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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Radiometric Model and Inter-Comparison Results of the SGLI-VNR On-Board Calibration

The Second Generation Global Imager (SGLI) on Global Change Observation Mission– Climate (GCOM-C) satellite empowers surface and atmospheric measurements related to the carbon cycle and radiation budget, with two radiometers of Visible and Near Infrared Radiometer (SGLI-VNR) and Infrared Scanning Radiometer (SGLI-IRS) that perform a wide-band (380 nm–12 µm) optical observation not only with as wide as a 1150–1400 km field of view (FOV), but also with as high as 0.25–0.5 km resolution. Additionally, polarization and along-track slant view observations are quite characteristic of SGLI. It is important to calibrate radiometers to provide the sensor data records for more than 28 standard products and 23 research products including clouds, aerosols, ocean color, vegetation, snow and ice, and other applications. In this paper, the radiometric model and the first results of on-board calibrations on the SGLI-VNR, which include weekly solar and light-emitting diode (LED) calibration and monthly lunar calibration, will be described. Each calibration data was obtained with corrections, where beta angle correction and avoidance of reflection from multilayer insulation (MLI) were applied for solar calibration; LED temperature correction was performed for LED calibration; and the GIRO (GSICS (Global Space-based Inter-Calibration System) Implementation of the ROLO (RObotic Lunar Observatory) model) model was used for lunar calibration. Results show that the inter-comparison of the relative degradation amount between these three calibrations agreed to within 1% or less.

Urabe, Tomoyuki↗
NASA NTRS2019

TPSAS-NF1676L-34012-DND

Earth’s climate system is highly interconnected, meaning that changes to the global climate influence the United States climatically and economically. In much the same way as European and Asian financial markets affect the U.S. economy, changes to ice sheet mass and energy flows in the far reaches of the planet affect our climate. Life on Earth is sensitive to climate conditions; human society is especially susceptible due to the climate-vulnerable, complex, and often fragile systems that provide food, water, energy, and security. Observed changes to the global climate affecting the United States include rising global temperatures, diminishing sea ice, melting ice sheets and glaciers, rising sea levels, etc. These documented changes have global economic and national security implications, including for the United States. For example, sea level rise alone is putting $100 billion dollars of U.S. military assets at risk, according to the Dept. of Defense. Arctic climate change continues to outpace the rest of the globe. Over the last 30 years, rapid and, in many cases, unprecedented changes to Arctic temperatures, sea ice, snow cover, land ice, and permafrost have occurred. While the Arctic may seem far away, changes in the Arctic climate system have a global reach, affecting sea level, the carbon cycle, atmospheric winds, ocean currents, and potentially the frequency of extreme weather. This presentation discusses the changes in the observed in the Arctic, the projected changes, and the potential impacts to us living the U.S.

Patrick C Taylor↗
NASA NTRSDate not supplied

TPSAS-NF1676L-32105-DND

NASA Langley Research Center is developing the High-Altitude Lidar Observatory (HALO) system to address the observational needs of NASA’s weather, climate, carbon cycle, and atmospheric composition focus areas. HALO is a multi-function airborne lidar being developed to measure atmospheric H2O and CH4 mixing ratios and aerosol/cloud/ocean optical properties using the Differential Absorption Lidar (DIAL) and High Spectral Resolution Lidar (HSRL) techniques, respectively. To respond to a wide range of airborne process studies, HALO can be rapidly reconfigured to provide either CH4 DIAL+HSRL, H2O DIAL+HSRL, or CH4 DIAL+H2O DIAL measurements using three different laser transmitters. During spring 2018 NASA Langley demonstrated the world’s first combined airborne CH4 DIAL and HSRL measurements from the Langley King Air aircraft during four test flights. The HALO methane configuration also participated in the Long Island Sound Tropospheric Ozone Study (LISTOS) air quality field campaign on the NASA B200 aircraft. The flown instrument configuration employed the DIAL technique at 1645 nm for column and multi-layer range resolved measurements of CH4 concentrations, and the HSRL technique at 532 nm to make independent, unambiguous retrievals of aerosol extinction and backscatter. It also employed the standard backscatter technique at 1064 nm and is polarization-sensitive at the 1064/532 nm wavelengths. The addition of the HSRL channels provides context to the airborne CH4 DIAL measurements such as source attribution, transport, and vertical mixing through mixed layer height retrievals, as well as providing the critical capability to validate aerosol induced biases from passive space-borne measurements of column CH4. In this presentation we focus on the instrument capabilities, initial CH4 measurements over oil and gas production sites in coordination with the ACT-America campaign, CH4 signatures over the Long Island Sound domain during the LISTOS campaign, and prospects for future airborne campaigns with cohosted payloads consisting of active and passive sensors.

