scholarly journals Estimating Aerosol Emissions by Assimilating Remote Sensing Observations into a Global Transport Model

2012 ◽  
Vol 4 (11) ◽  
pp. 3528-3543 ◽  
Author(s):  
Nick Schutgens ◽  
Makiko Nakata ◽  
Teruyuki Nakajima
Keyword(s):  
Remote Sensing ◽  
Transport Model ◽  
Global Transport ◽  
Aerosol Emissions

scholarly journals Retrieving global sources of aerosols from MODIS observations by inverting GOCART model

2007 ◽  
Vol 7 (2) ◽  
pp. 3629-3718 ◽  
Author(s):  
O. Dubovik ◽  
T. Lapyonok ◽  
Y. J. Kaufman ◽  
M. Chin ◽  
P. Ginoux ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Thickness ◽  
Transport Model ◽  
A Priori ◽  
Inversion Algorithm ◽  
Numerical Tests ◽  
Aerosol Emission ◽  
Coarse Mode ◽  
Aerosol Sources ◽  
Aerosol Emissions

Abstract. Knowledge of the global distribution of tropospheric aerosols is important for studying the effects of aerosols on global climate. Chemical transport models rely on archived meteorological fields, accounting for aerosol sources, transport and removal processes can simulate the global distribution of atmospheric aerosols. However, the accuracy of global aerosol modeling is limited. Uncertainty in location and strength of aerosol emission sources is a major factor in limiting modeling accuracy. This paper describes an effort to develop an algorithm for retrieving global sources of aerosol from satellite observations by inverting the GOCART aerosol transport model. To optimize inversion algorithm performance, the inversion was formulated as a generalized multi-term least-squares-type fitting. This concept uses the principles of statistical optimization and unites diverse retrieval techniques into a single flexible inversion procedure. It is particularly useful for choosing and refining a priori constraints in the retrieval algorithm. For example, it is demonstrated that a priori limitations on the partial derivatives of retrieved characteristics, which are widely used in atmospheric remote sensing, can also be useful in inverse modeling for constraining time and space variability of the retrieved global aerosol emissions. The similarities and differences with the standard "Kalman filter" inverse modeling approach and the "Phillips-Tikhonov-Twomey" constrained inversion widely used in remote sensing are discussed. In order to retain the originally high space and time resolution of the global model in the inversion of a long record of observations, the algorithm was expressed using adjoint operators in a form convenient for practical development of the inversion from codes implementing forward model simulations. The inversion algorithm was implemented using the GOCART aerosol transport model. The numerical tests we conducted showed successful retrievals of global aerosol emissions with a 2°×2.5° resolution by inverting the GOCART output. For achieving satisfactory retrieval from satellite sensors such as MODIS, the emissions were assumed constant within the 24 h diurnal cycle and aerosol differences in chemical composition were neglected. Such additional assumptions were needed to constrain the inversion due to limitations of satellite temporal coverage and sensitivity to aerosol parameters. As a result, the algorithm was defined for the retrieval of emission sources of fine and coarse mode aerosols from the MODIS fine and coarse mode aerosol optical thickness data respectively. Numerical tests showed that such assumptions are justifiable, taking into account the accuracy of the model and observations and that it provides valuable retrievals of the location and the strength of the aerosol emissions. The algorithm was applied to MODIS observations during two weeks in August 2000. The global placement of fine mode aerosol sources retrieved from inverting MODIS observations was coherent with available independent knowledge. This was particularly encouraging since the inverse method did not use any a priori information about the sources and it was initialized under a "zero aerosol emission" assumption. The retrieval reproduced the instantaneous global MODIS observations with a standard deviation in fitting of aerosol optical thickness of ~0.04. The optical thickness during high aerosol loading events was reproduced with a standard deviation of ~48%. Applications of the algorithm for the retrieval of coarse mode aerosol emissions were less successful, mainly due to the currently existing lack of MODIS data over high reflectance desert dust sources. Possibilities for enhancing the global satellite data inversion by using diverse a priori constraints on the retrieval are demonstrated. The potential and limitations of applying our approach for the retrieval of global aerosol sources from aerosol remote sensing are discussed.

