Bradley G. Henderson

Multiscale observations of CO2, 13CO2, and pollutants at Four Corners for emission verification and attribution

Significance Climate change and air pollution caused by fossil-energy-related CO2 and NOx emissions is a capstone societal issue. A critical barrier to an international treaty aimed toward controlling emissions is the inability to verify inventories and reduction of emissions claimed by individual nations following implementation of new technologies. We demonstrate for the first time, to our knowledge, that simultaneous remote observations of CO2, NO2, and CO regional column enhancements can be made with high fidelity and frequency. These can then be used to identify emissions from power plants and to distinguish them from other sources. Our findings represent a significant advancement in remote sensing monitoring methodology and can be used to develop an enforceable, transparent, and equitable climate treaty. There is a pressing need to verify air pollutant and greenhouse gas emissions from anthropogenic fossil energy sources to enforce current and future regulations. We demonstrate the feasibility of using simultaneous remote sensing observations of column abundances of CO2, CO, and NO2 to inform and verify emission inventories. We report, to our knowledge, the first ever simultaneous column enhancements in CO2 (3–10 ppm) and NO2 (1–3 Dobson Units), and evidence of δ13CO2 depletion in an urban region with two large coal-fired power plants with distinct scrubbing technologies that have resulted in ∆NOx/∆CO2 emission ratios that differ by a factor of two. Ground-based total atmospheric column trace gas abundances change synchronously and correlate well with simultaneous in situ point measurements during plume interceptions. Emission ratios of ∆NOx/∆CO2 and ∆SO2/∆CO2 derived from in situ atmospheric observations agree with those reported by in-stack monitors. Forward simulations using in-stack emissions agree with remote column CO2 and NO2 plume observations after fine scale adjustments. Both observed and simulated column ∆NO2/∆CO2 ratios indicate that a large fraction (70–75%) of the region is polluted. We demonstrate that the column emission ratios of ∆NO2/∆CO2 can resolve changes from day-to-day variation in sources with distinct emission factors (clean and dirty power plants, urban, and fires). We apportion these sources by using NO2, SO2, and CO as signatures. Our high-frequency remote sensing observations of CO2 and coemitted pollutants offer promise for the verification of power plant emission factors and abatement technologies from ground and space.

Aerosol optical depth retrieval over the NASA Stennis Space Center: MTI, MODIS, and AERONET

The Multispectral Thermal Imager (MTI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-based aerosol optical depth (AOD) retrievals are compared to each other and to the sun photometer AOD measurements at the National Aeronautics and Space Administrations Stennis Aerosol Robotic Network (AERONET) site. The overall accuracy of the MODIS AOD retrieval is approximately the same as the accuracy of the MTI with close-to-nadir view (at large scattering angles) when compared with AERONET measurements. The accuracy of the MODIS AOD retrieval is found to be a function of the scattering angle (the angle between the direction of the incoming and the scattered photons). The root mean square error of the MODIS AOD retrieval at the Stennis site is found to increase from 0.04 at scattering angles between 90/spl deg/ and 105/spl deg/ to over 0.12 at scattering angles from 140/spl deg/ to 165/spl deg/. Using only moderate scattering angles (<105/spl deg/) can significantly increase the accuracy of the MODIS AOD retrievals.

The effect of spatial resolution on satellite aerosol optical depth retrieval

We use data from the Multispectral Thermal Imager (MTI) to evaluate the effects of spatial resolution on the accuracy of aerosol optical depth (AOD) retrieval. Our results show that increasing the pixel size by itself has little effect on AOD retrieval accuracy at our chosen study site. However, increased pixel size does increase the error in AOD retrieval as a result of clouds. High-resolution sensors like MTI are able to avoid most clouds, but as the pixel size increases, subpixel clouds avoid the cloud mask, creep into selected pixels, and add a positive bias to the retrieved value of AOD. In accompanying work, we show that increasing pixel size has a small but noticeable impact on the normalized difference vegetation index (NDVI) and the 2.2-/spl mu/m reflectance, both used in the retrieval algorithm. We also examine the uniformity of the aerosol layer and show that the AOD varies by less than 0.02 in optical depth units over a 2.3/spl times/3.8 km/sup 2/ area. An analysis of the temporal variability of Aerosol Robotic Network-retrieved AOD shows a standard deviation of 0.02 on partly cloudy days and 0.004 on clear days.

