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Levy, Robert

Publications and source records attributed to Levy, Robert.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Can OMPS and VIIRS Be Used as a Single Virtual Sensor for Aerosol Retrieval?

Satellite earth observation of aerosols using a single sensor have been done for more than three decades. The retrieved global distribution of column integrated aerosol optical depth (AOD) has been refined with higher accuracy and finer resolutions. However, the other aerosol properties, such as aerosol absorption and aerosol height, haven not been mapped as extensively using passive sensors. The detectors sensitive to these properties (UV-based sensors such as Aura-OMI) lack the adequate visible channels to tease out the absorption, height and loading contributions. In addition, their spatial resolution is prone to frequent cloud contamination impacting aerosol loading retrievals using only these spectral ranges. However, the deployment of the VIIRS and OMPS-NM sensors on the same Suomi NPP platform provides a first opportunity to carry out retrievals of aerosol single scattering albedo (SSA) and AOD with little cloud contamination using an expanded spectral range from the UV to the near-IR wavelengths. After collocated VIIRS pixel within the large OMPS footprint during the high resolution OMPS observing mode, we compared the radiance from OMPS and VIIRS to ensure the calibration from individual sensors are comparable. Then a look up table (LUT) was created for a smoke case over central Africa. The LUT is generated using 10 channels from 0.357 to 2.1 micron with 44 combinations of aerosol models and 4 different aerosol heights. With CALIOP and AERONET observation nearby, the possibility of using OMPS and VIIRS radiance as a single virtual sensor to simultaneously and consistently retrieve AOD, SSA, and aerosol height is explored.

Shi, Yingxi↗
NASA NTRS2019

First Steps in the Creation of a Joint MISR/MODIS Ocean Color Atmospheric Correction Algorithm

We are creating a new algorithm that combines observations from MISR and MODIS (both on the NASA Terra spacecraft) to improve atmospheric correction and coverage for ocean color data products. The algorithm utilizes information rich, multi-angle MISR observations for atmospheric correction, applied to MODIS. Our goal is to produce atmospherically corrected Remote Sensing Reflectance from MODIS with enhanced coverage and accuracy, for input to downstream bio-optical ocean parameter retrieval algorithms.An important aspect of this work is the utilization of multi-angle views of the reflected ocean surface sun glint. Usually, such observations are avoided, since the intensity of the glint overwhelms any contribution from the ocean body. However, MISR's multi-angle observations see varying degrees of glint, which means they can be used to better determine aerosol optical properties (Kaufman et al., 2002, Ottaviani et al., 2013), and to identify surface wind speeds that govern the glint pattern. The latter could be utilized to replace the wind speeds taken from ancillary sources that are currently used to conservatively mask potential glint contamination in MODIS observations.To assess this capability, and to identify the appropriate parameterization, we present an analysis using the Generalized Nonlinear Retrieval Analysis (GENRA, Vukicevic et al., 2009) information content assessment. This technique is also easily modified to act as a Bayesian retrieval algorithm, for which initial results are discussed. Finally, we describe the status of integrating MISR data into the processing capabilities of the Ocean Biology Processing Group (OBPG) at NASA, and show the first ocean color vicarious calibration (Franz et al., 2007) of the MISR instrument.

Knobelspiesse, Kirk↗
NASA NTRS2019

NASA GEOS Aerosol Modeling and Assimilation Activities

The current assimilation of Aerosol Optical Depth (AOD) in GEOS involves very careful cloud screening and homogenization of the observing system by means of a neural network that translates satellite reflectances from MODIS into AERONET calibrated AOD. In this talk we will present an update of the GEOS aerosol assimilation system, with emphasis on the improved treatment of MODIS observations. We will then proceed to assess the impact of geostationary aerosol observations from the ABI and AHI sensors on the GOES-16 and Himawari-8 satellites. The GEOS assimilated aerosol fields will be validated by comparison to independent in-situ and remotely-sensed measurements (PM2.5 concentrations, surface dust concentrations, Maritime Aerosol Network, airborne and ground based lidars, UV based measurements, etc.).

