765 research outputs found
Matrix exponential in C/C++ version of vector radiative transfer code IPOL
No full textWe use only left eigenvectors to evaluate the matrix exponential in the method of discrete ordinates for the vector radiative transfer equation, which neglects circular polarization, in a plane-parallel atmosphere. This is contrary to a common practice of using the right eigenvectors to evaluate the matrix exponential combined with the left eigenvectors to avoid the inversion of the matrix of the right ones. Two numerical tests for Rayleigh and Aerosol scattering confirm our idea. For better explanation of our approach and for independent crosscheck of our results, we distribute an example in C/C++.The research of Alexei Lyapustin and Sergey Korkin was funded by NASA Science for Terra, Aqua and SNPP (17-TASNPP17-0116; solicitation NNH17ZDA001NTASNPP). The authors are thankful to an anonymous Reviewer for independent validation of our approach in his RT code and for valuable suggestions that helped clarify the paper.https://www.sciencedirect.com/science/article/pii/S002240731930012
The satellite-based remote sensing of particulate matter (PM) in support to urban air quality: PM variability and hot spots within the Cordoba city (Argentina) as revealed by the high-resolution MAIAC-algorithm retrievals applied to a ten-years dataset
No full textFil: Della Ceca, Lara Sofia. Comisión Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales Mario Gulich; Argentina.Fil: Carreras, Hebe A. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina.Fil: Carreras, Hebe A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Multidisciplinario de Biología Vegetal; Argentina.Fil: Lyapustin, Alexei I. GEST/UMBC, NASA Goddard Space Flight Center; Estados Unidos.Fil: Barnaba, Francesca. Italian National Research Council. Institute of Atmospheric Science and Climate; Italia.Particulate matter (PM) is one of the major harmful pollutants to public health and the environment [1]. In
developed countries, specific air-quality legislation establishes limit values for PM metrics (e.g., PM10, PM2.5)
to protect the citizens health (e.g., European Commission Directive 2008/50, US Clean Air Act). Extensive PM
measuring networks therefore exist in these countries to comply with the legislation. In less developed countries
air quality monitoring networks are still lacking and satellite-based datasets could represent a valid alternative to
fill observational gaps.Fil: Della Ceca, Lara Sofia. Comisión Nacional de Actividades Espaciales. Instituto de Altos Estudios Espaciales Mario Gulich; Argentina.Fil: Carreras, Hebe A. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina.Fil: Carreras, Hebe A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Multidisciplinario de Biología Vegetal; Argentina.Fil: Lyapustin, Alexei I. GEST/UMBC, NASA Goddard Space Flight Center; Estados Unidos.Fil: Barnaba, Francesca. Italian National Research Council. Institute of Atmospheric Science and Climate; Italia.Ciencias Medioambientales (los aspectos sociales van en 5.7 "Geografía Económica y Social
Satellite Observed Widespread Decline in Mongolian Grasslands Largely Due to Overgrazing
No full textThe Mongolian Steppe is one of the largest remaining grassland ecosystems. Recent studies have reported widespread decline of vegetation across the steppe and about 70 percent of this ecosystem is now considered degraded. Among the scientific community there has been an active debate about whether the observed degradation is related to climate, or overgrazing, or both. Here, we employ a new atmospheric correction and cloud screening algorithm (MAIAC) to investigate trends in satellite observed vegetation phenology. We relate these trends to changes in climate and domestic animal populations. A series of harmonic functions is fitted to MODIS observed phenological curves to quantify seasonal and inter-annual changes in vegetation. Our results show a widespread decline (of about 12 percent on average) in MODIS observed NDVI across the country but particularly in the transition zone between grassland and the Gobi desert, where recent decline was as much as 40 percent below the 2002 mean NDVI. While we found considerable regional differences in the causes of landscape degradation, about 80 percent of the decline in NDVI could be attributed to increase in livestock. Changes in precipitation were able to explain about 30 percent of degradation across the country as a whole but up to 50 percent in areas with denser vegetation cover (p0.05). Temperature changes, while significant, played only a minor role (r20.10, p0.05). Our results suggest that the cumulative effect of overgrazing is a primary contributor to the degradation of the Mongolian steppe and is at least partially responsible for desertification reported in previous studies
