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NASA NTRS2012

An Overview of Suomi NPP VIIRS Calibration Maneuvers

The first Visible Infrared Imager Radiometer Suite (VIIRS) instrument was successfully launched on-board the Suomi National Polar-orbiting Partnership (SNPP) spacecraft on October 28, 2011. Suomi NPP VIIRS observations are made in 22 spectral bands, from the visible (VIS) to the long-wave infrared (LWIR), and are used to produce 22 Environmental Data Records (EDRs) with a broad range of scientific applications. The quality of these VIIRS EDRs strongly depends on the quality of its calibrated and geo-located Sensor Date Records (SDRs). Built with a strong heritage to the NASA's EOS MODerate resolution Imaging Spectroradiometer (MODIS) instrument, the VIIRS is calibrated on-orbit using a similar set of on-board calibrators (OBC), including a solar diffuser (SD) and solar diffuser stability monitor (SDSM) system for the reflective solar bands (RSB) and a blackbody (BB) for the thermal emissive bands (TEB). On-orbit maneuvers of the SNPP spacecraft provide additional calibration and characterization data from the VIIRS instrument which cannot be obtained pre-launch and are required to produce the highest quality SDRs. These include multi-orbit yaw maneuvers for the characterization of SD and SDSM screen transmission, quasi-monthly roll maneuvers to acquire lunar observations to track sensor degradation in the visible through shortwave infrared, and a driven pitch-over maneuver to acquire multiple scans of deep space to determine TEB response versus scan angle (RVS). This paper pro-vides an overview of these three SNPP calibration maneuvers. Discussions are focused on their potential calibration and science benefits, pre-launch planning activities, and on-orbit scheduling and implementation strategies. Results from calibration maneuvers performed during the Intensive Calibration and Validation (ICV) period for the VIIRS sensor are illustrated. Also presented in this paper are lessons learned regarding the implementation of calibration spacecraft maneuvers on follow-on missions.

Butler, James J.↗

NASA NTRS2012

MODIS Radiometric Calibration Program, Methods and Results

As a key instrument for NASA s Earth Observing System (EOS), the Moderate Resolution Imaging Spectroradiometer (MODIS) has made significant contributions to the remote sensing community with its unprecedented amount of data products continuously generated from its observations and freely distributed to users worldwide. MODIS observations, covering spectral regions from visible (VIS) to long-wave infrared (LWIR), have enabled a broad range of research activities and applications for studies of the earth s interactive system of land, oceans, and atmosphere. In addition to extensive pre-launch measurements, developed to characterize sensor performance, MODIS carries a set of on-board calibrators (OBC) that can be used to track on-orbit changes of various sensor characteristics. Most importantly, dedicated and continuous calibration efforts have been made to maintain sensor data quality. This paper provides an overview of the MODIS calibration program, on-orbit calibration activities, methods, and performance. Key calibration results and lessons learned from the MODIS calibration effort are also presented in this paper.

Xiong, Xiaoxiong↗

NASA NTRS2012

Short-Term Prediction Research and Transition (SPoRT) Center: Transitioning Satellite Data to Operations

The Short-term Prediction Research and Transition (SPoRT) Center located at NASA Marshall Space Flight Center has been conducting testbed activities aimed at transitioning satellite products to National Weather Service operational end users for the last 10 years. SPoRT is a NASA/NOAA funded project that has set the bar for transition of products to operational end users through a paradigm of understanding forecast challenges and forecaster needs, displaying products in end users decision support systems, actively assessing the operational impact of these products, and improving products based on forecaster feedback. Aiming for quality partnerships rather than a large quantity of data users, SPoRT has become a community leader in training operational forecasters on the use of up-and-coming satellite data through the use of legacy instruments and proxy data. Traditionally, SPoRT has supplied satellite imagery and products from NASA instruments such as the Moderate-resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS). However, recently, SPoRT has been funded by the GOES-R and Joint Polar Satellite System (JPSS) Proving Grounds to accelerate the transition of selected imagery and products to help improve forecaster awareness of upcoming operational data from the Visible Infrared Imager Radiometer Suite (VIIRS), Cross-track Infrared Sounder (CrIS), Advanced Baseline Imager (ABI), and Geostationary Lightning Mapper (GLM). This presentation provides background on the SPoRT Center, the SPoRT paradigm, and some example products that SPoRT is excited to work with forecasters to evaluate.

