Search NASA⌕ Search

SEARCH · Search NASA

Results for “Atmospheric Forecast”

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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Magnetogram Forecast: An All-Clear Space Weather Forecasting System

Solar flares and coronal mass ejections (CMEs) are the drivers of severe space weather. Forecasting the probability of their occurrence is critical in improving space weather forecasts. The National Oceanic and Atmospheric Administration (NOAA) currently uses the McIntosh active region category system, in which each active region on the disk is assigned to one of 60 categories, and uses the historical flare rates of that category to make an initial forecast that can then be adjusted by the NOAA forecaster. Flares and CMEs are caused by the sudden release of energy from the coronal magnetic field by magnetic reconnection. It is believed that the rate of flare and CME occurrence in an active region is correlated with the free energy of an active region. While the free energy cannot be measured directly with present observations, proxies of the free energy can instead be used to characterize the relative free energy of an active region. The Magnetogram Forecast (MAG4) (output is available at the Community Coordinated Modeling Center) was conceived and designed to be a databased, all-clear forecasting system to support the operational goals of NASA's Space Radiation Analysis Group. The MAG4 system automatically downloads nearreal- time line-of-sight Helioseismic and Magnetic Imager (HMI) magnetograms on the Solar Dynamics Observatory (SDO) satellite, identifies active regions on the solar disk, measures a free-energy proxy, and then applies forecasting curves to convert the free-energy proxy into predicted event rates for X-class flares, M- and X-class flares, CMEs, fast CMEs, and solar energetic particle events (SPEs). The forecast curves themselves are derived from a sample of 40,000 magnetograms from 1,300 active region samples, observed by the Solar and Heliospheric Observatory Michelson Doppler Imager. Figure 1 is an example of MAG4 visual output

Barghouty, Nasser↗
NASA NTRS2018

Introduction to Data Assimilation

Atmospheric data assimilation is a class of techniques used for producing descriptions of fields of air temperature, pressure, humidity, wind, etc. on a spatial grid or in terms of a finite functional representation. These are then used to initialize numerical weather forecasts or to analyze the atmosphere for other purposes. The techniques combine past, present, and even future observations in an approximate statistically optimal way. Various types of statistical or physically-based models and their corresponding adjoints are employed to relate diverse fields in both time and space and to relate what is observed to what is being analyzed. Computationally, the problem is very demanding and onstraining on the techniques that can be employed on a routine basis.

Atmospheric data assimilation↗
NASA NTRS1985

Upper air forecasting for aviation in the United States

It is shown that the present forecast models used in the aviation digital forecast (ADF) product for automated flight planning in the U.S. did not improve in accuracy and precision over the past two decades. A new approach to the upper air forecasting is presented in the NASA/NOAA MERIT project. In MERIT, a concept of a tailored analysis is used, based on a high density automated aircraft report database (including radiosonde and satellite data), producing an accurate and precise description of the initial state of the atmosphere, and a relatively simple forecast model to move the forecast forward in time. The steadily updated 2-12 h forecasts provided every 3 h will be more efficient and accurate than the present 18 and 24 h forecasts provided every 12 hours.

Steinberg, R.↗
NASA NTRS2017

Earth Global Reference Atmospheric Model (GRAM) Overview and Updates: DOLWG Meeting

What is Earth-GRAM (Global Reference Atmospheric Model): Provides monthly mean and standard deviation for any point in atmosphere - Monthly, Geographic, and Altitude Variation; Earth-GRAM is a C++ software package - Currently distributed as Earth-GRAM 2016; Atmospheric variables included: pressure, density, temperature, horizontal and vertical winds, speed of sound, and atmospheric constituents; Used by engineering community because of ability to create dispersions in atmosphere at a rapid runtime - Often embedded in trajectory simulation software; Not a forecast model; Does not readily capture localized atmospheric effects.

