1. How to Improve Mesoscale Atmospheric-Modeling Results Bob Bornstein San Jose State University San Jose, CA pblmodel@hotmail.com Presented at UNAM Mx City 9 March 2007

2. Acknowledgements Dr. H. Taha, Altostratus & SJSU Dr. H. Taha, Altostratus & SJSU Dr. F. Freedman Dr. F. Freedman Dr. Erez Weinroth, HUJI & SJSU Dr. Erez Weinroth, HUJI & SJSU my M.S. (ex) STUDENTS: Dr. C. Lozej- Archer, T. Ghidey, K. Craig, R. Balmori my M.S. (ex) STUDENTS: Dr. C. Lozej- Archer, T. Ghidey, K. Craig, R. Balmori Funded by: NSF, USAID, DHS, LBL, NSF Funded by: NSF, USAID, DHS, LBL, NSF

3. OUTLINE Introduction Introduction Getting Mesomet models to work better Getting Mesomet models to work better SYNOPTIC FORCING SYNOPTIC FORCING INPUT DATA INPUT DATA SFC/PBL FORCING SFC/PBL FORCING uMM5: Houston ozone uMM5: Houston ozone Conclusions Conclusions (Future) uWRF efforts (Future) uWRF efforts

4. Basic-Theme 1 e.g., O 3 -EPISODES OCCUR NOT FROM CHANGING TOPOGRAPHY OR EMISSIONS NOT FROM CHANGING TOPOGRAPHY OR EMISSIONS BUT DUE TO CHANGING GC/SYNOPTIC PATTERNS, WHICH BUT DUE TO CHANGING GC/SYNOPTIC PATTERNS, WHICH ENTER MESO-SOLUTION FROM CORRECT (we hope) LARGER-SCALE MODEL-FIELDS, & WHICH ENTER MESO-SOLUTION FROM CORRECT (we hope) LARGER-SCALE MODEL-FIELDS, & WHICH THUS ALLOW SCF MESO THERMAL-FORCINGS (i.e., UP/DOWN SLOPE, LAND/SEA, URBAN, CLOUDS/FOG) TO DEVELOP CORRECTLY (we hope) THUS ALLOW SCF MESO THERMAL-FORCINGS (i.e., UP/DOWN SLOPE, LAND/SEA, URBAN, CLOUDS/FOG) TO DEVELOP CORRECTLY (we hope)

5. CORRECT-ORDER OF FORCINGS IN A MESO-MET MODEL IS THUS FIRST: UPPER-LEVEL Syn/GC FORCING FIRST: UPPER-LEVEL Syn/GC FORCING pressure (the GC/Syn driver)  Syn/GC winds Syn/GC winds NEXT: TOPOGRAPHY NEXT: TOPOGRAPHY grid spacing  flow-channeling LAST: MESO SFC-CONDITIONS LAST: MESO SFC-CONDITIONS temp (the meso-driver) & sfc z  temp (the meso-driver) & sfc z 0  mesoscale winds

6. Case Study 1: Atlanta Summer Thunderstorm (K. Craig) Obs: weak-cold front N of city Obs: weak-cold front N of city Large-scale IC/BC: front S of city Large-scale IC/BC: front S of city MM5 UHI-induced thunderstorm: MM5 UHI-induced thunderstorm: 5-km deep, w max 6-m/s, 8-cm precip 5-km deep, w max 6-m/s, 8-cm precip Should be: 9-km, 12-m/s, 14-cm Should be: 9-km, 12-m/s, 14-cm Source of problem: Source of problem: Wx model incorrectly put front S of City  MM5-storm formed in stable-flow from N (& not in unstable-flow from S) MM5-storm formed in stable-flow from N (& not in unstable-flow from S)

