Outlier Detection and Removal in Multivariate Time Series for a More Robust Machine Learning–based Solar Flare Prediction
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Junzhi Wen↗
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Using Bright Point Shapes to Constrain Wave Heating of the Solar Corona: Predictions for DKIST
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Samuel J. Van Kooten↗
Solar Magnetic Activity and Solar-Stellar Connections
Prediction of solar magnetic activity on various temporal scales is a fundamental element of space weather, which requires a wide range of theoretical and observational expertise in solar phenomena from the deep interior to the corona. Historical observations have revealed many features of cyclic variations of the solar activity; but these data are dramatically insufficient to draw a physical picture of global magnetic field evolution. New observational data, currently available from space missions and ground-based observatories, provide us with detailed information about solar dynamics and magnetism. However, because of the relatively short duration of data series and the great variety of data types and quality, it is challenging to assimilate these data in theoretical models and make reliable forecasts. The recent unexpectedly weak solar activity cycles, as well as observations of rotational and magnetic topology transitions in solar-type stars, suggest that the Sun and its magnetic dynamo are currently in a very interesting evolutionary stage. This could relate to the difficulty in getting a model of the Sun to produce solar-like – rather than anti-solar-like – differential rotation, to reproduce the rotation profile obtained from helioseismology, and to predict solar activity cycles.
magnetic↗
Cross-Scale Coupling in the Solar Atmosphere
For understanding and eventually predicting solar activity, the fundamental question that Solar-C must answer is: How does energy transfer from the large-scales at which it is injected into the solar atmosphere to the small scales at which it is dissipated? We show that this question of cross-scale coupling is fundamental to all activity, ranging from the smallest nanoflares that are postulated to power coronal heating and solar wind acceleration, to the largest coronal mass ejections and eruptive flares. For the solar atmosphere, the most important process that actually dissipates the energy is believed to be magnetic reconnection. We present results on recent calculations of reconnection in a variety of solar contexts and focus on the coupling between kinetic and MHD scales during reconnection. We discuss the implications of our results for present data and for future observations from Solar-C.
Antiochos, Spiro K.↗
Atmospheric drag model calibrations for spacecraft lifetime prediction
Although solar activity prediction uncertainty normally dominates decay prediction error budget for near-Earth spacecraft, the effect of drag force modeling errors for given levels of solar activity needs to be considered. Two atmospheric density models, the modified Harris-Priester model and the Jacchia-Roberts model, to reproduce the decay histories of the Solar Mesosphere Explorer (SME) and Solar Maximum Mission (SMM) spacecraft in the 490- to 540-kilometer altitude range were analyzed. Historical solar activity data were used in the input to the density computations. For each spacecraft and atmospheric model, a drag scaling adjustment factor was determined for a high-solar-activity year, such that the observed annual decay in the mean semimajor axis was reproduced by an averaged variation-of-parameters (VOP) orbit propagation. The SME (SMM) calibration was performed using calendar year 1983 (1982). The resulting calibration factors differ by 20 to 40 percent from the predictions of the prelaunch ballistic coefficients. The orbit propagations for each spacecraft were extended to the middle of 1988 using the calibrated drag models. For the Jaccia-Roberts density model, the observed decay in the mean semimajor axis of SME (SMM) over the 4.5-year (5.5-year) predictive period was reproduced to within 1.5 (4.4) percent. The corresponding figure for the Harris-Priester model was 8.6 (20.6) percent. Detailed results and conclusions regarding the importance of accurate drag force modeling for lifetime predictions are presented.
