OCS Study 

BOEMRE 2010-050 

 

Satellite Data Assimilation 

into Meteorological/Air Quality Models 

U.S. Department of the Interior

Bureau of Ocean Energy Management, 

 Regulation and Enforcement 

Gulf of Mexico OCS Region 
OCS Study 

BOEMRE 2010-050 

Satellite Data Assimilation 

into Meteorological/Air Quality Models 

Authors 

A. Pour Biazar 

R.T. McNider 

S. Mackaro 

Prepared under BOEMRE Cooperative Agreement 

1435-01-04-CA-37002 (M054AC12302) 

by 

University of Alabama in Huntsville 

National Space Science and Technology Center 

Earth System Science Center 

Huntsville, Alabama 35805 

Published by 

U.S. Department of the Interior 

Bureau of Ocean Energy Management, 

 Regulation and Enforcement 

Gulf of Mexico OCS Region 

New Orleans

December 2010 
iii

DISCLAIMER 

This report was prepared under contract between the Bureau of Ocean Energy Management, 

Regulation and Enforcement (BOEMRE) and the University of Alabama in Huntsville (UAH). 

This report has been technically reviewed by the BOEMRE, and it has been approved for 

publication. Approval does not signify that the contents necessarily reflect the views and policies 

of the BOEMRE, nor does mention of trade names or commercial products constitute 

endorsement or recommendation for use. It is, however, exempt from review and compliance 

with the BOEMRE editorial standards. 

REPORT AVAILABILITY 

This report is available only in compact disc format from the Bureau of Ocean Energy 

Management, Regulation and Enforcement, Gulf of Mexico OCS Region, at a charge of $15.00, 

by referencing OCS Study BOEMRE 2010-050. The report may be downloaded from the 

BOEMRE website through the Environmental Studies Program Information System (ESPIS). 

You will be able to obtain this report also from the National Technical Information Service in the 

near future. Here are the addresses. You may also inspect copies at selected Federal Depository 

Libraries. 

 Bureau of Ocean Energy Management, U.S. Department of Commerce 

 Regulation and Enforcement National Technical Information Service 

 Gulf of Mexico OCS Region 5285 Port Royal Road 

 Public Information Office (MS 5034) Springfield, Virginia 22161 

 1201 Elmwood Park Boulevard Phone: (703) 605-6040 

 New Orleans, Louisiana 70123-2394 Fax: (703) 605-6900 

 Telephone requests may be placed at Email: bookstore@ntis.gov

 (504) 736-2519, 1-800-200-GULF, or 

 Rush Orders: 1-800-553-6847 

 Fax: (504) 736-2620 

CITATION 

Biazar, A.P., R.T. McNider, and S. Mackaro. 2010. Satellite data assimilation into 

meteorological/air quality models. U.S. Dept. of the Interior, Bureau of Ocean Energy 

Management, Regulation and Enforcement, Gulf of Mexico OCS Region, New Orleans, 

LA. OCS Study BOEMRE 2010-050. 70 pp. 

ACKNOWLEDGMENTS 

This work was accomplished under partial support from Cooperative Agreement Between the 

University of Alabama in Huntsville and the Bureau of Ocean Energy Management, Regulation 

and Enforcement on Gulf of Mexico Issues, Texas Commission on Environmental Quality 

(TCEQ), and the following grants: U.S. Environmental Protection Agency (USEPA) Star Grant 

R-826770-01-0, Southern Oxidant Study, USEPA Cooperative Agreement R-82897701-0 and 

Texas Air Research Center/Lamar University contracts TARC/LU-052UAL0030A and 

123UAL2030A. Note the results in this study do not necessarily reflect policy or science 

positions by the funding agencies. 
v

TABLE OF CONTENTS 

Page

LIST OF FIGURES ...................................................................................................................... vii 

LIST OF TABLES......................................................................................................................... xi 

1. INTRODUCTION .............................................................................................................. 1 

2. IMPROVING PHOTOLYSIS RATES IN PHOTOCHEMICAL AIR QUALITY 

MODELS USING SATELLITE DATA............................................................................................ 3 

2.1. Methodology........................................................................................................... 4 

2.2. Retrieval of Goes Broadband Visible Transmission and Cloud Top Heights ........ 6 

2.3. Implementation Within CMAQ .............................................................................. 9 

2.4. Model Simulations................................................................................................ 10 

2.5. Results and Discussion ......................................................................................... 10 

2.5.1. Model-to-Model Comparisons................................................................ 11 

2.5.2. Verification of Model Results................................................................. 20 

3. IMPROVEMENT IN LAND SURFACE MODELING USING SATELLITE 

DATA ................................................................................................................................27

4. MODEL GROUND TEMPERATURE TENDENCY COMPARED WITH 

SATELLITE OBSERVATION .........................................................................................39

5. RECOVERING SKIN TEMPERATURE WITHIN MM5............................................... 41 

6. DEVELOPMENT OF A CONSISTENT THREE TEMPERATURE SYSTEM 

FOR ASSIMILATING SATELLITE SKIN TEMPERATURES .................................... 45 

6.1. Skin Temperature Assimilation.............................................................................. 46

6.2. Three-Temperature System .................................................................................... 49

6.3. 1DBLM Test Cases ................................................................................................ 53

6.4. Energy Balance Considerations.............................................................................. 58

6.5. Summary................................................................................................................. 62

7. SUMMARY AND CONCLUSIONS ............................................................................... 65

8. REFERENCES ................................................................................................................. 67
LIST OF FIGURES 

Page

Figure 1. CMAQ/MM5 predicted and GOES satellite observed cloud fields for (a) 

August 24, 2000, 21 GMT, and (b) August 28, 19 GMT. ...................................... 9

Figure 2. Cloud transmissivity and corresponding NO2 photolysis rates for August 

24, 2000, at 2100 GMT from CMAQ_base and CMAQ_sat simulations at 

the surface (first model layer). ............................................................................. 12

Figure 3. Image of Houston-Galveston Bay area. ............................................................... 14

Figure 4. Hourly differences in NOx and HNO3 surface removal from the two 

simulations (control vs. satellite assimilation) for point A over HoustonGalveston Bay area for August 24, 2000. ............................................................ 14

Figure 5. Differences between NO, NO2, and O3 (ppb) for August 24 through 

August 31 between satellite cloud assimilation and control simulations for 

grid cells A and B as marked in Figure 3 (A upper panel and B lower 

panel). ................................................................................................................... 15

Figure 6. Extreme differences in (a) NO and (b) NO2 between assimilation and 

control simulations (assim-control) for the entire period of study covering 

from 

0 GMT, August 24, 2000, to 0 GMT, September 1, 2000.................................... 17

Figure 7. Largest differences in (a) NOx and (b) O3 between assimilation and 

control simulations (assim-control) for the entire period of study covering 

from 0 GMT, August 24, 2000, to 0 GMT, September 1, 2000. .......................... 18

Figure 8. CMAQ Model predictions of ozone versus USEPA’s AIRS observations 

for the 12-km domain on August 24, 2000, 21:00 GMT...................................... 22

Figure 9. Time series of ozone predictions versus observations (OBS) for a location 

near New Orleans on August 26, 2000. . .............................................................. 23

Figure 10. Time series of ozone predictions versus observations (OBS) for a location 

near New Orleans on August 31, 2000. ............................................................... 24

Figure 11. Time series of ozone predictions versus observations (OBS) for a location 

in South Mississippi on August 30, 2000. . .......................................................... 25

Figure 12. Base reflectivities (dBz) at 0.5 degree tilt angle from the Mobile, AL 

(MOB; left) and Eglin Air Force Base (EVX; right) WSR-88D radars 

overlaid on standard National Weather Service ASOS plots................................ 28

vii
Figure 13. Base reflectivity (dBz) from EVX WSR-88D overlaid on CTRL 

simulated 1000 mb vertical velocity (cm s-1) valid 22 UTC 14 May 2001. ......... 29

Figure 14. Time series of CTRL simulation 2 m temperature (pink) and dew point 

(blue) bias valid 12 UTC 14 May 2001 to 00 UTC 15 May 2001........................ 29

Figure 15. Base reflectivity (dBz) from EVX WSR-88D overlaid on ASSIM 

simulated 1000mb vertical velocity (cm s-1) valid 22 Z 14 May 2001................ 30

Figure 16. Time series of CTRL (pink), ASSIM (blue) and OSU (green) bias for 

(a) air temperature (C) and (b) dew point (C) valid 12 UTC 14 May 

2001 to 00 UTC 15 May 2001. ............................................................................. 31

Figure 17. LST (C) valid 20 UTC 14 May 2001 as retrieved from (a) GOES and 

(b) ASSIM............................................................................................................. 32

Figure 18. Time series of simulated sensible (solid) and latent (dashed) heat fluxes at 

VPS from CTRL (blue), ASSIM (red), and OSU (green) valid 12 UTC 14 

May 2001 to 00 UTC 15 May 2001...................................................................... 33

Figure 19. 12-hour accumulated sensible and latent heat flux (KJ)....................................... 34

Figure 20. Moisture availability over simulation region valid 15 UTC 14 May 2001 

in (a) CTRL and (b) ASSIM. ................................................................................ 35

Figure 21. Moisture availability over simulation region valid 15 UTC 30 August 

2000 in (a) CTRL and (b) ASSIM. ....................................................................... 36

Figure 22. Skin temperature tendencies (K/s) valid 1245 UTC to 1545 UTC 30 

August 2000 retrieved from GOES satellite. ........................................................ 40

Figure 23. Skin temperature tendencies (K/s) valid 1245 UTC to 1545 UTC 30 

August 2000 retrieved from CTRL with no assimilation. .................................... 40

Figure 24. Skin temperature tendencies (K/s) valid 1245 UTC to 1545 UTC 30 

August 2000 as simulated by CTRL..................................................................... 42

Figure 25. Skin temperature tendencies (K/s) valid 1245 UTC to 1545 UTC 30 

August 2000 as simulated by CTRL but for ASSIM............................................ 42

Figure 26. Schematic of the use of ground temperature within the surface to 

boundary layer interface of the MM5 using a composite slab land surface 

model..................................................................................................................... 48

Figure 27. Schematic of a land surface to boundary layer interface that follows the 

traditional bulk aerodynamic formulation of surface layer fluxes........................ 49

viii
ix

Figure 28. Schematic flow chart of the iteration used to solve the three temperature 

system used in 1DBLM. ....................................................................................... 51

Figure 29. Observed (solid), CTRL simulated (dashed), and ASSIM simulated 

(dash-dot) variables valid 06 UTC 4 July 2003 to 12 UTC 5 July 2003 for 

NRMN................................................................................................................... 55

Figure 30. Observed (solid), CTRL simulated (dashed), and ASSIM simulated 

(dash-dot) variables valid 06 UTC 4 July 2003 to 12 UTC 5 July 2003 for 

FORA.................................................................................................................... 57

Figure 31. Observed (solid), CTRL simulated (dashed), and ASSIM simulated 

(dash-dot) variables valid 06 UTC 29 July 2005 to 12 UTC 5 July 2003 

for SGP-CF ........................................................................................................... 58

Figure 32. Time series of skin temperature tendency from CTRL (solid), ASSIM 

(small dash), and observations (dotted) along with time series of skin 

temperature from CTRL (dash-dot) and ASSIM (large dash).............................. 60

Figure 33. Time series of skin temperature tendency from CTRL (solid), ASSIM 

(small dash), and observations (dotted) along with time series of skin 

temperature from CTRL (dash-dot) and ASSIM (large dash).............................. 61

Figure 34. Observed (solid), CTRL simulated (dashed), and ASSIM simulated 

(dash-dot) variables valid 06 UTC 29 July 2005 to 12 UTC 5 July 2003 

for SGP-CF applying the MCN94 assimilation technique every 2 minutes......... 62
xi

LIST OF TABLES 

Page

Table 1. Pertinent Initial Model Parameters for the OK and SGP Model Simulations. ..... 54
1. INTRODUCTION 

Improving understanding of the near shore environment is important in order to assess 

the impact of offshore oil and gas exploration on the environment including air quality impacts. 

Regulators at the federal level and coastal states have a responsibility to ensure that State 

Implementation Plans (SIP) and new source review adequately addresses air quality impacts both 

onshore and in the coastal environment of offshore facilities. Since Prevention of Significant 

Deterioration (PSD) regulations incorporate offshore facilities and onshore new sources, this 

responsibility includes developing and assessing tools for sources offshore and in the onshore 

coastal environment. The Bureau of Ocean Energy Management, Regulation and Enforcement 

has a long history of carrying out studies and developing tools to improve air quality 

characterization and prediction in the coastal and near shore environment. 

Because of this responsibility, understanding of the coastal atmosphere including sea 

breezes, land breezes and nearshore atmospheric structure is important to properly assessing air 

quality impacts. The vertical shear in sea breeze regimes and timing of sea breeze events is 

important to plume spread and transport both for near shore facilities and on shore facilities. The 

behavior of sea breezes and coastal temperature and turbulence structure is tied to both sea 

surface temperatures and onshore land temperatures. Thus, the development of land surface 

parameterizations and parameter specifications (such as moisture availability, heat capacity, 

surface roughness) is important to proper specification of the atmospheric structure which is 

critical to air quality impacts assessment. Additionally, the coastal cloud structure is vitally 

important to surface heating rates, impacting boundary layer structure and temperature. Further, 

coastal cloud modification of photolysis fields in photochemical processes is important to ozone 

and aerosol formation and decay. 

The work presented in this report is directed at improving the specification of surface 

parameters such as insolation, soil moisture and surface heat capacity that control the developing 

land boundary layer through use of satellite data. The tools and techniques using the satellite data 

are tested in the context of the type boundary layer models used in air quality models. In 

particular, the techniques using satellite data for determining soil moisture availability (McNider 

et al., 1994), surface heat capacity (McNider et al., 2005), insolation (McNider et al., 1995) and 

photolysis fields (Pour-Biazar et al., 2007) are examined in the context of recent national level 

air quality studies undertaken along the Texas Gulf Coast (TexAQS2000). 

The University of Alabama in Hunstville (UAH) has had a long history of working with 

National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric 

Administration (NOAA) and applied agencies such as the National Weather Service, the U.S. 

Environmental Protection Agency (USEPA) and State air quality programs in developing and 

transferring satellite products and techniques to the user community. At the present UAH is 

supported by a NASA Applications Grant to transfer some of the techniques outlined in the 

present report into the USEPA Community Air Quality (CMAQ) modeling system. Through this 

process USEPA works with UAH and the user community in testing the techniques. The 

techniques then become part of the USEPA supported system that can be broadly disseminated 

1
2

and used by states and air quality consultants. The UAH expects that this synergistic relationship 

between NASA, NOAA and the user community to extend satellite applications will continue. 
3

2. IMPROVING PHOTOLYSIS RATES IN PHOTOCHEMICAL AIR 

QUALITY MODELS USING SATELLITE DATA 

Note the following discussion was derived from Pour-Biazar et al. (2007). 

A key component of air quality modeling is the correct estimation of photodissociation 

reaction rates (or photolysis rates) for chemical species. These rates (the rate at which 

photochemistry takes place) depend on the intensity of solar radiation reaching a given point in 

the atmosphere and the molecular properties of the molecule undergoing photodissociation. 

Therefore, attenuation or enhancement of radiant energy due to atmospheric absorption and 

scattering is an important factor in determining the photolysis rates. Since clouds can 

significantly alter the solar radiation in the wavelengths affecting the photolysis rates, they can 

have considerable impact on the photochemistry. 