Ep. #072 - Alexei Yurchak
No full textThis recording and transcript form part of a collection of podcasts conducted by the Cultures of Energy at Rice University. Cultures of Energy brings writers, artists and scholars together to talk, think and feel their way into the Anthropocene. We cover serious issues like climate change, species extinction and energy transition. But we also try to confront seemingly huge and insurmountable problems with insight, creativity and laughter.To help us sort through a week dominated by spiraling Russo-American political intrigue, we welcome (13:01) to the podcast Berkeley anthropologist, Alexei Yurchak, analyst extraordinaire of all things late Soviet and post Soviet, and author of the award-winning Everything was Forever Until It Was No More: The Last Soviet Generation (Princeton, 2005). We trace the connections between that project’s exploration of culture and politics at the end of state socialism and Alexei’s current research on the scientists who have been working to preserve Lenin’s body since 1924. We talk about the fascinating intersection of biopolitics and necropolitics involved in the effort to maintain Lenin’s body in a lifelike state for almost a century, how discursive hegemony of form in the late Soviet period also informed corporeal hegemony of form, the results of this science that you can find in your own pharmacy, and the network of political leaders’ bodies across the world that Soviet and now Russian scientists have worked to preserve. Alexei dispels the idea that cloning was ever on the table in this project; but explains that his interlocutors do believe that they can now keep Lenin’s body in a near-life state in perpetuity. We return from there to the contemporary political chaos and what Alexei makes of the Trump-Putin entanglement stories currently dominating the headlines. Alexei shares his concerns about the powerful return of Russophobia to the United States, about what popular characterizations of Russia get wrong, and about how anti-Russian sentiment may provide a convenient excuse to defer a serious examination of the root causes of Trumpism. Ready to take a break from the political hysteria? Then listen on
Seasonal monitoring and estimation of regional aerosol distribution over Po valley, northern Italy, using a high-resolution MAIAC product
No full textIn this work, the new 1 km-resolved Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is employed to characterize seasonal PM10 - AOD correlations over northern Italy. The accuracy of the new dataset is assessed compared to the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5.1 Aerosol Optical Depth (AOD) data, retrieved at 0.55 μm with spatial resolution of 10 km (MYD04_L2). We focused on evaluating the ability of these two products to characterize both temporal and spatial distributions of aerosols within urban and suburban areas. Ground PM10 measurements were obtained from 73 of the Italian Regional Agency for Environmental Protection (ARPA) monitoring stations, spread across northern Italy, during a three-year period from 2010 to 2012. The Po Valley area (northern Italy) was chosen as the study domain because of its severe urban air pollution, resulting from it having the highest population and industrial manufacturing density in the country, being located in a valley where two surrounding mountain chains favor the stagnation of pollutants. We found that the global correlations between the bin-averaged PM10 and AOD are R2 = 0.83 and R2 = 0.44 for MYD04_L2 and for MAIAC, respectively, suggesting a greater sensitivity of the high-resolution product to small-scale deviations. However, the introduction of Relative Humidity (RH) and Planetary Boundary Layer (PBL) depth corrections allowed for a significant improvement to the bin-averaged PM - AOD correlation, which led to a similar performance: R2 = 0.96 for MODIS and R2 = 0.95 for MAIAC. Furthermore, the introduction of the PBL information in the corrected AOD values was found to be crucial in order to capture the clear seasonal cycle shown by measured PM10 values. The study allowed us to define four seasonal linear correlations that estimate PM10 concentrations satisfactorily from the remotely sensed MAIAC AOD retrieval. Overall, the results show that the high resolution provided by MAIAC retrieval data is much more relevant than the 10 km MODIS data to characterize PM10 in this region of Italy which has a pretty limited geographical domain but a broad variety of land usages and consequent particulate concentrations