Zavodsky, Bradley↗

NASA NTRS2012

P69 Using the NASA-Unified WRF to Assess the Impacts of Real-Time Vegetation on Simulations of Severe Weather

Since June 2010, the NASA Short-term Prediction Research and Transition (SPoRT; Goodman et al. 2004; Darden et al. 2010; Stano et al. 2012; Fuell et al. 2012) Center has been generating a real-time Normalized Difference Vegetation Index (NDVI) and corresponding Green Vegetation Fraction (GVF) composite based on reflectances from NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. This dataset is generated at 0.01 resolution across the Continental United States (CONUS), and updated daily. The goal of producing such a vegetation dataset is to improve over the default climatological GVF dataset in land surface and numerical weather prediction models, in order to have better simulations of heat and moisture exchange between the land surface and the planetary boundary layer. Details on the SPoRT/MODIS vegetation composite algorithm are presented in Case et al. (2011). Vegetation indices such as GVF and Leaf Area Index (LAI) are used by land surface models (LSMs) to represent the horizontal and vertical density of plant vegetation (Gutman and Ignatov 1998), in order to calculate transpiration, interception and radiative shading. Both of these indices are related to the NDVI; however, there is an inherent ambiguity in determining GVF and LAI simultaneously from NDVI, as described in Gutman and Ignatov (1998). One practice is to specify the LAI while allowing the GVF to vary both spatially and temporally, as is done in the Noah LSM (Chen and Dudhia 2001; Ek et al. 2003). Operational versions of Noah within several of the National Centers for Environmental Prediction (NCEP) global and regional modeling systems hold the LAI fixed, while the GVF varies according to a global monthly climatology. This GVF climatology was derived from NDVI data on the NOAA Advanced Very High Resolution Radiometer (AVHRR) polar orbiting satellite, using information from 1985 to 1991 (Gutman and Ignatov 1998; Jiang et al. 2010). Representing data at the mid-point of every month, the climatological dataset is on a grid with 0.144 (~16 km) spatial resolution and is distributed with the community WRF model (Ek et al. 2003; Jiang et al. 2010; Skamarock et al. 2008).

Case, Jonathan L.↗

NASA NTRS2012

MODIS On-Orbit Calibration and Lessons Learned

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument for NASA's Earth Observing System (EOS) Terra and Aqua missions. Since launch, Terra and Aqua MODIS have successfully operated for more than 12 and 10 years, respectively, and generated an unprecedented amount of data products for the science and user community over a wide range of applications. MODIS was developed with improved design and stringent calibration requirements over its heritage sensors in order . to extend and enhance their long-term data records. Its follow-on instrument, the Visible/Infrared Imager Radiometer Suite (VIIRS), was launched on-board the Suomi National Polar-orbiting Partnership (NPP) spacecraft October 28, 2011. MODIS collects data in 36 spectral bands, covering wavelengths from 0.41 to 14.S!Jlll, and at 250m, SOOm, and lkm spatial resolutions (nadir). MODIS on-orbit calibration is provided by a set of onboard calibrators (OBC), including a solar diffuser (SO), a solar diffuser stability monitor (SDSM), a blackbody (BB), and a spectroradiometric calibration assembly (SRCA). In addition to the onboard calibrators, regular lunar observations are made by both Terra and Aqua MODIS to track their calibration stability in the reflective solar region. This tutorial session provides an overview of MODIS on-orbit calibration and characterization methodologies. It discusses challenging issues and lessons learned from sensor design, operation, calibration, and inter-comparisons. Examples of instrument on-orbit performance are illustrated with a focus on the improvements made based on various lessons learned. It is expected that MODIS experience and lessons will continue to provide valuable information for future earth observing missions/sensors.

