Tue 3/29/2016. Representation of clouds and precipitation:

Transcription

1 Tue 3/29/2016 Representation of clouds and precipitation: - Case study example of microphysics test - Bulk parameterizations: Process listing - WRF scheme options Convective parameterization paper summaries (last 5) Reminders/announcements: Upcoming: MP papers and experiments Plan to grade midterms soon, CP paper summaries first

2 MP papers: Will do sign-up sheet

3 Re-Cap from Thursday Bulk microphysics: Single, double (fixed and diagnosed α), and triple moment (predict shape parameter, or other) Why is the microphysics scheme called last in the physics sequence? Why are microphysics schemes listed as time-split physics? If you went into WSM6 and increased the intercept values, how do you think it would impact precipitation efficiency? If you increased the fall velocity of graupel by a factor of 10, what impacts would it have on the model atmosphere? Let s go through a worksheet example

4 Single-moment bulk Intercept (N 0 ) Mean particle diameter is related to λ -1 Number (ln N D ) Mass (M) Diameter (D)

5 Microphysics Section Outline Basics of microphysics schemes MP scheme responsibilities Distinguishing characteristics: Classes, Distribution, & Processes Why classes matter: Hurricane example Representation of number concentration: Bin vs. Bulk Single, double, and triple moment schemes The WRF schemes Calling sequence Defining characteristics: Warm and cold-cloud processes Scheme details: CCN, process representation Model simulated radar Case-study examples: Winter storm (lake effect) Convective storm Tropical cyclone

6 Case Examples Previous experiments: - Lake-effect storm (Oct 2006) Kevin Hill, AIR, former MEA 716 student - Tennessee Flood event (May 2010) My class project from TS Hanna (2008) Barrett Smith, NWS, former MEA 716 student

7 Case 1: Unusual early-season snow event, Buffalo, NY Friday 13 October 2006 Contributed by Dr. Kevin Hill Photo by Tom Niziol, NWS BUF

8 Lake Effect: Dr. Kevin Hill October storm exceptional: Depth of cold air almost unprecedented for early October Lake Erie: 62 degrees, 850 mb to surface δt ~ 24 C Main question: How could boundary layer be cold enough for snow with flow downwind of a 62 F lake?! Obviously, this event was not forecasted accurately

9 GFS 500-mb Z analysis, 00 UTC 10/11/06

10 GFS 500-mb Z analysis, 00 UTC 10/12/06

11 GFS 500-mb Z analysis, 00 UTC 10/13/06

12 GFS 500-mb Z analysis, 00 UTC 10/14/06

13 1000 mb winds, SLP, GOES-12 Visible satellite, 19 UTC Thursday 12 October

14 MEA 443, Monday 10/16/06 Unusually deep unstable layer for lake-effect situation 1

15 Sfc obs, radar mosaic, Th-Fri Oct

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27 Case 3: Buffalo, NY Friday 13 October 2006 Photo by Tom Niziol, NWS BUF

28 Wikipedia

29 Wikipedia Entry for Aphid

30 Thursday 8-9 pm: reports of trees falling and power outages Thursday night Friday morning: near constant thunder and lightning cloud tops reached an incredible thousand feet, about double the previously observed worst

31 Phase 1: Phase 2: Thursday 3 pm 12 midnight: 5 to 8, ratio ~6:1 Friday morning: ~12, ~12:1 ratio Total: 22.6 inches recorded at the Buffalo airport Blew away any October record (6" in 1909, only 4 falls of 2" or more in 100 years in October) 7 th greatest snowfall ever at any time in Buffalo!

32 Impacts (Wikipedia) 400,000 without power on 13 th, 100,000 without power for a week or more Initial estimates of clean-up $130M USD Schools closed for 6 straight days President G.W. Bush declared affected counties major disaster 90% of Buffalo s trees damaged, including Olmsted parks

33 Hypotheses What mechanisms cooled the boundary layer? Consider role of phase-change processes Melting: As snow fell from aloft and melted, would cool warm layer Evaporation/Sublimation: Falling hydrometeors with dry air near surface, initial evaporation and sublimation would cool lower troposphere Presentation by forecaster at NWS BUF stated that they didn't think evaporation or melting played a role

34 Methods Control simulation: See if WRF able to capture this remarkable mesoscale event Purpose: Quantify the contribution of evaporation/sublimation, and melting WRF model experiments: Remove sublimation/evaporation (NOEVAP) Remove melting in WSM6 (NOMELT)

35 Model Simulations WRF /9/3-km spacing GFS initial conditions WSM6, YSU, EC on domain 3

36 WSM6 modifications Make psmlt = 0.0 Reduce snow (qrs(*,*,2) Increase rain (qrs(*,*,1) Note: psmlt is a negative value when snow is melting

37 Control results

38 For a grid point at the downstream end of Lake Erie, near Buffalo, NY

39 Control simulation summary Respectable control simulation overall Enough precipitation, fairly realistic distribution Good enough to continue on with experiments...

