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NASA NTRS2019

A Compilation of Global Bio-Optical in Situ Data for Ocean-Colour Satellite Applications – Version Two

A global compilation of in situ data is useful to evaluate the quality of ocean-colour satellite data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (including, inter alia, MOBY, BOUSSOLE, AERONETOC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO) and span the period from 1997 to 2018. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficients and total suspended matter. The data were from multi-project archives acquired via open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were propagated throughout the work and made available in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. This paper also describes the changes that were made to the compilation in relation to the previous version (Valente et al., 2016). The compiled data are available at https://doi.org/10.1594/PANGAEA.898188.

Andre Valente↗

NASA NTRS2009

American Meteorological Society (AMS) - The Modern Era Retrospective-Analysis for Research and Applications (MERRA) Data and Accessibility

The AM Short Course on The Modern Era Retrospective-analysis for Research and Applications (MERRA) data and accessibility will be held on January 11, 2009 preceding the 89th Annual Meeting in Phoenix, Arizona. Preliminary programs, registration, hotel, and general information will be posted on the AMS Web site in mid-September 2008. Retrospective-analyses (or reanalyses) have been established as an important tool in weather and climate research over the last decade. As computer power increases, the data assimilation and modeling systems improve and become more advanced, the input data quality increases and so reanalyses become more reliable. In 2008, NASA Global Modeling and Assimilation Office began producing a new reanalysis called the Modem Era Retrospective-analysis for Research and Applications (MERRA). The initial data from the reanalysis has been made available to the community and should be complete through 30 years (1979-present) by Fall of 2009. MERRA has taken advantage of the advancement of computing resources to provide users more data than previously available. The native spatial resolution is nominally 1/2 degrees and the surface two dimensional data are one hourly frequency. In addition to the meteorological analysis data, complete mass, energy and momentum budget data and also stratospheric data are provided. The eventual data holdings will exceed 150Tb. In order to facilitate user accessibility to the data, it will be stored in online hard drives (not tape storage) and available through several portals. Subsetting tools will also be available to allow users to tailor their data requests. The goals of this short course are to provide hands on users of reanalyses instruction on MERRA systems and also interactive experience with the online data and access tools. The course is intended for students and research scientists who will be actively interested in accessing and applying MERRA data in their weather, climate or applications work. The course has three parts. There will be an overview of the MERRA system, the validation of the system and the native data format. Second, Instructors will provide examples of weather and climate data analysis using various software packages (primarily GrADS) as well as the online access tools for subsetting and download, as well as visualization (e.g. Giovanni and Google Earth). This will also include examples on changing the data format to fit user's preferences and also to regrid the data for comparisons to other reanalyses and observational data. Lastly, there will he time set aside for participants to have hands on access to the data and software while interacting with the instructors and other developers. The course convener is Dr. Michael Bosilovich, NASA GSFC Global Modeling and Assimilation Office (GMAO). He will be joined by several GMAO, Goddard Earth Science Data and information Services Center (GES DISC) and Software Integration and Visualization Office (SIVO) staff.

daSilva, Arlindo↗

NASA NTRS2016

A Compilation of Global Bio-Optical in Situ Data for Ocean-Colour Satellite Applications

A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GePCO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately. The compiled data are available at doi:10.1594PANGAEA.854832 (Valente et al., 2015).

Valente, Andre↗

NASA NTRS1998

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model: Overview - Part 1

Knowledge of the amount of water in the soil is of great importance to many earth science disciplines. Soil moisture is a key variable in controlling the exchange of water and energy between the land surface and the atmosphere. Thus, soil moisture information is valuable in a wide range of applications including weather and climate, runoff potential and flood control, early warning of droughts, irrigation, crop yield forecasting, soil erosion, reservoir management, geotechnical engineering, and water quality. Despite the importance of soil moisture information, widespread and continuous measurements of soil moisture are not possible today. Although many earth surface conditions can be measured from satellites, we still cannot adequately measure soil moisture from space. Research in soil moisture remote sensing began in the mid 1970s shortly after the surge in satellite development. Recent advances in remote sensing have shown that soil moisture can be measured, at least qualitatively, by several methods. Quantitative measurements of moisture in the soil surface layer have been most successful using both passive and active microwave remote sensing, although complications arise from surface roughness and vegetation type and density. Early attempts to measure soil moisture from space-borne microwave instruments were hindered by what is now considered sub-optimal wavelengths (shorter than 5 cm) and the coarse spatial resolution of the measurements. L-band frequencies between 1 and 3 GHz (10-30 cm) have been deemed optimal for detection of soil moisture in the upper few centimeters of soil. The Electronically Steered Thinned Array Radiometer (ESTAR), an aircraft-based instrument operating a 1,4 GHz, has shown great promise for soil moisture determination. Initiatives are underway to develop a similar instrument for space. Existing space-borne synthetic aperture radars (SARS) operating at C- and L-band have also shown some potential to detect surface wetness. The advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