Rory Barton-Grimley↗
NASA NTRSDate not supplied

TPSAS-NF1676L-27659-DND

The Arctic is warming fast, a trend expected to continue. Rapid Arctic warming has far-reaching implications affecting the global energy budget, sea level rise, the global carbon cycle, and potentially the atmospheric and oceanic circulation. Our understanding of the forcing mechanisms driving Arctic climate change has rapidly evolved in the last decade. For 40 years, the melting of sea ice and snow has been implicated as the primary driver of amplified Arctic warming, however our results indicate a shift, whereby changes in the atmospheric temperature, humidity, and clouds have surpassed the warming effects of snow and sea ice loss in the latest CMIP5 model simulations. We examine the impacts of climate feedbacks on the Arctic surface energy budget: namely surface albedo, clouds, shortwave clear-sky feedbacks, longwave clear-sky feedbacks, ocean heat content, and surface turbulent fluxes. Simulations from 16 CMIP5 models running RCP8.5, a high-emission scenario are used. We employ the methodology used in Lu and Cai (2009) on a single CMIP3 model allowing an analysis of changes between CMIP3 and CMIP5.

Robyn C Boeke↗
NASA NTRSDate not supplied

TPSAS-NF1676L-29139-DND

The Arctic has been warming at a rate outpacing globally-averaged warming by 2-3 times, a phenomenon known as "Arctic Amplification" (AA). AA is evident in observations of Arctic surface temperature over the last century as well as in model predictions. The accuracy of model predictions is crucial to determine our response to climate change, as Arctic changes have consequences for the atmospheric circulation, sea level rise, and the carbon cycle. Unfortunately, models show a wide range of possible futures and intermodel spread in projections of Arctic temperature is larger than for any other region: a 2°C global temperature increase- the limit set by the Paris Climate Agreement- would lead to an Arctic temperature change between 3-7°C according to Coupled Model Intercomparison 5 (CMIP5) models. This intermodel spread represents a key deficiency in the understanding of Arctic feedback processes and how changes in various physical parameters (e.g. sea ice cover, clouds, heat transport and surface fluxes) affect the temperature response. Using a process-oriented decomposition of the simulate Arctic surface energy budget, we attribute the largest contribution to the CMIP5 intermodel spead in projected Arctic warming to the Barents-Kara Sea region. Our results reveal that the increasing seasonal fluxing of ocean heat content is a significant part of the intermodel spread in AA and relates strongly to change in surface turbulent fluxes. Our results indicate that if we are to significantly reduce the intermodel spread in projected AA we must focus on understanding the behavior and physical processes that drive surface energy flows in the B-K sea region.

Patrick Taylor↗
NASA NTRS2020

Tropical rains controlling deposition of Saharan dust across the North Atlantic Ocean

Mineral dust plays an important role in the atmospheric radiation budget as well as in the ocean carbon cycle through fertilization and by ballasting of settling organic matter. However, observational records of open-ocean dust deposition are sparse. Here, we present the spatial and temporal evolution of Saharan dust deposition over 2 years from marine sediment traps across the North Atlantic, directly below the core of the Saharan dust plume, with highest dust fluxes observed in summer. We combined the observed deposition fluxes with model simulations and satellite observations and argue that dust deposition in the Atlantic is predominantly controlled by summer rains. The dominant depositional pathway changes from wet deposition in summer to dry deposition in winter. Wet deposition has previously been suggested to increase the release of dust-derived nutrients and their bioavailability, which may be a key contributor to surface-ocean productivity in remote and oligotrophic parts of the oceans.

Mineral dust↗
NASA NTRS2020

Assessment of Lidar XCH4 Measurements During ACT-America 2019 for Boundary Layer Methane and Emission Model Constraint

Atmospheric methane is a potent and abundant greenhouse gas (GHG) with increasing importance due to rising emissions and their subsequent impact on radiative forcing, indicating an important driving role in climate change. The recent Earth Science Decadal Survey called for further understanding of the sources and sinks of atmospheric methane, the processes that will affect their future concentrations, and identified the need for advanced observing capabilities to elucidate these issues. HALO, a combined methane Differential Absorption Lidar and Aerosol/Cloud High Spectral Resolution Lidar participated in the ACT-America summer 2019 campaign to help constrain the emissions and transport of GHGs in the atmosphere to address some of the aforementioned science goals. In this presentation we will discuss the utility of using an in-situ point measurement versus lidar column measurement for inventory and source identification applications. Comparisons between column measurements of methane from in-situ and lidar will be made to assess whether point measurements are useful when the assumption of a well-mixed boundary layer, a practice commonly used in the community, is valid. We will also show that a lidar column measurement can apportion boundary layer methane from the column and has the ability to identify boundary layer methane enhancements in fair weather conditions using atmospheric backscatter signals, a measurement capability that has the potential to transform the observational strategies of future airborne carbon cycle science investigations. We will also present on the differences and utility of constraining emission models using an in-situ versus lidar column and asses the resulting uncertainties of each.

