scholarly journals A novel methodology using MODIS and CERES for assessing the daily radiative forcing of smoke aerosols in large scale over the Amazonia

2014 ◽  
Vol 14 (22) ◽  
pp. 31515-31550
Author(s):  
E. T. Sena ◽  
P. Artaxo
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Radiative Transfer ◽  
Biomass Burning ◽  
Satellite Remote Sensing ◽  
Radiative Forcing ◽  
Large Scale ◽  
High Temporal Resolution ◽  
Modis Aod ◽  
The Impact

Abstract. A new methodology was developed for obtaining daily retrievals of the direct radiative forcing of aerosols (24h-DARF) at the top of the atmosphere (TOA) using satellite remote sensing. For that, simultaneous CERES (Clouds and Earth's Radiant Energy System) shortwave flux at the top of the atmosphere (TOA) and MODIS (Moderate Resolution Spectroradiometer) aerosol optical depth (AOD) retrievals were used. This methodology is applied over a large region of Brazilian Amazonia. We focused our studies on the peak of the biomass burning season (August to September) from 2000 to 2009 to analyse the impact of forest smoke on the radiation balance. To assess the spatial distribution of the DARF, background scenes without biomass burning impacts, were defined as scenes with MODIS AOD < 0.1. The fluxes at the TOA retrieved by CERES for those clean conditions (Fcl) were estimated as a function of the illumination geometry (θ0) for each 0.5° × 0.5° grid cell. The instantaneous DARF was obtained as the difference between clean Fcl (θ0) and the polluted mean flux at the TOA measured by CERES in each cell (Fpol (θ0)). The radiative transfer code SBDART (Santa Barbara DISORT Radiative Transfer model) was used to expand instantaneous DARFs to 24 h averages. With this methodology it is possible to assess the DARF both at large scale and at high temporal resolution. This new methodology also showed to be more robust, because it considerably reduces statistical sources of uncertainties in the estimates of the DARF, when compared to previous assessments of the DARF using satellite remote sensing. The spatial distribution of the 24h-DARF shows that, for some cases, the mean 24h-DARF presents local values as high as −30 W m−2. The temporal variability of the 24h-DARF along the biomass burning season was also studied and showed large intraseasonal and interannual variability. In an attempt to validate the radiative forcing obtained in this work using CERES and MODIS, those results were compared to coincident AERONET ground based estimates of the DARF. This analysis showed that CERES-MODIS and AERONET 24h-DARF are related as DARFCERES-MODIS24 h = (1.07 ± 0.04)DARFAERONET24 h −(0.0 ± 0.6). This is a significant result, considering that the 24h-DARF retrievals were obtained by applying completely different methodologies, and using different instruments. The instantaneous CERES-MODIS DARF was also compared with radiative transfer evaluations of the forcing. To validate the aerosol and surface models used in the simulations, downward shortwave fluxes at the surface evaluated using SBDART and measured by pyranometers were compared. The simulated and measured downward fluxes are related through FBOAPYRANOMETER = (1.00 ± 0.04)FBOASBDART −(20 ± 27), indicating that the models and parameters used in the simulations were consistent. The relationship between CERES-MODIS instantaneous DARF and calculated SBDART forcing was satisfactory, with DARFCERES-MODIS = (0.86 ± 0.06)DARFSBDART −(6 ± 2). Those analysis showed a good agreement between satellite remote sensing, ground-based and radiative transfer evaluated DARF, demonstrating the robustness of the new proposed methodology for calculated radiative forcing for biomass burning aerosols. To our knowledge, this was the first time satellite remote sensing assessments of the DARF were compared with ground based DARF estimates.

scholarly journals A novel methodology for large-scale daily assessment of the direct radiative forcing of smoke aerosols

2015 ◽  
Vol 15 (10) ◽  
pp. 5471-5483 ◽  
Author(s):  
E. T. Sena ◽  
P. Artaxo
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Radiative Transfer ◽  
Biomass Burning ◽  
Satellite Remote Sensing ◽  
Radiative Forcing ◽  
High Temporal Resolution ◽  
Radiation Balance ◽  
Direct Radiative Forcing ◽  
The Impact

