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NASA NTRS2012

Quasi-Global Precipitation as Depicted in the GPCPV2.2 and TMPA V7

After a lengthy incubation period, the year 2012 saw the release of the Global Precipitation Climatology Project (GPCP) Version 2.2 monthly dataset and the TRMM Multi-satellite Precipitation Analysis (TMPA) Version 7. One primary feature of the new data sets is that DMSP SSMIS data are now used, which entailed a great deal of development work to overcome calibration issues. In addition, the GPCP V2.2 included a slight upgrade to the gauge analysis input datasets, particularly over China, while the TMPA V7 saw more-substantial upgrades: 1) The gauge analysis record in Version 6 used the (older) GPCP monitoring product through April 2005 and the CAMS analysis thereafter, which introduced an inhomogeneity. Version 7 uses the Version 6 GPCC Full analysis, switching to the Version 4 Monitoring analysis thereafter. 2) The inhomogeneously processed AMSU record in Version 6 is uniformly processed in Version 7. 3) The TMI and SSMI input data have been upgraded to the GPROF2010 algorithm. The global-change, water cycle, and other user communities are acutely interested in how these data sets compare, as consistency between differently processed, long-term, quasi-global data sets provides some assurance that the statistics computed from them provide a good representation of the atmosphere's behavior. Within resolution differences, the two data sets agree well over land as the gauge data (which tend to dominate the land results) are the same in both. Over ocean the results differ more because the satellite products used for calibration are based on very different algorithms and the dominant input data sets are different. The time series of tropical (30 N-S) ocean average precipitation shows that the TMPA V7 follows the TMI-PR Combined Product calibrator, although running approximately 5% higher on average. The GPCP and TMPA time series are fairly consistent, although the GPCP runs approximately 10% lower than the TMPA, and has a somewhat larger interannual variation. As well, the GPCP and TMPA interannual variations have an apparent phase shift, with GPCP running a few months later. Additional diagnostics will include mean maps and selected scatter plots.

Huffman, George J.↗
NASA NTRS2020

Great Lakes Water Resources II: A Google Earth Engine Tool to Automate Wetland Mapping Using Optical and Radar Satellite Sensors in the Great Lakes Basin for Wetland Management and Monitoring

The Great Lakes Basin is one of the world’s largest freshwater ecosystems. The Basin harbors over 200,000 acres of wetlands. These wetlands provide a variety of environmental, ecological, and recreational functions to over 30 million people in the region. Some of these functions include improving water quality, mitigating flood impacts, providing wildlife habitat, and housing recreational activities. However, due to anthropogenic activities, habitat conversion and degradation threaten to disrupt or destroy remaining wetland ecosystems. Maps of wetland distribution based on ground surveys are costly and labor-intensive, prohibiting timely evaluations of wetland loss and gain. The Great Lakes Water Resources II team at the NASA Jet Propulsion Laboratory developed the Wetlands Extent Tool 2.0 (WET 2.0) in Google Earth Engine to automate mapping of wetland distribution in the Great Lakes Basin. The team partnered with the US Fish and Wildlife Service (USFWS), Environmental Protection Agency (EPA), Minnesota Department of Natural Resources (MDNR), the National Oceanic and Atmospheric Administration (NOAA), and Ducks Unlimited (DU). WET 2.0 incorporates Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) satellite data. WET 2.0 is trained to classify anywhere in the Great Lakes Basin. Utilizing a Random Forest classifier, WET 2.0 is capable of automatically mapping wetland extent in the entire Great Lakes Basin, achieving a mean overall accuracy of 80.12% when tested in Michigan. Findings and maps produced in WET 2.0 will enable our partners to identify areas of ecosystem degradation and wetland destruction in order to enact environmental practices and policy initiatives to maintain environmental and economic health in the area.

