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NASA NTRS2014

Comparison of Marine Boundary Layer Cloud Properties from CERES-MODIS Edition 4 and DOE ARM AMF Measurements at the Azores

Marine boundary layer (MBL) cloud properties derived from the NASA Clouds and the Earth's Radiant Energy System (CERES) project using Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data are compared with observations taken at the Department of Energy Atmospheric Radiation Measurement (ARM) Mobile Facility at the Azores (AMF-Azores) site from June 2009 through December 2010. Cloud properties derived from ARM ground-based observations were averaged over a 1 h interval centered at the satellite overpass time, while the CERES-MODIS (CM) results were averaged within a 30 km×30 km grid box centered over the Azores site. A total of 63 daytime and 92 nighttime single-layered overcast MBL cloud cases were selected from 19 months of ARM radar-lidar and satellite observations. The CM cloud top/base heights (Htop/Hbase) were determined from cloud top/base temperatures (Ttop/Tbase) using a regional boundary layer lapse rate method. For daytime comparisons, the CM-derived Htop (Hbase), on average, is 0.063 km (0.068 km) higher (lower) than its ARM radar-lidar-observed counterpart, and the CM-derived Ttop and Tbase are 0.9 K less and 2.5 K greater than the surface values with high correlations (R(sup 2) = 0.82 and 0.84, respectively). In general, the cloud top comparisons agree better than the cloud base comparisons, because the CM cloud base temperatures and heights are secondary products determined from cloud top temperatures and heights. No significant day-night difference was found in the analyses. The comparisons of MBL cloud microphysical properties reveal that when averaged over a 30 km× 30 km area, the CM-retrieved cloud droplet effective radius (re) at 3.7 micrometers is 1.3 micrometers larger than that from the ARM retrievals (12.8 micrometers), while the CM-retrieved cloud liquid water path (LWP) is 13.5 gm( exp -2) less than its ARM counterpart (114.2 gm( exp-2) due to its small optical depth (9.6 versus 13.7). The differences are reduced by 50% when the CM averages are computed only using the MODIS pixel nearest the AMF site. Using the effective radius retrieved using 2.1 micrometers channel to calculate LWP can reduce the difference between the CM and ARM microwave radiometer retrievals from 13.7 to 2.1 gm2. The 10% differences between the ARM and CERES-MODIS LWP and r(sub e) retrievals are within the uncertainties of the ARM LWP (approximately 20gm( exp -2)) and r(sub e) (approximately 10%) retrievals; however, the 30% difference in optical depth is significant. Possible reasons contributing to this discrepancy are increased sensitivities in optical depth from both surface retrievals when τ is approximately 10 and topography. The τ differences vary with wind direction and are consistent with the island orography.Much better agreement in τ is obtained when using only those data taken when the wind is from the northeast, where topographical effects on the sampled clouds are minimal.

Xi, Baike↗

NASA NTRS2015

Impact of Asian Aerosols on Precipitation Over California: An Observational and Model Based Approach

Dust and pollution emissions from Asia are often transported across the Pacific Ocean to over the western United States. Therefore, it is essential to fully understand the impact of these aerosols on clouds and precipitation forming over the eastern Pacific and western United States, especially during atmospheric river events that account for up to half of California's annual precipitation and can lead to widespread flooding. In order for numerical modeling simulations to accurately represent the present and future regional climate of the western United States, we must account for the aerosol-cloud-precipitation interactions associated with Asian dust and pollution aerosols. Therefore, we have constructed a detailed study utilizing multi-sensor satellite observations, NOAA-led field campaign measurements, and targeted numerical modeling studies where Asian aerosols interacted with cloud and precipitation processes over the western United States. In particular, we utilize aerosol optical depth retrievals from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), NOAA Geostationary Operational Environmental Satellite (GOES-11), and Japan Meteorological Agency (JMA) Multi-functional Transport Satellite (MTSAT) to effectively detect and monitor the trans-Pacific transport of Asian dust and pollution. The aerosol optical depth (AOD) retrievals are used in assimilating the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in order to provide the model with an accurate representation of the aerosol spatial distribution across the Pacific. We conduct WRF-Chem model simulations of several cold-season atmospheric river events that interacted with Asian aerosols and brought significant precipitation over California during February-March 2011 when the NOAA CalWater field campaign was ongoing. The CalWater field campaign consisted of aircraft and surface measurements of aerosol and precipitation processes that help extensively validate our WRF-Chem model simulations. After validating the capability of the WRF-Chem in realistically simulating the aerosol-cloud precipitation interactions, we conduct sensitivity studies where the AOD is doubled to diagnose whether an increasing concentration of Asian aerosols over the western United States will lead to further impacts on the cloud and precipitation processes over California. We also perform sensitivity studies where the aerosols will be partitioned into dust-only and pollution-only in order to separate the impacts of the differing Asian aerosol species. The results of our WRF-Chem model simulations aim to show that the trans-Pacific transport of Asian aerosols influence the precipitation associated with atmospheric river events that can ultimately impact the regional climate of the western United States. 1 University

