SEARCH · Engineering Papers

Results for “WEATHER FORECASTING”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18

All sources NASA NTRS DOE OSTI

NASA NTRS2013

Severe Weather Tool using 1500 UTC Cape Canaveral Air Force Station Soundings

People and property at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) are at risk when severe weather occurs. Strong winds, hail and tornadoes can injure individuals and cause costly damage to structures if not properly protected. NASA's Launch Services Program and Ground Systems Development and Operations Program and other KSC programs use the daily and weekly severe weather forecasts issued by the 45th Weather Squadron (45 WS) to determine if they need to limit an activity such as working on gantries, or protect property such as a vehicle on a pad. The 45 WS requested the Applied Meteorology Unit (AMU) develop a warm season (May-September) severe weather tool for use in the Meteorological Interactive Data Display System (MIDDS) based on the late morning, 1500 UTC (1100 local time), CCAFS (XMR) sounding. The 45 WS frequently makes decisions to issue a severe weather watch and other severe weather warning support products to NASA and the 45th Space Wing in the late morning, after the 1500 UTC sounding. The results of this work indicate that certain stability indices based on the late morning XMR soundings can depict differences between days with reported severe weather and days with no reported severe weather. The AMU determined a frequency of reported severe weather for the stability indices and implemented an operational tool in MIDDS.

Bauman, William H., III↗

NASA NTRS2014

A Sounding-based Severe Weather Tool to Support Daily Operations at Kennedy Space Center and Cape Canaveral Air Force Station

Hail↗

NASA NTRS2016

Wind Information Uplink to Aircraft Performing Interval Management Operations

Interval Management (IM) is an ADS-B-enabled suite of applications that use ground and flight deck capabilities and procedures designed to support the relative spacing of aircraft (Barmore et al., 2004, Murdoch et al. 2009, Barmore 2009, Swieringa et al. 2011; Weitz et al. 2012). Relative spacing refers to managing the position of one aircraft to a time or distance relative to another aircraft, as opposed to a static reference point such as a point over the ground or clock time. This results in improved inter-aircraft spacing precision and is expected to allow aircraft to be spaced closer to the applicable separation standard than current operations. Consequently, if the reduced spacing is used in scheduling, IM can reduce the time interval between the first and last aircraft in an overall arrival flow, resulting in increased throughput. Because IM relies on speed changes to achieve precise spacing, it can reduce costly, low-altitude, vectoring, which increases both efficiency and throughput in capacity-constrained airspace without negatively impacting controller workload and task complexity. This is expected to increase overall system efficiency. The Flight Deck Interval Management (FIM) equipment provides speeds to the flight crew that will deliver them to the achieve-by point at the controller-specified time, i.e., assigned spacing goal, after the target aircraft crosses the achieve-by point (Figure 1.1). Since the IM and target aircraft may not be on the same arrival procedure, the FIM equipment predicts the estimated times of arrival (ETA) for both the IM and target aircraft to the achieve-by point. This involves generating an approximate four-dimensional trajectory for each aircraft. The accuracy of the wind data used to generate those trajectories is critical to the success of the IM operation. There are two main forms of uncertainty in the wind information used by the FIM equipment. The first is the accuracy of the forecast modeling done by the weather provider. This is generally a global environmental prediction obtained from a weather model such as the Rapid Refresh (RAP) from the National Centers for Environmental Prediction (NCEP). The weather forecast data will have errors relative to the actual, or truth, winds that the aircraft will encounter. The second source of uncertainty is that only a small subset of the forecast data can be uplinked to the aircraft for use by the FIM equipment. This results in loss of additional information. The Federal Aviation Administration (FAA) and RTCA are currently developing standards for the communication of wind and atmospheric data to the aircraft for use in NextGen operations. This study examines the impact of various wind forecast sampling methods on IM performance metrics to inform the standards development.

