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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Wind farm structural response and wake dynamics for an evolving stable boundary layer: computational and experimental comparisons

Abstract. The wind turbine design process requires performing thousands of simulations for a wide range of inflow and control conditions, which necessitates computationally efficient yet time-accurate models, especially when considering wind farm settings. To this end, FAST.Farm is a dynamic-wake-meandering-based mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. This work is an extension of a study that addressed constructing a diurnal cycle evolution based on experimental data (Quon, 2024). Here, this inflow is used to validate the turbine structural and wake-meandering response between experimental data, FAST.Farm simulation results, and high-fidelity large-eddy simulation results from the coupled Simulator fOr Wind Farm Applications (SOWFA)–OpenFAST tool. The validation occurs within the nocturnal stable boundary layer when corresponding meteorological and turbine data are available. To this end, we compared the load results from FAST.Farm and SOWFA–OpenFAST to multi-turbine measurements from a subset of a full-scale wind farm. Computational predictions of blade-root and tower-base bending loads are compared to 10 min statistics of strain gauge measurements during 3.5 h of the evolving stable boundary layer, generally with good agreement. This time period coincided with an active wake-steering campaign of an upstream turbine, resulting in time-varying yaw positions of all turbines. Wake meandering was also compared between the computational solutions, generally with excellent agreement. Simulations were based on a high-fidelity precursor constructed from inflow measurements and using state-of-the-art mesoscale-to-microscale coupling.

17 WIND ENERGY↗
NASA NTRS2001

The Computational Complexity, Parallel Scalability, and Performance of Atmospheric Data Assimilation Algorithms

The computational complexity of algorithms for Four Dimensional Data Assimilation (4DDA) at NASA's Data Assimilation Office (DAO) is discussed. In 4DDA, observations are assimilated with the output of a dynamical model to generate best-estimates of the states of the system. It is thus a mapping problem, whereby scattered observations are converted into regular accurate maps of wind, temperature, moisture and other variables. The DAO is developing and using 4DDA algorithms that provide these datasets, or analyses, in support of Earth System Science research. Two large-scale algorithms are discussed. The first approach, the Goddard Earth Observing System Data Assimilation System (GEOS DAS), uses an atmospheric general circulation model (GCM) and an observation-space based analysis system, the Physical-space Statistical Analysis System (PSAS). GEOS DAS is very similar to global meteorological weather forecasting data assimilation systems, but is used at NASA for climate research. Systems of this size typically run at between 1 and 20 gigaflop/s. The second approach, the Kalman filter, uses a more consistent algorithm to determine the forecast error covariance matrix than does GEOS DAS. For atmospheric assimilation, the gridded dynamical fields typically have More than 10(exp 6) variables, therefore the full error covariance matrix may be in excess of a teraword. For the Kalman filter this problem can easily scale to petaflop/s proportions. We discuss the computational complexity of GEOS DAS and our implementation of the Kalman filter. We also discuss and quantify some of the technical issues and limitations in developing efficient, in terms of wall clock time, and scalable parallel implementations of the algorithms.

Lyster, Peter M.↗
NASA NTRS2012

Atmospheric Dust Modeling from Meso to Global Scales with the Online NMMB/BSC-Dust Model Part 2: Experimental Campaigns in Northern Africa

