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Добірка наукової літератури з теми "Wind Forecasts"

Автор: Grafiati
Опубліковано: 11 березня 2023

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Статті в журналах з теми "Wind Forecasts"

1

Sampson, Charles R., Paul A. Wittmann, Efren A. Serra, Hendrik L. Tolman, Jessica Schauer, and Timothy Marchok. "Evaluation of Wave Forecasts Consistent with Tropical Cyclone Warning Center Wind Forecasts." Weather and Forecasting 28, no. 1 (February 1, 2013): 287–94. http://dx.doi.org/10.1175/waf-d-12-00060.1.

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Abstract An algorithm to generate wave fields consistent with forecasts from the official U.S. tropical cyclone forecast centers has been made available in near–real time to forecasters since summer 2007. The algorithm removes the tropical cyclone from numerical weather prediction model surface wind field forecasts, replaces the removed winds with interpolated values from surrounding grid points, and then adds a surface wind field generated from the official forecast into the background. The modified wind fields are then used as input into the WAVEWATCH III model to provide seas consistent with the official tropical cyclone forecasts. Although this product is appealing to forecasters because of its consistency and its superior tropical cyclone track forecast, there has been only anecdotal evaluation of resulting wave fields to date. This study evaluates this new algorithm for two years’ worth of Atlantic tropical cyclones and compares results with those of WAVEWATCH III run with U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) surface winds alone. Results show that the new algorithm has generally improved forecasts of maximum significant wave heights and 12-ft seas’ radii in proximity to tropical cyclones when compared with forecasts produced using only the NOGAPS surface winds.
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Riordan, Allen J. "Forecasting for a Remote Island: A Class Exercise." Bulletin of the American Meteorological Society 84, no. 6 (June 1, 2003): 777–84. http://dx.doi.org/10.1175/bams-84-6-777.

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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°26′S, 3°24′E). 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.
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3

Tyner, Bryce, Anantha Aiyyer, Jonathan Blaes, and Donald Reid Hawkins. "An Examination of Wind Decay, Sustained Wind Speed Forecasts, and Gust Factors for Recent Tropical Cyclones in the Mid-Atlantic Region of the United States." Weather and Forecasting 30, no. 1 (February 1, 2015): 153–76. http://dx.doi.org/10.1175/waf-d-13-00125.1.

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Abstract In this study, several analyses were conducted that were aimed at improving sustained wind speed and gust forecasts for tropical cyclones (TCs) affecting coastal regions. An objective wind speed forecast analysis of recent TCs affecting the mid-Atlantic region was first conducted to set a benchmark for improvement. Forecasts from the National Digital Forecast Database were compared to observations and surface wind analyses in the region. The analysis suggests a general overprediction of sustained wind speeds, especially for areas affected by the strongest winds. Currently, National Weather Service Weather Forecast Offices use a software tool known as the Tropical Cyclone Forecast/Advisory (TCM) wind tool (TCMWindTool) to develop their wind forecast grids. The tool assumes linear decay in the sustained wind speeds when interpolating the National Hurricane Center 12–24-hourly TCM product to hourly grids. An analysis of postlandfall wind decay for recent TCs was conducted to evaluate this assumption. Results indicate that large errors in the forecasted wind speeds can emerge, especially for stronger storms. Finally, an analysis of gust factors for recent TCs affecting the region was conducted. Gust factors associated with weak sustained wind speeds are shown to be highly variable but average around 1.5. The gust factors decrease to values around 1.2 for wind speeds above 40 knots (kt; 1 kt = 0.51 m s−1) and are in general insensitive to the wind direction, suggesting local rather than upstream surface roughness largely dictates the gust factor at a given location. Forecasters are encouraged to increase land reduction factors used in the TCMWindTool and to modify gust factors to account for factors including the sustained wind speed and local surface roughness.
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Knaff, John A., and Charles R. Sampson. "After a Decade Are Atlantic Tropical Cyclone Gale Force Wind Radii Forecasts Now Skillful?" Weather and Forecasting 30, no. 3 (June 1, 2015): 702–9. http://dx.doi.org/10.1175/waf-d-14-00149.1.

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Abstract The National Hurricane Center (NHC) has a long history of forecasting the radial extent of gale force or 34-knot (kt; where 1 kt = 0.51 m s−1) winds for tropical cyclones in their area of responsibility. These are referred to collectively as gale force wind radii forecasts. These forecasts are generated as part of the 6-hourly advisory messages made available to the public. In 2004, NHC began a routine of postanalysis or “best tracking” of gale force wind radii that continues to this day. At approximately the same time, a statistical wind radii forecast, based solely on climatology and persistence, was implemented so that NHC all-wind radii forecasts could be evaluated for skill. This statistical wind radii baseline forecast is also currently used in several applications as a substitute for or to augment NHC wind radii forecasts. This investigation examines the performance of NHC gale force wind radii forecasts in the North Atlantic over the last decade. Results presented within indicate that NHC’s gale force wind radii forecasts have increased in skill relative to the best tracks by several measures, and now significantly outperform statistical wind radii baseline forecasts. These results indicate that it may be time to reinvestigate whether applications that depend on wind radii forecast information can be improved through better use of NHC wind radii forecast information.
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Roads, JO, K. Ueyoshi, SC Chen, J. Alpert, and F. Fujioka. "Medium-range fire weather forecasts." International Journal of Wildland Fire 1, no. 3 (1991): 159. http://dx.doi.org/10.1071/wf9910159.

