Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Deep Blue”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Absorption Properties of Mediterranean Aerosols Obtained from Multi-year Ground-based and Satellite Remote Sensing Observations

Aerosol absorption properties are of high importance to assess aerosol impact on regional climate. This study presents an analysis of aerosol absorption products obtained over the Mediterranean Basin or land stations in the region from multi-year ground-based AERONET and satellite observations with a focus on the Absorbing Aerosol Optical Depth (AAOD), Single Scattering Albedo (SSA) and their spectral dependence. The AAOD and Absorption Angstrom Exponent (AAE) data set is composed of daily averaged AERONET level 2 data from a total of 22 Mediterranean stations having long time series, mainly under the influence of urban-industrial aerosols and/or soil dust. This data set covers the 17 yr period 1996-2012 with most data being from 2003-2011 (approximately 89 percent of level-2 AAOD data). Since AERONET level-2 absorption products require a high aerosol load (AOD at 440 nm greater than 0.4), which is most often related to the presence of desert dust, we also consider level-1.5 SSA data, despite their higher uncertainty, and filter out data with an Angstrom exponent less than 1.0 in order to study absorption by carbonaceous aerosols. The SSA data set includes both AERONET level-2 and satellite level-3 products. Satellite-derived SSA data considered are monthly level 3 products mapped at the regional scale for the spring and summer seasons that exhibit the largest aerosol loads. The satellite SSA dataset includes the following products: (i) Multi-angle Imaging SpectroRadiometer (MISR) over 2000-2011, (ii) Ozone Monitoring Instrument (OMI) near-UV algorithm over 2004-2010, and (iii) MODerate resolution Imaging Spectroradiometer (MODIS) Deep-Blue algorithm over 2005-2011, derived only over land in dusty conditions. Sun-photometer observations show that values of AAOD at 440 nm vary between 0.024 +/- 0.01 (resp. 0.040 +/- 0.01) and 0.050 +/- 0.01 (0.055 +/- 0.01) for urban (dusty) sites. Analysis shows that the Mediterranean urban-industrial aerosols appear "moderately" absorbing with values of SSA close to approximately 0.94-0.95 +/- 0.04 (at 440 nm) in most cases except over the large cities of Rome and Athens, where aerosol appears more absorbing (SSA approximately 0.89-0.90 +/- 0.04). The aerosol Absorption Angstrom Exponent (AAE, estimated using 440 and 870 nm) is found to be larger than 1 for most sites over the Mediterranean, a manifestation of mineral dust (iron) and/or brown carbon producing the observed absorption. AERONET level-2 sun-photometer data indicate the existence of a moderate East-West gradient, with higher values over the eastern basin (AAEEast. = 1.39/AAEWest. = 1.33) due to the influence of desert dust. The North-South AAE gradient is more pronounced, especially over the western basin. Our additional analysis of AERONET level-1.5 data also shows that organic absorbing aerosols significantly affect some Mediterranean sites. These results indicate that current climate models treating organics as nonabsorbing over the Mediterranean certainly underestimate the warming effect due to carbonaceous aerosols. A~comparative analysis of the regional SSA variability has been attempted using satellite data. OMI and MODIS data show an absorbing zone (SSA approximately 0.90 at 470-500 nm) over Northeastern Africa that does not appear in the MISR retrievals. In contrast, MISR seems able to observe the East-West SSA gradient during summer, as also detected by AERONET. Also, the analysis of SSA provided by satellites indicates that the aerosol over the Mediterranean Sea appears less absorbing during spring (MAM) than summer (JJA).

satellite remote sensing observations↗
NASA NTRS2012

Top-down Estimate of Dust Emissions Through Integration of MODIS and MISR Aerosol Retrievals With the Geos-chem Adjoint Model