40 2-meter T at Buffalo noevap Little difference in T between NOMELT, control control Up to 1.5 C warmer in NOEVAP than control nomelt Model snowfall rate at Buffalo No snow in NOEVAP Similar amount of precipitation, too warm to be snow (LSM) Similar snowfall amounts in NOEVAP, control LSM in this older version didn t classify snowfall because T > 0 C!

41 Summary Used WRF to test 2 physical processes that may have contributed to cooling, allowing large amount of snow Eliminating evaporational and sublimational cooling from model led to no snow This is in part due to outdated, incorrect LSM setting Conclusion: based on model results, Evaporational/sublimational cooling played important role in event Melting didn t play a significant role (!)

42 A partial listing of microphysical processes Deposition/sublimation of graupel Melting of hail Deposition/sublimation of cloud ice Homogeneous freezing of cloud water Deposition/sublimation of snow Homogeneous freezing of rain water Deposition on ice nuclei (nucleation) Accretion of cloud water to graupel Deposition/sublimation of hail Accretion of cloud water to snow (rime) Condensation/evaporation of cloud water Coalescence of cloud water to rain Condensation/evaporation of rain water Cloud water to rain (autoconversion) Freezing of cloud water (immersion, contact) Accretion of cloud ice by snow Freezing of rain water (immersion, contact) Accretion of snow by snow (aggregation) Melting of cloud ice Accretion of cloud ice by graupel Melting of snow Accretion of rain water to graupel Melting of graupel Accretion of snow by graupel Breakup of rain drops Splintering of freezing droplets Breakup of snow crystals Accretion of snow by rain

43 WRF MP Options (first 11 of 22) (V3.7.1) mp_physics Characteristics Design 1: Kessler Bulk, single moment, 3 class Simplest possible, use for test case 2: Lin Bulk, single moment, 6 class Classic, ahead of its time. WSM6 updates 3: WSM3 Bulk, single moment, 3 class, simple ice All ice below freezing, all warm above 4: WSM5 Bulk, single moment, 5 class Probably okay for coarse grid spacing 5: Ferrier Bulk, single moment, NAM scheme Clever total ice advection for efficiency 6: WSM6 Bulk, single moment, 6 class, can add hail An updated Lin et al., standard/default 7: Goddard Bulk, single moment, 6 class, hail option Can set switch to have hail vs. graupel 8: Thompson Bulk, 6-class, double moment for ice, rain Computationally efficient part double moment 9: Milbrandt-Yau Bulk, 7-class, double moment for all Expensive. Double moment, fixed alpha 10: Morrison Bulk, 6-class, double moment for cloud ice, cloud water, rain, and snow. Can add hail Designed for arctic clouds, good radiation int. 11: CAM 5.1 Bulk, 5-class, double moment, NEW in WRF Some aspects not fully implemented in WRF

44 WRF MP Options (through 22) mp_physics Characteristics Design 13: SBU_Ylin Bulk, single-moment, 5-class, diagnosed riming intensity continuous spectrum No cutoff distinction between graupel/snow 14: WDM5 Bulk, single moment, 3 class Perhaps useful in special cases 16: WDM6 Bulk, single moment, 6 class, can add hail Double moment for CCN, cloud, rain 17: NSSL 2/4 Bulk, NSSL double moment 4 ice All NSSL schemes allow setting intercept 18: NSSL CCN Bulk, double moment, predicted CCN For double moment, can set shape param. 19: NSSL single 7 Bulk, single moment, 7 class Predicts graupel ρ 21: NSSL single 6 Bulk, single moment, 6 class, set intercepts Can set many namelist parameters 28: Thompson AA Bulk, Thompson aerosol aware Can utilize climatological aerosol values 30: HUJI Bin, fast version From Hebrew University of Jerusalem, Israel 32: HUJI all Bin, full version (expensive) Need to read up on these schemes 95: Ferrier old Bulk, old Eta version, still in NAM? Not sure why you would use this over 5

45 716 Midterm Part 2 Lindsay R. Blank March 17, 2016

46 Improving the behavior of the cumulus parameterization for tropical cyclone prediction: Convection trigger Authors: Lei-Ming Ma and Zhe-Min Tan Nanjing University, China Journal: Atmospheric Research, September 2009 Scientific Question(s): (1) Which CP scheme performs best in simulating tropical cyclones? (2) Is there a way to improve the parameterization of the best CP scheme as identified in (1)? Important Advance?: Yes

47 36 numerical simulations conducted to evaluate Betts- Miller, Grell, and Kain-Fritsch performance. Best CP Scheme: Kain-Fritsch Problems when large scale signals/forcing are weak! Solution: Implement a new trigger formula in which moisture advection is taken into effect. Conclusion: New Kain-Fritsch trigger scheme improves tropical cyclone simulations!

