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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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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Meteorology, Macrophysics, Microphysics, Microwaves, and Mesoscale Modeling of Mediterranean Mountain Storms: The M8 Laboratory

Comprehensive understanding of the microphysical nature of Mediterranean storms can be accomplished by a combination of in situ meteorological data analysis and radar-passive microwave data analysis, effectively integrated with numerical modeling studies at various scales, from synoptic scale down through the mesoscale, the cloud macrophysical scale, and ultimately the cloud microphysical scale. The microphysical properties of and their controls on severe storms are intrinsically related to meteorological processes under which storms have evolved, processes which eventually select and control the dominant microphysical properties themselves. This involves intense convective development, stratiform decay, orographic lifting, and sloped frontal lifting processes, as well as the associated vertical motions and thermodynamical instabilities governing physical processes that affect details of the size distributions and fall rates of the various types of hydrometeors found within the storm environment. Insofar as hazardous Mediterranean storms, highlighted in this study by three mountain storms producing damaging floods in northern Italy between 1992 and 2000, developing a comprehensive microphysical interpretation requires an understanding of the multiple phases of storm evolution and the heterogeneous nature of precipitation fields within a storm domain. This involves convective development, stratiform transition and decay, orographic lifting, and sloped frontal lifting processes. This also involves vertical motions and thermodynamical instabilities governing physical processes that determine details of the liquid/ice water contents, size disi:ributions, and fall rates of the various modes of hydrometeors found within hazardous storm environments.

Starr, David O.↗
NASA NTRS2005

Remote Sensing Characterization of the Urban Landscape for Improvement of Air Quality Modeling

The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, in moderating ground-level ozone and air temperature, compared to "business as usual" simulations in which heat island mitigation strategies are not applied. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the (CMAQ) modeling schemes. Use of these data has been found to better characterize low densityhburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for fiture scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the state Environmental Protection agency to evaluate how these transportation plans will affect fbture air quality.

Quattrochi, Dale A.↗
NASA NTRS1981

An algorithm to generate input data from meteorological and space shuttle observations to validate a CH4-CO model

Objective procedures to analyze data from meteorological and space shuttle observations to validate a three dimensional model were investigated. The transport and chemistry of carbon monoxide and methane in the troposphere were studied. Four aspects were examined: (1) detailed evaluation of the variational calculus procedure, with the equation of continuity as a strong constraint, for adjustment of global tropospheric wind fields; (2) reduction of the National Meteorological Center (NMC) data tapes for data input to the OSTA-1/MAPS Experiment; (3) interpolation of the NMC Data for input to the CH4-CO model; and (4) temporal and spatial interpolation procedures of the CO measurements from the OSTA-1/MAPS Experiment to generate usable contours of the data.

Peters, L. K.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-11382-DND

Long-range transport of ozone precursor species and carbonaceous aerosols emitted by wildfires may affect the atmospheric composition and regional climate far away from the location of the fires. Major outbreaks of fires occurred in 3 areas of Asia, Kazakhstan, Siberia, and Thailand, during the spring of 2008. Satellite and aircraft-based observations and trajectory studies have identified the transit of the plumes from these fires across the Pacific. We have conducted simulations of the effects of these fires on the atmospheric composition using the Real-time Air Quality Modeling System (RAQMS). RAQMS is a global scale meteorological and chemical modeling system with unified (stratosphere/troposphere) chemistry module. Model results, for example, indicate that increases of 10 ppbv in upper tropospheric ozone at 180W longitude and northern mid-latitudes in April, 2008 could be attributed to these fires. Large increases in aerosol optical depth due to carbonaceous aerosols are also seen nearer the fire location. Both tropospheric ozone and carbonaceous aerosols influence the atmospheric energy balance and climate. We use an off-line radiative transfer model along with the RAQMS chemical and aerosol analyses to calculate the direct radiative forcing during April, 2008, due to Asian wildfire emissions. Arctic indirect radiative forcing due to these emissions are evaluated using RAQMS aerosol extinction analyses in conjunction with MODIS retrievals of cloud liquid water and effective radius. We will discuss the radiative forcing results and the relative influences of the different fire events.

