1. Progress and Problems with Forecasting Orographic Precipitation over the Pacific Northwest and Southwest Canada Clifford F. Mass, University of Washington, Seattle, WA AMS Mountain Meteorology Conference, August 2008

2. Orographic Precipitation is an essential part of the regional meteorology

3. Few Areas of North America Experience Such Large Amounts and Gradients of Precipitation

5. Northwest Orographic Precipitation Has Major Societal Impacts Flood Control on Dozens of Dams (Wynochee Dam shown)

6. Billion-Dollar Storms Are All Associated with Orographic Precipitation

7. Mount Rainier National Park 18 inches in 36 hr (Nov 8, 2006)

8. Dec. 3, 2007 20 inches in two days over coastal terrain of SW Washington The results: massive landslides and river flooding

9. And, of course, the 2010 Olympics will depend on our understanding and predictive capabilities for orographic precipitation

10. Northwest U.S. and S.W. Canada an excellent testbed for studying orographic precipitation Relatively simple terrain of various configurations – Olympics—an orographic island – Vancouver Island and portions of Cascades (linear Undisturbed flow approaching the barriers Accessible with a large number of surface observing stations Major high resolution real-time simulation efforts at the UW and University of British Columbia. Lack of deep convection.

11. There have been major progress in understanding and predicting orographic precipitation over this region during the past several decades A number of regional field experiments have led to substantial advances in understanding.

12. Major Regional Orographic Precipitation Field Experiments CYCLES (1970s) COAST (Dec. 1993, Dec. 1995) IMPROVE 1 (Jan.-Feb. 2001) IMPROVE 2 (Nov.-Dec. 2001) COASTAL OLYMPICS (2003-2004) Proposed: OLYMPEX 2010

13. Progress Long-term real-time NWP and case-specific numerical experiments have examined the strengths and weaknesses of orographic NWP in the region. Prior to roughly 1995-2000 operational center models lacked the resolution and physics to even begin to handle the regional precipitation. NWP is now resolving major orographic precipitation features of the region.

14. NGM, 80 km, 1995

15. NGM, 1995

16. 2001: Eta Model, 22 km

17. 2007-2008 12-km UW MM5 Real-time 12-km WRF-ARW and WRF-NMM are similar December 3, 2007 0000 UTC Initial 12-h forecast 3-hr precip.

18. 2007-2008 4-km MM5 Real-time

19. NWS WRF-NMM 12-km

20. NWS WRF-NMM (12-km)

21. UW Real-Time Prediction System Running the MM5 and WRF-ARW at 36-12-4 km since 1996 Thompson Microphysics NOAH LSM Run twice a day to 72h Verified with thousands of stations from over 70 networks. Long record of model biases and issues over terrain.

22. Domains

23. A Few Major Lessons There are several key horizontal scales that influence orographic precipitation. The first is the scale of the major mesoscale barriers (e.g., west slopes of Cascades, mountains of Vancouver Island). In order to resolve the influence of the these features, one needs grid spacing of 12-15 km.

24. 100 km

25. 36-km 12-km

26. Major Lessons Then there are smaller scale features, produced by the corrugations in the terrain associated with the river valleys, and smaller-scale features forced by terrain such as the Puget Sound convergence zone. Such features require 4-km or better grid spacing to get a reasonable handle on the precipitation distributions.

27. 10-km

28. 12-km 4-km

29. Small-Scale Spatial Gradients in Climatological Precipitation on the Olympic Peninsula Alison M. Anders, Gerard H. Roe, Dale R. Durran, and Justin R. Minder Journal of Hydrometeorology Volume 8, Issue 5 (October 2007) pp. 1068–1081

30. Annual Climatologies of MM5 4- km domain

31. Verification of Small-Scale Orographic Effects

32. But not so perfect for individual events (issues of resolution, model physics, and initialization, among others)

33. Perhaps the most detailed look at this scale separation of orographic flows was presented by Garvert, Smull and Mass, 2007 (IMPROVE-2 paper)

34. Garvert et al. Used aircraft radar and in situ data from the IMPROVE-2 field experiment, as well as high resolution (1.3 km grid spacing) MM5 output. Documented and simulated small scale mountain waves and their microphysical/precipitation implications.