Naeger, Aaron R.↗

NASA NTRS2015

Development of a Near-Real Time Hail Damage Swath Identification Algorithm for Vegetation

The Midwest is home to one of the world's largest agricultural growing regions. Between the time period of late May through early September, and with irrigation and seasonal rainfall these crops are able to reach their full maturity. Using moderate to high resolution remote sensors, the monitoring of the vegetation can be achieved using the red and near-infrared wavelengths. These wavelengths allow for the calculation of vegetation indices, such as Normalized Difference Vegetation Index (NDVI). The vegetation growth and greenness, in this region, grows and evolves uniformly as the growing season progresses. However one of the biggest threats to Midwest vegetation during the time period is thunderstorms that bring large hail and damaging winds. Hail and wind damage to crops can be very expensive to crop growers and, damage can be spread over long swaths associated with the tracks of the damaging storms. Damage to the vegetation can be apparent in remotely sensed imagery and is visible from space after storms slightly damage the crops, allowing for changes to occur slowly over time as the crops wilt or more readily apparent if the storms strip material from the crops or destroy them completely. Previous work on identifying these hail damage swaths used manual interpretation by the way of moderate and higher resolution satellite imagery. With the development of an automated and near-real time hail swath damage identification algorithm, detection can be improved, and more damage indicators be created in a faster and more efficient way. The automated detection of hail damage swaths will examine short-term, large changes in the vegetation by differencing near-real time eight day NDVI composites and comparing them to post storm imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua and Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi NPP. In addition land surface temperatures from these instruments will be examined as for hail damage swath identification. Initial validation of the automated algorithm is based upon Storm Prediction Center storm reports but also the National Severe Storm Laboratory (NSSL) Maximum Estimated Size Hail (MESH) product. Opportunities for future work are also shown, with focus on expansion of this algorithm with pixel-based image classification techniques for tracking surface changes as a result of severe weather.

Bell, Jordan R.↗

NASA NTRS2011

ISSARS Aerosol Database : an Incorporation of Atmospheric Particles into a Universal Tool to Simulate Remote Sensing Instruments

The Instrument Simulator Suite for Atmospheric Remote Sensing (ISSARS) entered its third and final year of development with an overall goal of providing a unified tool to simulate active and passive space borne atmospheric remote sensing instruments. These simulations focus on the atmosphere ranging from UV to microwaves. ISSARS handles all assumptions and uses various models on scattering and microphysics to fill the gaps left unspecified by the atmospheric models to create each instrument's measurements. This will help benefit mission design and reduce mission cost, create efficient implementation of multi-instrument/platform Observing System Simulation Experiments (OSSE), and improve existing models as well as new advanced models in development. In this effort, various aerosol particles are incorporated into the system, and a simulation of input wavelength and spectral refractive indices related to each spherical test particle(s) generate its scattering properties and phase functions. These atmospheric particles being integrated into the system comprise the ones observed by the Multi-angle Imaging SpectroRadiometer(MISR) and by the Multiangle SpectroPolarimetric Imager(MSPI). In addition, a complex scattering database generated by Prof. Ping Yang (Texas A&M) is also incorporated into this aerosol database. Future development with a radiative transfer code will generate a series of results that can be validated with results obtained by the MISR and MSPI instruments; nevertheless, test cases are simulated to determine the validity of various plugin libraries used to determine or gather the scattering properties of particles studied by MISR and MSPI, or within the Single-scattering properties of tri-axial ellipsoidal mineral dust particles database created by Prof. Ping Yang.

