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The energy system needs a range of forecast types for its operation in addition to the narrow wind power forecast. Therefore, the group behind the former IEA Wind Task 36 Forecasting for Wind Energy (running 6 years, from 2016-2021) has broadened its perspective on forecasting issues in part by reaching out to other IEA Technology Collaboration Programmes such as the ones for PV, hydropower, system integration, hydrogen etc. The three existing Work Packages (WPs) on NWP Improvements (WP1), Power and Uncertainty Forecasting (WP2) and optimal use of Forecasting Solutions (WP3), are complemented by thirteen work streams in a matrix structure. The three work packages span three distinct areas of challenge in forecasting for the weather driven energy system. The first area is the continuing effort to improve the representation of physical processes in weather forecast models through both new high performance initializations and tailored parameterizations. The second area is the heterogeneity of the forecasters and end users, the full understanding of the uncertainties throughout the modelling chain and the incorporation of novel data into power forecasting algorithms. A third area is representation, communication, and use of these uncertainties to industry in forms that readily support decision-making in plant operations and electricity markets. Task 51 will focus on facilitating communication and collaborations among international research groups engaged in the improvement of the accuracy and applicability of forecast models and their utility for the stakeholders in the wind industry, in the power sector and in the energy system. The first two activities of Task 51 were (1) a workshop in Dublin on the State of the Art and Research Gaps for Forecasting. The results of the workshop will be compiled into a journal article, and (2) the publication of the IEA Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions as an open access book by Elsevier. Other planned activities include further workshops on seasonal forecasting with emphasis on Dunkelflaute, storage and hydro in May 2023, a workshop on minute-scale forecasting (2024), and a workshop on extreme power system events (2025). The results of these interactive workshops will be compiled into a journal articles. Additionally, the Recommended Practice on Forecast Solution Selection will be updated to reflect the broader perspective. Reference: Corinna Möhrlen, John Zack, Gregor Giebel (eds): IEA Wind Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions. Elsevier, 348 pages, Nov. 2022. ISBN: 9780443186813. Download the individual chapters from https://www.sciencedirect.com/book/9780443186813/iea-wind-recommended-practice-for-the-implementation-of-renewable-energy-forecasting-solutions.

During August and September 2012, a study was conducted to determine the sources of PM2.5 adjacent to the I-710 Long Beach Freeway. The site is directly affected by the emissions from heavy diesel traffic flowing from major container ports about 10 km south of the sampling site. Hourly average data were obtained for particulate species including PM2.5, black carbon and UV absorbing carbon, EC, fine particulate nonvolatile and semi-volatile organic material (NVOM and SVOM), sulfate, nitrate, chloride, ammonium ion, and Na ion, and for related factors including O3, CO, NOX, SO2, and total traffic flow on the I-710. A total of 520 hourly averaged data sets with 15 measured variables were analyzed by EPA-PMF v5.0. The data were best described by a 10-factor solution. Based on the composition and diurnal patterns of the factors, they were assigned to three diesel-related factors (two of which appeared to represent traffic from the ports and one general freeway diesel factor), a light-duty, spark-ignition vehicle-related factor, three secondary factors (one of which was associated with O3 formation processes), and three factors dominated by sulfate, SO2, and chloride, respectively. The diurnal patterns for these last three factors are strongly correlated. Meteorological and refinery upset data indicate that they are associated with emissions from a nearby refinery. The results of the PMF analysis were combined with nephelometer light scattering, corrected for coarse particle scattering and estimated aerosol water content in a multilinear regression analysis to identify visibility degradation sources. Major contributors were the aerosol water content, and the secondary PMF factors associated with either Nitrate and NVOM or NVOM and SVOM. The use of hourly average data made possible the identification of factors associated with gasoline vehicle emissions and both port and non-port diesel emissions.Implications: Hourly averaged data were obtained for PM2.5, its components and factors related to primary emissions and the formation of secondary material at a near freeway sampling location adjacent to the I-710 freeway just south of the Long Beach Boulevard entrance and 10 km north of the Ports of Long Beach and Los Angeles. The major objective of the study was to determine the impact of traffic from the ports at the monitoring site. This manuscript reports on the PMF analysis of the data set. Factors related to both diesel traffic originating from the ports and diesel traffic from non-port origins were identified. The diesel traffic originating from the ports was responsible for 9% of the total traffic and 95% of the BC measured at the sampling site. The non-port diesel traffic was responsible for 15% of the total traffic and 5% of the BC. While the Port 1 diesel traffic coming from the ports contributed a large fraction of the BC, this source contributed only 2% of the CO and 5% of the NOX at the sampling site. The impact of these traffic sources on light scattering was also small. Analysis of sources of sulfate and SO2 at the sampling site indicated that these species did not come from port activities of ships at or approaching the port, but rather from upset flare events at a nearby oil refinery.