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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 a wet bias during the winter and a slight dry bias during the summer, which has noticeable impact on forecasts of the derived fire weather index. The FWI forecasts are also strongly affected by near-surface wind forecast errors. Still, skillful forecasts of the fire weather index as well as the other relevant fire weather variables are made out to about 10 days. These forecasts could be utilized more extensively by fire weather forecasters.
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6

Jacobs, A. J. M., and N. Maat. "Numerical Guidance Methods for Decision Support in Aviation Meteorological Forecasting." Weather and Forecasting 20, no. 1 (February 1, 2005): 82–100. http://dx.doi.org/10.1175/waf-827.1.

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Abstract Numerical guidance methods for decision making support of aviation meteorological forecasters are presented. The methods have been developed to enhance the usefulness of numerical weather prediction (NWP) model data and local and upstream observations in the production of terminal aerodrome forecasts (TAFs) and trend-type forecasts (TRENDs) for airports. In this paper two newly developed methods are described and it is shown how they are used to derive numerical guidance products for aviation. The first is a combination of statistical and physical postprocessing of NWP model data and in situ observations. This method is used to derive forecasts for all aviation-related meteorological parameters at the airport. The second is a high-resolution wind transformation method, a technique used to derive local wind at airports from grid-box-averaged NWP model winds. For operational use of the numerical guidance products encoding software is provided for automatic production of an alphanumeric TAF and TREND code. A graphical user interface with an integrated code editor enables the forecaster to modify the suggested automatic codes. For aviation, the most important parameters in the numerical guidance are visibility and cloud-base height. Both have been subjected to a statistical verification analysis, together with their automatically produced codes. The results in terms of skill score are compared to the skill of the forecasters’ TAF and TREND code. The statistical measures suggest that the guidance has the best skill at lead times of +4 h and more. For the short term, mainly trend-type forecasts, the persistence forecast based on recent observations is difficult to beat. Verification has also shown that the wind transformation method, which has been applied to generate 10-m winds at Amsterdam Airport Schiphol, reduces the mean error in the (grid box averaged) NWP model wind significantly. Among the potential benefits of these numerical guidance methods is increasing forecast accuracy. As a result, the related numerical guidance products and encoding software have been integrated in the operational environment for the production of TAFs and TRENDs.
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7

Knaff, John A., Charles R. Sampson, and Galina Chirokova. "A Global Statistical–Dynamical Tropical Cyclone Wind Radii Forecast Scheme." Weather and Forecasting 32, no. 2 (March 20, 2017): 629–44. http://dx.doi.org/10.1175/waf-d-16-0168.1.

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Abstract Forecasts of tropical cyclone (TC) surface wind structure have recently begun to show some skill, but the number of reliable forecast tools, mostly regional hurricane and select global models, remains limited. To provide additional wind structure guidance, this work presents the development of a statistical–dynamical method to predict tropical cyclone wind structure in terms of wind radii, which are defined as the maximum extent of the 34-, 50-, and 64-kt (1 kt = 0.514 m s−1) winds in geographical quadrants about the center of the storm. The basis for TC size variations is developed from an infrared satellite-based record of TC size, which is homogenously calculated from a global sample. The change in TC size is predicted using a statistical–dynamical approach where predictors are based on environmental diagnostics derived from global model forecasts and observed storm conditions. Once the TC size has been predicted, the forecast intensity and track are used along with a parametric wind model to estimate the resulting wind radii. To provide additional guidance for applications and users that require forecasts of central pressure, a wind–pressure relationship that is a function of TC motion, intensity, wind radii (i.e., size), and latitude is then applied to these forecasts. This forecast method compares well with similar wind structure forecasts made by global forecast and regional hurricane models and when these forecasts are used as a member of a simple consensus; its inclusion improves the forecast performance of the consensus.
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Hallgren, Christoffer, Stefan Ivanell, Heiner Körnich, Ville Vakkari, and Erik Sahlée. "The smoother the better? A comparison of six post-processing methods to improve short-term offshore wind power forecasts in the Baltic Sea." Wind Energy Science 6, no. 5 (September 16, 2021): 1205–26. http://dx.doi.org/10.5194/wes-6-1205-2021.

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Abstract. With a rapidly increasing capacity of electricity generation from wind power, the demand for accurate power production forecasts is growing. To date, most wind power installations have been onshore and thus most studies on production forecasts have focused on onshore conditions. However, as offshore wind power is becoming increasingly popular it is also important to assess forecast quality in offshore locations. In this study, forecasts from the high-resolution numerical weather prediction model AROME was used to analyze power production forecast performance for an offshore site in the Baltic Sea. To improve the AROME forecasts, six post-processing methods were investigated and their individual performance analyzed in general as well as for different wind speed ranges, boundary layer stratifications, synoptic situations and in low-level jet conditions. In general, AROME performed well in forecasting the power production, but applying smoothing or using a random forest algorithm increased forecast skill. Smoothing the forecast improved the performance at all wind speeds, all stratifications and for all synoptic weather classes, and the random forest method increased the forecast skill during low-level jets. To achieve the best performance, we recommend selecting which method to use based on the forecasted weather conditions. Combining forecasts from neighboring grid points, combining the recent forecast with the forecast from yesterday or applying linear regression to correct the forecast based on earlier performance were not fruitful methods to increase the overall forecast quality.
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Lehr, William J., Debra Simecek-Beatty, and Marc Hodges. "Wind Uncertainty in Long Range Trajectory Forecasts." International Oil Spill Conference Proceedings 2003, no. 1 (April 1, 2003): 435–39. http://dx.doi.org/10.7901/2169-3358-2003-1-435.