Turbulence-induced spatial variation of surface temperature in high-resolution thermal IR satellite imagery

Atmospheric eddies cause transient spatial and temporal variations of surface temperature and can limit the precision of satellite surface temperature retrievals. If a thermal IR sensor has sufficiently high spatial resolution, the effects of these transient changes of temperature will be seen as variations of the thermal spatial pattern. Nine thermal IR images of a uniform emissivity area on Mauna Loa caldera are carefully compared to document spatial differences between them. These images were obtained from the Dept. of Energy Multispectral Thermal Imager satellite at about 20m GSD. Spatial patterns with a 1C - 6C magnitude are present but not repeated in any of the images. In order to better understand the characteristics and impact of turbulence induced temperature fluctuations for quantitative remote thermal IR sensing, an effort to model the spatial variation of surface temperature as driven by turbulent energy fluxes has been initiated. Stochastic models initially examined showed a close coupling between surface temperature and turbulent fluxes but were not successful. Traditional energy balance models used in this type of simulation are insufficient to model skin temperature because of the importance of the skin layer and its small depth compared to soil depths used in the models. A new treatment based on surface renewal theory is introduced.

Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics

ABSTRACT This study presents an approach for generating a global land mapping dataset of the satellite measurements of CO2 total column (XCO2) using spatio-temporal geostatistics, which makes full use of the joint spatial and temporal dependencies between observations. The mapping approach considers the latitude-zonal seasonal cycles and spatio-temporal correlation structure of XCO2, and obtains global land maps of XCO2, with a spatial grid resolution of 1° latitude by 1° longitude and temporal resolution of 3 days. We evaluate the accuracy and uncertainty of the mapping dataset in the following three ways: (1) in cross-validation, the mapping approach results in a high correlation coefficient of 0.94 between the predictions and observations, (2) in comparison with ground truth provided by the Total Carbon Column Observing Network (TCCON), the predicted XCO2 time series and those from TCCON sites are in good agreement, with an overall bias of 0.01 ppm and a standard deviation of the difference of 1.22 ppm and (3) in comparison with model simulations, the spatio-temporal variability of XCO2 between the mapping dataset and simulations from the CT2013 and GEOS-Chem are generally consistent. The generated mapping XCO2 data in this study provides a new global geospatial dataset in global understanding of greenhouse gases dynamics and global warming.

Geometrical constraint on shadowing in rough surfaces

By considering a one-dimensional cross-section through the rough surface, we derive a purely geometrical constraint on the statistical distribution of shadowed facet slopes that should be satisfied by any model of surface emissivity that includes the effect of self-shadowing. Our purpose is not to develop a single shadowing model, but to provide a condition that any valid shadowing model should satisfy. Although the emphasis of the presentation is theoretical, some practical ramifications also are discussed.

Comparison of a single-view and a double-view aerosol optical depth retrieval algorithm

We compare the results of a single-view and a double-view aerosol optical depth (AOD) retrieval algorithm applied to image pairs acquired over NASA Stennis Space Center, Mississippi. The image data were acquired by the Department of Energys (DOE) Multispectral Thermal Imager (MTI), a pushbroom satellite imager with 15 bands from the visible to the thermal infrared. MTI has the ability to acquire imagery in pairs in which the first image is a near-nadir view and the second image is off-nadir with a zenith angle of approximately 60°. A total of 15 image pairs were used in the analysis. For a given image pair, AOD retrieval is performed twice---once using a single-view algorithm applied to the near-nadir image, then again using a double-view algorithm. Errors for both retrievals are computed by comparing the results to AERONET AOD measurements obtained at the same time and place. The single-view algorithm showed an RMS error about the mean of 0.076 in AOD units, whereas the double-view algorithm showed a modest improvement with an RMS error of 0.06. The single-view errors show a positive bias which is presumed to be a result of the empirical relationship used to determine ground reflectance in the visible. A plot of AOD error of the double-view algorithm versus time shows a noticeable trend which is interpreted to be a calibration drift. When this trend is removed, the RMS error of the double-view algorithm drops to 0.030. The single-view algorithm qualitatively appears to perform better during the spring and summer whereas the double-view algorithm seems to be less sensitive to season.