The last 6 years, the International Energy Agency (IEA) Wind Task 36 “Forecasting for Wind Energy” has provided forecasting stakeholders (weather institutes, forecast service providers, end users and academics) a platform to discuss challenges and benefits of forecasting for wind power. These discussions have led to a number of activities and initiatives to overcome challenges and to broadcast the benefits of forecasting. Among the major outputs are an information portal with links to free data, a collection of use cases for probabilistic forecasts, and the IEA Recommended Practice on Forecast Solution Selection, including 4 chapters dealing with the (1) solution selection process, (2) benchmarks and trials, (3) verification process and use of online measurements from wind farms for real-time forecasting applications. In the future, we will no longer “integrate” wind and solar into existing power systems, but instead are wind and solar going to be the backbone of our power systems.To address those challenges in an integrated fashion, the IEA Task for Forecasting under the IEA Wind Technology Collaboration Programme (TCP) relaunched with a new Task number (51) and a new work program. The work packages (WPs) are still structured according to stakeholder topics: WP1 deals with weather forecasting, and mainly addresses meteorologists, WP2 deals with the conversion of the weather feeds to the application specific variables such as wind power and addresses forecast vendors, and WP3 deals with the applications and how to get most value out of the forecasts, and therefore addresses the forecast users, including recent advances in data science and digitalisation. However, many of the topics the new Task takes up are cross-cutting, and are therefore now Work Streams (WS):Atmospheric physics and modelling (lead by WP1) Airborne Wind Energy Systems (WP1) Seasonal forecasting (WP1) State of the Art for energy system forecasting (WP2) Forecasting for underserved areas (WP2) Minute scale forecasting (WP2) Uncertainty / probabilistic forecasting (WP3) Decision making under uncertainty (WP3) Extreme power system events (WP3) Data science and artificial intelligence (WP3) Privacy, data markets and sharing (WP3) Value of forecasting (WP3) Forecasting in the design phase (WP3) Most of these work streams require collaboration, and therefore have dedicated partners in other IEA Wind Tasks, or in IEA Tasks outside of the Wind TCP. Task 51 will therefore collaborate with IEA Wind Tasks 32, 44, 48 and 50, IEA PVPS Task 16, IEA Hydro, the IEA Hydrogen TCP, IEA Bioenergy Task 44 and WMO. A major activity of Task 51 will be four public workshops in the next four summers, starting with a workshop on the State of the Art and Research Gaps in 2022, on seasonal forecasting with a special emphasis on hydro power and storage in 2023, on minute scale forecasting in 2024, and on extreme power system events in 2025. We keep the community updated on events, new publications and other relevant information on our website ieawindforecasting.dk and via LinkedIn and Research gate.

This study demonstrates improvements in ensemble wind forecasts at hub height due to bias correction strategies and their impact on wind energy forecasts at the Alta II wind farm in southern California. The ensemble consists of twenty members that differ in physics schemes used. Ensemble wind forecasts are produced for three months. Hub-height sodar wind observations are used to evaluate forecast performance. Time-dependent bias correction (TBC) and probability bias correction (PBC) are proposed to calibrate hub-height ensemble wind forecasts. The root mean square errors (RMSEs) and biases of forecasted hub-height winds are significantly reduced using both bias correction methods. RMSEs were reduced by 20% and 15% for TBC and PBC, respectively. When evaluating forecast performance from a reliability perspective, PBC better corrects both high and low forecast probabilities for high-wind thresholds. The penalty associated with deviation of the wind energy forecasts from observed values is reduced by 8.7% and 8.0% for TBC and PBC, respectively. It is notable that PBC produces a greater penalty reduction during the high wind bias forecast period (6 p.m.–8 a.m. local standard time); during this time period, PBC reduces the penalty by 25.8% while TBC reduces it by just 19.2%.