Water Resources↗
NASA NTRS2024

Vertical Structure of a Springtime Smoky and Humid Troposphere Over the Southeast Atlantic From Aircraft and Reanalysis

The springtime atmosphere over the southeast Atlantic Ocean (SEA) is subjected to a consistent layer of biomass burning (BB) smoke from widespread fires on the African continent. An elevated humidity signal is coincident with this layer, consistently proportional to the amount of smoke present. The combined humidity and BB aerosol has potentially significant radiative and dynamic impacts. Here, we use aircraft-based observations from the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) deployments in conjunction with reanalyses to characterize covariations in humidity and BB smoke across the SEA. The observed plume–vapor relationship, and its agreement with the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) and Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, persists across all observations, although the magnitude of the relationship varies as the season progresses. Water vapor is well represented by the reanalyses, while CAMS tends to underestimate carbon monoxide especially under high BB. While CAMS aerosol optical depth (AOD) is generally overestimated relative to ORACLES AOD, the observations show a consistent relationship between carbon monoxide (CO) and aerosol extinction, demonstrating the utility of the CO tracer to understanding vertical aerosol distribution. We next use k-means clustering of the reanalyses to examine multi-year seasonal patterns and distributions. We identify canonical profile types of humidity and of CO, allowing us to characterize changes in vapor and BB atmospheric structures, and their impacts as they covary. While the humidity profiles show a range in both total water vapor concentration and in vertical structure, the CO profiles primarily vary in terms of maximum concentration, with similar vertical structures in each. The distribution of profile types varies spatiotemporally across the SEA region and through the season, ranging from largely one type in the northeast and southwest to more evenly distributed between multiple types where air masses meet in the middle of the SEA. These distributions follow patterns of transport from the humid, smoky source region (greatest influence in the northeast of the SEA) and the seasonal changes in both humidity and smoke (increasing and decreasing through the season, respectively). With this work, we establish a framework for a more complete analysis of the broader radiative and dynamical effects of humid aerosols over the SEA.

Kristina Pistone↗
NASA NTRS1988

Tracer water transport and subgrid precipitation variation within atmospheric general circulation models

A capability is developed for monitoring tracer water movement in the three-dimensional Goddard Institute for Space Science Atmospheric General Circulation Model (GCM). A typical experiment with the tracer water model follows water evaporating from selected grid squares and determines where this water first returns to the Earth's surface as precipitation or condensate, thereby providing information on the lateral scales of hydrological transport in the GCM. Through a comparison of model results with observations in nature, inferences can be drawn concerning real world water transport. Tests of the tracer water model include a comparison of simulated and observed vertically-integrated vapor flux fields and simulations of atomic tritium transport from the stratosphere to the oceans. The inter-annual variability of the tracer water model results is also examined.

Koster, Randal D.↗
NASA NTRS2004

[Analysis of Multiplatform CO (Carbon Monoxide) Measurements During Trace-P Mission]

Carbon monoxide is considered mission critical (TRACE-P NRA) because it is one of the gases involved in controlling the oxidizing power of the atmosphere and, as a tracer gas, is valuable in interpreting mission data sets. Carbon monoxide exhibits interannual differences, suggesting relatively short-term imbalances in sources and sinks. Sources of CO are dominated by fossil fuel combustion, biomass burning, and the photochemical oxidation of CH4 and nonmethane hydrocarbons while reaction with OH is believed to be the major sink for atmospheric CO, with additional losses due to soil uptake. Uncertainties in the magnitude and distribution of both sources and sinks remain fairly large however, and additional data are required to refine the global budget. Seasonal changes and a northern hemispheric latitudinal gradient have been described for a variety of Pacific basin sites through long-term monitoring of surface background levels. Latitudinal variations have also recently been described at upper tropospheric altitudes over a multi-year period by. TRACE-P will provide an aircraft survey of CO over the northern Pacific in the northern spring when CO concentrations are at their seasonal maximum in the northern hemisphere (NH) and at their seasonal minimum in the southern hemisphere (SH). Previous GTE missions, Le., PEM West-B and PEM Tropics-B, ground-based, and satellite observations (MAPS, April 1994) give us a general picture of the distribution of CO over the northern Pacific during this season. Based on these measurements, background CO levels over remote ocean areas are anticipated to be in the range of 110 - 180 ppbv, while those closer to the Asian continent may rise as high as 600 ppbv. These measurements also reveal high spatial variability (both horizontal and vertical) as well as temporal variations in CO over the area planned for the TRACE-P mission. This variability is a result of multiple CO sources, the meteorological complexity of transport processes, and the photochemical aging of air masses. The influence of biomass burning in the southern Pacific should be relatively small since the mission coincides with the southern tropical wet season when agricultural burning is at its seasonal low. The proposed CO measurements taken during TRACE-P should therefore largely be a function of the impact of various NH sources, primarily Asian and predominantly fossil fuel combustion and biomass burning. These processes are also major sources of many other atmospheric pollutants, consequently making accurate and precise CO measurements is one of the highest TRACE-P priorities [TRACE-P NRA]. The TRACE-P mission emphasizes the dual objectives of assessing the magnitude of the transport of chemically and radiatively important gases such as CO from Asia to the western Pacific, and determining how emissions change and are modified during this transport.