Remote Sensing of Tropical Ecosystems: Atmospheric Correction and Cloud Masking Matter
No full textTropical rainforests are significant contributors to the global cycles of energy, water and carbon. As a result, monitoring of the vegetation status over regions such as Amazonia has been a long standing interest of Earth scientists trying to determine the effect of climate change and anthropogenic disturbance on the tropical ecosystems and its feedback on the Earth's climate. Satellite-based remote sensing is the only practical approach for observing the vegetation dynamics of regions like the Amazon over useful spatial and temporal scales, but recent years have seen much controversy over satellite-derived vegetation states in Amaznia, with studies predicting opposite feedbacks depending on data processing technique and interpretation. Recent results suggest that some of this uncertainty could stem from a lack of quality in atmospheric correction and cloud screening. In this paper, we assess these uncertainties by comparing the current standard surface reflectance products (MYD09, MYD09GA) and derived composites (MYD09A1, MCD43A4 and MYD13A2 - Vegetation Index) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to results obtained from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. MAIAC uses a new cloud screening technique, and novel aerosol retrieval and atmospheric correction procedures which are based on time-series and spatial analyses. Our results show considerable improvements of MAIAC processed surface reflectance compared to MYD09/MYD13 with noise levels reduced by a factor of up to 10. Uncertainties in the current MODIS surface reflectance product were mainly due to residual cloud and aerosol contamination which affected the Normalized Difference Vegetation Index (NDVI): During the wet season, with cloud cover ranging between 90 percent and 99 percent, conventionally processed NDVI was significantly depressed due to undetected clouds. A smaller reduction in NDVI due to increased aerosol levels was observed during the dry season, with an inverse dependence of NDVI on aerosol optical thickness (AOT). NDVI observations processed with MAIAC showed highly reproducible and stable inter-annual patterns with little or no dependence on cloud cover, and no significant dependence on AOT (p less than 0.05). In addition to a better detection of cloudy pixels, MAIAC obtained about 20-80 percent more cloud free pixels, depending on season, a considerable amount for land analysis given the very high cloud cover (75-99 percent) observed at any given time in the area. We conclude that a new generation of atmospheric correction algorithms, such as MAIAC, can help to dramatically improve vegetation estimates over tropical rain forest, ultimately leading to reduced uncertainties in satellite-derived vegetation products globally
Assessing Uncertainties of a Geophysical Approach to Estimate Surface Fine Particulate Matter Distributions from Satellite-Observed Aerosol Optical Depth
No full textHealth impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM2.5). We use a forward geophysical approach to derive ground-level PM2.5 distributions from satellite AOD at 1 km2(exp) resolution for 2011 over the northeastern US by applying relationships between surface PM2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 1212 km2(exp) horizontal resolution). Seasonal average satellite-derived PM2.5 reveals more spatial detail and best captures observed surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5AOD relationships. Overall, we estimate that uncertainties in the modeled PM2.5AOD lead to an error of 11 g m3(exp) in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8 g m3(exp). Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM2.5AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model representation of relative humidity and aerosol vertical profile shape contributes some systematic biases. The parameterization of aerosol optical properties, which determines the mass extinction efficiency, also contributes to random uncertainty, with the size distribution being the largest source of uncertainty and hygroscopicity of inorganic salt the second largest. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5
MODIS Collection 6 MAIAC algorithm