Abstract. A new methodology was developed for obtaining daily retrievals of the direct radiative forcing of aerosols (24h-DARF) at the top of the atmosphere (TOA) using satellite remote sensing. Simultaneous CERES (Clouds and Earth's Radiant Energy System) shortwave flux at the top of the atmosphere and MODIS (Moderate Resolution Spectroradiometer) aerosol optical depth (AOD) retrievals were used. To analyse the impact of forest smoke on the radiation balance, this methodology was applied over the Amazonia during the peak of the biomass burning season from 2000 to 2009. To assess the spatial distribution of the DARF, background smoke-free scenes were selected. The fluxes at the TOA under clean conditions (Fcl) were estimated as a function of the illumination geometry (θ0) for each 0.5° × 0.5° grid cell. The instantaneous DARF was obtained as the difference between the clean (Fcl (θ0)) and the polluted flux at the TOA measured by CERES in each cell (Fpol (θ0)). The radiative transfer code SBDART (Santa Barbara DISORT Radiative Transfer model) was used to expand instantaneous DARFs to 24 h averages. This new methodology was applied to assess the DARF both at high temporal resolution and over a large area in Amazonia. The spatial distribution shows that the mean 24h-DARF can be as high as −30 W m−2 over some regions. The temporal variability of the 24h-DARF along the biomass burning season was also studied and showed large intraseasonal and interannual variability. We showed that our methodology considerably reduces statistical sources of uncertainties in the estimate of the DARF, when compared to previous approaches. DARF assessments using the new methodology agree well with ground-based measurements and radiative transfer models. This demonstrates the robustness of the new proposed methodology for assessing the radiative forcing for biomass burning aerosols. To our knowledge, this is the first time that satellite remote sensing assessments of the DARF have been compared with ground-based DARF estimates.

scholarly journals Reconciling Ground-Based and Space-Based Estimates of the Frequency of Occurrence and Radiative Effect of Clouds around Darwin, Australia

2014 ◽  
Vol 53 (2) ◽  
pp. 456-478 ◽  
Author(s):  
A. Protat ◽  
S. A. Young ◽  
S. A. McFarlane ◽  
T. L’Ecuyer ◽  
G. G. Mace ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Radiative Forcing ◽  
Large Scale ◽  
Radiative Heating ◽  
Orthogonal Polarization ◽  
Frequency Of Occurrence ◽  
Radiative Fluxes ◽  
Radiative Impact ◽  
Satellite Radar

AbstractThe objective of this paper is to investigate whether estimates of the cloud frequency of occurrence and associated cloud radiative forcing as derived from ground-based and satellite active remote sensing and radiative transfer calculations can be reconciled over a well-instrumented active remote sensing site located in Darwin, Australia, despite the very different viewing geometry and instrument characteristics. It is found that the ground-based radar–lidar combination at Darwin does not detect most of the cirrus clouds above 10 km (because of limited lidar detection capability and signal obscuration by low-level clouds) and that the CloudSat radar–Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) combination underreports the hydrometeor frequency of occurrence below 2-km height because of instrument limitations at these heights. The radiative impact associated with these differences in cloud frequency of occurrence is large on the surface downwelling shortwave fluxes (ground and satellite) and the top-of-atmosphere upwelling shortwave and longwave fluxes (ground). Good agreement is found for other radiative fluxes. Large differences in radiative heating rate as derived from ground and satellite radar–lidar instruments and radiative transfer calculations are also found above 10 km (up to 0.35 K day−1 for the shortwave and 0.8 K day−1 for the longwave). Given that the ground-based and satellite estimates of cloud frequency of occurrence and radiative impact cannot be fully reconciled over Darwin, caution should be exercised when evaluating the representation of clouds and cloud–radiation interactions in large-scale models, and limitations of each set of instrumentation should be considered when interpreting model–observation differences.

