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Biswadev (Dev) RoyAtmospheric Modeling Division
National Exposure Research LaboratoryRTP, North Carolina 27711
Currently: Air Modeler/Air Planning Section/EPA/Region-VI, Dallas, TX
September 20, 2007 AMD Seminar
C-111C, NERL, RTP, NC 27711
Application of Satellite Data to Improve Model Performance and Evaluation
EPA/NERL/AMD Post-doc Projects
MODIS
MOPITT
CMAQ
1. Improvement of fire emissions inventory using satellite information- study impact of wildfire emissions reallocations on CMAQ- compare CMAQ predicted CO columns with MOPITT
- use MODIS fire count information and ground observations record for creating 2005 fire emissions for NEI
2. Compare CMAQ optical depth with MODIS observations- compare CMAQ w/AERONET, MODIS and IMPROVE- develop PM2.5-AOD relationship
3. Evaluation of MM5 ground temperature output - compare with aircraft, GOES, and MODIS - inter-relate skin temperature errors with PBL height errors
Data Sources
• MODIS: Moderate Resolution Imaging Spectroradiometer
• MOPITT: Measurement of Pollution in the Troposphere (correlation radiometer)
• GOES: Geost. Operational Environmental Satellite
• AERONET: NASA/Aerosol Robotic Network
• IMPROVE: Interagency monitoring network for class I areas
• STN: monitoring network for urban areas
• MTP: microwave temperature profiler (JPL) TEXAQS I
• Heimann IR Probes: aircraft mounted sensor (JPL) TEXAQS I
1. Study the impact of fire emissions reallocation using
MODIS fire signatures
Objective:
• Reallocate NEI using MODIS fire signatures and check its impact on CMAQ using PM2.5 and Total Carbon data from IMPROVE
1. MODIS Fire-pixel counts were gridded into respective CMAQ grid cells
2. 90% of the NEI monthly prescribed burns and wildfire emissions for each state-month are distributed in space and time using the MODIS fire counts
-- State’s monthly emissions in the NEI were multiplied by fraction of pixel count for each grid cell over the monthly count for the state and by the fraction of each grid cell in that particular state
-- Spatially reallocated emissions were distributed temporally using the ratio of the pixel count per day and pixel count per month for each grid cell
Steps taken for “emissions reallocation”
CMAQ Options MM5-CMAQ
● Pre-release version of CMAQ 4.4 used
● Simulations using CB-IV chemical mechanism
● Modal Aerosol Model and ISORROPIA thermodynamic equilibrium model
● Chemical BC’s for CMAQ based on GEOS-CHEM
● Meteorological inputs from MM5, 34 vertical layers collapsed to 14 layers
● 36 km x 36 km horizontal grid
MODIS RR fire pixel counts
Reallocated minus base case PM2.5 emission rates in g s-1 & OC+EC
0
3
6
9
12
0 3 6 9 12
IMPROVE Average Total Carbon
Mod
eled
Ave
rage
Tot
al C
arbo
n
0
3
6
9
12
15
0 3 6 9 12 15
IMPROVE Average Total Carbon
Mod
eled
Ave
rage
Tot
al C
arbo
n
0
3
6
9
12
15
0 3 6 9 12 15
IMPROVE AVerage Total Carbon
Mod
eled
Ave
rage
Tot
al C
arbo
n
r=0.26
r=0.51
r=0.58
May (Base)
May (Reallocated)
August (Base)
August (Reallocated)
0
3
6
9
12
0 3 6 9 12
IMPROVE Average Total Carbon
Mod
eled
Ave
rage
Tot
al C
arbo
n
r=0.36
Monthly average spatial plot of CMAQ total
carbon before and after
emissions reallocation for May and August
2001
0
3
6
9
12
15
0 3 6 9 12 15
IMPROVE Averaged PM2.5
Mo
del
ed A
vera
ge
PM
2.5
0
3
6
9
12
15
0 3 6 9 12 15
IMPROVE Averaged PM2.5
Mo
del
ed A
vera
ge
PM
2.5
0
3
6
9
12
15
0 3 6 9 12 15
IMPROVE Average PM2.5
Mod
eled
Ave
rage
PM
2.5
r=0.64
r=0.75
r=0.84
r=0.82
Monthly average spatial plot of
CMAQ predicted PM2.5 before and after emissions reallocation for May and August
2001
1b. CMAQ CO evaluation using MOPITT• Improvement of fire emissions inventory using satellite
information- study impact of wildfire emissions reallocations on CMAQ- compare CMAQ predicted CO columns with MOPITT obs.
