Journal articles on the topic 'Wind Forecasts'
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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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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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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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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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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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Molinder, Jennie, Heiner Körnich, Esbjörn Olsson, Hans Bergström, and Anna Sjöblom. "Probabilistic forecasting of wind power production losses in cold climates: a case study." Wind Energy Science 3, no. 2 (October 9, 2018): 667–80. http://dx.doi.org/10.5194/wes-3-667-2018.
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Abstract. The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next-day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a 2-week period. Uncertainties in initial and boundary conditions are represented with an ensemble forecasting system, while uncertainties in the spatial representation are included with a neighbourhood method. Using probabilistic forecasting instead of one single forecast was shown to improve the forecast skill of the ice-related production loss forecasts and hence the icing forecasts. The spread of the multiple forecasts can be used as an estimate of the forecast uncertainty and of the likelihood for icing and severe production losses. Best results, both in terms of forecast skill and forecasted uncertainty, were achieved using both the ensemble forecast and the neighbourhood method combined. This demonstrates that the application of probabilistic forecasting for wind power in cold climates can be valuable when planning next-day energy production, in the usage of de-icing systems and for site safety.
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Shi, Jianqiu, Yubao Liu, Yang Li, Yuewei Liu, Gregory Roux, Lan Shi, and Xiaowei Fan. "Wind Speed Forecasts of a Mesoscale Ensemble for Large-Scale Wind Farms in Northern China: Downscaling Effect of Global Model Forecasts." Energies 15, no. 3 (January 26, 2022): 896. http://dx.doi.org/10.3390/en15030896.
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To facilitate wind power integration for the electric power grid operated by the Inner Mongolia Electric Power Corporation—a major electric power grid in China—a high-resolution (of 2.7 km grid intervals) mesoscale ensemble prediction system was developed that forecasts winds for 130 wind farms in the Inner Mongolia Autonomous Region. The ensemble system contains 39 forecasting members that are divided into 3 groups; each group is composed of the NCAR (National Center for Atmospheric Research) real-time four-dimensional data assimilation and forecasting model (RTFDDA) with 13 physical perturbation members, but driven by the forecasts of the GFS (Global Forecast System), GEM (Global Environmental Multiscale Model), and GEOS (Goddard Earth Observing System), respectively. The hub-height wind predictions of these three sub-ensemble groups at selected wind turbines across the region were verified against the hub-height wind measurements. The forecast performance and variations with lead time, wind regimes, and diurnal and regional changes were analyzed. The results show that the GFS group outperformed the other two groups with respect to correlation coefficient and mean absolute error. The GFS group had the most accurate forecasts in ~59% of sites, while the GEOS and GEM groups only performed the best on 34% and 2% of occasions, respectively. The wind forecasts were most accurate for wind speeds ranging from 3 to 12 m/s, but with an overestimation for low speeds and an underestimation for high speeds. The GEOS-driven members obtained the least bias error among the three groups. All members performed rather accurately in daytime, but evidently overestimated the winds during nighttime. The GFS group possessed the fewest diurnal errors, and the bias of the GEM group grew significantly during nighttime. The wind speed forecast errors of all three ensemble members increased with the forecast lead time, with the average absolute error increasing by ~0.3 m/s per day during the first 72 h of forecasts.
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Bi, Li, James A. Jung, Michael C. Morgan, and John F. Le Marshall. "Assessment of Assimilating ASCAT Surface Wind Retrievals in the NCEP Global Data Assimilation System." Monthly Weather Review 139, no. 11 (November 1, 2011): 3405–21. http://dx.doi.org/10.1175/2011mwr3391.1.
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Abstract A two-season Observing System Experiment (OSE) was used to quantify the impacts of assimilating the Advanced Scatterometer (ASCAT) surface winds product distributed by the European Organization for the Exploitation of Meteorological Satellites (EUMESAT) and the National Environmental Satellite, Data, and Information Service (NESDIS). The ASCAT wind retrievals were provided by the Royal Netherlands Meteorological Office (KNMI) and the 50-km resolution ASCAT products were assimilated. The impact of assimilating the ASCAT surface wind product in the National Centers for Environmental Prediction (NCEP) Global Data Assimilation/Global Forecast System (GDAS/GFS) was assessed by comparing the forecast results through 168 h for the months of August 2008 and January 2009. The NCEP GDAS/GFS was used, at a resolution of T382–64 layers, as the assimilation system and forecast model for these experiments. A control simulation utilizing all the data types assimilated in the operational GDAS was compared to an experimental simulation that added the ASCAT surface winds. Quality control procedures required to assimilate the ASCAT surface winds are discussed. Anomaly correlations (ACs) of geopotential height forecasts as well as geographic distribution of AC of geopotential height forecasts at 1000 and 500 hPa were evaluated for the control and experiment during both seasons. The geographical distribution of forecast impact (FI) on the wind and temperature fields near the surface is also presented. The results of this study show that assimilation of the surface wind retrievals from the ASCAT sensor improve the NCEP GFS wind and temperature forecasts. A positive FI, which suggests the error growth of the experiment is slower than the control, has been realized in the NCEP GDAS/GFS wind and temperature forecasts through 24 h. The ASCAT experiment AC scores show modest forecast improvements from days 4 through 7.
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Holman, Bryan P., Steven M. Lazarus, and Michael E. Splitt. "Statistically and Dynamically Downscaled, Calibrated, Probabilistic 10-m Wind Vector Forecasts Using Ensemble Model Output Statistics." Monthly Weather Review 146, no. 9 (August 13, 2018): 2859–80. http://dx.doi.org/10.1175/mwr-d-17-0338.1.
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Abstract A computationally efficient method is developed that performs gridded postprocessing of ensemble 10-m wind vector forecasts. An expansive set of idealized WRF Model simulations are generated to provide physically consistent, high-resolution winds over a coastal domain characterized by an intricate land/water mask. The ensemble model output statistics (EMOS) technique is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. In a yearlong study, the method is applied to 24-h wind forecasts from the Global Ensemble Forecast System (GEFS) at 28 east-central Florida stations. Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicates that the postprocessed forecasts are calibrated. A downscaling case study illustrates the method as applied to a quiescent easterly flow event. Strengths and weaknesses of the approach are presented and discussed.
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Ma, Yimin, Xinmei Huang, Graham A. Mills, and Kevin Parkyn. "Verification of Mesoscale NWP Forecasts of Abrupt Cold Frontal Wind Changes." Weather and Forecasting 25, no. 1 (February 1, 2010): 93–112. http://dx.doi.org/10.1175/2009waf2222259.1.
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Abstract During a wildfire, a sharp wind change can lead to an abrupt increase in fire activity and change the rate of spread, endangering firefighters working on what had been the flank of the fire. In southeastern Australia, routine forecast of cold-frontal wind change arrival times is a critical component of the fire weather forecasting service, and mesoscale NWP model predictions are integral to this forecast process. An event-based verification method has been developed in order to verify these mesoscale NWP model forecasts of wind changes. The approach is based on fuzzy-rule techniques and objectively determines the timing of significant (fire weather) wind changes from time series of observations at a single surface station. In this paper these rules are applied to observational and NWP model forecast time series at observation locations over five fire seasons to determine objective “observed wind change times” and “forecast wind change times” for significant frontal wind changes in southeastern Australia. These forecast wind change times are compared with those observed, and also with those determined subjectively by forecasters at the Victorian Regional Forecast Centre. This provides an objective verification of NWP wind change forecasts and a measure of contemporary NWP model skill against which future model improvements may be measured. Case studies of two wind change events at selected stations are also presented to demonstrate some of the strengths, weaknesses, and characteristics of this verification technique.