Predicting the influences of dust on atmospheric composition, climate, and human health requires accurate knowledge of dust emissions, but large uncertainties persist in quantifying mineral sources. This study presents a new method for combined use of satellite-measured radiances and inverse modeling to spatially constrain the amount and location of dust emissions. The technique is illustrated with a case study in May 2008; the dust emissions in Taklimakan and Gobi deserts are spatially optimized using the GEOSChem chemical transport model and its adjoint constrained by aerosol optical depth (AOD) that are derived over the downwind dark-surface region in China from MODIS (Moderate Resolution Imaging Spectroradiometer) reflectance with the aerosol single scattering properties consistent with GEOS-chem. The adjoint inverse modeling yields an overall 51% decrease in prior dust emissions estimated by GEOS-Chem over the Taklimakan-Gobi area, with more significant reductions south of the Gobi Desert. The model simulation with optimized dust emissions shows much better agreement with independent observations from MISR (Multi-angle Imaging SpectroRadiometer) AOD and MODIS Deep Blue AOD over the dust source region and surface PM10 concentrations. The technique of this study can be applied to global multi-sensor remote sensing data for constraining dust emissions at various temporal and spatial scales, and hence improving the quantification of dust effects on climate, air quality, and human health.

MODIS aerosol retrievals↗
NASA NTRS2014

Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data: Part III. Using Combined PCA to Compare Spatiotemporal Variability of MODIS, MISR and OMI Aerosol Optical Depth

Satellite measurements of global aerosol properties are very useful in constraining aerosol parameterization in climate models. The reliability of different data sets in representing global and regional aerosol variability becomes an essential question. In this study, we present the results of a comparison using combined principal component analysis (CPCA), applied to monthly mean, mapped (Level 3) aerosol optical depth (AOD) product from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Ozone Monitoring Instrument (OMI). This technique effectively finds the common space-time variability in the multiple data sets by decomposing the combined AOD field. The results suggest that all of the sensors capture the globally important aerosol regimes, including dust, biomass burning, pollution, and mixed aerosol types. Nonetheless, differences are also noted. Specifically, compared with MISR and OMI, MODIS variability is significantly higher over South America, India, and the Sahel. MODIS deep blue AOD has a lower seasonal variability in North Africa, accompanied by a decreasing trend that is not found in either MISR or OMI AOD data. The narrow swath of MISR results in an underestimation of dust variability over the Taklamakan Desert. The MISR AOD data also exhibit overall lower variability in South America and the Sahel. OMI does not capture the Russian wild fire in 2010 nor the phase shift in biomass burning over East South America compared to Central South America, likely due to cloud contamination and the OMI row anomaly. OMI also indicates a much stronger (boreal) winter peak in South Africa compared with MODIS and MISR.

satellite observation↗
NASA NTRS2014

Retrieval of Aerosol Optical Properties under Thin Cirrus from MODIS

Retrieval of aerosol optical properties using shortwave bands from passive satellite sensors, such as MODIS, is typically limited to cloud-free areas. However, if the clouds are thin enough (i.e. thin cirrus) such that the satellite-observed reflectance contains signals under the cirrus layer, and if the optical properties of this cirrus layer are known, the TOA reflectance can be corrected for the cirrus layer to be used for retrieving aerosol optical properties. To this end, we first correct the TOA reflectances in the aerosol bands (0.47, 0.55, 0.65, 0.86, 1.24, 1.63, and 2.12 micron for ocean algorithm and 0.412, 0.47, and 0.65 micron for deep blue algorithm) for the effects of thin cirrus using 1.38 micron reflectance and conversion factors that convert cirrus reflectance in 1.38 micron band to those in aerosol bands. It was found that the conversion factors can be calculated by using relationships between reflectances in 1.38 micron band and minimum reflectances in the aerosol bands (Gao et al., 2002). Refer to the example in the figure. Then, the cirrus-corrected reflectance can be calculated by subtracting the cirrus reflectance from the TOA reflectance in the optically thin case. A sensitivity study suggested that cloudy-sky TOA reflectances can be calculated with small errors in the form of simple linear addition of cirrus-only reflectances and clear-sky reflectances. In this study, we correct the cirrus signals up to TOA reflectance at 1.38 micron of 0.05 where the simple linear addition is valid without extensive radiative transfer simulations. When each scene passes the set of tests shown in the flowchart, the scene is corrected for cirrus contamination and passed into aerosol retrieval algorithms.