Instrument Simulator Suite for Atmospheric Remote ↗

NASA NTRS2011

Analysis of Ultra High Resolution Sea Surface Temperature Level 4 Datasets

Sea surface temperature (SST) studies are often focused on improving accuracy, or understanding and quantifying uncertainties in the measurement, as SST is a leading indicator of climate change and represents the longest time series of any ocean variable observed from space. Over the past several decades SST has been studied with the use of satellite data. This allows a larger area to be studied with much more frequent measurements being taken than direct measurements collected aboard ship or buoys. The Group for High Resolution Sea Surface Temperature (GHRSST) is an international project that distributes satellite derived sea surface temperatures (SST) data from multiple platforms and sensors. The goal of the project is to distribute these SSTs for operational uses such as ocean model assimilation and decision support applications, as well as support fundamental SST research and climate studies. Examples of near real time applications include hurricane and fisheries studies and numerical weather forecasting. The JPL group has produced a new 1 km daily global Level 4 SST product, the Multiscale Ultrahigh Resolution (MUR), that blends SST data from 3 distinct NASA radiometers: the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Very High Resolution Radiometer (AVHRR), and the Advanced Microwave Scanning Radiometer ? Earth Observing System(AMSRE). This new product requires further validation and accuracy assessment, especially in coastal regions.We examined the accuracy of the new MUR SST product by comparing the high resolution version and a lower resolution version that has been smoothed to 19 km (but still gridded to 1 km). Both versions were compared to the same data set of in situ buoy temperature measurements with a focus on study regions of the oceans surrounding North and Central America as well as two smaller regions around the Gulf Stream and California coast. Ocean fronts exhibit high temperature gradients (Roden, 1976), and thus satellite data of SST can be used in the detection of these fronts. In this case, accuracy is less of a concern because the primary focus is on the spatial derivative of SST. We calculated the gradients for both versions of the MUR data set and did statistical comparisons focusing on the same regions.

sea surface temperature SST)↗

NASA NTRS2014

Validation of Land Surface Temperature Products Derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) Using Ground-based and Heritage Satellite Measurements

Thermal infrared satellite observations of the Earth's surface are widely used to retrieve Land Surface Temperature (LST) and monitor LST changes around the world. Since January 2012, the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) has provided daily observations of LST with a spatial resolution of 750 m at nadir. Comparison of the standard VIIRS LST product with the equivalent daily standard product from the Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 and with ground-based measurements over vegetated and inland water surfaces showed good agreement. Analysis indicated the accuracy and precision of the VIIRS product over these cover types was 0.2 K and 2.0 K respectively provided the analyses included appropriate compensation for any spatial heterogeneity in LST within the validation site. However, comparisons between in situ LST and the VIIRS and MODIS LST.

Land surface temperature↗

NASA NTRS2013

High Resolution Aerosol Data from MODIS Satellite for Urban Air Quality Studies

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not suitable for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM(sub 2.5) as measured by the 27 EPA ground monitoring stations was investigated. These results were also compared to conventional MODIS 10 km AOD retrievals (MOD04) for the same days and locations. The coefficients of determination for MOD04 and for MAIAC are R(exp 2) =0.45 and 0.50 respectively, suggested that AOD is a reasonably good proxy for PM(sub 2.5) ground concentrations. Finally, we studied the relationship between PM(sub 2.5) and AOD at the intra-urban scale (10 km) in Boston. The fine resolution results indicated spatial variability in particle concentration at a sub-10 kilometer scale. A local analysis for the Boston area showed that the AOD-PM(sub 2.5) relationship does not depend on relative humidity and air temperatures below approximately 7 C. The correlation improves for temperatures above 7 - 16 C. We found no dependence on the boundary layer height except when the former was in the range 250-500 m. Finally, we apply a mixed effects model approach to MAIAC aerosol optical depth (AOD) retrievals from MODIS to predict PM(sub 2.5) concentrations within the greater Boston area. With this approach we can control for the inherent day-to-day variability in the AOD-PM(sub 2.5) relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance. Our results show that the model-predicted PM(sub 2.5) mass concentrations are highly correlated with the actual observations (out-of-sample R(exp 2) of 0.86). Therefore, adjustment for the daily variability in the AOD-PM(sub 2.5) relationship provides a means for obtaining spatially-resolved PM(sub 2.5) concentrations.

