Search NASA⌕ Search

SEARCH · Search NASA

Results for “weather years”

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.

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

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Technoeconomic Opportunity Analysis for Local Power Generation in Falls City, Nebraska

Falls City is a small community in Nebraska interested in understanding how energy from local energy systems could support the community's economic development planning. To address the current community needs and address the future energy demand technical assistance conducted through the Communities Local Energy Action Program (Communities LEAP) assessed the technical and economic opportunities of adding energy technologies to Falls City's municipally owned and operated electric utility system. The modeling performed considered the technical and economic feasibility of technologies using the System Advisor Model (SAM). The modeling explored three technology configurations using multiple years of historical weather and wholesale cost data (2015-2022 & a typical meteorological year), and two different wholesale escalation rates (0.3% and 2.5%). Wholesale energy prices were based on the Southwest Power Pool's (SPP) real-time energy market and the annual escalation rates of these rates based on historical SPP wholesale and national retail electricity price trends. Results from the modeling showed that at current CAPEX costs and SPP wholesale electricity costs no technology combination averaged across the scenarios run provide a positive net present value (NPV). External financial support, changes in market conditions, and additional revenue streams would help create more economically favorable projects. As conditions change re-evaluation may be necessary.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2017

AMSNEXRAD Automated Detection of Meteorite Strewnfields in Doppler Weather Radar

For several years meteorite recovery in the United States has been greatly enhanced by using Doppler weather radar images to determine possible fall zones for meteorites produced by witnessed fireballs. While most fireball events leave no record on the Doppler radar, some large fireballs do. Based on the successful recovery of 10 meteorite falls 'under the radar', and the discovery of radar on more than 10 historic falls, it is believed that meteoritic dust and or actual meteorites falling to the ground have been recorded on Doppler weather radar.

Michael Hankey↗
DOE OSTI2024

Explained: Causes of Three Recent Major Blackouts and What Is Being Done in Response

While the power grid is designed to have an adequate supply of electricity generating capacity under hot and cold conditions well above or below average, weather in recent years has approached the bounds of anticipated conditions, in both magnitude and duration. Extreme weather can result in unanticipated levels of electricity demand and correlated power plant outages driven by extreme temperatures. How power system planners account for the changing role of wind and solar also plays an important role in the power grid's ability to supply enough electricity during extreme weather. In response to recent significant outages, power system planners have been focusing on ensuring methods to evaluate reliability during periods of "net peak demand" consider availability of renewable resources and capture the impact of extreme weather on power plants of all types. In particular, there is significant emphasis on the ability of natural gas-fueled power plants to operate during periods of extreme cold temperatures. The impact of extreme weather on the need to change planning processes is demonstrated in this fact sheet that discusses three significant outages that occurred in recent years.

power grid↗
NASA NTRS1986

Terrestrial ages of Antarctic meteorites: Implications for concentration mechanisms

Antarctic meteorites differ from meteorites fallen in other places in their mean terrestrial ages. Boeckl estimated the terrestrial half-life for the disintegration of stone meteorites by weathering under the climatic conditions of the Western United States to be about 3600 years. Antarctic meteorites, however, have terrestrial ages up to 70000 years, indicating larger weathering half-lives. The terrestrial ages of meteorites are determined by their concentration of cosmic-ray-produced radionuclides with suitable half-lives (C-14, Al-26, and Cl-36). These radionuclides have yielded reliable ages for the Antarctic meteorites. The distribution of terrestrial ages of Allan Hills and Yamato meteorites are examined.

