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

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Observation Denial and Performance of a Local Mesoscale Model

Forecasters at the 45th Weather Squadron (45 WS) use observations from the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) wind tower network and the CCAFS (XMR) daily rawinsonde observations (RAOB) to issue and verify wind advisories and warnings for operations. These observations are also used by the Spaceflight Meteorology Group (SMG) in Houston, Texas and the Melbourne, Florida National Weather Service office to initialize their locally run mesoscale models. SMG also uses the observations to support shuttle landings at the KSC Shuttie Landing Facility. Due to impending budget cuts, some or all of the KSC/CCAFS wind towers on the east-central Florida mainland and the XMR RAOBs may be eliminated. The loss of these data may impact the forecast capability of the 45 WS and SMG. The Applied Meteorology Unit (AMU) was tasked to conduct a modeling study to determine how important these observations are to the accuracy of the model output used by the forecasters as input to their forecasts. To accomplish this, the AMU performed a sensitivity study using the Weather Research and Forecasting (NRF) model initialized with and without KSC/CCAFS wind tower and XMR RAOB observations. The AMU assessed the accuracy of model output by comparing peak wind forecasts with operationally significant wind advisory and warning criteria forecast by the 45 WS. To assess model performance when initialized with and without some of the wind tower and XMR RAOB observations, the AMU conducted a subjective analysis by displaying model wind forecasts graphically with the observations overlaid for comparison and they conducted an objective analysis by comparing the maximum peak wind forecast to the maximum peak wind observed within the KSC/CCAFS wind tower network. Data were collected for twelve warm season cases and eight cool season cases from June - September 2007 and November - January 2008, respectively. For each case chosen, the 45 WS must have issued a wind advisory or warning for the KSC/CCAFS area and the KSC/CCAFS wind towers must have recorded significant wind events, or winds greater than 18 kt.

Watson, Leela R.↗
NASA NTRS2010

Use of Quality Controlled AIRS Temperature Soundings to Improve Forecast Skill

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. Also included are the clear column radiances used to derive these products which are representative of the radiances AIRS would have seen if there were no clouds in the field of view. All products also have error estimates. The sounding goals of AIRS are to produce 1 km tropospheric layer mean temperatures with an rms error of 1K, and layer precipitable water with an rms error of 20 percent, in cases with up to 90 percent effective cloud cover. The products are designed for data assimilation purposes for the improvement of numerical weather prediction, as well as for the study of climate and meteorological processes. With regard to data assimilation, one can use either the products themselves or the clear column radiances from which the products were derived. The AIRS Version 5 retrieval algorithm is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates for retrieved quantities and clear column radiances, and the use of these error estimates for Quality Control. The temperature profile error estimates are used to determine a case-by-case characteristic pressure pbest, down to which the profile is considered acceptable for data assimilation purposes. The characteristic pressure p(sub best) is determined by comparing the case dependent error estimate (delta)T(p) to the threshold values (Delta)T(p). The AIRS Version 5 data set provides error estimates of T(p) at all levels, and also profile dependent values of pbest based on use of a Standard profile dependent threshold (Delta)T(p). These Standard thresholds were designed as a compromise between optimal use for data assimilation purposes, which requires highest accuracy (tighter Quality Control), and climate purposes, which requires more spatial coverage (looser Quality Control). Subsequent research using Version 5 sounding and error estimates showed that tighter Quality Control performs better for data assimilation proposes, while looser Quality Control better spatial coverage) performs better for climate purposes. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 degree latitude x 0.67 degree longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates (delta)T(p) were used as the uncertainty for each measurement in the data assimilation process.

Susskind, Joel↗
NASA NTRS1975

Policy issues and data communications for NASA earth observation missions until 1985

The series of LANDSAT sensors with the highest potential data rates of the missions were examined. An examination of LANDSAT imagery uses shows that relatively few require transmission of the full resolution data on a repetitive quasi real time basis. Accuracy of global crop size forecasting can possibly be improved through information derived from LANDSAT imagery. A current forecasting experiment uses the imagery for crop area estimation only, yield being derived from other data sources.