Crosson, William L.↗

NASA NTRS2015

Benchmark Data Set for Wheat Growth Models: Field Experiments and AgMIP Multi-Model Simulations.

The data set includes a current representative management treatment from detailed, quality-tested sentinel field experiments with wheat from four contrasting environments including Australia, The Netherlands, India and Argentina. Measurements include local daily climate data (solar radiation, maximum and minimum temperature, precipitation, surface wind, dew point temperature, relative humidity, and vapor pressure), soil characteristics, frequent growth, nitrogen in crop and soil, crop and soil water and yield components. Simulations include results from 27 wheat models and a sensitivity analysis with 26 models and 30 years (1981-2010) for each location, for elevated atmospheric CO2 and temperature changes, a heat stress sensitivity analysis at anthesis, and a sensitivity analysis with soil and crop management variations and a Global Climate Model end-century scenario.

climate change impact↗

NASA NTRS2008

NASA's Modern Era Retrospective-Analysis for Research and Applications (MERRA): Early Results and Future Directions

This talk will review the status and progress of the NASA/Global Modeling and Assimilation Office (GMAO) atmospheric global reanalysis project called the Modern Era Retrospective-Analysis for Research and Applications (MERRA). An overview of NASA's emerging capabilities for assimilating a variety of other Earth Science observations of the land, ocean, and atmospheric constituents will also be presented. MERRA supports NASA Earth science by synthesizing the current suite of research satellite observations in a climate data context (covering the period 1979-present), and by providing the science and applications communities with of a broad range of weather and climate data with an emphasis on improved estimates of the hydrological cycle. MERRA is based on a major new version of the Goddard Earth Observing System Data Assimilation System (GEOS-5), that includes the Earth System Modeling Framework (ESMF)-based GEOS-5 atmospheric general circulation model and the new NOAA National Centers for Environmental Prediction (NCEP) unified grid-point statistical interpolation (GST) analysis scheme developed as a collaborative effort between NCEP and the GMAO. In addition to MERRA, the GMAO is developing new capabilities in aerosol and constituent assimilation, ocean, ocean biology, and land surface assimilation. This includes the development of an assimilation capability for tropospheric air quality monitoring and prediction, the development of a carbon-cycle modeling and assimilation system, and an ocean data assimilation system for use in coupled short-term climate forecasting.

Schubert, Siegfried↗

NASA NTRSDate not supplied

The INSTEP Monitoring Network: Merging High-and-Low Cost Measurements to Characterize California Wildfires