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Frediani, Maria E. B., Thomas M. Hopson, Joshua P. Hacker, Emmanouil N. Anagnostou, Luca Delle Monache, and Francois Vandenberghe. "Object-Based Analog Forecasts for Surface Wind Speed." Monthly Weather Review 145, no. 12 (December 2017): 5083–102. http://dx.doi.org/10.1175/mwr-d-17-0012.1.
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Analogs are used as a forecast postprocessing technique, in which a statistical forecast is derived from past prognostic states. This study proposes a method to identify analogs through spatial objects, which are then used to create forecast ensembles. The object-analog technique preserves the field’s spatial relationships, reduces spatial dimensionality, and consequently facilitates the use of artificial intelligence algorithms to improve analog selection. Forecast objects are created with a three-step object selection, combining standard image processing algorithms. The resulting objects are used to find similar forecasts in a training set with a similarity measure based on object area intersection and magnitude. Storm-induced power outages in the Northeast United States motivated the method’s validation for 10-m AGL wind speed forecasts. The training set comprises reforecasts and reanalyses of events that caused damages to the utility infrastructure. The corresponding reanalyses of the best reforecast analogs are used to produce the object-analog ensemble forecasts. The forecasts are compared with other analog forecast methods. Analogs representing lower and upper predictability limits provide references to distinguish the method’s ability (to find good analogs) from the training set’s ability (to provide good analogs) to generate skillful ensemble forecasts. The object-analog forecasts are competitively skillful compared to simpler analog techniques with an advantage of lower spatial dimensionality, while generating reliable ensemble forecasts, with reduced systematic and random errors, maintaining correlation, and improving Brier scores.
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Hämäläinen, Karoliina, Elena Saltikoff, Otto Hyvärinen, Ville Vakkari, and Sami Niemelä. "Assessment of Probabilistic Wind Forecasts at 100 m Above Ground Level Using Doppler Lidar and Weather Radar Wind Profiles." Monthly Weather Review 148, no. 3 (March 1, 2019): 1321–34. http://dx.doi.org/10.1175/mwr-d-19-0184.1.
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Abstract Modern society is very dependent on electricity. In the energy sector, the amount of renewable energy is growing, especially wind energy. To keep the electricity network in balance we need to know how much, when, and where electricity is produced. To support this goal, the need for proper wind forecasts has grown. Compared to traditional deterministic forecasts, ensemble models can better provide the range of variability and uncertainty. However, probabilistic forecasts are often either under- or overdispersive and biased, thus not covering the true and full distribution of probabilities. Hence, statistical postprocessing is needed to increase the value of forecasts. However, traditional closer-to-surface wind observations do not support the verification of wind higher above the surface that is more relevant for wind energy production. Thus, the goal of this study was to test whether new types of observations like radar and lidar winds could be used for verification and statistical calibration of 100-m winds. According to our results, the calibration improved the forecast skill compared to a raw ensemble. The results are better for low and moderate winds, but for higher wind speeds more training data would be needed, either from a larger number of stations or using a longer training period.
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Drillet, Y., J. M. Lellouche, B. Levier, M. Drévillon, O. Le Galloudec, G. Reffray, C. Regnier, E. Greiner, and M. Clavier. "Forecasting the mixed layer depth in the north east Atlantic: an ensemble approach, with uncertainties based on data from operational oceanic systems." Ocean Science Discussions 11, no. 3 (June 11, 2014): 1435–72. http://dx.doi.org/10.5194/osd-11-1435-2014.
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Abstract. Operational systems operated by Mercator Océan provide daily ocean forecasts, and combining these forecasts we can produce ensemble forecast and uncertainty estimates. This study focuses on the mixed layer depth in the North East Atlantic near the Porcupine Abyssal Plain for May 2013. This period is of interest for several reasons: (1) four Mercator Océan operational systems provide daily forecasts at a horizontal resolution of 1/4°, 1/12° and 1/36° with different physics; (2) glider deployment under the OSMOSIS project provides observation of the changes in mixed layer depth; (3) the ocean stratifies in May, but mixing events induced by gale force wind are observed and forecasted by the systems. A statistical approach and forecast error quantification for each system and for the combined products are presented. Skill scores indicate that forecasts are in any case better than persistence, and temporal correlations between forecast and observations are greater than 0.8 even for the 4 day forecast. The impact of atmospheric forecast error, and for the wind field in particular, is also quantified in terms of the forecast time delay and the intensity of mixing or stratification events.
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Chen, Niya, Zheng Qian, and Xiaofeng Meng. "Multistep Wind Speed Forecasting Based on Wavelet and Gaussian Processes." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/461983.
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Accurate wind speed forecasts are necessary for the safety and economy of the renewable energy utilization. The wind speed forecasts can be obtained by statistical model based on historical data. In this paper, a novel W-GP model (wavelet decomposition based Gaussian process learning paradigm) is proposed for short-term wind speed forecasting. The nonstationary and nonlinear original wind speed series is first decomposed into a set of better-behaved constitutive subseries by wavelet decomposition. Then these sub-series are forecasted respectively by GP method, and the forecast results are summed to formulate an ensemble forecast for original wind speed series. Therefore, the previous process which obtains wind speed forecast result is named W-GP model. Finally, the proposed model is applied to short-term forecasting of the mean hourly and daily wind speed for a wind farm located in southern China. The prediction results indicate that the proposed W-GP model, which achieves a mean 13.34% improvement in RMSE (Root Mean Square Error) compared to persistence method for mean hourly data and a mean 7.71% improvement for mean daily wind speed data, shows the best forecasting accuracy among several forecasting models.
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Darby, Lisa S., Allen B. White, Daniel J. Gottas, and Timothy Coleman. "An Evaluation of Integrated Water Vapor, Wind, and Precipitation Forecasts Using Water Vapor Flux Observations in the Western United States." Weather and Forecasting 34, no. 6 (December 1, 2019): 1867–88. http://dx.doi.org/10.1175/waf-d-18-0159.1.
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Abstract Differences between forecasts and observations at eight atmospheric river observatories (AROs) in the western United States during winter 2015/16 are analyzed. NOAA’s operational RAP and HRRR 3-h forecasts of wind, integrated water vapor (IWV), integrated water vapor flux (IWV flux), and precipitation from the grid points nearest the AROs were paired with ARO observations presented in the NOAA/Physical Sciences Division’s water vapor flux tool (WVFT). The focus of this paper is to characterize and quantify the differences in the WVFT observations and forecasts. We used traditional forecast evaluation methods since they were compatible with the design of the tool: a near-real-time visual depiction of hourly observed and forecasted variables at a single location. Forecast root-mean-squared errors (RMSEs) and unbiased RMSEs, standard deviations of the observed and forecasted variables, and frequency bias scores (FBS) for all of the fields, plus equitable threat scores for precipitation, are presented. Both models forecasted IWV at all AROs and the winds that drive orographic precipitation at most AROs within a reasonable range of the observations as indicated by comparisons of the standard deviations and RMSEs of the forecasts with the standard deviations of the observations and FBS. These results indicated that forecasted advection of moisture to the stations was adequate for generating precipitation. At most stations and most hourly precipitation rates, the HRRR underpredicted precipitation. At several AROs the RAP precipitation forecasts more closely matched the observations at smaller (<1.27 mm h−1) precipitation rates, but underpredicted precipitation rates > 2 mm h−1.