White, Patrick↗
NASA NTRS1982

Variational methods in simultaneous optimum interpolation and initialization

The duality between optimum interpolation and variational objective analysis, is reviewed. This duality is used to set up a variational approach to objective analysis which uses prior information concerning the atmospheric spectral energy distribution, in the variational problem. In the wind analysis example, the wind field is partitioned into divergent and nondivergent parts, and a control parameter governing the relative energy in the two parts is estimated from the observational data being analyzed by generalized cross validation, along with a bandwidth parameter. A variational approach to combining objective analysis and initialization in a single step is proposed. In a simple example of this approach, data, forecast, and prior information concerning atmospheric energy distribution is combined into a single variational problem. This problem has (at least) one bandwidth parameter, one partitioning parameter governing the relative energy in fast slow modes, and one parameter governing the relative weight to be given to observational and forecast data.

Wahba, G.↗
NASA NTRS2018

Biogeochemical Effects of Rising Atmospheric CO2 on Terrestrial and Ocean Systems

Rising carbon dioxide (CO2) has decreased seawater pH at long-term observing stations around the world, including in the open ocean north of Oahu, Hawaii, near Alaska's Aleutian Islands, the Gulf of Maine shore, and on Gray's Reef in the southeastern United States. This ocean acidification process has already affected some marine species and altered fundamental ecosystem processes, and further effects are likely. While atmospheric CO rises at approximately the same rate all over the globe, its non-climate effects on land vary depending on climate and dominant species. In terrestrial ecosystems, rising atmospheric CO concentrations are expected to increase plant photosynthesis, growth, and water-use efficiency, though these effects are reduced when nutrients, drought or other factors limit plant growth. Rising CO would likely change carbon storage and influence terrestrial hydrology and biogeochemical cycling, but concomitant effects on vegetation composition and nutrient feedbacks are challenging to predict, making decadal forecasts uncertain. Consequences of rising atmospheric CO are expected to include difficult-to-predict changes in the ecosystem services that terrestrial and ocean systems provide to humans. For instance, ocean acidification resulting from rising CO has decreased the supply of larvae that sustains commercial shellfish production in the northwestern United States. In addition, CO fertilization (increases) plus warming (decreases) are changing terrestrial crop yields. Continued persistence of uptake of carbon by the land and ocean is uncertain. Climate and environmental change create complex feedbacks to the carbon cycle and it is not clear how feedbacks modulate future effects of rising CO on carbon sinks. These are several mechanisms that could reduce future sink capacity.

Biogeochemical↗
NASA NTRS2015

A Module for Assimilating Hyperspectral Infrared Retrieved Profiles into the Gridpoint Statistical Interpolation System for Unique Forecasting Applications

Hyperspectral infrared sounder radiance data are assimilated into operational modeling systems however the process is computationally expensive and only approximately 1% of available data are assimilated due to data thinning as well as the fact that radiances are restricted to cloud-free fields of view. In contrast, the number of hyperspectral infrared profiles assimilated is much higher since the retrieved profiles can be assimilated in some partly cloudy scenes due to profile coupling other data, such as microwave or neural networks, as first guesses to the retrieval process. As the operational data assimilation community attempts to assimilate cloud-affected radiances, it is possible that the use of retrieved profiles might offer an alternative methodology that is less complex and more computationally efficient to solve this problem. The NASA Short-term Prediction Research and Transition (SPoRT) Center has assimilated hyperspectral infrared retrieved profiles into Weather Research and Forecasting Model (WRF) simulations using the Gridpoint Statistical Interpolation (GSI) System. Early research at SPoRT demonstrated improved initial conditions when assimilating Atmospheric Infrared Sounder (AIRS) thermodynamic profiles into WRF (using WRF-Var and assigning more appropriate error weighting to the profiles) to improve regional analysis and heavy precipitation forecasts. Successful early work has led to more recent research utilizing WRF and GSI for applications including the assimilation of AIRS profiles to improve WRF forecasts of atmospheric rivers and assimilation of AIRS, Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI) profiles to improve model representation of tropopause folds and associated non-convective wind events. Although more hyperspectral infrared retrieved profiles can be assimilated into model forecasts, one disadvantage is the retrieved profiles have traditionally been assigned the same error values as the rawinsonde observations when assimilated with GSI. Typically, satellitederived profile errors are larger and more difficult to quantify than traditional rawinsonde observations (especially in the boundary layer), so it is important to appropriately assign observation errors within GSI to eliminate potential spurious innovations and analysis increments that can sometimes arise when using retrieved profiles. The goal of this study is to describe modifications to the GSI source code to more appropriately assimilate hyperspectral infrared retrieved profiles and outline preliminary results that show the differences between a model simulation that assimilated the profiles as rawinsonde observations and one that assimilated the profiles in a module with the appropriate error values.