Reliable estimates of photolysis rates are essential in reducing the uncertainty in air 

quality modeling. Air quality models rely on radiative transfer models for the prediction of 

photolysis rates. There are a suite of radiative transfer models [see Barker et al., 2003] that take 

extraterrestrial solar flux, optical properties of the atmosphere, and surface albedo as inputs to 

describe the propagation of radiation in the atmosphere. These models are widely used for both 

research and in weather and climate models. Barker et al. (2003) compared the performance of 

25 radiative transfer models with respect to the impact of unresolved clouds; most of the models 

used in their study underestimated atmospheric absorption of solar radiation. Other studies 

[Collins et al., 2000; Liao et al., 1999; Jacobson, 1998; Dickerson et al., 1997; Castro et al., 

1997; Ruggaber et al., 1994; Madronich, 1987] have investigated the effects of changes in 

atmospheric conditions and surface albedo on the estimates of photolysis rates. Most of these 

studies conclude that aerosols and clouds play an important role in modifying the photolysis rate 

either by enhancing it due to light scattering, or by reducing it due to absorption and attenuation 

of light. 

The Community Multiscale Air Quality modeling system (CMAQ) [USEPA, 1999] uses 

a two step approach for calculating the photolysis rates. This approach is similar to that of the 

Regional Acid Deposition Model (RADM) (Chang et al., 1987) and is a typical method used in 

most air quality models. First, in a preprocessor, a radiative transfer module (based on 

Madronich, 1987) is used to compute clear sky photolysis rates for a range of latitudes, altitudes, 

and zenith angles. Then, within the chemical transport model, the tabular photolysis rates are 

interpolated for each location and corrected for cloud cover. There are two major concerns with 

this approach as far as cloud correction is concerned. First, estimation of cloud transmissivity in 

models is highly parameterized and, therefore, introduces a large uncertainty. Second and most 

important, the cloud information is provided by a mesoscale model, which has difficulty with the 

spatial and temporal placement of clouds and their vertical extent. The mesoscale model used in 

the CMAQ modeling system is the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) 

(Grell et al., 1994; NCAR, 2003). 

Prediction of clouds in mesoscale models used for air quality modeling applications has 

always been a difficult problem. Cloud processes on grid cell sizes of 4 km and greater are 
4

highly parameterized and uncertain. One of the weakest areas of meteorological models is the 

correct prediction of clouds at the correct location at the correct time. In air quality case studies, 

observations could conceivably be used to improve the specification of clouds. Unfortunately, 

standard weather service observations are not sufficiently dense to be used for cloud 

specification. However, geostationary satellite data can provide the desirable coverage with 

sufficient spatial resolution. The Geostationary Operational Environmental Satellite (GOES) has 

the capability to measure cloud properties such as optical reflectance down to scales of 1 km and 

cloud top heights to 4 km, and for time scales down to an hour or less. Arola et al. (2002) 

evaluated satellite retrievals of ultraviolet radiation (UV) over Europe and did not find any 

significant systematic bias in many of the retrieval methods used. 

In a previous study, McNider et al. (1998) used satellite-derived broadband cloud 

transmittance to correct NO2 photolysis rates within the Regional Acid Deposition Model 

(RADM) [Chang et al., 1987]. The case study for August 3, 1988 episode focused on the eastern 

United States. They concluded that the overestimation of the clouds by the meteorological 

model significantly reduced the photolysis rates as compared to the satellite-derived rates. 

This discussion presents the results from incorporating satellite-derived transmissivity 

and cloud top height to provide the cloud properties needed in photolysis rate calculations, and 

then use these revised photolysis fields in the CMAQ model. This is a first-order incorporation 

of the cloud effects. The GOES visible and infrared (IR) data collected and processed during the 

Texas Air Quality Study, 2000 (TexAQS2000) period are utilized. The impact of the satellitebased photolysis fields versus MM5-derived photolysis fields on ozone production is examined. 

2.1. Methodology 

The cloud correction method is based on the current CMAQ formulation. The CMAQ 

uses a two step approach for photolysis rate calculations, clear sky photolysis rates are 

calculated, and then they are corrected for the cloud cover. While the implementation within 

CMAQ is described, the method can be applied to any air quality model that uses a similar two 

step approach for the calculation of photolysis rates. First a brief description of the current 

method used in CMAQ is presented, followed by a brief description of our approach. The main 

issue explored here is the use of satellite-derived clouds as opposed to model-generated clouds 

for cloud correction. Therefore, the technique presented here can be beneficial to any other 

model that uses model-generated clouds for cloud correction. 

Current Method for Cloud Correction in CMAQ 

The method used for photolysis rate calculation and the subsequent cloud correction to those 

rates are described in USEPA’s Models3 Science document [USEPA, 1999]. The Photolysis rate 

(s-1) is represented by: 






J dF 2

1

  )()()( (1) 

Where () (m

2

/molecule) is the absorption cross-section for the molecule undergoing 

photodissociation as a function of wavelength  (m); (), the quantum yield 

(molecules/photon), is the probability that the molecule photodissociates in the direction of the 

pertinent reaction upon absorbing the radiation of wavelength ; and F() is the actinic flux 

(photons/m2/s/m). 

By providing the actinic flux for clear sky, photolysis rates (Jclear) can be calculated by equation 

(1). In CMAQ, clear sky rates are then corrected for cloud cover. The cloud correction is based 

on Chang et al. (1987) and Madronich (1987) with some alterations as described in CMAQ 

Science Document [USEPA, 1999]. Below the cloud, the rate is corrected by: 

5

 tr61f1JJ 1))cos(.(  below clear  c c   (2) 

Where fc is the cloud fraction for a grid cell, trc is cloud transmissivity, and  is the zenith angle. 

The above formulation leads to a lower value for the photolysis rates below the cloud, where the 

cloud transmissivity is reduced. Above the cloud, the photolysis rate is modified as: 

 ))(cos( above clear c c     tr1f1JJ  (3) 

Here  is a reaction dependent coefficient that further modifies the rates above the cloud [Chang 

et al., 1987]. This is to allow for the photolysis rate enhancement resulting from the reflected 

radiation from the cloud top. Within the cloud, the photolysis rates are obtained by interpolating 

between cloud base and cloud top values (which is a deviation from Chang et al. 1987). 

Therefore, based on the formulation above, the cloud transmittance and cloud fraction are 

required for calculating cloud correction for photolysis rates. Since in-cloud photolysis rates are 

interpolated, cloud-base and cloud-top heights must also be known. 

In CMAQ, the calculation of cloud transmissivity is highly parameterized [USEPA, 1999] and is 

formulated based on a parameterization suggested by Stephens (1978). By obtaining cloud 

thickness (Hc) and liquid water content (w) the liquid water path (g/m2

) is calculated by: 

 wHLWP c (4) 

Then the broadband cloud optical depth (c) as a function of liquid water path, assuming that the 

drop-size distribution within the cloud column is uniform, is calculated as [Stephens, 1978]: 

709512633  LWP)(logln..

c

10 10     (5) 

Finally, cloud transmissivity is determined by: 

 )(





 



134

e5

tr

c

c

c

 (6) 
6

Where  is the scattering phase-function asymmetry factor [USEPA, 1999]. In Equation (6) it is 

further assumed that  is constant and has a value of 0.86. For optically thin clouds where c<5 

cloud correction is not performed. As evident from this formulation, even if the MM5-derived 

cloud fields were correct, there is some uncertainty in the calculation of cloud transmittance by 

equation (6) due to the assumptions used in different steps (as stated above). 

From GOES satellite observations, we are able to recover broadband cloud transmissivity and the 

cloud top height. Also, since the GOES cloud mask algorithm can detect clouds (and the impact 

of sub-scale clouds) at 4 km resolution, an observed cloud fraction can be calculated for coarser 

grid cells as the fraction of cloudy pixels within a grid cell. Cloud base height is estimated as the 

local condensation level (LCL) from the temperature and mixing ratio profiles simulated by the 

mesoscale model. In this study, we replaced trc and fc in equations (2) and (3) with the satelliteinferred quantities to perform the cloud correction. 

2.2. Retrieval of Goes Broadband Visible Transmission and Cloud 

Top Heights 

The Infrared Measurement and Processing Group (hereafter IR Group) at the National 

Space Science and Technology Center performed the satellite retrievals for this study. Currently, 

the IR group uses GOES Product Generation System (GPGS) to provide routine real-time 

retrievals of skin temperature, total precipitable water, cloud top pressure, cloud albedo, surface 

albedo and surface insolation for the use of meteorological and air quality models [Haines et al., 

2004]. As input, GPGS needs a first guess field for its retrievals and the model grid information 

if the product is to be used in a grid model. For this study, the MM5 simulation that was utilized 

for the CMAQ runs provided the required information to GPGS and the retrievals reflected the 

MM5 grid cell values. 

The algorithm used for the retrieval of albedo and surface insolation is the 

implementation of Gautier et al. (1980) method complemented by the improvements from Diak 

and Gautier (1983). The method uses information from the GOES Imager visible channel (0.52-

0.72 m) at 1 km resolution and employs a clear and a cloudy atmosphere to explain the 

observed upwelling radiant energy. The model (what model?) applies the effects of Rayleigh 

scattering, ozone absorption, water vapor absorption, cloud absorption, and cloud reflection. The 

effects of Rayleigh scattering are modeled after Coulson (1959) and Allen (1963) for the GOES 

visible band (radiant flux as viewed by the satellite) and for the bulk solar flux incident at the 

surface. Ozone absorption is modeled after Lacis and Hansen (1974). Water vapor absorption is 

assumed to be negligible in both the surface and cloud albedo calculations (explaining the 

observed radiance in the GOES visible band), but accounted for when applying the total solar 

flux in the surface insolation calculation. Water vapor absorption coefficients are obtained from 

Paltridge (1973), and total column water vapor is assumed to be 25 mm and adjusted for solar 

zenith angle. Cloud absorption (for thick clouds) is assumed to be a constant 7% of the incident 

flux at the top of the cloud (Diak and Gautier, 1983). 
The surface albedo for the entire domain is calculated by using the clear-sky composite 

image. For the current study, a 20-day composite centered on the period of the case study was 

used to generate the clear-sky composite image. The single composite image records the 

minimum albedo value for each pixel for a given hour. Assuming that for any given hour during 

the day (for the entire month) each pixel experiences clear-sky at least once, the minimum value 

would represent the clear-sky value for that pixel. This formulation assumes that the visible 

channel surface albedo does not vary significantly within the time period of composite. 

The insolation is calculated as the sum of solar radiation incident at the surface from both 

direct and diffuse sources and also includes the effect of attenuation by clouds. For the clear-sky 

case, the incident short-wave radiation at the surface is 1) the incident solar flux that is 

attenuated by Raleigh scattering, ozone and water vapor absorption, and 2) the surface reflected 

flux scattered back to the surface by Raleigh scattering. With the surface albedo known, and the 

absorption and scattering processes estimated, the surface insolation is calculated directly. 

For the cloudy-sky, the satellite-derived radiant energy is the sum of atmospheric 

backscatter, reflection of the incident solar flux from the cloud surface, backscatter within the 

cloud by Rayleigh scattering, and the amount of surface reflection that reaches satellite after 

attenuation. Since the radiance at the satellite, the surface albedo, and estimates of the scattering 

and absorption are known, the radiation formulation can then be solved for the cloud albedo. In 

practice, the algorithm calculates a surface insolation using both the clear-sky and cloudy-sky 

formulations for a given scene. If the cloudy-sky calculation is greater than or equal to the clearsky value, then the clear-sky value is used and the scene is assumed clear. This is consistent with 

the cloud albedo being near zero for clear-sky conditions. Since the effect of cloud albedo 

dominates in the insolation calculation, uncertainties in cloud thickness have been shown to 

produce only small effects on the surface insolation calculation [Haines et al., 2004]. 

Since the sum of cloud albedo (Ac), cloud absorption (ac), and cloud transmittance is 

equal to one, then the broadband cloud transmittance is calculated as: 

)(. c  aA1tr cc (7) 

The other needed vital information for our cloud correction is the cloud top height. A 

cloud top pressure is assigned to each cloudy pixel. The GOES 11-μm window channel (of 

either the Imager or the Sounder) brightness temperature is used for this purpose. The clouds are 

assumed to be uniform in coverage and height over the GOES pixel. The brightness temperature 

for each cloudy pixel is referenced to the corresponding thermodynamic profile for the closest 

model grid. No attempt is made to correct the brightness temperature for the effect of water 

vapor above the cloud. The pressure assignment is similar to that used by Fritz and Winston 

(1962) and applied by Jedlovec et al. (2000). Log-linear interpolation is used between model 

vertical pressure levels to assign a corresponding pressure for the cloud top temperature. 

The approach works well for opaque clouds where the cloud emissivity is close to unity 

and emission (measured by the satellite) comes primarily from the cloud top. Typical pressure 

assignment errors are on the order of 25-50 mb (0.5-2.0 K). For non-opaque clouds such as thin 

cirrus, emission from below the clouds is detected by the satellite and cannot be separated from 

7
8

cloud emission without knowledge of the cloud emissivity. This is true for all relatively 

transparent clouds (non-opaque) including low clouds. Therefore, the bias would be greatest for 

non-opaque clouds.

For air quality applications, however, since the focus is on the boundary layer, the error 

in the cloud top pressure for the opaque clouds does not pose a significant problem. 

Furthermore, in our technique the cloud top height is only used for determination of the 

atmospheric layer in which photolysis rates are being interpolated, and does not impact the 

correction made to the photolysis rates within the boundary layer (as the transmittance is 

estimated directly from the satellite observations). In addition, the determination of cloud-top in 

CMAQ is limited by the vertical resolution of the model, which usually is too coarse in the freetroposphere. For the non-opaque clouds, the cloud transmissivity is large and therefore the 

modifications to photolysis rates are small and thus the impact of the error in the cloud top height 

is further reduced. Figures 1a and 2a illustrate a situation on August 24, 2000, where the satellite 

observation indicates most of the domain is cloudy, yet in fact only the cloud mass over the 

Galveston Bay area is opaque. For most of the domain, the clouds are almost transparent and the 

retrieved cloud transmittance is close to 1. For low transparent clouds the estimate of cloud top 

pressure is not reliable. Thus, in such a situation we still make the necessary corrections and 

allow for a thin cloud above the cloud base by assuming that the cloud is only one model-layer 

thick. 
(a) August 24, 2000, 21 GMT 

 

(b) August 28, 2000, 19 GMT 

 

Figure 1. CMAQ/MM5 predicted and GOES satellite observed cloud fields for (a) August 24, 2000, 21 GMT, and 

(b) August 28, 19 GMT.

2.3. Implementation Within CMAQ 

The current setup for CMAQ calculates the clear sky photolysis rates in a preprocessor 

and provides a tabular input to the Chemical Transport Model (CTM) [USEPA, 1999]. The 

meteorological data, including cloud information derived from MM5 predictions, is prepared in a 

Meteorology-Chemistry Interface Processor (MCIP) for the use in CTM. Within the CTM the 

attenuation to clear sky photolysis rates due to the presence of clouds is performed based on the 

input information from the meteorological model. We have made modifications to the MCIP to 

replace the MM5-derived (hereafter referred to as MCIP clouds) cloud information with the 

satellite observations. 

Using satellite observations the cloud fraction in MCIP is replaced with the observed 

cloud fraction. From cloud top temperature (or pressure as discussed above), the corresponding 

9
10

CTM layer is identified as the cloud top layer. Model surface temperature and mixing ratio are 

used to calculate the lifting condensation level, and is used as the cloud base height. Within the 

CTM, when satellite-retrieved transmissivity is present, the standard parameterization is 

bypassed and the satellite observations are used directly in equations 2 and 3. 

