Improving WRF/CAMx Model Performance using Satellite Data Assimilation Technique for the

Uintah Basin

19th AMS Conference on Mountain MeteorologyJuly 15, 2020

Huy TranTrang Tran

AcknowledgementsUtah Division of Air Quality

Uintah Impact Mitigation Special Service DistrictUtah Legislature SB118

Manual and forcible corrections to WRF had to be made to capture surface albedo)

Corrected WRF Albedo in winter 2013 ozone episode

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(Ref.: Crosman et al., 2015; Neemann et al., 2015)

Neemann, E. M., Crosman, E. T., Horel, J. D., and Avey, L.: Simulations of a cold-air pool associated with elevated wintertime ozone in the Uintah Basin, Utah, Atmos. Chem. Phys., 15, 135–151, https://doi.org/10.5194/acp-15-135-2015, 2015.

Albedo

Surface albedo corrections are:• Performed on case by case basic, and there is no standard method

to perform the correction.• Heavily relies on knowledge and observations of modelers on the

actual surface conditions during simulated episode

• May results in unrealistic albedo estimations over model domain

Utilizing satellite observations (MODIS) to correct surface characteristics

Surface Shortwave Albedo (ALBEDO)• MCD43A1: MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global - 500m V006

• MCD19A1: MODIS/Terra+Aqua Land Surface BRF Daily L2G Global 500m, 1km and 10km SIN Grid V006

• MCD19A2: MODIS/Terra+Aqua Land Aerosol Optical Thickness Daily L2G Global 1km SIN Grid V006

Surface Snow Cover Fraction (SNOWC)• MOD10A1: MODIS (Terra) Snow Cover Daily L3 Global 500m Grid

Leaf Area Index (LAI)• MCD15A3H: MODIS/Terra+Aqua Leaf Area Index/FPAR 4-Day L4 Global 500 m (4 days composite)

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MODIS Data Assimilation Processes6SV RoutineMCD43A1

fiso(𝜆𝜆), fvol(𝜆𝜆), fgeo(𝜆𝜆)

BRDF Albedo Determination:

Black − sky Albedo 𝛼𝛼𝐵𝐵𝐵𝐵𝐵𝐵 𝜃𝜃𝐵𝐵𝑆𝑆𝑆𝑆, 𝜆𝜆 = 𝑓𝑓𝑖𝑖𝑖𝑖𝑖𝑖 𝜆𝜆 𝑔𝑔𝑖𝑖𝑖𝑖𝑖𝑖 𝜃𝜃𝐵𝐵𝑆𝑆𝑆𝑆 + 𝑓𝑓𝑣𝑣𝑖𝑖𝑣𝑣 𝜆𝜆 𝑔𝑔𝑣𝑣𝑖𝑖𝑣𝑣 𝜃𝜃𝐵𝐵𝑆𝑆𝑆𝑆 + 𝑓𝑓𝑔𝑔𝑔𝑔𝑖𝑖 𝜆𝜆 𝑔𝑔𝑔𝑔𝑔𝑔𝑖𝑖 𝜃𝜃𝐵𝐵𝑆𝑆𝑆𝑆

White-sky Albedo 𝛼𝛼𝑊𝑊𝐵𝐵𝐵𝐵 𝜃𝜃𝐵𝐵𝑆𝑆𝑆𝑆, 𝜆𝜆 = 𝑓𝑓𝑖𝑖𝑖𝑖𝑖𝑖 𝜆𝜆 𝑔𝑔3𝑖𝑖𝑖𝑖𝑖𝑖 + 𝑓𝑓𝑣𝑣𝑖𝑖𝑣𝑣 𝜆𝜆 𝑔𝑔3𝑣𝑣𝑖𝑖𝑣𝑣 + 𝑓𝑓𝑔𝑔𝑔𝑔𝑖𝑖 𝜆𝜆 𝑔𝑔3𝑔𝑔𝑔𝑔𝑖𝑖

Blue-sky Albedo 𝛼𝛼𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵= 𝛼𝛼𝐵𝐵𝐵𝐵𝐵𝐵 1 − 𝐷𝐷 + 𝛼𝛼𝑊𝑊𝐵𝐵𝐵𝐵𝐷𝐷