7. ATLANTA UHI-INITIATED STORM: OBS SAT & PRECIP (UPPER) & MM5 W & PRECIP (LOWER)

8. Case study 2: SFBA Summer O 3 - episode (T. ) Case study 2: SFBA Summer O 3 - episode (T. Ghidey) Obs: daily max- O 3 sequentially moved from Livermore to Sacramento to SJV Obs: daily max- O 3 sequentially moved from Livermore to Sacramento to SJV Large scale IC/BC: Large scale IC/BC: shifting meos-700 hPa high  shifting meos-sfc low  changing sfc-flow  max-O 3 changed location Results: good analysis-nudging in MM5  Results: good analysis-nudging in MM5  good mesoscale winds

9. H H L SAC episode day: D-1 700 hPa Syn H moved to Utah with coastal “bulge” & L in S-Cal  correct SW flow from SFBA to Sac

10. L H SJV episode day: D-3 700 hPa Fresno eddy moved N & H moves inland  flow around eddy blocks SFBA flow to SAC, but forces it S into SJV

11. Theme 2: MM5 Non-urban Sfc-IC/BC Issues Deep-soil temp: BC Deep-soil temp: BC Controls nighttime min-T Controls nighttime min-T Values unknown & MM5 estimation-method is flawed Values unknown & MM5 estimation-method is flawed Soil-moisture: IC Soil-moisture: IC Controls daytime max-T Controls daytime max-T Values unknown & MM5-table values are too specific Values unknown & MM5-table values are too specific SST: IC/BC SST: IC/BC Horiz coastal T-grad controls sea-breeze flow Horiz coastal T-grad controls sea-breeze flow But we usually focus only on land-sfc temps But we usually focus only on land-sfc temps IC/BC-SST values from large-scale model are IC/BC-SST values from large-scale model are too coarse & not f(t)

12. Theme 2A: MM5 deep-soil temp Calculated as average large-scale model-input surface-T during simulation-period Calculated as average large-scale model-input surface-T during simulation-period But this assumes zero time-lag b/t sfc & lower- level (about 1 m) soil-temps But this assumes zero time-lag b/t sfc & lower- level (about 1 m) soil-temps But obs show But obs show 2-3 month time-lag b/t these two temps 2-3 month time-lag b/t these two temps Larger-lag in low-conductivity dry-soils Larger-lag in low-conductivity dry-soils Thus MM5 min-temps always are too-high in summer & too-low in winter Thus MM5 min-temps always are too-high in summer & too-low in winter Need to develop tech (beyond current trial & error) to account for lag Need to develop tech (beyond current trial & error) to account for lag

13. Case Study 3: Mid-east 2-m air temp (S. Kasakech) July 29August 1August 2 July 31 Aug 1 Aug2 Standard-MM5 summer night-time min-T, But lower input deep-soil temp  better 2-m T results  better winds  better O 3 obs Run 1 MM5:Run 4 Obs Run 4: Reduced Seep-soil T First 2 days show GC/Syn trend not in MM5, as MM5-runs had no analysis nudging

14. Case study 4: SCOS96 2-m Temps (D. Boucouvual) RUN 1: has  No GC warming trend  Wrong max and min T 3-Aug4-Aug 5-Aug6-Aug RUN 5: corrected, as it used > Analysis nudging > Reduced deep-soil T

15. Theme 2B: MM5 input-table zproblems Theme 2B: MM5 input-table z 0 problems Water z = 0.01 cm Water z 0 = 0.01 cm Only IC  updated internally by Charnak eq = f(MM5 u) Only IC  updated internally by Charnak eq = f(MM5 u * ) But Eq only valid for open-sea, smooth-swell, conditions But Eq only valid for open-sea, smooth-swell, conditions Obs for rough-sea coastal-areas ~ 1 cm  Obs for rough-sea coastal-areas ~ 1 cm  MM5 coastal-winds are over-estimated Urban z = 80 cm Urban z 0 = 80 cm too low for tall cities: obs up to 3-4 m too low for tall cities: obs up to 3-4 m Urban-winds: too fast Urban-winds: too fast Must adjust input-value or use GIS/RS input as f(x,y) Must adjust input-value or use GIS/RS input as f(x,y)