Murali Natarajan↗
NASA NTRS2011

Assessment of NASA's Physiographic and Meteorological Datasets as Input to HSPF and SWAT Hydrological Models

This paper documents the use of simulated Moderate Resolution Imaging Spectroradiometer land use/land cover (MODIS-LULC), NASA-LIS generated precipitation and evapo-transpiration (ET), and Shuttle Radar Topography Mission (SRTM) datasets (in conjunction with standard land use, topographical and meteorological datasets) as input to hydrological models routinely used by the watershed hydrology modeling community. The study is focused in coastal watersheds in the Mississippi Gulf Coast although one of the test cases focuses in an inland watershed located in northeastern State of Mississippi, USA. The decision support tools (DSTs) into which the NASA datasets were assimilated were the Soil Water & Assessment Tool (SWAT) and the Hydrological Simulation Program FORTRAN (HSPF). These DSTs are endorsed by several US government agencies (EPA, FEMA, USGS) for water resources management strategies. These models use physiographic and meteorological data extensively. Precipitation gages and USGS gage stations in the region were used to calibrate several HSPF and SWAT model applications. Land use and topographical datasets were swapped to assess model output sensitivities. NASA-LIS meteorological data were introduced in the calibrated model applications for simulation of watershed hydrology for a time period in which no weather data were available (1997-2006). The performance of the NASA datasets in the context of hydrological modeling was assessed through comparison of measured and model-simulated hydrographs. Overall, NASA datasets were as useful as standard land use, topographical , and meteorological datasets. Moreover, NASA datasets were used for performing analyses that the standard datasets could not made possible, e.g., introduction of land use dynamics into hydrological simulations

Alacron, Vladimir J.↗
NASA NTRS2018

A Multi-Satellite Assimilation and Modeling Platform to Construct a Global and Complete view of the Hydrologic Cycle

What captivates me about the research field of satellite remote sensing of the earth system science is the opportunity to help human beings by addressing challenging questions related to the current and future availability of natural resources for domestic, agricultural, and industrial needs. For example, a continuously varying climate poses a threat to the water resource, not only in developing countries, but also in economically well-developed regions, creating the urgent need to characterize and predict its spatial and temporal availability. Specifically, my curiosity is driven by the challenge of merging cutting-edge space technology and earth observations (i.e., remote sensing) with state-of-the-art models for the purpose of improving our scientific knowledge about the variability and the change of the hydrologic cycle.The only practical way to observe the land surface processes (e.g., hydrologic cycle) on continental to global scales is via satellite remote sensing. Though remote sensing can make spatially comprehensive measurements of various components of the land surface system, it cannot provide direct information on the entire system, and the measurements represent only a snapshot in time. Physical based models may be used to continuously predict the temporal and spatial earth processes, but these predictions are often poor, due to model initialization, parameter and meteorological forcing errors, and inadequate model physics and/or resolution. Thus, an attractive prospect is to combine the strengths of land surface models and observations (and minimize the weaknesses of both) to provide a superior land surface state estimate. This is the goal of land surface data assimilation. Data assimilation provides a better estimate of the environmental states than either models or observations could individually do. The broad concept of data assimilation can be applied to various disciplines such as hydrology, ecology, environmental hazards, agriculture and economy.My research is targeted to integrating multiple satellite information having multi-sensor and multi-resolution assimilation within today's state-of-the-art hydrologic models. Multi-sensor and multi-resolution assimilation techniques represent a necessary milestone in future earth science applications because they offer the capability of comprehensively integrating disparate types of earth observations via deep-learning techniques. A multi-satellite assimilation and modeling platform will ultimately provide a robust and complete dataset to more fully understand earth's dynamics. In fact, the multi-satellite assimilation platforms build a comprehensive description of the earth processes that is geared toward accurately representing the complexity of natural and anthropogenic interactions in land surface processes. Most importantly, these platforms are suitable as decision support tools for applications across different aspects of earth system science.My recent work has focused on recent multi-sensor data assimilation techniques targeted at improving snow, soil moisture, groundwater, and terrestrial water storage hydrological states.

Girotto, Manuela↗
NASA NTRS2005

Chemical Modeling for Studies of GeoTRACE Capabilities

Geostationary measurements of tropospheric pollutants with high spatial and temporal resolution will revolutionize the understanding and predictions of the chemically linked global pollutants aerosols and ozone. However, the capabilities of proposed geostationary instruments, particularly GeoTRACE, have not been thoroughly studied with model simulations. Such model simulations are important to answer the questions and allay the concerns that have been expressed in the atmospheric sciences community about the feasibility of such measurements. We proposed a suite of chemical transport model simulations using the EPA Models 3 chemical transport model, which obtains its meteorology from the MM-5 mesoscale model. The model output consists of gridded abundances of chemical pollutants and meteorological parameters every 30-60 minutes for cases that have occurred in the Eastern United States. This output was intended to be used to test the GeoTRACE capability to retrieve the tropospheric columns of these pollutants.