- use MODIS fire count information and ground observations record for creating 2005 fire emissions for NEI
• Compare CMAQ optical depth with MODIS observations- compare CMAQ w/AERONET, MODIS and IMPROVE- develop PM2.5-AOD relationship
• Evaluation of MM5 ground temperature output - compare with aircraft, GOES, and MODIS - inter-relate skin temperature errors with PBL height errors
• While reallocating fire emissions does it improve CO comparison with data?
With: J. Szykman (EPA/NASA), C. Kittaka (NASA/LaRC/SAIC), Jim Godowitch, and Tom Pierce
1
)(
)(
artv
atrue
atrueartv
CCIA
xAIAx
xxAxx
I=Identity Matrix
A=Avg. kernel
C=error cov. matrix
Using ‘weighting function’ the mixing ratio is adjusted at each level due to effects of all possible levels.
Passive MOPITT does not match CMAQ vertical resolution hence weighting fn. used
CMAQ Column CO Base and Reallocated columns with MOPITT
Initial CMAQ MOPITT Data Revised CMAQ
CMAQ CO vs. MOPITT CO at MOPITT pressure levels Base Fire Emissions and Reallocated Fire Emissions -
August 22-31Pacific Northwest Domain
1c. 2005 fire emissions
• Develop relationship between ground-based area burned and MODIS fire counts for 2002 and use the same for creating 2005 fire emissions
• Improvement of fire emissions inventory using satellite information- study impact of wildfire emissions reallocations on CMAQ- compare CMAQ predicted CO columns with MOPITT
- use MODIS fire count information and ground observations record for creating 2005 fire emissions
• Compare CMAQ optical depth with MODIS observations- compare CMAQ w/AERONET, MODIS and IMPROVE- develop PM2.5-AOD relationship
• Evaluation of MM5 ground temperature output - compare with aircraft, GOES, and MODIS - inter-relate skin temperature errors with PBL height errors
With: George Pouliot, Tom Pace, David Mobley, and Tom Pierce
Terra collects data on “descending” node
Aqua collects data on “ascending” node
TERRA
AQUA
Estimate Burned Area using Np
),(),( tiNtiA pA is area burned in a spatial region labeled by index ‘i’ and during a fixed time period labeled by index ‘t’
Np = No. of fire pixels obs. within the same region during same time period
α=constant Area Burned/Np obtained Region-wise
Scheme for MODIS pixel clustering and match up
with ground-reports
Adjacency test:
L
fireday
n
p NN1 1
L = lifetime of the fire; n = no. of obs.
Burned Area in Acres/month and PM2.5
Emissions - 2002
Spring: Prescribed
Summer: wildfire
MODIS imagery August 12, 2002 and PM2.5 emissions from Biscuit Fire, OR.
using Np-Area burned relationshipMODIS Imagery, Aug. 12, 2002
C1
C2
C1
C2
C2
NEI: All emissions in 1 grid; Satellite : aids in spatial re-distribution: removal of excess NOx hence over-estimate of surface ozone
• Emissions reallocation has re-distributed the total carbon concentrations from state-wide extent to a more localized fashion
• Transport patterns suggest that the MM5 simulation captured shifts in wind direction adequately
• Reallocated CMAQ simulation adjusted with plume-rise predicts higher total carbon concentration
• Emissions reallocation can reduce biases in the base simulation of total carbon during non-fire periods
• Emissions reallocation yield a better correlation with IMPROVE data obtained from locations having a significant separation from the fire location
• CMAQ CO columns agree better after using MOPITT kernels• MODIS fire detect information can improve spatial and
temporal allocation of emissions from large fires with a high degree of confidence.
Summary on wildfire emissions study
2a, 2b CMAQ AOD comparison
To thoroughly characterize the performance of the emissions meteorological and chemical transport modeling components of the Models-3 system
• Improvement of fire emissions inventory using satellite information- study impact of wildfire emissions reallocations on CMAQ- compare CMAQ predicted CO columns with MOPITT
- use MODIS fire count information and ground observations record for creating 2005 fire emissions for NEI
• Compare CMAQ optical depth with MODIS observations- compare CMAQ w/AERONET, MODIS and IMPROVE- spatial variability of AOD and develop PM2.5-AOD relationship
• Evaluation of MM5 ground temperature output - compare with aircraft, GOES, and MODIS - inter-relate skin temperature errors with PBL height errors
2a: With Rohit Mathur, Alice Gilliland, and Steven Howard
2b: With Adam Reff, Brian Eder & Steven Howard
Two fold objective -- evaluation of CMAQ AOD
• To thoroughly characterize the performance of the emissions, meteorological and chemical/transport modeling components of the Models-3 system and build confidence within community.