Rory A Barton-Grimley↗
NASA NTRS2021

Advances in Land Surface Models and Indicators for Drought Monitoring and Prediction

Millions of people across the globe are affected by droughts every year, and recent droughts have highlighted the considerable agricultural impacts and economic costs of these events. Monitoring the state of droughts depends on integrating multiple indicators that each capture particular aspects of hydrologic impact and various types and phases of drought. As the capabilities of land surface models and remote sensing have improved, important physical processes such as dynamic, interactive vegetation phenology, groundwater, and snowpack evolution now support a range of drought indicators that better reflect coupled water, energy, and carbon cycle processes. In this work, we discuss these advances, including newer classes of indicators that can be applied to improve the characterization of drought onset, severity, and duration. We utilize a new model-based drought reconstruction to illustrate the role of dynamic phenology and groundwater in drought assessment. Further, through case studies on flash droughts, snow droughts, and drought recovery, we illustrate the potential advantages of advanced model physics and observational capabilities, especially from remote sensing, in characterizing droughts.

Christa D Peters-lidard↗
NASA NTRSDate not supplied

Space‐Based Observations for Understanding Changes in the Arctic‐Boreal Zone

A recent, interdisciplinary survey of more than 40 international remote sensing experts reviewed the strengths and limitations of current space-based observational capabilities of the Arctic Boreal Zone (ABZ) and made recommendations for improving upon these current capabilities (Duncan et al., 2019). General recommendations focused on the complementarity of sub-orbital and satellite observations, the need for measurement continuity, and the importance of stakeholder engagement. The development of a comprehensive and robust sub-orbital portion of an ABZ observing network can act to fill some temporal gaps in satellite coverage, provide detail unobtainable from space, and is necessary for validation and interpretation of satellite data. A priority across all types of observations is the continuation, enhancement, and/or creation of long-term, multi-satellite, climate-quality, and self-consistent data records of ABZ components, such as surface temperature, energy fluxes, or sea ice extent and volume, for improved determination of trends. Long-term passive satellite observations currently represent the only feasible option for monitoring change of the ABZ at broad spatial scales required to address pressing science challenges. Active sensors are particularly attractive for the ABZ as they do not depend on reflected sunlight and so join passive microwave sensors in having a significant advantage over passive visible and infrared sensors in the low-light conditions that are typical of the ABZ for several months of the year. Application of consistent retrieval algorithms to multiple data sets, as well as careful characterization of satellite instruments and their temporal evolution, helps to ensure data quality and consistency. Among the needs ranked as “Most Important” are those associated with gaining a process-based understanding of the ABZ carbon cycle and hydrologic cycle (which includes sea level rise) as they have the potential to affect a large portion of Earth’s population.

Remote sensing↗
NASA NTRSDate not supplied

Examining Soil Freeze/Thaw Remote Sensing Using NASA’s SLAPex Freeze/Thaw Airborne Campaign

Around half the exposed land of the Northern Hemisphere experiences seasonal freezing and thawing. The changes to the soil thermal, hydraulic, and mechanical properties are large and these, in turn, exert significant controls on the water, energy, and carbon cycles of the affected regions. Wide-area freeze/thaw discrimination via microwave remote sensing is possible using both passive and active techniques. We explore freeze/thaw remote sensing using observations from NASA’s Scanning L-band Active Passive (SLAP) airborne sensor during the SLAPex Freeze/Thaw campaign near Winnipeg, Canada in November, 2015. SLAP is an airborne simulator of the Soil Moisture Active Passive (SMAP) satellite mission. The SLAPex Freeze/Thaw dataset is a unique dataset that allows comparison of airborne-derived freeze/thaw state (plus soil moisture for thawed areas) against in-situ ground truth, satellite-derived freeze/thaw from SMAP, as well as tower-based passive microwave freeze/thaw observations. The sensitivity of passive and active microwave sensing to different depths of freezing and different dielectric models (when soil moisture is retrieved) is examined, and the spatial scaling of heterogeneous frozen/thawed pixels is also explored. Implications for the accuracy of satellite-based freeze/thaw discrimination are discussed.

Edward Kim↗
NASA NTRS2013

Soil Moisture Active Passive (SMAP) observer power system architecture

The Soil Moisture Active and Passive (SMAP) observer is scheduled for launch in 2014. The primary science objective of SMAP is the high-resolution mapping of earth’s soil moisture. These measurements will be used to enhance understanding of processes that link the water, energy and carbon cycles, and to extend the capabilities of weather and climate prediction models. The SMAP orbit, sun-synchronous with an altitude of 680 km, was chosen to optimize the sampling characteristics of the instruments while ensuring that the resolution requirements are met. The combined active radar and passive radiometer measurement approach takes advantage of the spatial resolutions of the radar (3 km x 1000 km) and the radiometer (39 km x 47 km).