scholarly journals Radiative impact of an extreme Arctic biomass-burning event

2018 ◽  
Vol 18 (12) ◽  
pp. 8829-8848 ◽  
Author(s):  
Justyna Lisok ◽  
Anna Rozwadowska ◽  
Jesper G. Pedersen ◽  
Krzysztof M. Markowicz ◽  
Christoph Ritter ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Biomass Burning ◽  
Radiative Forcing ◽  
Radiation Budget ◽  
Plane Parallel ◽  
Direct Radiative Forcing ◽  
The Mean ◽  
The Impact

Abstract. The aim of the presented study was to investigate the impact on the radiation budget of a biomass-burning plume, transported from Alaska to the High Arctic region of Ny-Ålesund, Svalbard, in early July 2015. Since the mean aerosol optical depth increased by the factor of 10 above the average summer background values, this large aerosol load event is considered particularly exceptional in the last 25 years. In situ data with hygroscopic growth equations, as well as remote sensing measurements as inputs to radiative transfer models, were used, in order to estimate biases associated with (i) hygroscopicity, (ii) variability of single-scattering albedo profiles, and (iii) plane-parallel closure of the modelled atmosphere. A chemical weather model with satellite-derived biomass-burning emissions was applied to interpret the transport and transformation pathways. The provided MODTRAN radiative transfer model (RTM) simulations for the smoke event (14:00 9 July–11:30 11 July) resulted in a mean aerosol direct radiative forcing at the levels of −78.9 and −47.0 W m−2 at the surface and at the top of the atmosphere, respectively, for the mean value of aerosol optical depth equal to 0.64 at 550 nm. This corresponded to the average clear-sky direct radiative forcing of −43.3 W m−2, estimated by radiometer and model simulations at the surface. Ultimately, uncertainty associated with the plane-parallel atmosphere approximation altered results by about 2 W m−2. Furthermore, model-derived aerosol direct radiative forcing efficiency reached on average −126 W m-2/τ550 and −71 W m-2/τ550 at the surface and at the top of the atmosphere, respectively. The heating rate, estimated at up to 1.8 K day−1 inside the biomass-burning plume, implied vertical mixing with turbulent kinetic energy of 0.3 m2 s−2.

scholarly journals Evaluation of climate model aerosol seasonal and spatial variability over Africa using AERONET

2017 ◽  
Vol 17 (22) ◽  
pp. 13999-14023 ◽  
Author(s):  
Hannah M. Horowitz ◽  
Rebecca M. Garland ◽  
Marcus Thatcher ◽  
Willem A. Landman ◽  
Zane Dedekind ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Radiative Forcing ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Aerosol Particles ◽  
Atmospheric Model ◽  
Model Aerosol ◽  
The Impact

Abstract. The sensitivity of climate models to the characterization of African aerosol particles is poorly understood. Africa is a major source of dust and biomass burning aerosols and this represents an important research gap in understanding the impact of aerosols on radiative forcing of the climate system. Here we evaluate the current representation of aerosol particles in the Conformal Cubic Atmospheric Model (CCAM) with ground-based remote retrievals across Africa, and additionally provide an analysis of observed aerosol optical depth at 550 nm (AOD550 nm) and Ångström exponent data from 34 Aerosol Robotic Network (AERONET) sites. Analysis of the 34 long-term AERONET sites confirms the importance of dust and biomass burning emissions to the seasonal cycle and magnitude of AOD550 nm across the continent and the transport of these emissions to regions outside of the continent. In general, CCAM captures the seasonality of the AERONET data across the continent. The magnitude of modeled and observed multiyear monthly average AOD550 nm overlap within ±1 standard deviation of each other for at least 7 months at all sites except the Réunion St Denis Island site (Réunion St. Denis). The timing of modeled peak AOD550 nm in southern Africa occurs 1 month prior to the observed peak, which does not align with the timing of maximum fire counts in the region. For the western and northern African sites, it is evident that CCAM currently overestimates dust in some regions while others (e.g., the Arabian Peninsula) are better characterized. This may be due to overestimated dust lifetime, or that the characterization of the soil for these areas needs to be updated with local information. The CCAM simulated AOD550 nm for the global domain is within the spread of previously published results from CMIP5 and AeroCom experiments for black carbon, organic carbon, and sulfate aerosols. The model's performance provides confidence for using the model to estimate large-scale regional impacts of African aerosols on radiative forcing, but local feedbacks between dust aerosols and climate over northern Africa and the Mediterranean may be overestimated.