Pougatchev, Nikita S.↗
NASA NTRSDate not supplied

Using Landsat 8 Satellite Imagery to Analyze Biogeochemical Constituents in the Waters of the San Francisco Bay Area and Beyond

The ocean's coastal zones play a key role in our planet's health and mitigate the adverse effects of climate change. Building on the success of NASA satellite imagery in mapping land cover changes around the globe in response to climate change, deforestation, and natural disasters, I have worked to determine how aquatic reflectance images from the Landsat 8 sensor could be correlated with biogeochemical constituents within the waters of the San Francisco Bay Area and California coastline. By analyzing Landsat 8 satellite imagery from the years 2013-2020 in collaboration with data from the United States Geological Survey (USGS), San Francisco Estuary Institute (SFEI), and universities involved with the Harmful Algal Bloom Monitoring and Alert Program (HABMAP), I investigated which areas of the San Francisco Bay and California coastline demonstrated significant correlations with values extracted from the satellite imagery. Using a Chlorophyll Index (CI) ratio calculated from two Landsat 8 satellite reflectance bands, it was found that there is a positive correlation between the CI and calculated oxygen, suspended particulate matter, and silicate from several USGS sampling points. In contrast, there is a significant negative correlation between the CI and salinity, nitrite, and phosphate at those same locations. Moreover, large suspected harmful algal blooms (HABs) along the California coastline seen in the Landsat 8 imagery were corroborated with HAB data from various institutions. Understanding the effectiveness of Landsat 8 aquatic reflectance images will allow scientists to predict HABs and other nutrient cycling that may be harmful to aquatic ecosystems around the world.

Landsat↗
NASA NTRS2004

Classification of Tropical Oceanic Precipitation using High-Altitude Aircraft Microwave and Electric Field Measurements

During the 1998 and 2001 hurricane seasons of the western Atlantic Ocean and Gulf of Mexico, the Advanced Microwave Precipitation Radiometer (AMPR), the ER-2 Doppler (EDOP) radar, and the Lightning Instrument Package (LIP) were flown aboard the NASA ER-2 high-altitude aircraft as part of the Third Convection and Moisture Experiment (CAMEX-3) and the Fourth Convection and Moisture Experiment (CAMEX-4). Several hurricanes, tropical storms, and other precipitation systems were sampled during these experiments. An oceanic rainfall screening technique has been developed using AMPR passive microwave observations of these systems collected at frequencies of 10.7, 19.35, 37.1, and 85.5 GHz. This technique combines the information content of the four AMPR frequencies regarding the gross vertical structure of hydrometeors into an intuitive and easily executable precipitation mapping format. The results have been verified using vertical profiles of EDOP reflectivity and lower-altitude horizontal reflectivity scans collected by the NOAA WP3D Orion radar. Matching the rainfall classification results with coincident electric field information collected by the LIP readily identifies convective rain regions within the precipitation fields. This technique shows promise as a real-time research and analysis tool for monitoring vertical updraft strength and convective intensity from airborne platforms such as remotely operated or uninhabited aerial vehicles. The technique is analyzed and discussed for a wide variety of precipitation types using the 26 August 1998 observations of Hurricane Bonnie near landfall.