Despite challenges with data quality and scope, low-cost sensor networks have skyrocketed in popularity over the last 15 years, making air quality data available on refined spatial scales. More recently, studies have leveraged both high and low-quality instruments to create stronger “hybrid” models, with most studies focusing on particulate matter. Low-cost measurements typically represent ground-level emissions only, providing context for human health issues from climate change-driven events such as wildfires. Since low-cost sensors’ capabilities are localized, daily events and microclimates tend to dominate the data rather than larger regional or atmospheric trends. Likewise, their low cost explains their high uncertainty. In contrast, some regulatory-grade instruments produce column measurements as well, providing reliable information on a broader scope. To bridge this gap while expanding into gas-phase measurements, we deployed 12 air quality sensor packages in California, USA during the 2022 wildfire season. These INSTEP (Inexpensive Network Sensor Technology Exploring Pollution) monitors measure carbon monoxide (CO), carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2), and several hydrocarbons including methane (CH4) and formaldehyde (HCHO). Half of the monitors were co-located with remote sensing spectrometers: NASA Pandora and Total Column Carbon Observing Network (TCCON). The overlap in pollutants includes NO2, O3, and HCHO between the INSTEP monitors and the Pandora column measurements. TCCON covers column CO, CO2, and CH4, rounding out our comparison. Most of the monitors were distributed throughout the San Francisco Bay area, and an additional three were located within 100 km of Los Angeles. The sites ranged in geographic and population characteristics, including desert, mountainous, coastal, and urban locations. Since varying environmental conditions such as temperature and pressure are known to challenge sensor performance, we will apply newer sensor “calibration” techniques meant to combat this. We will normalize our sensor signals by z-scoring them prior to applying a single calibration model in the form of multivariate linear regression or an artificial neural network. While this technique has been validated for the hydrocarbon and ozone sensor types (metal oxide), it has not yet been tested on electrochemical and non-dispersive infrared sensors, which are also used in the INSTEP monitors. This will serve as a test to see if this normalization technique – or another – is most effective in accounting for environmental differences among sensors. Related data analysis efforts have found success with a variety of geospatial analysis techniques, including weighted network models in which high-quality instruments are given higher weights than their low-cost counterparts. Our preliminary analysis will focus on kriging, which uses a Gaussian algorithm to assign weights, providing estimated pollution levels at locations between monitors. Smoke trajectory and evolution will also be considered using both measurement types. We also aim to baseline subtract our emission estimates from each region to determine which portion of emissions are regional and local, further characterizing burn differences in northern and southern California fires. Future directions include using INSTEP jointly with TEMPO satellite data, and mobile deployments on aircraft and uncrewed aerial vehicles (UAV).

Low-cost sensors↗

NASA NTRS2017

Airborne Measurements of Western U.S. Wildfire Emissions: Comparison with Prescribed Burning and Air Quality Implications

Wildfires emit significant amounts of pollutants that degrade air quality. Plumes from three wildfires in the western U.S. were measured from aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) and the Biomass Burning Observation Project (BBOP), both in summer 2013. This study reports an extensive set of emission factors (EFs) for over 80 gases and 5 components of submicron particulate matter (PM1) from these temperate wildfires. These include rarely, or never before, measured oxygenated volatile organic compounds and multifunctional organic nitrates. The observed EFs are compared with previous measurements of temperate wildfires, boreal forest fires, and temperate prescribed fires. The wildfires emitted high amounts of PM1 (with organic aerosol (OA) dominating the mass) with an average EF that is more than 2 times the EFs for prescribed fires. The measured EFs were used to estimate the annual wildfire emissions of carbon monoxide, nitrogen oxides, total non methane organic compounds, and PM1 from 11 western U.S. states. The estimated gas emissions are generally comparable with the 2011 National Emissions Inventory (NEI). However, our PM1 emission estimate (1530 +/- 570 Gg/yr) is over 3 times that of the NEI PM2.5 estimate and is also higher than the PM2.5 emitted from all other sources in these states in the NEI. This study indicates that the source of OA from biomass burning in the western states is significantly underestimated. In addition, our results indicate that prescribed burning may be an effective method to reduce fine particle emissions.

measured oxygenated volatile organic compounds↗

NASA NTRS2023

Potentially Underestimated Gas Flaring Activities—A New Approach to Detect Combustion Using Machine Learning and NASA’s Black Marble Product Suite

Monitoring changes in greenhouse gas (GHG) emission is critical for assessing climate mitigation efforts towards the Paris Agreement goal. A crucial aspect of science-based GHG monitoring is to provide objective information for quality assurance and uncertainty assessment of the reported emissions. Emission estimates from combustion events (gas flaring and biomass burning) are often calculated based on activity data (AD) from satellite observations, such as those detected from the visible infrared imaging radiometer suite (VIIRS) onboard the Suomi-NPP and NOAA-20 satellites. These estimates are often incorporated into carbon models for calculating emissions and removals. Consequently, errors and uncertainties associated with AD propagate into these models and impact emission estimates. Deriving uncertainty of AD is therefore crucial for transparency of emission estimates but remains a challenge due to the lack of evaluation data or alternate estimates. This work proposes a new approach using machine learning (ML) for combustion detection from NASA's Black Marble product suite and explores the assessment of potential uncertainties through comparison with existing detections. We jointly characterize combustion using thermal and light emission signals, with the latter improving detection of probable weaker combustion with less distinct thermal signatures. Being methodologically independent, the differences in ML-derived estimates with existing approaches can indicate the potential uncertainties in detection. The approach was applied to detect gas flares over the Eagle Ford Shale, Texas. We analyzed the spatio-temporal variations in detections and found that approximately 79.04% and 72.14% of the light emission-based detections are missed by ML-derived detections from VIIRS thermal bands and existing datasets, respectively. This improvement in combustion detection and scope for uncertainty assessment is essential for comprehensive monitoring of resulting emissions and we discuss the steps for extending this globally.