Berndt, Emily↗
NASA NTRS1981

A stochastic-dynamical approach to the study of the natural variability of the climate

A method, suggested by Leith (1975), which employed stochastic-dynamic forecasts obtained from a general circulation model in such a way as to satisfy the definition of climatic noise, was used to validate assumptions accounting for the effects of external influences in estimating the climatic noise. Two assumptions were investigated: (1) that the weather fluctuations can be represented as a Markov process, and (2) that changing external conditions do not influence the atmosphere's statistical properties on short time scales. The general circulation model's simulation of the daily weather fluctuations was generated by performing integrations with prescribed climatological boundary conditions for random initial atmospheric states, with resulting dynamical forecasts providing an ensemble of simulated data for the autoregressive modeling of weather fluctuations. To estimate the climatic noise from the observational data (consisting of hourly values of sea level pressure and surface temperature at 54 U.S. stations for the month of January for the years 1949-1975) use of the short time-scale assumption is made. The simulated and observed data were found not to be consistent with either white noise or a Markov process of weather fluctuations. Good agreement was found between the results of the hypothetical testing of the simulated and the observed surface temperatures; and only partial support was found for the short time-scale assumption, i.e., for sea level pressure.

Straus, D. M.↗
NASA NTRS1984

Advective forecasts of VAS imagery for use in anticipating convective destabilization

Wind field forecasts, based on data from the Visible Atmospheric Sounder (VAS) instrument on board the most recent GOES satellite, are described. The forecasts were used to generate a series of water vapor images for the Central U.S. according to an isentropic prediction scheme. The ability of VAS imagery to detect regions of mesoscale convective instability was contrasted with data from a 9-12 hr advective forecast and the results are discussed. It is shown that the VAS imagery was free from masking by convective outflows and lends itself to repeated applications for updating throughout the day. The incorporation of surface data into the VAS forecast is recommended in order to identify areas of persistent moisture convergence. Some examples of the VAS imagery are provided.

Petersen, R. A.↗
NASA NTRSDate not supplied

The Impact of Assimilating Large Volumes of GNSS Radio Occultation Observations from Spire’s Commercial Constellation into NASA’s Global Earth Observing System

The upcoming retrospective analysis for the 21st century (R21C) reanalysis product from NASA will include the full dataset of GNSS Radio Occultation (RO) observations collected by Spire with their constellation of smallsats. The Spire RO dataset, which was purchased by NASA for its Commercial Smallsat Data Acquisition (CSDA) archive, has global coverage and includes approximately 6 thousand RO profiles per day during 2019 increasing to nearly 20 thousand in 2022-2023. This more than doubles the volume of RO profiles from all other routinely assimilated RO missions combined during 2023. The increase in the number of RO profiles available is particularly important in the extratropics as the next largest RO constellation, the state-of-the-science Formosa Satellite Mission 7 (FORMOSAT-7)/Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) mission (hereafter COSMIC-2), is focused on the Tropics and only provides approximately 4 thousand RO profiles per day. The large number of observations in the Spire RO dataset have great potential to improve analyses and forecasts of the Earth’s atmosphere produced by numerical weather prediction systems. Their impact is assessed through numerical experiments using NASA’s Global Earth Observing System (GEOS) Atmospheric Data Assimilation System (ADAS) with and without the observations from the COSMIC-2 mission. The ability of the Spire RO observations to make up for the omission of the RO observations from COSMIC-2 is directly assessed over the tropics and the impact of Spire over the extratropics is compared to that of the available RO missions that sample over this region. Finally, the quality of the Spire RO observations is compared to that of the routinely assimilated RO missions using Forecast Sensitivity-based Observation Impact (FSOI).