The new NMMB/BSC-Dust model is intended to provide short to medium-range weather and dust forecasts from regional to global scales. It is an online model in which the dust aerosol dynamics and physics are solved at each model time step. The companion paper (Perez et al., 2011) develops the dust model parameterizations and provides daily to annual evaluations of the model for its global and regional configurations. Modeled aerosol optical depth (AOD) was evaluated against AERONET Sun photometers over Northern Africa, Middle East and Europe with correlations around 0.6-0.7 on average without dust data assimilation. In this paper we analyze in detail the behavior of the model using data from the Saharan Mineral dUst experiment (SAMUM-1) in 2006 and the Bodele Dust Experiment (BoDEx) in 2005. AOD from satellites and Sun photometers, vertically resolved extinction coefficients from lidars and particle size distributions at the ground and in the troposphere are used, complemented by wind profile data and surface meteorological measurements. All simulations were performed at the regional scale for the Northern African domain at the expected operational horizontal resolution of 25 km. Model results for SAMUM-1 generally show good agreement with satellite data over the most active Saharan dust sources. The model reproduces the AOD from Sun photometers close to sources and after long-range transport, and the dust size spectra at different height levels. At this resolution, the model is not able to reproduce a large haboob that occurred during the campaign. Some deficiencies are found concerning the vertical dust distribution related to the representation of the mixing height in the atmospheric part of the model. For the BoDEx episode, we found the diurnal temperature cycle to be strongly dependant on the soil moisture, which is underestimated in the NCEP analysis used for model initialization. The low level jet (LLJ) and the dust AOD over the Bodélé are well reproduced. The remaining negative AOD bias (due to underestimated surface wind speeds) can be substantially reduced by decreasing the threshold friction velocity in the model.

photometers↗
DOE OSTI2014

Combined experimental and numerical evaluation of a prototype nano-PCM enhanced wallboard

In the United States, forty-eight (48) percent of the residential end-use energy consumption is spent on space heating and air conditioning. Reducing envelope-generated heating and cooling loads through application of phase change material (PCM)-enhanced building envelopes can facilitate maximizing the energy efficiency of buildings. Combined experimental testing and numerical modeling of PCM-enhanced envelope components are two important aspects of the evaluation of their energy benefits. An innovative phase change material (nano-PCM) was developed with PCM encapsulated with expanded graphite (interconnected) nanosheets, which is highly conductive for enhanced thermal storage and energy distribution, and is shape-stable for convenient incorporation into lightweight building components. A wall with cellulose cavity insulation and prototype PCM-enhanced interior wallboards was built and tested in a natural exposure test (NET) facility in a hot-humid climate location. The test wall contained PCM wallboards and regular gypsum wallboard, for a side-by-side annual comparison study. Further, numerical modeling of the walls containing the nano-PCM wallboard was performed to determine its actual impact on wall-generated heating and cooling loads. The model was first validated using experimental data, and then used for annual simulations using Typical Meteorological Year (TMY3) weather data. This article presents the measured performance and numerical analysis evaluating the energy-saving potential of the nano-PCM-enhanced wallboard.

finite element analysis↗
DOE OSTI2014

Low-cost phase change material as an energy storage medium in building envelopes: Experimental and numerical analyses

A promising approach to increasing the energy efficiency of buildings is the implementation of a phase change material (PCM) in the building envelope. Numerous studies over the last two decades have reported the energy saving potential of PCMs in building envelopes, but their wide application has been inhibited, in part, by their high cost. This article describes a novel PCM made of naturally occurring fatty acids/glycerides trapped into high density polyethylene (HDPE) pellets and its performance in a building envelope application. The PCM-HDPE pellets were mixed with cellulose insulation and then added to an exterior wall of a test building in a hot and humid climate, and tested over a period of several months, To demonstrate the efficacy of the PCM-enhanced cellulose insulation in reducing the building envelope heat gains and losses, side-by-side comparison was performed with another wall section filled with cellulose-only insulation. Further, numerical modeling of the test wall was performed to determine the actual impact of the PCM-HDPE pellets on wall-generated heating and cooling loads and the associated electricity consumption. The model was first validated using experimental data and then used for annual simulations using typical meteorological year (TMY3) weather data. Furthermore, this article presents the experimental data and numerical analyses showing the energy-saving potential of the new PCM.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Commercial and Residential Hourly Load Profiles for all TMY3 Locations in the United States