ESMF Re-gridding

MCD19A1𝜃𝜃𝐵𝐵𝑆𝑆𝑆𝑆 , 𝜗𝜗𝐵𝐵𝑆𝑆𝑆𝑆, 𝜃𝜃𝑉𝑉𝑖𝑖𝑔𝑔𝑉𝑉 ,

𝜗𝜗𝑉𝑉𝑖𝑖𝑔𝑔𝑉𝑉

MCD19A2AOD

Lookup Table for Diffuse Radiation

Fraction𝐷𝐷 (𝜆𝜆, 𝜃𝜃𝐵𝐵𝑆𝑆𝑆𝑆 , 𝜗𝜗𝐵𝐵𝑆𝑆𝑆𝑆, 𝜃𝜃𝑉𝑉𝑖𝑖𝑔𝑔𝑉𝑉, 𝜗𝜗𝑉𝑉𝑖𝑖𝑔𝑔𝑉𝑉,𝐴𝐴𝐴𝐴𝐷𝐷)

MCD15A3HLeaf Area Index

WRF Surface Nudgingwrfsfdda CAMx

MOD10A1Snow Cover

WRF

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wrfcamx

wrfinput

Modifications to WRF source codes are required

/RegistryRegistry.EM_COMMON

/physmodule_physics_init.Fmodule_fddagd_driver.Fmodule_fdda_psufddagd.F

/shareoutput_wrf.F

/dyn_em.module_first_rk_step_part1.F

/run/namelist.input…&fddaif_ramping = 1,dtramp_min = 60,grid_fdda = 1,grid_sfdda = 1,sgfdda_interval_m = 360, galb_sfc = 0.950,gsnc_sfc = 1.000,/

WRF

Input files:wrfinput (LAI)wrfsfdda (ALBEDO, SNOWC)

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WRF Model Configurations

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Parameters Values

Grid size (x,y) 298 x 322 in 1.3km horizontal resolution

Vertical levels 37

Vertical coordinates Terrain-following Eta (non-hybrid)

Vertical grid spacing 12-16 m in the boundary-layer

Topographic dataset USGS GTOPO30Land use data set modified NLCD2011Veg parm table variables modified for winter simulations

SNUP, MAXALB

Snow cover initialization SNODAS

Re-initialize Every 5 days

WRF Treatment Option SelectedMicrophysics ThompsonLongwave radiation RRTMGShortwave radiation RRTMGLand surface model (LSM) NOAHPlanetary boundary layer (PBL) scheme

MYJ

MODIS data assimilation results in better estimations of ALBEDO

Default WRF (REF)

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MODIS assimilation

Modeling episode Feb 01- 28 2011Albedo

MODIS data assimilation results in “better” estimations of Snow Cover (SNOWC)

Default WRF (REF)

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MODIS assimilation

Modeling episode Feb 01- 28 2011

SNOWC

Effect of MODIS data assimilation

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ow D

epth

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) REF MODIS

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Effect of MODIS data assimilation (Cont.)

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GL) PBLH at Ouray (OU) REF MODIS

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e Ra

te a

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/km

)

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extremely stable

moderate stable

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neutral

unstable

MODIS data assimilation does NOT necessarily result in better overall WRF performance

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MODIS data assimilation does NOT necessarily result in better overall WRF performance

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Modifications to CAMx source codes are also required

CAMx /Includescamxfld.inc

/Mod_srccamxfld.f

/IO_binsreadinp.fmetinit.f

/CAMxgetalbedo.fstartup.ftstep_init.f

wrfcamx(ALBEDO, SNOWC)

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• CAMx calculates its own ALBEDO and SNOWC as the function of snow water equivalent, snow age, and landuse types

• At the initialization of CAMx simulation, CAMx-ALBEDO maybe higher than WRF-ALBEDO

Higher ALBEDO obtained in WRF-MODIS did not translate to higher photolysis rate in CAMx

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ne P

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e (h

r-1) OURAY (OU)

REF MODIS

MODIS data assimilation is not the magic wand to solve ozone underperformance issue

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Ouray Observation NAAQS REF MODIS

Summaries

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• We have developed a methodological approach in improving WRF/CAMxperformance using satellite data.

• MODIS data assimilation “improves” WRF and CAMx model performance.

• Positive effect of MODIS assimilation varies with different simulation episode and length of the episode.

• Effect of the technique could be more substantial in direct coupling meteorology-chemistry model.

• Better resolution and more frequent satellite dataset in future will enhance the benefit of satellite- base data assimilation technique.

• The same approach could be applied for assimilating other satellite data product to improve performances of both meteorology and chemistry model.