Michael J. Murphy, Jr.↗
NASA NTRSDate not supplied

The Impact of Assimilating Large Volumes of GNSS Radio Occultation Observations from Spire’s Commercial Constellation into NASA’s GEOS

The upcoming retrospective analysis for the 21st century (R21C) reanalysis product from NASA will include the full dataset of GNSS Radio Occultation (RO) observations collected by Spire with their constellation of smallsats. The Spire RO dataset, which was purchased by NASA for its Commercial Smallsat Data Acquisition (CSDA) archive, has global coverage and includes approximately 6 thousand RO profiles per day during 2019 increasing to nearly 20 thousand in 2022-2023. This more than doubles the volume of RO profiles from all other routinely assimilated RO missions combined during 2023. The increase in the number of RO profiles available is particularly important in the extratropics as the next largest RO constellation, the state-of-the-science Formosa Satellite Mission 7 (FORMOSAT-7)/Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) mission (hereafter COSMIC-2), is focused on the Tropics and only provides approximately 4 thousand RO profiles per day. The large number of observations in the Spire RO dataset have great potential to improve analyses and forecasts of the Earth’s atmosphere produced by numerical weather prediction systems. Their impact is assessed through numerical experiments using NASA’s Global Earth Observing System (GEOS) Atmospheric Data Assimilation System (ADAS) with and without the observations from the COSMIC-2 mission. The ability of the Spire RO observations to make up for the omission of the RO observations from COSMIC-2 is directly assessed over the tropics and the impact of Spire over the extratropics is compared to that of the available RO missions that sample over this region. Finally, the quality of the Spire RO observations is compared to that of the routinely assimilated RO missions using Forecast Sensitivity-based Observation Impact (FSOI).

Michael J. Murphy↗
NASA NTRSDate not supplied

The Impact of Assimilating Large Volumes of GNSS Radio Occultation Observations from Spire’s Commercial Constellation into NASA’s GEOS

The upcoming retrospective analysis for the 21st century (R21C) reanalysis product from NASA will include the full dataset of GNSS Radio Occultation (RO) observations collected by Spire with their constellation of smallsats. The Spire RO dataset, which was purchased by NASA for its Commercial Smallsat Data Acquisition (CSDA) archive, has global coverage and increases in number from approximately 6 thousand RO profiles per day during 2019 to approximately 20 thousand in 2022-2023. This more than doubles the volume of RO profiles from all other routinely assimilated RO missions combined during 2022. The increase in the number of RO profiles available is particularly important in the extratropics as the next largest RO constellation, the state-of-the-science Formosa Satellite Mission 7 (FORMOSAT-7)/Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) mission (hereafter COSMIC-2), is focused on the Tropics and only provides approximately 4 thousand RO profiles per day. The large number of observations in the Spire RO dataset have great potential to improve analyses and forecasts of the Earth’s atmosphere produced by numerical weather prediction systems. Their impact is assessed through numerical experiments using NASA’s Global Earth Observing System (GEOS) Atmospheric Data Assimilation System (ADAS) with and without the observations from the COSMIC-2 mission. The ability of the Spire RO observations to make up for the omission of the RO observations from COSMIC-2 is directly assessed over the tropics and the impact of Spire over the extratropics is compared to that of the available RO missions that sample over this region. Finally, the quality of the Spire RO observations is compared to that of the routinely assimilated RO missions using Forecast Sensitivity-based Observation Impact (FSOI).

Michael J. Murphy↗
NASA NTRSDate not supplied

The noise dose range of the X-59 estimated from propagation simulations

NASA’s X-59 aircraft will soon be flown in a series of low-boom community noise surveys across the USA. Community test experimental designs will include noise dose schedules, which require an estimate of the noise dose range that X-59 can produce. To determine the dose range, propagation simulations using NASA’s PCBoom code were completed using CFD-generated X-59 C612A near-field pressure data for an on-design condition (Mach 1.4 at 53,200 feet) and two off-design conditions (Mach 1.4 at 46,000 ft and Mach 1.3 at 43,000 ft). Near-field data at four cardinal aircraft headings were propagated through one year of realistic atmospheric profiles from the Climate Forecast System Version 2 database at several USA locations. Statistics of the mean and range of the Perceived Level (PL) of the booms in the inner 40 km of each carpet as well as the carpet width were computed. Results indicate the typical inner carpet mean PL is approximately 70-87 dB depending on the aircraft condition. The typical PL range across the inner carpet is 6-8 dB (on-design) and 5-13 dB (off-designs). The carpet widths ranged from 32-55 km. Understanding these quantities for various flight and atmospheric conditions is important for planning X-59 community noise surveys.

