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DOE OSTI2025

Huge ensembles – Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators

Abstract. Simulating low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1000–10 000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part 1, we construct an ensemble weather forecasting system based on spherical Fourier neural operators (SFNOs), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest-growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. With large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states during the rollout, and the ML ensemble passes a crucial spectral test in the literature. The IFS and ML ensembles have similar extreme forecast indices, and we show that the ML extreme weather forecasts are reliable and discriminating. These diagnostics ensure that the ensemble can reliably simulate the time evolution of the atmosphere, including low-likelihood high-impact extremes. In Part 2, we generate a huge ensemble initialized each day in summer 2023, and we characterize the simulations of extremes.

Mahesh, Ankur↗

NASA NTRS1984

Forecasts of the 500 mb height using a dynamically oriented statistical model

The forecast skill of a simple dynamically inspired statistical model of the Northern Hemisphere 500 mb height field is evaluated in spectral and physical space for a variety of forecast lead times (1-32 days) and predictand averaging times (1-32 days). The model includes viscous damping, wave propagation, climatology and implicit stochastic forcing. The largest model skill was found for forecasts of the zonal flow and the largest waves. In general, the largest forecast skills were also associated with the largest forecast error, there being a slight geographic phase shift of the skill with respect to the error. Model skills for climate (time-averaged) forecasts are greater when using instantaneous rather than time averages to forecast time averages. Analysis of model errors suggests areas for improvement in representing forcing terms and model physics. However, the model error fields are largely 'white noise' which suggests that global forecast skills substantially larger than those obtained here are unlikely to be achieved by more sophisticated models.

Roads, J. O.↗

NASA NTRS1985

Error growth in operational ECMWF forecasts

A parameterization scheme used at the European Centre for Medium Range Forecasting to model the average growth of the difference between forecasts on consecutive days was extended by including the effect of error growth on forecast model deficiencies. Error was defined as the difference between the forecast and analysis fields during the verification time. Systematic and random errors were considered separately in calculating the error variance for a 10 day operational forecast. A good fit was obtained with measured forecast errors and a satisfactory trend was achieved in the difference between forecasts. Fitting six parameters to forecast errors and differences that were performed separately for each wavenumber revealed that the error growth rate grew with wavenumber. The saturation error decreased with the total wavenumber and the limit of predictability, i.e., when error variance reaches 95 percent of saturation, decreased monotonically with the total wavenumber.

Kalnay, E.↗

NASA NTRS1986

Impact of the initial specification of moisture and vertical motion on precipitation forecasts with a mesoscale model Implications for a satellite mesoscale data base

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.

Mlynczak, Pamela E.↗

NASA NTRS2000

Effect of Combined Spaceborne Microwave and Continuous Lightning Measurements on Precipitation Forecasts of the 1998 Ground-Hog Day Storm

We evaluated the impact of several newly available sources of meteorological data on mesoscale model forecasts of precipitation produced by the extra-tropical cyclone that struck Florida on February 2, 1998. Precipitation distributions of convective rainfall events were derived from Special Sensor Microwave Imager (SSM/I) and Multi-Channel Passive Microwave Sensor (TMI) microwave radiometric data by means of the Goddard PROFiling (GPROF) algorithm. Continuous lightning distributions were obtained from sferics measurements obtained from a network of VLF radio receivers. Histograms of coincident sferics frequency distributions were matched to those of precipitation to derive bogus convective rainfall rates from the continuously available sferics measurements. SSM/I and TMI microwave data were used to derive Integrated Precipitable Water (IPW) distributions. The TMI also provided sea surface temperatures (SSTS) of the Loop Current and Gulf Stream with improved structural detail. A series of experiments assimilated IPW and latent heating from the bogus convective rainfall for six-hours in the MM5 mesoscale forecast model to produce nine-hour forecasts of all rainfall as well as other weather parameters. Although continuously assimilating latent heating only slightly improved the surface pressure distribution forecast, it significantly improved the precipitation forecasts. Correctly locating convective rainfall was found critical for assimilating latent heating in the forecast model, but measurement of the rainfall intensity proved to be less important. The improved SSTs also had a positive impact on rainfall forecasts for this case. Assimilating bogus rainfall in the model produced nine-hour forecasts of radar reflectivity distributions that agreed well with coincident observations from the TRMM spaceborne precipitation radar, ground based radar and spaceborne microwave measurements.