LANL MTI science team experience

The Multispectral Thermal Imager (MTI) is a technology test and demonstration satellite whose primary mission involved a finite number of technical objectives. MTI was not designed, or supported, to become a general purpose operational satellite. The role of the MTI science team is to provide a core group of system-expert scientists who perform the scientific development and technical evaluations needed to meet programmatic objectives. Another mission for the team is to develop algorithms to provide atmospheric compensation and quantitative retrieval of surface parameters to a relatively small community of MTI users. Finally, the science team responds and adjusts to unanticipated events in the life of the satellite. Broad or general lessons learned include the value of working closely with the people who perform the calibration of the data as well as those providing archived image and retrieval products. Close interaction between the Los Alamos National Laboratory (LANL) teams was very beneficial to the overall effort as well as the science effort. Secondly, as time goes on we make increasing use of gridded global atmospheric data sets which are products of global weather model data assimilation schemes. The Global Data Assimilation System information is available globally every six hours and the Rapid Update Cycle products are available over much of the North America and its coastal regions every hour. Additionally, we did not anticipate the quantity of validation data or time needed for thorough algorithm validation. Original validation plans called for a small number of intensive validation campaigns soon after launch. One or two intense validation campaigns are needed but are not sufficient to define performance over a range of conditions or for diagnosis of deviations between ground and satellite products. It took more than a year to accumulate a good set of validation data. With regard to the specific programmatic objectives, we feel that we can do a reasonable job on retrieving surface water temperatures well within the 1°C objective under good observing conditions. Before the loss of the onboard calibration system, sea surface retrievals were usually within 0.5°C. After that, the retrievals are usually within 0.8°C during the day and 0.5°C at night. Daytime atmospheric water vapor retrievals have a scatter that was anticipated: within 20%. However, there is error in using the Aerosol Robotic Network retrievals as validation data which may be due to some combination of calibration uncertainties, errors in the ground retrievals, the method of comparison, and incomplete physics. Calibration of top-of-atmosphere radiance measurements to surface reflectance has proven daunting. We are not alone here: it is a difficult problem to solve generally and the main issue is proper compensation for aerosol effects. Getting good reflectance validation data over a number of sites has proven difficult but, when assumptions are met, the algorithm usually performs quite well. Aerosol retrievals for off-nadir views seem to perform better than near-nadir views and the reason for this is under investigation. Land surface temperature retrieval and temperature-emissivity separations are difficult to perform accurately with multispectral sensors. An interactive cloud masking system was implemented for production use. Clouds are so spectrally and spatially variable that users are encouraged to carefully evaluate the delivered mask for their own needs. The same is true for the water mask. This mask is generated from a spectral index that works well for deep, clear water, but there is much variability in water spectral reflectance inland and along coasts. The value of the second-look maneuvers has not yet been fully or systematically evaluated. Early experiences indicated that the original intentions have marginal value for MTI objectives, but potentially important new ideas have been developed. Image registration (the alignment of data from different focal planes) and band-to-band registration has been a difficult problem to solve, at least for mass production of the images in a processing pipeline. The problems, and their solutions, are described in another paper.

Concurrent measurements of directional reflectance and temperature of a wintertime coniferous forest from space

We measure directional reflectance and daytime temperature of a wintertime coniferous forest from space using data acquired by the Department of Energys Multispectral Thermal Imager (MTI). The study site is the Howland experimental forest in central Maine. The data include measurements from all seasons over a one-year period from 2001-2002 but with a concentration in late winter and early spring. The results show variation in both reflectance and temperature with direction and season. The reflectance results compare favorably with previous bidirectional measurements performed at the Howland site. Near-nadir reflectance in the visible bands varies periodically over the year with a high in summer and a low in winter. Near-infrared (NIR) reflectance shows dual variation. The canopy reflectance varies as a function of solar and satellite zenith angle, presumably due to a changing proportion of shadows. Furthermore, a NIR pseudo-BRDF (bidirectional reflectance distribution function) shows that the canopy brightens in the NIR during fall and winter. Retrieved canopy temperatures are consistently warmer in the off-nadir view by about 2°C, with a small seasonal variation. The seasonal canopy temperature trend is well exhibited, and days with snow on the ground are easily distinguished from days with no snow on the ground. The results also show that the retrieved temperatures are consistently warmer than above-canopy air temperature by about 4°C. This difference is greater for off-nadir views and also appears to be larger in the spring and summer than in the fall and winter.