The International Energy Agency (IEA) Wind Task 36 on Wind Power Forecasting organises international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, ...), forecast vendors and forecast users to facilitate scientific exchange to be prepared for future challenges.The talk discusses the general setup of the Task, and the latest developments. Among those are decision making under uncertainty. To this aim, a series of forecasting experiments are being developed and one initial experiment was tested by a wide audience. The forecasting experiments took the form of a game, during which the participants could experience the benefit of probabilistic information on their decisions to trade. Other results include an information portal for meteorological data, and the IEA Recommended Practice for Forecast Solution Selection which is divided into 3 parts: (1) "Forecast Solution Selection Process", (2) "Designing and Executing Forecasting Benchmarks and Trials", and (3) "Evaluation of Forecasts and Forecast Solutions". The Recommended Practice guideline encourages forecast users to establish a framework of metrics that help identify, whether the user's forecast performance criteria effectively incentivize the forecast provider to optimize towards the forecast target variable that has the most value for the user's application(s). For this year, we intend to update the guideline in the light of the experiences throughout the industry in its initial application, and after collecting this experience at 3 Open Space workshops.Collaboration is open to IEA Wind member states; 12 countries are already actively collaborating.The Task is divided in three work packages: Work Package (WP) 1 is a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction (NWP) model physics, but also widely distributed information on accessible datasets. This WP also currently organises a benchmark for NWP models, based on the Wind Forecast Improvement Project 2 (WFIP2) datasets. WP2 deals with the power conversion from the wind speed forecasts and the associated vendor issues. Amongst other things, WP2 published the IEA Recommended Practice on how to select an optimal wind power forecast solution for a specific application. The focus of WP3 is on the engagement of end users to disseminate the best practice in the use of wind power predictions, especially probabilistic forecasts and also what kind of measurements are required in real-time environmentsA major activity of the Task is the organisation of workshops and special sessions at conferences, like this one. Previous workshops on e.g. forecasting on the minute scale including lidars, a workshop on the value of forecasts, or special sessions on the Wind Energy Science Conference, the Wind Integration Workshop, the ESIG Meteorology and Market Design for Grid Services workshops are still visible online from the IEAWindForecasting YouTube channel.

Wind power forecasts have been operationally used for over 25 years. Despite this fact, there are still many possibilities to improve and enhance forecasts, both from the weather prediction side and in the use of the forecasts. Until now, most applications have focused on deterministic forecast methods. This is likely to change in the future as penetration levels increase and weather conditions become more unstable due to climate change. Probabilistic methods are therefore receiving more attention from users. The International Energy Agency (IEA) Wind Task 36 on Wind Power Forecasting organises international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, ...), forecast vendors and forecast users to facilitate scientific exchange to be prepared for future challenges.Collaboration is open to IEA Wind member states; 12 countries are already actively collaborating. The Task is divided in three work packages: Work Package (WP) 1 is a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction (NWP) model physics, but also widely distributed information on accessible datasets. This WP will also organise benchmarks for NWP models. The efforts of WP2 resulted in the publication of an international pre-standard (an IEA Recommended Practice) on how to select an optimal wind power forecast solution for a specific application. The focus of WP3 is on the engagement of end users to disseminate the best practice in the use of wind power predictions, especially probabilistic forecasts.The paper presents an overview of the recently completed first phase and the ongoing second phase of IEA Task 36 on Wind Power Forecasting, which provides a forum forinternational collaboration in this important field for meteorologists, wind power forecasters and end users. For collaboration, please contact the author (grgi@dtu.dk) and seethe website at www.ieawindforecasting.dk.

During August and September 2012, a study was conducted to determine the sources of PM2.5 adjacent to the I-710 Long Beach Freeway. The site is directly affected by the emissions from heavy diesel traffic flowing from major container ports about 10 km south of the sampling site. Hourly average data were obtained for particulate species including PM2.5, black carbon and UV absorbing carbon, EC, fine particulate nonvolatile and semi-volatile organic material (NVOM and SVOM), sulfate, nitrate, chloride, ammonium ion, and Na ion, and for related factors including O3, CO, NOX, SO2, and total traffic flow on the I-710. A total of 520 hourly averaged data sets with 15 measured variables were analyzed by EPA-PMF v5.0. The data were best described by a 10-factor solution. Based on the composition and diurnal patterns of the factors, they were assigned to three diesel-related factors (two of which appeared to represent traffic from the ports and one general freeway diesel factor), a light-duty, spark-ignition vehicle-related factor, three secondary factors (one of which was associated with O3 formation processes), and three factors dominated by sulfate, SO2, and chloride, respectively. The diurnal patterns for these last three factors are strongly correlated. Meteorological and refinery upset data indicate that they are associated with emissions from a nearby refinery. The results of the PMF analysis were combined with nephelometer light scattering, corrected for coarse particle scattering and estimated aerosol water content in a multilinear regression analysis to identify visibility degradation sources. Major contributors were the aerosol water content, and the secondary PMF factors associated with either Nitrate and NVOM or NVOM and SVOM. The use of hourly average data made possible the identification of factors associated with gasoline vehicle emissions and both port and non-port diesel emissions.