Binebrink, A. L.↗
Arcing rates for High Voltage Solar Arrays - Theory, experiment, and predictions
All solar arrays have biased surfaces that can be exposed to the space environment. It has been observed that when the array bias is less than a few hundred volts negative, then the exposed conductive surfaces may undergo arcing in the space plasma. A theory for arcing is developed on these high voltage solar arrays that ascribes the arcing to electric field runaway at the interface of the plasma, conductor, and solar cell dielectric. Experiments were conducted in the laboratory for the High Voltage Solar Array experiment that will fly on the Japanese Space Flyer Unit (SFU) in 1994. The theory was compared in detail with the experiment and shown to give a reasonable explanation for the data. The combined theory and ground experiments were then used to develop predictions for the SFU flight.
Hastings, Daniel E.↗
The arcing rate for a High Voltage Solar Array - Theory, experiment and predictions
All solar arrays have biased surfaces which can be exposed to the space environment. It has been observed that when the array bias is less than a few hundred volts negative then the exposed conductive surfaces may undergo arcing in the space plasma. A theory for arcing is developed on these high voltage solar arrays which ascribes the arcing to electric field runaway at the interface of the plasma, conductor and solar cell dielectric. Experiments were conducted in the laboratory for the High Voltage Solar Array (HVSA) experiment which will fly on the Japanese Space Flyer Unit (SFU) in 1994. The theory was compared in detail to the experiment and shown to give a reasonable explanation for the data. The combined theory and ground experiments were then used to develop predictions for the SFU flight.
Hastings, Daniel E.↗
A Short-Term Solar Forecasting Platform Using a Physics-Based Smart Persistence Model and Data Imputation Method
Electrical energy plays vital role in our socio-economic activity and therefore ensuring the reliability of the electric grid, from the generation, transmission and distribution level is critical. In order to maintain the power system parameter viz., frequency, voltage, etc., optimally, balancing of generation and consumption is very much essential. However, solar energy is infirm power by nature this is due to cloud cover / other local phenomena. Hence, Photovoltaic (PV) power generation brings a significant challenge to the grid operator due to the variability of the solar energy. The complexity of this challenge in terms of planning and dispatch ability of PV resources, aggravates with the high penetration of solar energy into the electric grid. In this setting, reliable solar radiation forecasting models based on accurate and quality input data become essential. In order to develop a suitable model for predicting solar radiation, quality historical / real time measurement is also needed. Under this study NIWE and NREL jointly developed / tested short-term solar forecasting frameworks using a smart persistence and physics-based smart persistence models for intra-hour forecasting of solar radiation (PSPI) and benchmarked 9 different data imputation techniques in 15 Solar Radiation Resource Assessment (SRRA) stations, located at different parts of India. During any measurement campaign, due to various technical reasons, we may miss few observations. However, the missing observation often reduce the performance of any forecasting model. Therefore, suitable data imputation method would assist us to obtain continuous observation of solar radiation. A station-by-station and method-by-method analysis was carried out to understand the performance of each model. Based on our analysis, among all the data imputation methods, the Kalman data imputation method is better for Indian Weather condition. In addition, Kalman StructTS, Linear, Stine and Arima methods yield slightly inferior accuracy compared to Kalman, but outperform the other methods. The extended solar radiation data are used by solar forecasting models to provide the prediction of solar radiation at 15 SRRA stations. As far as short term forecasting model is concerned, the PSPI model outperforms the Smart Persistence model. However, the forecast error is increases with the forecasting horizon.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Lifetime predictions for the Solar Maximum Mission (SMM) and San Marco spacecraft
Lifetime prediction techniques developed by the Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD) are described. These techniques were developed to predict the Solar Maximum Mission (SMM) spacecraft orbit, which is decaying due to atmospheric drag, with reentry predicted to occur before the end of 1989. Lifetime predictions were also performed for the Long Duration Exposure Facility (LDEF), which was deployed on the 1984 SMM repair mission and is scheduled for retrieval on another Space Transportation System (STS) mission later this year. Concepts used in the lifetime predictions were tested on the San Marco spacecraft, which reentered the Earth's atmosphere on December 6, 1988. Ephemerides predicting the orbit evolution of the San Marco spacecraft until reentry were generated over the final 90 days of the mission when the altitude was less than 380 kilometers. The errors in the predicted ephemerides are due to errors in the prediction of atmospheric density variations over the lifetime of the satellite. To model the time dependence of the atmospheric densities, predictions of the solar flux at the 10.7-centimeter wavelength were used in conjunction with Harris-Priester (HP) atmospheric density tables. Orbital state vectors, together with the spacecraft mass and area, are used as input to the Goddard Trajectory Determination System (GTDS). Propagations proceed in monthly segments, with the nominal atmospheric drag model scaled for each month according to the predicted monthly average value of F10.7. Calibration propagations are performed over a period of known orbital decay to obtain the effective ballistic coefficient. Progagations using plus or minus 2 sigma solar flux predictions are also generated to estimate the despersion in expected reentry dates. Definitive orbits are compared with these predictions as time expases. As updated vectors are received, these are also propagated to reentryto continually update the lifetime predictions.