Xiong, Jack↗

NASA NTRS2012

Distributed Assimilation of Satellite-based Snow Extent for Improving Simulated Streamflow in Mountainous, Dense Forests: An Example Over the DMIP2 Western Basins

Snow cover area affects snowmelt, soil moisture, evapotranspiration, and ultimately streamflow. For the Distributed Model Intercomparison Project - Phase 2 Western basins, we assimilate satellite-based fractional snow cover area (fSCA) from the Moderate Resolution Imaging Spectroradiometer, or MODIS, into the National Weather Service (NWS) SNOW-17 model. This model is coupled with the NWS Sacramento Heat Transfer (SAC-HT) model inside the National Aeronautics and Space Administration's (NASA) Land Information System. SNOW-17 computes fSCA from snow water equivalent (SWE) values using an areal depletion curve. Using a direct insertion, we assimilate fSCAs in two fully distributed ways: 1) we update the curve by attempting SWE preservation, and 2) we reconstruct SWEs using the curve. The preceding are refinements of an existing simple, conceptually-guided NWS algorithm. Satellite fSCA over dense forests inadequately accounts for below-canopy snow, degrading simulated streamflow upon assimilation during snowmelt. Accordingly, we implement a below-canopy allowance during assimilation. This simplistic allowance and direct insertion are found to be inadequate for improving calibrated results, still degrading them as mentioned above. However, for streamflow volume for the uncalibrated runs, we obtain: (1) substantial to major improvements (64-81 %) as a percentage of the control run residuals (or distance from observations), and (2) minor improvements (16-22 %) as a percentage of observed values. We highlight the need for detailed representations of canopy-snow optical radiative transfer processes in mountainous, dense forest regions if assimilation-based improvements are to be seen in calibrated runs over these areas.

Yatheendradas, Soni↗

NASA NTRS2012

Derivation of a New Smoke Emissions Inventory using Remote Sensing, and Its Implications for Near Real-Time Air Quality Applications

A new emissions inventory of particulate matter (PM) is being derived mainly from remote sensing data using fire radiative power (FRP) and aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, as well as wind data from the Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis dataset, which spans the satellite era. This product is generated using a coefficient of emission, C(sub e), that has been produced on a 1x1 degree global grid such that, when it is multiplied with satellite measurements of FRP or its time-integrated equivalent fire radiative energy (FRE) retrieved over a given area and time period, the corresponding PM emissions are estimated. This methodology of using C(sub e) to derive PM emissions is relatively new and advantageous for near real-time air quality applications compared to current methods based on post-fire burned area that may not provide emissions in a timely manner. Furthermore, by using FRP to characterize a fire s output, it will represent better accuracy than the use of raw fire pixel counts, since fires in individual pixels can differ in size and strength by orders of magnitude, resulting in similar differences in emission rates. Here we will show examples of this effect and how this new emission inventory can properly account for the differing emission rates from fires of varying strengths. We also describe the characteristics of the new emissions inventory, and propose the process chain of incorporating it into models for air quality applications.

Ellison, Luke↗

NASA NTRS2012

Determining Aerosol Plume Height from Two GEO Imagers: Lessons from MISR and GOES

Aerosol plume height is a key parameter to determine impacts of particulate matters generated from biomass burning, wind-blowing dust, and volcano eruption. Retrieving cloud top height from stereo imageries from two GOES (Geostationary Operational Environmental Satellites) have been demonstrated since 1970's and the principle should work for aerosol plumes if they are optically thick. The stereo technique has also been used by MISR (Multiangle Imaging SpectroRadiometer) since 2000 that has nine look angles along track to provide aerosol height measurements. Knowing the height of volcano aerosol layers is as important as tracking the ash plume flow for aviation safety. Lack of knowledge about ash plume height during the 2010 Eyja'rjallajokull eruption resulted in the largest air-traffic shutdown in Europe since World War II. We will discuss potential applications of Asian GEO satellites to make stereo measurements for dust and volcano plumes.