37. Proposed Olympex 2010-2011 will hopefully continue this work

38. During the 1990’s it became clear that there were problems with the simulated precipitation and microphysical distributions over Northwest terrain Apparent in the daily UW real-time MM5 forecasts at 12 and 4-km Also obvious in research simulations of major storm events.

39. Early Work-1995-2000 (mainly MM5, but results are more general) Relatively simple microphysics: water, ice/snow, no supercooled water, no graupel. (explicit moisture scheme of Hsie et al. 1984, with ice-phase microphysics below 0°C Dudhia 1989) was applied in for 36, 12, and 4-km domains. Tendency for overprediction on the windward slopes, even after considering undercatchment. Only for heaviest observed amounts was there no overprediction. Tendency for underprediction to the lee of the barrier and in major gaps. Colle and Mass, 1999;Colle, Mass and Westrick,2000

40. MM5 Precip Bias for 24-h 90% and 160% lines are contoured with dashed and solid lines For entire Winter season

41. Problems Were Obvious in the Lee of the Olympics Lack of clouds and precipitation in model on the lee side in light to moderate events. Too much precipitation moving over mountains under strong winds.

42. Testing more sophisticated schemes and higher resolution ~2000 Testing of ultra-high resolution (~1 km) and better microphysics schemes (e.g., with supercooled water and graupel), showed some improvements but fundamental problems remained: e.g., lee dry bias, overprediction for light to moderate events, but not the heaviest. Example: simulations of the 5-9 February 1996 flood of Colle and Mass 2000.

43. 5-9 February 1996

44. Colle and Mass, 2000 Little Windward Bias, Too Dry in Lee Bias: 100%-no bias Windward slope Lee

45. Higher Resolution: changes lee precipitation, but lee bulls eyes of heavy precip develop mountain waves too strong?

46. Varying Microphysics Modest changes, with graupel causing high intensity areas in the immediate lee. Most sophisticated microphysics did not necessarily produce the best verification

47. Flying Blind

48. IMPROVE Clearly, progress in improving the simulation of orographic precipitation demanded better observations: – High quality insitu observations aloft of cloud and precipitation species. – Comprehensive radar coverage above the barrier – High quality basic state information (e.g., wind, humidity, temperature) The IMPROVE field experiment (2001) was designed and to a significant degree achieved this.

49. Olympic Mts. British Columbia Washington Cascade Mts. Oregon California Orographic Study Area Washington Oregon Coastal Mts. S-Pol Radar Range Santiam Pass OSA ridge crest Cascade Mts. < 100 m 100-500 m 500-1000 m 1000-1500 m 1500-2000 m 2000-3000 m > 3000 m Terrain Heights Portland Salem Newport Medford UW Convair-580 Airborne Doppler Radar S-Pol Radar BINET Antenna NEXRAD Radar Wind Profiler Rawinsonde Legend Ground Observer 0 100 km WSRP Dropsondes Columbia R. Rain Gauge Sites in OSA Vicinity Santiam Pass SNOTEL sites CO-OP rain gauge sites 50 km Orographic Study Area S-Pol Radar Range Olympic Mts. S-Pol Radar Range Westport 90 nm (168 km) Offshore Frontal Study Area Paine Field Univ. of Washington Area of Multi- Doppler Coverage Special Raingauges PNNL Remote Sensing Site Two IMPROVE observational campaigns: I. Offshore Frontal Study (Wash. Coast, Jan-Feb 2001) II. Orographic Study (Oregon Cascades, Nov-Dec 2001)