Carr, Gregory↗
NASA NTRS2018

The Tropospheric Emission Spectrometer: From Discovery Mission to Earth System Sounding

The Tropospheric Emission Spectrometer (TES) was launched aboard the Aura spacecraft in 2004 and decommissioned in February 2018. Over more than a decade of observations, TES has shown the potential of high-resolution Fourier Transform Spectrometer (FTS) measurements to alter our understanding of atmospheric composition and its relationship to the Earth System. We survey some of the major contributions TES has made to understanding air quality, chemistry-climate interactions, atmospheric hydrology, and carbon cycle science. We then show how innovations in retrieval methods pioneered by the TES team are now being applied to multi-spectral, multi-instrument records, establishing a new paradigm for composition measurements into the future.

Bowman, Kevin W.↗
NASA NTRS2021

Advances in Land Surface Models and Indicators for Drought Monitoring and Prediction

Millions of people across the globe are affected by droughts every year, and recent droughts have highlighted the considerable agricultural impacts and economic costs of these events. Monitoring the state of droughts depends on integrating multiple indicators that each capture particular aspects of hydrologic impact and various types and phases of drought. As the capabilities of land-surface models and remote sensing have improved, important physical processes such as dynamic, interactive vegetation phenology, groundwater, and snow pack evolution now support a range of drought indicators that better reflect coupled water, energy and carbon cycle processes. In this work, we discuss these advances, including newer classes of indicators that can be applied to improve the characterization of drought onset, severity and duration. We utilize a new model-based drought reconstruction to illustrate the role of dynamic phenology and groundwater in drought assessment. Further, through case studies on flash droughts, snow droughts, and drought recovery, we illustrate the potential advantages of advanced model physics and observational capabilities, especially from remote sensing, in characterizing droughts.

Drought↗
NASA NTRS2019

Modeling Phenological Controls on Carbon Dynamics in Dryland Sagebrush Ecosystems

Dryland ecosystems play an important role in determining how precipitation anomalies affect terrestrial carbon fluxes at regional to global scales. Thus, to understand how climate change may affect the global carbon cycle, we must also be able to understand and model its effects on dryland vegetation. Dynamic Global Vegetation Models (DGVMs) are an important tool for modeling ecosystem dynamics, but they often struggle to reproduce seasonal patterns of plant productivity. Because the phenological niche of many plant species is linked to both total productivity and competitive interactions with other plants, errors in how process-based models represent phenology hinder our ability to predict climate change impacts. This may be particularly problematic in dryland ecosystems where many species have developed a complex phenology in response to seasonal variability in both moisture and temperature. Here, we examine how uncertainty in key parameters as well as the structure of existing phenology routines affect the ability of a DGVM to match seasonal patterns of leaf area index (LAI) and gross primary productivity (GPP) across a temperature and precipitation gradient. First, we optimized model parameters using a combination of site-level eddy covariance data and remotely-sensed LAI data. Second, we modified the model to include a semi-deciduous phenology type and added flexibility to the representation of grass phenology. While optimizing parameters reduced model bias, the largest gains in model performance were associated with the development of our new representation of phenology. This modified model was able to better capture seasonal patterns of both leaf area index (R(exp 2) = 0.75) and gross primary productivity (R(exp 2) = 0.84), though its ability to estimate total annual GPP depended on using eddy covariance data for optimization. The new model also resulted in a more realistic outcome of modeled competition between grass and shrubs. These findings demonstrate the importance of improving how DGVMs represent phenology in order to accurately forecast climate change impacts in dryland ecosystems.

Ecosystem model↗
NASA NTRS2020

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

radiative transfer↗
NASA NTRS2019

Satellite Ocean Colour: Current Status and Future Perspective

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and inter-annual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.

ocean colour↗
NASA NTRS2019

Chapter Five - Remote sensing techniques for estimating evaporation

Evaporation of water from the Earth’s surface into the atmosphere is central to the terrestrial energy, water, and carbon cycle. Remote sensing approaches to measure evaporation (ET) combine observable inputs to the energy and water balance within statistical or process-based methodologies. These ET products draw on satellite observations from across the electromagnetic spectrum. The ET retrievals are grouped in two main categories. One category includes approaches that combine meteorological descriptions of potential evaporation with a range of strategies to estimate evaporative stress. A second category includes surface energy balance approaches that retrieve latent heat from the thermal signatures. For each category a practical implementation example is described, including an outlook on progress towards multimodel assessment of global evaporation.

Evaporation↗