Wu, Dong L.↗

NASA NTRS2012

Analysis of Fine-Mode Aerosol Retrieval Capabilities by Different Passive Remote Sensing Instrument Designs

Remote sensing of aerosol optical properties is difficult, but multi-angle, multi-spectral, polarimetric instruments have the potential to retrieve sufficient information about aerosols that they can be used to improve global climate models. However, the complexity of these instruments means that it is difficult to intuitively understand the relationship between instrument design and retrieval success. We apply a Bayesian statistical technique that relates instrument characteristics to the information contained in an observation. Using realistic simulations of fine size mode dominated spherical aerosols, we investigate three instrument designs. Two of these represent instruments currently in orbit: the Multiangle Imaging SpectroRadiometer (MISR) and the POLarization and Directionality of the Earths Reflectances (POLDER). The third is the Aerosol Polarimetry Sensor (APS), which failed to reach orbit during recent launch, but represents a viable design for future instruments. The results show fundamental differences between the three, and offer suggestions for future instrument design and the optimal retrieval strategy for current instruments. Generally, our results agree with previous validation efforts of POLDER and airborne prototypes of APS, but show that the MISR aerosol optical thickness uncertainty characterization is possibly underestimated.

Knobelspiesse, Kirk↗

NASA NTRS2012

Accuracy Assessment of Aqua-MODIS Aerosol Optical Depth Over Coastal Regions: Importance of Quality Flag and Sea Surface Wind Speed

Coastal regions around the globe are a major source for anthropogenic aerosols in the atmosphere, but the underlying surface characteristics are not favorable for the Moderate Resolution Imaging Spectroradiometer (MODIS) algorithms designed for retrieval of aerosols over dark land or open-ocean surfaces. Using data collected from 62 coastal stations worldwide from the Aerosol Robotic Network (AERONET) from approximately 2002-2010, accuracy assessments are made for coastal aerosol optical depth (AOD) retrieved from MODIS aboard Aqua satellite. It is found that coastal AODs (at 550 nm) characterized respectively by the MODIS Dark Land (hereafter Land) surface algorithm, the Open-Ocean (hereafter Ocean) algorithm, and AERONET all exhibit a log-normal distribution. After filtering by quality flags, the MODIS AODs respectively retrieved from the Land and Ocean algorithms are highly correlated with AERONET (with R(sup 2) is approximately equal to 0.8), but only the Land algorithm AODs fall within the expected error envelope greater than 66% of the time. Furthermore, the MODIS AODs from the Land algorithm, Ocean algorithm, and combined Land and Ocean product show statistically significant discrepancies from their respective counterparts from AERONET in terms of mean, probability density function, and cumulative density function, which suggest a need for future improvement in retrieval algorithms. Without filtering with quality flag, the MODIS Land and Ocean AOD dataset can be degraded by 30-50% in terms of mean bias. Overall, the MODIS Ocean algorithm overestimates the AERONET coastal AOD by 0.021 for AOD less than 0.25 and underestimates it by 0.029 for AOD greater than 0.25. This dichotomy is shown to be related to the ocean surface wind speed and cloud contamination effects on the satellite aerosol retrieval. The Modern Era Retrospective-Analysis for Research and Applications (MERRA) reveals that wind speeds over the global coastal region 25 (with a mean and median value of 2.94 meters per second and 2.66 meters per second, respectively) are often slower than 6 meters per second assumed in the MODIS Ocean algorithm. As a result of high correlation (R(sup 2) greater than 0.98) between the bias in binned MODIS AOD and the corresponding binned wind speed over the coastal sea surface, an empirical scheme for correcting the bias of AOD retrieved from the MODIS Ocean algorithm is formulated and is shown to be effective over the majority of the coastal AERONET stations, and hence can be used in future analysis of AOD trend and MODIS AOD data assimilation.

