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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1975

A study of application of remote sensing to river forecasting. Volume 1: Executive summary

A project is described whose goal was to define, implement and evaluate a pilot demonstration test to show the practicability of applying remotely sensed data to operational river forecasting in gaged or previously ungaged watersheds. A secondary objective was to provide NASA with documentation describing the computer programs that comprise the streamflow forecasting simulation model used. A computer-based simulation model was adapted to a streamflow forecasting application and implemented in an IBM System/360 Model 44 computer, operating in a dedicated mode, with operator interactive control through a Model 2250 keyboard/graphic CRT terminal. The test site whose hydrologic behavior was simulated is a small basin (365 square kilometers) designated Town Creek near Geraldine, Alabama.

Source record↗
NASA NTRS1997

Evaluation of the 29-km Eta Model for Weather Support to the United States Space Program

The Applied Meteorology Unit (AMU) conducted a year-long evaluation of NCEP's 29-km mesoscale Eta (meso-eta) weather prediction model in order to identify added value to forecast operations in support of the United States space program. The evaluation was stratified over warm and cool seasons and considered both objective and subjective verification methodologies. Objective verification results generally indicate that meso-eta model point forecasts at selected stations exhibit minimal error growth in terms of RMS errors and are reasonably unbiased. Conversely, results from the subjective verification demonstrate that model forecasts of developing weather events such as thunderstorms, sea breezes, and cold fronts, are not always as accurate as implied by the seasonal error statistics. Sea-breeze case studies reveal that the model generates a dynamically-consistent thermally direct circulation over the Florida peninsula, although at a larger scale than observed. Thunderstorm verification reveals that the meso-eta model is capable of predicting areas of organized convection, particularly during the late afternoon hours but is not capable of forecasting individual thunderstorms. Verification of cold fronts during the cool season reveals that the model is capable of forecasting a majority of cold frontal passages through east central Florida to within +1-h of observed frontal passage.

Manobianco, John↗
NASA NTRS2009

Application of TRMM PR and TMI Measurements to Assess Cloud Microphysical Schemes in the MM5 Model for a Winter Storm

Seen by the human eye, precipitation particles are commonly drops of rain, flakes of snow, or lumps of hail that reach the ground. Remote sensors and numerical models usually deal with information about large collections of rain, snow, and hail (or graupel --also called soft hail ) in a volume of air. Therefore, the size and number of the precipitation particles and how particles interact, evolve, and fall within the volume of air need to be represented using physical laws and mathematical tools, which are often implemented as cloud and precipitation microphysical parameterizations in numerical models. To account for the complexity of the precipitation physical processes, scientists have developed various types of such schemes in models. The accuracy of numerical weather forecasting may vary dramatically when different types of these schemes are employed. Therefore, systematic evaluations of cloud and precipitation schemes are of great importance for improvement of weather forecasts. This study is one such endeavor; it pursues quantitative assessment of all the available cloud and precipitation microphysical schemes in a weather model (MM5) through comparison with the observations obtained by National Aeronautics and Space Administration (NASA) s and Japan Aerospace Exploration Agency (JAXA) s Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and microwave imager (TMI). When satellite sensors (like PR or TMI) detect information from precipitation particles, they cannot directly observe the microphysical quantities (e.g., water species phase, density, size, and amount etc.). Instead, they tell how much radiation is absorbed by rain, reflected away from the sensor by snow or graupel, or reflected back to the satellite. On the other hand, the microphysical quantities in the model are usually well represented in microphysical schemes and can be converted to radiative properties that can be directly compared to the corresponding PR and TMI observations. This study employs this method to evaluate the accuracy of the simulated radiative properties by the MM5 model with different microphysical schemes. It is found that the representations of particle density, size, and mass in the different schemes in the MM5 model determine the model s performance when predicting a winter storm over the eastern Pacific Ocean. Schemes lacking moderate density particles (i.e. graupel), with snow flakes that are too large, or with excessive mass of snow or graupel lead to degraded prediction of the radiative properties as observed by the TRMM satellite. This study demonstrates the uniqueness of the combination of both an active microwave sensor (PR) and passive microwave sensor (TMI) onboard TRMM on assessing the accuracy of numerical weather forecasting. It improves our understanding of the physical and radiative properties of different types of precipitation particles and provides suggestions for better representation of cloud and precipitation processes in numerical models. It would, ultimately, contribute to answering questions like "Why did it not rain when the forecast says it would?"