Smith, E. A.↗
Sensitivity Study for Forecasting Variables of WRF-Solar Using a Tangent Linear Approach
Integrating solar generation in recent years has highlighted the need for improved accuracy in predicting solar power. Confidence in solar power forecasting can be achieved by designing an ensemble that provides reliable probabilistic information for solar radiation with reduced uncertainty and error. Ideally, ensemble members are created through the optimized perturbation of the initial conditions in numerical weather prediction (NWP) models. Tangent linear models are capable of efficiently investigating the sensitivity of solar radiation to model input parameters because they do not require individual perturbation of each variable. This sensitivity study using tangent linear models provide us the capability to identify the right variables to perturb in an ensemble prediction system. In this study, we developed tangent linear models for WRF-Solar modules that directly impact the computation of solar radiation and the simulation of cloud formation and dissipation including the Fast All-sky Model for Solar Applications (FARMS), the Noah land surface model (LSM), the Thompson microphysics, the Mello-Yamada-Nakanishi-Niino (MYNN) boundary layer parameterization, and the Deng scheme for a shallow-convection parameterization. A sensitivity analysis was conducted under various scenarios based on satellite observations and model simulations from the National Solar Radiation Data Base (NSRDB) and WRF-Solar, respectively. Critical forecasting variables that are highly sensitive to the forecasting of global horizontal irradiance (GHI), direct normal irradiance (DNI), cloud mixing ratio, cloud tendency, cloud fraction, and sensible and latent heat fluxes were determined using the relevant WRF-Solar module. This study will be used as a guidance on future research leading to high-quality probabilistic solar forecasting. In this presentation, we discuss the validation of tangent linear approach for WRF-Solar modules and illustrate how the sensitivity results are valuable in the improvement of probabilistic solar prediction.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Operation of the computer model for microenvironment solar exposure
A computer model for microenvironmental solar exposure was developed to predict solar exposure to satellite surfaces which may shadow or reflect on one another. This document describes the technical features of the model as well as instructions for the installation and use of the program.
Gillis, J. R.↗
Comparative Study of foF2 Measurements with IRI-2007 Model Predictions During Extended Solar Minimum
The unusually deep and extended solar minimum of cycle 2324 made it very difficult to predict the solar indices 1 or 2 years into the future. Most of the predictions were proven wrong by the actual observed indices. IRI gets its solar, magnetic, and ionospheric indices from an indices file that is updated twice a year. In recent years, due to the unusual solar minimum, predictions had to be corrected downward with every new indices update. In this paper we analyse how much the uncertainties in the predictability of solar activity indices affect the IRI outcome and how the IRI values calculated with predicted and observed indices compared to the actual measurements.Monthly median values of F2 layer critical frequency (foF2) derived from the ionosonde measurements at the mid-latitude ionospheric station Juliusruh were compared with the International Reference Ionosphere (IRI-2007) model predictions. The analysis found that IRIprovides reliable results that compare well with actual measurements, when the definite (observed and adjusted) indices of solar activityare used, while IRI values based on earlier predictions of these indices noticeably overestimated the measurements during the solar minimum.One of the principal objectives of this paper is to direct attention of IRI users to update their solar activity indices files regularly.Use of an older index file can lead to serious IRI overestimations of F-region electron density during the recent extended solar minimum.