Ahmad, Nashat N.↗

NASA NTRS2017

NASA Weather Support 2017

In the mid to late 1980's, as NASA was studying ways to improve weather forecasting capabilities to reduce excessive weather launch delays and to reduce excessive weather Launch Commit Criteria (LCC) waivers, the Challenger Accident occurred and the AC-67 Mishap occurred.[1] NASA and USAF weather personnel had advance knowledge of extremely high levels of weather hazards that ultimately caused or contributed to both of these accidents. In both cases, key knowledge of the risks posed by violations of weather LCC was not in the possession of final decision makers on the launch teams. In addition to convening the mishap boards for these two lost missions, NASA convened expert meteorological boards focusing on weather support. These meteorological boards recommended the development of a dedicated organization with the highest levels of weather expertise and influence to support all of American spaceflight. NASA immediately established the Weather Support Office (WSO) in the Office of Space Flight (OSF), and in coordination with the United Stated Air Force (USAF), initiated an overhaul of the organization and an improvement in technology used for weather support as recommended. Soon after, the USAF established a senior civilian Launch Weather Officer (LWO) position to provide meteorological support and continuity of weather expertise and knowledge over time. The Applied Meteorology Unit (AMU) was established by NASA, USAF, and the National Weather Service to support initiatives to place new tools and methods into an operational status. At the end of the Shuttle Program, after several weather office reorganizations, the WSO function had been assigned to a weather branch at Kennedy Space Center (KSC). This branch was dismantled in steps due to further reorganization, loss of key personnel, and loss of budget line authority. NASA is facing the loss of sufficient expertise and leadership required to provide current levels of weather support. The recommendation proposed herein is to re-establish the WSO under a high level office, with funding set at about the same levels as today, with a revitalized charter and focus to allow for the WSO to operate as originally intended.

lessons learned↗

NASA NTRS1963

Tiros achievements

Tiros satellite cloud photographs for weather forecastings

METEOROLOGY↗

NASA NTRS1966

The future of space travel

Space exploration and applications to earth sciences, weather forecasting, and related topics

SPACE EXPLORATION↗

NASA NTRS2003

Adaptation of Mesoscale Weather Models to Local Forecasting

Methodologies have been developed for (1) configuring mesoscale numerical weather-prediction models for execution on high-performance computer workstations to make short-range weather forecasts for the vicinity of the Kennedy Space Center (KSC) and the Cape Canaveral Air Force Station (CCAFS) and (2) evaluating the performances of the models as configured. These methodologies have been implemented as part of a continuing effort to improve weather forecasting in support of operations of the U.S. space program. The models, methodologies, and results of the evaluations also have potential value for commercial users who could benefit from tailoring their operations and/or marketing strategies based on accurate predictions of local weather. More specifically, the purpose of developing the methodologies for configuring the models to run on computers at KSC and CCAFS is to provide accurate forecasts of winds, temperature, and such specific thunderstorm-related phenomena as lightning and precipitation. The purpose of developing the evaluation methodologies is to maximize the utility of the models by providing users with assessments of the capabilities and limitations of the models. The models used in this effort thus far include the Mesoscale Atmospheric Simulation System (MASS), the Regional Atmospheric Modeling System (RAMS), and the National Centers for Environmental Prediction Eta Model ( Eta for short). The configuration of the MASS and RAMS is designed to run the models at very high spatial resolution and incorporate local data to resolve fine-scale weather features. Model preprocessors were modified to incorporate surface, ship, buoy, and rawinsonde data as well as data from local wind towers, wind profilers, and conventional or Doppler radars. The overall evaluation of the MASS, Eta, and RAMS was designed to assess the utility of these mesoscale models for satisfying the weather-forecasting needs of the U.S. space program. The evaluation methodology includes objective and subjective verification methodologies. Objective (e.g., statistical) verification of point forecasts is a stringent measure of model performance, but when used alone, it is not usually sufficient for quantifying the value of the overall contribution of the model to the weather-forecasting process. This is especially true for mesoscale models with enhanced spatial and temporal resolution that may be capable of predicting meteorologically consistent, though not necessarily accurate, fine-scale weather phenomena. Therefore, subjective (phenomenological) evaluation, focusing on selected case studies and specific weather features, such as sea breezes and precipitation, has been performed to help quantify the added value that cannot be inferred solely from objective evaluation.

Manobianco, John T.↗

DOE OSTI2018

A Solar Time-based Analog Ensemble Method for Regional Solar Power Forecasting

This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Further, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American Mesoscale Forecast System, the Global Forecast System, and the Short-Range Ensemble Forecast, for both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. In conclusion, the normalized root mean square error (NRMSE) has been reduced by 13.80%-61.21% when compared with three tested baselines.

14 SOLAR ENERGY↗

NASA NTRS2013

Evaluation of Model Microphysics Within Precipitation Bands of Extratropical Cyclones