Hood, Robbie E.↗
NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗
NASA NTRS1997

Scatterometer Observes Extratropical Transition of Pacific Typhoons

From September 15 to 25, 1996, NASA's scatterometer (NSCAT) monitored the evolution of twin typhoons-Violet and Tom-as they moved north from the western tropical Pacific, acquiring features of mid-latitude storms. The typhoons developed frontal structures, increased asymmetry, and dry air was introduced into their cores. Violet hit Japan, causing death and destruction, and Tom merged with a mid-latitude trough and evolved into a large extratropical storm with gale-force winds. We understand relatively little about the extratropical transition of tropical cyclones because of the complex thermodynamics involved, but we do know that the mid-latitude storms resulting from tropical cyclones usually generate strong winds and heavy precipitation. Since the transition usually occurs over the ocean, few measurements have been made. The transition is a fascinating science problem, but it also has important economic consequences. The transition occurs over the busiest trans-ocean shipping lanes, and when the resulting storms hit land, they usually devastate populated areas. NSCAT was successfully launched into a near-polar, sunsynchronous orbit on the Japanese Advanced Earth Observing Satellite (ADEOS) in August 1996 from Tanegashima Space Center in Japan. NSCAT's six antennas send microwave pulses at a frequency of 14 GHz to the Earth's surface and measure the backscatter.

Liu, W. Timothy↗
NASA NTRS1994

Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application

One essential parameter used in the estimation of radiative and turbulent heat fluxes from satellite data is surface temperature. Sea and land surface temperature (SST and LST) retrieval algorithms that utilize the thermal infrared portion of the spectrum have been developed, with the degree of success dependent primarily upon the variability of the surface and atmospheric characteristics. However, little effort has been directed to the retrieval of the sea ice surface temperature (IST) in the Arctic and Antarctic pack ice or the ice sheet surface temperature over Antarctica and Greenland. The reason is not one of methodology, but rather our limited knowledge of atmospheric temperature, humidity, and aerosol vertical, spatial and temporal distributions, the microphysical properties of polar clouds, and the spectral characteristics of snow, ice, and water surfaces. Over the open ocean the surface is warm, dark, and relatively homogeneous. This makes SST retrieval, including cloud clearing, a fairly straightforward task. Over the ice, however, the surface within a single satellite pixel is likely to be highly heterogeneous, a mixture of ice of various thicknesses, open water, and snow cover in the case of sea ice. Additionally, the Arctic is cloudy - very cloudy - with typical cloud cover amounts ranging from 60-90 percent. There are few observations of cloud cover amounts over Antarctica. The goal of this research is to increase our knowledge of surface temperature patterns and magnitudes in both polar regions, by examining existing data and improving our ability to use satellite data as a monitoring tool. Four instruments are of interest in this study: the AVHRR, ATSR, SMMR, and SSM/I. Our objectives are as follows. Refine the existing AVHRR retrieval algorithm defined in Key and Haefliger (1992; hereafter KH92) and applied elsewhere. Develop a method for IST retrieval from ATSR data similar to the one used for SST. Further investigate the possibility of estimating surface temperature from passive microwave data (in conjunction with AVHRR clear sky samples) through the use of 'effective emissivities' and physical relationships between skin temperature and subsurface temperature. Use the general method outlined in MK93 to calculate a 12-year record of clear sky equivalent surface temperatures, or possibly all-sky snow-ice interface physical temperatures, from SMMR and SSM/I, compare these temperatures to climatologies, ECMWF modeled surface temperatures, and surface temperatures predicted by a 2-D ice model. And intercompare several ice surface retrieval methods and validate them against ground measurements from the Swiss Camp on the Greenland ice sheet. Additionally, we intend to develop a surface temperature product based on AVHRR data and possibly blended with drifting buoy and meteorological station temperatures.

Key, Jeff↗
NASA NTRS1972

Studies of the inner shelf and coastal sedimentation environment of the Beaufort Sea from ERTS-1

