Forecasting intense geomagnetic activity using interplanetary magnetic field data

NASA Astrophysics Data System (ADS)

Saiz, E.; Cid, C.; Cerrato, Y.

2008-12-01

Southward interplanetary magnetic fields are considered traces of geoeffectiveness since they are a main agent of magnetic reconnection of solar wind and magnetosphere. The first part of this work revises the ability to forecast intense geomagnetic activity using different procedures available in the literature. The study shows that current methods do not succeed in making confident predictions. This fact led us to develop a new forecasting procedure, which provides trustworthy results in predicting large variations of Dst index over a sample of 10 years of observations and is based on the value Bz only. The proposed forecasting method appears as a worthy tool for space weather purposes because it is not affected by the lack of solar wind plasma data, which usually occurs during severe geomagnetic activity. Moreover, the results obtained guide us to provide a new interpretation of the physical mechanisms involved in the interaction between the solar wind and the magnetosphere using Faraday's law.

Cost-Loss Analysis of Ensemble Solar Wind Forecasting: Space Weather Use of Terrestrial Weather Tools

NASA Astrophysics Data System (ADS)

Henley, E. M.; Pope, E. C. D.

2017-12-01

This commentary concerns recent work on solar wind forecasting by Owens and Riley (2017). The approach taken makes effective use of tools commonly used in terrestrial weather—notably, via use of a simple model—generation of an "ensemble" forecast, and application of a "cost-loss" analysis to the resulting probabilistic information, to explore the benefit of this forecast to users with different risk appetites. This commentary aims to highlight these useful techniques to the wider space weather audience and to briefly discuss the general context of application of terrestrial weather approaches to space weather.

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

NASA Technical Reports Server (NTRS)

Balikhin, M. A.; Rodriguez, J. V.; Boynton, R. J.; Walker, S. N.; Aryan, Homayon; Sibeck, D. G.; Billings, S. A.

2016-01-01

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.

Comparative analysis of NOAA REFM and SNB3GEO tools for the forecast of the fluxes of high-energy electrons at GEO.

PubMed

Balikhin, M A; Rodriguez, J V; Boynton, R J; Walker, S N; Aryan, H; Sibeck, D G; Billings, S A

2016-01-01

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 SNB 3 GEO model has been operating online, providing a 24 h ahead forecast of the same fluxes. In addition to solar wind speed, the SNB 3 GEO forecasts use solar wind density and interplanetary magnetic field B 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 SNB 3 GEO 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 SNB 3 GEO forecast.

Toward the Probabilistic Forecasting of High-latitude GPS Phase Scintillation

NASA Technical Reports Server (NTRS)

Prikryl, P.; Jayachandran, P.T.; Mushini, S. C.; Richardson, I. G.

2012-01-01

The phase scintillation index was obtained from L1 GPS data collected with the Canadian High Arctic Ionospheric Network (CHAIN) during years of extended solar minimum 2008-2010. Phase scintillation occurs predominantly on the dayside in the cusp and in the nightside auroral oval. We set forth a probabilistic forecast method of phase scintillation in the cusp based on the arrival time of either solar wind corotating interaction regions (CIRs) or interplanetary coronal mass ejections (ICMEs). CIRs on the leading edge of high-speed streams (HSS) from coronal holes are known to cause recurrent geomagnetic and ionospheric disturbances that can be forecast one or several solar rotations in advance. Superposed epoch analysis of phase scintillation occurrence showed a sharp increase in scintillation occurrence just after the arrival of high-speed solar wind and a peak associated with weak to moderate CMEs during the solar minimum. Cumulative probability distribution functions for the phase scintillation occurrence in the cusp are obtained from statistical data for days before and after CIR and ICME arrivals. The probability curves are also specified for low and high (below and above median) values of various solar wind plasma parameters. The initial results are used to demonstrate a forecasting technique on two example periods of CIRs and ICMEs.

Data Assimilation in the Solar Wind: Challenges and First Results.

PubMed

Lang, Matthew; Browne, Philip; van Leeuwen, Peter Jan; Owens, Mathew

2017-11-01

Data assimilation (DA) is used extensively in numerical weather prediction (NWP) to improve forecast skill. Indeed, improvements in forecast skill in NWP models over the past 30 years have directly coincided with improvements in DA schemes. At present, due to data availability and technical challenges, DA is underused in space weather applications, particularly for solar wind prediction. This paper investigates the potential of advanced DA methods currently used in operational NWP centers to improve solar wind prediction. To develop the technical capability, as well as quantify the potential benefit, twin experiments are conducted to assess the performance of the Local Ensemble Transform Kalman Filter (LETKF) in the solar wind model ENLIL. Boundary conditions are provided by the Wang-Sheeley-Arge coronal model and synthetic observations of density, temperature, and momentum generated every 4.5 h at 0.6 AU. While in situ spacecraft observations are unlikely to be routinely available at 0.6 AU, these techniques can be applied to remote sensing of the solar wind, such as with Heliospheric Imagers or interplanetary scintillation. The LETKF can be seen to improve the state at the observation location and advect that improvement toward the Earth, leading to an improvement in forecast skill in near-Earth space for both the observed and unobserved variables. However, sharp gradients caused by the analysis of a single observation in space resulted in artificial wavelike structures being advected toward Earth. This paper is the first attempt to apply DA to solar wind prediction and provides the first in-depth analysis of the challenges and potential solutions.

Data Assimilation in the Solar Wind: Challenges and First Results

NASA Astrophysics Data System (ADS)

Lang, Matthew; Browne, Philip; van Leeuwen, Peter Jan; Owens, Mathew

2017-11-01

Data assimilation (DA) is used extensively in numerical weather prediction (NWP) to improve forecast skill. Indeed, improvements in forecast skill in NWP models over the past 30 years have directly coincided with improvements in DA schemes. At present, due to data availability and technical challenges, DA is underused in space weather applications, particularly for solar wind prediction. This paper investigates the potential of advanced DA methods currently used in operational NWP centers to improve solar wind prediction. To develop the technical capability, as well as quantify the potential benefit, twin experiments are conducted to assess the performance of the Local Ensemble Transform Kalman Filter (LETKF) in the solar wind model ENLIL. Boundary conditions are provided by the Wang-Sheeley-Arge coronal model and synthetic observations of density, temperature, and momentum generated every 4.5 h at 0.6 AU. While in situ spacecraft observations are unlikely to be routinely available at 0.6 AU, these techniques can be applied to remote sensing of the solar wind, such as with Heliospheric Imagers or interplanetary scintillation. The LETKF can be seen to improve the state at the observation location and advect that improvement toward the Earth, leading to an improvement in forecast skill in near-Earth space for both the observed and unobserved variables. However, sharp gradients caused by the analysis of a single observation in space resulted in artificial wavelike structures being advected toward Earth. This paper is the first attempt to apply DA to solar wind prediction and provides the first in-depth analysis of the challenges and potential solutions.

Potential Improvements in Space Weather Forecasting using New Products Developed for the Upcoming DSCOVR Solar Wind Mission

NASA Astrophysics Data System (ADS)

Cash, M. D.; Biesecker, D. A.; Reinard, A. A.

2013-05-01

The Deep Space Climate Observatory (DSCOVR) mission, which is scheduled for launch in late 2014, will provide real-time solar wind thermal plasma and magnetic measurements to ensure continuous monitoring for space weather forecasting. DSCOVR will be located at the L1 Lagrangian point and will include a Faraday cup to measure the proton and alpha components of the solar wind and a triaxial fluxgate magnetometer to measure the magnetic field in three dimensions. The real-time data provided by DSCOVR will be used to generate space weather applications and products that have been demonstrated to be highly accurate and provide actionable information for customers. We present several future space weather products currently under evaluation for development. New potential space weather products for use with DSCOVR real-time data include: automated shock detection, more accurate L1 to Earth delay time, automatic solar wind regime identification, and prediction of rotations in solar wind Bz within magnetic clouds. Additional ideas from the community on future space weather products are encouraged.

Evaluation of the performance of a meso-scale NWP model to forecast solar irradiance on Reunion Island for photovoltaic power applications

NASA Astrophysics Data System (ADS)

Kalecinski, Natacha; Haeffelin, Martial; Badosa, Jordi; Periard, Christophe

2013-04-01

Solar photovoltaic power is a predominant source of electrical power on Reunion Island, regularly providing near 30% of electrical power demand for a few hours per day. However solar power on Reunion Island is strongly modulated by clouds in small temporal and spatial scales. Today regional regulations require that new solar photovoltaic plants be combined with storage systems to reduce electrical power fluctuations on the grid. Hence cloud and solar irradiance forecasting becomes an important tool to help optimize the operation of new solar photovoltaic plants on Reunion Island. Reunion Island, located in the South West of the Indian Ocean, is exposed to persistent trade winds, most of all in winter. In summer, the southward motion of the ITCZ brings atmospheric instabilities on the island and weakens trade winds. This context together with the complex topography of Reunion Island, which is about 60 km wide, with two high summits (3070 and 2512 m) connected by a 1500 m plateau, makes cloudiness very heterogeneous. High cloudiness variability is found between mountain and coastal areas and between the windward, leeward and lateral regions defined with respect to the synoptic wind direction. A detailed study of local dynamics variability is necessary to better understand cloud life cycles around the island. In the presented work, our approach to explore the short-term solar irradiance forecast at local scales is to use the deterministic output from a meso-scale numerical weather prediction (NWP) model, AROME, developed by Meteo France. To start we evaluate the performance of the deterministic forecast from AROME by using meteorological measurements from 21 meteorological ground stations widely spread around the island (and with altitudes from 8 to 2245 m). Ground measurements include solar irradiation, wind speed and direction, relative humidity, air temperature, precipitation and pressure. Secondly we study in the model the local dynamics and thermodynamics that control cloud development and solar irradiance in order to define new predictors to improve probabilistic forecast of solar irradiance.

Forecasting of Radiation Belts: Results From the PROGRESS Project.

NASA Astrophysics Data System (ADS)

Balikhin, M. A.; Arber, T. D.; Ganushkina, N. Y.; Walker, S. N.

2017-12-01

Forecasting of Radiation Belts: Results from the PROGRESS Project. The overall goal of the PROGRESS project, funded in frame of EU Horizon2020 programme, is to combine first principles based models with the systems science methodologies to achieve reliable forecasts of the geo-space particle radiation environment.The PROGRESS incorporates three themes : The propagation of the solar wind to L1, Forecast of geomagnetic indices, and forecast of fluxes of energetic electrons within the magnetosphere. One of the important aspects of the PROGRESS project is the development of statistical wave models for magnetospheric waves that affect the dynamics of energetic electrons such as lower band chorus, hiss and equatorial noise. The error reduction ratio (ERR) concept has been used to optimise the set of solar wind and geomagnetic parameters for organisation of statistical wave models for these emissions. The resulting sets of parameters and statistical wave models will be presented and discussed. However the ERR analysis also indicates that the combination of solar wind and geomagnetic parameters accounts for only part of the variance of the emissions under investigation (lower band chorus, hiss and equatorial noise). In addition, advances in the forecast of fluxes of energetic electrons, exploiting empirical models and the first principles IMPTAM model achieved by the PROGRESS project is presented.

Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Siyuan; Hwang, Youngdeok; Khabibrakhmanov, Ildar

With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual modelmore » has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.« less

An empirical model to forecast solar wind velocity through statistical modeling

NASA Astrophysics Data System (ADS)

Gao, Y.; Ridley, A. J.

2013-12-01

The accurate prediction of the solar wind velocity has been a major challenge in the space weather community. Previous studies proposed many empirical and semi-empirical models to forecast the solar wind velocity based on either the historical observations, e.g. the persistence model, or the instantaneous observations of the sun, e.g. the Wang-Sheeley-Arge model. In this study, we use the one-minute WIND data from January 1995 to August 2012 to investigate and compare the performances of 4 models often used in literature, here referred to as the null model, the persistence model, the one-solar-rotation-ago model, and the Wang-Sheeley-Arge model. It is found that, measured by root mean square error, the persistence model gives the most accurate predictions within two days. Beyond two days, the Wang-Sheeley-Arge model serves as the best model, though it only slightly outperforms the null model and the one-solar-rotation-ago model. Finally, we apply the least-square regression to linearly combine the null model, the persistence model, and the one-solar-rotation-ago model to propose a 'general persistence model'. By comparing its performance against the 4 aforementioned models, it is found that the accuracy of the general persistence model outperforms the other 4 models within five days. Due to its great simplicity and superb performance, we believe that the general persistence model can serve as a benchmark in the forecast of solar wind velocity and has the potential to be modified to arrive at better models.

Impacts of Short-Term Solar Power Forecasts in System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibanez, Eduardo; Krad, Ibrahim; Hodge, Bri-Mathias

2016-05-05

Solar generation is experiencing an exponential growth in power systems worldwide and, along with wind power, is posing new challenges to power system operations. Those challenges are characterized by an increase of system variability and uncertainty across many time scales: from days, down to hours, minutes, and seconds. Much of the research in the area has focused on the effect of solar forecasting across hours or days. This paper presents a methodology to capture the effect of short-term forecasting strategies and analyzes the economic and reliability implications of utilizing a simple, yet effective forecasting method for solar PV in intra-daymore » operations.« less

Optimization of Evaporative Demand Models for Seasonal Drought Forecasting

NASA Astrophysics Data System (ADS)

McEvoy, D.; Huntington, J. L.; Hobbins, M.

2015-12-01

Providing reliable seasonal drought forecasts continues to pose a major challenge for scientists, end-users, and the water resources and agricultural communities. Precipitation (Prcp) forecasts beyond weather time scales are largely unreliable, so exploring new avenues to improve seasonal drought prediction is necessary to move towards applications and decision-making based on seasonal forecasts. A recent study has shown that evaporative demand (E0) anomaly forecasts from the Climate Forecast System Version 2 (CFSv2) are consistently more skillful than Prcp anomaly forecasts during drought events over CONUS, and E0 drought forecasts may be particularly useful during the growing season in the farming belts of the central and Midwestern CONUS. For this recent study, we used CFSv2 reforecasts to assess the skill of E0 and of its individual drivers (temperature, humidity, wind speed, and solar radiation), using the American Society for Civil Engineers Standardized Reference Evapotranspiration (ET0) Equation. Moderate skill was found in ET0, temperature, and humidity, with lesser skill in solar radiation, and no skill in wind. Therefore, forecasts of E0 based on models with no wind or solar radiation inputs may prove to be more skillful than the ASCE ET0. For this presentation we evaluate CFSv2 E0 reforecasts (1982-2009) from three different E0 models: (1) ASCE ET0; (2) Hargreaves and Samani (ET-HS), which is estimated from maximum and minimum temperature alone; and (3) Valiantzas (ET-V), which is a modified version of the Penman method for use when wind speed data are not available (or of poor quality) and is driven only by temperature, humidity, and solar radiation. The University of Idaho's gridded meteorological data (METDATA) were used as observations to evaluate CFSv2 and also to determine if ET0, ET-HS, and ET-V identify similar historical drought periods. We focus specifically on CFSv2 lead times of one, two, and three months, and season one forecasts; which are time scales with moderate skill and are more likely to be used in hydro-climatic applications and decision-making.

WIRE: Weather Intelligence for Renewable Energies

NASA Astrophysics Data System (ADS)

Heimo, A.; Cattin, R.; Calpini, B.

2010-09-01

Renewable energies such as wind and solar energy will play an important, even decisive role in order to mitigate and adapt to the projected dramatic consequences to our society and environment due to climate change. Due to shrinking fossil resources, the transition to more and more renewable energy shares is unavoidable. But, as wind and solar energy are strongly dependent on highly variable weather processes, increased penetration rates will also lead to strong fluctuations in the electricity grid which need to be balanced. Proper and specific forecasting of ‘energy weather' is a key component for this. Therefore, it is today appropriate to scientifically address the requirements to provide the best possible specific weather information for forecasting the energy production of wind and solar power plants within the next minutes up to several days. Towards such aims, Weather Intelligence will first include developing dedicated post-processing algorithms coupled with weather prediction models and with past and/or online measurement data especially remote sensing observations. Second, it will contribute to investigate the difficult relationship between the highly intermittent weather dependent power production and concurrent capacities such as transport and distribution of this energy to the end users. Selecting, resp. developing surface-based and satellite remote sensing techniques well adapted to supply relevant information to the specific post-processing algorithms for solar and wind energy production short-term forecasts is a major task with big potential. It will lead to improved energy forecasts and help to increase the efficiency of the renewable energy productions while contributing to improve the management and presumably the design of the energy grids. The second goal will raise new challenges as this will require first from the energy producers and distributors definitions of the requested input data and new technologies dedicated to the management of power plants and electricity grids and second from the meteorological measurement community to deliver suitable, short term high quality forecasts to fulfill these requests with emphasis on highly variable weather conditions and spatially distributed energy productions often located in complex terrain. This topic has been submitted for a new COST Action under the title "Short-Term High Resolution Wind and Solar Energy Production Forecasts".

A Comparative Verification of Forecasts from Two Operational Solar Wind Models

DTIC Science & Technology

2010-12-16

knowing how much confidence to place on predicted parameters. Cost /benefit information is provided to administrators who decide to sustain or...components of the magnetic field vector in the geocentric solar magnetospheric (GSM) coordinate system at each hour of forecast time. For an example of a

Evaluation of a Revised Interplanetary Shock Prediction Model: 1D CESE-HD-2 Solar-Wind Model

NASA Astrophysics Data System (ADS)

Zhang, Y.; Du, A. M.; Du, D.; Sun, W.

2014-08-01

We modified the one-dimensional conservation element and solution element (CESE) hydrodynamic (HD) model into a new version [ 1D CESE-HD-2], by considering the direction of the shock propagation. The real-time performance of the 1D CESE-HD-2 model during Solar Cycle 23 (February 1997 - December 2006) is investigated and compared with those of the Shock Time of Arrival Model ( STOA), the Interplanetary-Shock-Propagation Model ( ISPM), and the Hakamada-Akasofu-Fry version 2 ( HAFv.2). Of the total of 584 flare events, 173 occurred during the rising phase, 166 events during the maximum phase, and 245 events during the declining phase. The statistical results show that the success rates of the predictions by the 1D CESE-HD-2 model for the rising, maximum, declining, and composite periods are 64 %, 62 %, 57 %, and 61 %, respectively, with a hit window of ± 24 hours. The results demonstrate that the 1D CESE-HD-2 model shows the highest success rates when the background solar-wind speed is relatively fast. Thus, when the background solar-wind speed at the time of shock initiation is enhanced, the forecasts will provide potential values to the customers. A high value (27.08) of χ 2 and low p-value (< 0.0001) for the 1D CESE-HD-2 model give considerable confidence for real-time forecasts by using this new model. Furthermore, the effects of various shock characteristics (initial speed, shock duration, background solar wind, longitude, etc.) and background solar wind on the forecast are also investigated statistically.

Solar and Heliospheric Data Requirements: Going Further Than L1

NASA Technical Reports Server (NTRS)

Szabo, A.

2011-01-01

Current operational space weather forecasting relies on solar wind observations made by the ACE spacecraft located at the L1 point providing 30-40 minutes warning time. Some use is also made of SOHO and STEREO solar imaging that potentially can give multiple days of warning time. However, our understanding of the propagation and evolution of solar wind transients is still limited resulting in a typical timing uncertainty of approximately 10 hours. In order to improve this critical understanding, a number of NASA missions are being planned. Specifically the Solar Probe Plus and Solar Orbiter missions will investigate the inner Heliospheric evolution of coronal mass ejections and the acceleration and propagation of solar energetic particles. In addition, a number of multi-spacecraft concepts have been studied that have the potential to significantly improve the accuracy of long-term space weather forecasts.

Solar Atmosphere to Earth's Surface: Long Lead Time dB/dt Predictions with the Space Weather Modeling Framework

NASA Astrophysics Data System (ADS)

Welling, D. T.; Manchester, W.; Savani, N.; Sokolov, I.; van der Holst, B.; Jin, M.; Toth, G.; Liemohn, M. W.; Gombosi, T. I.

2017-12-01

The future of space weather prediction depends on the community's ability to predict L1 values from observations of the solar atmosphere, which can yield hours of lead time. While both empirical and physics-based L1 forecast methods exist, it is not yet known if this nascent capability can translate to skilled dB/dt forecasts at the Earth's surface. This paper shows results for the first forecast-quality, solar-atmosphere-to-Earth's-surface dB/dt predictions. Two methods are used to predict solar wind and IMF conditions at L1 for several real-world coronal mass ejection events. The first method is an empirical and observationally based system to estimate the plasma characteristics. The magnetic field predictions are based on the Bz4Cast system which assumes that the CME has a cylindrical flux rope geometry locally around Earth's trajectory. The remaining plasma parameters of density, temperature and velocity are estimated from white-light coronagraphs via a variety of triangulation methods and forward based modelling. The second is a first-principles-based approach that combines the Eruptive Event Generator using Gibson-Low configuration (EEGGL) model with the Alfven Wave Solar Model (AWSoM). EEGGL specifies parameters for the Gibson-Low flux rope such that it erupts, driving a CME in the coronal model that reproduces coronagraph observations and propagates to 1AU. The resulting solar wind predictions are used to drive the operational Space Weather Modeling Framework (SWMF) for geospace. Following the configuration used by NOAA's Space Weather Prediction Center, this setup couples the BATS-R-US global magnetohydromagnetic model to the Rice Convection Model (RCM) ring current model and a height-integrated ionosphere electrodynamics model. The long lead time predictions of dB/dt are compared to model results that are driven by L1 solar wind observations. Both are compared to real-world observations from surface magnetometers at a variety of geomagnetic latitudes. Metrics are calculated to examine how the simulated solar wind drivers impact forecast skill. These results illustrate the current state of long-lead-time forecasting and the promise of this technology for operational use.

Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Florita, A.; Hodge, B. M.; Milligan, M.

2012-08-01

The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites andmore » for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.« less

The Impact of Discontinuity Front Orientation on the Accuracy of L1 Space Weather Forecasting

NASA Astrophysics Data System (ADS)

Szabo, A.

2013-12-01

Current space weather forecasting from the Sun-Earth first Lagrange (L1) point assumes that all observed solar wind discontinuity fronts (interplanetary shocks, ICME boundaries) are perpendicular to the Sun-Earth line and are propagating radially out from eh Sun. In reality, these weather fronts can have significantly tilted orientation. Combined ACE, Wind and Soho observations allow the quantification of this effect. With the launch of the DSCOVR spacecraft in early 2015, dual real-time solar wind measurements will become available (at least at some time). Algorithms and their impact exploiting this unique scenario will be discussed.

Validation of Model Forecasts of the Ambient Solar Wind

NASA Technical Reports Server (NTRS)

Macneice, P. J.; Hesse, M.; Kuznetsova, M. M.; Rastaetter, L.; Taktakishvili, A.

2009-01-01

Independent and automated validation is a vital step in the progression of models from the research community into operational forecasting use. In this paper we describe a program in development at the CCMC to provide just such a comprehensive validation for models of the ambient solar wind in the inner heliosphere. We have built upon previous efforts published in the community, sharpened their definitions, and completed a baseline study. We also provide first results from this program of the comparative performance of the MHD models available at the CCMC against that of the Wang-Sheeley-Arge (WSA) model. An important goal of this effort is to provide a consistent validation to all available models. Clearly exposing the relative strengths and weaknesses of the different models will enable forecasters to craft more reliable ensemble forecasting strategies. Models of the ambient solar wind are developing rapidly as a result of improvements in data supply, numerical techniques, and computing resources. It is anticipated that in the next five to ten years, the MHD based models will supplant semi-empirical potential based models such as the WSA model, as the best available forecast models. We anticipate that this validation effort will track this evolution and so assist policy makers in gauging the value of past and future investment in modeling support.

High-quality weather data for grid integration studies

NASA Astrophysics Data System (ADS)

Draxl, C.

2016-12-01

As variable renewable power penetration levels increase in power systems worldwide, renewable integration studies are crucial to ensure continued economic and reliable operation of the power grid. In this talk we will shed light on requirements for grid integration studies as far as wind and solar energy are concerned. Because wind and solar plants are strongly impacted by weather, high-resolution and high-quality weather data are required to drive power system simulations. Future data sets will have to push limits of numerical weather prediction to yield these high-resolution data sets, and wind data will have to be time-synchronized with solar data. Current wind and solar integration data sets will be presented. The Wind Integration National Dataset (WIND) Toolkit is the largest and most complete grid integration data set publicly available to date. A meteorological data set, wind power production time series, and simulated forecasts created using the Weather Research and Forecasting Model run on a 2-km grid over the continental United States at a 5-min resolution is now publicly available for more than 126,000 land-based and offshore wind power production sites. The Solar Integration National Dataset (SIND) is available as time synchronized with the WIND Toolkit, and will allow for combined wind-solar grid integration studies. The National Solar Radiation Database (NSRDB) is a similar high temporal- and spatial resolution database of 18 years of solar resource data for North America and India. Grid integration studies are also carried out in various countries, which aim at increasing their wind and solar penetration through combined wind and solar integration data sets. We will present a multi-year effort to directly support India's 24x7 energy access goal through a suite of activities aimed at enabling large-scale deployment of clean energy and energy efficiency. Another current effort is the North-American-Renewable-Integration-Study, with the aim of providing a seamless data set across borders for a whole continent, to simulate and analyze the impacts of potential future large wind and solar power penetrations on bulk power system operations.

915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jensen, M.; Bartholomew, M. J.; Giangrande, S.

When considering the amount of shortwave radiation incident on a photovoltaic solar array and, therefore, the amount and stability of the energy output from the system, clouds represent the greatest source of short-term (i.e., scale of minutes to hours) variability through scattering and reflection of incoming solar radiation. Providing estimates of this short-term variability is important for determining and regulating the output from large solar arrays as they connect with the larger power infrastructure. In support of the installation of a 37-MW solar array on the grounds of Brookhaven National Laboratory (BNL), a study of the impacts of clouds onmore » the output of the solar array has been undertaken. The study emphasis is on predicting the change in surface solar radiation resulting from the observed/forecast cloud field on a 5-minute time scale. At these time scales, advection of cloud elements over the solar array is of particular importance. As part of the BNL Aerosol Life Cycle Intensive Operational Period (IOP), a 915-MHz Radar Wind Profiler (RWP) was deployed to determine the profile of low-level horizontal winds and the depth of the planetary boundary layer. The initial deployment mission of the 915-MHz RWP for cloud forecasting has been expanded the deployment to provide horizontal wind measurements for estimating and constraining cloud advection speeds. A secondary focus is on the observation of dynamics and microphysics of precipitation during cold season/winter storms on Long Island. In total, the profiler was deployed at BNL for 1 year from May 2011 through May 2012.« less

915-Mhz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jensen, M.; Bartholomew, M. J.; Giangrande, S.

When considering the amount of shortwave radiation incident on a photovoltaic solar array and, therefore, the amount and stability of the energy output from the system, clouds represent the greatest source of short-term (i.e., scale of minutes to hours) variability through scattering and reflection of incoming solar radiation. Providing estimates of this short-term variability is important for determining and regulating the output from large solar arrays as they connect with the larger power infrastructure. In support of the installation of a 37-MW solar array on the grounds of Brookhaven National Laboratory (BNL), a study of the impacts of clouds onmore » the output of the solar array has been undertaken. The study emphasis is on predicting the change in surface solar radiation resulting from the observed/forecast cloud field on a 5-minute time scale. At these time scales, advection of cloud elements over the solar array is of particular importance. As part of the BNL Aerosol Life Cycle Intensive Operational Period (IOP), a 915-MHz Radar Wind Profiler (RWP) was deployed to determine the profile of low-level horizontal winds and the depth of the planetary boundary layer. The initial deployment mission of the 915-MHz RWP for cloud forecasting has been expanded the deployment to provide horizontal wind measurements for estimating and constraining cloud advection speeds. A secondary focus is on the observation of dynamics and microphysics of precipitation during cold season/winter storms on Long Island. In total, the profiler was deployed at BNL for 1 year from May 2011 through May 2012.« less

Transitioning the Rice Realtime Forecast Models to DSCOVR

NASA Astrophysics Data System (ADS)

Bala, R.; Reiff, P. H.

2016-12-01

The Rice realtime forecast models of global magnetospheric indices Kp, Dst and AE have been actively running at mms.rice.edu/realtime/forecast.html for nearly a decade now. These neural network models were trained using the ACE archival solar wind data while the near-realtime forecasts are provided using instantaneous upwind solar wind data stream measured at the L1 point through ACE. Additionally, the webpage also provide status of the current space weather condition as an additional resource, updating every ten minutes. Furthermore, the subscribers of our space weather alert system, called `spacalrt', have been receiving email notices based on predefined thresholds. One of the gaps that is currently seen in the Rice neural network models lies in the density dependent models using variants of the solar wind pressure. The anomalous behavior in reporting densities in ACE has been a common issue for some time now. Often such behavior is observed when the solar energetic particle that are associated with solar flares or CMEs are Earth directed. Therefore, it is understood that the subsequent measures of the density reported by ACE will be either very low or, at a minimum, contaminated. Under these circumstances, the density-based Rice models typically underpredict. However, the newly launched DSCOVR satellite will help enhance our prediction models with high-quality data; it has real time space weather data available through the NOAA's Space Weather Prediction Center as of July, 2016. We are in the process of transitioning our forecast operations to include data from DSCOVR while running the original ACE data stream in parallel until it lasts. This paper will compare and contrast the forecasted values from the two satellites. Finally, we will discuss our efforts in providing the forecast products for the Rice space weather website that will be a part of the book on "Machine Learning Techniques for Space Weather" to be published by Elsiever.

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheung, WanYin; Zhang, Jie; Florita, Anthony

2015-12-08

Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance,more » cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.« less

Geomagnetic storm forecasting service StormFocus: 5 years online

NASA Astrophysics Data System (ADS)

Podladchikova, Tatiana; Petrukovich, Anatoly; Yermolaev, Yuri

2018-04-01

Forecasting geomagnetic storms is highly important for many space weather applications. In this study, we review performance of the geomagnetic storm forecasting service StormFocus during 2011-2016. The service was implemented in 2011 at SpaceWeather.Ru and predicts the expected strength of geomagnetic storms as measured by Dst index several hours ahead. The forecast is based on L1 solar wind and IMF measurements and is updated every hour. The solar maximum of cycle 24 is weak, so most of the statistics are on rather moderate storms. We verify quality of selection criteria, as well as reliability of real-time input data in comparison with the final values, available in archives. In real-time operation 87% of storms were correctly predicted while the reanalysis running on final OMNI data predicts successfully 97% of storms. Thus the main reasons for prediction errors are discrepancies between real-time and final data (Dst, solar wind and IMF) due to processing errors, specifics of datasets.

Big Data Analytics for Modelling and Forecasting of Geomagnetic Field Indices

NASA Astrophysics Data System (ADS)

Wei, H. L.

2016-12-01

A massive amount of data are produced and stored in research areas of space weather and space climate. However, the value of a vast majority of the data acquired every day may not be effectively or efficiently exploited in our daily practice when we try to forecast solar wind parameters and geomagnetic field indices using these recorded measurements or digital signals, probably due to the challenges stemming from the dealing with big data which are characterized by the 4V futures: volume (a massively large amount of data), variety (a great number of different types of data), velocity (a requirement of quick processing of the data), and veracity (the trustworthiness and usability of the data). In order to obtain more reliable and accurate predictive models for geomagnetic field indices, it requires that models should be developed from the big data analytics perspective (or it at least benefits from such a perspective). This study proposes a few data-based modelling frameworks which aim to produce more efficient predictive models for space weather parameters forecasting by means of system identification and big data analytics. More specifically, it aims to build more reliable mathematical models that characterise the relationship between solar wind parameters and geomagnetic filed indices, for example the dependent relationship of Dst and Kp indices on a few solar wind parameters and magnetic field indices, namely, solar wind velocity (V), southward interplanetary magnetic field (Bs), solar wind rectified electric field (VBs), and dynamic flow pressure (P). Examples are provided to illustrate how the proposed modelling approaches are applied to Dst and Kp index prediction.

Comparative analysis of GPS-derived TEC estimates and foF2 observations during storm conditions towards the expansion of ionospheric forecasting capabilities over Europe

NASA Astrophysics Data System (ADS)

Tsagouri, Ioanna; Belehaki, Anna; Elias, Panagiotis

2017-04-01

This paper builts the discussion on the comparative analysis of the variations in the peak electron density at F2 layer and the TEC parameter during a significant number of geomagnetic storm events that occurred in the present solar cycle 24. The ionospheric disturbances are determined through the comparison of actual observations of the foF2 critical frequency and GPS-TEC estimates obtained over European locations with the corresponding median estimates, and they are analysed in conjunction to the solar wind conditions at L1 point that are monitored by the ACE spacecraft. The quantification of the storm impact on the TEC parameter in terms of possible limitations introduced by different TEC derivation methods is carefully addressed.The results reveal similarities and differences in the response of the two parameters with respect to the solar wind drivers of the storms, as well as the local time and the latitude of the observation point. The aforementioned dependences drive the storm-time forecasts of the SWIF model (Solar Wind driven autorgressive model for Ionospheric short-term Forecast), which is operationally implemented in the DIAS system (http://dias.space.noa.gr) and extensively tested in performance at several occassions. In its present version, the model provides alerts and warnings for upcoming ionospheric disturbances, as well as single site and regional forecasts of the foF2 characteristic over Europe up to 24 hours ahead based on the assesment of the solar wind conditions at ACE location. In that respect, the results obtained above support the upgrade of the SWIF's modeling technique in forecasting the storm-time TEC variation within an operational environment several hours in advance. Preliminary results on the evaluation of the model's efficiency in TEC prediction are also discussed, giving special attention in the assesment of the capabilities through the TEC-derivation uncertanties for future discussions.

Assessment of Predictive Capabilities of L1 Orbiters using Realtime Solar Wind Data

NASA Astrophysics Data System (ADS)

Holmes, J.; Kasper, J. C.; Welling, D. T.

2017-12-01

Realtime measurements of solar wind conditions at L1 point allow us to predict geomagnetic activity at Earth up to an hour in advance. These predictions are quantified in the form of geomagnetic indices such as Kp and Ap, allowing for a concise, standardized prediction and measurement system. For years, the Space Weather Prediction Center used ACE realtime solar wind data to develop its one and four-hour Kp forecasts, but has in the past year switched to using DSCOVR data as its source. In this study, the performance of both orbiters in predicting Kp over the course of one month was assessed in an attempt to determine whether or not switching to DSCOVR data has resulted in improved forecasts. The period of study was chosen to encompass a time when the satellites were close to each other, and when moderate to high activity was observed. Kp predictions were made using the Geospace Model, part of the Space Weather Modeling Framework, to simulate conditions based on observed solar wind parameters. The performance of each satellite was assessed by comparing the model output to observed data.

Perturbed-input-data ensemble modeling of magnetospheric dynamics

NASA Astrophysics Data System (ADS)

Morley, S.; Steinberg, J. T.; Haiducek, J. D.; Welling, D. T.; Hassan, E.; Weaver, B. P.

2017-12-01

Many models of Earth's magnetospheric dynamics - including global magnetohydrodynamic models, reduced complexity models of substorms and empirical models - are driven by solar wind parameters. To provide consistent coverage of the upstream solar wind these measurements are generally taken near the first Lagrangian point (L1) and algorithmically propagated to the nose of Earth's bow shock. However, the plasma and magnetic field measured near L1 is a point measurement of an inhomogeneous medium, so the individual measurement may not be sufficiently representative of the broader region near L1. The measured plasma may not actually interact with the Earth, and the solar wind structure may evolve between L1 and the bow shock. To quantify uncertainties in simulations, as well as to provide probabilistic forecasts, it is desirable to use perturbed input ensembles of magnetospheric and space weather forecasting models. By using concurrent measurements of the solar wind near L1 and near the Earth, we construct a statistical model of the distributions of solar wind parameters conditioned on their upstream value. So that we can draw random variates from our model we specify the conditional probability distributions using Kernel Density Estimation. We demonstrate the utility of this approach using ensemble runs of selected models that can be used for space weather prediction.

High Quality Data for Grid Integration Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clifton, Andrew; Draxl, Caroline; Sengupta, Manajit

As variable renewable power penetration levels increase in power systems worldwide, renewable integration studies are crucial to ensure continued economic and reliable operation of the power grid. The existing electric grid infrastructure in the US in particular poses significant limitations on wind power expansion. In this presentation we will shed light on requirements for grid integration studies as far as wind and solar energy are concerned. Because wind and solar plants are strongly impacted by weather, high-resolution and high-quality weather data are required to drive power system simulations. Future data sets will have to push limits of numerical weather predictionmore » to yield these high-resolution data sets, and wind data will have to be time-synchronized with solar data. Current wind and solar integration data sets are presented. The Wind Integration National Dataset (WIND) Toolkit is the largest and most complete grid integration data set publicly available to date. A meteorological data set, wind power production time series, and simulated forecasts created using the Weather Research and Forecasting Model run on a 2-km grid over the continental United States at a 5-min resolution is now publicly available for more than 126,000 land-based and offshore wind power production sites. The National Solar Radiation Database (NSRDB) is a similar high temporal- and spatial resolution database of 18 years of solar resource data for North America and India. The need for high-resolution weather data pushes modeling towards finer scales and closer synchronization. We also present how we anticipate such datasets developing in the future, their benefits, and the challenges with using and disseminating such large amounts of data.« less

Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.

2010-01-01

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the loadmore » and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. Currently, uncertainties associated with wind and load forecasts, as well as uncertainties associated with random generator outages and unexpected disconnection of supply lines, are not taken into account in power grid operation. Thus, operators have little means to weigh the likelihood and magnitude of upcoming events of power imbalance. In this project, funded by the U.S. Department of Energy (DOE), a framework has been developed for incorporating uncertainties associated with wind and load forecast errors, unpredicted ramps, and forced generation disconnections into the energy management system (EMS) as well as generation dispatch and commitment applications. A new approach to evaluate the uncertainty ranges for the required generation performance envelope including balancing capacity, ramping capability, and ramp duration has been proposed. The approach includes three stages: forecast and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence levels. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis, incorporating all sources of uncertainties of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the “flying brick” technique has been developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation algorithm has been developed to validate the accuracy of the confidence intervals.« less

Coronal hole evolution from multi-viewpoint data as input for a STEREO solar wind speed persistence model

NASA Astrophysics Data System (ADS)

Temmer, Manuela; Hinterreiter, Jürgen; Reiss, Martin A.

2018-03-01

We present a concept study of a solar wind forecasting method for Earth, based on persistence modeling from STEREO in situ measurements combined with multi-viewpoint EUV observational data. By comparing the fractional areas of coronal holes (CHs) extracted from EUV data of STEREO and SoHO/SDO, we perform an uncertainty assessment derived from changes in the CHs and apply those changes to the predicted solar wind speed profile at 1 AU. We evaluate the method for the time period 2008-2012, and compare the results to a persistence model based on ACE in situ measurements and to the STEREO persistence model without implementing the information on CH evolution. Compared to an ACE based persistence model, the performance of the STEREO persistence model which takes into account the evolution of CHs, is able to increase the number of correctly predicted high-speed streams by about 12%, and to decrease the number of missed streams by about 23%, and the number of false alarms by about 19%. However, the added information on CH evolution is not able to deliver more accurate speed values for the forecast than using the STEREO persistence model without CH information which performs better than an ACE based persistence model. Investigating the CH evolution between STEREO and Earth view for varying separation angles over ˜25-140° East of Earth, we derive some relation between expanding CHs and increasing solar wind speed, but a less clear relation for decaying CHs and decreasing solar wind speed. This fact most likely prevents the method from making more precise forecasts. The obtained results support a future L5 mission and show the importance and valuable contribution using multi-viewpoint data.

Power Flow Simulations of a More Renewable California Grid Utilizing Wind and Solar Insolation Forecasting

NASA Astrophysics Data System (ADS)

Hart, E. K.; Jacobson, M. Z.; Dvorak, M. J.

2008-12-01

Time series power flow analyses of the California electricity grid are performed with extensive addition of intermittent renewable power. The study focuses on the effects of replacing non-renewable and imported (out-of-state) electricity with wind and solar power on the reliability of the transmission grid. Simulations are performed for specific days chosen throughout the year to capture seasonal fluctuations in load, wind, and insolation. Wind farm expansions and new wind farms are proposed based on regional wind resources and time-dependent wind power output is calculated using a meteorological model and the power curves of specific wind turbines. Solar power is incorporated both as centralized and distributed generation. Concentrating solar thermal plants are modeled using local insolation data and the efficiencies of pre-existing plants. Distributed generation from rooftop PV systems is included using regional insolation data, efficiencies of common PV systems, and census data. The additional power output of these technologies offsets power from large natural gas plants and is balanced for the purposes of load matching largely with hydroelectric power and by curtailment when necessary. A quantitative analysis of the effects of this significant shift in the electricity portfolio of the state of California on power availability and transmission line congestion, using a transmission load-flow model, is presented. A sensitivity analysis is also performed to determine the effects of forecasting errors in wind and insolation on load-matching and transmission line congestion.

Real-time Kp predictions from ACE real time solar wind

NASA Astrophysics Data System (ADS)

Detman, Thomas; Joselyn, Joann

1999-06-01

The Advanced Composition Explorer (ACE) spacecraft provides nearly continuous monitoring of solar wind plasma, magnetic fields, and energetic particles from the Sun-Earth L1 Lagrange point upstream of Earth in the solar wind. The Space Environment Center (SEC) in Boulder receives ACE telemetry from a group of international network of tracking stations. One-minute, and 1-hour averages of solar wind speed, density, temperature, and magnetic field components are posted on SEC's World Wide Web page within 3 to 5 minutes after they are measured. The ACE Real Time Solar Wind (RTSW) can be used to provide real-time warnings and short term forecasts of geomagnetic storms based on the (traditional) Kp index. Here, we use historical data to evaluate the performance of the first real-time Kp prediction algorithm to become operational.

Ionospheric ion temperature forecasting in multiples of 27 days

NASA Astrophysics Data System (ADS)

Sojka, Jan J.; Schunk, Robert W.; Nicolls, Michael J.

2014-03-01

The ionospheric variability found at auroral locations is usually assumed to be unpredictable. The magnetosphere, which drives this ionospheric variability via storms and substorms, is at best only qualitatively describable. In this study we demonstrate that over a 3 year period, ionospheric variability observed from Poker Flat, Alaska, has, in fact, a high degree of long-term predictability. The observations used in this study are (a) the solar wind high speed stream velocity measured by the NASA Advanced Composition Explorer satellite, used to define the corotating interaction region (CIR), and (b) the ion temperature at 300 km altitude measured by the National Science Foundation Poker Flat Incoherent Scatter Radar over Poker Flat, Alaska. After determining a seasonal and diurnal climatology for the ion temperature, we show that the residual ion temperature heating events occur synchronously with CIR-geospace interactions. Furthermore, we demonstrate examples of ion temperature forecasting at 27, 54, and 81 days. A rudimentary operational forecasting scenario is described for forecasting recurrence 27 days ahead for the CIR-generated geomagnetic storms. These forecasts apply specifically to satellite tracking operations (thermospheric drag) and emergency HF-radio communications (ionospheric modifications) in the polar regions. The forecast is based on present-day solar and solar wind observations that can be used to uniquely identify the coronal hole and its CIR. From this CIR epoch, a 27 day forecast is then made.

Turbulent Heating and Wave Pressure in Solar Wind Acceleration Modeling: New Insights to Empirical Forecasting of the Solar Wind

NASA Astrophysics Data System (ADS)

Woolsey, L. N.; Cranmer, S. R.

2013-12-01

The study of solar wind acceleration has made several important advances recently due to improvements in modeling techniques. Existing code and simulations test the competing theories for coronal heating, which include reconnection/loop-opening (RLO) models and wave/turbulence-driven (WTD) models. In order to compare and contrast the validity of these theories, we need flexible tools that predict the emergent solar wind properties from a wide range of coronal magnetic field structures such as coronal holes, pseudostreamers, and helmet streamers. ZEPHYR (Cranmer et al. 2007) is a one-dimensional magnetohydrodynamics code that includes Alfven wave generation and reflection and the resulting turbulent heating to accelerate solar wind in open flux tubes. We present the ZEPHYR output for a wide range of magnetic field geometries to show the effect of the magnetic field profiles on wind properties. We also investigate the competing acceleration mechanisms found in ZEPHYR to determine the relative importance of increased gas pressure from turbulent heating and the separate pressure source from the Alfven waves. To do so, we developed a code that will become publicly available for solar wind prediction. This code, TEMPEST, provides an outflow solution based on only one input: the magnetic field strength as a function of height above the photosphere. It uses correlations found in ZEPHYR between the magnetic field strength at the source surface and the temperature profile of the outflow solution to compute the wind speed profile based on the increased gas pressure from turbulent heating. With this initial solution, TEMPEST then adds in the Alfven wave pressure term to the modified Parker equation and iterates to find a stable solution for the wind speed. This code, therefore, can make predictions of the wind speeds that will be observed at 1 AU based on extrapolations from magnetogram data, providing a useful tool for empirical forecasting of the sol! ar wind.

Forecasting Global Horizontal Irradiance Using the LETKF and a Combination of Advected Satellite Images and Sparse Ground Sensors

NASA Astrophysics Data System (ADS)

Harty, T. M.; Lorenzo, A.; Holmgren, W.; Morzfeld, M.

2017-12-01

The irradiance incident on a solar panel is the main factor in determining the power output of that panel. For this reason, accurate global horizontal irradiance (GHI) estimates and forecasts are critical when determining the optimal location for a solar power plant, forecasting utility scale solar power production, or forecasting distributed, behind the meter rooftop solar power production. Satellite images provide a basis for producing the GHI estimates needed to undertake these objectives. The focus of this work is to combine satellite derived GHI estimates with ground sensor measurements and an advection model. The idea is to use accurate but sparsely distributed ground sensors to improve satellite derived GHI estimates which can cover large areas (the size of a city or a region of the United States). We use a Bayesian framework to perform the data assimilation, which enables us to produce irradiance forecasts and associated uncertainties which incorporate both satellite and ground sensor data. Within this framework, we utilize satellite images taken from the GOES-15 geostationary satellite (available every 15-30 minutes) as well as ground data taken from irradiance sensors and rooftop solar arrays (available every 5 minutes). The advection model, driven by wind forecasts from a numerical weather model, simulates cloud motion between measurements. We use the Local Ensemble Transform Kalman Filter (LETKF) to perform the data assimilation. We present preliminary results towards making such a system useful in an operational context. We explain how localization and inflation in the LETKF, perturbations of wind-fields, and random perturbations of the advection model, affect the accuracy of our estimates and forecasts. We present experiments showing the accuracy of our forecasted GHI over forecast-horizons of 15 mins to 1 hr. The limitations of our approach and future improvements are also discussed.

An improved empirical model of electron and ion fluxes at geosynchronous orbit based on upstream solar wind conditions

DOE PAGES

Denton, M. H.; Henderson, M. G.; Jordanova, V. K.; ...

2016-07-01

In this study, a new empirical model of the electron fluxes and ion fluxes at geosynchronous orbit (GEO) is introduced, based on observations by Los Alamos National Laboratory (LANL) satellites. The model provides flux predictions in the energy range ~1 eV to ~40 keV, as a function of local time, energy, and the strength of the solar wind electric field (the negative product of the solar wind speed and the z component of the magnetic field). Given appropriate upstream solar wind measurements, the model provides a forecast of the fluxes at GEO with a ~1 h lead time. Model predictionsmore » are tested against in-sample observations from LANL satellites and also against out-of-sample observations from the Compact Environmental Anomaly Sensor II detector on the AMC-12 satellite. The model does not reproduce all structure seen in the observations. However, for the intervals studied here (quiet and storm times) the normalized root-mean-square deviation < ~0.3. It is intended that the model will improve forecasting of the spacecraft environment at GEO and also provide improved boundary/input conditions for physical models of the magnetosphere.« less

The DSCOVR Solar Wind Mission and Future Space Weather Products

NASA Astrophysics Data System (ADS)

Cash, M. D.; Biesecker, D. A.; Reinard, A. A.

2012-12-01

The Deep Space Climate Observatory (DSCOVR) mission, scheduled for launch in mid-2014, will provide real-time solar wind thermal plasma and magnetic measurements to ensure continuous monitoring for space weather forecasting. DSCOVR will orbit L1 and will serve as a follow-on mission to NASA's Advanced Composition Explorer (ACE), which was launched in 1997. DSCOVR will have a total of six instruments, two of which will provide real-time data necessary for space weather forecasting: a Faraday cup to measure the proton and alpha components of the solar wind, and a triaxial fluxgate magnetometer to measure the magnetic field in three dimensions. Real-time data provided by DSCOVR will include Vx, Vy, Vz, n, T, Bx, By, and Bz. Such real-time L1 data is used in generating space weather applications and products that have been demonstrated to be highly accurate and provide actionable information for customers. We evaluate current space weather products driven by ACE and discuss future products under development for DSCOVR. New space weather products under consideration include: automated shock detection, more accurate L1 to Earth delay time, and prediction of rotations in solar wind Bz within magnetic clouds. Suggestions from the community on product ideas are welcome.

Solving the Meteorological Challenges of Creating a Sustainable Energy System (Invited)

NASA Astrophysics Data System (ADS)

Marquis, M.

2010-12-01

Global energy demand is projected to double from 13 TW at the start of this century to 28 TW by the middle of the century. This translates into obtaining 1000 MW (1 GW, the amount produced by an average nuclear or coal power plant) of new energy every single day for the next 40 years. The U.S. Department of Energy has conducted three feasibility studies in the last two years identifying the costs, challenges, impacts, and benefits of generating large portions of the nation’s electricity from wind and solar energy, in the new two decades. The 20% Wind by 2030 report found that the nation could meet one-fifth of its electricity demand from wind energy by 2030. The second report, the Eastern Wind Integration and Transmission Study, considered similar costs, challenges, and benefits, but considered 20% wind energy in the Eastern Interconnect only, with a target date of 2024. The third report, the Western Wind and Solar Integration Study, considered the operational impact of up to 35% penetration of wind, photovoltaics (PVs) and, concentrating solar power (CSP) on the power system operated by the WestConnect group, with a target date of 2017. All three studies concluded that it is technically feasible to obtain these high penetration levels of renewable energy, but that increases in the balancing area cooperation or coordination, increased utilization of transmission and building of transmission in some cases, and improved weather forecasts are needed. Current energy systems were designed for dispatchable fuels, such as coal, natural gas and nuclear energy. Fitting weather-driven renewable energy into today's energy system is like fitting a square peg into a round hole. If society chooses to meet a significant portion of new energy demand from weather-driven renewable energy, such as wind and solar energy, a number of obstacles must be overcome. Some of these obstacles are meteorological and climatological issues that are amenable to scientific research. For variable renewable energy sources to reach high penetration levels, electric system operators and utilities need better atmo¬spheric observations, models, and forecasts. Current numerical weather prediction models have not been optimized to help the nation use renewable energy. Improved meteorological observations (e.g., wind turbine hub-height wind speeds, surface direct and diffuse solar radiation), as well as observations through a deeper layer of the atmosphere for assimilation into NWP models, are needed. Particularly urgent is the need for improved forecasts of ramp events. Longer-term predictions of renewable resources, on the seasonal to decadal scale, are also needed. Improved understanding of the variability and co-variability of wind and solar energy, as well as their correlations with large-scale climate drivers, would assist decision-makers in long-term planning. This talk with discuss the feasibility and benefits of developing enhanced weather forecasts and climate information specific to the needs of a growing renewable energy infrastructure.

Case Studies of Forecasting Ionospheric Total Electron Content

NASA Astrophysics Data System (ADS)

Mannucci, A. J.; Meng, X.; Verkhoglyadova, O. P.; Tsurutani, B.; McGranaghan, R. M.

2017-12-01

We report on medium-range forecast-mode runs of ionosphere-thermosphere coupled models that calculate ionospheric total electron content (TEC), focusing on low-latitude daytime conditions. A medium-range forecast-mode run refers to simulations that are driven by inputs that can be predicted 2-3 days in advance, for example based on simulations of the solar wind. We will present results from a weak geomagnetic storm caused by a high-speed solar wind stream on June 29, 2012. Simulations based on the Global Ionosphere Thermosphere Model (GITM) and the Thermosphere Ionosphere Electrodynamic General Circulation Model (TIEGCM) significantly over-estimate TEC in certain low latitude daytime regions, compared to TEC maps based on observations. We will present the results from a more intense coronal mass ejection (CME) driven storm where the simulations are closer to observations. We compare high latitude data sets to model inputs, such as auroral boundary and convection patterns, to assess the degree to which poorly estimated high latitude drivers may be the largest cause of discrepancy between simulations and observations. Our results reveal many factors that can affect the accuracy of forecasts, including the fidelity of empirical models used to estimate high latitude precipitation patterns, or observation proxies for solar EUV spectra, such as the F10.7 index. Implications for forecasts with few-day lead times are discussed

Integrated Modeling of the Battlespace Environment

DTIC Science & Technology

2010-10-01

Office of Counsel.Code 1008.3 ADOR/Director NCST E. R. Franchi , 7000 Public Affairs (Unclassified/ Unlimited Only). Code 7030 4 Division, Code...ESMF: the Hakamada- Akasofu-Fry version 2 (HAFv2) solar wind model and the global assimilation of ionospheric mea- surements (GAIM1) forecast...ground-truth measurements for comparison with the solar wind predictions. Global Assimilation of Ionospheric Measurements The GAIMv2.3 effort

Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2015-09-01

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

A Real-time 3D Visualization of Global MHD Simulation for Space Weather Forecasting

NASA Astrophysics Data System (ADS)

Murata, K.; Matsuoka, D.; Kubo, T.; Shimazu, H.; Tanaka, T.; Fujita, S.; Watari, S.; Miyachi, H.; Yamamoto, K.; Kimura, E.; Ishikura, S.

2006-12-01

Recently, many satellites for communication networks and scientific observation are launched in the vicinity of the Earth (geo-space). The electromagnetic (EM) environments around the spacecraft are always influenced by the solar wind blowing from the Sun and induced electromagnetic fields. They occasionally cause various troubles or damages, such as electrification and interference, to the spacecraft. It is important to forecast the geo-space EM environment as well as the ground weather forecasting. Owing to the recent remarkable progresses of super-computer technologies, numerical simulations have become powerful research methods in the solar-terrestrial physics. For the necessity of space weather forecasting, NICT (National Institute of Information and Communications Technology) has developed a real-time global MHD simulation system of solar wind-magnetosphere-ionosphere couplings, which has been performed on a super-computer SX-6. The real-time solar wind parameters from the ACE spacecraft at every one minute are adopted as boundary conditions for the simulation. Simulation results (2-D plots) are updated every 1 minute on a NICT website. However, 3D visualization of simulation results is indispensable to forecast space weather more accurately. In the present study, we develop a real-time 3D webcite for the global MHD simulations. The 3-D visualization results of simulation results are updated every 20 minutes in the following three formats: (1)Streamlines of magnetic field lines, (2)Isosurface of temperature in the magnetosphere and (3)Isoline of conductivity and orthogonal plane of potential in the ionosphere. For the present study, we developed a 3-D viewer application working on Internet Explorer browser (ActiveX) is implemented, which was developed on the AVS/Express. Numerical data are saved in the HDF5 format data files every 1 minute. Users can easily search, retrieve and plot past simulation results (3D visualization data and numerical data) by using the STARS (Solar-terrestrial data Analysis and Reference System). The STARS is a data analysis system for satellite and ground-based observation data for solar-terrestrial physics.

Solar wind driven empirical forecast models of the time derivative of the ground magnetic field

NASA Astrophysics Data System (ADS)

Wintoft, Peter; Wik, Magnus; Viljanen, Ari

2015-03-01

Empirical models are developed to provide 10-30-min forecasts of the magnitude of the time derivative of local horizontal ground geomagnetic field (|dBh/dt|) over Europe. The models are driven by ACE solar wind data. A major part of the work has been devoted to the search and selection of datasets to support the model development. To simplify the problem, but at the same time capture sudden changes, 30-min maximum values of |dBh/dt| are forecast with a cadence of 1 min. Models are tested both with and without the use of ACE SWEPAM plasma data. It is shown that the models generally capture sudden increases in |dBh/dt| that are associated with sudden impulses (SI). The SI is the dominant disturbance source for geomagnetic latitudes below 50° N and with minor contribution from substorms. However, at occasions, large disturbances can be seen associated with geomagnetic pulsations. For higher latitudes longer lasting disturbances, associated with substorms, are generally also captured. It is also shown that the models using only solar wind magnetic field as input perform in most cases equally well as models with plasma data. The models have been verified using different approaches including the extremal dependence index which is suitable for rare events.

Solar wind structure out of the ecliptic plane over solar cycles

NASA Astrophysics Data System (ADS)

Sokol, J. M.; Bzowski, M.; Tokumaru, M.

2017-12-01

Sun constantly emits a stream of plasma known as solar wind. Ground-based observations of the solar wind speed through the interplanetary scintillations (IPS) of radio flux from distant point sources and in-situ measurements by Ulysses mission revealed that the solar wind flow has different characteristics depending on the latitude. This latitudinal structure evolves with the cycle of solar activity. The knowledge on the evolution of solar wind structure is important for understanding the interaction between the interstellar medium surrounding the Sun and the solar wind, which is responsible for creation of the heliosphere. The solar wind structure must be taken into account in interpretation of most of the observations of heliospheric energetic neutral atoms, interstellar neutral atoms, pickup ions, and heliospheric backscatter glow. The information on the solar wind structure is not any longer available from direct measurements after the termination of Ulysses mission and the only source of the solar wind out of the ecliptic plane is the IPS observations. However, the solar wind structure obtained from this method contains inevitable gaps in the time- and heliolatitude coverage. Sokół et al 2015 used the solar wind speed data out of the ecliptic plane retrieved from the IPS observations performed by Institute for Space-Earth Environmental Research (Nagoya University, Japan) and developed a methodology to construct a model of evolution of solar wind speed and density from 1985 to 2013 that fills the data gaps. In this paper we will present a refined model of the solar wind speed and density structure as a function of heliographic latitude updated by the most recent data from IPS observations. And we will discuss methods of extrapolation of the solar wind structure out of the ecliptic plane for the past solar cycles, when the data were not available, as well as forecasting for few years upward.

Origin of the Wang-Sheeley-Arge solar wind model

NASA Astrophysics Data System (ADS)

Sheeley, Neil R., Jr.

2017-03-01

A correlation between solar wind speed at Earth and the amount of magnetic field line expansion in the corona was verified in 1989 using 22 years of solar and interplanetary observations. We trace the evolution of this relationship from its birth 15 years earlier in the Skylab era to its current use as a space weather forecasting technique. This paper is the transcript of an invited talk at the joint session of the Historical Astronomy Division and the Solar Physics Division of the American Astronomical Society during its 224th meeting in Boston, MA, on 3 June 2014.

Assessing Space Weather Applications and Understanding: IMF Bz at L1

NASA Astrophysics Data System (ADS)

Riley, P.; Savani, N.; Mays, M. L.; Austin, H. J.

2017-12-01

The CCMC - International (CCMC-I) is designed as a self-organizing informal forum for facilitating novel global initiatives on space weather research, development, forecasting and education. Here we capitalize on CCMC'AGUs experience in providing highly utilized web-based services, leadership and trusted relationships with space weather model developers. One of the CCMC-I initiatives is the International Forum for Space Weather Capabilities Assessment. As part of this initiative, within the solar and heliosphere domain, we focus our community discussion on forecasting the magnetic structure of interplanetary CMEs and the ambient solar wind. During the International CCMC-LWS Working Meeting in April 2017 the group instigated open communication to agree upon a standardized process by which all current and future models can be compared under an unbiased test. In this poster, we present our initial findings how we expect different models will move forward with validating and forecasting the magnetic vectors of the solar wind at L1. We also present a new IMF Bz Score-board which will be used to assist in the transitioning of research models into more operational settings.

A new short-term forecasting model for the total electron content storm time disturbances

NASA Astrophysics Data System (ADS)

Tsagouri, Ioanna; Koutroumbas, Konstantinos; Elias, Panagiotis

2018-06-01

This paper aims to introduce a new model for the short-term forecast of the vertical Total Electron Content (vTEC). The basic idea of the proposed model lies on the concept of the Solar Wind driven autoregressive model for Ionospheric short-term Forecast (SWIF). In its original version, the model is operationally implemented in the DIAS system (http://dias.space.noa.gr) and provides alerts and warnings for upcoming ionospheric disturbances, as well as single site and regional forecasts of the foF2 critical frequency over Europe up to 24 h in advance. The forecasts are driven by the real time assessment of the solar wind conditions at ACE location. The comparative analysis of the variations in foF2 and vTEC during eleven geomagnetic storm events that occurred in the present solar cycle 24 reveals similarities but also differences in the storm-time response of the two characteristics with respect to the local time and the latitude of the observation point. Since the aforementioned dependences drive the storm-time forecasts of the SWIF model, the results obtained here support the upgrade of the SWIF's modeling technique in forecasting the storm-time vTEC variation from its onset to full development and recovery. According to the proposed approach, the vTEC storm-time response can be forecasted from 1 to 12-13 h before its onset, depending on the local time of the observation point at storm onset at L1. Preliminary results on the assessment of the performance of the proposed model and further considerations on its potential implementation in operational mode are also discussed.

Application of data assimilation to solar wind forecasting models

NASA Astrophysics Data System (ADS)

Innocenti, M.; Lapenta, G.; Vrsnak, B.; Temmer, M.; Veronig, A.; Bettarini, L.; Lee, E.; Markidis, S.; Skender, M.; Crespon, F.; Skandrani, C.; Soteria Space-Weather Forecast; Data Assimilation Team

2010-12-01

Data Assimilation through Kalman filtering [1,2] is a powerful statistical tool which allows to combine modeling and observations to increase the degree of knowledge of a given system. We apply this technique to the forecast of solar wind parameters (proton speed, proton temperature, absolute value of the magnetic field and proton density) at 1 AU, using the model described in [3] and ACE data as observations. The model, which relies on GOES 12 observations of the percentage of the meridional slice of the sun covered by coronal holes, grants 1-day and 6-hours in advance forecasts of the aforementioned quantities in quiet times (CMEs are not taken into account) during the declining phase of the solar cycle and is tailored for specific time intervals. We show that the application of data assimilation generally improves the quality of the forecasts during quiet times and, more notably, extends the periods of applicability of the model, which can now provide reliable forecasts also in presence of CMEs and for periods other than the ones it was designed for. Acknowledgement: The research leading to these results has received funding from the European Commission’s Seventh Framework Programme (FP7/2007-2013) under the grant agreement N. 218816 (SOTERIA project: http://www.soteria-space.eu). References: [1] R. Kalman, J. Basic Eng. 82, 35 (1960); [2] G. Welch and G. Bishop, Technical Report TR 95-041, University of North Carolina, Department of Computer Science (2001); [3] B. Vrsnak, M. Temmer, and A. Veronig, Solar Phys. 240, 315 (2007).

Deflection and Distortion of CME internal magnetic flux rope due to the interaction with a structured solar wind

NASA Astrophysics Data System (ADS)

Shiota, D.; Iju, T.; Hayashi, K.; Fujiki, K.; Tokumaru, M.; Kusano, K.

2016-12-01

CMEs are the most violent driver of geospace disturbances, and therefore their arrival to the Earth position is an important factor in space weather forecast. The dynamics of CME propagation is strongly affected by the interaction with background solar wind. To understand the interaction between a CME and background solar wind, we performed three-dimensional MHD simulations of the propagation of a CME with internal twisted magnetic flux rope into a structured bimodal solar wind. We compared three different cases in which an identical CME is launched into an identical bimodal solar wind but the launch dates of the CME are different. Each position relative to the boundary between slow and fast solar winds becomes almost in the slow wind stream region, almost in the fast wind stream region, or in vicinity of the boundary of the fast and slow solar wind stream (that grows to CIR). It is found that the CME is most distorted and deflected eastward in the case near the CIR, in contrast to the other two cases. The maximum strength of southward magnetic field at the Earth position is also highest in the case near CIR. The results are interpreted that the dynamic pressure gradient due to the back reaction from pushing the ahead slow wind stream and due to the collision behind fast wind stream hinders the expansion of the CME internal flux rope into the direction of the solar wind velocity gradient. As a result, the expansion into the direction to the velocity gradient is slightly enhanced and results in the enhanced deflection and distortion of the CME and its internal flux rope. These results support the pileup accident hypothesis proposed by Kataoka et al. (2015) to form unexpectedly geoeffective solar wind structure.

Towards more accurate wind and solar power prediction by improving NWP model physics

NASA Astrophysics Data System (ADS)

Steiner, Andrea; Köhler, Carmen; von Schumann, Jonas; Ritter, Bodo

2014-05-01

The growing importance and successive expansion of renewable energies raise new challenges for decision makers, economists, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the errors and provide an a priori estimate of remaining uncertainties associated with the large share of weather-dependent power sources. For this purpose it is essential to optimize NWP model forecasts with respect to those prognostic variables which are relevant for wind and solar power plants. An improved weather forecast serves as the basis for a sophisticated power forecasts. Consequently, a well-timed energy trading on the stock market, and electrical grid stability can be maintained. The German Weather Service (DWD) currently is involved with two projects concerning research in the field of renewable energy, namely ORKA*) and EWeLiNE**). Whereas the latter is in collaboration with the Fraunhofer Institute (IWES), the project ORKA is led by energy & meteo systems (emsys). Both cooperate with German transmission system operators. The goal of the projects is to improve wind and photovoltaic (PV) power forecasts by combining optimized NWP and enhanced power forecast models. In this context, the German Weather Service aims to improve its model system, including the ensemble forecasting system, by working on data assimilation, model physics and statistical post processing. This presentation is focused on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. First steps leading to improved physical parameterization schemes within the NWP-model are presented. Wind mast measurements reaching up to 200 m height above ground are used for the estimation of the (NWP) wind forecast error at heights relevant for wind energy plants. One particular problem is the daily cycle in wind speed. The transition from stable stratification during nighttime to well mixed conditions during the day presents a big challenge to NWP models. Fast decrease and successive increase in hub-height wind speed after sunrise, and the formation of nocturnal low level jets will be discussed. For PV, the life cycle of low stratus clouds and fog is crucial. Capturing these processes correctly depends on the accurate simulation of diffusion or vertical momentum transport and the interaction with other atmospheric and soil processes within the numerical weather model. Results from Single Column Model simulations and 3d case studies will be presented. Emphasis is placed on wind forecasts; however, some references to highlights concerning the PV-developments will also be given. *) ORKA: Optimierung von Ensembleprognosen regenerativer Einspeisung für den Kürzestfristbereich am Anwendungsbeispiel der Netzsicherheitsrechnungen **) EWeLiNE: Erstellung innovativer Wetter- und Leistungsprognosemodelle für die Netzintegration wetterabhängiger Energieträger, www.projekt-eweline.de

Short-term load and wind power forecasting using neural network-based prediction intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2014-02-01

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Statistical Similarities Between WSA-ENLIL+Cone Model and MAVEN in Situ Observations From November 2014 to March 2016

NASA Astrophysics Data System (ADS)

Lentz, C. L.; Baker, D. N.; Jaynes, A. N.; Dewey, R. M.; Lee, C. O.; Halekas, J. S.; Brain, D. A.