Anderson, J. C.↗

NASA NTRS2012

Eyjafjallajokull Volcano Plume Particle-Type Characterization from Space-Based Multi-angle Imaging

The Multi-angle Imaging SpectroRadiometer (MISR) Research Aerosol algorithm makes it possible to study individual aerosol plumes in considerable detail. From the MISR data for two optically thick, near-source plumes from the spring 2010 eruption of the Eyjafjallaj kull volcano, we map aerosol optical depth (AOD) gradients and changing aerosol particle types with this algorithm; several days downwind, we identify the occurrence of volcanic ash particles and retrieve AOD, demonstrating the extent and the limits of ash detection and mapping capability with the multi-angle, multi-spectral imaging data. Retrieved volcanic plume AOD and particle microphysical properties are distinct from background values near-source, as well as for overwater cases several days downwind. The results also provide some indication that as they evolve, plume particles brighten, and average particle size decreases. Such detailed mapping offers context for suborbital plume observations having much more limited sampling. The MISR Standard aerosol product identified similar trends in plume properties as the Research algorithm, though with much smaller differences compared to background, and it does not resolve plume structure. Better optical analogs of non-spherical volcanic ash, and coincident suborbital data to validate the satellite retrieval results, are the factors most important for further advancing the remote sensing of volcanic ash plumes from space.

Kahn, Ralph A.↗

NASA NTRS2011

Asian Dust Weather Categorization with Satellite and Surface Observations

This study categorizes various dust weather types by means of satellite remote sensing over central Asia. Airborne dust particles can be identified by satellite remote sensing because of the different optical properties exhibited by coarse and fine particles (i.e. varying particle sizes). If a correlation can be established between the retrieved aerosol optical properties and surface visibility, the intensity of dust weather can be more effectively and consistently discerned using satellite rather than surface observations. In this article, datasets consisting of collocated products from Moderate Resolution Imaging Spectroradiometer Aqua and surface measurements are analysed. The results indicate an exponential relationship between the surface visibility and the satellite-retrieved aerosol optical depth, which is subsequently used to categorize the dust weather. The satellite-derived spatial frequency distributions in the dust weather types are consistent with China s weather station reports during 2003, indicating that dust weather classification using satellite data is highly feasible. Although the period during the springtime from 2004 to 2007 may be not sufficient for statistical significance, our results reveal an increasing tendency in both intensity and frequency of dust weather over central Asia during this time period.

Lin, Tang-Huang↗

NASA NTRS2012

Satellite and Surface Perspectives of Snow Extent in the Southern Appalachian Mountains

Assessing snow cover patterns in mountain regions remains a challenge for a variety of reasons. Topography (e.g., elevation, exposure, aspect, and slope) strongly influences snowfall accumulation and subsequent ablation processes, leading to pronounced spatial variability of snow cover. In-situ observations are typically limited to open areas at lower elevations (<1000 m). In this paper, we use several products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess snow cover extent in the Southern Appalachian Mountains (SAM). MODIS daily snow cover maps and true color imagery are analyzed after selected snow events (e.g., Gulf/Atlantic Lows, Alberta Clippers, and Northwest Upslope Flow) from 2006 to 2012 to assess the spatial patterns of snowfall across the SAM. For each event, we calculate snow cover area across the SAM using MODIS data and compare with the Interactive Multi-sensor Snow and ice mapping system (IMS) and available in-situ observations. Results indicate that Gulf/Atlantic Lows are typically responsible for greater snow extent across the entire SAM region due to intensified cyclogenesis associated with these events. Northwest Upslope Flow events result in snow cover extent that is limited to higher elevations (>1000 m) across the SAM, but also more pronounced along NW aspects. Despite some limitations related to the presence of ephemeral snow or cloud cover immediately after each event, we conclude that MODIS products are useful for assessing the spatial variability of snow cover in heavily forested mountain regions such as the SAM.

Sugg, Johnathan W.↗

NASA NTRS2011

Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

The NASA Short-term Prediction Research and Transition (SPoRT) Center develops new products and techniques that can be used in operational meteorology. The majority of these products are derived from NASA polar-orbiting satellite imagery from the Earth Observing System (EOS) platforms. One such product is a Greenness Vegetation Fraction (GVF) dataset, which is produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the new SPoRT-MODIS GVF dataset on land surface models apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. The second phase of the project is to examine the impacts of the SPoRT GVF dataset on NWP using the Weather Research and Forecasting (WRF) model. Two separate WRF model simulations were made for individual severe weather case days using the NCEP GVF (control) and SPoRT GVF (experimental), with all other model parameters remaining the same. Based on the sensitivity results in these case studies, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). The opposite was true for areas with lower GVF in the SPoRT model runs. These differences in the heating and evaporation rates produced subtle yet quantifiable differences in the simulated convective precipitation systems for the selected severe weather case examined.