Recent studies evaluating the bulk microphysical schemes (BMPs) within cloud resolving models (CRMs) have indicated large uncertainties and errors in the amount and size distributions of snow and cloud ice aloft. The snow prediction is sensitive to the snow densities, habits, and degree of riming within the BMPs. Improving these BMPs is a crucial step toward improving both weather forecasting and climate predictions. Several microphysical schemes in the Weather Research and Forecasting (WRF) model down to 1.33‐km grid spacing are evaluated using aircraft, radar, and ground in situ data from the Global Precipitation Mission Coldseason Precipitation Experiment (GCPEx) experiment, as well as a few years (15 winter storms) of surface measurements of riming, crystal habit, snow density, and radar measurements at Stony Brook, NY (SBNY on north shore of Long Island) during the 2009-2012 winter seasons. Surface microphysical measurements at SBNY were taken every 15 to 30 minutes using a stereo microscope and camera, and snow depth and snow density were also recorded. During these storms, a vertically-pointing Ku-band radar was used to observe the vertical evolution of reflectivity and Doppler vertical velocities. A Particle Size and Velocity (PARSIVEL) disdrometer was also used to measure the surface size distribution and fall speeds of snow at SBNY. For the 15 cases at SBNY, the WSM6, Morrison (MORR), Thompson (THOM2), and Stony Brook (SBU-YLIN) BMPs were validated. A non-spherical snow assumption (THOM2 and SBU-YLIN) simulated a more realistic distribution of reflectivity than spherical snow assumptions in the WSM6 and MORR schemes. The MORR, WSM6, and SBU-YLIN schemes are comparable to the observed velocity distribution in light and moderate riming periods. The THOM2 is ~0.25 meters per second too slow with its velocity distribution in these periods. In heavier riming, the vertical Doppler velocities in the WSM6, THOM2, and MORR schemes were ~0.25 meters per second too slow, while the SBU-YLIN was 0.25 to 0.5 meters per second too fast. Overall, the BMPs simulate a size distribution close to the observed for D < 4 mm in the dendritic, plates, and mixed habit periods. The model BMPs underestimate the size distribution when large aggregates were observed. For D > 6 mm in the dendrites, side planes, and mixed habit periods, the BMPs are likely not simulating enough aggregation to create a larger size distribution, although the MORR (double moment) scheme seemed to perform best. These SBNY results will be compared with some results from GCPEx for a warm frontal snow band observed at 18 February 2012.

Colle, Brian A.↗

NASA NTRS2013

Evaluation of Model Microphysics within Precipitation Bands of Extratropical Cyclones

Recent studies evaluating the bulk microphysical schemes (BMPs) within cloud resolving models (CRMs) have indicated large uncertainties and errors in the amount and size distributions of snow and cloud ice aloft. The snow prediction is sensitive to the snow densities, habits, and degree of riming within the BMPs. Improving these BMPs is a crucial step toward improving both weather forecasting and climate predictions. Several microphysical schemes in the Weather Research and Forecasting (WRF) model down to 1.33‐km grid spacing are evaluated using aircraft, radar, and ground in situ data from the Global Precipitation Mission Coldseason Precipitation Experiment (GCPEx) experiment, as well as a few years (15 winter storms) of surface measurements of riming, crystal habit, snow density, and radar measurements at Stony Brook, NY (SBNY on north shore of Long Island) during the 2009-2012 winter seasons. Surface microphysical measurements at SBNY were taken every 15 to 30 minutes using a stereo microscope and camera, and snow depth and snow density were also recorded. During these storms, a vertically-pointing Ku-band radar was used to observe the vertical evolution of reflectivity and Doppler vertical velocities. A Particle Size and Velocity (PARSIVEL) disdrometer was also used to measure the surface size distribution and fall speeds of snow at SBNY. For the 15 cases at SBNY, the WSM6, Morrison (MORR), Thompson (THOM2), and Stony Brook (SBU-YLIN) BMPs were validated. A non-spherical snow assumption (THOM2 and SBU-YLIN) simulated a more realistic distribution of reflectivity than spherical snow assumptions in the WSM6 and MORR schemes. The MORR, WSM6, and SBU-YLIN schemes are comparable to the observed velocity distribution in light and moderate riming periods. The THOM2 is approx 0.25 m/s too slow with its velocity distribution in these periods. In heavier riming, the vertical Doppler velocities in the WSM6, THOM2, and MORR schemes were approx 0.25 m/s too slow, while the SBU-YLIN was 0.25 to 0.5 m/s too fast. Overall, the BMPs simulate a size distribution close to the observed for D < 4 mm in the dendritic, plates, and mixed habit periods. The model BMPs underestimate the size distribution when large aggregates were observed. For D > 6 mm in the dendrites, side planes, and mixed habit periods, the BMPs are likely not simulating enough aggregation to create a larger size distribution, although the MORR (double moment) scheme seemed to perform best. These SBNY results will be compared with some results from GCPEx for a warm frontal snow band observed at 18 February 2012.