The author has identified the following significant results. Field studies of the initiation of river flow onto the frozen Arctic Ocean were made by a group of scientists from May 15 to June 10, 1972. A time lapse camera mounted on a 40-ft. tower near the mouth of the Kaparuk River, west of Prudhoe Bay, provided a detailed 10-day record (24 hours a day) of flow direction and water level. It is believed that wind build-up, discharge variance of the river, and through-ice drainage rates are the prime factors influencing overflow onto the sea ice. Current meter, transmissometer, temperature, salinity, and thermoprobe data were collected from holes drilled in the shorefast ice from seal holes and from the river water overflow. Depth and a real distribution of the overflow water were monitored from the ice using snowmobiles, and from the air using helicopters and fixed-wing aircraft. Photographic records were made on 35mm KII, KX, Ektachrome IR, Plus X, and IR black and white film. A simple two camera frame permitted simultaneous exposures of two types of film. Preliminary comparisons of the IR and conventional films, both color and black and white, showed no advantage to using the infrared film. However, the low altitude photography taken during this study will be very helpful in the interpretation of ERTS-1 imagery.

Reimnitz, E.↗
NASA NTRS2001

Initial Results from the Micro-pulse Lidar Network (MPL-Net)

The micro-pulse lidar system (MPL) was developed in the early 1990s and was the first small, eye-safe, and autonomous lidar built for full time monitoring of cloud and aerosol vertical distributions. In 2000, a new project using MPL systems was started at NASA Goddard Space Flight Center. This new project, the Micro-pulse Lidar Network or MPL-Net, was created to provide long-term observations of aerosol and cloud vertical profiles at key sites around the world. This is accomplished using both NASA operated sites and partnerships with other organizations owning MPL systems. The MPL-Net sites are co-located with NASA AERONET sunphotometers to provide aerosol optical depth data needed for calibration of the MPL. In addition to the long-term sites, MPL-Net provides lidar support for a limited number of field experiments and ocean cruises each year. We will present an overview of the MPL-Net project and show initial results from the first two MPL-Net sites at the South Pole and at Goddard Space Flight Center. Observations of dust layers transported from the Gobi desert, across the Pacific Ocean, to the east coast of the United States will also be shown. MPL-Net affiliated instruments were in place at the desert source region in China, on a research vessel in the Sea of Japan, at ARM sites in Alaska and Oklahoma, and finally at our home site in Maryland (GSFC) during the massive dust storms that occurred in April 2001. The MPL observations of dust layers at each location are shown in comparison to dust layers predicted using the Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport model (GOCART). Finally, the MPL-Net project is the primary ground-validation program for the Geo-Science Laser Altimeter System (GLAS) satellite lidar project (launch date 2002). We will present an overview demonstrating how MPL-Net results are used to help prepare the GLAS data processing algorithms and assist in the calibration/validation of the GLAS data products.

Welton, Ellsworth J.↗
NASA NTRS2018

Climate Reanalysis: Progress and Future Prospects

Reanalysis is the process whereby an unchanging data assimilation system is used to provide a consistent reprocessing of observations, typically spanning an extended segment of the historical data record. The process relies on an underlying model to combine often-disparate observations in a physically consistent manner, enabling production of gridded data sets for a broad range of applications including the study of historical weather events, preparation of climatologies, business sector development and, more recently, climate monitoring. Over the last few decades, several generations of reanalyses of the global atmosphere have been produced by various operational and research centers, focusing more or less on the period of regular conventional and satellite observations beginning in the mid to late twentieth century. There have also been successful efforts to extend atmospheric reanalyses back to the late nineteenth and early twentieth centuries, using mostly surface observations. Much progress has resulted from (and contributed to) advancements in numerical weather prediction, especially improved models and data assimilation techniques, increased computing capacity, the availability of new observation types and efforts to recover and improve the quality of historical ones. The recent extension of forecast systems that allow integrated modeling of meteorological, oceanic, land surface, and chemical variables provide the basic elements for coupled data assimilation. This has opened the door to the development of a new generation of coupled reanalyses of the Earth system, or integrated Earth system analyses (IESA). Evidence so far suggests that this approach can improve the analysis of currently uncoupled components of the Earth system, especially at their interface, and lead to increased predictability. However, extensive analysis coupling as envisioned for IESA, while progressing, still presents significant challenges. These include model biases that can be exacerbated when coupled, component systems with different physical characteristics and different spatial and temporal scales, and component observations in different media with different spatial and temporal frequencies and different latencies. Quantification of uncertainty in reanalyses is also a critical challenge and is important for expanding their utility as a tool for climate change assessment. This talk provides a brief overview of the progress of reanalysis development during recent decades, and describes remaining challenges in the progression toward coupled Earth system reanalyses.