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and residential building stock. The End-Use Load Profiles project website includes links to data viewers for this new dataset. For documentation of dataset validation, model calibration, and uncertainty quantification, see Wilson et al. (2022). These data were first created around 2012 as a byproduct of various analyses of solar photovoltaics and solar water heating (see references below for are two examples). This dataset contains several errors and limitations. It is recommended that users of this dataset transition to the updated version of the dataset posted in the resources. This dataset contains weather data, commercial load profile data, and residential load profile data. Weather The Typical Meteorological Year 3 (TMY3) provides one year of hourly data for around 1,000 locations. The TMY weather represents 30-year normals, which are typical weather conditions over a 30-year period. Commercial The commercial load profiles included are the 16 ASHRAE 90.1-2004 DOE Commercial Prototype Models simulated in all TMY3 locations, with building insulation levels changing based on ASHRAE 90.1-2004 requirements in each climate zone. The folder names within each resource represent the weather station location of the profiles, whereas the file names represent the building type and the representative city for the ASHRAE climate zone that was used to determine code compliance insulation levels. As indicated by the file names, all building models represent construction that complied with the ASHRAE 90.1-2004 building energy code requirements. No older or newer vintages of buildings are represented. Residential The BASE residential load profiles are five EnergyPlus models (one per climate region) representing 2009 IECC construction single-family detached homes simulated in all TMY3 locations. No older or newer vintages of buildings are represented. Each of the five climate regions include only one heating fuel type; electric heating is only found in the Hot-Humid climate. Air conditioning is not found in the Marine climate region. One major issue with the residential profiles is that for each of the five climate zones, certain location-specific algorithms from one city were applied to entire climate zones. For example, in the Hot-Humid files, the heating season calculated for Tampa, FL (December 1 - March 31) was unknowingly applied to all other locations in the Hot-Humid zone, which restricts heating operation outside of those days (for example, heating is disabled in Dallas, TX during cold weather in November). This causes the heating energy to be artificially low in colder parts of that climate zone, and conversely the cooling season restriction leads to artificially low cooling energy use in hotter parts of each climate zone. Additionally, the ground temperatures for the representative city were used across the entire climate zone. This affects water heating energy use (because inlet cold water temperature depends on ground temperature) and heating/cooling energy use (because of ground heat transfer through foundation walls and floors). Representative cities were Tampa, FL (Hot-Humid), El Paso, TX (Mixed-Dry/Hot-Dry), Memphis, TN (Mixed-Humid), Arcata, CA (Marine), and Billings, MT (Cold/Very-Cold). The residential dataset includes a HIGH building load profile that was intended to provide a rough approximation of older home vintages, but it combines poor thermal insulation with larger house size, tighter thermostat setpoints, and less efficient HVAC equipment. Conversely, the LOW building combines excellent thermal insulation with smaller house size, wider thermostat setpoints, and more efficient HVAC equipment. However, it is not known how well these ...

Array↗
NASA NTRS1977

An error analysis of tropical cyclone divergence and vorticity fields derived from satellite cloud winds on the Atmospheric and Oceanographic Information Processing System (AOIPS)

An advanced Man-Interactive image and data processing system (AOIPS) was developed to extract basic meteorological parameters from satellite data and to perform further analyses. The errors in the satellite derived cloud wind fields for tropical cyclones are investigated. The propagation of these errors through the AOIPS system and their effects on the analysis of horizontal divergence and relative vorticity are evaluated.

Hasler, A. F.↗
NASA NTRS2016

Multispacecraft Observations and Modeling of the 22/23 June 2015 Geomagnetic Storm

The magnetic storm of 22-23 June 2015 was one of the largest in the current solar cycle. We present in situ observations from the Magnetospheric Multiscale Mission (MMS) and the Van Allen Probes (VAP) in the magnetotail, field-aligned currents from AMPERE (Active Magnetosphere and Planetary Electrodynamics Response), and ionospheric flow data from Defense Meteorological Satellite Program (DMSP). Our real-time space weather alert system sent out a "red alert," correctly predicting Kp indices greater than 8. We show strong outflow of ionospheric oxygen, dipolarizations in the MMS magnetometer data, and dropouts in the particle fluxes seen by the MMS Fast Plasma Instrument suite. At ionospheric altitudes, the AMPERE data show highly variable currents exceeding 20 MA. We present numerical simulations with the Block Adaptive Tree-Solarwind - Roe - Upwind Scheme (BATS-R-US) global magnetohydrodynamic model linked with the Rice Convection Model. The model predicted the magnitude of the dipolarizations, and varying polar cap convection patterns, which were confirmed by DMSP measurements.