remote sensing↗

NASA NTRS2015

Effect of Cloud Fraction on Near-Cloud Aerosol Behavior in the MODIS Atmospheric Correction Ocean Color Product

Characterizing the way satellite-based aerosol statistics change near clouds is important for better understanding both aerosol-cloud interactions and aerosol direct radiative forcing. This study focuses on the question of whether the observed near-cloud increases in aerosol optical thickness and particle size may be explained by a combination of two factors: (i) Near-cloud data coming from areas with higher cloud fractions than far-from-cloud data and (ii) Cloud fraction being correlated with aerosol optical thickness and particle size. This question is addressed through a statistical analysis of aerosol parameters included in the MODIS (MODerate resolution Imaging Spectroradiometer) ocean color product. Results from ten Septembers (2002-2011) over part of the northeast Atlantic Ocean confirm that the combination of these two factors working together explains a significant but not dominant part (in our case, 15%-30%) of mean optical thickness changes near clouds. Overall, the findings show that cloud fraction plays a large role in shaping the way aerosol statistics change with distance to clouds. This implies that both cloud fraction and distance to clouds are important to consider when aerosol-cloud interactions or aerosol direct radiative effects are examined in satellite or modeling studies.

Remote sensing↗

NASA NTRS2010

Ten Years of MISR Observations from Terra: Looking Back, Ahead, and in Between

The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been collecting global Earth data from NASA's Terra satellite since February 2000. With its nine along-track view angles, four visible/near-infrared spectral bands, intrinsic spatial resolution of 275 m, and stable radiometric and geometric calibration, no instrument that combines MISR's attributes has previously flown in space. The more than 10-year (and counting) MISR data record provides unprecedented opportunities for characterizing long-term trends in aerosol, cloud, and surface properties, and includes 3-D textural information conventionally thought to be accessible only to active sensors.

remote sensing↗

NASA NTRS2014

A Physically Based Algorithm for Non-Blackbody Correction of Cloud-Top Temperature and Application to Convection Study

Cloud-top temperature (CTT) is an important parameter for convective clouds and is usually different from the 11-micrometers brightness temperature due to non-blackbody effects. This paper presents an algorithm for estimating convective CTT by using simultaneous passive [Moderate Resolution Imaging Spectroradiometer (MODIS)] and active [CloudSat 1 Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] measurements of clouds to correct for the non-blackbody effect. To do this, a weighting function of the MODIS 11-micrometers band is explicitly calculated by feeding cloud hydrometer profiles from CloudSat and CALIPSO retrievals and temperature and humidity profiles based on ECMWF analyses into a radiation transfer model.Among 16 837 tropical deep convective clouds observed by CloudSat in 2008, the averaged effective emission level (EEL) of the 11-mm channel is located at optical depth; approximately 0.72, with a standard deviation of 0.3. The distance between the EEL and cloud-top height determined by CloudSat is shown to be related to a parameter called cloud-top fuzziness (CTF), defined as the vertical separation between 230 and 10 dBZ of CloudSat radar reflectivity. On the basis of these findings a relationship is then developed between the CTF and the difference between MODIS 11-micrometers brightness temperature and physical CTT, the latter being the non-blackbody correction of CTT. Correction of the non-blackbody effect of CTT is applied to analyze convective cloud-top buoyancy. With this correction, about 70% of the convective cores observed by CloudSat in the height range of 6-10 km have positive buoyancy near cloud top, meaning clouds are still growing vertically, although their final fate cannot be determined by snapshot observations.