2018-02-01

Normal solar wind flows and intense solar transient events interact directly with the upper Martian atmosphere due to the absence of an intrinsic global planetary magnetic field. Since the launch of the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission, there are now new means to directly observe solar wind parameters at the planet's orbital location for limited time spans. Due to MAVEN's highly elliptical orbit, in situ measurements cannot be taken while MAVEN is inside Mars' magnetosheath. To model solar wind conditions during these atmospheric and magnetospheric passages, this research project utilized the solar wind forecasting capabilities of the WSA-ENLIL+Cone model. The model was used to simulate solar wind parameters that included magnetic field magnitude, plasma particle density, dynamic pressure, proton temperature, and velocity during a four Carrington rotation-long segment. An additional simulation that lasted 18 Carrington rotations was then conducted. The precision of each simulation was examined for intervals when MAVEN was in the upstream solar wind, that is, with no exospheric or magnetospheric phenomena altering in situ measurements. It was determined that generalized, extensive simulations have comparable prediction capabilities as shorter, more comprehensive simulations. Generally, this study aimed to quantify the loss of detail in long-term simulations and to determine if extended simulations can provide accurate, continuous upstream solar wind conditions when there is a lack of in situ measurements.

A Comparative Verification of Forecasts from Two Operational Solar Wind Models (Postprint)

DTIC Science & Technology

2012-02-08

much confidence to place on predicted parameters. Cost /benefit information is provided to administrators who decide to sustain or replace existing...magnetic field magnitude and three components of the magnetic field vector in the geocentric solar magnetospheric (GSM) coordinate system at each hour of

Electron Flux Models for Different Energies at Geostationary Orbit

NASA Technical Reports Server (NTRS)

Boynton, R. J.; Balikhin, M. A.; Sibeck, D. G.; Walker, S. N.; Billings, S. A.; Ganushkina, N.

2016-01-01

Forecast models were derived for energetic electrons at all energy ranges sampled by the third-generation Geostationary Operational Environmental Satellites (GOES). These models were based on Multi-Input Single-Output Nonlinear Autoregressive Moving Average with Exogenous inputs methodologies. The model inputs include the solar wind velocity, density and pressure, the fraction of time that the interplanetary magnetic field (IMF) was southward, the IMF contribution of a solar wind-magnetosphere coupling function proposed by Boynton et al. (2011b), and the Dst index. As such, this study has deduced five new 1 h resolution models for the low-energy electrons measured by GOES (30-50 keV, 50-100 keV, 100-200 keV, 200-350 keV, and 350-600 keV) and extended the existing >800 keV and >2 MeV Geostationary Earth Orbit electron fluxes models to forecast at a 1 h resolution. All of these models were shown to provide accurate forecasts, with prediction efficiencies ranging between 66.9% and 82.3%.

Wind Energy Management System Integration Project Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.

2010-09-01

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation) and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and windmore » forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. In order to improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively, by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. In this report, a new methodology to predict the uncertainty ranges for the required balancing capacity, ramping capability and ramp duration is presented. Uncertainties created by system load forecast errors, wind and solar forecast errors, generation forced outages are taken into account. The uncertainty ranges are evaluated for different confidence levels of having the actual generation requirements within the corresponding limits. The methodology helps to identify system balancing reserve requirement based on a desired system performance levels, identify system “breaking points”, where the generation system becomes unable to follow the generation requirement curve with the user-specified probability level, and determine the time remaining to these potential events. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (California ISO) real life data have shown the effectiveness of the proposed approach. A tool developed based on the new methodology described in this report will be integrated with the California ISO systems. Contractual work is currently in place to integrate the tool with the AREVA EMS system.« less

SMILE: A Novel and Global Way to Explore Solar-Terrestrial Relationships

NASA Astrophysics Data System (ADS)

Branduardi-Raymont, G.; Wang, C.; Sembay, S.; Dai, L.; Li, L.; Donovan, E.; Sun, T.; Kataria, D. O.; Eastwood, J. P.; Yang, H.; Read, A.; Whittaker, I. C.; Spanswick, E.; Sibeck, D. G.; Kuntz, K. D.; Escoubet, C. P.; Rebuffat, D.; Raab, W.; Zheng, J.

2016-12-01

SMILE (Solar wind Magnetosphere Ionosphere Link Explorer) aims to investigate the dynamic coupling of the solar wind with the Earth's magnetosphere in a novel and global manner, never tried so far. From a highly elliptical Earth orbit, SMILE will combine soft X-ray imaging of the Earth's magnetic boundaries and polar cusps with simultaneous UV imaging of the northern aurora, while self-sufficiently measuring solar wind/magnetosheath plasma and magnetic field conditions in situ. X-ray imaging of the dayside magnetosheath and cusps is an innovative technique arising from the discovery of solar wind charge exchange X-ray emission, first observed at comets, and subsequently found to occur in the vicinity of the Earth's magnetosphere. SMILE is a scientific precursor of space weather operational satellites which are expected to forecast the arrival and impact of solar storms on the terrestrial environment in the future. SMILE does not provide such forecasting capabilities, rather its measurements will inform the science underpinning our still limited understanding of space weather and of its fundamental drivers. For the first time we will be able to trace and link the processes of solar wind injection in the magnetosphere with those acting on the charged particles precipitating into the cusps and eventually the aurora. While the basic theory of magnetospheric circulation is well known and the microscale has been explored by many in situ measurements, the reality of how this complex interaction takes place on a global scale, and how it evolves, is still poorly understood. SMILE will answer scientific questions such as: What are the large-scale structure and fundamental modes of the dayside solar wind/magnetosphere interaction? What defines the substorm cycle? How do CME-driven storms arise and how do they relate to substorms? SMILE is a joint space mission between the European Space Ageny and the Chinese Academy of Sciences, due for launch at the end of 2021. This presentation will cover the science that will be delivered by SMILE and its impact on our understanding of the way the solar wind interacts with the Earth's environment. The presentation will provide an overview of SMILE's payload and mission development, and demonstrate the scientific potential of SMILE through simulations of the data that it will return.

Wind speed time series reconstruction using a hybrid neural genetic approach

NASA Astrophysics Data System (ADS)

Rodriguez, H.; Flores, J. J.; Puig, V.; Morales, L.; Guerra, A.; Calderon, F.

2017-11-01

Currently, electric energy is used in practically all modern human activities. Most of the energy produced came from fossil fuels, making irreversible damage to the environment. Lately, there has been an effort by nations to produce energy using clean methods, such as solar and wind energy, among others. Wind energy is one of the cleanest alternatives. However, the wind speed is not constant, making the planning and operation at electric power systems a difficult activity. Knowing in advance the amount of raw material (wind speed) used for energy production allows us to estimate the energy to be generated by the power plant, helping the maintenance planning, the operational management, optimal operational cost. For these reasons, the forecast of wind speed becomes a necessary task. The forecast process involves the use of past observations from the variable to forecast (wind speed). To measure wind speed, weather stations use devices called anemometers, but due to poor maintenance, connection error, or natural wear, they may present false or missing data. In this work, a hybrid methodology is proposed, and it uses a compact genetic algorithm with an artificial neural network to reconstruct wind speed time series. The proposed methodology reconstructs the time series using a ANN defined by a Compact Genetic Algorithm.

Implementation and Verification of the Chen Prediction Technique for Forecasting Large Nonrecurrent Storms*

NASA Astrophysics Data System (ADS)

Arge, C. N.; Chen, J.; Slinker, S.; Pizzo, V. J.

2000-05-01

The method of Chen et al. [1997, JGR, 101, 27499] is designed to accurately identify and predict the occurrence, duration, and strength of largegeomagnetic storms using real-time solar wind data. The method estimates the IMF and the geoeffectiveness of the solar wind upstream of a monitor and can provide warning times that range from a few hours to more than 10 hours. The model uses physical features of solar wind structures that cause large storms: long durations of southward interplanetary magnetic field. It is currently undergoing testing, improvement, and validation at NOAA/SEC in effort to transition it into a real-time space weather forecasting tool. The original version of the model has modified so that it now makes hourly (as opposed to daily) predictions and has been improved in effort to enhance both its predictive capability and reliability. In this paper, we report on the results of a 2-year historical verification study of the model using ACE real-time data. The prediction performances of the original and improved versions of the model are then compared. A real-time prediction web page has been developed and is on line at NOAA/SEC. *Work supported by ONR.

Hourly Wind Speed Interval Prediction in Arid Regions

NASA Astrophysics Data System (ADS)

Chaouch, M.; Ouarda, T.

2013-12-01

The long and extended warm and dry summers, the low rate of rain and humidity are the main factors that explain the increase of electricity consumption in hot arid regions. In such regions, the ventilating and air-conditioning installations, that are typically the most energy-intensive among energy consumption activities, are essential for securing healthy, safe and suitable indoor thermal conditions for building occupants and stored materials. The use of renewable energy resources such as solar and wind represents one of the most relevant solutions to overcome the increase of the electricity demand challenge. In the recent years, wind energy is gaining more importance among the researchers worldwide. Wind energy is intermittent in nature and hence the power system scheduling and dynamic control of wind turbine requires an estimate of wind energy. Accurate forecast of wind speed is a challenging task for the wind energy research field. In fact, due to the large variability of wind speed caused by the unpredictable and dynamic nature of the earth's atmosphere, there are many fluctuations in wind power production. This inherent variability of wind speed is the main cause of the uncertainty observed in wind power generation. Furthermore, producing wind power forecasts might be obtained indirectly by modeling the wind speed series and then transforming the forecasts through a power curve. Wind speed forecasting techniques have received substantial attention recently and several models have been developed. Basically two main approaches have been proposed in the literature: (1) physical models such as Numerical Weather Forecast and (2) statistical models such as Autoregressive integrated moving average (ARIMA) models, Neural Networks. While the initial focus in the literature has been on point forecasts, the need to quantify forecast uncertainty and communicate the risk of extreme ramp events has led to an interest in producing probabilistic forecasts. In short term context, probabilistic forecasts might be more relevant than point forecasts for the planner to build scenarios In this paper, we are interested in estimating predictive intervals of the hourly wind speed measures in few cities in United Arab emirates (UAE). More precisely, given a wind speed time series, our target is to forecast the wind speed at any specific hour during the day and provide in addition an interval with the coverage probability 0

Forecasting Geomagnetic Activity Using Kalman Filters

NASA Astrophysics Data System (ADS)

Veeramani, T.; Sharma, A.

2006-05-01

The coupling of energy from the solar wind to the magnetosphere leads to the geomagnetic activity in the form of storms and substorms and are characterized by indices such as AL, Dst and Kp. The geomagnetic activity has been predicted near-real time using local linear filter models of the system dynamics wherein the time series of the input solar wind and the output magnetospheric response were used to reconstruct the phase space of the system by a time-delay embedding technique. Recently, the radiation belt dynamics have been studied using a adaptive linear state space model [Rigler et al. 2004]. This was achieved by assuming a linear autoregressive equation for the underlying process and an adaptive identification of the model parameters using a Kalman filter approach. We use such a model for predicting the geomagnetic activity. In the case of substorms, the Bargatze et al [1985] data set yields persistence like behaviour when a time resolution of 2.5 minutes was used to test the model for the prediction of the AL index. Unlike the local linear filters, which are driven by the solar wind input without feedback from the observations, the Kalman filter makes use of the observations as and when available to optimally update the model parameters. The update procedure requires the prediction intervals to be long enough so that the forecasts can be used in practice. The time resolution of the data suitable for such forecasting is studied by taking averages over different durations.

An hybrid neuro-wavelet approach for long-term prediction of solar wind

NASA Astrophysics Data System (ADS)

Napoli, Christian; Bonanno, Francesco; Capizzi, Giacomo

2011-06-01

Nowadays the interest for space weather and solar wind forecasting is increasing to become a main relevance problem especially for telecommunication industry, military, and for scientific research. At present the goal for weather forecasting reach the ultimate high ground of the cosmos where the environment can affect the technological instrumentation. Some interests then rise about the correct prediction of space events, like ionized turbulence in the ionosphere or impacts from the energetic particles in the Van Allen belts, then of the intensity and features of the solar wind and magnetospheric response. The problem of data prediction can be faced using hybrid computation methods so as wavelet decomposition and recurrent neural networks (RNNs). Wavelet analysis was used in order to reduce the data redundancies so obtaining representation which can express their intrinsic structure. The main advantage of the wavelet use is the ability to pack the energy of a signal, and in turn the relevant carried informations, in few significant uncoupled coefficients. Neural networks (NNs) are a promising technique to exploit the complexity of non-linear data correlation. To obtain a correct prediction of solar wind an RNN was designed starting on the data series. As reported in literature, because of the temporal memory of the data an Adaptative Amplitude Real Time Recurrent Learning algorithm was used for a full connected RNN with temporal delays. The inputs for the RNN were given by the set of coefficients coming from the biorthogonal wavelet decomposition of the solar wind velocity time series. The experimental data were collected during the NASA mission WIND. It is a spin stabilized spacecraft launched in 1994 in a halo orbit around the L1 point. The data are provided by the SWE, a subsystem of the main craft designed to measure the flux of thermal protons and positive ions.

Using Heliospheric Imaging for Storm Forecasting - SMEI CME Observations as a Tool for Operational Forecasting at AFWA

NASA Astrophysics Data System (ADS)

Webb, D. F.; Johnston, J. C.; Fry, C. D.; Kuchar, T. A.

2008-12-01

Observations of coronal mass ejections (CMEs) from heliospheric imagers such as the Solar Mass Ejection Imager (SMEI) can lead to significant improvements in operational space weather forecasting. We are working with the Air Force Weather Agency (AFWA) to ingest SMEI all-sky imagery with appropriate tools to help forecasters improve their operational space weather forecasts. We describe two approaches: 1) Near- real time analysis of propagating CMEs from SMEI images alone combined with near-Sun observations of CME onsets and, 2) Using these calculations of speed as a mid-course correction to the HAFv2 solar wind model forecasts. HAFv2 became operational at AFWA in late 2006. The objective is to determine a set of practical procedures that the duty forecaster can use to update or correct a solar wind forecast using heliospheric imager data. SMEI observations can be used inclusively to make storm forecasts, as recently discussed in Webb et al. (Space Weather, in press, 2008). We have developed a point-and-click analysis tool for use with SMEI images and are working with AFWA to ensure that timely SMEI images are available for analyses. When a frontside solar eruption occurs, especially if within about 45 deg. of Sun center, a forecaster checks for an associated CME observed by a coronagraph within an appropriate time window. If found, especially if the CME is a halo type, the forecaster checks SMEI observations about a day later, depending on the apparent initial CME speed, for possibly associated CMEs. If one is found, then the leading edge is measured over several successive frames and an elongation-time plot constructed. A minimum of three data points, i.e., over 3-4 orbits or about 6 hours, are necessary for such a plot. Using the solar source location and onset time of the CME from, e.g., SOHO observations, and assuming radial propagation, a distance-time relation is calculated and extrapolated to the 1 AU distance. As shown by Webb et al., the storm onset time is then expected to be about 3 hours after this 1 AU arrival time (AT). The prediction program is updated as more SMEI data become available. Currently when an appropriate solar event occurs, AFWA routinely runs the HAFv2 model to make a forecast of the shock and ejecta arrival times at Earth. SMEI data can be used to improve this prediction. The HAFv2 model can produce synthetic sky maps of predicted CME brightness for comparison with SMEI images. The forecaster uses SMEI imagery to observe and track the CME. The forecaster then measures the CME location and speed using the SMEI imagery and the HAFv2 synthetic sky maps. After comparing the SMEI and HAFv2 results, the forecaster can adjust a key input to HAFv2, such as the initial speed of the disturbance at the Sun or the mid-course speed. The forecaster then iteratively runs HAFv2 until the observed and forecast sky maps match. The final HAFv2 solution becomes the new forecast. When the CME/shock arrives at (or does not reach) Earth, the forecaster verifies the forecast and updates the forecast skill statistics. Eventually, we plan to develop a more automated version of this procedure.

Comparing Temporally-Separated Solar Wind Structures at 1 AU (STEREO A and OMNI)

NASA Astrophysics Data System (ADS)

Galvin, A. B.; Farrugia, C. J.; Jian, L. K.

2017-12-01

One may use the longitudinal coverage of different spacecraft assets, or the same asset over sequential Carrington Rotations, to study the solar wind behavior from long-lived structures (coronal holes, active regions), or occasionally observe the extent of transient structures (Farrugia et al., 2011). This is of interest as the evolution of the extent and persistence of interplanetary coronal mass ejections (ICMEs) and of stream interaction regions (SIRs) have implications for space weather forecasting. One challenge is that one must be aware of the temporal evolution of the structure on the Sun and the affect of `sampling' different solar sources due to different solar latitudes of the in-situ spacecraft observations. Here we look at case studies of recent event time intervals during 2015-2017 where solar wind emanating from long-lived coronal-hole structures are observed both at STEREO A and at near-Earth assets (OMNI2). The observations are taken at similar solar latitudes and longitudes but temporally separated by several days or weeks.

Solar wind modulation of UK lightning

NASA Astrophysics Data System (ADS)

Davis, Chris; Harrison, Giles; Lockwood, Mike; Owens, Mathew; Barnard, Luke

2013-04-01

The response of lightning rates in the UK to arrival of high speed solar wind streams at Earth is investigated using a superposed epoch analysis. The fast solar wind streams' arrivals are determined from modulation of the solar wind Vy component, measured by the Advanced Composition Explorer (ACE) spacecraft. Lightning rate changes around these event times are then determined from the very low frequency Arrival Time Difference (ATD) system of the UK Met Office. Arrival of high speed streams at Earth is found to be preceded by a decrease in total solar irradiance and an increase in sunspot number and Mg II emissions. These are consistent with the high speed stream's source being co-located with an active region appearing on the Eastern solar limb and rotating at the 27 day rate of the Sun. Arrival of the high speed stream at Earth also coincides with a rapid decrease in cosmic ray flux and an increase in lightning rates over the UK, persisting for around 40 days. The lightning rate increase is corroborated by an increase in the total number of thunder days observed by UK Met stations, again for around 40 days after the arrival of a high speed solar wind stream. This increase in lightning may be beneficial to medium range forecasting of hazardous weather.

Sensor network based solar forecasting using a local vector autoregressive ridge framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, J.; Yoo, S.; Heiser, J.

2016-04-04

The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations duemore » to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.« less

Comparison between three algorithms for Dst predictions over the 2003 2005 period

NASA Astrophysics Data System (ADS)

Amata, E.; Pallocchia, G.; Consolini, G.; Marcucci, M. F.; Bertello, I.

2008-02-01

We compare, over a two and half years period, the performance of a recent artificial neural network (ANN) algorithm for the Dst prediction called EDDA [Pallocchia, G., Amata, E., Consolini, G., Marcucci, M.F., Bertello, I., 2006. Geomagnetic Dst index forecast based on IMF data only. Annales Geophysicae 24, 989-999], based on IMF inputs only, with the performance of the ANN Lundstedt et al. [2002. Operational forecasts of the geomagnetic Dst index. Geophysical Research Letters 29, 341] algorithm and the Wang et al. [2003. Influence of the solar wind dynamic pressure on the decay and injection of the ring current. Journal of Geophysical Research 108, 51] algorithm based on differential equations, which both make use of both IMF and plasma inputs. We show that: (1) all three algorithms perform similarly for "small" and "moderate" storms; (2) the EDDA and Wang algorithms perform similarly and considerably better than the Lundstedt et al. [2002. Operational forecasts of the geomagnetic Dst index. Geophysical Research Letters 29, 341] algorithm for "intense" and for "severe" storms; (3) the EDDA algorithm has the clear advantage, for space weather operational applications, that it makes use of IMF inputs only. The advantage lies in the fact that plasma data are at times less reliable and display data gaps more often than IMF measurements, especially during large solar disturbances, i.e. during periods when space weather forecast are most important. Some considerations are developed on the reasons why EDDA may forecast the Dst index without making use of solar wind density and velocity data.

Nonlinear dynamics of the magnetosphere and space weather

NASA Technical Reports Server (NTRS)

Sharma, A. Surjalal

1996-01-01

The solar wind-magnetosphere system exhibits coherence on the global scale and such behavior can arise from nonlinearity on the dynamics. The observational time series data were used together with phase space reconstruction techniques to analyze the magnetospheric dynamics. Analysis of the solar wind, auroral electrojet and Dst indices showed low dimensionality of the dynamics and accurate prediction can be made with an input/output model. The predictability of the magnetosphere in spite of the apparent complexity arises from its dynamical synchronism with the solar wind. The electrodynamic coupling between different regions of the magnetosphere yields its coherent, low dimensional behavior. The data from multiple satellites and ground stations can be used to develop a spatio-temporal model that identifies the coupling between different regions. These nonlinear dynamical models provide space weather forecasting capabilities.

A Space Weather Forecasting System with Multiple Satellites Based on a Self-Recognizing Network

PubMed Central

Tokumitsu, Masahiro; Ishida, Yoshiteru

2014-01-01

This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing. PMID:24803190

A space weather forecasting system with multiple satellites based on a self-recognizing network.

PubMed

Tokumitsu, Masahiro; Ishida, Yoshiteru

2014-05-05

This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.

Time-dependent MHD simulations of the solar wind outflow using interplanetary scintillation observations

DOE PAGES

Kim, Tae K.; Pogorelov, Nikolai V.; Borovikov, Sergey N.; ...

2012-11-20

Numerical modeling of the heliosphere is a critical component of space weather forecasting. The accuracy of heliospheric models can be improved by using realistic boundary conditions and confirming the results with in situ spacecraft measurements. To accurately reproduce the solar wind (SW) plasma flow near Earth, we need realistic, time-dependent boundary conditions at a fixed distance from the Sun. We may prepare such boundary conditions using SW speed and density determined from interplanetary scintillation (IPS) observations, magnetic field derived from photospheric magnetograms, and temperature estimated from its correlation with SW speed. In conclusion, we present here the time-dependent MHD simulationmore » results obtained by using the 2011 IPS data from the Solar-Terrestrial Environment Laboratory as time-varying inner boundary conditions and compare the simulated data at Earth with OMNI data (spacecraft-interspersed, near-Earth solar wind data).« less

How Reliable Is the Prediction of Solar Wind Background?

NASA Astrophysics Data System (ADS)

Jian, Lan K.; MacNeice, Peter; Taktakishvili, Aleksandre; Odstrcil, Dusan; Jackson, Bernard; Yu, Hsiu-Shan; Riley, Pete; Sokolov, Igor

2015-04-01

The prediction of solar wind background is a necessary part of space weather forecasting. Multiple coronal and heliospheric models have been installed at the Community Coordinated Modeling Center (CCMC) to produce the solar wind, including the Wang-Sheely-Arge (WSA)-Enlil model, MHD-Around-a-Sphere (MAS)-Enlil model, Space Weather Modeling Framework (SWMF), and heliospheric tomography using interplanetary scintillation (IPS) data. By comparing the modeling results with the OMNI data over 7 Carrington rotations in 2007, we have conducted a third-party validation of these models for the near-Earth solar wind. This work will help the models get ready for the transition from research to operation. Besides visual comparison, we have quantitatively assessed the models’ capabilities in reproducing the time series and statistics of solar wind parameters. Using improved algorithms, we have identified magnetic field sector boundaries (SBs) and slow-to-fast stream interaction regions (SIRs) as focused structures. The success rate of capturing them and the time offset vary largely with models. For this period, the 2014 version of MAS-Enlil model works best for SBs, and the heliospheric tomography works best for SIRs. General strengths and weaknesses for each model are identified to provide an unbiased reference to model developers and users.

NREL Integrate: RCS-4-42326

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hudgins, Andrew P.; Waight, Jim; Grover, Shailendra

OMNETRIC Corp., Duke Energy, CPS Energy, and the University of Texas at San Antonio (UTSA) created a project team to execute the project 'OpenFMB Reference Architecture Demonstration.' The project included development and demonstration of concepts that will enable the electric utility grid to host larger penetrations of renewable resources. The project concept calls for the aggregation of renewable resources and loads into microgrids and the control of these microgrids with an implementation of the OpenFMB Reference Architecture. The production of power from the renewable resources that are appearing on the grid today is very closely linked to the weather. Themore » difficulty of forecasting the weather, which is well understood, leads to difficulty in forecasting the production of renewable resources. The current state of the art in forecasting the power production from renewables (solar PV and wind) are accuracies in the range of 12-25 percent NMAE. In contrast the demand for electricity aggregated to the system level, is easier to predict. The state of the art of demand forecasting done, 24 hours ahead, is about 2-3% MAPE. Forecasting the load to be supplied from conventional resources (demand minus generation from renewable resources) is thus very hard to forecast. This means that even a few hours before the time of consumption, there can be considerable uncertainty over what must be done to balance supply and demand. Adding to the problem of difficulty of forecasting, is the reality of the variability of the actual production of power from renewables. Due to the variability of wind speeds and solar insolation, the actual output of power from renewable resources can vary significantly over a short period of time. Gusts of winds result is variation of power output of wind turbines. The shadows of clouds moving over solar PV arrays result in the variation of power production of the array. This compounds the problem of balancing supply and demand in real time. Establishing a control system that can manage distribution systems with large penetrations of renewable resources is difficult due to two major issues: (1) the lack of standardization and interoperability between the vast array of equipment in operation and on the market, most of which use different and proprietary means of communication and (2) the magnitude of the network and the information it generates and consumes. The objective of this project is to provide the industry with a design concept and tools that will enable the electric power grid to overcome these barriers and support a larger penetration of clean energy from renewable resources.« less

A Semi-Empirical Model for Forecasting Relativistic Electrons at Geostationary Orbit

NASA Technical Reports Server (NTRS)

Lyatsky, Wladislaw; Khazanov, George V.

2008-01-01

We developed a new prediction model for forecasting relativistic (>2MeV) electrons, which provides a VERY HIGH correlation between predicted and actually measured electron fluxes at geostationary orbit. This model implies the multi-step particle acceleration and is based on numerical integrating two linked continuity equations for primarily accelerated particles and relativistic electrons. The model includes a source and losses, and used solar wind data as only input parameters. We used the coupling function which is a best-fit combination of solar wind/Interplanetary Magnetic Field parameters, responsible for the generation of geomagnetic activity, as a source. The loss function was derived from experimental data. We tested the model for four year period 2004-2007. The correlation coefficient between predicted and actual values of the electron fluxes for whole four year period as well as for each of these years is about 0.9. The high and stable correlation between the computed and actual electron fluxes shows that the reliable forecasting these electrons at geostationary orbit is possible. The correlation coefficient between predicted and actual electron fluxes is stable and incredibly high.

Spring-fall asymmetry of substorm strength, geomagnetic activity and solar wind: Implications for semiannual variation and solar hemispheric asymmetry

USGS Publications Warehouse

Marsula, K.; Tanskanen, E.; Love, J.J.

2011-01-01

We study the seasonal variation of substorms, geomagnetic activity and their solar wind drivers in 1993–2008. The number of substorms and substorm mean duration depict an annual variation with maxima in Winter and Summer, respectively, reflecting the annual change of the local ionosphere. In contradiction, substorm mean amplitude, substorm total efficiency and global geomagnetic activity show a dominant annual variation, with equinoctial maxima alternating between Spring in solar cycle 22 and Fall in cycle 23. The largest annual variations were found in 1994 and 2003, in the declining phase of the two cycles when high-speed streams dominate the solar wind. A similar, large annual variation is found in the solar wind driver of substorms and geomagnetic activity, which implies that the annual variation of substorm strength, substorm efficiency and geomagnetic activity is not due to ionospheric conditions but to a hemispherically asymmetric distribution of solar wind which varies from one cycle to another. Our results imply that the overall semiannual variation in global geomagnetic activity has been seriously overestimated, and is largely an artifact of the dominant annual variation with maxima alternating between Spring and Fall. The results also suggest an intimate connection between the asymmetry of solar magnetic fields and some of the largest geomagnetic disturbances, offering interesting new pathways for forecasting disturbances with a longer lead time to the future.

Origin and Ion Charge State Evolution of Solar Wind Transients 4 - 7 August 2011

NASA Astrophysics Data System (ADS)

Rodkin, Denis; Goryaev, Farid; Pagano, Paolo; Gibb, Gordon; Slemzin, Vladimir; Shugay, Yulia; Veselovsky, Igor; Mackay, Duncan

2017-04-01

Identification of transients and their origins on the Sun is one of the most important problems of the space weather forecasting. In our work, we present a case study of the complex event consisting of several solar wind transients detected by ACE on 4 - 7 August 2011, that caused a geomagnetic storm with Dst= - 110 nT. The supposed coronal sources - three flares and coronal mass ejections (CMEs) occurred on 2 - 4 August 2011 in the active region AR 11261. To investigate the solar origins and formation of these transients, we studied kinematic and thermodynamic properties of expanding coronal structures using the SDO/AIA EUV images and the differential emission measure (DEM) diagnostics. The Helioseismic and Magnetic Imager (HMI) magnetic field maps were used as the input data for the 3D numerical model to describe the flux rope ejection. We characterize the early phase of the flux rope ejection in the corona, where the usual three-component CME structure formed. The flux rope ejected with the speed about 200 km/s to the height of 0.25 Rsun. The kinematics of the modeled CME front well agrees with the STEREO EUV measurements. Using the results of the plasma diagnostics and MHD modeling, we calculated the ion charge ratios of carbon and oxygen as well as the mean charge state of iron ions of the 2 August 2011 CME taking into account the processes of heating, cooling, expansion, ionization and recombination of the moving plasma in the corona up to the freeze-in region. We estimated a probable heating rate of the CME plasma in the low corona by matching the calculated ion composition parameters of the CME with that measured in-situ parameters of the solar wind transients. We also consider the similarities and discrepancies between the results of the MHD simulation and the observation of the event. Our results show that analysis of the ion composition of CMEs enables to disclose a relationship between parameters of the solar wind transients and properties of their solar origins, which opens new possibilities to validate and improve the solar wind forecasting models.

Relationship between coronal holes and high speed streams at L1: arrival times, durations, and intensities

NASA Astrophysics Data System (ADS)

Luo, B.; Bu, X.; Liu, S.; Gong, J.

2017-12-01

Coronal holes are sources of high-speed steams (HSS) of solar wind. When coronal holes appear at mid/low latitudes on the Sun, consequential HSSs may impact Earth and cause recurrent geospace environment disturbances, such as geomagnetic storms, relativistic electron enhancements at the geosynchronous orbit, and thermosphere density enhancements. Thus, it is of interests for space weather forecasters to predict when (arrival times), how long (time durations), and how severe (intensities) HSSs may impact Earth when they notice coronal holes on the sun and are anticipating their geoeffectiveness. In this study, relationship between coronal holes and high speed streams will be statistically investigated. Several coronal hole parameters, including passage times of solar central meridian, coronal hole longitudinal widths, intensities reflected by mean brightness, are derived using Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images for years 2011 to 2016. These parameters will be correlated with in-situ solar wind measurements measured at the L1 point by the ACE spacecraft, which can give some results that are useful for space weather forecaster in predicting the arrival times, durations, and intensities of coronal hole high-speed streams in about 3 days advance.

On improvement to the Shock Propagation Model (SPM) applied to interplanetary shock transit time forecasting

NASA Astrophysics Data System (ADS)

Li, H. J.; Wei, F. S.; Feng, X. S.; Xie, Y. Q.

2008-09-01

This paper investigates methods to improve the predictions of Shock Arrival Time (SAT) of the original Shock Propagation Model (SPM). According to the classical blast wave theory adopted in the SPM, the shock propagating speed is determined by the total energy of the original explosion together with the background solar wind speed. Noting that there exists an intrinsic limit to the transit times computed by the SPM predictions for a specified ambient solar wind, we present a statistical analysis on the forecasting capability of the SPM using this intrinsic property. Two facts about SPM are found: (1) the error in shock energy estimation is not the only cause of the prediction errors and we should not expect that the accuracy of SPM to be improved drastically by an exact shock energy input; and (2) there are systematic differences in prediction results both for the strong shocks propagating into a slow ambient solar wind and for the weak shocks into a fast medium. Statistical analyses indicate the physical details of shock propagation and thus clearly point out directions of the future improvement of the SPM. A simple modification is presented here, which shows that there is room for improvement of SPM and thus that the original SPM is worthy of further development.

Near-term Forecasting of Solar Total and Direct Irradiance for Solar Energy Applications

NASA Astrophysics Data System (ADS)

Long, C. N.; Riihimaki, L. D.; Berg, L. K.

2012-12-01

Integration of solar renewable energy into the power grid, like wind energy, is hindered by the variable nature of the solar resource. One challenge of the integration problem for shorter time periods is the phenomenon of "ramping events" where the electrical output of the solar power system increases or decreases significantly and rapidly over periods of minutes or less. Advance warning, of even just a few minutes, allows power system operators to compensate for the ramping. However, the ability for short-term prediction on such local "point" scales is beyond the abilities of typical model-based weather forecasting. Use of surface-based solar radiation measurements has been recognized as a likely solution for providing input for near-term (5 to 30 minute) forecasts of solar energy availability and variability. However, it must be noted that while fixed-orientation photovoltaic panel systems use the total (global) downwelling solar radiation, tracking photovoltaic and solar concentrator systems use only the direct normal component of the solar radiation. Thus even accurate near-term forecasts of total solar radiation will under many circumstances include inherent inaccuracies with respect to tracking systems due to lack of information of the direct component of the solar radiation. We will present examples and statistical analyses of solar radiation partitioning showing the differences in the behavior of the total/direct radiation with respect to the near-term forecast issue. We will present an overview of the possibility of using a network of unique new commercially available total/diffuse radiometers in conjunction with a near-real-time adaptation of the Shortwave Radiative Flux Analysis methodology (Long and Ackerman, 2000; Long et al., 2006). The results are used, in conjunction with persistence and tendency forecast techniques, to provide more accurate near-term forecasts of cloudiness, and both total and direct normal solar irradiance availability and variability. This new system could be a long term economical solution for solar energy applications.xample of SW Flux Analysis global hemispheric (light blue) and direct (yellow) clear-sky shortwave (SW) along with corresponding actual global hemispheric (blue) and direct (red) SW, and the corresponding fractional sky cover (black, right Y-axis). Note in afternoon about 40-50% of the global SW is available, yet most times there is no direct SW.

Operational Numerical Weather Prediction at the Met Office and potential ways forward for operational space weather prediction systems

NASA Astrophysics Data System (ADS)

Jackson, David

NICT (National Institute of Information and Communications Technology) has been in charge of space weather forecast service in Japan for more than 20 years. The main target region of the space weather is the geo-space in the vicinity of the Earth where human activities are dominant. In the geo-space, serious damages of satellites, international space stations and astronauts take place caused by energetic particles or electromagnetic disturbances: the origin of the causes is dynamically changing of solar activities. Positioning systems via GPS satellites are also im-portant recently. Since the most significant effect of positioning error comes from disturbances of the ionosphere, it is crucial to estimate time-dependent modulation of the electron density profiles in the ionosphere. NICT is one of the 13 members of the ISES (International Space Environment Service), which is an international assembly of space weather forecast centers under the UNESCO. With help of geo-space environment data exchanging among the member nations, NICT operates daily space weather forecast service every day to provide informa-tion on forecasts of solar flare, geomagnetic disturbances, solar proton event, and radio-wave propagation conditions in the ionosphere. The space weather forecast at NICT is conducted based on the three methodologies: observations, simulations and informatics (OSI model). For real-time or quasi real-time reporting of space weather, we conduct our original observations: Hiraiso solar observatory to monitor the solar activity (solar flare, coronal mass ejection, and so on), domestic ionosonde network, magnetometer HF radar observations in far-east Siberia, and south-east Asia low-latitude ionosonde network (SEALION). Real-time observation data to monitor solar and solar-wind activities are obtained through antennae at NICT from ACE and STEREO satellites. We have a middle-class super-computer (NEC SX-8R) to maintain real-time computer simulations for solar and solar-wind, magnetosphere and ionosphere. The three simulations are directly or indirectly connected each other based on real-time observa-tion data to reproduce a virtual geo-space region on the super-computer. Informatics is a new methodology to make precise forecast of space weather. Based on new information and communication technologies (ICT), it provides more information in both quality and quantity. At NICT, we have been developing a cloud-computing system named "space weather cloud" based on a high-speed network system (JGN2+). Huge-scale distributed storage (1PB), clus-ter computers, visualization systems and other resources are expected to derive new findings and services of space weather forecasting. The final goal of NICT space weather service is to predict near-future space weather conditions and disturbances which will be causes of satellite malfunctions, tele-communication problems, and error of GPS navigations. In the present talk, we introduce our recent activities on the space weather services and discuss how we are going to develop the services from the view points of space science and practical uses.

Activities of NICT space weather project

NASA Astrophysics Data System (ADS)

Murata, Ken T.; Nagatsuma, Tsutomu; Watari, Shinichi; Shinagawa, Hiroyuki; Ishii, Mamoru

NICT (National Institute of Information and Communications Technology) has been in charge of space weather forecast service in Japan for more than 20 years. The main target region of the space weather is the geo-space in the vicinity of the Earth where human activities are dominant. In the geo-space, serious damages of satellites, international space stations and astronauts take place caused by energetic particles or electromagnetic disturbances: the origin of the causes is dynamically changing of solar activities. Positioning systems via GPS satellites are also im-portant recently. Since the most significant effect of positioning error comes from disturbances of the ionosphere, it is crucial to estimate time-dependent modulation of the electron density profiles in the ionosphere. NICT is one of the 13 members of the ISES (International Space Environment Service), which is an international assembly of space weather forecast centers under the UNESCO. With help of geo-space environment data exchanging among the member nations, NICT operates daily space weather forecast service every day to provide informa-tion on forecasts of solar flare, geomagnetic disturbances, solar proton event, and radio-wave propagation conditions in the ionosphere. The space weather forecast at NICT is conducted based on the three methodologies: observations, simulations and informatics (OSI model). For real-time or quasi real-time reporting of space weather, we conduct our original observations: Hiraiso solar observatory to monitor the solar activity (solar flare, coronal mass ejection, and so on), domestic ionosonde network, magnetometer HF radar observations in far-east Siberia, and south-east Asia low-latitude ionosonde network (SEALION). Real-time observation data to monitor solar and solar-wind activities are obtained through antennae at NICT from ACE and STEREO satellites. We have a middle-class super-computer (NEC SX-8R) to maintain real-time computer simulations for solar and solar-wind, magnetosphere and ionosphere. The three simulations are directly or indirectly connected each other based on real-time observa-tion data to reproduce a virtual geo-space region on the super-computer. Informatics is a new methodology to make precise forecast of space weather. Based on new information and communication technologies (ICT), it provides more information in both quality and quantity. At NICT, we have been developing a cloud-computing system named "space weather cloud" based on a high-speed network system (JGN2+). Huge-scale distributed storage (1PB), clus-ter computers, visualization systems and other resources are expected to derive new findings and services of space weather forecasting. The final goal of NICT space weather service is to predict near-future space weather conditions and disturbances which will be causes of satellite malfunctions, tele-communication problems, and error of GPS navigations. In the present talk, we introduce our recent activities on the space weather services and discuss how we are going to develop the services from the view points of space science and practical uses.

Neural net forecasting for geomagnetic activity

NASA Technical Reports Server (NTRS)

Hernandez, J. V.; Tajima, T.; Horton, W.

1993-01-01

We use neural nets to construct nonlinear models to forecast the AL index given solar wind and interplanetary magnetic field (IMF) data. We follow two approaches: (1) the state space reconstruction approach, which is a nonlinear generalization of autoregressive-moving average models (ARMA) and (2) the nonlinear filter approach, which reduces to a moving average model (MA) in the linear limit. The database used here is that of Bargatze et al. (1985).

A GEOS-Based OSSE for the "MISTiC Winds" Concept

NASA Technical Reports Server (NTRS)

McCarty, W.; Blaisdell, J.; Fuentes, M.; Carvalho, D.; Errico, R.; Gelaro, R.; Kouvaris, L.; Moradi, I.; Pawson, S.; Prive, N.;

What If We Had A Magnetograph at Lagrangian L5?

NASA Technical Reports Server (NTRS)

Pevtsov, Alexei A.; Bertello, Luca; MacNeice, Peter; Petrie, Gordon

2016-01-01

Synoptic Carrington charts of magnetic field are routinely used as an input for modelings of solar wind and other aspects of space weather forecast. However, these maps are constructed using only the observations from the solar hemisphere facing Earth. The evolution of magnetic flux on the "farside" of the Sun, which may affect the topology of coronal field in the "nearside," is largely ignored. It is commonly accepted that placing a magnetograph in Lagrangian L5 point would improve the space weather forecast. However, the quantitative estimates of anticipated improvements have been lacking. We use longitudinal magnetograms from the Synoptic Optical Long-term Investigations of the Sun (SOLIS) to investigate how adding data from L5 point would affect the outcome of two major models used in space weather forecast.

Towards a More Accurate Solar Power Forecast By Improving NWP Model Physics

NASA Astrophysics Data System (ADS)

Köhler, C.; Lee, D.; Steiner, A.; Ritter, B.

2014-12-01

The growing importance and successive expansion of renewable energies raise new challenges for decision makers, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the uncertainties associated with the large share of weather-dependent power sources. Precise power forecast, well-timed energy trading on the stock market, and electrical grid stability can be maintained. The research project EWeLiNE is a collaboration of the German Weather Service (DWD), the Fraunhofer Institute (IWES) and three German transmission system operators (TSOs). Together, wind and photovoltaic (PV) power forecasts shall be improved by combining optimized NWP and enhanced power forecast models. The conducted work focuses on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. Not only the representation of the model cloud characteristics, but also special events like Sahara dust over Germany and the solar eclipse in 2015 are treated and their effect on solar power accounted for. An overview of the EWeLiNE project and results of the ongoing research will be presented.

Spring-fall asymmetry of substorm strength, geomagnetic activity and solar wind: Implications for semiannual variation and solar hemispheric asymmetry

USGS Publications Warehouse

Mursula, K.; Tanskanen, E.; Love, J.J.

2011-01-01

We study the seasonal variation of substorms, geomagnetic activity and their solar wind drivers in 1993-2008. The number of substorms and substorm mean duration depict an annual variation with maxima in Winter and Summer, respectively, reflecting the annual change of the local ionosphere. In contradiction, substorm mean amplitude, substorm total efficiency and global geomagnetic activity show a dominant annual variation, with equinoctial maxima alternating between Spring in solar cycle 22 and Fall in cycle 23. The largest annual variations were found in 1994 and 2003, in the declining phase of the two cycles when high-speed streams dominate the solar wind. A similar, large annual variation is found in the solar wind driver of substorms and geomagnetic activity, which implies that the annual variation of substorm strength, substorm efficiency and geomagnetic activity is not due to ionospheric conditions but to a hemispherically asymmetric distribution of solar wind which varies from one cycle to another. Our results imply that the overall semiannual variation in global geomagnetic activity has been seriously overestimated, and is largely an artifact of the dominant annual variation with maxima alternating between Spring and Fall. The results also suggest an intimate connection between the asymmetry of solar magnetic fields and some of the largest geomagnetic disturbances, offering interesting new pathways for forecasting disturbances with a longer lead time to the future. Copyright ?? 2011 by the American Geophysical Union.

Validation for Global Solar Wind Prediction Using Ulysses Comparison: Multiple Coronal and Heliospheric Models Installed at the Community Coordinated Modeling Center

NASA Technical Reports Server (NTRS)

Jian, L. K.; MacNeice, P. J.; Mays, M. L.; Taktakishvili, A.; Odstrcil, D.; Jackson, B.; Yu, H.-S.; Riley, P.; Sokolov, I. V.

2016-01-01

The prediction of the background global solar wind is a necessary part of space weather forecasting. Several coronal and heliospheric models have been installed and/or recently upgraded at the Community Coordinated Modeling Center (CCMC), including the Wang-Sheely-Arge (WSA)-Enlil model, MHD-Around-a-Sphere (MAS)-Enlil model, Space Weather Modeling Framework (SWMF), and Heliospheric tomography using interplanetary scintillation data. Ulysses recorded the last fast latitudinal scan from southern to northern poles in 2007. By comparing the modeling results with Ulysses observations over seven Carrington rotations, we have extended our third-party validation from the previous near-Earth solar wind to middle to high latitudes, in the same late declining phase of solar cycle 23. Besides visual comparison, wehave quantitatively assessed the models capabilities in reproducing the time series, statistics, and latitudinal variations of solar wind parameters for a specific range of model parameter settings, inputs, and grid configurations available at CCMC. The WSA-Enlil model results vary with three different magnetogram inputs.The MAS-Enlil model captures the solar wind parameters well, despite its underestimation of the speed at middle to high latitudes. The new version of SWMF misses many solar wind variations probably because it uses lower grid resolution than other models. The interplanetary scintillation-tomography cannot capture the latitudinal variations of solar wind well yet. Because the model performance varies with parameter settings which are optimized for different epochs or flow states, the performance metric study provided here can serve as a template that researchers can use to validate the models for the time periods and conditions of interest to them.

Validation for global solar wind prediction using Ulysses comparison: Multiple coronal and heliospheric models installed at the Community Coordinated Modeling Center

NASA Astrophysics Data System (ADS)

Jian, L. K.; MacNeice, P. J.; Mays, M. L.; Taktakishvili, A.; Odstrcil, D.; Jackson, B.; Yu, H.-S.; Riley, P.; Sokolov, I. V.

2016-08-01

The prediction of the background global solar wind is a necessary part of space weather forecasting. Several coronal and heliospheric models have been installed and/or recently upgraded at the Community Coordinated Modeling Center (CCMC), including the Wang-Sheely-Arge (WSA)-Enlil model, MHD-Around-a-Sphere (MAS)-Enlil model, Space Weather Modeling Framework (SWMF), and heliospheric tomography using interplanetary scintillation data. Ulysses recorded the last fast latitudinal scan from southern to northern poles in 2007. By comparing the modeling results with Ulysses observations over seven Carrington rotations, we have extended our third-party validation from the previous near-Earth solar wind to middle to high latitudes, in the same late declining phase of solar cycle 23. Besides visual comparison, we have quantitatively assessed the models' capabilities in reproducing the time series, statistics, and latitudinal variations of solar wind parameters for a specific range of model parameter settings, inputs, and grid configurations available at CCMC. The WSA-Enlil model results vary with three different magnetogram inputs. The MAS-Enlil model captures the solar wind parameters well, despite its underestimation of the speed at middle to high latitudes. The new version of SWMF misses many solar wind variations probably because it uses lower grid resolution than other models. The interplanetary scintillation-tomography cannot capture the latitudinal variations of solar wind well yet. Because the model performance varies with parameter settings which are optimized for different epochs or flow states, the performance metric study provided here can serve as a template that researchers can use to validate the models for the time periods and conditions of interest to them.

Preconditioning of Interplanetary Space Due to Transient CME Disturbances

DOE Office of Scientific and Technical Information (OSTI.GOV)

Temmer, M.; Reiss, M. A.; Hofmeister, S. J.

Interplanetary space is characteristically structured mainly by high-speed solar wind streams emanating from coronal holes and transient disturbances such as coronal mass ejections (CMEs). While high-speed solar wind streams pose a continuous outflow, CMEs abruptly disrupt the rather steady structure, causing large deviations from the quiet solar wind conditions. For the first time, we give a quantification of the duration of disturbed conditions (preconditioning) for interplanetary space caused by CMEs. To this aim, we investigate the plasma speed component of the solar wind and the impact of in situ detected interplanetary CMEs (ICMEs), compared to different background solar wind modelsmore » (ESWF, WSA, persistence model) for the time range 2011–2015. We quantify in terms of standard error measures the deviations between modeled background solar wind speed and observed solar wind speed. Using the mean absolute error, we obtain an average deviation for quiet solar activity within a range of 75.1–83.1 km s{sup −1}. Compared to this baseline level, periods within the ICME interval showed an increase of 18%–32% above the expected background, and the period of two days after the ICME displayed an increase of 9%–24%. We obtain a total duration of enhanced deviations over about three and up to six days after the ICME start, which is much longer than the average duration of an ICME disturbance itself (∼1.3 days), concluding that interplanetary space needs ∼2–5 days to recover from the impact of ICMEs. The obtained results have strong implications for studying CME propagation behavior and also for space weather forecasting.« less

University of Arizona Compressed Air Energy Storage

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simmons, Joseph; Muralidharan, Krishna

2012-12-31

Boiled down to its essentials, the grant’s purpose was to develop and demonstrate the viability of compressed air energy storage (CAES) for use in renewable energy development. While everyone agrees that energy storage is the key component to enable widespread adoption of renewable energy sources, the development of a viable scalable technology has been missing. The Department of Energy has focused on expanded battery research and improved forecasting, and the utilities have deployed renewable energy resources only to the extent of satisfying Renewable Portfolio Standards. The lack of dispatchability of solar and wind-based electricity generation has drastically increased the costmore » of operation with these components. It is now clear that energy storage coupled with accurate solar and wind forecasting make up the only combination that can succeed in dispatchable renewable energy resources. Conventional batteries scale linearly in size, so the price becomes a barrier for large systems. Flow batteries scale sub-linearly and promise to be useful if their performance can be shown to provide sufficient support for solar and wind-base electricity generation resources. Compressed air energy storage provides the most desirable answer in terms of scalability and performance in all areas except efficiency. With the support of the DOE, Tucson Electric Power and Science Foundation Arizona, the Arizona Research Institute for Solar Energy (AzRISE) at the University of Arizona has had the opportunity to investigate CAES as a potential energy storage resource.« less

Mapping 3D plasma structure in the solar wind with the L1 constellation: joint observations from Wind, ACE, DSCOVR, and SoHO

NASA Astrophysics Data System (ADS)

Stevens, M. L.; Kasper, J. C.; Case, A. W.; Korreck, K. E.; Szabo, A.; Biesecker, D. A.; Prchlik, J.

2017-12-01

At this moment in time, four observatories with similar instrumentation- Wind, ACE, DSCOVR, and SoHO- are stationed directly upstream of the Earth and making continuous observations. They are separated by drift-time baselines of seconds to minutes, timescales on which MHD instabilities in the solar wind are known to grow and evolve, and spatial baselines of tens to 200 earth radii, length scales relevant to the Earth's magnetosphere. By comparing measurements of matched solar wind structures from the four vantage points, the form of structures and associated dynamics on these scales is illuminated. Our targets include shocks and MHD discontinuities, stream fronts, locii of reconnection and exhaust flow boundary layers, plasmoids, and solitary structures born of nonlinear instability. We use the tetrahedral quality factors and other conventions adopted for Cluster to identify periods where the WADS constellation is suitably non-degenerate and arranged in such a way as to enable specific types of spatial, temporal, or spatiotemporal inferences. We present here an overview of the geometries accessible to the L1 constellation and timing-based and plasma-based observations of solar wind structures from 2016-17. We discuss the unique potential of the constellation approach for space physics and space weather forecasting at 1 AU.

Solar Drivers for Space Weather Operations (Invited)

NASA Astrophysics Data System (ADS)

White, S. M.

2013-12-01

Most space weather effects can be tied back to the Sun, and major research efforts are devoted to understanding the physics of the relevant phenomena with a long-term view of predicting their occurrence. This talk will focus on the current state of knowledge regarding the solar drivers of space weather, and in particular the connection between the science and operational needs. Topics covered will include the effects of solar ionizing flux on communications and navigation, radio interference, flare forecasting, the solar wind and the arrival of coronal mass ejections at Earth.

Multi-spacecraft testing of time-dependent interplanetary MHD models for operational forecasting of geomagnetic storms

NASA Technical Reports Server (NTRS)

Dryer, M.; Smith, Z. K.

1989-01-01

An MHD 2-1/2D, time-dependent model is used, together with observations of six solar flares during February 3-7, 1986, to demonstrate global, large-scale, compound disturbances in the solar wind over a wide range of heliolongitudes. This scenario is one that is likely to occur many times during the cruise, possibly even encounter, phases of the Multi-Comet Mission. It is suggested that a model such as this one should be tested with multi-spacecraft data (such as the MCM and earth-based probes) with several goals in view: (1) utility of the model for operational real-time forecasting of geomagnetic storms, and (2) scientific interpretation of certain forms of cometary activities and their possible association with solar-generated activity.

Solar-wind predictions for the Parker Solar Probe orbit. Near-Sun extrapolations derived from an empirical solar-wind model based on Helios and OMNI observations

NASA Astrophysics Data System (ADS)

Venzmer, M. S.; Bothmer, V.

2018-03-01

Context. The Parker Solar Probe (PSP; formerly Solar Probe Plus) mission will be humanitys first in situ exploration of the solar corona with closest perihelia at 9.86 solar radii (R⊙) distance to the Sun. It will help answer hitherto unresolved questions on the heating of the solar corona and the source and acceleration of the solar wind and solar energetic particles. The scope of this study is to model the solar-wind environment for PSPs unprecedented distances in its prime mission phase during the years 2018 to 2025. The study is performed within the Coronagraphic German And US SolarProbePlus Survey (CGAUSS) which is the German contribution to the PSP mission as part of the Wide-field Imager for Solar PRobe. Aim. We present an empirical solar-wind model for the inner heliosphere which is derived from OMNI and Helios data. The German-US space probes Helios 1 and Helios 2 flew in the 1970s and observed solar wind in the ecliptic within heliocentric distances of 0.29 au to 0.98 au. The OMNI database consists of multi-spacecraft intercalibrated in situ data obtained near 1 au over more than five solar cycles. The international sunspot number (SSN) and its predictions are used to derive dependencies of the major solar-wind parameters on solar activity and to forecast their properties for the PSP mission. Methods: The frequency distributions for the solar-wind key parameters, magnetic field strength, proton velocity, density, and temperature, are represented by lognormal functions. In addition, we consider the velocity distributions bi-componental shape, consisting of a slower and a faster part. Functional relations to solar activity are compiled with use of the OMNI data by correlating and fitting the frequency distributions with the SSN. Further, based on the combined data set from both Helios probes, the parameters frequency distributions are fitted with respect to solar distance to obtain power law dependencies. Thus an empirical solar-wind model for the inner heliosphere confined to the ecliptic region is derived, accounting for solar activity and for solar distance through adequate shifts of the lognormal distributions. Finally, the inclusion of SSN predictions and the extrapolation down to PSPs perihelion region enables us to estimate the solar-wind environment for PSPs planned trajectory during its mission duration. Results: The CGAUSS empirical solar-wind model for PSP yields dependencies on solar activity and solar distance for the solar-wind parameters' frequency distributions. The estimated solar-wind median values for PSPs first perihelion in 2018 at a solar distance of 0.16 au are 87 nT, 340 km s-1, 214 cm-3, and 503 000 K. The estimates for PSPs first closest perihelion, occurring in 2024 at 0.046 au (9.86 R⊙), are 943 nT, 290 km s-1, 2951 cm-3, and 1 930 000 K. Since the modeled velocity and temperature values below approximately 20 R⊙appear overestimated in comparison with existing observations, this suggests that PSP will directly measure solar-wind acceleration and heating processes below 20 R⊙ as planned.

A space weather information service based upon remote and in-situ measurements of coronal mass ejections heading for Earth

NASA Astrophysics Data System (ADS)

Hartkorn, O. A.; Ritter, B.; Meskers, A. J. H.; Miles, O.; Russwurm, M.; Scully, S.; Roldan, A.; Juestel, P.; Reville, V.; Lupu, S.; Ruffenach, A.

2014-12-01

The Earth's magnetosphere is formed as a consequence of the interaction between the planet's magnetic field and the solar wind, a continuous plasma stream from the Sun. A number of different solar wind phenomena have been studied over the past forty years with the intention of understandingand forcasting solar behavior and space weather. In particular, Earth-bound interplanetary coronal mass ejections (CMEs) can significantly disturb the Earth's magnetosphere for a short time and cause geomagnetic storms. We present a mission concept consisting of six spacecraft that are equally spaced in a heliocentric orbit at 0.72 AU. These spacecraft will monitor the plasma properties, the magnetic field's orientation and magnitude, and the 3D-propagation trajectory of CMEs heading for Earth. The primary objective of this mission is to increase space weather forecasting time by means of a near real-time information service, that is based upon in-situ and remote measurements of the CME properties. The mission secondary objective is the improvement of scientific space weather models. In-situ measurements are performed using a Solar Wind Analyzer instrumentation package and flux gate magnetometers. For remote measurements, coronagraphs are employed. The proposed instruments originate from other space missions with the intention to reduce mission costs and to streamline the mission design process. Communication with the six identical spacecraft is realized via a deep space network consisting of six ground stations. This network provides an information service that is in uninterrupted contact with the spacecraft, allowing for continuos space weather monitoring. A dedicated data processing center will handle all the data, and forward the processed data to the SSA Space Weather Coordination Center. This organization will inform the general public through a space weather forecast. The data processing center will additionally archive the data for the scientific community. This concept mission allows for major advances in space weather forecasting and the scientific modeling of space weather.

Impacts of Severe Weather, Climate Zone, and Energy Factors on Base Realignment and Closure (BRAC)

DTIC Science & Technology

2015-03-26

hydroelectric, solar photovoltaic , and wind power . Aside from locations and facilities that use electricity to heat, natural gas is the only...have large photovoltaic solar arrays with unique buy-back contracts or power -purchase agreements. These renewable energy projects benefit primarily...these costs, a Monte Carlo simulation is used to forecast annual costs and account for uncertainty with tornado and hurricane risks, along with

Verification of short lead time forecast models: applied to Kp and Dst forecasting

NASA Astrophysics Data System (ADS)

Wintoft, Peter; Wik, Magnus

2016-04-01

In the ongoing EU/H2020 project PROGRESS models that predicts Kp, Dst, and AE from L1 solar wind data will be used as inputs to radiation belt models. The possible lead times from L1 measurements are shorter (10s of minutes to hours) than the typical duration of the physical phenomena that should be forecast. Under these circumstances several metrics fail to single out trivial cases, such as persistence. In this work we explore metrics and approaches for short lead time forecasts. We apply these to current Kp and Dst forecast models. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 637302.

Prediction Model for Relativistic Electrons at Geostationary Orbit

NASA Technical Reports Server (NTRS)

Khazanov, George V.; Lyatsky, Wladislaw

2008-01-01

We developed a new prediction model for forecasting relativistic (greater than 2MeV) electrons, which provides a VERY HIGH correlation between predicted and actually measured electron fluxes at geostationary orbit. This model implies the multi-step particle acceleration and is based on numerical integrating two linked continuity equations for primarily accelerated particles and relativistic electrons. The model includes a source and losses, and used solar wind data as only input parameters. We used the coupling function which is a best-fit combination of solar wind/interplanetary magnetic field parameters, responsible for the generation of geomagnetic activity, as a source. The loss function was derived from experimental data. We tested the model for four year period 2004-2007. The correlation coefficient between predicted and actual values of the electron fluxes for whole four year period as well as for each of these years is stable and incredibly high (about 0.9). The high and stable correlation between the computed and actual electron fluxes shows that the reliable forecasting these electrons at geostationary orbit is possible.

Relativistic Electrons at Geostationary Orbit: Modeling Results

NASA Technical Reports Server (NTRS)

Khazanov, George V.; Lyatsky, Wladislaw

2008-01-01

We developed a new prediction model for forecasting relativistic (greater than 2MeV) electrons, which provides a VERY HIGH correlation between predicted and actually measured electron fluxes at geostationary orbit. This model implies the multi-step particle acceleration and is based on numerical integrating two linked continuity equations for primarily accelerated particles and relativistic electrons. The model includes a source and losses, and used solar wind data as only input parameters. We used the coupling function which is a best-fit combination of solar wind/interplanetary magnetic field parameters, responsible for the generation of geomagnetic activity, as a source. The loss function was derived from experimental data. We tested the model for four year period 2004-2007. The correlation coefficient between predicted and actual values of the electron fluxes for whole four year period as well as for each of these years is stable and incredibly high (about 0.9). The high and stable correlation between the computed and actual electron fluxes shows that the reliable forecasting these electrons at geostationary orbit is possible.

A Study of the Solar Wind-Magnetosphere Coupling Using Neural Networks

NASA Astrophysics Data System (ADS)

Wu, Jian-Guo; Lundstedt, Henrik

1996-12-01

The interaction between solar wind plasma and interplanetary magnetic field (IMF) and Earth's magnetosphere induces geomagnetic activity. Geomagnetic storms can cause many adverse effects on technical systems in space and on the Earth. It is therefore of great significance to accurately predict geomagnetic activity so as to minimize the amount of disruption to these operational systems and to allow them to work as efficiently as possible. Dynamic neural networks are powerful in modeling the dynamics encoded in time series of data. In this study, we use partially recurrent neural networks to study the solar wind-magnetosphere coupling by predicting geomagnetic storms (as measured by the Dstindex) from solar wind measurements. The solar wind, the IMF and the geomagnetic index Dst data are hourly averaged and read from the National Space Science Data Center's OMNI database. We selected these data from the period 1963 to 1992, which cover 10552h and contain storm time periods 9552h and quiet time periods 1000h. The data are then categorized into three data sets: a training set (6634h), across-validation set (1962h), and a test set (1956h). The validation set is used to determine where the training should be stopped whereas the test set is used for neural networks to get the generalization capability (the out-of-sample performance). Based on the correlation analysis between the Dst index and various solar wind parameters (including various combinations of solar wind parameters), the best coupling functions can be found from the out-of-sample performance of trained neural networks. The coupling functions found are then used to forecast geomagnetic storms one to several hours in advance. The comparisons are made on iterating the single-step prediction several times and on making a non iterated, direct prediction. Thus, we will present the best solar wind-magnetosphere coupling functions and the corresponding prediction results. Interesting Links: Lund Space Weather and AI Center

Magnetohydrodynamic modelling of solar disturbances in the interplanetary medium

NASA Astrophysics Data System (ADS)

Dryer, M.

1985-12-01

A scientifically constructed series of interplanetary magnetohydrodynamic models is made that comprise the foundations for a composite solar terrestrial environment model. These models, unique in the field of solar wind physics, include both 2-1/2D as well as 3D time dependent codes that will lead to future operational status. We have also developed a geomagnetic storm forecasting strategy, referred to as the Solar Terrestrial Environment Model (STEM/2000), whereby these models would be appended in modular fashion to solar, magnetosphere, ionosphere, thermosphere, and neutral atmosphere models. We stress that these models, while still not appropriate at this date for operational use, outline a strategy or blueprint for the future. This strategy, if implemented in its essential features, offers a high probability for technology transfer from theory to operational testing within, approximately, a decade. It would ensure that real time observations would be used to drive physically based models that outputs of which would be used by space environment forecasters.

Data Assimilation in the Solar Wind: Challenges and First Results

NASA Astrophysics Data System (ADS)

Lang, Matthew; Browne, Phil; van Leeuwen, Peter Jan; Owens, Matt

2017-04-01

Data assimilation (DA) is currently underused in the solar wind field to improve the modelled variables using observations. Data assimilation has been used in Numerical Weather Prediction (NWP) models with great success, and it can be seen that the improvement of DA methods in NWP modelling has led to improvements in forecasting skill over the past 20-30 years. The state of the art DA methods developed for NWP modelling have never been applied to space weather models, hence it is important to implement the improvements that can be gained from these methods to improve our understanding of the solar wind and how to model it. The ENLIL solar wind model has been coupled to the EMPIRE data assimilation library in order to apply these advanced data assimilation methods to a space weather model. This coupling allows multiple data assimilation methods to be applied to ENLIL with relative ease. I shall discuss twin experiments that have been undertaken, applying the LETKF to the ENLIL model when a CME occurs in the observation and when it does not. These experiments show that there is potential in the application of advanced data assimilation methods to the solar wind field, however, there is still a long way to go until it can be applied effectively. I shall discuss these issues and suggest potential avenues for future research in this area.

The Solar Wind and Geomagnetic Activity as a Function of Time Relative to Corotating Interaction Regions

NASA Technical Reports Server (NTRS)

McPherron, Robert L.; Weygand, James

2006-01-01

Corotating interaction regions during the declining phase of the solar cycle are the cause of recurrent geomagnetic storms and are responsible for the generation of high fluxes of relativistic electrons. These regions are produced by the collision of a high-speed stream of solar wind with a slow-speed stream. The interface between the two streams is easily identified with plasma and field data from a solar wind monitor upstream of the Earth. The properties of the solar wind and interplanetary magnetic field are systematic functions of time relative to the stream interface. Consequently the coupling of the solar wind to the Earth's magnetosphere produces a predictable sequence of events. Because the streams persist for many solar rotations it should be possible to use terrestrial observations of past magnetic activity to predict future activity. Also the high-speed streams are produced by large unipolar magnetic regions on the Sun so that empirical models can be used to predict the velocity profile of a stream expected at the Earth. In either case knowledge of the statistical properties of the solar wind and geomagnetic activity as a function of time relative to a stream interface provides the basis for medium term forecasting of geomagnetic activity. In this report we use lists of stream interfaces identified in solar wind data during the years 1995 and 2004 to develop probability distribution functions for a variety of different variables as a function of time relative to the interface. The results are presented as temporal profiles of the quartiles of the cumulative probability distributions of these variables. We demonstrate that the storms produced by these interaction regions are generally very weak. Despite this the fluxes of relativistic electrons produced during those storms are the highest seen in the solar cycle. We attribute this to the specific sequence of events produced by the organization of the solar wind relative to the stream interfaces. We also show that there are large quantitative differences in various parameters between the two cycles.

Atmospheric boundary layer effects induced by the 20 March 2015 solar eclipse

NASA Astrophysics Data System (ADS)

Gray, Suzanne L.; Harrison, R. Giles

2016-04-01

The British Isles benefits from dense meteorological observation networks, enabling insights into the still-unresolved effects of solar eclipse events on the near-surface wind field. The near-surface effects of the solar eclipse of 20 March 2015 are derived through comparison of output from the Met Office's operational weather forecast model (which is ignorant of the eclipse) with data from two meteorological networks: the Met Office's land surface station (MIDAS) network and a roadside measurement network operated by Vaisala. Synoptic-evolution relative calculations reveal the cooling and increase in relative humidity almost universally attributed to eclipse events. In addition, a slackening of wind speeds by up to about 2 knots in already weak winds and backing in wind direction of about 20 degrees under clear skies across middle England are attributed to the eclipse event. The slackening of wind speed is consistent with the previously reported boundary layer stabilisation during eclipse events. Wind direction changes have previously been attributed to a large-scale `eclipse-induced cold-cored cyclone', mountain slope flows, and changes in the strength of sea breezes. A new explanation is proposed here by analogy with nocturnal wind changes at sunset and shown to predict direction changes consistent with those observed.

Geoeffectiveness (D (sub st) and K (sub p)) of Interplanetary Coronal Mass Ejections During 1995-2009 and Implications for Storm Forecasting

NASA Technical Reports Server (NTRS)

Richardson, I. G.; Cane, H. V.