Reiff, P. H.↗
NASA NTRS2013

Future Extreme Heat Scenarios to Enable the Assessment of Climate Impacts on Public Health over the Coterminous U.S.

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, which contributes to local-scale urban heat islands, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. This research seeks to provide historical and future measures of climate-driven extreme heat events to enable assessments of the impacts of heat on public health over the coterminous U.S. We use atmospheric temperature and humidity information from meteorological reanalysis and from Global Climate Models (GCMs) to provide data on past and future heat events. The focus of research is on providing assessments of the magnitude, frequency and geographic distribution of extreme heat in the U.S. to facilitate public health studies. In our approach, long-term climate change is captured with GCM outputs, and the temporal and spatial characteristics of short-term extremes are represented by the reanalysis data. Two future time horizons for 2040 and 2090 are compared to the recent past period of 1981- 2000. We characterize regional-scale temperature and humidity conditions using GCM outputs for two climate change scenarios (A2 and A1B) defined in the Special Report on Emissions Scenarios (SRES). For each future period, 20 years of multi-model GCM outputs are analyzed to develop a 'heat stress climatology' based on statistics of extreme heat indicators. Differences between the two future and the past period are used to define temperature and humidity changes on a monthly time scale and regional spatial scale. These changes are combined with the historical meteorological data, which is hourly and at a spatial scale (12 km), to create future climate realizations. From these realizations, we compute the daily heat stress measures and related spatially-specific climatological fields, such as the mean annual number of days above certain thresholds of maximum and minimum air temperatures, heat indices and a new heat stress variable developed as part of this research that gives an integrated measure of heat stress (and relief) over the course of a day. Comparisons are made between projected (2040 and 2090) and past (1990) heat stress statistics. Outputs are aggregated to the county level, which is a popular scale of analysis for public health interests. County-level statistics are made available to public health researchers by the Centers for Disease Control and Prevention (CDC) via the Wideranging Online Data for Epidemiologic Research (WONDER) system. This addition of heat stress measures to CDC WONDER allows decision and policy makers to assess the impact of alternative approaches to optimize the public health response to EHEs. Through CDC WONDER, users are able to spatially and temporally query public health and heat-related data sets and create county-level maps and statistical charts of such data across the coterminous U.S

Quattrochi, Dale A.↗
NASA NTRS2014

Linking Excessive Heat with Daily Heat-Related Mortality over the Coterminous United States

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, which contributes to local-scale urban heat islands, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. This research seeks to provide historical and future measures of climate-driven extreme heat events to enable assessments of the impacts of heat on public health over the coterminous U.S. We use atmospheric temperature and humidity information from meteorological reanalysis and from Global Climate Models (GCMs) to provide data on past and future heat events. The focus of research is on providing assessments of the magnitude, frequency and geographic distribution of extreme heat in the U.S. to facilitate public health studies. In our approach, long-term climate change is captured with GCM outputs, and the temporal and spatial characteristics of short-term extremes are represented by the reanalysis data. Two future time horizons for 2040 and 2090 are compared to the recent past period of 1981- 2000. We characterize regional-scale temperature and humidity conditions using GCM outputs for two climate change scenarios (A2 and A1B) defined in the Special Report on Emissions Scenarios (SRES). For each future period, 20 years of multi-model GCM outputs are analyzed to develop a 'heat stress climatology' based on statistics of extreme heat indicators. Differences between the two future and the past period are used to define temperature and humidity changes on a monthly time scale and regional spatial scale. These changes are combined with the historical meteorological data, which is hourly and at a spatial scale (12 km) much finer than that of GCMs, to create future climate realizations. From these realizations, we compute the daily heat stress measures and related spatially-specific climatological fields, such as the mean annual number of days above certain thresholds of maximum and minimum air temperatures, heat indices, and a new heat stress variable developed as part of this research that gives an integrated measure of heat stress (and relief) over the course of a day. Comparisons are made between projected (2040 and 2090) and past (1990) heat stress statistics. Outputs are aggregated to the county level, which is a popular scale of analysis for public health interests. County-level statistics are made available to public health researchers by the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This addition of heat stress measures to CDC WONDER allows decision and policy makers to assess the impact of alternative approaches to optimize the public health response to EHEs. Through CDC WONDER, users are able to spatially and temporally query public health and heat-related data sets and create county-level maps and statistical charts of such data across the coterminous U.S.