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ABSTRACT The essential equations of oil spill trajectory modeling have not changed in four decades. The vast majority of existing trajectory models divide the slick up into pieces, called Lagrangian Elements (LE's). The models move the LE's by summing movement vectors related to three somewhat interrelated components; surface water currents, turbulent diffusion, and surface wind stress. Improvement in modeling has consisted, chiefly, of better refining these three components. While such trajectory models generate predictions, the real world is filled with uncertainty. As the spill response community seeks longer-range forecasts, spill models must be able to not only provide a best-guess prediction but also to estimate the likelihood and extent of any errors in the prediction. Wind forecast error often is the major contributor to trajectory error for longer-range forecasts because, more so than currents, wind is subject to rapid change in magnitude and direction. This paper provides a formula to identify a possible practical limit of predictability for dynamical wind models. It also describes a method to estimate probability bounds on slick location based on a selected set of past wind records that vary within a prescribed variance from the present and forecasted winds. Based on the degree of confidence in the wind forecast and the sensitivity to risk from a bad guess, the user can adjust the size of this variance. By extrapolating the set of selected wind records, longer-range trajectory bounds can be given that extend past any deterministic wind forecast and give long-range trajectory analysis to the spill responder.
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10

Theuer, Frauke, Andreas Rott, Jörge Schneemann, Lueder von Bremen, and Martin Kühn. "Observer-based power forecast of individual and aggregated offshore wind turbines." Wind Energy Science 7, no. 5 (October 24, 2022): 2099–116. http://dx.doi.org/10.5194/wes-7-2099-2022.

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Abstract. Due to the increasing share of wind energy in the power system, minute-scale wind power forecasts have gained importance. Remote-sensing-based approaches have proven to be a promising alternative to statistical methods and thus need to be further developed towards an operational use, aiming to increase their forecast availability and skill. Therefore, the contribution of this paper is to extend lidar-based forecasts to a methodology for observer-based probabilistic power forecasts of individual wind turbines and aggregated wind farm power. To do so, lidar-based forecasts are combined with supervisory control and data acquisition (SCADA)-based forecasts that advect wind vectors derived from wind turbine operational data. After a calibration, forecasts of individual turbines are aggregated to a probabilistic power forecast of turbine subsets by means of a copula approach. We found that combining the lidar- and SCADA-based forecasts significantly improved both forecast skill and forecast availability of a 5 min ahead probabilistic power forecast at an offshore wind farm. Calibration further increased the forecast skill. Calibrated observer-based forecasts outperformed the benchmark persistence for unstable atmospheric conditions. The aggregation of probabilistic forecasts of turbine subsets revealed the potential of the copula approach. We discuss the skill, robustness and dependency on atmospheric conditions of the individual forecasts, the value of the observer-based forecast, its calibration and aggregation, and more generally the value of minute-scale power forecasts of offshore wind. In conclusion, combining different data sources to an observer-based forecast is beneficial in all regarded cases. For an operational use one should distinguish between and adapt to atmospheric stability.
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Дисертації з теми "Wind Forecasts"

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Mason, Jesse Cheyenne. "On improving wind-turbine hub-height wind-speed forecasts." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46558.

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Siuta, David. "Improving hub-height wind forecasts in complex terrain." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/61055.

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Wind-speed forecasts from numerical-weather-prediction (NWP) models are important for daily wind-resource generation planning. However, NWP models are imperfect. The ability of energy planners to efficiently manage resources is a function of the accuracy of deterministic wind forecasts and of the associated probability estimates of forecast uncertainty. As the amount of energy generated from wind increases to significant levels, improving forecast accuracy and representation of forecast uncertainty is a key area of active research. This dissertation advances wind forecasting over regions of complex topography using the Weather Research and Forecasting (WRF) model. The optimal WRF-model configuration is a function of planetary-boundary-layer (PBL) physics, grid length, and initial-condition choice. The first component of this work determines which of these three factors most influences forecast accuracy over complex terrain. The two largest factors influencing forecast accuracy are the PBL-physics scheme and the grid length, with the dominant factor being a function of location, season, and time of day. The second component of the research addresses the need for probability-based forecast information, which is only recently being used within the industry. Wind forecasts from an ensemble using eight PBL schemes, three grid lengths, and two initial-conditions sources are converted into probability models that are then evaluated. Using the full, empirical ensemble distribution produces uncalibrated probabilistic forecasts. Prescribing a Gaussian probability distribution based on statistical moments of a past training dataset results in calibrated and sharp probabilistic forecasts. Such a method is also computationally cheap. The final aspect of this study evaluates the role of boundary-layer static stability on forecast performance. Traditionally, empirical surface-layer similarity theory has been used to relate surface fluxes of heat, momentum, and moisture to vertical profiles of temperature and wind. To evaluate and improve surface-layer similarity theories over mountain ridges, a year-long field campaign of temperature and wind measurements was conducted at wind farms in British Columbia. New empirical equations for complex terrain are proposed based on the field data, and found to perform well at an independent test location.
Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
Graduate
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3

Welsh, David J. S. "The improvement of wind-wave forecasts in the Great Lakes /." The Ohio State University, 1997. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487948807587679.

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Nchaba, Teboho. "Verification of gridded seasonal wind speed forecasts over South Africa." Master's thesis, University of Cape Town, 2013. http://hdl.handle.net/11427/4970.

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Includes abstract.
Includes bibliographical references.
The Climate System Analysis Group (CSAG) at the University of Cape Town produces provisional global and Southern African seasonal wind forecasts generated using the United Kingdom Meteorological Office Atmospheric General Circulation Model (AGCM) HadAM3P (non-standard version of HadAM3). This study examines the quality of the seasonal wind speed forecasts through a forecast verification process for continuous variables using reanalysis products of the National Centers for Environmental Prediction and the Department of Energy (NCEP-DOE) as observations data. The verification analyses are performed using summary measures Mean Error (ME), Mean Absolute Error (MAE), Mean Squared Error (MSE), correlation coefficients, Linear Error in Probability Space (LEPS) and exploratory methods, scatter and conditional quantile plots. These methods are used to determine the aspects of forecast quality namely, bias, accuracy, reliability, resolution, and skill over a 20 year period (1991 to 2010). The results of the study have determined that the use of both accuracy and skill measures for the verification analyses provide more information about the quality of the forecasts, as opposed only one of these. In all provinces, the highest quality seasonal wind speed forecasts are made at 500 hPa and the lowest quality forecasts at 1000 hPa. Furthermore regions, pressure levels, and seasons with the highest forecast quality share the common characteristic that their wind speeds are relatively high. The forecasts add value to the climatology and thus are a useful tool for wind assessment at a seasonal scale. It is suggested that adding spatial resolution to the forecasts through downscaling may prepare them for use in applications such as wind power output forecasting.
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5

Lau, Ada. "Probabilistic wind power forecasts : from aggregated approach to spatiotemporal models." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:f5a66568-baac-4f11-ab1e-dc79061cfb0f.