2.4. Model Simulations 

We implemented the technique, described above, in the CMAQ modeling system to 

perform a set of simulations for 12-km and 4-km resolution domains over Texas for the period of 

August 24 to August 31, 2000. The 12-km domain covers the eastern half of Texas, Louisiana, 

Mississippi, southern part of Oklahoma and Arkansas, and the southwestern corner of Tennessee. 

The first set of simulations utilizes CMAQ in its standard configuration, and is used as the 

control case (hereafter referred to as CMAQ_base or control simulation) for comparison. The 

second set of simulations (hereafter referred to as CMAQ_sat) uses the satellite-derived cloud 

information. Both sets of simulations use the same meteorological information from a single 

MM5 run. 

The control MM5 simulation was configured to use FDDA gridded nudging, Dudhia 

moisture scheme, Grell convective parameterization, Medium Range Forecast (MRF) PBL 

scheme, RRTM radiation scheme, shallow convection scheme, and 5-layer soil model. Grell 

cumulus parameterization has proven to be useful for smaller grid sizes (10-30 km) as it tends to 

allow a balance between resolved scale rainfall and convective rainfall (Grell et al., 1991; Grell, 

1993).

The CMAQ (version 4.3) was configured to use piecewise parabolic method for 

advection, multiscale horizontal diffusion and eddy vertical diffusion, 3rd generation aerosol 

model and 2nd generation aerosol deposition model, RADM cloud model, and SMVGEAR 

chemical solver. Carbon bond IV (CB4) chemical mechanism [Gery et al., 1989], including 

aerosol and aqueous chemistry is utilized to describe atmospheric reactions. The model uses 21 

layers, with about 10 layers within the daytime boundary layer. The emissions for this study are 

based on USEPA’s 1999 National Emissions Inventory (NEI99, version 2). 

2.5. Results and Discussion 

As described in the previous section, the meteorological information to drive CMAQ was 

obtained from a single MM5 run. This means that there is no change in the dynamic fields for 

the CMAQ simulations and the differences between CMAQ_base and CMAQ_sat simulations 

are only due to the impact of observed clouds on the photochemistry. This inconsistency also 

impacts the heterogeneous processes in the model. In the areas where the model is underpredicting clouds, use of observed clouds reduces the errors in the gas-phase chemistry but the 

accompanying heterogeneous chemistry in the cloud layer is non-existence in the model. On the 
11

other hand, when the model over-predicts clouds, our technique will increase photolysis rates 

throughout the atmospheric column while the heterogeneous processes in the model are still 

active. Such errors in the current study are unavoidable (as they are inherent from the control 

MM5 simulation) and can only be corrected if the model is dynamically consistent with the 

observations. The current study is only focusing on the radiation impact of clouds on the 

photochemistry. The impact of cloud dynamics will be pursued in the subsequent papers. 

It should be noted, however, that the uncertainty due to the impact of cloud dynamics on 

the vertical transport of the pollutants is also important and needs to be investigated. For 

example, on the afternoon of August 24, 2000, convective clouds developed over the Galveston 

Bay and expanded toward north/northwest. This feature was absent in the MCIP cloud fields, 

meaning that the vertical transport of pollutants over the Bay area into these convective cells is 

missing in our simulations. While our method corrects for the impact of the observed convective 

clouds on the photochemistry, there are still errors arising from the lack of accurate vertical 

distribution of pollutants due to errors in the dynamics. Therefore, here we only emphasize on 

model-to-model comparisons to illustrate the first-order photochemical impact of including the 

observed clouds. In the second part, however, we present comparisons with selected 

observations to illustrate that the large differences seen in the model-to-model comparison are 

indeed real and our technique is greatly improving the model performance. 

2.5.1. Model-to-Model Comparisons 

Texas and surrounding areas were extremely dry for the period of this study. With few 

clouds observed, this case study perhaps was not the best case to show the benefits of utilizing 

GOES information. Nevertheless, there was sufficient cloudiness to illustrate the impact of 

observed clouds on the photochemical model predictions. Figure 1 display two different cases in 

which the disagreement between CMAQ MCIP cloud fields and GOES satellite observations are 

depicted. In the case of August 24, 2000, MCIP indicated clouds in the south/southeastern part 

of the domain with most of it being subgrid scale (with cloud fractions less than 1) with only a 

few small areas of grid scale clouds over land. In contrast, satellite observations indicated a 

large area of cloudiness extending from south/southeast to the northwest part of the domain. 

Satellite observation also indicated clouds in the northeast and northern parts of the domain that 

were absent in the MCIP fields. However, as indicated in Figure 2, the broadband transmissivity 

for most of the observed clouds for this day is high, meaning that most of the clouds are not 

opaque and should not affect the photolysis rates significantly. But the area around Galveston 

Bay, including Houston, is covered with thick clouds that are missing in the MCIP fields. This is 

significant since this area is the major source of emissions for ozone precursors. 

An error in the prediction of opaque clouds over the emission sources has major 

consequences. Opaque clouds (as seen in Figure 2) can significantly alter the cloud 

transmissivity and, thus, the photolysis rates. Over the source regions, an alteration (reduction in 

this case) in the photolysis rate has both a direct and an indirect impact on ozone chemistry. 

First, by slowing down the photochemistry, lower photolysis rates inhibit ozone production in 

the immediate vicinity of emission sources (direct impact). Second, due to the suppression of 

photochemistry, lifetime of ozone precursors is increased and the precursors can be transported 
to the regions where the air mass has a different chemical composition (indirect impact). The 

indirect impact can take many forms depending on the type of the cloud and the time of 

occurrence. These include the impact on the boundary layer air further downwind (for the clouds 

with weak vertical motion during the day), the accumulation of the precursors in the residual 

layer (clouds late in the day), or alteration in the chemical composition of free troposphere 

(convective cells with strong vertical motion). 

 

 

Figure 2. Cloud transmissivity and corresponding NO2 photolysis rates for August 24, 2000, at 2100 GMT from 

CMAQ_base and CMAQ_sat simulations at the surface (first model layer). CNTRL simulation shows 

clouds in the general area as the satellite observations as indicated in the transmissivity plots (upper 

panel); however, they are spread out and are not as opaque as the observed clouds as indicated in both 

transmissivity plots and the resulting NO2 photolysis rates (lower panel). 

In the case of Galveston Bay region, nitrogen oxides (NOx=NO+NO2) and volatile 

organic compounds (VOC) are co-emitted (on the regional scale). Therefore, in this region 

under clear skies, ozone is rapidly produced while NOx is transformed to products such as nitric 

acid (HNO3) and peroxyacetyl nitrate (PAN). The inhibition of the photochemistry in the 

12
13

presence of clouds on the other hand directly impacts the rapid formation of ozone in this area 

and by doing so both NOx and VOCs remain active for a longer period of time. In short, such an 

event alters the chemical aging of the air mass, and the air mass continues to have the potential 

of producing ozone for a longer period of time during transport. 

Another indirect impact of the clouds in this area is the alteration in partitioning of 

nitrogen oxides and the impact on nitrogen budget due to surface removal. This is caused by the 

disparity between the deposition velocity of NOx and the nitrates that are produced from 

oxidation of NOx. Under clear skies, as indicated before, NOx in this region undergoes a 

chemical transformation and produces nitrates such as HNO3 and PAN. In the presence of thick 

clouds, due to the reduction in the photochemical activities, nitrogen monoxide (NO) rapidly 

consumes ozone (O3) and produces nitrogen dioxide (NO2) while the production of HNO3 and 

loss of NOx due to chemical transformation is reduced. In this case while the partitioning of NOx

between NO and NO2 has been altered, there is a net increase in NOx due to its direct emissions. 

Therefore in one case, under clear conditions over the Galveston Bay area in the control 

case, more O3, HNO3, PAN and other nitrates are produced in the expense of NOx. But under 

cloudy conditions (satellite assimilation case), due to the slowing down of the photochemistry, 

most of the NOx will remain intact and will not be lost in the ozone production to produce 

nitrates. The rate of surface removal for NOx is an order of magnitude less than that of nitric 

acid [Biazar, 1995]. Therefore, in control simulation there is a much larger loss of total reactive 

nitrogen (NOy = NOx + HNO3 + PAN + other compounds produced from the oxidation of NOx) 

than the assimilation simulation. 

To show such an indirect impact, a grid point close to the bay (southeast of Houston at 

29.7N, 95.3W (marked A on the map in Figure 3) was examined. On August 24, the control 

simulation indicates a small overprediction followed by a substantial underprediction of clouds 

for this location. Comparing the accumulated hourly surface deposition from the two 

simulations (CMAQ_base vs. CMAQ_sat) for NOx and HNO3 reveals that the absence of clouds 

(in the control case) increased the surface removal of HNO3 for several hours for up to 9 

g/hectare/hr (Figure 4). The loss of nitric acid positively correlates with the increased ozone 

production and increased NO2 photolysis rate at this location (as shown in Figure 5a) and is a 

result of the increased HNO3 production due to active photochemistry. The inclusion of clouds 

resulted in less than 1 g/hectare loss of NOx in this case, as in the CMAQ_sat case NO2

photolysis rate decreases, and consequently O3 decreases and NO2 increases. 

In contrast to the August 24th case, on August 28, MCIP indicates a large area of 

cloudiness over western Mississippi, southern Arkansas, and Louisiana extending to the south 

Texas (Figure 1b). This is absent in the GOES observations. GOES observations indicate 

subgrid cloudiness in the western part of Texas. From Figure 2b it can be seen that these clouds 

are highly transparent and do not alter the photolysis rates significantly. Therefore, in this case 

we have a significant ozone formation in the vicinity of the emission sources that would be 

absent in the control simulation. 
Figure 3. Image of Houston-Galveston Bay area. Locations at (A) 29.7N, 95.3W and (B) 30N, 95.6W are 

marked with red circles. 

Difference in Dry Deposition (sat-cntrl)

(Point A: x=39, y=31, lon=-95.3, lat=29.7) 

-10

-8

-6

-4

-2

0

2

8/24/00

12:00

8/24/00

13:00

8/24/00

14:00

8/24/00

15:00

8/24/00

16:00

8/24/00

17:00

8/24/00

18:00

8/24/00

19:00

8/24/00

20:00

8/24/00

21:00

8/24/00

22:00

8/24/00

23:00

Date/Time (GMT)

Difference (g/hectare/hr)

NOx HNO3

14

Figure 4. Hourly differences in NOx and HNO3 surface removal from the two simulations (control vs. satellite 

assimilation) for point A over Houston-Galveston Bay area for August 24, 2000. Control and base are 

used interchangeably throughout this document. 
 (a) 

NO, NO2, O3 & JNO2 Differences (Satellite-Control)

(Point A: x=38:39, y=30:31, lon=-95.3, lat=29.7)

-25

-20

-15

-10

-5

0

5

10

15

20

25

8/24/00 0:00 8/25/00 0:00 8/26/00 0:00 8/27/00 0:00 8/28/00 0:00 8/29/00 0:00 8/30/00 0:00 8/31/00 0:00 9/1/00 0:00

Date/Time (GMT)

Concentration (ppb)

NO NO2 O3 JNO2 (/min)

(b) 

NO, NO2, O3 & JNO2 Differences (Satellite-Control)

(Point B: x=35:36, y=33:34, lon=-95.6, lat=30)

-25

-20

-15

-10

-5

0

5

10

15

20

25

8/24/00 0:00 8/25/00 0:00 8/26/00 0:00 8/27/00 0:00 8/28/00 0:00 8/29/00 0:00 8/30/00 0:00 8/31/00 0:00 9/1/00 0:00

Date/Time (GMT)

Concentration (ppb)

NO NO2 O3 JNO2 (/min)

Figure 5. Differences between NO, NO2, and O3 (ppb) for August 24 through August 31 between satellite cloud 

assimilation and control simulations for grid cells A and B as marked in Figure 3 (A upper panel and B 

lower panel). JNO2 (/min) is only presented for August 24 and August 28. In the text the emphasis is on 

the O3 decrease on August 24 for point A. 

15
16

The impact of such alterations in the photolysis rates on the local atmospheric chemical 

composition can be substantial, especially on the chemical species with the shorter 

photochemical lifetime. Figures 6 and 7 exhibit the largest differences in NO, NO2, NOx, and 

ozone between the assimilation and control simulations over the entire period of study for the 12-

km domain. The figures represent the extreme cases of discrepancy between control and 

assimilation simulations, and these extremes need not occur at the same time. However, 

examining the time series of larger values indicated that they occur about the same time and 

represent a shift in NOx partitioning. This is evident in spatial patterns in Figure 6 as the 

negative/positive values for NO are co-located with the positive/negative values of NO2. The 

areas marked with a large negative NO difference between the assimilation and control 

correspond to the situation where MCIP indicates over-prediction of clouds and therefore most 

of NO is converted to NO2 (and vise versa). These areas are confined to the large source regions, 

as evident for example over the Houston-Galveston Bay area, indicating a much faster 

photochemical activity and rapid ozone formation. 

For the NO2 case (Figure 6b) there are broader areas of large discrepancy. Over the 

Texas region, this indicates the transport of NOx outside the source region where the lifetime of 

NO2 is increased. This is perhaps due to the transport and dilution of the air mass outside the 

source region and mixing with an air mass of lower VOC where the rapid ozone formation is 

inhibited. The evidence for the above statement can be seen in Figure 7b in which largest ozone 

differences are depicted. Also, the large discrepancy in NOx (Figure 7a) to the north of Houston 

is indicative of NOx transport out of the source area due to inhibition of photochemistry in the 

presence of clouds. 
(a) 

(b) 

Figure 6. Extreme differences in (a) NO and (b) NO2 between assimilation and control simulations (assim-control) 

for the entire period of study covering from 0 GMT, August 24, 2000, to 0 GMT, September 1, 2000.

17
(a) 

(b) 

Figure 7. Largest differences in (a) NOx and (b) O3 between assimilation and control simulations (assim-control) for 

the entire period of study covering from 0 GMT, August 24, 2000, to 0 GMT, September 1, 2000. 

18
19

Figure 7b also indicates that there are times that the impact of our method on ozone 

concentration can be quite high (as much as 60 ppb). While these extreme cases are mostly 

localized in space and time, sustained differences of several ppb over broader areas are more 

common. Comparing the extreme values of NOx and ozone, there is a good correlation between 

higher ozone concentrations in the assimilation run and lower NOx concentrations (and lower 

NO2 concentrations). This indicates the presence of observed clear sky in contrast to MCIP 

indicating over-predictions of clouds. Therefore, the assimilation run produces more ozone and 

nitrates at the expense of NOx. On the other hand, under-predictions of clouds in MCIP cloud 

fields resulted in higher ozone values in the control run for the east/southeast and northern part of 

Louisiana and a large part of central Texas. 

As indicated in Figures 6 and 7, the impact of alterations in the photolysis rates on the 

local atmospheric chemical composition can be substantial, especially on the chemical species 

with the shorter photochemical lifetime. It should be noted, however, that the domain-averaged 

differences only show a maximum of 2 ppb for August 26 and are mostly between +/- 1 ppb for 

other days. Domain-averaged differences also exhibit a diurnal variation with higher predicted 

ozone for the assimilation run. This indicates that in this case study the overall impact of clouds 

in the two simulations over the 12 km domain is not drastically different, meaning that we have 

had as much under-predictions as we had over-predictions. This is an indirect way of comparing 

the impact of total cloud cover in the two simulations and concluding that they are not very 

different. But the large differences in ozone concentration in Figure 7, for example, indicate that 

the two simulations are very different in the temporal and spatial distribution of the clouds. 