gas flaring↗

NASA NTRS2014

Assessment of Satellite Ocean Colour Radiometry and Derived Geophysical Products

Standardization of methods to assess and assign quality metrics to satellite ocean color radiometry and derived geophysical products has become paramount with the inclusion of the marine reflectance and chlorophyll-a concentration (Chla) as essential climate variables (ECV; [1]) and the recognition that optical remote sensing of the oceans can only contribute to climate research if and when a continuous succession of satellite missions can be shown to collectively provide a consistent, long-term record with known uncertainties. In 20 years, the community has made significant advancements toward that objective, but providing a complete uncertainty budget for all products and for all conditions remains a daunting task. In the retrieval of marine water-leaving radiance from observed top-of-atmosphere radiance, the sources of uncertainties include those associated with propagation of sensor noise and radiometric calibration and characterization errors, as well as a multitude of uncertainties associated with the modeling and removal of effects from the atmosphere and sea surface. This chapter describes some common approaches used to assess quality and consistency of ocean color satellite products and reviews the current status of uncertainty quantification in the field. Its focus is on the primary ocean color product, the spectrum of marine reflectance Rrs, but uncertainties in some derived products such as the Chla or inherent optical properties (IOPs) will also be considered.

Ocean↗

NASA NTRS2004

NASA GEOS-3/TRMM Re-analysis: Capturing Observed Tropical Rainfall Variability in Global Analysis for Climate Research

Understanding climate variability over a wide range of space-time scales requires a comprehensive description of the earth system. Global analyses produced by a fixed assimilation system (i.e., re-analyses) - as their quality continues to improve - have the potential of providing a vital tool for meeting this challenge. But at the present time, the usefulness of re-analyses is limited by uncertainties in such basic fields as clouds, precipitation, and evaporation - especially in the tropics, where observations are relatively sparse. Analyses of the tropics have long been shown to be sensitive to. the treatment of cloud precipitation processes, which remains a major source of uncertainty in current models. Yet, for many climate studies it is crucial that analyses can accurately reproduce the observed rainfall intensity and variability since a small error of 1 mm/d in surface rain translates into an error of approx. 30 W/sq m in energy (latent heat) flux. Currently, discrepancies between the observed and analyzed monthly-mean rain rates averaged to 100 km x 100 km resolution can exceed 4 mm/d (or 120 W/sq m ), compared to uncertainties in surface radiative fluxes of approx. 10-20 W/sq m . Improving precipitation in analyses would reduce a major source of uncertainty in the global energy budget. Uncertainties in tropical precipitation have also been a major impediment in understanding how the tropics interact with other regions, including the remote response to El Nino/Southern Oscillation (ENSO) variability on interannual time scales, the influence of Madden-Julian Oscillation (MJO) and monsoons on intraseasonal time scales. A global analysis that can replicate the observed precipitation variability together with physically consistent estimates of other atmospheric variables provides the key to breaking this roadblock. NASA Goddard Space Flight Center has been exploring the use of satellite-based microwave rainfall measurements in improving global analyses and has recently produced a multi-year, 1 x 1 TRMM re-analysis , which assimilates 6-hourly TMI and SSM/I surface rain rates over tropical oceans using a ID variational continuous assimilation (VCA) procedure in the GEOS-3 global data assimilation system. The analysis period extends from 1 November 1997 through 3 1 December 2002. The goal is to produce a multi-year global analysis that is dynamically consistent with available tropical precipitation observations for the community to assess its utility in climate applications and identify areas for further improvements. A distinct feature of the GEOS-3RRMh4 re-analysis is that its precipitation analysis is not derived from a short-term forecast (as done in most operational systems) but is given by a time- continuous model integration constrained by precipitation observations within a 6-h analysis window, while the wind, temperature, and pressure fields are allowed to directly respond to the improved precipitation and associated latent heating structures within the same analysis window. In this talk, I will assess the impact VCA precipitation assimilation on analyses of climate signals ranging from a few weeks to interannual time scales and compare results against other operational and reanalysis products.

