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NASA NTRS2022

Development of a “Nature Run” for Observing System Simulation Experiments (OSSEs) for Snow Mission Development

Snow is a fundamental component of global and regional water budgets, particularly in mountainous areas and regions downstream that rely on snowmelt for water resources. Land surface models (LSMs) are commonly used to develop spatially distributed estimates of snow water equivalent (SWE) and runoff. However, LSMs are limited by uncertainties in model physics and parameters, among other factors. In this study, we describe the use of model calibration tools to improve snow simulations within the Noah-MP LSM as the first step in an observing system simulation experiment (OSSE). Noah-MP is calibrated against the University of Arizona (UA) SWE product over a western Colorado domain. With spatially varying calibrated parameters, we run calibrated and default Noah-MP simulations for water years 2010–20. By evaluating both simulations against the UA dataset, we show that calibration decreases domain averaged temporal RMSE and bias for snow depth from 0.15 to 0.13 m and from −0.036 to −0.0023 m, respectively, and improves the timing of snow ablation. Increased snow simulation performance also improves estimates of model-simulated runoff in four of six study basins, though only one has statistically significant improvement. Spatially distributed Noah-MP snow parameters perform better than default uniform values. We demonstrate that calibrating variables related to snow albedo calculations and rain–snow partitioning, among other processes, is a necessary step for creating a nature run that reasonably approximates true snow conditions for the OSSEs. Additionally, the inclusion of a snowfall scaling term can address biases in precipitation from meteorological forcing datasets, further improving the utility of LSMs for generating reliable spatiotemporal estimates of snow.

Melissa L. Wrzesien↗

NASA NTRS2022

Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System

The NASA LIS/WRF-Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi-scale surface hydrological modeling capabilities of the WRF-Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub-surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF-Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open-loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF-Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.

Timothy M. Lahmers↗

NASA NTRS2022

Evaluation of High Mountain Asia-Land Data Assimilation System (version 1) from 2003 to 2016: 2. The impact of assimilating satellite-based snow cover and freeze/thaw observations into a land surface model

This second paper of the two-part series focuses on demonstrating the impact of assimilating satellite-based snow cover and freeze/thaw observations into the hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point-scale, basin-scale, and domain-scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point-scale assessment is mainly conducted via evaluating against ground-based measurements. In general, the assimilation enabled estimates are better than no-assimilation counterparts. Second, the basin-scale runoff assessment demonstrates that across three snow-dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness-of-fit statistics through mitigating the negative effects brought by the fixed long-term precipitation correction factors. For example, when forced by the bias-corrected precipitation, the assimilation-enabled experiment improves the bias by 69%, the root-mean-squared error by 30%, and the unbiased root-mean-squared error by 18% (relative to the no-assimilation counterpart). Finally, the domainscale assessment is conducted via evaluating against satellite-based SWE and skin temperature products. Both sets of domain-scale analysis further corroborate the findings in the point-scale evaluations. Overall, this study suggests the benefits of the proposed multi-variate assimilation system in improving the cryospherichydrological process within a land surface model for use in HMA.

Yuan Xue↗

NASA NTRS2022

Evaluation of High Mountain Asia-Land Data Assimilation System (version 1) from 2003 to 2016: 2. The impact of assimilating satellite-based snow cover and freeze/thaw observations into a land surface model

This second paper of the two-part series focuses on demonstrating the impact of assimilating satellite-based snow cover and freeze/thaw observations into the hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point-scale, basin-scale, and domain-scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point-scale assessment is mainly conducted via evaluating against ground-based measurements. In general, the assimilation enabled estimates are better than no-assimilation counterparts. Second, the basin-scale runoff assessment demonstrates that across three snow-dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness-of-fit statistics through mitigating the negative effects brought by the fixed long-term precipitation correction factors. For example, when forced by the bias-corrected precipitation, the assimilation-enabled experiment improves the bias by 69%, the root-mean-squared error by 30%, and the unbiased root-mean-squared error by 18% (relative to the no-assimilation counterpart). Finally, the domain-scale assessment is conducted via evaluating against satellite-based SWE and skin temperature products. Both sets of domain-scale analysis further corroborate the findings in the point-scale evaluations. Overall, this study suggests the benefits of the proposed multi-variate assimilation system in improving the cryospheric-hydrological process within a land surface model for use in HMA.