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Wind power is one of the most promising renewable energy resources to replace conventional generation which carries high carbon footprints. Due to the abundance of wind and its relatively cheap installation costs, it is likely that wind power will become the most important energy resource in the near future. The successful development of wind power relies heavily on the ability to integrate wind power effciently into electricity grids. To optimize the value of wind power through careful power dispatches, techniques in forecasting the level of wind power and the associated variability are critical. Ideally, one would like to obtain reliable probability density forecasts for the wind power distributions. As wind is intermittent and wind turbines have non-linear power curves, this is a challenging task and many ongoing studies relate to the topic of wind power forecasting. For this reason, this thesis aims at contributing to the literature on wind power forecasting by constructing and analyzing various time series models and spatiotemporal models for wind power production. By exploring the key features of a portfolio of wind power data from Ireland and Denmark, we investigate different types of appropriate models. For instance, we develop anisotropic spatiotemporal correlation models to account for the propagation of weather fronts. We also develop twostage models to accommodate the probability masses that occur in wind power distributions due to chains of zeros. We apply the models to generate multi-step probability forecasts for both the individual and aggregated wind power using extensive data sets from Ireland and Denmark. From the evaluation of probability forecasts, valuable insights are obtained and deeper understanding of the strengths of various models could be applied to improve wind power forecasts in the future.
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6

de, Almeida Francisco M. S. C. "The influence of wind on HF radar surface current forecasts." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://edocs.nps.edu/npspubs/scholarly/theses/2008/Dec/08Dec%5Fde%5FAlmeida.pdf.

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Анотація:
Thesis (M.S. in Physical Oceanography)--Naval Postgraduate School, December 2008.
Thesis Advisor(s): Paduan, Jeffrey. "December 2008." Description based on title screen as viewed on January 29, 2009. Includes bibliographical references (p. 67-69). Also available in print.
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7

Sjöberg, Ludvig. "Wind Forecasts Using Large Eddy Simulations for Stratospheric Balloon Applications." Thesis, Luleå tekniska universitet, Rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74457.

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The launch of large stratospheric balloons is highly dependant on the meteorological conditions at ground level, including wind speed. The balloon launch base Esrange Space Center in northern Sweden currently uses forecasts delivered through the Swedish Meteorological and Hydrological Institute to predict opportunities for balloon launches. However the staff at Esrange Space Center experience that the current forecasts are not accurate enough. For that reason the Weather Research and Forecasting model is used to improve the forecast. The model performs a Large Eddy Simulation over the area closest to Esrange Space Center to predict wind speed and turbulence. During twelve hypothetical launch days the improved forecast have an overall accuracy of 93% compared to the old forecast accuracy of 69%. With some improvements and the right computational power the system is thought to be operationally viable.
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Gallaher, Shawn G. "Performance of a high resolution diagnostic model for short range mesoscale wind forecasts in complex terrain." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FGallaher.pdf.

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Анотація:
Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, September 2002.
Thesis advisor(s): Douglas K. Miller, Wendell A. Nuss. Includes bibliographical references (p. 125-128). Also available online.
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Olaofe, Zaccheus Olaniyi. "Wind energy generation and forecasts: a case study of Darling and Vredenburg sites." Master's thesis, University of Cape Town, 2013. http://hdl.handle.net/11427/16831.

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Анотація:
This research study presents the wind resource assessment at two potential onshore wind sites at the Western Cape of South Africa for small and large scale wind energy generation. It is anticipated that by virtue of the enormous wind resources prevalent along the South Africa West Coast, it is economical and cost effective to generate electricity from the wind to offset the increasing cost of energy generation from non-renewable sources (coal-fired, nuclear, gas etc.) which are the major source of power generation. Despite the environmental benefit and economic potentials of the wind energy, its variability and the inability to accurately predict (estimate) the long term energy generation potentials usually lead to difficulties in the selection and development of a suitable wind site for any proposed wind farm project(s) in the country.
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Ive, Federica. "Improving numerical simulation methods for the assessment of wind source availability and related power production for wind farms over complex terrain." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/350981.