Han, Mei↗
NASA NTRS1983

Evaluation of the synoptic and mesoscale predictive capabilities of a mesoscale atmospheric simulation system

The overall performance characteristics of a limited area, hydrostatic, fine (52 km) mesh, primitive equation, numerical weather prediction model are determined in anticipation of satellite data assimilations with the model. The synoptic and mesoscale predictive capabilities of version 2.0 of this model, the Mesoscale Atmospheric Simulation System (MASS 2.0), were evaluated. The two part study is based on a sample of approximately thirty 12h and 24h forecasts of atmospheric flow patterns during spring and early summer. The synoptic scale evaluation results benchmark the performance of MASS 2.0 against that of an operational, synoptic scale weather prediction model, the Limited area Fine Mesh (LFM). The large sample allows for the calculation of statistically significant measures of forecast accuracy and the determination of systematic model errors. The synoptic scale benchmark is required before unsmoothed mesoscale forecast fields can be seriously considered.

Koch, S. E.↗
NASA NTRS2023

Interpretation of Probabilistic Surface Ozone Forecasts: A Case Study for Philadelphia

The use of probabilistic forecasting has been growing in a variety of disciplines because of its potential to emphasize the degree of uncertainty inherent in a prediction. Interpretation of probabilistic forecasts, however, is oftentimes difficult, deterring users who may benefit from such forecasts. To encourage broader use of probabilistic forecasts in the field of air quality, a process for interpreting forecasts from a statistical probabilistic air quality surface ozone model [the Regression in Self Organizing Map (REGiS)] is demonstrated. Four procedures to convert probabilistic to deterministic forecasts are explored for the Philadelphia, Pennsylvania, metropolitan area. These procedures calibrate the predicted probability of daily maximum 8-h-average ozone exceeding a standard value by 1) estimating climatological relative frequency, 2) establishing a probability of an exceedance threshold as 50%, 3) maximizing the threat score, and 4) determining the unit bias ratio. REGiS is trained using 2000–11 ozone-season (1 May–30 September) data, calibrated using 2012–14 data, and evaluated using 2015–18 data. Assessment of the calibration data with the Pierce skill score suggests an exceedance threshold based on climatological relative frequency for the conversion from probabilistic to deterministic forecasts. Calibrated REGiS generally compares well to predictions from the U.S. national air quality model and operational “expert” forecasts over the evaluation period. For other probabilistic models and situations, different procedures of converting probabilistic to deterministic forecasts may be more beneficial. The methods presented in this paper represent an approach for operational air quality forecasters seeking to use probabilistic model output to support forecasts designed to protect public health.

Nikolay Balashov↗
NASA NTRS2001

Observing System Forecast Experiments at the DAO

Since the advent of meteorological satellites in the 1960's, numerous experiments have been conducted in order to evaluate the impact of these and other data on atmospheric analysis and prediction. Such studies have included both OSE'S and OSSE's. The OSE's were conducted to evaluate the impact of specific observations or classes of observations on analyses and forecasts. Such experiments have been performed for selected types of conventional data and for various satellite data sets as they became available. (See for example the 1989 ECMWF/EUMETSAT workshop proceedings on "The use of satellite data in operational numerical weather prediction" and the references contained therein.) The ODYSSEY were conducted to evaluate the potential for future observing systems to improve Numerical Weather Prediction NWP and to plan for the Global Weather Experiment and more recently for EVANS (Atlas et al., 1985a; Arnold and Day, 1986; Hoffman et al., 1990). In addition, OSSE's have been run to evaluate trade-offs in the design of observing systems and observing networks (Atlas and Emmitt, 1991; Rohaly and Krishnamurti, 1993), and to test new methodology for data assimilation (Atlas and Bloom, 1989).

Atlas, Robert↗
NASA NTRS2003

A Global Aerosol Model Forecast for the ACE-Asia Field Experiment

We present the results of aerosol forecast during the Aerosol Characterization Experiment (ACE-Asia) field experiment in spring 2001, using the Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model and the meteorological forecast fields from the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The aerosol model forecast provides direct information on aerosol optical thickness and concentrations, enabling effective flight planning, while feedbacks from measurements constantly evaluate the model, making successful model improvements. We verify the model forecast skill by comparing model predicted total aerosol extinction, dust, sulfate, and SO2 concentrations with those quantities measured by the C-130 aircraft during the ACE-Asia intensive operation period. The GEOS DAS meteorological forecast system shows excellent skills in predicting winds, relative humidity, and temperature for the ACE-Asia experiment area as well as for each individual flight, with skill scores usually above 0.7. The model is also skillful in forecast of pollution aerosols, with most scores above 0.5. The model correctly predicted the dust outbreak events and their trans-Pacific transport, but it constantly missed the high dust concentrations observed in the boundary layer. We attribute this missing dust source to the desertification regions in the Inner Mongolia Province in China, which have developed in recent years but were not included in the model during forecasting. After incorporating the desertification sources, the model is able to reproduce the observed high dust concentrations at low altitudes over the Yellow Sea. Two key elements for a successful aerosol model forecast are correct source locations that determine where the emissions take place, and realistic forecast winds and convection that determine where the aerosols are transported. We demonstrate that our global model can not only account for the large-scale intercontinental transport, but also produce the small-scale spatial and temporal variations that are adequate for aircraft measurements planning.