2011-01-01

We summarize the geoeffectiveness (based on the Dst and Kp indices) of the more than 300 interplanetary coronal mass ejections (ICMEs) that passed the Earth during 1996-2009, encompassing solar cycle 23. We subsequently estimate the probability that an ICME will generate geomagnetic activity that exceeds certain thresholds of Dst or Kp, including the NOAA "G" storm scale, based on maximum values of the southward magnetic field component (Bs), the solar wind speed (V), and the y component (Ey) of the solar wind convective electric field E = -V x B, in the ICME or sheath ahead of the ICME. Consistent with previous studies, the geoeffectiveness of an ICME is correlated with Bs or Ey approx.= VBs in the ICME or sheath, indicating that observations from a solar wind monitor upstream of the Earth are likely to provide the most reliable forecasts of the activity associated with an approaching ICME. There is also a general increase in geoeffectiveness with ICME speed, though the overall event-to-event correlation is weaker than for Bs and Ey. Nevertheless, using these results, we suggest that the speed of an ICME approaching the Earth inferred, for example, from routine remote sensing by coronagraphs on spacecraft well separated from the Earth or by all-sky imagers, could be used to estimate the likely geoeffectiveness of the ICME (our "comprehensive" ICME database provides a proxy for ICMEs identified in this way) with a longer lead time than may be possible using an upstream monitor

Space Monitoring Data Center at Moscow State University

NASA Astrophysics Data System (ADS)

Kalegaev, Vladimir; Bobrovnikov, Sergey; Barinova, Vera; Myagkova, Irina; Shugay, Yulia; Barinov, Oleg; Dolenko, Sergey; Mukhametdinova, Ludmila; Shiroky, Vladimir

Space monitoring data center of Moscow State University provides operational information on radiation state of the near-Earth space. Internet portal http://swx.sinp.msu.ru/ gives access to the actual data characterizing the level of solar activity, geomagnetic and radiation conditions in the magnetosphere and heliosphere in the real time mode. Operational data coming from space missions (ACE, GOES, ELECTRO-L1, Meteor-M1) at L1, LEO and GEO and from the Earth’s surface are used to represent geomagnetic and radiation state of near-Earth environment. On-line database of measurements is also maintained to allow quick comparison between current conditions and conditions experienced in the past. The models of space environment working in autonomous mode are used to generalize the information obtained from observations on the whole magnetosphere. Interactive applications and operational forecasting services are created on the base of these models. They automatically generate alerts on particle fluxes enhancements above the threshold values, both for SEP and relativistic electrons using data from LEO orbits. Special forecasting services give short-term forecast of SEP penetration to the Earth magnetosphere at low altitudes, as well as relativistic electron fluxes at GEO. Velocities of recurrent high speed solar wind streams on the Earth orbit are predicted with advance time of 3-4 days on the basis of automatic estimation of the coronal hole areas detected on the images of the Sun received from the SDO satellite. By means of neural network approach, Dst and Kp indices online forecasting 0.5-1.5 hours ahead, depending on solar wind and the interplanetary magnetic field, measured by ACE satellite, is carried out. Visualization system allows representing experimental and modeling data in 2D and 3D.

Short-term solar irradiance forecasting via satellite/model coupling

DOE PAGES

Miller, Steven D.; Rogers, Matthew A.; Haynes, John M.; ...

2017-12-01

The short-term (0-3 h) prediction of solar insolation for renewable energy production is a problem well-suited to satellite-based techniques. The spatial, spectral, temporal and radiometric resolution of instrumentation hosted on the geostationary platform allows these satellites to describe the current cloud spatial distribution and optical properties. These properties relate directly to the transient properties of the downwelling solar irradiance at the surface, which come in the form of 'ramps' that pose a central challenge to energy load balancing in a spatially distributed network of solar farms. The short-term evolution of the cloud field may be approximated to first order simplymore » as translational, but care must be taken in how the advection is handled and where the impacts are assigned. In this research, we describe how geostationary satellite observations are used with operational cloud masking and retrieval algorithms, wind field data from Numerical Weather Prediction (NWP), and radiative transfer calculations to produce short-term forecasts of solar insolation for applications in solar power generation. The scheme utilizes retrieved cloud properties to group pixels into contiguous cloud objects whose future positions are predicted using four-dimensional (space + time) model wind fields, selecting steering levels corresponding to the cloud height properties of each cloud group. The shadows associated with these clouds are adjusted for sensor viewing parallax displacement and combined with solar geometry and terrain height to determine the actual location of cloud shadows. For mid/high-level clouds at mid-latitudes and high solar zenith angles, the combined displacements from these geometric considerations are non-negligible. The cloud information is used to initialize a radiative transfer model that computes the direct and diffuse-sky solar insolation at both shadow locations and intervening clear-sky regions. Here, we describe the formulation of the algorithm and validate its performance against Surface Radiation (SURFRAD; Augustine et al., 2000, 2005) network observations. Typical errors range from 8.5% to 17.2% depending on the complexity of cloud regimes, and an operational demonstration outperformed persistence-based forecasting of Global Horizontal Irradiance (GHI) under all conditions by ~10 W/m2.« less

Short-term solar irradiance forecasting via satellite/model coupling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miller, Steven D.; Rogers, Matthew A.; Haynes, John M.

The short-term (0-3 h) prediction of solar insolation for renewable energy production is a problem well-suited to satellite-based techniques. The spatial, spectral, temporal and radiometric resolution of instrumentation hosted on the geostationary platform allows these satellites to describe the current cloud spatial distribution and optical properties. These properties relate directly to the transient properties of the downwelling solar irradiance at the surface, which come in the form of 'ramps' that pose a central challenge to energy load balancing in a spatially distributed network of solar farms. The short-term evolution of the cloud field may be approximated to first order simplymore » as translational, but care must be taken in how the advection is handled and where the impacts are assigned. In this research, we describe how geostationary satellite observations are used with operational cloud masking and retrieval algorithms, wind field data from Numerical Weather Prediction (NWP), and radiative transfer calculations to produce short-term forecasts of solar insolation for applications in solar power generation. The scheme utilizes retrieved cloud properties to group pixels into contiguous cloud objects whose future positions are predicted using four-dimensional (space + time) model wind fields, selecting steering levels corresponding to the cloud height properties of each cloud group. The shadows associated with these clouds are adjusted for sensor viewing parallax displacement and combined with solar geometry and terrain height to determine the actual location of cloud shadows. For mid/high-level clouds at mid-latitudes and high solar zenith angles, the combined displacements from these geometric considerations are non-negligible. The cloud information is used to initialize a radiative transfer model that computes the direct and diffuse-sky solar insolation at both shadow locations and intervening clear-sky regions. Here, we describe the formulation of the algorithm and validate its performance against Surface Radiation (SURFRAD; Augustine et al., 2000, 2005) network observations. Typical errors range from 8.5% to 17.2% depending on the complexity of cloud regimes, and an operational demonstration outperformed persistence-based forecasting of Global Horizontal Irradiance (GHI) under all conditions by ~10 W/m2.« less

Ionospheric Data Assimilation and Targeted Observation Strategies: Proof of Concept Analysis in a Geomagnetic Storm Event

NASA Astrophysics Data System (ADS)

Kostelich, Eric; Durazo, Juan; Mahalov, Alex

2017-11-01

The dynamics of the ionosphere involve complex interactions between the atmosphere, solar wind, cosmic radiation, and Earth's magnetic field. Geomagnetic storms arising from solar activity can perturb these dynamics sufficiently to disrupt radio and satellite communications. Efforts to predict ``space weather,'' including ionospheric dynamics, require the development of a data assimilation system that combines observing systems with appropriate forecast models. This talk will outline a proof-of-concept targeted observation strategy, consisting of the Local Ensemble Transform Kalman Filter, coupled with the Thermosphere Ionosphere Electrodynamics Global Circulation Model, to select optimal locations where additional observations can be made to improve short-term ionospheric forecasts. Initial results using data and forecasts from the geomagnetic storm of 26-27 September 2011 will be described. Work supported by the Air Force Office of Scientific Research (Grant Number FA9550-15-1-0096) and by the National Science Foundation (Grant Number DMS-0940314).

Predicting the magnetic vectors within coronal mass ejections arriving at Earth: 1. Initial architecture

NASA Astrophysics Data System (ADS)

Savani, N. P.; Vourlidas, A.; Szabo, A.; Mays, M. L.; Richardson, I. G.; Thompson, B. J.; Pulkkinen, A.; Evans, R.; Nieves-Chinchilla, T.

2015-06-01

The process by which the Sun affects the terrestrial environment on short timescales is predominately driven by the amount of magnetic reconnection between the solar wind and Earth's magnetosphere. Reconnection occurs most efficiently when the solar wind magnetic field has a southward component. The most severe impacts are during the arrival of a coronal mass ejection (CME) when the magnetosphere is both compressed and magnetically connected to the heliospheric environment. Unfortunately, forecasting magnetic vectors within coronal mass ejections remain elusive. Here we report how, by combining a statistically robust helicity rule for a CME's solar origin with a simplified flux rope topology, the magnetic vectors within the Earth-directed segment of a CME can be predicted. In order to test the validity of this proof-of-concept architecture for estimating the magnetic vectors within CMEs, a total of eight CME events (between 2010 and 2014) have been investigated. With a focus on the large false alarm of January 2014, this work highlights the importance of including the early evolutionary effects of a CME for forecasting purposes. The angular rotation in the predicted magnetic field closely follows the broad rotational structure seen within the in situ data. This time-varying field estimate is implemented into a process to quantitatively predict a time-varying Kp index that is described in detail in paper II. Future statistical work, quantifying the uncertainties in this process, may improve the more heuristic approach used by early forecasting systems.

Planning a Target Renewable Portfolio using Atmospheric Modeling and Stochastic Optimization

NASA Astrophysics Data System (ADS)

Hart, E.; Jacobson, M. Z.

2009-12-01

A number of organizations have suggested that an 80% reduction in carbon emissions by 2050 is a necessary step to mitigate climate change and that decarbonization of the electricity sector is a crucial component of any strategy to meet this target. Integration of large renewable and intermittent generators poses many new problems in power system planning. In this study, we attempt to determine an optimal portfolio of renewable resources to meet best the fluctuating California load while also meeting an 80% carbon emissions reduction requirement. A stochastic optimization scheme is proposed that is based on a simplified model of the California electricity grid. In this single-busbar power system model, the load is met with generation from wind, solar thermal, photovoltaic, hydroelectric, geothermal, and natural gas plants. Wind speeds and insolation are calculated using GATOR-GCMOM, a global-through-urban climate-weather-air pollution model. Fields were produced for California and Nevada at 21km SN by 14 km WE spatial resolution every 15 minutes for the year 2006. Load data for 2006 were obtained from the California ISO OASIS database. Maximum installed capacities for wind and solar thermal generation were determined using a GIS analysis of potential development sites throughout the state. The stochastic optimization scheme requires that power balance be achieved in a number of meteorological and load scenarios that deviate from the forecasted (or modeled) data. By adjusting the error distributions of the forecasts, the model describes how improvements in wind speed and insolation forecasting may affect the optimal renewable portfolio. Using a simple model, we describe the diversity, size, and sensitivities of a renewable portfolio that is best suited to the resources and needs of California and that contributes significantly to reduction of the state’s carbon emissions.

The Ensemble Space Weather Modeling System (eSWMS): Status, Capabilities and Challenges

NASA Astrophysics Data System (ADS)

Fry, C. D.; Eccles, J. V.; Reich, J. P.

2010-12-01

Marking a milestone in space weather forecasting, the Space Weather Modeling System (SWMS) successfully completed validation testing in advance of operational testing at Air Force Weather Agency’s primary space weather production center. This is the first coupling of stand-alone, physics-based space weather models that are currently in operations at AFWA supporting the warfighter. Significant development effort went into ensuring the component models were portable and scalable while maintaining consistent results across diverse high performance computing platforms. Coupling was accomplished under the Earth System Modeling Framework (ESMF). The coupled space weather models are the Hakamada-Akasofu-Fry version 2 (HAFv2) solar wind model and GAIM1, the ionospheric forecast component of the Global Assimilation of Ionospheric Measurements (GAIM) model. The SWMS was developed by team members from AFWA, Explorations Physics International, Inc. (EXPI) and Space Environment Corporation (SEC). The successful development of the SWMS provides new capabilities beyond enabling extended lead-time, data-driven ionospheric forecasts. These include ingesting diverse data sets at higher resolution, incorporating denser computational grids at finer time steps, and performing probability-based ensemble forecasts. Work of the SWMS development team now focuses on implementing the ensemble-based probability forecast capability by feeding multiple scenarios of 5 days of solar wind forecasts to the GAIM1 model based on the variation of the input fields to the HAFv2 model. The ensemble SWMS (eSWMS) will provide the most-likely space weather scenario with uncertainty estimates for important forecast fields. The eSWMS will allow DoD mission planners to consider the effects of space weather on their systems with more advance warning than is currently possible. The payoff is enhanced, tailored support to the warfighter with improved capabilities, such as point-to-point HF propagation forecasts, single-frequency GPS error corrections, and high cadence, high-resolution Space Situational Awareness (SSA) products. We present the current status of eSWMS, its capabilities, limitations and path of transition to operational use.

Quantifying variability in fast and slow solar wind: From turbulence to extremes

NASA Astrophysics Data System (ADS)

Tindale, E.; Chapman, S. C.; Moloney, N.; Watkins, N. W.

2017-12-01

Fast and slow solar wind exhibit variability across a wide range of spatiotemporal scales, with evolving turbulence producing fluctuations on sub-hour timescales and the irregular solar cycle modulating the system over many years. Here, we apply the data quantile-quantile (DQQ) method [Tindale and Chapman 2016, 2017] to over 20 years of Wind data, to study the time evolution of the statistical distribution of plasma parameters in fast and slow solar wind. This model-independent method allows us to simultaneously explore the evolution of fluctuations across all scales. We find a two-part functional form for the statistical distributions of the interplanetary magnetic field (IMF) magnitude and its components, with each region of the distribution evolving separately over the solar cycle. Up to a value of 8nT, turbulent fluctuations dominate the distribution of the IMF, generating the approximately lognormal shape found by Burlaga [2001]. The mean of this core-turbulence region tracks solar cycle activity, while its variance remains constant, independent of the fast or slow state of the solar wind. However, when we test the lognormality of this core-turbulence component over time, we find the model provides a poor description of the data at solar maximum, where sharp peaks in the distribution dominate over the lognormal shape. At IMF values higher than 8nT, we find a separate, extremal distribution component, whose moments are sensitive to solar cycle phase, the peak activity of the cycle and the solar wind state. We further investigate these `extremal' values using burst analysis, where a burst is defined as a continuous period of exceedance over a predefined threshold. This form of extreme value statistics allows us to study the stochastic process underlying the time series, potentially supporting a probabilistic forecast of high-energy events. Tindale, E., and S.C. Chapman (2016), Geophys. Res. Lett., 43(11) Tindale, E., and S.C. Chapman (2017), submitted Burlaga, L.F. (2001), J. Geophys. Res., 106(A8)

The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Finley, Cathy

2014-04-30

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements inmore » wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.« less

The scientific challenges to forecasting and nowcasting the magnetospheric response to space weather (Invited)

NASA Astrophysics Data System (ADS)

Hesse, M.; Kuznetsova, M. M.; Birn, J.; Pulkkinen, A. A.

2013-12-01

Space weather is different from terrestrial weather in an essential way. Terrestrial weather has benefitted from a long history of research, which has led to a deep and detailed level of understanding. In comparison, space weather is scientifically in its infancy. Many key processes in the causal chains from processes on the Sun to space weather effects in various locations in the heliosphere remain either poorly understood or not understood at all. Space weather is therefore, and will remain in the foreseeable future, primarily a research field. Extensive further research efforts are needed before we can reasonably expect the precision and fidelity of weather forecasts. For space weather within the Earth's magnetosphere, the coupling between solar wind and magnetosphere is of crucial importance. While past research has provided answers, often on qualitative levels, to some of the most fundamental questions, answers to some of the latter and the ability to predict quantitatively remain elusive. This presentation will provide an overview of pertinent aspects of solar wind-magnetospheric coupling, its importance for space weather near the Earth, and it will analyze the state of our ability to describe and predict its efficiency. It will conclude with a discussion of research activities, which are aimed at improving our ability to quantitatively forecast coupling processes.

Real-time forecasting of ICME shock arrivals at L1 during the "April Fool’s Day" epoch: 28 March  21 April 2001

NASA Astrophysics Data System (ADS)

Sun, W.; Dryer, M.; Fry, C. D.; Deehr, C. S.; Smith, Z.; Akasofu, S.-I.; Kartalev, M. D.; Grigorov, K. G.

2002-07-01

The Sun was extremely active during the "April Fool’s Day" epoch of 2001. We chose this period between a solar flare on 28 March 2001 to a final shock arrival at Earth on 21 April 2001. The activity consisted of two presumed helmet-streamer blowouts, seven M-class flares, and nine X-class flares, the last of which was behind the west limb. We have been experimenting since February 1997 with real-time, end-to-end forecasting of interplanetary coronal mass ejection (ICME) shock arrival times. Since August 1998, these forecasts have been distributed in real-time by e-mail to a list of interested scientists and operational USAF and NOAA forecasters. They are made using three different solar wind models. We describe here the solar events observed during the April Fool’s 2001 epoch, along with the predicted and actual shock arrival times, and the ex post facto correction to the real-time coronal shock speed observations. It appears that the initial estimates of coronal shock speeds from Type II radio burst observations and coronal mass ejections were too high by as much as 30%. We conclude that a 3-dimensional coronal density model should be developed for application to observations of solar flares and their Type II radio burst observations.

Eclipse-induced wind changes over the British Isles on the 20 March 2015

PubMed Central

2016-01-01

The British Isles benefits from dense meteorological observation networks, enabling insights into the still-unresolved effects of solar eclipse events on the near-surface wind field. The near-surface effects of the solar eclipse of 20 March 2015 are derived through comparison of output from the Met Office’s operational weather forecast model (which is ignorant of the eclipse) with data from two meteorological networks: the Met Office’s land surface station (MIDAS) network and a roadside measurement network operated by Vaisala. Synoptic-evolution relative calculations reveal the cooling and increase in relative humidity almost universally attributed to eclipse events. In addition, a slackening of wind speeds by up to about 2 knots in already weak winds and backing in wind direction of about 20° under clear skies across middle England are attributed to the eclipse event. The slackening of wind speed is consistent with the previously reported boundary layer stabilization during eclipse events. Wind direction changes have previously been attributed to a large-scale ‘eclipse-induced cold-cored cyclone’, mountain slope flows, and changes in the strength of sea breezes. A new explanation is proposed here by analogy with nocturnal wind changes at sunset and shown to predict direction changes consistent with those observed. This article is part of the themed issue ‘Atmospheric effects of solar eclipses stimulated by the 2015 UK eclipse’. PMID:27550759

Real-time Ensemble Forecasting of Coronal Mass Ejections using the WSA-ENLIL+Cone Model

NASA Astrophysics Data System (ADS)

Mays, M. L.; Taktakishvili, A.; Pulkkinen, A. A.; MacNeice, P. J.; Rastaetter, L.; Kuznetsova, M. M.; Odstrcil, D.

2013-12-01

Ensemble forecasting of coronal mass ejections (CMEs) provides significant information in that it provides an estimation of the spread or uncertainty in CME arrival time predictions due to uncertainties in determining CME input parameters. Ensemble modeling of CME propagation in the heliosphere is performed by forecasters at the Space Weather Research Center (SWRC) using the WSA-ENLIL cone model available at the Community Coordinated Modeling Center (CCMC). SWRC is an in-house research-based operations team at the CCMC which provides interplanetary space weather forecasting for NASA's robotic missions and performs real-time model validation. A distribution of n (routinely n=48) CME input parameters are generated using the CCMC Stereo CME Analysis Tool (StereoCAT) which employs geometrical triangulation techniques. These input parameters are used to perform n different simulations yielding an ensemble of solar wind parameters at various locations of interest (satellites or planets), including a probability distribution of CME shock arrival times (for hits), and geomagnetic storm strength (for Earth-directed hits). Ensemble simulations have been performed experimentally in real-time at the CCMC since January 2013. We present the results of ensemble simulations for a total of 15 CME events, 10 of which were performed in real-time. The observed CME arrival was within the range of ensemble arrival time predictions for 5 out of the 12 ensemble runs containing hits. The average arrival time prediction was computed for each of the twelve ensembles predicting hits and using the actual arrival time an average absolute error of 8.20 hours was found for all twelve ensembles, which is comparable to current forecasting errors. Some considerations for the accuracy of ensemble CME arrival time predictions include the importance of the initial distribution of CME input parameters, particularly the mean and spread. When the observed arrivals are not within the predicted range, this still allows the ruling out of prediction errors caused by tested CME input parameters. Prediction errors can also arise from ambient model parameters such as the accuracy of the solar wind background, and other limitations. Additionally the ensemble modeling setup was used to complete a parametric event case study of the sensitivity of the CME arrival time prediction to free parameters for ambient solar wind model and CME.

Major Geomagnetic Storms in Solar Cycle 24

NASA Astrophysics Data System (ADS)

Zheng, Y.

2013-12-01

Solar Cycle 24 has produced 11 major geomagnetic storms (where Dstmin < -100 nT) with three in 2011, six in 2012 and two in 2013 (as of 7 August 2013). Detailed analysis of each event will be given in terms of its solar driver(s): CME, coronal hole high speed solar wind stream (HSS), multiple CMEs or interactions between CME and HSS. While some of these storms are associated with a fast and wide CME, the few cases involving slow or common CMEs and interactions with HSS are particularly interesting. These events pose great challenges for accurate space weather forecasting, since operationally the slower or average CMEs tend to receive less attention and are sometimes overlooked altogether. The characteristics of such challenging, not-so-fast yet geoeffective CME events (such as their coronal signatures and interactions with surrounding solar wind structure(s), etc) will be examined in detail, with the goal of extracting common and telltale features, if any, of these CMEs that distinguish them from CMEs in a similar category.

Real­-Time Ensemble Forecasting of Coronal Mass Ejections Using the Wsa-Enlil+Cone Model

NASA Astrophysics Data System (ADS)

Mays, M. L.; Taktakishvili, A.; Pulkkinen, A. A.; Odstrcil, D.; MacNeice, P. J.; Rastaetter, L.; LaSota, J. A.

2014-12-01

Ensemble forecasting of coronal mass ejections (CMEs) provides significant information in that it provides an estimation of the spread or uncertainty in CME arrival time predictions. Real-time ensemble modeling of CME propagation is performed by forecasters at the Space Weather Research Center (SWRC) using the WSA-ENLIL+cone model available at the Community Coordinated Modeling Center (CCMC). To estimate the effect of uncertainties in determining CME input parameters on arrival time predictions, a distribution of n (routinely n=48) CME input parameter sets are generated using the CCMC Stereo CME Analysis Tool (StereoCAT) which employs geometrical triangulation techniques. These input parameters are used to perform n different simulations yielding an ensemble of solar wind parameters at various locations of interest, including a probability distribution of CME arrival times (for hits), and geomagnetic storm strength (for Earth-directed hits). We present the results of ensemble simulations for a total of 38 CME events in 2013-2014. For 28 of the ensemble runs containing hits, the observed CME arrival was within the range of ensemble arrival time predictions for 14 runs (half). The average arrival time prediction was computed for each of the 28 ensembles predicting hits and using the actual arrival time, an average absolute error of 10.0 hours (RMSE=11.4 hours) was found for all 28 ensembles, which is comparable to current forecasting errors. Some considerations for the accuracy of ensemble CME arrival time predictions include the importance of the initial distribution of CME input parameters, particularly the mean and spread. When the observed arrivals are not within the predicted range, this still allows the ruling out of prediction errors caused by tested CME input parameters. Prediction errors can also arise from ambient model parameters such as the accuracy of the solar wind background, and other limitations. Additionally the ensemble modeling sysem was used to complete a parametric event case study of the sensitivity of the CME arrival time prediction to free parameters for ambient solar wind model and CME. The parameter sensitivity study suggests future directions for the system, such as running ensembles using various magnetogram inputs to the WSA model.

Evidence for solar wind modulation of lightning

NASA Astrophysics Data System (ADS)

Scott, C. J.; Harrison, R. G.; Owens, M. J.; Lockwood, M.; Barnard, L.

2014-05-01

The response of lightning rates over Europe to arrival of high speed solar wind streams at Earth is investigated using a superposed epoch analysis. Fast solar wind stream arrival is determined from modulation of the solar wind V y component, measured by the Advanced Composition Explorer spacecraft. Lightning rate changes around these event times are determined from the very low frequency arrival time difference (ATD) system of the UK Met Office. Arrival of high speed streams at Earth is found to be preceded by a decrease in total solar irradiance and an increase in sunspot number and Mg II emissions. These are consistent with the high speed stream’s source being co-located with an active region appearing on the Eastern solar limb and rotating at the 27 d period of the Sun. Arrival of the high speed stream at Earth also coincides with a small (˜1%) but rapid decrease in galactic cosmic ray flux, a moderate (˜6%) increase in lower energy solar energetic protons (SEPs), and a substantial, statistically significant increase in lightning rates. These changes persist for around 40 d in all three quantities. The lightning rate increase is corroborated by an increase in the total number of thunder days observed by UK Met stations, again persisting for around 40 d after the arrival of a high speed solar wind stream. This result appears to contradict earlier studies that found an anti-correlation between sunspot number and thunder days over solar cycle timescales. The increase in lightning rates and thunder days that we observe coincides with an increased flux of SEPs which, while not being detected at ground level, nevertheless penetrate the atmosphere to tropospheric altitudes. This effect could be further amplified by an increase in mean lightning stroke intensity that brings more strokes above the detection threshold of the ATD system. In order to remove any potential seasonal bias the analysis was repeated for daily solar wind triggers occurring during the summer months (June to August). Though this reduced the number of solar wind triggers to 32, the response in both lightning and thunder day data remained statistically significant. This modulation of lightning by regular and predictable solar wind events may be beneficial to medium range forecasting of hazardous weather.

The scientific challenges to forecasting the propagation of space weather through the heliosphere (Invited)

NASA Astrophysics Data System (ADS)

van der Holst, B.; Manchester, W.; Sokolov, I.; Toth, G.; Gombosi, T. I.

2013-12-01

Coronal mass ejections (CMEs) are a major source of potentially destructive space weather conditions. Understanding and forecasting these events are of utmost importance. In this presentation we discuss the progress towards a physics-based predictive capability within the Space Weather Modeling Framework (SWMF). We demonstrate our latest development in the AWSoM (Alfven Wave Solar Model) global model of the solar corona and inner heliosphere. This model accounts for the coupled thermodynamics of the electrons and protons via single fluid magnetohydrodynamics. The coronal heating and solar wind acceleration are addressed with Alfvén wave turbulence. The realistic 3D magnetic field is simulated using data from the photospheric magnetic field measurements. The AWSoM model serves as a workhorse for modeling CMEs from initial eruption to prediction at 1AU. With selected events we will demonstrate the complexity and challenges associated with CME propagation.

Improving medium-range and seasonal hydroclimate forecasts in the southeast USA

NASA Astrophysics Data System (ADS)

Tian, Di

Accurate hydro-climate forecasts are important for decision making by water managers, agricultural producers, and other stake holders. Numerical weather prediction models and general circulation models may have potential for improving hydro-climate forecasts at different scales. In this study, forecast analogs of the Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) based on different approaches were evaluated for medium-range reference evapotranspiration (ETo), irrigation scheduling, and urban water demand forecasts in the southeast United States; the Climate Forecast System version 2 (CFSv2) and the North American national multi-model ensemble (NMME) were statistically downscaled for seasonal forecasts of ETo, precipitation (P) and 2-m temperature (T2M) at the regional level. The GFS mean temperature (Tmean), relative humidity, and wind speed (Wind) reforecasts combined with the climatology of Reanalysis 2 solar radiation (Rs) produced higher skill than using the direct GFS output only. Constructed analogs showed slightly higher skill than natural analogs for deterministic forecasts. Both irrigation scheduling driven by the GEFS-based ETo forecasts and GEFS-based ETo forecast skill were generally positive up to one week throughout the year. The GEFS improved ETo forecast skill compared to the GFS. The GEFS-based analog forecasts for the input variables of an operational urban water demand model were skillful when applied in the Tampa Bay area. The modified operational models driven by GEFS analog forecasts showed higher forecast skill than the operational model based on persistence. The results for CFSv2 seasonal forecasts showed maximum temperature (Tmax) and Rs had the greatest influence on ETo. The downscaled Tmax showed the highest predictability, followed by Tmean, Tmin, Rs, and Wind. The CFSv2 model could better predict ETo in cold seasons during El Nino Southern Oscillation (ENSO) events only when the forecast initial condition was in ENSO. Downscaled P and T2M forecasts were produced by directly downscaling the NMME P and T2M output or indirectly using the NMME forecasts of Nino3.4 sea surface temperatures to predict local-scale P and T2M. The indirect method generally showed the highest forecast skill which occurs in cold seasons. The bias-corrected NMME ensemble forecast skill did not outperform the best single model.

Analysis of the balancing of the wind and solar energy resources in Andalusia (Southern Spain)

NASA Astrophysics Data System (ADS)

Santos-Alamillos, F. J.; Pozo-Vazquez, D.; Lara-Fanego, V.; Ruiz-Arias, J. A.; Hernandez-Alvaro, J.; Tova-Pescador, J.

2010-09-01

A higher penetration of the renewable energy in the electric system in the future will be conditioned to a reduction of the uncertainty of the yield. A way to obtain this goal is to analyze the balancing between the productions of different sources of renewable energy, trying to combine these productions. In this work we analyze, from a meteorological point of view, the balancing between wind and solar energy resources in Andalusia (southern Iberian Peninsula). To this end, wind speed and global radiation data corresponding to an one year integration of the Weather Research and Forecasting (WRF) Numerical Weather Prediction (NWP) model were analyzed. Two method of analysis were used: a point correlation analysis and a Canonical Correlation Analysis (CCA). Results from these analyses allow obtaining, eventually, areas of local and distributed balancing between the wind and solar energy resources. The analysis was carried out separately for the different seasons of the year. Results showed, overall, a considerable balancing effect between the wind and solar resources in the mountain areas of the interior of the region, along the coast of the central part of the region and, specially, in the coastal area near the Gibraltar strait. Nevertheless, considerable differences were found between the seasons of the year, which may lead to compensating effects. Autumn proved to be the season with the most significant results.

The Interaction of Solar wind Discontinuities with the Earth's Bow Shock

NASA Technical Reports Server (NTRS)

Sibeck, David G.

2000-01-01

Funding from NASA Grant No. NAG54679 was received in three installments. The first year's installment amounted to only one month of salary support and was used to prepare survey plots. The second year's installment allowed us to complete two research papers concerning the interaction of solar wind discontinuities with the Earth's bow shock. In the first (published) paper, we reported that the discontinuities launch slow mode waves into the magnetosheath and the slow mode waves always propagate antisunward through the flank magnetosheath. Because the sunward/antisunward sense of the magnetosheath magnetic field reverses across local noon, so does the (north/south or east/west) sense of the velocity fluctuations associated with the waves. Wind, Geotail, and IMP-8 observations were used for this study. In the second study, we used Wind and Interball-1 observations to demonstrate that pressure pulses in the magnetosheath occur in pairs and that they bound pressure cavities and/or brief intervals of outward magnetopause motion. This paper is now in press. Funding from the third year's installment has been used to investigate the two aspects of the foreshock. Two manuscripts are now in preparation for submission to the Journal of Geophysical Research. The first reports that waves within the foreshock account for many instances of poor correlations between two solar wind monitors. Remaining cases of poor correlation occur during intervals of nearly constant IMF orientations and magnetic field strengths. While the former category pose a significant difficulty for space weather forecasts, the latter do not. The second study surveys IMP-8 observations of the foreshock. We find that diamagnetic cavities are common, particularly during periods of high solar wind velocity and low solar wind density. Plasma densities, temperatures, and magnetic field strengths fall during intervals of enhanced energetic particle fluxes. The cavities are bounded by regions of decelerated solar wind plasma and enhanced densities and magnetic field strengths.

New Space Weather Forecasting at NOAA with Michigan's Geospace Model

NASA Astrophysics Data System (ADS)

Singer, H. J.; Millward, G. H.; Balch, C. C.; Cash, M. D.; Onsager, T. G.; Toth, G.; Welling, D. T.; Gombosi, T. I.

2016-12-01

We will present first results from the University of Michigan's Geospace model that is transitioning, during 2016, from a research capability into operations at the NOAA Space Weather Prediction Center. The first generation of space weather products from this model will be described. These initial products will support power grid operators, as well as other users, with both global and regional, short-term predictions of geomagnetic activity. The Geospace model is a coupled system including three components: the BATS-R-US magnetohydrodynamic (MHD) model of the magnetosphere; the Ridley ionosphere electrodynamics model (RIM); and the Rice Convection Model (RCM), an inner magnetosphere ring-current model developed at Rice University. The model is driven by solar wind data from a satellite at L1 (now NOAA's DSCOVR satellite) and F10.7, a proxy for solar extreme ultra-violet radiation. The Geospace model runs continuously, driven by the 1-minute cadence real-time L1 data that is propagated to the inflow boundary of the MHD code. The model steps back to an earlier time and then continues forward if high-speed solar wind overtakes slower solar wind. This mode of operation is unique among the models at NOAA's National Center for Environment Prediction's Central Operations (NCO), and it is also different from the typical scientific simulation mode. All of this work has involved 3D graphical model displays and validation tools that are being developed to support forecasters and web-based external users. Lessons learned during the transition process will be described, as well as the iterative process that occurs between Research and Operations and the scientific challenges for future model and product improvements.

AI techniques in geomagnetic storm forecasting

NASA Astrophysics Data System (ADS)

Lundstedt, Henrik

This review deals with how geomagnetic storms can be predicted with the use of Artificial Intelligence (AI) techniques. Today many different Al techniques have been developed, such as symbolic systems (expert and fuzzy systems) and connectionism systems (neural networks). Even integrations of AI techniques exist, so called Intelligent Hybrid Systems (IHS). These systems are capable of learning the mathematical functions underlying the operation of non-linear dynamic systems and also to explain the knowledge they have learned. Very few such powerful systems exist at present. Two such examples are the Magnetospheric Specification Forecast Model of Rice University and the Lund Space Weather Model of Lund University. Various attempts to predict geomagnetic storms on long to short-term are reviewed in this article. Predictions of a month to days ahead most often use solar data as input. The first SOHO data are now available. Due to the high temporal and spatial resolution new solar physics have been revealed. These SOHO data might lead to a breakthrough in these predictions. Predictions hours ahead and shorter rely on real-time solar wind data. WIND gives us real-time data for only part of the day. However, with the launch of the ACE spacecraft in 1997, real-time data during 24 hours will be available. That might lead to the second breakthrough for predictions of geomagnetic storms.

Innovative second-generation wavelets construction with recurrent neural networks for solar radiation forecasting.

PubMed

Capizzi, Giacomo; Napoli, Christian; Bonanno, Francesco

2012-11-01

Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature.

The main pillar: Assessment of space weather observational asset performance supporting nowcasting, forecasting, and research to operations.

PubMed

Posner, A; Hesse, M; St Cyr, O C

2014-04-01

Space weather forecasting critically depends upon availability of timely and reliable observational data. It is therefore particularly important to understand how existing and newly planned observational assets perform during periods of severe space weather. Extreme space weather creates challenging conditions under which instrumentation and spacecraft may be impeded or in which parameters reach values that are outside the nominal observational range. This paper analyzes existing and upcoming observational capabilities for forecasting, and discusses how the findings may impact space weather research and its transition to operations. A single limitation to the assessment is lack of information provided to us on radiation monitor performance, which caused us not to fully assess (i.e., not assess short term) radiation storm forecasting. The assessment finds that at least two widely spaced coronagraphs including L4 would provide reliability for Earth-bound CMEs. Furthermore, all magnetic field measurements assessed fully meet requirements. However, with current or even with near term new assets in place, in the worst-case scenario there could be a near-complete lack of key near-real-time solar wind plasma data of severe disturbances heading toward and impacting Earth's magnetosphere. Models that attempt to simulate the effects of these disturbances in near real time or with archival data require solar wind plasma observations as input. Moreover, the study finds that near-future observational assets will be less capable of advancing the understanding of extreme geomagnetic disturbances at Earth, which might make the resulting space weather models unsuitable for transition to operations. Manuscript assesses current and near-future space weather assetsCurrent assets unreliable for forecasting of severe geomagnetic stormsNear-future assets will not improve the situation.

The main pillar: Assessment of space weather observational asset performance supporting nowcasting, forecasting, and research to operations

PubMed Central

Posner, A; Hesse, M; St Cyr, O C

2014-01-01

Space weather forecasting critically depends upon availability of timely and reliable observational data. It is therefore particularly important to understand how existing and newly planned observational assets perform during periods of severe space weather. Extreme space weather creates challenging conditions under which instrumentation and spacecraft may be impeded or in which parameters reach values that are outside the nominal observational range. This paper analyzes existing and upcoming observational capabilities for forecasting, and discusses how the findings may impact space weather research and its transition to operations. A single limitation to the assessment is lack of information provided to us on radiation monitor performance, which caused us not to fully assess (i.e., not assess short term) radiation storm forecasting. The assessment finds that at least two widely spaced coronagraphs including L4 would provide reliability for Earth-bound CMEs. Furthermore, all magnetic field measurements assessed fully meet requirements. However, with current or even with near term new assets in place, in the worst-case scenario there could be a near-complete lack of key near-real-time solar wind plasma data of severe disturbances heading toward and impacting Earth's magnetosphere. Models that attempt to simulate the effects of these disturbances in near real time or with archival data require solar wind plasma observations as input. Moreover, the study finds that near-future observational assets will be less capable of advancing the understanding of extreme geomagnetic disturbances at Earth, which might make the resulting space weather models unsuitable for transition to operations. Key Points Manuscript assesses current and near-future space weather assets Current assets unreliable for forecasting of severe geomagnetic storms Near-future assets will not improve the situation PMID:26213516

The role of MEXART in the National Space Weather Laboratory of Mexico: Detection of solar wind, CMEs, ionosphere, active regions and flares.

NASA Astrophysics Data System (ADS)

Mejia-Ambriz, J.; Gonzalez-Esparza, A.; De la Luz, V.; Villanueva-Hernandez, P.; Andrade, E.; Aguilar-Rodriguez, E.; Chang, O.; Romero Hernandez, E.; Sergeeva, M. A.; Perez Alanis, C. A.; Reyes-Marin, P. A.

2017-12-01

The National Space Weather Laboratory - Laboratorio Nacional de Clima Espacial (LANCE) - of Mexico has different ground based instruments to study and monitor the space weather. One of these instruments is the Mexican Array Radio Telescope (MEXART) which is principally dedicated to remote sensing the solar wind and coronal mass ejections (CMEs) at 140 MHz, the instrument can detect solar wind densities and speeds from about 0.4 to 1 AU by modeling observations of interplanetary scintillation (IPS). MEXART is also able to detect ionospheric disturbances associated with transient space weather events by the analysis of ionospheric scintillation (IONS) . Additionally, MEXART has followed the Sun since the beginning of the current Solar Cycle 24 with records of 8 minutes per day, and occasionally, has partially detected the process of strong solar flares. Here we show the contributions of MEXART to the LANCE by reporting recent detections of CMEs by IPS, the arrive of transient events at Earth by IONS, the influence of active regions in the flux of the Sun at 140 MHz and the detection of a M6.5 class flare. Furthermore we report the status of a near real time analysis of IPS data for forecast purposes and the potential contribution to the Worldwide IPS Stations network (WIPSS), which is an effort to achieve a better coverage of the solar wind observations in the inner heliosphere.

Wind and Solar Energy Resource Assessment for Navy Installations in the Midwestern US

NASA Astrophysics Data System (ADS)

Darmenova, K.; Apling, D.; Higgins, G. J.; Carnes, J.; Smith, C.

2012-12-01

A stable supply of energy is critical for sustainable economic development and the ever-increasing demand for energy resources drives the need for alternative weather-driven renewable energy solutions such as solar and wind-generated power. Recognizing the importance of energy as a strategic resource, the Department of the Navy has focused on energy efficient solutions aiming to increase tactical and shore energy security and reduce greenhouse gas emissions. Implementing alternative energy solutions will alleviate the Navy installations demands on the National power grid, however transitioning to renewable energy sources is a complex multi-stage process that involves initial investment in resource assessment and feasibility of building solar and wind power systems in Navy's facilities. This study focuses on the wind and solar energy resource assessment for Navy installations in the Midwestern US. We use the dynamically downscaled datasets at 12 km resolution over the Continental US generated with the Weather Research and Forecasting (WRF) model to derive the wind climatology in terms of wind speed, direction, and wind power at 20 m above the surface for 65 Navy facilities. In addition, we derived the transmissivity of the atmosphere, diffuse radiation fraction, cloud cover and seasonal energy potential for a zenith facing surface with unobstructed horizon for each installation location based on the results of a broadband radiative transfer model and our cloud database based on 17-years of GOES data. Our analysis was incorporated in a GIS framework in combination with additional infrastructure data that enabled a synergistic resource assessment based on the combination of climatological and engineering factors.

Forecast for solar cycle 23 activity: a progress report

NASA Astrophysics Data System (ADS)

Ahluwalia, H. S.

2001-08-01

At the 25th International Cosmic Ray Conference (ICRC) at Durban, South Africa, I announced the discovery of a three cycle quasi-periodicity in the ion chamber data string assembled by me, for the 1937 to 1994 period (Conf. Pap., v. 2, p. 109, 1997). It corresponded in time with a similar quasi-periodicity observed in the dataset for the planetary index Ap. At the 26th ICRC at Salt Lake City, UT, I reported on our analysis of the Ap data to forecast the amplitude of solar cycle 23 activity (Conf. Pap., v. 2, pl. 260, 1999). I predicted that cycle 23 will be moderate (a la cycle 17), notwithstanding the early exuberant forecasts of some solar astronomers that cycle 23, "may be one of the greatest cycles in recent times, if not the greatest." Sunspot number data up to April 2001 indicate that our forecast appears to be right on the mark. We review the solar, interplanetary and geophysical data and describe the important lessons learned from this experience. 1. Introduction Ohl (1971) was the first to realize that Sun may be sending us a subliminal message as to its intent for its activity (Sunspot Numbers, SSN) in the next cycle. He posited that the message was embedded in the geomagnetic activity (given by sum Kp). Schatten at al (1978) suggested that Ohl hypothesis could be understood on the basis of the model proposed by Babcock (1961) who suggested that the high latitude solar poloidal fields, near a minimum, emerge as the toroidal fields on opposite sides of the solar equator. This is known as the Solar Dynamo Model. One can speculate that the precursor poloidal solar field is entrained in the high speed solar wind streams (HSSWS) from the coronal holes which are observed at Earth's orbit during the descending phase of the previous cycle. The interaction

Space Environment Modelling with the Use of Artificial Intelligence Methods

NASA Astrophysics Data System (ADS)

Lundstedt, H.; Wintoft, P.; Wu, J.-G.; Gleisner, H.; Dovheden, V.

1996-12-01

Space based technological systems are affected by the space weather in many ways. Several severe failures of satellites have been reported at times of space storms. Our society also increasingly depends on satellites for communication, navigation, exploration, and research. Predictions of the conditions in the satellite environment have therefore become very important. We will here present predictions made with the use of artificial intelligence (AI) techniques, such as artificial neural networks (ANN) and hybrids of AT methods. We are developing a space weather model based on intelligence hybrid systems (IHS). The model consists of different forecast modules, each module predicts the space weather on a specific time-scale. The time-scales range from minutes to months with the fundamental time-scale of 1-5 minutes, 1-3 hours, 1-3 days, and 27 days. Solar and solar wind data are used as input data. From solar magnetic field measurements, either made on the ground at Wilcox Solar Observatory (WSO) at Stanford, or made from space by the satellite SOHO, solar wind parameters can be predicted and modelled with ANN and MHD models. Magnetograms from WSO are available on a daily basis. However, from SOHO magnetograms will be available every 90 minutes. SOHO magnetograms as input to ANNs will therefore make it possible to even predict solar transient events. Geomagnetic storm activity can today be predicted with very high accuracy by means of ANN methods using solar wind input data. However, at present real-time solar wind data are only available during part of the day from the satellite WIND. With the launch of ACE in 1997, solar wind data will on the other hand be available during 24 hours per day. The conditions of the satellite environment are not only disturbed at times of geomagnetic storms but also at times of intense solar radiation and highly energetic particles. These events are associated with increased solar activity. Predictions of these events are therefore also handled with the modules in the Lund Space Weather Model. Interesting Links: Lund Space Weather and AI Center

Wind power forecasting: IEA Wind Task 36 & future research issues

NASA Astrophysics Data System (ADS)

Giebel, G.; Cline, J.; Frank, H.; Shaw, W.; Pinson, P.; Hodge, B.-M.; Kariniotakis, G.; Madsen, J.; Möhrlen, C.

2016-09-01

This paper presents the new International Energy Agency Wind Task 36 on Forecasting, and invites to collaborate within the group. Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national meteorological centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, MetOffice, met.no, DMI,...), operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions. As first results, an overview of current issues for research in short-term forecasting of wind power is presented.

Comparative study of predicted and experimentally detected interplanetary shocks

NASA Astrophysics Data System (ADS)

Kartalev, M. D.; Grigorov, K. G.; Smith, Z.; Dryer, M.; Fry, C. D.; Sun, Wei; Deehr, C. S.

2002-03-01

We compare the real time space weather prediction shock arrival times at 1 AU made by the USAF/NOAA Shock Time of Arrival (STOA) and Interplanetary Shock Propagation Model (ISPM) models, and the Exploration Physics International/University of Alaska Hakamada-Akasofu-Fry Solar Wind Model (HAF-v2) to a real time analysis analysis of plasma and field ACE data. The comparison is made using an algorithm that was developed on the basis of wavelet data analysis and MHD identification procedure. The shock parameters are estimated for selected "candidate events". An appropriate automatically performing Web-based interface periodically utilizes solar wind observations made by the ACE at L1. Near real time results as well an archive of the registered interesting events are available on a specially developed web site. A number of events are considered. These studies are essential for the validation of real time space weather forecasts made from solar data.

A New Polar Magnetic Index of Geomagnetic Activity and its Application to Monitoring Ionospheric Parameters

NASA Technical Reports Server (NTRS)

Lyatsky, Wladislaw; Khazanov, George V.

2008-01-01

For improving the reliability of Space Weather prediction, we developed a new, Polar Magnetic (PM) index of geomagnetic activity, which shows high correlation with both upstream solar wind data and related events in the magnetosphere and ionosphere. Similarly to the existing polar cap PC index, the new, PM index was computed from data from two near-pole geomagnetic observatories; however, the method for computing the PM index is different. The high correlation of the PM index with both solar wind data and events in Geospace environment makes possible to improve significantly forecasting geomagnetic disturbances and such important parameters as the cross-polar-cap voltage and global Joule heating in high latitude ionosphere, which play an important role in the development of geomagnetic, ionospheric and thermospheric disturbances. We tested the PM index for 10-year period (1995-2004). The correlation between PM index and upstream solar wind data for these years is very high (the average correlation coefficient R approximately equal to 0.86). The PM index also shows the high correlation with the cross-polar-cap voltage and hemispheric Joule heating (the correlation coefficient between the actual and predicted values of these parameters is approximately 0.9), which results in significant increasing the prediction reliability of these parameters. Using the PM index of geomagnetic activity provides a significant increase in the forecasting reliability of geomagnetic disturbances and related events in Geospace environment. The PM index may be also used as an important input parameter in modeling ionospheric, magnetospheric, and thermospheric processes.

The Main Pillar: Assessment of Space Weather Observational Asset Performance Supporting Nowcasting, Forecasting and Research to Operations

NASA Technical Reports Server (NTRS)

Posner, Arik; Hesse, Michael; SaintCyr, Chris

2014-01-01

Space weather forecasting critically depends upon availability of timely and reliable observational data. It is therefore particularly important to understand how existing and newly planned observational assets perform during periods of severe space weather. Extreme space weather creates challenging conditions under which instrumentation and spacecraft may be impeded or in which parameters reach values that are outside the nominal observational range. This paper analyzes existing and upcoming observational capabilities for forecasting, and discusses how the findings may impact space weather research and its transition to operations. A single limitation to the assessment is lack of information provided to us on radiation monitor performance, which caused us not to fully assess (i.e., not assess short term) radiation storm forecasting. The assessment finds that at least two widely spaced coronagraphs including L4 would provide reliability for Earth-bound CMEs. Furthermore, all magnetic field measurements assessed fully meet requirements. However, with current or even with near term new assets in place, in the worst-case scenario there could be a near-complete lack of key near-real-time solar wind plasma data of severe disturbances heading toward and impacting Earth's magnetosphere. Models that attempt to simulate the effects of these disturbances in near real time or with archival data require solar wind plasma observations as input. Moreover, the study finds that near-future observational assets will be less capable of advancing the understanding of extreme geomagnetic disturbances at Earth, which might make the resulting space weather models unsuitable for transition to operations.

Validation of Community Models: 2. Development of a Baseline, Using the Wang-Sheeley-Arge Model

NASA Technical Reports Server (NTRS)

MacNeice, Peter

2009-01-01

This paper is the second in a series providing independent validation of community models of the outer corona and inner heliosphere. Here I present a comprehensive validation of the Wang-Sheeley-Arge (WSA) model. These results will serve as a baseline against which to compare the next generation of comparable forecasting models. The WSA model is used by a number of agencies to predict Solar wind conditions at Earth up to 4 days into the future. Given its importance to both the research and forecasting communities, it is essential that its performance be measured systematically and independently. I offer just such an independent and systematic validation. I report skill scores for the model's predictions of wind speed and interplanetary magnetic field (IMF) polarity for a large set of Carrington rotations. The model was run in all its routinely used configurations. It ingests synoptic line of sight magnetograms. For this study I generated model results for monthly magnetograms from multiple observatories, spanning the Carrington rotation range from 1650 to 2074. I compare the influence of the different magnetogram sources and performance at quiet and active times. I also consider the ability of the WSA model to forecast both sharp transitions in wind speed from slow to fast wind and reversals in the polarity of the radial component of the IMF. These results will serve as a baseline against which to compare future versions of the model as well as the current and future generation of magnetohydrodynamic models under development for forecasting use.

Wind power forecasting: IEA Wind Task 36 & future research issues

DOE PAGES

Giebel, G.; Cline, J.; Frank, H.; ...

2016-10-03

Here, this paper presents the new International Energy Agency Wind Task 36 on Forecasting, and invites to collaborate within the group. Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national meteorological centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, MetOffice, met.no, DMI,...), operational forecaster and forecast users. The Taskmore » is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions. As first results, an overview of current issues for research in short-term forecasting of wind power is presented.« less

Peak Wind Forecasts for the Launch-Critical Wind Towers on Kennedy Space Center/Cape Canaveral Air Force Station, Phase IV

NASA Technical Reports Server (NTRS)

Crawford, Winifred

2011-01-01

This final report describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The peak winds arc an important forecast clement for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to update the statistics in the current peak-wind forecast tool to assist in forecasting LCC violations. The tool includes onshore and offshore flow climatologies of the 5-minute mean and peak winds and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

Solar Wind Acceleration: Modeling Effects of Turbulent Heating in Open Flux Tubes

NASA Astrophysics Data System (ADS)

Woolsey, Lauren N.; Cranmer, Steven R.

2014-06-01

We present two self-consistent coronal heating models that determine the properties of the solar wind generated and accelerated in magnetic field geometries that are open to the heliosphere. These models require only the radial magnetic field profile as input. The first code, ZEPHYR (Cranmer et al. 2007) is a 1D MHD code that includes the effects of turbulent heating created by counter-propagating Alfven waves rather than relying on empirical heating functions. We present the analysis of a large grid of modeled flux tubes (> 400) and the resulting solar wind properties. From the models and results, we recreate the observed anti-correlation between wind speed at 1 AU and the so-called expansion factor, a parameterization of the magnetic field profile. We also find that our models follow the same observationally-derived relation between temperature at 1 AU and wind speed at 1 AU. We continue our analysis with a newly-developed code written in Python called TEMPEST (The Efficient Modified-Parker-Equation-Solving Tool) that runs an order of magnitude faster than ZEPHYR due to a set of simplifying relations between the input magnetic field profile and the temperature and wave reflection coefficient profiles. We present these simplifying relations as a useful result in themselves as well as the anti-correlation between wind speed and expansion factor also found with TEMPEST. Due to the nature of the algorithm TEMPEST utilizes to find solar wind solutions, we can effectively separate the two primary ways in which Alfven waves contribute to solar wind acceleration: 1) heating the surrounding gas through a turbulent cascade and 2) providing a separate source of wave pressure. We intend to make TEMPEST easily available to the public and suggest that TEMPEST can be used as a valuable tool in the forecasting of space weather, either as a stand-alone code or within an existing modeling framework.

Using Information Processing Techniques to Forecast, Schedule, and Deliver Sustainable Energy to Electric Vehicles

NASA Astrophysics Data System (ADS)

Pulusani, Praneeth R.

As the number of electric vehicles on the road increases, current power grid infrastructure will not be able to handle the additional load. Some approaches in the area of Smart Grid research attempt to mitigate this, but those approaches alone will not be sufficient. Those approaches and traditional solution of increased power production can result in an insufficient and imbalanced power grid. It can lead to transformer blowouts, blackouts and blown fuses, etc. The proposed solution will supplement the ``Smart Grid'' to create a more sustainable power grid. To solve or mitigate the magnitude of the problem, measures can be taken that depend on weather forecast models. For instance, wind and solar forecasts can be used to create first order Markov chain models that will help predict the availability of additional power at certain times. These models will be used in conjunction with the information processing layer and bidirectional signal processing components of electric vehicle charging systems, to schedule the amount of energy transferred per time interval at various times. The research was divided into three distinct components: (1) Renewable Energy Supply Forecast Model, (2) Energy Demand Forecast from PEVs, and (3) Renewable Energy Resource Estimation. For the first component, power data from a local wind turbine, and weather forecast data from NOAA were used to develop a wind energy forecast model, using a first order Markov chain model as the foundation. In the second component, additional macro energy demand from PEVs in the Greater Rochester Area was forecasted by simulating concurrent driving routes. In the third component, historical data from renewable energy sources was analyzed to estimate the renewable resources needed to offset the energy demand from PEVs. The results from these models and components can be used in the smart grid applications for scheduling and delivering energy. Several solutions are discussed to mitigate the problem of overloading transformers, lack of energy supply, and higher utility costs.

The solar polar radio telescope mission: an overview

NASA Astrophysics Data System (ADS)

Sun, Weiying; Zhang, Cheng; Zheng, Jianhua; Wu, Ji; Wang, C. B.; Wang, Chi; Wang, S.

: The solar polar orbit telescope (SPORT) is a mission proposed for the observation of ICMEs. The main payload is a synthetic aperture radiometer working at meter wave band taking images of the high density interplanetary plasma clouds formed by ICMEs and follows the propagation if it from the surface of the Sun all the way to as far as 0.5 AU or even further. With such a capability of observation, also the SPORT will study transient high energy phenomenon, the magnetic topology, temperature and density as well as velocity of the solar wind in the inner interplanetary heliosphere. In the practical part, the mission is also very useful for space weather forecast in advance of the geo-storm events. Other instruments are also selected to be on board of the solar polar orbit mission for in-situ measurement, such as fluxgate magnetometer, solar wind ion detector and high energy particle detectors. In this paper, we describe the scientific objective, basic principles and feasibility of the interferometric radiometer, general mission design and the status of the SPORT mission.

Statistical Short-Range Guidance for Peak Wind Speed Forecasts on Kennedy Space Center/Cape Canaveral Air Force Station: Phase I Results

NASA Technical Reports Server (NTRS)

Lambert, Winifred C.; Merceret, Francis J. (Technical Monitor)

2002-01-01

This report describes the results of the ANU's (Applied Meteorology Unit) Short-Range Statistical Forecasting task for peak winds. The peak wind speeds are an important forecast element for the Space Shuttle and Expendable Launch Vehicle programs. The Keith Weather Squadron and the Spaceflight Meteorology Group indicate that peak winds are challenging to forecast. The Applied Meteorology Unit was tasked to develop tools that aid in short-range forecasts of peak winds at tower sites of operational interest. A 7 year record of wind tower data was used in the analysis. Hourly and directional climatologies by tower and month were developed to determine the seasonal behavior of the average and peak winds. In all climatologies, the average and peak wind speeds were highly variable in time. This indicated that the development of a peak wind forecasting tool would be difficult. Probability density functions (PDF) of peak wind speed were calculated to determine the distribution of peak speed with average speed. These provide forecasters with a means of determining the probability of meeting or exceeding a certain peak wind given an observed or forecast average speed. The climatologies and PDFs provide tools with which to make peak wind forecasts that are critical to safe operations.

A short-term ensemble wind speed forecasting system for wind power applications

NASA Astrophysics Data System (ADS)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

Developing a Peak Wind Probability Forecast Tool for Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Lambert, WInifred; Roeder, William

2007-01-01

This conference presentation describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) in east-central Florida. The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violations. The tool will include climatologies of the 5-minute mean and peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

Statistical Short-Range Guidance for Peak Wind Forecasts on Kennedy Space Center/Cape Canaveral Air Force Station, Phase III

NASA Technical Reports Server (NTRS)

Crawford, Winifred

2010-01-01

This final report describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violations.The tool includes climatologies of the 5-minute mean and peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

A Peak Wind Probability Forecast Tool for Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Crawford, Winifred; Roeder, William

2008-01-01

This conference abstract describes the development of a peak wind forecast tool to assist forecasters in determining the probability of violating launch commit criteria (LCC) at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) in east-central Florida. The peak winds are an important forecast element for both the Space Shuttle and Expendable Launch Vehicle (ELV) programs. The LCC define specific peak wind thresholds for each launch operation that cannot be exceeded in order to ensure the safety of the vehicle. The 45th Weather Squadron (45 WS) has found that peak winds are a challenging parameter to forecast, particularly in the cool season months of October through April. Based on the importance of forecasting peak winds, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a short-range peak-wind forecast tool to assist in forecasting LCC violatioas.The tool will include climatologies of the 5-minute mean end peak winds by month, hour, and direction, and probability distributions of the peak winds as a function of the 5-minute mean wind speeds.

Post-processing method for wind speed ensemble forecast using wind speed and direction

NASA Astrophysics Data System (ADS)

Sofie Eide, Siri; Bjørnar Bremnes, John; Steinsland, Ingelin

2017-04-01

Statistical methods are widely applied to enhance the quality of both deterministic and ensemble NWP forecasts. In many situations, like wind speed forecasting, most of the predictive information is contained in one variable in the NWP models. However, in statistical calibration of deterministic forecasts it is often seen that including more variables can further improve forecast skill. For ensembles this is rarely taken advantage of, mainly due to that it is generally not straightforward how to include multiple variables. In this study, it is demonstrated how multiple variables can be included in Bayesian model averaging (BMA) by using a flexible regression method for estimating the conditional means. The method is applied to wind speed forecasting at 204 Norwegian stations based on wind speed and direction forecasts from the ECMWF ensemble system. At about 85 % of the sites the ensemble forecasts were improved in terms of CRPS by adding wind direction as predictor compared to only using wind speed. On average the improvements were about 5 %, but mainly for moderate to strong wind situations. For weak wind speeds adding wind direction had more or less neutral impact.

KSC-2015-1304

NASA Image and Video Library

2015-02-07

CAPE CANAVERAL, Fla. – Mike McAleenan, launch weather officer with the U.S. Air Force 45th Weather Squadron, provides an on the launch-day forecast during a briefing regarding NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR. DSCOVR will launch aboard a SpaceX Falcon 9 rocket. The mission is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Kim Shiflett

A Novel Wind Speed Forecasting Model for Wind Farms of Northwest China

NASA Astrophysics Data System (ADS)

Wang, Jian-Zhou; Wang, Yun

2017-01-01

Wind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon's Signed-Rank test, and Morgan-Granger-Newbold test tell us that the proposed model is different from the compared models.

DSCOVR Satellite Deploy & Light Test

NASA Image and Video Library

2014-11-24

Workers deploy the solar arrays on NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, in the Building 1 high bay at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is targeted for early 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida.

DSCOVR Satellite Deploy & Light Test

NASA Image and Video Library

2014-11-24

The solar arrays on NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, are unfurled in the Building 1 high bay at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is targeted for early 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida.

Can We Predict CME Deflections Based on Solar Magnetic Field Configuration Alone?

NASA Astrophysics Data System (ADS)

Kay, C.; Opher, M.; Evans, R. M.

2013-12-01

Accurate space weather forecasting requires knowledge of the trajectory of coronal mass ejections (CMEs), including predicting CME deflections close to the Sun and through interplanetary space. Deflections of CMEs occur due to variations in the background magnetic field or solar wind speed, magnetic reconnection, and interactions with other CMEs. Using our newly developed model of CME deflections due to gradients in the background solar magnetic field, ForeCAT (Kay et al. 2013), we explore the questions: (a) do all simulated CMEs ultimately deflect to the minimum in the background solar magnetic field? (b) does the majority of the deflection occur in the lower corona below 4 Rs? ForeCAT does not include temporal variations in the magnetic field of active regions (ARs), spatial variations in the background solar wind speed, magnetic reconnection, or interactions with other CMEs. Therefore we focus on the effects of the steady state solar magnetic field. We explore two different Carrington Rotations (CRs): CR 2029 (April-May 2005) and CR 2077 (November-December 2008). Little is known about how the density and magnetic field fall with distance in the lower corona. We consider four density models derived from observations (Chen 1996, Mann et al. 2003, Guhathakurta et al. 2006, Leblanc et al. 1996) and two magnetic field models (PFSS and a scaled model). ForeCAT includes drag resulting from both CME propagation and deflection through the background solar wind. We vary the drag coefficient to explore the effect of drag on the deflection at 1 AU.

Forecasting Kp from solar wind data: input parameter study using 3-hour averages and 3-hour range values

NASA Astrophysics Data System (ADS)

Wintoft, Peter; Wik, Magnus; Matzka, Jürgen; Shprits, Yuri

2017-11-01

We have developed neural network models that predict Kp from upstream solar wind data. We study the importance of various input parameters, starting with the magnetic component Bz, particle density n, and velocity V and then adding total field B and the By component. As we also notice a seasonal and UT variation in average Kp we include functions of day-of-year and UT. Finally, as Kp is a global representation of the maximum range of geomagnetic variation over 3-hour UT intervals we conclude that sudden changes in the solar wind can have a big effect on Kp, even though it is a 3-hour value. Therefore, 3-hour solar wind averages will not always appropriately represent the solar wind condition, and we introduce 3-hour maxima and minima values to some degree address this problem. We find that introducing total field B and 3-hour maxima and minima, derived from 1-minute solar wind data, have a great influence on the performance. Due to the low number of samples for high Kp values there can be considerable variation in predicted Kp for different networks with similar validation errors. We address this issue by using an ensemble of networks from which we use the median predicted Kp. The models (ensemble of networks) provide prediction lead times in the range 20-90 min given by the time it takes a solar wind structure to travel from L1 to Earth. Two models are implemented that can be run with real time data: (1) IRF-Kp-2017-h3 uses the 3-hour averages of the solar wind data and (2) IRF-Kp-2017 uses in addition to the averages, also the minima and maxima values. The IRF-Kp-2017 model has RMS error of 0.55 and linear correlation of 0.92 based on an independent test set with final Kp covering 2 years using ACE Level 2 data. The IRF-Kp-2017-h3 model has RMSE = 0.63 and correlation = 0.89. We also explore the errors when tested on another two-year period with real-time ACE data which gives RMSE = 0.59 for IRF-Kp-2017 and RMSE = 0.73 for IRF-Kp-2017-h3. The errors as function of Kp and for different years are also studied.

Integrating Solar PV in Utility System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mills, A.; Botterud, A.; Wu, J.

2013-10-31

This study develops a systematic framework for estimating the increase in operating costs due to uncertainty and variability in renewable resources, uses the framework to quantify the integration costs associated with sub-hourly solar power variability and uncertainty, and shows how changes in system operations may affect these costs. Toward this end, we present a statistical method for estimating the required balancing reserves to maintain system reliability along with a model for commitment and dispatch of the portfolio of thermal and renewable resources at different stages of system operations. We estimate the costs of sub-hourly solar variability, short-term forecast errors, andmore » day-ahead (DA) forecast errors as the difference in production costs between a case with “realistic” PV (i.e., subhourly solar variability and uncertainty are fully included in the modeling) and a case with “well behaved” PV (i.e., PV is assumed to have no sub-hourly variability and can be perfectly forecasted). In addition, we highlight current practices that allow utilities to compensate for the issues encountered at the sub-hourly time frame with increased levels of PV penetration. In this analysis we use the analytical framework to simulate utility operations with increasing deployment of PV in a case study of Arizona Public Service Company (APS), a utility in the southwestern United States. In our analysis, we focus on three processes that are important in understanding the management of PV variability and uncertainty in power system operations. First, we represent the decisions made the day before the operating day through a DA commitment model that relies on imperfect DA forecasts of load and wind as well as PV generation. Second, we represent the decisions made by schedulers in the operating day through hour-ahead (HA) scheduling. Peaking units can be committed or decommitted in the HA schedules and online units can be redispatched using forecasts that are improved relative to DA forecasts, but still imperfect. Finally, we represent decisions within the operating hour by schedulers and transmission system operators as real-time (RT) balancing. We simulate the DA and HA scheduling processes with a detailed unit-commitment (UC) and economic dispatch (ED) optimization model. This model creates a least-cost dispatch and commitment plan for the conventional generating units using forecasts and reserve requirements as inputs. We consider only the generation units and load of the utility in this analysis; we do not consider opportunities to trade power with neighboring utilities. We also do not consider provision of reserves from renewables or from demand-side options. We estimate dynamic reserve requirements in order to meet reliability requirements in the RT operations, considering the uncertainty and variability in load, solar PV, and wind resources. Balancing reserve requirements are based on the 2.5th and 97.5th percentile of 1-min deviations from the HA schedule in a previous year. We then simulate RT deployment of balancing reserves using a separate minute-by-minute simulation of deviations from the HA schedules in the operating year. In the simulations we assume that balancing reserves can be fully deployed in 10 min. The minute-by-minute deviations account for HA forecasting errors and the actual variability of the load, wind, and solar generation. Using these minute-by-minute deviations and deployment of balancing reserves, we evaluate the impact of PV on system reliability through the calculation of the standard reliability metric called Control Performance Standard 2 (CPS2). Broadly speaking, the CPS2 score measures the percentage of 10-min periods in which a balancing area is able to balance supply and demand within a specific threshold. Compliance with the North American Electric Reliability Corporation (NERC) reliability standards requires that the CPS2 score must exceed 90% (i.e., the balancing area must maintain adequate balance for 90% of the 10-min periods). The combination of representing DA forecast errors in the DA commitments, using 1-min PV data to simulate RT balancing, and estimates of reliability performance through the CPS2 metric, all factors that are important to operating systems with increasing amounts of PV, makes this study unique in its scope.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Giebel, G.; Cline, J.; Frank, H.

Here, this paper presents the new International Energy Agency Wind Task 36 on Forecasting, and invites to collaborate within the group. Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national meteorological centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, MetOffice, met.no, DMI,...), operational forecaster and forecast users. The Taskmore » is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions. As first results, an overview of current issues for research in short-term forecasting of wind power is presented.« less

A Wind Forecasting System for Energy Application

NASA Astrophysics Data System (ADS)

Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

2010-05-01

Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated probabilistic wind forecasts which will be invaluable in wind energy management. In brief, this method turns the ensemble forecasts into a calibrated predictive probability distribution. Each ensemble member is provided with a 'weight' determined by its relative predictive skill over a training period of around 30 days. Verification of data is carried out using observed wind data from operational wind farms. These are then compared to existing forecasts produced by ECMWF and Met Eireann in relation to skill scores. We are developing decision-making models to show the benefits achieved using the data produced by our wind energy forecasting system. An energy trading model will be developed, based on the rules currently used by the Single Electricity Market Operator for energy trading in Ireland. This trading model will illustrate the potential for financial savings by using the forecast data generated by this research.