IES↗
NASA NTRS1997

Scatterometer Observes Extratropical Transition of Pacific Typhoons

From September 15 to 25, 1996, NASA's scatterometer (NSCAT) monitored the evolution of twin typhoons, Violet and Tom, as they moved north from the western tropical Pacific, acquiring features of mid-latitude storms. The typhoons developed frontal structures, increased asymmetry, and dry air was introduced into their cores. Violet hit Japan, causing death and destruction (Figure 1), and Tom merged with a mid-latitude trough and evolved into a large extratropical storm with gale-force winds (Figure 2). We understand relatively little about the extratropical transition of tropical cyclones because of the complex thermodynamics involved [e.g., Sinclair, 1993], but we do know that the mid-latitude storms resulting from tropical cyclones usually generate strong winds and heavy precipitation. Since the transition usually occurs over the ocean, few measurements have been made. The transition is a fascinating science problem, but it also has important economic consequences. The transition occurs over the busiest trans-ocean shipping lanes, and when the resulting storms hit land, they usually devastate populated areas. NSCAT was successfully launched into a near-polar, sun-synchronous orbit on the Japanese Advanced Earth Observing Satellite (ADEOS) in August 1996 from Tanegashima Space Center in Japan. NSCAT's six antennas send microwave pulses at a frequency of 14 GHz to the Earth's surface and measure the backscatter. The antennas scan two 600-km bands of the ocean, which are separated by a 330-km data gap. From NSCAT observations, surface wind vectors can be derived at 25-km spatial resolution, covering 77% of the ice-free ocean in one day and 97% of the ocean in two days, under both clear and cloudy conditions.

Liu, W. Timothy↗
NASA NTRS2010

Seagrass Health Modeling and Prediction with NASA Science Data

Previous research has demonstrated that MODIS data products can be used as inputs into the seagrass productivity model developed by Fong and Harwell (1994). To further explore this use to predict seagrass productivity, Moderate Resolution Imaging Spectroradiometer (MODIS) custom data products, including Sea Surface Temperature, Light Attenuation, and Chlorophyll-a have been created for use as model parameter inputs. Coastal researchers can use these MODIS data products and model results in conjunction with historical and daily assessment of seagrass conditions to assess variables that affect the productivity of the seagrass beds. Current monitoring practices involve manual data collection (typically on a quarterly basis) and the data is often insufficient for evaluating the dynamic events that influence seagrass beds. As part of a NASA-funded research grant, the University of Mississippi, is working with researchers at NASA and Radiance Technologies to develop methods to deliver MODIS derived model output for the northern Gulf of Mexico (GOM) to coastal and environmental managers. The result of the project will be a data portal that provides access to MODIS data products and model results from the past 5 years, that includes an automated process to incorporate new data as it becomes available. All model parameters and final output will be available through the use National Oceanic and Atmospheric Administration?s (NOAA) Environmental Research Divisions Data Access Program (ERDDAP) tools as well as viewable using Thematic Realtime Environmental Distributed Data Services (THREDDS) and the Integrated Data Viewer (IDV). These tools provide the ability to create raster-based time sequences of model output and parameters as well as create graphs of model parameters versus time. This tool will provide researchers and coastal managers the ability to analyze the model inputs so that the factors influencing a change in seagrass productivity can be determined over time.

Robinson, Harold D.↗
NASA NTRS2003

Discoveries about Tropical Tropospheric Ozone from Satellite and SHADOZ (Southern Hemisphere Additional Ozonesondes) and a Future Perspective on NASA's Ozone Sensors