Schultz, L.↗
DOE OSTI2023

Dynamically Downscaled Hourly Future Weather Data with 12-km Resolution Covering Most of North America

This is an hourly future weather dataset for energy modeling applications. The dataset is primarily based on the output of a regional climate model (RCM), i.e., the Weather Research and Forecasting (WRF) model version 3.3.1. The WRF simulations are driven by the output of a general circulation model (GCM), i.e., the Community Climate System Model version 4 (CCSM4). This dataset is in the EPW format, which can be read or translated by more than 25 building energy modeling programs (e.g., EnergyPlus, ESP-r, and IESVE), energy system modeling programs (e.g., System Advisor Model (SAM)), indoor air quality analysis programs (e.g., CONTAM), and hygrothermal analysis programs (e.g., WUFI). It contains 13 weather variables, which are the Dry-Bulb Temperature, Dew Point Temperature, Relative Humidity, Atmospheric Pressure, Horizontal Infrared Radiation Intensity from Sky, Global Horizontal Irradiation, Direct Normal Irradiation, Diffuse Horizontal Irradiation, Wind Speed, Wind Direction, Sky Cover, Albedo, and Liquid Precipitation Depth. The weather data is created for two emissions scenarios: RCP4.5 and RCP8.5 and spans two 10-year time slices in the future: 2045 - 2054 and 2085 - 2094. It offers a spatial resolution of 12 km by 12 km with extensive coverage across most of North America. Due to the enormous size of the entire dataset, in the first stage of its distribution, we provide 20 years of future weather data for the centroid of each Public Use Microdata Area (PUMA), excluding Hawaii. PUMAs are non-overlapping, statistical geographic areas that partition each state or equivalent entity into geographic areas containing no fewer than 100,000 people each. The 2,378 PUMAs as a whole cover the entirety of the U.S. The weather data can be utilized alongside the large-scale energy analysis tools, ResStock and ComStock, developed by National Renewable Energy Laboratory, whose smallest resolution is at the PUMA scale. The data for RCP4.5 is still being processed and will be published soon.

Array↗
NASA NTRS2023

Integrating NASA Aqua AIRS in a Real-Time NUCAPS Science-to-Applications System to Support Severe Weather Forecasting

In recent years, National Oceanic and Atmospheric Administration (NOAA) Unique Combined Atmospheric Processing System (NUCAPS) hyperspectral infrared satellite sounding retrievals derived from Joint Polar Satellite System (JPSS) polar-orbiting satellites have been documented as observations that add value to weather forecasting applications. NUCAPS is currently the operational algorithm delivering JPSS satellite sounding retrievals to the NOAA National Weather Service (NWS) and is based on the heritage Atmospheric Infrared Sounder (AIRS) Science Team algorithm for processing vertical temperature, moisture, and trace gas retrievals. For the Special Collection on “Twenty Years of Observations from AIRS,” we highlight the methodology we implemented to develop a prototype science-to-applications system to enable real-time processing of AIRS satellite sounding retrievals through the NUCAPS algorithm (i.e., NUCAPS-Aqua) to support weather forecasting applications. The addition of NUCAPS-Aqua to experimental real-time pathways alongside operational JPSS NUCAPS soundings, facilitated assessment of NUCAPS-Aqua at the 2022 Hazardous Weather Testbed (HWT) Spring Experiment. Development of NUCAPS-Aqua described in this technical report includes preservation of microwave observations and calculation of a-priori regression coefficients. Additionally, the real-time processing and challenges with implementing a science-to-applications system are discussed. Two illustrative pre-convective forecasting examples analyzed by NWS forecasters during the 2022 HWT Spring Experiment are highlighted to demonstrate the benefit of NUCAPS-Aqua as (a) special afternoon soundings, (b) an additional observation to assess temporal trends using multiple satellites, and (c) a complement to observational and model analysis.