Weinman, James A.↗

NASA NTRS2001

Statistical Short-Range Forecast Guidance for Cloud Ceilings Over the Shuttle Landing Facility

This report describes the results of the AMU's Short-Range Statistical Forecasting task. The cloud ceiling forecast over the Shuttle Landing Facility (SLF) is a critical element in determining whether a Shuttle should land. Spaceflight Meteorology Group (SMG) forecasters find that ceilings at the SLF are challenging to forecast. The AMU was tasked to develop ceiling forecast equations to minimize the challenge. Studies in the literature that showed success in improving short-term forecasts of ceiling provided the basis for the AMU task. A 20-year record of cool-season hourly surface observations from stations in east-central Florida was used for the equation development. Two methods were used: an observations-based (OBS) method that incorporated data from all stations, and a persistence climatology (PCL) method used as the benchmark. Equations were developed for 1-, 2-, and 3-hour lead times at each hour of the day. A comparison between the two methods indicated that the OBS equations performed well and produced an improvement over the PCL equations. Therefore, the conclusion of the AMU study is that OBS equations produced more accurate forecasts than the PCL equations, and can be used in operations. They provide another tool with which to make the ceiling forecasts that are critical to safe Shuttle landings at KSC.

Lambert, Winifred C.↗

NASA NTRS2002

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

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.

Lambert, Winifred C.↗

NASA NTRS2008

Use of High-Resolution WRF Simulations to Forecast Lightning Threat

Recent observational studies have confirmed the existence of a robust statistical relationship between lightning flash rates and the amount of large precipitating ice hydrometeors aloft in storms. This relationship is exploited, in conjunction with the capabilities of cloud-resolving forecast models such as WRF, to forecast explicitly the threat of lightning from convective storms using selected output fields from the model forecasts. The simulated vertical flux of graupel at -15C and the shape of the simulated reflectivity profile are tested in this study as proxies for charge separation processes and their associated lightning risk. Our lightning forecast method differs from others in that it is entirely based on high-resolution simulation output, without reliance on any climatological data. short [6-8 h) simulations are conducted for a number of case studies for which three-dmmensional lightning validation data from the North Alabama Lightning Mapping Array are available. Experiments indicate that initialization of the WRF model on a 2 km grid using Eta boundary conditions, Doppler radar radial velocity fields, and METAR and ACARS data y&eld satisfactory simulations. __nalyses of the lightning threat fields suggests that both the graupel flux and reflectivity profile approaches, when properly calibrated, can yield reasonable lightning threat forecasts, although an ensemble approach is probably desirable in order to reduce the tendency for misplacement of modeled storms to hurt the accuracy of the forecasts. Our lightning threat forecasts are also compared to other more traditional means of forecasting thunderstorms, such as those based on inspection of the convective available potential energy field.

McCaul, E. W., Jr.↗

NASA NTRS2012

Two Topics in Seasonal Streamflow Forecasting: Soil Moisture Initialization Error and Precipitation Downscaling

Continental-scale offline simulations with a land surface model are used to address two important issues in the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which the downscaling of seasonal precipitation forecasts, if it could be done accurately, would improve streamflow forecasts. The reduction in streamflow forecast skill (with forecasted streamflow measured against observations) associated with adding noise to a soil moisture field is found to be, to first order, proportional to the average reduction in the accuracy of the soil moisture field itself. This result has implications for streamflow forecast improvement under satellite-based soil moisture measurement programs. In the second and more idealized ("perfect model") analysis, precipitation downscaling is found to have an impact on large-scale streamflow forecasts only if two conditions are met: (i) evaporation variance is significant relative to the precipitation variance, and (ii) the subgrid spatial variance of precipitation is adequately large. In the large-scale continental region studied (the conterminous United States), these two conditions are met in only a somewhat limited area.

Koster, Randal↗

NASA NTRS2014

Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

A case study and monthly statistical analysis using sounder data assimilation to improve the Alaska regional weather forecast model are presented. Weather forecast in Alaska faces challenges as well as opportunities. Alaska has a large land with multiple types of topography and coastal area. Weather forecast models must be finely tuned in order to accurately predict weather in Alaska. Being in the high-latitudes provides Alaska greater coverage of polar orbiting satellites for integration into forecasting models than the lower 48. Forecasting marine low stratus clouds is critical to the Alaska aviation and oil industry and is the current focus of the case study. NASA AIRS/CrIS sounder profiles data are used to do data assimilation for the Alaska regional weather forecast model to improve Arctic marine stratus clouds forecast. Choosing physical options for the WRF model is discussed. Preprocess of AIRS/CrIS sounder data for data assimilation is described. Local observation data, satellite data, and global data assimilation data are used to verify and/or evaluate the forecast results by the MET tools Model Evaluation Tools (MET).