scholarly journals Can We Detect the Brownness or Greenness of the Congo Rainforest Using Satellite-Derived Surface Albedo? A Study on the Role of Aerosol Uncertainties

2019 ◽  
Vol 11 (5) ◽  
pp. 1410 ◽  
Author(s):  
Suman Moparthy ◽  
Dominique Carrer ◽  
Xavier Ceamanos
Keyword(s):  
Remote Sensing ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Satellite Observations ◽  
Atmospheric Effects ◽  
Forest Regions ◽  
Aerosol Emissions

The ability of spatial remote sensing in the visible domain to properly detect the slow transitions in the Earth’s vegetation is often a subject of debate. The reason behind this is that the satellite products often used to calculate vegetation indices such as surface albedo or reflectance, are not always correctly decontaminated from atmospheric effects. In view of the observed decline in vegetation over the Congo during the last decade, this study investigates how effectively satellite-derived variables can contribute to the answering of this question. In this study, we use two satellite-derived surface albedo products, three satellite-derived aerosol optical depth (AOD) products, two model-derived AOD products, and synthetic observations from radiative transfer simulations. The study discusses the important discrepancies (of up to 70%) found between these satellite surface albedo products in the visible domain over this region. We conclude therefore that the analysis of trends in vegetation properties based on satellite observations in the visible domain such as NDVI (normalized difference vegetation index), calculated from reflectance or albedo variables, is still quite questionable over tropical forest regions such as the Congo. Moreover, this study demonstrates that there is a significant increase (of up to 14%) in total aerosols within the last decade over the Congo. We note that if these changes in aerosol loads are not correctly taken into account in the retrieval of surface albedo, a greenness change of the surface properties (decrease of visible albedo) of around 8% could be artificially detected. Finally, the study also shows that neglecting strong aerosol emissions due to volcano eruptions could lead to an artificial increase of greenness over the Congo of more than 25% in the year of the eruptions and up to 16% during the 2–3 years that follow.

scholarly journals Variations in Subpixel Fire Properties with Season and Land Cover in Southern Africa

2010 ◽  
Vol 14 (6) ◽  
pp. 1-29 ◽  
Author(s):  
Ted C. Eckmann ◽  
Christopher J. Still ◽  
Dar A. Roberts ◽  
Joel C. Michaelsen
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Southern Africa ◽  
Ecological Impacts ◽  
Fire Season ◽  
Remotely Sensed Data ◽  
Radiative Power ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Fire Properties ◽  
Aerosol Emissions

Abstract Some of the most widely used datasets for monitoring the world’s fires come from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard NASA’s Terra and Aqua satellites. For virtually all remote sensing systems, including MODIS, pixels that contain fires comprise a mix of burning and nonburning components, each with sizes and temperatures that vary between pixels. Current remote sensing products provide little information about these subpixel components, severely limiting estimates of the gas and aerosol emissions and ecological impacts from the world’s fires. This study shows how multiple endmember spectral mixture analysis (MESMA) can estimate subpixel fire sizes and temperatures from MODIS and can overcome many limitations of existing methods for characterizing fire intensities from remotely sensed data, such as the fire radiative power (FRP) approach. This study used MESMA to estimate subpixel fire sizes and temperatures for MODIS scenes in southern Africa, analyzed how these sizes and temperatures varied with season and land cover, and compared these to analyses made with FRP. This study could be the first to analyze fire sizes and temperatures on a spatial scale as large as a MODIS scene and a temporal scale as large as a full fire season. The variations in MESMA estimates of fire temperature with season and land cover were more consistent than the FRP estimates. Based on these findings, MESMA appears to be more effective than FRP at capturing some variations in fire temperatures, which strongly influence the gas and aerosol emissions from fires, along with their effects on ecosystems.

scholarly journals The Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) – Part 1: Model description and evaluation