Hou, Arthur Y.↗

NASA NTRS2023

Potomac River Basin Water Resources: Assessing Water Quality and Quantity in the National Capital Region Using NASA Earth Observations

The Potomac River Basin (PRB) is responsible for providing drinking water to over 5 million residents and plays a significant role in the health of the Chesapeake Bay. Therefore, it is important to understand the relationship between water quality, landcover, and the hydrological cycle within the PRB. The National Park Service (NPS) has monitored 37 streams within the National Park Units in Maryland, Virginia, West Virginia and Washington, D.C. This project aimed to help the NPS better understand trends in water quality to supplement their ability to monitor changes in the National Capital Region Network (NCRN). Google Earth Engine, ArcGIS Pro, R, and Python were used for data retrieval, visualization, and analysis. Earth observations included Landsat 5 TM and Landsat 8 OLI/TIRS imagery. Ancillary data included the USDA Cropland Data Layer, Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), and soil moisture data from the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). We compared Land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), precipitation and soil moisture data to water quality data provided by the NPS at a watershed level. LULC change maps were also generated for the PRB between 2008 and 2022. We found significant correlations between precipitation, soil moisture, NDVI, and water quality. Correlations were found between certain land use types and water quality metrics, but findings varied greatly between watersheds. These insights emphasize the imperative of strategic watershed management in preserving the integrity of key aquatic systems.

Landsat↗

NASA NTRS2007

High Spatial Resolution Thermal Infrared Remote Sensing Data for Analysis of the Atlanta, Georgia, Urban Heat Island Effect and Its Impacts on the Environment

The twenty-first century is the first "urban century" according to the United Nations Development Program. The focus of cities reflects awareness of the growing percentage of the world's population that lives in urban areas. In environmental terms, cities are the original producers of many of the global problems related to waste disposal, air and water pollution, and associated environmental and ecological challenges. Expansion of cities, both in population and areal extent, is a relentless process. In 2000, approximately 3 billion people representing about 40% of the global population, resided in urban areas. Urban population will continue to rise substantially over the next several decades according to UN estimates, and most of this growth will Occur in developing countries. The UN estimates that by 2025, 60% of the world's population will live in urban areas. As a consequence, the number of"megacities" (those cities with populations of 10 million inhabitants or more) will increase by 100 by 2025. Thus, there is a critical need to understand urban areas and what their impacts are on environmental, ecological and hydrologic resources, as well as on the local, regional, and even global climate. One of the more egregious side effects of urbanization is the increase in surface and air temperatures that lead to deterioration in air quality. In the United States, under the more stringent air quality guidelines established by the U.S. Environmental Protection Agency in 1997, nearly 300 counties in 34 states will not meet these new air quality standards for ground level ozone. Mitigation of the urban heat island (UHI) effect is actively being evaluated as a possible way to reduce ground ozone levels in cities and assist states in improving air quality. Foremost in the analysis of how the UHI affects air quality and other environmental factors is the use of remote sensing technology and data to characterize urban land covers in sufficient detail to quantifiably measure the impact of increased urban heating on air quality. The urban landscape impacts surface thermal energy exchanges that determine development of the UHI. This paper will illustrate how we are using high spatial remote sensing data collected over the Atlanta, Georgia metropolitan area in conjunction with other geographic information, to perform a detailed urban land cover classification and to determine the contribution of these land covers to the urban heat island effect. Also, the spatial arrangement of the land covers and the impact on urban heating from these selected patterns of development are evaluated. Additionally, this paper will show how these data are being used as inputs to improve air quality modeling for Atlanta, including potential benefits from UHI mitigation.