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Ancell, Brian C., Erin Kashawlic, and John L. Schroeder. "Evaluation of Wind Forecasts and Observation Impacts from Variational and Ensemble Data Assimilation for Wind Energy Applications." Monthly Weather Review 143, no. 8 (August 1, 2015): 3230–45. http://dx.doi.org/10.1175/mwr-d-15-0001.1.
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Abstract The U.S. Department of Energy Wind Forecast Improvement Project (WFIP) has recently been completed with the aim of 1) understanding the performance of different mesoscale data assimilation systems for lower-atmospheric wind prediction and 2) determining the observation impacts on wind forecasts within the different assimilation systems. Here an ensemble Kalman filter (EnKF) was tested against a three-dimensional variational data assimilation (3DVAR) technique. Forecasts lasting 24 hours were produced for a month-long period to determine the day-to-day performance of each system, as well as over 10 individual wind ramp cases. The observation impacts from surface mesonet and profiler/sodar wind observations aloft were also tested in each system for both the month-long run and the ramp forecasts. It was found that EnKF forecasts verified over a domain including Texas and Oklahoma were better than those of 3DVAR for the month-long experiment throughout the forecast window, presumably from the use of flow-dependent covariances in the EnKF. The assimilation of mesonet data improved both EnKF and 3DVAR early forecasts, but sodar/profiler data showed a degradation (EnKF) or had no effect (3DVAR), with the degradation apparently resulting from a lower-atmospheric wind bias. For the wind ramp forecasts, ensemble averaging appears to overwhelm any improvements flow-dependent assimilation may have on ramp forecasts, leading to better 3DVAR ramp prediction. This suggests that best member techniques within the EnKF may be necessary for improved performance over 3DVAR for forecasts of sharp features such as wind ramps. Observation impacts from mesonet and profiler/sodar observations generally improved EnKF ramp forecasts, but either had little effect on or degraded 3DVAR forecasts.
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Tiriolo, L., R. C. Torcasio, S. Montesanti, A. M. Sempreviva, C. R. Calidonna, C. Transerici, and S. Federico. "Forecasting wind power production from a wind farm using the RAMS model." Advances in Science and Research 12, no. 1 (April 9, 2015): 37–44. http://dx.doi.org/10.5194/asr-12-37-2015.
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Abstract. The importance of wind power forecast is commonly recognized because it represents a useful tool for grid integration and facilitates the energy trading. This work considers an example of power forecast for a wind farm in the Apennines in Central Italy. The orography around the site is complex and the horizontal resolution of the wind forecast has an important role. To explore this point we compared the performance of two 48 h wind power forecasts using the winds predicted by the Regional Atmospheric Modeling System (RAMS) for the year 2011. The two forecasts differ only for the horizontal resolution of the RAMS model, which is 3 km (R3) and 12 km (R12), respectively. Both forecasts use the 12 UTC analysis/forecast cycle issued by the European Centre for Medium range Weather Forecast (ECMWF) as initial and boundary conditions. As an additional comparison, the results of R3 and R12 are compared with those of the ECMWF Integrated Forecasting System (IFS), whose horizontal resolution over Central Italy is about 25 km at the time considered in this paper. v Because wind observations were not available for the site, the power curve for the whole wind farm was derived from the ECMWF wind operational analyses available at 00:00, 06:00, 12:00 and 18:00 UTC for the years 2010 and 2011. Also, for R3 and R12, the RAMS model was used to refine the horizontal resolution of the ECMWF analyses by a two-years hindcast at 3 and 12 km horizontal resolution, respectively. The R3 reduces the RMSE of the predicted wind power of the whole 2011 by 5% compared to R12, showing an impact of the meteorological model horizontal resolution in forecasting the wind power for the specific site.
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Wilczak, James, Cathy Finley, Jeff Freedman, Joel Cline, Laura Bianco, Joseph Olson, Irina Djalalova, et al. "The Wind Forecast Improvement Project (WFIP): A Public–Private Partnership Addressing Wind Energy Forecast Needs." Bulletin of the American Meteorological Society 96, no. 10 (October 1, 2015): 1699–718. http://dx.doi.org/10.1175/bams-d-14-00107.1.
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Abstract 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.
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Stuart, Neil A., and Richard H. Grumm. "Using Wind Anomalies to Forecast East Coast Winter Storms." Weather and Forecasting 21, no. 6 (December 1, 2006): 952–68. http://dx.doi.org/10.1175/waf964.1.
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Abstract Forecasting major winter storms is a critical function for all weather services. Conventional model-derived fields from numerical weather prediction models most frequently utilized by operational forecasters, such as pressure level geopotential height, temperature fields, quantitative precipitation forecasts, and model output statistics, are often insufficient to determine whether a winter storm represents a large departure from normal, or has the potential to produce significant snowfall. This paper presents a method, using normalized departures from climatology, to assist forecasters in identifying long-duration and potentially significant winter storms. The focus of this paper is on anomalous low- and upper-level wind anomalies associated with winter storms along the U.S. east coast. Observed and forecast low-level (850 hPa) and upper-level (300 and 250 hPa) easterly wind anomalies are compared with a 30-yr (1961–90) reanalysis climatology. Anomalous easterly low-level winds are correlated with enhanced low-level forcing and frontogenesis. Strong low-level winds can also contribute to enhanced precipitation rates. Upper-level winds that are anomalously below normal, represented as easterly wind anomalies, are also correlated with systems that are cut off from the main belt of westerlies, which may result in slower movement of the system, leading to long-duration events. The proposed method of evaluating easterly wind anomalies is shown to assist in identifying potentially slow-moving storms with extended periods of enhanced precipitation. To illustrate this method, winter storms on 25–26 December 2002 and 2–4 January 2003 will be compared with past historical winter storms. The results suggest that the low- and upper-level wind anomalies in the two recent snowstorms share common characteristics with several record snowstorms over the past 52 yr. Many of these storms were associated with easterly wind anomalies that departed significantly (2 or more standard deviations) from normal. The examination of climatic anomalies from model forecasts may assist forecasters in identifying significant winter storms in the short range (2–3 days) and potentially out to ranges as long as 7 days when ensemble forecast guidance is utilized.
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Thorarinsdottir, Thordis L., and Matthew S. Johnson. "Probabilistic Wind Gust Forecasting Using Nonhomogeneous Gaussian Regression." Monthly Weather Review 140, no. 3 (February 1, 2012): 889–97. http://dx.doi.org/10.1175/mwr-d-11-00075.1.
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Abstract A joint probabilistic forecasting framework is proposed for maximum wind speed, the probability of gust, and, conditional on gust being observed, the maximum gust speed in a setting where only the maximum wind speed forecast is available. The framework employs the nonhomogeneous Gaussian regression (NGR) statistical postprocessing method with appropriately truncated Gaussian predictive distributions. For wind speed, the distribution is truncated at zero, the location parameter is a linear function of the wind speed ensemble forecast, and the scale parameter is a linear function of the ensemble variance. The gust forecasts are derived from the wind speed forecast using a gust factor, and the predictive distribution for gust speed is truncated according to its definition. The framework is applied to 48-h-ahead forecasts of wind speed over the North American Pacific Northwest obtained from the University of Washington mesoscale ensemble. The resulting density forecasts for wind speed and gust speed are calibrated and sharp, and offer substantial improvement in predictive performance over the raw ensemble or climatological reference forecasts.
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Lynch, Kieran J., David J. Brayshaw, and Andrew Charlton-Perez. "Verification of European Subseasonal Wind Speed Forecasts." Monthly Weather Review 142, no. 8 (August 1, 2014): 2978–90. http://dx.doi.org/10.1175/mwr-d-13-00341.1.