Ionosphere↗
Forecasting Day-Ahead Solar Irradiance for Puerto Rico Using the WRF Model and NSRDB
Accurately predicting solar energy resources is a major challenge in integrating photovoltaics generation on the electric grid. Numerical weather prediction has been recognized by the solar energy community as a major approach to provide solar resource forecasts at various locations and for a variety of timescales. In this study, as a part of the Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100), we develop day-head solar irradiance forecast data using the Weather Research and Forecasting (WRF) model at 3 km and hourly/5-minute. The global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts simulated from the WRF model are postprocessed by a simple optimization method using satellite-derived gridded observations from the National Solar Radiation Data Base (NSRDB) to reduce error and bias of the solar irradiance forecasts covering 2018-2020. The NSRDB contributes to improving the GHI and DNI forecasts and also offers the opportunity for an in-depth analysis to evaluate their accuracy over a wide range of Puerto Rico regions. Preliminary results show overall improvements of GHI forecasts up to 37% (DNI: 15%) for mean absolute error and 97% (DNI: 76%) for mean bias error by applying a postprocessing technique to WRF model output.
data models↗
Initial On-Orbit Spectral Calibration of the PACE Ocean Color Instrument
The NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission Project Science Team has used Ocean Color Instrument (OCI) measurements of Fraunhofer lines in spectra of sunlight reflected by the solar diffuser and measurements of atmospheric absorption bands in cloudtop and ocean spectra to characterize the spectral calibration of OCI on orbit. Multiple lines have been analyzed for both the ultraviolet to visible (UVVIS, 340−607 nm) and visible to near-infrared (VISNIR, 597−897 nm) grating spectrographs. The spectrographs yield hyperspectral observations with 5 nm bandwidths and 0.625 nm sampling intervals. The on-orbit observations have been compared with the prelaunch spectral calibration of OCI performed by the Goddard Laser for Absolute Measurement of Radiance (GLAMR) during thermal vacuum testing to track any changes in the calibration since launch. The calibration analyzed the line positions and strengths for the Fraunhofer lines for each spectrograph by comparing the solar spectra measured by OCI with predicted solar spectra derived from the solar reference spectrum and the BRDF of the solar diffuser, convolved with the OCI relative spectral responses. The calibration also compared the line positions of the atmospheric absorption bands with the model transmissions used by the PACE Project. The line position comparisons show that the root mean square (RMS) spectral difference between the measured and predicted spectra is 0.15 nm, the average spectral shift is 0.062 nm, and the residual spectral dispersion over the wavelength range of the Fraunhofer lines is 0.17 nm. All three estimates of the spectral accuracy of OCI meet the instrument functional requirement of a spectral accuracy of 0.5 nm and are well within the 0.625 nm sampling interval of the data. The line strength comparisons between measured and predicted spectra are essentially the same. These results show that the spectral calibration of OCI on orbit has not drifted since the prelaunch calibration of OCI by GLAMR and that the on-orbit spectral calibration of OCI is stable over time. These results also provide a baseline for monitoring the future spectral performance of OCI on orbit.