No full textThis paper describes the latest version of the algorithm MAIAC used for processing the MODIS Collection 6 data record. Since initial publication in 2011–2012, MAIAC has changed considerably to adapt to global processing and improve cloud/snow detection, aerosol retrievals and atmospheric correction of MODIS data. The main changes include (1) transition from a 25 to 1 km scale for retrieval of the spectral regression coefficient (SRC) which helped to remove occasional blockiness at 25 km scale in the aerosol optical depth (AOD) and in the surface reflectance, (2) continuous improvements of cloud detection, (3) introduction of smoke and dust tests to discriminate absorbing fine- and coarse-mode aerosols, (4) adding over-water processing, (5) general optimization of the LUT-based radiative transfer for the global processing, and others. MAIAC provides an interdisciplinary suite of atmospheric and land products, including cloud mask (CM), column water vapor (CWV), AOD at 0.47 and 0.55 µm, aerosol type (background, smoke or dust) and fine-mode fraction over water; spectral bidirectional reflectance factors (BRF), parameters of Ross-thick Li-sparse (RTLS) bidirectional reflectance distribution function (BRDF) model and instantaneous albedo. For snow-covered surfaces, we provide subpixel snow fraction and snow grain size. All products come in standard HDF4 format at 1 km resolution, except for BRF, which is also provided at 500 m resolution on a sinusoidal grid adopted by the MODIS Land team. All products are provided on per-observation basis in daily files except for the BRDF/Albedo product, which is reported every 8 days. Because MAIAC uses a time series approach, BRDF/Albedo is naturally gap-filled over land where missing values are filled-in with results from the previous retrieval. While the BRDF model is reported for MODIS Land bands 1–7 and ocean band 8, BRF is reported for both land and ocean bands 1–12. This paper focuses on MAIAC cloud detection, aerosol retrievals and atmospheric correction and describes MCD19 data products and quality assurance (QA) flags.The research of Alexei Lyapustin, Yujie Wang
and Sergey Korkin was funded by NASA Science for Terra, Aqua
and SNPP (17-TASNPP17-0116; solicitation NNH17ZDA001NTASNPP). Alexei Lyapustin was additionally supported by the
NASA GeoCAPE program. The work of Dong Huang was funded
by the NASA DSCOVR program. We appreciate the large amount
of work from the MODAPS team on MAIAC integration, in
particular the support of Ed Masuoka and Sadashiva Devadiga, and
the support of LP DAAC. The lasting support of the NASA Center
for Climate Simulations in continental-scale testing and multiple
internal releases of MAIAC data has been invaluable. We are
grateful to the AERONET team for providing validation data. We
appreciate help of Andy Sayer, comments/edits by Jeff Reid and an
anonymous reviewer who helped to improve the paper. Lastly, we
would like to express gratitude to multiple users and user groups
in the land and air quality communities whose continuous analysis
of MAIAC MODIS data helped to bring MAIAC to its current level.https://amt.copernicus.org/articles/11/5741/2018
Consistency of vegetation index seasonality across the Amazon rainforest
No full textVegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing
A practical guide to coding line-by-line trace gas absorption in Earth's atmosphere
No full textWe present two new open-source codes, in the C language, for simulation of the line-by-line molecular (gas) absorption in the solar spectral region with wavelengths up to ∼2500 (nm). The first one, gcell, simulates absorption spectroscopy in a gas cell for a given length of the cell, temperature, and pressure. The second one, aspect, is for spectroscopy in Earth's atmosphere - a common need for remote sensing applications. Both use the HITRAN database for line shape (Voigt) modeling. Aspect adapts height variations of the thermodynamic parameters (profiles) from MODTRAN. Separate discussion of the gas cell and the atmospheric modes simplifies software development, documentation, and support, and ultimately the transfer of knowledge between generations of scientists. These are the main goals of the current paper. Despite the existence of numerous computer programs for absorption spectroscopy, the code development process is poorly covered in literature. As a result, it is difficult for a non-developer to confidently modify an existing code or create a new tool within a reasonable amount of time.This work received no target funding (see Conclusion: last paragraph). However, the work of S. Korkin was partially supported by NASA Atmosphere Observing System (AOS) mission; the work of A. Lyapustin and S. Korkin was partially supported by NASA VIIRS, DSCOVR, and PACE programs via respective ROSES proposals (PI: A. Lyapustin); the work of A. M. Sayer and A. Ibrahim was partially supported by NASA PACE Project Science.https://www.sciencedirect.com/science/article/pii/S002240732500007
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