Castellanos, Patricia↗
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

Impact of MODIS SWIR Band Calibration Improvements on Level-3 Atmospheric Products

The spectral reflectance measured by the MODIS reflective solar bands (RSB) is used for retrieving many atmospheric science products. The accuracy of these products depends on the accuracy of the calibration of the RSB. To this end, the RSB of the MODIS instruments are primarily calibrated on-orbit using regular solar diffuser (SD) observations. For lambda < 0.94 microns the SDs on-orbit bi-directional reflectance factor (BRF) change is tracked using solar diffuser stability monitor (SDSM) observations. For lambda > 0.94 microns, the MODIS Characterization Support Team (MCST) developed, in MODIS Collection 6 (C6), a time-dependent correction using observations from pseudo-invariant earth-scene targets. This correction has been implemented in C6 for the Terra MODIS 1.24 micron band over the entire mission, and for the 1.375 micron band in the forward processing. As the instruments continue to operate beyond their design lifetime of six years, a similar correction is planned for other short-wave infrared (SWIR) bands as well. MODIS SWIR bands are used in deriving atmosphere products, including aerosol optical thickness, atmospheric total column water vapor, cloud fraction and cloud optical depth. The SD degradation correction in Terra bands 5 and 26 impact the spectral radiance and therefore the retrieval of these atmosphere products. Here, we describe the corrections to Bands 5 (1.24 microns) and 26 (1.375 microns), and produce three sets (B5, B26 correction on/on, on/off, and off/off) of Terra-MODIS Level 1B (calibrated radiance product) data. By comparing products derived from these corrected and uncorrected Terra MODIS Level 1B (L1B) calibrations, dozens of L3 atmosphere products are surveyed for changes caused by the corrections, and representative results are presented. Aerosol and water vapor products show only small local changes, while some cloud products can change locally by > 10%, which is a large change.

Calibration↗
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

Using High-Resolution Airborne Remote Sensing to Study Aerosol Near Clouds

The horizontal space in between clear and cloudy air is very complex. This so-called twilight zone includes activated aerosols that are not quite clouds, thin cloud fragments that are not easily observable, and dying clouds that have not quite disappeared. This is a huge challenge for satellite remote sensing, specifically for retrieval of aerosol properties. Identifying what is cloud versus what is not cloud is critically important for attributing radiative effects and forcings to aerosols. At the same time, the radiative interactions between clouds and the surrounding media (molecules, surface and aerosols themselves) will contaminate retrieval of aerosol properties, even in clear skies. Most studies on aerosol cloud interactions are relevant to moderate resolution imagery (e.g. 500 m) from sensors such as MODIS. Since standard aerosol retrieval algorithms tend to keep a distance (e.g. 1 km) from the nearest detected cloud, it is impossible to evaluate what happens closer to the cloud. During Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS), the NASA ER-2 flew with the enhanced MODIS Airborne Simulator (eMAS), providing MODIS-like spectral observations at high (50 m) spatial resolution. We have applied MODIS-like aerosol retrieval for the eMAS data, providing new detail to characterization of aerosol near clouds. Interpretation and evaluation of these eMAS aerosol retrievals is aided by independent MODIS-like cloud retrievals, as well as profiles from the co-flying Cloud Physics Lidar (CPL). Understanding aerosolcloud retrieval at high resolution will lead to better characterization and interpretation of long-term, global products from lower resolution (e.g.MODIS) satellite retrievals.

aerosols near clouds↗
NASA NTRS2014

Current and Future Perspectives of Aerosol Research at NASA Goddard Space Flight Center