MODIS↗
NASA NTRS2013

The Collection 6 'dark-target' MODIS Aerosol Products

Aerosol retrieval algorithms are applied to Moderate resolution Imaging Spectroradiometer (MODIS) sensors on both Terra and Aqua, creating two streams of decade-plus aerosol information. Products of aerosol optical depth (AOD) and aerosol size are used for many applications, but the primary concern is that these global products are comprehensive and consistent enough for use in climate studies. One of our major customers is the international modeling comparison study known as AEROCOM, which relies on the MODIS data as a benchmark. In order to keep up with the needs of AEROCOM and other MODIS data users, while utilizing new science and tools, we have improved the algorithms and products. The code, and the associated products, will be known as Collection 6 (C6). While not a major overhaul from the previous Collection 5 (C5) version, there are enough changes that there are significant impacts to the products and their interpretation. In its entirety, the C6 algorithm is comprised of three sub-algorithms for retrieving aerosol properties over different surfaces: These include the dark-target DT algorithms to retrieve over (1) ocean and (2) vegetated-dark-soiled land, plus the (3) Deep Blue (DB) algorithm, originally developed to retrieve over desert-arid land. Focusing on the two DT algorithms, we have updated assumptions for central wavelengths, Rayleigh optical depths and gas (H2O, O3, CO2, etc.) absorption corrections, while relaxing the solar zenith angle limit (up to 84) to increase pole-ward coverage. For DT-land, we have updated the cloud mask to allow heavy smoke retrievals, fine-tuned the assignments for aerosol type as function of season location, corrected bugs in the Quality Assurance (QA) logic, and added diagnostic parameters such as topographic altitude. For DT-ocean, improvements include a revised cloud mask for thin-cirrus detection, inclusion of wind speed dependence in the retrieval, updates to logic of QA Confidence flag (QAC) assignment, and additions of important diagnostic information. At the same time as we have introduced algorithm changes, we have also accounted for upstream changes including: new instrument calibration, revised land-sea masking, and changed cloud masking. Upstream changes also impact the coverage and global statistics of the retrieved AOD. Although our responsibility is to the DT code and products, we have also added a product that merges DT and DB product over semi-arid land surfaces to provide a more gap-free dataset, primarily for visualization purposes. Preliminary validation shows that compared to surface-based sunphotometer data, the C6, Level 2 (along swath) DT-products compare at least as well as those from C5. C6 will include new diagnostic information about clouds in the aerosol field, including an aerosol cloud mask at 500 m resolution, and calculations of the distance to the nearest cloud from clear pixels. Finally, we have revised the strategy for aggregating and averaging the Level 2 (swath) data to become Level 3 (gridded) data. All together, the changes to the DT algorithms will result in reduced global AOD (by 0.02) over ocean and increased AOD (by 0.02) over land, along with changes in spatial coverage. Changes in calibration will have more impact to Terras time series, especially over land. This will result in a significant reduction in artificial differences in the Terra and Aqua datasets, and will stabilize the MODIS data as a target for AEROCOM studie

Aerosol retrieval algorithms↗
NASA NTRS2014

Model-Based Estimation of Sampling-Caused Uncertainty in Aerosol Remote Sensing for Climate Research Applications