Quattrochi, Dale A.↗
DOE OSTI2021

OpenET: Filling a Critical Data Gap in Water Management for the Western United States

The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data-driven irrigation management strategies, and expanding incentive-driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field-scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community-driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well-established satellite-based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web-based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite-driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

STFM: Accurate Spatio-Temporal Fusion Model for Weather Forecasting

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

It's the Heat AND the Humidity -- Assessment of Extreme Heat Scenarios to Enable the Assessment of Climate Impacts on Public Health

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, which contributes to local-scale urban heat islands, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. In a NASA-funded project supporting the National Climate Assessment, we are providing historical and future measures of extreme heat to enable assessments of the impacts of heat on public health over the coterminous U.S. We use atmospheric temperature and humidity information from meteorological reanalysis and from Global Climate Models (GCMs) to provide data on past and future heat events. The project s emphasis is on providing assessments of the magnitude, frequency and geographic distribution of extreme heat in the U.S. to facilitate public health studies. In our approach, long-term climate change is captured with GCM output, and the temporal and spatial characteristics of short-term extremes are represented by the reanalysis data. Two future time horizons, 2040 and 2090, are the focus of future assessments; these are compared to the recent past period of 1981-2000. We are characterizing regional-scale temperature and humidity conditions using GCM output for two climate change scenarios (A2 and A1B) defined in the Special Report on Emissions Scenarios (SRES). For each future period, 20 years of multi-model GCM output have been analyzed to develop a heat stress climatology based on statistics of extreme heat indicators. Differences between the two future and past periods have been used to define temperature and humidity changes on a monthly time scale and regional spatial scale. These changes, combined with hourly historical meteorological data at a spatial scale (12 km) much finer than that of GCMs, enable us to create future climate realizations, from which we compute the daily heat stress measures and related spatially-specific climatological fields. These include the mean annual number of days above certain thresholds of maximum and minimum air temperatures, heat indices and a new heat stress variable that gives an integrated measure of heat stress (and relief) over the course of a day. Comparisons are made between projected (2040 and 2090) and past (1990) heat stress statistics. All output is being provided at the 12 km spatial scale and will also be aggregated to the county level, which is a popular scale of analysis for public health researchers. County-level statistics will be made available by our collaborators at the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. CDC WONDER makes the information resources of the CDC available to public health professionals and the general public. This addition of heat stress measures to CDC WONDER will allow decision and policy makers to assess the impact of alternative approaches to optimize the public health response to EHEs. It will also allow public health researchers and policy makers to better include such heat stress measures in the context of national health data available in the CDC WONDER system. The users will be able to spatially and temporally query public health and heat-related data sets and create county-level maps and statistical charts of such data across the coterminous U.S.

Crosson, William L↗
NASA NTRS1994

The analysis of rapidly developing fog at the Kennedy Space Center

This report documents fog precursors and fog climatology at Kennedy Space Center (KSC) Florida from 1986 to 1990. The major emphasis of this report focuses on rapidly developing fog events that would affect the less than 7-statute mile visibility rule for End-Of-Mission (EOM) Shuttle landing at KSC (Rule 4-64(A)). The Applied Meteorology Unit's (AMU's) work is to: develop a data base for study of fog associated weather conditions relating to violations of this landing constraint; develop forecast techniques or rules-of-thumb to determine whether or not current conditions are likely to result in an acceptable condition at landing; validate the forecast techniques; and transition techniques to operational use. As part of the analysis the fog events were categorized as either advection, pre-frontal or radiation. As a result of these analyses, the AMU developed a fog climatological data base, identified fog precursors and developed forecaster tools and decision trees. The fog climatological analysis indicates that during the fog season (October to April) there is a higher risk for a visibility violation at KSC during the early morning hours (0700 to 1200 UTC), while 95 percent of all fog events have dissipated by 1600 UTC. A high number of fog events are characterized by a westerly component to the surface wind at KSC (92 percent) and 83 percent of the fog events had fog develop west of KSC first (up to 2 hours). The AMU developed fog decision trees and forecaster tools that would help the forecaster identify fog precursors up to 12 hours in advance. Using the decision trees as process tools ensures the important meteorological data are not overlooked in the forecast process. With these tools and a better understanding of fog formation in the local KSC area, the Shuttle weather support forecaster should be able to give the Launch and Flight Directors a better KSC fog forecast with more confidence.