Zhu, Jiang↗

NASA NTRS2015

Spectral Analysis of Forecast Error Investigated with an Observing System Simulation Experiment

The spectra of analysis and forecast error are examined using the observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASAGMAO). A global numerical weather prediction model, the Global Earth Observing System version 5 (GEOS-5) with Gridpoint Statistical Interpolation (GSI) data assimilation, is cycled for two months with once-daily forecasts to 336 hours to generate a control case. Verification of forecast errors using the Nature Run as truth is compared with verification of forecast errors using self-analysis; significant underestimation of forecast errors is seen using self-analysis verification for up to 48 hours. Likewise, self analysis verification significantly overestimates the error growth rates of the early forecast, as well as mischaracterizing the spatial scales at which the strongest growth occurs. The Nature Run-verified error variances exhibit a complicated progression of growth, particularly for low wave number errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realization of the control. The forecast errors of the two experiments become more correlated during the early forecast period, with correlations increasing for up to 72 hours before beginning to decrease.

OSSE↗

NASA NTRS2018

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GMAO S2S Forecast System

El Nino/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would be of great benefit for society. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean density. Satellite Sea Surface Salinity (SSS), combined with temperature, help to identify ocean density changes and associated mixing near the ocean surface. We assess the impact of satellite SSS observations for improving near-surface dynamics within ocean analyses and how these impact dynamical ENSO forecasts using the NASA GMAO (Global Modeling and Assimilation Office) Sub-seasonal to Seasonal (S2S_v2.1) coupled forecast system (Molod et al. 2018 - i.e. NASA's contribution to the NMME (National Multi-Model Ensemble) project). For all initialization experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF ( Local Ensemble Transform Kalman Filter) scheme similar to Penny et al., 2013. A separate reanalysis additionally assimilates Aquarius V5 (September 2011 to June 2015) and SMAP (Soil Moisture Active Passive) V4 (March 2015 to present) along-track data.We highlight the impact of satellite SSS on ocean reanalyses by comparing validation statistics of experiments that assimilate SSS versus our current prediction system that withholds SSS. We find that near-surface validation versus observed statistics for salinity are slightly degraded when assimilating SSS. This is an expected result due to known biases between SSS (measured by the satellite at approximately 1 centimeter) and in situ measurements (typically measured by Argo floats at 3 meters). On the other hand, a very encouraging result is that both temperature, absolute dynamic topography, and mixed layer statistics are improved with SSS assimilation. Previous work has shown that correcting near-surface density structure via gridded SSS assimilation can improve coupled forecasts. Here we present results of coupled forecasts that are initialized from the GMAO S2S reanalyses that assimilates/withholds along-track (L2) SSS. In particular, we contrast forecasts of the overestimated 2014 El Nino, the big 2015 El Nino, and the minor 2016 La Nina. For each of these ENSO scenarios, assimilation of satellite SSS improves the forecast validation. Improved SSS and density upgrades the mixed layer depth leading to more accurate coupled air/sea interaction.

Hackert, E.↗

NASA NTRS2018

Forecasting the Arrival Time of Coronal Mass Ejections: Analysis of the CCMC CME Scoreboard

Accurate forecasting of the properties of coronal mass ejections (CMEs) as they approach Earth is now recognized as an important strategic objective for both NOAA and NASA. The time of arrival of such events is a key parameter, one that had been anticipated to be relatively straightforward to constrain. In this study, we analyze forecasts submitted to the Community Coordinated Modeling Center at NASA's Goddard Space Flight Center over the last 6 years to answer the following questions: (1) How well do these models forecast the arrival time of CME-driven shocks? (2) What are the uncertainties associated with these forecasts? (3) Which model(s) perform best? (4) Have the models become more accurate during the past 6 years? We analyze all forecasts made by 32 models from 2013 through mid-2018, and additionally focus on 28 events, all of which were forecasted by six models. We find that the models are generally able to predict CME-shock arrival times, in an average sense, to within Â±10 hr, but with standard deviations often exceeding 20 hr. The best performers, on the other hand, maintained a mean error (bias) of 1 hr, a mean absolute error of 13 hr, and a precision (standard deviation) of 15 hr. Finally, there is no evidence that the forecasts have become more accurate during this interval. We discuss the intrinsic simplifications of thevarious models analyzed, the limitations of this investigation, and suggest possible paths to improve these forecasts in the future.