• To pursue inter-relating satellite AOD with PM2.5 (modeled and measured).
● Satellite Aerosol Optical Depth (AOD) products offer new and challenging opportunities for studying regional distribution of particulate matter and scopes for rigorous operational evaluation of modeling systems
● EPA standards are based on total PM2.5 hence it is important to assess model performance of total PM2.5 and the impact of CMAQ model performance for individual species on the total.
-- First need to establish whether AOD satellite data can be useful as additional information for PM2.5 model evaluation.
-- Summer period of 2001 selected
CMAQ and Terra/MODIS AOD comparison
CMAQ-Terra/MODIS comparison
14 Layer
CMAQ AOD Method Based on Reconstructed Mass-Extinction Method (Malm et al. 1994, Binkowski & Roselle, 2003)‘Reconstructed’ extinction coefficients are based on assumption that organic mass is soluble up to 50% by mass
])[01.0(
])[0006.0(])[001.0(])[004.0(])[()003.0( 344
LAC
CMFSOMNOSONHRHf
ap
tsp
● OM=Organic mass, FS=Fine Soil, LAC=Light Absorbing Carbon (elemental carbon), CM=Coarse mass. Concentration are in mg m-3
● The specific scattering coefficient 0.003, 0.004, 0.001 and 0.0006 are based on assuming log-normal particle size distribution.
● Modeled pressure, water-vapor mixing ratio and temperature are used to compute the vapor pressure and RH.
● Layer RH value is used to calculate the exact humidity growth factor from an LUT (Malm et al. 1994; Binkowski & Roselle, 2003)
CMAQ AOD vs MODIS AOD on some eventful days
Regional Pattern ---Frontal activity
Time-series of CMAQ AOD, SSA and MODIS AOD
CMAQ Grid-cell [114, 30] having large fire in FL
(May 19-29)
CMAQ Grid-cell [30, 90] having large fire in WA.
(Aug 11-21)
August 2001
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1 3 5 7 9 11 13 15 17 19 21 23 25 27
Day
AO
D
May 2001
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Day
AO
D
AOD CMAQ Method 2AOD MODISSSA CMAQ
MODIS AOD (0-4 scale)
MAY 24, 2001
MODIS AOD (0-4 scale)
MAY 25, 2001
Wildfire signature on MODIS AOD
Fractional SO4 AOD
Fractional NO3 AOD
Fractional NH4 AOD
Fractional OC AOD
Fractional BC AOD
Fractional A25 AOD
Fractional CM AOD
August 2001
Fract. AOD
14
1
14
1
)(
)( 4
ispall
iSO
sp
f
Z
ZAOD
Sulfate contributes ~ 40%
2*cmaq aod
CMAQ AOD X 2
JJA 2001
MODIS Avg. AOD JJA 2001
AOD Correlation Modis ~ CMAQ JJA 2001
AOD NMB and NME: JJA 2001
Normalized mean error :
(ΣABS(Model-Obs)/ΣObs) * 100:
Normalized mean bias :
(Σ(Model-Obs)/ΣObs) * 100
Good Days & Bad Days JJA 2001
NME % (50-100 range) Model-MODIS AOD E USA JJA 2001
50
60
70
80
90
100
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91
Day
NM
E%
GOOD Days (NME minimum)
Bad Days (NME maximum)
Satellite AOD Imputation performed for cloudy days
• Non-cloudy: (Modis-AOD/Cmaq-AOD) Ratio
• Mean Ratio for each Land Use Type
• Gamma distribute Ratio for each LUSE
• Cloudy Day: Use distribution to draw Ratio for LUSE
• Ratio * Cmaq-AOD = Imputed AOD
Attempt to relate AOD satellite with modeled PM2.5
New PM2.5 = f(NonDim. Met. factor)*(PM2.5_CMAQ)*AODMODIS
…..being developed… currently R ~ 0.63
012
34567
89
101112
131415
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4
PM25 at Z/PM25 at Sfc
Lay
er N
um
ber
He-Fold
NonDimensional met factor constructed out of using:
Obukhov length |Lo-1|, Stress: u*2, Convective velocity w*, Sfc.