Chin, Mian↗
NASA NTRS2013

Regional Climate and Variability of the Summertime Continental United States in Reanalyses

Understanding climate variability at regional scales is an important for research and societal needs. Atmospheric retrospective-analyses (or reanalyses) integrate multitudes of observing systems with numerical models to produce continuous data that include variables not easily observed, if at all. The breadth of variables as well as observational influence included in reanalyses make them ideal for investigating climate variability. In this paper, we assess NASA s Modern Era Retrospective-analysis for Research and Application (MERRA) regional variability in North America, specifically the United States, in conjunction with current satellite data reanalyses. Emphasis is placed on summertime precipitation because 1) it is a difficult parameter to capture in the most difficult season, 2) significant observational resources exist to benchmark comparisons, and 3) accurate assessment of precipitation variability is crucial to a multitude of sectors and applications. Likewise, we have also begun to evaluate surface air temperature. While precipitation biases are identified, year to year variability of the precipitation variations, in many cases, are quite reasonable. However, some spurious long term trends and sudden shifts in the time series are also identified. In surface air temperature, analysis of station observations provides ERA Interim a clear overall advantage. However, in a number of regions, all the reanalyses are quite comparable in variability and trend. In other regions, significant precipitation biases may occur, which has implications for the ancillary process data in a reanalysis, such as surface fluxes. We also characterize the reanalyses ability to capture variability related to ENSO. In general, the summertime variations of precipitation in the reanalyses are more highly correlated (positively) to ENSO (using ENSO34) than are the observations. The Northwestern US shows the largest positive correlations to ENSO34, and reanalyses agree with that, and correlate highly with the gauge observations there. Additional evaluation of the data assimilation using the MERRA Gridded Innovations and Observations (GIO) data, which consists of the assimilated observations as well as forecast an analysis error fields. This work represents an initial evaluation of using the MERRA reanalysis to study regional climate variations of the United States, and identifying its applied limitations

Bosilovich, M G.↗
NASA NTRS1981

Applications systems verification and transfer project. Volume 3: Operational applications of satellite snow cover observations in California

Five southern Sierra snowmelt basins and two northern Sierra-Southern Cascade snowmelt basins were used to evaluate the effect on operational water supply forecasting from satellite imagery. Manual photointerpretation techniques were used to obtain SCA and equivalent snow line for the years 1973 to 1979 for the seven test basins using LANDSAT imagery and GOES imagery. The use of SCA was tested operationally in 1977-79. Results indicate the addition of SCA improve the water supply forecasts during the snowmelt phase for these basins where there may be an unusual distribution of snowpack throughout the basin, or where there is a limited amount of real time data available. A high correlation to runoff was obtained when SCA was combined with snow water content data obtained from reporting snow sensors.

Brown, A. J.↗
NASA NTRS1982

USDA registration and rectification requirements

Some of the requirements of the United States Department of Agriculture for accuracy of aerospace acquired data, and specifically, requirements for registration and rectification of remotely sensed data are discussed. Particular attention is given to foreign and domestic crop estimation and forecasting, forestry information applications, and rangeland condition evaluations.

Allen, R.↗
NASA NTRS1985

Seasat data applications in ocean industries

It is pointed out that the world population expansion and resulting shortages of food, minerals, and fuel have focused additional attention on the world's oceans. In this context, aspects of weather prediction and the monitoring/prediction of long-range climatic anomalies become more important. In spite of technological advances, the commercial ocean industry and the naval forces suffer now from inadequate data and forecast products related to the oceans. The Seasat Program and the planned Navy-Remote Oceanographic Satellite System (N-ROSS) represent major contributions to improved observational coverage and the processing needed to achieve better forecasts. The Seasat Program was initiated to evaluate the effectiveness of the remote sensing of oceanographic phenomena from a satellite platform. Possible oceanographic satellite applications are presented in a table, and the impact of Seasat data on industry sectors is discussed. Attention is given to offshore oil development, deep-ocean mining, fishing, and marine transportation.