Ramping and Uncertainty Prediction Tool - Analysis and Visualization of Wind Generation Impact on Electrical Grid

DOE Office of Scientific and Technical Information (OSTI.GOV)

Etingov, Pavel; Makarov, PNNL Yuri; Subbarao, PNNL Kris

RUT software is designed for use by the Balancing Authorities to predict and display additional requirements caused by the variability and uncertainty in load and generation. The prediction is made for the next operating hours as well as for the next day. The tool predicts possible deficiencies in generation capability and ramping capability. This deficiency of balancing resources can cause serious risks to power system stability and also impact real-time market energy prices. The tool dynamically and adaptively correlates changing system conditions with the additional balancing needs triggered by the interplay between forecasted and actual load and output of variablemore » resources. The assessment is performed using a specially developed probabilistic algorithm incorporating multiple sources of uncertainty including wind, solar and load forecast errors. The tool evaluates required generation for a worst case scenario, with a user-specified confidence level.« less

Near real time wind energy forecasting incorporating wind tunnel modeling

NASA Astrophysics Data System (ADS)

Lubitz, William David

A series of experiments and investigations were carried out to inform the development of a day-ahead wind power forecasting system. An experimental near-real time wind power forecasting system was designed and constructed that operates on a desktop PC and forecasts 12--48 hours in advance. The system uses model output of the Eta regional scale forecast (RSF) to forecast the power production of a wind farm in the Altamont Pass, California, USA from 12 to 48 hours in advance. It is of modular construction and designed to also allow diagnostic forecasting using archived RSF data, thereby allowing different methods of completing each forecasting step to be tested and compared using the same input data. Wind-tunnel investigations of the effect of wind direction and hill geometry on wind speed-up above a hill were conducted. Field data from an Altamont Pass, California site was used to evaluate several speed-up prediction algorithms, both with and without wind direction adjustment. These algorithms were found to be of limited usefulness for the complex terrain case evaluated. Wind-tunnel and numerical simulation-based methods were developed for determining a wind farm power curve (the relation between meteorological conditions at a point in the wind farm and the power production of the wind farm). Both methods, as well as two methods based on fits to historical data, ultimately showed similar levels of accuracy: mean absolute errors predicting power production of 5 to 7 percent of the wind farm power capacity. The downscaling of RSF forecast data to the wind farm was found to be complicated by the presence of complex terrain. Poor results using the geostrophic drag law and regression methods motivated the development of a database search method that is capable of forecasting not only wind speeds but also power production with accuracy better than persistence.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Cui, Mingjian; Hodge, Bri-Mathias

The large variability and uncertainty in wind power generation present a concern to power system operators, especially given the increasing amounts of wind power being integrated into the electric power system. Large ramps, one of the biggest concerns, can significantly influence system economics and reliability. The Wind Forecast Improvement Project (WFIP) was to improve the accuracy of forecasts and to evaluate the economic benefits of these improvements to grid operators. This paper evaluates the ramp forecasting accuracy gained by improving the performance of short-term wind power forecasting. This study focuses on the WFIP southern study region, which encompasses most ofmore » the Electric Reliability Council of Texas (ERCOT) territory, to compare the experimental WFIP forecasts to the existing short-term wind power forecasts (used at ERCOT) at multiple spatial and temporal scales. The study employs four significant wind power ramping definitions according to the power change magnitude, direction, and duration. The optimized swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental WFIP forecasts improve the accuracy of the wind power ramp forecasting. This improvement can result in substantial costs savings and power system reliability enhancements.« less

3D electron density distributions in the solar corona during solar minima: assessment for more realistic solar wind modeling

NASA Astrophysics Data System (ADS)

de Patoul, J.; Foullon, C.; Riley, P.

2015-12-01

Knowledge of the electron density distribution in the solar corona put constraints on the magnetic field configurations for coronal modeling, and on initial conditions for solar wind modeling. We work with polarized SOHO/LASCO-C2 images from the last two recent minima of solar activity (1996-1997 and 2008-2010), devoid of coronal mass ejections. We derive the 4D electron density distributions in the corona by applying a newly developed time-dependent tomographic reconstruction method. First we compare the density distributions obtained from tomography with magnetohydrodynamic (MHD) solutions. The tomography provides more accurate distributions of electron densities in the polar regions, and we find that the observed density varies with the solar cycle in both polar and equatorial regions. Second, we find that the highest-density structures do not always correspond to the predicted large-scale heliospheric current sheet or its helmet streamer but can follow the locations of pseudo-streamers. We conclude that tomography offers reliable density distribution in the corona, reproducing the slow time evolution of coronal structures, without prior knowledge of the coronal magnetic field over a full rotation. Finally, we suggest that the highest-density structures show a differential rotation well above the surface depending on how it is magnetically connected to the surface. Such valuable information on the rotation of large-scale structures could help to connect the sources of the solar wind to their in-situ counterparts in future missions such as Solar Orbiter and Solar Probe Plus. This research combined with the MHD coronal modeling efforts has the potential to increase the reliability for future space weather forecasting.

Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station (CCAFS)

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III; Roeder, William P.

2010-01-01

Peak wind speed is important element in 24-Hour and Weekly Planning Forecasts issued by 45th Weather Squadron (45 WS). Forecasts issued for planning operations at KSC/CCAFS. 45 WS wind advisories issued for wind gusts greater than or equal to 25 kt. 35 kt and 50 kt from surface to 300 ft. AMU developed cool-season (Oct - Apr) tool to help 45 WS forecast: daily peak wind speed, 5-minute average speed at time of peak wind, and probability peak speed greater than or equal to 25 kt, 35 kt, 50 kt. AMU tool also forecasts daily average wind speed from 30 ft to 60 ft. Phase I and II tools delivered as a Microsoft Excel graphical user interface (GUI). Phase II tool also delivered as Meteorological Interactive Data Display System (MIDDS) GUI. Phase I and II forecast methods were compared to climatology, 45 WS wind advisories and North American Mesoscale model (MesoNAM) forecasts in a verification data set.

Statistical Short-Range Guidance for Peak Wind Speed Forecasts at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Dreher, Joseph G.; Crawford, Winifred; Lafosse, Richard; Hoeth, Brian; Burns, Kerry

2009-01-01

The peak winds near the surface are an important forecast element for space shuttle landings. As defined in the Flight Rules (FR), there are peak wind thresholds that cannot be exceeded in order to ensure the safety of the shuttle during landing operations. The National Weather Service Spaceflight Meteorology Group (SMG) is responsible for weather forecasts for all shuttle landings, and is required to issue surface average and 10-minute peak wind speed forecasts. They indicate peak winds are a challenging parameter to forecast. To alleviate the difficulty in making such wind forecasts, the Applied Meteorology Unit (AMU) developed a PC-based graphical user interface (GUI) for displaying peak wind climatology and probabilities of exceeding peak wind thresholds for the Shuttle Landing Facility (SLF) at Kennedy Space Center (KSC; Lambert 2003). However, the shuttle occasionally may land at Edwards Air Force Base (EAFB) in southern California when weather conditions at KSC in Florida are not acceptable, so SMG forecasters requested a similar tool be developed for EAFB.

Impact of Lidar Wind Sounding on Mesoscale Forecast

NASA Technical Reports Server (NTRS)

Miller, Timothy L.; Chou, Shih-Hung; Goodman, H. Michael (Technical Monitor)

2001-01-01

An Observing System Simulation Experiment (OSSE) was conducted to study the impact of airborne lidar wind sounding on mesoscale weather forecast. A wind retrieval scheme, which interpolates wind data from a grid data system, simulates the retrieval of wind profile from a satellite lidar system. A mesoscale forecast system based on the PSU/NCAR MM5 model is developed and incorporated the assimilation of the retrieved line-of-sight wind. To avoid the "identical twin" problem, the NCEP reanalysis data is used as our reference "nature" atmosphere. The simulated space-based lidar wind observations were retrieved by interpolating the NCEP values to the observation locations. A modified dataset obtained by smoothing the NCEP dataset was used as the initial state whose forecast was sought to be improved by assimilating the retrieved lidar observations. Forecasts using wind profiles with various lidar instrument parameters has been conducted. The results show that to significantly improve the mesoscale forecast the satellite should fly near the storm center with large scanning radius. Increasing lidar firing rate also improves the forecast. Cloud cover and lack of aerosol degrade the quality of the lidar wind data and, subsequently, the forecast.

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Florita, Anthony R; Krishnan, Venkat K

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power and currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced.more » The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start-time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.« less

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Florita, Anthony R; Krishnan, Venkat K

2017-08-31

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) ismore » analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.« less

Wind Power Forecasting Error Distributions: An International Comparison; Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, B. M.; Lew, D.; Milligan, M.

2012-09-01

Wind power forecasting is expected to be an important enabler for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that do occur can be critical to system operation functions, such as the setting of operating reserve levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data. The paper concludes with an assessment of similarities and differences between the errors observed in different locations.

Solar Probe Plus: Report of the Science and Technology Definition Team

NASA Technical Reports Server (NTRS)

2008-01-01

Solar Probe+ will be an extraordinary and historic mission, exploring what is arguably the last region of the solar system to be visited by a spacecraft, the Sun s outer atmosphere or corona as it extends out into space. Approaching as close as 9.5 RS* (8.5 RS above the Sun s surface), Solar Probe+ will repeatedly sample the near-Sun environment, revolutionizing our knowledge and understanding of coronal heating and of the origin and evolution of the solar wind and answering critical questions in heliophysics that have been ranked as top priorities for decades. Moreover, by making direct, in-situ measurements of the region where some of the most hazardous solar energetic particles are energized, Solar Probe+ will make a fundamental contribution to our ability to characterize and forecast the radiation environment in which future space explorers will work and live.

New Model of a Solar Wind Airplane for Geomatic Operations

NASA Astrophysics Data System (ADS)

Achachi, A.; Benatia, D.

2015-08-01

The ability for an aircraft to fly during a much extended period of time has become a key issue and a target of research, both in the domain of civilian aviation and unmanned aerial vehicles. This paper describes a new design and evaluating of solar wind aircraft with the objective to assess the impact of a new system design on overall flight crew performance. The required endurance is in the range of some hours in the case of law enforcement, border surveillance, forest fire fighting or power line inspection. However, other applications at high altitudes, such as geomatic operations for delivering geographic information, weather research and forecast, environmental monitoring, would require remaining airborne during days, weeks or even months. The design of GNSS non precision approach procedure for different airports is based on geomatic data.

Nowcasting and forecasting of the magnetopause and bow shock—A status update

NASA Astrophysics Data System (ADS)

Petrinec, S. M.; Redmon, R. J.; Rastaetter, L.

2017-01-01

There has long been interest in knowing the shape and location of the Earth's magnetopause and of the standing fast-mode bow shock upstream of the Earth's magnetosphere. This quest for knowledge spans both the research and operations arenas. Pertinent to the latter, nowcasting and near-term forecasting are important for determining the extent to which the magnetosphere is compressed or expanded due to the influence of the solar wind bulk plasma and fields and the coupling to other magnetosphere-ionosphere processes with possible effects on assets. This article provides an update to a previous article on the same topic published 15 years earlier, with focus on studies that have been conducted, the current status of nowcasting and forecasting of geophysical boundaries, and future endeavors.

Application and verification of ECMWF seasonal forecast for wind energy

NASA Astrophysics Data System (ADS)

Žagar, Mark; Marić, Tomislav; Qvist, Martin; Gulstad, Line

2015-04-01

A good understanding of long-term annual energy production (AEP) is crucial when assessing the business case of investing in green energy like wind power. The art of wind-resource assessment has emerged into a scientific discipline on its own, which has advanced at high pace over the last decade. This has resulted in continuous improvement of the AEP accuracy and, therefore, increase in business case certainty. Harvesting the full potential output of a wind farm or a portfolio of wind farms depends heavily on optimizing operation and management strategy. The necessary information for short-term planning (up to 14 days) is provided by standard weather and power forecasting services, and the long-term plans are based on climatology. However, the wind-power industry is lacking quality information on intermediate scales of the expected variability in seasonal and intra-annual variations and their geographical distribution. The seasonal power forecast presented here is designed to bridge this gap. The seasonal power production forecast is based on the ECMWF seasonal weather forecast and the Vestas' high-resolution, mesoscale weather library. The seasonal weather forecast is enriched through a layer of statistical post-processing added to relate large-scale wind speed anomalies to mesoscale climatology. The resulting predicted energy production anomalies, thus, include mesoscale effects not captured by the global forecasting systems. The turbine power output is non-linearly related to the wind speed, which has important implications for the wind power forecast. In theory, the wind power is proportional to the cube of wind speed. However, due to the nature of turbine design, this exponent is close to 3 only at low wind speeds, becomes smaller as the wind speed increases, and above 11-13 m/s the power output remains constant, called the rated power. The non-linear relationship between wind speed and the power output generally increases sensitivity of the forecasted power to the wind speed anomalies. On the other hand, in some cases and areas where turbines operate close to, or above the rated power, the sensitivity of power forecast is reduced. Thus, the seasonal power forecasting system requires good knowledge of the changes in frequency of events with sufficient wind speeds to have acceptable skill. The scientific background for the Vestas seasonal power forecasting system is described and the relationship between predicted monthly wind speed anomalies and observed wind energy production are investigated for a number of operating wind farms in different climate zones. Current challenges will be discussed and some future research and development areas identified.

Short-Term Load Forecasting Error Distributions and Implications for Renewable Integration Studies: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, B. M.; Lew, D.; Milligan, M.

2013-01-01

Load forecasting in the day-ahead timescale is a critical aspect of power system operations that is used in the unit commitment process. It is also an important factor in renewable energy integration studies, where the combination of load and wind or solar forecasting techniques create the net load uncertainty that must be managed by the economic dispatch process or with suitable reserves. An understanding of that load forecasting errors that may be expected in this process can lead to better decisions about the amount of reserves necessary to compensate errors. In this work, we performed a statistical analysis of themore » day-ahead (and two-day-ahead) load forecasting errors observed in two independent system operators for a one-year period. Comparisons were made with the normal distribution commonly assumed in power system operation simulations used for renewable power integration studies. Further analysis identified time periods when the load is more likely to be under- or overforecast.« less

Predicting Near-surface Winds with WindNinja for Wind Energy Applications

NASA Astrophysics Data System (ADS)

Wagenbrenner, N. S.; Forthofer, J.; Shannon, K.; Butler, B.

2016-12-01

WindNinja is a high-resolution diagnostic wind model widely used by operational wildland fire managers to predict how near-surface winds may influence fire behavior. Many of the features which have made WindNinja successful for wildland fire are also important for wind energy applications. Some of these features include flexible runtime options which allow the user to initialize the model with coarser scale weather model forecasts, sparse weather station observations, or a simple domain-average wind for what-if scenarios; built-in data fetchers for required model inputs, including gridded terrain and vegetation data and operational weather model forecasts; relatively fast runtimes on simple hardware; an extremely user-friendly interface; and a number of output format options, including KMZ files for viewing in Google Earth and GeoPDFs which can be viewed in a GIS. The recent addition of a conservation of mass and momentum solver based on OpenFOAM libraries further increases the utility of WindNinja to modelers in the wind energy sector interested not just in mean wind predictions, but also in turbulence metrics. Here we provide an evaluation of WindNinja forecasts based on (1) operational weather model forecasts and (2) weather station observations provided by the MesoWest API. We also compare the high-resolution WindNinja forecasts to the coarser operational weather model forecasts. For this work we will use the High Resolution Rapid Refresh (HRRR) model and the North American Mesoscale (NAM) model. Forecasts will be evaluated with data collected in the Birch Creek valley of eastern Idaho, USA between June-October 2013. Near-surface wind, turbulence data, and vertical wind and temperature profiles were collected at very high spatial resolution during this field campaign specifically for use in evaluating high-resolution wind models like WindNinja. This work demonstrates the ability of WindNinja to generate very high-resolution wind forecasts for wind energy applications and evaluates the forecasts produced by two different initialization methods with data collected in a broad valley surrounded by complex terrain.

A computer vision approach for solar radiation nowcasting using MSG images

NASA Astrophysics Data System (ADS)

Álvarez, L.; Castaño Moraga, C. A.; Martín, J.

2010-09-01

Cloud structures and haze are the two main atmospheric phenomena that reduce the performance of solar power plants, since they absorb solar energy reaching terrestrial surface. Thus, accurate forecasting of solar radiation is a challenging research area that involves both a precise localization of cloud structures and haze, as well as the attenuation introduced by these artifacts. Our work presents a novel approach for nowcasting services based on image processing techniques applied to MSG satellite images provided by the EUMETSAT Rapid Scan Service (RSS) service. These data are an interesting source of information for our purposes since every 5 minutes we obtain actual information of the atmospheric state in nearly real time. However, a workaround must be given in order to forecast solar radiation. To that end, we synthetically forecast MSG images forecasts from past images applying computer vision techniques adapted to fluid flows in order to evolve atmospheric state. First, we classify cloud structures on two different layers, corresponding to top and bottom clouds, which includes haze. This two-level classification responds to the dominant climate conditions found in our region of interest, the Canary Islands archipelago, regulated by the Gulf Stream and Trade Winds. Vertical structure of Trade Winds consists of two layers, the bottom one, which is fresh and humid, and the top one, which is warm and dry. Between these two layers a thermal inversion appears that does not allow bottom clouds to go up and naturally divides clouds in these two layers. Top clouds can be directly obtained from satellite images by means of a segmentation algorithm on histogram heights. However, bottom clouds are usually overlapped by the former, so an inpainting algorithm is used to recover overlapped areas of bottom clouds. For each layer, cloud motion is estimated through a correlation based optic flow algorithm that provides a vector field that describes the displacement field in each layer between two consecutive images in a sequence. Since RSS service from EUMETSAT provides images every 5 minutes (Δt), the cloud motion vector field between images at time t0 and (t0 - Δt) is quite similar to that between (t0 - Δt) and (t0 - 2Δt). Under this assumption, we infer the motion vector field for the next image in order to build a synthetic version of the image at time (t0 + Δt). The computation of this future motion vector field takes into account terrain orography in order to produce more realistic forecasts. In this sense, we are currently working on the integration of information from NWP outputs in order to introduce other atmospheric phenomena. Applying this algorithm several times we are able to produce short-term forecasts up to 6 hours with encouraging performance. To validate our results, we use both, comparison of synthetically generated images with the corresponding images at a given time, and direct solar radiation measurement with the set of meteorological stations located at several points of the canarian archipelago.

A Statistical Study of the Magnetic Structure of Magnetic Clouds Downstream of the Earth's Bow Shock

NASA Astrophysics Data System (ADS)

Turc, L.; Fontaine, D.; Kilpua, E.; Escoubet, C. P.

2015-12-01

Magnetic clouds (MCs) are large-scale solar wind transients characterized primarily by an enhanced and smoothly-rotating magnetic field over periods of the order of one day. They are the drivers of the most intense geomagnetic storms, therefore understanding their interaction with the Earth's environment is of major interest for space weather forecasting. The first steps of this complex chain of processes are their interaction with the terrestrial bow shock and the ensuing propagation in the magnetosheath. Recent studies have shown that under certain conditions the distinctive magnetic structure of MCs can be significantly altered downstream of the bow shock. In such case, the magnetic field impinging on the magnetosphere strongly differs from that in the upstream solar wind and could lead to a reconnection pattern very different from that expected from the solar wind observations. The aim of the present work is to substantiate and generalize these results, obtained from a few MC events, in performing a statistical study. For this purpose, a comprehensive database of MC events, covering about 15 years of data, from 2000 to 2014, has been compiled. It lists presently 151 MCs observed in L1 by either Wind or ACE. Using the events during which spacecraft observations in the magnetosheath are simultaneously available, we investigate the evolution of the magnetic structure of MCs from the solar wind to the magnetosheath. The influence of the upstream solar wind parameters, such as the plasma beta, the Alfven Mach number or the magnetic field strength, is examined. Using a semi-analytical model, we estimate the local shock properties encountered upon entering the magnetosheath and find that the alteration of the magnetic structure of MCs strongly depend on the shock geometry. The large dataset allows us to assess the limitations of the magnetosheath model. The consequences of our results in terms of the geoeffectivity of MCs are discussed.

Forecasting for a Remote Island: A Class Exercise.

NASA Astrophysics Data System (ADS)

Riordan, Allen J.

2003-06-01

Students enrolled in a satellite meteorology course at North Carolina State University, Raleigh, recently had an unusual opportunity to apply their forecast skills to predict wind and weather conditions for a remote site in the Southern Hemisphere. For about 40 days starting in early February 2001, students used satellite and model guidance to develop forecasts to support a research team stationed on Bouvet Island (54°26S, 3°24E). Internet products together with current output from NCEP's Aviation (AVN) model supported the activity. Wind forecasts were of particular interest to the Bouvet team because violent winds often developed unexpectedly and posed a safety hazard.Results were encouraging in that 24-h wind speed forecasts showed reasonable reliability over a wide range of wind speeds. Forecasts for 48 h showed only marginal skill, however. Two critical events were well forecasted-the major February storm with wind speeds of over 120 kt and a brief calm period following several days of strong winds in early March. The latter forecast proved instrumental in recovering the research team.

DSCOVR Satellite Deploy & Light Test

NASA Image and Video Library

2014-11-24

Workers conduct a light test on the solar arrays on NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, in the Building 1 high bay at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is targeted for early 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida.

Evaluation of Wind Power Forecasts from the Vermont Weather Analytics Center and Identification of Improvements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Optis, Michael; Scott, George N.; Draxl, Caroline

The goal of this analysis was to assess the wind power forecast accuracy of the Vermont Weather Analytics Center (VTWAC) forecast system and to identify potential improvements to the forecasts. Based on the analysis at Georgia Mountain, the following recommendations for improving forecast performance were made: 1. Resolve the significant negative forecast bias in February-March 2017 (50% underprediction on average) 2. Improve the ability of the forecast model to capture the strong diurnal cycle of wind power 3. Add ability for forecast model to assess internal wake loss, particularly at sites where strong diurnal shifts in wind direction are present.more » Data availability and quality limited the robustness of this forecast assessment. A more thorough analysis would be possible given a longer period of record for the data (at least one full year), detailed supervisory control and data acquisition data for each wind plant, and more detailed information on the forecast system input data and methodologies.« less

Short-term Wind Forecasting at Wind Farms using WRF-LES and Actuator Disk Model

NASA Astrophysics Data System (ADS)

Kirkil, Gokhan

2017-04-01

Short-term wind forecasts are obtained for a wind farm on a mountainous terrain using WRF-LES. Multi-scale simulations are also performed using different PBL parameterizations. Turbines are parameterized using Actuator Disc Model. LES models improved the forecasts. Statistical error analysis is performed and ramp events are analyzed. Complex topography of the study area affects model performance, especially the accuracy of wind forecasts were poor for cross valley-mountain flows. By means of LES, we gain new knowledge about the sources of spatial and temporal variability of wind fluctuations such as the configuration of wind turbines.

The use of real-time off-site observations as a methodology for increasing forecast skill in prediction of large wind power ramps one or more hours ahead of their impact on a wind plant.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin Wilde, Principal Investigator

2012-12-31

ABSTRACT Application of Real-Time Offsite Measurements in Improved Short-Term Wind Ramp Prediction Skill Improved forecasting performance immediately preceding wind ramp events is of preeminent concern to most wind energy companies, system operators, and balancing authorities. The value of near real-time hub height-level wind data and more general meteorological measurements to short-term wind power forecasting is well understood. For some sites, access to onsite measured wind data - even historical - can reduce forecast error in the short-range to medium-range horizons by as much as 50%. Unfortunately, valuable free-stream wind measurements at tall tower are not typically available at most windmore » plants, thereby forcing wind forecasters to rely upon wind measurements below hub height and/or turbine nacelle anemometry. Free-stream measurements can be appropriately scaled to hub-height levels, using existing empirically-derived relationships that account for surface roughness and turbulence. But there is large uncertainty in these relationships for a given time of day and state of the boundary layer. Alternatively, forecasts can rely entirely on turbine anemometry measurements, though such measurements are themselves subject to wake effects that are not stationary. The void in free-stream hub-height level measurements of wind can be filled by remote sensing (e.g., sodar, lidar, and radar). However, the expense of such equipment may not be sustainable. There is a growing market for traditional anemometry on tall tower networks, maintained by third parties to the forecasting process (i.e., independent of forecasters and the forecast users). This study examines the value of offsite tall-tower data from the WINDataNOW Technology network for short-horizon wind power predictions at a wind farm in northern Montana. The presentation shall describe successful physical and statistical techniques for its application and the practicality of its application in an operational setting. It shall be demonstrated that when used properly, the real-time offsite measurements materially improve wind ramp capture and prediction statistics, when compared to traditional wind forecasting techniques and to a simple persistence model.« less

Wind power application research on the fusion of the determination and ensemble prediction

NASA Astrophysics Data System (ADS)

Lan, Shi; Lina, Xu; Yuzhu, Hao

2017-07-01

The fused product of wind speed for the wind farm is designed through the use of wind speed products of ensemble prediction from the European Centre for Medium-Range Weather Forecasts (ECMWF) and professional numerical model products on wind power based on Mesoscale Model5 (MM5) and Beijing Rapid Update Cycle (BJ-RUC), which are suitable for short-term wind power forecasting and electric dispatch. The single-valued forecast is formed by calculating the different ensemble statistics of the Bayesian probabilistic forecasting representing the uncertainty of ECMWF ensemble prediction. Using autoregressive integrated moving average (ARIMA) model to improve the time resolution of the single-valued forecast, and based on the Bayesian model averaging (BMA) and the deterministic numerical model prediction, the optimal wind speed forecasting curve and the confidence interval are provided. The result shows that the fusion forecast has made obvious improvement to the accuracy relative to the existing numerical forecasting products. Compared with the 0-24 h existing deterministic forecast in the validation period, the mean absolute error (MAE) is decreased by 24.3 % and the correlation coefficient (R) is increased by 12.5 %. In comparison with the ECMWF ensemble forecast, the MAE is reduced by 11.7 %, and R is increased 14.5 %. Additionally, MAE did not increase with the prolongation of the forecast ahead.

Forecasting Space Weather Events for a Neighboring World

NASA Technical Reports Server (NTRS)

Zheng, Yihua; Mason, Tom; Wood, Erin L.

2015-01-01

Shortly after NASA's Mars Atmosphere and Volatile EvolutioN mission (MAVEN) spacecraft entered Mars' orbit on 21 September 2014, scientists glimpsed the Martian atmosphere's response to a front of solar energetic particles (SEPs) and an associated coronal mass ejection (CME). In response to some solar flares and CMEs, streams of SEPs burst from the solar atmosphere and are further accelerated in the interplanetary medium between the Sun and the planets. These particles deposit their energy and momentum into anything in their path, including the Martian atmosphere and MAVEN particle detectors. MAVEN scientists had been alerted to the likely CME-Mars encounter by a space weather prediction system that had its origins in space weather forecasting for Earth but now forecasts space weather for Earth's neighboring planets. The two Solar Terrestrial Relations Observatory spacecraft and Solar Heliospheric Observatory observed a CME on 26 September, with a trajectory that suggested a Mars intercept. A computer model developed for solar wind prediction, the Wang-Sheeley-Arge-Enlil cone model [e.g., Zheng et al., 2013; Parsons et al., 2011], running in real time at the Community Coordinated Modeling Center (CCMC) located at NASA Goddard since 2006, showed the CME propagating in the direction of Mars (Figure 1). According to MAVEN particle detectors, the disturbance and accompanying SEP enhancement at the leading edge of the CME reached Mars at approximately 17 hours Universal Time on 29 September 2014. Such SEPs may have a profound effect on atmospheric escape - they are believed to be a possible means for driving atmospheric loss. SEPs can cause loss of Mars' upper atmosphere through several loss mechanisms including sputtering of the atmosphere. Sputtering occurs when atoms are ejected from the atmosphere due to impacts with energetic particles.

Appendix I1-2 to Wind HUI Initiative 1: Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

John Zack; Deborah Hanley; Dora Nakafuji

This report is an appendix to the Hawaii WindHUI efforts to dev elop and operationalize short-term wind forecasting and wind ramp event forecasting capabilities. The report summarizes the WindNET field campaign deployment experiences and challenges. As part of the WindNET project on the Big Island of Hawaii, AWS Truepower (AWST) conducted a field campaign to assess the viability of deploying a network of monitoring systems to aid in local wind energy forecasting. The data provided at these monitoring locations, which were strategically placed around the Big Island of Hawaii based upon results from the Oahu Wind Integration and Transmission Studymore » (OWITS) observational targeting study (Figure 1), provided predictive indicators for improving wind forecasts and developing responsive strategies for managing real-time, wind-related system events. The goal of the field campaign was to make measurements from a network of remote monitoring devices to improve 1- to 3-hour look ahead forecasts for wind facilities.« less

Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations.

NASA Astrophysics Data System (ADS)

Ángel Prósper Fernández, Miguel; Casal, Carlos Otero; Canoura Fernández, Felipe; Miguez-Macho, Gonzalo

2017-04-01

Regional meteorological models are becoming a generalized tool for forecasting wind resource, due to their capacity to simulate local flow dynamics impacting wind farm production. This study focuses on the production forecast and validation of a real onshore wind farm using high horizontal and vertical resolution WRF (Weather Research and Forecasting) model simulations. The wind farm is located in Galicia, in the northwest of Spain, in a complex terrain region with high wind resource. Utilizing the Fitch scheme, specific for wind farms, a period of one year is simulated with a daily operational forecasting set-up. Power and wind predictions are obtained and compared with real data provided by the management company. Results show that WRF is able to yield good wind power operational predictions for this kind of wind farms, due to a good representation of the planetary boundary layer behaviour of the region and the good performance of the Fitch scheme under these conditions.

Understanding Space Weather: The Sun as a Variable Star

NASA Technical Reports Server (NTRS)

Strong, Keith; Saba, Julia; Kucera, Therese

2011-01-01

The Sun is a complex system of systems and until recently, less than half of its surface was observable at any given time and then only from afar. New observational techniques and modeling capabilities are giving us a fresh perspective of the solar interior and how our Sun works as a variable star. This revolution in solar observations and modeling provides us with the exciting prospect of being able to use a vastly increased stream of solar data taken simultaneously from several different vantage points to produce more reliable and prompt space weather forecasts. Solar variations that cause identifiable space weather effects do not happen only on solar-cycle timescales from decades to centuries; there are also many shorter-term events that have their own unique space weather effects and a different set of challenges to understand and predict, such as flares, coronal mass ejections, and solar wind variations

Understanding Space Weather: The Sun as a Variable Star

NASA Technical Reports Server (NTRS)

Strong, Keith; Saba, Julia; Kucera, Therese

2012-01-01

The Sun is a complex system of systems and until recently, less than half of its surface was observable at any given time and then only from afar. New observational techniques and modeling capabilities are giving us a fresh perspective of the solar interior and how our Sun works as a variable star. This revolution in solar observations and modeling provides us with the exciting prospect of being able to use a vastly increased stream of solar data taken simultaneously from several different vantage points to produce more reliable and prompt space weather forecasts. Solar variations that cause identifiable space weather effects do not happen only on solar-cycle timescales from decades to centuries; there are also many shorter-term events that have their own unique space weather effects and a different set of challenges to understand and predict, such as flares, coronal mass ejections, and solar wind variations.

The Wind Forecast Improvement Project (WFIP). A Public-Private Partnership Addressing Wind Energy Forecast Needs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wilczak, James M.; Finley, Cathy; Freedman, Jeff

The Wind Forecast Improvement Project (WFIP) is a public-private research program, the goals of which are to improve the accuracy of short-term (0-6 hr) wind power forecasts for the wind energy industry and then to quantify the economic savings that accrue from more efficient integration of wind energy into the electrical grid. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that include the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collectionmore » of special observations to be assimilated into forecast models to improve model initial conditions; and second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the U.S. (the upper Great Plains, and Texas), and included 12 wind profiling radars, 12 sodars, 184 instrumented tall towers and over 400 nacelle anemometers (provided by private industry), lidar, and several surface flux stations. Results demonstrate that a substantial improvement of up to 14% relative reduction in power root mean square error (RMSE) was achieved from the combination of improved NOAA numerical weather prediction (NWP) models and assimilation of the new observations. Data denial experiments run over select periods of time demonstrate that up to a 6% relative improvement came from the new observations. The use of ensemble forecasts produced even larger forecast improvements. Based on the success of WFIP, DOE is planning follow-on field programs.« less

GUMICS-4 Year Run: Ground Magnetic Field Predictions

NASA Astrophysics Data System (ADS)

Honkonen, I. J.; Viljanen, A.; Juusola, L.; Facsko, G.; Vanhamäki, H.

2013-12-01

Space weather can have severe effects even at ground level when Geomagnetically Induced Currents (GIC) disrupt power transmission networks, the worst case being a complete blackout affecting millions of people. The importance of space weather forecasting as well as the need for model improvement and validation has been recognized internationally. The recently concluded GUMICS-4 one year run, in which solar wind observations obtained from OMNIWeb for the period 2002-01-29 to 2003-02-02 were given as input to the model, will allow GUMICS to be validated against observations on an unprecedented scale. The performance of GUMICS can be quantified statistically, as a function of, for example, the solar wind driver, various geomagnetic indices, magnetic local time and other parameters. Here we concentrate on the ability of GUMICS to predict ground magnetic field observations for one year of simulated results. The ground magnetic field predictions are compared to observations of the mainland IMAGE magnetometer stations located at CGM latitudes 54-68 N. Furthermore the GIC derived from ground magnetic field predictions are compared to observations along the natural gas pipeline at Mäntsälä, South Finland. Various metrics are used to objectively evaluate the performance of GUMICS as a function of different parameters, thereby providing significant insight into the space weather forecasting ability of models based on first principles.

Model documentation: Renewable Fuels Module of the National Energy Modeling System

NASA Astrophysics Data System (ADS)

1994-04-01

This report documents the objectives, analytical approach, and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it related to the production of the 1994 Annual Energy Outlook (AEO94) forecasts. The report catalogues and describes modeling assumptions, computational methodologies, data inputs, and parameter estimation techniques. A number of offline analyses used in lieu of RFM modeling components are also described. This documentation report serves two purposes. First, it is a reference document for model analysts, model users, and the public interested in the construction and application of the RFM. Second, it meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its models. The RFM consists of six analytical submodules that represent each of the major renewable energy resources -- wood, municipal solid waste (MSW), solar energy, wind energy, geothermal energy, and alcohol fuels. Of these six, four are documented in the following chapters: municipal solid waste, wind, solar and biofuels. Geothermal and wood are not currently working components of NEMS. The purpose of the RFM is to define the technological and cost characteristics of renewable energy technologies, and to pass these characteristics to other NEMS modules for the determination of mid-term forecasted renewable energy demand.

Testing ElEvoHI on a multi-point in situ detected Coronal Mass Ejection

NASA Astrophysics Data System (ADS)

Amerstorfer, Tanja; Möstl, Christian; Hess, Phillip; Mays, M. Leila; Temmer, Manuela

2017-04-01

The Solar TErrestrial RElations Observatory (STEREO) has provided us a deep insight into the interplanetary propagation of coronal mass ejections (CMEs). Especially the wide-angle heliospheric imagers (HI) enabled the development of a multitude of methods for analyzing the evolution of CMEs through interplanetary (IP) space. Methods able to forecast arrival times and speeds at Earth (or other targets) use the advantage of following a CME's path of propagation up to 1 AU. However, these methods were not able to reduce today's errors in arrival time forecasts to less than ±6 hours, arrival speeds are mostly overestimated by some 100 km s-1. One reason for that is the assumption of constant propagation speed, which is clearly incorrect for most CMEs—especially for those being faster than the ambient solar wind. ElEvoHI, the Ellipse Evolution model (ElEvo) based on HI observations, is a new prediction tool, which uses the benefits of different methods and observations. It provides the possibility to adjust the CME frontal shape (angular width, ellipse aspect ratio) and the direction of motion for each CME event individually. This information can be gained from Graduated Cylindrical Shell (GCS) flux-rope fitting within coronagraph images. Using the Ellipse Conversion (ElCon) method, the observed HI elongation angle is converted into a unit of distance, which reveals the kinematics of the event. After fitting the time-distance profile of the CME using the drag-based equation of motion, where real-time in situ solar wind speed from 1 AU is used as additional input, we receive all input parameters needed to run a forecast using the ElEvo model and to predict arrival times and speeds at any target of interest in IP space. Here, we present a test on a slow CME event of 3 November 2010, in situ detected by the lined-up spacecraft MESSENGER and STEREO Behind. We gain the shape of the CME front from a cut of the 3D GCS CME shape with the ecliptic plane, resulting in an almost ideal ElEvoHI forecast of arrival time and speed at 1 AU.

Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III; Roeder, William P.

2010-01-01

The expected peak wind speed for the day is an important element in the daily morning forecast for ground and space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron (45 WS) must issue forecast advisories for KSC/CCAFS when they expect peak gusts for >= 25, >= 35, and >= 50 kt thresholds at any level from the surface to 300 ft. In Phase I of this task, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a cool-season (October - April) tool to help forecast the non-convective peak wind from the surface to 300 ft at KSC/CCAFS. During the warm season, these wind speeds are rarely exceeded except during convective winds or under the influence of tropical cyclones, for which other techniques are already in use. The tool used single and multiple linear regression equations to predict the peak wind from the morning sounding. The forecaster manually entered several observed sounding parameters into a Microsoft Excel graphical user interface (GUI), and then the tool displayed the forecast peak wind speed, average wind speed at the time of the peak wind, the timing of the peak wind and the probability the peak wind will meet or exceed 35, 50 and 60 kt. The 45 WS customers later dropped the requirement for >= 60 kt wind warnings. During Phase II of this task, the AMU expanded the period of record (POR) by six years to increase the number of observations used to create the forecast equations. A large number of possible predictors were evaluated from archived soundings, including inversion depth and strength, low-level wind shear, mixing height, temperature lapse rate and winds from the surface to 3000 ft. Each day in the POR was stratified in a number of ways, such as by low-level wind direction, synoptic weather pattern, precipitation and Bulk Richardson number. The most accurate Phase II equations were then selected for an independent verification. The Phase I and II forecast methods were compared using an independent verification data set. The two methods were compared to climatology, wind warnings and advisories issued by the 45 WS, and North American Mesoscale (NAM) model (MesoNAM) forecast winds. The performance of the Phase I and II methods were similar with respect to mean absolute error. Since the Phase I data were not stratified by precipitation, this method's peak wind forecasts had a large negative bias on days with precipitation and a small positive bias on days with no precipitation. Overall, the climatology methods performed the worst while the MesoNAM performed the best. Since the MesoNAM winds were the most accurate in the comparison, the final version of the tool was based on the MesoNAM winds. The probability the peak wind will meet or exceed the warning thresholds were based on the one standard deviation error bars from the linear regression. For example, the linear regression might forecast the most likely peak speed to be 35 kt and the error bars used to calculate that the probability of >= 25 kt = 76%, the probability of >= 35 kt = 50%, and the probability of >= 50 kt = 19%. The authors have not seen this application of linear regression error bars in any other meteorological applications. Although probability forecast tools should usually be developed with logistic regression, this technique could be easily generalized to any linear regression forecast tool to estimate the probability of exceeding any desired threshold . This could be useful for previously developed linear regression forecast tools or new forecast applications where statistical analysis software to perform logistic regression is not available. The tool was delivered in two formats - a Microsoft Excel GUI and a Tool Command Language/Tool Kit (Tcl/Tk) GUI in the Meteorological Interactive Data Display System (MIDDS). The Microsoft Excel GUI reads a MesoNAM text file containing hourly forecasts from 0 to 84 hours, from one model run (00 or 12 UTC). The GUI then displays e peak wind speed, average wind speed, and the probability the peak wind will meet or exceed the 25-, 35- and 50-kt thresholds. The user can display the Day-1 through Day-3 peak wind forecasts, and separate forecasts are made for precipitation and non-precipitation days. The MIDDS GUI uses data from the NAM and Global Forecast System (GFS), instead of the MesoNAM. It can display Day-1 and Day-2 forecasts using NAM data, and Day-1 through Day-5 forecasts using GFS data. The timing of the peak wind is not displayed, since the independent verification showed that none of the forecast methods performed significantly better than climatology. The forecaster should use the climatological timing of the peak wind (2248 UTC) as a first guess and then adjust it based on the movement of weather features.

Extended Statistical Short-Range Guidance for Peak Wind Speed Analyses at the Shuttle Landing Facility: Phase II Results

NASA Technical Reports Server (NTRS)

Lambert, Winifred C.

2003-01-01

This report describes the results from Phase II of the AMU's Short-Range Statistical Forecasting task for peak winds at the Shuttle Landing Facility (SLF). The peak wind speeds are an important forecast element for the Space Shuttle and Expendable Launch Vehicle programs. The 45th Weather Squadron and the Spaceflight Meteorology Group indicate that peak winds are challenging to forecast. The Applied Meteorology Unit was tasked to develop tools that aid in short-range forecasts of peak winds at tower sites of operational interest. A seven year record of wind tower data was used in the analysis. Hourly and directional climatologies by tower and month were developed to determine the seasonal behavior of the average and peak winds. Probability density functions (PDF) of peak wind speed were calculated to determine the distribution of peak speed with average speed. These provide forecasters with a means of determining the probability of meeting or exceeding a certain peak wind given an observed or forecast average speed. A PC-based Graphical User Interface (GUI) tool was created to display the data quickly.

A Review of Alfvénic Turbulence in High-Speed Solar Wind Streams: Hints From Cometary Plasma Turbulence

NASA Astrophysics Data System (ADS)

Tsurutani, Bruce T.; Lakhina, Gurbax S.; Sen, Abhijit; Hellinger, Petr; Glassmeier, Karl-Heinz; Mannucci, Anthony J.

2018-04-01

Solar wind turbulence within high-speed streams is reviewed from the point of view of embedded single nonlinear Alfvén wave cycles, discontinuities, magnetic decreases (MDs), and shocks. For comparison and guidance, cometary plasma turbulence is also briefly reviewed. It is demonstrated that cometary nonlinear magnetosonic waves phase-steepen, with a right-hand circular polarized foreshortened front and an elongated, compressive trailing edge. The former part is a form of "wave breaking" and the latter that of "period doubling." Interplanetary nonlinear Alfvén waves, which are arc polarized, have a 180° foreshortened front and with an elongated trailing edge. Alfvén waves have polarizations different from those of cometary magnetosonic waves, indicating that helicity is a durable feature of plasma turbulence. Interplanetary Alfvén waves are noted to be spherical waves, suggesting the possibility of additional local generation. They kinetically dissipate, forming MDs, indicating that the solar wind is partially "compressive" and static. The 2 MeV protons can nonresonantly interact with MDs leading to rapid cross-field ( 5.5% Bohm) diffusion. The possibility of local ( 1 AU) generation of Alfvén waves may make it difficult to forecast High-Intensity, Long-Duration AE Activity and relativistic magnetospheric electrons with great accuracy. The future Solar Orbiter and Solar Probe Plus missions should be able to not only test these ideas but to also extend our knowledge of plasma turbulence evolution.

An introduction to Space Weather Integrated Modeling

NASA Astrophysics Data System (ADS)

Zhong, D.; Feng, X.

2012-12-01

The need for a software toolkit that integrates space weather models and data is one of many challenges we are facing with when applying the models to space weather forecasting. To meet this challenge, we have developed Space Weather Integrated Modeling (SWIM) that is capable of analysis and visualizations of the results from a diverse set of space weather models. SWIM has a modular design and is written in Python, by using NumPy, matplotlib, and the Visualization ToolKit (VTK). SWIM provides data management module to read a variety of spacecraft data products and a specific data format of Solar-Interplanetary Conservation Element/Solution Element MHD model (SIP-CESE MHD model) for the study of solar-terrestrial phenomena. Data analysis, visualization and graphic user interface modules are also presented in a user-friendly way to run the integrated models and visualize the 2-D and 3-D data sets interactively. With these tools we can locally or remotely analysis the model result rapidly, such as extraction of data on specific location in time-sequence data sets, plotting interplanetary magnetic field lines, multi-slicing of solar wind speed, volume rendering of solar wind density, animation of time-sequence data sets, comparing between model result and observational data. To speed-up the analysis, an in-situ visualization interface is used to support visualizing the data 'on-the-fly'. We also modified some critical time-consuming analysis and visualization methods with the aid of GPU and multi-core CPU. We have used this tool to visualize the data of SIP-CESE MHD model in real time, and integrated the Database Model of shock arrival, Shock Propagation Model, Dst forecasting model and SIP-CESE MHD model developed by SIGMA Weather Group at State Key Laboratory of Space Weather/CAS.

Gridded Calibration of Ensemble Wind Vector Forecasts Using Ensemble Model Output Statistics

NASA Astrophysics Data System (ADS)

Lazarus, S. M.; Holman, B. P.; Splitt, M. E.

2017-12-01

A computationally efficient method is developed that performs gridded post processing of ensemble wind vector forecasts. An expansive set of idealized WRF model simulations are generated to provide physically consistent high resolution winds over a coastal domain characterized by an intricate land / water mask. Ensemble model output statistics (EMOS) is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. Using data withdrawal and 28 east central Florida stations, the method is applied to one year of 24 h wind forecasts from the Global Ensemble Forecast System (GEFS). Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicate the post processed forecasts are calibrated. Two downscaling case studies are presented, a quiescent easterly flow event and a frontal passage. Strengths and weaknesses of the approach are presented and discussed.

Comparison of the economic impact of different wind power forecast systems for producers

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Davò, F.; Sperati, S.; Benini, M.; Delle Monache, L.

2014-05-01

Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a probabilistic energy forecast system.

Exploring the calibration of a wind forecast ensemble for energy applications

NASA Astrophysics Data System (ADS)

Heppelmann, Tobias; Ben Bouallegue, Zied; Theis, Susanne

2015-04-01

In the German research project EWeLiNE, Deutscher Wetterdienst (DWD) and Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) are collaborating with three German Transmission System Operators (TSO) in order to provide the TSOs with improved probabilistic power forecasts. Probabilistic power forecasts are derived from probabilistic weather forecasts, themselves derived from ensemble prediction systems (EPS). Since the considered raw ensemble wind forecasts suffer from underdispersiveness and bias, calibration methods are developed for the correction of the model bias and the ensemble spread bias. The overall aim is to improve the ensemble forecasts such that the uncertainty of the possible weather deployment is depicted by the ensemble spread from the first forecast hours. Additionally, the ensemble members after calibration should remain physically consistent scenarios. We focus on probabilistic hourly wind forecasts with horizon of 21 h delivered by the convection permitting high-resolution ensemble system COSMO-DE-EPS which has become operational in 2012 at DWD. The ensemble consists of 20 ensemble members driven by four different global models. The model area includes whole Germany and parts of Central Europe with a horizontal resolution of 2.8 km and a vertical resolution of 50 model levels. For verification we use wind mast measurements around 100 m height that corresponds to the hub height of wind energy plants that belong to wind farms within the model area. Calibration of the ensemble forecasts can be performed by different statistical methods applied to the raw ensemble output. Here, we explore local bivariate Ensemble Model Output Statistics at individual sites and quantile regression with different predictors. Applying different methods, we already show an improvement of ensemble wind forecasts from COSMO-DE-EPS for energy applications. In addition, an ensemble copula coupling approach transfers the time-dependencies of the raw ensemble to the calibrated ensemble. The calibrated wind forecasts are evaluated first with univariate probabilistic scores and additionally with diagnostics of wind ramps in order to assess the time-consistency of the calibrated ensemble members.

Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

NASA Astrophysics Data System (ADS)

Radziukynas, V.; Klementavičius, A.

2016-04-01

The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).

Using Analog Ensemble to generate spatially downscaled probabilistic wind power forecasts

NASA Astrophysics Data System (ADS)

Delle Monache, L.; Shahriari, M.; Cervone, G.

2017-12-01

We use the Analog Ensemble (AnEn) method to generate probabilistic 80-m wind power forecasts. We use data from the NCEP GFS ( 28 km resolution) and NCEP NAM (12 km resolution). We use forecasts data from NAM and GFS, and analysis data from NAM which enables us to: 1) use a lower-resolution model to create higher-resolution forecasts, and 2) use a higher-resolution model to create higher-resolution forecasts. The former essentially increases computing speed and the latter increases forecast accuracy. An aggregated model of the former can be compared against the latter to measure the accuracy of the AnEn spatial downscaling. The AnEn works by taking a deterministic future forecast and comparing it with past forecasts. The model searches for the best matching estimates within the past forecasts and selects the predictand value corresponding to these past forecasts as the ensemble prediction for the future forecast. Our study is based on predicting wind speed and air density at more than 13,000 grid points in the continental US. We run the AnEn model twice: 1) estimating 80-m wind speed by using predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind, 2) estimating air density by using predictors such as temperature, pressure, and relative humidity. We use the air density values to correct the standard wind power curves for different values of air density. The standard deviation of the ensemble members (i.e. ensemble spread) will be used as the degree of difficulty to predict wind power at different locations. The value of the correlation coefficient between the ensemble spread and the forecast error determines the appropriateness of this measure. This measure is prominent for wind farm developers as building wind farms in regions with higher predictability will reduce the real-time risks of operating in the electricity markets.

Performance of Trajectory Models with Wind Uncertainty

NASA Technical Reports Server (NTRS)

Lee, Alan G.; Weygandt, Stephen S.; Schwartz, Barry; Murphy, James R.

2009-01-01

Typical aircraft trajectory predictors use wind forecasts but do not account for the forecast uncertainty. A method for generating estimates of wind prediction uncertainty is described and its effect on aircraft trajectory prediction uncertainty is investigated. The procedure for estimating the wind prediction uncertainty relies uses a time-lagged ensemble of weather model forecasts from the hourly updated Rapid Update Cycle (RUC) weather prediction system. Forecast uncertainty is estimated using measures of the spread amongst various RUC time-lagged ensemble forecasts. This proof of concept study illustrates the estimated uncertainty and the actual wind errors, and documents the validity of the assumed ensemble-forecast accuracy relationship. Aircraft trajectory predictions are made using RUC winds with provision for the estimated uncertainty. Results for a set of simulated flights indicate this simple approach effectively translates the wind uncertainty estimate into an aircraft trajectory uncertainty. A key strength of the method is the ability to relate uncertainty to specific weather phenomena (contained in the various ensemble members) allowing identification of regional variations in uncertainty.

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, J.; Hodge, B. M.; Florita, A.

2013-10-01

Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The resultsmore » show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.« less

A hybrid wavelet transform based short-term wind speed forecasting approach.

PubMed

Wang, Jujie

2014-01-01

It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

A Hybrid Wavelet Transform Based Short-Term Wind Speed Forecasting Approach

PubMed Central

Wang, Jujie

2014-01-01

It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy. PMID:25136699

Overview and Meteorological Validation of the Wind Integration National Dataset toolkit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Draxl, C.; Hodge, B. M.; Clifton, A.

2015-04-13

The Wind Integration National Dataset (WIND) Toolkit described in this report fulfills these requirements, and constitutes a state-of-the-art national wind resource data set covering the contiguous United States from 2007 to 2013 for use in a variety of next-generation wind integration analyses and wind power planning. The toolkit is a wind resource data set, wind forecast data set, and wind power production and forecast data set derived from the Weather Research and Forecasting (WRF) numerical weather prediction model. WIND Toolkit data are available online for over 116,000 land-based and 10,000 offshore sites representing existing and potential wind facilities.

Solar Wind Driven Autoregression Model for Ionospheric Short Term Forecast (SWIF)

DTIC Science & Technology

2008-06-01

for GCAM; previous two months for TSAR). However the quality of the autoscaling software ( ARTIST ) performance determines largely the accuracy of...showing the SWIF synthesis. Figure 17: Athens Digisonde ionograms autoscaled with ARTIST4.0 (top) and with ARTIST4.5 (bottom) during fall quiet...intervals Figure 18: Athens Digisonde ionograms autoscaled with ARTIST4.0 (top) and with ARTIST4.5 (bottom) during summer quiet intervals Figure 19

Five Indisputable Facts on Modern Power Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bloom, Aaron P; Brinkman, Gregory L; Lopez, Anthony J

This presentation overviews five indisputable facts about modern power systems: Fact one: The grid can handle more renewable generation than previously thought. Fact two: Geographic and resource diversity provide additional reliability to the system. Fact three: Wind and solar forecasting provide significant value. Fact four: Our electric power markets were not originally designed for variable renewables -- but they could be adapted. Fact five: Modern power electronics are creating new sources of essential reliability services.

Assessing the Operational Robustness of the Homer Model for Marine Corps Use in Expeditionary Environments

DTIC Science & Technology

2014-06-01

systems. It can model systems including both conventional, diesel powered generators and renewable power sources such as photovoltaic arrays and wind...conducted an experiment where he assessed the capabilities of the HOMER model in forecasting the power output of a solar panel at NPS [32]. In his ex...energy efficiency in expeditionary operations, the HOMER micropower optimization model provides potential to serve as a powerful tool for improving

ICARUS Mission, Next Step of Coronal Exploration after Solar Orbiter and Solar Probe Plus

NASA Astrophysics Data System (ADS)

Krasnoselskikh, V.; Tsurutani, B.; Velli, M.; Maksimovic, M.; Balikhin, M. A.; Dudok de Wit, T.; Kretzschmar, M.

2017-12-01

The primary scientific goal of ICARUS, a mother-daughter satellite mission, will be to determine how the magnetic field and plasma dynamics in the outer solar atmosphere give rise to the corona, the solar wind and the heliosphere. Reaching this goal will be a Rosetta-stone step, with results broadly applicable in the fields of space plasma and astrophysics. Within ESA's Cosmic Vision roadmap, these goals address Theme 2: How does the solar system work ?" Investigating basic processes occurring from the Sun to the edge of the Solar System". ICARUS will not only advance our understanding of the plasma environment around the Sun, but also of the numerous magnetically active stars with hot plasma coronae. ICARUS I will perform the firstever direct in situ measurements of electromagnetic fields, particle acceleration, wave activity, energy distribution and flows directly in the regions where the solar wind emerges from the coronal plasma. ICARUS I will have a perihelion at 1 Solar radius from its surface, it will cross the region where the major energy deposition occurs. The polar orbit of ICARUS I will enable crossing the regions where both the fast and slow wind are generated. It will probe local characteristics of the plasma and provide unique information about the processes involved in the creation of the solar wind. ICARUS II will observe this region using remote-sensing instruments, providing simultaneous information about regions crossed by ICARUS I and the solar atmosphere below as observed by solar telescopes. It will provide bridges for understanding the magnetic links between heliosphere and solar atmosphere. Such information is crucial to understanding of the physics and electrodynamics of the solar atmosphere. ICARUS II will also play an important relay role, enabling the radio-link with ICARUS I. It will receive, collect and store information transmitted from ICARUS I during its closest approach to the Sun. It will perform preliminary data processing and transmit it to the Earth. Performing such unique in situ measurements in the region where deadly solar energetic particles are energized, ICARUS will make fundamental contributions to our ability to monitor and forecast the space radiation environment. Such knowledge is extremely important for space explorations, especially for long-term manned space missions.

An application of ensemble/multi model approach for wind power production forecast.

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Decimi, G.; Hagedorn, R.; Sperati, S.

2010-09-01

The wind power forecast of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast is based on a mesoscale meteorological models that provides the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. The corrected wind data are then used as input in the wind farm power curve to obtain the power forecast. These computations require historical time series of wind measured data (by an anemometer located in the wind farm or on the nacelle) and power data in order to be able to perform the statistical analysis on the past. For this purpose a Neural Network (NN) is trained on the past data and then applied in the forecast task. Considering that the anemometer measurements are not always available in a wind farm a different approach has also been adopted. A training of the NN to link directly the forecasted meteorological data and the power data has also been performed. The normalized RMSE forecast error seems to be lower in most cases by following the second approach. We have examined two wind farms, one located in Denmark on flat terrain and one located in a mountain area in the south of Italy (Sicily). In both cases we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by using two or more models (RAMS, ECMWF deterministic, LAMI, HIRLAM). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error of at least 1% compared to the singles models approach. Moreover the use of a deterministic global model, (e.g. ECMWF deterministic model) seems to reach similar level of accuracy of those of the mesocale models (LAMI and RAMS). Finally we have focused on the possibility of using the ensemble model (ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first day ahead period. In fact low spreads often correspond to low forecast error. For longer forecast horizon the correlation between RMSE and ensemble spread decrease becoming too low to be used for this purpose.

Forecast of geomagnetic storms using CME parameters and the WSA-ENLIL model

NASA Astrophysics Data System (ADS)

Moon, Y.; Lee, J.; Jang, S.; Na, H.; Lee, J.

2013-12-01

Intense geomagnetic storms are caused by coronal mass ejections (CMEs) from the Sun and their forecast is quite important in protecting space- and ground-based technological systems. The onset and strength of geomagnetic storms depend on the kinematic and magnetic properties of CMEs. Current forecast techniques mostly use solar wind in-situ measurements that provide only a short lead time. On the other hand, techniques using CME observations near the Sun have the potential to provide 1-3 days of lead time before the storm occurs. Therefore, one of the challenging issues is to forecast interplanetary magnetic field (IMF) southward components and hence geomagnetic storm strength with a lead-time on the order of 1-3 days. We are going to answer the following three questions: (1) when does a CME arrive at the Earth? (2) what is the probability that a CME can induce a geomagnetic storm? and (3) how strong is the storm? To address the first question, we forecast the arrival time and other physical parameters of CMEs at the Earth using the WSA-ENLIL model with three CME cone types. The second question is answered by examining the geoeffective and non-geoeffective CMEs depending on CME observations (speed, source location, earthward direction, magnetic field orientation, and cone-model output). The third question is addressed by examining the relationship between CME parameters and geomagnetic indices (or IMF southward component). The forecast method will be developed with a three-stage approach, which will make a prediction within four hours after the solar coronagraph data become available. We expect that this study will enable us to forecast the onset and strength of a geomagnetic storm a few days in advance using only CME parameters and the physics-based models.

Correlation of Magnetic Fields with Solar Wind Plasma Parameters at 1AU

NASA Astrophysics Data System (ADS)

Shen, F.

2017-12-01

The physical parameters of the solar wind observed in-situ near 1AU have been studied for several decades, and relationships between them, such as the positive correlation between the solar wind plasma temperature T and velocity V, and the negative correlation between density N and velocity V, are well known. However, the magnetic field intensity does not appear to be well correlated with any individual plasma parameter. In this paper, we discuss previously under-reported correlations between B and the combined plasma parameters √NV2 as well as between B and √NT. These two correlations are strong during the periods of corotating interaction regions and high speed streams, moderate during intervals of slow solar wind, and rather poor during the passage of interplanetary coronal mass ejections. The results indicate that the magnetic pressure in the solar wind is well correlated both with the plasma dynamic pressure and the thermal pressure. Then, we employ a 3D MHD model to simulate the formation of the relationships between the magnetic strength B and √NV2 as well as √NT observed at 1AU. The inner boundary condition is derived by empirical models, with the magnetic field and density are optional. Five kinds of boundary conditions at the inner boundary of heliosphere are tested. In the cases that the magnetic field is related to speed at the inner boundary, the correlation coefficients between B and √NV2 as well as between B and √NT are even higher than that in the observational results. At 1AU the simulated radial magnetic field shows little latitude dependence, which matches the observation of Ulysses. Most of the modeled characters in these cases are closer to observation than others. This inner boundary condition may more accurately characterize Sun's magnetic influence on the heliosphere. The new input may be able to improve the simulation of CME propagation in the inner heliosphere and the space weather forecasting.

The probability forecast evaluation of hazard and storm wind over the territories of Russia and Europe

NASA Astrophysics Data System (ADS)

Perekhodtseva, E. V.

2012-04-01

The results of the probability forecast methods of summer storm and hazard wind over territories of Russia and Europe are submitted at this paper. These methods use the hydrodynamic-statistical model of these phenomena. The statistical model was developed for the recognition of the situation involving these phenomena. For this perhaps the samples of the values of atmospheric parameters (n=40) for the presence and for the absence of these phenomena of storm and hazard wind were accumulated. The compressing of the predictors space without the information losses was obtained by special algorithm (k=7<19m/s, the values of 65%24m/s, the values of 75%29m/s or the area of the tornado and strong squalls. The evaluation of this probability forecast was provided by criterion of Brayer. The estimation was successful and was equal for the European part of Russia B=0,37. The application of the probability forecast of storm and hazard winds allows to mitigate the economic losses when the errors of the first and second kinds of storm wind categorical forecast are not so small. A lot of examples of the storm wind probability forecast are submitted at this report.

Space Weather Forecasting Operational Needs: A view from NOAA/SWPC

NASA Astrophysics Data System (ADS)

Biesecker, D. A.; Onsager, T. G.; Rutledge, R.

2017-12-01

The gaps in space weather forecasting are many. From long lead time forecasts, to accurate warnings with lead time to take action, there is plenty of room for improvement. Significant numbers of new observations would improve this picture, but it's also important to recognize the value of numerical modeling. The obvious interplanetary mission concepts that would be ideal would be 1) to measure the in-situ solar wind along the entire Sun-Earth line from as near to the Sun as possible all the way to Earth 2) a string of spacecraft in 1 AU heliocentric orbits making in-situ measurements as well as remote-sensing observations of the Sun, corona, and heliosphere. Even partially achieving these ideals would benefit space weather services, improving lead time and providing greater accuracy further into the future. The observations alone would improve forecasting. However, integrating these data into numerical models, as boundary conditions or via data assimilation, would provide the greatest improvements.

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias

With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by firstmore » layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.« less

Application of global weather and climate model output to the design and operation of wind-energy systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Curry, Judith

This project addressed the challenge of providing weather and climate information to support the operation, management and planning for wind-energy systems. The need for forecast information is extending to longer projection windows with increasing penetration of wind power into the grid and also with diminishing reserve margins to meet peak loads during significant weather events. Maintenance planning and natural gas trading is being influenced increasingly by anticipation of wind generation on timescales of weeks to months. Future scenarios on decadal time scales are needed to support assessment of wind farm siting, government planning, long-term wind purchase agreements and the regulatorymore » environment. The challenge of making wind forecasts on these longer time scales is associated with a wide range of uncertainties in general circulation and regional climate models that make them unsuitable for direct use in the design and planning of wind-energy systems. To address this challenge, CFAN has developed a hybrid statistical/dynamical forecasting scheme for delivering probabilistic forecasts on time scales from one day to seven months using what is arguably the best forecasting system in the world (European Centre for Medium Range Weather Forecasting, ECMWF). The project also provided a framework to assess future wind power through developing scenarios of interannual to decadal climate variability and change. The Phase II research has successfully developed an operational wind power forecasting system for the U.S., which is being extended to Europe and possibly Asia.« less

Development and Application of Advanced Weather Prediction Technologies for the Wind Energy Industry (Invited)

NASA Astrophysics Data System (ADS)

Mahoney, W. P.; Wiener, G.; Liu, Y.; Myers, W.; Johnson, D.

2010-12-01

Wind energy decision makers are required to make critical judgments on a daily basis with regard to energy generation, distribution, demand, storage, and integration. Accurate knowledge of the present and future state of the atmosphere is vital in making these decisions. As wind energy portfolios expand, this forecast problem is taking on new urgency because wind forecast inaccuracies frequently lead to substantial economic losses and constrain the national expansion of renewable energy. Improved weather prediction and precise spatial analysis of small-scale weather events are crucial for renewable energy management. In early 2009, the National Center for Atmospheric Research (NCAR) began a collaborative project with Xcel Energy Services, Inc. to perform research and develop technologies to improve Xcel Energy's ability to increase the amount of wind energy in their generation portfolio. The agreement and scope of work was designed to provide highly detailed, localized wind energy forecasts to enable Xcel Energy to more efficiently integrate electricity generated from wind into the power grid. The wind prediction technologies are designed to help Xcel Energy operators make critical decisions about powering down traditional coal and natural gas-powered plants when sufficient wind energy is predicted. The wind prediction technologies have been designed to cover Xcel Energy wind resources spanning a region from Wisconsin to New Mexico. The goal of the project is not only to improve Xcel Energy’s wind energy prediction capabilities, but also to make technological advancements in wind and wind energy prediction, expand our knowledge of boundary layer meteorology, and share the results across the renewable energy industry. To generate wind energy forecasts, NCAR is incorporating observations of current atmospheric conditions from a variety of sources including satellites, aircraft, weather radars, ground-based weather stations, wind profilers, and even wind sensors on individual wind turbines. The information is utilized by several technologies including: a) the Weather Research and Forecasting (WRF) model, which generates finely detailed simulations of future atmospheric conditions, b) the Real-Time Four-Dimensional Data Assimilation System (RTFDDA), which performs continuous data assimilation providing the WRF model with continuous updates of the initial atmospheric state, 3) the Dynamic Integrated Forecast System (DICast®), which statistically optimizes the forecasts using all predictors, and 4) a suite of wind-to-power algorithms that convert wind speed to power for a wide range of wind farms with varying real-time data availability capabilities. In addition to these core wind energy prediction capabilities, NCAR implemented a high-resolution (10 km grid increment) 30-member ensemble RTFDDA prediction system that provides information on the expected range of wind power over a 72-hour forecast period covering Xcel Energy’s service areas. This talk will include descriptions of these capabilities and report on several topics including initial results of next-day forecasts and nowcasts of wind energy ramp events, influence of local observations on forecast skill, and overall lessons learned to date.

Challenges in Understanding and Forecasting Winds in Complex Terrain.

NASA Astrophysics Data System (ADS)

Mann, J.; Fernando, J.; Wilczak, J. M.

2017-12-01

An overview will be given of some of the challenges in understanding and forecasting winds in complex terrain. These challenges can occur for several different reasons including 1) gaps in our understanding of fundamental physical boundary layer processes occurring in complex terrain; 2) a lack of adequate parameterizations and/or numerical schemes in NWP models; and 3) inadequate observations for initialization of NWP model forecasts. Specific phenomena that will be covered include topographic wakes/vortices, cold pools, gap flows, and mountain-valley winds, with examples taken from several air quality and wind energy related field programs in California as well as from the recent Second Wind Forecast Improvement Program (WFIP2) field campaign in the Columbia River Gorge/Basin area of Washington and Oregon States. Recent parameterization improvements discussed will include those for boundary layer turbulence, including 3D turbulence schemes, and gravity wave drag. Observational requirements for improving wind forecasting in complex terrain will be discussed, especially in the context of forecasting pressure gradient driven gap flow events.

Forecasting Cool Season Daily Peak Winds at Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Barrett, Joe, III; Short, David; Roeder, William

2008-01-01

The expected peak wind speed for the day is an important element in the daily 24-Hour and Weekly Planning Forecasts issued by the 45th Weather Squadron (45 WS) for planning operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The morning outlook for peak speeds also begins the warning decision process for gusts ^ 35 kt, ^ 50 kt, and ^ 60 kt from the surface to 300 ft. The 45 WS forecasters have indicated that peak wind speeds are a challenging parameter to forecast during the cool season (October-April). The 45 WS requested that the Applied Meteorology Unit (AMU) develop a tool to help them forecast the speed and timing of the daily peak and average wind, from the surface to 300 ft on KSC/CCAFS during the cool season. The tool must only use data available by 1200 UTC to support the issue time of the Planning Forecasts. Based on observations from the KSC/CCAFS wind tower network, surface observations from the Shuttle Landing Facility (SLF), and CCAFS upper-air soundings from the cool season months of October 2002 to February 2007, the AMU created multiple linear regression equations to predict the timing and speed of the daily peak wind speed, as well as the background average wind speed. Several possible predictors were evaluated, including persistence, the temperature inversion depth, strength, and wind speed at the top of the inversion, wind gust factor (ratio of peak wind speed to average wind speed), synoptic weather pattern, occurrence of precipitation at the SLF, and strongest wind in the lowest 3000 ft, 4000 ft, or 5000 ft. Six synoptic patterns were identified: 1) surface high near or over FL, 2) surface high north or east of FL, 3) surface high south or west of FL, 4) surface front approaching FL, 5) surface front across central FL, and 6) surface front across south FL. The following six predictors were selected: 1) inversion depth, 2) inversion strength, 3) wind gust factor, 4) synoptic weather pattern, 5) occurrence of precipitation at the SLF, and 6) strongest wind in the lowest 3000 ft. The forecast tool was developed as a graphical user interface with Microsoft Excel to help the forecaster enter the variables, and run the appropriate regression equations. Based on the forecaster's input and regression equations, a forecast of the day's peak and average wind is generated and displayed. The application also outputs the probability that the peak wind speed will be ^ 35 kt, 50 kt, and 60 kt.