2009 ◽  
Vol 9 (8) ◽  
pp. 2843-2861 ◽  
Author(s):  
S. R. Freitas ◽  
K. M. Longo ◽  
M. A. F. Silva Dias ◽  
R. Chatfield ◽  
P. Silva Dias ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Monoxide ◽  
Biomass Burning ◽  
Transport Model ◽  
Aerosol Particles ◽  
Atmospheric Modeling ◽  
Tracer Transport ◽  
Regional Atmospheric Modeling System ◽  
Biomass Burning Aerosol ◽  
Regional Atmospheric Modeling

Abstract. We introduce the Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS). CATT-BRAMS is an on-line transport model fully consistent with the simulated atmospheric dynamics. Emission sources from biomass burning and urban-industrial-vehicular activities for trace gases and from biomass burning aerosol particles are obtained from several published datasets and remote sensing information. The tracer and aerosol mass concentration prognostics include the effects of sub-grid scale turbulence in the planetary boundary layer, convective transport by shallow and deep moist convection, wet and dry deposition, and plume rise associated with vegetation fires in addition to the grid scale transport. The radiation parameterization takes into account the interaction between the simulated biomass burning aerosol particles and short and long wave radiation. The atmospheric model BRAMS is based on the Regional Atmospheric Modeling System (RAMS), with several improvements associated with cumulus convection representation, soil moisture initialization and surface scheme tuned for the tropics, among others. In this paper the CATT-BRAMS model is used to simulate carbon monoxide and particulate material (PM2.5) surface fluxes and atmospheric transport during the 2002 LBA field campaigns, conducted during the transition from the dry to wet season in the southwest Amazon Basin. Model evaluation is addressed with comparisons between model results and near surface, radiosondes and airborne measurements performed during the field campaign, as well as remote sensing derived products. We show the matching of emissions strengths to observed carbon monoxide in the LBA campaign. A relatively good comparison to the MOPITT data, in spite of the fact that MOPITT a priori assumptions imply several difficulties, is also obtained.

scholarly journals Using Remote Sensing to Identify Changes in Land Use and Sources of Fecal Bacteria to Support a Watershed Transport Model

Water ◽  
2014 ◽  
Vol 6 (7) ◽  
pp. 1925-1944 ◽  
Author(s):  
Sean Butler ◽  
Tim Webster ◽  
Anna Redden ◽  
Jennie Rand ◽  
Nathan Crowell ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Transport Model ◽  
Fecal Bacteria

scholarly journals Evaluation of balloon and satellite water vapour measurements in the Southern tropical UTLS during the HIBISCUS campaign

2007 ◽  
Vol 7 (3) ◽  
pp. 6037-6075 ◽  
Author(s):  
N. Montoux ◽  
A. Hauchecorne ◽  
J.-P. Pommereau ◽  
G. Durry ◽  
B. Morel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Vapour ◽  
Transport Model ◽  
Tunable Diode Laser ◽  
Upper Troposphere ◽  
Satellite Systems ◽  
Mixing Ratios ◽  
Tropical Tropopause ◽  
Dry Bias

