Results for “Energy forecasting” — Page 21

NASA NTRS2014

Operational Dust Prediction

Over the last few years, numerical prediction of dust aerosol concentration has become prominent at several research and operational weather centres due to growing interest from diverse stakeholders, such as solar energy plant managers, health professionals, aviation and military authorities and policymakers. Dust prediction in numerical weather prediction-type models faces a number of challenges owing to the complexity of the system. At the centre of the problem is the vast range of scales required to fully account for all of the physical processes related to dust. Another limiting factor is the paucity of suitable dust observations available for model, evaluation and assimilation. This chapter discusses in detail numerical prediction of dust with examples from systems that are currently providing dust forecasts in near real-time or are part of international efforts to establish daily provision of dust forecasts based on multi-model ensembles. The various models are introduced and described along with an overview on the importance of dust prediction activities and a historical perspective. Assimilation and evaluation aspects in dust prediction are also discussed.

assimilation↗

NASA NTRS2023

Tensor-to-scalar ratio forecasts for extended LiteBIRD frequency configurations

LiteBIRD is a planned JAXA-led cosmic microwave background (CMB) B -mode satellite experiment aiming for launch in the late 2020s, with a primary goal of detecting the imprint of primordial inflationary gravitational waves. Its current baseline focal-plane configuration includes 15 frequency bands between 40 and 402 GHz, fulfilling the mission requirements to detect the amplitude of gravitational waves with the total uncertainty on the tensor-to-scalar ratio, δr , down to δr < 0.001. A key aspect of this performance is accurate astrophysical component separation, and the ability to remove polarized thermal dust emission is particularly important. In this paper we note that the CMB frequency spectrum falls off nearly exponentially above 300 GHz relative to the thermal dust spectral energy distribution, and a relatively minor high frequency extension can therefore result in even lower uncertainties and better model reconstructions. Specifically, we compared the baseline design with five extended configurations, while varying the underlying dust modeling, in each of which the High-Frequency Telescope (HFT) frequency range was shifted logarithmically toward higher frequencies, with an upper cutoff ranging between 400 and 600 GHz. In each case, we measured the tensor-to-scalar ratio r uncertainty and bias using both parametric and minimum-variance component-separation algorithms. When the thermal dust sky model includes a spatially varying spectral index and temperature, we find that the statistical uncertainty on r after foreground cleaning may be reduced by as much as 30–50% by extending the upper limit of the frequency range from 400 to 600 GHz, with most of the improvement already gained at 500 GHz. We also note that a broader frequency range leads to higher residuals when fitting an incorrect dust model, but also it is easier to discriminate between models through higher χ 2 sensitivity. Even in the case in which the fitting procedure does not correspond to the underlying dust model in the sky, and when the highest frequency data cannot be modeled with sufficient fidelity and must be excluded from the analysis, the uncertainty on r increases by only about 5% for a 500 GHz configuration compared to the baseline.

U. Fuskeland↗

NASA NTRSDate not supplied

Assimilation of Soil Moisture Observations Over Land Improves Analysis and Prediction of Tropical Cyclone Idai

Soil moisture conditions can impact the circulation and structure of a tropical cyclone (TC) when part or all of the circulation is over land. Dry land surface conditions may lead to faster dissipation of a TC over land, whereas very wet conditions may lead to a prolonged maintenance of its intensity. While this relationship is relatively well understood in theory, applications of these findings in the context of numerical weather prediction (NWP) have been limited. Here we present a case study that explores the potential of improving TC predictions through an improved soil moisture initialization in an NWP framework. Specifically, we examine the impact of assimilating observations from the NASA Soil Moisture Active Passive (SMAP) mission into the NASA Goddard Earth Observing System (GEOS) global weather model on the prediction of South-West Indian Ocean TC Idai (2019). SMAP provides accurate L-band (1.4 GHz) brightness temperatures (Tb) observations that are sensitive to soil moisture globally and at high revisit times of 2-3 days. It has previously been shown that the assimilation of SMAP Tbs significantly improves modeled land surface states. Here we evaluate: (i) forecasts initialized from an analysis that is comparable to the GEOS operational analysis (without SMAP Tb assimilation) and (ii) forecasts initialized from an analysis that additionally assimilates SMAP Tb observations. We find that in the analysis with SMAP assimilation, the TC has a better-defined, more aligned vertical structure over land relative to the control run; moreover, the analyzed TC size, as measured by the wind speed radius, better matches the observed TC size. We further find significant reductions in the forecast intensity error and the forecast along-track error, measured against observations. The largest error reductions occur at lead times of 36 to 72 hours, suggesting that the land with its longer memory gains in importance as a source of predictability at this timescale. An investigation of the underlying mechanisms leading to the skill improvements from SMAP data assimilation revealed that the assimilation of SMAP leads to wetter soil moisture conditions and an increased latent heat flux in the SMAP analysis, which results in a TC with higher column-integrated total moisture content and total energy compared to the control analysis.