"Ask Argonne" - Edwin Campos, Research Meteorologist, Part 1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edwin Campos

2013-05-08

Dr. Edwin Campos is a Research Meteorologist at Argonne National Laboratory. For the last two decades, he has studied weather, and in particular, clouds. Clouds are one of the most uncertain variables in climate predictions and are often related to transportation hazards. Clouds can also impact world-class sporting events like the Olympics. You may have questions about the role of clouds, or weather, on our daily lives. How is severe weather monitored for airports? What is the impact of clouds and wind on the generation of electricity? One of the projects Edwin is working on is short-term forecasting as itmore » relates to solar electricity. For this, Edwin's team is partnering with industry and academia to study new ways of forecasting clouds, delivering technologies that will allow the incorporation of more solar power into the electric grid. Post a question for Edwin as a comment below, and it might get answered in the follow-up video we'll post in the next few weeks.« less

"Ask Argonne" - Edwin Campos, Research Meteorologist, Part 1

ScienceCinema

Edwin Campos

2017-12-09

Dr. Edwin Campos is a Research Meteorologist at Argonne National Laboratory. For the last two decades, he has studied weather, and in particular, clouds. Clouds are one of the most uncertain variables in climate predictions and are often related to transportation hazards. Clouds can also impact world-class sporting events like the Olympics. You may have questions about the role of clouds, or weather, on our daily lives. How is severe weather monitored for airports? What is the impact of clouds and wind on the generation of electricity? One of the projects Edwin is working on is short-term forecasting as it relates to solar electricity. For this, Edwin's team is partnering with industry and academia to study new ways of forecasting clouds, delivering technologies that will allow the incorporation of more solar power into the electric grid. Post a question for Edwin as a comment below, and it might get answered in the follow-up video we'll post in the next few weeks.

Winds over Japan.

NASA Astrophysics Data System (ADS)

Plumley, William J.

1994-01-01

Before World War II, weather forecasters had little knowledge of upper-air wind patterns above 20000 feet. Data were seldom avai able at these heights, and the need was not great because commercial aircraft seldom flew at these altitudes. The war in the Pacific changed all that. Wind forecasts for 30000 feet plus became urgent to support the XXI Bomber Command in its bombing mission over Japan.The U.S. Army Air Force Pacific Ocean Area (AAFPOA) placed a Weather Central in the Marianas Islands in 1944 (Saipan in 1944 and Guam in 1945) to provide forecasting support for this mission. A forecasting procedure was put into operation that combined the elements known as "single-station forecasting" and an advanced procedure that used "altirmeter corrections" to analyze upper-airdata and make prognoses. Upper-air charts were drawn for constant pressure surfaces rather than constant height surfaces. The constant pressure surfaces were tied together by means of the atmospheric temperature field represented by specific temperature anomalies between pressure surfaces. Wind forecasts over the Marianas-Japan route made use of space cross sections that provided the data to forecast winds at each 5000-ft level to 35000 ft along the mission flight path. The new procedures allowed the forecaster to construct internally consistent meteorological charts in three dimensions in regions of sparse data.Army air force pilots and their crews from the Marianas were among the first to experience the extreme wind conditions now known as the "jet stream". Air force forecasters demonstrated that, with experience, such winds could reasonably be forecast under difficult operational conditions.

Major Nor'easter Set to Impact Northeast U.S.

NASA Image and Video Library

2015-01-26

This image was taken by the Suomi NPP satellite's VIIRS instrument at 1825Z on January 26, 2015. A low pressure system currently forming off the mid-Atlantic coast will rapidly strengthen into a major nor'easter today and affect parts of the Northeast U.S. through early Wednesday. This system will be responsible for heavy to intense snowfall and strong winds, with blizzard conditions expected from eastern New Jersey to eastern Massachusetts where Blizzard Warnings are in effect. Accumulations will likely exceed one foot from eastern New Jersey through eastern Maine by late Tuesday. The heaviest snow accumulations, perhaps exceeding two feet, are forecast across portions of Connecticut, Rhode Island and Massachusetts, including the Boston area. Currently, New York City is forecast to receive 18-24 inches of snow, and Boston is forecast to receive 24-36 inches of snow. Wind gusts of 45 to 60 mph will be common from eastern New Jersey to eastern Massachusetts, leading to widespread blizzard conditions. Wind gusts up to 70 mph are possible in far eastern Massachusetts, including Cape Cod and Nantucket. Credit: NASA/NOAA/NPP/VIIRS Via: NASA/NOAA via NOAA Environmental Visualization Laboratory NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Comparison of Wind Power and Load Forecasting Error Distributions: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, B. M.; Florita, A.; Orwig, K.

2012-07-01

The introduction of large amounts of variable and uncertain power sources, such as wind power, into the electricity grid presents a number of challenges for system operations. One issue involves the uncertainty associated with scheduling power that wind will supply in future timeframes. However, this is not an entirely new challenge; load is also variable and uncertain, and is strongly influenced by weather patterns. In this work we make a comparison between the day-ahead forecasting errors encountered in wind power forecasting and load forecasting. The study examines the distribution of errors from operational forecasting systems in two different Independent Systemmore » Operator (ISO) regions for both wind power and load forecasts at the day-ahead timeframe. The day-ahead timescale is critical in power system operations because it serves the unit commitment function for slow-starting conventional generators.« less

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan

2015-10-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reductionmore » in the amount of reserves that must be held to accommodate the uncertainty of solar power output.« less

Using Bayes Model Averaging for Wind Power Forecasts

NASA Astrophysics Data System (ADS)

Preede Revheim, Pål; Beyer, Hans Georg

2014-05-01

For operational purposes predictions of the forecasts of the lumped output of groups of wind farms spread over larger geographic areas will often be of interest. A naive approach is to make forecasts for each individual site and sum them up to get the group forecast. It is however well documented that a better choice is to use a model that also takes advantage of spatial smoothing effects. It might however be the case that some sites tends to more accurately reflect the total output of the region, either in general or for certain wind directions. It will then be of interest giving these a greater influence over the group forecast. Bayesian model averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensembles. Raftery et al. [1] show how BMA can be used for statistical post processing of forecast ensembles, producing PDFs of future weather quantities. The BMA predictive PDF of a future weather quantity is a weighted average of the ensemble members' PDFs, where the weights can be interpreted as posterior probabilities and reflect the ensemble members' contribution to overall forecasting skill over a training period. In Revheim and Beyer [2] the BMA procedure used in Sloughter, Gneiting and Raftery [3] were found to produce fairly accurate PDFs for the future mean wind speed of a group of sites from the single sites wind speeds. However, when the procedure was attempted applied to wind power it resulted in either problems with the estimation of the parameters (mainly caused by longer consecutive periods of no power production) or severe underestimation (mainly caused by problems with reflecting the power curve). In this paper the problems that arose when applying BMA to wind power forecasting is met through two strategies. First, the BMA procedure is run with a combination of single site wind speeds and single site wind power production as input. This solves the problem with longer consecutive periods where the input data does not contain information, but it has the disadvantage of nearly doubling the number of model parameters to be estimated. Second, the BMA procedure is run with group mean wind power as the response variable instead of group mean wind speed. This also solves the problem with longer consecutive periods without information in the input data, but it leaves the power curve to also be estimated from the data. [1] Raftery, A. E., et al. (2005). Using Bayesian Model Averaging to Calibrate Forecast Ensembles. Monthly Weather Review, 133, 1155-1174. [2]Revheim, P. P. and H. G. Beyer (2013). Using Bayesian Model Averaging for wind farm group forecasts. EWEA Wind Power Forecasting Technology Workshop,Rotterdam, 4-5 December 2013. [3]Sloughter, J. M., T. Gneiting and A. E. Raftery (2010). Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging. Journal of the American Statistical Association, Vol. 105, No. 489, 25-35

Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

NASA Astrophysics Data System (ADS)

Huang, Xin; Wang, Huaning; Xu, Long; Liu, Jinfu; Li, Rong; Dai, Xinghua

2018-03-01

Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.

A new aircraft hurricane wind climatology and applications in assessing the predictive skill of tropical cyclone intensity using high-resolution ensemble forecasts

NASA Astrophysics Data System (ADS)

Judt, Falko; Chen, Shuyi S.

2015-07-01

Hurricane surface wind is a key measure of storm intensity. However, a climatology of hurricane winds is lacking to date, largely because hurricanes are relatively rare events and difficult to observe over the open ocean. Here we present a new hurricane wind climatology based on objective surface wind analyses, which are derived from Stepped Frequency Microwave Radiometer measurements acquired by NOAA WP-3D and U.S. Air Force WC-130J hurricane hunter aircraft. The wind data were collected during 72 aircraft reconnaissance missions into 21 western Atlantic hurricanes from 1998 to 2012. This climatology provides an opportunity to validate hurricane intensity forecasts beyond the simplistic maximum wind speed metric and allows evaluating the predictive skill of probabilistic hurricane intensity forecasts using high-resolution model ensembles. An example of application is presented here using a 1.3 km grid spacing Weather Research and Forecasting model ensemble forecast of Hurricane Earl (2010).

Evaluation of NOAA's High Resolution Rapid Refresh (HRRR), 12 km North America Model (NAM12) and 4km North America Model (NAM 4) hub-height wind speed forecasts

NASA Astrophysics Data System (ADS)

Pendergrass, W.; Vogel, C. A.

2013-12-01

As an outcome of discussions between Duke Energy Generation and NOAA/ARL following the 2009 AMS Summer Community Meeting, in Norman Oklahoma, ARL and Duke Energy Generation (Duke) signed a Cooperative Research and Development Agreement (CRADA) which allows NOAA to conduct atmospheric boundary layer (ABL) research using Duke renewable energy sites as research testbeds. One aspect of this research has been the evaluation of forecast hub-height winds from three NOAA atmospheric models. Forecasts of 10m (surface) and 80m (hub-height) wind speeds from (1) NOAA/GSD's High Resolution Rapid Refresh (HRRR) model, (2) NOAA/NCEP's 12 km North America Model (NAM12) and (3) NOAA/NCEP's 4k high resolution North America Model (NAM4) were evaluated against 18 months of surface-layer wind observations collected at the joint NOAA/Duke Energy research station located at Duke Energy's West Texas Ocotillo wind farm over the period April 2011 through October 2012. HRRR, NAM12 and NAM4 10m wind speed forecasts were compared with 10m level wind speed observations measured on the NOAA/ATDD flux-tower. Hub-height (80m) HRRR , NAM12 and NAM4 forecast wind speeds were evaluated against the 80m operational PMM27-28 meteorological tower supporting the Ocotillo wind farm. For each HRRR update, eight forecast hours (hour 01, 02, 03, 05, 07, 10, 12, 15) plus the initialization hour (hour 00), evaluated. For the NAM12 and NAM4 models forecast hours 00-24 from the 06z initialization were evaluated. Performance measures or skill score based on absolute error 50% cumulative probability were calculated for each forecast hour. HRRR forecast hour 01 provided the best skill score with an absolute wind speed error within 0.8 m/s of observed 10m wind speed and 1.25 m/s for hub-height wind speed at the designated 50% cumulative probability. For both NAM4 and NAM12 models, skill scores were diurnal with comparable best scores observed during the day of 0.7 m/s of observed 10m wind speed and 1.1 m/s for hub-height wind speed at the designated 50% cumulative probability level.

Space weather: Why are magnetospheric physicists interested in solar explosive phenomena

NASA Astrophysics Data System (ADS)

Koskinen, H. E. J.; Pulkkinen, T. I.

That solar activity drives magnetospheric dynamics has for a long time been the basis of solar-terrestrial physics. Numerous statistical studies correlating sunspots, 10.7 cm radiation, solar flares, etc., with various magnetospheric and geomagnetic parameters have been performed. However, in studies of magnetospheric dynamics the role of the Sun has often remained in the background and only the actual solar wind impinging the magnetosphere has gained most of the attention. During the last few years a new applied field of solar-terrestrial physics, space weather, has emerged. The term refers to variable particle and field conditions in our space environment, which may be hazardous to space-borne or ground-based technological systems and can endanger human life and health. When the modern society is becoming increasingly dependent on space technology, the need for better modelling and also forecasting of space weather becomes urgent. While for post analysis of magnetospheric phenomena it is quite sufficient to include observations from the magnetospheric boundaries out to L1 where SOHO is located, these observations do not provide enough lead-time to run space weather forecasting models and to distribute the forecasts to potential customers. For such purposes we need improved physical understanding and models to predict which active processes on the Sun will impact the magnetosphere and what their expected consequences are. An important change of view on the role of the Sun as the origin of magnetospheric disturbances has taken place during last 10--20 years. For a long time, the solar flares were thought to be the most geoeffective solar phenomena. Now the attention has shifted much more towards coronal mass ejections and the SOHO coronal observations seem to have turned the epoch irreversibly. However, we are not yet ready to make reliable perdictions of the terrestrial environment based on CME observations. From the space weather viewpoint, the key questions are when a CME will be ejected, will it hit the Earth, what will its density and speed be, and how the magnetic field will be wrapped around the plasma cloud. This is clearly an enormous modelling task, but very forthwhile to carry further. Also forecasting of the solar energetic particle events would be very usefule as they form the most hazardous single effect on spaceflight, be that on the Space Station, on the Moon, or even further. We illustrate the chain of effects from the solar atmosphere to near-Earth space using some of the CME-associated magnetic storm events from the SOHO era.

Geodetic Space Weather Monitoring by means of Ionosphere Modelling

NASA Astrophysics Data System (ADS)

Schmidt, Michael

2017-04-01

The term space weather indicates physical processes and phenomena in space caused by radiation of energy mainly from the Sun. Manifestations of space weather are (1) variations of the Earth's magnetic field, (2) the polar lights in the northern and southern hemisphere, (3) variations within the ionosphere as part of the upper atmosphere characterized by the existence of free electrons and ions, (4) the solar wind, i.e. the permanent emission of electrons and photons, (5) the interplanetary magnetic field, and (6) electric currents, e.g. the van Allen radiation belt. It can be stated that ionosphere disturbances are often caused by so-called solar storms. A solar storm comprises solar events such as solar flares and coronal mass ejections (CMEs) which have different effects on the Earth. Solar flares may cause disturbances in positioning, navigation and communication. CMEs can effect severe disturbances and in extreme cases damages or even destructions of modern infrastructure. Examples are interruptions to satellite services including the global navigation satellite systems (GNSS), communication systems, Earth observation and imaging systems or a potential failure of power networks. Currently the measurements of solar satellite missions such as STEREO and SOHO are used to forecast solar events. Besides these measurements the Earth's ionosphere plays another key role in monitoring the space weather, because it responses to solar storms with an increase of the electron density. Space-geodetic observation techniques, such as terrestrial GNSS, satellite altimetry, space-borne GPS (radio occultation), DORIS and VLBI provide valuable global information about the state of the ionosphere. Additionally geodesy has a long history and large experience in developing and using sophisticated analysis and combination techniques as well as empirical and physical modelling approaches. Consequently, geodesy is predestinated for strongly supporting space weather monitoring via modelling the ionosphere and detecting and forecasting its disturbances. At present a couple of nations, such as the US, UK, Japan, Canada and China, are taken the threats from extreme space weather events seriously and support the development of observing strategies and fundamental research. However, (extreme) space weather events are in all their consequences on the modern highly technologized society, causative global problems which have to be treated globally and not regionally or even nationally. Consequently, space weather monitoring must include (1) all space-geodetic observation techniques and (2) geodetic evaluation methods such as data combination, real-time modelling and forecast. In other words, geodetic space weather monitoring comprises the basic ideas of GGOS and will provide products such as forecasts of severe solar events in order to initiate necessary activities to protect the infrastructure of modern society.

Wind-Farm Forecasting Using the HARMONIE Weather Forecast Model and Bayes Model Averaging for Bias Removal.

NASA Astrophysics Data System (ADS)

O'Brien, Enda; McKinstry, Alastair; Ralph, Adam

2015-04-01

Building on previous work presented at EGU 2013 (http://www.sciencedirect.com/science/article/pii/S1876610213016068 ), more results are available now from a different wind-farm in complex terrain in southwest Ireland. The basic approach is to interpolate wind-speed forecasts from an operational weather forecast model (i.e., HARMONIE in the case of Ireland) to the precise location of each wind-turbine, and then use Bayes Model Averaging (BMA; with statistical information collected from a prior training-period of e.g., 25 days) to remove systematic biases. Bias-corrected wind-speed forecasts (and associated power-generation forecasts) are then provided twice daily (at 5am and 5pm) out to 30 hours, with each forecast validation fed back to BMA for future learning. 30-hr forecasts from the operational Met Éireann HARMONIE model at 2.5km resolution have been validated against turbine SCADA observations since Jan. 2014. An extra high-resolution (0.5km grid-spacing) HARMONIE configuration has been run since Nov. 2014 as an extra member of the forecast "ensemble". A new version of HARMONIE with extra filters designed to stabilize high-resolution configurations has been run since Jan. 2015. Measures of forecast skill and forecast errors will be provided, and the contributions made by the various physical and computational enhancements to HARMONIE will be quantified.

Peak Wind Tool for General Forecasting

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III

2010-01-01

The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded again by six years, from October 1996 to April 2002, by interpolating 1000-ft sounding data to 100-ft increments. The Phase II developmental data set included observations for the cool season months of October 1996 to February 2007. The AMU calculated 68 candidate predictors from the XMR soundings, to include 19 stability parameters, 48 wind speed parameters and one wind shear parameter. Each day in the data set was stratified by synoptic weather pattern, low-level wind direction, precipitation and Richardson Number, for a total of 60 stratification methods. Linear regression equations, using the 68 predictors and 60 stratification methods, were created for the tool's three forecast parameters: the highest peak wind speed of the day (PWSD), 5-minute average speed at the same time (A WSD), and timing of the PWSD. For PWSD and A WSD, 30 Phase II methods were selected for evaluation in the verification data set. For timing of the PWSD, 12 Phase\\I methods were selected for evaluation. The verification data set contained observations for the cool season months of March 2007 to April 2009. The data set was used to compare the Phase I and II forecast methods to climatology, model forecast winds and wind advisories issued by the 45 WS. The model forecast winds were derived from the 0000 and 1200 UTC runs of the 12-km North American Mesoscale (MesoNAM) model. The forecast methods that performed the best in the verification data set were selected for the Phase II version of the tool. For PWSD and A WSD, linear regression equations based on MesoNAM forecasts performed significantly better than the Phase I and II methods. For timing of the PWSD, none of the methods performed significantly bener than climatology. The AMU then developed the Microsoft Excel and MIDDS GUls. The GUIs display the forecasts for PWSD, AWSD and the probability the PWSD will meet or exceed 25 kt, 35 kt and 50 kt. Since none of the prediction methods for timing of the PWSD performed significantly better thanlimatology, the tool no longer displays this predictand. The Excel and MIDDS GUIs display forecasts for Day-I to Day-3 and Day-I to Day-5, respectively. The Excel GUI uses MesoNAM forecasts as input, while the MIDDS GUI uses input from the MesoNAM and Global Forecast System model. Based on feedback from the 45 WS, the AMU added the daily average wind speed from 30 ft to 60 ft to the tool, which is one of the parameters in the 24-Hour and Weekly Planning Forecasts issued by the 45 WS. In addition, the AMU expanded the MIDDS GUI to include forecasts out to Day-7.

An application of ensemble/multi model approach for wind power production forecasting

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Pinson, P.; Hagedorn, R.; Decimi, G.; Sperati, S.

2011-02-01

The wind power forecasts of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast applied in this study is based on meteorological models that provide the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. For this purpose a training of a Neural Network (NN) to link directly the forecasted meteorological data and the power data has been performed. One wind farm has been examined located in a mountain area in the south of Italy (Sicily). First we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by the combination of models (RAMS, ECMWF deterministic, LAMI). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error (normalized by nominal power) of at least 1% compared to the singles models approach. Finally we have focused on the possibility of using the ensemble model system (EPS by ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first three days ahead period.

How Interplanetary Scintillation Data Can Improve Modeling of Coronal Mass Ejection Propagation

NASA Astrophysics Data System (ADS)

Taktakishvili, A.; Mays, M. L.; Manoharan, P. K.; Rastaetter, L.; Kuznetsova, M. M.

2017-12-01

Coronal mass ejections (CMEs) can have a significant impact on the Earth's magnetosphere-ionosphere system and cause widespread anomalies for satellites from geosynchronous to low-Earth orbit and produce effects such as geomagnetically induced currents. At the NASA/GSFC Community Coordinated Modeling Center we have been using ensemble modeling of CMEs since 2012. In this presnetation we demonstrate that using of interplanetary scintillation (IPS) observations from the Ooty Radio Telescope facility in India can help to track CME propagaion and improve ensemble forecasting of CMEs. The observations of the solar wind density and velocity using IPS from hundreds of distant sources in ensemble modeling of CMEs can be a game-changing improvement of the current state of the art in CME forecasting.

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Bauman, William H., III

2008-01-01

NASA prefers to land the space shuttle at Kennedy Space Center (KSC). When weather conditions violate Flight Rules at KSC, NASA will usually divert the shuttle landing to Edwards Air Force Base (EAFB) in Southern California. But forecasting surface winds at EAFB is a challenge for the Spaceflight Meteorology Group (SMG) forecasters due to the complex terrain that surrounds EAFB, One particular phenomena identified by SMG is that makes it difficult to forecast the EAFB surface winds is called "wind cycling". This occurs when wind speeds and directions oscillate among towers near the EAFB runway leading to a challenging deorbit bum forecast for shuttle landings. The large-scale numerical weather prediction models cannot properly resolve the wind field due to their coarse horizontal resolutions, so a properly tuned high-resolution mesoscale model is needed. The Weather Research and Forecasting (WRF) model meets this requirement. The AMU assessed the different WRF model options to determine which configuration best predicted surface wind speed and direction at EAFB, To do so, the AMU compared the WRF model performance using two hot start initializations with the Advanced Research WRF and Non-hydrostatic Mesoscale Model dynamical cores and compared model performance while varying the physics options.

SUN-TO-EARTH CHARACTERISTICS OF THE 2012 JULY 12 CORONAL MASS EJECTION AND ASSOCIATED GEO-EFFECTIVENESS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hu, Huidong; Liu, Ying D.; Wang, Rui

We analyze multi-spacecraft observations associated with the 2012 July 12 coronal mass ejection (CME), covering the source region on the Sun from the Solar Dynamics Observatory , stereoscopic imaging observations from the Solar Terrestrial Relations Observatory ( STEREO ), magnetic field characteristics from Mercury Surface, Space Environment, Geochemistry, and Ranging ( MESSENGER ), and type II radio burst and in situ measurements from Wind . A triangulation method based on STEREO stereoscopic observations is employed to determine the kinematics of the CME, and the outcome is compared with the results derived from the type II radio burst using a solarmore » wind electron density model. A Grad–Shafranov technique is applied to Wind in situ data to reconstruct the flux-rope structure and compare it with the observations of the solar source region, which helps in understanding the geo-effectiveness associated with the CME structure. Our conclusions are as follows: (1) the CME undergoes an impulsive acceleration, a rapid deceleration before reaching MESSENGER , and then a gradual deceleration out to 1 au, which should be considered in CME kinematics models; (2) the type II radio burst was probably produced from a high-density interaction region between the CME-driven shock and a nearby streamer or from the shock flank with lower heights, which implies uncertainties in the determination of CME kinematics using solely type II radio bursts; (3) the flux-rope orientation and chirality deduced from in situ reconstructions at Wind agree with those obtained from solar source observations; (4) the prolonged southward magnetic field near the Earth is mainly from the axial component of the largely southward inclined flux rope, which indicates the importance of predicting both the flux-rope orientation and magnetic field components in geomagnetic activity forecasting.« less

IEA Wind Task 36 Forecasting

NASA Astrophysics Data System (ADS)

Giebel, Gregor; Cline, Joel; Frank, Helmut; Shaw, Will; Pinson, Pierre; Hodge, Bri-Mathias; Kariniotakis, Georges; Sempreviva, Anna Maria; Draxl, Caroline

2017-04-01

Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Wind Power Forecasting tries to organise international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, …) and operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets for verification. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts aiming at industry and forecasters alike. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions, especially probabilistic ones. The Operating Agent is Gregor Giebel of DTU, Co-Operating Agent is Joel Cline of the US Department of Energy. Collaboration in the task is solicited from everyone interested in the forecasting business. We will collaborate with IEA Task 31 Wakebench, which developed the Windbench benchmarking platform, which this task will use for forecasting benchmarks. The task runs for three years, 2016-2018. Main deliverables are an up-to-date list of current projects and main project results, including datasets which can be used by researchers around the world to improve their own models, an IEA Recommended Practice on performance evaluation of probabilistic forecasts, a position paper regarding the use of probabilistic forecasts, and one or more benchmark studies implemented on the Windbench platform hosted at CENER. Additionally, spreading of relevant information in both the forecasters and the users community is paramount. The poster also shows the work done in the first half of the Task, e.g. the collection of available datasets and the learnings from a public workshop on 9 June in Barcelona on Experiences with the Use of Forecasts and Gaps in Research. Participation is open for all interested parties in member states of the IEA Annex on Wind Power, see ieawind.org for the up-to-date list. For collaboration, please contact the author grgi@dtu.dk).

Stratospheric wind errors, initial states and forecast skill in the GLAS general circulation model

NASA Technical Reports Server (NTRS)

Tenenbaum, J.

1983-01-01

Relations between stratospheric wind errors, initial states and 500 mb skill are investigated using the GLAS general circulation model initialized with FGGE data. Erroneous stratospheric winds are seen in all current general circulation models, appearing also as weak shear above the subtropical jet and as cold polar stratospheres. In this study it is shown that the more anticyclonic large-scale flows are correlated with large forecast stratospheric winds. In addition, it is found that for North America the resulting errors are correlated with initial state jet stream accelerations while for East Asia the forecast winds are correlated with initial state jet strength. Using 500 mb skill scores over Europe at day 5 to measure forecast performance, it is found that both poor forecast skill and excessive stratospheric winds are correlated with more anticyclonic large-scale flows over North America. It is hypothesized that the resulting erroneous kinetic energy contributes to the poor forecast skill, and that the problem is caused by a failure in the modeling of the stratospheric energy cycle in current general circulation models independent of vertical resolution.

Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

DOE PAGES

Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; ...

2015-11-10

Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based onmore » state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.« less

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

PubMed

Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

PubMed Central

Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627

Impact of scatterometer wind (ASCAT-A/B) data assimilation on semi real-time forecast system at KIAPS

NASA Astrophysics Data System (ADS)

Han, H. J.; Kang, J. H.

2016-12-01

Since Jul. 2015, KIAPS (Korea Institute of Atmospheric Prediction Systems) has been performing the semi real-time forecast system to assess the performance of their forecast system as a NWP model. KPOP (KIAPS Protocol for Observation Processing) is a part of KIAPS data assimilation system and has been performing well in KIAPS semi real-time forecast system. In this study, due to the fact that KPOP would be able to treat the scatterometer wind data, we analyze the effect of scatterometer wind (ASCAT-A/B) on KIAPS semi real-time forecast system. O-B global distribution and statistics of scatterometer wind give use two information which are the difference between background field and observation is not too large and KPOP processed the scatterometer wind data well. The changes of analysis increment because of O-B global distribution appear remarkably at the bottom of atmospheric field. It also shows that scatterometer wind data cover wide ocean where data would be able to short. Performance of scatterometer wind data can be checked through the vertical error reduction against IFS between background and analysis field and vertical statistics of O-A. By these analysis result, we can notice that scatterometer wind data will influence the positive effect on lower level performance of semi real-time forecast system at KIAPS. After, long-term result based on effect of scatterometer wind data will be analyzed.

Tools, Resources, and Innovations for Active Learning of Solar and Geospace Environment Content in the Undergraduate Classrooms

NASA Astrophysics Data System (ADS)

Knipp, D. J.

2013-12-01

An undergraduate course in solar and geospace (helio) physics should link fundamental principles from introductory physics and astronomy courses to concepts that appear unique, or are uniquely named in the heliophysics course. This paper discusses short topics and activities that can be addressed in an approximately 15-min class segment, that introduce students to aspects of solar, solar wind, and geospace storms that are a step beyond, or a special application of, an introductory physics concept. Some of these activities could be assigned as pre- or post- class activities as well. Many of the actives are aligned with images or diagrams in textbook, "Understanding Space Weather and the Physics Behind It," but could be easily adapted to other texts. We also address activities that link to information from space weather forecasting and/or modeling websites.

Verification of operational solar flare forecast: Case of Regional Warning Center Japan

NASA Astrophysics Data System (ADS)

Kubo, Yûki; Den, Mitsue; Ishii, Mamoru

2017-08-01

In this article, we discuss a verification study of an operational solar flare forecast in the Regional Warning Center (RWC) Japan. The RWC Japan has been issuing four-categorical deterministic solar flare forecasts for a long time. In this forecast verification study, we used solar flare forecast data accumulated over 16 years (from 2000 to 2015). We compiled the forecast data together with solar flare data obtained with the Geostationary Operational Environmental Satellites (GOES). Using the compiled data sets, we estimated some conventional scalar verification measures with 95% confidence intervals. We also estimated a multi-categorical scalar verification measure. These scalar verification measures were compared with those obtained by the persistence method and recurrence method. As solar activity varied during the 16 years, we also applied verification analyses to four subsets of forecast-observation pair data with different solar activity levels. We cannot conclude definitely that there are significant performance differences between the forecasts of RWC Japan and the persistence method, although a slightly significant difference is found for some event definitions. We propose to use a scalar verification measure to assess the judgment skill of the operational solar flare forecast. Finally, we propose a verification strategy for deterministic operational solar flare forecasting. For dichotomous forecast, a set of proposed verification measures is a frequency bias for bias, proportion correct and critical success index for accuracy, probability of detection for discrimination, false alarm ratio for reliability, Peirce skill score for forecast skill, and symmetric extremal dependence index for association. For multi-categorical forecast, we propose a set of verification measures as marginal distributions of forecast and observation for bias, proportion correct for accuracy, correlation coefficient and joint probability distribution for association, the likelihood distribution for discrimination, the calibration distribution for reliability and resolution, and the Gandin-Murphy-Gerrity score and judgment skill score for skill.

A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen

The National Center for Atmospheric Research (NCAR) is pleased to have led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast™ Solar Power Forecasting System. The project has included cutting edge research, testing in several geographically- and climatologically-diverse high penetration solar utilities and Independent System Operators (ISOs), and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, and industry partners. This public-private-academic team has worked in concert to perform use-inspired research to advance solarmore » power forecasting through cutting-edge research to advance both the necessary forecasting technologies and the metrics for evaluating them. The project has culminated in a year-long, full-scale demonstration of provide irradiance and power forecasts to utilities and ISOs to use in their operations. The project focused on providing elements of a value chain, beginning with the weather that causes a deviation from clear sky irradiance and progresses through monitoring and observations, modeling, forecasting, dissemination and communication of the forecasts, interpretation of the forecast, and through decision-making, which produces outcomes that have an economic value. The system has been evaluated using metrics developed specifically for this project, which has provided rich information on model and system performance. Research was accomplished on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system. The shortest range forecasts are based on observations in the field. The shortest range system, built by Brookhaven National Laboratory (BNL) and based on Total Sky Imagers (TSIs) is TSICast, which operates on the shortest time scale with a latency of only a few minutes and forecasts that currently go out to about 15 min. This project has facilitated research in improving the hardware and software so that the new high definition cameras deployed at multiple nearby locations allow discernment of the clouds at varying levels and advection according to the winds observed at those levels. Improvements over “smart persistence” are about 29% for even these very short forecasts. StatCast is based on pyranometer data measured at the site as well as concurrent meteorological observations and forecasts. StatCast is based on regime-dependent artificial intelligence forecasting techniques and has been shown to improve on “smart persistence” forecasts by 15-50%. A second category of short-range forecasting systems employ satellite imagery and use that information to discern clouds and their motion, allowing them to project the clouds, and the resulting blockage of irradiance, in time. CIRACast (the system produced by the Cooperative Institute for Atmospheric Research [CIRA] at Colorado State University) was already one of the more advanced cloud motion systems, which is the reason that team was brought to this project. During the project timeframe, the CIRA team was able to advance cloud shadowing, parallax removal, and implementation of better advecting winds at different altitudes. CIRACast shows generally a 25-40% improvement over Smart Persistence between sunrise and approximately 1600 UTC (Coordinated Universal Time) . A second satellite-based system, MADCast (Multi-sensor Advective Diffusive foreCast system), assimilates data from multiple satellite imagers and profilers to assimilate a fully three-dimensional picture of the cloud into the dynamic core of WRF. During 2015, MADCast (provided at least 70% improvement over Smart Persistence, with most of that skill being derived during partly cloudy conditions. That allows advection of the clouds via the Weather Research and Forecasting (WRF) model dynamics directly. After WRF-Solar™ showed initial success, it was also deployed in nowcasting mode with coarser runs out to 6 hours made hourly. It provided improvements on the order of 50-60% over Smart Persistence for forecasts up to 1600 UTC. The advantages of WRF-Solar-Nowcasting and MADCast were then blended to develop the new MAD-WRF model that incorporates the most important features of each of those models, both assimilating satellite cloud fields and using WRF-So far physics to develop and dissipate clouds. MAE improvements for MAD-WRF for forecasts from 3-6 hours are improved over WRF-Solar-Now by 20%. While all the Nowcasting system components by themselves provide improvement over Smart Persistence, the largest benefit is derived when they are smartly blended together by the Nowcasting Integrator to produce an integrated forecast. The development of WRF-Solar™ under this project has provided the first numerical weather prediction (NWP) model specifically designed to meet the needs of irradiance forecasting. The first augmentation improved the solar tracking algorithm to account for deviations associated with the eccentricity of the Earth’s orbit and the obliquity of the Earth. Second, WRF-Solar™ added the direct normal irradiance (DNI) and diffuse (DIF) components from the radiation parameterization to the model output. Third, efficient parameterizations were implemented to either interpolate the irradiance in between calls to the expensive radiative transfer parameterization, or to use a fast radiative transfer code that avoids computing three-dimensional heating rates but provides the surface irradiance. Fourth, a new parameterization was developed to improve the representation of absorption and scattering of radiation by aerosols (aerosol direct effect). A fifth advance is that the aerosols now interact with the cloud microphysics, altering the cloud evolution and radiative properties, an effect that has been traditionally only implemented in atmospheric computationally costly chemistry models. A sixth development accounts for the feedbacks that sub-grid scale clouds produce in shortwave irradiance as implemented in a shallow cumulus parameterization Finally, WRF-Solar™ also allows assimilation of infrared irradiances from satellites to determine the three dimensional cloud field, allowing for an improved initialization of the cloud field that increases the performance of short-range forecasts. We find that WRF-Solar™ can improve clear sky irradiance prediction by 15-80% over a standard version of WRF, depending on location and cloud conditions. In a formal comparison to the NAM baseline, WRF-Solar™ showed improvements in the Day-Ahead forecast of 22-42%. The SunCast™ system requires substantial software engineering to blend all of the new model components as well as existing publically available NWP model runs. To do this we use an expert system for the Nowcasting blender and the Dynamic Integrated foreCast (DICast®) system for the NWP models. These two systems are then blended, we use an empirical power conversion method to convert the irradiance predictions to power, then apply an analog ensemble (AnEn) approach to further tune the forecast as well as to estimate its uncertainty. The AnEn module decreased RMSE (root mean squared error) by 17% over the blended SunCast™ power forecasts and provided skill in the probabilistic forecast with a Brier Skill Score of 0.55. In addition, we have also developed a Gridded Atmospheric Forecast System (GRAFS) in parallel, leveraging cost share funds. An economic evaluation based on Production Cost Modeling in the Public Service Company of Colorado showed that the observed 50% improvement in forecast accuracy will save their customers $819,200 with the projected MW deployment for 2024. Using econometrics, NCAR has scaled this savings to a national level and shown that an annual expected savings for this 50% forecast error reduction ranges from $11M in 2015 to $43M expected in 2040 with increased solar deployment. This amounts to a $455M discounted savings over the 26 year period of analysis.« less

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan

2015-08-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reductionmore » in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.« less

KSC-2014-4580

NASA Image and Video Library

2014-11-24

CAPE CANAVERAL, Fla. – Workers conduct a light test on the solar arrays on NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, in the Building 1 high bay at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is targeted for early 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Ben Smegelsky

KSC-2014-4578

NASA Image and Video Library

2014-11-24

CAPE CANAVERAL, Fla. – The solar arrays on NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, are unfurled in the Building 1 high bay at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is targeted for early 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Ben Smegelsky

KSC-2014-4582

NASA Image and Video Library

2014-11-24

CAPE CANAVERAL, Fla. – Workers conduct a light test on the solar arrays on NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, in the Building 1 high bay at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is targeted for early 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Ben Smegelsky

KSC-2014-4581

NASA Image and Video Library

2014-11-24

CAPE CANAVERAL, Fla. – Workers conduct a light test on the solar arrays on NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, in the Building 1 high bay at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is targeted for early 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Ben Smegelsky

Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jensen, Michael P; Giangrande, Scott E; Bartholomew, Mary Jane

The Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) [http://www.arm.gov/campaigns/osc2013rwpcf] campaign was scheduled to take place from 15 July 2013 through 15 July 2015 (or until shipped for the next U.S. Department of Energy Atmospheric Radiation Measurement [ARM] Climate Research Facility first Mobile Facility [AMF1] deployment). The campaign involved the deployment of the AMF1 Scintec 915 MHz Radar Wind Profiler (RWP) at BNL, in conjunction with several other ARM, BNL and National Weather Service (NWS) instruments. The two main scientific foci of the campaign were: 1) To provide profiles of the horizontal wind to be used tomore » test and validate short-term cloud advection forecasts for solar-energy applications and 2) to provide vertical profiling capabilities for the study of dynamics (i.e., vertical velocity) and hydrometeors in winter storms. This campaign was a serendipitous opportunity that arose following the deployment of the RWP at the Two-Column Aerosol Project (TCAP) campaign in Cape Cod, Massachusetts and restriction from participation in the Green Ocean Amazon 2014/15 (GoAmazon 2014/15) campaign due to radio-frequency allocation restriction for international deployments. The RWP arrived at BNL in the fall of 2013, but deployment was delayed until fall of 2014 as work/safety planning and site preparation were completed. The RWP further encountered multiple electrical failures, which eventually required several shipments of instrument power supplies and the final amplifier to the vendor to complete repairs. Data collection began in late January 2015. The operational modes of the RWP were changed such that in addition to collecting traditional profiles of the horizontal wind, a vertically pointing mode was also included for the purpose of precipitation sensing and estimation of vertical velocities. The RWP operated well until the end of the campaign in July 2015 and collected observations for more than 20 precipitation events.« less

Three-model ensemble wind prediction in southern Italy

NASA Astrophysics Data System (ADS)

Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo

2016-03-01

Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

Short-term wind speed prediction based on the wavelet transformation and Adaboost neural network

NASA Astrophysics Data System (ADS)

Hai, Zhou; Xiang, Zhu; Haijian, Shao; Ji, Wu

2018-03-01

The operation of the power grid will be affected inevitably with the increasing scale of wind farm due to the inherent randomness and uncertainty, so the accurate wind speed forecasting is critical for the stability of the grid operation. Typically, the traditional forecasting method does not take into account the frequency characteristics of wind speed, which cannot reflect the nature of the wind speed signal changes result from the low generality ability of the model structure. AdaBoost neural network in combination with the multi-resolution and multi-scale decomposition of wind speed is proposed to design the model structure in order to improve the forecasting accuracy and generality ability. The experimental evaluation using the data from a real wind farm in Jiangsu province is given to demonstrate the proposed strategy can improve the robust and accuracy of the forecasted variable.

Applied Meteorology Unit (AMU) Quarterly Report First Quarter FY-14

NASA Technical Reports Server (NTRS)

Bauman, William Henry; Crawford, Winifred C.; Shafer, Jaclyn A.; Watson, Leela R.; Huddleston, Lisa L.; Decker, Ryan K.

2014-01-01

NASA's LSP and other programs at Vandenberg Air Force Base (VAFB) use wind forecasts issued by the 30th Operational Support Squadron (30 OSS) to determine if they need to limit activities or protect property such as a launch vehicle due to the occurrence of warning level winds at VAFB in California. The 30 OSS tasked the AMU to provide a wind forecasting capability to improve wind warning forecasts and enhance the safety of their customers' operations. This would allow 30 OSS forecasters to evaluate pressure gradient thresholds between pairs of regional observing stations to help determine the onset and duration of warning category winds. Development of such a tool will require that solid relationships exist between wind speed and the pressure gradient of one or more station pairs. As part of this task, the AMU will also create a statistical climatology of meteorological observations from the VAFB wind towers.

The impact of wind power on electricity prices

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brancucci Martinez-Anido, Carlo; Brinkman, Greg; Hodge, Bri-Mathias

This paper investigates the impact of wind power on electricity prices using a production cost model of the Independent System Operator - New England power system. Different scenarios in terms of wind penetration, wind forecasts, and wind curtailment are modeled in order to analyze the impact of wind power on electricity prices for different wind penetration levels and for different levels of wind power visibility and controllability. The analysis concludes that electricity price volatility increases even as electricity prices decrease with increasing wind penetration levels. The impact of wind power on price volatility is larger in the shorter term (5-minmore » compared to hour-to-hour). The results presented show that over-forecasting wind power increases electricity prices while under-forecasting wind power reduces them. The modeling results also show that controlling wind power by allowing curtailment increases electricity prices, and for higher wind penetrations it also reduces their volatility.« less

Arab energy: prospects to 2000

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1982-01-01

The energy situation of 21 Arab countries for the period between 1960 and 2000 is examined. Attempts to forecast the demand and supply of energy in the Arab world for 1985, 1990 and 2000 are discussed. Following a description of the methodology employed, crude petroleum, petroleum production, natural gas and electricity are explored in detail. The national programs of the Arab countries for electric-power generation include conventional thermal electricity, hydroelectricity, nuclear power, solar energy, biomass conversion, and geothermal and wind energy. 23 references.

Short time ahead wind power production forecast

NASA Astrophysics Data System (ADS)

Sapronova, Alla; Meissner, Catherine; Mana, Matteo

2016-09-01

An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast.

Challenges in Measuring External Currents Driven by the Solar Wind-Magnetosphere Interaction

NASA Technical Reports Server (NTRS)

Le, Guan; Slavin, James A.; Pfaff, Robert F.

2014-01-01

In studying the Earth's geomagnetism, it has always been a challenge to separate magnetic fields from external currents originating from the ionosphere and magnetosphere. While the internal magnetic field changes very slowly in time scales of years and more, the ionospheric and magnetospheric current systems driven by the solar wind -magnetosphere interaction are very dynamic. They are intimately controlled by the ionospheric electrodynamics and ionospheremagnetosphere coupling. Single spacecraft observations are not able to separate their spatial and temporal variations, and thus to accurately describe their configurations. To characterize and understand the external currents, satellite observations require both good spatial and temporal resolutions. This paper reviews our observations of the external currents from two recent LEO satellite missions: Space Technology 5 (ST-5), NASA's first three-satellite constellation mission in LEO polar orbit, and Communications/Navigation Outage Forecasting System (C/NOFS), an equatorial satellite developed by US Air Force Research Laboratory. We present recommendations for future geomagnetism missions based on these observations.

ICARUS mission, next step of coronal exploration after Solar Orbiter and Solar Probe Plus

NASA Astrophysics Data System (ADS)

Krasnoselskikh, Vladimir; Tsurutani, Bruce T.; Velli, Marco; Maksimovic, Milan; Balikhin, Mikhael; Dudok de Wit, Thierry; Kretzschmar, Matthieu

2017-04-01

The primary scientific goal of ICARUS (Investigation of Coronal AcceleRation and heating Up to the Sun), a mother-daughter satellite mission, will be to determine how the magnetic _field and plasma dynamics in the outer solar atmosphere give rise to the corona, the solar wind and the entire heliosphere. Reaching this goal will be a Rosetta-stone step, with results broadly applicable within the fields of space plasma physics and astrophysics. Within ESA's Cosmic Vision roadmap, these science goals address Theme 2: How does the solar system work ?" by investigating basic processes occurring From the Sun to the edge of the Solar System". ICARUS will not only advance our understanding of the plasma environment around our the Sun, but also of the numerous magnetically active stars with hot plasma coronae. ICARUS I will perform the first-ever direct in situ measurements of electromagnetic fields, particle acceleration, wave activity, energy distribution and flows directly in the regions where the solar wind emerges from the coronal plasma. ICARUS I will have a perihelion at 1 Solar radius from its surface, it will cross the region where the major energy deposition occurs. The polar orbit of ICARUS I will enable crossing the regions where both the fast and slow wind are generated. It will probe local characteristics of the plasma and provide unique information about the physical processes involved in the creation of the solar wind. ICARUS II will observe this region using remote-sensing instruments, providing simultaneous information about regions crossed by ICARUS I and the solar atmosphere below as observed by solar telescopes. It will thus provide bridges for understanding the magnetic links between the heliosphere and the solar atmosphere. Such information is crucial to our understanding of the plasma physics and electrodynamics of the solar atmosphere. ICARUS II will also play a very important relay role, enabling the radio-link with ICARUS I. It will receive, collect and store information transmitted from ICARUS I during its closest approach to the Sun. It will also perform preliminary data processing before transmitting it to the Earth. Performing such unique in situ measurements in the region where presumably deadly solar energetic particles are energized, ICARUS will make fundamental contributions to our ability to monitor and forecast the space radiation environment. Such a knowledge is extremely important for future space explorations, especially for long-term manned space missions.

Characterizing and analyzing ramping events in wind power, solar power, load, and netload

DOE PAGES

Cui, Mingjian; Zhang, Jie; Feng, Cong; ...

2017-04-07

Here, one of the biggest concerns associated with integrating a large amount of renewable energy into the power grid is the ability to handle large ramps in the renewable power output. For the sake of system reliability and economics, it is essential for power system operators to better understand the ramping features of renewable, load, and netload. An optimized swinging door algorithm (OpSDA) is used and extended to accurately and efficiently detect ramping events. For wind power ramps detection, a process of merging 'bumps' (that have a different changing direction) into adjacent ramping segments is included to improve the performancemore » of the OpSDA method. For solar ramps detection, ramping events that occur in both clear-sky and measured (or forecasted) solar power are removed to account for the diurnal pattern of solar generation. Ramping features are extracted and extensively compared between load and netload under different renewable penetration levels (9.77%, 15.85%, and 51.38%). Comparison results show that (i) netload ramp events with shorter durations and smaller magnitudes occur more frequently when renewable penetration level increases, and the total number of ramping events also increases; and (ii) different ramping characteristics are observed in load and netload even with a low renewable penetration level.« less

Initialization of a mesoscale model for April 10, 1979, using alternative data sources

NASA Technical Reports Server (NTRS)

Kalb, M. W.

1984-01-01

A 35 km grid limited area mesoscale model was initialized with high density SESAME radiosonde data and high density TIROS-N satellite temperature profiles for April 10, 1979. These data sources were used individually and with low level wind fields constructed from surface wind observations. The primary objective was to examine the use of satellite temperature data for initializing a mesoscale model by comparing the forecast results with similar experiments employing radiosonde data. The impact of observed low level winds on the model forecasts was also investigated with experiments varying the method of insertion. All forecasts were compared with each other and with mesoscale observations for precipitation, mass and wind structure. Several forecasts produced convective precipitation systems with characteristics satisfying criteria for a mesoscale convective complex. High density satellite temperature data and balanced winds can be used in a mesoscale model to produce forecasts which verify favorably with observations.

Solar wind classification from a machine learning perspective

NASA Astrophysics Data System (ADS)

Heidrich-Meisner, V.; Wimmer-Schweingruber, R. F.

2017-12-01

It is a very well known fact that the ubiquitous solar wind comes in at least two varieties, the slow solar wind and the coronal hole wind. The simplified view of two solar wind types has been frequently challenged. Existing solar wind categorization schemes rely mainly on different combinations of the solar wind proton speed, the O and C charge state ratios, the Alfvén speed, the expected proton temperature and the specific proton entropy. In available solar wind classification schemes, solar wind from stream interaction regimes is often considered either as coronal hole wind or slow solar wind, although their plasma properties are different compared to "pure" coronal hole or slow solar wind. As shown in Neugebauer et al. (2016), even if only two solar wind types are assumed, available solar wind categorization schemes differ considerably for intermediate solar wind speeds. Thus, the decision boundary between the coronal hole and the slow solar wind is so far not well defined.In this situation, a machine learning approach to solar wind classification can provide an additional perspective.We apply a well-known machine learning method, k-means, to the task of solar wind classification in order to answer the following questions: (1) How many solar wind types can reliably be identified in our data set comprised of ten years of solar wind observations from the Advanced Composition Explorer (ACE)? (2) Which combinations of solar wind parameters are particularly useful for solar wind classification?Potential subtypes of slow solar wind are of particular interest because they can provide hints of respective different source regions or release mechanisms of slow solar wind.

Solar flare predictions and warnings

NASA Technical Reports Server (NTRS)

White, K. P., III; Mayfield, E. B.

1973-01-01

The real-time solar monitoring information supplied to support SPARCS-equipped rocket launches, the routine collection and analysis of 3.3-mm solar radio maps, short-term flare forecasts based on these maps, longer-term forecasts based on the recurrence of active regions, and results of the synoptic study of solar active regions at 3.3-mm wavelength are presented. Forecasted flares in the 24-hour forecasts were 81% accurate, and those in the 28-day forecasts were 97% accurate. Synoptic radio maps at 3.3-mm wavelength are presented for twenty-three solar rotations in 1967 and 1968, as well as synoptic flare charts for the same period.

Characterizing Time Series Data Diversity for Wind Forecasting: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Chartan, Erol Kevin; Feng, Cong

Wind forecasting plays an important role in integrating variable and uncertain wind power into the power grid. Various forecasting models have been developed to improve the forecasting accuracy. However, it is challenging to accurately compare the true forecasting performances from different methods and forecasters due to the lack of diversity in forecasting test datasets. This paper proposes a time series characteristic analysis approach to visualize and quantify wind time series diversity. The developed method first calculates six time series characteristic indices from various perspectives. Then the principal component analysis is performed to reduce the data dimension while preserving the importantmore » information. The diversity of the time series dataset is visualized by the geometric distribution of the newly constructed principal component space. The volume of the 3-dimensional (3D) convex polytope (or the length of 1D number axis, or the area of the 2D convex polygon) is used to quantify the time series data diversity. The method is tested with five datasets with various degrees of diversity.« less

Minimum Energy Routing through Interactive Techniques (MERIT) modeling

NASA Technical Reports Server (NTRS)

Wylie, Donald P.

1988-01-01

The MERIT program is designed to demonstrate the feasibility of fuel savings by airlines through improved route selection using wind observations from their own fleet. After a discussion of weather and aircraft data, manually correcting wind fields, automatic corrections to wind fields, and short-range prediction models, it is concluded that improvements in wind information are possible if a system is developed for analyzing wind observations and correcting the forecasts made by the major models. One data handling system, McIDAS, can easily collect and display wind observations and model forecasts. Changing the wind forecasts beyond the time of the most recent observations is more difficult; an Australian Mesoscale Model was tested with promising but not definitive results.

Statistical Short-Range Guidance for Peak Wind Speed Forecasts at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Dreher, Joseph; Crawford, Winifred; Lafosse, Richard; Hoeth, Brian; Burns, Kerry

2008-01-01

The peak winds near the surface are an important forecast element for Space Shuttle landings. As defined in the Shuttle Flight Rules (FRs), there are peak wind thresholds that cannot be exceeded in order to ensure the safety of the shuttle during landing operations. The National Weather Service Spaceflight Meteorology Group (SMG) is responsible for weather forecasts for all shuttle landings. They indicate peak winds are a challenging parameter to forecast. To alleviate the difficulty in making such wind forecasts, the Applied Meteorology Unit (AMTJ) developed a personal computer based graphical user interface (GUI) for displaying peak wind climatology and probabilities of exceeding peak-wind thresholds for the Shuttle Landing Facility (SLF) at Kennedy Space Center. However, the shuttle must land at Edwards Air Force Base (EAFB) in southern California when weather conditions at Kennedy Space Center in Florida are not acceptable, so SMG forecasters requested that a similar tool be developed for EAFB. Marshall Space Flight Center (MSFC) personnel archived and performed quality control of 2-minute average and 10-minute peak wind speeds at each tower adjacent to the main runway at EAFB from 1997- 2004. They calculated wind climatologies and probabilities of average peak wind occurrence based on the average speed. The climatologies were calculated for each tower and month, and were stratified by hour, direction, and direction/hour. For the probabilities of peak wind occurrence, MSFC calculated empirical and modeled probabilities of meeting or exceeding specific 10-minute peak wind speeds using probability density functions. The AMU obtained and reformatted the data into Microsoft Excel PivotTables, which allows users to display different values with point-click-drag techniques. The GUT was then created from the PivotTables using Visual Basic for Applications code. The GUI is run through a macro within Microsoft Excel and allows forecasters to quickly display and interpret peak wind climatology and likelihoods in a fast-paced operational environment. A summary of how the peak wind climatologies and probabilities were created and an overview of the GUT will be presented.

Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning, volume 3

NASA Technical Reports Server (NTRS)

Keitz, J. F.

1982-01-01

The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This volume of the report discusses the results of Task 3 of the four major tasks included in the study. Task 3 compares flight plans developed on the Suitland forecast with actual data observed by the aircraft (and averaged over 10 degree segments). The results show that the average difference between the forecast and observed wind speed is 9 kts. without considering direction, and the average difference in the component of the forecast wind parallel to the direction of the observed wind is 13 kts. - both indicating that the Suitland forecast underestimates the wind speeds. The Root Mean Square (RMS) vector error is 30.1 kts. The average absolute difference in direction between the forecast and observed wind is 26 degrees and the temperature difference is 3 degree Centigrade. These results indicate that the forecast model as well as the verifying analysis used to develop comparison flight plans in Tasks 1 and 2 is a limiting factor and that the average potential fuel savings or penalty are up to 3.6 percent depending on the direction of flight.

Development of a satellite-based nowcasting system for surface solar radiation

NASA Astrophysics Data System (ADS)

Limbach, Sebastian; Hungershoefer, Katja; Müller, Richard; Trentmann, Jörg; Asmus, Jörg; Schömer, Elmar; Groß, André

2014-05-01

The goal of the RadNowCast project was the development of a tool-chain for a satellite-based nowcasting of the all sky global and direct surface solar radiation. One important application of such short-term forecasts is the computation of the expected energy yield of photovoltaic systems. This information is of great importance for an efficient balancing of power generation and consumption in large, decentralized power grids. Our nowcasting approach is based on an optical-flow analysis of a series of Meteosat SEVIRI satellite images. For this, we extended and combined several existing software tools and set up a series of benchmarks for determining the optimal forecasting parameters. The first step in our processing-chain is the determination of the cloud albedo from the HRV (High Resolution Visible)-satellite images using a Heliosat-type method. The actual nowcasting is then performed by a commercial software system in two steps: First, vector fields characterizing the movement of the clouds are derived from the cloud albedo data from the previous 15 min to 2 hours. Next, these vector fields are combined with the most recent cloud albedo data in order to extrapolate the cloud albedo in the near future. In the last step of the processing, the Gnu-Magic software is used to calculate the global and direct solar radiation based on the forecasted cloud albedo data. For an evaluation of the strengths and weaknesses of our nowcastig system, we analyzed four different benchmarks, each of which covered different weather conditions. We compared the forecasted data with radiation data derived from the real satellite images of the corresponding time steps. The impact of different parameters on the cloud albedo nowcasting and the surface radiation computation has been analysed. Additionally, we could show that our cloud-albedo-based forecasts outperform forecasts based on the original HRV images. Possible future extension are the incorporation of additional data sources, for example NWC-SAF high resolution wind fields, in order to improve the quality of the atmospheric motion fields, and experiments with custom, optimized software components for the optical-flow estimation and the nowcasting.

Building the Sun4Cast System: Improvements in Solar Power Forecasting

DOE PAGES

Haupt, Sue Ellen; Kosovic, Branko; Jensen, Tara; ...

2017-06-16

The Sun4Cast System results from a research-to-operations project built on a value chain approach, and benefiting electric utilities’ customers, society, and the environment by improving state-of-the-science solar power forecasting capabilities. As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers.more » The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, the basis of the system beyond about 6 h. For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. As a result, this paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.« less

Building the Sun4Cast System: Improvements in Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen; Kosovic, Branko; Jensen, Tara

The Sun4Cast System results from a research-to-operations project built on a value chain approach, and benefiting electric utilities’ customers, society, and the environment by improving state-of-the-science solar power forecasting capabilities. As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers.more » The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, the basis of the system beyond about 6 h. For short-range (0-6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short to mid-term irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach, and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. As a result, this paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.« less

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-07-25

This paper presents four algorithms to generate random forecast error time series. The performance of four algorithms is compared. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets used in power grid operation to study the net load balancing need in variable generation integration studies. The four algorithms are truncated-normal distribution models, state-space based Markov models, seasonal autoregressive moving average (ARMA) models, and a stochastic-optimization based approach. The comparison is made using historical DA load forecast and actual load valuesmore » to generate new sets of DA forecasts with similar stoical forecast error characteristics (i.e., mean, standard deviation, autocorrelation, and cross-correlation). The results show that all methods generate satisfactory results. One method may preserve one or two required statistical characteristics better the other methods, but may not preserve other statistical characteristics as well compared with the other methods. Because the wind and load forecast error generators are used in wind integration studies to produce wind and load forecasts time series for stochastic planning processes, it is sometimes critical to use multiple methods to generate the error time series to obtain a statistically robust result. Therefore, this paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Statistical Post-Processing of Wind Speed Forecasts to Estimate Relative Economic Value

NASA Astrophysics Data System (ADS)

Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

2013-04-01

The objective of this research is to get the best possible wind speed forecasts for the wind energy industry by using an optimal combination of well-established forecasting and post-processing methods. We start with the ECMWF 51 member ensemble prediction system (EPS) which is underdispersive and hence uncalibrated. We aim to produce wind speed forecasts that are more accurate and calibrated than the EPS. The 51 members of the EPS are clustered to 8 weighted representative members (RMs), chosen to minimize the within-cluster spread, while maximizing the inter-cluster spread. The forecasts are then downscaled using two limited area models, WRF and COSMO, at two resolutions, 14km and 3km. This process creates four distinguishable ensembles which are used as input to statistical post-processes requiring multi-model forecasts. Two such processes are presented here. The first, Bayesian Model Averaging, has been proven to provide more calibrated and accurate wind speed forecasts than the ECMWF EPS using this multi-model input data. The second, heteroscedastic censored regression is indicating positive results also. We compare the two post-processing methods, applied to a year of hindcast wind speed data around Ireland, using an array of deterministic and probabilistic verification techniques, such as MAE, CRPS, probability transform integrals and verification rank histograms, to show which method provides the most accurate and calibrated forecasts. However, the value of a forecast to an end-user cannot be fully quantified by just the accuracy and calibration measurements mentioned, as the relationship between skill and value is complex. Capturing the full potential of the forecast benefits also requires detailed knowledge of the end-users' weather sensitive decision-making processes and most importantly the economic impact it will have on their income. Finally, we present the continuous relative economic value of both post-processing methods to identify which is more beneficial to the wind energy industry of Ireland.

On the Relationship Between High Speed Solar Wind Streams and Radiation Belt Electron Fluxes

NASA Technical Reports Server (NTRS)

Zheng, Yihua

2011-01-01

Both past and recent research results indicate that solar wind speed has a close connection to radiation belt electron fluxes [e.g., Paulikas and Blake, 1979; Reeves et aI., 2011]: a higher solar wind speed is often associated with a higher level of radiation electron fluxes. But the relationship can be very complex [Reeves et aI., 2011]. The study presented here provides further corroboration of this viewpoint by emphasizing the importance of a global perspective and time history. We find that all the events during years 2010 and 2011 where the >0.8 MeV integral electron flux exceeds 10(exp 5) particles/sq cm/sr/s (pfu) at GEO orbit are associated with the high speed streams (HSS) following the onset of the Stream Interaction Region (SIR), with most of them belonging to the long-lasting Corotating Interaction Region (CIR). Our preliminary results indicate that during HSS events, a maximum speed of 700 km/s and above is a sufficient but not necessary condition for the > 0.8 MeV electron flux to reach 10(exp 5) pfu. But in the exception cases of HSS events where the electron flux level exceeds the 10(exp 5) pfu value but the maximum solar wind speed is less than 700 km/s, a prior impact can be noted either from a CME or a transient SIR within 3-4 days before the arrival of the HSS - stressing the importance of time history. Through superposed epoch analysis and studies providing comparisons with the CME events and the HSS events where the flux level fails to reach the 10(exp 5) pfu, we will present the quantitative assessment of behaviors and relationships of various quantities, such as the time it takes to reach the flux threshold value from the stream interface and its dependence on different physical parameters (e.g., duration of the HSS event, its maximum or average of the solar wind speed, IMF Bz, Kp). The ultimate goal is to apply what is derived to space weather forecasting.

Integrating Solar Power onto the Electric Grid - Bridging the Gap between Atmospheric Science, Engineering and Economics

NASA Astrophysics Data System (ADS)

Ghonima, M. S.; Yang, H.; Zhong, X.; Ozge, B.; Sahu, D. K.; Kim, C. K.; Babacan, O.; Hanna, R.; Kurtz, B.; Mejia, F. A.; Nguyen, A.; Urquhart, B.; Chow, C. W.; Mathiesen, P.; Bosch, J.; Wang, G.

2015-12-01

One of the main obstacles to high penetrations of solar power is the variable nature of solar power generation. To mitigate variability, grid operators have to schedule additional reliability resources, at considerable expense, to ensure that load requirements are met by generation. Thus despite the cost of solar PV decreasing, the cost of integrating solar power will increase as penetration of solar resources onto the electric grid increases. There are three principal tools currently available to mitigate variability impacts: (i) flexible generation, (ii) storage, either virtual (demand response) or physical devices and (iii) solar forecasting. Storage devices are a powerful tool capable of ensuring smooth power output from renewable resources. However, the high cost of storage is prohibitive and markets are still being designed to leverage their full potential and mitigate their limitation (e.g. empty storage). Solar forecasting provides valuable information on the daily net load profile and upcoming ramps (increasing or decreasing solar power output) thereby providing the grid advance warning to schedule ancillary generation more accurately, or curtail solar power output. In order to develop solar forecasting as a tool that can be utilized by the grid operators we identified two focus areas: (i) develop solar forecast technology and improve solar forecast accuracy and (ii) develop forecasts that can be incorporated within existing grid planning and operation infrastructure. The first issue required atmospheric science and engineering research, while the second required detailed knowledge of energy markets, and power engineering. Motivated by this background we will emphasize area (i) in this talk and provide an overview of recent advancements in solar forecasting especially in two areas: (a) Numerical modeling tools for coastal stratocumulus to improve scheduling in the day-ahead California energy market. (b) Development of a sky imager to provide short term forecasts (0-20 min ahead) to improve optimization and control of equipment on distribution feeders with high penetration of solar. Leveraging such tools that have seen extensive use in the atmospheric sciences supports the development of accurate physics-based solar forecast models. Directions for future research are also provided.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mendes, J.; Bessa, R.J.; Keko, H.

Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highlymore » dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios (with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.« less

Do Solar Coronal Holes Affect the Properties of Solar Energetic Particle Events?

NASA Technical Reports Server (NTRS)

Kahler, S. W.; Arge, C. N.; Akiyama, S.; Gopalswamy, N.

2013-01-01

The intensities and timescales of gradual solar energetic particle (SEP) events at 1 AU may depend not only on the characteristics of shocks driven by coronal mass ejections (CMEs), but also on large-scale coronal and interplanetary structures. It has long been suspected that the presence of coronal holes (CHs) near the CMEs or near the 1-AU magnetic footpoints may be an important factor in SEP events. We used a group of 41 E (is) approx. 20 MeV SEP events with origins near the solar central meridian to search for such effects. First we investigated whether the presence of a CH directly between the sources of the CME and of the magnetic connection at 1 AU is an important factor. Then we searched for variations of the SEP events among different solar wind (SW) stream types: slow, fast, and transient. Finally, we considered the separations between CME sources and CH footpoint connections from 1 AU determined from four-day forecast maps based on Mount Wilson Observatory and the National Solar Observatory synoptic magnetic-field maps and the Wang-Sheeley-Arge model of SW propagation. The observed in-situ magnetic-field polarities and SW speeds at SEP event onsets tested the forecast accuracies employed to select the best SEP/CH connection events for that analysis. Within our limited sample and the three analytical treatments, we found no statistical evidence for an effect of CHs on SEP event peak intensities, onset times, or rise times. The only exception is a possible enhancement of SEP peak intensities in magnetic clouds.

Solar flare predictions and warnings

NASA Technical Reports Server (NTRS)

White, K. P., III

1972-01-01

The real-time solar monitoring information supplied to support SPARCS equipped rocket launches, the routine collection and analysis of 3.3-mm solar radio maps, short-term flare forecasts based on these maps, longer-term forecasts based on the recurrence of active regions, and an extension of the flare forecasting technique are summarized. Forecasts for expectation of a solar flare of class or = 2F are given and compared with observed flares. A total of 52 plage regions produced all the flares of class or = 1N during the study period. The following results are indicated: of the total of 21 positive forecasts, 3 were correct and 18 were incorrect; of the total of 31 negative forecasts, 3 were incorrect and 28 were correct; of a total of 6 plage regions producing large flares, 3 were correctly forecast and 3 were missed; and of 46 regions not producing any large flares, 18 were incorrectly forecast and 28 were correctly forecast.

Three-Dimensional Evolution of Flux-Rope CMEs and Its Relation to the Local Orientation of the Heliospheric Current Sheet

NASA Astrophysics Data System (ADS)

Isavnin, A.; Vourlidas, A.; Kilpua, E. K. J.

2014-06-01

Flux ropes ejected from the Sun may change their geometrical orientation during their evolution, which directly affects their geoeffectiveness. Therefore, it is crucial to understand how solar flux ropes evolve in the heliosphere to improve our space-weather forecasting tools. We present a follow-up study of the concepts described by Isavnin, Vourlidas, and Kilpua ( Solar Phys. 284, 203, 2013). We analyze 14 coronal mass ejections (CMEs), with clear flux-rope signatures, observed during the decay of Solar Cycle 23 and rise of Solar Cycle 24. First, we estimate initial orientations of the flux ropes at the origin using extreme-ultraviolet observations of post-eruption arcades and/or eruptive prominences. Then we reconstruct multi-viewpoint coronagraph observations of the CMEs from ≈ 2 to 30 R⊙ with a three-dimensional geometric representation of a flux rope to determine their geometrical parameters. Finally, we propagate the flux ropes from ≈ 30 R⊙ to 1 AU through MHD-simulated background solar wind while using in-situ measurements at 1 AU of the associated magnetic cloud as a constraint for the propagation technique. This methodology allows us to estimate the flux-rope orientation all the way from the Sun to 1 AU. We find that while the flux-ropes' deflection occurs predominantly below 30 R⊙, a significant amount of deflection and rotation happens between 30 R⊙ and 1 AU. We compare the flux-rope orientation to the local orientation of the heliospheric current sheet (HCS). We find that slow flux ropes tend to align with the streams of slow solar wind in the inner heliosphere. During the solar-cycle minimum the slow solar-wind channel as well as the HCS usually occupy the area in the vicinity of the solar equatorial plane, which in the past led researchers to the hypothesis that flux ropes align with the HCS. Our results show that exceptions from this rule are explained by interaction with the Parker-spiraled background magnetic field, which dominates over the magnetic interaction with the HCS in the inner heliosphere at least during solar-minimum conditions.