Quattrochi, Dale A.↗

NASA NTRS2010

Performance Analysis of Scientific and Engineering Applications Using MPInside and TAU

In this paper, we present performance analysis of two NASA applications using performance tools like Tuning and Analysis Utilities (TAU) and SGI MPInside. MITgcmUV and OVERFLOW are two production-quality applications used extensively by scientists and engineers at NASA. MITgcmUV is a global ocean simulation model, developed by the Estimating the Circulation and Climate of the Ocean (ECCO) Consortium, for solving the fluid equations of motion using the hydrostatic approximation. OVERFLOW is a general-purpose Navier-Stokes solver for computational fluid dynamics (CFD) problems. Using these tools, we analyze the MPI functions (MPI_Sendrecv, MPI_Bcast, MPI_Reduce, MPI_Allreduce, MPI_Barrier, etc.) with respect to message size of each rank, time consumed by each function, and how ranks communicate. MPI communication is further analyzed by studying the performance of MPI functions used in these two applications as a function of message size and number of cores. Finally, we present the compute time, communication time, and I/O time as a function of the number of cores.

Saini, Subhash↗

NASA NTRS2019

Global Validation of Columnar Water Vapor Derived from EOS MODIS-MAIAC Algorithm Against the Ground-Based AERONET Observations

The water vapor is a relevant greenhouse gas in the Earth's climate system, and satellite products become one of the most effective way to characterize and monitor the columnar water vapor (CWV) content at global scale. Recently, a new product (MCD19) was released as part of MODIS (Moderate Resolution Imaging Spectroradiometer) Collection 6 (C6). This operational product from the Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm includes a high 1-kilometer resolution CWV retrievals. This study presents the first global validation of MAIAC C6 CWV obtained from MODIS MCD19A2 product. This evaluation was performed using Aerosol Robotic Network (AERONET) observations at 265 sites (2000-2017). Overall, the results show a good agreement between MAIAC/AERONET CWV retrievals, with correlation coefficient higher than 0.95 and RMS (Root Mean Square) error lower than 0.250 centimeters. The binned error analysis revealed an underestimation (approximately 10 percent) of Aqua CWV retrievals with negative bias for CWV higher than 3.0 centimeters. In contrast, Terra CWV retrievals show a slope of regression close to unity and a low mean bias of 0.075 centimeters. While the accuracy is relatively similar between 1.0 and 5.0 centimeters for both sensor products, Terra dataset is more reliable for applications in humid tropical areas (less than 5.0 centimeters). The expected error was defined as plus or minus 15 percent, with less than 68 percent of retrievals falling within this envelope. However, the accuracy is regionally dependent, and lower error should be expected in some regions, such as South America and Oceania. Since MODIS instruments have exceeded their design lifetime, time series analysis was also presented for both sensor products. The temporal analysis revealed a systematic offset of global average between Terra and Aqua CWV records. We also found an upward trend (approximately 0.2 centimeters per decade) in Terra CWV retrievals, while Aqua CWV retrievals remain stable over time. The sensor degradation influences the ability to detect climate signals, and this study indicates the need for revisiting calibration of the MODIS bands 17-19, mainly for Terra instrument, to assure the quality of the MODIS water vapor product. Finally, this study presents a comprehensive validation analysis of MAIAC CWV over land, raising the understanding of its overall quality.

MAIAC↗

NASA NTRS2010

Use of Land Use Land Cover Change Mapping Products in Aiding Coastal Habitat Conservation and Restoration Efforts of the Mobile Bay NEP

The Mobile Bay region has undergone significant land use land cover change (LULC) over the last 35 years, much of which is associated with urbanization. These changes have impacted the region s water quality and wildlife habitat availability. In addition, much of the region is low-lying and close to the Gulf, which makes the region vulnerable to hurricanes, climate change (e.g., sea level rise), and sometimes man-made disasters such as the Deepwater Horizon (DWH) oil spill. Land use land cover change information is needed to help coastal zone managers and planners to understand and mitigate the impacts of environmental change on the region. This presentation discusses selective results of a current NASA-funded project in which Landsat data over a 34-year period (1974-2008) is used to produce, validate, refine, and apply land use land cover change products to aid coastal habitat conservation and restoration needs of the Mobile Bay National Estuary Program (MB NEP). The project employed a user defined classification scheme to compute LULC change mapping products for the entire region, which includes the majority of Mobile and Baldwin counties. Additional LULC change products have been computed for select coastal HUC-12 sub-watersheds adjacent to either Mobile Bay or the Gulf of Mexico, as part of the MB NEP watershed profile assessments. This presentation will include results of additional analyses of LULC change for sub-watersheds that are currently high priority areas, as defined by MB NEP. Such priority sub-watersheds include those that are vulnerable to impacts from the DWH oil spill, as well as sub-watersheds undergoing urbanization. Results demonstrating the nature and permanence of LULC change trends for these higher priority sub-watersheds and results characterizing change for the entire 34-year period and at approximate 10-year intervals across this period will also be presented. Future work will include development of value-added coastal habitat quality assessment products that will be used by the MB NEP and its partners in the planning of coastal conservation and restoration activities.