Physical Algorithm↗

NASA NTRS2015

Chlorophyll Variability in the Oligotrophic Gyres: Mechanisms, Seasonality and Trends

A 16-year (1998-2013) analysis of trends and seasonal patterns was conducted for the 5 subtropical ocean gyres using chlorophyll-a (Chl-a) retrievals from ocean color satellite data, sea surface temperature (SST) obtained from optimally interpolated Advanced Very High Resolution Radiometer (AVHRR) data, and sea-level anomaly (SLA) from Aviso multi-sensor altimetry data. Trend analysis was also performed on mixed-layer data derived from gridded temperature and salinity profiles (1998-2010) from the Simple Ocean Data Assimilation (SODA) model. The Chl-a monthly composites were constructed from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate-resolution Imaging Spectroradiometer (MODIS) on Aqua using two different algorithms: the standard algorithm (STD) that has been in use since the start of the SeaWiFS mission in 1997, and a more recently developed Ocean Color Index (OCI) algorithm that is purported to provide improved accuracy in low chlorophyll waters such as the oligotrophic regions of the subtropical gyres. Trends were obtained for all gyres using both STD and OCI algorithms, which demonstrated generally consistent results. The North Pacific, Indian Ocean, North Atlantic and South Atlantic gyres showed significant downward trends in Chl-a, while the South Pacific gyre has a much weaker upward trend with no statistical significance. Time series of satellite-derived net primary production (NPP) showed downward trends for all the gyres, while all 5 gyres exhibited positive trends in SST and SLA. The seasonal variability of Chl-a in each gyre is tightly coupled to the variability in mixed layer depth (MLD) with peak values in winter in both hemispheres when vertical mixing is more vigorous, reaching depths approaching the nutricline (ZNO3, here defined as the depth of the 0.2 micron nitrate concentration). On a seasonal basis, Chl-a concentrations increase when the MLD approaches or is deeper than the nutricline depth, in agreement with the concept that vertical mixing is the major driving mechanism for phytoplankton photosynthesis in the interior of the gyres. In addition, MLD and SST seasonal changes are well correlated indicating that SST is a reasonable index of vertical mixing in the gyres. The combination of surface warming trends and biomass reduction over the 16-year period has the potential to reduce atmospheric CO2 uptake by the gyres and therefore influence the global carbon cycle.

Long-term trends↗

NASA NTRS2014

Observation of Mountain Lee Waves with MODIS NIR Column Water Vapor

Mountain lee waves have been previously observed in data from the Moderate Resolution Imaging Spectroradiometer (MODIS) "water vapor" 6.7 micrometers channel which has a typical peak sensitivity at 550 hPa in the free troposphere. This paper reports the first observation of mountain waves generated by the Appalachian Mountains in the MODIS total column water vapor (CWV) product derived from near-infrared (NIR) (0.94 micrometers) measurements, which indicate perturbations very close to the surface. The CWV waves are usually observed during spring and late fall or some summer days with low to moderate CWV (below is approx. 2 cm). The observed lee waves display wavelengths from3-4 to 15kmwith an amplitude of variation often comparable to is approx. 50-70% of the total CWV. Since the bulk of atmospheric water vapor is confined to the boundary layer, this indicates that the impact of thesewaves extends deep into the boundary layer, and these may be the lowest level signatures of mountain lee waves presently detected by remote sensing over the land.

satellite remote sensing↗

NASA NTRS2014

Air Pollution Over the Ganges Basin and Northwest Bay of Bengal in the Early Postmonsoon Season Based on NASA MERRAero Data

The MERRA Aerosol Reanalysis (MERRAero) has been recently developed at NASA's Global Modeling Assimilation Office. This reanalysis is based on a version of the Goddard Earth Observing System-5 (GEOS-5) model radiatively coupled with Goddard Chemistry, Aerosol, Radiation, and Transport aerosols, and it includes assimilation of bias-corrected aerosol optical thickness (AOT) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on both Terra and Aqua satellites. In October over the period 2002-2009, MERRAero showed that AOT was lower over the east of the Ganges basin than over the northwest of the Ganges basin: this was despite the fact that the east of the Ganges basin should have produced higher anthropogenic aerosol emissions because of higher population density, increased industrial output, and transportation. This is evidence that higher aerosol emissions do not always correspond to higher AOT over the areas where the effects of meteorological factors on AOT dominate those of aerosol emissions. MODIS AOT assimilation was essential for correcting modeled AOT mainly over the northwest of the Ganges basin, where AOT increments were maximal. Over the east of the Ganges basin and northwest Bay of Bengal (BoB), AOT increments were low and MODIS AOT assimilation did not contribute significantly to modeled AOT. Our analysis showed that increasing AOT trends over northwest BoB (exceeding those over the east of the Ganges basin) were reproduced by GEOS-5, not because of MODIS AOT assimilation butmainly because of the model capability of reproducing meteorological factors contributing to AOT trends. Moreover, vertically integrated aerosol mass flux was sensitive to wind convergence causing aerosol accumulation over northwest BoB.