We have been producing near-real tropical tropospheric ozone ('TTO') data from TOMS since 1997 with Prof. Hudson and students at the University of Maryland. Maps for 1996-2000 for the operational Earth-Probe instrument reside at: . We also have archived 'TTO' data from the Nimbus 7/TOMS satellite (1979-1992). The tropics is a region strongly influenced by natural variability and anthropogenic activity and the satellite data have been used to track biomass burning pollution and to detect interannual variability and climate signals in ozone. We look forward to future ozone sensors from NASA; four will be launched in 2004 as part of the EOS AURA Mission. The satellite view of chemical-dynamical interactions in tropospheric ozone is not adequate to capture vertical variability. Thus, in 1998, NASA's Goddard Space Flight Center, NOAA's Climate Monitoring and Diagnostics Laboratory (CMDL) and a team of international sponsors established the SHADOZ (Southern Hemisphere ADditional OZonesondes) project to address the gap in tropical ozone soundings. SHADOZ augments launches at selected sites and provides a public archive of ozonesonde data from twelve tropical and subtropical stations at http://croc.nsfc.nasa.gov/shadoz. The stations are: Ascension Island; Nairobi, Kenya; Irene, South Africa; R,union Island; Watukosek, Java; Fiji; Tahiti; American Samoa; San Cristobal, Galapagos; Natal, Brazil, Malindi, Kenya; Paramaribo, Surinam. From the first 3-4 years of data (presently greater than 1700 sondes), the following features emerge: (a) highly variable tropospheric ozone; (b) a zonal wave-one pattern in tropospheric column ozone; (c) tropospheric ozone variability over the Indian and Pacific Ocean displays strong convective signatures.

Thompson, Anne↗
NASA NTRS2023

A Multi-Pixel Split-Window Approach to Sea Surface Temperature Retrieval from Thermal Imagers with Relatively High Radiometric Noise: Preliminary Studies

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA), Land Surface Temperature Monitoring (LSTM) and NASA-JPL/ASI Surface Biology and Geology Thermal (SBG) missions or the secondary payload on board the ESA Earth Explorer 10 Harmony. The instruments on board these missions are expected to be characterized by an NEdT of ⪆0.1 K. In order to reduce the impact of radiometric noise on the retrieved sea surface temperature (SST), this study investigates the possibility of applying a multi-pixel atmospheric correction based on the hypotheses that (i) the spatial variability scales of radiatively active atmospheric variables are, on average, larger than those of the SST and (ii) the effect of atmosphere is accounted for via the split window (SW) difference. Based on 32 Sentinel 3 SLSTR case studies selected in oceanic regions where SST features are mainly driven by meso to sub-mesoscale turbulence (e.g., corresponding to major western boundary currents), this study documents that the local spatial variability of the SW difference term on the scale of ≃3 × 3 km2 is comparable with the noise associated with the SW difference. Similarly, the power spectra of the SW term are shown to have, for small scales, the behavior of white noise spectra. On this basis, we suggest to average the SW term and to use it for the atmospheric correction procedure to reduce the impact of radiometric noise. In principle, this methodology can be applied on proper scales that can be dynamically defined for each pixel. The applicability of our findings to high-resolution TIR missions is discussed and an example of an application to ECOSTRESS data is reported.

sea surface temperature↗
NASA NTRS2011

Monitoring Aerosols from Space: What We can Say, and What We Can't

Aerosols are understood to play a significant role is the global energy balance, and especially on atmospheric as well as surface energy balances regionally. A combination of direct radiative cooling of the surface, atmospheric warming through diabatic heating, and indirect effects of aerosol on clouds are all thought to contribute to the net aerosol effect, though the magnitudes of each are both highly variable in space and time, and highly uncertain. Passive space-based remote sensing is a key tool for constraining these effects, due to the frequent, global coverage satellites can provide. However, information from such observations about total-column aerosol amount (i.e., aerosol optical depth or AOD), and especially about aerosol type, is limited. The current generation of passive aerosol remote-sensing instruments, including the Multi-angle Imaging SpectroRadiometer (MISR) and the MODerate resolution Imaging Spectroradiometer (MODIS) offer vast improvements over previous instruments, including AOD over water and much of the land surface, fine vs. coarse particle type over ocean from MODIS, and discrimination of about a dozen aerosol types from MISR under good retrieval conditions, based on particle size, shape, and single-scattering albedo (SSA) constraints. This presentation will summarize the capabilities and expected improvements in the currently available aerosol products, in light of required energy budget constraints. Ways of addressing the need for detailed information about particle microphysical properties, especially SSA, unobtainable from MISR or MODIS, will be discussed.

Kahn, Ralph A.↗