X-59↗
NASA NTRSDate not supplied

4pPA1 - Turbulence Effects on Shaped Booms: Central Composite Design of Modeled Atmospheric Turbulence Parameters for Sonic Boom Propagation

Propagation of sonic booms through turbulence reduces mean sonic boom perception metric levels and also causes considerable variability. NASA’s PCBoom suite of sonic boom acoustic propagation modules includes an approximate method for accounting for the effects of turbulence on traditional N-wave sonic booms. The current implementation is ineffective for shaped sonic booms or low-booms, and it also has limited values for turbulence and ambient input parameters. NASA’s future X-59 low-boom community noise surveys require an accurate estimate of the effects of turbulence in regions across the USA, so the module must be improved. This work presents the methods of selecting which ambient and turbulence parameters should be included in an improved PCBoom turbulence module. Turbulence and ambient data were collected from two atmospheric model databases, the Climate Forecast System Version 2 and European Centre for Medium-Range Weather Forecast Reanalysis Version 5 (ERA5), hourly from 7 AM to 7 PM local time for 10 years at 19 locations across the USA. A fully-factorial propagation analysis using these parameters would be exceedingly computationally expensive. Instead, a central composite design was chosen resulting in 45 combinations of ambient and turbulence parameters. These 45 cases effectively sample the space balancing computational burden.

turbulence↗
NASA NTRSDate not supplied

TPSAS-NF1676L-20351-DND

Characterization of atmospheric variability is crucial to the design of the Entry, Descent, and Landing (EDL) activities at Mars. The distribution of dust in the Martian atmosphere has a significant impact on the atmospheric structure and variability. Information obtained through reconstruction of EDL data from past missions can be used in studying the performance of a model used in preflight atmospheric characterization for future missions. We use the Mars Weather Research and Forecasting (MarsWRF) General Circulation Model to simulate the atmospheric temperature during the landing time periods of Mars Exploration Rovers MER-A and MER-B in 2004. The multi-scale capability of MarsWRF is useful for conducting high- resolution nested simulation centered at the landing site. This version of MarsWRF includes a high spectral accuracy k-distribution radiative transfer model. We use the IR dust opacity profile data from the Thermal Emission spectrometer (TES) limb scan data as well as the TES Nadir data to prescribe the dust opacity distribution. Data from MY 26, which corresponds to the year of MER landing, and data from a climatology based on the median of 4 years (MY24 to MY27) of TES data are used. Model temperature profiles are compared with the reconstructions from EDL data and TES observations. Sensitivity of the model simulation to variations in the input opacity data are shown.

Murali Natarajan↗
NASA NTRSDate not supplied

Assimilation of TROPICS Radiance Data in Nasa Geos System and Impact Assessments Through Observing System Experiments

Since the first full-scale weather satellite Television Infrared Observation Satellite (TIROS)-1 was launched in 1960 to measure weather patterns from space, the number of satellites carrying various sensors to measure atmospheric properties has increased rapidly. Data from these meteorological satellites have been crucial to the advancement of the NWP forecasts. Especially, space-borne measurements of atmospheric temperature and moisture information provided by microwave sounders were reported to contribute most to positive impacts on global NWP forecasts. Developing and launching an operational weather satellite is a daunting and tremendously expensive mission that requires many years of planning, developments, and maintenance after the launch. Small satellites with low size, weight, and power requirements can reduce the cost associated with the construction and launch of large bus platforms and draw attention of several space agencies and weather technology companies that started investing their resources to develop small satellites and measure the potential benefits and weaknesses. The NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission is a constellation of small satellites carrying state-of-art microwave temperature and humidity sounders with 12 channels between 91 GHz and 205 GHz frequency. Currently five TROPICS cubsats, including TROPICS-pathfinder, are in space and provide the temperature and humidity data from space to the meteorological community. This study seeks to assess the potential impact that the constellation of TROPICS satellites may bring to the global NWP analysis and forecasts by assimilating all-sky TROPICS data and implementing observing system experiments (OSE) using the NASA GEOS system. Various evaluations metrics including forecast skills, fit to other observations such as radiosondes and microwave and infrared sounder are used. Along with the NWP impact assessment, the quality of the TROPICS data is evaluated by looking at observation minus forecast statistics in comparisons with other conventional satellite temperature and humidity sounders.