Jana Kolassa↗

NASA NTRS1983

A probabilistic analysis of silicon cost

Silicon materials costs represent both a cost driver and an area where improvement can be made in the manufacture of photovoltaic modules. The cost from three processes for the production of low-cost silicon being developed under the U.S. Department of Energy's (DOE) National Photovoltaic Program is analyzed. The approach is based on probabilistic inputs and makes use of two models developed at the Jet Propulsion Laboratory: SIMRAND (SIMulation of Research ANd Development) and IPEG (Improved Price Estimating Guidelines). The approach, assumptions, and limitations are detailed along with a verification of the cost analyses methodology. Results, presented in the form of cumulative probability distributions for silicon cost, indicate that there is a 55% chance of reaching the DOE target of $16/kg for silicon material. This is a technically achievable cost based on expert forecasts of the results of ongoing research and development and do not imply any market prices for a given year.

Reiter, L. J.↗

NASA NTRS1994

The value of NDBC observations during March 1993's 'Storm of the Century'

National Data Buoy Center (NDBC) observations from the western Gulf of Mexico Provided several early indications of more rapid cyclogenesis on 12 March 1993 than was forecast by numerical weather prediction (NWP) models. Observations demostrated a closed circulation with gale-force winds shortly after the storm entered the Gulf of Mexico. Pressure measurements at two buoys off the Texas coast were 4-6 hPa lower than the 12-h NWP forecasts, a significant forecast error. Observations from NDBC's moored buoys and Coastal-Marine Automated Network stations revealed that the developing storm was significantly deeper than was indicated on the National Meteorological Center's automated surface analyses. Ocean wave observations reveal some of the steepest waves NDBC has ever measured, indicating phenomenonal wave growth and a high potential for damage to vessels and structures. A warm eddy caused sea surface temperatures (SST) to be several degrees above normal under the track of the storm, creating a strong SST gradient to the north. This provided ample energy and strengthened the baroclinity. NDBC observations showed the eddy to be somewhat larger and warmer than indicated by the most recent National Hurricane Center analysis. This event demonstrates the tremendous value of NDBC marine observations for accurately detecting the occurrence and strength of coastal cyclogenesis events.

Gilhousen, David B.↗

NASA NTRS1983

Satellite freeze forecast system: Executive summary

A satellite-based temperature monitoring and prediction system consisting of a computer controlled acquisition, processing, and display system and the ten automated weather stations called by that computer was developed and transferred to the national weather service. This satellite freeze forecasting system (SFFS) acquires satellite data from either one of two sources, surface data from 10 sites, displays the observed data in the form of color-coded thermal maps and in tables of automated weather station temperatures, computes predicted thermal maps when requested and displays such maps either automatically or manually, archives the data acquired, and makes comparisons with historical data. Except for the last function, SFFS handles these tasks in a highly automated fashion if the user so directs. The predicted thermal maps are the result of two models, one a physical energy budget of the soil and atmosphere interface and the other a statistical relationship between the sites at which the physical model predicts temperatures and each of the pixels of the satellite thermal map.