Jacobian Jsfc, and e-Folding Height for PM2.5
Summary on AOD study for JJA 2001
• CMAQ surface extinction due to particle scattering compares well with the IMPROVE nephelometer data
• Ratio of MODIS to CMAQ AOD is most of the time a factor of 1 to 10 higher than ratio of MODIS mass concentration to CMAQ PM2.5
mass concentration data
• Mean difference between MODIS and CMAQ AOD columns is 0.2
• Sulfate is found to be a dominant contributor to CMAQ AOD
• CMAQ AOD patterns reflect synoptic activities very clearly
3a. MM5 skin temperature evaluation
• Comparison of MM5 GT with MODIS, GOES and aircraft obs. over Houston during TexAQS-2000
• Improvement of fire emissions inventory using satellite information- study impact of wildfire emissions reallocations on CMAQ- compare CMAQ predicted CO columns with MOPITT
- use MODIS fire count information and ground observations record for creating 2005 fire emissions for NEI
• Compare CMAQ optical depth with MODIS observations- compare CMAQ w/AERONET, MODIS and IMPROVE- develop PM2.5-AOD relationship
• Evaluation of MM5 ground temperature output - compare GT with aircraft, GOES, and MODIS - inter-relate skin temperature errors with PBL height errors
• MOD11A1 1 km gridded day, night global data.
• Provides per-pixel temperature in Kelvin with a cross track view-angle dependent algorithm applied to observations.
• Accuracy: ~ 1oK for land use (IGBP) with known emissivity
MOD11A1 1km LST Product
Footnote: Processing & comparison with GOES & aircraft LST product:
Data in integerized sinusoidal (ISIN) projection re-sampled to geographic system using MODIS Reprojection Tool v3.3. Environment for Visualization (ENVI v4.2) used for geo-referencing re-sampled data over the Texas domain.
Terra/MODIS land surface temperature product
MM5 GT compares with GOES 4km windowed and
MODIS 1km native GOES TG night-time at 4 km
windowed over HD1MM5 T5 night-time at 1 km
native over HD1 MODIS TM night-time at 1 km
native over HD1
GOES TG day-time at 4 km windowed over HD1
MM5 T5 day-time at 1 km native over HD1
MODIS TM day-time at 1 km native over HD1
08/19 08/19 08/19
08/19 08/19 08/19
(a) (b)(c)
(d) (e) (f)
(a)
+3.0
-2.5
-6.0
-4.0
-4.0
-2.5
≈ 0.0
+0.5
-1.0
≈ 0.0
-2.0
+1.0
NIGHT DAY
MODIS – Model (MM5)(1)
MODIS-GOES(1, 2)
NW
SW
NE NW NE
SW(a) (b)
UCP data-rich zone
(heavy built-up area)
Sector-wise difference in thermal property
Important point:
Low Day/Night temperature differences in the model domain with detailed urban canopy information
Skin temperatures from MODIS provides a diagnostic indicator of model performance.
• “Inside” Morphology database region: Urbanized model predicts urban heat island successfully; i.e., model bias is
small in urban sector when compared to MODIS. Standard MM5 using Roughness approach produces poor description of
the Houston heat island. Model bias is high in urban area.
• “Outside” Morphology database region:
Model predictions of skin temperatures are problematic; an avenue to explore is the possibility of inaccurate land use specification.
Model UCP extrapolation methodology, reexamination of designation of land use in mesoscale models and their physical properties are needed.
• Other simulation days, and nighttime results exhibit similar features
• Improvement of fire emissions inventory using satellite information- study impact of wildfire emissions reallocations on CMAQ- compare CMAQ predicted CO columns with MOPITT
- use MODIS fire count information and ground observations record for creating 2005 fire emissions for NEI
• Compare CMAQ optical depth with MODIS observations- compare CMAQ w/AERONET, MODIS and IMPROVE- develop PM2.5-AOD relationship
• Evaluation of MM5 ground temperature output - compare with aircraft, GOES, and MODIS - inter-relate skin temperature errors with PBL height errors
3b. Relate skin temperature error and PBL height error
• Infer inter-relation between skin temperature and PBL height error using EMD/HT method
Block avg T, PBL Height & Spectra
Temperature
PBL Height
TFE Spectra - Temperature
TFE Spectra -PBL Height
Heimann Probe
MM5 GT
• Time --
Obs
Model
Obs
Model
Hilbert Spectra to ascertain Tskin-Mixing Ht. Relation
Treating skin temperature & PBL height error (obs. Minus model) series as being non-stationary
MTP PBL Height minus MM5
Heimann Skin Temp. minus MM5 Skin Temp.