48 Control Simulation precipitation break down after 84 hours. (a) total precipitation, (b) convective precipitation, and ( c) non-convective precipitation a. b. c.

49 As in slide 3, but for the experimental simulation. a. b. c.

50 Old Simplified Arakawa-Shubert Sensitivity of Hurricane Intensity Forecast to Convective Momentum Transport Parameterization, Han and Pan 2006 Presented by Laura McGee Pressure gradient force (pgf) in convective momentum transport included Note: Only activated in WRF-NMM! Mass-flux scheme Momentum exchange calculated through mass flux formulation Quasi-equilibrium closure, based on destabilization rate Triggers with CAPE Shallow mixing included

51 Pressure Gradient Force Assumed proportional to cloud mass flux times vertical wind shear Accounts for effect of convection-induced pressure gradients on convective momentum transport empirically by increasing entrainment for momentum in updraft Useful in hurricane simulations: helps increase hurricane intensity, though not enough, more realistic precipitation patterns, less track error, no spurious hurricanes Still weakens hurricanes (too much vertical mixing), largely under-predicts observed intensity, intensity changes slower

52 Precipitation for Control Run (BMJ)

53 Precipitation for old SAS

54 RTHCUTEN final time step

55 RQVCUTEN final time step

56 Revision of Convection and Vertical Diffusion Schemes Han and Pan 2011 in the NCEP Global Forecast System

57 Motivation Stratocumulus clouds Systematic underestimation, especially over nearshore regions in the eastern Pacific and Atlantic Oceans Attributed to the old SC scheme which uses a turbulent diffusion approach Gridpoint storms Excessively heavy precipitation at the grid scales during convective season Attributed to the DC scheme which cannot fully eliminate instability and consequently cause explicit convective ascent to occur on the grid scale

58 Modification Shallow convection: Mass flux parameterization Based on the previous SAS deep convection scheme: cloud-base mass flux, entrainment, and also detrainment specifications have been changed Deep convection: An entrainment rate approach with the rate being dependent on environmental moisture is used Make cumulus convection deeper and stronger

59 Modification (Cont d) Cumulus momentum transport due to convection-induced pressure gradient force are included in SC and DC Nonlocal PBL: A stratocumulus cloud-top-driven vertical diffusion scheme has been incorporated into the MRF PBL model Triggering mechanism: A parcel lifted from CSL without entrainment must reach its LFC within hPa of ascent Effects of environmental humidity in the subcloud layer, may be a deficiency as vertical model resolution change Empirical coefficients of cloud cover calculation

60 Accumulated precipitation from BMJ (top) and New-SAS (bottom)

61 Isosurfaces: 100 Pa m s -2 Warming Moist Cooling Dry

62 Assessing the performance of the Modified Tiedtke CP Scheme Hans P Taylor

63 Modified Tiedtke CP Scheme Characteristics Detrainment of condensates CAPE-based closure Mass-Flux Scheme Momentum Transport Shallow Convection Trigger Checks for unstable parcel in the lowest 350-hPa layer Cloud-top height determined from vertical velocity of parcel Cloud thickness must exceed 200 hpa Modifications Replaces moisture convergence with CAPE closure Prevent deep convection in dry regions, by establishing a threshold RH value (vertically averaged >= 0.8) Decreases fraction of cloud ensemble of shallow convection that penetrates into inversion layer Increases Entrainment-Detrainment rate for shallow convection (3) And (6) from Gregory et al., 2000

64 Performance comparison of CP Schemes

65 Sensitivity to choice of PBL Scheme

66 Figure 1: Spatial distribution of Convective Precipitation (Left) and Total Precipitation (Right) at 84th hour of forecast using the Betts-Miller-Janjic convective parameterization scheme. Figure 2: Spatial distribution of Convective Precipitation (Left) and Total Precipitation (Right) at 84th hour of forecast using the modified Tiedtke convective parameterization scheme

67 Comparing Tendencies Cross-sections tendencies of potential temperature (bottom) and moisture mixing ratio (top) using the BMJ CP scheme (right) and modified Tiedtke CP scheme (left), with distance along the horizontal axis.