Perhaps further analysis of these results with respect to daytime ozone on particular days would 

be needed to examine the impact of cloud correction on peak ozone. 

Houston-Galveston Bay Area and the Case of August 24 

As evident from Figure 6, there are large differences between the two simulations over 

Houston-Galveston Bay area. In particular, there seems to be a sharp contrast between the air to 

the southeast of Houston and that of north/northwest of Houston. We picked two representative 

grid cells for these areas to be examined in more detail. The cells are marked with red circles in 

Figure 3. The coordinate for the cell to the southeast of Houston is 29.7N, 95.3W (marked A), 

and the cell to the northwest has a coordinate of 30N, 95.6W (marked B). 

Figure 5 illustrates the differences in NO, NO2, and O3 between the two simulations for 

these two grid cells (CMAQ_base-CMAQ-sat or control-sat). The figure also shows the 

differences in NO2 photolysis rates between the two simulations for August 24 and August 28. 

Since the photolysis rates are not one of the standard outputs from the model, they were not 

saved for the entire period of simulation. However, the available data for both days clearly 

emphasizes the direct impact of the clouds as a positive difference in photolysis rate corresponds 

to a positive difference in O3 concentration and a negative difference in NO2 concentration. 

Interestingly, the extreme differences noted in Figure 6 for cell A appear to be from 

August 24. This also coincides with a large difference in O3 (Figure 7b) for this cell. While the 

differences on August 24 are extreme for cell A, large differences are observed on many days for 
20

both grid cells. In almost all cases a good negative correlation exists between O3 differences and 

that of NO2, indicating that most of these daytime differences are due to a discrepancy between 

modeled and observed clouds, and are the result of alterations in photochemical activity. The 

difference in O3 is more pronounced than that of NO and NO2, since in the control run not only 

O3 production has been abated, but also at the same time O3 is being consumed by NO to 

produce NO2. In some cases, as in the case of August 24, most of the difference seen is due to 

O3 consumption by NO and the additional photochemical production is negligible. It should be 

noted, however, that our emissions over Houston-Galveston area could be low with respect to 

anthropogenic hydrocarbon emissions [Allen et al., 2002]. If this proves to be the case, an 

increase in the hydrocarbon emissions would result in fast photochemical reaction and even 

higher near source ozone production and will lead to a higher discrepancy between control and 

assimilation simulations. The increase in the hydrocarbon emissions would expedite the 

photochemical activity near NOx sources with respect to ozone formation in the cloud-free areas. 

Therefore, abating the photochemistry in such a case would enhance the ozone decrease. 

On August 24, in mid-morning to early-afternoon period, model dynamics indicate a 

nicely formed see breeze that extends deep (over 200 km) inland. The flow generally has a 

curvature, starting as an easterly/southeasterly flow offshore and turning to a southerly flow over 

land. Later in the afternoon, the inland flow becomes westerly and a convergence zone forms 

along the coast. In particular, over west/southwest of Houston-Galveston (HG) area the winds 

are calm after 20:00 GMT. About this time, satellite observations indicate the formation of the 

convective cells from south/southeast of HG area which later advances inland toward 

north/northwest. This created a situation in which the emissions to the southeast of HG were 

accumulating in the model as the cloud correction (according to the satellite observations) took 

place. The extreme values for point A occur at 21:00 GMT. 

In the control simulation, only about 5 ppb of ozone is produced (net change due to all 

the processes) from 18:00 to 21:00 GMT (going from 18 ppb to 23 ppb). In the presence of 

clouds in the assimilation simulation, most of the ozone is consumed by NO producing NO2 and 

creating large differences seen in Figure 5. As mentioned earlier, since the surface removal of 

NOx is slower than that of HNO3, most of the NO2 in this air mass (50 ppb for the grid cell A) 

remains intact and will be converted instantly back to O3 as soon as it is exposed to sunlight. 

2.5.2. Verification of Model Results 

Up to this point we have compared the results from the satellite assimilation simulation 

against the control simulation, in which CMAQ in its standard configuration was applied. Now, 

the question is that while the differences in concentrations of ozone and nitrogen oxides between 

the two simulations are large, are these differences real and have we been able to correct model 

errors of the same magnitude? In other words, can we verify these results against observations 

and show that model predictions have improved? 

We acknowledge that the emissions used in this study need improvement and the 

uncertainties arising from the problems with the emissions are high. Nevertheless, for an area 

impacted by the cloud cover (or lack of it), we expect to see a variation in the concentrations that 
21

is more in line with the observations. Our hypothesis was that, for the areas impacted, the errors 

due to incorrect cloud cover in the model far exceed the errors caused by inaccurate emissions. 

To test this hypothesis, we compared ozone concentrations from the two simulations 

(CMAQ_base vs. CMAQ_sat) with USEPA’s AQS (Air Quality System, 

http://www.epa.gov/ttn/airs/airsaqs) observations for the entire period of simulations. 

The overall large-scale spatial distribution of the predicted ozone for both the control and 

satellite assimilation simulations generally agreed with observations. Figure 8 depicts a snapshot 

of CMAQ_sat predictions at 21:00 GMT (15:00 CST) on August 24, 2000. The model is able to 

predict the low ozone concentrations next to Galveston Bay as well as the high ozone 

concentrations in the Dallas area. However, model predictions of high ozone concentrations in 

many rural areas and smaller towns cannot be substantiated due to the large gaps in the 

observational network. 

By using satellite clouds, the bias (mean error) for surface ozone predictions was reduced 

by 26%, from -4.05 to -2.99, while the RMSE was reduced by 3%. The predictions of peak 

ozone were improved by 1%. The domain-average predictions of peak ozone exhibit an 

insignificant improvement, but examination of the individual sites impacted by cloud 

misalignment indicate a much greater improvement. While these statistics indicate an 

improvement in ozone predictions, they are unable to show the full impact of satellite 

assimilation. Several factors affect large-scale statistical evaluation for this study. First, there 

are large data voids in the observational network, and since there are large spatial variations in 

surface ozone, performing objective analysis to fill-in the gaps carries significant uncertainties. 

Second, most of the monitors are located in the vicinity of urban centers where they are largely 

impacted by local emissions and local weather. Therefore, several monitors that may reside 

within one model grid can exhibit large variations (up to 50 ppb for ozone). In such cases doing 

a simple averaging for the cluster of observations will not suffice, especially since the model also 

indicates a large spatial gradient from one cell to another (urban to rural). Additionally, the 

problems with emissions, lateral boundary conditions for a relatively small domain, and lack of 

clouds for a significant part of this study also contributed to errors over the entire domain. Such 

errors will lead to modest statistics that conceal the improvements at individual sites impacted by 

observed clouds. Therefore, to test our hypothesis we evaluated model predictions over selected 

locations where the cloud impact was significant. 

For the selection of locations we referred to Figure 7b and identified the areas where the 

differences between the two simulations were the largest. In those areas, we picked the grid 

boxes that contained an observation site. Some of the largest differences occurred in the eastern 

part of the domain and over southern Mississippi and southeastern Louisiana. We could identify 

three grid boxes in that region fulfilling our requirements, namely two locations over New 

Orleans area and one over South Mississippi. As evident from Figure 7b, some of the extreme 

under- and over-predictions of ozone by the base simulation occurs over this region. While these 

extremes do not occur at the same time, having the largest under- and over-predictions of ozone 

in the same area indicates the importance of cloud effects in the source regions. 
Figure 8. CMAQ Model predictions of ozone versus USEPA’s AIRS observations for the 12-km domain on 

August 24, 2000, 21:00 GMT. 

22
Figure 9 shows the time series of ozone concentrations from the two CMAQ simulations 

plotted alongside observations on August 26, 2000, for a location over the New Orleans area. 

Just before the sunrise, both model simulations drop to values close to the observation. But after 

the sunrise, ozone concentration in the control run does not increase at the same rate as the 

observation. The slow rate of the increase and the subsequent decrease in the ozone 

concentration is due to the over-prediction of clouds in MM5-derived fields for this location 

(based on examining the cloud cover for the simulations, not shown here). Since this location is 

impacted by high NOx concentration, a reduction in the photochemical activity due to the 

overcast sky causes ozone consumption and therefore a reduction in ozone concentration. On the 

other hand, the satellite assimilation simulation (CMAQ_sat) for this location on August 26 

indicates a better agreement with the observations. Clearly in this case the under-prediction of 

up to 35 ppb in ozone concentration is due to the MCIP indicating over-prediction of clouds and 

the use of satellite observed clouds has been able to correct this error. 

Observed O3 vs Model Predictions

(New Orleans: x=71, y=41, lon=-91.13, lat=30.5)

-50

-30

-10

10

30

50

70

90

110

8/26/00 3:00 8/26/00 6:00 8/26/00 9:00 8/26/00 12:00 8/26/00 15:00 8/26/00 18:00

Date/Time (CST)

Concentration (ppb)

CMAQ_base

CMAQ_sat

(base - sat)

OBS

Figure 9. Time series of ozone predictions versus observations (OBS) for a location near New Orleans on 

August 26, 2000. CMAQ_base is the control simulation and CMAQ_sat is the satellite assimilation 

simulation. The light blue line shows the difference between the two model simulations. The control 

simulation under-predicted ozone by about 35 ppb. 

23
The second example, as depicted in Figure 10, shows a scenario in which CMAQ_base is 

over-predicting ozone concentrations due to the lack of clouds in MCIP fields while in fact the 

sky is cloudy. In this case both the control and assimilation simulations over-predict ozone 

concentrations for most of the day over a location near New Orleans on August 31. This is 

perhaps due to the errors in the emissions for this location. However, in the afternoon, as the 

clouds move over this location, CMAQ_sat exhibits a sharp decrease in ozone concentration 

similar to that of the observation while the concentrations in the control run remain high. At 

17:00, ozone concentration from the assimilation run agrees with the observation while the 

control simulation over-predicts ozone by 58 ppb. This also speaks to the impact of cloud 

correction and indicates that a reduction in photochemical activities in this location is enough to 

correct the large model over-prediction of ozone. 

Observed O3 vs Model Predictions

(New Orleans: x=74, y=40, lon=-90.77, lat=30.36)

-20

0

20

40

60

80

100

120

8/31/00 3:00 8/31/00 6:00 8/31/00 9:00 8/31/00 12:00 8/31/00 15:00 8/31/00 18:00

Date/Time (CST)

Concentration (ppb)

CMAQ_base

CMAQ_sat

(base - sat)

OBS

Figure 10. Time series of ozone predictions versus observations (OBS) for a location near New Orleans on 

August 31, 2000. CMAQ_base is the control simulation and CMAQ_sat is the satellite assimilation 

simulation. The light blue line shows the difference between the two model simulations. Control 

simulation over-predicted ozone by about 58 ppb. 

24
The third case is from a location in southern Mississippi on August 31. As depicted in 

Figure 11, this case indicates under-predictions of ozone by the CMAQ_base run while the 

CMAQ_sat run again shows a better agreement with the observations. For this location the 

observations exhibit some variations which could be due to passing plumes that are not captured 

well in the simulations. But the largest discrepancy, which is an under-prediction of about 35 

ppb at 16:00 in the control run, is due to the over-prediction of clouds in MM5-derived fields. 

Again in this case we observe a reasonable agreement between CMAQ_sat and observations at 

that time. 

For all these locations during the nights CMAQ_base and CMAQ_sat are generally in 

agreement and their deviation from measured concentrations (that are due to other errors in the 

model) is smaller than the errors introduced due to incorrect cloud cover specification. Indeed in 

most of the domain, when there was a discrepancy between MCIP cloud fields and that of the 

observations, the largest errors could be attributed to the impact of clouds. 

Observed O3 vs Model Predictions

(South MISS., lon=-89.57, lat=30.23)

-40

-20

0

20

40

60

80

100

8/30/00 3:00 8/30/00 6:00 8/30/00 9:00 8/30/00 12:00 8/30/00 15:00 8/30/00 18:00

Date/Time (CST)

Ozone Concentration (ppb)

CMAQ_base

CMAQ_sat

(base - sat)

OBS

Figure 11. Time series of ozone predictions versus observations (OBS) for a location in South Mississippi on 

August 30, 2000. CMAQ_base is the control simulation and CMAQ_sat is the satellite assimilation 

simulation. The light blue line shows the difference between the two model simulations. Control 

simulation under-predicted ozone by about 35 ppb. 

25
27

3. IMPROVEMENT IN LAND SURFACE MODELING USING SATELLITE 

DATA 

While BOEMRE is concerned with the near shore and offshore environment, 

characteristics of the land surface are important to the development of the coastal environment. 

Development of the thermal structure over land impacts sea breeze and land breeze structure and 

the transport and dispersion of near shore air pollution sources. The following chapter is based 

on Mackaro (2008), and Mackaro et al. (2008), and it describes the problem encountered when 

the technique of McNider et al. 1994 (MCN94) was implemented in the Fifth-Generation Penn 

State/NCAR Mesoscale Model (MM5) [Grell et al., 1994; NCAR, 2003]. 

As an example 14 May 2001, a sea breeze circulation (SBC) developed along the coast of 

the northern Gulf of Mexico (Figure 12). The event, described in detail in Mackaro (2003), was 

simulated using the PSU/NCAR MM5 v3.A. The primary purpose of this study was to evaluate 

the possible benefits of land surface data assimilation to simulations of sea breeze circulations. 

The McN94 method was applied for which Land Surface Temperature (LST) tendencies 

retrieved from the Geostationary Operational Environmental Satellite (GOES) were dynamically 

assimilated into the surface energy budget of MM5. Three additional experiments were 

accomplished with varying land surface schemes to examine the sensitivity of the SBC to 

differential land surface forcings. The SBC developed in all simulations and was found to be 

very sensitive to the magnitude of land surface heating resulting from the partitioning of sensible 

and latent heat flux within the simulated surface energy budget. 

The control experiment (CTRL), using a simplified slab Land Surface Model (LSM), 

simulated a SBC that did not have the inland penetration as observed by radar data (Figure 13). 

This indicates that the circulation strength was underrepresented. Simulated MM5 air 

temperatures along the coast were an average of 4K less than observations and there was a 

gridwide cold bias of 2K (Figure 14). This resulted in a land-sea thermal gradient that was 

weaker than necessary to initiate the SBC and allow it to fully develop. Examination of the 

simulated surface energy budget indicated that the majority of solar insolation reaching the 

surface was partitioned into latent heat flux, limiting the sensible heat flux needed to heat the 

near surface air sufficiently. One possible explanation for this discrepancy is that the 

specification of moisture availability values that were too large. 

 

The MCN94 technique was specifically designed to correct such discrepancies in surface 

moisture specification. This is accomplished by adjusting the surface moisture in the land surface 

so that ground temperatures agree with the observed satellite tendencies. An overview of the 

technique is provided in a later section. In essence when surface tendencies are less than 

observed by satellite then surface moisture is decreased allowing ground temperatures to heat up 

faster. This appears to be the case here. 

 

The MCN94 assimilation run (ASSIM) was capable of simulating a SBC which 

penetrated further inland than that simulated by CTRL and which was more consistent with 

observations (Figure 15). Closer inspection indicated the land surface forcing was responsible 

for heating the ground sufficiently so that the air temperature rise along the coast closely 
matched the observations. The SBC fully developed in response. The assimilation technique 

reduced the moisture availability within the model and produced LST tendencies to 

larger than observed in CTRL. 