Wheeler, Mark M.↗
NASA NTRS1999

Local Data Integration in East Central Florida

The Applied Meteorology Unit has configured a Local Data Integration System (LDIS) for east central Florida which assimilates in-situ and remotely-sensed observational data into a series of high-resolution gridded analyses. The ultimate goal for running LDIS is to generate products that may enhance weather nowcasts and short-range (less than 6 h) forecasts issued in support of the 45th Weather Squadron (45 WS), Spaceflight Meteorology Group (SMG), and the Melbourne National Weather Service (NWS MLB) operational requirements. LDIS has the potential to provide added value for nowcasts and short-ten-n forecasts for two reasons. First, it incorporates all data operationally available in east central Florida. Second, it is run at finer spatial and temporal resolutions than current national-scale operational models such as the Rapid Update Cycle and Eta models. LDIS combines all available data to produce grid analyses of primary variables (wind, temperature, etc.) at specified temporal and spatial resolutions. These analyses of primary variables can be used to compute diagnostic quantities such as vorticity and divergence. This paper demonstrates the utility of LDIS over east central Florida for a warm season case study. The evolution of a significant thunderstorm outflow boundary is depicted through horizontal and vertical cross section plots of wind speed, divergence, and circulation. In combination with a suitable visualization too], LDIS may provide users with a more complete and comprehensive understanding of evolving mesoscale weather than could be developed by individually examining the disparate data sets over the same area and time.

Case, Jonathan L.↗
NASA NTRS2016

Preparations for Integrating Space-Based Total Lightning Observations into Forecast Operations

NASA's Short-term Prediction Research and Transition (SPoRT) Center has been a leader in collaborating with the United States National Weather Service (NWS) offices to integrate ground-based total lightning (intra-cloud and cloud-to-ground) observations into the real-time operational environment. For much of these collaborations, the emphasis has been on training, dissemination of data to the NWS AWIPS system, and focusing on the utility of these data in the warning decision support process. A shift away from this paradigm has occurred more recently for several reasons. For one, SPoRT's collaborations have expanded to new partners, including emergency managers and the aviation community. Additionally, and most importantly, is the impending launch of the GOES-R Geostationary Lightning Mapper (GLM). This has led to collaborative efforts to focus on additional forecast needs, new data displays, develop training for GLM uses based on the lessons learned from ground-based lightning mapping arrays, and ways to better relate total lightning data to other meteorological parameters. This presentation will focus on these efforts to prepare the operational end user community for GLM with an eye towards sharing lessons learned as EUMETSAT prepares for the Meteosat Third Generation Lightning Imager. This will focus on both software and training needs. In particular, SPoRT has worked closely with the Meteorological Development Laboratory to create the total lightning tracking tool. This software allows for NWS forecasters to manually track storms of interest and display a time series trend of observations. This tool also has been expanded to work on any gridded data set allowing for easy visual comparisons of multiple parameters in addition to total lightning. A new web display has been developed for the ground-based observations that can be easily extended to satellite observations. This paves the way for new collaborations outside of the NWS, both domestically and internationally, as the web display will be functional on PCs and mobile devices. Furthermore, SPoRT has helped developed the software plug-in to visualize GLM data. Examples using the official GLM proxy product will be used to provide a glimpse as to what real-time GLM and likely MTG-LI data will be in the near future.

GLM↗