To evaluate the effect of sampling frequency on the global monthly mean aerosol optical thickness (AOT), we use 6 years of geographical coordinates of Moderate Resolution Imaging Spectroradiometer (MODIS) L2 aerosol data, daily global aerosol fields generated by the Goddard Institute for Space Studies General Circulation Model and the chemical transport models Global Ozone Chemistry Aerosol Radiation and Transport, Spectral Radiationtransport Model for Aerosol Species and Transport Model 5, at a spatial resolution between 1.125 deg × 1.125 deg and 2 deg × 3◦: the analysis is restricted to 60 deg S-60 deg N geographical latitude. We found that, in general, the MODIS coverage causes an underestimate of the global mean AOT over the ocean. The long-term mean absolute monthly difference between all and dark target (DT) pixels was 0.01-0.02 over the ocean and 0.03-0.09 over the land, depending on the model dataset. Negative DT biases peak during boreal summers, reaching 0.07-0.12 (30-45% of the global long-term mean AOT). Addition of the Deep Blue pixels tempers the seasonal dependence of the DT biases and reduces the mean AOT difference over land by 0.01-0.02. These results provide a quantitative measure of the effect the pixel exclusion due to cloud contamination, ocean sun-glint and land type has on the MODIS estimates of the global monthly mean AOT. We also simulate global monthly mean AOT estimates from measurements provided by pixel-wide along-track instruments such as the Aerosol Polarimetry Sensor and the Cloud-Aerosol LiDAR with Orthogonal Polarization. We estimate the probable range of the global AOT standard error for an along-track sensor to be 0.0005-0.0015 (ocean) and 0.0029-0.01 (land) or 0.5-1.2% and 1.1-4% of the corresponding global means. These estimates represent errors due to sampling only and do not include potential retrieval errors. They are smaller than or comparable to the published estimate of 0.01 as being a climatologically significant change in the global mean AOT, suggesting that sampling density is unlikely to limit the use of such instruments for climate applications at least on a global, monthly scale.

long-term variability↗
NASA NTRS2014

Assessment of 10 Year Record of Aerosol Optical Depth from OMI UV Observations

The Ozone Monitoring Instrument (OMI) onboard the EOS-Aura satellite provides information on aerosol optical properties by making use of the large sensitivity to aerosol absorption in the near-ultraviolet (UV) spectral region. Another important advantage of using near UV observations for aerosol characterization is the low surface albedo of all terrestrial surfaces in this spectral region that reduces retrieval errors associated with land surface reflectance characterization. In spite of the 13 × 24 square kilometers coarse sensor footprint, the OMI near UV aerosol algorithm (OMAERUV) retrieves aerosol optical depth (AOD) and single-scattering albedo under cloud-free conditions from radiance measurements at 354 and 388 nanometers. We present validation results of OMI AOD against space and time collocated Aerosol Robotic Network measured AOD values over multiple stations representing major aerosol episodes and regimes. OMAERUV's performance is also evaluated with respect to those of the Aqua-MODIS Deep Blue and Terra-MISR AOD algorithms over arid and semi-arid regions in Northern Africa. The outcome of the evaluation analysis indicates that in spite of the "row anomaly" problem, affecting the sensor since mid-2007, the long-term aerosol record shows remarkable sensor stability.

aerosol optical depth↗
NASA NTRS2014

Seeing Earth Through the Eyes of an Astronaut

The Human Exploration Science Office within the ARES Directorate has undertaken a new class of handheld camera photographic observations of the Earth as seen from the International Space Station (ISS). For years, astronauts have attempted to describe their experience in space and how they see the Earth roll by below their spacecraft. Thousands of crew photographs have documented natural features as diverse as the dramatic clay colors of the African coastline, the deep blues of the Earth's oceans, or the swirling Aurora Borealis of Australia in the upper atmosphere. Dramatic recent improvements in handheld digital single-lens reflex (DSLR) camera capabilities are now allowing a new field of crew photography: night time-lapse imagery.

Dawson, Melissa↗
NASA NTRS2014

A-Train Aerosol Observations Preliminary Comparisons with AeroCom Models and Pathways to Observationally Based All-Sky Estimates