/3 hK

/4 hK

Figure 12. Base reflectivities (dBz) at 0.5 degree tilt angle from the Mobile, AL (MOB; left) and Eglin Air Force 

Base (EVX; right) WSR-88D radars overlaid on standard National Weather Service ASOS plots. 

Observations valid 22 UTC 14 May 2001. The color difference is a result of MOB radar in clear air 

mode, and EVX in precipitation mode. MOB color bar is on the left, EVX is on the right. 

28
Figure 13. Base reflectivity (dBz) from EVX WSR-88D overlaid on CTRL simulated 1000 mb vertical velocity 

(cm s-1) valid 22 UTC 14 May 2001. 

CTRL Bias

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0 1 2 3 4 5 6 7 8 9 10 11 12

Forecast Hour

Bias (C)

Figure 14. Time series of CTRL simulation 2 m temperature (pink) and dew point (blue) bias 

valid 12 UTC 14 May 2001 to 00 UTC 15 May 2001. 

29
Figure 15. Base reflectivity (dBz) from EVX WSR-88D overlaid on ASSIM simulated 1000mb vertical velocity 

(cm s-1) valid 22 Z 14 May 2001. 

An experiment utilizing the Oregon State University (OSU) soil parameterization scheme 

was run for comparison with the ASSIM. Note that OSU LSM later developed into the National 

Oceanic and Atmospheric Administration (NOAA) NOAH LSM. The name NOAH represents 

the recognition of collaborators for this community model, N for National Centers for 

Environmental Prediction (NCEP), O for Oregon State University (Dept of Atmospheric 

Sciences), A for Air Force, and H for National Weather Service’s Hydrologic Research 

Laboratory; 

http://www.emc.ncep.noaa.gov/mmb/gcp/noahlsm/Noah_LSM_USERGUIDE_2.7.1.htm. The 

model, using the OSU LSM simulated a SBC similar in magnitude to the CTRL over the Florida 

panhandle and to ASSIM over the Mississippi coast. This finding was traced to the specification 

of the volumetric soil moisture content in the upper soil layer of the OSU scheme initialized from 

the NCEP Eta-based 4-D Data Assimilation System (EDAS) analyses (Eta is one of NCEP's 

mesoscale numerical weather prediction models and the name "Eta" derives from the model's 

vertical coordinate known as the "eta" or "step-mountain" coordinate). The soil moisture was 

50% less along the Mississippi coast than further east along the Florida panhandle. The result 

was sensible heat fluxes that sufficiently heated the ground allowing the SBC to fully develop. It 

was determined that over the entire grid, ASSIM improved the temperature bias while degrading 

the dew point bias (Figure 16). OSU did just the opposite, degrading the temperature bias while 

improving the dew point bias. It was also found that ASSIM produced land surface temperatures 

that were much closer in magnitude to the satellite observations than the CTRL or OSU 

simulations (Figure 17). 

30
(a) 

Temperature Bias

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0 1 2 3 4 5 6 7 8 9 10 11 12

Forecast Hour

Bias (C)

(b) 

Dew point Bias

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0 1 2 3 4 5 6 7 8 9 10 11 12

Forecast Hour

Bias (C)

Figure 16. Time series of CTRL (pink), ASSIM (blue) and OSU (green) bias for (a) air temperature (C) and (b) 

dew point (C) valid 12 UTC 14 May 2001 to 00 UTC 15 May 2001. 

31
(a)

(b) 

Figure 17. LST (C) valid 20 UTC 14 May 2001 as retrieved from (a) GOES and (b) ASSIM. 

32
The results of this research indicate that the assimilation of GOES derived LST 

tendencies have the potential to improve simulations of the SBC. This research demonstrated the 

ability of the McN94 technique to improve the land surface forcing within the MM5 without the 

use of advanced soil parameterization techniques. 

Upon further review, it was determined that while the ASSIM improved the model 

results, the success of the simulation is limited by the drastic increase in sensible heat flux 

needed to arrive at the improved solution (Figure 18, Figure 19). This problem results from 

over-drying of the land surface within the model as moisture availability values were decreased 

to 0.05 (Figure 20). This value is actually a hard coded minimum that was implemented within 

the MM5 so that the latent heat flux would never go to zero. The intense drying found here 

caused some concern in regards to the way the technique had been implemented within the 

framework of the model since the original simulation performed in McN94 did not exhibit this 

problem. 

Surface Energy Budget 

0

100

200

300

400

500

600

0 1 2 3 4 5 6 7 8 9 10 11 12

Forecast Hour

Wm-2

Figure 18. Time series of simulated sensible (solid) and latent (dashed) heat fluxes at 

VPS from CTRL (blue), ASSIM (red), and OSU (green) valid 12 UTC 14 

May 2001 to 00 UTC 15 May 2001. 

The intense drying observed in the SBC case was also found in a separate study. MM5 

simulations with the same model options as the SBC case of 14 May 2001 were performed for 30 

August 2000. The simulations took place during the time period of the 2000 Texas Air Quality 

(TexAQS 2000) Study using the same model set up as was used for the 14 May 2001 case. 

These simulations were not part of the actual field experiment and were only performed to 

examine if the drying observed for the 14 May 2001 case could be reproduced for a different 

region. 

It was found that the McN94 method caused the MM5 to dry out the surface to the hard 

coded minimum value of moisture availability (0.05). Figure 21 shows the moisture availability 

for a control run, for which most of the region has a moisture availability of 0.30, and an 

33
assimilation run, for which the moisture availability has been reduced to 0.05 for much of the 

domain. 

a. CTRL b. ASSIM

a. CTRL b. ASSIM

Figure 19. 12-hour accumulated sensible and latent heat flux KJ )( . 

34
(a) 

(b)

Figure 20. Moisture availability over simulation region valid 15 UTC 14 May 2001 in (a) CTRL and 

(b) ASSIM. 

35
(a) 

 (b) 

Figure 21. Moisture availability over simulation region valid 15 UTC 30 August 2000 in (a) CTRL and 

(b) ASSIM. 

36
The goal of the McN94 assimilation technique was to provide more realistic land surface 

forcing by adjusting the moisture availability within the latent heat flux term of the surface 

energy budget. Thus, the technique should theoretically improve the moisture specification not 

degrade it. In fact the original testing of the model in McN94 showed this to be the case. Under 

the initial phase of this BOEMRE activity, a concerted effort was made to determine what aspect 

or assumptions in the McN94 technique might be leading to this discrepancy. It has been 

determined that a discrepancy exists when implementing the McN94 technique within the 

framework of the MM5. The root of this problem lies within a physical inconsistency in the 

formulation of surface temperature for which the ground temperature is used for all processes 

that require a near surface or surface temperature. The importance of the consistent use of 

aerodynamic temperature is well studied within the boundary layer community but has not 

always been adhered to in the mesoscale model community as evidenced by developments 

within MM5. The inconsistency typically only surfaces when an investigator attempts to use 

measurements of skin temperature or recover that temperature from a diagnostic surface energy 

budget (Sun and Mahrt 1995; Beljaars and Holtslag 1991). 

An investigation took place to make a first attempt to recover a model skin temperature 

within the MM5 framework for direct comparison with the information provided by the satellite 

retrievals. The hope is that by recovering a skin temperature from the model, we could apply the 

McN94 technique and improve model simulated heating rates along with consistent surface 

fluxes. The results from this feasibility study follow. 

37
4. MODEL GROUND TEMPERATURE TENDENCY COMPARED WITH 

SATELLITE OBSERVATION 

The first step in this study was to compare the ground temperature tendency with the 

satellite skin temperature tendencies to ensure that they are in fact largely different. A case day 

was selected and two simulations were performed using the MM5. The simulation was centered 

over Houston, TX, initialized 00UTC 30 August 2000 and integrated forward in time for 24 h. 

This case day was chosen in support of research being performed for the Texas air quality study 

of 2000. Four kilometer horizontal grid spacing was used and applied with the Blackadar PBL 

parameterization and the Dudhia 5-layer soil model (Dudhia, 1996). The simulation was 

performed with (ASSIM) and without (CTRL) the McN94 assimilation technique activated 

between 13 and 15 UTC. Since the moisture availability is most uncertain during the mid 

morning hours, the adjustment takes place during this time period. The hours from 1245 UTC to 

1545 UTC will be examined here. 

Comparing the tendencies for 4 h beginning 1245 UTC and ending 1545 UTC from the 

satellite (Figure 22) and CTRL (Figure 23), it is evident that the surface, as observed by the 

satellite, is heating much faster than that simulated in CTRL. The satellite retrievals indicate 

skin temperature tendencies on the order of while CTRL is simulating tendencies 

no larger than ASSIM produced temperature tendencies that were closer to the 

satellite observed tendencies (not shown), however, as previously found, the improvements were 

at the expense of moisture and unreasonably large sensible heat fluxes. This finding reaffirms 

that comparisons of satellite skin temperature tendencies and MM5 ground temperature 

tendencies are in fact inconsistent. 

/100.3 sKx 3

./100.1 3 sKx 

39
Figure 22. Skin temperature tendencies (K/s) valid 1245 UTC to 1545 UTC 30 August 2000 retrieved from GOES 

satellite. 

Figure 23. Skin temperature tendencies (K/s) valid 1245 UTC to 1545 UTC 30 August 2000 retrieved from CTRL 

with no assimilation. 

40
5. RECOVERING SKIN TEMPERATURE WITHIN MM5 

The goal of this feasibility study was to recover a model skin temperature for use in the 

McN94 technique. We will employ the use of an existing relationship first suggested by 

Zilitinkevich (1970). We begin with the theoretical background of this first attempt. 

Zilitinkevich (1970) suggested relating the aerodynamic temperature to the surface skin 

temperature value using an expression in the form 

   45.0  GZo    zuk o //0962.0  , (13) 

in which  is the kinematic viscosity of air, and k is the Von Karman’s constant,  Zo is the 

aerodynamic potential temperature, and  G is the surface skin temperature and not ground 

temperature (even though the subscript G is present). This equation, the Zilitinkevich 

adjustment, adjusts the surface skin temperature using the existing similarity functions. The sign 

of the adjustment is dictated by the sign of  . For a typical unstable condition, where the 

surface is warmer than the overlying atmosphere  will be negative and the aerodynamic 

temperature will be cooler than the surface temperature. 

Given the fact that we have all of the information needed from the Zilitinkevich 

adjustment with the exception of the surface skin and aerodynamic temperature, we make the 

assumption that the ground temperature is representative of the aerodynamic temperature. This 

assumption, while not physically consistent, is closer in validity then assuming the ground 

temperature is the same as the skin temperature and will be used in this feasibility study. The 

Zilitinkevich adjustment is then inverted and applied as, 

   45.0

RAD  Zo    zuk o //0962.0  . (14) 

The recovered model skin temperature was then used in place of the ground temperature 

when sent into the McN94 assimilation code. The temperature tendency calculated will then be a 

skin temperature tendency. 

Examining the temperature tendency of the recovered model skin temperature from 

CTRL (Figure 24) indicated a tendency field that was warming faster than the ground 

temperature (Figure 23). The temperature tendencies are further improved when the McN94 

technique is applied as temperature tendencies valid 1245 UTC and 1545 UTC closely resemble 

those from the satellite (Figure 25). We now have temperature tendencies that are now more 

closely linked to the satellite. 

41
Figure 24. Skin temperature tendencies (K/s) valid 1245 UTC to 1545 UTC 30 August 2000 as simulated by CTRL. 

Figure 25. Skin temperature tendencies (K/s) valid 1245 UTC to 1545 UTC 30 August 2000 as simulated by CTRL 

but for ASSIM. 

42
43

Comparing the model skin temperature field recovered by ASSIM (Figure 25) to that 

observed by the satellite (Figure 22) clearly indicates an improved field of surface temperatures 

over that of CTRL (Figure 24). While it is evident that we have recovered a model skin 

temperature that closely matches the satellite, it is of equal importance that we recover surface 

fluxes that are realistic. This is examined below. 

The results of this study indicate the use of model skin temperature in the McN94 

technique does provide favorable results. This method was described as a first attempt because 

the model skin temperature was only being used in the McN94 technique, and not in other parts 

of the model framework. This feasibility study has indicated that the over-drying found in 

previous studies was a result of the inconsistency in the temperature tendency comparisons. As 

such, a skin temperature must be made available within the model for the McN94 technique to be 

implemented in a physically consistent manner. This will be accomplished by use of a three 

temperature system for which model skin temperature, ground temperature, and aerodynamic 

temperature are calculated at each time step and used in a physically consistent manner. The 

system developed in this document is done so within a one-dimensional model to allow for full 

control over the model physics. That model and the three-temperature system are described in 

the next section. 
6. DEVELOPMENT OF A CONSISTENT THREE TEMPERATURE 

SYSTEM FOR ASSIMILATING SATELLITE SKIN 

TEMPERATURES 

Over the last few decades, investigators have embraced more physically complete land 

surface models and land surface satellite assimilation techniques as viable methods to better 

represent how a model surface reacts to changes in incoming or outgoing energy. Originally this 

representation was made through parameter specification that was based on climatological means 

containing no information about short term variations. As mesoscale models have moved to 

higher resolutions, it became clear that improving land surface representation was needed. 

Employing more physically complete land surface models by including vegetative layers and/or 

multiple soil layers, while more sophisticated, require detailed land use characteristics and initial 

conditions for parameters such as stomatal resistance or vegetative layer conductance for which 

measurements are not routinely conducted. It is for this reason that the recovery of surface 

parameters through the use of satellite data has been performed (Wetzel et al., 1984; Wetzel and 

Woodward, 1987; Carlson et al., 1981, Carlson, 1986; McNider et al., 1994; Anderson et al. 

1997). One such technique, MCN94, has been directly employed within several mesoscale 

model frameworks to recover surface moisture availability with improvements in mesoscale 

forecasts. Its framework spurred other investigations to recover other parameters e.g. stomatal 

resistance (Jones et al. 1998a) and grid scale heat capacity (McNider et al. 2005). 

Skin temperatures that reflect the radiating temperature of a surface observed by infrared 

radiometers are one of the most widely available products from polar orbiting and geostationary 

satellites and the most commonly used satellite data in land surface assimilation. Within a pixel 

of a satellite footprint, the skin temperature is the radiating temperature of everything within that 

particular field of view (trees, buildings, roads, etc.). While this intrinsic spatial averaging 

smears out detail in the surface, it is exactly the type of averaging that models need and as such 

may be better than land surface classification schemes which require weighting or averaging to 

be consistent with the model grid. As surface skin temperatures have become more widely used 

in land surface assimilation, boundary layer investigators began to explore the issue of 

inconsistencies in the use of skin temperatures in similarity flux forms and issues with using 

radiometric satellite data in land surface models (Zilitinkevich 1970; Lhomme et al. 1988; Kustas 

et al. 1989; Sugita and Brutsaert 1990; Brutsaert, 1982). 

Beljaars and Holtslag (1991) and Sun and Mahrt (1995) recognized that the original 

similarity forms for surface layer fluxes could not use these skin temperatures directly. Rather 

than a skin value, the similarity forms required an aerodynamic temperature. The aerodynamic 

temperature is not directly measured but rather inferred through an extrapolation of a similarity 

prediction of the temperature profile to the roughness height. Choudhury et al. (1986) and 

Beljaars and Holtslag (1991) showed that the temperature difference between the surface skin 

temperature and the temperature at the roughness height can range from 2 K to 6 K in stable 

conditions to -2 to -6 K in unstable conditions. Thus, extrapolations of the Monin-Obukhov 

temperature profiles (Monin and Obukhov, 1954) to the surface boundary rather than the 

roughness height can lead to errors in the estimation of the lower boundary temperature used in 

the surface flux calculations (Beljaars and Holtslag 1991). This indicates that using skin 

45
temperature data directly must be done so in a manner which is physically consistent with the 

bulk aerodynamic formulation of similarity functions. 

