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APPLICATIONS OF SATELLITE OBSERVATIONS FOR AIR QUALITY RESEARCH

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APPLICATIONS OF SATELLITE OBSERVATIONS FOR AIR QUALITY RESEARCH

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    1. APPLICATIONS OF SATELLITE OBSERVATIONS FOR AIR QUALITY RESEARCH

    2. SATELLITE OBSERVATIONS OF TROPOSPHERIC COMPOSITION

    3. CONSTRAINING NOx AND VOC EMISSIONS USING SPACE-BASED MEASUREMENTS OF SOLAR BACKSCATTER

    4. GOME CONSTRAINTS ON NOx EMISSIONS

    5. TOP-DOWN MAPPING OF ISOPRENE EMISSIONS USING FORMALDEHYDE COLUMNS FROM GOME This slide illustrates the capability of GOME for top-down emission estimates of isoprene. We compare the GOME vertical HCHO column data for July 1996 to the corresponding GEOS-CHEM columns (for the GOME 10:30 am observation time and using GOME air mass factors). The structure of HCHO over the U.S. in the model is mainly due to isoprene, consistent with previous analyses of surface HCHO measurements (we KNOW that HCHO over the U.S. during the growing season is mainly from isoprene). The same structure is found in the GOME data; GOME is clearly seeing isoprene emission. The GEOS-CHEM model in this figure uses isoprene emissions from the IGAC GEIA inventory of Alex Guenther; comparison with GOME for the data in the figure shows a mean model bias of +11% and R2 = 0.70. There is no direct validation of the HCHO column product for GOME, and such validation would be indeed difficult to achieve in view of the low reliability of HCHO measurements from aircraft. We examined the consistency between GOME and the in situ HCHO surface data in the U.S. by using GEOS-CHEM as a transfer function. GEOS-CHEM with the GEIA isoprene emissions simulates the surface HCHO observations with a mean bias of +17% and R2 = 0.47; the bias is consistent with GOME and provides some quantitative support for the GOME observations. We then converted the GOME HCHO column data into a “GOME isoprene emission inventory” by using scaling factors from GEOS-CHEM for the relationship between HCHO columns and isoprene emissions (the scaling factor depends on the HCHO yield from isoprene oxidation and on the HCHO lifetime). The GOME inventory is 20% lower than GEIA but 50% higher than BEIS2 (the “official” EPA inventory). We then ingested the GOME inventory into GEOS-CHEM and reran the model. Results show improved simulation of the surface HCHO data (bias = -9%, R2 = 0.71), providing independent support for the GOME inventory. This slide illustrates the capability of GOME for top-down emission estimates of isoprene. We compare the GOME vertical HCHO column data for July 1996 to the corresponding GEOS-CHEM columns (for the GOME 10:30 am observation time and using GOME air mass factors). The structure of HCHO over the U.S. in the model is mainly due to isoprene, consistent with previous analyses of surface HCHO measurements (we KNOW that HCHO over the U.S. during the growing season is mainly from isoprene). The same structure is found in the GOME data; GOME is clearly seeing isoprene emission. The GEOS-CHEM model in this figure uses isoprene emissions from the IGAC GEIA inventory of Alex Guenther; comparison with GOME for the data in the figure shows a mean model bias of +11% and R2 = 0.70. There is no direct validation of the HCHO column product for GOME, and such validation would be indeed difficult to achieve in view of the low reliability of HCHO measurements from aircraft. We examined the consistency between GOME and the in situ HCHO surface data in the U.S. by using GEOS-CHEM as a transfer function. GEOS-CHEM with the GEIA isoprene emissions simulates the surface HCHO observations with a mean bias of +17% and R2 = 0.47; the bias is consistent with GOME and provides some quantitative support for the GOME observations. We then converted the GOME HCHO column data into a “GOME isoprene emission inventory” by using scaling factors from GEOS-CHEM for the relationship between HCHO columns and isoprene emissions (the scaling factor depends on the HCHO yield from isoprene oxidation and on the HCHO lifetime). The GOME inventory is 20% lower than GEIA but 50% higher than BEIS2 (the “official” EPA inventory). We then ingested the GOME inventory into GEOS-CHEM and reran the model. Results show improved simulation of the surface HCHO data (bias = -9%, R2 = 0.71), providing independent support for the GOME inventory.