remote sensing↗
NASA NTRS2007

Uncertainty Analysis of Historical Hurricane Data

An analysis of variance (ANOVA) study was conducted for historical hurricane data dating back to 1851 that was obtained from the U. S. Department of Commerce National Oceanic and Atmospheric Administration (NOAA). The data set was chosen because it is a large, publicly available collection of information, exhibiting great variability which has made the forecasting of future states, from current and previous states, difficult. The availability of substantial, high-fidelity validation data, however, made for an excellent uncertainty assessment study. Several factors (independent variables) were identified from the data set, which could potentially influence the track and intensity of the storms. The values of these factors, along with the values of responses of interest (dependent variables) were extracted from the data base, and provided to a commercial software package for processing via the ANOVA technique. The primary goal of the study was to document the ANOVA modeling uncertainty and predictive errors in making predictions about hurricane location and intensity 24 to 120 hours beyond known conditions, as reported by the data set. A secondary goal was to expose the ANOVA technique to a broader community within NASA. The independent factors considered to have an influence on the hurricane track included the current and starting longitudes and latitudes (measured in degrees), and current and starting maximum sustained wind speeds (measured in knots), and the storm starting date, its current duration from its first appearance, and the current year fraction of each reading, all measured in years. The year fraction and starting date were included in order to attempt to account for long duration cyclic behaviors, such as seasonal weather patterns, and years in which the sea or atmosphere were unusually warm or cold. The effect of short duration weather patterns and ocean conditions could not be examined with the current data set. The responses analyzed were the storm latitude, longitude and intensity, as recorded in the data set, 24 or 120 hours beyond the current state. Several ANOVA modeling schemes were examined. Two forms of validation were used: 1) comparison with official hurricane prediction performance metrics and 2) cases studies conducted on hurricanes from the 2005 season, which were not included within the model construction and ANOVA assessment. In general, the ANOVA technique did not perform as well as the established official prediction performance metrics published by NOAA; still, the technique did remarkably well in this demonstration with a difficult data set and could probably be made to perform better with more knowledge of hurricane development and dynamics applied to the problem. The technique provides a repeatable prediction process that eliminates the need for judgment in the forecast.

Green, Lawrence L.↗
NASA NTRS1995

The effect of moonlight on observation of cloud cover at night, and application to cloud climatology

Ten years of nighttime weather observations from the Northern Hemisphere in December were classified according to the illuminance of moonlight or twilight on the cloud tops, and a threshold level of illuminance was determined, above which the clouds are apparently detected adequately. This threshold corresponds to light from a full moon at an elevation angle of 6 deg, light from a partial moon at higher elevation, or twilight from the sun less than 9 deg bvelow the horizon. It permits the use of about 38% of the observations made with the sun below the horizon. The computed diurnal cycles of total cloud cover are altered considerably when this moonlight criterion is imposed. Maximum cloud cover over much of the ocean is now found to be at night or in the morning, whereas computations obtained without benefit of the moonlight criterion, as in our published atlases, showed the time of maximum to be noon or early afternoon in many regions. The diurnal cycles of total cloud cover we obtain are compared with those of the International Satellite Cloud Climatology Project (ISCCP) for a few regions; they are generally in better agreement if the moonlight criterion is imposed on the surface observations. Using the moonlight criterion, we have analyzed 10 years (1982-91) of surface weather observations over land and ocean, worldwide, for total cloud cover and for the frequency of occurrence of clear sky, fog, and precipitation. The global average cloud cover (average of day and night) is about 2% higher if the moonlight criterion is imposed than if all observations are used. The difference is greater in winter than in summer, because of the fewer hours of darkness in summer. The amplitude of the annual cycle of total cloud cover over the Arctic Ocean and at the South Pole is diminished by a few percent when the moonlight criterion is imposed. The average cloud cover for 1982-91 is found to be 55% for Northern Hemisphere land, 53% for Southern Hemisphere land, 66% for Northern Hemisphere ocean, and 70% for Southern Hemisphere ocean, giving a global average of 64%. The global average for daytime is 64.6%; for nighttime 63.3%.