It is important of attention to pay attention to details such as consistent use of skin and 

aerodynamic temperature and iteration when incorporating satellite assimilation techniques into 

mesoscale models, whether for forecasting or air quality simulations. Simply using existing 

model frameworks that do not always have the consistency in skin temperatures and 

aerodynamic temperatures can lead to significant problems as satellite data is introduced. This is 

illustrated through issues encountered in applying the MCN94 satellite assimilation technique 

within MM5. While this is only one model, other models share similar approaches in their 

surface formulations, so these techniques may be instructive to the general modeling community. 

This discussion will also clarify applications of the MCN94 satellite assimilation technique and 

follow-on use in light of these aforementioned papers on skin temperatures and aerodynamic 

temperatures. Also included here is the importance in understanding the adjustments that take 

place when making changes in parameters in the surface energy balance and the use of 

appropriate computational approaches. The surface energy budget equation is a highly non-linear 

that though often viewed as a balanced state is changing quickly with time during the day. 

Changes to any term in the equation means that other terms will change so that a balanced state 

can be maintained. Thus, one cannot simply change one term and assume that other terms stay 

the same in an attempt to move from one energy state to another. 

 

6.1. Skin Temperature Assimilation 

One of the first methods proposed for using satellite skin temperatures to recover surface 

variables was that of Carlson (1986) who proposed the use of twice daily data from polar 

orbiting satellites to recover surface moisture availability and thermal inertia. Wetzel et al. 

(1984) also hypothesized that skin temperature tendencies from satellite might be able to be used 

to recover surface moisture availability. As new sensors have been put into space, Carlson and 

others made continuing progress in coupling surface skin temperatures and other surface 

properties to refine the recovery of surface moisture availability. However, these studies were 

diagnostic and did not take the step of using the results in a mesoscale model. One of the first 

attempts to use satellite data directly in the surface energy budget of a mesoscale model was 

McN94 followed by the extension of Jones et al. (1998a). 

In the Jones 1998b approach, Geostationary Operational Environmental Satellite 

(GOES)-derived skin temperature tendencies were assimilated into the surface energy budget 

equation of a mesoscale model so that the simulated rate of temperature change closely agreed 

with the satellite observations. At approximately the same time Norman et al. (1995) were also 

beginning to assimilate similar skin temperature tendencies in an offline boundary layer model. 

The adjustments made using the MCN94 technique are performed within the framework of a 

simple slab model. A simple slab model assumes all of the vegetative and soil properties that 

dictate land surface forcings can be described with a limited set of parameters. Note that in the 

context of the scheme in which satellite data is used, to determine moisture availability and 

surface resistance, the slab is a composite of vegetation and soil. Thus, even though soils are 

46
used to specify initial conditions, it is more than a bare soil slab. It includes both soil 

evaporation and plant transpiration. 

As discussed in McN94 and Norman et al. (1995), assimilating skin temperature 

tendencies rather than absolute temperatures is advantageous in that errors in the assumed 

emissivity of the surface, instrument errors from sensor degradation, as well as drift are largely 

negated. In the MCN94 assimilation method, a prognostic form of the surface energy budget is 

defined for both the satellite and model; 

  N m m

m

G

b EGHR

dt

dT

c   

 



 (8) 

  N S S

S

G

b EGHR

dt

dT

c   

 



 (9) 

where is the net radiation, including net shortwave, net longwave, and surface radiance, RN H , 

E , and G are, in order, the sensible, latent, and soil heat flux and the subscript   and m  S are 

used to infer the model and satellite derived variables respectively. Here a critical assumption is 

invoked for the mid morning hours, when surface heating is most rapid, the largest uncertainty is 

due to the moisture availability (embedded within the latent heat energy term) and all the other 

terms can be considered to have the same magnitude. Taking the difference allows us to obtain 

an equation relating the satellite and model latent heating terms to the difference in the surface 

temperature heat rates; 

m

m

G

S

G

bS E

dt

dT

dt

dT CE  



 



 



 



    



  (10) 

This formulation stems from the use of the prognostic surface energy budget equation for a 

ground temperature. An inconsistency arises here in that the ground temperature is that for a 

surface with a finite depth and heat capacity. Fundamentally this is not the same as the radiating 

skin observed by the satellite. The temperature tendency retrieved from the satellite is that of the 

skin temperature, and not the ground temperature. 

The ground temperature evolution as dictated by the prognostic equation of (8) and (9) 

allows storage dependency on soil or canopy characteristics through the bulk heat capacity ( ), 

but does not produce a skin temperature or an aerodynamic temperature. However, this 

temperature often works well in similarity flux formulations since it doesn’t have the dynamic 

range of a skin temperature yet can reasonably replace the aerodynamic temperature needed in 

the prognostic equation. In fact, this is the form employed in the slab formulation of MM5 in that 

the ground temperature is used as the aerodynamic temperature in flux calculations and in 

determination of the surface longwave outward radiation (Figure 26). The use of such 

formulations in MM5 and other models has produced realistic fluxes in real world applications. 

However, when true skin temperatures are needed for satellite assimilation techniques or in 

comparison with tower radiometer skin temperatures, the need for consistency is evident. 

b c

47
48

Figure 26. Schematic of the use of ground temperature within the surface to boundary layer interface of the 

MM5 using a composite slab land surface model. 

The original implementation of McN94 serendipitously avoided part of this inconsistency 

in that in the mesoscale model in which they applied the moisture recovery, a true skin 

temperature was calculated from a surface energy balance model. The model energy balance 

skin temperature was calculated by finding the root of a balanced energy budget for an infinitely 

thing surface. However, the inconsistency was promulgated in that the skin temperatures were 

used in equation (8). Later applications of the McN94 technique, when used within MM5, 

indicated problems with mixing the satellite skin temperatures and the slab temperatures. During 

the morning hours, skin temperature tendencies from the satellite was often much larger than the 

ground temperature tendencies resulting in erroneously large adjustments. Results from case 

studies which indicated that using model slab temperatures mixed with satellite skin 

temperatures, while often improving model performance, usually produced an over drying of the 

surface (e.g., Mackaro 2003). 

In order to correct the problems with mixing skin, aerodynamic and ground temperatures 

a new slab formulation for use within the MM5 or other mesoscale model was formulated. This 

is described in the following sections. The need for this formulation is in light of skin 

temperature research that has made the community aware of issues with its use since the 

development of the MCN94 technique. 

SWNet LWNet H E

G

TG

TM
6.2. Three-Temperature System 

A one dimensional boundary layer model (hereafter referred to as 1DBLM) was 

developed for this study. It uses a simple slab model, that is a surface energy budget assuming 

that the all vegetative and soil properties can be described using a set of surface parameters. The 

slab takes on a specific land surface type based on the specification of characteristics including 

albedo, soil heat capacity, moisture availability, roughness length, thermal conductivity, 

emissivity, soil depth, and soil density. The surface to boundary layer interface in consistency 

with the traditional bulk aerodynamic formulation of similarity fluxes in which each boundary 

has its own explicit value of temperature (Figure 27). 

1 z

o z

T1

TAERO

TR

Figure 27. Schematic of a land surface to boundary layer interface that follows the traditional bulk 

aerodynamic formulation of surface layer fluxes. 

The surface energy budget is based on the diagnostic energy balance formulation, 

  0 4 1    

 



LWS R p  V  hTTkcqMuLucTRR effGRsss (12) 

where the first two terms represent the net shortwave and incoming longwave radiation, the 

outgoing longwave radiation is given as a function of the modeled skin temperature to the fourth 

SWNet

LWNet

H

E

TG

TM

G

49
power, , 

4 TR M is the moisture availability which describes the total amount of water available 

for evaporation from 0 to 100%,  is the air density, and  u and are the similarity 

relationship formulations representing the fluxes of heat and moisture. The soil density, 

qu

 s , 

specific heat capacity, , thermal diffusivity, , and effective slab depth, , combine to 

describe the soils heat storage capability, while the sign and magnitude of the difference between 

model skin temperature, , and the ground temperature, , dictate the direction and 

magnitude of the heat flux from the radiating surface to the slab (Figure 27). 

s c s k 1 h

TR TG

We convert to a prognostic surface energy balance for the temperature of the slab in the 

form of equation (8). The formulation of the resistance term comes from the simple slab model 

formulation of Blackadar (1979), where is the thermal heat capacity of the slab per unit area 

and is related to the thermal inertia of the slab. 

Cb

 

To obtain an aerodynamic temperature from information available in 1DBLM, we 

employ a method suggested by Zilitinkevich (1970) and Deardorff (1972) to relate the 

temperature at to the model skin temperature; o z

 

   45.0 TTT //0962.0 vzuk Aero RZo    o (13) 

 

where v is the kinematic viscosity of air. 

The process of solving for the model skin, ground, and aerodynamic temperature involves 

the use of the iterative process shown in Figure 28. is first retrieved by use of a root finding 

technique. The aerodynamic temperature is then recovered by use of Equation (13) and is then 

used to update fluxes for each iteration. The final step of the process involves calculating the 

ground temperature as it is time dependent. 

TR

50
R

OLD T

SWNET R EHTLW   4 0 

Figure 28. Schematic flow chart of the iteration used to solve the three temperature system used in 1DBLM. 

With a physically consistent method of determining aerodynamic, model skin, and 

ground temperature in place, we now want to invoke the use of the McN94 assimilation 

technique. In order to do so, we must allow for the fact that the only information from the 

satellite is the satellite skin temperature not the ground temperature. Using the time rate of 

change of the satellite skin temperature, we can arrive at the tendency used by the McN94 

technique, dt

dTR . The technique was developed from a prognostic form of the surface energy 

G

Step 1 

Step 2 

   45.0 TTT //0962.0 vzuk Aero RZo    o

   MGmGRsss

effb

G TTTTkc

hCt

T    

 

1

R

NEW

R

OLD  TT > Tolerance 

R

NEW

R

OLD  TT <Tolerance 

TAERO

New **  ,u

untilTAERO < Tolerance 

Root Finding 

R

NEW T

Step 3 

Step 4 

TG

51
budget and simply replacing dt

dTG with 

dt

dTR is not valid. A relationship between dt

dTG and 

dt

dTR

is thus necessary. Since we are using a closed system of equations for our three temperatures, a 

relationship then exists between and . We relate the tendency of the ground temperature 

and skin temperature by 

TG TR

 given as, 

dt

dTR 

dt

dTG  (14) 

 Since both 

dt

dTG and 

dt

dTR are available from 1DBLM, we assume that the ratio of 

the two is the same for both the satellite and model, and solve for the relationship factor,  as 

dt

dT

dt

dTG (t / R )  (15) 

The behavior of  was found to be smooth and non-linear during the mid morning period 

for most types of land surfaces. While it is recognized that there may be a more elegant method 

of relating the radiative and ground temperature, the relationship used above is easily determined 

and is internally consistent at least within the framework of 1DBLM. 

The next step is to replace dt

dTG with 

dt

dT

t R  )( in equation (10) to arrive at a form which 

is physically consistent; 

m

m

G

S

R

dt

dT 





b E

dt

dT

tc  



 ES   ) 











( (16) 

From this we can continue as described in Lapenta (1999) with the model latent heat flux 

defined as, 

 

H

o

VS TZ AIR

z

z

o qqM



Em

u

 



 



  ) 

ln

(  (17) 

 By replacing with the latent heat flux recovered for the satellite, , we can 

invert the equation and arrive at an adjusted moisture availability, ; 

Em ES

M S

  GSAT AIR

H

o

S



MS qTqu

z

z

E 

 



 







 )(

ln 

. (18) 

52
The adjusted moisture availability then replaces that specified as the initial 

condition. The adjustment typically takes place on the hour of each subsequent hour within the 

assimilation period during the mid morning hours. It is the previous hour’s temperature 

tendency used in the adjustment so that no a-priori information is necessary. 

6.3. 1DBLM Test Cases 

A series of simulations were performed to evaluate 1DBLM’s physically consistent 

framework and its ability to simulate the surface to boundary layer interactions. Of specific 

interest is the ability of the model to reproduce values of observed skin temperature, air 

temperature, and surface fluxes of heat and moisture. The impact of the McN94 technique on 

these modeled variables is also of interest. In order to validate the 1DBLM simulations, surface 

based observations were obtained from Oklahoma Mesonet (Brock et al.,1995) sites that are part 

of the Oklahoma Atmospheric Surface Layer Instrumentation System (OASIS; Brotzge and 

Duchon, 2000) and from the U.S. Department of Energy’s Atmospheric Radiation Measurement 

(ARM) Cloud and Radiation Testbed (CART) Southern Great Plains Central facility (SGP-CF) 

site. From each site, skin temperature and net radiation from an Infrared thermometer (IRT; 

Fiebrich et al., 2003) and net radiation, air temperature, wind speed, sensible and latent heat flux 

from tower mounted instruments were collected. Ground heat flux from ground instruments was 

also collected. 

Results for 4 July 2003 from the Norman, OK (NRMN), and Foraker, OK (FORA) 

OASIS sites and the ARM-CART SGP-CF site for 28 July 2005 are described here. Clear sky 

conditions persisted during the morning hours across the state of OK on both days while clouds 

developed over many of the sites during the afternoon of 4 July 2003, and remained clear on 28 

July 2005. 

Two sets of simulations were performed utilizing 1DBLM, a control simulation in which 

MM5 surface properties were used, and an assimilation run in which moisture availability was 

recovered by utilizing the observations from MESONET sites. For initializing 1DBLM, initial 

profiles of potential temperature, u- and v-component wind, and specific humidity for 06UTC 

were obtained from an MM5 4km horizontal resolution forecast. Initial conditions for the MM5 

were specified at 00UTC for each day from the NCEP Eta Data Assimilation System 40-km 

analyses. For MM5 simulations the Blackadar boundary layer scheme and a 5-layer soil model 

were employed. The closest grid point to the mesonet station was used to initialize 1DBLM. 

The land use category specified within the MM5 was used to determine the initial land surface 

specifications for 1DBLM, namely moisture availability, thermal inertia, and roughness length. 

Table 1 provides values of pertinent parameters for each site. The observed net shortwave 

radiation, as calculated from the explicit tower measurements of the net radiation components, 

was used as input to 1DBLM to ensure consistency in the amount of solar radiation input to the 

system. This also allows us to represent the decrease in incoming radiation resulting from the 

presence of clouds. 

53
Table 1 

Pertinent Initial Model Parameters for the OK and SGP Model Simulations. 

Site Lat Lon  s c  s  MA o z  TM ug g v

NRMN 35.24 97.46 0.04 1256 1850 1.23 0.30 0.15 1.00 298 -7.24 4.40 

FORA 36.84 96.43 0.04 1256 1850 1.23 0.30 0.15 1.00 295.7 -2.69 2.97 

SGPCF 36.62 97.50 0.04 1256 1850 1.23 0.30 0.15 1.00 295.0 -2.10 -2.31 

A Control (CTRL) simulation was performed at each location for which the 1DBLM 

model is allowed to run using only the observed net shortwave radiation and the given initial 

parameters. An assimilation simulation (ASSIM) was then performed for which the McN94 

moisture availability adjustment is invoked over a three hour period from 1300UTC to 

1500UTC. For the assimilation simulation over each site, the surface skin temperatures from the 

corresponding IRT were used. 