Spruce, Joseph P.↗

NASA NTRS2000

Lightweight Lidar Telescopes for Space Applications

NASA is intent on exploiting the unique perspective of space-based remote optical instruments to observe and study large-scale environmental processes. Emphasis on smaller and more affordable missions continues to force the remote sensing instruments to find innovative ways to reduce the size, weight, and cost of the sensor package. This is a challenge because many of the proposed instruments incorporate a high quality meter-class telescope that can be a significant driver of total instrument costs. While various methods for telescope weight reduction have been achieved, many of the current approaches rely on exotic materials and specialized manufacturing techniques that limit availability or substantially increase costs. A competitive lightweight telescope technology that is especially well suited to space-based coherent Doppler wind lidar has been developed through a collaborative effort involving NASA Marshall Space Flight Center (MSFC) through the Global Hydrology and Climate Center (GHCC) and the University of Alabama in Huntsville (UAH) at the Center for Applied Optics (CAO). The new lightweight optics using metal alloy shells and surfaces (LOMASS) fabrication approach is suitable for high quality metal mirrors and meter-class telescopes. Compared to alternative materials and fabrication methods the new approach promises to reduce the areal density of a meter-class telescope to less than 15-kg/sq m; deliver a minimum VIO-RMS surface optical quality; while using commercial materials and equipment to lower procurement costs. The final optical figure and finish is put into the mirrors through conventional diamond turning and polishing techniques. This approach is especially advantageous for a coherent lidar instrument because the reduced telescope weight permits the rotation of the telescope to scan the beam without requiring heavy wedges or additional large mirrors. Ongoing investigations and preliminary results show promise for the LOMASS approach to be successful in demonstrating a novel alternative approach to fabricating lightweight mirrors with performance parameters comparable with the Space Readiness Coherent Lidar Experiment (SPARCLE). Development and process characterization is continuing with the design and fabrication of mirrors for a 25-cm telescope suitable for a lidar instrument.

Peters, Bruce R.↗

NASA NTRS2007

MODIS Aerosol Observations used to Constrain Dust Distributions and Lifecycle in the NASA GEOS-5 Model

Approximately 240 Tg of mineral dust aerosol are transported annually from Saharan Africa to the Atlantic Ocean. Dust affects the Earth radiation budget, and plays direct (through scattering and absorption of radiation) and indirect (through modification of cloud properties and environment) roles in climate. Deposition of dust to the surface provides an important nutrient source to terrestrial and oceanic ecosystems. Dust is additionally a contributor to adverse air quality. Among the tools toward understanding the lifecycle and impacts of mineral dust aerosols are numerical models. Important constraints on these models come from quantitative satellite observations, like those from the space-based Moderate Resolution Imaging Spectroradiometer (MODIS). In particular, Kauhan et al. [2005] used MODIS aerosol observations to infer transport and deposition fluxes of Saharan dust over the Atlantic, Caribbean, and Amazonian basins. Those observations are used here to constrain the transport of dust and its interannual variability simulated in the NASA GEOS-5 general circulation model and data assimilation system. Significant uncertainty exists in the MODIS-derived fluxes, however, due to uncertainty in the wind fields provided by meteorological analyses in this region. That same uncertainty in the wind fields is manifest in our GEOS-5 simulations of dust distributions. Here we use MODIS observations to investigate the seasonality and location of the Saharan dust plume and explore through sensitivity analysis of our model the meteorological controls on the dust distribution, including dust direct radiative effects and sub-gridscale source and sink processes.

Colarco, P.↗