We have developed a technique for combining CALIOP aerosol backscatter, MODIS spectral AOD (aerosol optical depth), and OMI AAOD (absorption aerosol optical depth) retrievals for the purpose of estimating full spectral sets of aerosol radiative properties, and ultimately for calculating the 3-D distribution of direct aerosol radiative forcing. We present results using one year of data collected in 2007 and show comparisons of the aerosol radiative property estimates to collocated AERONET retrievals. Use of the recently released MODIS Collection 6 data for aerosol optical depths derived with the dark target and deep blue algorithms has extended the coverage of the multi-sensor estimates towards higher latitudes. We compare the spatio-temporal distribution of our multi-sensor aerosol retrievals and calculations of seasonal clear-sky aerosol radiative forcing based on the aerosol retrievals to values derived from four models that participated in the latest AeroCom model intercomparison initiative. We find significant inter-model differences, in particular for the aerosol single scattering albedo, which can be evaluated using the multi-sensor A-Train retrievals. We discuss the major challenges that exist in extending our clear-sky results to all-sky conditions. On the basis of comparisons to suborbital measurements, we present some of the limitations of the MODIS and CALIOP retrievals in the presence of adjacent or underlying clouds. Strategies for meeting these challenges are discussed.

Aerosol↗
NASA NTRS2016

Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China 2004-2013

Three decades of rapid economic development is causing severe and widespread PM2.5(particulate matter (is) less than 2.5 ) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data. We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1 deg resolution using the most recent satellite data and evaluated model performance with available ground observations. We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model predicted PM2.5 concentrations from 2004 to early 2014 using ground observations. The overall model cross-validation R(sup 2) and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM(sub 2.5) concentrations with little bias at the monthly (R(sup 2) = 0.73), regression slope = 0.91) and seasonal (R(sup 2) = 0.79), regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 micro-g/cu cm between 2004 and 2007 and a decrease of 0.46 micro-g/cu cm between 2008 and 2013. Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.

satellite remote sensing↗
NASA NTRS2016

Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors

We estimated global fine particulate matter (PM(sub 2.5)) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to global geophysically-based satellite-derived PM(sub 2.5) estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998−2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM(sub 2.5) estimates were highly consistent (R(sup 2) equals 0.81) with out-of-sample cross-validated PM(sub 2.5) concentrations from monitors. The global population-weighted annual average PM(sub 2.5) concentrations were 3-fold higher than the 10 micrograms per cubic meter WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM(sub 2.5) data sources can yield valuable improvements to PM(sub 2.5) characterization on a global scale.

aerosols↗
NASA NTRS2016

Combing Visible and Infrared Spectral Tests for Dust Identification

The MODIS Dark Target aerosol algorithm over Ocean (DT-O) uses spectral reflectance in the visible, near-IR and SWIR wavelengths to determine aerosol optical depth (AOD) and Angstrom Exponent (AE). Even though DT-O does have "dust-like" models to choose from, dust is not identified a priori before inversion. The "dust-like" models are not true "dust models" as they are spherical and do not have enough absorption at short wavelengths, so retrieved AOD and AE for dusty regions tends to be biased. The inference of "dust" is based on postprocessing criteria for AOD and AE by users. Dust aerosol has known spectral signatures in the near-UV (Deep blue), visible, and thermal infrared (TIR) wavelength regions. Multiple dust detection algorithms have been developed over the years with varying detection capabilities. Here, we test a few of these dust detection algorithms, to determine whether they can be useful to help inform the choices made by the DT-O algorithm. We evaluate the following methods: The multichannel imager (MCI) algorithm uses spectral threshold tests in (0.47, 0.64, 0.86, 1.38, 2.26, 3.9, 11.0, 12.0 micrometer) channels and spatial uniformity test [Zhao et al., 2010]. The NOAA dust aerosol index (DAI) uses spectral contrast in the blue channels (412nm and 440nm) [Ciren and Kundragunta, 2014]. The MCI is already included as tests within the "Wisconsin" (MOD35) Cloud mask algorithm.

aerosol optical depth↗
NASA NTRS2016

Evaluation of Different MODIS AOD Retrieval Algorithms for PM (sub 2.5) Estimation in the Western, Midwestern and Southeastern United States with Implications for Public Health