Colle, Brian A.↗

NASA NTRS2014

Evaluation of Mixed-Phase Microphysics Within Winter Storms Using Field Data and In Situ Observations

Snow prediction within models is sensitive to the snow densities, habits, and degree of riming within the BMPs. Improving these BMPs is a crucial step toward improving both weather forecasting and climate predictions. Several microphysical schemes in the Weather Research and Forecasting (WRF) model down to 1.33-km grid spacing are evaluated using aircraft, radar, and ground in situ data from the Global Precipitation Mission Cold-season Precipitation Experiment (GCPEx) experiment over southern Ontario, as well as a few years (12 winter storms) of surface measurements of riming, crystal habit, snow density, and radar measurements at Stony Brook, NY (SBNY on north shore of Long Island) during the 2009-2012 winter seasons. Surface microphysical measurements at SBNY were taken every 15 to 30 minutes using a stereo microscope and camera, and snow depth and snow density were also recorded. During these storms, a vertically-pointing Ku band radar was used to observe the vertical evolution of reflectivity and Doppler vertical velocities. The GCPex presentation will focus on verification using aircraft spirals through warm frontal snow band event on 18 February 2012. All the BMPs realistically simulated the structure of the band and the vertical distribution of snow/ice aloft, except the SBU-YLIN overpredicted slightly and Thompson (THOM) underpredicted somewhat. The Morrison (MORR) scheme produced the best slope size distribution for snow, while the Stony Brook (SBU) underpredicted and the THOM slightly overpredicted. Those schemes that have the slope intercept a function of temperature (SBU and WSM6) tended to perform better for that parameter than others, especially the fixed intercept in Goddard. Overall, the spread among BMPs was smaller than in other studies, likely because there was limited riming with the band. For the 15 cases at SBNY, which include moderate and heavy riming events, the non-spherical snow assumption (THOM and SBU-YLIN) simulated a more realistic distribution of reflectivity than spherical snow assumptions in the WSM6 and MORR schemes. The MORR, WSM6, and SBU schemes are comparable to the observed velocity distribution in light and moderate riming periods. The THOM is approx. 0.25 m/s too slow with its velocity distribution in these periods. In heavier riming, the vertical Doppler velocities in the WSM6, THOM, and MORR schemes were approx. 0.25 m/s too slow, while the SBU was 0.25 to 0.5 m/s too fast because of some excessive cloud water issues.

Colle, Brian A.↗

NASA NTRS1963

NIMBUS SPACECRAFT DEVELOPMENT

Nimbus meteorological satellite system for data on worldwide atmospheric processes - real-time weather forecasting and research

NIMBUS SATELLITE↗

NASA NTRS1963

NIMBUS SPACECRAFT DEVELOPMENT

Development and design of the nimbus meteorological satellite system which provides data on worldwide atmospheric processes for use in weather forecasting

SATELLITE DESIGN↗

NASA NTRS2003

Atmospheric Electrical Activity and the Prospects for Improving Short-Term, Weather Forcasting

How might lightning measurements be used to improve short-term (0-24 hr) weather forecasting? We examine this question under two different prediction strategies. These include integration of lightning data into short-term forecasts (nowcasts) of convective (including severe) weather hazards and the assimilation of lightning data into cloud-resolving numerical weather prediction models. In each strategy we define specific metrics of forecast improvement and a progress assessment. We also address the conventional observing system deficiencies and potential gap-filling information that can be addressed through the use of the lightning measurement.

Goodman, Steven J.↗

NASA NTRS2011

Evaluating the Performance of Single and Double Moment Microphysics Schemes During a Synoptic-Scale Snowfall Event

Increases in computing resources have allowed for the utilization of high-resolution weather forecast models capable of resolving cloud microphysical and precipitation processes among varying numbers of hydrometeor categories. Several microphysics schemes are currently available within the Weather Research and Forecasting (WRF) model, ranging from single-moment predictions of precipitation content to double-moment predictions that include a prediction of particle number concentrations. Each scheme incorporates several assumptions related to the size distribution, shape, and fall speed relationships of ice crystals in order to simulate cold-cloud processes and resulting precipitation. Field campaign data offer a means of evaluating the assumptions present within each scheme. The Canadian CloudSat/CALIPSO Validation Project (C3VP) represented collaboration among the CloudSat, CALIPSO, and NASA Global Precipitation Measurement mission communities, to observe cold season precipitation processes relevant to forecast model evaluation and the eventual development of satellite retrievals of cloud properties and precipitation rates. During the C3VP campaign, widespread snowfall occurred on 22 January 2007, sampled by aircraft and surface instrumentation that provided particle size distributions, ice water content, and fall speed estimations along with traditional surface measurements of temperature and precipitation. In this study, four single-moment and two double-moment microphysics schemes were utilized to generate hypothetical WRF forecasts of the event, with C3VP data used in evaluation of their varying assumptions. Schemes that incorporate flexibility in size distribution parameters and density assumptions are shown to be preferable to fixed constants, and that a double-moment representation of the snow category may be beneficial when representing the effects of aggregation. These results may guide forecast centers in optimal configurations of their forecast models for winter weather and identify best practices present within these various schemes.