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 International Energy Agency (IEA) Task on Wind Power Forecasting organises international collaboration, among national weather centres, forecast vendors and forecast users. The Task looks back on the first 3 years, and just started the second three-year period. Collaboration is open to IEA Wind member states, 12 countries are already therein.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 and a benchmark. Secondly, we try to improve the derived power forecasts and deal with forecast vendor related matters to bring the entire industry forward. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions.The main result of the first phase is the IEA Recommended Practice for Selecting Renewable Power Forecasting Solutions.This document in three parts (forecast solution selection process, designing and executing forecasting benchmarks and trials and evaluation of forecasts and forecast solutions) takes its outset from the recurrent problem of forecast users of how to choose a forecast solution. The first report describes how to tackle the general situation, while the second report specifically describes how to set up a forecasting trial so that the result is what the client intended. Many of the pitfalls we have seen over the years, are avoided. Other results of the first phase include a comprehensive review paper on the use of uncertainty forecasts in the power industry and an information portal related to forecasting.In the second phase of the Task, we will take up additional topics such as the uncertainty propagation through the modelling chain, the use of distributed measurements for forecasting, and some initial standardisation of data flows and formats.

Much of the electric system is weather dependent; thus, our ability to forecast the weather contributes to its efficient and economical operation. Climatological forecasts of meteorological variables are used for long-term planning, capturing changing frequencies of extreme events, such as cold and hot periods, and identifying suitable locations for deploying new resources. Planning for fuel delivery and maintenance relies on subseasonal to seasonal forecasts. On shorter timescales of days, the weather affects both energy demand and supply. Electrical load depends critically on weather because electricity is used for heating and cooling. As more renewable energy is deployed, it becomes increasingly important to understand how these energy sources vary with atmospheric conditions; thus, predictions are necessary for planning unit commitments. On the scales of minutes to hours, shortterm nowcasts aid in the real-time grid integration of these variable energy resources (VERs).

The deployment of solar-based electricity generation, especially in the form of photovoltaics (PVs), has increased markedly in recent years due to a wide range of factors including concerns over greenhouse gas emissions, supportive government policies, and lower equipment costs. Still, a number of challenges remain for reliable, efficient integration of solar energy. Chief among them will be developing new tools and practices that manage the variability and uncertainty of solar power.

The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included 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 collection of special observations to be assimilated into forecast models 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 United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemometers. Results demonstrate that a substantial reduction (12%–5% for forecast hours 1–12) in power RMSE was achieved from the combination of improved numerical weather prediction models and assimilation of new observations, equivalent to the previous decade’s worth of improvements found for low-level winds in NOAA/National Weather Service (NWS) operational weather forecast models. Data-denial experiments run over select periods of time demonstrate that up to a 6% improvement came from the new observations. Ensemble forecasts developed by the private sector partners also produced significant improvements in power production and ramp prediction. Based on the success of WFIP, DOE is planning follow-on field programs.

With the higher levels of wind power and other renewable generation now included in the North American electrical system, operational information plays an increasing role in the decisions that grid operators are asked to make on a daily basis. While wind forecasting was something that was considered a luxury only a few years ago, it is critical to today's operation. There are other solutions that the industry is developing to incorporate high levels of wind, such as demand-side management, developing flexible resources to accommodate ramping and cycling needs, and developing larger BAs and market tools to allow the development of ancillary services. But wind forecasting will always be a crucial part of the solution and the primary tool that grid operators will rely on to implement policies and procedures to reliably and economically operate the system.

Policy makers, responsible for the economic well-being of regions and states, have sought a variety of tools for predicting global change. One set of tools for simulating climate change, at a global scale, are the general circulation models (GCMs). Current versions of GCMs are limited to global studies, which are spatially crude and have output that is of minimal use to decision-makers accustomed to day-to-day, synoptic-scale changes in weather events. This paper discusses the use of a mesoscale model to “downscale” GCM output to the regional and local scales desired by policy makers. It presents a “nested” dynamical modeling approach. The paper also addresses several issues associated with the dynamical linking of GCMs and mesoscale models. Issues having to do with the choice of an atmospheric system, a variable as the best indicator of climate change, or having to do with validation of mesoscale simulations, which were based on GCM output, are considered.