This study was part of the research activities of the Center for Applied Atmospheric Research and Education (CAARE) funded by the NASA MUREP (Minority University Research and Education Project) Institutional Research Opportunity (MIRO) Program. Satellite measurements of Aerosol Optical Depth (AOD) have been shown to be correlated with ground measurements of fine particulate matter less than 2.5 microns PM (sub 2.5), which in turn has been linked to respiratory and heart diseases. The strength of the correlation between AOD and PM (sub 2.5) varies for different AOD retrieval algorithms and geographic regions. We evaluated several Moderate Resolution Imaging Spectrometer (MODIS) AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target versus Deep Blue), Collections (5.1 versus 6) and spatial resolutions (10-kilometers versus 3-kilometers) for cities in the Western, Midwestern and Southeastern U.S. We developed and validated PM (sub 2.5) prediction models using remotely-sensed AOD data, which were improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind speed, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the predictive power of all the PM (sub 2.5) models, especially in the Western U.S. Temperature, relative humidity and wind speed were the most significant meteorological variables throughout the year in the Western U.S. Wind speed was the most significant meteorological variable for the cold season while temperature was the most significant variable for the warm season in the Midwestern and Southeastern U.S. Our study re-establishes the connection between PM (sub 2.5) and public health concerns including respiratory and cardiovascular diseases (asthma, high blood pressure, coronary heart disease, heart attack, and stroke). Using PM (sub 2.5) data and health data from the Centers for Disease Control and Prevention (CDC)'s Behavioral Risk Factor Surveillance System (BRFSS), our statistical analysis showed that heart attack and stroke occurrences had the strongest correlations with PM (sub 2.5).

Environmental Public Health↗
NASA NTRS2016

GOCI Yonsei Aerosol Retrieval (YAER) Algorithm and Validation During the DRAGON-NE Asia 2012 Campaign

The Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) is the first multi-channel ocean color imager in geostationary orbit. Hourly GOCI top-of-atmosphere radiance has been available for the retrieval of aerosol optical properties over East Asia since March 2011. This study presents improvements made to the GOCI Yonsei Aerosol Retrieval (YAER) algorithm together with validation results during the Distributed Regional Aerosol Gridded Observation Networks - Northeast Asia 2012 campaign (DRAGONNE Asia 2012 campaign). The evaluation during the spring season over East Asia is important because of high aerosol concentrations and diverse types of Asian dust and haze. Optical properties of aerosol are retrieved from the GOCI YAER algorithm including aerosol optical depth (AOD) at 550 nm, fine-mode fraction (FMF) at 550 nm, single-scattering albedo (SSA) at 440 nm, Angstrom exponent (AE) between 440 and 860 nm, and aerosol type. The aerosol models are created based on a global analysis of the Aerosol Robotic Networks (AERONET) inversion data, and covers a broad range of size distribution and absorptivity, including nonspherical dust properties. The Cox-Munk ocean bidirectional reflectance distribution function (BRDF) model is used over ocean, and an improved minimum reflectance technique is used over land. Because turbid water is persistent over the Yellow Sea, the land algorithm is used for such cases. The aerosol products are evaluated against AERONET observations and MODIS Collection 6 aerosol products retrieved from Dark Target (DT) and Deep Blue (DB) algorithms during the DRAGON-NE Asia 2012 campaign conducted from March to May 2012. Comparison of AOD from GOCI and AERONET resulted in a Pearson correlation coefficient of 0.881 and a linear regression equation with GOCI AOD = 1.083 x AERONET AOD - 0.042. The correlation between GOCI and MODIS AODs is higher over ocean than land. GOCI AOD shows better agreement with MODIS DB than MODIS DT. The other GOCI YAER products (AE, FMF, and SSA) show lower correlation with AERONET than AOD, but still show some skills for qualitative use.