Abstract. Among the objectives of the HIBISCUS campaign was the study of water vapour in the tropical upper troposphere and lower stratosphere (UTLS) by balloon borne in situ and remote sensing, offering a unique opportunity for evaluating the performances of balloon and satellite water vapour data available at the southern tropics in February-April 2004. Instruments evaluated include balloon borne in situ tunable diode laser spectrometer (μ SDLA) and surface acoustic wave hygrometer (SAW), and remote sensing with a near IR spectrometer (SAOZ) flown on a circumnavigating long duration balloon. The satellite systems available are those of AIRS/AMSU (v4), SAGE-II (v6.2), HALOE (v19), MIPAS (v4.62) and GOMOS (v6.0). In the stratosphere between 20–25 km, three satellite instruments, HALOE, SAGE-II and MIPAS, are showing very consistent results (nearly constant mixing ratios), while AIRS, GOMOS and the SAOZ balloon are displaying a slight increase with altitude. Considering the previous studies, the first three appear the most precise at this level, HALOE being the less variable (5%), close to the atmospheric variability shown by the REPROBUS/ECMWF Chemistry-Transport model. The three others are showing significantly larger variability, AIRS being the most variable (35%), followed by GOMOS (25%) and SAOZ (20%). Lower down in the Tropical Tropopause Layer between 14–20 km, HALOE and SAGE-II are showing marked minimum mixing ratios around 17–19 km, not seen by all others. For HALOE, this might be related to an altitude registration error already identified on ozone, while for SAGE-II, a possible explanation could be the persistence of the dry bias displayed by previous retrieval versions, not completely removed in version 6.2. On average, MIPAS is consistent with AIRS, GOMOS and SAOZ, not displaying the dry bias observed in past versions, but a fast degradation of precision below 20 km. Compared to satellites, the μ SDLA measurements shows systematically larger humidity although this conclusion may be biased by the fact that the balloon flights were carried out intentionally next or above strong convective systems where remote observations from space are difficult. In the upper troposphere below 14 km, all remote sensing measurements (except MIPAS of limited precision, and AIRS/AMSU) become rare, dry biased and less variable compared to ECMWF, but particularly HALOE and SAGE-II. The main reason for that is the frequent masking by clouds within which no remote measurements could be performed except by the AMSU microwave. Water vapour remote sensing profiles are representative of cloud free conditions only and thus dryer and less variable on average than ECMWF and AIRS/AMSU. Always in the upper troposphere, two in-situ instruments, μ SDLA and SAW, flown on the same balloon agree each other, displaying water vapour mixing ratios 100–200% larger than that of HALOE and MIPAS, which could be explained by the large ice supersaturation of the layer up to the tropopause, hardly detectable from the orbit.

scholarly journals The Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) – Part 1: Model description and evaluation

2007 ◽  
Vol 7 (3) ◽  
pp. 8525-8569 ◽  
Author(s):  
S. R. Freitas ◽  
K. M. Longo ◽  
M. A. F. Silva Dias ◽  
R. Chatfield ◽  
P. Silva Dias ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Monoxide ◽  
Transport Model ◽  
Aerosol Particles ◽  
Atmospheric Modeling ◽  
Wave Radiation ◽  
Model Description ◽  
Tracer Transport ◽  
Regional Atmospheric Modeling System ◽  
Regional Atmospheric Modeling

Abstract. We introduce the Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS). CATT-BRAMS is an on-line transport model fully consistent with the simulated atmospheric dynamics. Emission sources from biomass burning and urban-industrial-vehicular activities for trace gases and aerosol particles are obtained from several published datasets and remote sensing information. The tracer and aerosol mass concentration prognostic includes the effects of sub-grid scale turbulence in the planetary boundary layer, convective transport by shallow and deep moist convection, wet and dry deposition, and plume rise associated with vegetation fires in addition to the grid scale transport. The radiation parameterization takes into account the interaction between aerosol particles and short and long wave radiation. The atmospheric model BRAMS is based on the Regional Atmospheric Modeling System (RAMS), with several improvements associated with cumulus convection representation, soil moisture initialization and surface scheme tuned for the tropics, among others. In this paper the CATT-BRAMS model is used to simulate carbon monoxide and particulate material (PM2.5) surface fluxes and atmospheric transport during the 2002 LBA field campaigns, conducted during the transition from the dry to wet season in the southwest Amazon Basin. Model evaluation is addressed with comparisons between model results and near surface, radiosonde and airborne measurements performed during the field campaign, as well as remote sensing derived products. We show the matching of emissions strengths to observed carbon monoxide in the LBA campaign. A relatively good comparison to the MOPITT data, in spite of the fact that MOPITT a priori assumptions imply several difficulties, is also obtained.

Land-based remote sensing of snow for the validation of a snow transport model

2004 ◽  
Vol 39 (2-3) ◽  
pp. 93-104 ◽  
Author(s):  
Javier G. Corripio ◽  
Yves Durand ◽  
Gilbert Guyomarc'h ◽  
Laurent Mérindol ◽  
Dominique Lecorps ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Transport Model ◽  
Snow Transport
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