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Анотація:
One of the Sustainable Development Goals set in 2015 by the United Nations aims to ensure access to affordable, reliable, sustainable, and modern energy for all, increasing the global share of renewable energy to 32-35% by 2030. Moving towards this goal, the University of Trento funded the interdepartmental strategic project ERiCSol (Energie Rinnovabili e Combustibili Solari), in order to promote the research on renewable energy storage and solar fuels. The research activity presented in this thesis lies in the framework of this project, focusing on the development of new advanced simulation approaches to improve the estimation of the wind resource availability and the related power production for Italian wind farms in complex terrain. The wind farms, operated by the company AGSM S.p.A., are located in two different geographical contexts: Rivoli Veronese and Affi are at the inlet of the Adige Valley, while Casoni di Romagna and Carpinaccio Firenzuola, are on the crest of the Apennines close to the borders between the provinces of Bologna e Firenze. The analysis of data from year-long field measurements highlighted the different peculiarities of these areas. The wind farms at the mouth of the Adige Valley are influenced by a daily periodic thermally-driven circulation, characterised by a nocturnal intense down-valley wind alternating with a diurnal weaker up-valley wind, while the Apennines wind farms are primarily affected by synoptic-scale winds. Simulations, with the mesoscale Weather Research and Forecasting (WRF) model, are performed and compared with field measurements in both cases, to highlight strengths and weaknesses. The results show that the model is able to capture with good accuracy wind speed and direction in the Apennines wind farms, while larger errors arise for Rivoli Veronese and Affi wind farms, where the intensity of the nocturnal down-valley wind is generally underestimated. Considering the former case, modelled and observed yearly wind speed density distributions are compared, in order to evaluate the impact of model errors in the estimation of the wind resource at these sites. Since reliable simulations of the wind resource are also essential to ensure the security in power transmission and to prevent penalties to energy operators, an analysis of the power production is also performed, to evaluate how errors in the estimate of the resource translate into errors in the estimate of the production. Considering the wind farms at the mouth of the Adige Valley, the research work mainly focuses on the evaluation of the impact of data assimilation by means of observational nudging on model results, in order to optimize the setup for operational forecasts. Different configurations are tested and compared, varying the temporal window for the assimilation of local data.
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Книги з теми "Wind Forecasts"

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G, Benjamin Stanley, and Forecast Systems Laboratory (U.S.), eds. A study of the accuracy of RUC-1 and RUC-2 wind and aircraft trajectory forecasts using ACARS observations. Boulder, Colo: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Forecast Systems Laboratory, 1998.

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2

Grasping for the wind: The search for meaning in the 20th century. Grand Rapids, Mich: Zondervan, 2001.

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John F. Kennedy Space Center. and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. Statistical short-range guidance for peak wind speed forecasts on Kennedy Space Center/Cape Canaveral Air Force Station: Phase 1 results. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 2002.

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John F. Kennedy Space Center. and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. Statistical short-range guidance for peak wind speed forecasts on Kennedy Space Center/Cape Canaveral Air Force Station: Phase 1 results. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 2002.

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5

Inc, Icon Group International, ed. The 2002 world market forecasts for imported wine. San Diego, Calif: Icon Group, 2002.

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6

K, Sahai A., and Indian Institute of Tropical Meteorology., eds. Experimental seasonal forecast of Indian summer monsoon 2007: Statistical and dynamical models. Pune: Indian Institute of Tropical Meteorology, 2007.

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Mandke, S. K. Dynamical ensemble seasonal forecast experiments of recent Indian summer monsoons: An assessment using new approach. Pune: Indian Institute of Tropical Meteorology, 2005.

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8

Space Environment Center (U.S.), ed. Wang and Sheeley medium-range planetary A index forecast verification statistics. Boulder, Colo: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Space Environment Center, 1996.

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Space Environment Center (U.S.), ed. Wang and Sheeley medium-range planetary A index forecast verification statistics. Boulder, Colo: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Space Environment Center, 1996.

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Space Environment Center (U.S.), ed. Wang and Sheeley medium-range planetary A index forecast verification statistics. Boulder, Colo: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Space Environment Center, 1996.

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Частини книг з теми "Wind Forecasts"

1

Kay, Merlinde, and Iain MacGill. "Improving NWP Forecasts for the Wind Energy Sector." In Weather Matters for Energy, 413–28. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-9221-4_20.

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Kirthika, N., K. I. Ramachandran, and Sasi K. Kottayil. "Deep Quantile Regression Based Wind Generation and Demand Forecasts." In Advances in Intelligent Systems and Computing, 112–22. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49345-5_12.

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Moritz, Richard E. "Accuracy of Surface Geostrophic Wind Forecasts in the Central Arctic." In The Geophysics of Sea Ice, 1135–61. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-5352-0_19.

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4

Giannitrapani, Antonio, Simone Paoletti, Antonio Vicino, and Donato Zarrilli. "Optimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts." In 2013 Proceedings of the Conference on Control and its Applications, 13–20. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973273.3.

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Abel, Rafael, Lutz Pegel, and Andreas Waldmann. "On the Importance of Highly Resolved Wind Forecasts for Range Estimation." In Proceedings, 187–96. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37011-4_16.

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Di Napoli, Claudia, Alessandro Messeri, Martin Novák, João Rio, Joanna Wieczorek, Marco Morabito, Pedro Silva, Alfonso Crisci, and Florian Pappenberger. "The Universal Thermal Climate Index as an Operational Forecasting Tool of Human Biometeorological Conditions in Europe." In Applications of the Universal Thermal Climate Index UTCI in Biometeorology, 193–208. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76716-7_10.

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AbstractIn operational weather forecasting standard environmental parameters, such as air temperature and humidity, are traditionally used to predict thermal conditions in the future. These parameters, however, are not enough to describe the thermal stress induced by the outdoor environment to the human body as they neglect the human heat budget and personal characteristics (e.g. clothing). The Universal Thermal Climate Index (UTCI) overcomes these limitations by using an advanced thermo-physiological model coupled with a state-of-the-art clothing model. Several systems have been recently developed to operationally forecast human biometeorological conditions via the UTCI, i.e. by computing UTCI from the forecasts of air temperature, humidity, wind speed and radiation as provided by numerical weather prediction models. Here we describe the UTCI-based forecasting systems developed in Czech Republic, Italy, Poland, Portugal and at the pan-European scale. Their characteristics are illustrated and their potential as warning systems for thermal hazards discussed.
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Alonzo, Bastien, Riwal Plougonven, Mathilde Mougeot, Aurélie Fischer, Aurore Dupré, and Philippe Drobinski. "From Numerical Weather Prediction Outputs to Accurate Local Surface Wind Speed: Statistical Modeling and Forecasts." In Springer Proceedings in Mathematics & Statistics, 23–44. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99052-1_2.