Hahn, Carole J.↗
NASA NTRS1994

Global surface-based cloud observation for ISCCP

Visual observations of cloud cover are hindered at night due to inadequate illumination of the clouds. This usually leads to an underestimation of the average cloud cover at night, especially for the amounts of middle and high clouds, in climatologies on surface observations. The diurnal cycles of cloud amounts, if based on all the surface observations, are therefore in error, but they can be obtained more accurately if the nighttime observations are screened to select those made under sufficient moonlight. Ten years of nighttime weather observations from the northern hemisphere in December were classified according to the illuminance of moonlight or twilight on the cloud tops, and a threshold level of illuminance was determined, above which the clouds are apparently detected adequately. This threshold corresponds to light from a full moon at an elevation angle of 6 degrees or from a partial moon at higher elevation, or twilight from the sun less than 9 degrees below the horizon. It permits the use of about 38% of the observations made with the sun below the horizon. The computed diurnal cycles of total cloud cover are altered considerably when this moonlight criterion is imposed. Maximum cloud cover over much of the ocean is now found to be at night or in the morning, whereas computations obtained without benefit of the moonlight criterion, as in our published atlases, showed the time of maximum to be noon or early afternoon in many regions. Cloud cover is greater at night than during the day over the open oceans far from the continents, particularly in summer. However, near noon maxima are still evident in the coastal regions, so that the global annual average oceanic cloud cover is still slightly greater during the day than at night, by 0.3%. Over land, where daytime maxima are still obtained but with reduced amplitude, average cloud cover is 3.3% greater during the daytime. The diurnal cycles of total cloud cover we obtain are compared with those of ISCCP for a few regions; they are generally in better agreement if the moonlight criterion is imposed on the surface observations. Using the moonlight criterion, we have analyzed ten years (1982-1991) of surface weather observations over land and ocean, worldwide, for total cloud cover and for the frequency of occurrence of clear sky, fog and precipitation The global average cloud cover (average of day and night) is about 2% higher if we impose the moonlight criterion than if we use all observations. The difference is greater in winter than in summer, because of the fewer hours of darkness in the summer. The amplitude of the annual cycle of total cloud cover over the Arctic Ocean and at the South Pole is diminished by a few percent when the moonlight criterion is imposed. The average cloud cover for 1982-1991 is found to be 55% for northern hemisphere land, 53% for southern hemisphere land, 66% for northern hemisphere ocean, and 70% for southern hemisphere ocean, giving a global average of 64%. The global average for daytime is 64.6% for nighttime 63.3%.

Source record↗
DOE OSTI2025

Predicting weather impacts on corn production in a data-limited region using a transfer learning approach

The stability of food supply and prices may depend more on annual changes in yields from year-to-year variability in weather than on longer-term average changes from changing climatic conditions. However, the absence of high-quality data on crop yields at fine spatial resolutions in many regions of the world makes it challenging to statistically model their response to interannual variability in weather patterns. Therefore, there is a need for empirical methods that can project annual crop yield changes even in limited data regions. Here, we propose a transfer learning algorithm that uses high spatial resolution data from one region to project yields in another region with more limited data. The goal of our work is to understand what data types can be beneficial for transferring learning from a source region to a very different target region with more limited data. We utilize Long Short-Term Memory to develop a transfer learning model that is trained on historical county-level corn yield in the United States and predicts district-level corn yield variations in India. Even using smaller amounts of data in India, simulating a data-scarce region, we achieve an average root mean square error of 0.48 bu acre−1 in predicting interannual yield variations. Using Shapley values to interpret results, we explore the contribution of the different weather parameters to interannual yield variability and find a larger influence of precipitation-related variables. Our study demonstrates the usefulness of this method for transferring models of weather impacts on crop yields trained on a data-rich country to one with more limited data. It suggests the potential of applying the transfer learning model to mitigate the need for extensive raw data globally.