16. Case study 5: Houston GIS/RS z o (S. Stetson) Values up 3 m Values too large, as they were f(h) and not f(ơ h )

17. Theme 2C: MM5 SST-problems Current Wx Model SSTs  MM5 Current Wx Model SSTs  MM5 Only every 6 or 12 hours Only every 6 or 12 hours Lack small scale SST-variations Lack small scale SST-variations Thus produces poor land-sea T-gradients  Thus produces poor land-sea T-gradients  poor sea-breeze flows Solution: satellite-derived SSTs Solution: satellite-derived SSTs More frequent More frequent More detailed More detailed

18. Case Study 6: NYC SST+currents (J. Pullen) COAMPS input satellite: SST + sfc currents L

19. Theme 3: Model-Urbanization History Need to urbanize momentum, thermo, & TKE Eqs Need to urbanize momentum, thermo, & TKE Eqs At surface & in SBL: diagnostic Eqs At surface & in SBL: diagnostic Eqs In PBL: prognostic Eqs In PBL: prognostic Eqs Newest from veg-canopy model of Yamada (1982) Newest from veg-canopy model of Yamada (1982) But Veg-param’s replaced with GIS/RS urban param’s But Veg-param’s replaced with GIS/RS urban param’s Brown and Williams (1998) Brown and Williams (1998) Masson (2000) Masson (2000) Martilli et al. (2001) in TVM/URBMET Martilli et al. (2001) in TVM/URBMET Dupont, Ching, et al. (2003) in EPA/MM5 Dupont, Ching, et al. (2003) in EPA/MM5 Taha et al. (2005), Balmori et al. (2006b) in uMM5 Taha et al. (2005), Balmori et al. (2006b) in uMM5 Input: detailed urban-parameters as f(x,y)

20. From EPA uMM5: Mason + Martilli (by Dupont) Within Gayno-Seaman PBL/TKE scheme

21.  Advanced urbanization scheme from Masson (2000) ______ _________ 3 new terms in each prog equation

22. New GIS/RS inputs for uMM5 as f (x, y, z)  land use (38 categories)  roughness elements  anthropogenic heat as f (t)  vegetation and building heights  paved-surface fractions  drag-force coefficients for buildings & vegetation  building height-to-width, wall-plan, & impervious- area ratios  building frontal, plan, & and rooftop area densities  wall and roof: ε, cρ, α, etc.  vegetation: canopies, root zones, stomatal resistances

23. Case study 7: SJSU uMM5 performance by CPU  With 1 CPU: MM5 is 10x faster than uMM5  With 96 CPU: MM5 is only 3x faster than uMM5 (< 12 CPU not shown) With 96 CPU: MM5 is still gaining, but MM5 has ceased to gain at 48 CPU & then it starts to loose

24. Performance by physics sound waves & PBL schemes take most CPU in both urban/PBL scheme in uMM5 takes almost 50% of all time

25. Urbanization  day& nite on same line  stability effects not important  mechanical effects are important Is it worth it?: Case study 7 (A. Martilli) MM5: uMM5

26. (Last) Case Study 8: uMM5 for Houston: Balmori (2006) Goal: Accurate uMM5 Houston urban/rural temps & winds for Aug 2000 O 3 -episode via Texas2000 field-study data Texas2000 field-study data Taha/SJSU modification of LU/LC & urban morphology parameters by Taha/SJSU modification of LU/LC & urban morphology parameters by processing Burian parameters processing Burian parameters modifying uMM5 to accept them modifying uMM5 to accept them USFS urban-reforestation scenarios  USFS urban-reforestation scenarios  lower daytime max-UHI-intensity & O  lower daytime max-UHI-intensity & O 3  EPA emission-reduction credits  $’s saved

