodology follows Clark and Slater (2006) but for CONUS
• Ensemble has:
• Realistic probability of precipitation (PoP)
• Estimate of forcing uncertainty
• Quantification of uncertainty for data assimilation
• Potential Applications:
• Propagation of forcing uncertainty into model states
• Useful for state uncertainty estimation (e.g. SWE)
• Ensemble calibrations can allow for estimates of parameter
uncertainty (impact of noise on parameter estimation)
Motivation
Methodology
Input Data
Example Output
Validation
Summary
Next Steps
• Include downward shortwave and humidity in ensemble
• Improved downward shortwave radiation product (Slater et al.,
in prep)
• Stations across US at many elevations, climatic conditions
• Will allow for improved temperature/moisture – shortwave
relationships
• Include ensemble into data assimilation system(s)
• Particle filter and ensemble Kalman filter
• Can we improve daily to seasonal streamflow prediction using
automated systems?