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Abstract Analysis of the forecasts and hindcasts from the ECMWF 32-day forecast model reveals that there is statistically significant skill in predicting weekly mean wind speeds over areas of Europe at lead times of at least 14–20 days. Previous research on wind speed predictability has focused on the short- to medium-range time scales, typically finding that forecasts lose all skill by the later part of the medium-range forecast. To the authors’ knowledge, this research is the first to look beyond the medium-range time scale by taking weekly mean wind speeds, instead of averages at hourly or daily resolution, for the ECMWF monthly forecasting system. It is shown that the operational forecasts have high levels of correlation (~0.6) between the forecasts and observations over the winters of 2008–12 for some areas of Europe. Hindcasts covering 20 winters show a more modest level of correlation but are still skillful. Additional analysis examines the probabilistic skill for the United Kingdom with the application of wind power forecasting in mind. It is also shown that there is forecast “value” for end users (operating in a simple cost/loss ratio decision-making framework). End users that are sensitive to winter wind speed variability over the United Kingdom, Germany, and some other areas of Europe should therefore consider forecasts beyond the medium-range time scale as it is clear there is useful information contained within the forecast.
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Traiteur, Justin J., David J. Callicutt, Maxwell Smith, and Somnath Baidya Roy. "A Short-Term Ensemble Wind Speed Forecasting System for Wind Power Applications." Journal of Applied Meteorology and Climatology 51, no. 10 (October 2012): 1763–74. http://dx.doi.org/10.1175/jamc-d-11-0122.1.
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AbstractThis study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 h ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model and persistence, autoregressive, and autoregressive moving-average models. The ensemble is calibrated against observations for a 6-month period (January–June 2006) at a potential wind-farm site in Illinois using the Bayesian model averaging technique. The forecasting system is evaluated against observations for the July 2006–December 2007 period at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble as well the time series models under all environmental stability conditions. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 min. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.
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Griffiths, Deryn, Nicholas Loveday, Benjamin Price, Michael Foley, and Alistair McKelvie. "Circular Flip-Flop Index: quantifying revision stability of forecasts of direction." Journal of Southern Hemisphere Earth Systems Science 71, no. 3 (2021): 266. http://dx.doi.org/10.1071/es21010.
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The Flip-Flop Index, designed to quantify the extent to which a forecast changes from one issue time to the next, is extended to a Circular Flip-Flop Index for use with forecasts of wind direction, swell direction or similar. The index was devised so we could understand the degree of stability in wind direction forecasts. The Circular Flip Flop Index is independent of observations, has a relatively simple definition and does not penalise a sequence of forecasts that show a trend as long as the forecasts stay within a 180° sector. The Circular Flip-Flop Index is interpreted in terms of the impact of changing forecasts on decisions made by users of the forecast. The Circular Flip-Flop Index has been used to compare the stability of sequences of automated forecast guidance to the official Australian Bureau of Meteorology forecasts, which are prepared manually. It is the first objective assessment of the stability of forecasts of direction. The results show that the forecasts of wind direction from the automated forecast guidance, itself a consensus of many numerical weather models, are more stable than the official, manual forecasts. The Circular Flip-Flop Index does not measure skill but can play a complementary role in characterising and evaluating a forecasting system.
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Bi, Li, James A. Jung, Michael C. Morgan, and John F. Le Marshall. "A Two-Season Impact Study of the WindSat Surface Wind Retrievals in the NCEP Global Data Assimilation System." Weather and Forecasting 25, no. 3 (June 1, 2010): 931–49. http://dx.doi.org/10.1175/2010waf2222377.1.
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Abstract A two-season observing system experiment (OSE) was used to quantify the impacts of assimilating the WindSat surface winds product developed by the Naval Research Laboratory (NRL). The impacts of assimilating these surface winds were assessed by comparing the forecast results through 168 h for the months of October 2006 and March 2007. The National Centers for Environmental Prediction’s (NCEP) Global Data Assimilation/Global Forecast System (GDAS/GFS) was used, at a resolution of T382-64 layers, as the assimilation system and forecast model for these experiments. A control simulation utilizing all the data types assimilated in the operational GDAS was compared to an experimental simulation that added the WindSat surface winds. Quality control procedures required to assimilate the surface winds are discussed. Anomaly correlations (ACs) of geopotential heights at 1000 and 500 hPa were evaluated for the control and experiment during both seasons. The geographical distribution of the forecast impacts (FIs) on the wind field and temperature fields at 10-m height and 500 hPa is also discussed. The results of this study show that assimilating the surface wind retrievals from the WindSat satellite improve the NCEP GFS wind and temperature forecasts. A positive FI, which suggests that the error growth of the experiment is slower than the control, has been realized in the NCEP GDAS/GFS wind and temperature forecasts through 24 h. The WindSat experiment AC scores are similar to the control simulation AC scores until the day 6 forecasts, when the improvements in the WindSat experiment become greater for both seasons and in most of the cases.
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Dar, Arslan Salim, and Lüder von Bremen. "Short-Term Forecasting of Wake-Induced Fluctuations in Offshore Wind Farms." Energies 12, no. 14 (July 23, 2019): 2833. http://dx.doi.org/10.3390/en12142833.
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The increasing share of offshore wind energy traded at the spot market requires short term wind direction forecasts to determine wake losses and increased power fluctuations due to multiple wakes in certain wind directions. The information on potential power fluctuations can be used to issue early warnings to grid operators. The current work focuses on analyzing wind speed and power fluctuation time series for a German offshore wind farm. By associating these fluctuations with wind directions, it is observed that the turbines in double or multiple wake situations yield higher fluctuations in wind speed and power compared to the turbines in free flow. The wind direction forecasts of the European Center for Medium-Range Weather Forecast model are compared with Supervisory Control and Data Acquisition (SCADA) data observations of the turbine yaw. The cumulative probability distribution of the difference in forecasted and observed wind directions shows that for a tolerance of +/−10 ∘ , 71% of the observations are correctly forecasted for a lead time of 1 day, which drops to 54% for a lead time of 3 days. The circular continuous rank probability score of the observed wind directions doubles over the lead time of 72 h.
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Möhrlen, Corinna, Gregor Giebel, Ricardo J. Bessa, and Nadine Fleischhut. "How do Humans decide under Wind Power Forecast Uncertainty — an IEA Wind Task 36 Probabilistic Forecast Games and Experiments initiative." Journal of Physics: Conference Series 2151, no. 1 (January 1, 2022): 012014. http://dx.doi.org/10.1088/1742-6596/2151/1/012014.
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Abstract The need to take into account and explicitly model forecast uncertainty is today at the heart of many scientific and applied enterprises. For instance, the ever-increasing accuracy of weather forecasts has been driven by the development of ensemble forecasts, where a large number of forecasts are generated either by generating forecasts from different models or by repeatedly perturbing the initial conditions of a single forecast model. Importantly, this approach provides robust estimates of forecast uncertainty, which supports human judgement and decision-making. Although weather forecasts and their uncertainty are also crucial for the weather-to-power conversion for RES forecasting in system operation, power trading and balancing, the industry has been reluctant to adopt ensemble methods and other new technologies that can help manage highly variable and uncertain power feed-ins, especially under extreme weather conditions. In order to support the energy industry in the adaptation of uncertainty forecasts into their business practices, the IEA Wind Task 36 has started an initiative in collaboration with the Max Planck Institute for Human Development and Hans-Ertel Center for Weather Research to investigate the existing barriers in the industry to the adoption of such forecasts into decision processes. In the first part of the initiative, a forecast game was designed as a demonstration of a typical decision-making task in the power industry. The game was introduced in an IEA Wind Task 36 workshop and thereafter released to the public. When closed, it had been played by 120 participants. We will discuss the results of our first experience with the experiment and introduce some new features of the second generation of experiments as a continuation of the initiative. We will also discuss specific questions that emerged when we started and after analysing the experiments. Lastly we will discuss the trends we found and how we will fit these into the overall objective of the initiative which is to provide training tools to demonstrate the use and benefit of uncertainty forecasts by simulating decision scenarios with feedback and allowing people to learn from experience, rather than reading articles, how to use such forecasts.