    6. MAPPING OF ISOPRENE EMISSIONS USING GOME HCHO COLUMN DATA: We continue our ongoing work investigating the potential of satellite observations of column HCHO to provide a better understanding of the factors that control the emission of isoprene. This slide concentrates on the mid-summer months during 2001. We use the GEOS-CHEM global 3-D model (2x2.5 degree grid) driven by the latest isoprene emission algorithm (Model of Emissions of Gases and Aerosols in Nature, MEGAN) to simulate HCHO columns sampled along GOME orbits (overpass time 10-12 LT). The model reproduces a similar spatial pattern to the observed HCHO columns over North America (correlation coefficients typically > 0.75 for the months shown), providing confidence in the MEGAN emission inventory. There are also notable discrepancies between the model and the observations concerning the magnitude and temporal variability of the HCHO columns. Investigating these discrepancies illustrates the importance of ground-truthing satellite observations with in situ data. Hourly isoprene concentration data are available from the EPA PAMS network for the GOME record studied (1996-2001). We show monthly mean PAMS data sampled between 10-12 LT. In addition to the seasonal variation in isoprene concentration there is also a high level of interannual variability that the GOME HCHO columns reproduce well; outliers include isoprene concentration values > 10 ppbC. The PROPHET long-term monitoring station in Michigan provides valuable information on the daily course of isoprene flux during the growing season. MEGAN isoprene fluxes generally agree with observations. The modeled regression between isoprene flux and HCHO column is used to estimate isoprene fluxes from the GOME data. GOME is broadly consistent with the observed seasonal isoprene fluxes. Periods of enhanced isoprene flux are also captured by GOME they tend to be positively biased. We continue our ongoing work investigating the potential of satellite observations of column HCHO to provide a better understanding of the factors that control the emission of isoprene. This slide concentrates on the mid-summer months during 2001. We use the GEOS-CHEM global 3-D model (2x2.5 degree grid) driven by the latest isoprene emission algorithm (Model of Emissions of Gases and Aerosols in Nature, MEGAN) to simulate HCHO columns sampled along GOME orbits (overpass time 10-12 LT). The model reproduces a similar spatial pattern to the observed HCHO columns over North America (correlation coefficients typically > 0.75 for the months shown), providing confidence in the MEGAN emission inventory. There are also notable discrepancies between the model and the observations concerning the magnitude and temporal variability of the HCHO columns. Investigating these discrepancies illustrates the importance of ground-truthing satellite observations with in situ data. Hourly isoprene concentration data are available from the EPA PAMS network for the GOME record studied (1996-2001). We show monthly mean PAMS data sampled between 10-12 LT. In addition to the seasonal variation in isoprene concentration there is also a high level of interannual variability that the GOME HCHO columns reproduce well; outliers include isoprene concentration values > 10 ppbC. The PROPHET long-term monitoring station in Michigan provides valuable information on the daily course of isoprene flux during the growing season. MEGAN isoprene fluxes generally agree with observations. The modeled regression between isoprene flux and HCHO column is used to estimate isoprene fluxes from the GOME data. GOME is broadly consistent with the observed seasonal isoprene fluxes. Periods of enhanced isoprene flux are also captured by GOME they tend to be positively biased.

