Post on 28-Jan-2016
Improving Medium-Range Ensemble-Based QPF over the Western United
States
Trevor Alcott and Jon RutzNOAA/NWS WR-STID
Jim SteenburghUniversity of Utah
jim.steenburgh@utah.edu
The Challenges2008–2014 GEFS Day 1 Mean Climo 1981–2010 PRISM Climo
Precipitation variability is inherently sub-grid scale
The Challenges
LCC
BCC
48”
93”
Salt LakeCity
~10 km
650+” 100”
509”
404” 300”
316”
<200”ParkCity
Due to SLR and snow fraction, snow is even worse
Source: http://sharewhat.blogspot.com/2010_11_01_archive.html; Data: PRISM, WRCC
EstimatedWRCC/COOP
The Challenges
Precipitation frequently wind-direction dependent
Source: PRISM, Dunn (1983)
Ogden 190º–240º
Alta: 300º–330º
The Challenges
It also depends on blocking
Source: Neiman et al. (2002)
OR<1
The Challenges
It also depends on sub-cloud effects
Source: Neiman et al. (2002)
Cloud Base
The Challenges
It also depends on synoptic context
Source: Steenburgh (2003, 2004)
The Challenges
Interior precipitation features inherently small scale
Source: Serreze et al. (2001)
“Large midwinter snowfall events inThe marine sectors, Idaho, Arizona/
New Mexico are [more] spatially coherent...Large events are less
spatially coherent for drier inland regions”–Serreze et al. (2001)
Low Coherence Leftovers
High Coherence
The Challenges
Interior model skill is inherently low
Source: Brill (2012), Williams and Heck (1972)
“The scattered nature of precipitationin [northwest Utah] is shown to have apronounced effect on Brier scores for
Forecasts of probability of precipitaiton. ”–Williams and Heck (1972)
West Coast Western
InteriorSoutheast US
Key Questions
• What can we really squeeze out of statistical downscaling?
• How can we better identify heavy precipitation events
• Emphasis on western U.S.
Statistical Downscaling
Simple Statistical Downscaling
Similar to Mountain Mapper/WPC Approach
Example (subset of NAEFS)KSLC
Alta
Because We Can!
Does Downscaling Work?
Day 3 Reliability @ Mt SitesD
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Underlying Issues
Leeward wet bias
Neutral/dry bias over mts
Underlying Issues
Regardless of situation, downscaling with climo yields a climatological“orographic ratio” (OR)
Possible Pathways to Improvement
• Wait for high-res 1-km super ensemble
• Develop OR “parameterization” that can be applied ex post facto– Challenge: Need a reliable relationship between large-scale
conditions and orographic enhancement across wide range of regional climates and topographic scales
• Dynamical downscaling– Use single high-res run applied to one ensemble member to
scale precipitation• Issue: Large spread, what member do I pick?
– Use a simple model that can be applied to each ensemble member
• Rhea model works OK over broad topographic features, not so well at finer scales
Identifying Heavy Precipitation Events
Question #1
• It is generally thought that medium-range QPFs have limited skill
• Recent studies show that spatially coherent “proxy” variables, such as IWV and IVT are highly correlated with precipitation over complex terrain
• Question: Are forecasts of these proxy variables more skillful for predicting observed precipitation than model QPF itself?
Methodology
• Quantify relationship between cool-season (Oct-Mar) GEFS reforecast data (QPF, IWV, and IVT) and analyzed QPE
• QPE from the CPC Unified Precip Analysis– 0.25º resolution– 24-h totals valid at 1200 UTC
• IWV and IVT forecasts from 0000 UTC and 24-h QPF are compared to QPEs valid 1200–1200 UTC
Results
Question #2
• Question: Model QPF suffers from low absolute accuracy, but can “outlier QPF” reliably predict “outlier QPE”?
• Use an M-Climate approach to identify event intensity– M-Climate: The percentile rank of an ensemble
mean forecast for a given variable and lead time (relative to all forecasts at that lead time) is compared to the percentile rank of an observation/analysis (relative to all analyses)
Example
Reliability
Results
WR Situation Awareness Table
http://ssd.wrh.noaa.gov/satable/Select “Output: GEFS QPF M-Climate”
Summary
• Simple downscaling appears to be better statistically than NWS forecasts and raw model QPF– Still numerous problems
• Ensemble mean GEFS QPF correlates better with QPE than IWV or IVT
• Over the west, forecast skill and reliability are generally larger (smaller) along and upstream (downstream) of major topographical barriers
Day 1 Reliability @ Mt SitesD
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