This paper describes the capabilities and operational utility of a version of the Mesoscale Atmospheric Simulation System (MASS) that has been developed to support operational weather forecasting at the Kennedy Space Center (KSC) and Cape Canaveral Air Station (CCAS). The implementation of local, mesoscale modeling systems at KSC/CCAS is designed to provide detailed short-range (less than 24 h) forecasts of winds, clouds, and hazardous weather such as thunderstorms. Short-range forecasting is a challenge for daily operations, and manned and unmanned launches since KSC/CCAS is located in central Florida where the weather during the warm season is dominated by mesoscale circulations like the sea breeze. For this application, MASS has been modified to run on a Stardent 3000 workstation. Workstation-based, real-time numerical modeling requires a compromise between the requirement to run the system fast enough so that the output can be used before expiration balanced against the desire to improve the simulations by increasing resolution and using more detailed physical parameterizations. It is now feasible to run high-resolution mesoscale models such as MASS on local workstations to provide timely forecasts at a fraction of the cost required to run these models on mainframe supercomputers. MASS has been running in the Applied Meteorology Unit (AMU) at KSC/CCAS since January 1994 for the purpose of system evaluation. In March 1995, the AMU began sending real-time MASS output to the forecasters and meteorologists at CCAS, Spaceflight Meteorology Group (Johnson Space Center, Houston, Texas), and the National Weather Service (Melbourne, Florida). However, MASS is not yet an operational system. The final decision whether to transition MASS for operational use will depend on a combination of forecaster feedback, the AMU's final evaluation results, and the life-cycle costs of the operational system.

The grant The Application of High Resolution Dynamical-Numerical Models as a Tool to Infer Climate Statistics was designed to investigate the use of dynamical-numerical models as a tool for the inference of climate statistics. In addition this grant would develop a procedure that could be implemented at department of defense centers (particularly AFCCC). The main objective of this work was to determine the feasibility of using a dynamical-numerical model for the generation of climate statistics. Other objectives of the pro-posed work were: (1) Estimate the quality of climate statistics generated from a dynamical-numerical model, (2) Determine the change in quality of the statistics with varying amounts of observational data assimilated into the model, (3) Estimate the sensitivity of the statistics to the choice of numerical model. (4) Determine the variations in the quality of the statistics with differing simulation strategies, and (5) Investigate algorithms to estimate quantities not directly generated by the model (i.e., visibility, icing, etc.). The major effort of this work involved the simulations for January and July for ten years. The details of the results of these simulations are found in the accompanying technical reports. Here we will mainly address the objectives listed above.

The International Geosphere-Biosphere Program — Biological Aspects of the Hydrological Cycle (IGBP-BAHC) is concerned with the role of the biosphere in the hydrological cycle over a range of space and time scales. One crucial emphasis in this exercise involves ‘upscaling’ or aggregating the processes in the soil-vegetation-atmosphere interface from the patch scale of ecology to the mesoscale, larger regional scales and the continental scale. In time the upscaling must move from hours to decades and even longer time periods. A second emphasis involves the ‘downscaling’ of low-resolution climatic and meteorological data to high-resolution grids that are suitable for ecological and hydrological research. These two emphases are described along with prototype models for upscaling and downscaling. Other issues such as the incorporation of land surface heterogeneity in upscaling and the classification of atmospheric circulation patterns in downscaling are discussed along with other aspects related to these emphases are presented in greater detail. In particular the discussion of the downscaling component in BAHC summarizes the results of the most recent workshops (BAHC, 1993a and b).

This poster gives an overview of IEA Wind Task 36 for Wind Power Forecasting. The Operating Agent is Gregor Giebel of DTU, Co-Operating Agent is Will Shaw of PNNL. Collaboration in the task is solicited from everyone interested in the forecasting business. The second phase of Task 36 runs for three years, 2019-2021. The main deliverables from phase 1 (2016-2018) were (1) an IEA Wind Recommended Practice on the forecast solution selection and evaluation process, (2) an up-to-date list of current wind forecasting projects and their main project results, including datasets which can be used by researchers around the world to improve their own models, and (3) two review journal papers regarding the use of probabilistic forecasts and minute-scale forecasting and one Power & Energy magazine publication. Through workshops, webinars and meetings, dissemination of relevant information in both forecaster and end-user communities has been a focus area. Participation is open for all organisations in member states of the IEA Annex on Wind Power, see ieawind.org for the up-to-date list.