Martsolf, J. D.↗

NASA NTRS2004

A Case Study of the Impact of AIRS Temperature Retrievals on Numerical Weather Prediction

Large errors in numerical weather prediction are often associated with explosive cyclogenesis. Most studes focus on the under-forecasting error, i.e. cases of rapidly developing cyclones which are poorly predicted in numerical models. However, the over-forecasting error (i.e., to predict an explosively developing cyclone which does not occur in reality) is a very common error that severely impacts the forecasting skill of all models and may also present economic costs if associated with operational forecasting. Unnecessary precautions taken by marine activities can result in severe economic loss. Moreover, frequent occurrence of over-forecasting can undermine the reliance on operational weather forecasting. Therefore, it is important to understand and reduce the prdctions of extreme weather associated with explosive cyclones which do not actually develop. In this study we choose a very prominent case of over-forecasting error in the northwestern Pacific. A 960 hPa cyclone develops in less than 24 hour in the 5-day forecast, with a deepening rate of about 30 hPa in one day. The cyclone is not versed in the analyses and is thus a case of severe over-forecasting. By assimilating AIRS data, the error is largely eliminated. By following the propagation of the anomaly that generates the spurious cyclone, it is found that a small mid-tropospheric geopotential height negative anomaly over the northern part of the Indian subcontinent in the initial conditions, propagates westward, is amplified by orography, and generates a very intense jet streak in the subtropical jet stream, with consequent explosive cyclogenesis over the Pacific. The AIRS assimilation eliminates this anomaly that may have been caused by erroneous upper-air data, and represents the jet stream more correctly. The energy associated with the jet is distributed over a much broader area and as a consequence a multiple, but much more moderate cyclogenesis is observed.

Reale, O.↗

NASA NTRS2003

Barometric Tides from ECMWF Operational Analyses

The solar diurnal and semidiurnal tidal oscillations in surface pressure are extracted from the the operational analysis product of the European Centre for Medium Range Weather Forecasting (ECMWF). For the semidiurnal tide this involves a special temporal interpolation, following Van den Dool and colleagues. The resulting tides are compared with a ground truth tide dataset, a compilation of well-determined tide estimates deduced from long time series of station barometer measurements. These comparisons show that the ECMWF tides are significantly more accurate than the tides deduced from two other widely available reanalysis products. Spectral analysis of ECMWF pressure series shows that the tides consist of sharp central peaks with modulating sidelines at integer multiples of 1 cycle/year, superimposed on a broad cusp of stochastic energy. The integrated energy in the cusp dominates that of the sidelines. This complicates development of a simple model that can characterize the full temporal variability of the tides.

Ray, R. D.↗

NASA NTRS1999

Characterization of Climate Change and Variability with GPS

We compared zonal mean specific humidity derived from the 21 June-4 July 1995 Global Positioning System (GPS)/MET occultation observations with that derived from the European Center for Medium-Range Weather Forecasts (ECMWF) global analyses. The GPS/MET results indicate a drier troposphere, especially near the subtropical tradewind inversion. A small, moist bias in the GPS/MET upper northern-hemisphere troposphere compared to ECMWF may be due to a small radiosonde temperature bias. A diagram shows the difference (g/kg) between the GPS/MET zonal mean specific humidity and that for June-August derived from 1963-1973 radiosondes. Although the observing period is short, GPS and ECMWF results both indicate a significantly wetter boundary layer at most latitudes consistent with decadal trends observed in radiosonde data. GPS/MET results exhibit higher tropical convective available potential energy (CAPE), suggesting a more vigorous tropical Hadley circulation. Drier, free troposphere air in the descending branches of the Hadley circulation is due in part to a moist radiosonde bias but may also reflect some negative moisture feedback. Using 1992-1997 ground GPS observations and recent advancements in GPS technology, we removed an apparent altimetric drift (-1.2 +/- 0.4 mm/yr) due to columnar water vapor from the Topography (Ocean) Experiment (TOPEX) microwave radiometer, which brought the TOPEX mean sea level change estimates into better agreement with historical tide gauge records, suggesting global mean sea level is rising at a rate of 1.5-2.0 mm/yr. We can also discern a statistically significant increase of 0.2 +/- 0.1 kg/square m/yr in mean columnar water vapor over the ocean from 1992-1997. Optimal fingerprinting can be used for the detection and attribution of tropospheric warming due to an anthropogenic greenhouse. Optimal fingerprinting distinguishes between different types of signals according to their spatial and temporal patterns, while minimizing the influence of natural climate variability. S. Leroy concludes that the signal-to-noise ratio of global warming detection increases by unity approximately every 10 years if a single oceanic region is chosen. Less time for detection is likely when many global regions are considered simultaneously. GPS occultation constellations allow the possibility of detecting small changes in upper air temperature with inconsequential calibration errors, making occultation an ideal data type for global warming detection studies. Our initial study of a 22-GHz satellite-satellite occultation system predicts upper troposphere moisture sensitivities of 3-5 ppmv and 1-2 percent in the middle and lower troposphere. Additional information contained in original.