50. The NOAA P3 Research Aircraft Dual Doppler Tail Radar Surveillance Radar Cloud Physics and Standard Met. Sensors Convair 580 Cloud Physics and Standard Met. Sensors

52. PARSL Site Terrain ht. (m) Distance (km) 050100 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 S-POL Radar Santiam Junction Santiam Pass Camp Sherman -50-100 20-40 inches/year 40-60 inches/year 60-80 inches/year 80-100 inches/year > 100 inches/year < 20inches/year 60 km100 km Slope matches that of an ice crystal falling at 0.5 m/s in a mean cross-barrier flow of 10 m/s, which takes ~3 h. Total flight time: 3.4 h Convair-580 Flight Strategy

53. The S-Pol Doppler Radar

55. S-Band Vertically Pointing Radar Pacific Northwest National Lab (PNNL) Atmospheric Remote Sensing Laboratory (PARSL) 94 GHz Cloud Radar 35 GHz Scanning Cloud Radar Micropulse LIDAR Microwave Radiometer Broadband radiometers Multi-Filter Rotating Shadowband Radiometer (MFRSR) Infrared Thermometer (IRT) Ceilometer Surface MET Total Sky Imager

56. An IMPROVE-2 Sample: Dec. 13-14, 2001 Strong, extremely well sampled event on the Oregon Cascades Varied biases on the windward slopes, and now overprediction over the lee. Overprediction at valley stations on windward side Little bias on windward crest stations Garvert et al., 2005a 1.3 km 4 -km

57. But now, we had the microphysical data aloft to determine what was happening Model Observations

58. The Diagnosis Too much snow being produced aloft Too much snow blowing over the mountains, providing overprediction in the lee Too much cloud liquid water on the lower windward slopes Too little cloud liquid water near crest level. Problems with the snow size distribution (too few small particles) Several others!

59. In Comparison: The Weaker Dec. 4-5, 2001 Event Yanluan and Colle 2008 Based on WRF Model Overprediction over windward slopes Too much precip in the immediate lee of the crest Underprediction to the east of the Cascades Excessive generation of snow aloft

60. Lots of activity in improving microphysical parameterizations New Thompson Scheme for WRF that includes a number of significant improvements. Higher moment schemes are being tested. (e.g., new Morrison two-moment scheme) Microphysical schemes are being modified to consider the different density and fall speed characteristics of varying ice habits and degrees of riming (work of Woods, Hafen, and Stoelinga, UW)

61. Another Major Question What is the importance of unresolved small scale orographic features and sub-grid scale motions on mesoscale orographic precipitation? Do these features enhance precipitation? Do they need to be parameterized for coarser simulations? Or do we need ultra high resolution to get the orographic precipitation right?

62. The Influence of Small Scale Ridges (Colle 2008) Small net windward enhancement by small scale features

63. The Influence of Shear-Induced Turbulence on Microphysics Houze and Medina, JAS, 2005

64. The problems with the simulation of orographic precipitation are not limited to microphysics and resolution The MM5 and WRF V1-2.1 lacked positive definite advection schemes for moisture variables. The result of such numerics is a lack of conservation of moisture, producing essentially an unphysical source of water. Thus, lack of PD advection explains part of the overprediction problem in MM5/WRF COAMPS and CSU RAMS have PD schemes.

65. Recent Work of Robert Hahn, UW, for Dec. 13-14, 2001 IMPROVE 2 event PD-NOPD Domain36km12km4km1.33km Coast Water-4.0%-2.5%-6.5%-6.6% Coast Mountains-4.1%-4.4%-7.9%-9.8% Willamette Valley-3.5%-3.9%-13.0%-15.6% Cascade Windward-4.1%-5.0%-13.5%-17.2% Cascade Leeward-4.3%-8.0%-10.2%-11.4% DOMAIN TOTAL-3.9%-4.4%-10.9%-13.4%

66. Positive Definite Advection Initiated MM5 and WRF have similar bias WRF has lesser bias Benefits Appear to Be Apparent In UW Real-Time Prediction

67. Problems and deficiencies of boundary layer and diffusion schemes can significantly affect precipitation and microphysics Boundary layer parameterizations are generally considered one of the major weaknesses of mesoscale models (as noted at recent WRF users group meeting in Boulder). Deficiencies in the PBL structures were noted during IMPROVE. Errors in boundary layer structure can substantially alter mountain waves and resultant precipitation.