Yuan Xue↗

NASA NTRS2023

Quantifying the Importance of Selected Drought Indicators for the United States Drought Monitor

Using information theory, our study quantifies the importance of selected indicators for the U.S. Drought Monitor (USDM) maps. We use the technique of mutual information (MI) to measure the importance of any indicator to the USDM, and because MI is derived solely from the data, our findings are independent of any model structure (conceptual, physically-based, or empirical). We also compare these MIs against the drought representation effectiveness ratings in the North America Drought Indices and Indicators Assessment (NADIIA) survey for Koeppen climate zones. This reveals: [1] agreement between some ratings and our MI values (high for example indicators like Standardized Precipitation-Evapotranspiration Index or SPEI); [2] some divergences (for example, soil moisture has high ratings but near-zero MIs for ESA-CCI soil moisture in the Western U.S., indicating the need of another remotely sensed soil moisture source); and [3] new insights into the importance of variables such as Snow Water Equivalent (SWE) that are not included in sources like NADIIA. Further analysis of the MI results yields findings related to: [1] hydrological mechanisms (summertime SWE domination during individual drought events through snowmelt into the water-scarce soil); [2] hydroclimatic types (the top pair of inputs in the Western and non-Western regions are SPEIs and soil moistures respectively); and [3] predictability (high for the California 2012-2017 event, with longer-timescale indicators dominating). Finally, the high MIs between multiple indicators jointly and the USDM indicate potentially high drought forecasting accuracies achievable using only model-based inputs, and the potential for global drought monitoring using only remotely sensed inputs, especially for locations having insufficient in situ observations.

Drought↗

NASA NTRS2023

Can Remotely Sensed Snow Disappearance Explain Seasonal Water Supply?

Understanding the relationship between remotely sensed snow disappearance and seasonal water supply may become vital in coming years to supplement limited ground based, in situ measurements of snow in a changing climate. For the period 2001–2019, we investigated the relationship between satellite derived Day of Snow Disappearance (DSD)—the date at which snow has completely disappeared—and the seasonal water supply, i.e., the April—July total streamflow volume, for 15 snow dominated basins across the western U.S. A Monte Carlo framework was applied, using linear regression models to evaluate the predictive skill—defined here as a model’s ability to accurately predict seasonal flow volumes—of varied predictors, including DSD and in situ snow water equivalent (SWE), across a range of spring forecast dates. In all basins there is a statistically significant relationship between mean DSD and seasonal water supply (p ≤ 0.05), with mean DSD explaining roughly half of the variance. Satellite-based model skill improves later in the forecast season, surpassing the skill of in-situ-based (SWE) models in skill in 10 of the 15 basins by the latest forecast date. We found little to no correlation between model error and basin characteristics such as elevation and the ratio of snow water equivalent to total precipitation. Despite a relatively short data record, this exploratory analysis shows promise for improving seasonal water supply prediction, in particular for snow dominated basins lacking in situ observations.

snow remote sensing↗

NASA NTRS2024

Vermont Wildland Fires: Investigating the Role of Antecedent Conditions and Recent Environmental Trends in Exacerbating Fire Risk and Potential in Vermont

Under a changing climate, increases in dry conditions and extreme heat events are projected to exacerbate wildfire risk in the northeastern U.S. In recent years, Vermont has observed higher annual temperatures, more frequent heatwaves, increased annual precipitation, extreme flood events, and decreased snowfall. The mechanisms through which environmental factors contribute to increased fire risk in humid environments, such as Vermont, are poorly understood. The team partnered with the National Weather Service, the Vermont Division of Forests, and the University of Vermont to investigate phenological trends and antecedent conditions influencing wildland fire risk. For the phenological analysis, from 2001 to 2023, vegetation data, phenological dates, and snow water equivalent (SWE) values were accessed from Landsat 5Thematic Mapper (TM), Landsat 7Enhanced Thematic Mapper Plus (ETM+), Landsat 8Operational Land Imager (OLI), Landsat 9OLI-2, the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Snow Data Assimilation System (SNODAS), respectively. For the antecedent condition analysis, from 2008 to 2023, soil moisture, Environmental Stress Index, wind speed, minimum relative humidity, and daily precipitation data were obtained from the Global Land Data Assimilation System (GLDAS), SERVIR, gridMET Wind, gridMET Humidity, and NClimGrid, respectively. The study found that green-up dates over the study period remain relatively stable, while snowmelt dates appear increasingly variable. Minimum relative humidity was the most significant environmental variable correlated with wildfire risk in Vermont. Results from this study will inform the National Weather Service's preparation of fire forecasts before prescribed burns and support community outreach by the Vermont Agency of Natural Resources.

Wildland Fire↗

NASA NTRS1985

Light-induced effects-impacts to module performance measurements and reliability testing: An overview

The stability of solar cells is a key factor in determining the reliability of photovoltaic modules and is of great interest in the case of solar cells having a new technology which has not yet been fully developed. In particular this question arises with hydrogenated amorphous silicon (a-Si) solar cells because a-Si exhibits reversible light induced changes in its electronic properties, commonly referred to as the Staebler-Wronski effect (SWE). Continuous progress is being made in the peak conversion efficiencies of a-Si solar cells and efficiencies in excess of 11% have been achieved. However, stability is still a problem. ARCO Solar reports results on solar cells which, after over a year's exposure to sunlight, under open circuit conditions, still have about 7% conversion efficiency. Other results show a region of fast degradation for about a month, after which the degradation diminishes rapidly.