    7. COMPARATIVE INVERSE ANALYSIS OF ASIAN CO SOURCES USING DAILY MOPITT AND TRACE-P DATA This slide compares the estimates of CO sources in Asia obtained using satellite observations versus aircraft observations. The objective of the TRACE-P mission during spring 2001 was to characterize Asian outflow. The figure at the left shows mean CO concentrations observed over the Western Pacific. Simultaneously, the MOPITT instrument aboard EOS-Terra was globally measuring CO. The figure on the right shows mean CO columns over Asia and the Western Pacific as measured by MOPITT during TRACE-P, and illustrates the main outflow pathway from Asia. MOPITT validation exercises during TRACE-P established confidence in MOPITT’s ability to observe the variability in Asian export. Bayesian synthesis inverse methods were applied to estimate Asian sources in 2001 based on these two sets of observations. The GEOS-CHEM CTM was used as the forward model and customized a priori emission estimates were obtained from a year 2000 anthropogenic emission inventory by Streets et al., and a year 2001 biomass burning inventory using fire count data by Heald et al. The central figure compares the a posteriori emission estimates from TRACE-P (red) and MOPITT (blue) to a priori (black). MOPITT observations provide greater information to regionally disaggregate 11 regions, compared to the 6 constrained by the aircraft. This is due to the ability of the satellite to make observations over the source regions and over the Indian Ocean. The aircraft and satellite provide generally consistent estimates of Asian sources, where regions dominated by anthropogenic emissions, such as China are underestimated, and regions dominated by biomass burning emissions, such as SE Asia are overestimated. MOPITT observations support a more modest decrease in emissions in SE Asia than suggested by the aircraft observations, likely due to the satellite’s ability to observe over the continental region and thus better characterize SE Asian outflow over the source region. The inverse solution is sensitive to error specification and data selection. In particular, averaging the MOPITT data degrades the information which can be used to constrain regional sources. By conducting ensemble modeling with the MOPITT observations the uncertainty on retrieved sources is estimated to be 10-40%. The best estimate of total Asian CO sources using MOPITT observations is 361 Tgyr-1, over half of which is attributed to East Asia.This slide compares the estimates of CO sources in Asia obtained using satellite observations versus aircraft observations. The objective of the TRACE-P mission during spring 2001 was to characterize Asian outflow. The figure at the left shows mean CO concentrations observed over the Western Pacific. Simultaneously, the MOPITT instrument aboard EOS-Terra was globally measuring CO. The figure on the right shows mean CO columns over Asia and the Western Pacific as measured by MOPITT during TRACE-P, and illustrates the main outflow pathway from Asia. MOPITT validation exercises during TRACE-P established confidence in MOPITT’s ability to observe the variability in Asian export. Bayesian synthesis inverse methods were applied to estimate Asian sources in 2001 based on these two sets of observations. The GEOS-CHEM CTM was used as the forward model and customized a priori emission estimates were obtained from a year 2000 anthropogenic emission inventory by Streets et al., and a year 2001 biomass burning inventory using fire count data by Heald et al. The central figure compares the a posteriori emission estimates from TRACE-P (red) and MOPITT (blue) to a priori (black). MOPITT observations provide greater information to regionally disaggregate 11 regions, compared to the 6 constrained by the aircraft. This is due to the ability of the satellite to make observations over the source regions and over the Indian Ocean. The aircraft and satellite provide generally consistent estimates of Asian sources, where regions dominated by anthropogenic emissions, such as China are underestimated, and regions dominated by biomass burning emissions, such as SE Asia are overestimated. MOPITT observations support a more modest decrease in emissions in SE Asia than suggested by the aircraft observations, likely due to the satellite’s ability to observe over the continental region and thus better characterize SE Asian outflow over the source region. The inverse solution is sensitive to error specification and data selection. In particular, averaging the MOPITT data degrades the information which can be used to constrain regional sources. By conducting ensemble modeling with the MOPITT observations the uncertainty on retrieved sources is estimated to be 10-40%. The best estimate of total Asian CO sources using MOPITT observations is 361 Tgyr-1, over half of which is attributed to East Asia.