AOT↗

NASA NTRS2014

Land, Cryosphere, and Nighttime Environmental Products from Suomi NPP VIIRS: Overview and Status

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar-orbiting Partnership (S-NPP: http://npp.gsfc.nasa.gov/). VIIRS was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer (AVHRR) and provide observation continuity with NASA's Earth Observing System's (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS). Since the VIIRS first-light images were received in November 2011, NASA and NOAA funded scientists have been working to evaluate the instrument performance and derived products to meet the needs of the NOAA operational users and the NASA science community. NOAA's focus has been on refining a suite of operational products known as Environmental Data Records (EDRs), which were developed according to project specifications under the former National Polar-orbiting Environmental Satellite System (NPOESS). The NASA S-NPP Science Team has focused on evaluating the EDRs for science use, developing and testing additional products to meet science data needs and providing MODIS data product continuity. This paper will present to-date findings of the NASA Science Team's evaluation of the VIIRS Land and Cryosphere EDRs, specifically Surface Reflectance, Land Surface Temperature, Surface Albedo, Vegetation Indices, Surface Type, Active Fires, Snow Cover, Ice Surface Temperature, and Sea Ice Characterization (http://viirsland.gsfc.nasa.gov/index.html). The paper will also discuss new capabilities being developed at NASA's Land Product Evaluation and Test Element (http://landweb.nascom.nasa.gov/NPP_QA/); including downstream data and products derived from the VIIRS Day/Night Band (DNB).

land cover land use↗

NASA NTRS2015

Next-Generation Angular Distribution Models for Top-of-Atmosphere Radiative Flux Calculation from the CERES Instruments: Methodology

The top-of-atmosphere (TOA) radiative fluxes are critical components to advancing our understanding of the Earth's radiative energy balance, radiative effects of clouds and aerosols, and climate feedback. The Clouds and the Earth's Radiant Energy System (CERES) instruments provide broadband shortwave and longwave radiance measurements. These radiances are converted to fluxes by using scene-type-dependent angular distribution models (ADMs). This paper describes the next-generation ADMs that are developed for Terra and Aqua using all available CERES rotating azimuth plane radiance measurements. Coincident cloud and aerosol retrievals, and radiance measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological parameters from Goddard Earth Observing System (GEOS) data assimilation version 5.4.1 are used to define scene type. CERES radiance measurements are stratified by scene type and by other parameters that are important for determining the anisotropy of the given scene type. Anisotropic factors are then defined either for discrete intervals of relevant parameters or as a continuous functions of combined parameters, depending on the scene type. Significant differences between the ADMs described in this paper and the existing ADMs are over clear-sky scene types and polar scene types. Over clear ocean, we developed a set of shortwave (SW) ADMs that explicitly account for aerosols. Over clear land, the SW ADMs are developed for every 1 latitude1 longitude region for every calendar month using a kernel-based bidirectional reflectance model. Over clear Antarctic scenes, SW ADMs are developed by accounting the effects of sastrugi on anisotropy. Over sea ice, a sea-ice brightness index is used to classify the scene type. Under cloudy conditions over all surface types, the longwave (LW) and window (WN) ADMs are developed by combining surface and cloud-top temperature, surface and cloud emissivity, cloud fraction, and precipitable water. Compared to the existing ADMs, the new ADMs change the monthly mean instantaneous fluxes by up to 5Wm(exp -2) on a regional scale of 1 deg. latitude x 1 deg. longitude, but the flux changes are less than 0.5Wm(exp -2) on a global scale.