Corte, A. B.↗
NASA NTRS1980

Possible influence of solar rotation on tropospheric circulation

A large-scale structure observed in the photospheric magnetic field is carried out into the heliosphere by the solar wind. At Earth the resulting interplanetary magnetic field has polarity away from the Sun for several consecutive days followed by an abrupt reversal and several days with field polarity toward the Sun. Low-pressure troughs near the Gulf of Alaska appear to have significantly larger area when the interplanetary field is away from the Sun than when it is toward the Sun. This relation persisted during most of the winters of 1951 to 1973. Surface pressures around the Gulf of Alaska during some winters were in anti-phase for interplanetary field toward and away from the Sun. During the two days after a polarity reversal, the accuracy of the best weather forecasts for the continental United States appears to be significantly lower than at other times. Polarity reversals that are accompanied by energetic interplanetary conditions appear to be associated with a larger decrease in the area of the low pressure troughs.

Wilcox, J. M.↗
NASA NTRS1988

General-aviation's view of progress in the aviation weather system

For all its activity statistics, general-aviation is the most vulnerable to hazardous weather. Of concern to the general aviation industry are: (1) the slow pace of getting units of the Automated Weather Observation System (AWOS) to the field; (2) the efforts of the National Weather Service to withdraw from both the observation and dissemination roles of the aviation weather system; (3) the need for more observation points to improve the accuracy of terminal and area forecasts; (4) the need for improvements in all area forecasts, terminal forecasts, and winds aloft forecasts; (5) slow progress in cockpit weather displays; (6) the erosion of transcribed weather broadcasts (TWEB) and other deficiencies in weather information dissemination; (7) the need to push to make the Direct User Access Terminal (DUAT) a reality; and (7) the need to improve severe weather (thunderstorm) warning systems.

Lundgren, Douglas J.↗
NASA NTRS1994

Incorporation of an evaporative cooling scheme into a dynamic model of orographic precipitation

A simple evaporative cooling scheme was incorporated into a dynamic model to estimate orographic precipitation in mountainous regions. The orographic precipitation model is based on the transport of atmospheric moisture and the quantification of preciptable water across a 3D representation of the terrain from the surface up to 250 hPa. Advective wind fields are computed independently and boundary conditions are extracted from radiosonde data. Precipitation rates are obtained through calibration of a spatially distributed precipitation efficiency parameter. The model was applied to the central Sierra Nevada. Results show a gain of the order of 20% in threat-score coefficients designed to measure the forecast ability of the model. Accuracy gains are largest at high elevations and during intense storms associated with warm air masses.

Barros, Ana Paula↗
NASA NTRS2002

Impact of Quikscat Data on Numerical Weather Prediction

One of the important applications of satellite surface wind observations is to increase the accuracy of weather analyses and forecasts. Satellite surface wind data can improve numerical weather prediction (NWP) model forecasts by contributing to improved analyses of the surface wind field and air sea fluxes. Through the data assimilation process,these data can also improve atmospheric mass and motion fields in the free atmosphere above the surface. The SeaWinds scatterometer on the QuikScat satellite was launched in July 1999 and represented a dramatic departure in design from the other scatterometer instruments launched during the past decade (ERS-1,2 and NSCAT). The NASA Data Assimilation Office (DAO) was the first data assimilation center to assimilate QuikScat Seawinds data and evaluate their impact on numerical weather prediction. Following the launch of QuikScat, a detailed evaluation of the initial surface wind data sets was performed as part of a collaborative project between the Environmental Modeling Center of NCEP, NESDIS and the DAO. More recently, the impact of Quikscat data was evaluated in detailed experiments using the NCEP operational data assimilation system. As a result of the beneficial impact obtained, NCEP began operational utilization of Quikscat data. Results from these experiments as well as recent DAO assimilation experiments showing the impact of Quikscat data on stratospheric analyses and forecasts will be presented at the meeting.

Atlas, Robert↗
NASA NTRS2003

Instrument concept of NEXRAD In Space (NIS) - a geostationary radar for hurricane studies

The current Geostationary Operational Environmental Satellites (GOES) are eqipped to make cloud top measurements only. In contrast, a millimeter-wave radar allows 3-D measurements of precipitation associated with hurricanes and other convective systems. It also provides important inputs for numerical weather prediction models for improving the accuracy of weather nowcasting and forecasting.