Wronski, C. R.↗

NASA NTRS1994

1993 Technical Paper Contest for Women. Gear Up 2000: Women in Motion

The NASA Ames Research Center Advisory Committee for Women (ACW) sponsored the second ACW Technical paper Contest for Ames women in order to increase the visibility of, and to encourage writing for publication by Ames women scientists, engineers, and technicians. The topics of the contest paper mirrored in the topics of the 1993 Society for Women Engineers (SWE) National Convention, which included technological, workplace, global, and family issues.

Orans, Robin↗

NASA NTRS2000

Production of Agglutinates in the Lunar Regolith

Shock melting of microtargets produces homogeneous domains (<10 micron) from the finer fractions of lunar soils, incorporating SWE and vapor-deposit enriched fine grains. Finest grains may or may not be assimilated in such agglutinate-forming melts.

Basu, A.↗

NASA NTRS2000

Electrons In The Low Density Solar Wind

The recent occurrence of an interval (May 9th to May 12th, 1999) of abnormally low density solar wind has drawn attention to such events. The SWE instrument on the Wind spacecraft observed nine similar events between launch (November 1994) and August 1999: one in 1997, three in 1998, and five in January-August 1999. No such events were observed in 1996, the year of solar minimum. This already suggests a strong dependence upon solar activity. In this paper we discuss observations of the electron strahl, a strong anisotropy in the solar wind electrons above 60 eV directed along the magnetic field and observed continuously during the periods of low density in 1998 and 1999. When the solar wind density was less than 2/cc, the angular width of the strahl was below 3.5 degrees and the temperature deduced from the slope of the electron strahl phase density (as a function of energy in the energy range 200 to 800 eV) was 100 to 150 eV, equivalent to a typical coronal electron temperature. Three examples of this phenomenon, observed on Feb. 20- 22, April 26-27 and May 9-12, 1999, are discussed to show their similarity to one another. These electron observations are interpreted to show that the strahl occurs as a result of the conservation of the first adiabatic invariant, combined with the lack of coulomb collisions as suggested by Fairfield and Scudder, 1985.

Ogilvie, Keith W.↗

NASA NTRS2002

MO and DA on the SWIE Instrument on the Wind Spacecraft

The construction of the Faraday Cup portion of the SWIE instrument on the Wind spacecraft, participation in Mission Operations, and Data Analysis (MO and DA) of observations of the solar wind has been supported by a sequence of grants. This 'final' Report represents work done on Mission Operations and Data Analysis for the Faraday Cup portion of the SWE. The work reported here was supported under NASA Grant NAG5-7359 (OSP 6701100) from June 1998 to October 2001. It should be noted that this work is continuing under NASA Grant NAG-10915, and therefore this report is 'final' only in the sense that the Grant has changed its number; a future report will cover the entire period of work. We have two types of obligations under these contracts: (1) To provide and assure the validity of "Key Parameters" which describe the basic properties of the solar wind on a daily basis. We have provided our 92 second observations daily via plots and parameters available from our Web site: http://web.mit.edu/space/www/wind/wind.html (2). To carry out scientific studies based on our observations. To document the extent of our research, we are including below a list of publications and presentations related to this project. The observations from Wind have made a major contribution to the study of the solar wind, and have every indication of continuing to do so.

Lazarus, Alan J.↗

NASA NTRS2002

IMAGE-EUV Observation of Large Scale Standing Wave Pattern in the Nightside Plasmasphere

We present analyses of a nightside plasmaspheric pattern of bifurcated, filamentary He(+) 30.4-nm emission enhancements observed by IMAGE EUV between approximately 19:40-22:13 UT on 28 June 2000 that indicate the presence of a large-scale, global ULF standing wave pattern. Analysis of coincident IMAGE magnetometer chain data reveals that these ULF waves extend across the magnetic latitude-longitude range of the chain and possess multiple spectral features between 0.6-5-mHz (3-30 minute period). Additionally, analysis of ACE SWE data reveals similarly structured spectral components in the solar wind. Collectively, these analyses lead to the conclusion that the observed large-scale ULF wave pattern is the result of solar wind pressure pulses 'ringing' the inner-magnetosphere.