Figure 29 shows the CTRL and ASSIM-case model predictions for surface skin 

temperature, 2-meter temperature, sensible and latent heat flux, and wind speed as compared 

with observations from the NRMN site. The net radiation from both the IRT and tower are also 

plotted along with the model predicted value to show the relationship between the tower 

measurements and the model. The CTRL, using the physically consistent 3-temperature method 

produced a time series of model skin and 2-m temperature which matches the observations 

reasonably well. Thus, since the difference between the model skin temperature and the 

observed skin temperature is negligible, the adjustment made by our technique is also negligible 

and in the figure the plots for both CNTRL and ASSIM-case are on top of each other. 

54
Figure 29. Observed (solid), CTRL simulated (dashed), and ASSIM simulated (dash-dot) variables valid 06 UTC 4 

July 2003 to 12 UTC 5 July 2003 for NRMN. Net radiation as measured by the IRT (solid) and tower 

instruments (dotted line) are given. 

The two instruments at the NRMN site (IRT and the sensors on the tower) show some 

discrepancies with respect to measured net radiation. This is attributed to a combination of 

differences in the field of view and emissivity of the measurements (Basara, 2007, personal 

communication) and to differences in incident solar insolation. Since we have been using the 

tower measurements of insolation in this study, these discrepancies also manifest themselves in 

the skin temperature plot. The overestimation of winds near the surface, as predicted by the 

model, is responsible for warmer 2-meter temperatures during the day since the surface shear 

stress leads to a more well mixed environment. The surface sensible and latent heat fluxes were 

well represented by the model (both simulations), deviating from the observations on the order or 

55
30 to 50 W/m2

. Perhaps lowering wind speed would have lead to a larger temperature gradient 

(between skin and the atmosphere) and consequently a larger sensible heat flux, and accordingly 

a lower latent heat flux. The relative success of the model for NRMN site means that the 

climatological values for the surface parameters including roughness length, moisture 

availability, and thermal inertia are representative. Since the observed rise of skin temperature 

during the assimilation period was well represented by the CTRL run, there was little adjustment 

made within ASSIM, as indicated by little visible difference between the CTRL and ASSIM time 

series (Figure 29). The results for NRMN site shows that 1DBLM provided a reasonable 

representation of the surface to Boundary layer interactions and boundary layer evolution. It also 

indicated the limitation of the assimilation technique as it was unable to correct errors in 

partitioning of fluxes and the Bowen ratio (the ratio of sensible heat flux to latent heat flux) 

when these errors are due to factors (e.g., wind speed) other than surface moisture. 

Figure 30 shows the same parameters as Figure 18 except for the FORA site. The FORA 

site exhibited a surface that was fairly moist, resulting from convective precipitation on the 

previous days. As a result the CTRL simulation, using the MM5 specified land use parameters, 

simulated a skin temperature which was much warmer than observed during the daytime, and a 

2-meter temperature curve which was slightly warmer than observed. The CTRL simulated a 

maximum skin temperature 6 degrees K warmer than the observation. In response, the CTRL 

simulated sensible heat flux was overpredicted, and the latent heat flux underpredicted. When 

the McN94 technique was applied, the moisture availability increased to 0.48 and a 5 degree K 

improvement (reduction) in the maximum model skin temperature. This resulted in a reduction 

of sensible heat flux and an increase in latent heat flux, nearly matching the observations. The 

success of the assimilation technique for FORA confirms the validity of the assumptions made. 

The model predicted wind speeds show a much better agreement with the observations; therefore 

most of the error in the Bowen ratio must be due to the specification of the model surface 

moisture. 

Figure 31 shows the same information as the previous figures but for the SGP-CF site. 

The CTRL simulated a skin temperature which reached a daytime maximum 6 degrees K cooler 

than observed. This is in part attributed to an over-representation of the initial moisture 

availability in the model as compared to the extremely dry surface at the site (indicated by the 

measured fractional water index surrounding the SGP CF site for 29 July 2005). Consequently, 

the sensible heat flux was underpredicted by about 50%, and the latent heat flux overpredicted by 

about twice the observation. When the McN94 technique was applied, the moisture availability 

was reduced, resulting in a 2.5 degree K improvement to the maximum daytime model skin 

temperature which was still below the observations. While both sensible and latent heat fluxes 

were improved, both the sensible and latent heat fluxes were nudged towards the observations. 

The assimilation technique as implemented here did not retrieve the exact observed skin 

temperature or even the rate of temperature change, but does nudge the results in the correct 

direction. In addition, both the predicted sensible and latent heat fluxes partitioning were nudged 

in the correct direction. It has never been a requirement that the McN94 technique provide exact 

results, as one of the major assumptions made in the technique is that all other model parameters 

are represented well, leaving the largest uncertainty within the moisture availability. The results 

56
indicated here are similar to those found with McN94, however the results were accomplished 

using a physically consistent method, unlike the previous implementation. 

After the relative success of the 4 July 2003 cases, the inability of 1DBLM, with the 

moisture availability adjustment active, to replicate the large heating rates through the entire 

heating period of 29 July 2005 lead to an investigation that examined how the surface energy 

balance was reacting to the changes in surface moisture availability. This is described in the 

following section. 

Figure 30. Observed (solid), CTRL simulated (dashed), and ASSIM simulated (dash-dot) variables valid 06 UTC 4 

July 2003 to 12 UTC 5 July 2003 for FORA. Net radiation as measured by the IRT (solid) and tower 

instruments (dotted line) are given. 

57
Figure 31. Observed (solid), CTRL simulated (dashed), and ASSIM simulated (dash-dot) variables valid 06 UTC 29 

July 2005 to 12 UTC 5 July 2003 for SGP-CF. Net radiation as measure by the IRT (solid) and tower 

instruments (dotted line) are given. 

6.4. Energy Balance Considerations 

The simulations at different sites, as described in the previous section, show that the use 

of observational data can lead to better simulations of an earth-atmosphere system. The 

observations are not used directly, but rather indirectly to provide some difference measure. 

Simply replacing parameters within a model system with observations, while conceptually seems 

the easiest thing to do, can be problematic since actual values of those parameters are typically a 

product of a different equilibrium state. A direct replacement can shock a system such that it 

58
may lead to numerical instability and ultimately a model crash. For this reason investigators 

began by attempting to nudge a model state towards observations. 

Early problems with attempts to insert surface skin temperatures directly into the surface 

energy budget were recognized by Stauffer et al. (1991). They encountered difficulties in that the 

terms in the energy budget no longer supported the temperatures being inserted. Thus the system 

became unbalanced leading to numerical instability and erroneous results. MCN94, building on 

the experience of Stauffer et al. (1991), recognized that direct insertion of skin temperature 

would not work. Rather MCN94, following Wetzel and Woodward (1987) and Carlson et al. 

(1981), took the approach that fundamental parameters within the surface energy budget such as 

surface moisture availability or heat capacity must be changed to support the desired temperature 

result. In this regard they analytically inverted a surface energy model to solve for the moisture 

availability that would produce the desired temperature change in the morning hours. McNider et 

al. (2005) (hereafter MCN05) employed a similar technique to solve for the slab heat capacity 

that would give the desired temperature response in the early evening. Other investigators such 

as Jones et al. (1998a) have used this analytical inversion to solve for other parameters in more 

complex surface models to solve for stomatal resistance. However, in all of these analytical 

inversions the assumption is that other terms remain the same as when the desired parameter is 

recovered. In reality, once one parameter is changed other terms in the budget equation also 

change. Thus, attention is required in solution techniques and iteration strategies so that the 

desired recovery is made. 

An examination of the observed, control model, and assimilation simulated heating rate 

for the SGP-CF case uncovered interesting findings. Figure 32 gives a graphic representation of 

these heating rates along with the subsequent impact on the skin temperature. The high 

frequency changes of the surface energy balance can be seen in the time series of temperature 

tendencies. As expected, the model simulated heating rate was less than the observations. When 

the moisture availability adjustment takes place, the result was a jump in the surface temperature 

tendencies and an increase in skin temperature. However; the model very quickly returns to a 

heating rate close to its previous one. Even with the initial jump in tendency, it can be seen that 

the average hourly tendency would be below the observation. In order to test the impact of 

increasing the assimilation frequency, a test case was conducted where the moisture availability 

was adjusted every 2 minutes. Figure 33 provides the same information as Figure 32 but for this 

higher frequency assimilation test run. The result is an hourly average tendency that is more 

closely matched to the observations. A portion of the hourly tendency is much less than 

previously found, resulting from the fact that the observed tendency was calculated from hourly 

observations and not the two minute interval. While the time series of tendencies appears to be 

quite chaotic, the result on the surface skin temperature and fluxes was much improved over the 

hourly adjustment. This can be seen in Figure 34, which indicates skin temperatures that nearly 

match the observations. The surface fluxes were further nudged towards observation and could 

be further improved if more attention was given in correcting the boundary layer processes 

responsible for the near surface temperature representation and winds. 

The results, as depicted in Figure 32-Figure 34, provide an example of how sensitive the surface 

energy balance is to changes in any of its components. In order to capture the changes in an 

59
energetically consistent manner, the use of very small time steps was employed. The use of 

more sophisticated solvers may allow for larger time steps and improve numerical stability. 

Figure 32. Time series of skin temperature tendency from CTRL (solid), ASSIM (small dash), and observations 

(dotted) along with time series of skin temperature from CTRL (dash-dot) and ASSIM (large dash). The 

assimilation is implemented every 60 minutes. 

60
Figure 33. Time series of skin temperature tendency from CTRL (solid), ASSIM (small dash), and observations 

(dotted) along with time series of skin temperature from CTRL (dash-dot) and ASSIM (large dash). The 

assimilation is implemented every 2 minutes. 

61
Figure 34. Observed (solid), CTRL simulated (dashed), and ASSIM simulated (dash-dot) variables valid 06 UTC 29 

July 2005 to 12 UTC 5 July 2003 for SGP-CF applying the MCN94 assimilation technique every 2 

minutes. 

6.5. Summary 

This discussion has outlined the importance of consistent use of skin and aerodynamic 

temperature representation and iteration processes when incorporating surface information from 

satellite data into mesoscale models. The three-temperature system used here can be directly 

implemented within currently forecasting systems in which the boundary layer parameterization 

can interact with a slab land surface model. While this is a limited set of model configurations 

the implications of inconsistent temperature use is likely applicable to all situations where 

surface information from satellites is used. 

The MCN94 satellite assimilation technique was applied in this study. In light of 

research that took place since its development, some of the problems faced when incorporating 

this technique within the MM5 were alleviated. This was accomplished by using the 

aforementioned three-temperature system so that satellite observations could be directly 

compared with the physically consistent model simulated counterpart. The results of the tests 

presented here look promising, and provide results similar to those originally produced in 

MCN94 but within a physically consistent system. 

62
63

The impact of making adjustments to parameters within a surface energy balance on that 

balance was discussed. Results from test cases indicated how sensitive the system is to these 

changes. Future work might examine the impacts of using more elegant and numerically stable 

solvers in this system, as well as reevaluating how operator splitting is done within models. 
7. SUMMARY AND CONCLUSIONS 

In this study, satellite-retrieved cloud transmissivity, cloud top height, and observed 

cloud fraction were used to correct photolysis rates for cloud cover in CMAQ. The results from 

CMAQ simulations using this method were compared with simulations that used standard MM5-

derived cloud fields as input. The simulations were performed with 4-km and 12-km grid cell 

sizes over Texas, extending east to Mississippi, for the period of August 24 to August 31, 2000. 

The results reveal that lack of observed clouds in the model can drastically alter the 

predicted atmospheric chemical composition within the boundary layer and exaggerate or underpredict ozone concentrations. Cloud impact is acute and more pronounced over the emission 

source regions and can lead to large errors in the model predictions of ozone and its by-products. 

Clouds also increased the lifetime of ozone precursors leading to their transport out of the source 

regions and causing ozone production farther downwind. Longer lifetime for NOx and its 

transport out of the source regions and over regions high in biogenic hydrocarbon emissions (in 

the eastern part of the domain) led to increased ozone production that was missing in the control 

simulation. Over Houston-Galveston Bay area, the presence of clouds altered the chemical 

composition of the atmosphere and reduced the net surface removal of reactive nitrogen 

compounds. 

It should be noted that there are many sources of errors in these simulations (e.g., 

emissions, lateral boundary conditions for a relatively small domain) and the technique presented 

here only corrects one of the errors. It should also be noted that the modeling domain was 

extremely dry during the period of this study. Therefore, the impact of inclusion of observed 

clouds on photochemistry during other periods with more cloud formation could be even more 

dramatic than presented in this study. This is evident when the statistical analyses of the results 

are compared to the large error reduction at the individual sites impacted by clouds. The 

statistics for the entire domain generally show a moderate improvement. Such large errors can 

lead to major problems in the use of photochemical models for case studies as well as in air 

quality forecasting. In case studies, simply an inconsistency between the observed cloud field 

and that of the model can result in erroneous concentrations that cannot be explained by the insitu measurements. Air quality forecast models often use the model results from the previous 

forecast (or some adjusted form of it) to initialize the model for the new forecast. Therefore, the 

errors arising from an inconsistency in the cloud fields can propagate into the future forecasts. 

Therefore, the use of observed clouds in the preparation of initial concentrations for air quality 

forecasting is beneficial. 

This study showed that at some locations the errors in ozone concentration arising from 

inaccurate cloud cover specification reached as high as 60 ppb which was mostly corrected by 

the use of our technique. Such errors are significant and can have considerable impact on air 

quality modeling efforts. However, other sources of error in the model due to inadequate cloud 

specification are as important and need to be addressed. The assimilation technique presented 

here only corrected the photolysis rates and did not account for the inconsistencies in dynamics 

and aqueous-phase chemistry. The discrepancy in dynamics affects the vertical mixing, which 

65
66

can lead to over-/under-prediction of pollutants. Such discrepancies also affect the chemistry as 

the heterogeneous processes in the model will be affected by an inconsistent photolysis rate. 

These problems require further research for improving the existing photolytic rate 

calculations in the current air quality models. Even though in this study the technique was 

implemented within CMAQ, any model that uses model-generated clouds suffers from the same 

problems and may benefit equally from using satellite-derived clouds. The method presented 

here addresses a problem in the chemical transport model while the source of this problem is the 

inadequate cloud prediction in the model. One approach to resolve this issue would be the 

assimilation of observed clouds in a dynamically consistent manner in the model. 

With respect to the land surface modeling using satellite data, the results of this study 

have indicated that the highly uncertain specification of surface moisture availability can be 

improved using satellite data. However, it was also shown that the application of assimilation 

techniques, such as the MCN94 technique, must be carefully done within a model framework. 

The original tests of MCN94 showed that it could effectively alter surface moisture. This was 

done in a model system which explicitly calculated explicitly a surface radiating skin 

temperature and an aerodynamic temperature. In the transfer of this technique to MM5, it was 

found that the radiating skin temperature was in fact not a skin temperature and that MM5 used 

the ground temperature rather than a aerodynamic temperature to calculate surface fluxes, 

leading to an overadjustment of surface moisture. The study also pointed out inconsistencies in 

the technique of MCN94 in that in its formulation of using satellite temperature tendencies it also 

mixed radiating and aerodynamic temperatures. 