NEXRAD hurricane precipitation radar↗
NASA NTRS2003

Conceptual design of a geostationary radar for monitoring hurricanes

The current Geostationary Operational Environmental Satellites (GOES) are equipped to make cloud top measurements only. In contrast, a millimeter-wave radar allows 3-D measurements of precipitation associated with hurricanes and other convective systems. It also provides important inputs to numerical weather prediction models for improving the accuracy in weather nowcasting and forecasting.

hurricane precipitation radar↗
NASA NTRS2013

Assimilation of Quality Controlled AIRS Temperature Profiles using the NCEP GFS

We have previously conducted a number of data assimilation experiments using AIRS Version-5 quality controlled temperature profiles as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The data assimilation and forecast system we used was the Goddard Earth Observing System Model , Version-5 (GEOS-5) Data Assimilation System (DAS), which represents a combination of the NASA GEOS-5 forecast model with the National Centers for Environmental Prediction (NCEP) operational Grid Point Statistical Interpolation (GSI) global analysis scheme. All analyses and forecasts were run at a 0.5deg x 0.625deg spatial resolution. Data assimilation experiments were conducted in four different seasons, each in a different year. Three different sets of data assimilation experiments were run during each time period: Control; AIRS T(p); and AIRS Radiance. In the "Control" analysis, all the data used operationally by NCEP was assimilated, but no AIRS data was assimilated. Radiances from the Aqua AMSU-A instrument were also assimilated operationally by NCEP and are included in the "Control". The AIRS Radiance assimilation adds AIRS observed radiance observations for a select set of channels to the data set being assimilated, as done operationally by NCEP. In the AIRS T(p) assimilation, all information used in the Control was assimilated as well as Quality Controlled AIRS Version-5 temperature profiles, i.e., AIRS T(p) information was substituted for AIRS radiance information. The AIRS Version-5 temperature profiles were presented to the GSI analysis as rawinsonde profiles, assimilated down to a case-by-case appropriate pressure level p(sub best) determined using the Quality Control procedure. Version-5 also determines case-by-case, level-by-level error estimates of the temperature profiles, which were used as the uncertainty of each temperature measurement. These experiments using GEOS-5 have shown that forecasts resulting from analyses using the AIRS T(p) assimilation system were superior to those from the Radiance assimilation system, both with regard to global 7 day forecast skill and also the ability to predict storm tracks and intensity.

Susskind, Joel↗
NASA NTRSDate not supplied

Assimilation of microwave satellite observations over the rainbands of tropical cyclones

The potential of microwave observations in improving the weather forecasts is limited by the accuracy of all-sky radiative transfer calculations. We introduce a novel Bayesian MonteCarlo technique to improve the assimilation of microwave observations over the rainbands of tropical cyclones. The BMCI technique eliminates the need for a forward model in the data assimilation system. The technique includes three steps, (i) generating a comprehensive dataset using in-situ cloud measurements and atmospheric profiles, (ii) generating synthetic ATMS observations from the training dataset, and (iii) using real observations to estimate the geophysical variables over the rainbands of tropical cyclones. The retrieved profiles of temperature, relative humidity, and cloud water content as well assurface information such as SST were then assimilated into the model. The results show that assimilating the BMCI retrievals can influence the dynamical features of the cyclone, including a stronger warm core, a symmetric eye, and vertically aligned wind columns.

Monte Carlo↗
NASA NTRS1975

Relationships between stratospheric clear air turbulence and synoptic meteorological parameters over the western United States between 12-20 km altitude

Procedures for forecasting clear air turbulence in the stratosphere over the western United States from rawinsonde data are described and results presented. Approaches taken to relate meteorological parameters to regions of turbulence and nonturbulence encountered by the XB-70 during 46 flights at altitudes between 12-20 km include: empirical probabilities, discriminant function analysis, and mountainwave theory. Results from these techniques were combined into a procedure to forecast regions of clear air turbulence with an accuracy of 70-80 percent. A computer program was developed to provide an objective forecast directly from the rawinsonde sounding data.