Short Term Exogenic Climate Change Forcing

NASA Astrophysics Data System (ADS)

Krahenbuhl, Daniel

Several short term exogenic forcings affecting Earth's climate are but recently identified. Lunar nutation periodicity has implications for numerical meteorological prediction. Abrupt shifts in solar wind bulk velocity, particle density, and polarity exhibit correlation with terrestrial hemispheric vorticity changes, cyclonic strengthening and the intensification of baroclinic disturbances. Galactic Cosmic ray induced tropospheric ionization modifies cloud microphysics, and modulates the global electric circuit. This dissertation is constructed around three research questions: (1): What are the biweekly declination effects of lunar gravitation upon the troposphere? (2): How do United States severe weather reports correlate with heliospheric current sheet crossings? and (3): How does cloud cover spatially and temporally vary with galactic cosmic rays? Study 1 findings show spatial consistency concerning lunar declination extremes upon Rossby longwaves. Due to the influence of Rossby longwaves on synoptic scale circulation, our results could theoretically extend numerical meteorological forecasting. Study 2 results indicate a preference for violent tornadoes to occur prior to a HCS crossing. Violent tornadoes (EF3+) are 10% more probable to occur near, and 4% less probable immediately after a HCS crossing. The distribution of hail and damaging wind reports do not mirror this pattern. Polarity is critical for the effect. Study 3 results confirm anticorrelation between solar flux and low-level marine-layer cloud cover, but indicate substantial regional variability between cloud cover altitude and GCRs. Ultimately, this dissertation serves to extend short term meteorological forecasting, enhance climatological modeling and through analysis of severe violent weather and heliospheric events, protect property and save lives.

Test of wind predictions for peak fire-danger stations in Oregon and Washington.

Treesearch

Owen P. Cramer

1957-01-01

Relative accuracy of several wind-speed forecasting methods was tested during the forest fire seasons of 1950 and 1951. For the study, three fire-weather forecast centers of the U. S. Weather Bureau prepared individual station forecasts for 11 peak stations within the national. forests of Oregon and Washington. These spot forecasts were considered...

The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Wu, Hongyu; Florita, Anthony R.

The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was comparedmore » through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.« less

The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

DOE PAGES

Wang, Qin; Wu, Hongyu; Florita, Anthony R.; ...

2016-11-11

The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was comparedmore » through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.« less

Added value of non-calibrated and BMA calibrated AEMET-SREPS probabilistic forecasts: the 24 January 2009 extreme wind event over Catalonia

NASA Astrophysics Data System (ADS)

Escriba, P. A.; Callado, A.; Santos, D.; Santos, C.; Simarro, J.; García-Moya, J. A.

2009-09-01

At 00 UTC 24 January 2009 an explosive ciclogenesis originated over the Atlantic Ocean reached its maximum intensity with observed surface pressures lower than 970 hPa on its center and placed at Gulf of Vizcaya. During its path through southern France this low caused strong westerly and north-westerly winds over the Iberian Peninsula higher than 150 km/h at some places. These extreme winds leaved 10 casualties in Spain, 8 of them in Catalonia. The aim of this work is to show whether exists an added value in the short range prediction of the 24 January 2009 strong winds when using the Short Range Ensemble Prediction System (SREPS) of the Spanish Meteorological Agency (AEMET), with respect to the operational forecasting tools. This study emphasizes two aspects of probabilistic forecasting: the ability of a 3-day forecast of warn an extreme windy event and the ability of quantifying the predictability of the event so that giving value to deterministic forecast. Two type of probabilistic forecasts of wind are carried out, a non-calibrated and a calibrated one using Bayesian Model Averaging (BMA). AEMET runs daily experimentally SREPS twice a day (00 and 12 UTC). This system consists of 20 members that are constructed by integrating 5 local area models, COSMO (COSMO), HIRLAM (HIRLAM Consortium), HRM (DWD), MM5 (NOAA) and UM (UKMO), at 25 km of horizontal resolution. Each model uses 4 different initial and boundary conditions, the global models GFS (NCEP), GME (DWD), IFS (ECMWF) and UM. By this way it is obtained a probabilistic forecast that takes into account the initial, the contour and the model errors. BMA is a statistical tool for combining predictive probability functions from different sources. The BMA predictive probability density function (PDF) is a weighted average of PDFs centered on the individual bias-corrected forecasts. The weights are equal to posterior probabilities of the models generating the forecasts and reflect the skill of the ensemble members. Here BMA is applied to provide probabilistic forecasts of wind speed. In this work several forecasts for different time ranges (H+72, H+48 and H+24) of 10 meters wind speed over Catalonia are verified subjectively at one of the instants of maximum intensity, 12 UTC 24 January 2009. On one hand, three probabilistic forecasts are compared, ECMWF EPS, non-calibrated SREPS and calibrated SREPS. On the other hand, the relationship between predictability and skill of deterministic forecast is studied by looking at HIRLAM 0.16 deterministic forecasts of the event. Verification is focused on location and intensity of 10 meters wind speed and 10-minutal measures from AEMET automatic ground stations are used as observations. The results indicate that SREPS is able to forecast three days ahead mean winds higher than 36 km/h and that correctly localizes them with a significant probability of ocurrence in the affected area. The probability is higher after BMA calibration of the ensemble. The fact that probability of strong winds is high allows us to state that the predictability of the event is also high and, as a consequence, deterministic forecasts are more reliable. This is confirmed when verifying HIRLAM deterministic forecasts against observed values.

How important is getting the land surface energy exchange correct in WRF for wind energy forecasting?

NASA Astrophysics Data System (ADS)

Wharton, S.; Simpson, M.; Osuna, J. L.; Newman, J. F.; Biraud, S.

2013-12-01

Wind power forecasting is plagued with difficulties in accurately predicting the occurrence and intensity of atmospheric conditions at the heights spanned by industrial-scale turbines (~ 40 to 200 m above ground level). Better simulation of the relevant physics would enable operational practices such as integration of large fractions of wind power into power grids, scheduling maintenance on wind energy facilities, and deciding design criteria based on complex loads for next-generation turbines and siting. Accurately simulating the surface energy processes in numerical models may be critically important for wind energy forecasting as energy exchange at the surface strongly drives atmospheric mixing (i.e., stability) in the lower layers of the planetary boundary layer (PBL), which in turn largely determines wind shear and turbulence at heights found in the turbine rotor-disk. We hypothesize that simulating accurate a surface-atmosphere energy coupling should lead to more accurate predictions of wind speed and turbulence at heights within the turbine rotor-disk. Here, we tested 10 different land surface model configurations in the Weather Research and Forecasting (WRF) model including Noah, Noah-MP, SSiB, Pleim-Xiu, RUC, and others to evaluate (1) the accuracy of simulated surface energy fluxes to flux tower measurements, (2) the accuracy of forecasted wind speeds to observations at rotor-disk heights, and (3) the sensitivity of forecasting hub-height rotor disk wind speed to the choice of land surface model. WRF was run for four, two-week periods covering both summer and winter periods over the Southern Great Plains ARM site in Oklahoma. Continuous measurements of surface energy fluxes and lidar-based wind speed, direction and turbulence were also available. The SGP ARM site provided an ideal location for this evaluation as it centrally located in the wind-rich Great Plains and multi-MW wind farms are rapidly expanding in the area. We found significant differences in simulated wind speeds at rotor-disk heights from WRF which indicated, in part, the sensitivity of lower PBL winds to surface energy exchange. We also found significant differences in energy partitioning between sensible heat and latent energy depending on choice of land surface model. Overall, the most consistent, accurate model results were produced using Noah-MP. Noah-MP was most accurate at simulating energy fluxes and wind shear. Hub-height wind speed, however, was predicted with most accuracy with Pleim-Xiu. This suggests that simulating wind shear in the surface layer is consistent with accurately simulating surface energy exchange while the exact magnitudes of wind speed may be more strongly influenced by the PBL dynamics. As the nation is working towards a 20% wind energy goal by 2030, increasing the accuracy of wind forecasting at rotor-disk heights becomes more important considering that utilities require wind farms to estimate their power generation 24 to 36 hours ahead and face penalties for inaccuracies in those forecasts.

An Experimental Real-Time Ocean Nowcast/Forecast System for Intra America Seas

NASA Astrophysics Data System (ADS)

Ko, D. S.; Preller, R. H.; Martin, P. J.

2003-04-01

An experimental real-time Ocean Nowcast/Forecast System has been developed for the Intra America Seas (IASNFS). The area of coverage includes the Caribbean Sea, the Gulf of Mexico and the Straits of Florida. The system produces nowcast and up to 72 hours forecast the sea level variation, 3D ocean current, temperature and salinity fields. IASNFS consists an 1/24 degree (~5 km), 41-level sigma-z data-assimilating ocean model based on NCOM. For daily nowcast/forecast the model is restarted from previous nowcast. Once model is restarted it continuously assimilates the synthetic temperature/salinity profiles generated by a data analysis model called MODAS to produce nowcast. Real-time data come from satellite altimeter (GFO, TOPEX/Poseidon, ERS-2) sea surface height anomaly and AVHRR sea surface temperature. Three hourly surface heat fluxes, including solar radiation, wind stresses and sea level air pressure from NOGAPS/FNMOC are applied for surface forcing. Forecasts are produced with available NOGAPS forecasts. Once the nowcast/forecast are produced they are distributed through the Internet via the updated web pages. The open boundary conditions including sea surface elevation, transport, temperature, salinity and currents are provided by the NRL 1/8 degree Global NCOM which is operated daily. An one way coupling scheme is used to ingest those boundary conditions into the IAS model. There are 41 rivers with monthly discharges included in the IASNFS.

Modeling solar energetic particle events using ENLIL heliosphere simulations

NASA Astrophysics Data System (ADS)

Luhmann, J. G.; Mays, M. L.; Odstrcil, D.; Li, Yan; Bain, H.; Lee, C. O.; Galvin, A. B.; Mewaldt, R. A.; Cohen, C. M. S.; Leske, R. A.; Larson, D.; Futaana, Y.

2017-07-01

Solar energetic particle (SEP) event modeling has gained renewed attention in part because of the availability of a decade of multipoint measurements from STEREO and L1 spacecraft at 1 AU. These observations are coupled with improving simulations of the geometry and strength of heliospheric shocks obtained by using coronagraph images to send erupted material into realistic solar wind backgrounds. The STEREO and ACE measurements in particular have highlighted the sometimes surprisingly widespread nature of SEP events. It is thus an opportune time for testing SEP models, which typically focus on protons 1-100 MeV, toward both physical insight to these observations and potentially useful space radiation environment forecasting tools. Some approaches emphasize the concept of particle acceleration and propagation from close to the Sun, while others emphasize the local field line connection to a traveling, evolving shock source. Among the latter is the previously introduced SEPMOD treatment, based on the widely accessible and well-exercised WSA-ENLIL-cone model. SEPMOD produces SEP proton time profiles at any location within the ENLIL domain. Here we demonstrate a SEPMOD version that accommodates multiple, concurrent shock sources occurring over periods of several weeks. The results illustrate the importance of considering longer-duration time periods and multiple CME contributions in analyzing, modeling, and forecasting SEP events.

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, Martin; Giebel, Gregor; Skov Nielsen, Torben; Hahmann, Andrea; Sørensen, Poul; Madsen, Henrik

2013-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the title "Integrated Wind Power Planning Tool". The goal is to integrate a mesoscale numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. Using the state-of-the-art mesoscale NWP model Weather Research & Forecasting model (WRF) the forecast error is sought quantified in dependence of the time scale involved. This task constitutes a preparative study for later implementation of features accounting for NWP forecast errors in the DTU Wind Energy maintained Corwind code - a long term wind power planning tool. Within the framework of PSO 10464 research related to operational short term wind power prediction will be carried out, including a comparison of forecast quality at different mesoscale NWP model resolutions and development of a statistical wind power prediction tool taking input from WRF. The short term prediction part of the project is carried out in collaboration with ENFOR A/S; a Danish company that specialises in forecasting and optimisation for the energy sector. The integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated short term prediction tool constitutes scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator. The need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2025 from the current 20%.

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

DOE PAGES

Bessa, Ricardo; Möhrlen, Corinna; Fundel, Vanessa; ...

2017-09-14

Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding ofmore » its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. Our paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. Furthermore, a set of recommendations for standardization and improved training of operators are provided along with examples of best practices.« less

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bessa, Ricardo; Möhrlen, Corinna; Fundel, Vanessa

Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding ofmore » its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. Our paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. Furthermore, a set of recommendations for standardization and improved training of operators are provided along with examples of best practices.« less

An Evaluation of QuikSCAT data over Tropical Cyclones as Determined in an Operational Environment

NASA Astrophysics Data System (ADS)

Hawkins, J. D.; Edson, R. T.

2001-12-01

QuikSCAT data over all global tropical cyclones were examined during the past 3 1/2 years in conjunction with the development of a user¡_s guide to the forecasters at the Joint Typhoon Warning Center, Pearl Harbor, Hawaii. The active microwave scatterometer has greatly enhanced the forecaster's ability to evaluate surface winds over the data poor regions of the tropical oceans. The QuikSCAT scatterometer¡_s unique ability to provide both wind speed and direction on a nearly bi-daily basis has greatly increased the forecaster¡_s near real-time knowledge of tropical cyclone genesis, intensification potential, outer wind structure, and a ¡rminimum estimate¡_ for a tropical cyclone¡_s maximum sustained winds. Scatterometer data were compared with data available to the forecasters in a near real-time environment including ship, land and buoy reports. In addition, comparisons were also made with aircraft measurements (for Atlantic and East Pacific systems), numerical weather model wind fields, and various remote sensing techniques. Wind speeds were found to be extremely useful, especially for the radius of gale force winds. However, in rain-contaminated areas, light winds were often greatly overestimated while in heavy winds, wind speeds were often quite reasonable if not slightly underestimated. The largest issues are still focused on the correct wind direction selection. In these cases, rain-flagged wind vector cells greatly affected the results from the direction ambiguity selection procedure. The ambiguity selection algorithm often had difficulties resolving a circulation center when large areas of the tropical cyclone¡_s center were flagged. Often a block of winds would occur perpendicular to the swath irregardless of the circulation¡_s position. These winds caused considerable confusion for the operational forecasters. However, it was determined that in many cases, an accurate center position could still be obtained by using methods to incorporate the more accurate wind speeds and the outer wind field vectors that were not as seriously affected. Quantitative results and comparisons will be shown in this presentation. In addition, guides to the operational forecasters to determine system centers inspite of the ambiguity selection problems will also be discussed.

A preliminary study of the impact of the ERS 1 C band scatterometer wind data on the European Centre for Medium-Range Weather Forecasts global data assimilation system

NASA Technical Reports Server (NTRS)

Hoffman, Ross N.

1993-01-01

A preliminary assessment of the impact of the ERS 1 scatterometer wind data on the current European Centre for Medium-Range Weather Forecasts analysis and forecast system has been carried out. Although the scatterometer data results in changes to the analyses and forecasts, there is no consistent improvement or degradation. Our results are based on comparing analyses and forecasts from assimilation cycles. The two sets of analyses are very similar except for the low level wind fields over the ocean. Impacts on the analyzed wind fields are greater over the southern ocean, where other data are scarce. For the most part the mass field increments are too small to balance the wind increments. The effect of the nonlinear normal mode initialization on the analysis differences is quite small, but we observe that the differences tend to wash out in the subsequent 6-hour forecast. In the Northern Hemisphere, analysis differences are very small, except directly at the scatterometer locations. Forecast comparisons reveal large differences in the Southern Hemisphere after 72 hours. Notable differences in the Northern Hemisphere do not appear until late in the forecast. Overall, however, the Southern Hemisphere impacts are neutral. The experiments described are preliminary in several respects. We expect these data to ultimately prove useful for global data assimilation.

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

DOE PAGES

Staid, Andrea; Watson, Jean -Paul; Wets, Roger J. -B.; ...

2017-07-11

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Staid, Andrea; Watson, Jean -Paul; Wets, Roger J. -B.

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less

The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations. The Southern Study Area, Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Freedman, Jeffrey M.; Manobianco, John; Schroeder, John

This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP) -- Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute - 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10more » - 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems’ ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 - 3 hours.« less

An Approach to Comprehensive and Sustainable Solar Wind Model Validation

NASA Astrophysics Data System (ADS)

Rastaetter, L.; MacNeice, P. J.; Mays, M. L.; Boblitt, J. M.; Wiegand, C.

2017-12-01

The number of models of the corona and inner heliosphere and of their updates and upgrades grows steadily, as does the number and character of the model inputs. Maintaining up to date validation of these models, in the face of this constant model evolution, is a necessary but very labor intensive activity. In the last year alone, both NASA's LWS program and the CCMC's ongoing support of model forecasting activities at NOAA SWPC have sought model validation reports on the quality of all aspects of the community's coronal and heliospheric models, including both ambient and CME related wind solutions at L1. In this presentation I will give a brief review of the community's previous model validation results of L1 wind representation. I will discuss the semi-automated web based system we are constructing at the CCMC to present comparative visualizations of all interesting aspects of the solutions from competing models.This system is designed to be easily queried to provide the essential comprehensive inputs to repeat andupdate previous validation studies and support extensions to them. I will illustrate this by demonstrating how the system is being used to support the CCMC/LWS Model Assessment Forum teams focused on the ambient and time dependent corona and solar wind, including CME arrival time and IMF Bz.I will also discuss plans to extend the system to include results from the Forum teams addressing SEP model validation.

The MSFC Solar Activity Future Estimation (MSAFE) Model

NASA Technical Reports Server (NTRS)

Suggs, Ron

2017-01-01

The Natural Environments Branch of the Engineering Directorate at Marshall Space Flight Center (MSFC) provides solar cycle forecasts for NASA space flight programs and the aerospace community. These forecasts provide future statistical estimates of sunspot number, solar radio 10.7 cm flux (F10.7), and the geomagnetic planetary index, Ap, for input to various space environment models. For example, many thermosphere density computer models used in spacecraft operations, orbital lifetime analysis, and the planning of future spacecraft missions require as inputs the F10.7 and Ap. The solar forecast is updated each month by executing MSAFE using historical and the latest month's observed solar indices to provide estimates for the balance of the current solar cycle. The forecasted solar indices represent the 13-month smoothed values consisting of a best estimate value stated as a 50 percentile value along with approximate +/- 2 sigma values stated as 95 and 5 percentile statistical values. This presentation will give an overview of the MSAFE model and the forecast for the current solar cycle.

Prediction of the solar modulation of galactic cosmic rays and radiation dose of aircrews up to the solar cycle 26

NASA Astrophysics Data System (ADS)

Miyake, S.; Kataoka, R.; Sato, T.

2016-12-01

The solar modulation of galactic cosmic rays (GCRs), which is the variation of the terrestrial GCR flux caused by the heliospheric environmental change, is basically anti-correlated with the solar activity with so-called 11-year periodicity. In the current weak solar cycle 24, we expect that the flux of GCRs is getting higher than that in the previous solar cycles, leading to the increase in the radiation exposure in the space and atmosphere. In order to quantitatively evaluate the possible solar modulation of GCRs and resultant radiation exposure at flight altitude during the solar cycles 24, 25, and 26, we have developed the time-dependent and three-dimensional model of the solar modulation of GCRs. Our model can give the flux of GCRs anywhere in the heliosphere by assuming the variation of the solar wind velocity, the strength of the interplanetary magnetic field, and its tilt angle. We solve the curvature and gradient drift motion of GCRs in the heliospheric magnetic field, and therefore reproduce the 22-year variation of the solar modulation of GCRs. It is quantitatively confirmed that our model reproduces the energy spectra observed by BESS and PAMELA. We then calculate the variation of the GCR energy spectra during the solar cycles 24, 25, and 26, by extrapolating the solar wind parameters and tilt angle. We also calculate the neutron monitor counting rate and the radiation dose of aircrews at flight altitude, by the air-shower simulation performed by PHITS (Particle and Heavy Ion Transport code System). In this presentation, we report the quantitative forecast values of the solar modulation of GCRs, neutron monitor counting rate, and the radiation dose at flight altitude up to the cycle 26, including the discussion of the charge sign dependence on those results.

Improvements and Lingering Challenges with Modeling Low-Level Winds Over Complex Terrain during the Wind Forecast Improvement Project 2

NASA Astrophysics Data System (ADS)

Olson, J.; Kenyon, J.; Brown, J. M.; Angevine, W. M.; Marquis, M.; Pichugina, Y. L.; Choukulkar, A.; Bonin, T.; Banta, R. M.; Bianco, L.; Djalalova, I.; McCaffrey, K.; Wilczak, J. M.; Lantz, K. O.; Long, C. N.; Redfern, S.; McCaa, J. R.; Stoelinga, M.; Grimit, E.; Cline, J.; Shaw, W. J.; Lundquist, J. K.; Lundquist, K. A.; Kosovic, B.; Berg, L. K.; Kotamarthi, V. R.; Sharp, J.; Jiménez, P.

2017-12-01

The Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) are NOAA real-time operational hourly updating forecast systems run at 13- and 3-km grid spacing, respectively. Both systems use the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) as the model component of the forecast system. During the second installment of the Wind Forecast Improvement Project (WFIP 2), the RAP/HRRR have been targeted for the improvement of low-level wind forecasts in the complex terrain within the Columbia River Basin (CRB), which requires much finer grid spacing to resolve important terrain peaks in the Cascade Mountains as well as the Columbia River Gorge. Therefore, this project provides a unique opportunity to test and develop the RAP/HRRR physics suite within a very high-resolution nest (Δx = 750 m) over the northwestern US. Special effort is made to incorporate scale-aware aspects into the model physical parameterizations to improve RAP/HRRR wind forecasts for any application at any grid spacing. Many wind profiling and scanning instruments have been deployed in the CRB in support the WFIP 2 field project, which spanned 01 October 2015 to 31 March 2017. During the project, several forecast error modes were identified, such as: (1) too-shallow cold pools during the cool season, which can mix-out more frequently than observed and (2) the low wind speed bias in thermal trough-induced gap flows during the warm season. Development has been focused on the column-based turbulent mixing scheme to improve upon these biases, but investigating the effects of horizontal (and 3D) mixing has also helped improve some of the common forecast failure modes. This presentation will highlight the testing and development of various model components, showing the improvements over original versions for temperature and wind profiles. Examples of case studies and retrospective periods will be presented to illustrate the improvements. We will demonstrate that the improvements made in WFIP 2 will be extendable to other regions, complex or flat terrain. Ongoing and future challenges in RAP/HRRR physics development will be touched upon.

Impacts of Typhoon Megi (2010) on the South China Sea

DTIC Science & Technology

2014-06-01

investigations. To obtain realistic typhoon-strength atmospheric forcing, the EASNFS applied typhoon-resolving Weather Research and Forecasting ( WRF ) model wind...EASNFS applied typhoon-resolving Weather Research and Forecasting ( WRF ) model wind field blended with global weather forecast winds from the U.S. Navy...only 1C. Sequential SST snapshots, of which only a Figure 1. The EASNFS model domain with topography and an inset covered by WRF model. Typhoon Megi’s

Quantifying the Economic and Grid Reliability Impacts of Improved Wind Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Martinez-Anido, Carlo Brancucci; Wu, Hongyu

Wind power forecasting is an important tool in power system operations to address variability and uncertainty. Accurately doing so is important to reducing the occurrence and length of curtailment, enhancing market efficiency, and improving the operational reliability of the bulk power system. This research quantifies the value of wind power forecasting improvements in the IEEE 118-bus test system as modified to emulate the generation mixes of Midcontinent, California, and New England independent system operator balancing authority areas. To measure the economic value, a commercially available production cost modeling tool was used to simulate the multi-timescale unit commitment (UC) and economicmore » dispatch process for calculating the cost savings and curtailment reductions. To measure the reliability improvements, an in-house tool, FESTIV, was used to calculate the system's area control error and the North American Electric Reliability Corporation Control Performance Standard 2. The approach allowed scientific reproducibility of results and cross-validation of the tools. A total of 270 scenarios were evaluated to accommodate the variation of three factors: generation mix, wind penetration level, and wind fore-casting improvements. The modified IEEE 118-bus systems utilized 1 year of data at multiple timescales, including the day-ahead UC, 4-hour-ahead UC, and 5-min real-time dispatch. The value of improved wind power forecasting was found to be strongly tied to the conventional generation mix, existence of energy storage devices, and the penetration level of wind energy. The simulation results demonstrate that wind power forecasting brings clear benefits to power system operations.« less

Role of Meteorology in Flights of a Solar-Powered Airplane

NASA Technical Reports Server (NTRS)

Donohue, Casey

2004-01-01

In the summer of 2001, the Helios prototype solar-powered uninhabited aerial vehicle (UAV) [a lightweight, remotely piloted airplane] was deployed to the Pacific Missile Range Facility (PMRF), at Kauai, Hawaii, in an attempt to fly to altitudes above 100,000 ft (30.48 km). The goal of flying a UAV to such high altitudes has been designated a level-I milestone of the NASA Environmental Research Aircraft and Sensor Technology (ERAST) program. In support of this goal, meteorologists from NASA Dryden Flight Research Center were sent to PMRF, as part of the flight crew, to provide current and forecast weather information to the pilots, mission directors, and planners. Information of this kind is needed to optimize flight conditions for peak aircraft performance and to enable avoidance of weather conditions that could adversely affect safety. In general, the primary weather data of concern for ground and flight operations are wind speeds (see Figure 1). Because of its long wing span [247 ft (.75 m)] and low weight [1,500 to 1,600 lb (about 680 to 726 kg)], the Helios airplane is sensitive to wind speeds exceeding 7 kn (3.6 m/s) at the surface. Also, clouds are of concern because they can block sunlight needed to energize an array of solar photovoltaic cells that provide power to the airplane. Vertical wind shear is very closely monitored in order to prevent damage or loss of control due to turbulence.

Saturn and Earth polar oval position forecast by IMPEx InfrastructureWeb Services based on the Paraboloid magnetospheric model

NASA Astrophysics Data System (ADS)

Blokhina, M. S.; Alexeev, I. I.; Belenkaya, E. S.; Kalegaev, V. V.; Barinova, V. O.; Khodachenko, M. L.; Topf, F.

2012-09-01

The Saturn and Earth auroral emissions have different generation mechanisms, however, both mechanisms are not understood very well till now (see [1]). Both of these phenomena have a long history of observations. For Saturn these are Hubble images and big onground telescope images, as well as the Cassini ones in recent time. For Earth these are the satellite visible and UV camera images and onground observations. In course of the EU-FP7 Project "Integrated Medium for Planetary Exploration" the Web services based on the paraboloid magnetospheric models were constructed . The model field lines tracing gives us a possibility to distinguish the closed and open field line bundles. Additionally, we can find a boundary between the dipole type field lines and determine a region of the tail-like field lines crossing the equatorial plane tailward from the inner edge of the tail current sheet. Projections of this boundary and of the boundary between open and closed field lines at the ionospheric level mark the terrestrial auroral oval boundaries. The final result depends on the solar wind parameters and the magnetospheric state. In the Earth's case we have the ACE solar wind monitoring data which should be used to determine the magnetospheric state (http://smdc.sinp.msu.ru/index.py? nav=paraboloid/index [Interactive Earth]). For Saturn we use the three levels of the solar wind dynamic pressure (http://smdc.sinp. msu.ru/index.py?nav=paraboloid/index [Interactive Saturn]).

Online Analysis of Wind and Solar Part I: Ramping Tool

DOE Office of Scientific and Technical Information (OSTI.GOV)

Etingov, Pavel V.; Ma, Jian; Makarov, Yuri V.

2012-01-31

To facilitate wider penetration of renewable resources without compromising system reliability concerns arising from the lack of predictability of intermittent renewable resources, a tool for use by California Independent System Operator (CAISO) power grid operators was developed by Pacific Northwest National Laboratory (PNNL) in conjunction with CAISO with funding from California Energy Commission. This tool predicts and displays additional capacity and ramping requirements caused by uncertainties in forecasts of loads and renewable generation. The tool is currently operational in the CAISO operations center. This is one of two final reports on the project.

KSC-2015-1316

NASA Image and Video Library

2015-02-08

Gaseous oxygen vents away from the SpaceX Falcon 9 rocket standing at Space Launch Complex 40 at Florida’s Cape Canaveral Air Force Station during the first launch attempt for NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR. The mission is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Ben Smegelsky

KSC-2015-1307

NASA Image and Video Library

2015-02-08

CAPE CANAVERAL, Fla. – The SpaceX Falcon 9 rocket set to launch NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, is flanked by lightning masts at Space Launch Complex 40 at Cape Canaveral Air Force Station in Florida. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Kim Shiflett

KSC-2015-1305

NASA Image and Video Library

2015-02-08

CAPE CANAVERAL, Fla. – The SpaceX Falcon 9 rocket set to launch NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, awaits liftoff at 6:10 p.m. EST from Space Launch Complex 40 at Cape Canaveral Air Force Station in Florida. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Kim Shiflett

KSC-2015-1315

NASA Image and Video Library

2015-02-08

The SpaceX Falcon 9 rocket set to launch NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, stands at Space Launch Complex 40 at Florida’s Cape Canaveral Air Force Station during the mission’s first launch attempt. The mission is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Ben Smegelsky

KSC-2015-1309

NASA Image and Video Library

2015-02-08

CAPE CANAVERAL, Fla. – The SpaceX Falcon 9 rocket set to launch NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, awaits liftoff at 6:10 p.m. EST from Space Launch Complex 40 at Cape Canaveral Air Force Station in Florida. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Kim Shiflett

Coupling West WRF to GSSHA with GSSHApy

NASA Astrophysics Data System (ADS)

Snow, A. D.

2017-12-01

The West WRF output data is in the gridded NetCDF output format containing the required forcing data needed to run a GSSHA simulation. These data include precipitation, pressure, temperature, relative humidity, cloud cover, wind speed, and solar radiation. Tools to reproject, resample, and reformat the data for GSSHA have recently been added to the open source Python library GSSHApy (https://github.com/ci-water/gsshapy). These tools have created a connection that has made it possible to run forecasts using the West WRF forcing data with GSSHA to produce both streamflow and lake level predictions.

DSCOVR Spacecraft Arrival, Offload, & Unpacking

NASA Image and Video Library

2014-11-20

Workers align NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, wrapped in plastic, onto a portable work stand at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is currently scheduled for January 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida.

DSCOVR Spacecraft Arrival, Offload, & Unpacking

NASA Image and Video Library

2014-11-20

Preparations are underway to remove a protective shipping container from around NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is currently scheduled for January 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida.

DSCOVR Spacecraft Arrival, Offload, & Unpacking

NASA Image and Video Library

2014-11-20

NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, enclosed in a protective shipping container, is delivered by truck to the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is currently scheduled for January 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida.

Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parks, K.; Wan, Y. H.; Wiener, G.

2011-10-01

The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'),more » or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability. The downside is costs are higher. In organized electricity markets, units that are committed for reliability reasons are paid their offer price even when prevailing market prices are lower. Often, these uplift charges are allocated to market participants that caused the inefficient dispatch in the first place. Thus, wind energy facilities are burdened with their share of costs proportional to their forecast errors. For Xcel Energy, wind energy uncertainty costs manifest depending on specific market structures. In the Public Service of Colorado (PSCo), inefficient commitment and dispatch caused by wind uncertainty increases fuel costs. Wind resources participating in the Midwest Independent System Operator (MISO) footprint make substantial payments in the real-time markets to true-up their day-ahead positions and are additionally burdened with deviation charges called a Revenue Sufficiency Guarantee (RSG) to cover out of market costs associated with operations. Southwest Public Service (SPS) wind plants cause both commitment inefficiencies and are charged Southwest Power Pool (SPP) imbalance payments due to wind uncertainty and variability. Wind energy forecasting helps mitigate these costs. Wind integration studies for the PSCo and Northern States Power (NSP) operating companies have projected increasing costs as more wind is installed on the system due to forecast error. It follows that reducing forecast error would reduce these costs. This is echoed by large scale studies in neighboring regions and states that have recommended adoption of state-of-the-art wind forecasting tools in day-ahead and real-time planning and operations. Further, Xcel Energy concluded reduction of the normalized mean absolute error by one percent would have reduced costs in 2008 by over $1 million annually in PSCo alone. The value of reducing forecast error prompted Xcel Energy to make substantial investments in wind energy forecasting research and development.« less

Microgrid optimal scheduling considering impact of high penetration wind generation

NASA Astrophysics Data System (ADS)

Alanazi, Abdulaziz

The objective of this thesis is to study the impact of high penetration wind energy in economic and reliable operation of microgrids. Wind power is variable, i.e., constantly changing, and nondispatchable, i.e., cannot be controlled by the microgrid controller. Thus an accurate forecasting of wind power is an essential task in order to study its impacts in microgrid operation. Two commonly used forecasting methods including Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) have been used in this thesis to improve the wind power forecasting. The forecasting error is calculated using a Mean Absolute Percentage Error (MAPE) and is improved using the ANN. The wind forecast is further used in the microgrid optimal scheduling problem. The microgrid optimal scheduling is performed by developing a viable model for security-constrained unit commitment (SCUC) based on mixed-integer linear programing (MILP) method. The proposed SCUC is solved for various wind penetration levels and the relationship between the total cost and the wind power penetration is found. In order to reduce microgrid power transfer fluctuations, an additional constraint is proposed and added to the SCUC formulation. The new constraint would control the time-based fluctuations. The impact of the constraint on microgrid SCUC results is tested and validated with numerical analysis. Finally, the applicability of proposed models is demonstrated through numerical simulations.

Solar energetic particle events in different types of solar wind

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kahler, S. W.; Vourlidas, A., E-mail: stephen.kahler@kirtland.af.mil

2014-08-10

We examine statistically some properties of 96 20 MeV gradual solar energetic proton (SEP) events as a function of three different types of solar wind (SW) as classified by Richardson and Cane. Gradual SEP (E > 10 MeV) events are produced in shocks driven by fast (V ≳ 900 km s{sup –1}) and wide (W > 60°) coronal mass ejections (CMEs). We find no differences among the transient, fast, and slow SW streams for SEP 20 MeV proton event timescales. It has recently been found that the peak intensities Ip of these SEP events scale with the ∼2 MeV protonmore » background intensities, which may be a proxy for the near-Sun shock seed particles. Both the intensities Ip and their 2 MeV backgrounds are significantly enhanced in transient SW compared to those of fast and slow SW streams, and the values of Ip normalized to the 2 MeV backgrounds only weakly correlate with CME V for all SW types. This result implies that forecasts of SEP events could be improved by monitoring both the Sun and the local SW stream properties and that the well known power-law size distributions of Ip may differ between transient and long-lived SW streams. We interpret an observed correlation between CME V and the 2 MeV background for SEP events in transient SW as a manifestation of enhanced solar activity.« less

The Third Solar Wind Conference: A summary

NASA Technical Reports Server (NTRS)

Russell, C. T.

1974-01-01

The Third Solar Wind Conference consisted of nine sessions. The following subjects were discussed: (1) solar abundances; (2) the history and evolution of the solar wind; (3) the structure and dynamics of the solar corona; (4) macroscopic and microscopic properties of the solar wind; (5) cosmic rays as a probe of the solar wind; (6) the structure and dynamics of the solar wind; (7) spatial gradients; (8) stellar winds; and (9) interactions with objects in the solar wind. The invited and contributed talks presented at the conference are summarized.

Recent Progress of Solar Weather Forecasting at Naoc

NASA Astrophysics Data System (ADS)

He, Han; Wang, Huaning; Du, Zhanle; Zhang, Liyun; Huang, Xin; Yan, Yan; Fan, Yuliang; Zhu, Xiaoshuai; Guo, Xiaobo; Dai, Xinghua

The history of solar weather forecasting services at National Astronomical Observatories, Chinese Academy of Sciences (NAOC) can be traced back to 1960s. Nowadays, NAOC is the headquarters of the Regional Warning Center of China (RWC-China), which is one of the members of the International Space Environment Service (ISES). NAOC is responsible for exchanging data, information and space weather forecasts of RWC-China with other RWCs. The solar weather forecasting services at NAOC cover short-term prediction (within two or three days), medium-term prediction (within several weeks), and long-term prediction (in time scale of solar cycle) of solar activities. Most efforts of the short-term prediction research are concentrated on the solar eruptive phenomena, such as flares, coronal mass ejections (CMEs) and solar proton events, which are the key driving sources of strong space weather disturbances. Based on the high quality observation data of the latest space-based and ground-based solar telescopes and with the help of artificial intelligence techniques, new numerical models with quantitative analyses and physical consideration are being developed for the predictions of solar eruptive events. The 3-D computer simulation technology is being introduced for the operational solar weather service platform to visualize the monitoring of solar activities, the running of the prediction models, as well as the presenting of the forecasting results. A new generation operational solar weather monitoring and forecasting system is expected to be constructed in the near future at NAOC.

Scaling forecast models for wind turbulence and wind turbine power intermittency

NASA Astrophysics Data System (ADS)

Duran Medina, Olmo; Schmitt, Francois G.; Calif, Rudy

2017-04-01

The intermittency of the wind turbine power remains an important issue for the massive development of this renewable energy. The energy peaks injected in the electric grid produce difficulties in the energy distribution management. Hence, a correct forecast of the wind power in the short and middle term is needed due to the high unpredictability of the intermittency phenomenon. We consider a statistical approach through the analysis and characterization of stochastic fluctuations. The theoretical framework is the multifractal modelisation of wind velocity fluctuations. Here, we consider three wind turbine data where two possess a direct drive technology. Those turbines are producing energy in real exploitation conditions and allow to test our forecast models of power production at a different time horizons. Two forecast models were developed based on two physical principles observed in the wind and the power time series: the scaling properties on the one hand and the intermittency in the wind power increments on the other. The first tool is related to the intermittency through a multifractal lognormal fit of the power fluctuations. The second tool is based on an analogy of the power scaling properties with a fractional brownian motion. Indeed, an inner long-term memory is found in both time series. Both models show encouraging results since a correct tendency of the signal is respected over different time scales. Those tools are first steps to a search of efficient forecasting approaches for grid adaptation facing the wind energy fluctuations.

Temporal and spatial variability of wind resources in the United States as derived from the Climate Forecast System Reanalysis

Treesearch

Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman

2015-01-01

This study examines the spatial and temporal variability of wind speed at 80m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the...

Wind effect on PV module temperature: Analysis of different techniques for an accurate estimation.

NASA Astrophysics Data System (ADS)

Schwingshackl, Clemens; Petitta, Marcello; Ernst Wagner, Jochen; Belluardo, Giorgio; Moser, David; Castelli, Mariapina; Zebisch, Marc; Tetzlaff, Anke

2013-04-01

In this abstract a study on the influence of wind to model the PV module temperature is presented. This study is carried out in the framework of the PV-Alps INTERREG project in which the potential of different photovoltaic technologies is analysed for alpine regions. The PV module temperature depends on different parameters, such as ambient temperature, irradiance, wind speed and PV technology [1]. In most models, a very simple approach is used, where the PV module temperature is calculated from NOCT (nominal operating cell temperature), ambient temperature and irradiance alone [2]. In this study the influence of wind speed on the PV module temperature was investigated. First, different approaches suggested by various authors were tested [1], [2], [3], [4], [5]. For our analysis, temperature, irradiance and wind data from a PV test facility at the airport Bolzano (South Tyrol, Italy) from the EURAC Institute of Renewable Energies were used. The PV module temperature was calculated with different models and compared to the measured PV module temperature at the single panels. The best results were achieved with the approach suggested by Skoplaki et al. [1]. Preliminary results indicate that for all PV technologies which were tested (monocrystalline, amorphous, microcrystalline and polycrystalline silicon and cadmium telluride), modelled and measured PV module temperatures show a higher agreement (RMSE about 3-4 K) compared to standard approaches in which wind is not considered. For further investigation the in-situ measured wind velocities were replaced with wind data from numerical weather forecast models (ECMWF, reanalysis fields). Our results show that the PV module temperature calculated with wind data from ECMWF is still in very good agreement with the measured one (R² > 0.9 for all technologies). Compared to the previous analysis, we find comparable mean values and an increasing standard deviation. These results open a promising approach for PV module temperature estimation using meteorological parameters. References: [1] Skoplaki, E. et al., 2008: A simple correlation for the operating temperature of photovoltaic modules of arbitrary mounting, Solar Energy Materials & Solar Cells 92, 1393-1402 [2] Skoplaki, E. et al., 2008: Operating temperature of photovoltaic modules: A survey of pertinent correlations, Renewable Energy 34, 23-29 [3] Koehl, M. et al., 2011: Modeling of the nominal operating cell temperature based on outdoor weathering, Solar Energy Materials & Solar Cells 95, 1638-1646 [4] Mattei, M. et al., 2005: Calculation of the polycrystalline PV module temperature using a simple method of energy balance, Renewable Energy 31, 553-567 [5] Kurtz, S. et al.: Evaluation of high-temperature exposure of rack-mounted photovoltaic modules

Renewable energy alternatives to mega hydropower: a case study of Inga 3 for Southern Africa

NASA Astrophysics Data System (ADS)

Deshmukh, R.; Mileva, A.; Wu, G. C.

2018-06-01

We assess the feasibility and cost-effectiveness of renewable energy alternatives to Inga 3, a 4.8-GW hydropower project on the Congo River, to serve the energy needs of the host country, the Democratic Republic of Congo (DRC), and the main buyer, South Africa. To account for a key uncertainty in the literature regarding the additional economic impacts of managing variable wind and solar electricity, we built a spatially and temporally detailed power system investment model for South Africa. We find that a mix of wind, solar photovoltaics, and some natural gas is more cost-effective than Inga 3 to meet future demand except in scenarios with pessimistic assumptions about wind technology performance. If a low load growth forecast is used, including Inga 3 in the power mix results in higher system cost across all sensitivities. In our scenarios, the effect of Inga 3 deployment on South African power system cost ranges from an increase of ZAR 4300 (US 330) million annually to savings of ZAR 1600 (US 120) million annually by 2035. A cost overrun as low as 20% makes the Inga 3 scenarios more expensive in all sensitivity cases. Including time and cost overruns and losses in transmission from DRC to South Africa make Inga 3 an even less attractive investment. For DRC, through analysis of spatial datasets representing technical, physical, and environmental constraints, we find abundant renewable energy potential: 60 GW of solar photovoltaic and 0.6–2.3 GW of wind located close to transmission infrastructure have levelized costs less than US 0.07 per kWh, or the anticipated cost of Inga 3 to residential consumers.

Operational Real-time Forecast of MeV Electrons at Geosynchronous Orbit Based on ACE and GOES-10 Measurements

NASA Astrophysics Data System (ADS)

Li, X.; Temerin, M. A.; Monk, S.; Baker, D. N.; Reeves, G. D.

2002-05-01

The MeV electrons, also known as `killer electrons', have a deleterious impact on satellites through deep dielectric charging and the bodies of astronauts through radiation damage during extravehicular activity. Using a recently developed model based on the standard radial diffusion equation [Li et al., 2001], we show that the intensity of these MeV electrons at geosynchronous orbit can be quantitatively predicted 1-2 days in advance given knowledge of the solar wind. Our current model is operating in real-time, using real-time data from ACE and GOES-10, to make forecast of >2 MeV eletrons at geosynchronous orbit up to 48 hours in advance, the results are available on the web, currently updated every two hours (http://lasp.colorado.edu/~monk/xlf2.html).

Probabilistic Space Weather Forecasting: a Bayesian Perspective

NASA Astrophysics Data System (ADS)

Camporeale, E.; Chandorkar, M.; Borovsky, J.; Care', A.

2017-12-01

Most of the Space Weather forecasts, both at operational and research level, are not probabilistic in nature. Unfortunately, a prediction that does not provide a confidence level is not very useful in a decision-making scenario. Nowadays, forecast models range from purely data-driven, machine learning algorithms, to physics-based approximation of first-principle equations (and everything that sits in between). Uncertainties pervade all such models, at every level: from the raw data to finite-precision implementation of numerical methods. The most rigorous way of quantifying the propagation of uncertainties is by embracing a Bayesian probabilistic approach. One of the simplest and most robust machine learning technique in the Bayesian framework is Gaussian Process regression and classification. Here, we present the application of Gaussian Processes to the problems of the DST geomagnetic index forecast, the solar wind type classification, and the estimation of diffusion parameters in radiation belt modeling. In each of these very diverse problems, the GP approach rigorously provide forecasts in the form of predictive distributions. In turn, these distributions can be used as input for ensemble simulations in order to quantify the amplification of uncertainties. We show that we have achieved excellent results in all of the standard metrics to evaluate our models, with very modest computational cost.

Evidence of active region imprints on the solar wind structure

NASA Technical Reports Server (NTRS)

Hick, P.; Jackson, B. V.

1995-01-01

A common descriptive framework for discussing the solar wind structure in the inner heliosphere uses the global magnetic field as a reference: low density, high velocity solar wind emanates from open magnetic fields, with high density, low speed solar wind flowing outward near the current sheet. In this picture, active regions, underlying closed magnetic field structures in the streamer belt, leave little or no imprint on the solar wind. We present evidence from interplanetary scintillation measurements of the 'disturbance factor' g that active regions play a role in modulating the solar wind and possibly contribute to the solar wind mass output. Hence we find that the traditional view of the solar wind, though useful in understanding many features of solar wind structure, is oversimplified and possibly neglects important aspects of solar wind dynamics

Balancing Authority Cooperation Concepts to Reduce Variable Generation Integration Costs in the Western Interconnection: Consolidating Balancing Authorities and Sharing Balancing Reserves

DOE Office of Scientific and Technical Information (OSTI.GOV)

Samaan, Nader A.; Makarov, Yuri V.; Nguyen, Tony B.

2017-05-07

The study described in this chapter demonstrates the benefits of BA consolidation with the help of a detailed WECC system model and advanced methodology, which is also described in this chapter. The study aims to determine the potential savings in production cost and reduction in balancing reserve requirements in the WECC system. The study has found that effective use of the diversity in load and variable generation over a wide area can indeed help to achieve significant savings. The implementation cost for the consolidation was beyond the scope of this study. The analysis was performed for two different scenarios ofmore » VG penetration: 11% (8% wind and 3% solar) and 33% (24% wind and 9% solar) of WECC projected energy demand in 2020. In analysis of balancing reserves, the objective was to determine the reduction in balancing reserve requirements due to BA consolidation, in terms of required capacity and ramp-rates. Hour-ahead and 10-minute ahead forecast errors for load, wind, and solar were simulated. In addition, 1-minute resolution load, wind and solar data were used to derive balancing reserve requirements i.e. load-following and regulation requirements for each individual BA and for the consolidated BA (CBA). The reduction in balancing reserves was determined by calculating the difference between total reserve requirements that need to be carried by different BAs if they operate individually, and reserve requirements that need to be carried by the CBA. The study results show that the consolidated WECC system would have about a 50% overall reduction in balancing reserves for the 11% penetration scenario and a 65% reduction for the 33% penetration scenario in comparison with total reserve requirements that need to be carried by different BAs if they operate individually.« less

Accuracy of National Weather Service wind-direction forecasts at Macon and Augusta, Georgia

Treesearch

Leonidas G. Lavdas

1997-01-01

National Weather Service wind forecasts and observations over a nine-year period (1985 to 1993) were analyzed to determine the usefulness of these forecasts for forestry smoke management. Data from Macon, GA indicated that forecasts were accurate to within plus or minus 22.5E about 38 percent of the time. When a wider plus or minus 67.5E window was used, accuracy...

Complementarity among climate related energy sources: Sensitivity study to climate characteristics across Europe

NASA Astrophysics Data System (ADS)

Francois, Baptiste; Hingray, Benoit; Creutin, Jean-Dominique; Raynaud, Damien; Borga, Marco; Vautard, Robert

2015-04-01

Climate related energy sources like solar-power, wind-power and hydro-power are important contributors to the transitions to a low-carbon economy. Past studies, mainly based on solar and wind powers, showed that the power from such energy sources fluctuates in time and space following their driving climatic variables. However, when combining different energy sources together, their intermittent feature is smoothed, resulting to lower time variability of the produced power and to lower storage capacity required for balancing. In this study, we consider solar, wind and hydro energy sources in a 100% renewable Europe using a set of 12 regions following two climate transects, the first one going from the Northern regions (Norway, Finland) to the Southern ones (Greece, Andalucía, Tunisia) and the second one going from the oceanic climate (West of France, Galicia) to the continental one (Romania, Belorussia). For each of those regions, we combine wind and solar irradiance data from the Weather Research and Forecasting Model (Vautard et al., 2014), temperature data from the European Climate Assessment & Dataset (Haylock et al., 2008) and runoff from the Global Runoff Data Center (GRDC, 1999) for estimating solar-power, wind-power, run-of-the-river hydro-power and the electricity demand over a time period of 30 years. The use of this set of 12 regions across Europe allows integrating knowledge about time and space variability for each different energy sources. We then assess the optimal share of each energy sources, aiming to decrease the time variability of the regional energy balance at different time scales as well as the energy storage required for balancing within each region. We also evaluate how energy transport among regions contributes for smoothing out both the energy balance and the storage requirement. The strengths of this study are i) to handle with run-of-the-river hydro power in addition to wind and solar energy sources and ii) to carry out this analysis over a long time period while past studies, to our knowledge, have used less than 10 year time period. References: Vautard, R., Thais, F., Tobin, I., Bréon, F.-M., de Lavergne, J.-G.D., Colette, A., Yiou, P., and Ruti, P.M. (2014). Regional climate model simulations indicate limited climatic impacts by operational and planned European wind farms. Nat. Commun. 5, 3196. Haylock, M.R., Hofstra, N., Tank, A.M.G.K., Klok, E.J., Jones, P.D., New, M., 2008. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. J. Geophys. Res.-Atmos. 113. doi:10.1029/2008JD010201 GRDC (Global Runoff Data Center), 1999. Long-term mean monthly discharges of selected GRDC stations, Global Runoff Data Centre, Koblenz, Germany.

The Physical Processes of CME/ICME Evolution

NASA Astrophysics Data System (ADS)

Manchester, Ward; Kilpua, Emilia K. J.; Liu, Ying D.; Lugaz, Noé; Riley, Pete; Török, Tibor; Vršnak, Bojan

2017-11-01

As observed in Thomson-scattered white light, coronal mass ejections (CMEs) are manifest as large-scale expulsions of plasma magnetically driven from the corona in the most energetic eruptions from the Sun. It remains a tantalizing mystery as to how these erupting magnetic fields evolve to form the complex structures we observe in the solar wind at Earth. Here, we strive to provide a fresh perspective on the post-eruption and interplanetary evolution of CMEs, focusing on the physical processes that define the many complex interactions of the ejected plasma with its surroundings as it departs the corona and propagates through the heliosphere. We summarize the ways CMEs and their interplanetary CMEs (ICMEs) are rotated, reconfigured, deformed, deflected, decelerated and disguised during their journey through the solar wind. This study then leads to consideration of how structures originating in coronal eruptions can be connected to their far removed interplanetary counterparts. Given that ICMEs are the drivers of most geomagnetic storms (and the sole driver of extreme storms), this work provides a guide to the processes that must be considered in making space weather forecasts from remote observations of the corona.

Theoretical Technology Research for ISTP/SOLARMAX

NASA Technical Reports Server (NTRS)

Ashour-Abdalla, Maha; Acuna, Mario (Technical Monitor)

2000-01-01

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.

3D cloud detection and tracking system for solar forecast using multiple sky imagers

DOE PAGES

Peng, Zhenzhou; Yu, Dantong; Huang, Dong; ...

2015-06-23

We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together intomore » larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less

Additional Arctic observations improve weather and sea-ice forecasts for the Northern Sea Route

PubMed Central

Inoue, Jun; Yamazaki, Akira; Ono, Jun; Dethloff, Klaus; Maturilli, Marion; Neuber, Roland; Edwards, Patti; Yamaguchi, Hajime

2015-01-01

During ice-free periods, the Northern Sea Route (NSR) could be an attractive shipping route. The decline in Arctic sea-ice extent, however, could be associated with an increase in the frequency of the causes of severe weather phenomena, and high wind-driven waves and the advection of sea ice could make ship navigation along the NSR difficult. Accurate forecasts of weather and sea ice are desirable for safe navigation, but large uncertainties exist in current forecasts, partly owing to the sparse observational network over the Arctic Ocean. Here, we show that the incorporation of additional Arctic observations improves the initial analysis and enhances the skill of weather and sea-ice forecasts, the application of which has socioeconomic benefits. Comparison of 63-member ensemble atmospheric forecasts, using different initial data sets, revealed that additional Arctic radiosonde observations were useful for predicting a persistent strong wind event. The sea-ice forecast, initialised by the wind fields that included the effects of the observations, skilfully predicted rapid wind-driven sea-ice advection along the NSR. PMID:26585690

Nonparametric Stochastic Model for Uncertainty Quantifi cation of Short-term Wind Speed Forecasts

NASA Astrophysics Data System (ADS)

AL-Shehhi, A. M.; Chaouch, M.; Ouarda, T.

2014-12-01

Wind energy is increasing in importance as a renewable energy source due to its potential role in reducing carbon emissions. It is a safe, clean, and inexhaustible source of energy. The amount of wind energy generated by wind turbines is closely related to the wind speed. Wind speed forecasting plays a vital role in the wind energy sector in terms of wind turbine optimal operation, wind energy dispatch and scheduling, efficient energy harvesting etc. It is also considered during planning, design, and assessment of any proposed wind project. Therefore, accurate prediction of wind speed carries a particular importance and plays significant roles in the wind industry. Many methods have been proposed in the literature for short-term wind speed forecasting. These methods are usually based on modeling historical fixed time intervals of the wind speed data and using it for future prediction. The methods mainly include statistical models such as ARMA, ARIMA model, physical models for instance numerical weather prediction and artificial Intelligence techniques for example support vector machine and neural networks. In this paper, we are interested in estimating hourly wind speed measures in United Arab Emirates (UAE). More precisely, we predict hourly wind speed using a nonparametric kernel estimation of the regression and volatility functions pertaining to nonlinear autoregressive model with ARCH model, which includes unknown nonlinear regression function and volatility function already discussed in the literature. The unknown nonlinear regression function describe the dependence between the value of the wind speed at time t and its historical data at time t -1, t - 2, … , t - d. This function plays a key role to predict hourly wind speed process. The volatility function, i.e., the conditional variance given the past, measures the risk associated to this prediction. Since the regression and the volatility functions are supposed to be unknown, they are estimated using nonparametric kernel methods. In addition, to the pointwise hourly wind speed forecasts, a confidence interval is also provided which allows to quantify the uncertainty around the forecasts.

Climatological attribution of wind power ramp events in East Japan and their probabilistic forecast based on multi-model ensembles downscaled by analog ensemble using self-organizing maps

NASA Astrophysics Data System (ADS)

Ohba, Masamichi; Nohara, Daisuke; Kadokura, Shinji

2016-04-01

Severe storms or other extreme weather events can interrupt the spin of wind turbines in large scale that cause unexpected "wind ramp events". In this study, we present an application of self-organizing maps (SOMs) for climatological attribution of the wind ramp events and their probabilistic prediction. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors. The SOM is applied to analyze and connect the relationship between atmospheric patterns over Japan and wind power generation. SOM is employed on sea level pressure derived from the JRA55 reanalysis over the target area (Tohoku region in Japan), whereby a two-dimensional lattice of weather patterns (WPs) classified during the 1977-2013 period is obtained. To compare with the atmospheric data, the long-term wind power generation is reconstructed by using a high-resolution surface observation network AMeDAS (Automated Meteorological Data Acquisition System) in Japan. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of wind ramp events. Probabilistic forecasts to wind power generation and ramps are conducted by using the obtained SOM. The probability are derived from the multiple SOM lattices based on the matching of output from TIGGE multi-model global forecast to the WPs on the lattices. Since this method effectively takes care of the empirical uncertainties from the historical data, wind power generation and ramp is probabilistically forecasted from the forecasts of global models. The predictability skill of the forecasts for the wind power generation and ramp events show the relatively good skill score under the downscaling technique. It is expected that the results of this study provides better guidance to the user community and contribute to future development of system operation model for the transmission grid operator.

Modeling AWSoM CMEs with EEGGL: A New Approach for Space Weather Forecasting

NASA Astrophysics Data System (ADS)

Jin, M.; Manchester, W.; van der Holst, B.; Sokolov, I.; Toth, G.; Vourlidas, A.; de Koning, C. A.; Gombosi, T. I.

2015-12-01

The major source of destructive space weather is coronal mass ejections (CMEs). However, our understanding of CMEs and their propagation in the heliosphere is limited by the insufficient observations. Therefore, the development of first-principals numerical models plays a vital role in both theoretical investigation and providing space weather forecasts. Here, we present results of the simulation of CME propagation from the Sun to 1AU by combining the analytical Gibson & Low (GL) flux rope model with the state-of-art solar wind model AWSoM. We also provide an approach for transferring this research model to a space weather forecasting tool by demonstrating how the free parameters of the GL flux rope can be prescribed based on remote observations via the new Eruptive Event Generator by Gibson-Low (EEGGL) toolkit. This capability allows us to predict the long-term evolution of the CME in interplanetary space. We perform proof-of-concept case studies to show the capability of the model to capture physical processes that determine CME evolution while also reproducing many observed features both in the corona and at 1 AU. We discuss the potential and limitations of this model as a future space weather forecasting tool.

Integrating Wind Profiling Radars and Radiosonde Observations with Model Point Data to Develop a Decision Support Tool to Assess Upper-Level Winds for Space Launch

NASA Technical Reports Server (NTRS)

Bauman, William H., III; Flinn, Clay

2013-01-01

On the day of launch, the 45th Weather Squadron (45 WS) Launch Weather Officers (LWOs) monitor the upper-level winds for their launch customers. During launch operations, the payload/launch team sometimes asks the LWOs if they expect the upper-level winds to change during the countdown. The LWOs used numerical weather prediction model point forecasts to provide the information, but did not have the capability to quickly retrieve or adequately display the upper-level observations and compare them directly in the same display to the model point forecasts to help them determine which model performed the best. The LWOs requested the Applied Meteorology Unit (AMU) develop a graphical user interface (GUI) that will plot upper-level wind speed and direction observations from the Cape Canaveral Air Force Station (CCAFS) Automated Meteorological Profiling System (AMPS) rawinsondes with point forecast wind profiles from the National Centers for Environmental Prediction (NCEP) North American Mesoscale (NAM), Rapid Refresh (RAP) and Global Forecast System (GFS) models to assess the performance of these models. The AMU suggested adding observations from the NASA 50 MHz wind profiler and one of the US Air Force 915 MHz wind profilers, both located near the Kennedy Space Center (KSC) Shuttle Landing Facility, to supplement the AMPS observations with more frequent upper-level profiles. Figure 1 shows a map of KSC/CCAFS with the locations of the observation sites and the model point forecasts.

Use of ground-based wind profiles in mesoscale forecasting

NASA Technical Reports Server (NTRS)

Schlatter, Thomas W.

1985-01-01

A brief review is presented of recent uses of ground-based wind profile data in mesoscale forecasting. Some of the applications are in real time, and some are after the fact. Not all of the work mentioned here has been published yet, but references are given wherever possible. As Gage and Balsley (1978) point out, sensitive Doppler radars have been used to examine tropospheric wind profiles since the 1970's. It was not until the early 1980's, however, that the potential contribution of these instruments to operational forecasting and numerical weather prediction became apparent. Profiler winds and radiosonde winds compare favorably, usually within a few m/s in speed and 10 degrees in direction (see Hogg et al., 1983), but the obvious advantage of the profiler is its frequent (hourly or more often) sampling of the same volume. The rawinsonde balloon is launched only twice a day and drifts with the wind. In this paper, I will: (1) mention two operational uses of data from a wind profiling system developed jointly by the Wave Propagation and Aeronomy Laboratories of NOAA; (2) describe a number of displays of these same data on a workstation for mesoscale forecasting developed by the Program for Regional Observing and Forecasting Services (PROFS); and (3) explain some interesting diagnostic calculations performed by meteorologists of the Wave Propagation Laboratory.

The propagation of wind errors through ocean wave hindcasts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holthuijsen, L.H.; Booij, N.; Bertotti, L.

1996-08-01

To estimate uncertainties in wave forecast and hindcasts, computations have been carried out for a location in the Mediterranean Sea using three different analyses of one historic wind field. These computations involve a systematic sensitivity analysis and estimated wind field errors. This technique enables a wave modeler to estimate such uncertainties in other forecasts and hindcasts if only one wind analysis is available.

On Using the Weimer Statistical Model for Real-Time Ionospheric Specifications and Forecasts

NASA Astrophysics Data System (ADS)

Bekerat, H. A.; Schunk, R. W.; Scherliess, L.

2002-12-01

The Weimer statistical model (Weimer, 2001) for the high-latitude convection pattern was tested with regard to its ability to produce real-time convection patterns. This work is being conducted under the polar section of GAIM (Global Assimilation of Ionospheric Measurements). The method adopted involves the comparison of the cross-track ion drift velocities measured by DMSP satellites with those calculated from the Weimer model. Starting with a Weimer pattern obtained using real-time IMF and solar wind data at the time of a DMSP satellite pass in the high-latitude ionosphere, the cross-track ion drift velocities along the DMSP track were calculated from the Weimer convection model and compared to those measured by the DMSP satellite. Then, in order to improve the agreement between the measurement and the model, two of the input parameters to the model, the IMF clock-angle and the solar wind speed, were varied to get the pattern that gives the best agreement with the DMSP satellite measurements. Four months of data (March, July, September, and December 1998) were used to test the Weimer model. The result shows that the agreement between the measurement and the Weimer model is improved by using this procedure. The Weimer model is good in a statistical sense, it was able to produce the large-scale structure in most cases. However, it is not good enough to be used for real-time ionospheric specifications and forecasts because it failed to produce a lot of the mesoscale structure measured along most DMSP satellite passes. Reference Weimer, D. R., J. Geophys. Res., 106, 407,2001

Superposed Epoch Studies of the Response of the High-Latitude Magnetosphere-Ionosphere-Thermosphere System to Solar Wind High-Speed Stream Driving

NASA Astrophysics Data System (ADS)

Grandin, M.; Aikio, A. T.; Kozlovsky, A.; Ulich, T.; Raita, T.

2016-12-01

During the declining phase of the solar cycle, the Earth's magnetosphere-ionosphere-thermosphere system is mainly disturbed by solar wind high-speed streams (HSSs). Their ionospheric response, especially at high latitudes, is not fully understood yet. The perturbations in the ionosphere last for several days. We have examined the effect of HSS in two studies, which apply the superposed epoch method to data to reveal the statistical response in the ionospheric F, E and D regions to such perturbations. We use ionosonde, geomagnetic and cosmic noise absorption data obtained from Finnish stations during 95 high-speed stream events detected between 2006 and 2008. Results show a long-lasting decrease in the F layer critical frequency foF2 between 12 and 23 MLT in summer and equinox. This depletion of the F layer is interpreted as a result of enhanced electric fields inducing ion-neutral frictional heating in the auroral and subauroral regions. The response near noon is different, since foF2 is increased shortly upon arrival of the co-rotating stream interaction region (CIR), possibly because of precipitation of particles from the dayside plasma sheet provoked by the associated solar wind pressure pulse. In the morning sector, both foF2 and foEs show increases for several days, indicating particle precipitation having a soft component. In the study of cosmic noise absorption (CNA), we observe a different response depending on the L-value of the station. Within the auroral oval (L=5-6), CNA gets maximum values in the morning sector 0-12 MLT during the first and second day following the zero epoch. Values are greater during events with longer-lasting high solar wind speed. The CNA maximum shifts to later MLT at lower L values, and in JYV (L=3.8), the maximum takes place at 14 MLT during day 4. Substorm energization events dominate during the first days at 00-01 MLT. We also address the role of Pc5 geomagnetic pulsations observed in association with CNA events. These results may contribute to improve nowcasting and forecasting of space weather activity during high-speed stream events.

77 FR 61597 - Avalon Wind, LLC; Avalon Wind 2, LLC; Catalina Solar, LLC; Catalina Solar 2, LLC; Pacific Wind...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-10

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Docket No. EL12-109-000] Avalon Wind, LLC; Avalon Wind 2, LLC; Catalina Solar, LLC; Catalina Solar 2, LLC; Pacific Wind Lessee, LLC; Pacific Wind 2, LLC; Valentine Solar, LLC; EDF Renewable Development, Inc.; Notice of Petition for Declaratory...

Assessment of wind energy potential in Poland

NASA Astrophysics Data System (ADS)

Starosta, Katarzyna; Linkowska, Joanna; Mazur, Andrzej

2014-05-01

The aim of the presentation is to show the suitability of using numerical model wind speed forecasts for the wind power industry applications in Poland. In accordance with the guidelines of the European Union, the consumption of wind energy in Poland is rapidly increasing. According to the report of Energy Regulatory Office from 30 March 2013, the installed capacity of wind power in Poland was 2807MW from 765 wind power stations. Wind energy is strongly dependent on the meteorological conditions. Based on the climatological wind speed data, potential energy zones within the area of Poland have been developed (H. Lorenc). They are the first criterion for assessing the location of the wind farm. However, for exact monitoring of a given wind farm location the prognostic data from numerical model forecasts are necessary. For the practical interpretation and further post-processing, the verification of the model data is very important. Polish Institute Meteorology and Water Management - National Research Institute (IMWM-NRI) runs an operational model COSMO (Consortium for Small-scale Modelling, version 4.8) using two nested domains at horizontal resolutions of 7 km and 2.8 km. The model produces 36 hour and 78 hour forecasts from 00 UTC, for 2.8 km and 7 km domain resolutions respectively. Numerical forecasts were compared with the observation of 60 SYNOP and 3 TEMP stations in Poland, using VERSUS2 (Unified System Verification Survey 2) and R package. For every zone the set of statistical indices (ME, MAE, RMSE) was calculated. Forecast errors for aerological profiles are shown for Polish TEMP stations at Wrocław, Legionowo and Łeba. The current studies are connected with a topic of the COST ES1002 WIRE-Weather Intelligence for Renewable Energies.

Integrating Wind Profiling Radars and Radiosonde Observations with Model Point Data to Develop a Decision Support Tool to Assess Upper-Level Winds for Space Launch

NASA Technical Reports Server (NTRS)

Bauman, William H., III; Flinn, Clay

2013-01-01

On the day-of-launch, the 45th Weather Squadron (45 WS) Launch Weather Officers (LWOs) monitor the upper-level winds for their launch customers to include NASA's Launch Services Program and NASA's Ground Systems Development and Operations Program. They currently do not have the capability to display and overlay profiles of upper-level observations and numerical weather prediction model forecasts. The LWOs requested the Applied Meteorology Unit (AMU) develop a tool in the form of a graphical user interface (GUI) that will allow them to plot upper-level wind speed and direction observations from the Kennedy Space Center (KSC) 50 MHz tropospheric wind profiling radar, KSC Shuttle Landing Facility 915 MHz boundary layer wind profiling radar and Cape Canaveral Air Force Station (CCAFS) Automated Meteorological Processing System (AMPS) radiosondes, and then overlay forecast wind profiles from the model point data including the North American Mesoscale (NAM) model, Rapid Refresh (RAP) model and Global Forecast System (GFS) model to assess the performance of these models. The AMU developed an Excel-based tool that provides an objective method for the LWOs to compare the model-forecast upper-level winds to the KSC wind profiling radars and CCAFS AMPS observations to assess the model potential to accurately forecast changes in the upperlevel profile through the launch count. The AMU wrote Excel Visual Basic for Applications (VBA) scripts to automatically retrieve model point data for CCAFS (XMR) from the Iowa State University Archive Data Server (http://mtarchive.qeol.iastate.edu) and the 50 MHz, 915 MHz and AMPS observations from the NASA/KSC Spaceport Weather Data Archive web site (http://trmm.ksc.nasa.gov). The AMU then developed code in Excel VBA to automatically ingest and format the observations and model point data in Excel to ready the data for generating Excel charts for the LWO's. The resulting charts allow the LWOs to independently initialize the three models 0-hour forecasts against the observations to determine which is the best performing model and then overlay the model forecasts on time-matched observations during the launch countdown to further assess the model performance and forecasts. This paper will demonstrate integration of observed and predicted atmospheric conditions into a decision support tool and demonstrate how the GUI is implemented in operations.