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Sampson, Charles R., and John A. Knaff. "A Consensus Forecast for Tropical Cyclone Gale Wind Radii." Weather and Forecasting 30, no. 5 (October 1, 2015): 1397–403. http://dx.doi.org/10.1175/waf-d-15-0009.1.
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Abstract The National Hurricane Center (NHC) has been forecasting gale force wind radii for many years, and more recently (starting in 2004) began routine postanalysis or “best tracking” of the maximum radial extent of gale [34 knots (kt; 1 kt = 0.514 m s−1)] force winds in compass quadrants surrounding the tropical cyclone (wind radii). At approximately the same time, a statistical wind radii forecast, based solely on climatology and persistence, was implemented so that wind radii forecasts could be evaluated for skill. If the best-track gale radii are used as ground truth (even accounting for random errors in the analyses), the skill of the NHC forecasts appears to be improving at 2- and 3-day lead times, suggesting that the guidance has also improved. In this paper several NWP models are evaluated for their skill, an equally weighted average or “consensus” of the model forecasts is constructed, and finally the consensus skill is evaluated. The results are similar to what is found with tropical cyclone track and intensity in that the consensus skill is comparable to or better than that of the individual models. Furthermore, the consensus skill is high enough to be of potential use as forecast guidance or as a proxy for official gale force wind radii forecasts at the longer lead times.
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Lyu, Yang, Xiefei Zhi, Hong Wu, Hongmei Zhou, Dexuan Kong, Shoupeng Zhu, Yingxin Zhang, and Cui Hao. "Analyses on the Multimodel Wind Forecasts and Error Decompositions over North China." Atmosphere 13, no. 10 (October 10, 2022): 1652. http://dx.doi.org/10.3390/atmos13101652.
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In this study, wind forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), the Japan Meteorological Agency (JMA) and the United Kingdom Meteorological Office (UKMO) are evaluated for lead times of 1–7 days at the 10 m and multiple isobaric surfaces (500 hPa, 700 hPa, 850 hPa and 925 hPa) over North China for 2020. The straightforward multimodel ensemble mean (MME) method is utilized to improve forecasting abilities. In addition, the forecast errors are decomposed to further diagnose the error sources of wind forecasts. Results indicated that there is little difference in the performances of the four models in terms of wind direction forecasts (DIR), but obvious differences occur in the meridional wind (U), zonal wind (V) and wind speed (WS) forecasts. Among them, the ECMWF and NCEP showed the highest and lowest abilities, respectively. The MME effectively improved wind forecast abilities, and showed more evident superiorities at higher levels for longer lead times. Meanwhile, all of the models and the MME manifested consistent trends of increasing (decreasing) errors for U, V and WS (DIR) with rising height. On the other hand, the main source of errors for wind forecasts at both 10 m and isobaric surfaces was the sequence component (SEQU), which rose rapidly with increasing lead times. The deficiency of the less proficient NCEP model at the 10 m and isobaric surfaces could mainly be attributed to the bias component (BIAS) and SEQU, respectively. Furthermore, the MME tended to produce lower SEQU than the models at all layers, which was more obvious at longer lead times. However, the MME showed a slight deficiency in reducing BIAS and the distribution component of forecast errors. The results not only recognized the model forecast performances in detail, but also provided important references for the use of wind forecasts in business departments and associated scientific researches.
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Ma, Hui, Xiaolei Ma, Shengwei Mei, Fei Wang, and Yanwei Jing. "Improving the Near-Surface Wind Forecast around the Turpan Basin of the Northwest China by Using the WRF_TopoWind Model." Atmosphere 12, no. 12 (December 6, 2021): 1624. http://dx.doi.org/10.3390/atmos12121624.
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Wind energy is a type of renewable and clean energy which has attracted more and more attention all over the world. The Northwest China is a region with the most abundant wind energy not only in China, but also in the whole world. To achieve the goal of carbon neutralization, there is an urgent need to make full use of wind energy in Northwest China and to improve the efficiency of wind power generation systems in this region. As forecast accuracy of the near-surface wind is crucial to wind-generated electricity efficiency, improving the near-surface wind forecast is of great importance. This study conducted the first test to incorporate the subgrid surface drag into the near-surface wind forecast under the complex terrain conditions over Northwest China by using two TopoWind models added by newer versions of the Weather Research and Forecasting (WRF) model. Based on three groups (each group had 28 runs) of forecasts (i.e., Control run, Test 01 and Test 02) started at 12:00 UTC of each day (ran for 48 h) during the period of 1–28 October 2020, it was shown that, overall, both TopoWind models could improve the near-surface wind speed forecasts under the complex terrain conditions over Northwest China, particularly for reducing the errors associated with the forecast of the wind-speed’s magnitude. In addition to wind forecast, the forecasts of sea level pressure and 2-m temperature were also improved. Different geographical features (wind-farm stations located south of the mountain tended to have more accurate forecast) and weather systems were found to be crucial to forecast accuracy. Good forecasts tended to appear when the simulation domain was mainly controlled by the high-pressure systems with the upper-level jet far from it.
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Tennant, Warren J., Zoltan Toth, and Kevin J. Rae. "Application of the NCEP Ensemble Prediction System to Medium-Range Forecasting in South Africa: New Products, Benefits, and Challenges." Weather and Forecasting 22, no. 1 (February 1, 2007): 18–35. http://dx.doi.org/10.1175/waf979.1.
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Abstract The National Centers for Environmental Prediction (NCEP) Ensemble Forecasting System (EFS) is used operationally in South Africa for medium-range forecasts up to 14 days ahead. The use of model-generated probability forecasts has a clear benefit in the skill of the 1–7-day forecasts. This is seen in the forecast probability distribution being more successful in spanning the observed space than a single deterministic forecast and, thus, substantially reducing the instances of missed events in the forecast. In addition, the probability forecasts generated using the EFS are particularly useful in estimating confidence in forecasts. During the second week of the forecast the EFS is used as a heads-up for possible synoptic-scale events and also for predicting average weather conditions and probability density distributions of some elements such as maximum temperature and wind. This paper assesses the medium-range forecast process and the application of the NCEP EFS at the South African Weather Service. It includes a description of the various medium-range products, adaptive bias-correction methods applied to the forecasts, verification of the forecast products, and a discussion on the various challenges that face researchers and forecasters alike.
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Pu, Zhaoxia, Ying Wang, Xin Li, Christopher Ruf, Li Bi, and Avichal Mehra. "Impacts of Assimilating CYGNSS Satellite Ocean-Surface Wind on Prediction of Landfalling Hurricanes with the HWRF Model." Remote Sensing 14, no. 9 (April 28, 2022): 2118. http://dx.doi.org/10.3390/rs14092118.