Choi, Myungje↗
NASA NTRS2017

An Example of Aerosol Pattern Variability Over Bright Surface Using High Resolution MODIS MAIAC: The Eastern and Western Areas of the Dead Sea and Environs

The extreme rate of evaporation of the Dead Sea (DS) has serious implications for the surrounding area, including atmospheric conditions. This study analyzes the aerosol properties over the western and eastern parts of the DS during the year 2013, using MAIAC (Multi-Angle Implementation of Atmospheric Correction) for MODIS, which retrieves aerosol optical depth (AOD) data at a resolution of 1km. The main goal of the study is to evaluate MAIAC over the study area and determine, for the first time, theprevailing aerosol spatial patterns. First, the MAIAC-derived AOD data was compared with data from three nearby AERONET sites (Nes Ziona - an urban site, and Sede Boker and Masada - two arid sites), and with the conventional Dark Target (DT) and Deep Blue (DB) retrievals for the same days and locations, on a monthly basis throughout 2013. For the urban site, the correlation coefficient (r) for DTDB products showed better performance than MAIAC (r = 0.80, 0.75, and 0.64 respectively) year-round. However, in the arid zones, MAIAC showed better correspondence to AERONET sites than the conventional retrievals (r = 0.58 - 0.60 and 0.48 - 0.50 respectively). We investigated the difference in AOD levels, and its variability, between the Dead Sea coasts on a seasonal basis and calculated monthlyseasonal AOD averages for presenting AOD patterns over arid zones. Thus, we demonstrated that aerosol concentrations show a strong preference for the western coast, particularly during the summer season. This preference, is most likely a result of local anthropogenic emissions combined with the typical seasonal synoptic conditions, the Mediterranean Sea breeze, and the region complex topography. Our results also indicate that a large industrial zone showed higher AOD levels compared to an adjacent reference-site, i.e., 13 percent during the winter season.

AOD↗
NASA NTRS2017

Use of A-Train Aerosol Observations to Constrain Direct Aerosol Radiative Effects (DARE) Comparisons with Aerocom Models and Uncertainty Assessments

We describe a technique for combining multiple A-Train aerosol data sets, namely MODIS spectral AOD (aerosol optical depth), OMI AAOD (absorption aerosol optical depth) and CALIOP aerosol backscatter retrievals (hereafter referred to as MOC retrievals) to estimate full spectral sets of aerosol radiative properties, and ultimately to calculate the 3-D distribution of direct aerosol radiative effects (DARE). We present MOC results using almost two years of data collected in 2007 and 2008, and show comparisons of the aerosol radiative property estimates to collocated AERONET retrievals. Use of the MODIS Collection 6 AOD data derived with the dark target and deep blue algorithms has extended the coverage of the MOC retrievals towards higher latitudes. The MOC aerosol retrievals agree better with AERONET in terms of the single scattering albedo (ssa) at 441 nm than ssa calculated from OMI and MODIS data alone, indicating that CALIOP aerosol backscatter data contains information on aerosol absorption. We compare the spatio-temporal distribution of the MOC retrievals and MOC-based calculations of seasonal clear-sky DARE to values derived from four models that participated in the Phase II AeroCom model intercomparison initiative. Overall, the MOC-based calculations of clear-sky DARE at TOA over land are smaller (less negative) than previous model or observational estimates due to the inclusion of more absorbing aerosol retrievals over brighter surfaces, not previously available for observationally-based estimates of DARE. MOC-based DARE estimates at the surface over land and total (land and ocean) DARE estimates at TOA are in between previous model and observational results. Comparisons of seasonal aerosol property to AeroCom Phase II results show generally good agreement best agreement with forcing results at TOA is found with GMI-MerraV3. We discuss sampling issues that affect the comparisons and the major challenges in extending our clear-sky DARE results to all-sky conditions. We present estimates of clear-sky and all-sky DARE and show uncertainties that stem from the assumptions in the spatial extrapolation and accuracy of aerosol and cloud properties, in the diurnal evolution of these properties, and in the radiative transfer calculations.

aerosol↗
NASA NTRS2016

In-Situ and Remotely-Sensed Observations of Biomass Burning Aerosols at Doi Ang Khang, Thailand During 7-SEAS BASELInE 2015