Also shown is that with careful attention to surface details, satellite data can be 

effectively assimilated to recover surface moisture and potentially surface heat capacity. 

However, tests showed that even with these improvements the final skin temperatures, though 

improved, still did not fully follow the satellite data. Additional work is needed regarding the 

theory of how to adjust parameters within the complex nonlinear dynamical system at the 

surface/air interface. Part of the problem is that atmospheric scientists have not been as careful as 

mathematicians in applying operator splitting. For example, most all atmospheric models split 

operators by separately carrying out calculations of surface temperature and first level air 

temperature. At the next time step there is interaction but this step-wize approach might lead to 

oscillations as previously shown. A better technique may be to solve for the surface and near 

surface air temperature together, especially if assimilation is going to take place, which disrupts 

the system balance. 
67

8. REFERENCES 

Allen, C.W. 1963. Astrophysical quantities, 2nd ed. London: University of London, Athlone 

Press. 291 pp. 

Allen, D., C. Durrenberger, and TNRCC Technical Analysis Division. 2002. Accelerated science 

evaluation of ozone formation in the Houston-Galveston Area: Photochemical air quality 

modeling. Technical Report. Texas Commission on Environmental Management. 47 pp. 

Internet website: 

http://www.utexas.edu/research/ceer/texaqsarchive/pdfs/Modeling02_17_02.PDF

Anderson, M.C., J.M. Norman, G.R. Diak, W.P. Kustas, and J.R. Mecikalski. 1997. A twosource time-integrated model for estimating surface fluxes using thermal infrared remote 

sensing. Remote Sens. Env. 60:195-216. 

Arola, A., S. Kalliskota, P.N. den Outer, K. Edvardsen, G. Hansen, T. Koskela, T.J. Martin, J. 

Matthijsen, R. Meerkoetter, P. Peeters, G. Seckmeyer, P.C. Simon, H. Slaper, P. Taalas, and 

J. Verdebout. 2002. Assessment of four methods to estimate surface UV radiation using 

satellite data, by comparison with ground measurements from four stations in Europe. J. 

Geophys. Res. 107(D16):4310. doi:10.1029/2001JD000462. 

Barker, H.W., G.L. Stephens, P.T. Partain, J.W. Bergman, B. Bonnel, K. Campana, E.E. 

Clothiaux, S. Clough, S. Cusack, J. Delamere, J. Edwards, K.F. Evans, Y. Fouquart, S. 

Freidenreich, V. Galin, Y. Hou, S. Kato, J. Li, E. Mlawer, J.J. Morcrette, W. O'Hirok, P. 

Räisänen, V. Ramaswamy, B. Ritter, E. Rozanov, M. Schlesinger, K. Shibata, P. Sporyshev, 

Z. Sun, M. Wendisch, N. Wood, and F. Yang. 2003. Assessing 1D atmospheric solar 

radiative transfer models: Interpretation and handling of unresolved clouds. Journal of 

Climate 16(16):2676–2699. 

Beljaars, A.C.M. and A.A.M. Holtslag.1991.Flux parameterization over land surfaces for 

atmospheric models. Journal of Applied Meteorology 30:327-341. 

Biazar, A.P. 1995. The role of natural nitrogen oxides in ozone production in the southeastern 

environment. Ph.D. Dissertation, Atmospheric Science Department, University of Alabama 

in Huntsville, AL. 271 pp. 

Blackadar, A.K. 1979. High Resolution Models of the Planetary Boundary Layer. Adv. Environ. 

Sci. Eng. 1:50-85. 

Brock, F.V., K.C. Crawford, R.L. Elliott, G.W. Cuperus, S.J. Stadler, H.L. Johnson, and M.D. 

Eilts. 1995. The Oklahoma mesonet: A technical overview. Journal of Atmospheric and 

Oceanic Technology 12:5-19. 

Brotzge, J.A. and C.E. Duchon. 2000. A field comparison among a domeless net radiometer, two 

four-component net radiometers, and a domed net radiometer. Journal of Atmospheric and 

Oceanic Technology 17:1569-1582. 

Brutsaert, W. 1982. Evaporation into the atmosphere-theory, history, and applications. Higham, 

MT, USA: D. Reidel Publishing Company. 299 pp. 

Carlson, T.N. 1986. Regional scale estimates of surface moisture availability and thermal inertia 

using remote thermal measurements. Remote Sens. Rev. 1:197-246. 

Carlson, T.N., J.K. Dodd, S.G. Benjamin, and J.N. Cooper. 1981. Satellite estimation of the 

surface energy balance, moisture availability and thermal inertia. Journal of Applied 

Meteorology 20:67-87. 
68

Castro, T., L.G. Ruiz-Suarez, J.C. Ruiz-Suarez, M.J. Molina, and M. Montero. 1997. Sensitivity 

analysis of a UV radiation transfer model and experimental photolysis rates of NO2 in the 

atmosphere of Mexico City. Atmospheric Environment 31:609-620. 

Chang, J.S., R.A. Brost, I.S.A. Isaksen, S. Madronich, P. Middleton, W.R. Stockwell, and C.J. 

Walcek. 1987. A three-dimensional Eulerian acid deposition model: Physical concepts and 

formulation. J. Geophys. Res. 92(D12):14681-14700. 

Choudhury, B.J., R.J. Reginato, and S.B. Idso. 1986. An analysis of infrared temperature 

observation of wheat and calculation of latent heat flux. Agric. For. Meteor. 37:75-88. 

Collins, D.R.; H.H. Jonsson, H. Liao, R.C. Flagan, J.H. Seinfeld, K.J. Noone, and S.V. Hering. 

2000. Airborne analysis of the Los Angeles aerosol. Atmospheric Environment 34(24): 

4155-4173. 

Coulson, K.L. 1959. Characteristics of the radiation emerging from the top of a Rayleigh 

atmosphere, 1 and 2. Planet. Space Sci. 1:256-284. 

Deardorff, J.W. 1972. Theoretical expression for the countergradient vertical heat flux. J. 

Geophys. Res. 77:5900-5904. 

Diak, G.R., and C. Gautier. 1983. Improvements to a simple physical model for estimating 

insolation from GOES data. J. Appl. Meteor. 22:505-508. 

Diak, G. and M. Whipple. 1995. Note on estimating surface sensible heat fluxes using surface 

temperatures measured from geostationary satellite. J. Geophys. Res. 100:25453-25461 

Dickerson, R.R., S. Kondragunta, G. Stenchikov, K.L. Civerolo, B.G. Doddridge, and B.N. 

Holben. 1997. The impact of aerosols on solar ultraviolet radiation and photochemical smog. 

Science 278:827-830. 

Dudhia, J. 1996. A multi-layer soil temperature model for MM5. The Sixth PSU/NCAR 

Mesoscale Model Users' Workshop, Boulder, Colorado, National Center for Atmospheric 

Research. Pp. 49-50. 

Fiebrich, C.A., J.E. Martinez, J.A. Brotzge, and J.B. Basara. 2003. The Oklahoma Mesonet's skin 

temperature network. Journal of Atmospheric and Oceanic Technology 20:1496-1504. 

Fritz, S., and J.S. Winston. 1962. Synoptic use of radiation measurements from satellite 

TIROS-II. Mon. Weather Review 90:1-9. 

Gautier, C., G. R. Diak, and S. Mass. 1980. A simple physical model for estimating incident 

solar radiation at the surface from GOES satellite data. J. Appl. Meteor. 19:1005-1012. 

Gery, M.W., G.Z. Whitten, J.P. Killus, and M.C. Dodge. 1989. A photochemical kinetics 

mechanism for urban and regional scale computer modeling. J. Geophys. Res. 

94(D10):12,925–12,956. 

Grell, G.A. 1993. Prognostic evaluation of assumptions used by cumulus parameterizations. 

Mon. Wea. Rev. 121:764–787. 

Grell, G.A., Y.H. Kuo, and R. Pasch. 1991. Semi-prognostic tests of cumulus parameterization 

schemes in the middle latitudes. Mon. Wea. Rev. 119:5–31. 

Grell, G.A., J. Dudhia, and D.R. Stauffer. 1994. A description of the fifth-generation Penn 

State/NCAR mesoscale model (MM5). NCAR Technical Note NCAR/TN-398+STR, 

National Center for Atmospheric Research, Boulder, Colorado. 138 pp. 

Haines, S.L., R.J. Suggs, and G.J. Jedlovec. 2004. The GOES product generation system. NASA 

Technical Memorandum, NASA/Marshall Space Flight Center. Document ID: 20050019524. 

Report Number: M-1118, NASA TM-2004-213286. 

(http://hdl.handle.net/2060/20050019524) 
69

Jacobson, M.Z. 1998. Studying the effects of aerosols on vertical photolysis rate coefficient and 

temperature profiles over an urban airshed. J. Geophys. Res. 103(D9):10593-10604. 

doi:10.1029/98JD00287. 

Jedlovec, G.J., J.A. Lerner, and R.J. Atkinson. 2000. A satellite-derived upper-tropospheric 

water vapor transport index for climate studies. J. Appl. Meteor. 39:15-41. 

Jones, A.S., I.C. Guch, and T.H. Vonder Haar. 1998a. Data assimilation of satellite-derived 

heating rates as proxy surface wetness data into a regional atmospheric mesoscale model. 

Part II: A case study. Monthly Weather Review 126:646-667. 

Jones, A.S., I.C. Guch, and T.H. Vonder Haar. 1998b. Data assimilation of satellite-derived 

heating rates as proxy surface wetness data into a regional atmospheric mesoscale model. 

Part I: Methodology. Monthly Weather Review 126:634-645. 

Kustas, W.P., B.J. Choudhury, M.S. Moran, R.J. Reginato, R.D. Jackson, L.W. Gary, and H.L. 

Weaver. 1989. Determination of sensible heat flux over sparse canopy using thermal infrared 

data. Agric. For. Meteor. 44:197-216. 

Lacis, A.A. and J.E. Hansen. 1974. A parameterization for absorption of solar radiation in the 

earth’s atmosphere. J. Atmos. Sci. 31:118-133. 

Lhomme, J.P., N. Katerji, A. Perrier, and J.M. Bertolini. 1988. Radiative surface temperature and 

convective flux calculation over crop canopies. Boundary-Layer Meteorology 43:383-392. 

Liao, H., Y.L. Yung, and J.H. Seinfeld. 1999. Effects of aerosols on tropospheric photolysis rates 

in clear and cloudy atmospheres. J. Geophys. Res. 104(D19):23697-23708. 

doi:10.1029/1999JD900409. 

Mackaro, S. 2003. Applications of land surface data assimilation to simulations of sea breeze 

circulations. M.S. Thesis, Atmospheric Science Department, University of Alabama in 

Huntsville. 86 pp. 

Mackaro, Scott. 2008. The importance of physically consistent temperature representation in 

land surface satellite assimilation. Ph.D. Dissertation, Atmospheric Science Department, 

University of Alabama in Huntsville. 157pp. 

Mackaro, Scott M., R.T. McNider, and A.P. Biazar. 2008. A physically consistent method for the 

assimilation of land surface temperature tendencies. Proceedings of American 

Meteorological Society 88th Annual Meeting, 12th Conference on Integrated Observing and 

Assimilation Systems-Assimilation of Ocean and Land Surface Observations into Models-I

(12IOAS-AOLS), New Orleans, LA, January 20-24. 

Madronich, S. 1987. Photodissociation in the atmosphere: 1. actinic flux and the effects of 

ground reflections and clouds. J. Geophys. Research 92:9740-9752. 

McNider, R.T., A.J. Song, D.M. Casey, P.J. Wetzel, W.L. Crosson, and R.M. Rabin. 1994. 

Toward a dynamic thermodynamic assimilation of satellite surface temperature in numerical 

atmospheric models. Mon. Wea. Rev. 122:2784-2803. 

McNider, R.T., J.A. Song, and S.Q. Kidder. 1995. Assimilation of GOES-derived solar 

insolation into a mesoscale model for studies of cloud shading effects. Int. J. Remote Sens. 

16:2207-2231. 

McNider, R.T., W.B. Norris, D.M. Casey, J.E. Pleim, S.J. Roselle, and W.M. Lapenta. 1998. 

Assimilation of satellite data in regional air quality models. Pp. 25-35, Air Pollution 

Modeling and Its Application XII, S.E. Gryning and N. Chaumerliac (eds.), NATO/CCMS, 

Plenum Press, NY. 
70

McNider, R.T., W.M. Lapenta; A.P. Biazar, G.J. Jedlovec, R.J. Suggs, and J. Pleim. 2005. 

Retrieval of model grid-scale heat capacity using geostationary satellite products. Part I: First 

case-study application. Journal of Applied Meteorology 44(9):1346-1360. (AN18622838). 

Monin, A.S. and A.M. Obukhov. 1954. Basicl of turbulent mixing in the ground layer of the 

atmosphere. Trans. Geophys. Inst. Akad. Nauk. USSR, 151:163-187. 

NCAR. 2003. MM5 Modeling System Version 3, NCAR. Internet website: 

http://www.mmm.ucar.edu/mm5/doc1.html. 

Norman, J.M., W.P Kustas, and K.S. Humes. 1995. A two-source approach for estimating soil 

and vegetation energy fluxes in observations of directional radiometric surface temperature. 

Agricultural and Forest Meteorology 77:263−293. 

Paltridge, G.W. 1973. Direct measurement of water vapor absorption of solar radiation in the 

free atmosphere. J. Atmos. Sci. 30:156-160. 

Pour-Biazar, A., R.T. McNider, S.J. Roselle, R. Suggs, G. Jedlovec, S. Kim , D.W. Byun, J.C. 

Lin, T.C. Ho, S. Haines, B. Dornblaser, and R. Cameron. 2007. Correcting photolysis rates 

on the basis of satellite observed clouds, J. Geophys. Res. 112:D10302. 17 pp. 

doi:10.1029/2006JD007422. 

Ruggaber, A., R. Dlugi, and T. Nakajima. 1994. Modeling radiation quantities and photolysis 

frequencies in the troposphere. Journal of Atmospheric Chemistry 18 :171–210. 

Stauffer, D.R., N.L. Seaman, and F.S. Binkowski. 1991. Use of four-dimensional data 

assimilation in a limited-area mesoscale model. Part II: Effects of data assimilation within the 

planetary boundary layer. Monthly Weather Review 119:734-754. 

Stephens, G.L. 1978. Radiation profiles in extended water clouds. II: Parameterization Schemes. 

Journal of the Atmospheric Sciences 35(11):2123–2132. 

Sugita, M. and W. Brutsaert. 1990. How similar are temperature and humidity profiles in the 

unstable boundary layer. Journal of Applied Meteorology 29:489-497. 

Sun, J. and L. Mahrt. 1995. Determination of surface fluxes from the surface radiative 

temperature. Journal of the Atmospheric Sciences 52:1096-1106. 

U.S. Enviromental Protection Agency. 1999. Science algorithms of the EPA Models-3 

Community Multiscale Air Quality (CMAQ) modeling system. EPA-600/R-99/030. 

Wetzel, P.J. and R.H. Woodward. 1987. Soil moisture estimation using GOES-VISSR infrared 

data: A case study with a simple statistical method. Journal of Applied Meteorology 26:107-

117. 

Wetzel, P.J., D. Atlas, and R.H. Woodward. 1984. Determining soil moisture from 

geosynchronous satellite infrared data: A feasibility study. Journal of Applied Meteorology 

23:375-391. 

Zilitinkevich, S.S. 1970. Dynamics of the atmospheric boundary layer. Leningrad Gidrometeor. 

291 pp.