The MSFC Solar Activity Future Estimation (MSAFE) Model

NASA Technical Reports Server (NTRS)

Suggs, Ron

2017-01-01

The MSAFE model provides forecasts for the solar indices SSN, F10.7, and Ap. These solar indices are used as inputs to space environment models used in orbital spacecraft operations and space mission analysis. Forecasts from the MSAFE model are provided on the MSFC Natural Environments Branch's solar web page and are updated as new monthly observations become available. The MSAFE prediction routine employs a statistical technique that calculates deviations of past solar cycles from the mean cycle and performs a regression analysis to calculate the deviation from the mean cycle of the solar index at the next future time interval. The forecasts are initiated for a given cycle after about 8 to 9 monthly observations from the start of the cycle are collected. A forecast made at the beginning of cycle 24 using the MSAFE program captured the cycle fairly well with some difficulty in discerning the double peak that occurred at solar cycle maximum.

Tower Mesonetwork Climatology and Interactive Display Tool

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Bauman, William H., III

2004-01-01

Forecasters at the 45th Weather Squadron and Spaceflight Meteorology Group use data from the tower network over the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) to evaluate Launch Commit Criteria, and issue and verify forecasts for ground operations. Systematic biases in these parameters could adversely affect an analysis, forecast, or verification. Also, substantial geographical variations in temperature and wind speed can occur under specific wind directions. To address these concerns, the Applied Meteorology Unit (AMU) developed a climatology of temperatures and winds from the tower network, and identified the geographical variation and significant tower biases. The mesoclimate is largely driven by the complex land-water interfaces across KSC/CCAFS. Towers with close proximity to water typically had much warmer nocturnal temperatures and higher wind speeds throughout the year. The strongest nocturnal wind speeds occurred from October to March whereas the strongest mean daytime wind speeds occurred from February to May. These results of this project can be viewed by forecasters through an interactive graphical user interface developed by the AMU. The web-based interface includes graphical and map displays of mean, standard deviation, bias, and data availability for any combination of towers, variables, months, hours, and wind directions.

Implementation of a state of the art automated system for the production of cloud/water vapor motion winds from geostationary satellites

NASA Technical Reports Server (NTRS)

Velden, Christopher S.

1994-01-01

The thrust of the proposed effort under this contract is aimed at improving techniques to track water vapor data in sequences of imagery from geostationary satellites. In regards to this task, significant testing, evaluation, and progress was accomplished during this period. Sets of winds derived from Meteosat data were routinely produced during Atlantic hurricane events in the 1993 season. These wind sets were delivered via Internet in real time to the Hurricane Research Division in Miami for their evaluation in a track forecast model. For eighteen cases in which 72-hour forecasts were produced, thirteen resulted in track forecast improvements (some quite significant). In addition, quality-controlled Meteosat water vapor winds produced by NESDIS were validated against rawinsondes, yielding an 8 m/s RMS. This figure is comparable to upper-level cloud drift wind accuracies. Given the complementary horizontal coverage in cloud-free areas, we believe that water vapor vectors can supplement cloud-drift wind information to provide good full-disk coverage of the upper tropospheric flow. The impact of these winds on numerical analysis and forecasts will be tested in the next reporting period.

On the Origins of the Intercorrelations Between Solar Wind Variables

NASA Astrophysics Data System (ADS)

Borovsky, Joseph E.

2018-01-01

It is well known that the time variations of the diverse solar wind variables at 1 AU (e.g., solar wind speed, density, proton temperature, electron temperature, magnetic field strength, specific entropy, heavy-ion charge-state densities, and electron strahl intensity) are highly intercorrelated with each other. In correlation studies of the driving of the Earth's magnetosphere-ionosphere-thermosphere system by the solar wind, these solar wind intercorrelations make determining cause and effect very difficult. In this report analyses of solar wind spacecraft measurements and compressible-fluid computer simulations are used to study the origins of the solar wind intercorrelations. Two causes are found: (1) synchronized changes in the values of the solar wind variables as the plasma types of the solar wind are switched by solar rotation and (2) dynamic interactions (compressions and rarefactions) in the solar wind between the Sun and the Earth. These findings provide an incremental increase in the understanding of how the Sun-Earth system operates.

How MAG4 Improves Space Weather Forecasting

NASA Technical Reports Server (NTRS)

Falconer, David; Khazanov, Igor; Barghouty, Nasser

2013-01-01

Dangerous space weather is driven by solar flares and Coronal Mass Ejection (CMEs). Forecasting flares and CMEs is the first step to forecasting either dangerous space weather or All Clear. MAG4 (Magnetogram Forecast), developed originally for NASA/SRAG (Space Radiation Analysis Group), is an automated program that analyzes magnetograms from the HMI (Helioseismic and Magnetic Imager) instrument on NASA SDO (Solar Dynamics Observatory), and automatically converts the rate (or probability) of major flares (M- and X-class), Coronal Mass Ejections (CMEs), and Solar Energetic Particle Events.

Imaging of near-Earth space plasma.

PubMed

Mitchell, Cathryn N

2002-12-15

This paper describes the technique of imaging the ionosphere using tomographic principles. It reports on current developments and speculates on the future of this research area. Recent developments in computing and ionospheric measurement, together with the sharing of data via the internet, now allow us to envisage a time when high-resolution, real-time images and 'movies' of the ionosphere will be possible for radio communications planning. There is great potential to use such images for improving our understanding of the physical processes controlling the behaviour of the ionosphere. While real-time images and movies of the electron concentration are now almost possible, forecasting of ionospheric morphology is still in its early stages. It has become clear that the ionosphere cannot be considered as a system in isolation, and consequently new research projects to link together models of the solar-terrestrial system, including the Sun, solar wind, magnetosphere, ionosphere and thermosphere, are now being proposed. The prospect is now on the horizon of assimilating data from the entire solar-terrestrial system to produce a real-time computer model and 'space weather' forecast. The role of tomography in imaging beyond the ionosphere to include the whole near-Earth space-plasma realm is yet to be realized, and provides a challenging prospect for the future. Finally, exciting possibilities exist in applying such methods to image the atmospheres and ionospheres of other planets.

The Scientific Foundations of Forecasting Magnetospheric Space Weather

NASA Astrophysics Data System (ADS)

Eastwood, J. P.; Nakamura, R.; Turc, L.; Mejnertsen, L.; Hesse, M.

2017-11-01

The magnetosphere is the lens through which solar space weather phenomena are focused and directed towards the Earth. In particular, the non-linear interaction of the solar wind with the Earth's magnetic field leads to the formation of highly inhomogenous electrical currents in the ionosphere which can ultimately result in damage to and problems with the operation of power distribution networks. Since electric power is the fundamental cornerstone of modern life, the interruption of power is the primary pathway by which space weather has impact on human activity and technology. Consequently, in the context of space weather, it is the ability to predict geomagnetic activity that is of key importance. This is usually stated in terms of geomagnetic storms, but we argue that in fact it is the substorm phenomenon which contains the crucial physics, and therefore prediction of substorm occurrence, severity and duration, either within the context of a longer-lasting geomagnetic storm, but potentially also as an isolated event, is of critical importance. Here we review the physics of the magnetosphere in the frame of space weather forecasting, focusing on recent results, current understanding, and an assessment of probable future developments.

Forecasting of global solar radiation using anfis and armax techniques

NASA Astrophysics Data System (ADS)

Muhammad, Auwal; Gaya, M. S.; Aliyu, Rakiya; Aliyu Abdulkadir, Rabi'u.; Dauda Umar, Ibrahim; Aminu Yusuf, Lukuman; Umar Ali, Mudassir; Khairi, M. T. M.

2018-01-01

Procurement of measuring device, maintenance cost coupled with calibration of the instrument contributed to the difficulty in forecasting of global solar radiation in underdeveloped countries. Most of the available regressional and mathematical models do not capture well the behavior of the global solar radiation. This paper presents the comparison of Adaptive Neuro Fuzzy Inference System (ANFIS) and Autoregressive Moving Average with eXogenous term (ARMAX) in forecasting global solar radiation. Full-Scale (experimental) data of Nigerian metrological agency, Sultan Abubakar III international airport Sokoto was used to validate the models. The simulation results demonstrated that the ANFIS model having achieved MAPE of 5.34% outperformed the ARMAX model. The ANFIS could be a valuable tool for forecasting the global solar radiation.

Forecasts and Warnings of Extreme Solar Storms at the Sun

NASA Astrophysics Data System (ADS)

Lundstedt, H.

2015-12-01

The most pressing space weather forecasts and warnings are those of the most intense solar flares and coronal mass ejections. However, in trying to develop these forecasts and warnings, we are confronted to many fundamental questions. Some of those are: How to define an observable measure for an extreme solar storm? How extreme can a solar storm become and how long is the build up time? How to make forecasts and warnings? Many have contributed to clarifying these general questions. In his presentation we will describe our latest results on the topological complexity of magnetic fields and the use of SDO SHARP parameters. The complexity concept will then be used to discuss the second question. Finally we will describe probability estimates of extreme solar storms.

Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

NASA Astrophysics Data System (ADS)

Lee, Joseph C. Y.; Lundquist, Julie K.

2017-11-01

Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind-downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

DOE PAGES

Lee, Joseph C. Y.; Lundquist, Julie K.

2017-11-23

Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. Our paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustratemore » with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.« less

Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Joseph C. Y.; Lundquist, Julie K.

Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. Our paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustratemore » with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.« less

Intra-Hour Dispatch and Automatic Generator Control Demonstration with Solar Forecasting - Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coimbra, Carlos F. M.

2016-02-25

In this project we address multiple resource integration challenges associated with increasing levels of solar penetration that arise from the variability and uncertainty in solar irradiance. We will model the SMUD service region as its own balancing region, and develop an integrated, real-time operational tool that takes solar-load forecast uncertainties into consideration and commits optimal energy resources and reserves for intra-hour and intra-day decisions. The primary objectives of this effort are to reduce power system operation cost by committing appropriate amount of energy resources and reserves, as well as to provide operators a prediction of the generation fleet’s behavior inmore » real time for realistic PV penetration scenarios. The proposed methodology includes the following steps: clustering analysis on the expected solar variability per region for the SMUD system, Day-ahead (DA) and real-time (RT) load forecasts for the entire service areas, 1-year of intra-hour CPR forecasts for cluster centers, 1-year of smart re-forecasting CPR forecasts in real-time for determination of irreducible errors, and uncertainty quantification for integrated solar-load for both distributed and central stations (selected locations within service region) PV generation.« less

Scientific motivation for ADM/Aeolus mission

NASA Astrophysics Data System (ADS)

Källén, Erland

2018-04-01

The ADM/Aeolus wind lidar mission will provide a global coverage of atmospheric wind profiles. Atmospheric wind observations are required for initiating weather forecast models and for predicting and monitoring long term climate change. Improved knowledge of the global wind field is widely recognised as fundamental to advancing the understanding and prediction of weather and climate. In particular over tropical areas there is a need for better wind data leading to improved medium range (3-10 days) weather forecasts over the whole globe.

The MSFC Solar Activity Future Estimation (MSAFE) Model

NASA Technical Reports Server (NTRS)

Suggs, Ronnie J.

2017-01-01

The MSAFE model provides forecasts for the solar indices SSN, F10.7, and Ap. These solar indices are used as inputs to many space environment models used in orbital spacecraft operations and space mission analysis. Forecasts from the MSAFE model are provided on the MSFC Natural Environments Branch's solar webpage and are updated as new monthly observations come available. The MSAFE prediction routine employs a statistical technique that calculates deviations of past solar cycles from the mean cycle and performs a regression analysis to predict the deviation from the mean cycle of the solar index at the next future time interval. The prediction algorithm is applied recursively to produce monthly smoothed solar index values for the remaining of the cycle. The forecasts are initiated for a given cycle after about 8 to 12 months of observations are collected. A forecast made at the beginning of cycle 24 using the MSAFE program captured the cycle fairly well with some difficulty in discerning the double peak that occurred at solar cycle maximum.

A stochastic post-processing method for solar irradiance forecasts derived from NWPs models

NASA Astrophysics Data System (ADS)

Lara-Fanego, V.; Pozo-Vazquez, D.; Ruiz-Arias, J. A.; Santos-Alamillos, F. J.; Tovar-Pescador, J.

2010-09-01

Solar irradiance forecast is an important area of research for the future of the solar-based renewable energy systems. Numerical Weather Prediction models (NWPs) have proved to be a valuable tool for solar irradiance forecasting with lead time up to a few days. Nevertheless, these models show low skill in forecasting the solar irradiance under cloudy conditions. Additionally, climatic (averaged over seasons) aerosol loading are usually considered in these models, leading to considerable errors for the Direct Normal Irradiance (DNI) forecasts during high aerosols load conditions. In this work we propose a post-processing method for the Global Irradiance (GHI) and DNI forecasts derived from NWPs. Particularly, the methods is based on the use of Autoregressive Moving Average with External Explanatory Variables (ARMAX) stochastic models. These models are applied to the residuals of the NWPs forecasts and uses as external variables the measured cloud fraction and aerosol loading of the day previous to the forecast. The method is evaluated for a set one-moth length three-days-ahead forecast of the GHI and DNI, obtained based on the WRF mesoscale atmospheric model, for several locations in Andalusia (Southern Spain). The Cloud fraction is derived from MSG satellite estimates and the aerosol loading from the MODIS platform estimates. Both sources of information are readily available at the time of the forecast. Results showed a considerable improvement of the forecasting skill of the WRF model using the proposed post-processing method. Particularly, relative improvement (in terms of the RMSE) for the DNI during summer is about 20%. A similar value is obtained for the GHI during the winter.

Impacts of subgrid-scale orography parameterization on simulated atmospheric fields over Korea using a high-resolution atmospheric forecast model

NASA Astrophysics Data System (ADS)

Lim, Kyo-Sun Sunny; Lim, Jong-Myoung; Shin, Hyeyum Hailey; Hong, Jinkyu; Ji, Young-Yong; Lee, Wanno

2018-06-01

A substantial over-prediction bias at low-to-moderate wind speeds in the Weather Research and Forecasting (WRF) model has been reported in the previous studies. Low-level wind fields play an important role in dispersion of air pollutants, including radionuclides, in a high-resolution WRF framework. By implementing two subgrid-scale orography parameterizations (Jimenez and Dudhia in J Appl Meteorol Climatol 51:300-316, 2012; Mass and Ovens in WRF model physics: problems, solutions and a new paradigm for progress. Preprints, 2010 WRF Users' Workshop, NCAR, Boulder, Colo. http://www.mmm.ucar.edu/wrf/users/workshops/WS2010/presentations/session%204/4-1_WRFworkshop2010Final.pdf, 2010), we tried to compare the performance of parameterizations and to enhance the forecast skill of low-level wind fields over the central western part of South Korea. Even though both subgrid-scale orography parameterizations significantly alleviated the positive bias at 10-m wind speed, the parameterization by Jimenez and Dudhia revealed a better forecast skill in wind speed under our modeling configuration. Implementation of the subgrid-scale orography parameterizations in the model did not affect the forecast skills in other meteorological fields including 10-m wind direction. Our study also brought up the problem of discrepancy in the definition of "10-m" wind between model physics parameterizations and observations, which can cause overestimated winds in model simulations. The overestimation was larger in stable conditions than in unstable conditions, indicating that the weak diurnal cycle in the model could be attributed to the representation error.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nakafuji, Dora; Gouveia, Lauren

This project supports development of the next generation, integrated energy management infrastructure (EMS) able to incorporate advance visualization of behind-the-meter distributed resource information and probabilistic renewable energy generation forecasts to inform real-time operational decisions. The project involves end-users and active feedback from an Utility Advisory Team (UAT) to help inform how information can be used to enhance operational functions (e.g. unit commitment, load forecasting, Automatic Generation Control (AGC) reserve monitoring, ramp alerts) within two major EMS platforms. Objectives include: Engaging utility operations personnel to develop user input on displays, set expectations, test and review; Developing ease of use and timelinessmore » metrics for measuring enhancements; Developing prototype integrated capabilities within two operational EMS environments; Demonstrating an integrated decision analysis platform with real-time wind and solar forecasting information and timely distributed resource information; Seamlessly integrating new 4-dimensional information into operations without increasing workload and complexities; Developing sufficient analytics to inform and confidently transform and adopt new operating practices and procedures; Disseminating project lessons learned through industry sponsored workshops and conferences;Building on collaborative utility-vendor partnership and industry capabilities« less

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, themore » samples are shown to produce realistic wind scenarios based on sample spectra and space-time correlation structure.« less

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

DOE PAGES

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

2018-03-01

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, themore » samples are shown to produce realistic wind scenarios based on sample spectra and space-time correlation structure.« less

The combined value of wind and solar power forecasting improvements and electricity storage

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Bri-Mathias; Brancucci Martinez-Anido, Carlo; Wang, Qin

As the penetration rates of variable renewable energy increase, the value of power systems operation flexibility technology options, such as renewable energy forecasting improvements and electricity storage, is also assumed to increase. In this work, we examine the value of these two technologies, when used independently and concurrently, for two real case studies that represent the generation mixes for the California and Midcontinent Independent System Operators (CAISO and MISO). Since both technologies provide additional system flexibility they reduce operational costs and renewable curtailment for both generation mixes under study. Interestingly, the relative impacts are quite similar when both technologies aremore » used together. Though both flexibility options can solve some of the same issues that arise with high penetration levels of renewables, they do not seem to significantly increase or decrease the economic potential of the other technology.« less

Variable Generation Power Forecasting as a Big Data Problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen; Kosovic, Branko

To blend growing amounts of power from renewable resources into utility operations requires accurate forecasts. For both day ahead planning and real-time operations, the power from the wind and solar resources must be predicted based on real-time observations and a series of models that span the temporal and spatial scales of the problem, using the physical and dynamical knowledge as well as computational intelligence. Accurate prediction is a Big Data problem that requires disparate data, multiple models that are each applicable for a specific time frame, and application of computational intelligence techniques to successfully blend all of the model andmore » observational information in real-time and deliver it to the decision makers at utilities and grid operators. This paper describes an example system that has been used for utility applications and how it has been configured to meet utility needs while addressing the Big Data issues.« less

The combined value of wind and solar power forecasting improvements and electricity storage

DOE PAGES

Hodge, Bri-Mathias; Brancucci Martinez-Anido, Carlo; Wang, Qin; ...

2018-02-12

As the penetration rates of variable renewable energy increase, the value of power systems operation flexibility technology options, such as renewable energy forecasting improvements and electricity storage, is also assumed to increase. In this work, we examine the value of these two technologies, when used independently and concurrently, for two real case studies that represent the generation mixes for the California and Midcontinent Independent System Operators (CAISO and MISO). Since both technologies provide additional system flexibility they reduce operational costs and renewable curtailment for both generation mixes under study. Interestingly, the relative impacts are quite similar when both technologies aremore » used together. Though both flexibility options can solve some of the same issues that arise with high penetration levels of renewables, they do not seem to significantly increase or decrease the economic potential of the other technology.« less

Variable Generation Power Forecasting as a Big Data Problem

DOE PAGES

Haupt, Sue Ellen; Kosovic, Branko

2016-10-10

To blend growing amounts of power from renewable resources into utility operations requires accurate forecasts. For both day ahead planning and real-time operations, the power from the wind and solar resources must be predicted based on real-time observations and a series of models that span the temporal and spatial scales of the problem, using the physical and dynamical knowledge as well as computational intelligence. Accurate prediction is a Big Data problem that requires disparate data, multiple models that are each applicable for a specific time frame, and application of computational intelligence techniques to successfully blend all of the model andmore » observational information in real-time and deliver it to the decision makers at utilities and grid operators. This paper describes an example system that has been used for utility applications and how it has been configured to meet utility needs while addressing the Big Data issues.« less

Solar wind and extreme ultraviolet modulation of the lunar ionosphere/exosphere

NASA Technical Reports Server (NTRS)

Freeman, J. W.

1976-01-01

The ALSEP/SIDE detectors routinely monitor the dayside lunar ionosphere. Variations in the ionosphere are found to correlate with both the 2800 MHz radio index which can be related to solar EUV and with the solar wind proton flux. For the solar wind, the ionospheric variation is proportionately greater than that of the solar wind. This suggests an amplification effect on the lunar atmosphere due perhaps to sputtering of the surface or, less probably, an inordinate enhancement of noble gases in the solar wind. The surface neutral number density is calculated under the assumption of neon gas. During a quiet solar wind this number agrees with or is slightly above that expected for neon accreted from the solar wind. During an enhanced solar wind the neutral number density is much higher.

SASS wind forecast impact studies using the GLAS and NEPRF systems: Preliminary conclusions

NASA Technical Reports Server (NTRS)

Kalnay, E.; Atlas, R.; Baker, W. E.; Duffy, D.; Halem, M.; Helfand, M.

1984-01-01

For this project, a version of the GLAS Analysis/Forecast System was developed that includes an objective dealiasing scheme as an integral part of the analysis cycle. With this system the (100 sq km) binned SASS wind data generated by S. Peteherych of AER, Canada corresponding of the period 0000 GMT 7 September 1978 to 1200 GMT 13 September 1978 was objectively dealiased. The dealiased wind fields have been requested and received by JPL, NMC and the British Meteorological Office. The first 3.5 days of objectively dealiased fields were subjectively enhanced on the McIDAS system. Approximately 20% of the wind directions were modified, and of these, about 70% were changed by less than 90 deg. Two SASS forecast impact studies, were performed using the dealiased fields, with the GLAS and the NEPRF (Navy Environmental Prediction Research Facility) analysis/forecast systems.

Research on wind field algorithm of wind lidar based on BP neural network and grey prediction

NASA Astrophysics Data System (ADS)

Chen, Yong; Chen, Chun-Li; Luo, Xiong; Zhang, Yan; Yang, Ze-hou; Zhou, Jie; Shi, Xiao-ding; Wang, Lei

2018-01-01

This paper uses the BP neural network and grey algorithm to forecast and study radar wind field. In order to reduce the residual error in the wind field prediction which uses BP neural network and grey algorithm, calculating the minimum value of residual error function, adopting the residuals of the gray algorithm trained by BP neural network, using the trained network model to forecast the residual sequence, using the predicted residual error sequence to modify the forecast sequence of the grey algorithm. The test data show that using the grey algorithm modified by BP neural network can effectively reduce the residual value and improve the prediction precision.

KSC-2015-1314

NASA Image and Video Library

2015-02-08

Sen. Bill Nelson, left, and former Vice President Al Gore greet singer Jimmy Buffett, right, at NASA’s Kennedy Space Center in Florida prior to the planned liftoff of NOAA’s Deep Space Climate Observatory mission, or DSCOVR. DSCOVR will launch aboard a SpaceX Falcon 9 rocket. The mission is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Kim Shiflett

KSC-2015-1298

NASA Image and Video Library

2015-02-07

CAPE CANAVERAL, Fla. – Stephen Volz, assistant administrator of the NOAA Satellite and Information Service, prepares to brief media on preparations for the liftoff of NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR. DSCOVR will launch aboard a SpaceX Falcon 9 rocket. The mission is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Jim Grossman

KSC-2015-1312

NASA Image and Video Library

2015-02-08

CAPE CANAVERAL, Fla. – Former Vice President Al Gore speaks to news media at NASA’s Kennedy Space Center in Florida prior to the planned liftoff of NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR. DSCOVR will launch aboard a SpaceX Falcon 9 rocket. The mission is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Kim Shiflett

KSC-2015-1303

NASA Image and Video Library

2015-02-07

CAPE CANAVERAL, Fla. – Hans Koenigsmann, vice president of mission assurance at SpaceX, listens to a question from a member of the news media during a briefing regarding NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR. DSCOVR will launch aboard a SpaceX Falcon 9 rocket. The mission is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Kim Shiflett

KSC-2015-1297

NASA Image and Video Library

2015-02-07

CAPE CANAVERAL, Fla. – Steven Clarke, NASA Joint Agency Satellite Division director for the agency’s Science Mission Directorate, prepares to brief media on preparations for the liftoff of NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR. DSCOVR will launch aboard a SpaceX Falcon 9 rocket. The mission is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts.To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Jim Grossman

KSC-2015-1296

NASA Image and Video Library

2015-02-07

CAPE CANAVERAL, Fla. – Tom Berger, director of the NOAA Space Weather Prediction Center, prepares to brief media on preparations for the liftoff of NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR. DSCOVR will launch aboard a SpaceX Falcon 9 rocket. The mission is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Jim Grossman

KSC-2015-1306

NASA Image and Video Library

2015-02-08

CAPE CANAVERAL, Fla. – The payload fairing protecting NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, tops the SpaceX Falcon 9 rocket set to lift off at 6:10 p.m. EST from Space Launch Complex 40 at Cape Canaveral Air Force Station in Florida. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Kim Shiflett

DSCOVR Spacecraft Arrival, Offload, & Unpacking

NASA Image and Video Library

2014-11-20

Workers remove the plastic cover from NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, in the high bay of Building 1 at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is currently scheduled for January 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida.

DSCOVR Spacecraft Arrival, Offload, & Unpacking

NASA Image and Video Library

2014-11-20

Preparations are underway to lift NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, wrapped in plastic, from its transportation pallet at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is currently scheduled for January 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida.

DSCOVR Spacecraft Arrival, Offload, & Unpacking

NASA Image and Video Library

2014-11-20

NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, has been uncovered and is ready for processing in the high bay of Building 1 at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is currently scheduled for January 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida.

DSCOVR Spacecraft Arrival, Offload, & Unpacking

NASA Image and Video Library

2014-11-20

A lifting device is attached to NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, wrapped in plastic, to remove it from its transportation pallet at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is currently scheduled for January 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida.

DSCOVR Spacecraft Arrival, Offload, & Unpacking

NASA Image and Video Library

2014-11-20

NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, wrapped in plastic, comes into view as the protective shipping container is lifted from around the spacecraft at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is currently scheduled for January 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida.

DSCOVR Spacecraft Arrival, Offload, & Unpacking

NASA Image and Video Library

2014-11-20

NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR, wrapped in plastic, is transferred from its transportation pallet to a portable work stand at the Astrotech payload processing facility in Titusville, Florida, near Kennedy Space Center. DSCOVR is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. Launch is currently scheduled for January 2015 aboard a SpaceX Falcon 9 v 1.1 launch vehicle from Cape Canaveral Air Force Station, Florida.

Climatology and trend of wind power resources in China and its surrounding regions: a revisit using Climate Forecast System Reanalysis data

Treesearch

Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman

2015-01-01

The mean climatology, seasonal and interannual variability and trend of wind speeds at the hub height (80 m) of modern wind turbines over China and its surrounding regions are revisited using 33-year (1979–2011) wind data from the Climate Forecast System Reanalysis (CFSR) that has many improvements including higher spatial resolution over previous global reanalysis...

Elemental and charge state composition of the fast solar wind observed with SMS instruments on WIND

NASA Technical Reports Server (NTRS)

Gloeckler, G.; Galvin, A. B.; Ipavich, F. M.; Hamilton, D. C.; Bochsler, P.; Geiss, J.; Fisk, L. A.; Wilken, B.

1995-01-01

The elemental composition and charge state distributions of heavy ions of the solar wind provide essential information about: (1) atom-ion separation processes in the solar atmosphere leading to the 'FIP effect' (the overabundance of low First Ionization potential (FIP) elements in the solar wind compared to the photosphere); and (2) coronal temperature profiles, as well as mechanisms which heat the corona and accelerate the solar wind. This information is required for solar wind acceleration models. The SWICS instrument on Ulysses measures for all solar wind flow conditions the relative abundance of about 8 elements and 20 charge states of the solar wind. Furthermore, the Ulysses high-latitude orbit provides an unprecedented look at the solar wind from the polar coronal holes near solar minimum conditions. The MASS instrument on the WIND spacecraft is a high-mass resolution solar wind ion mass spectrometer that will provide routinely not only the abundances and charge state of all elements easily measured with SWICS, but also of N, Mg, S. The MASS sensor was fully operational at the end of 1994 and has sampled the in-ecliptic solar wind composition in both the slow and the corotating fast streams. This unique combination of SWICS on Ulysses and MASS on WIND allows us to view for the first time the solar wind from two regions of the large coronal hole. Observations with SWICS in the coronal hole wind: (1) indicate that the FIP effect is small; and (2) allow us determine the altitude of the maximum in the electron temperature profile, and indicate a maximum temperature of approximately 1.5 MK. New results from the SMS instruments on Wind will be compared with results from SWICS on Ulysses.

Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoff, Thomas Hoff; Kankiewicz, Adam

Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California Independent System Operator’s (ISO) load forecasts by integrating behind-the-meter (BTM) PV forecasts. The three, new, state-of-the-art solar irradiance forecasting models included: the infrared (IR) satellite-based cloud motion vector (CMV) model; the WRF-SolarCA model and variants; and the Optimized Deep Machine Learning (ODML)-training model. The first two forecasting models targeted known weaknesses in current operational solar forecasts. They were benchmarked against existing operational numerical weather prediction (NWP)more » forecasts, visible satellite CMV forecasts, and measured PV plant power production. IR CMV, WRF-SolarCA, and ODML-training forecasting models all improved the forecast to a significant degree. Improvements varied depending on time of day, cloudiness index, and geographic location. The fourth objective was to demonstrate that the California ISO’s load forecasts could be improved by integrating BTM PV forecasts. This objective represented the project’s most exciting and applicable gains. Operational BTM forecasts consisting of 200,000+ individual rooftop PV forecasts were delivered into the California ISO’s real-time automated load forecasting (ALFS) environment. They were then evaluated side-by-side with operational load forecasts with no BTM-treatment. Overall, ALFS-BTM day-ahead (DA) forecasts performed better than baseline ALFS forecasts when compared to actual load data. Specifically, ALFS-BTM DA forecasts were observed to have the largest reduction of error during the afternoon on cloudy days. Shorter term 30 minute-ahead ALFS-BTM forecasts were shown to have less error under all sky conditions, especially during the morning time periods when traditional load forecasts often experience their largest uncertainties. This work culminated in a GO decision being made by the California ISO to include zonal BTM forecasts into its operational load forecasting system. The California ISO’s Manager of Short Term Forecasting, Jim Blatchford, summarized the research performed in this project with the following quote: “The behind-the-meter (BTM) California ISO region forecasting research performed by Clean Power Research and sponsored by the Department of Energy’s SUNRISE program was an opportunity to verify value and demonstrate improved load forecast capability. In 2016, the California ISO will be incorporating the BTM forecast into the Hour Ahead and Day Ahead load models to look for improvements in the overall load forecast accuracy as BTM PV capacity continues to grow.”« less

Predictability of Solar Radiation for Photovoltaics systems over Europe: from short-term to seasonal time-scales

NASA Astrophysics Data System (ADS)

De Felice, Matteo; Petitta, Marcello; Ruti, Paolo

2014-05-01

Photovoltaic diffusion is steadily growing on Europe, passing from a capacity of almost 14 GWp in 2011 to 21.5 GWp in 2012 [1]. Having accurate forecast is needed for planning and operational purposes, with the possibility to model and predict solar variability at different time-scales. This study examines the predictability of daily surface solar radiation comparing ECMWF operational forecasts with CM-SAF satellite measurements on the Meteosat (MSG) full disk domain. Operational forecasts used are the IFS system up to 10 days and the System4 seasonal forecast up to three months. Forecast are analysed considering average and variance of errors, showing error maps and average on specific domains with respect to prediction lead times. In all the cases, forecasts are compared with predictions obtained using persistence and state-of-art time-series models. We can observe a wide range of errors, with the performance of forecasts dramatically affected by orography and season. Lower errors are on southern Italy and Spain, with errors on some areas consistently under 10% up to ten days during summer (JJA). Finally, we conclude the study with some insight on how to "translate" the error on solar radiation to error on solar power production using available production data from solar power plants. [1] EurObserver, "Baromètre Photovoltaïque, Le journal des énergies renouvables, April 2012."

A study of the solar wind deceleration in the Earth's foreshock region

NASA Technical Reports Server (NTRS)

Zhang, T.-L.; Schwingenschuh, K.; Russell, C. T.

1995-01-01

Previous observations have shown that the solar wind is decelerated and deflected in the earth's upstream region populated by long-period waves. This deceleration is corelated with the 'diffuse' but not with the 'reflected' ion population. The speed of the solar wind may decrease tens of km/s in the foreshock region. The solar wind dynamic pressure exerted on the magnetopause may vary due to the fluctuation of the solar wind speed and density in the foreshock region. In this study, we examine this solar wind deceleration and determine how the solar wind deceleration varies in the foreshock region.

Solar Corona/Wind Composition and Origins of the Solar Wind

NASA Astrophysics Data System (ADS)

Lepri, S. T.; Gilbert, J. A.; Landi, E.; Shearer, P.; von Steiger, R.; Zurbuchen, T.

2014-12-01

Measurements from ACE and Ulysses have revealed a multifaceted solar wind, with distinctly different kinetic and compositional properties dependent on the source region of the wind. One of the major outstanding issues in heliophysics concerns the origin and also predictability of quasi-stationary slow solar wind. While the fast solar wind is now proven to originate within large polar coronal holes, the source of the slow solar wind remains particularly elusive and has been the subject of long debate, leading to models that are stationary and also reconnection based - such as interchange or so-called S-web based models. Our talk will focus on observational constraints of solar wind sources and their evolution during the solar cycle. In particular, we will point out long-term variations of wind composition and dynamic properties, particularly focused on the abundance of elements with low First Ionization Potential (FIP), which have been routinely measured on both ACE and Ulysses spacecraft. We will use these in situ observations, and remote sensing data where available, to provide constraints for solar wind origin during the solar cycle, and on their correspondence to predictions for models of the solar wind.

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-12-18

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and comparesmore » the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Automated Statistical Forecast Method to 36-48H ahead of Storm Wind and Dangerous Precipitation at the Mediterranean Region

NASA Astrophysics Data System (ADS)

Perekhodtseva, E. V.

2009-09-01

Development of successful method of forecast of storm winds, including squalls and tornadoes and heavy rainfalls, that often result in human and material losses, could allow one to take proper measures against destruction of buildings and to protect people. Well-in-advance successful forecast (from 12 hours to 48 hour) makes possible to reduce the losses. Prediction of the phenomena involved is a very difficult problem for synoptic till recently. The existing graphic and calculation methods still depend on subjective decision of an operator. Nowadays in Russia there is no hydrodynamic model for forecast of the maximal precipitation and wind velocity V> 25m/c, hence the main tools of objective forecast are statistical methods using the dependence of the phenomena involved on a number of atmospheric parameters (predictors). Statistical decisive rule of the alternative and probability forecast of these events was obtained in accordance with the concept of "perfect prognosis" using the data of objective analysis. For this purpose the different teaching samples of present and absent of this storm wind and rainfalls were automatically arranged that include the values of forty physically substantiated potential predictors. Then the empirical statistical method was used that involved diagonalization of the mean correlation matrix R of the predictors and extraction of diagonal blocks of strongly correlated predictors. Thus for these phenomena the most informative predictors were selected without loosing information. The statistical decisive rules for diagnosis and prognosis of the phenomena involved U(X) were calculated for choosing informative vector-predictor. We used the criterion of distance of Mahalanobis and criterion of minimum of entropy by Vapnik-Chervonenkis for the selection predictors. Successful development of hydrodynamic models for short-term forecast and improvement of 36-48h forecasts of pressure, temperature and others parameters allowed us to use the prognostic fields of those models for calculations of the discriminant functions in the nodes of the grid 150x150km and the values of probabilities P of dangerous wind and thus to get fully automated forecasts. In order to change to the alternative forecast the author proposes the empirical threshold values specified for this phenomenon and advance period 36 hours. In the accordance to the Pirsey-Obukhov criterion (T), the success of these automated statistical methods of forecast of squalls and tornadoes to 36 -48 hours ahead and heavy rainfalls in the warm season for the territory of Italy, Spain and Balkan countries is T = 1-a-b=0,54: 0,78 after author experiments. A lot of examples of very successful forecasts of summer storm wind and heavy rainfalls over the Italy and Spain territory are submitted at this report. The same decisive rules were applied to the forecast of these phenomena during cold period in this year too. This winter heavy snowfalls in Spain and in Italy and storm wind at this territory were observed very often. And our forecasts are successful.

Assessing Upper-Level Winds on Day-of-Launch

NASA Technical Reports Server (NTRS)

Bauman, William H., III; Wheeler, Mark M.

2012-01-01

On the day-or-launch. the 45th Weather Squadron Launch Weather Officers (LWOS) monitor the upper-level winds for their launch customers to include NASA's Launch Services Program (LSP). During launch operations, the payload launch team sometimes asks the LWO if they expect the upper level winds to change during the countdown but the LWOs did not have the capability to quickly retrieve or display the upper-level observations and compare them to the numerical weather prediction model point forecasts. The LWOs requested the Applied Meteorology Unit (AMU) develop a capability in the form of a graphical user interface (GUI) that would allow them to plot upper-level wind speed and direction observations from the Kennedy Space Center Doppler Radar Wind Profilers and Cape Canaveral Air Force Station rawinsondes and then overlay model point forecast profiles on the observation profiles to assess the performance of these models and graphically display them to the launch team. The AMU developed an Excel-based capability for the LWOs to assess the model forecast upper-level winds and compare them to observations. They did so by creating a GUI in Excel that allows the LWOs to first initialize the models by comparing the O-hour model forecasts to the observations and then to display model forecasts in 3-hour intervals from the current time through 12 hours.

Benefits of an ultra large and multiresolution ensemble for estimating available wind power

NASA Astrophysics Data System (ADS)

Berndt, Jonas; Hoppe, Charlotte; Elbern, Hendrik

2016-04-01

In this study we investigate the benefits of an ultra large ensemble with up to 1000 members including multiple nesting with a target horizontal resolution of 1 km. The ensemble shall be used as a basis to detect events of extreme errors in wind power forecasting. Forecast value is the wind vector at wind turbine hub height (~ 100 m) in the short range (1 to 24 hour). Current wind power forecast systems rest already on NWP ensemble models. However, only calibrated ensembles from meteorological institutions serve as input so far, with limited spatial resolution (˜10 - 80 km) and member number (˜ 50). Perturbations related to the specific merits of wind power production are yet missing. Thus, single extreme error events which are not detected by such ensemble power forecasts occur infrequently. The numerical forecast model used in this study is the Weather Research and Forecasting Model (WRF). Model uncertainties are represented by stochastic parametrization of sub-grid processes via stochastically perturbed parametrization tendencies and in conjunction via the complementary stochastic kinetic-energy backscatter scheme already provided by WRF. We perform continuous ensemble updates by comparing each ensemble member with available observations using a sequential importance resampling filter to improve the model accuracy while maintaining ensemble spread. Additionally, we use different ensemble systems from global models (ECMWF and GFS) as input and boundary conditions to capture different synoptic conditions. Critical weather situations which are connected to extreme error events are located and corresponding perturbation techniques are applied. The demanding computational effort is overcome by utilising the supercomputer JUQUEEN at the Forschungszentrum Juelich.

Flexible reserve markets for wind integration

NASA Astrophysics Data System (ADS)

Fernandez, Alisha R.

The increased interconnection of variable generation has motivated the use of improved forecasting to more accurately predict future production with the purpose to lower total system costs for balancing when the expected output exceeds or falls short of the actual output. Forecasts are imperfect, and the forecast errors associated with utility-scale generation from variable generators need new balancing capabilities that cannot be handled by existing ancillary services. Our work focuses on strategies for integrating large amounts of wind generation under the flex reserve market, a market that would called upon for short-term energy services during an under or oversupply of wind generation to maintain electric grid reliability. The flex reserve market would be utilized for time intervals that fall in-between the current ancillary services markets that would be longer than second-to-second energy services for maintaining system frequency and shorter than reserve capacity services that are called upon for several minutes up to an hour during an unexpected contingency on the grid. In our work, the wind operator would access the flex reserve market as an energy service to correct for unanticipated forecast errors, akin to paying the generators participating in the market to increase generation during a shortfall or paying the other generators to decrease generation during an excess of wind generation. Such a market does not currently exist in the Mid-Atlantic United States. The Pennsylvania-New Jersey-Maryland Interconnection (PJM) is the Mid-Atlantic electric grid case study that was used to examine if a flex reserve market can be utilized for integrating large capacities of wind generation in a lowcost manner for those providing, purchasing and dispatching these short-term balancing services. The following work consists of three studies. The first examines the ability of a hydroelectric facility to provide short-term forecast error balancing services via a flex reserve market, identifying the operational constraints that inhibit a multi-purpose dam facility to meet the desired flexible energy demand. The second study transitions from the hydroelectric facility as the decision maker providing flex reserve services to the wind plant as the decision maker purchasing these services. In this second study, methods for allocating the costs of flex reserve services under different wind policy scenarios are explored that aggregate farms into different groupings to identify the least-cost strategy for balancing the costs of hourly day-ahead forecast errors. The least-cost strategy may be different for an individual wind plant and for the system operator, noting that the least-cost strategy is highly sensitive to cost allocation and aggregation schemes. The latter may also cause cross-subsidies in the cost for balancing wind forecast errors among the different wind farms. The third study builds from the second, with the objective to quantify the amount of flex reserves needed for balancing future forecast errors using a probabilistic approach (quantile regression) to estimating future forecast errors. The results further examine the usefulness of separate flexible markets PJM could use for balancing oversupply and undersupply events, similar to the regulation up and down markets used in Europe. These three studies provide the following results and insights to large-scale wind integration using actual PJM wind farm data that describe the markets and generators within PJM. • Chapter 2 provides an in-depth analysis of the valuable, yet highly-constrained, energy services multi-purpose hydroelectric facilities can provide, though the opportunity cost for providing these services can result in large deviations from the reservoir policies with minimal revenue gain in comparison to dedicating the whole of dam capacity to providing day-ahead, baseload generation. • Chapter 3 quantifies the system-wide efficiency gains and the distributive effects of PJM's decision to act as a single balancing authority, which means that it procures ancillary services across its entire footprint simultaneously. This can be contrasted to Midwest Independent System Operator (MISO), which has several balancing authorities operating under its footprint. • Chapter 4 uses probabilistic methods to estimate the uncertainty in the forecast errors and the quantity of energy needed to balance these forecast errors at a certain percentile. Current practice is to use a point forecast that describes the conditional expectation of the dependent variable at each time step. The approach here uses quantile regression to describe the relationship between independent variable and the conditional quantiles (equivalently the percentiles) of the dependent variable. An estimate of the conditional density is performed, which contains information about the covariate relationship of the sign of the forecast errors (negative for too much wind generation and positive for too little wind generation) and the wind power forecast. This additional knowledge may be implemented in the decision process to more accurately schedule day-ahead wind generation bids and provide an example for using separate markets for balancing an oversupply and undersupply of generation. Such methods are currently used for coordinating large footprints of wind generation in Europe.

An operational integrated short-term warning solution for solar radiation storms: introducing the Forecasting Solar Particle Events and Flares (FORSPEF) system

NASA Astrophysics Data System (ADS)

Anastasiadis, Anastasios; Sandberg, Ingmar; Papaioannou, Athanasios; Georgoulis, Manolis; Tziotziou, Kostas; Jiggens, Piers; Hilgers, Alain

2015-04-01

We present a novel integrated prediction system, of both solar flares and solar energetic particle (SEP) events, which is in place to provide short-term warnings for hazardous solar radiation storms. FORSPEF system provides forecasting of solar eruptive events, such as solar flares with a projection to coronal mass ejections (CMEs) (occurrence and velocity) and the likelihood of occurrence of a SEP event. It also provides nowcasting of SEP events based on actual solar flare and CME near real-time alerts, as well as SEP characteristics (peak flux, fluence, rise time, duration) per parent solar event. The prediction of solar flares relies on a morphological method which is based on the sophisticated derivation of the effective connected magnetic field strength (Beff) of potentially flaring active-region (AR) magnetic configurations and it utilizes analysis of a large number of AR magnetograms. For the prediction of SEP events a new reductive statistical method has been implemented based on a newly constructed database of solar flares, CMEs and SEP events that covers a large time span from 1984-2013. The method is based on flare location (longitude), flare size (maximum soft X-ray intensity), and the occurrence (or not) of a CME. Warnings are issued for all > C1.0 soft X-ray flares. The warning time in the forecasting scheme extends to 24 hours with a refresh rate of 3 hours while the respective warning time for the nowcasting scheme depends on the availability of the near real-time data and falls between 15-20 minutes. We discuss the modules of the FORSPEF system, their interconnection and the operational set up. The dual approach in the development of FORPSEF (i.e. forecasting and nowcasting scheme) permits the refinement of predictions upon the availability of new data that characterize changes on the Sun and the interplanetary space, while the combined usage of solar flare and SEP forecasting methods upgrades FORSPEF to an integrated forecasting solution. This work has been funded through the "FORSPEF: FORecasting Solar Particle Events and Flares", ESA Contract No. 4000109641/13/NL/AK

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Zhang, Xinmin; Li, Yuan

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

The interaction of the solar wind with the interstellar medium

NASA Technical Reports Server (NTRS)

Axford, W. I.

1972-01-01

The expected characteristics of the solar wind, extrapolated from the vicinity of the earth are described. Several models are examined for the interaction of the solar wind with the interstellar plasma and magnetic field. Various aspects of the penetration of neutral interstellar gas into the solar wind are considered. The dynamic effects of the neutral gas on the solar wind are described. Problems associated with the interaction of cosmic rays with the solar wind are discussed.

Neutral Solar Wind Generated by Lunar Exospheric Dust at the Terminator

NASA Technical Reports Server (NTRS)

Collier, Michael R.; Stubbs, Timothy J.

2007-01-01

We calculate the flux of neutral solar wind observed on the lunar surface at the terminator due to solar wind protons penetrating exospheric dust with: (1) grains larger that 0.1 microns and (2) grains larger than 0.01 microns. For grains larger than 0.1 microns, the ratio of the neutral solar wind to solar wind flux is estimated to be approx.10(exp -4)-10(exp -3) at solar wind speeds in excess of 800 km/s, but much lower (less than 10(exp -5) at average to low solar wind speeds. However, when the smaller grain sizes are considered, the ratio of the neutral solar wind flux to solar wind flux is estimated to be greater than or equal to 10(exp -5) at all speeds and at speeds in excess of 700 km/s reaches 10(exp -3)-10(exp -2). These neutral solar wind fluxes are easily measurable with current low energy neutral atom instrumentation. Observations of neutral solar wind from the surface of the Moon could provide a very sensitive determination of the distribution of very small dust grains in the lunar exosphere and would provide data complementary to optical measurements at ultraviolet and visible wavelengths. Furthermore, neutral solar wind, unlike its ionized counterpart, is .not held-off by magnetic anomalies, and may contribute to greater space weathering than expected in certain lunar locations.

A Comparison of the Performance of Advanced Statistical Techniques for the Refinement of Day-ahead and Longer NWP-based Wind Power Forecasts

NASA Astrophysics Data System (ADS)

Zack, J. W.

2015-12-01

Predictions from Numerical Weather Prediction (NWP) models are the foundation for wind power forecasts for day-ahead and longer forecast horizons. The NWP models directly produce three-dimensional wind forecasts on their respective computational grids. These can be interpolated to the location and time of interest. However, these direct predictions typically contain significant systematic errors ("biases"). This is due to a variety of factors including the limited space-time resolution of the NWP models and shortcomings in the model's representation of physical processes. It has become common practice to attempt to improve the raw NWP forecasts by statistically adjusting them through a procedure that is widely known as Model Output Statistics (MOS). The challenge is to identify complex patterns of systematic errors and then use this knowledge to adjust the NWP predictions. The MOS-based improvements are the basis for much of the value added by commercial wind power forecast providers. There are an enormous number of statistical approaches that can be used to generate the MOS adjustments to the raw NWP forecasts. In order to obtain insight into the potential value of some of the newer and more sophisticated statistical techniques often referred to as "machine learning methods" a MOS-method comparison experiment has been performed for wind power generation facilities in 6 wind resource areas of California. The underlying NWP models that provided the raw forecasts were the two primary operational models of the US National Weather Service: the GFS and NAM models. The focus was on 1- and 2-day ahead forecasts of the hourly wind-based generation. The statistical methods evaluated included: (1) screening multiple linear regression, which served as a baseline method, (2) artificial neural networks, (3) a decision-tree approach called random forests, (4) gradient boosted regression based upon an decision-tree algorithm, (5) support vector regression and (6) analog ensemble, which is a case-matching scheme. The presentation will provide (1) an overview of each method and the experimental design, (2) performance comparisons based on standard metrics such as bias, MAE and RMSE, (3) a summary of the performance characteristics of each approach and (4) a preview of further experiments to be conducted.

On the skill of various ensemble spread estimators for probabilistic short range wind forecasting

NASA Astrophysics Data System (ADS)

Kann, A.

2012-05-01

A variety of applications ranging from civil protection associated with severe weather to economical interests are heavily dependent on meteorological information. For example, a precise planning of the energy supply with a high share of renewables requires detailed meteorological information on high temporal and spatial resolution. With respect to wind power, detailed analyses and forecasts of wind speed are of crucial interest for the energy management. Although the applicability and the current skill of state-of-the-art probabilistic short range forecasts has increased during the last years, ensemble systems still show systematic deficiencies which limit its practical use. This paper presents methods to improve the ensemble skill of 10-m wind speed forecasts by combining deterministic information from a nowcasting system on very high horizontal resolution with uncertainty estimates from a limited area ensemble system. It is shown for a one month validation period that a statistical post-processing procedure (a modified non-homogeneous Gaussian regression) adds further skill to the probabilistic forecasts, especially beyond the nowcasting range after +6 h.

Increasing the temporal resolution of direct normal solar irradiance forecasted series

NASA Astrophysics Data System (ADS)

Fernández-Peruchena, Carlos M.; Gastón, Martin; Schroedter-Homscheidt, Marion; Marco, Isabel Martínez; Casado-Rubio, José L.; García-Moya, José Antonio

2017-06-01

A detailed knowledge of the solar resource is a critical point in the design and control of Concentrating Solar Power (CSP) plants. In particular, accurate forecasting of solar irradiance is essential for the efficient operation of solar thermal power plants, the management of energy markets, and the widespread implementation of this technology. Numerical weather prediction (NWP) models are commonly used for solar radiation forecasting. In the ECMWF deterministic forecasting system, all forecast parameters are commercially available worldwide at 3-hourly intervals. Unfortunately, as Direct Normal solar Irradiance (DNI) exhibits a great variability due to the dynamic effects of passing clouds, 3-h time resolution is insufficient for accurate simulations of CSP plants due to their nonlinear response to DNI, governed by various thermal inertias due to their complex response characteristics. DNI series of hourly or sub-hourly frequency resolution are normally used for an accurate modeling and analysis of transient processes in CSP technologies. In this context, the objective of this study is to propose a methodology for generating synthetic DNI time series at 1-h (or higher) temporal resolution from 3-h DNI series. The methodology is based upon patterns as being defined with help of the clear-sky envelope approach together with a forecast of maximum DNI value, and it has been validated with high quality measured DNI data.

Learning-based Wind Estimation using Distant Soundings for Unguided Aerial Delivery

NASA Astrophysics Data System (ADS)

Plyler, M.; Cahoy, K.; Angermueller, K.; Chen, D.; Markuzon, N.

2016-12-01

Delivering unguided, parachuted payloads from aircraft requires accurate knowledge of the wind field inside an operational zone. Usually, a dropsonde released from the aircraft over the drop zone gives a more accurate wind estimate than a forecast. Mission objectives occasionally demand releasing the dropsonde away from the drop zone, but still require accuracy and precision. Barnes interpolation and many other assimilation methods do poorly when the forecast error is inconsistent in a forecast grid. A machine learning approach can better leverage non-linear relations between different weather patterns and thus provide a better wind estimate at the target drop zone when using data collected up to 100 km away. This study uses the 13 km resolution Rapid Refresh (RAP) dataset available through NOAA and subsamples to an area around Yuma, AZ and up to approximately 10km AMSL. RAP forecast grids are updated with simulated dropsondes taken from analysis (historical weather maps). We train models using different data mining and machine learning techniques, most notably boosted regression trees, that can accurately assimilate the distant dropsonde. The model takes a forecast grid and simulated remote dropsonde data as input and produces an estimate of the wind stick over the drop zone. Using ballistic winds as a defining metric, we show our data driven approach does better than Barnes interpolation under some conditions, most notably when the forecast error is different between the two locations, on test data previously unseen by the model. We study and evaluate the model's performance depending on the size, the time lag, the drop altitude, and the geographic location of the training set, and identify parameters most contributing to the accuracy of the wind estimation. This study demonstrates a new approach for assimilating remotely released dropsondes, based on boosted regression trees, and shows improvement in wind estimation over currently used methods.

Solar wind dynamic pressure and electric field as the main factors controlling Saturn's aurorae.

PubMed

Crary, F J; Clarke, J T; Dougherty, M K; Hanlon, P G; Hansen, K C; Steinberg, J T; Barraclough, B L; Coates, A J; Gérard, J-C; Grodent, D; Kurth, W S; Mitchell, D G; Rymer, A M; Young, D T

2005-02-17

The interaction of the solar wind with Earth's magnetosphere gives rise to the bright polar aurorae and to geomagnetic storms, but the relation between the solar wind and the dynamics of the outer planets' magnetospheres is poorly understood. Jupiter's magnetospheric dynamics and aurorae are dominated by processes internal to the jovian system, whereas Saturn's magnetosphere has generally been considered to have both internal and solar-wind-driven processes. This hypothesis, however, is tentative because of limited simultaneous solar wind and magnetospheric measurements. Here we report solar wind measurements, immediately upstream of Saturn, over a one-month period. When combined with simultaneous ultraviolet imaging we find that, unlike Jupiter, Saturn's aurorae respond strongly to solar wind conditions. But in contrast to Earth, the main controlling factor appears to be solar wind dynamic pressure and electric field, with the orientation of the interplanetary magnetic field playing a much more limited role. Saturn's magnetosphere is, therefore, strongly driven by the solar wind, but the solar wind conditions that drive it differ from those that drive the Earth's magnetosphere.

Advancing solar energy forecasting through the underlying physics

NASA Astrophysics Data System (ADS)

Yang, H.; Ghonima, M. S.; Zhong, X.; Ozge, B.; Kurtz, B.; Wu, E.; Mejia, F. A.; Zamora, M.; Wang, G.; Clemesha, R.; Norris, J. R.; Heus, T.; Kleissl, J. P.

2017-12-01

As solar power comprises an increasingly large portion of the energy generation mix, the ability to accurately forecast solar photovoltaic generation becomes increasingly important. Due to the variability of solar power caused by cloud cover, knowledge of both the magnitude and timing of expected solar power production ahead of time facilitates the integration of solar power onto the electric grid by reducing electricity generation from traditional ancillary generators such as gas and oil power plants, as well as decreasing the ramping of all generators, reducing start and shutdown costs, and minimizing solar power curtailment, thereby providing annual economic value. The time scales involved in both the energy markets and solar variability range from intra-hour to several days ahead. This wide range of time horizons led to the development of a multitude of techniques, with each offering unique advantages in specific applications. For example, sky imagery provides site-specific forecasts on the minute-scale. Statistical techniques including machine learning algorithms are commonly used in the intra-day forecast horizon for regional applications, while numerical weather prediction models can provide mesoscale forecasts on both the intra-day and days-ahead time scale. This talk will provide an overview of the challenges unique to each technique and highlight the advances in their ongoing development which come alongside advances in the fundamental physics underneath.

Solar-Terrestrial Predictions

NASA Astrophysics Data System (ADS)

Thompson, R. J.; Cole, D. G.; Wilkinson, P. J.; Shea, M. A.; Smart, D.

1990-11-01

Volume 1: The following subject areas are covered: the magnetosphere environment; forecasting magnetically quiet periods; radiation hazards to human in deep space (a summary with special reference to large solar particle events); solar proton events (review and status); problems of the physics of solar-terrestrial interactions; prediction of solar proton fluxes from x-ray signatures; rhythms in solar activity and the prediction of episodes of large flares; the role of persistence in the 24-hour flare forecast; on the relationship between the observed sunspot number and the number of solar flares; the latitudinal distribution of coronal holes and geomagnetic storms due to coronal holes; and the signatures of flares in the interplanetary medium at 1 AU. Volume 2: The following subject areas were covered: a probability forecast for geomagnetic activity; cost recovery in solar-terrestrial predictions; magnetospheric specification and forecasting models; a geomagnetic forecast and monitoring system for power system operation; some aspects of predicting magnetospheric storms; some similarities in ionospheric disturbance characteristics in equatorial, mid-latitude, and sub-auroral regions; ionospheric support for low-VHF radio transmission; a new approach to prediction of ionospheric storms; a comparison of the total electron content of the ionosphere around L=4 at low sunspot numbers with the IRI model; the French ionospheric radio propagation predictions; behavior of the F2 layer at mid-latitudes; and the design of modern ionosondes.

How can monthly to seasonal forecasts help to better manage power systems? (Invited)

NASA Astrophysics Data System (ADS)

Dubus, L.; Troccoli, A.

2013-12-01

The energy industry increasingly depends on weather and climate, at all space and time scales. This is especially true in countries with volunteer renewable energies development policies. There is no doubt that Energy and Meteorology is a burgeoning inter-sectoral discipline. It is also clear that the catalyst for the stronger interaction between these two sectors is the renewed and fervent interest in renewable energies, especially wind and solar power. Recent progress in meteorology has led to a marked increase in the knowledge of the climate system and in the ability to forecast climate on monthly to seasonal time scales. Several studies have already demonstrated the effectiveness of using these forecasts for energy operations, for instance for hydro-power applications. However, it is also obvious that scientific progress on its own is not sufficient to increase the value of weather forecasts. The process of integration of new meteorological products into operational tools and decision making processes is not straightforward but it is at least as important as the scientific discovery. In turn, such integration requires effective communication between users and providers of these products. We will present some important aspects of energy systems in which monthly to seasonal forecasts can bring useful, if not vital, information, and we will give some examples of encouraging energy/meteorology collaborations. We will also provide some suggestions for a strengthened collaboration into the future.

Spatial optimization of an ideal wind energy system as a response to the intermittency of renewable energies?

NASA Astrophysics Data System (ADS)

Lassonde, Sylvain; Boucher, Olivier; Breon, François-Marie; Tobin, Isabelle; Vautard, Robert

2016-04-01

The share of renewable energies in the mix of electricity production is increasing worldwide. This trend is driven by environmental and economic policies aiming at a reduction of greenhouse gas emissions and an improvement of energy security. It is expected to continue in the forthcoming years and decades. Electricity production from renewables is related to weather and climate factors such as the diurnal and seasonal cycles of sunlight and wind, but is also linked to variability on all time scales. The intermittency in the renewable electricity production (solar, wind power) could eventually hinder their future deployment. Intermittency is indeed a challenge as demand and supply of electricity need to be balanced at any time. This challenge can be addressed by the deployment of an overcapacity in power generation (from renewable and/or thermal sources), a large-scale energy storage system and/or improved management of the demand. The main goal of this study is to optimize a hypothetical renewable energy system at the French and European scales in order to investigate if spatial diversity of the production (here electricity from wind energy) could be a response to the intermittency. We use ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-interim meteorological reanalysis and meteorological fields from the Weather Research and Forecasts (WRF) model to estimate the potential for wind power generation. Electricity demand and production are provided by the French electricity network (RTE) at the scale of administrative regions for years 2013 and 2014. Firstly we will show how the simulated production of wind power compares against the measured production at the national and regional scale. Several modelling and bias correction methods of wind power production will be discussed. Secondly, we will present results from an optimization procedure that aims to minimize some measure of the intermittency of wind energy. For instance we estimate the optimal distribution between French regions (with or without cross-border inputs) that minimizes the impact of low-production periods computed in a running mean sense and its sensitivity to the period considered. We will also assess which meteorological situations are the most problematic over the 35-year ERA-interim climatology(1980-2015).

Detection and forecasting of oyster norovirus outbreaks: recent advances and future perspectives.

PubMed

Wang, Jiao; Deng, Zhiqiang

2012-09-01

Norovirus is a highly infectious pathogen that is commonly found in oysters growing in fecally contaminated waters. Norovirus outbreaks can cause the closure of oyster harvesting waters and acute gastroenteritis in humans associated with consumption of contaminated raw oysters. Extensive efforts and progresses have been made in detection and forecasting of oyster norovirus outbreaks over the past decades. The main objective of this paper is to provide a literature review of methods and techniques for detecting and forecasting oyster norovirus outbreaks and thereby to identify the future directions for improving the detection and forecasting of norovirus outbreaks. It is found that (1) norovirus outbreaks display strong seasonality with the outbreak peak occurring commonly in December-March in the U.S. and April-May in the Europe; (2) norovirus outbreaks are affected by multiple environmental factors, including but not limited to precipitation, temperature, solar radiation, wind, and salinity; (3) various modeling approaches may be employed to forecast norovirus outbreaks, including Bayesian models, regression models, Artificial Neural Networks, and process-based models; and (4) diverse techniques are available for near real-time detection of norovirus outbreaks, including multiplex PCR, seminested PCR, real-time PCR, quantitative PCR, and satellite remote sensing. The findings are important to the management of oyster growing waters and to future investigations into norovirus outbreaks. It is recommended that a combined approach of sensor-assisted real time monitoring and modeling-based forecasting should be utilized for an efficient and effective detection and forecasting of norovirus outbreaks caused by consumption of contaminated oysters. Copyright © 2012 Elsevier Ltd. All rights reserved.

Features of solar wind streams on June 21-28, 2015 as a result of interactions between coronal mass ejections and recurrent streams from coronal holes

NASA Astrophysics Data System (ADS)

Shugay, Yu. S.; Slemzin, V. A.; Rod'kin, D. G.

2017-11-01

Coronal sources and parameters of solar wind streams during a strong and prolonged geomagnetic disturbance in June 2015 have been considered. Correspondence between coronal sources and solar wind streams at 1 AU has been determined using an analysis of solar images, catalogs of flares and coronal mass ejections, solar wind parameters including the ionic composition. The sources of disturbances in the considered period were a sequence of five coronal mass ejections that propagated along the recurrent solar wind streams from coronal holes. The observed differences from typical in magnetic and kinetic parameters of solar wind streams have been associated with the interactions of different types of solar wind. The ionic composition has proved to be a good additional marker for highlighting components in a mixture of solar wind streams, which can be associated with different coronal sources.

76 FR 76153 - Notice of Effectiveness of Exempt Wholesale Generator Status; Caney River Wind Project, LLC...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-06

...] Notice of Effectiveness of Exempt Wholesale Generator Status; Caney River Wind Project, LLC, Mesquite Solar 1, LLC, Copper Crossing Solar LLC, Copper Mountain Solar 1, LLC, Pinnacle Wind, LLC, Bellevue Solar, LLC, Yamhill Solar, LLC, Osage Wind, LLC, Minco Wind II, LLC Take notice that during the month of...

Flare forecasting at the Met Office Space Weather Operations Centre

NASA Astrophysics Data System (ADS)

Murray, S. A.; Bingham, S.; Sharpe, M.; Jackson, D. R.

2017-04-01

The Met Office Space Weather Operations Centre produces 24/7/365 space weather guidance, alerts, and forecasts to a wide range of government and commercial end-users across the United Kingdom. Solar flare forecasts are one of its products, which are issued multiple times a day in two forms: forecasts for each active region on the solar disk over the next 24 h and full-disk forecasts for the next 4 days. Here the forecasting process is described in detail, as well as first verification of archived forecasts using methods commonly used in operational weather prediction. Real-time verification available for operational flare forecasting use is also described. The influence of human forecasters is highlighted, with human-edited forecasts outperforming original model results and forecasting skill decreasing over longer forecast lead times.

Wind Information Uplink to Aircraft Performing Interval Management Operations

NASA Technical Reports Server (NTRS)

Ahmad, Nashat; Barmore, Bryan; Swieringa, Kurt

2015-01-01

The accuracy of the wind information used to generate trajectories for aircraft performing Interval Management (IM) operations is critical to the success of an IM operation. There are two main forms of uncertainty in the wind information used by the Flight Deck Interval Management (FIM) equipment. The first is the accuracy of the forecast modeling done by the weather provider. The second is that only a small subset of the forecast data can be uplinked to the aircraft for use by the FIM equipment, resulting in loss of additional information. This study focuses on what subset of forecast data, such as the number and location of the points where the wind is sampled should be made available to uplink to the aircraft.

Impact of the initial specification of moisture and vertical motion on precipitation forecasts with a mesoscale model Implications for a satellite mesoscale data base

NASA Technical Reports Server (NTRS)

Mlynczak, Pamela E.; Houghton, David D.; Diak, George R.

1986-01-01

Using a numerical mesoscale model, four simulations were performed to determine the effects of suppressing the initial mesoscale information in the moisture and wind fields on the precipitation forecasts. The simulations included a control forecast 12-h simulation that began at 1200 GMT March 1982 and three experiment simulations with modifications to the moisture and vertical motion fields incorporated at 1800 GMT. The forecasts from 1800 GMT were compared to the second half of the control forecast. It was found that, compared to the control forecast, suppression of the moisture and/or wind initial field(s) produces a drier forecast. However, the characteristics of the precipitation forecasts of the experiments were not different enough to conclude that either mesoscale moisture or mesoscale vertical velocity at the initial time are more important for producing a forecast closer to that of the control.

Tracing the Solar Wind to its Origin: New Insights from ACE/SWICS Data and SO/HIS Performance Predictions

NASA Astrophysics Data System (ADS)

Stakhiv, Mark

The solar wind is a hot tenuous plasma that continuously streams off of the Sun into the heliosphere. The solar wind is the medium through which coronal mass ejections (CMEs) travel from the Sun to the Earth, where they can disrupt vital space-based technologies and wreak havoc on terrestrial infrastructure. Understanding the solar wind can lead to improved predications of CME arrival time as well as their geoeffectiveness. The solar wind is studied in this thesis through in situ measurements of heavy ions. Several outstanding questions about the solar wind are addressed in this thesis: What is the origin of the solar wind? How is the solar wind heated and accelerated? The charge state distribution and abundance of heavy ions in the solar wind record information about their source location and heating mechanism. This information is largely unchanged from the Sun to the Earth, where it is collected in situ with spacecraft. In this thesis we use data from the Solar Wind Ion Composition Spectrometer (SWICS) that flew on two spacecraft: Ulysses (1990 - 2009) and ACE (1998 - present). We analyze the kinetic and compositional properties of the solar wind with heavy ion data and lay out a unified wind scenario, which states that the solar wind originates from two different sources and regardless of its release mechanism the solar wind is then accelerated by waves. The data from these instruments are the best available to date but still lack the measurement cadence and distribution resolution to fully answer all of the solar wind questions. To address these issues a new heavy ion sensor is being developed to be the next generation of in situ heavy ion measurements. This thesis supports the development of this instrument through the analysis of the sensors measurement properties and the characterization of its geometric factor and efficiencies.