Source record↗
NASA NTRS2016

Transitioning Enhanced Land Surface Initialization and Model Verification Capabilities to the Kenya Meteorological Department (KMD)

Flooding, severe weather, and drought are key forecasting challenges for the Kenya Meteorological Department (KMD), based in Nairobi, Kenya. Atmospheric processes leading to convection, excessive precipitation and/or prolonged drought can be strongly influenced by land cover, vegetation, and soil moisture content, especially during anomalous conditions and dry/wet seasonal transitions. It is thus important to represent accurately land surface state variables (green vegetation fraction, soil moisture, and soil temperature) in Numerical Weather Prediction (NWP) models. The NASA SERVIR and the Short-term Prediction Research and Transition (SPoRT) programs in Huntsville, AL have established a working partnership with KMD to enhance its regional modeling capabilities. SPoRT and SERVIR are providing experimental land surface initialization datasets and model verification capabilities for capacity building at KMD. To support its forecasting operations, KMD is running experimental configurations of the Weather Research and Forecasting (WRF; Skamarock et al. 2008) model on a 12-km/4-km nested regional domain over eastern Africa, incorporating the land surface datasets provided by NASA SPoRT and SERVIR. SPoRT, SERVIR, and KMD participated in two training sessions in March 2014 and June 2015 to foster the collaboration and use of unique land surface datasets and model verification capabilities. Enhanced regional modeling capabilities have the potential to improve guidance in support of daily operations and high-impact weather and climate outlooks over Eastern Africa. For enhanced land-surface initialization, the NASA Land Information System (LIS) is run over Eastern Africa at ~3-km resolution, providing real-time land surface initialization data in place of interpolated global model soil moisture and temperature data available at coarser resolutions. Additionally, real-time green vegetation fraction (GVF) composites from the Suomi-NPP VIIRS instrument is being incorporated into the KMD-WRF runs, using the product generated by NOAA/NESDIS. Model verification capabilities are also being transitioned to KMD using NCAR's Model *Corresponding author address: Jonathan Case, ENSCO, Inc., 320 Sparkman Dr., Room 3008, Huntsville, AL, 35805. Email: Jonathan.Case-1@nasa.gov Evaluation Tools (MET; Brown et al. 2009) software in conjunction with a SPoRT-developed scripting package, in order to quantify and compare errors in simulated temperature, moisture and precipitation in the experimental WRF model simulations. This extended abstract and accompanying presentation summarizes the efforts and training done to date to support this unique regional modeling initiative at KMD. To honor the memory of Dr. Peter J. Lamb and his extensive efforts in bolstering weather and climate science and capacity-building in Africa, we offer this contribution to the special Peter J. Lamb symposium. The remainder of this extended abstract is organized as follows. The collaborating international organizations involved in the project are presented in Section 2. Background information on the unique land surface input datasets is presented in Section 3. The hands-on training sessions from March 2014 and June 2015 are described in Section 4. Sample experimental WRF output and verification from the June 2015 training are given in Section 5. A summary is given in Section 6, followed by Acknowledgements and References.

land surface modeling↗
NASA NTRS1982

A comparison of measured and estimated meteorological data for use in crop growth modeling

Gridded spatial estimates of maximum temperature, minimum temperature, precipitation, and solar radiation, prepared for agricultural use from World Meteorological Organization surface reports and enhanced by polar orbiting satellites were compared with daily meteorological data measured at various agricultural research facilities across the United States to determine their level of accuracy. Preliminary results indicate that daily maximum temperature can be determined to within 9.1 degrees Celsius with ninety percent confidence. With similar levels of confidence, daily minimum temperature can be determined to within 6.7 degrees Celsius, daily solar radiation to within 231.2 cal/sq cm min, and daily precipitation to within 9.7 millimeters.