scholarly journals Understanding the seasonality and climatology of aerosols in Africa through evaluation of CCAM aerosol simulations against AERONET measurements

2017 ◽  
Author(s):  
Hannah M. Horowitz ◽  
Rebecca M. Garland ◽  
Marcus Thatcher ◽  
Willem A. Landman ◽  
Zane Dedekind ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Radiative Forcing ◽  
Large Scale ◽  
Climate Models ◽  
Aerosol Particles ◽  
Atmospheric Model ◽  
Reunion Island ◽  
Réunion Island ◽  
The Impact

Abstract. The sensitivity of climate models to the characterization of African aerosol particles is poorly understood. Africa is a major source of dust and biomass burning aerosols and so this represents an important research gap in understanding the impact of aerosols on radiative forcing of the climate system. Here we evaluate the current representation of aerosol particles in the Conformal Cubic Atmospheric Model (CCAM) with ground-based observations across Africa, and additionally provide an analysis of aerosol optical depth at 550 nm (AOD550nm) and Ångström exponent data from thirty-four Aerosol Robotic Network (AERONET) sites. Analysis of the 34 long-term AERONET sites confirms the importance of dust and biomass burning emissions to the seasonal cycle and magnitude of AOD550nm across the continent and the transport of these emissions to regions outside of the continent. Western African sites had the largest AOD550nm values, on average, with the timing and magnitude of AOD550nm maxima dominated by desert dust. The impact of dust on aerosol loading is also apparent at northern African sites, with peak AOD550nm occurring later than the western sites. The seasonal variation in the location of the intertropical convergence zone and associated northward shift in dust transport may be responsible for the shift in timing of maximum AOD550nm between the western and northern African sites. Southern African sites have the lowest AOD550nm values on average, and peak during the biomass burning period. The outflow of these aerosol particles was observed at Ascension Island and Reunion Island AERONET stations. In general, CCAM captures well the seasonality of the AERONET data across the continent. The magnitude of modeled and observed multi-year monthly average AOD550nm overlap within ±1 standard deviation of each other for at least 7 months at all sites except Reunion Island. The timing of peak AOD550nm in southern Africa in the model occurs one month prior to the observed peak, which does not align with the timing of maximum fire counts in the region. For the western and northern African sites, it is evident that CCAM currently overestimates dust in some regions while others (e.g., the Arabian Peninsula) are better characterized. This may be due to overestimated dust lifetime, or that the characterization of the soil for these areas needs to be updated with local information. The CCAM simulated AOD550nm for the global domain is within the spread of previously published results from CMIP5 and AeroCom experiments for black carbon, organic carbon and sulfate aerosols. The model’s performance provides confidence for using the model to estimate large-scale regional impacts of African aerosols on radiative forcing, but local feedbacks between dust aerosols and climate over northern Africa and the Mediterranean may be overestimated.

scholarly journals Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3982
Author(s):  
Giacomo Lazzeri ◽  
William Frodella ◽  
Guglielmo Rossi ◽  
Sandro Moretti
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Fire Severity ◽  
Experimental Methodology ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

2021 ◽  
Author(s):  
Ilaria Clemenzi ◽  
David Gustafsson ◽  
Jie Zhang ◽  
Björn Norell ◽  
Wolf Marchand ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Accurate Estimation ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Distribution ◽  
The Impact

&lt;p&gt;Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake &amp;#214;veruman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km&lt;sup&gt;2&lt;/sup&gt; grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.&lt;/p&gt;

scholarly journals Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3232 ◽  
Author(s):  
Yan Liu ◽  
Qirui Ren ◽  
Jiahui Geng ◽  
Meng Ding ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Training Data ◽  
Land Resources ◽  
Remote Sensing Images ◽  
Training Strategy ◽  
The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

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