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This study examines the impacts of assimilating ocean-surface winds derived from the NASA Cyclone Global Navigation Satellite System (CYGNSS) on improving the short-range numerical simulations and forecasts of landfalling hurricanes using the NCEP operational Hurricane Weather Research and Forecasting (HWRF) model. A series of data assimilation experiments are performed using HWRF and a Gridpoint Statistical Interpolation (GSI)-based hybrid 3-dimensional ensemble-variational (3DEnVar) data assimilation system. The influence of CYGNSS data on hurricane forecasts is compared with that of Advanced Scatterometer (ASCAT) wind products that have already been assimilated into the HWRF forecast system in a series of assimilation experiments. The effects of different versions of CYGNSS data (V2.1 vs. V3.0) on hurricane forecasts are evaluated. The results indicate that CYGNSS ocean-surface wind can lead to improved numerical simulations and forecasts of hurricane track and intensity, asymmetric wind structure, and precipitation. The impacts of CYGNSS on hurricane forecasts are comparable and complementary to the operational use of ASCAT satellite data products. The dependence of the relative impacts of different versions of CYGNSS data on optimal thinning distances is evident.
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Ohba, Masamichi, Shinji Kadokura, and Daisuke Nohara. "Medium-Range Probabilistic Forecasts of Wind Power Generation and Ramps in Japan Based on a Hybrid Ensemble." Atmosphere 9, no. 11 (October 29, 2018): 423. http://dx.doi.org/10.3390/atmos9110423.
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This study shows the application of self-organizing maps (SOMs) to probabilistic forecasts of wind power generation and ramps in Japan. SOMs are applied to atmospheric variables obtained from the Japanese 55-year atmospheric Reanalysis JRA-55 over the region, thus deriving classified weather patterns (WPs). Probabilistic relationships are established between the synoptic-scale atmospheric variables over East Japan and the generation of regionally integrated wind power in East Japan. Medium-range probabilistic wind power predictions are derived by SOM as analog ensembles based on the WPs of the multi-center ensemble forecasts. As this analog approach handles stochastic uncertainties effectively, probabilistic wind power forecasts are rapidly generated from a very large number of forecast ensembles. The use of a multi-model ensemble provides better results than a one-forecast model. The hybrid ensemble forecasts further improve the probabilistic predictability skill of wind power generation compared with non-hybrid methods. It is expected that long-term wind forecasts will provide better guidance to transmission grid operators. The advantage of this method is that it can include an interpretative analysis of meteorological factors for variations in renewable energy.
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Beck, Jeffrey, Mathieu Nuret, and Olivier Bousquet. "Model Wind Field Forecast Verification Using Multiple-Doppler Syntheses from a National Radar Network." Weather and Forecasting 29, no. 2 (April 1, 2014): 331–48. http://dx.doi.org/10.1175/waf-d-13-00068.1.
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Abstract Model verification has traditionally relied upon in situ observations, which typically exist on a sparse network, making nonsurface model forecast verification difficult. Given increasing model resolution, supplemental observational datasets are needed. Multiple-Doppler wind retrievals using a national network of radars provide an opportunity to assess the accuracy of wind forecasts at multiple levels, as well as verification within a three-dimensional domain. Wind speed and direction verification results are presented for a 9-day period of forecasts from the French Application of Research to Operations at Mesoscale-Western Mediterranean (AROME-WMED) model using multiple-Doppler retrievals from the French Application Radar à la Météorologie Infrasynoptique (ARAMIS) network. For the analyzed period, relationships were found that suggest that errors are not only linked to forecasted evolution of meteorological phenomena, but are sensitive to terrain height below the analyzed level as well as mesoscale processes. The spatial distribution of errors at initialization and forecast times shows that biases are generally independent of location and terrain height at initialization, but that the impact of terrain below the analysis level affects the forecasted wind magnitude and direction over time. These comparisons illustrate that multiple-Doppler wind retrieval measurements accurately identify model error and can serve as an invaluable dataset for model verification.
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Leidner, S. Mark, Bachir Annane, Brian McNoldy, Ross Hoffman, and Robert Atlas. "Variational Analysis of Simulated Ocean Surface Winds from the Cyclone Global Navigation Satellite System (CYGNSS) and Evaluation Using a Regional OSSE." Journal of Atmospheric and Oceanic Technology 35, no. 8 (August 2018): 1571–84. http://dx.doi.org/10.1175/jtech-d-17-0136.1.
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AbstractA positive impact of adding directional information to observations from the Cyclone Global Navigation Satellite System (CYNGSS) constellation of microsatellites is observed in simulation using a high-resolution nature run of an Atlantic hurricane for a 4-day period. Directional information is added using a two-dimensional variational analysis method (VAM) for near-surface vector winds that blends simulated CYGNSS wind speeds with an a priori background vector wind field at 6-h analysis times. The resulting wind vectors at CYGNSS data locations are more geophysically self-consistent when using high-resolution 6-h forecast backgrounds from a Hurricane Weather Research and Forecast Model (HWRF) control observing system simulation experiment (OSSE) compared to low-resolution 6-h forecasts from an associated Global Forecast System (GFS) model control OSSE. An important contributing factor is the large displacement error in the center of circulation in the GFS background wind fields that produces asymmetric circulations in the associated VAM analyses. Results of a limited OSSE indicate that CYGNSS winds reduce forecast error in hurricane intensity in 0–48-h forecasts compared to using no CYGNSS data. Assimilation of VAM-CYGNSS vector winds reduces maximum wind speed error by 2–5 kt (1 kt = 0.51 m s−1) and reduces minimum central pressure error by 2–5 hPa. The improvement in forecast intensity is notably larger and more consistent than the reduction in track error. The assimilation of VAM-CYGNSS wind vectors constrains analyses of surface wind field structures during OSSE more effectively than wind speeds alone. Because of incomplete sampling and the limitations of the data assimilation system used, CYGNSS scalar winds produce unwanted wind/pressure imbalances and asymmetries more often than the assimilation of VAM-CYGNSS data.
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Banta, Robert M., Yelena L. Pichugina, W. Alan Brewer, Eric P. James, Joseph B. Olson, Stanley G. Benjamin, Jacob R. Carley, et al. "Evaluating and Improving NWP Forecast Models for the Future: How the Needs of Offshore Wind Energy Can Point the Way." Bulletin of the American Meteorological Society 99, no. 6 (June 2018): 1155–76. http://dx.doi.org/10.1175/bams-d-16-0310.1.
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AbstractTo advance the understanding of meteorological processes in offshore coastal regions, the spatial variability of wind profiles must be characterized and uncertainties (errors) in NWP model wind forecasts quantified. These gaps are especially critical for the new offshore wind energy industry, where wind profile measurements in the marine atmospheric layer spanned by wind turbine rotor blades, generally 50–200 m above mean sea level (MSL), have been largely unavailable. Here, high-quality wind profile measurements were available every 15 min from the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL)’s high-resolution Doppler lidar (HRDL) during a monthlong research cruise in the Gulf of Maine for the 2004 New England Air Quality Study. These measurements were compared with retrospective NWP model wind forecasts over the area using two NOAA forecast-modeling systems [North American Mesoscale Forecast System (NAM) and Rapid Refresh (RAP)]. HRDL profile measurements quantified model errors, including their dependence on height above sea level, diurnal cycle, and forecast lead time. Typical model wind speed errors were ∼2.5 m s−1, and vector-wind errors were ∼4 m s−1. Short-term forecast errors were larger near the surface—30% larger below 100 m than above and largest for several hours after local midnight (biased low). Longer-term, 12-h forecasts had the largest errors after local sunset (biased high). At more than 3-h lead times, predictions from finer-resolution models exhibited larger errors. Horizontal variability of winds, measured as the ship traversed the Gulf of Maine, was significant and raised questions about whether modeled fields, which appeared smooth in comparison, were capturing this variability. If not, horizontal arrays of high-quality, vertical-profiling devices will be required for wind energy resource assessment offshore. Such measurement arrays are also needed to improve NWP models.