Kursinski, R.↗

NASA NTRS2016

C/NOFS Observations of Electromagnetic Coupling Between Magnetically Conjugate MSTID Structures

This report demonstrates empirically that couplings between magnetically conjugate medium-scale traveling ionospheric disturbances (MSTIDs) are electromagnetic in nature. This is accomplished by comparing plasma density, electric, and magnetic perturbations sampled simultaneously by sensors on the Communication Navigation Outage Forecasting System (CNOFS) satellite. During the period of interest on 17 February 2010, CNOFS made three consecutive orbits while magnetically conjugate to the field of view of an all-sky imager located at El Leoncito, Argentina (31.8degS, 69.3degW). Imaged 630.0 nm airglow was characterized by alternating bands of relatively bright and dark emissions that were aligned from northeast to southwest and propagated toward the northwest, characteristic of MSTIDs in the southern hemisphere. Measurable Poynting fluxes flow along the Earths magnetic field (S) from generator to load hemispheres. While S was predominantly away from the ionosphere above El Leoncito, interhemispheric energy flows were not one-way streets. Measured Poynting flux intensities diminished with time over the three CNOFS passes, suggesting that source mechanisms of MSTIDs were absent or that initial impedance mismatches between the two hemispheres approached an equilibrium status.

Burke, W. J.↗

NASA NTRS2022

History and Development of the USAF Agriculture Meteorology Modeling System and resulting USAF-NASA Strategic Partnership

The USAF Weather (AFW) supports a number of military and U.S. government agencies by providing authoritative weather analysis and forecast products for any location globally, including soil moisture analyses. The long history of supporting soil moisture products and partnering with other U.S. government agencies led to the partnering between the U.S. Air Force (USAF) and NASA Goddard Space Flight Center, resulting in a merger of those organizations’ modeling systems, collaborative development of the Land Information System (LIS), and operational fielding of the system within the USAF 557th Weather Wing [557 WW; formerly, Headquarters Air Force Weather Agency (HQ AFWA)]. In 2009, the USAF implemented the NASA LIS and later made it the primary software system to generate global soil hydrology and energy budget products. The implementation of LIS delivered a significant upgrade over the existing Land Data Assimilation System (LDAS) the USAF operated, the Agriculture Meteorology (AGRMET) system. Implementation enabled the rapid integration of new LDAS technology into USAF operations, and led to a long-term NASA–USAF partnership resulting in continued development, integration, and implementation of new LIS capabilities. This paper documents both the history of the USAF Weather organization capabilities enabling the generation of soil moisture and other land surface analysis products, and describes the USAF–NASA partnership leading to the development of the merged LIS-AGRMET system. The article also presents a successful example of a mutually beneficial partnership that has enabled cutting-edge land analysis capabilities at the USAF, while transitioning NASA software and satellite data into USAF operations.