Min-Jeong Kim↗
NASA NTRSDate not supplied

Assimilation of TROPICS Radiance Data in NASA GEOS System and Impact Assessments Through Observing System Experiments

Since the first full-scale weather satellite Television Infrared Observation Satellite (TIROS)-1 was launched in 1960 to measure weather patterns from space, the number of satellites carrying various sensors to measure atmospheric properties has increased rapidly. Data from these meteorological satellites have been crucial to the advancement of the NWP forecasts. Especially, space-borne measurements of atmospheric temperature and moisture information provided by microwave sounders were reported to contribute most to positive impacts on global NWP forecasts. Developing and launching an operational weather satellite is a daunting and tremendously expensive mission that requires many years of planning, developments, and maintenance after the launch. Small satellites with low size, weight, and power requirements can reduce the cost associated with the construction and launch of large bus platforms and draw attention of several space agencies and weather technology companies that started investing their resources to develop small satellites and measure the potential benefits and weaknesses. The NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission is a constellation of small satellites carrying state-of-art microwave temperature and humidity sounders with 12 channels between 91 GHz and 205 GHz frequency. Currently five TROPICS cubsats, including TROPICS-pathfinder, are in space and provide the temperature and humidity data from space to the meteorological community. This study seeks to assess the potential impact that the constellation of TROPICS satellites may bring to the global NWP analysis and forecasts by assimilating all-sky TROPICS data and implementing observing system experiments (OSE) using the NASA GEOS system. Various evaluations metrics including forecast skills, fit to other observations such as radiosondes and microwave and infrared sounder are used. Along with the NWP impact assessment, the quality of the TROPICS data is evaluated by looking at observation minus forecast statistics in comparisons with other conventional satellite temperature and humidity sounders.

Min-Jeong Kim↗
NASA NTRS1994

Experiments with the Mesoscale Atmospheric Simulation System (MASS) using the synthetic relative humidity

This study is intended to examine the impact of the synthetic relative humidity on the model simulation of mesoscale convective storm environment. The synthetic relative humidity is derived from the National Weather Services surface observations, and non-conventional sources including aircraft, radar, and satellite observations. The latter sources provide the mesoscale data of very high spatial and temporal resolution. The synthetic humidity data is used to complement the National Weather Services rawinsonde observations. It is believed that a realistic representation of initial moisture field in a mesoscale model is critical for the model simulation of thunderstorm development, and the formation of non-convective clouds as well as their effects on the surface energy budget. The impact will be investigated based on a real-data case study using the mesoscale atmospheric simulation system developed by Mesoscale Environmental Simulations Operations, Inc. The mesoscale atmospheric simulation system consists of objective analysis and initialization codes, and the coarse-mesh and fine-mesh dynamic prediction models. Both models are a three dimensional, primitive equation model containing the essential moist physics for simulating and forecasting mesoscale convective processes in the atmosphere. The modeling system is currently implemented at the Applied Meteorology Unit, Kennedy Space Center. Two procedures involving the synthetic relative humidity to define the model initial moisture fields are considered. It is proposed to perform several short-range (approximately 6 hours) comparative coarse-mesh simulation experiments with and without the synthetic data. They are aimed at revealing the model sensitivities should allow us both to refine the specification of the observational requirements, and to develop more accurate and efficient objective analysis schemes. The goal is to advance the MASS (Mesoscal Atmospheric Simulation System) modeling expertise so that the model output can provide reliable guidance for thunderstorm forecasting.

Chang, Chia-Bo↗