Vishwakarma, Srishti [ORNL] (ORCID:000000031674419↗
NASA NTRS2014

Analysis of Airport Ground Delay Program Decisions Using Data Mining Techniques

Air traffic service providers have to make decisions regarding changes to air traffic flow in the event of major weather disturbances and traffic congestions to maintain safety of the system. The behavior of the air traffic management system will be more predictable if consistent decisions are made under similar traffic and weather conditions. Consistency of deciding on control action depends on the weather and traffic conditions as well as accuracy in predicting these conditions. Weather parameters (defined in terms of forecast and actual weather and traffic conditions) on different days can be used to categorize days into days with little decision consistency, days with moderate decision consistency and days with high decision consistency. Four years of traffic, weather and ground delay program decisions data at major airports in the United States are used in the analysis. This paper examines performance of different data mining methods in the three regions of decision consistency. Not surprisingly, data mining methods have the best performance in the region of most decision consistency and have the poorest performance in the region of little decision consistency. In applications where data mining methods have differing performance in differing regions, it would be more useful to characterize the region specific performance instead of characterizing performance by a single parameter. Finally, the results show no significant variation in the performance of different data mining methods for this particular problem. The fact that different mining methods show no significant variation also provides further confidence in the results of data mining methods. Work in this abstract discusses initial results. This paper describes the results in terms of both forecast and actual environmental conditions and discusses how prediction errors impact decision consistency.

Kulkarni, Deepak↗
DOE OSTI2023

The environmental controls on efficiency of enhanced rock weathering in soils

Abstract Enhanced rock weathering (ERW) in soils is a promising carbon removal technology, but the realistically achievable efficiency, controlled primarily by in situ weathering rates of the applied rocks, is highly uncertain. Here we explored the impacts of coupled biogeochemical and transport processes and a set of primary environmental and operational controls, using forsterite as a proxy mineral in soils and a multiphase multi-component reactive transport model considering microbe-mediated reactions. For a onetime forsterite application of ~ 16 kg/m 2 , complete weathering within five years can be achieved, giving an equivalent carbon removal rate of ~ 2.3 kgCO 2 /m 2 /yr. However, the rate is highly variable based on site-specific conditions. We showed that the in situ weathering rate can be enhanced by conditions and operations that maintain high CO 2 availability via effective transport of atmospheric CO 2 (e.g. in well-drained soils) and/or sufficient biogenic CO 2 supply (e.g. stimulated plant–microbe processes). Our results further highlight that the effect of increasing surface area on weathering rate can be significant—so that the energy penalty of reducing the grain size may be justified—only when CO 2 supply is nonlimiting. Therefore, for ERW practices to be effective, siting and engineering design (e.g. optimal grain size) need to be co-optimized.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Severe Weather Forecast Decision Aid

This report presents a 15-year climatological study of severe weather events and related severe weather atmospheric parameters. Data sources included local forecast rules, archived sounding data, Cloud-to-Ground Lightning Surveillance System (CGLSS) data, surface and upper air maps, and two severe weather event databases covering east-central Florida. The local forecast rules were used to set threat assessment thresholds for stability parameters that were derived from the sounding data. The severe weather events databases were used to identify days with reported severe weather and the CGLSS data was used to differentiate between lightning and non-lightning days. These data sets provided the foundation for analyzing the stability parameters and synoptic patterns that were used to develop an objective tool to aid in forecasting severe weather events. The period of record for the analysis was May - September, 1989 - 2003. The results indicate that there are certain synoptic patterns more prevalent on days with severe weather and some of the stability parameters are better predictors of severe weather days based on locally tuned threat values. The results also revealed the stability parameters that did not display any skill related to severe weather days. An interactive web-based Severe Weather Decision Aid was developed to assist the duty forecaster by providing a level of objective guidance based on the analysis of the stability parameters, CGLSS data, and synoptic-scale dynamics. The tool will be tested and evaluated during the 2005 warm season.

Bauman, William H., III↗
NASA NTRSDate not supplied

Predicting Air Traffic Management Initiatives Using Supervised Learning

Terminal Traffic Management Initiatives (TMIs) such as Ground Stops (GS) and Ground Delay Programs (GDP) are implemented to manage excess demand or lowered capacity at an airport. Air Traffic Flow Management (TFM) specialists identify situations such as aviation constraints, current and forecasted weather conditions, airport demand and capacity, and initiate TMIs for safe and orderly movement of air traffic. In this paper, we outline supervised learning techniques that can be used to predict and recommend TMIs at an airport based on current weather and airport conditions. Our research involves building classic Machine Learning (ML) models such as Logistic Regression, K-Nearest Neighbor, Random Forest and XGBoost, as well as Long short-term memory (LSTM) networks. We trained the models on 3-year historical data (weather, airport demand, capacity and TMIs) from Newark (EWR) airport which was selected based on its higher TMI implementation rates and varied weather conditions. Although Random Forest and XGBoost algorithms are able to predict if a TMI is needed or not, they have difficulty in predicting specific program type. For this purpose, we found that LSTM time-series forecasting models performed better as they also learn from past TMI program type sequences. This study also lays down the foundation for advanced modeling techniques and architectures to predict TMIs in advance for future periods. The ability to predict TMIs in advance will be highly beneficial to the traffic controllers and managers as this will help them to prepare for and manage TMIs more efficiently.

