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CORRECTABLE BC-ERRORS WITHIN MESO-MET MODELS R. Bornstein San Jose State University San Jose, CA Presented at 86th AMS Annual Meeting,

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Presentation on theme: "CORRECTABLE BC-ERRORS WITHIN MESO-MET MODELS R. Bornstein San Jose State University San Jose, CA Presented at 86th AMS Annual Meeting,"— Presentation transcript:

1 CORRECTABLE BC-ERRORS WITHIN MESO-MET MODELS R. Bornstein San Jose State University San Jose, CA pblmodel@hotmail.com Presented at 86th AMS Annual Meeting, Atlanta, GA 30 January 2006

2 Acknowledgements H. Taha, Altostratus & SJSU H. Taha, Altostratus & SJSU D. Hitchock & P. Smith, State of Texas D. Hitchock & P. Smith, State of Texas D. Byun, U. of Houston D. Byun, U. of Houston J. Ching & S. Dupont, US EPA J. Ching & S. Dupont, US EPA S. Stetson, SWS Inc. S. Stetson, SWS Inc. S. Burian, U. of Utah S. Burian, U. of Utah D. Nowak, USFS D. Nowak, USFS Funded by: NSF, USAID, DHS, LBL, LMMS, NASA Funded by: NSF, USAID, DHS, LBL, LMMS, NASA MY M.S. (ex) STUDENTS: J. Cheng, C. Lozej, F. Freedman, T. Ghidey, K. Craig, S. Kasakseh, R. Balmori MY M.S. (ex) STUDENTS: J. Cheng, C. Lozej, F. Freedman, T. Ghidey, K. Craig, S. Kasakseh, R. Balmori

3 OUTLINE INTRODUCTION INTRODUCTION SYNOPTIC FORCING SYNOPTIC FORCING POORLY (AT BEST) KNOWN INPUT DATA POORLY (AT BEST) KNOWN INPUT DATA DEEP SOIL TEMP IC/C DEEP SOIL TEMP IC/C SOIL MOISTURE IC SOIL MOISTURE IC SST IC/BC SST IC/BC SFC/PBL FORCING SFC/PBL FORCING NON-URBAN NON-URBAN URBAN URBAN CONCLUSION CONCLUSION

4 Theme of Talk: MESO-MET ATM-MODELS MUST CAPTURE B.C. FORCINGS IN CORRECT ORDER (1 of 2) e.g., AN O 3 EPISODES OCCURS ON A GIVEN DAY: NOT FROM CHANGING: TOPOGRAPHY & EMISSIONS NOT FROM CHANGING: TOPOGRAPHY & EMISSIONS BUT DUE TO CHANGING (UPPER-LEVEL &/OR SFC) BUT DUE TO CHANGING (UPPER-LEVEL &/OR SFC) GC/SYNOPTIC PRESSURE-PATTERNS, WHICH ENTER OUR MESO-SOLUTIONS FROM EITHER CORRECT OR IMPRECISE LARGER-SCALE MODEL-VALUES & WHICH ENTER OUR MESO-SOLUTIONS FROM EITHER CORRECT OR IMPRECISE LARGER-SCALE MODEL-VALUES & WHICH THUS ALLOW SCF MESO THERMAL-FORCINGS (i.e., UP/DOWN SLOPE, LAND/SEA, URBAN, CLOUDS/FOG) TO DEVELOP CORRECTLY OR INCORRECTLY THUS ALLOW SCF MESO THERMAL-FORCINGS (i.e., UP/DOWN SLOPE, LAND/SEA, URBAN, CLOUDS/FOG) TO DEVELOP CORRECTLY OR INCORRECTLY

5 CORRECT ORDER (2 of 2) MUST THUS CORRECTLY REPRODUCE: UPPER-LEVEL Syn/GC FORCING FIRST UPPER-LEVEL Syn/GC FORCING FIRST pressure (the GC/Syn driver), which produces Syn/GC winds TOPOGRAPHY NEXT TOPOGRAPHY NEXT grid spacing  flow-channeling MESO SFC-CONDITIONS LAST MESO SFC-CONDITIONS LAST temp (the meso-driver) & sfc roughness  Meso-winds

6 Correct GC/Synoptic forcing Methodology: Methodology: Check large-scale forcing before simulations: Check large-scale forcing before simulations: NWS charts vs. large-scale model input-fields NWS charts vs. large-scale model input-fields If correct  use analysis-nudging FDDA  If correct  use analysis-nudging FDDA  correct synoptic-trends Case studies: Case studies: SFBA Winter synoptic-storm (Lozej 1996) SFBA Winter synoptic-storm (Lozej 1996) Atlanta urban-thunderstorm (Craig 2000) Atlanta urban-thunderstorm (Craig 2000) Ozone-episodes Ozone-episodes LA (Boucouvala et al. 2003) LA (Boucouvala et al. 2003) SFBA (Ghidey 2005) SFBA (Ghidey 2005)