Scoggins, J. R.↗
NASA NTRS2016

Comparative Analysis of NOAA REFM and SNB3GEO Tools for the Forecast of the Fluxes of High-Energy Electrons at GEO

Reliable forecasts of relativistic electrons at geostationary orbit (GEO) are important for the mitigation of their hazardous effects on spacecraft at GEO. For a number of years the Space Weather Prediction Center at NOAA has provided advanced online forecasts of the fluence of electrons with energy >2 MeV at GEO using the Relativistic Electron Forecast Model (REFM). The REFM forecasts are based on real-time solar wind speed observations at L1. The high reliability of this forecasting tool serves as a benchmark for the assessment of other forecasting tools. Since 2012 the Sheffield SNB3GEO model has been operating online, providing a 24 h ahead forecast of the same fluxes. In addition to solar wind speed, the SNB3GEO forecasts use solar wind density and interplanetary magnetic field B(sub z) observations at L1. The period of joint operation of both of these forecasts has been used to compare their accuracy. Daily averaged measurements of electron fluxes by GOES 13 have been used to estimate the prediction efficiency of both forecasting tools. To assess the reliability of both models to forecast infrequent events of very high fluxes, the Heidke skill score was employed. The results obtained indicate that SNB3GEO provides a more accurate 1 day ahead forecast when compared to REFM. It is shown that the correction methodology utilized by REFM potentially can improve the SNB3GEO forecast.

Balikhin, M. A.↗
NASA NTRSDate not supplied

Optimization of Airport Runway Configuration with Forecast-Augmented Offline Reinforcement Learning

Runway configuration Management (RCM) governs the optimal utilization of runways based on variables such as traffic and meteorological conditions, making it a daunting task in air traffic management due to its dependency on volatile operational and environmental factors. This paper improves upon our previous work [1] on using offline model-free reinforcement learning for creating a Runway Configuration Assistance (RCA) decision-support tool. A novel integration of forecast data from LAMP (Localized Aviation Model Output Statistics Program) and TAF (Terminal Area Forecast) is introduced, enhancing the tool’s accuracy and also its adaptability to quick wind changes. The performance is evaluated using two major US airports, Charlotte Douglas International Airport (CLT) and Denver International Airport (DEN). To counter scalability issues presented by the addition of discrete forecast variables, we transitioned to a continuous state space model, ensuring scalability and inclusion of longer forecast data. The results of our experiments reflect significant improvements in the RCA tool’s prediction accuracy.

Sumanth Nethi↗
NASA NTRS2022

Estimating groundwater use and demand in arid Kenya through assimilation of satellite data and in-situ sensors with machine learning toward drought early action

Groundwater is an important source of water for people, livestock, and agriculture during drought in the Horn of Africa. In this work, areas of high groundwater use and demand in drought-prone Kenya were identified and forecasted prior to the dry season. Estimates of groundwater use were extended from a sentinel network of 69 in-situ sensored mechanical boreholes to the region with satellite data and a machine learning model. The sensors contributed 756 site-month observations from June 2017 to September 2021 for model building and validation at a density of approximately one sensor per 3700 sq.km. An ensemble of 19 parameterized algorithms was informed by features including satellite-derived precipitation, surface water availability, vegetation indices, hydrologic land surface modeling, and site characteristics to dichotomize high groundwater pump utilization. Three operational definitions of high demand on groundwater infrastructure were considered: 1) mechanical runtime of pumps greater than a quarter of a day (6+ hr) and daily per capita volume extractions indicative of 2) domestic water needs (35+ L), and 3) intermediate needs including livestock (75+ L). Gridded interpolation of localized groundwater use and demand was provided from 2017 to 2020 and forecasted for the 2021 dry season, June–September 2021. Cross-validated skill for contemporary estimates of daily pump runtime and daily volume extraction to meet domestic and intermediate water needs was 68%, 69%, and 75%, respectively. Forecasts were externally validated with an accuracy of at least 56%, 70%, or 72% for each groundwater use definition. The groundwater maps are accessible to stakeholders including the Kenya National Drought Management Authority (NDMA) and the Famine Early Warning Systems Network (FEWS NET). These maps represent the first operational spatially-explicit sub-seasonal to seasonal (S2S) estimates of groundwater use and demand in the literature. Knowledge of historical and forecasted groundwater use is anticipated to improve decision-making and resource allocation for a range of early warning early action applications.

