Presentation on theme: "Problems With Model Physics in Mesoscale Models Clifford F"— Presentation transcript:
1Problems With Model Physics in Mesoscale Models Clifford F Problems With Model Physics in Mesoscale Models Clifford F. Mass, University of Washington, Seattle, WA
2Major Improvements in Mesoscale Prediction Major improvements in the skill of mesoscale models as resolution has increased to 3-15 km.Since mesoscale predictability is highly dependent on synoptic predictability, advances in synoptic observations and data assimilation have produced substantial forecast skill benefits.Although model physics has improved there are still major weaknesses that need to be overcome.
3Important to Know the Strengths and Weaknesses of Our Tools
4Very Complex Because Model Physics Interaction With Each Other—AND Model Dynamics
5Some Physics Issues with the WRF Model that Are Shared With Virtually All Other Mesoscale Models
6Overmixing in Mesoscale Models Most mesoscale models have problems in maintaining shallow, stable cool layers near the surface.Excessive mixing in the vertical results in excessive temperatures at the surface and excessive winds under stable conditions.Such periods are traditionally ones in which weather forecasters can greatly improve over the models or models/statistical post-processing
7Cold spellTime series of bias in MAX-T over the U.S., 1 August 2003 – 1 August Mean temperature over all stations is shown with a dotted line. 3-day smoothing is performed on the data.
8Shallow Fog…Nov 19, 2005 Held in at low levels for days. Associated with a shallow cold, moist layer with an inversion above.MM5 and WRF predicted the inversion…generally without the shallow mixed layer of cold air a few hundred meters deepMM5 or WRF could not maintain the moisture at low levels
15So What is the Problem?We are using the Yonsei University (YSU) scheme in most work. We have tried all available WRF PBL schemes…no obvious solution in any of them. Same behavior obvious in other models and PBL parameterizations.Doesn’t improve going from 36 to 12 km resolution, 1.3 km slightly better.There appears to be common flaws in most boundary layer schemes especially under stable conditions.
16Problems with WRF surface winds WRF generally has a substantial overprediction bias for all but the lightest winds.Not enough light winds.Winds are generally too geostrophic over land.Not enough contrast between winds over land and water.This problem is evident virtually everywhere and appears to occur in all PBL schemes available with WRF.Worst in stable conditions.
30Surface Wind ProblemsClearly, there are flaws in current planetary boundary layer schemes.But there also be another problem?—the inability to consider sub-grid scale variability in terrain and land use.
32A new drag surface drag parameterization Determine the subgrid terrain variance and make surface drag or roughness used in model dependent on it.Consulting with Jimy Dudhia of NCAR came up with an approach—enhancing u* and only in the boundary layer scheme (YSU).For our 12-km and 36-km runs used the variance of 1-km grid spacing terrain.
44During the 1990’s it became clear that there were problems with the simulated precipitation and microphysical distributionsApparent in the MM5 forecasts at 12 and 4-kmAlso obvious in research simulations of major storm events.
45Early Work-1995-2000 (mainly MM5, but results are more general) Relatively simple microphysics: water, ice/snow, no supercooled water, no graupelTendency for overprediction on the windward slopes of mountain barriers. Only for heaviest observed amounts was there no overprediction.Tendency for underprediction to the lee of mountains
46MM5 Precip Bias for 24-h 90% and 160% lines are contoured with dashed and solid lines For entireWinterseason
47Testing 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.
51IMPROVEClearly, progress in improving the simulation of precipitation and clouds demanded better observations:High quality insitu observations aloft of cloud and precipitation species.Comprehensive radar coverageHigh quality basic state information (e.g., wind, humidity, temperature)The IMPROVE field experiment (2001) was designed and to a significant degree achieved this.
55Convair-580 Flight Strategy 900020-40 inches/year40-60 inches/year60-80 inches/yearinches/year> 100 inches/year< 20inches/year800060 km7000Slope 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.60005000Terrain ht. (m)40003000100 kmTotal flight time: 3.4 h20001000S-POLRadarSantiamJunctionSantiamPassCampShermanPARSLSite-100-5050100Distance (km)
59We now had the microphysical data aloft to determine what was happening ModelObservations
60The Diagnosis Too much snow being produced aloft Too much snow blowing over the mountains, providing overprediction in the leeToo much cloud liquid water on the lower windward slopesToo little cloud liquid water near crest level.Problems with the snow size distribution (too few small particles)Several others!
61Problems and deficiencies of boundary layer and diffusion schemes can significantly affect precipitation and microphysicsBoundary layer parameterizations are generally considered one of the major weaknesses of mesoscale modelsDeficiencies in the PBL structures were noted during IMPROVE.Errors in boundary layer structure can substantially alter mountain waves and resultant precipitation.
62Impacts of Boundary Layer Parameterization on Microphysics Snow-diffCLW-diffGraupel-diffMicrophysics Differences ETA - MRF
63Lots 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.
64Convective Parameterization The need for convective parameterization declines at models gain enough resolution to explicitly model convection.Appears that one starts getting useful explicit convective predictions at 4-km grid spacing.In the future, they is one problem that will go away as we move to sub-4km grid spacing.
65Composite NEXRAD Radar Real-time 12 h WRF Reflectivity ForecastValid 6/10/03 12Z4 km BAMEX forecast10 km BAMEX forecast22 km CONUS forecastComposite NEXRAD Radar
66Example: Radar reflectivity, 24 h fcst vs obs, valid 0000 UTC May 13, 2005 WRF 4kmNMM 4.5kmWRF 2kmobserved
67Hurricane RainbandsUltra high resolution (< 2 km grid spacing) result in better structures and intensity predictions.15-km grid spacing1.67 km grid spacing
68More Physics IssuesSerious deficiencies in many land surface modeling schemes, particularly in the areas of snow physics and soil moistureNeed to characterize uncertainties in physics schemes and the development of stochastic physics.Require physics schemes applicable to a wide range of resolutions for the next generation of unified models.
69Resolution Was EasyWe have had a lot of fun increasing resolution over the past few decades.Now we have to put much more emphasis on doing the research and operational testing required to improve model physics and describing the uncertainties in our schemes.This work is made more difficult by the interactions among the physics parameterizations.
76Improvement?Next step—could have the parameterizaton fade out for higher winds speeds and lower stability, possibility by depending on Richardson number.Actually, this makes some sense…sometimes the atmosphere is well-mixed, and at these times variations in sub-grid roughness would be less important.