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Sampson, Charles R., James S. Goerss, John A. Knaff, Brian R. Strahl, Edward M. Fukada, and Efren A. Serra. "Tropical Cyclone Gale Wind Radii Estimates, Forecasts, and Error Forecasts for the Western North Pacific." Weather and Forecasting 33, no. 4 (August 1, 2018): 1081–92. http://dx.doi.org/10.1175/waf-d-17-0153.1.
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Abstract In 2016, the Joint Typhoon Warning Center extended forecasts of gale-force and other wind radii to 5 days. That effort and a thrust to perform postseason analysis of gale-force wind radii for the “best tracks” (the quality controlled and documented tropical cyclone track and intensity estimates released after the season) have prompted requirements for new guidance to address the challenges of both. At the same time, operational tools to estimate and predict wind radii continue to evolve, now forming a quality suite of gale-force wind radii analysis and forecasting tools. This work provides an update to real-time estimates of gale-force wind radii (a mean/consensus of gale-force individual wind radii estimates) that includes objective scatterometer-derived estimates. The work also addresses operational gale-force wind radii forecasting in that it provides an update to a gale-force wind radii forecast consensus, which now includes gale-force wind radii forecast error estimates to accompany the gale-force wind radii forecasts. The gale-force wind radii forecast error estimates are computed using predictors readily available in real time (e.g., consensus spread, initial size, and forecast intensity) so that operational reliability and timeliness can be ensured. These updates were all implemented in operations at the Joint Typhoon Warning Center by January 2018, and more updates should be expected in the coming years as new and improved guidance becomes available.
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Page, Wesley G., Natalie S. Wagenbrenner, Bret W. Butler, Jason M. Forthofer, and Chris Gibson. "An Evaluation of NDFD Weather Forecasts for Wildland Fire Behavior Prediction." Weather and Forecasting 33, no. 1 (February 1, 2018): 301–15. http://dx.doi.org/10.1175/waf-d-17-0121.1.
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Abstract Wildland fire managers in the United States currently utilize the gridded forecasts from the National Digital Forecast Database (NDFD) to make fire behavior predictions across complex landscapes during large wildfires. However, little is known about the NDFDs performance in remote locations with complex topography for weather variables important for fire behavior prediction, including air temperature, relative humidity, and wind speed. In this study NDFD forecasts for calendar year 2015 were evaluated in fire-prone locations across the conterminous United States during periods with the potential for active fire spread using the model performance statistics of root-mean-square error (RMSE), mean fractional bias (MFB), and mean bias error (MBE). Results indicated that NDFD forecasts of air temperature and relative humidity performed well with RMSEs of about 2°C and 10%–11%, respectively. However, wind speed was increasingly underpredicted when observed wind speeds exceeded about 4 m s−1, with MFB and MBE values of approximately −15% and −0.5 m s−1, respectively. The importance of accurate wind speed forecasts in terms of fire behavior prediction was confirmed, and the forecast accuracies needed to achieve “good” surface head fire rate-of-spread predictions were estimated as ±20%–30% of the observed wind speed. Weather station location, the specific forecast office, and terrain complexity had the largest impacts on wind speed forecast error, although the relatively low variance explained by the model (~37%) suggests that other variables are likely to be important. Based on these results it is suggested that wildland fire managers should use caution when utilizing the NDFD wind speed forecasts if high wind speed events are anticipated.
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Smith, Craig M., Darko Koračin, and Kristian Horvath. "Day-Ahead Predictability of Complex Terrain Flows for Wind Resource Production: A Case Study of the Washoe Zephyr." Weather and Forecasting 29, no. 6 (December 1, 2014): 1343–55. http://dx.doi.org/10.1175/waf-d-14-00021.1.
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Abstract A detailed description of the meteorological conditions of the Washoe Valley (Nevada) and simulations that examine the predictability of the westerly high wind event known as the Washoe Zephyr are presented. Numerical weather model prediction skill is computed for day-ahead (24–48 h) forecasts of wind speed at a meteorological tower on the Virginia Hills range relative to a persistence forecast based on a seasonal climatology constructed of hourly mean observations. The model predictions are shown to be more skillful than a climatology based on seasonal and hourly means during winter and less skillful than the seasonal-hourly climatology (SHC) during summer. Overall skill of the forecasted winds tends to increase with finer horizontal grid spacing. Phase errors compose the largest component of the error decomposition and large phase errors are associated with the onset and decay of the diurnally forced Washoe Zephyr during summer and synoptically forced high wind events and valley rotors during winter. The correlation coefficient between forecasts and observations for all forecast horizontal grid spacings considered is shown to depend roughly linearly on the ratio of the integrated power spectral density in the synoptic band to the integrated power spectral density in the combined diurnal and subdiurnal band.
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Bédard, Joël, Stéphane Laroche, and Pierre Gauthier. "Near-Surface Wind Observation Impact on Forecasts: Temporal Propagation of the Analysis Increment." Monthly Weather Review 145, no. 4 (April 1, 2017): 1549–64. http://dx.doi.org/10.1175/mwr-d-16-0310.1.
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Abstract This study examines the assimilation of near-surface wind observations over land to improve wind nowcasting and short-term tropospheric forecasts. A new geostatistical operator based on geophysical model output statistics (GMOS) is compared with a bilinear interpolation scheme (Bilin). The multivariate impact on forecasts and the temporal evolution of the analysis increments produced are examined as well as the influence of background error covariances on different components of the prediction system. Results show that Bilin significantly degrades surface and upper-air fields when assimilating only wind data from 4942 SYNOP stations. GMOS on the other hand produces smaller increments that are in better agreement with the model state. It leads to better short-term near-surface wind forecasts and does not deteriorate the upper-air forecasts. The information persists longer in the system with GMOS, although the local improvements do not propagate beyond 6-h lead time. Initial model tendencies indicate that the mass field is not significantly altered when using static error covariances and the boundary layer parameterizations damp the poorly balanced increment locally. Conversely, most of the analysis increment is propagated when using flow-dependent error statistics. It results in better balanced wind and mass fields and provides a more persistent impact on the forecasts. Forecast accuracy results from observing system experiments (assimilating SYNOP winds with all observations used operationally) are generally neutral. Nevertheless, forecasts and analyses from GMOS are more self-consistent than those from both Bilin and a control experiment (not assimilating near-surface winds over land) and the information from the observations persists up to 12-h lead time.
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Langland, Rolf H., Christopher Velden, Patricia M. Pauley, and Howard Berger. "Impact of Satellite-Derived Rapid-Scan Wind Observations on Numerical Model Forecasts of Hurricane Katrina." Monthly Weather Review 137, no. 5 (May 1, 2009): 1615–22. http://dx.doi.org/10.1175/2008mwr2627.1.
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Abstract The impacts of special Geostationary Operational Environmental Satellite (GOES) rapid-scan (RS) wind observations on numerical model 24–120-h track forecasts of Hurricane Katrina are examined in a series of data assimilation and forecast experiments. The RS wind vectors are derived from geostationary satellites by tracking cloud motions through successive 5-min images. In these experiments, RS wind observations are added over the area 15°–60°N, 60°–110°W, and they supplement the observations used in operational forecasts. The inclusion of RS wind observations reduces errors in numerical forecasts of the Katrina landfall position at 1200 UTC 29 August 2005 by an average of 12% compared to control cases that include “targeted” dropsonde observations in the Katrina environment. The largest average improvements are made to the 84- to 120-h Katrina track forecasts, rather than to the short-range track forecasts. These results suggest that RS wind observations can potentially be used in future cases to improve track forecasts of tropical cyclones.