Aerosols are tiny atmospheric particles that are emitted from various natural and anthropogenic sources. They affect climate through direct and indirect interactions with solar and thermal radiation, clouds, and atmospheric circulation (Solomon et al. 2007). The launch of a variety of sophisticated satellite-based observing systems aboard the Terra, Aqua, Aura, SeaWiFS (see appendix for all acronym expansions), CALIPSO, and other satellites in the late 1990s to mid-2000s through the NASA EOS and other U.S. and non-U.S. programs ushered in a golden era in aerosol research. NASA has been a leader in providing global aerosol characterizations through observations from satellites, ground networks, and field campaigns, as well as from global and regional modeling. AeroCenter (http://aerocenter.gsfc.nasa.gov/), which was formed in 2002 to address the many facets of aerosol research in a collaborative manner, is an interdisciplinary union of researchers (~200 members) at NASA GSFC and other nearby institutions, including NOAA, several universities, and research laboratories. AeroCenter hosts a web-accessible regular seminar series and an annual meeting to present up-to-date aerosol research, including measurement techniques; remote sensing algorithms; modeling development; field campaigns; and aerosol interactions with radiation, clouds, precipitation, climate, biosphere, atmospheric chemistry, air quality, and human health. The 2013 annual meeting was held at the NASA GSFC Visitor Center on 31 May 2013, which coincided with the seventh anniversary of the passing of Yoram Kaufman, a modern pioneer in satellite-based aerosol science and the founder of AeroCenter. The central theme of this year's meeting was "current and future perspectives" of NASA's aerosol science and satellite missions.

aerocenter↗
NASA NTRS2012

Impact of Sensor Degradation on the MODIS NDVI Time Series

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, the impact of sensor degradation on trend detection was evaluated using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470 nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004 yr−1 decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends in MODIS NDVI over North America were consistentwith simulated results,with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in detection of NDVI trends.

NDVI↗
NASA NTRS2012

Implications of Satellite Swath Width on Global Aerosol Optical Thickness Statistics

We assess the impact of swath width on the statistics of aerosol optical thickness (AOT) retrieved by satellite as inferred from observations made by the Moderate Resolution Imaging Spectroradiometer (MODIS). We sub-sample the year 2009 MODIS data from both the Terra and Aqua spacecraft along several candidate swaths of various widths. We find that due to spatial sampling there is an uncertainty of approximately 0.01 in the global, annual mean AOT. The sub-sampled monthly mean gridded AOT are within +/- 0.01 of the full swath AOT about 20% of the time for the narrow swath sub-samples, about 30% of the time for the moderate width sub-samples, and about 45% of the time for the widest swath considered. These results suggest that future aerosol satellite missions with only a narrow swath view may not sample the true AOT distribution sufficiently to reduce significantly the uncertainty in aerosol direct forcing of climate.

Colarco, Peter↗
NASA NTRS2011

Impact of Sensor Degradation on the MODIS NDVI Time Series

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, we evaluated the impact of sensor degradation on trend detection using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004/yr decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends MODIS NDVI over North America were consistent with simulated results, with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in NDVI trends over vegetation.

Wang, Dongdong↗
NASA NTRS2004

Aerosol Sources, Absorption, and Intercontinental Transport: Synergies Among Models, Remote Sensing, and Atmospheric Measurements

Aerosol climate forcing is one of the largest uncertainties in assessing the anthropogenic impact on the global climate system. This uncertainty arises from the poorly quantified aerosol sources, especially black carbon emissions, our limited knowledge of aerosol mixing state and optical properties, and the consequences of intercontinental transport of aerosols and their precursors. Here we use a global model GOCART to simulate atmospheric aerosols, including sulfate, black carbon, organic carbon, dust, and sea salt, from anthropogenic, .biomass burning, and natural sources. We compare the model calculated aerosol extinction and absorption with those quantities from the ground-based sun photometer measurements from AERON" at several different wavelengths and the field observations from ACE-Asia, and model calculated total aerosol optical depth and fine mode fractions with the MODIS satellite retrieval. We will also estimate the intercontinental transport of pollution and dust aerosols from their source regions to other areas in different seasons.

Chin, Mian↗