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Almeida, Vânia, and João Gama. "Collaborative Wind Power Forecast." In Adaptive and Intelligent Systems, 162–71. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11298-5_17.

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Zjavka, Ladislav, Stanislav Mišák, and Lukáš Prokop. "Post-processing of Wind-Speed Forecasts Using the Extended Perfect Prog Method with Polynomial Neural Networks to Elicit PDE Models." In Hybrid Intelligent Systems, 11–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14347-3_2.

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10

Nielsen, Henrik Aalborg. "Short Term Forecast of Wind Power." In Advances in Wind Energy Conversion Technology, 107–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-540-88258-9_4.

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Тези доповідей конференцій з теми "Wind Forecasts"

1

Kemper, Jason J., Mark F. Bielecki, and Thomas L. Acker. "Modeling of Wind Power Production Forecast Errors for Wind Integration Studies." In ASME 2010 4th International Conference on Energy Sustainability. ASMEDC, 2010. http://dx.doi.org/10.1115/es2010-90441.

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In wind integration studies, accurate representations of the wind power output from potential wind power plants and corresponding representations of wind power forecasts are needed, and typically used in a production cost simulation. Two methods for generating “synthetic” wind power forecasts that capture the statistical trends and characteristics found in commercial forecasting techniques are presented. These two methods are based on auto-regressive moving average (ARMA) models and the Markov random walk method. Statistical criteria are suggested for evaluation of wind power forecast performance, and both synthetic forecast methods proposed are evaluated quantitatively and qualitatively. The forecast performance is then compared with a commercial forecast used for an operational wind power plant in the Northwestern United States evaluated using the same statistical performance measures. These quantitative evaluation parameters are monitored during specific months of the year, during rapid ramping events, and at all times. The best ARMA based models failed to replicate the auto-regressive decay of forecast errors associated with commercial forecasts. A modification to the Markov method, consisting of adding a dimension to the state transition array, allowed the forecast time series to depend on multiple inputs. This improvement lowered the artificial variability in the original time series. The overall performance of this method was better than for the ARMA based models, and provides a suitable technique for use in creating a synthetic wind forecast for a wind integration study.
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2

Zhang, Jie, Anthony Florita, Bri-Mathias Hodge, and Jeffrey Freedman. "Ramp Forecasting Performance From Improved Short-Term Wind Power Forecasting." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34775.

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The variable and uncertain nature of wind generation presents a new concern to power system operators. One of the biggest concerns associated with integrating a large amount of wind power into the grid is the ability to handle large ramps in wind power output. Large ramps can significantly influence system economics and reliability, on which power system operators place primary emphasis. The Wind Forecasting Improvement Project (WFIP) was performed to improve wind power forecasts and determine the value of these improvements to grid operators. This paper evaluates the performance of improved short-term wind power ramp forecasting. The study is performed for the Electric Reliability Council of Texas (ERCOT) by comparing the experimental WFIP forecast to the current short-term wind power forecast (STWPF). Four types of significant wind power ramps are employed in the study; these are based on the power change magnitude, direction, and duration. The swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental short-term wind power forecasts improve the accuracy of the wind power ramp forecasting, especially during the summer.
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Haupt, Sue Ellen, Gerry Wiener, Yubao Liu, Bill Myers, Juanzhen Sun, David Johnson, and William Mahoney. "A Wind Power Forecasting System to Optimize Power Integration." In ASME 2011 5th International Conference on Energy Sustainability. ASMEDC, 2011. http://dx.doi.org/10.1115/es2011-54773.

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The National Center for Atmospheric Research (NCAR) has developed a wind prediction system for Xcel Energy, the power company with the largest wind capacity in the United States. The wind power forecasting system includes advanced modeling capabilities, data assimilation, nowcasting, and statistical post-processing technologies. The system ingests both external model data and observations. NCAR produces a deterministic mesoscale wind forecast of hub height winds on a very fine resolution grid using the Weather Research and Forecasting (WRF) model, run using the Real Time Four Dimensional Data Assimilation (RTFDDA) system. In addition, a 30 member ensemble system is run to both improve forecast accuracy and provide an indication of forecast uncertainty. The deterministic and ensemble model output plus data from various global and regional models are ingested by NCAR’s Dynamic, Integrated, Forecast System (DICast®), a statistical learning algorithm. DICast® produces forecasts of wind speed for each wind turbine. These wind forecasts are then fed into a power conversion algorithm that has been empirically derived for each Xcel power connection node. In addition, a ramp forecasting technology fine-tunes the capability to accurately predict the time, magnitude, and duration of a ramping event. This basic system has consistently improved Xcel’s ability to optimize the economics of incorporating wind energy into their power system.
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4

Bai, Li, Emanuele Crisostomi, Marco Raugi, and Mauro Tucci. "Wind Power Forecast Using Wind Forecasts at Different Altitudes in Convolutional Neural Networks." In 2019 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2019. http://dx.doi.org/10.1109/pesgm40551.2019.8973938.

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Sroga, J., A. Rosenberg, L. O’Hara, P. Hays та B. Kennedy. "Ground Based 0.53 μm Wind Sensor". У Optical Remote Sensing. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/ors.1985.wc30.

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Laser based sensors have recently received increasing attention as systems to remotely measure basic state parameters of the atmosphere such as temperature, moisture, pressure and winds. Ground based lidar systems are currently in various stages of development to demonstrate these measurement capabilities and spaceborne lidar systems have been proposed for obtaining global coverage of data required to improve numerical weather forecasts. Measurements of the wind field have been shown to be particularly important in improving numerical forecasts (Halem, 1983).
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Chagas, F. M., B. R. F. Rachid, B. G. Ambrosio, A. A. Luz, C. B. Gramcianinov, P. F. Serrao, R. Camargo, and E. Siegle. "Assessment of Wind and Wave High-Resolution Forecasts During High-Energy Weather Events in the Brazilian Coast." In ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/omae2021-62030.