space weather↗

NASA NTRS2021

Evaluation and Intercomparison of Wildfire Smoke Forecasts from Multiple Modeling Systems for the 2019 Williams Flats Fire

Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from 12 state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, US, August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within 1 d are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models, with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by fire radiative power (FRP)-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019, mainly over the transported smoke plumes, owing to the underestimated emissions on 7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center's Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper, with a day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models, with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported smoke that is 1 d old. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated for by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

AOD↗

NASA NTRSDate not supplied

Examination of L-Band Brightness Temperature Forecasts in the SMAP Level-4 Soil Moisture Analysis

The NASA Soil Moisture Active Passive (SMAP) mission [4] has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. By assimilating the Tb observations into the NASA Catchment land surface model [5] using a spatially distributed ensemble Kalman filter, the NASA Global Modeling and Assimilation Office generates the SMAP Level-4 Soil Moisture (L4_SM) product, which provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with ~2.5-day latency for use in research and applications [6]. The L4_SM product also includes estimates of soil temperature, land surface fluxes, and assimilation diagnostics such as the model forecast and observed Tb values [7]. The output from the L4_SM system is routinely monitored by the L4_SM team. Such monitoring provides valuable information; instances of unusually large Tb observation-minus-forecast (O-F) residuals can indicate events during which soil moisture conditions are poorly described in the land modeling system [7,8]. For example, repeated occurrences of very large Tb O-F values in central Australia were traced back to deficiencies in the gauge-based precipitation product used in the land modeling system through L4_SM Version 5; this discovery prompted the use, beginning in Version 6, of satellite- and gauge-based precipitation observations outside of North America [9]. Recently, a statistical analysis of the Tb O-F residuals revealed systematic errors in the “tau-omega” L-band radiative transfer model that converts the land-model simulated soil moisture and temperature into the forecast Tb prior to the L4_SM analysis. The L4_SM analysis is built on the ensemble Kalman filter and assumes unbiased forecast errors. The presence of systematic Tb forecast errors could thus adversely impact the quality of the analyzed soil moisture. In this paper, we examine the Tb O-F residuals of the latest Version 7 L4_SM data (Science Version ID Vv7030 and Vv7032) [10,11,12]. In the L4_SM Version 7 algorithm, key parameters of the L-band radiative transfer model, including soil roughness, scattering albedo, and a (seasonally varying) climatology of vegetation opacity, are obtained from the SMAP Level-2 Radiometer retrieval product (Version 5) [1]. Additionally, we also use ground measurements of surface soil moisture and soil temperature from the SMAP core validation sites as in situ reference of soil conditions [2,3]. As part of the L4_SM system calibration, the seasonally-varying bias between the model forecast Tb and the observed values is removed prior to the assimilation of the SMAP Tb observations [7]. That is, the L4_SM system is designed to only correct errors in synoptic-scale and interannual variations from the long-term mean seasonal cycle while maintaining the model’s (potentially erroneous) climatology. The Tb O-F residuals examined here are thus computed using SMAP Tb observations after they are rescaled to the mean seasonal cycle of Tb from the modeling system without SMAP data assimilation. Consequently, the long-term average of the Tb O-F residuals has a global mean of only 0.13 K and locally small values, ranging from -1 to 3 K. Despite the small time-average values of the Tb O-F residuals, the model forecast Tb was nevertheless found to exhibit undesirable systematic errors. At some locations, the time-average Tb O-F values strongly depend on surface soil moisture (SM). At the Yanco SMAP core validation site, for example, the Tb O-F residuals typically range from 5 to 15 K under dry soil moisture conditions (SM < 0.15 m3 m-3) yet are predominantly negative under wet soil moisture conditions (SM > 0.25 m3 m-3), with values ranging from 0 to -40 K. This results in soil moisture analysis increments that persistently make the soil drier under dry SM conditions and persistently make the soil wetter under wet SM conditions, suggesting an error in the dynamic range of the simulated Tb, soil moisture or soil temperature. In this paper, we describe the higher-order systematic Tb forecast errors in more detail, examine their impact on the L4_SM product quality, and explore potential avenues to further improve the L4_SM algorithm.