27.  GC influences are small  Early-AM along-shore flow (from east) from N-edge of off-shore cold-core L  Flow is then sequentially:  from Ship Channel to Houston by Bay Breeze  into Houston by UHI-convergence (time of O 3 -max)  and finally beyond Houston to NW by Gulf Breeze Domain-5, episode-day, obs O 3 -transport: sea breeze + UHI-convergence influences

28. L H C Urban min + UHI Conv H Start of N-flow H L H L over-Houston: due to titration H Near-max O 3

29. uMM5 simulation for 22-26 August case Model configuration Model configuration 5 domains, with Δx = 108, 36, 12, 4, and 1 km 5 domains, with Δx = 108, 36, 12, 4, and 1 km (x, y) grid-pts: 43x53, 55x55, 100x100, 136x151, 133x141 (x, y) grid-pts: 43x53, 55x55, 100x100, 136x151, 133x141 full-  levels: 29 (Domains 1-4) & 49 (Domain 5) full-  levels: 29 (Domains 1-4) & 49 (Domain 5) lowest ½  level= 7 m lowest ½  level= 7 m 2-way feedback in Domians 1-4 2-way feedback in Domians 1-4 Parameterizations/physics options Parameterizations/physics options > Grell cumulus (D 1-2) > ETA or MRF PBL (D 1-4) > Grell cumulus (D 1-2) > ETA or MRF PBL (D 1-4) > Gayno-Seaman PBL (D 5) > Simple ice moisture > Gayno-Seaman PBL (D 5) > Simple ice moisture > Urbanization module > NOAH LSM > Urbanization module > NOAH LSM > RRTM radiative cooling Inputs Inputs > NNRP Reanalysis fields > ADP observational data > NNRP Reanalysis fields > ADP observational data > S. Burian urban-morphology LIDAR building-data (D-5) > S. Burian urban-morphology LIDAR building-data (D-5) > LU/LC modifications (from D. Byun )

30. Episode-day Synoptics: 8/25, 12 UTC (08 DST) H H 700 hPa Surface 700 hPa & sfc GC H’s: at their weakest (no gradient over Texas)  meso-scale forcing (sea breeze & UHI convergence) will dominate

31. Concurrent NNRP fields at 700 hPa & sfc H H NNRP-input to MM5 (as IC/BC) captured GC/synoptic features, as location & strength of H were similar to NWS charts (previous slide) D p=2 hPa

32. MM5: episode day, 3 PM > D–1: reproduces weak GC p-grad & flow > D-2: weak coastal-L > D-3: well-formed L produces along-shore V L D-1 D-2 D-3

33. Domain 4 (3 PM) : L is off of Houston only on O 3 day (25 th ) L L  Episode day day

34. Urbanized Domain 5: near-sfc 3-PM V, 4-days  Episode day day Cold-L Hot Cool

35. Along-shore flow, 8/25 (episode day): obs at 15-UTC vs uMM5 (Domain-5) at 20-UTC Tx2000 HGA uMM5 (D-5, red box) cap-tured along-shore V HGA uMM5

36. 1-km uMM5 Houston UHI: 8 PM, 21 Aug Upper, L: MM5 UHI (2.0 K) Upper, L: MM5 UHI (2.0 K) Upper,R: uMM5 UHI (3.5 K) Upper,R: uMM5 UHI (3.5 K) Lower L: (uMM5-MM5) UHI Lower L: (uMM5-MM5) UHI LU/LC error

37. 8/23 Daytime 2-m UHI: obs vs uMM5 (D-5) H OBS: 1 PM uMM5: 3 PM Cold UHI

38. Along –shore V: due to Cold-Core L : D-3 MM5 vs. Obs-T MM5: produces coastal cold-core low Obs (18 UTC): > Cold-core L (only 1 ob) > Urban area (blue-dot clump) retards cold-air penetration C H H