7 1996 SFBA Winter-Storm (Lozej) Obs storm went over SFBA Obs storm went over SFBA Wrong input large-scale IC/BC caused storm to Wrong input large-scale IC/BC caused storm to move too zonally and thus too fast move too zonally and thus too fast pass (and precipitate) too far north pass (and precipitate) too far north Note: IC/BC more important for synoptic storms than for meso-systems (they are driven by surface-conditions) Note: IC/BC more important for synoptic storms than for meso-systems (they are driven by surface-conditions) Obs and MM5: next 3 slides Obs and MM5: next 3 slides

8 GOES IR & SFC NWS 12 March, 12 UTC: Storm over SFBA SFBA

9 NCEP (2.5 0 )/MM5 (27 km) (solid blue) & ETA (dash pink) 500 mb heights (dam) at 12 UTC  Left: slight IC/BC errors in NCEP  ETA: digs deeper vs  NCEP/MM5: more zonal  Right: storm goes too far N of SFBA & moves too quickly 11 March 12 March L L

10 MM5 (upper) 3 hr precip max is thus N of observed precip (lower) max (at 50-km S of SFBA) SFBA

11 Atlanta Summer Thunderstorm (Craig) Obs: weak-cold front N of Atlanta Obs: weak-cold front N of Atlanta Large-scale IC/BC: front S of city Large-scale IC/BC: front S of city MM5 UHI-induced thunderstorm: 5-km deep, w max 6-m/s, 8-cm precip MM5 UHI-induced thunderstorm: 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: MM5-storm formed in stable-flow from N & not in unstable-flow from S Data & MM5 results: next slide Data & MM5 results: next slide

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

13 LA Summer O 3 -episode (Boucouvula) Obs of large scale IC/BC: Obs of large scale IC/BC: Shift of meso-700 hPa high  upper-level flow from N  NW-moving sea-breeze & max-O 3 was blocked by sfc-flow from N was blocked by sfc-flow from N stayed in San Fernando Valley stayed in San Fernando Valley MM5: analysis nudging  MM5: analysis nudging  got front and O 3 right (next slide)

14 Upper: analysis nudging  MM5 sea-breeze front (blue line) & O 3 -max blocked from passing to-N b/t 2 Mts by strong opposing N-S large-scale flow (as in obs) Mt Lower: no analysis nudging  MM5 sea-breeze front & O 3- max not blocked from pass- ing to North b/t 2 Mts, as N-S opposing large-scale flow is weak Mt

15 SFBA Summer O 3 - episode () SFBA Summer O 3 - episode (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 MM5 (next 2 slides): MM5 (next 2 slides): good analysis-nudging  good sfc-wind

16 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

17 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

18 Topographic-Channeling (J. Cheng) Horiz grid-spacing too-large  Horiz grid-spacing too-large  Mt-passes not resolved  Mt-passes not resolved  flow-direction is wrong Mt-passes are too wide  Mt-passes are too wide  speeds underestimated Solution: Solution: decrease-spacing until w max is unchanged Case study (not shown): Case study (not shown): SFBA Richmond toxic-spill

19 MM5 Non-urban Sfc-IC/BC Issues Deep-soil temp: BC Deep-soil temp: BC Controls min-T Controls min-T Values unknown & MM5-estimation is flawed Values unknown & MM5-estimation is flawed Soil-moisture: IC Soil-moisture: IC Controls max-T Controls max-T Values unknown & MM5-table values too specific Values unknown & MM5-table values too specific SST: IC/BC SST: IC/BC Horiz coastal T-grad controls sea-breeze flow Horiz coastal T-grad controls sea-breeze flow We usually focus only on land-sfc temp We usually focus only on land-sfc temp IC/BC SST values from large-scale model  IC/BC SST values from large-scale model  too coarse & not f(t)

20 Summary for 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 This assumes a zero time-lag b/t sfc and lower- level (about 1 m) soil-temps This assumes a zero time-lag b/t sfc and lower- level (about 1 m) soil-temps But obs show But obs show 2-3 month time-lag b/t these 2 temps 2-3 month time-lag b/t these 2 temps Larger-lag in low-conductivity dry-soils Larger-lag in low-conductivity dry-soils Thus MM5 min-temps will always be too-high in summer and too-low in winter Thus MM5 min-temps will always be too-high in summer and too-low in winter We need to develop tech (beyond current trial and error) to account for lag: next 2 slides We need to develop tech (beyond current trial and error) to account for lag: next 2 slides