The spring 2015 deployment of a suite of instrumentation at Doi Ang Khang (DAK) in northwestern Thailand enabled the characterization of air masses containing smoke aerosols from burning predominantly in Myanmar. Aerosol Robotic Network (AERONET) Sun photometer data were used to validate Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 "Deep Blue" aerosol optical depth (AOD) retrievals; MODIS Terra and Aqua provided results of similar quality, with correlation coefficients of 0.93-0.94 and similar agreement within expected uncertainties to global-average performance. Scattering and absorption measurements were used to compare surface and total column aerosol single scatter albedo (SSA); while the two were well-correlated, and showed consistent positive relationships with moisture (increasing SSA through the season as surface relative humidity and total columnar water vapor increased), in situ surface-level SSA was nevertheless significantly lower by 0.12-0.17. This could be related to vertical heterogeneity and/or instrumental issues. DAK is at approximately 1,500 meters above sea level in heterogeneous terrain, and the resulting strong diurnal variability in planetary boundary layer depth above the site leads to high temporal variability in both surface and column measurements, and acts as a controlling factor to the ratio between surface particulate matter (PM) levels and column AOD. In contrast, while some hygroscopic effects were observed relating to aerosol particle size and Angstrom exponent, relative humidity variations appear to be less important for this ratio here. As part of the Seven South-East Asian Studies (7-SEAS) project, the Biomass-burning Aerosols & Stratocumulus Environment: Lifecycles and Interactions Experiment (BASELInE) was intended to probe physicochemical processes, interactions, and feedbacks related to biomass burning aerosols and clouds during the spring burning season (February-April) in southeast Asia (SEA).

biomass burning↗
NASA NTRS2016

Retrieval of Daily Maps of PM2.5 Aerosol in the Problematic California Valleys: Bright, Speckled Reflectances, Thin AOT, but High Pollution

The San Joaquin Valley suffers from severe episodes of respirable aerosol (PM2.5) in wintertime.We provide maps of aerosol episodes using daily snapshots of PM2.5 and its changing features despite numerous difficulties inherent to sampling the region. Linear relationships relating aerosol optical thickness (AOT) to PM2.5 give an explained variance of approximately 3.The GEO-CAPE mission has as a goal the provision of relevant measures of respirable aerosol to the community,but has not formulated a science goal beyond the limited goal of retrieval of AOT, bringing the usefulness of GEO-CAPE into doubt.Our special focus was on the DISCOVER-AQ period, Jan-Feb 2013, which had many supporting measurements.Both high pollution and retrieval difficulties tend to occur in many Mediterranean agricultural regions like the San Joauin. One difficulty is the relatively bright surfaces with considerable exposed soil. NASAs MAIAC and MODIS Deep Blue retrieval techniques are shown to have considerable skill even at low aerosol optical thickness (AOT) values, as evaluated by concurrent AERONET sunphotometer measurements.More significantly, these AOT values can correspond to high daytime PM2.5 since aerosol mixed layer depth is thin and variable, 200m 600 m. The thin layers derive from typical subsidence of dry air between more stormy periods. This situation provides an advantage: water vapor column is also almost completely limited to a similar mixed layer depth, and can thus serve as a measure of aerosol dilution.Using the MAIAC Water Vapor Column:In order to make the maps below, we used the MAIAC data but subtracted partial water-vapor columns estimated from MERRA Reanalysis Data availabe from the GSFC GMAO using kriging. We did not use the mixed-layer estimates from MERRA, since such analyses were found problematic during our forecasting exercises for DISCOVER-AQ. Observations from the aircraft soundings suggested that this overlying moisture was mostly due to larger scale flows, not ML venting.However, the specific humidity at the surface and a nearly well-mixed ML was analyzed by kriging from the surface network (MesoWest, University of Utah). These were thought to be truer, uninfluenced by physical process modeling that combines with data observations. (TBD: How different are they?) Procedure: Subtract overlying partial water columns from MAIAC column water and divide this by a surface value of water vapor. (MAIAC column is expressed in cm of water, i.e., water vapor at surface conditions.This method appears to bring out useful details in the distribution of submicron particles in the very problematic Wintertime San Joaquin Valley, and allow analysis of pollution episodes throughout the valley, rather than long-term averages.

san joaquin valley↗