39. UHI-Induced Convergence: obs vs. uMM5 OBSERVEDuMM5 C C

40. Obs speeds (D-5): large z  speed-decrease over city Obs speeds (D-5): large z 0  speed-decrease over city - - - + + + + V

41. Current base-case Veg-cover (in 0.1’s), with an urban min of 0.2-0.3 Future case (from USFS) Increases in veg-cover (in 0.01’s), with max increases (in urban areas) of about 0.1 uMM5 urban reforestation & rural deforestation simulations

42. Soil moisture increase for: Run 12 (entire area, left) and Run 13 (urban area only, right)

43. Run 12 (urban-max reforestation) minus Run 10 (base case): near-sfc ∆T at 4 PM reforested central urban-area cools & surrounding deforested rural-areas warm

44. D UHI(t) for Base-case minus Runs 15-18 D UHI(t) for Base-case minus Runs 15-18 U1 sea Ru U2 UHI = Temp in Box-Urban minus Temp in Box-Rural UHI = Temp in Box-Urban minus Temp in Box-Rural Runs 15-18: different urban re-forestation scenarios Runs 15-18: different urban re-forestation scenarios D UHI=Run-17 UHI –Run-13 UHI (max effect, green line) D UHI=Run-17 UHI –Run-13 UHI (max effect, green line) Reduced UHI  lower max-O 3 (not shown)  Reduced UHI  lower max-O 3 (not shown)  EPA emission-reduction credits  Max-impact of –0.9 K of a 3.5 K Noon-UHI, of which a 3.5 K Noon-UHI, of which 1.5 K was from uMM5

45. Overall Lessons Models can’t be assumed to be Models can’t be assumed to be perfect perfect black boxes black boxes If obs not available  OK to make reasonable educated estimates, e.g., for If obs not available  OK to make reasonable educated estimates, e.g., for Deep-soil temp Deep-soil temp Soil moisture Soil moisture Need data for comparisons with simulated fields Need data for comparisons with simulated fields Need good urbanization, e.g., uMM5 Need good urbanization, e.g., uMM5 Need to develop better PBL parameterizations Need to develop better PBL parameterizations

46. FUTURE WORK: uWRF uWRF with NCAR (F. Chen) for DTRA uWRF with NCAR (F. Chen) for DTRA Martilli-Dupont-Taha urbanization Martilli-Dupont-Taha urbanization Freedman turbulence Freedman turbulence Applications (current + will propose*) Applications (current + will propose*) Urban canyon dispersion for DTRA Urban canyon dispersion for DTRA Urban climate (with UNAM) Urban climate (with UNAM) *NYC ozone for EPA *NYC ozone for EPA *Calif ozone for CARB *Calif ozone for CARB *Urban thunderstorms for NSF *Urban thunderstorms for NSF *Urban wx forecasting for NWS *Urban wx forecasting for NWS

47. PROG* APPROACH FOR LENGTH SCALE Freedman & Jacobson (2002 & 2003, BLM) + Freedman at SJSU *2 prog Eqs.: TKE & DISSIPATION RATE ε Where ℓ = c ε E 3/2 /ε Values of σ ε & σ E are reversed in Mellor & Yamada  reversed in all atm models  K & TKE in upper PBL were wrong! __

48. CALIBRATION TO NEUTRAL ABL: ℓ vs. z Lines: various values of κ = c ε2 σ ε /σ E x = COLEMAN (‘99) DNS New (R-panel) best-fit κ = 1.3 (dashed line), w/ better results (ℓ↓) in upper PBL Standard approach (left panel) best fit with κ = 2.5, w/ poor results in upper PBL newold

49. Same, but for K (z) x = COLEMAN (‘99) DNS New (R-panel) best-fit κ = 1.3 (dashed line), w/ better results in lower PBL & K ↓ aloft Standard approach (left panel) best fit with κ = 2.5, w/ poor results in lower PBL new old

50. Thanks Any questions?