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Abstract We present a high-resolution metocean forecast model (Aimar), which provides 24/7 results for the Brazilian coast. The model integrates global model boundary conditions and detailed coastal models, especially for complex geometry areas near ports and major coastal cities. The aim of this paper is to assess the forecast reliability and to present model data compared to in-situ measurements under high energy weather events. Mean wind velocity and direction were investigated during the occurrence of an extratropical cyclone near Brazilian coast. The model has been assessed by comparing its results to two specific events, one for winds and one for waves. Results of the tested wind event show that Aimar results predict the high energy winds in advance of 5 days, while NCEP’s Global Forecast System Ensemble (GFSe) predicted the same event in advance of 2–3 days, for the region of Santos city. Results of the tested wave event show that Aimar forecasts properly represent the wave propagation for complex geometry coasts. The high-resolution coastal model could predict the nearshore state of sea agitation caused by the passage of a cold front. Model agreement with in-situ wave measurements adjacent to Rio de Janeiro-RJ city were considered Excellent and Good, according to statistical parameters R and RMAE. These results show that high-resolution coastal forecast models can be applied to increase the efficiency, resource uses and reduce the risks for marine operations and engineering works.
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Wu, Yuan-Kang, Po-En Su, and Jing-Shan Hong. "An overview of wind power probabilistic forecasts." In 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 2016. http://dx.doi.org/10.1109/appeec.2016.7779540.

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Kaso, Mathias, Felix Musgens, and Oliver Grothe. "Dynamic forecast combinations of improved individual forecasts for the prediction of wind energy." In 2016 13th International Conference on the European Energy Market (EEM). IEEE, 2016. http://dx.doi.org/10.1109/eem.2016.7521228.

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Campos, Ricardo Martins, Andressa D’Agostini, Leandro Machado Cruz, Bruna Reis Leite França, and C. Guedes Soares. "Extreme Wind and Wave Predictability From Operational Forecasts at the Drake Passage." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-96626.

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Abstract The Drake Passage is an ocean area between the South America and Antarctic with very extreme wind and wave climates. The forecast accuracy of surface winds and significant wave heights is analyzed in the present paper, in order to study the behavior and distribution of model uncertainties as a function of: forecast range, severity, location, and numerical model. The operational forecast considered is run twice a day by the Brazilian Navy, and a period of one year (2017) is selected for the assessment. Observations consist of four satellite missions: JASON2, JASON3, CRYOSAT, and SARAL. The numerical atmospheric models with 10-m winds are GFS (Global Forecast System) and ICON (Icosahedral Nonhydrostatic Model). They force wave simulations using the model WAVEWATCH III with a mosaic of two grids. The forecast horizon analyzed is five days, which is made publicly available by the Brazilian Navy. Results show that under calm to moderate conditions, within the first two days of forecast, the wind and wave model skill is very high. However, above the 90th percentile and beyond the third forecast day, the predictability drops significantly. It highlights specific contours of forecast range versus percentiles where the wind and wave modelers should focus, in order to anticipate and to improve the predictability of extreme events at the Drake Passage.
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Frate, Guido Francesco, Lorenzo Ferrari, and Umberto Desideri. "Impact of Forecast Uncertainty on Wind Farm Profitability." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91523.

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Abstract The great amount of support schemes that initially fueled the fast, and often uncontrollable, Renewable Energy Sources (RESs) growth have been strongly reduced or revoked in many countries. Currently, the general trend is to try to equate the RESs to the traditional power plants. From the energy market point of view, this entails exposing the RESs more to the market competition and mechanics. This could be done, for example, requiring the stochastic RESs to submit a production schedule in advance and to be financially responsible for any deviation from this. This could push the Wind Farm (WF) operators to make accurate forecasts, fostering the electric system resiliency and an efficient use of balancing resources. From the forecasting point of view this is not a trivial problem, since the schedule submission is often due 10–12 hours before the actual delivery. Since forecast errors are unavoidable, the submitted schedule could turn out to be infeasible, forcing the WF to recur to correcting actions which are generally costly. Focusing on this, the analysis estimates the revenue reduction which would affect a WF operating in the energy market due to forecast errors. To do this in a realistic way, a case study is selected, and realistic forecast scenarios are generated using a copula approach. Important forecast error features like autocorrelation and dependency on forecasted power level and forecast lead-time are modeled. The revenue reduction due to balancing actions is calculated on an annual basis, using typical days, derived through the production data clustering. Losses ranging from 5% to 35% has been found, depending on the days and on the market prices. A sensitivity analysis to the costs of balancing actions is performed. In this way, the effect of different market architectures and, possibly, of different RESs penetration level is considered in the analysis. Finally, the effectiveness of the curtailment as a technique to reduce the impact of forecast errors in highly penalizing market environments is assessed.
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Звіти організацій з теми "Wind Forecasts"

1

Pennock, K. Updated Eastern Interconnect Wind Power Output and Forecasts for ERGIS: July 2012. Office of Scientific and Technical Information (OSTI), October 2012. http://dx.doi.org/10.2172/1056126.

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Norquist, Donald C., and Warner C. Meeks. A Comparative Verification of Forecasts from Two Operational Solar Wind Models (Postprint). Fort Belvoir, VA: Defense Technical Information Center, February 2012. http://dx.doi.org/10.21236/ada558506.

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Piwko, Richard, and Gary Jordan. Impacts of Improved Day-Ahead Wind Forecasts on Power Grid Operations: September 2011. Office of Scientific and Technical Information (OSTI), November 2011. http://dx.doi.org/10.2172/1031400.