Molthan, Andrew L.↗

DOE OSTI2021

Review of Onsite Temperature and Solar Forecasting Models to Enable Better Building Design and Operations

Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data. Traditionally, most studies utilize airport weather information as the decision inputs. However, most buildings are in environments that are quite different than those at the airport miles away. Tree cover, adjacent buildings, and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature, humidity, solar irradiance, and wind that are large enough to influence design and operation decisions. In order to overcome this challenge, there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales. Additionally, this paper reviews and complies knowledge on common weather data resources, data processing methodologies and forecasting techniques of weather information. Commonly used statistical, machine learning and physical-based models are discussed and presented as two major categories: deterministic forecasting and probabilistic forecasting. Finally, evaluation metrics for forecasting errors are listed and discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2012

Effects of Real-Time NASA Vegetation Data on Model Forecasts of Severe Weather

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA-EOS Aqua and Terra satellites. NASA SPoRT started generating daily real-time GVF composites at 1-km resolution over the Continental United States beginning 1 June 2010. A companion poster presentation (Bell et al.) primarily focuses on impact results in an offline configuration of the Noah land surface model (LSM) for the 2010 warm season, comparing the SPoRT/MODIS GVF dataset to the current operational monthly climatology GVF available within the National Centers for Environmental Prediction (NCEP) and Weather Research and Forecasting (WRF) models. This paper/presentation primarily focuses on individual case studies of severe weather events to determine the impacts and possible improvements by using the real-time, high-resolution SPoRT-MODIS GVFs in place of the coarser-resolution NCEP climatological GVFs in model simulations. The NASA-Unified WRF (NU-WRF) modeling system is employed to conduct the sensitivity simulations of individual events. The NU-WRF is an integrated modeling system based on the Advanced Research WRF dynamical core that is designed to represents aerosol, cloud, precipitation, and land processes at satellite-resolved scales in a coupled simulation environment. For this experiment, the coupling between the NASA Land Information System (LIS) and the WRF model is utilized to measure the impacts of the daily SPoRT/MODIS versus the monthly NCEP climatology GVFs. First, a spin-up run of the LIS is integrated for two years using the Noah LSM to ensure that the land surface fields reach an equilibrium state on the 4-km grid mesh used. Next, the spin-up LIS is run in two separate modes beginning on 1 June 2010, one continuing with the climatology GVFs while the other uses the daily SPoRT/MODIS GVFs. Finally, snapshots of the LIS land surface fields are used to initialize two different simulations of the NU-WRF, one running with climatology LIS and GVFs, and the other running with experimental LIS and NASA/SPoRT GVFs. In this paper/presentation, case study results will be highlighted in regions with significant differences in GVF between the NCEP climatology and SPoRT product during severe weather episodes.

Case, Jonathan L.↗

NASA NTRS2020

Convection Parametrization and Multi-Nesting Dependence of a Heavy Rainfall Event over Namibia with Weather Research and Forecasting (WRF) Model

Namibia is considered as one of the countries that are most vulnerable to climate change due to its generally dry climate and the percentage of its population that rely on subsistence agriculture for their livelihoods. Early warning systems are an important aspect of adapting to climate change. Weather forecasting relies on the use of numerical weather prediction models and these need to be configured properly. In this study we investigate the effects of using multi-nests and a convection scheme on the simulation of a heavy rainfall event over the Northwestern region of Kunene, Namibia. The event which was associated with a cut-off low system, was short-lived and resulted in over 45 mm of rainfall in one hour. For the multi-nest, a 9 km grid length parent domain is nested within the Global Forecast System (GFS) simulations, which in turn forces a 3 km grid spacing child domain. A different set of simulations are produced using a single nest of 3 km grid spacing, nested directly inside the GFS data. The simulations are produced with the convection scheme switched on and off. The impact of a single versus multi nest is found to be small in general, with slight differences in the location of high rainfall intensity. Switching off the convection schemes results in high rainfall intensity and increased detail in the simulations, including when a grid spacing of 9 km mis used. Using a grid spacing of 3 km with the convection scheme on, results in a loss of detail in the simulations as well as lower rainfall amounts. The study shows a need for different configurations to be tested before an optimum configuration can be selected for operational forecasting. We recommend further tests with different synoptic forcing to be conducted to identify a suitable configuration for Namibia.

flooding↗