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Davis, Christopher, Wei Wang, Jimy Dudhia, and Ryan Torn. "Does Increased Horizontal Resolution Improve Hurricane Wind Forecasts?" Weather and Forecasting 25, no. 6 (December 1, 2010): 1826–41. http://dx.doi.org/10.1175/2010waf2222423.1.
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Abstract The representation of tropical cyclone track, intensity, and structure in a set of 69 parallel forecasts performed at each of two horizontal grid increments with the Advanced Research Hurricane (AHW) component of the Weather and Research and Forecasting Model (WRF) is evaluated. These forecasts covered 10 Atlantic tropical cyclones: 6 from the 2005 season and 4 from 2007. The forecasts were integrated from identical initial conditions produced by a cycling ensemble Kalman filter. The high-resolution forecasts used moving, storm-centered nests of 4- and 1.33-km grid spacing. The coarse-resolution forecasts consisted of a single 12-km domain (which was identical to the outer domain in the forecasts with nests). Forecasts were evaluated out to 120 h. Novel verification techniques were developed to evaluate forecasts of wind radii and the degree of storm asymmetry. Intensity (maximum wind) and rapid intensification, as well as wind radii, were all predicted more accurately with increased horizontal resolution. These results were deemed to be statistically significant based on the application of bootstrap confidence intervals. No statistically significant differences emerged regarding storm position errors between the two forecasts. Coarse-resolution forecasts tended to overpredict the extent of winds compared to high-resolution forecasts. The asymmetry of gale-force winds was better predicted in the coarser-resolution simulation, but asymmetry of hurricane-force winds was predicted better at high resolution. The skill of the wind radii forecasts decayed gradually over 120 h, suggesting a synoptic-scale control of the predictability of outer winds.
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Li, Xin, Zhaoxia Pu, Jun A. Zhang, and George David Emmitt. "Combined Assimilation of Doppler Wind Lidar and Tail Doppler Radar Data over a Hurricane Inner Core for Improved Hurricane Prediction with the NCEP Regional HWRF System." Remote Sensing 14, no. 10 (May 13, 2022): 2367. http://dx.doi.org/10.3390/rs14102367.
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Accurate specification of hurricane inner-core structure is critical to predicting the evolution of a hurricane. However, observations over hurricane inner cores are generally lacking. Previous studies have emphasized Tail Doppler radar (TDR) data assimilation to improve hurricane inner-core representation. Recently, Doppler wind lidar (DWL) has been used as an observing system to sample hurricane inner-core and environmental conditions. The NOAA P3 Hurricane Hunter aircraft has DWL installed and can obtain wind data over a hurricane’s inner core when the aircraft passes through the hurricane. In this study, we examine the impact of assimilating DWL winds and TDR radial winds on the prediction of Hurricane Earl (2016) with the NCEP operational Hurricane Weather Research and Forecasting (HWRF) system. A series of data assimilation experiments are conducted with the Gridpoint Statistical Interpolation (GSI)-based ensemble-3DVAR hybrid system to identify the best way to assimilate TDR and DWL data into the HWRF forecast system. The results show a positive impact of DWL data on hurricane analysis and prediction. Compared with the assimilation of u and v components, assimilation of DWL wind speed provides better hurricane track and intensity forecasts. Proper choices of data thinning distances (e.g., 5 km horizontal thinning and 70 hPa vertical thinning for DWL) can help achieve better analysis in terms of hurricane vortex representation and forecasts. In the analysis and forecast cycles, the combined TDR and DWL assimilation (DWL wind speed and TDR radial wind, along with other conventional data, e.g., NCEP Automated Data Processing (ADP) data) offsets the downgrade analysis from the absence of DWL observations in an analysis cycle and outperforms assimilation of a single type of data (either TDR or DWL) and leads to improved forecasts of hurricane track, intensity, and structure. Overall, assimilation of DWL observations has been beneficial for analysis and forecasts in most cases. The outcomes from this study demonstrate the great potential of including DWL wind profiles in the operational HWRF system for hurricane forecast improvement.
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Dong, Xue, Xiaowen Tang, Jiajia Tang, Shengxiao Zhao, Yanyan Lu, and Xiaofeng Chen. "Impact of Assimilating Conventional Observations on Short-Term Nearshore Wind Forecast over the East China Sea." Atmosphere 14, no. 1 (December 27, 2022): 47. http://dx.doi.org/10.3390/atmos14010047.
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This study investigates the impact of assimilating conventional weather observations on the wind forecast over the nearshore region of the East China Sea. Multi-level wind measurements in the boundary layer from five masts near the coast were used to verify the numerical model forecasts. Four numerical experiments with a rapid update cycle were performed to forecast the wind field over the masts. The observation shows that the characteristics of the wind field are distinct between the onshore and offshore masts. The numerical forecasts were able to reproduce the main features of the observed wind field both onshore and offshore. However, the wind forecasts of the offshore masts showed larger BIAS and MAE than those onshore. The forecast skill was shown to be sensitive to different weather events and the choice of control variables in the assimilation. The use of new momentum control variables allows a smaller observation-minus-analysis field compared with the traditional control variables, and the resultant wind forecast showed significant improvements. Further tuning of the new control variable scheme showed little improvement of the wind forecast which demonstrates the importance of maintaining the balance between large-scale and small-scale fields in the analysis. The larger forecast error at the offshore masts was likely due to the distribution of conventional observations and the uncertainties in representing the marine boundary layer in numerical models.
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Schuhen, Nina, Thordis L. Thorarinsdottir, and Tilmann Gneiting. "Ensemble Model Output Statistics for Wind Vectors." Monthly Weather Review 140, no. 10 (October 1, 2012): 3204–19. http://dx.doi.org/10.1175/mwr-d-12-00028.1.
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Abstract A bivariate ensemble model output statistics (EMOS) technique for the postprocessing of ensemble forecasts of two-dimensional wind vectors is proposed, where the postprocessed probabilistic forecast takes the form of a bivariate normal probability density function. The postprocessed means and variances of the wind vector components are linearly bias-corrected versions of the ensemble means and ensemble variances, respectively, and the conditional correlation between the wind components is represented by a trigonometric function of the ensemble mean wind direction. In a case study on 48-h forecasts of wind vectors over the North American Pacific Northwest with the University of Washington Mesoscale Ensemble, the bivariate EMOS density forecasts were calibrated and sharp, and showed considerable improvement over the raw ensemble and reference forecasts, including ensemble copula coupling.
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Wilks, Daniel S., Charles J. Neumann, and Miles B. Lawrence. "Statistical Extension of the National Hurricane Center 5-Day Forecasts." Weather and Forecasting 24, no. 4 (August 1, 2009): 1052–63. http://dx.doi.org/10.1175/2009waf2222189.1.
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Abstract U.S. National Hurricane Center (NHC) forecasts for tropical cyclone tracks and wind speeds are extended in time to produce spatially disaggregated probability forecasts for landfall location and intensity, using a weighted bootstrap procedure. Historical analogs, with respect to the forecast characteristics (location, heading, and wind speed) of a current storm, are selected. These are resampled by translating their locations to random positions consistent with the current forecast, and recent NHC forecast accuracy statistics. The result is a large number of plausible Monte Carlo realizations that jointly approximate a probability distribution for the future track and intensity of the storm. Performance of the resulting forecasts is assessed for U.S. tropical cyclone landfall probabilities during 1998–2006, and the forecasts are shown to be skillful and exhibit excellent reliability, even beyond the 120-h forecast horizon of the NHC advisory forecasts upon which they are based.