1. Improving Medium-Range Ensemble-Based QPF over the Western United States Trevor Alcott and Jon Rutz NOAA/NWS WR-STID Jim Steenburgh University of Utah jim.steenburgh@utah.edu

2. The Challenges 2008–2014 GEFS Day 1 Mean Climo1981–2010 PRISM Climo Precipitation variability is inherently sub-grid scale

3. The Challenges LCC BCC 48” 93” Salt Lake City ~10 km 650+” 100” 509” 404” 300” 316” <200” Park City Due to SLR and snow fraction, snow is even worse Source: http://sharewhat.blogspot.com/2010_11_01_archive.html; Data: PRISM, WRCC Estimated WRCC/COOP

4. The Challenges Precipitation frequently wind-direction dependent Source: PRISM, Dunn (1983) Ogden 190º–240º Alta: 300º–330º

5. The Challenges It also depends on blocking Source: Neiman et al. (2002) OR<1

6. The Challenges It also depends on sub-cloud effects Source: Neiman et al. (2002) Cloud Base

7. The Challenges It also depends on synoptic context Source: Steenburgh (2003, 2004)

8. The Challenges Interior precipitation features inherently small scale Source: Serreze et al. (2001) “Large midwinter snowfall events in The 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

9. The Challenges Interior model skill is inherently low Source: Brill (2012), Williams and Heck (1972) “The scattered nature of precipitation in [northwest Utah] is shown to have a pronounced effect on Brier scores for Forecasts of probability of precipitaiton. ” –Williams and Heck (1972) West Coast Western Interior Southeast US

11. Key Questions What can we really squeeze out of statistical downscaling? How can we better identify heavy precipitation events Emphasis on western U.S.

12. Statistical Downscaling

13. Simple Statistical Downscaling Similar to Mountain Mapper/WPC Approach

14. Example (subset of NAEFS) KSLC Alta

15. Because We Can!

16. Does Downscaling Work?

17. Day 3 Reliability @ Mt Sites Downsclaed RAW

18. Underlying Issues Leeward wet bias Neutral/dry bias over mts

19. Underlying Issues Regardless of situation, downscaling with climo yields a climatological “orographic ratio” (OR)

20. 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

21. Identifying Heavy Precipitation Events

22. 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?

23. 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

24. Results

25. 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)

26. Example

27. Reliability

28. Results

29. WR Situation Awareness Table http://ssd.wrh.noaa.gov/satable/ Select “Output: GEFS QPF M-Climate”

30. 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

31. Day 1 Reliability @ Mt Sites Downsclaed RAW