Su, W.↗

NASA NTRS2014

Estimation of Crop Gross Primary Production (GPP): Fapar(sub Chl) Versus MOD15A2 FPAR

Photosynthesis (PSN) is a pigment level process in which antenna pigments (predominately chlorophylls) in chloroplasts absorb photosynthetically active radiation (PAR) for the photochemical process. PAR absorbed by foliar non-photosynthetic components is not used for PSN. The fraction of PAR absorbed (fAPAR) by a canopy/vegetation (i.e., fAPAR(sub canopy)) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) images, referred to as MOD15A2 FPAR, has been used to compute absorbed PAR (APAR) for PSN (APARPSN) which is utilized to produce the standard MODIS gross primary production (GPP) product, referred to as MOD17A2 GPP. In this study, the fraction of PAR absorbed by chlorophyll throughout the canopy (fAPAR(sub chl)) was retrieved from MODIS images for three AmeriFlux crop fields in Nebraska. There are few studies in the literature that compare the performance of MOD15A2 FPAR versus fAPAR(sub chl) in GPP estimation. In our study MOD15A2 FPAR and the retrieved fAPAR(sub chl) were compared with field fAPAR(sub canopy) and the fraction of PAR absorbed by green leaves of the vegetation (fAPAR(sub green)). MOD15A2 FPAR overestimated field fAPAR(sub canopy) in spring and in fall, and underestimated field fAPAR(sub canopy) in midsummer whereas fAPAR(sub chl) correctly captured the seasonal phenology. The retrieved fAPAR(sub chl) agreed well with field fAPAR(sub green) at early crop growth stage in June, and was less than field fAPAR(sub green) in late July, August and September. GPP estimates with fAPAR(sub chl) and with MOD15A2 FPAR were compared to tower flux GPP. GPP simulated with fAPAR(sub chl) was corroborated with tower flux GPP. Improvements in crop GPP estimation were achieved by replacing MOD15A2 FPAR with fAPAR(sub chl) which also reduced uncertainties of crop GPP estimates by 1.12-2.37 g C m(sup −2) d(sup −1).

GPM↗

NASA NTRS2014

Management and Climate Contributions to Satellite-Derived Active Fire Trends in the Contiguous United States

Fires in croplands, plantations, and rangelands contribute significantly to fire emissions in the United States, yet are often overshadowed by wildland fires in efforts to develop inventories or estimate responses to climate change. Here we quantified decadal trends, interannual variability, and seasonality of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) observations of active fires (thermal anomalies) as a function of management type in the contiguous U.S. during 2001-2010. We used the Monitoring Trends in Burn Severity database to identify active fires within the perimeter of large wildland fires and land cover maps to identify active fires in croplands. A third class of fires defined as prescribed/other included all residual satellite active fire detections. Large wildland fires were the most variable of all three fire types and had no significant annual trend in the contiguous U.S. during 2001-2010. Active fires in croplands, in contrast, increased at a rate of 3.4 percent per year. Cropland and prescribed/other fire types combined were responsible for 77 percent of the total active fire detections within the U.S and were most abundant in the south and southeast. In the west, cropland active fires decreased at a rate of 5.9 percent per year, likely in response to intensive air quality policies. Potential evaporation was a dominant regulator of the interannual variability of large wildland fires, but had a weaker influence on the other two fire types. Our analysis suggests it may be possible to modify landscape fire emissions within the U.S. by influencing the way fires are used in managed ecosystems.

Satellite-derived↗

NASA NTRS2015

Retrieving the Height of Smoke and Dust Aerosols by Synergistic Use of VIIRS, OMPS, and CALIOP Observations

Aerosol Single scattering albedo and Height Estimation (ASHE) algorithm was first introduced in Jeong and Hsu (2008) to provide aerosol layer height as well as single scattering albedo (SSA) for biomass burning smoke aerosols. One of the advantages of this algorithm was that the aerosol layer height can be retrieved over broad areas, which had not been available from lidar observations only. The algorithm utilized aerosol properties from three different satellite sensors, i.e., aerosol optical depth (AOD) and Ångström exponent (AE) from Moderate Resolution Imaging Spectroradiometer (MODIS), UV aerosol index (UVAI) from Ozone Monitoring Instrument (OMI), and aerosol layer height from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Here, we extend the application of the algorithm to Visible Infrared Imaging Radiometer Suite (VIIRS) and Ozone Mapping and Profiler Suite (OMPS) data. We also now include dust layers as well as smoke. Other updates include improvements in retrieving the AOD of nonspherical dust from VIIRS, better determination of the aerosol layer height from CALIOP, and more realistic input aerosol profiles in the forward model for better accuracy.

aerosol↗