Inventory of File dvrtma.t12z.ndgd_alaska.grib2

Science.gov Websites

Number of Records: 6 Number Level/Layer Parameter Forecast Valid Description 001 anl PRES ENS=low-res c Pressure [Pa]:surface analysis/forecast error 002 anl UGRD ENS=low-res c U-Component of Wind [m/s]:10 m above ground analysis/forecast error 003 anl VGRD ENS=low-res c V-Component of Wind [m/s]:10 m above

Inventory of File dvrtma.t12z.ndgd_conus.grib2

Science.gov Websites

Number of Records: 6 Number Level/Layer Parameter Forecast Valid Description 001 anl PRES ENS=low-res c Pressure [Pa]:surface analysis/forecast error 002 anl UGRD ENS=low-res c U-Component of Wind [m/s]:10 m above ground analysis/forecast error 003 anl VGRD ENS=low-res c V-Component of Wind [m/s]:10 m above

Using Seasonal Forecasting Data for Vessel Routing

NASA Astrophysics Data System (ADS)

Bell, Ray; Kirtman, Ben

2017-04-01

We present an assessment of seasonal forecasting of surface wind speed, significant wave height and ocean surface current speed in the North Pacific for potential use of vessel routing from Singapore to San Diego. WaveWatchIII is forced with surface winds and ocean surface currents from the Community Climate System Model 4 (CCSM4) retrospective forecasts for the period of 1982-2015. Several lead time forecasts are used from zero months to six months resulting in 2,720 model years, ensuring the findings from this study are robust. July surface wind speed and significant wave height can be skillfully forecast with a one month lead time, with the western North Pacific being the most predictable region. Beyond May initial conditions (lead time of two months) the El Niño Southern Oscillation (ENSO) Spring predictability barrier limits skill of significant wave height but there is skill for surface wind speed with January initial conditions (lead time of six months). In a separate study of vessel routing between Norfolk, Virginia and Gibraltar we demonstrate the benefit of a multimodel approach using the North American Multimodel Ensemble (NMME). In collaboration with Charles River Analytics an all-encompassing forecast is presented by using machine learning on the various ensembles which can be using used for industry applications.

An Experimental High-Resolution Forecast System During the Vancouver 2010 Winter Olympic and Paralympic Games

NASA Astrophysics Data System (ADS)

Mailhot, J.; Milbrandt, J. A.; Giguère, A.; McTaggart-Cowan, R.; Erfani, A.; Denis, B.; Glazer, A.; Vallée, M.

2014-01-01

Environment Canada ran an experimental numerical weather prediction (NWP) system during the Vancouver 2010 Winter Olympic and Paralympic Games, consisting of nested high-resolution (down to 1-km horizontal grid-spacing) configurations of the GEM-LAM model, with improved geophysical fields, cloud microphysics and radiative transfer schemes, and several new diagnostic products such as density of falling snow, visibility, and peak wind gust strength. The performance of this experimental NWP system has been evaluated in these winter conditions over complex terrain using the enhanced mesoscale observing network in place during the Olympics. As compared to the forecasts from the operational regional 15-km GEM model, objective verification generally indicated significant added value of the higher-resolution models for near-surface meteorological variables (wind speed, air temperature, and dewpoint temperature) with the 1-km model providing the best forecast accuracy. Appreciable errors were noted in all models for the forecasts of wind direction and humidity near the surface. Subjective assessment of several cases also indicated that the experimental Olympic system was skillful at forecasting meteorological phenomena at high-resolution, both spatially and temporally, and provided enhanced guidance to the Olympic forecasters in terms of better timing of precipitation phase change, squall line passage, wind flow channeling, and visibility reduction due to fog and snow.

Empirical model for the electron density peak height disturbance in response to solar wind conditions

NASA Astrophysics Data System (ADS)

Blanch, E.; Altadill, D.

2009-04-01

Geomagnetic storms disturb the quiet behaviour of the ionosphere, its electron density and the electron density peak height, hmF2. Many works have been done to predict the variations of the electron density but few efforts have been dedicated to predict the variations the hmF2 under disturbed helio-geomagnetic conditions. We present the results of the analyses of the F2 layer peak height disturbances occurred during intense geomagnetic storms for one solar cycle. The results systematically show a significant peak height increase about 2 hours after the beginning of the main phase of the geomagnetic storm, independently of both the local time position of the station at the onset of the storm and the intensity of the storm. An additional uplift is observed in the post sunset sector. The duration of the uplift and the height increase are dependent of the intensity of the geomagnetic storm, the season and the local time position of the station at the onset of the storm. An empirical model has been developed to predict the electron density peak height disturbances in response to solar wind conditions and local time which can be used for nowcasting and forecasting the hmF2 disturbances for the middle latitude ionosphere. This being an important output for EURIPOS project operational purposes.

Improved Ionospheric Electrodynamic Models and Application to Calculating Joule Heating Rates

NASA Technical Reports Server (NTRS)

Weimer, D. R.

2004-01-01

Improved techniques have been developed for empirical modeling of the high-latitude electric potentials and magnetic field aligned currents (FAC) as a function of the solar wind parameters. The FAC model is constructed using scalar magnetic Euler potentials, and functions as a twin to the electric potential model. The improved models have more accurate field values as well as more accurate boundary locations. Non-linear saturation effects in the solar wind-magnetosphere coupling are also better reproduced. The models are constructed using a hybrid technique, which has spherical harmonic functions only within a small area at the pole. At lower latitudes the potentials are constructed from multiple Fourier series functions of longitude, at discrete latitudinal steps. It is shown that the two models can be used together in order to calculate the total Poynting flux and Joule heating in the ionosphere. An additional model of the ionospheric conductivity is not required in order to obtain the ionospheric currents and Joule heating, as the conductivity variations as a function of the solar inclination are implicitly contained within the FAC model's data. The models outputs are shown for various input conditions, as well as compared with satellite measurements. The calculations of the total Joule heating are compared with results obtained by the inversion of ground-based magnetometer measurements. Like their predecessors, these empirical models should continue to be a useful research and forecast tools.

Western Wind and Solar Integration Study | Grid Modernization | NREL

Science.gov Websites

Western Wind and Solar Integration Study Western Wind and Solar Integration Study Can we integrate large amounts of wind and solar energy into the electric power system of the West? That's the question explored by the Western Wind and Solar Integration Study, one of the largest such regional studies to date

Impact of the Assimilation of Hyperspectral Infrared Profiles on Advanced Weather and Research Model Simulations of a Non-Convective Wind Event

NASA Technical Reports Server (NTRS)

Berndt, Emily B.; Zavodsky, Bradley T; Jedlovec, Gary J.; Elmer, Nicholas J.

2013-01-01

Non-convective wind events commonly occur with passing extratropical cyclones and have significant societal and economic impacts. Since non-convective winds often occur in the absence of specific phenomena such as a thunderstorm, tornado, or hurricane, the public are less likely to heed high wind warnings and continue daily activities. Thus non-convective wind events result in as many fatalities as straight line thunderstorm winds. One physical explanation for non-convective winds includes tropopause folds. Improved model representation of stratospheric air and associated non-convective wind events could improve non-convective wind forecasts and associated warnings. In recent years, satellite data assimilation has improved skill in forecasting extratropical cyclones; however errors still remain in forecasting the position and strength of extratropical cyclones as well as the tropopause folding process. The goal of this study is to determine the impact of assimilating satellite temperature and moisture retrieved profiles from hyperspectral infrared (IR) sounders (i.e. Atmospheric Infrared Sounder (AIRS), Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI)) on the model representation of the tropopause fold and an associated high wind event that impacted the Northeast United States on 09 February 2013. Model simulations using the Advanced Research Weather Research and Forecasting Model (ARW) were conducted on a 12-km grid with cycled data assimilation mimicking the operational North American Model (NAM). The results from the satellite assimilation run are compared to a control experiment (without hyperspectral IR retrievals), North American Regional Reanalysis (NARR) reanalysis, and Rapid Refresh analyses.

Short-Term Forecasting of Radiation Belt and Ring Current

NASA Technical Reports Server (NTRS)

Fok, Mei-Ching

2007-01-01

A computer program implements a mathematical model of the radiation-belt and ring-current plasmas resulting from interactions between the solar wind and the Earth s magnetic field, for the purpose of predicting fluxes of energetic electrons (10 keV to 5 MeV) and protons (10 keV to 1 MeV), which are hazardous to humans and spacecraft. Given solar-wind and interplanetary-magnetic-field data as inputs, the program solves the convection-diffusion equations of plasma distribution functions in the range of 2 to 10 Earth radii. Phenomena represented in the model include particle drifts resulting from the gradient and curvature of the magnetic field; electric fields associated with the rotation of the Earth, convection, and temporal variation of the magnetic field; and losses along particle-drift paths. The model can readily accommodate new magnetic- and electric-field submodels and new information regarding physical processes that drive the radiation-belt and ring-current plasmas. Despite the complexity of the model, the program can be run in real time on ordinary computers. At present, the program can calculate present electron and proton fluxes; after further development, it should be able to predict the fluxes 24 hours in advance

A correlation study regarding the AE index and ACE solar wind data for Alfvénic intervals using wavelet decomposition and reconstruction

NASA Astrophysics Data System (ADS)

Guarnieri, Fernando L.; Tsurutani, Bruce T.; Vieira, Luis E. A.; Hajra, Rajkumar; Echer, Ezequiel; Mannucci, Anthony J.; Gonzalez, Walter D.

2018-01-01

The purpose of this study is to present a wavelet interactive filtering and reconstruction technique and apply this to the solar wind magnetic field components detected at the L1 Lagrange point ˜ 0.01 AU upstream of the Earth. These filtered interplanetary magnetic field (IMF) data are fed into a model to calculate a time series which we call AE∗. This model was adjusted assuming that magnetic reconnection associated with southward-directed IMF Bz is the main mechanism transferring energy into the magnetosphere. The calculated AE∗ was compared to the observed AE (auroral electrojet) index using cross-correlation analysis. The results show correlations as high as 0.90. Empirical removal of the high-frequency, short-wavelength Alfvénic component in the IMF by wavelet decomposition is shown to dramatically improve the correlation between AE∗ and the observed AE index. It is envisioned that this AE∗ can be used as the main input for a model to forecast relativistic electrons in the Earth's outer radiation belts, which are delayed by ˜ 1 to 2 days from intense AE events.

Response of the dynamic and thermodynamic structure of the stratosphere to the solar cycle in the boreal winter

NASA Astrophysics Data System (ADS)

Shi, Chunhua; Gao, Yannan; Cai, Juan; Guo, Dong; Lu, Yan

2018-04-01

The response of the dynamic and thermodynamic structure of the stratosphere to the solar cycle in the boreal winter is investigated based on measurements of the solar cycle by the Spectral Irradiance Monitor onboard the SORCE satellite, monthly ERA-Interim Reanalysis data from the European Center for Medium-Range Weather Forecasts, the radiative transfer scheme of the Beijing Climate Center (BCC-RAD) and a multiple linear regression model. The results show that during periods of strong solar activity, the solar shortwave heating anomaly from the climatology in the tropical upper stratosphere triggers a local warm anomaly and strong westerly winds in mid-latitudes, which strengthens the upward propagation of planetary wave 1 but prevents that of wave 2. The enhanced westerly jet makes a slight adjustment to the propagation path of wave 1, but prevents wave 2 from propagating upward, decreases the dissipation of wave 2 in the extratropical upper stratosphere and hence weakens the Brewer-Dobson circulation. The adiabatic heating term in relation to the Brewer-Dobson circulation shows anomalous warming in the tropical lower stratosphere and anomalous cooling in the mid-latitude upper stratosphere.

Solar Corona Explorer: A mission for the physical diagnosis of the solar corona

NASA Technical Reports Server (NTRS)

1981-01-01

Mission objectives and spacecraft requirements for the Solar Corona Explorer (SCE), a proposed free flying, unmanned solar research craft to be tenatively launched in 1987, were defined. The SCE's purpose is to investigate structure, dynamics and evolution of the corona, globally and in the required physical detail, to study the close coupling between the inner corona and the heliosphere. Investigative objectives are: (1) to understand the corona as the source of varying interplanetary plasma and of varying solar X-ray and extreme ultraviolet fluxes; (2) to develop the capabilities to model the corona with sufficient precision to forecast the Earth's variable environment in space, on the scales from weeks to years; (3) to develop an understanding of the physical processes that determine the dynamics and physical state of the coronal plasma, particularly acceleration processes; and (4) to develop insight and test theory on the Sun applicable to stellar coronae and winds, and in particular, to understand why cool stars put such a large fraction of their energy into X-rays. Considered related factors are: (1) duration of the mission; (2) onboard measuring instrumentation; (3) ground support equipment and procedures; and (4) programs of interpretation and modeling.

Improving wind energy forecasts using an Ensemble Kalman Filter data assimilation technique in a fully coupled hydrologic and atmospheric model

NASA Astrophysics Data System (ADS)

Williams, J. L.; Maxwell, R. M.; Delle Monache, L.

2012-12-01

Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its propensity to change speed and direction over short time scales. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. Using the PF.WRF model, a fully-coupled hydrologic and atmospheric model employing the ParFlow hydrologic model with the Weather Research and Forecasting model coupled via mass and energy fluxes across the land surface, we have explored the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture and wind speed, and demonstrated that reductions in uncertainty in these coupled fields propagate through the hydrologic and atmospheric system. We have adapted the Data Assimilation Research Testbed (DART), an implementation of the robust Ensemble Kalman Filter data assimilation algorithm, to expand our capability to nudge forecasts produced with the PF.WRF model using observational data. Using a semi-idealized simulation domain, we examine the effects of assimilating observations of variables such as wind speed and temperature collected in the atmosphere, and land surface and subsurface observations such as soil moisture on the quality of forecast outputs. The sensitivities we find in this study will enable further studies to optimize observation collection to maximize the utility of the PF.WRF-DART forecasting system.

78 FR 76609 - Genesis Solar, LLC; NRG Delta LLC; Mountain View Solar, LLC; Pheasant Run Wind, LLC; Pheasant Run...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-18

... Delta LLC; Mountain View Solar, LLC; Pheasant Run Wind, LLC; Pheasant Run Wind II, LLC; Tuscola Wind II, LLC; Mountain Wind Power, LLC; Mountain Wind Power II, LLC; Summerhaven Wind, LP; Notice of...

Magnetogram Forecast: An All-Clear Space Weather Forecasting System

NASA Technical Reports Server (NTRS)

Barghouty, Nasser; Falconer, David

2015-01-01

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

Mass-loading of the solar wind at 67P/Churyumov-Gerasimenko. Observations and modelling

NASA Astrophysics Data System (ADS)

Behar, E.; Lindkvist, J.; Nilsson, H.; Holmström, M.; Stenberg-Wieser, G.; Ramstad, R.; Götz, C.

2016-11-01

Context. The first long-term in-situ observation of the plasma environment in the vicinity of a comet, as provided by the European Rosetta spacecraft. Aims: Here we offer characterisation of the solar wind flow near 67P/Churyumov-Gerasimenko (67P) and its long term evolution during low nucleus activity. We also aim to quantify and interpret the deflection and deceleration of the flow expected from ionization of neutral cometary particles within the undisturbed solar wind. Methods: We have analysed in situ ion and magnetic field data and combined this with hybrid modeling of the interaction between the solar wind and the comet atmosphere. Results: The solar wind deflection is increasing with decreasing heliocentric distances, and exhibits very little deceleration. This is seen both in observations and in modeled solar wind protons. According to our model, energy and momentum are transferred from the solar wind to the coma in a single region, centered on the nucleus, with a size in the order of 1000 km. This interaction affects, over larger scales, the downstream modeled solar wind flow. The energy gained by the cometary ions is a small fraction of the energy available in the solar wind. Conclusions: The deflection of the solar wind is the strongest and clearest signature of the mass-loading for a small, low-activity comet, whereas there is little deceleration of the solar wind.

Hydrodaynamic - Statistical Forecast Method To 36-48h Ahead Of Storm Wind And Tornadoes Over The Territory Of Europe And Siberia

NASA Astrophysics Data System (ADS)

Perekhodtseva, Elvira V.

2010-05-01

Development of successful method of forecast of storm winds, including squalls and tornadoes, that often result in human and material losses, could allow one to take proper measures against destruction of buildings and to protect people. Well-in-advance successful forecast (from 12 hours to 48 hour) makes possible to reduce the losses. Prediction of the phenomena involved is a very difficult problem for synoptic till recently. The existing graphic and calculation methods still depend on subjective decision of an operator. Nowadays in Russia there is no hydrodynamic model for forecast of the maximal wind velocity V> 25m/c, hence the main tools of objective forecast are statistical methods using the dependence of the phenomena involved on a number of atmospheric parameters (predictors). . Statistical decisive rule of the alternative and probability forecast of these events was obtained in accordance with the concept of "perfect prognosis" using the data of objective analysis. For this purpose the different teaching samples of present and absent of this storm wind and rainfalls were automatically arranged that include the values of forty physically substantiated potential predictors. Then the empirical statistical method was used that involved diagonalization of the mean correlation matrix R of the predictors and extraction of diagonal blocks of strongly correlated predictors. Thus for these phenomena the most informative predictors were selected without loosing information. The statistical decisive rules for diagnosis and prognosis of the phenomena involved U(X) were calculated for choosing informative vector-predictor. We used the criterion of distance of Mahalanobis and criterion of minimum of entropy by Vapnik-Chervonenkis for the selection predictors. Successful development of hydrodynamic models for short-term forecast and improvement of 36-48h forecasts of pressure, temperature and others parameters allowed us to use the prognostic fields of those models for calculations of the discriminant functions in the nodes of the grid 75x75km and the values of probabilities P of dangerous wind and thus to get fully automated forecasts. . In order to apply the alternative forecast to European part of Russia and Europe the author proposes the empirical threshold values specified for this phenomenon and advance period 36 hours. According to the Pirsey-Obukhov criterion (T), the success of this hydrometeorological-statistical method of forecast of storm wind and tornadoes to 36 -48 hours ahead in the warm season for the territory of Europe part of Russia and Siberia is T = 1-a-b=0,54-0,78 after independent and author experiments during the period 2004-2009 years. A lot of examples of very successful forecasts are submitted at this report for the territory of Europe and Russia. The same decisive rules were applied to the forecast of these phenomena during cold period in 2009-2010 years too. On the first month of 2010 a lot of cases of storm wind with heavy snowfall were observed and were forecasting over the territory of France, Italy and Germany.

Latest View of Hurricane Joaquin

NASA Image and Video Library

2017-12-08

Hurricane Joaquin continued to intensify in the Bahamas on October 1 and NASA and NOAA satellites have been providing valuable data on the storm. NASA's GPM and Terra satellites and NOAA's GOES-East satellite provided rainfall, cloud extent, cloud height and other data to forecasters. Joaquin became a major hurricane today, October 1, reaching Category 3 status on the Saffir-Simpson Wind Scale. On October 1 at 1330 UTC (9:30 a.m. EDT) NOAA's GOES-East satellite captured this visible image of Hurricane Joaquin covering the southern Bahamas and extending over southeastern Cuba, and the island of Hispaniola (which includes Haiti and the Dominican Republic). Joaquin's eye had become completely visible now that the storm had reached Category 3 status. On October 1, a Hurricane Warning was in effect for the Central Bahamas, Northwestern Bahamas including the Abacos, Berry Islands, Eleuthera, Grand Bahama Island, and New Providence, The Acklins, Crooked Island, and Mayaguana in the southeastern Bahamas. A Hurricane Watch was in effect for Bimini and Andros Island, and a Tropical Storm Warning was in effect for the remainder of the southeastern Bahamas excluding the Turks and Caicos Islands and Andros Island. According to NHC, at 8 a.m. EDT (1200 UTC), the center of Hurricane Joaquin was located near latitude 23.2 North, longitude 73.7 West. That's just 10 miles (15 km) north of Samana Cays, Bahamas and about 75 miles (120 km) southeast of San Salvador, Bahamas. Joaquin was moving toward the west-southwest near 5 mph (7 kph), and this motion is expected to continue today. NHC noted that a turn toward the west- northwest is forecast tonight (Oct. 1), followed by a turn toward the north and an increase in forward speed on Friday, Oct. 2. On the forecast track, the center of Joaquin will move near or over portions of the central Bahamas today and tonight and pass near or over portions of the northwestern Bahamas on Friday. Maximum sustained winds are near 120 mph (195 km/h) with higher gusts. Joaquin is a category 3 hurricane on the Saffir-Simpson Hurricane Wind Scale. Some strengthening is forecast in the next day or so, with some fluctuations in intensity possible on Friday. Hurricane force winds extend outward up to 35 miles (55 km) from the center and tropical storm force winds extend outward up to 140 miles (220 km). The minimum central pressure just extrapolated by an Air Force Reserve Hurricane Hunter aircraft is 942 millibars. For updated forecasts, watches and warnings visit the National Hurricane Center (NHC) website: www.nhc.noaa.gov. Credit: NASA/NOAA GOES Project NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

30 WS North Base Wind Study

NASA Technical Reports Server (NTRS)

Wheeler, Mark

2011-01-01

The 30 Weather Squadron (30 WS) is concerned about strong winds observed at their northern towers without advance warning. They state that terrain influences along the extreme northern fringes of Vandenberg Air Force Base (VAFB) make it difficult for forecasters to issue timely and accurate high wind warnings for northeasterly wind events. These events tend to occur during the winter or early spring when they are under the influence of the Great Basin high pressure weather regime. The Launch Weather Officers (LWOs) have seen these rapid wind increases in the current northern Towers 60, 70 and 71 in excess of their 35 kt operational warning threshold. For this task, the 30 WS requested the Applied Meteorology Unit (AMU) analyze data from days when these towers reported winds in excess of 35 kt and determine if there were any precursors in the observations that would allow the LWOs to better forecast and warn their operational customers for these wind events. The 30 WS provided wind tower data for the cool season (October - March) from the period January 2004-March 20 IO. The AMU decoded and evaluated the wind tower data for 66 days identified by the 30 WS as having high-wind events. Out of the 66 event days, only 30 had wind speed observations of > or =35 kt from at least one of the three northern towers. The AMU analyzed surface and upper air charts to determine the synoptic conditions for each event day along with tower peak wind speed and direction time series and wind rose charts for all 30 event days. The analysis revealed a trend on all event days in which the tower winds shifted to the northeast for a period of time before the first recorded > or =35 kt wind speed. The time periods for the 30 event days ranged from 20 minutes to several hours, with a median value of 110 minutes. This trend, if monitored, could give the 30 WS forecasters a precursor to assist in issuing an operational warning before a high wind event occurs. The AMU recommends developing a high-wind alert capability for VAFB using a local mesoscale model to forecast these wind events. The model should incorporate all of the VAFB local data sets and have a forecast capability of between 2 to 24 hours. Such a model would allow the meteorologists at VAFB to alert the operational customers of high wind events in a timely manner so protective action could be taken.

Improved Modeling Tools Development for High Penetration Solar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Washom, Byron; Meagher, Kevin

2014-12-11

One of the significant objectives of the High Penetration solar research is to help the DOE understand, anticipate, and minimize grid operation impacts as more solar resources are added to the electric power system. For Task 2.2, an effective, reliable approach to predicting solar energy availability for energy generation forecasts using the University of California, San Diego (UCSD) Sky Imager technology has been demonstrated. Granular cloud and ramp forecasts for the next 5 to 20 minutes over an area of 10 square miles were developed. Sky images taken every 30 seconds are processed to determine cloud locations and cloud motionmore » vectors yielding future cloud shadow locations respective to distributed generation or utility solar power plants in the area. The performance of the method depends on cloud characteristics. On days with more advective cloud conditions, the developed method outperforms persistence forecasts by up to 30% (based on mean absolute error). On days with dynamic conditions, the method performs worse than persistence. Sky Imagers hold promise for ramp forecasting and ramp mitigation in conjunction with inverter controls and energy storage. The pre-commercial Sky Imager solar forecasting algorithm was documented with licensing information and was a Sunshot website highlight.« less

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Bauman, William H., III; Hoeth, Brian

2009-01-01

This abstract describes work that will be done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting "wind cycling" cases at Edwards Air Force Base, CA (EAFB), in which the wind speeds and directions oscillate among towers near the EAFB runway. The Weather Research and Forecasting (WRF) model allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and determine which configuration will best predict surface wind speed and direction at EAFB.

Land-Breeze Forecasting

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Wheeler, Mark M.; Merceret, Francis J. (Technical Monitor)

2002-01-01

The nocturnal land breeze at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) is both operationally significant and challenging to forecast. The occurrence and timing of land breezes impact low-level winds, atmospheric stability, low temperatures, and fog development. Accurate predictions of the land breeze are critical for toxic material dispersion forecasts associated with space launch missions, since wind direction and low-level stability can change noticeably with the onset of a land breeze. This report presents a seven-year observational study of land breezes over east-central Florida from 1995 to 2001. This comprehensive analysis was enabled by the high-resolution tower observations over KSC/CCAFS. Five-minute observations of winds, temperature, and moisture along with 9 15-MHz Doppler Radar Wind Profiler data were used to analyze specific land-breeze cases, while the tower data were used to construct a composite climatology. Utilities derived from this climatology were developed to assist forecasters in determining the land-breeze occurrence, timing, and movement based on predicted meteorological conditions.

Analysis of Numerical Weather Predictions of Reference Evapotranspiration and Precipitation

NASA Astrophysics Data System (ADS)

Bughici, Theodor; Lazarovitch, Naftali; Fredj, Erick; Tas, Eran

2017-04-01

This study attempts to improve the forecast skill of the evapotranspiration (ET0) and Precipitation for the purpose of crop irrigation management over Israel using the Weather Research and Forecasting (WRF) Model. Optimized crop irrigation, in term of timing and quantities, decreases water and agrochemicals demand. Crop water demands depend on evapotranspiration and precipitation. The common method for computing reference evapotranspiration, for agricultural needs, ET0, is according to the FAO Penman-Monteith equation. The weather variables required for ET0 calculation (air temperature, relative humidity, wind speed and solar irradiance) are estimated by the WRF model. The WRF Model with two-way interacting domains at horizontal resolutions of 27, 9 and 3 km is used in the study. The model prediction was performed in an hourly time resolution and a 3 km spatial resolution, with forecast lead-time of up to four days. The WRF prediction of these variables have been compared against measurements from 29 meteorological stations across Israel for the year 2013. The studied area is small but with strong climatic gradient, diverse topography and variety of synoptic conditions. The forecast skill that was used for forecast validation takes into account the prediction bias, mean absolute error and root mean squared error. The forecast skill of the variables was almost robust to lead time, except for precipitation. The forecast skill was tested across stations with respect to topography and geographic location and for all stations with respect to seasonality and synoptic weather system determined by employing a semi-objective synoptic systems classification to the forecasted days. It was noticeable that forecast skill of some of the variables was deteriorated by seasonality and topography. However, larger impacts in the ET0 skill scores on the forecasted day are achieved by a synoptic based forecast. These results set the basis for increasing the robustness of ET0 to synoptic effects and for more precise crop irrigation over Israel.

Numerical simulation of wind loads on solar panels

NASA Astrophysics Data System (ADS)

Su, Kao-Chun; Chung, Kung-Ming; Hsu, Shu-Tsung

2018-05-01

Solar panels mounted on the roof of a building or ground are often vulnerable to strong wind loads. This study aims to investigate wind loads on solar panels using computational fluid dynamic (CFD). The results show good agreement with wind tunnel data, e.g. the streamwise distribution of mean surface pressure coefficient of a solar panel. Wind uplift for solar panels with four aspect ratios is evaluated. The effect of inclined angle and clearance (or height) of a solar panel is addressed. It is found that wind uplift of a solar panel increases when there is an increase in inclined angle and the clearance above ground shows an opposite effect.

Strategic siting and regional grid interconnections key to low-carbon futures in African countries

PubMed Central

Deshmukh, Ranjit; Ndhlukula, Kudakwashe; Radojicic, Tijana; Reilly-Moman, Jessica; Phadke, Amol; Kammen, Daniel M.; Callaway, Duncan S.

2017-01-01

Recent forecasts suggest that African countries must triple their current electricity generation by 2030. Our multicriteria assessment of wind and solar potential for large regions of Africa shows how economically competitive and low-environmental–impact renewable resources can significantly contribute to meeting this demand. We created the Multicriteria Analysis for Planning Renewable Energy (MapRE) framework to map and characterize solar and wind energy zones in 21 countries in the Southern African Power Pool (SAPP) and the Eastern Africa Power Pool (EAPP) and find that potential is several times greater than demand in many countries. Significant fractions of demand can be quickly served with “no-regrets” options—or zones that are low-cost, low-environmental impact, and highly accessible. Because no-regrets options are spatially heterogeneous, international interconnections are necessary to help achieve low-carbon development for the region as a whole, and interconnections that support the best renewable options may differ from those planned for hydropower expansion. Additionally, interconnections and selecting wind sites to match demand reduce the need for SAPP-wide conventional generation capacity by 9.5% in a high-wind scenario, resulting in a 6–20% cost savings, depending on the avoided conventional technology. Strategic selection of low-impact and accessible zones is more cost effective with interconnections compared with solutions without interconnections. Overall results are robust to multiple load growth scenarios. Together, results show that multicriteria site selection and deliberate planning of interconnections may significantly increase the economic and environmental competitiveness of renewable alternatives relative to conventional generation. PMID:28348209

Strategic siting and regional grid interconnections key to low-carbon futures in African countries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Grace C.; Deshmukh, Ranjit; Ndhlukula, Kudakwashe

2017-03-27

Recent forecasts suggest that African countries must triple their current electricity generation by 2030. Our multicriteria assessment of wind and solar potential for large regions of Africa shows how economically competitive and low-environmental– impact renewable resources can significantly contribute to meeting this demand. We created the Multicriteria Analysis for Planning Renewable Energy (MapRE) framework to map and characterize solar and wind energy zones in 21 countries in the Southern African Power Pool (SAPP) and the Eastern Africa Power Pool (EAPP) and find that potential is several times greater than demand in many countries. Significant fractions of demand can be quicklymore » served with “no-regrets” options—or zones that are low-cost, low-environmental impact, and highly accessible. Because no-regrets options are spatially heterogeneous, international interconnections are necessary to help achieve low-carbon development for the region as a whole, and interconnections that support the best renewable options may differ from those planned for hydropower expansion. Additionally, interconnections and selecting wind sites to match demand reduce the need for SAPP-wide conventional generation capacity by 9.5% in a high-wind scenario, resulting in a 6–20% cost savings, depending on the avoided conventional technology. Strategic selection of low-impact and accessible zones is more cost effective with interconnections compared with solutions without interconnections. In conclusion, the overall results are robust to multiple load growth scenarios. Together, results show that multicriteria site selection and deliberate planning of interconnections may significantly increase the economic and environmental competitiveness of renewable alternatives relative to conventional generation.« less

Strategic siting and regional grid interconnections key to low-carbon futures in African countries.

PubMed

Wu, Grace C; Deshmukh, Ranjit; Ndhlukula, Kudakwashe; Radojicic, Tijana; Reilly-Moman, Jessica; Phadke, Amol; Kammen, Daniel M; Callaway, Duncan S

2017-04-11

Recent forecasts suggest that African countries must triple their current electricity generation by 2030. Our multicriteria assessment of wind and solar potential for large regions of Africa shows how economically competitive and low-environmental-impact renewable resources can significantly contribute to meeting this demand. We created the Multicriteria Analysis for Planning Renewable Energy (MapRE) framework to map and characterize solar and wind energy zones in 21 countries in the Southern African Power Pool (SAPP) and the Eastern Africa Power Pool (EAPP) and find that potential is several times greater than demand in many countries. Significant fractions of demand can be quickly served with "no-regrets" options-or zones that are low-cost, low-environmental impact, and highly accessible. Because no-regrets options are spatially heterogeneous, international interconnections are necessary to help achieve low-carbon development for the region as a whole, and interconnections that support the best renewable options may differ from those planned for hydropower expansion. Additionally, interconnections and selecting wind sites to match demand reduce the need for SAPP-wide conventional generation capacity by 9.5% in a high-wind scenario, resulting in a 6-20% cost savings, depending on the avoided conventional technology. Strategic selection of low-impact and accessible zones is more cost effective with interconnections compared with solutions without interconnections. Overall results are robust to multiple load growth scenarios. Together, results show that multicriteria site selection and deliberate planning of interconnections may significantly increase the economic and environmental competitiveness of renewable alternatives relative to conventional generation.

Improvement of background solar wind predictions

NASA Astrophysics Data System (ADS)

Dálya, Zsuzsanna; Opitz, Andrea

2016-04-01

In order to estimate the solar wind properties at any heliospheric positions propagation tools use solar measurements as input data. The ballistic method extrapolates in-situ solar wind observations to the target position. This works well for undisturbed solar wind, while solar wind disturbances such as Corotating Interaction Regions (CIRs) and Coronal Mass Ejections (CMEs) need more consideration. We are working on dedicated ICME lists to clean these signatures from the input data in order to improve our prediction accuracy. These ICME lists are created from several heliospheric spacecraft measurements: ACE, WIND, STEREO, SOHO, MEX and VEX. As a result, we are able to filter out these events from the time series. Our corrected predictions contribute to the investigation of the quiet solar wind and space weather studies.

Microphysics of Waves and Instabilities in the Solar Wind and Their Macro Manifestations in the Corona and Interplanetary Space

NASA Technical Reports Server (NTRS)

Habbal, Shadia Rifai

2005-01-01

Investigations of the physical processes responsible for coronal heating and the acceleration of the solar wind were pursued with the use of our recently developed 2D MHD solar wind code and our 1D multifluid code. In particular, we explored: (1) the role of proton temperature anisotropy in the expansion of the solar (2) the role of plasma parameters at the coronal base in the formation of high (3) a three-fluid model of the slow solar wind (4) the heating of coronal loops (5) a newly developed hybrid code for the study of ion cyclotron resonance in wind, speed solar wind streams at mid-latitudes, the solar wind.

Market-Based Indian Grid Integration Study Options: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stoltenberg, B.; Clark, K.; Negi, S. K.

2012-03-01

The Indian state of Gujarat is forecasting solar and wind generation expansion from 16% to 32% of installed generation capacity by 2015. Some states in India are already experiencing heavy wind power curtailment. Understanding how to integrate variable generation (VG) into the grid is of great interest to local transmission companies and India's Ministry of New and Renewable Energy. This paper describes the nature of a market-based integration study and how this approach, while new to Indian grid operation and planning, is necessary to understand how to operate and expand the grid to best accommodate the expansion of VG. Second,more » it discusses options in defining a study's scope, such as data granularity, generation modeling, and geographic scope. The paper also explores how Gujarat's method of grid operation and current system reliability will affect how an integration study can be performed.« less

11- and 22-year variations of the cosmic ray density and of the solar wind speed

NASA Technical Reports Server (NTRS)

Chirkov, N. P.

1985-01-01

Cosmic ray density variations for 17-21 solar activity cycles and the solar wind speed for 20-21 events are investigated. The 22-year solar wind speed recurrence was found in even and odd cycles. The 22-year variations of cosmic ray density were found to be opposite that of solar wind speed and solar activity. The account of solar wind speed in 11-year variations significantly decreases the modulation region of cosmic rays when E = 10-20 GeV.

Correlations between solar wind parameters and auroral kilometric radiation intensity

NASA Technical Reports Server (NTRS)

Gallagher, D. L.; Dangelo, N.

1981-01-01

The relationship between solar wind properties and the influx of energy into the nightside auroral region as indicated by the intensity of auroral kilometric radiation is investigated. Smoothed Hawkeye satellite observations of auroral radiation at 178, 100 and 56.2 kHz for days 160 through 365 of 1974 are compared with solar wind data from the composite Solar Wind Plasma Data Set, most of which was supplied by the IMP-8 spacecraft. Correlations are made between smoothed daily averages of solar wind ion density, bulk flow speed, total IMF strength, electric field, solar wind speed in the southward direction, solar wind speed multiplied by total IMF strength, the substorm parameter epsilon and the Kp index. The greatest correlation is found between solar wind bulk flow speed and auroral radiation intensity, with a linear correlation coefficient of 0.78 for the 203 daily averages examined. A possible mechanism for the relationship may be related to the propagation into the nightside magnetosphere of low-frequency long-wavelength electrostatic waves produced in the magnetosheath by the solar wind.

Modelling Solar Energetic Particle Events Using the iPATH Model

NASA Astrophysics Data System (ADS)

Li, G.; Hu, J.; Ao, X.; Zank, G. P.; Verkhoglyadova, O. P.

2016-12-01

Solar Energetic Particles (SEPs) is the No. 1 space weather hazard. Understanding how particles are energized and propagated in these events is of practical concerns to the manned space missions. In particular, both the radial evolution and the longitudinal extent of a gradual solarenergetic particle (SEP) event are central topics for space weather forecasting. In this talk, I discuss the improved Particle Acceleration and Transport in the Heliosphere (iPATH) model. The iPATH model consists of three parts: (1) an updated ZEUS3D V3.5 MHD module that models thebackground solar wind and the initiation of a CME in a 2D domain; (2) an updated shock acceleration module where we investigate particle acceleration at different longitudinal locations along the surface of a CME-driven shock. Accelerated particle spectrum are obtained at the shock under the diffusive shock acceleration mechanism. Shock parameters and particle distributions are recorded and used as inputs for the later part. (3) an updated transport module where we follow the transport of accelerated particles from the shock to any destinations (Earth and/or Mars, e.g.) using a Monte-Carlo method. Both pitch angle scattering due to MHD turbulence and perpendicular diffusion across magnetic field are included. Our iPATH model is therefore intrinsically 2D in nature. The model is capable of generating time intensity profiles and instantaneous particle spectra atvarious locations and can greatly improve our current space weather forecasting capability.

The Radiation, Interplanetary Shocks, and Coronal Sources (RISCS) Toolset

NASA Technical Reports Server (NTRS)

Zank, G. P.; Spann, J.

2014-01-01

We outline a plan to develop a physics based predictive toolset RISCS to describe the interplanetary energetic particle and radiation environment throughout the inner heliosphere, including at the Earth. To forecast and "nowcast" the radiation environment requires the fusing of three components: 1) the ability to provide probabilities for incipient solar activity; 2) the use of these probabilities and daily coronal and solar wind observations to model the 3D spatial and temporal heliosphere, including magnetic field structure and transients, within 10 AU; and 3) the ability to model the acceleration and transport of energetic particles based on current and anticipated coronal and heliospheric conditions. We describe how to address 1) - 3) based on our existing, well developed, and validated codes and models. The goal of RISCS toolset is to provide an operational forecast and "nowcast" capability that will a) predict solar energetic particle (SEP) intensities; b) spectra for protons and heavy ions; c) predict maximum energies and their duration; d) SEP composition; e) cosmic ray intensities, and f) plasma parameters, including shock arrival times, strength and obliquity at any given heliospheric location and time. The toolset would have a 72 hour predicative capability, with associated probabilistic bounds, that would be updated hourly thereafter to improve the predicted event(s) and reduce the associated probability bounds. The RISCS toolset would be highly adaptable and portable, capable of running on a variety of platforms to accommodate various operational needs and requirements.

Using the WSA Model to Test the Parker Spiral Approximation for SEP Event Magnetic Connections

NASA Astrophysics Data System (ADS)

Kahler, S. W.; Arge, C. N.; Smith, D. A.

2016-08-01

In studies of solar energetic (E > 10 MeV) particle (SEP) events the Parker spiral (PS) field approximation, based only on the measured 1 AU solar wind (SW) speed Vsw, is nearly always used to determine the coronal or photospheric source locations of the 1 AU magnetic fields. There is no objective way to validate that approximation, but here we seek guidelines for optimizing its application. We first review recent SEP studies showing the extensive use of the PS approximation with various assumptions about coronal and photospheric source fields. We then run the Wang-Sheeley-Arge (WSA) model over selected Carrington rotations (CRs) to track both the photospheric and 5 R_{⊙} source locations of the forecasted 1 AU SW, allowing us to compare those WSA sources with the PS sources inferred from the WSA Vsw forecast. We compile statistics of the longitude differences (WSA-PS) for all the CRs and discuss the limitations of using the WSA model to validate the PS approximation. Over nearly all of each CR the PS and WSA source longitudes agree to within several degrees. The agreement is poor only in the slow-fast SW interaction regions characterized by high-speed events (HSEs), where the longitude differences can reach several tens of degrees. This result implies that SEP studies should limit use of the PS approximation around HSEs and use magnetic field polarities as an additional check of solar source connections.

Space Weather Forecasting at IZMIRAN

NASA Astrophysics Data System (ADS)

Gaidash, S. P.; Belov, A. V.; Abunina, M. A.; Abunin, A. A.

2017-12-01

Since 1998, the Institute of Terrestrial Magnetism, Ionosphere, and Radio Wave Propagation (IZMIRAN) has had an operating heliogeophysical service—the Center for Space Weather Forecasts. This center transfers the results of basic research in solar-terrestrial physics into daily forecasting of various space weather parameters for various lead times. The forecasts are promptly available to interested consumers. This article describes the center and the main types of forecasts it provides: solar and geomagnetic activity, magnetospheric electron fluxes, and probabilities of proton increases. The challenges associated with the forecasting of effects of coronal mass ejections and coronal holes are discussed. Verification data are provided for the center's forecasts.

Evaluation of regression and neural network models for solar forecasting over different short-term horizons

DOE PAGES

Inanlouganji, Alireza; Reddy, T. Agami; Katipamula, Srinivas

2018-04-13

Forecasting solar irradiation has acquired immense importance in view of the exponential increase in the number of solar photovoltaic (PV) system installations. In this article, analyses results involving statistical and machine-learning techniques to predict solar irradiation for different forecasting horizons are reported. Yearlong typical meteorological year 3 (TMY3) datasets from three cities in the United States with different climatic conditions have been used in this analysis. A simple forecast approach that assumes consecutive days to be identical serves as a baseline model to compare forecasting alternatives. To account for seasonal variability and to capture short-term fluctuations, different variants of themore » lagged moving average (LMX) model with cloud cover as the input variable are evaluated. Finally, the proposed LMX model is evaluated against an artificial neural network (ANN) model. How the one-hour and 24-hour models can be used in conjunction to predict different short-term rolling horizons is discussed, and this joint application is illustrated for a four-hour rolling horizon forecast scheme. Lastly, the effect of using predicted cloud cover values, instead of measured ones, on the accuracy of the models is assessed. Results show that LMX models do not degrade in forecast accuracy if models are trained with the forecast cloud cover data.« less

Evaluation of regression and neural network models for solar forecasting over different short-term horizons

DOE Office of Scientific and Technical Information (OSTI.GOV)

Inanlouganji, Alireza; Reddy, T. Agami; Katipamula, Srinivas

Forecasting solar irradiation has acquired immense importance in view of the exponential increase in the number of solar photovoltaic (PV) system installations. In this article, analyses results involving statistical and machine-learning techniques to predict solar irradiation for different forecasting horizons are reported. Yearlong typical meteorological year 3 (TMY3) datasets from three cities in the United States with different climatic conditions have been used in this analysis. A simple forecast approach that assumes consecutive days to be identical serves as a baseline model to compare forecasting alternatives. To account for seasonal variability and to capture short-term fluctuations, different variants of themore » lagged moving average (LMX) model with cloud cover as the input variable are evaluated. Finally, the proposed LMX model is evaluated against an artificial neural network (ANN) model. How the one-hour and 24-hour models can be used in conjunction to predict different short-term rolling horizons is discussed, and this joint application is illustrated for a four-hour rolling horizon forecast scheme. Lastly, the effect of using predicted cloud cover values, instead of measured ones, on the accuracy of the models is assessed. Results show that LMX models do not degrade in forecast accuracy if models are trained with the forecast cloud cover data.« less

Predicting tropical cyclone intensity using satellite measured equivalent blackbody temperatures of cloud tops. [regression analysis

NASA Technical Reports Server (NTRS)

Gentry, R. C.; Rodgers, E.; Steranka, J.; Shenk, W. E.

1978-01-01

A regression technique was developed to forecast 24 hour changes of the maximum winds for weak (maximum winds less than or equal to 65 Kt) and strong (maximum winds greater than 65 Kt) tropical cyclones by utilizing satellite measured equivalent blackbody temperatures around the storm alone and together with the changes in maximum winds during the preceding 24 hours and the current maximum winds. Independent testing of these regression equations shows that the mean errors made by the equations are lower than the errors in forecasts made by the peristence techniques.

The impact of scatterometer wind data on global weather forecasting

NASA Technical Reports Server (NTRS)

Atlas, D.; Baker, W. E.; Kalnay, E.; Halem, M.; Woiceshyn, P. M.; Peteherych, S.

1984-01-01

The impact of SEASAT-A scatterometer (SASS) winds on coarse resolution atmospheric model forecasts was assessed. The scatterometer provides high resolution winds, but each wind can have up to four possible directions. One wind direction is correct; the remainder are ambiguous or "aliases'. In general, the effect of objectively dealiased-SASS data was found to be negligible in the Northern Hemisphere. In the Southern Hemisphere, the impact was larger and primarily beneficial when vertical temperature profile radiometer (VTPR) data was excluded. However, the inclusion of VTPR data eliminates the positive impact, indicating some redundancy between the two data sets.

Better hurricane forecasts

NASA Astrophysics Data System (ADS)

Friebele, Elaine

People living in coastal areas can rely on better hurricane predictions because forecasters now have nearly instant access to global wind data. Measurements of wind speed and direction over the world's oceans are available within 3 hours of measurement from the Japanese satellite ADEOS (Advanced Earth Observing Satellite).Wind parameters at 25-km resolution are being measured by NASA's scatterometer traveling on the Japanese satellite ADEOS (Advanced Earth Observing Satellite). “The high accuracy and spatial resolution of the data were quickly recognized by our forecasters, who have been starved for data over significant expanses of the world's oceans,” said Jim Hoke, director of NOAA's Marine Prediction Center.

On using scatterometer and altimeter data to improve storm surge forecasting in the Adriatic Sea

NASA Astrophysics Data System (ADS)

Bajo, Marco; Umgiesser, Georg; De Biasio, Francesco; Vignudelli, Stefano; Zecchetto, Stefano

2017-04-01

Satellite data are seldom used in storm surge forecasting. Among the most important issues related to the storm surge forecasting are the quality of the model wind forcing and the initial condition of the sea surface elevation. In this work, focused on storm surge forecasting in the Adriatic Sea, satellite scatterometer wind data are used to correct the wind speed and direction biases of the ECMWF global atmospheric model by tuning the spatial fields, as an alternative to data assimilation. The capability of such an unbiased wind is tested against that of a high resolution wind, produced by a regional non-hydrostatic model. On the other hand, altimeter Total Water Level Envelope (TWLE) data, which provide the sea level elevation, are used to improve the accuracy of the initial state of the model simulations. This is done by assimilating into a storm surge model the TWLE obtained by the altimeter observations along ground tracks, after subtraction of the tidal components. In order to test the methodology, eleven storm surge events recorded in Venice, from 2008 to 2012, have been simulated using different configurations of forcing wind and altimeter data assimilation. Results show that the relative error on the estimation of the maximum surge peak, averaged over the cases considered, decreases from 13% to 7% using both the unbiased wind and the altimeter data assimilation, while forcing the hydrodynamic model with the high resolution wind (no tuning), the altimeter data assimilation reduces the error from 9% to 6%.

Combining Remote and In Situ Observations with MHD models to Understand the Formation of the Slow Solar Wind

NASA Astrophysics Data System (ADS)

Viall, N. M.; Kepko, L.; Antiochos, S. K.; Lepri, S. T.; Vourlidas, A.; Linker, J.

2017-12-01

Connecting the structure and variability in the solar corona to the Heliosphere and solar wind is one of the main goals of Heliophysics and space weather research. The instrumentation and viewpoints of the Parker Solar Probe and Solar Orbiter missions will provide an unprecedented opportunity to combine remote sensing with in situ data to determine how the corona drives the Heliosphere, especially as it relates to the origin of the slow solar wind. We present analysis of STEREO coronagraph and heliospheric imager observations and of in situ ACE and Wind measurements that reveal an important connection between the dynamics of the corona and of the solar wind. We show observations of quasi-periodic release of plasma into the slow solar wind occurring throughout the corona - including regions away from the helmet streamer and heliospheric current sheet - and demonstrate that these observations place severe constraints on the origin of the slow solar wind. We build a comprehensive picture of the dynamic evolution by combining remote imaging data, in situ composition and magnetic connectivity information, and MHD models of the solar wind. Our results have critical implications for the magnetic topology involved in slow solar wind formation and magnetic reconnection dynamics. Crucially, this analysis pushes the limits of current instrument resolution and sensitivity, showing the enormous potential science to be accomplished with the Parker Solar Probe and Solar Orbiter missions.

Coronal holes as sources of solar wind

NASA Technical Reports Server (NTRS)

Nolte, J. T.; Krieger, A. S.; Timothy, A. F.; Gold, R. E.; Roelof, E. C.; Vaiana, G.; Lazarus, A. J.; Sullivan, J. D.; Mcintosh, P. S.

1976-01-01

We investigate the association of high-speed solar wind with coronal holes during the Skylab mission by: (1) direct comparison of solar wind and coronal X-ray data; (2) comparison of near-equatorial coronal hole area with maximum solar wind velocity in the associated streams; and (3) examination of the correlation between solar and interplanetary magnetic polarities. We find that all large near-equatorial coronal holes seen during the Skylab period were associated with high-velocity solar wind streams observed at 1 AU.

Electrostatic Solitary Waves in the Solar Wind: Evidence for Instability at Solar Wind Current Sheets

NASA Technical Reports Server (NTRS)

Malaspina, David M.; Newman, David L.; Wilson, Lynn Bruce; Goetz, Keith; Kellogg, Paul J.; Kerstin, Kris

2013-01-01

A strong spatial association between bipolar electrostatic solitary waves (ESWs) and magnetic current sheets (CSs) in the solar wind is reported here for the first time. This association requires that the plasma instabilities (e.g., Buneman, electron two stream) which generate ESWs are preferentially localized to solar wind CSs. Distributions of CS properties (including shear angle, thickness, solar wind speed, and vector magnetic field change) are examined for differences between CSs associated with ESWs and randomly chosen CSs. Possible mechanisms for producing ESW-generating instabilities at solar wind CSs are considered, including magnetic reconnection.

Near-surface wind speed statistical distribution: comparison between ECMWF System 4 and ERA-Interim

NASA Astrophysics Data System (ADS)

Marcos, Raül; Gonzalez-Reviriego, Nube; Torralba, Verónica; Cortesi, Nicola; Young, Doo; Doblas-Reyes, Francisco J.

2017-04-01

In the framework of seasonal forecast verification, knowing whether the characteristics of the climatological wind speed distribution, simulated by the forecasting systems, are similar to the observed ones is essential to guide the subsequent process of bias adjustment. To bring some light about this topic, this work assesses the properties of the statistical distributions of 10m wind speed from both ERA-Interim reanalysis and seasonal forecasts of ECMWF system 4. The 10m wind speed distribution has been characterized in terms of the four main moments of the probability distribution (mean, standard deviation, skewness and kurtosis) together with the coefficient of variation and goodness of fit Shapiro-Wilks test, allowing the identification of regions with higher wind variability and non-Gaussian behaviour at monthly time-scales. Also, the comparison of the predicted and observed 10m wind speed distributions has been measured considering both inter-annual and intra-seasonal variability. Such a comparison is important in both climate research and climate services communities because it provides useful climate information for decision-making processes and wind industry applications.

A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Krishnan, Venkat K; Zhang, Jie

Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic wind power ramp forecast (cp-WPRF) model based on Copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by considering the coupling effects among different ramp features. To accurately model the marginal distributions with a copula, a Gaussian mixture model (GMM) is adopted to characterize the WPR uncertaintymore » and features. The Canonical Maximum Likelihood (CML) method is used to estimate parameters of the multivariable copula. The optimal copula model is chosen based on the Bayesian information criterion (BIC) from each copula family. Finally, the best conditions based cp-WPRF model is determined by predictive interval (PI) based evaluation metrics. Numerical simulations on publicly available wind power data show that the developed copula-based cp-WPRF model can predict WPRs with a high level of reliability and sharpness.« less

Analysis of the solar/wind resources in Southern Spain for optimal sizing of hybrid solar-wind power generation systems

NASA Astrophysics Data System (ADS)

Quesada-Ruiz, S.; Pozo-Vazquez, D.; Santos-Alamillos, F. J.; Lara-Fanego, V.; Ruiz-Arias, J. A.; Tovar-Pescador, J.

2010-09-01

A drawback common to the solar and wind energy systems is their unpredictable nature and dependence on weather and climate on a wide range of time scales. In addition, the variation of the energy output may not match with the time distribution of the load demand. This can partially be solved by the use of batteries for energy storage in stand-alone systems. The problem caused by the variable nature of the solar and wind resources can be partially overcome by the use of energy systems that uses both renewable resources in a combined manner, that is, hybrid wind-solar systems. Since both resources can show complementary characteristics in certain location, the independent use of solar or wind systems results in considerable over sizing of the batteries system compared to the use of hybrid solar-wind systems. Nevertheless, to the day, there is no single recognized method for properly sizing these hybrid wind-solar systems. In this work, we present a method for sizing wind-solar hybrid systems in southern Spain. The method is based on the analysis of the wind and solar resources on daily scale, particularly, its temporal complementary characteristics. The method aims to minimize the size of the energy storage systems, trying to provide the most reliable supply.

Heavy ion composition in the inner heliosphere: Predictions for Solar Orbiter

NASA Astrophysics Data System (ADS)

Lepri, S. T.; Livi, S. A.; Galvin, A. B.; Kistler, L. M.; Raines, J. M.; Allegrini, F.; Collier, M. R.; Zurbuchen, T.

2014-12-01

The Heavy Ion Sensor (HIS) on SO, with its high time resolution, will provide the first ever solar wind and surpathermal heavy ion composition and 3D velocity distribution function measurements inside the orbit of Mercury. These measurements will provide us the most in depth examination of the origin, structure and evolution of the solar wind. The near co-rotation phases of the orbiter will enable the most accurate mapping of in-situ structures back to their solar sources. Measurements of solar wind composition and heavy ion kinetic properties enable characterization of the sources, transport mechanisms and acceleration processes of the solar wind. This presentation will focus on the current state of in-situ studies of heavy ions in the solar wind and their implications for the sources of the solar wind, the nature of structure and variability in the solar wind, and the acceleration of particles. Additionally, we will also discuss opportunities for coordinated measurements across the payloads of Solar Orbiter and Solar Probe in order to answer key outstanding science questions of central interest to the Solar and Heliophysics communities.

Depth profiling analysis of solar wind helium collected in diamond-like carbon film from Genesis

DOE PAGES

Bajo, Ken-ichi; Olinger, Chad T.; Jurewicz, Amy J.G.; ...

2015-01-01

The distribution of solar-wind ions in Genesis mission collectors, as determined by depth profiling analysis, constrains the physics of ion solid interactions involving the solar wind. Thus, they provide an experimental basis for revealing ancient solar activities represented by solar-wind implants in natural samples. We measured the first depth profile of ⁴He in a collector; the shallow implantation (peaking at <20 nm) required us to use sputtered neutral mass spectrometry with post-photoionization by a strong field. The solar wind He fluence calculated using depth profiling is ~8.5 x 10¹⁴ cm⁻². The shape of the solar wind ⁴He depth profile ismore » consistent with TRIM simulations using the observed ⁴He velocity distribution during the Genesis mission. It is therefore likely that all solar-wind elements heavier than H are completely intact in this Genesis collector and, consequently, the solar particle energy distributions for each element can be calculated from their depth profiles. Ancient solar activities and space weathering of solar system objects could be quantitatively reproduced by solar particle implantation profiles.« less

Evaluating the spatio-temporal performance of sky imager based solar irradiance analysis and forecasts

NASA Astrophysics Data System (ADS)

Schmidt, T.; Kalisch, J.; Lorenz, E.; Heinemann, D.

2015-10-01

Clouds are the dominant source of variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the world-wide electric power supply increases the need for accurate solar radiation forecasts. In this work, we present results of a shortest-term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A two month dataset with images from one sky imager and high resolutive GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series in different cloud scenarios. Overall, the sky imager based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depend strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1-2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

Medium-range fire weather forecasts

Treesearch

J.O. Roads; K. Ueyoshi; S.C. Chen; J. Alpert; F. Fujioka

1991-01-01

The forecast skill of theNational Meteorological Center's medium range forecast (MRF) numerical forecasts of fire weather variables is assessed for the period June 1,1988 to May 31,1990. Near-surface virtual temperature, relative humidity, wind speed and a derived fire weather index (FWI) are forecast well by the MRF model. However, forecast relative humidity has...

An Initial Assessment of the Impact of CYGNSS Ocean Surface Wind Assimilation on Navy Global and Mesoscale Numerical Weather Prediction

NASA Astrophysics Data System (ADS)

Baker, N. L.; Tsu, J.; Swadley, S. D.

2017-12-01

We assess the impact of assimilation of CYclone Global Navigation Satellite System (CYGNSS) ocean surface winds observations into the NAVGEM[i] global and COAMPS®[ii] mesoscale numerical weather prediction (NWP) systems. Both NAVGEM and COAMPS® used the NRL 4DVar assimilation system NAVDAS-AR[iii]. Long term monitoring of the NAVGEM Forecast Sensitivity Observation Impact (FSOI) indicates that the forecast error reduction for ocean surface wind vectors (ASCAT and WindSat) are significantly larger than for SSMIS wind speed observations. These differences are larger than can be explained by simply two pieces of information (for wind vectors) versus one (wind speed). To help understand these results, we conducted a series of Observing System Experiments (OSEs) to compare the assimilation of ASCAT wind vectors with the equivalent (computed) ASCAT wind speed observations. We found that wind vector assimilation was typically 3 times more effective at reducing the NAVGEM forecast error, with a higher percentage of beneficial observations. These results suggested that 4DVar, in the absence of an additional nonlinear outer loop, has limited ability to modify the analysis wind direction. We examined several strategies for assimilating CYGNSS ocean surface wind speed observations. In the first approach, we assimilated CYGNSS as wind speed observations, following the same methodology used for SSMIS winds. The next two approaches converted CYGNSS wind speed to wind vectors, using NAVGEM sea level pressure fields (following Holton, 1979), and using NAVGEM 10-m wind fields with the AER Variational Analysis Method. Finally, we compared these methods to CYGNSS wind speed assimilation using multiple outer loops with NAVGEM Hybrid 4DVar. Results support the earlier studies suggesting that NAVDAS-AR wind speed assimilation is sub-optimal. We present detailed results from multi-month NAVGEM assimilation runs along with case studies using COAMPS®. Comparisons include the fit of analyses and forecasts with in-situ observations and analyses from other NWP centers (e.g. ECMWF and GFS). [i] NAVy Global Environmental Model [ii] COAMPS® is a registered trademark of the Naval Research Laboratory for the Navy's Coupled Ocean Atmosphere Mesoscale Prediction System. [iii] NRL Atmospheric Variational Data Assimilation System

Asymmetric Solar Wind driven substorms from ballooning-interchange and magnetic reconnection

NASA Astrophysics Data System (ADS)

Horton, W.

2013-12-01

For nonsymmetric currents closing in the northern and southern magnetopause, we find new onset conditions for the ballooning-interchange and magnetic reconnections modes. While these two eigenmodes have opposite symmetries in a classic symmetric geotail geometry as in Prichett-Coroniti-Pellat [GRL1997], this symmetry is broken for real solar winds and a tilted Earth magnetic dipole. Extending earlier work, we show a new model that includes distinct north I_[N] and south I_[S] magnetopause return currents and distinct N-S magnetopause boundary boundary conditions. These conditions drive asymmetric wave functions within the geotail. The wave functions in the high β magnetopause give new onset conditions for substorms. The nonlinear growth rates are estimated and nonlinear FLR-fluid simulations are performed. FLR fluid models with 5 to 7 pde's, are compared qualitatively with the PIC simulations of Prichett-Coroniti [ P-C 2013 and 2011] which used 4 billion particles on a Cray XT5 NSF computer. The P-C 2013 simulations capture some features of the THEMIS data and we look for the corresponding features in the FLR-fluid simulations. The classic reconnection parameter Delta^{'} has a complex generalization for the asymmetric solar wind and IMF on the magnetopause [Horton and Tajima, JGR 1988]. When the mid-tail B_z(x) is such as to give the ballooning-interchange instability we show that in the late stage of the evolutions the nonlinear convective derivatives in the pde-system change the symmetry of the structures producing large magnetic islands of the scale observed by CLUSTER substorm data [ Nakamura et al. 2006]. We conclude that asymmetric models are needed to give reliable forecasting of the onset of subtorms and storms.

The Solar Wind Ion Composition Spectrometer

NASA Technical Reports Server (NTRS)

Gloeckler, G.; Geiss, J.; Balsiger, H.; Bedini, P.; Cain, J. C.; Fisher, J.; Fisk, L. A.; Galvin, A. B.; Gliem, F.; Hamilton, D. C.

1992-01-01

The Solar Wind Ion Composition Spectrometer (SWICS) on Ulysses is designed to determine uniquely the elemental and ionic-charge composition, and the temperatures and mean speeds of all major solar-wind ions, from H through Fe, at solar wind speeds ranging from 175 km/s (protons) to 1280 km/s (Fe(8+)). The instrument, which covers an energy per charge range from 0.16 to 59.6 keV/e in about 13 min, combines an electrostatic analyzer with postacceleration, followed by a time-of-flight and energy measurement. The measurements made by SWICS will have an impact on many areas of solar and heliospheric physics, in particular providing essential and unique information on: (1) conditions and processes in the region of the corona where the solar wind is accelerated; (2) the location of the source regions of the solar wind in the corona; (3) coronal heating processes; (4) the extent and causes of variations in the composition of the solar atmosphere; (5) plasma processes in the solar wind; (6) the acceleration of energetic particles in the solar wind; (7) the thermalization and acceleration of interstellar ions in the solar wind, and their composition; and (8) the composition, charge states, and behavior of the plasma in various regions of the Jovian magnetosphere.

National Weather Service Forecast Office - Honolulu, Hawai`i

Science.gov Websites

Locations - Coastal Forecast Kauai Northwest Waters Kauai Windward Waters Kauai Leeward Waters Kauai Channel Oahu Forecast Oahu Surf Forecast Coastal Wind Observations Buoy Reports, and current weather conditions for selected locations tides, sunrise and sunset information Coastal Waters Forecast general weather

Mixture EMOS model for calibrating ensemble forecasts of wind speed.

PubMed

Baran, S; Lerch, S

2016-03-01

Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather prediction models in order to produce calibrated predictive probability density functions. The EMOS predictive probability density function is given by a parametric distribution with parameters depending on the ensemble forecasts. We propose an EMOS model for calibrating wind speed forecasts based on weighted mixtures of truncated normal (TN) and log-normal (LN) distributions where model parameters and component weights are estimated by optimizing the values of proper scoring rules over a rolling training period. The new model is tested on wind speed forecasts of the 50 member European Centre for Medium-range Weather Forecasts ensemble, the 11 member Aire Limitée Adaptation dynamique Développement International-Hungary Ensemble Prediction System ensemble of the Hungarian Meteorological Service, and the eight-member University of Washington mesoscale ensemble, and its predictive performance is compared with that of various benchmark EMOS models based on single parametric families and combinations thereof. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison with the raw ensemble and climatological forecasts. The mixture EMOS model significantly outperforms the TN and LN EMOS methods; moreover, it provides better calibrated forecasts than the TN-LN combination model and offers an increased flexibility while avoiding covariate selection problems. © 2016 The Authors Environmetrics Published by JohnWiley & Sons Ltd.

NOAA SWPC / NASA CCMC Space Weather Modeling Assessment Project: Toward the Validation of Advancements in Heliospheric Space Weather Prediction Within WSA-Enlil

NASA Astrophysics Data System (ADS)

Adamson, E. T.; Pizzo, V. J.; Biesecker, D. A.; Mays, M. L.; MacNeice, P. J.; Taktakishvili, A.; Viereck, R. A.

2017-12-01

In 2011, NOAA's Space Weather Prediction Center (SWPC) transitioned the world's first operational space weather model into use at the National Weather Service's Weather and Climate Operational Supercomputing System (WCOSS). This operational forecasting tool is comprised of the Wang-Sheeley-Arge (WSA) solar wind model coupled with the Enlil heliospheric MHD model. Relying on daily-updated photospheric magnetograms produced by the National Solar Observatory's Global Oscillation Network Group (GONG), this tool provides critical predictive knowledge of heliospheric dynamics such as high speed streams and coronal mass ejections. With the goal of advancing this predictive model and quantifying progress, SWPC and NASA's Community Coordinated Modeling Center (CCMC) have initiated a collaborative effort to assess improvements in space weather forecasts at Earth by moving from a single daily-updated magnetogram to a sequence of time-dependent magnetograms to drive the ambient inputs for the WSA-Enlil model as well as incorporating the newly developed Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model. We will provide a detailed overview of the scope of this effort and discuss preliminary results from the first phase focusing on the impact of time-dependent magnetogram inputs to the WSA-Enlil model.

Forecast Method of Solar Irradiance with Just-In-Time Modeling

NASA Astrophysics Data System (ADS)

Suzuki, Takanobu; Goto, Yusuke; Terazono, Takahiro; Wakao, Shinji; Oozeki, Takashi

PV power output mainly depends on the solar irradiance which is affected by various meteorological factors. So, it is required to predict solar irradiance in the future for the efficient operation of PV systems. In this paper, we develop a novel approach for solar irradiance forecast, in which we introduce to combine the black-box model (JIT Modeling) with the physical model (GPV data). We investigate the predictive accuracy of solar irradiance over wide controlled-area of each electric power company by utilizing the measured data on the 44 observation points throughout Japan offered by JMA and the 64 points around Kanto by NEDO. Finally, we propose the application forecast method of solar irradiance to the point which is difficulty in compiling the database. And we consider the influence of different GPV default time on solar irradiance prediction.

Forecasting surface-layer atmospheric parameters at the Large Binocular Telescope site

NASA Astrophysics Data System (ADS)

Turchi, Alessio; Masciadri, Elena; Fini, Luca

2017-04-01

In this paper, we quantify the performance of an automated weather forecast system implemented on the Large Binocular Telescope (LBT) site at Mt Graham (Arizona) in forecasting the main atmospheric parameters close to the ground. The system employs a mesoscale non-hydrostatic numerical model (Meso-Nh). To validate the model, we compare the forecasts of wind speed, wind direction, temperature and relative humidity close to the ground with the respective values measured by instrumentation installed on the telescope dome. The study is performed over a large sample of nights uniformly distributed over 2 yr. The quantitative analysis is done using classical statistical operators [bias, root-mean-square error (RMSE) and σ] and contingency tables, which allows us to extract complementary key information, such as the percentage of correct detections (PC) and the probability of obtaining a correct detection within a defined interval of values (POD). The results of our study indicate that the model performance in forecasting the atmospheric parameters we have just cited are very good, in some cases excellent: RMSE for temperature is below 1°C, for relative humidity it is 14 per cent and for the wind speed it is around 2.5 m s-1. The relative error of the RMSE for wind direction varies from 9 to 17 per cent depending on the wind speed conditions. This work is performed in the context of the ALTA (Advanced LBT Turbulence and Atmosphere) Center project, whose final goal is to provide forecasts of all the atmospheric parameters and the optical turbulence to support LBT observations, adaptive optics facilities and interferometric facilities.

KSC-2015-1302

NASA Image and Video Library

2015-02-07

CAPE CANAVERAL, Fla. – Col. D. Jason Cothern, Space Demonstrations Division chief at Kirtland Air Force Base in Albuquerque, New Mexico, listens to a question from a member of the news media during a briefing regarding NOAA’s Deep Space Climate Observatory spacecraft, or DSCOVR. DSCOVR will launch aboard a SpaceX Falcon 9 rocket. The mission is a partnership between NOAA, NASA and the U.S. Air Force. DSCOVR will maintain the nation's real-time solar wind monitoring capabilities which are critical to the accuracy and lead time of NOAA's space weather alerts and forecasts. To learn more about DSCOVR, visit http://www.nesdis.noaa.gov/DSCOVR. Photo credit: NASA/Kim Shiflett