68. Garvert, Mass, and Smull, 2007 Improve-2 Dec13-14, 2001 Changes in PBL Schemes substantially change PBL structures, with none being correct.

69. Impacts of Boundary Layer Parameterization on Microphysics Snow-diffCLW-diffGraupel-diff Microphysics Differences ETA - MRF

70. The Next Major Challenge: Probabilistic Orographic Precipitation Prediction The atmosphere is not deterministic and there are substantial uncertainties in initial conditions and physics parameterizations, and continued approximations in the numerics. Over the next several years, we need to perfect approaches for probabilistic prediction of orographic precip that produce sharp and reliable probability density functions.

71. Special Challenges and Advantages of Probabilistic Prediction Over Terrain Less observations that over flatland, making calibration more difficult. (disadvantage) More frequent precipitation (an advantage). Less of a phase space, since orography does constrain possible atmospheric states. Orographic flow often controlled by interaction of synoptic scale flow with mesoscale terrain. (advantage).

72. Probabilistic NWP over NW terrain is already well along Current Operational Systems – University of Washington UWME system (36-12 km) – University of Washington EnKF System (36- 12km) – NWS Multi-Model SREF System (32 km)

73. UWME – Core : 8 members, 00 and 12Z Each uses different synoptic scale initial and boundary conditions All use same physics – Physics : 8 members, 00Z only Each uses different synoptic scale initial and boundary conditions Each uses different physics Each uses different SST perturbations Each uses different land surface characteristic perturbations – Centroid, 00 and 12Z Average of 8 core members used for initial and boundary conditions

74. Resolution ( ~ @ 45  N ) Objective Abbreviation/Model/Source Type Computational Distributed Analysis gfs, Global Forecast System, SpectralT254 / L641.0  / L14 SSI National Centers for Environmental Prediction~55km~80km3D Var cmcg, Global Environmental Multi-scale (GEM),Finite0.9  / L281.25  / L113D Var Canadian Meteorological Centre Diff. ~70km ~100km eta, Eta limited-area mesoscale model, Finite12km / L60 90km / L37SSI National Centers for Environmental Prediction Diff.3D Var gasp, Global AnalysiS and Prediction model,SpectralT239 / L291.0  / L11 3D Var Australian Bureau of Meteorology~60km~80km jma, Global Spectral Model (GSM),SpectralT106 / L211.25  / L13OI Japan Meteorological Agency~135km~100km ngps, Navy Operational Global Atmos. Pred. System,SpectralT239 / L301.0  / L14OI Fleet Numerical Meteorological & Oceanographic Cntr. ~60km~80km tcwb, Global Forecast System,SpectralT79 / L181.0  / L11 OI Taiwan Central Weather Bureau~180km~80km ukmo, Unified Model, Finite5/6  5/9  /L30same / L123D Var United Kingdom Meteorological Office Diff.~60km Current International Multi-Analysis Collection

75. Ensemble domain

76. Post-Processing of Ensembles Uses Bayesian Model Averaging to optimally combine the various ensemble members to produce reliable and sharp probabilistic forecasts. The output provides spatially varying PDFs of precipitation and other parameters.

78. Ensemble-Based Probabilistic Products Probability Density Function at one point

79. Work Cut Out for Us Large amount of work yet to be done to perfect ensemble-based probabilistic prediction of orographic precipitation. Quantification of uncertainty in parameterizations Higher resolution Many others.

80. The End

81. High (4-km or higher) resolution also need for some small scale orographically forced precipitation features