Perry, C. R., Jr.↗
NASA NTRS2007

Coupled Stochastic Time-Inverted Lagrangian Transport/Weather Forecast and Research/Vegetation Photosynthesis and Respiration Model: Simulations of Tower-Based and Airborne CO2 Measurements - Part II

This paper evaluates simulations of atmospheric CO2 measured in 2004 at continental surface and airborne receptors, intended to test the capability to use data with high temporal and spatial resolution for analyses of carbon sources and sinks at regional and continental scales. The simulations were performed using the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by the Weather Forecast and Research (WRF) model, and linked to surface fluxes from the satellite-driven Vegetation Photosynthesis and Respiration Model (VPRM). The simulations provide detailed representations of hourly CO2 tower data and reproduce the shapes of airborne vertical profiles with high fidelity. WRF meteorology gives superior model performance compared with standard meteorological products, and the impact of including WRF convective mass fluxes in the STILT trajectory calculations is significant in individual cases. Important biases in the simulation are associated with the nighttime CO2 build-up and subsequent morning transition to convective conditions, and with errors in the advected lateral boundary condition. Comparison of STILT simulations driven by the WRF model against those driven by the Brazilian variant of the Regional Atmospheric Modeling System (BRAMS) shows that model-to-model differences are smaller than between an individual transport model and observations, pointing to systematic errors in the simulated transport. Future developments in the WRF model s data assimilation capabilities, basic research into the fundamental aspects of trajectory calculations, and intercomparison studies involving other transport models, are possible venues for reducing these errors. Overall, the STILT/WRF/VPRM offers a powerful tool for continental and regional scale carbon flux estimates.

Eluszkiewicz, Janusz↗
NASA NTRS1996

Numerical Simulation of Wake Vortices Measured During the Idaho Falls and Memphis Field Programs

A numerical large-eddy simulation model is under modification and testing for application to aircraft wake vortices. The model, having a meteorological framework, permits the interaction of wake vortices with environments characterized by crosswind shear, stratification, and humidity. As part of the validation process, model results are compared with measured field data from the 1990 Idaho Falls and the 1994-1995 Memphis field experiments. Cases are selected that represent different aircraft and a cross section of meteorological environments. Also included is one case with wake vortex generation in ground effect. The model simulations are initialized with the appropriate meteorological conditions and a post roll-up vortex system. No ambient turbulence is assumed in our initial set of experiments, although turbulence can be self generated by the interaction of the model wakes with the ground and environment.

Proctor, Fred H.↗
NASA NTRS2019

North American Extreme Precipitation Events and Related Large-Scale Meteorological Patterns: a Review of Statistical Methods, Dynamics, Modeling, and Trends

This paper surveys the current state of knowledge regarding Large-Scale Meteorological Patterns (LSMPs) associated with short-duration (less than one week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of “extreme” are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some 4 promising analysis techniques have been identified and the LSMP perspective is useful for evaluating model dynamics.

Mathew Barlow↗
NASA NTRS1984

Numerical simulation of mesoscale precipitation

The numerical simulation of mesoscale precipitation as well as the development of software and appropriate computer techniques are investigated. The development of a mesoscale model and the means to incorporate meteorological data into the model are examined.

Kalb, M. W.↗
NASA NTRS2000

Delta 2 Explosion Plume Analysis Report

A Delta II rocket exploded seconds after liftoff from Cape Canaveral Air Force Station (CCAFS) on 17 January 1997. The cloud produced by the explosion provided an opportunity to evaluate the models which are used to track potentially toxic dispersing plumes and clouds at CCAFS. The primary goal of this project was to conduct a case study of the dispersing cloud and the models used to predict the dispersion resulting from the explosion. The case study was conducted by comparing mesoscale and dispersion model results with available meteorological and plume observations. This study was funded by KSC under Applied Meteorology Unit (AMU) option hours. The models used in the study are part of the Eastern Range Dispersion Assessment System (ERDAS) and include the Regional Atmospheric Modeling System (RAMS), HYbrid Particle And Concentration Transport (HYPACT), and Rocket Exhaust Effluent Dispersion Model (REEDM). The primary observations used for explosion cloud verification of the study were from the National Weather Service's Weather Surveillance Radar 1988-Doppler (WSR-88D). Radar reflectivity measurements of the resulting cloud provided good estimates of the location and dimensions of the cloud over a four-hour period after the explosion. The results indicated that RAMS and HYPACT models performed reasonably well. Future upgrades to ERDAS are recommended.

Evans, Randolph J.↗