John Eylander↗

NASA NTRS2007

Improved Modeling of Land-Atmosphere Interactions using a Coupled Version of WRF with the Land Information System

The exchange of energy and moisture between the Earth's surface and the atmospheric boundary layer plays a critical role in many hydrometeorological processes. Accurate and high-resolution representations of surface properties such as sea-surface temperature (SST), vegetation, soil temperature and moisture content, and ground fluxes are necessary to better understand the Earth-atmosphere interactions and improve numerical predictions of weather and climate phenomena. The NASA/NWS Short-term Prediction Research and Transition (SPORT) Center is currently investigating the potential benefits of assimilating high-resolution datasets derived from the NASA moderate resolution imaging spectroradiometer (MODIS) instruments using the Weather Research and Forecasting (WRF) model and the Goddard Space Flight Center Land Information System (LIS). The LIS is a software framework that integrates satellite and ground-based observational and modeled data along with multiple land surface models (LSMs) and advanced computing tools to accurately characterize land surface states and fluxes. The LIS can be run uncoupled to provide a high-resolution land surface initial condition, and can also be run in a coupled mode with WRF to integrate surface and soil quantities using any of the LSMs available in LIS. The LIS also includes the ability to optimize the initialization of surface and soil variables by tuning the spin-up time period and atmospheric forcing parameters, which cannot be done in the standard WRF. Among the datasets available from MODIS, a leaf-area index field and composite SST analysis are used to improve the lower boundary and initial conditions to the LIS/WRF coupled model over both land and water. Experiments will be conducted to measure the potential benefits from using the coupled LIS/WRF model over the Florida peninsula during May 2004. This month experienced relatively benign weather conditions, which will allow the experiments to focus on the local and mesoscale impacts of the high-resolution MODIS datasets and optimized soil and surface initial conditions. Follow-on experiments will examine the utility of such an optimized WRF configuration for more complex weather scenarios such as convective initiation. This paper will provide an overview of the experiment design and present preliminary results from selected cases in May 2004.

Case, Jonathan L.↗

NASA NTRSDate not supplied

Assimilation of SMAP Observations Over Land Improves the Simulation and Prediction of Tropical Cyclone Idai

This work is focused on the role of soil moisture in the prediction of tropical cyclones (TCs) approaching land and after landfall. Soil moisture conditions can impact the circulation and structure of an existing tropical cyclone (TC) when part or all of the circulation is over land. For example, dry land surface conditions may lead to faster dissipation of a TC over land (often associated with changes in precipitation structure), whereas very wet conditions may help sustain or in rare cases re-intensify a TC. Moreover, the presence of strong soil moisture gradients may affect the symmetry and development of the TC circulation leading to changes in its over-land track. While the link between soil moisture conditions and TC evolution in proximity to land is relatively well understood in theory, applications of these findings in the context of numerical weather prediction (NWP) have been limited. Here we present a case study that explores the potential of improving TC predictions through an improved soil moisture initialization in an NWP framework. Specifically, we examine the impact of assimilating observations from the NASA Soil Moisture Active Passive (SMAP) mission into the NASA Goddard Earth Observing System (GEOS) global weather model on the prediction of South-West Indian Ocean TC Idai (2019). SMAP provides accurate L-band (1.4 GHz) brightness temperatures (Tb) observations that are sensitive to soil moisture globally and at high revisit times of 2-3 days. It has previously been shown that the assimilation of SMAP Tb observations significantly improves modeled land surface states. Thus, it is expected that SMAP can be used to constrain land surface initial conditions and potentially benefit TC forecasts. Here we present two sets of retrospective forecasts of TC Idai that are compared in an Observing System Experiment framework at ¼ degree resolution: (i) forecasts initialized from an analysis that is comparable to the GEOS operational analysis (without SMAP Tb assimilation) and (ii) forecasts initialized from an analysis that additionally assimilates SMAP brightness temperature observations over land using a weakly-coupled land analysis. We find that the assimilation of SMAP meaningfully improves the representation of TC Idai’s structure as well as the prediction of its intensity and track. The analyzed TC size, as measured by the wind speed radius, is improved by up to 18% in the analysis with SMAP assimilation relative to the control run. The forecast intensity error, measured against the observed intensity, is reduced by up to 23%. At the 1/4-degree resolution used here, GEOS unavoidably under-estimates TC intensity and over-estimates TC size. The SMAP assimilation therefore corrects the model in the right direction, leading to a storm that is more energetic and more compact. Furthermore, we find that the along-track forecast error is reduced by up to 34%, indicating a more accurate propagation speed, which is consistent with the fact that TC speed over land is strongly affected by surface processes. The impact of SMAP assimilation on the forecast cross-track error is neutral. Across the TC forecast skill metrics used here, the improvements from SMAP DA are largest at lead times of 36 to 72 hours, suggesting that the predictability of forecasts at shorter lead times may be dominated by short-term convective processes, while the land and its longer memory gains in importance as a source of predictability on a 2-3 day timescale. We further investigated the underlying mechanisms leading to the skill improvements from SMAP data assimilation by isolating the land areas that directly influence TC Idai using a back trajectory analysis. We find that the assimilation of SMAP leads to wetter soil moisture conditions that cause an increased latent heat flux, which ultimately results in TC analyzed representation that has higher column-integrated total moisture content and total energy compared to the analysis in the control run without SMAP assimilation. Overall, the results highlight that the assimilation of SMAP observations into a global numerical weather prediction model can lead to pronounced improvements of TC predictions. This is a crucial step towards a better mitigation of the socio-economic impact of landfalling TCs and thus safeguarding human lives. Finally, our study presents an event-based approach that assesses the impact of land data assimilation for a particular weather event rather than by globally averaging differences in skill. We argue that global skill assessments – while necessary – can mute the impact of land data assimilation, because the land’s influence on the atmosphere is constrained to certain locations and certain times. Instead, the event-based approach better highlights the true potential of land data assimilation in the context of NWP, especially for extreme events when accurate predictions are critical.