Katie Fankhauser↗
NASA NTRS2024

Use of Forecast Atmosphere for Earth Entry, Descent, and Landing Modeling

Flight mechanics simulations are used to characterize the performance of Earth entry, descent, and landing vehicles. Atmospheric prediction models are often a key component of these simulations. Global atmospheric models are used for both engineering design due to their ability to define atmospheric properties over a large swath of locations and times as well as used for the availability of atmospheric uncertainties in the models that can be used in statistical performance analysis. However for day-of-flight operations, the use of forecast atmospheres based on more current measurements are beneficial to the accuracy of the performance prediction, including landing locations. Reanalysis of these forecasts can also be used for post-flight analysis, including trajectory reconstruction. This paper describes how forecast atmospheres can be beneficial for Earth entry, descent, and landing analysis and ways these models can be implemented in simulations.

Soumyo Dutta↗
NASA NTRS2019

ISEP: A Joint SRAG/CCMC Collaboration to Improve Mitigation of Space Weather Effects on Crew Health in the Exo-LEO Era

The Space Radiation Analysis Group (SRAG) at Johnson Space Center (JSC) is tasked with monitoring changes to space weather and mitigating any resultant impacts to crew health and safety. As human spaceflight goals extend from Low-Earth Orbit (LEO) missions like the International Space Station (ISS) to the moon, Mars and beyond, SRAG will need to update their current approach for crew monitoring of and protection from radiation exposure due to energetic Solar Particle Events (ESPEs). Challenges faced in planning exo-LEO missions include the lack of protection from the Earth’s geomagnetic field employed by the ISS in addition to limited communication capability between the crew and the ground. In the event of an ESPE, the current ISS trajectory ensures that the vehicle is only traveling through fields of higher radiation exposure for a brief period of time; the Earth’s geomagnetic field prevents the penetration of the high-energy particles of concern throughout the majority of the orbit. Exo-LEO missions, on the other hand, require that the vehicle travel through free space, exposing vehicle and crew to the full impact of the ESPE. NASA has combined multiple approaches to resolve this radiation exposure issue. New vehicles are designed to take advantage of advances in particle transport modeling capabilities and shielding technology, allowing redistribution of mass throughout the vehicle to areas of thinner shielding when the energetic particle flux has increased to levels of concern. Although vehicle shielding is an important aspect of radiation exposure protection, there is a continued requirement to monitor and predict the space weather environment. To this end, SRAG maintains a console position in Mission Control with 24/7 mission support capability. In the event of increased solar activity, SRAG collaborates with the Flight Control Team (FCT) to determine if crew action (i.e., shelter) is required. During any increase in solar activity, the FCT needs three pieces of information to effectively decide the crew response in light of other required mission tasks: if an event (ESPE) will occur, how ‘intense’ an observed event will be, and how long will an observed event will last. An ideal alert system limits false alarms, therefore causing the crew to take action unnecessarily, without ignoring events that pose a hazard to the crew. SRAG’s current operational concept for ISS missions focuses on short-term forecasts, best described as ‘now-casting’. Console operators are in daily communication with the Space Weather Prediction Center (SWPC) for situational awareness purposes. When conditions exist that may lead to increased solar activity, operators receive notifications from SWPC. In the case of a well-connected ESPE, the console operator may only have on the order of minutes to several hours to notify the FCT of the event and provide a recommendation for crew action. As NASA shifts to exo-LEO missions, the increased time in free space as well as the reduced ability to communicate with the crew will force a transition in crew protection strategy that emphasizes improvments to both the accuracy and the lead time in forecasting capabilities.

Barzilla, Janet E.↗
NASA NTRS1975

Utilization of LANDSAT monitoring capabilities for snowcover depletion analysis

LANDSAT images for three snowmelt seasons were utilized to map and analyze snowcover depletion on a small river basin in southeastern Wyoming. Results indicate that snowcover-runoff curves established from repetitive LANDSAT coverage may be used in conjunction with streamflow data to provide low-cost seasonal runoff forecasts having a high degree of accuracy. Additionally, detectable variations within a snowpack might provide temporal estimates of peak flows.

Thompson, A. G.↗