Rolf Reichle↗

DOE OSTI2021

Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

Residential electricity load profiles and their diversity have become increasingly important to realize the benefits of Smart or Transactive Energy Networks (TENs). An important element of TENs will be practical, accurate, and implementable residential load forecasting techniques. While there have been many approaches to short-term load forecasting, few have included forecasting for individual households, partly because the high volatility and idiosyncrasies present in individual household load data can pose significant challenges. In this study, we develop a Convolutional Long Short-Term Memory-based neural network with Selected Autoregressive Features (termed a CLSAF model) to improve short-term household electricity load forecasting accuracy by employing three strategies: autoregressive features selection, exogenous features selection, and a “default” state to avoid overfitting at times of high load volatility. We include aggregations of apartments to floor and building level, because utilities may favor transactive approaches that rely on aggregator models, e.g., a cluster of consumers as opposed to an individual. We demonstrate that the CLSAF model, by virtue of its enhanced feature representation and modest computational resources, can accomplish load forecasting in a multi-family residential building across three spatial granularities (individual apartment/household, floor, and building levels), with an accuracy improvement of up to 25% compared to a persistence model. We propose a data screening technique to characterize time-series electricity-load data. This technique is suitable for integration into a TEN ecosystem and allows one to estimate confidence levels of the load forecasts to optimize computational resources and the risks associated with uncertain forecasts.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Evaluating WRF-Solar EPS cloud mask forecast using the NSRDB

Improving the accuracy of day-ahead solar forecasts using numerical weather prediction models requires improving the forecasting of cloud occurrence and properties. Validating cloud forecasts is challenging because this requires evaluating different types of clouds over a wide variety of regions. This study analyzes the cloud occurrence (or cloud mask) over the contiguous United States (CONUS), predicted by the Weather Research and Forecasting-Solar Ensemble Prediction System (WRF-Solar EPS), to identify the strengths and limitations of the model in reproducing cloud fields. To enable the in-depth analysis of cloud mask forecasts covering CONUS, we use satellite observations from the National Solar Radiation Database (NSRDB). Two evaluation methods are implemented to consider all clouds and partially removed clouds from the 2-km NSRDB in evaluating the 9-km WRF-Solar EPS cloud mask. Cloud detection metrics as well as the frequency of cloud occurrence are used to quantify the monthly performance of WRF-Solar EPS. Mismatched cloud frequency (MCF) is used to assess the model's capability to predict different types of clouds, which are classified using three levels of cloud optical depth (COD) and cloud top height (CTH). The day-ahead forecasts covering the full year of 2018 demonstrate that WRF-Solar EPS produces MCFs ranging from 27%-46%, 13%-34%, and 8%-19% for thin, mid-thickness, and thick clouds, respectively. For three CTH levels, the model shows MCFs ranging from 19%-46%, 16%-33%, and 8%-27% for low-level, middle-level, and high-level clouds, respectively. This comprehensive characterization of model performance helps identify model weakness and will eventually lead to improvements in cloud and solar radiation forecasting.

14 SOLAR ENERGY↗

DOE OSTI2022

How Can Probabilistic Solar Power Forecasts Be Used to Lower Costs and Improve Reliability in Power Spot Markets? A Review and Application to Flexiramp Requirements

Net load uncertainty in electricity spot markets is rapidly growing. There are five general approaches by which system operators and market participants can use probabilistic forecasts of wind, solar, and load to help manage this uncertainty. These include operator situation awareness, resource risk hedging, reserves procurement, definition of contingencies, and explicit stochastic optimization. We review these approaches, and then provide a case study in which a method for using probabilistic solar forecasts to define needs for reserves is developed and evaluated. The case study has three parts. First, we describe building blocks for enhancing the Watt-Sun solar forecasting system to produce probabilistic irradiance and power forecasts. Second, relationships between Watt-Sun forecasts for multiple sites in California and the system's need for flexible ramp capability (flexiramp) are defined by machine learning and statistical methods. Third, the performance of present methods to defining flexiramp requirements, which are not conditioned on weather and renewables forecasts, is compared with that of probabilistic solar forecast-based requirements, using a multi-timescale production costing model with an 1820-bus representation of the WECC power system. Significant potential savings in fuel and flexiramp procurement costs from using solar-informed reserve requirements are found.

14 SOLAR ENERGY↗