Jana Kolassa↗

NASA NTRSDate not supplied

Assimilation of SMAP Observations Over Land Improves the Simulation and Prediction of Tropical Cyclone Idai

This work is focused on the role of soil moisture in the prediction of tropical cyclones (TCs) approaching land and after landfall. Soil moisture conditions can impact the circulation and structure of an existing tropical cyclone (TC) when part or all of the circulation is over land. For example, dry land surface conditions may lead to faster dissipation of a TC over land (often associated with changes in precipitation structure), whereas very wet conditions may help sustain or in rare cases re-intensify a TC. Moreover, the presence of strong soil moisture gradients may affect the symmetry and development of the TC circulation leading to changes in its over-land track. While the link between soil moisture conditions and TC evolution in proximity to land is relatively well understood in theory, applications of these findings in the context of numerical weather prediction (NWP) have been limited. Here we present a case study that explores the potential of improving TC predictions through an improved soil moisture initialization in an NWP framework. Specifically, we examine the impact of assimilating observations from the NASA Soil Moisture Active Passive (SMAP) mission into the NASA Goddard Earth Observing System (GEOS) global weather model on the prediction of South-West Indian Ocean TC Idai (2019). SMAP provides accurate L-band (1.4 GHz) brightness temperatures (Tb) observations that are sensitive to soil moisture globally and at high revisit times of 2-3 days. It has previously been shown that the assimilation of SMAP Tb observations significantly improves modeled land surface states. Thus, it is expected that SMAP can be used to constrain land surface initial conditions and potentially benefit TC forecasts. Here we present two sets of retrospective forecasts of TC Idai that are compared in an Observing System Experiment framework at ¼ degree resolution: (i) forecasts initialized from an analysis that is comparable to the GEOS operational analysis (without SMAP Tb assimilation) and (ii) forecasts initialized from an analysis that additionally assimilates SMAP brightness temperature observations over land using a weakly-coupled land analysis. We find that the assimilation of SMAP meaningfully improves the representation of TC Idai’s structure as well as the prediction of its intensity and track. The analyzed TC size, as measured by the wind speed radius, is improved by up to 18% in the analysis with SMAP assimilation relative to the control run. The forecast intensity error, measured against the observed intensity, is reduced by up to 23%. At the 1/4-degree resolution used here, GEOS unavoidably under-estimates TC intensity and over-estimates TC size. The SMAP assimilation therefore corrects the model in the right direction, leading to a storm that is more energetic and more compact. Furthermore, we find that the along-track forecast error is reduced by up to 34%, indicating a more accurate propagation speed, which is consistent with the fact that TC speed over land is strongly affected by surface processes. The impact of SMAP assimilation on the forecast cross-track error is neutral. Across the TC forecast skill metrics used here, the improvements from SMAP DA are largest at lead times of 36 to 72 hours, suggesting that the predictability of forecasts at shorter lead times may be dominated by short-term convective processes, while the land and its longer memory gains in importance as a source of predictability on a 2-3 day timescale. We further investigated the underlying mechanisms leading to the skill improvements from SMAP data assimilation by isolating the land areas that directly influence TC Idai using a back trajectory analysis. We find that the assimilation of SMAP leads to wetter soil moisture conditions that cause an increased latent heat flux, which ultimately results in TC analyzed representation that has higher column-integrated total moisture content and total energy compared to the analysis in the control run without SMAP assimilation. Overall, the results highlight that the assimilation of SMAP observations into a global numerical weather prediction model can lead to pronounced improvements of TC predictions. This is a crucial step towards a better mitigation of the socio-economic impact of landfalling TCs and thus safeguarding human lives. Finally, our study presents an event-based approach that assesses the impact of land data assimilation for a particular weather event rather than by globally averaging differences in skill. We argue that global skill assessments – while necessary – can mute the impact of land data assimilation, because the land’s influence on the atmosphere is constrained to certain locations and certain times. Instead, the event-based approach better highlights the true potential of land data assimilation in the context of NWP, especially for extreme events when accurate predictions are critical.