    8. CONSTRAINING PM SOURCES WITH MODIS AOD DATA

    9. MAPPING SURFACE PM2.5 USING MISR (2001 data)

    10. USING MOPITT TO OBSERVE TRANSPACIFIC POLLUTION TRANSPORT This slide illustrates the potential for continuous observation of transpacific transport of pollution using MOPITT measurements of CO columns. Major transpacific pollution plumes (CO approaching 300 ppb) were observed on the TRACE-P outgoing transit flights over the NE Pacific from Dryden to Hawaii and Hawaii to Guam on Feb 26-27. Sampling of these plumes was guided by the chemical forecasts, which sent the planes to the right places. The locations of the two main plumes sampled by TP are superimposed in the top figure on the GEOS-CHEM fields of CO columns for Feb 26. The northern plume (40N) was centered at 5 km altitude while the southern plume (20N) was centered at 2 km altitude. The plumes originated from a single Asian outflow event from a WCB that swept across the China coast on Feb 23 and lifted pollution to the free troposphere. That outflow encountered a blocking high over the north central Pacific and split into the two plumes shown here. The northern plume had no significant ozone enhancement, reflecting the lack of photochemical activity in that season. The southerly plume had large ozone production (80 ppb ozone) because of subsidence (which led to PAN decomposition) and the tropical trajectory. Also shown in the top figure are the MOPITT CO columns for Feb 26. High values are observed over the NE Pacific. By sampling the GEOS-CHEM fields with the MOPITT averaging kernels (center panel) we can better appreciate the structure that is seen in the MOPITT data. The bottom figure shows time series of MOPITT vs. GEOS-CHEM CO columns over the NW and NE Pacific for the TP period. We use this figure to demonstrate the capability of MOPITT for continuous monitoring of transpacific transport of pollution. We use averaging over large regions to overcome MOPITT data gaps (orbit track, clouds). MOPITT identifies four major transpacific pollution events over the NE Pacific during the TP period (the first one corresponded to the TP transit flight). These are indicated by arrows on the bottom figure. The first three events were clearly tracked by MOPITT over the NW Pacific 4-5 days prior, as shown in the figure; the last one was not as easily tracked and appeared to have a major European contribution. Comparison of the model vs. observed time series shows that the model can reproduce the outflow events over the NW Pacific with high fidelity; over the NE Pacific the correlation is poorer, probably because of accumulation of transport errors. This slide illustrates the potential for continuous observation of transpacific transport of pollution using MOPITT measurements of CO columns. Major transpacific pollution plumes (CO approaching 300 ppb) were observed on the TRACE-P outgoing transit flights over the NE Pacific from Dryden to Hawaii and Hawaii to Guam on Feb 26-27. Sampling of these plumes was guided by the chemical forecasts, which sent the planes to the right places. The locations of the two main plumes sampled by TP are superimposed in the top figure on the GEOS-CHEM fields of CO columns for Feb 26. The northern plume (40N) was centered at 5 km altitude while the southern plume (20N) was centered at 2 km altitude. The plumes originated from a single Asian outflow event from a WCB that swept across the China coast on Feb 23 and lifted pollution to the free troposphere. That outflow encountered a blocking high over the north central Pacific and split into the two plumes shown here. The northern plume had no significant ozone enhancement, reflecting the lack of photochemical activity in that season. The southerly plume had large ozone production (80 ppb ozone) because of subsidence (which led to PAN decomposition) and the tropical trajectory. Also shown in the top figure are the MOPITT CO columns for Feb 26. High values are observed over the NE Pacific. By sampling the GEOS-CHEM fields with the MOPITT averaging kernels (center panel) we can better appreciate the structure that is seen in the MOPITT data. The bottom figure shows time series of MOPITT vs. GEOS-CHEM CO columns over the NW and NE Pacific for the TP period. We use this figure to demonstrate the capability of MOPITT for continuous monitoring of transpacific transport of pollution. We use averaging over large regions to overcome MOPITT data gaps (orbit track, clouds). MOPITT identifies four major transpacific pollution events over the NE Pacific during the TP period (the first one corresponded to the TP transit flight). These are indicated by arrows on the bottom figure. The first three events were clearly tracked by MOPITT over the NW Pacific 4-5 days prior, as shown in the figure; the last one was not as easily tracked and appeared to have a major European contribution. Comparison of the model vs. observed time series shows that the model can reproduce the outflow events over the NW Pacific with high fidelity; over the NE Pacific the correlation is poorer, probably because of accumulation of transport errors.

    11. APPLICATION OF TES TROPOSPHERIC OZONE TO INVESTIGATE INTERCONTINENTAL TRANSPORT

    12. TRANSPACIFIC TRANSPORT OF ASIAN AEROSOL POLLUTION AS SEEN BY MODIS

    13. POLLUTION AND BIOMASS BURNING OUTFLOW DURING THE ICARTT AIRCRAFT MISSION (Jul-Aug 2004)

    14. LOOKING TOWARD THE FUTURE: GEOSTATIONARY AND L1 MISSION CONCEPTS

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