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

owns

clae

d

R

AW

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

owns

clae

d

R

AW