Jana Kolassa↗

NASA NTRS2008

High-Resolution Specification of the Land and Ocean Surface for Improving Regional Mesoscale Model Predictions

The exchange of energy and moisture between the Earth's surface and the atmospheric boundary layer plays a critical role in many meteorological processes. High-resolution, accurate representations of surface properties such as sea-surface temperature (SST), soil temperature and moisture content, ground fluxes, and vegetation are necessary to better understand the Earth-atmosphere interactions and improve numerical predictions of sensible weather. The NASA Short-term Prediction Research and Transition (SPoRT) Center has been conducting separate studies to examine the impacts of high-resolution land-surface initialization data from the Goddard Space Flight Center Land Information System (LIS) on subsequent WRF forecasts, as well as the influence of initializing WRF with SST composites derived from the MODIS instrument. This current project addresses the combined impacts of using high-resolution lower boundary data over both land (LIS data) and water (MODIS SSTs) on the subsequent daily WRF forecasts over Florida during May 2004. For this experiment, the WRF model is configured to run on a nested domain with 9- km and 3-kin grid spacing, centered on the Florida peninsula and adjacent coastal waters of the Gulf of Mexico and Atlantic Ocean. A control configuration of WRF is established to take all initial condition data from the NCEP Eta model. Meanwhile, two WRF experimental runs are configured to use high-resolution initialization data from (1) LIS land-surface data only, and (2) a combination of LIS data and high-resolution MODIS SST composites. The experiment involves running 24-hour simulations of the control WRF configuration, the MS-initialized WRF, and the LIS+MODIS-initialized WRF daily for the entire month of May 2004. All atmospheric data for initial and boundary conditions for the Control, LIS, and LIS+MODIS runs come from the NCEP Eta model on a 40-km grid. Verification statistics are generated at land surface observation sites and buoys, and the impacts of the high-resolution lower boundary data on the development and evolution of mesoscale circulations such as sea and land breezes are examined, This paper will present the results of these WRF modeling experiments using LIS and MODIS lower boundary datasets over the Florida peninsula during May 2004.

Case, Jonathan L.↗

NASA NTRS2012

How are Space Weather and Space Climate Connected to Solar Phenomena?