Montgomery, D. R.↗
NASA NTRS2002

The NASA Thunderstorm Observations and Research (ThOR) Mission: Lightning Mapping from Space to Improve the Short-term Forecasting of Severe Storms

The ThOR mission uses a lightning mapping sensor in geostationary Earth orbit to provide continuous observations of thunderstorm activity over the Americas and nearby oceans. The link between lightning activity and cloud updrafts is the basis for total lightning observations indicating the evolving convective intensification and decay of storms. ThOR offers a national operational demonstration of the utility of real-time total lightning mapping for earlier and more reliable identification of potentially severe and hazardous storms. Regional pilot projects have already demonstrated that the dominance in-cloud lightning and increasing in-cloud lash rates are known to precede severe weather at the surface by tens of minutes. ThOR is currently planned for launch in 2005 on a commercial or research satellite. Real-time data will be provided to selected NWS Weather Forecast Offices and National Centers (EMC/AWC/SPC) for evaluation.

Goodman, S. J.↗
NASA NTRS2009

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data.

Duda, David P.↗
NASA NTRS2011

Performance of the IRI-2007 Model for Topside Ion Density and Composition Profiles During the 23/24 Solar Minimum

The recent solar minimum between cycles 23 and 24 was unusually extended and deep, resulting in an ionosphere that is significantly different from that expected based on previous solar minima. The ion density and composition estimates from the Communication/Navigation Outage Forecast System (C/NOFS) satellite are used to evaluate the performance of the IRI-2007 model between 400 and 850 kIn altitude in equatorial regions. The current model is shown to typically overestimate the expected topside density of 0+ and underestimate the density of H+ during 2008 and 2009. The overestimation of ion density by IRI-2007 is found to vary with local time and longitude.

Klenzing, J. H.↗
NASA NTRS2016

A Portfolio Analysis Tool for Measuring NASAs Aeronautics Research Progress toward Planned Strategic Outcomes

Description of a tool for portfolio analysis of NASA's Aeronautics research progress toward planned community strategic Outcomes is presented. The strategic planning process for determining the community Outcomes is also briefly described. Stakeholder buy-in, partnership performance, progress of supporting Technical Challenges, and enablement forecast are used as the criteria for evaluating progress toward Outcomes. A few illustrative examples are also presented.

Portfolio Analysis↗
NASA NTRS2016

A Tool for Measuring NASA's Aeronautics Research Progress Toward Planned Strategic Community Outcomes

Description of a tool for portfolio analysis of NASA's Aeronautics research progress toward planned community strategic Outcomes is presented. For efficiency and speed, the tool takes advantage of a function developed in Excels Visual Basic for Applications. The strategic planning process for determining the community Outcomes is also briefly discussed. Stakeholder buy-in, partnership performance, progress of supporting Technical Challenges, and enablement forecast are used as the criteria for evaluating progress toward Outcomes. A few illustrative examples of using the tool are also presented.

Thrusts↗
NASA NTRS2017

Converging Climate Sensitivities of European Forests Between Observed Radial Tree Growth and Vegetation Models

The impacts of climate variability and trends on European forests are unevenly distributed across different bioclimatic zones and species. Extreme climate events are also becoming more frequent and it is unknown how they will affect feed backs of CO2 between forest ecosystems and the atmosphere. An improved understanding of species differences at the regional scale of the response of forest productivity to climate variation and extremes is thus important for forecasting forest dynamics. In this study, we evaluate the climate sensitivity of above ground net primary production (NPP) simulated by two dynamic global vegetation models (DGVM; ORCHIDEE and LPJ-wsl) against tree ring width (TRW) observations from about1000 sites distributed across Europe. In both the model simulations and the TRW observations, forests in northern Europe and the Alps respond positively to warmer spring and summer temperature, and their overall temperature sensitivity is larger than that of the soil-moisture-limited forests in central Europe and Mediterranean regions. Compared with TRW observations, simulated NPP from ORCHIDEE and LPJ-wsl appear to be overly sensitive to climatic factors. Our results indicate that the models lack biological processes that control time lags, such as carbohydrate storage and remobilization, that delay the effects of radial growth dynamics to climate. Our study highlights the need for re-evaluating the physiological controls on the climate sensitivity of NPP simulated by DGVMs. In particular, DGVMs could be further enhanced by a more detailed representation of carbon reserves and allocation that control year-to year variation in plant growth.

Zhang, Zhen↗
NASA NTRS2018

Impact of Aquarius and SMAP Sea Surface Salinity Observations on Seasonal Predictions of the 2015 El Nino

We assess the impact of satellite sea surface salinity (SSS) observations on dynamical ENSO forecasts for the big 2015 El Nino event. From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP (Soil Moisture Active Passive Mission), allows a unique opportunity to compare and contrast forecasts generated with the benefit of these two satellite SSS observation types. Four distinct experiments are presented that include 1) freely evolving model SSS (i.e. no satellite SSS), relaxation to 2) climatological SSS (i.e. WOA13 SSS), 3) Aquarius, and 4) SMAP initialization. Coupled hindcasts are then generated from these initial conditions for March 2015. These forecasts are then validated against observations and evaluated with respect to the observed El Nino development.

Hackert, E.↗