21 Mid-east Obs vs. MM5: 2 m temp (Kasakech ’06 AMS) 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

22 SCOS96 LA Temps (Boucouvual et al.) 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

23 MM5 input-table values: zproblems MM5 input-table values: z 0 problems Water z = 0.01 cm Water z 0 = 0.01 cm Only IC  updated internally by eq = f(MM5 u) Only IC  updated internally by eq = f(MM5 u * ) But Eq only valid for open-sea smooth-swell conditions But Eq only valid for open-sea smooth-swell conditions Observed values for rough-sea coastal-areas ~ 1 cm  Observed values 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 input GIS/RS f(x,y) Must adjust input value or input GIS/RS f(x,y) See next 2 slides See next 2 slides

24 25-MM5 category (USGS) vegetation categories and physical parameters VegetationIntegerIdentificationVegetationDescription Albedo(%)Moisture Avail. (%) Emissivity (% at 9 μ m) Roughness Length (cm) Thermal Inertia (cal cm-2 k-1 s-1/2) SumWinSumWinSumWinSumWinSumWin 1Urban15151010888880800.030.03 2 Drylnd Crop. Past. 1723306098.5921550.040.04 3 Irrg. Crop. Past. 1823505098.5921550.040.04 4 Mix. Dry/Irrg.C.P. 1823255098.5921550.040.04 5 Crop./Grs. Mosaic 1823254099921450.040.04 6 Crop./Wood Mosc 1620356098.59320200.040.04 7Grassland1923153098.59212100.030.04 8Shrubland22251020888810100.030.04 9 Mix Shrb./Grs. 20241525909011100.030.04 10Savanna20201515929215150.030.03 11 Decids. Broadlf. 16173060939350500.040.05 12 Decids. Needlf. 14153060949350500.040.05 13 Evergrn. Braodlf. 12125050959550500.050.05 14 Evergrn. Needlf. 12123060959550500.040.05 15 Mixed Forest 13143060949450500.040.06 16 Water Bodies 881001009898.01.010.060.06 17 Herb. Wetland 14146075959520200.060.06 18 Wooded wetland 14143570959540400.050.06 19 Bar. Sparse Veg. 252525858510100.020.02 20 Herb. Tundra 15605090929210100.050.05 21 Wooden Tundra 15505090939330300.050.05 22 Mixed Tundra 15555090929215150.050.05 23 Bare Grnd. Tundra 257029585951050.020.05 24 Snow or Ice 557095959595550.050.05 25 No data

25 S. Stetson: Houston GIS/RS z o input Values up 3 m

26 Importance of: detailed SST as f(x,y,t) Importance of: detailed SST as f(x,y,t) Theory Theory Along-shore winds  off-shore Ekman ocean-transport  cold-water upwelling  atm & ocean cold-core Lows  altered atm pollutant-transport *Need: detailed satellite SST-input *Need: detailed satellite SST-input Case studies: see next 4 slides Case studies: see next 4 slides Houston (Balmori, 2006 AMS) Houston (Balmori, 2006 AMS) NYC (Pullen et al., 2006 AMS) NYC (Pullen et al., 2006 AMS)

27 Houston MM5 2-m Temps at 4 PM: cold-core L from SST-eddy? L

28 MM5 2-m cold-core L (in 3 domains)  along-shore coastal-V  Houston ozone-episode L L D-2 D-1 D-3

29 NYC SST + currents: Pullen et al. (2006 AMS) L

30 Satellite SST Over Gulf of Mx: lots of details http://www7333.nrlssc.navy.mil/

31 Model-Urbanization Techniques Urbanize momentum, thermoynamic, & TKE Urbanize momentum, thermoynamic, & TKE surface & SBL: diagnostic eqs surface & SBL: diagnostic eqs PBL: prognostic eqs PBL: prognostic eqs From veg-canopy model (Yamada 1982) From veg-canopy model (Yamada 1982) Veg-param replaced with GIS/RS urban-param/data Veg-param replaced with GIS/RS urban-param/data 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 Detailed input urban-parameters as f(x,y) Next: 2 slides

32 max urban effect Urbanized meso-met model TKE (z) _________________ h c =building top

33 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

34 Summary of how to obtain good meso-met model results 1st capture trends in large-scale forcing via 1st capture trends in large-scale forcing via validated large-scale model input validated large-scale model input analysis nudging analysis nudging Then simulate correct meso sfc-T via correct Then simulate correct meso sfc-T via correct IC/BC deep soil-T (for min-T) IC/BC deep soil-T (for min-T) IC soil-moisture (for max-T) IC soil-moisture (for max-T) Get good SSTs (from obs or ocean-models) for good sea-breeze flows Get good SSTs (from obs or ocean-models) for good sea-breeze flows Use good urbanizations (scheme & inputs) for good temps, turbulence, & winds Use good urbanizations (scheme & inputs) for good temps, turbulence, & winds

35 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, it is OK to make reasonable educated estimates, e.g., for If obs not available, it is 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

36 Any questions?

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