Six, N. Frank↗

NASA NTRS2004

Ground Based Remote Sensing of Upper Atmosphere Dynamics, Thermodynamics and Composition in Support of TIMED Satellites Scientific Mission

The following research work was accomplished: 1. We operated high throughput spectrophotometers and interferometers at eight observatories in the Arctic, Antarctic and mid-latitude regions to record relatively high-resolution spectra of very low light level airglow and auroral line as well as band emissions. 2. Our Polar observations of auroral emissions from N2 and O emissions have been analyzed to derive the O/N2 ratios around 110 km height in the Polar thermosphere during different auroral events triggered by the precipitation of auroral electrons with average energy of about 10 keV. These results have been compared with similar ratios derived from TIMED satellite s GUVI measurements of N2 LBH and 01 1356A emissions. 3. Our airglow measurements show MLT density and temperature modulations by Planetary, Tidal and Gravity Waves. They also indicate Mesopause cooling preceding a Stratospheric Warming Event (SWE).

Source record↗

NASA NTRS2005

A New Model of Size-graded Soil Veneer on the Lunar Surface

Introduction. We propose a new model of distribution of submillimeter sized lunar soil grains on the lunar surface. We propose that in the uppermost millimeter or two of the lunar surface, soil-grains are size graded with the finest nanoscale dust on top and larger micron-scale particles below. This standard state is perturbed by ejecta deposition of larger grains at the lunar surface, which have a coating of dusty layer that may not have substrates of intermediate sizes. Distribution of solar wind elements (SWE), agglutinates, vapor deposited nanophase Fe0 in size fractions of lunar soils and ir spectra of size fractions of lunar soils are compatible with this model. A direct test of this model requires bringing back glue-impregnated tubes of lunar soil samples to be dissected and examined on Earth.

Basu, Abhijit↗

NASA NTRS2005

[Multi-Scale Convergence of Cold-Land Process Representation in Land-Surface Models, Microwave Remote Sensing, and Field Observations]

The cryosphere is a major component of the hydrosphere and interacts significantly with the global climate system, the geosphere, and the biosphere. Measurement of the amount of water stored in the snow pack and forecasting the rate of melt are thus essential for managing water supply and flood control systems. Snow hydrologists are confronted with the dual problems of estimating both the quantity of water held by seasonal snow packs and time of snow melt. Monitoring these snow parameters is essential for one of the objectives of the Earth Science Enterprise-understanding of the global hydrologic cycle. Measuring spatially distributed snow properties, such as snow water equivalence (SWE) and wetness, from space is a key component for improvement of our understanding of coupled atmosphere-surface processes. Through the GWEC project, we have significantly advanced our understandings and improved modeling capabilities of the microwave signatures in response to snow and underground properties.

Shi, Jiancheng↗

NASA NTRS2007

A Web 2.0 and OGC Standards Enabled Sensor Web Architecture for Global Earth Observing System of Systems

This paper will describe the progress of a 3 year research award from the NASA Earth Science Technology Office (ESTO) that began October 1, 2006, in response to a NASA Announcement of Research Opportunity on the topic of sensor webs. The key goal of this research is to prototype an interoperable sensor architecture that will enable interoperability between a heterogeneous set of space-based, Unmanned Aerial System (UAS)-based and ground based sensors. Among the key capabilities being pursued is the ability to automatically discover and task the sensors via the Internet and to automatically discover and assemble the necessary science processing algorithms into workflows in order to transform the sensor data into valuable science products. Our first set of sensor web demonstrations will prototype science products useful in managing wildfires and will use such assets as the Earth Observing 1 spacecraft, managed out of NASA/GSFC, a UASbased instrument, managed out of Ames and some automated ground weather stations, managed by the Forest Service. Also, we are collaborating with some of the other ESTO awardees to expand this demonstration and create synergy between our research efforts. Finally, we are making use of Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) suite of standards and some Web 2.0 capabilities to Beverage emerging technologies and standards. This research will demonstrate and validate a path for rapid, low cost sensor integration, which is not tied to a particular system, and thus be able to absorb new assets in an easily evolvable, coordinated manner. This in turn will help to facilitate the United States contribution to the Global Earth Observation System of Systems (GEOSS), as agreed by the U.S. and 60 other countries at the third Earth Observation Summit held in February of 2005.

Mandl, Daniel↗

NASA NTRS2008

Using Sensor Web Processes and Protocols to Assimilate Satellite Data into a Forecast Model

The goal of the Sensor Management Applied Research Technologies (SMART) On-Demand Modeling project is to develop and demonstrate the readiness of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) capabilities to integrate both space-based Earth observations and forecast model output into new data acquisition and assimilation strategies. The project is developing sensor web-enabled processing plans to assimilate Atmospheric Infrared Sounding (AIRS) satellite temperature and moisture retrievals into a regional Weather Research and Forecast (WRF) model over the southeastern United States.

Goodman, H. Michael↗