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4

Finley, Cathy. 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. Office of Scientific and Technical Information (OSTI), April 2014. http://dx.doi.org/10.2172/1129929.

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5

Nachmani, Gil. Minimum-Energy Flight Paths for UAVs Using Mesoscale Wind Forecasts and Approximate Dynamic Programming. Fort Belvoir, VA: Defense Technical Information Center, December 2007. http://dx.doi.org/10.21236/ada475720.

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Freedman, Jeffrey M., John Manobianco, John Schroeder, Brian Ancell, Keith Brewster, Sukanta Basu, Venkat Banunarayanan, Bri-Mathias Hodge, and Isabel Flores. 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. Office of Scientific and Technical Information (OSTI), April 2014. http://dx.doi.org/10.2172/1129905.

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7

Optis, Michael, George N. Scott, and Caroline Draxl. Evaluation of Wind Power Forecasts from the Vermont Weather Analytics Center and Identification of Improvements. Office of Scientific and Technical Information (OSTI), February 2018. http://dx.doi.org/10.2172/1419925.

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8

Dumais, Robert E., John W. Raby, Yansen Wang, Yasmina R. Raby, and David Knapp. Performance Assessment of the Three-Dimensional Wind Field Weather Running Estimate - Nowcast and the Three-Dimensional Wind Field Air Force Weather Agency Weather Research and Forecasting Wind Forecasts. Fort Belvoir, VA: Defense Technical Information Center, December 2012. http://dx.doi.org/10.21236/ada573289.

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Gallagher, Alex, Sandra LeGrand, Taylor Hodgdon, and Theodore Letcher. Simulating environmental conditions for Southwest United States convective dust storms using the Weather Research and Forecasting Model v4.1. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/44963.

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Dust aerosols can pose a significant detriment to public health, transportation, and tactical operations through reductions in air quality and visibility. Thus, accurate model forecasts of dust emission and transport are essential to decision makers. While a large number of studies have advanced the understanding and predictability of dust storms, the majority of existing literature considers dust production and forcing conditions of the underlying meteorology independently of each other. Our study works to-wards filling this research gap by inventorying dust-event case studies forced by convective activity in the Desert Southwest United States, simulating select representative case studies using several configurations of the Weather Research and Forecasting (WRF) model, testing the sensitivity of forecasts to essential model parameters, and assessing overall forecast skill using variables essential to dust production and transport. We found our control configuration captured the initiation, evolution, and storm structure of a variety of convective features admirably well. Peak wind speeds were well represented, but we found that simulated events arrived up to 2 hours earlier or later than observed. Our results show that convective storms are highly sensitive to initialization time and initial conditions that can preemptively dry the atmosphere and suppress the growth of convective storms.
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Venäläinen, Ari, Sanna Luhtala, Mikko Laapas, Otto Hyvärinen, Hilppa Gregow, Mikko Strahlendorff, Mikko Peltoniemi, et al. Sää- ja ilmastotiedot sekä uudet palvelut auttavat metsäbiotaloutta sopeutumaan ilmastonmuutokseen. Finnish Meteorological Institute, January 2021. http://dx.doi.org/10.35614/isbn.9789523361317.

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Climate change will increase weather induced risks to forests, and thus effective adaptation measures are needed. In Säätyö project funded by the Ministry of Agriculture and Forestry, we have summarized the data that facilitate adaptation measures, developed weather and climate services that benefit forestry, and mapped what kind of new weather and climate services are needed in forestry. In addition, we have recorded key further development needs to promote adaptation. The Säätyö project developed a service product describing the harvesting conditions of trees based on the soil moisture assessment. The output includes an analysis of the current situation and a 10-day forecast. In the project we also tested the usefulness of long forecasts beyond three months. The weather forecasting service is sidelined and supplemented by another co-operation project between the Finnish Meteorological Institute and Metsäteho called HarvesterSeasons (https://harvesterseasons.com/). The HarvesterSeasons service utilizes long-term forecasts of up to 6 months to assess terrain bearing conditions. A test version of a wind damage risk tool was developed in cooperation with the Department of Forest Sciences of the University of Eastern Finland and the Finnish Meteorological Institute. It can be used to calculate the wind speeds required in a forest area for wind damage (falling trees). It is currently only suitable for researcher use. In the Säätyö project the possibility of locating the most severe wind damage areas immediately after a storm was also tested. The method is based on the spatial interpolation of wind observations. The method was used to analyze storms that caused forest damages in the summer and fall of 2020. The produced maps were considered illustrative and useful to those responsible for compiling the situational picture. The accumulation of snow on tree branches, can be modeled using weather data such as rainfall, temperature, air humidity, and wind speed. In the Säätyö project, the snow damage risk assessment model was further developed in such a way that, in addition to the accumulated snow load amount, the characteristics of the stand and the variations in terrain height were also taken into account. According to the verification performed, the importance of abiotic factors increased under extreme snow load conditions (winter 2017-2018). In ordinary winters, the importance of biotic factors was emphasized. According to the comparison, the actual snow damage could be explained well with the tested model. In the interviews and workshop, the uses of information products, their benefits, the conditions for their introduction and development opportunities were mapped. According to the results, diverse uses and benefits of information products and services were seen. Information products would make it possible to develop proactive forest management, which would reduce the economic costs caused by wind and snow damages. A more up-to-date understanding of harvesting conditions, enabled by information products, would enhance the implementation of harvesting and harvesting operations and the management of timber stocks, as well as reduce terrain, trunk and root damage. According to the study, the introduction of information is particularly affected by the availability of timeliness. Although the interviewees were not currently willing to pay for the information products developed in the project, the interviews highlighted several suggestions for the development of information products, which could make it possible to commercialize them.
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