Radiometric Calibration↗
Status of Cycle 23 Forecasts
A number of techniques for predicting solar activity on a solar cycle time scale are identified, described, and tested with historical data. Some techniques, e.g,, regression and curve-fitting, work well as solar activity approaches maximum and provide a month- by-month description of future activity, while others, e.g., geomagnetic precursors, work well near solar minimum but provide an estimate only of the amplitude of the cycle. A synthesis of different techniques is shown to provide a more accurate and useful forecast of solar cycle activity levels. A combination of two uncorrelated geomagnetic precursor techniques provides the most accurate prediction for the amplitude of a solar activity cycle at a time well before activity minimum. This precursor method gave a smoothed sunspot number maximum of 154+21 for cycle 23. A mathematical function dependent upon the time of cycle initiation and the cycle amplitude then describes the level of solar activity for the complete cycle. As the time of cycle maximum approaches a better estimate of the cycle activity is obtained by including the fit between recent activity levels and this function. This Combined Solar Cycle Activity Forecast now gives a smoothed sunspot maximum of 140+20 for cycle 23. The success of the geomagnetic precursors in predicting future solar activity suggests that solar magnetic phenomena at latitudes above the sunspot activity belts are linked to solar activity, which occurs many years later in the lower latitudes.
Hathaway, D. H.↗
Evaluation of WRF-Solar Cloud Forecast Using the NSRDB: Preprint
Cloud forecast is a crucial component in predicting solar irradiance from numerical weather prediction (NWP) models. Assessing cloud properties from the NWP models requires significant work due to the need for high-quality data, spatial analysis covering model extent, and detailed analysis of model performance for different types of clouds. This study presents an evaluation of the WRF-Solar cloud forecast using the National Solar Radiation Database (NSRDB). We propose an evaluation framework applied to a single model prediction as well as ensemble-based forecasts. Various cloud detection metrics are calculated when comparing with the satellite-derived dataset. The mismatched clouds from the WRF-Solar model are quantified using nine cloud types classified by cloud top height and cloud optical depth. The results based on the WRF-Solar forecasts covering the entire U.S. for the full year of 2018 shows mismatched cloud frequency in the range of 8% - 46% for thick and high-level (deep convective) to thin and low-level (cumulus) clouds.
cloud mask forecast↗
The SAMEX Vector Magnetograph: A Design Study for a Space-Based Solar Vector Magnetograph
This report presents the results of a pre-phase A study performed by the Marshall Space Flight Center (MSFC) for the Air Force Geophysics Laboratory (AFGL) to develop a design concept for a space-based solar vector magnetograph and hydrogen-alpha telescope. These are two of the core instruments for a proposed Air Force mission, the Solar Activities Measurement Experiments (SAMEX). This mission is designed to study the processes which give rise to activity in the solar atmosphere and to develop techniques for predicting solar activity and its effects on the terrestrial environment.
Hagyard, M. J.↗
Effects of heavy-element settling on solar neutrino fluxes and interior structure
We consider the effects of gravitational settling of both He and heavier elements on the predicted solar neutrino fluxes and interior sound speed and density profiles. We find that while the structural changes that result from the inclusion of both He and heavy-element settling are only slightly larger than the changes resulting from the inclusion of He settling alone, the additional increases in expected neutrino fluxes are of comparable size. Our preferred model with both He and heavy-element settling has neutrino count rates of 9.0 SNU for Cl-37 detectors and 137 SNU for Ga-71 detectors, as compared to 7.1 and 127 SNU for a comparable model without any diffusive separation, or 8.0 and 132 SNU for a model that includes He settling alone. We suggest that the correction factors by which the predicted neutrino fluxes of solar models calculated without including the effects of diffusion should be multiplied are 1.25 +/- 0.08 for Cl detectors, 1.07 +/- 0.02 for Ga detectors, and 1.28 +/- 0.09 for the B-8 flux (1 sigma errors). Comparison of internal sound speed and density profiles strongly suggests that the additional changes in calculated p-mode oscillation frequencies due to the inclusion of heavy-element settling will be small compared to the changes that result from He settling alone, especially for the higher degree modes. All models with diffusive separation give much better agreement with the observed depth of the convection zone than do nondiffusive models. The model that includes both He and heavy-element settling requires an initial He mass fraction Y = 0.280 and has a surface He abundance of Y = 0.251 at the solar age.
Proffitt, Charles R.↗