Bell, Jordan R.↗

NASA NTRS2012

Using NASA Environmental Data to Enhance Public Health Decision Making

The Universities Space Research Association at the NASA Marshall Space Flight Center is collaborating with the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) to address issues of environmental health and enhance public health decision making by utilizing NASA remotely sensed data and products. The objectives of this collaboration are to develop high-quality spatial data sets of environmental variables, and deliver the data sets and associated analyses to local, state and federal end-user groups. These data can be linked spatially and temporally to public health data, such as mortality and disease morbidity, for further analysis and decision making. Three daily environmental data sets have been developed for the conterminous U.S. on different spatial resolutions for the time period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid utilizing the US Environmental Protection Agency (EPA) ground observations and NASA s MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Solar Insolation (SI) and maximum and minimum air temperature using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental data sets will be linked with public health data from the UAB REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline and other health outcomes. These environmental datasets and public health linkage analyses will be made available to public health professionals, researchers and the general public through the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system and through peer reviewed publications. To date, two of the data sets have been released to the public in CDC WONDER, Daily Air Temperature and Heat Index for years 1979-2010, and Daily Fine Particulate Matter (PM2.5) air quality measures for years 2003-2008. These data in CDC WONDER can be aggregated to the county-level, state-level, or regional-level as per users need and downloaded in tabular, graphical, and map formats. The summary statistical output are available to web and app developers via the WONDER Application Programming Interface (API). The linkage of these data with the CDC WONDER system provides a significant addition to CDC WONDER, allowing public health researchers and policy makers to better include environmental exposure data in the context of other health data available in CDC WONDER online system. It also substantially expands public access to NASA environmental data, making their use by a wide range of decision makers feasible.

Al-Hamdan, Mohammad↗

NASA NTRS2012

A Traceable Ground to On-Orbit Radiometric Calibration System for the Solar Reflective Wavelength Region

This paper describes the combination of a Mie scattering spectral BSDF and BTDF albedo standard whose calibration is traceable to the NIST SIRCUS Facility or the NIST STARR II Facility. The Space-based Calibration Transfer Spectroradiometer (SCATS) sensor uses a simple, invariant optical configuration and dedicated narrow band spectral channel modules to provide very accurate, polarization-insensitive, stable measurements of earth albedo and lunar disk albedo. Optical degradation effects on calibration stability are eliminated through use of a common optical system for observations of the Sun, Earth, and Moon. The measurements from space would be traceable to SI units through preflight calibrations of radiance and irradiance at NIST's SIRCUS facility and the invariant optical system used in the sensor. Simultaneous measurements are made in multiple spectral channels covering the solar reflective wavelength range of 300 nm to 2.4 microns. The large dynamic range of signals is handled by use of single-element, highly-linear detectors, stable discrete electronic components, and a non imaging optical configuration. Up to 19 spectral modules can be mounted on a single-axis drive to give direct pointing at the Earth and at least once per orbit view of the Sun and Moon. By observing the Sun on every orbit, the most stringent stability requirements of the system are limited to short time periods. The invariant optical system for both radiance and irradiance measurements also give excellent transfer to-orbit SI traceability. Emerging instrumental requirements for remotely sensing tropospheric trace species have led to a rethinking by some of the paradigm for Systeme International d'Unites (SI) traceability of the spectral irradiance and radiance radiometric calibrations to spectral albedo (sr(exp -1)) which is not a SI unit. In the solar reflective wavelength region the spectral albedo calibrations are tied often to either the spectral albedo of a solar diffuser or the Moon. This new type of Mie scattering diffuser (MSD) is capable of withstanding high temperatures, and is more Lambertian than Spectralon(tm). It has the potential of covering the entire solar reflective wavelength region. Laboratory measurements have shown that the specular reflectance component is negligible, and indicate that internal absorption by multiple scattering is small. This MSD, a true volume diffuser, exhibits a high degree of radiometric stability which suggests that measurements at the National Institute of Standards and Technology (NIST) could provide a spectral albedo standard. Measurements have been made of its radiometric stability under a simulated space environment of high energy gamma rays, high energy protons, and UV radiation from ambient down to the vacuum ultraviolet H Lyman alpha at 121.6 nm for its eventual use in space as a solar diffuser.