Many aspects of modern society are susceptible to space weather effects. Pertinent space weather effects include high-energy electromagnetic and particle radiation, changes of atmospheric drag, reduction of GPS accuracy or complete loss of GPS signals, communication outages, and the generation of potentially harmful DC currents in our electric power grid. Beginning in the early 1990s, researchers and government have been increasingly aware of the need to understand the causes of space weather, and to find ways to mitigate deleterious effects associated with it. New research and development programs have been created to address space weather primarily at NASA but also at other agencies. This investment has been very fruitful by generating a new class of entirely new space weather specification and forecast capabilities. This presentation provides an overview of space weather causes and effects, as well as of research and development to forecast and mitigate space weather effects. It will include a discussion of modern space weather analysis and forecasting, and conclude by pointing out paths into the future.

Hesse, Michael↗

NASA NTRS2000

Theoretical Technology Research for ISTP/SOLARMAX

During the last decade, we have been developing theoretical tools to support the scientific objectives of the International Solar Terrestrial Physics (ISTP) program. Results from our mission-oriented theory program have contributed significantly to the development of predictive capabilities by using real upstream solar wind conditions as input to our models and forecasting events observed downstream near Earth. We also developed the capability to unravel the complex information contained in ion velocity distribution functions measured near the Earth to determine their origin and energization process. During solar maximum, solar flares and coronal mass ejections (CMEs) dominate the sun's activity. It is now widely accepted that the impact of CMEs (or magnetic clouds) with the Earth's magnetosphere is the cause of most magnetic storms during solar maximum. One important aspect of a CME is the occurrence of solar energetic particle (SEP) events. During these events, protons, electrons, and heavy ions of solar origin are accelerated to very high energies by shock waves driven out from the sun. We carried out a series of large-scale kinetic (LSK) simulations to model the effect of SEPs on the near-Earth environment and the accessibility of these high-energy particles to the inner magnetosphere. We present the results of these studies.

Ashour-Abdalla, Maha↗

NASA NTRS2017

The 2017 Meteor Shower Activity Forecast for Earth Orbit

Most meteor showers will display typical activity levels in 2017. Perseid activity is expected to be higher than normal but less than in 2016; rates may reach 80% of the peak ZHR in 2016. Despite this enhancement, the Perseids rank 4th in flux for 0.04-cm-equivalent meteoroids: the Geminids (GEM), Daytime Arietids (ARI), and Southern delta Aquariids (SDA) all produce higher fluxes. Aside from heightened Perseid activity, the 2017 forecast includes a number of changes. In 2016, the Meteoroid Environment Office used 14 years of shower flux data to revisit the activity profiles of meteor showers included in the annual forecast. Both the list of showers and the shape of certain major showers have been revised. The names and three-letter shower codes were updated to match those in the International Astronomical Union (IAU) Meteor Data Center, and a number of defunct or insignificant showers were removed. The most significant of these changes are the increased durations of the Daytime Arietid (ARI) and Geminid (GEM) meteor showers. This document is designed to supplement spacecraft risk assessments that incorporate an annual averaged meteor shower flux (as is the case with all NASA meteor models). Results are presented relative to this baseline and are weighted to a constant kinetic energy. Two showers - the Daytime Arietids (ARI) and the Geminids (GEM) - attain flux levels approaching that of the baseline meteoroid environment for 0.1-cm-equivalent meteoroids. This size is the threshold for structural damage. These two showers, along with the Quadrantids (QUA) and Perseids (PER), exceed the baseline flux for 0.3-cm-equivalent particles, which is near the limit for pressure vessel penetration. Please note, however, that meteor shower fluxes drop dramatically with increasing particle size. As an example, the Arietids contribute a flux of about 5x10(exp -6) meteoroids m(exp -2) hr-1 in the 0.04-cm-equivalent range, but only 1x10(exp -8) meteoroids m(sub -2) hr-1 for the 0.3-cmequivalent and larger size regime. Thus, a PNP risk assessment should use the flux and flux enhancements corresponding to the smallest particle capable of penetrating a component, because the flux at this size will be the dominant contributor to the risk.

Moorhead, Althea↗