Manoj Agrawal↗
DOE OSTI2025

Five Years of Dissolved Oxygen, Temperature, Salinity, Depth, Weather Data from a Transitioning Wetland at Beaver Creek, Washington, USA

Groundwater dissolved oxygen (DO) variability in coastal system remains poorly understood despite its importance for biogeochemical cycling and ecosystem modeling. Here we investigate the temporal variability in groundwater DO and its hydro-climatic drivers across hourly to seasonal timescales in a transitioning wetland at Beaver Creek, Washington, USA. The site is transitioning from a freshwater forest to a brackish tidal wetland following removal of a barrier in 2014 that prevented tides from accessing the freshwater creek. By utilizing novel optical dissolved oxygen instrumentation (Opti O2, LLC) we obtained continuous, high-frequency (5-minute), in-situ measurements of DO from the flood-plain from June 26th, 2019 through September 30th, 2024. This 63 month dataset is comprised of groundwater dissolved oxygen, temperature, water level and salinity timeseries from the floodplain. This dataset also includes rainfall, air pressure, air temperature, and solar radiation data collected with a co-located Campbell ClimaVUE50 weather sensor. All data is contained within a single csv (2019-06-26 to 2024-09-30 Beaver Creek DO, saln, BGS, temp, weather.csv) that can easily be viewed either using software such as Excel or using any text editor.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Integrating NASA Satellite Data Into USDA World Agricultural Outlook Board Decision Making Environment To Improve Agricultural Estimates

The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. The goal of the current project is to improve WAOB estimates by integrating NASA satellite precipitation and soil moisture observations into WAOB's decision making environment. Precipitation (Level 3 gridded) is from the TRMM Multi-satellite Precipitation Analysis (TMPA). Soil moisture (Level 2 swath and Level 3 gridded) is generated by the Land Parameter Retrieval Model (LPRM) and operationally produced by the NASA Goddard Earth Sciences Data and Information Services Center (GBS DISC). A root zone soil moisture (RZSM) product is also generated, via assimilation of the Level 3 LPRM data by a land surface model (part of a related project). Data services to be available for these products include GeoTIFF, GDS (GrADS Data Server), WMS (Web Map Service), WCS (Web Coverage Service), and NASA Giovanni. Project benchmarking is based on retrospective analyses of WAOB analog year comparisons. The latter are between a given year and historical years with similar weather patterns and estimated crop yields. An analog index (AI) was developed to introduce a more rigorous, statistical approach for identifying analog years. Results thus far show that crop yield estimates derived from TMPA precipitation data are closer to measured yields than are estimates derived from surface-based precipitation measurements. Work is continuing to include LPRM surface soil moisture data and model-assimilated RZSM.

Teng, William↗
NASA NTRS2002

NASA's Sentinels Monitoring Weather and Climate: Past, Present, and Future

Weatherwise is probably the most popular newstand magazine focusing on the subject of weather. It is published six times per year and includes features on weather, climate, and technology. This article (to appear in the January/February Issue) provides a comprehensive review of NASA s past, present, and future contributions in satellite remote sensing for weather and climate processes. The article spans the historical strides of the TIROS program through the scientific and technological innovation of Earth Observer-3 and Global Precipitation Measurement (GPM). It is one of the most thorough reviews of NASA s weather and climate satellite efforts to appear in the popular literature.

Shepherd, J. Marshall↗