Heath, Donald F.↗

NASA NTRS2012

Estimating Marine Aerosol Particle Volume and Number from Maritime Aerosol Network Data

As well as spectral aerosol optical depth (AOD), aerosol composition and concentration (number, volume, or mass) are of interest for a variety of applications. However, remote sensing of these quantities is more difficult than for AOD, as it is more sensitive to assumptions relating to aerosol composition. This study uses spectral AOD measured on Maritime Aerosol Network (MAN) cruises, with the additional constraint of a microphysical model for unpolluted maritime aerosol based on analysis of Aerosol Robotic Network (AERONET) inversions, to estimate these quantities over open ocean. When the MAN data are subset to those likely to be comprised of maritime aerosol, number and volume concentrations obtained are physically reasonable. Attempts to estimate surface concentration from columnar abundance, however, are shown to be limited by uncertainties in vertical distribution. Columnar AOD at 550 nm and aerosol number for unpolluted maritime cases are also compared with Moderate Resolution Imaging Spectroradiometer (MODIS) data, for both the present Collection 5.1 and forthcoming Collection 6. MODIS provides a best-fitting retrieval solution, as well as the average for several different solutions, with different aerosol microphysical models. The average solution MODIS dataset agrees more closely with MAN than the best solution dataset. Terra tends to retrieve lower aerosol number than MAN, and Aqua higher, linked with differences in the aerosol models commonly chosen. Collection 6 AOD is likely to agree more closely with MAN over open ocean than Collection 5.1. In situations where spectral AOD is measured accurately, and aerosol microphysical properties are reasonably well-constrained, estimates of aerosol number and volume using MAN or similar data would provide for a greater variety of potential comparisons with aerosol properties derived from satellite or chemistry transport model data.

Sayer, A. M.↗

NASA NTRS2012

Multi-Angle Implementation of Atmospheric Correction for MODIS (MAIAC). Part 3: Atmospheric Correction

This paper describes the atmospheric correction (AC) component of the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) which introduces a new way to compute parameters of the Ross-Thick Li-Sparse (RTLS) Bi-directional reflectance distribution function (BRDF), spectral surface albedo and bidirectional reflectance factors (BRF) from satellite measurements obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS). MAIAC uses a time series and spatial analysis for cloud detection, aerosol retrievals and atmospheric correction. It implements a moving window of up to 16 days of MODIS data gridded to 1 km resolution in a selected projection. The RTLS parameters are computed directly by fitting the cloud-free MODIS top of atmosphere (TOA) reflectance data stored in the processing queue. The RTLS retrieval is applied when the land surface is stable or changes slowly. In case of rapid or large magnitude change (as for instance caused by disturbance), MAIAC follows the MODIS operational BRDF/albedo algorithm and uses a scaling approach where the BRDF shape is assumed stable but its magnitude is adjusted based on the latest single measurement. To assess the stability of the surface, MAIAC features a change detection algorithm which analyzes relative change of reflectance in the Red and NIR bands during the accumulation period. To adjust for the reflectance variability with the sun-observer geometry and allow comparison among different days (view geometries), the BRFs are normalized to the fixed view geometry using the RTLS model. An empirical analysis of MODIS data suggests that the RTLS inversion remains robust when the relative change of geometry-normalized reflectance stays below 15%. This first of two papers introduces the algorithm, a second, companion paper illustrates its potential by analyzing MODIS data over a tropical rainforest and assessing errors and uncertainties of MAIAC compared to conventional MODIS products.

Lyapustin, A.↗