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1
Berkovich, L. V., and Yu V. Tkacheva. "Operative short-range hydrodynamic weather forecasting at the points and its efficiency." Russian Meteorology and Hydrology 35, no. 12 (December 2010): 813–16. http://dx.doi.org/10.3103/s1068373910120046.
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Mariani, S., M. Casaioli, and P. Malguzzi. "Towards a new BOLAM-MOLOCH suite for the SIMM forecasting system: implementation of an optimised configuration for the HyMeX Special Observation Periods." Natural Hazards and Earth System Sciences Discussions 2, no. 1 (January 22, 2014): 649–80. http://dx.doi.org/10.5194/nhessd-2-649-2014.
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Abstract. In this work, the performance of two versions of the Sistema Idro-Meteo-Mare (SIMM) forecasting system, aiming at predicting weather, waves and sea surge in the Mediterranean basin and, in particular, around the Italian coasts, are compared for two high-impact case studies corresponding to the Intense Operation Period (IOP) 16 and 18 of the first monitoring campaign of the HYdrological cycle in Mediterranean EXperiment (HyMeX). The first SIMM version tested – currently operational – is based on the meteorological hydrostatic BOlogna Limited Area Model (BOLAM) one-way nested over two domains, the Mediterranean-embedded Costal WAve Forecasting system (Mc-WAF), and the Shallow water HYdrodynamic Finite Element Model (SHYFEM). The second version tested is the one initially implemented for the HyMeX monitoring campaigns, which is composed by an optimised new configuration of BOLAM defined over a wider, higher-resolution domain, the nonhydrostatic convection permitting model MOLOCH, the Mc-WAF component, and SHYFEM. Both SIMM versions are initialised with data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). The accumulated precipitation obtained by applying all the above meteorological model chains at the two case studies is compared with observations. In general, the precipitation forecast quality turns out to improve with increasing resolution, the best result being obtained with the MOLOCH model. Storm surge and tidal forecasts, obtained by forcing SHYFEM with the BOLAM and ECMWF IFS surface wind and atmospheric pressure fields, are compared with observations of tidal elevation measured at the ISPRA "Punta della Salute" tide-gauge, located in the Lagoon of Venice. Results indicate that, for the IOP18, short-term forecasts obtained with BOLAM outperform the ECMWF IFS one, while the opposite seems apparently true for longer-term predictions.
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Dawson, C. ,., J. F. V. Vincent, and A.-M. Rocca. "Short-range weather forecasting." Trends in Plant Science 3, no. 2 (February 1998): 45. http://dx.doi.org/10.1016/s1360-1385(97)01189-8.
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Murray, R. "SHORT-RANGE WEATHER FORECASTING." Weather 42, no. 11 (November 1987): 346–50. http://dx.doi.org/10.1002/j.1477-8696.1987.tb04869.x.
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Hewage, Pradeep, Ardhendu Behera, Marcello Trovati, Ella Pereira, Morteza Ghahremani, Francesco Palmieri, and Yonghuai Liu. "Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station." Soft Computing 24, no. 21 (April 23, 2020): 16453–82. http://dx.doi.org/10.1007/s00500-020-04954-0.
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Abstract Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.
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Altaf, M. U., T. Butler, X. Luo, C. Dawson, T. Mayo, and I. Hoteit. "Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation." Monthly Weather Review 141, no. 8 (July 25, 2013): 2705–20. http://dx.doi.org/10.1175/mwr-d-12-00310.1.
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Abstract This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H∞ filter. By design, an H∞ filter is more robust than the common Kalman filter in the sense that the estimation error in the H∞ filter has, in general, a finite growth rate with respect to the uncertainties in assimilation. The computational hydrodynamical model used in this study is the Advanced Circulation (ADCIRC) model. The authors assimilate data obtained from Hurricanes Katrina and Ike as test cases. The results clearly show that the H∞-based SEIK filter provides more accurate short-range forecasts of storm surge compared to recently reported data assimilation results resulting from the standard SEIK filter.
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McCandless, T. C., G. S. Young, S. E. Haupt, and L. M. Hinkelman. "Regime-Dependent Short-Range Solar Irradiance Forecasting." Journal of Applied Meteorology and Climatology 55, no. 7 (July 2016): 1599–613. http://dx.doi.org/10.1175/jamc-d-15-0354.1.
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AbstractThis paper describes the development and testing of a cloud-regime-dependent short-range solar irradiance forecasting system for predictions of 15-min-average clearness index (global horizontal irradiance). This regime-dependent artificial neural network (RD-ANN) system classifies cloud regimes with a k-means algorithm on the basis of a combination of surface weather observations, irradiance observations, and GOES-East satellite data. The ANNs are then trained on each cloud regime to predict the clearness index. This RD-ANN system improves over the mean absolute error of the baseline clearness-index persistence predictions by 1.0%, 21.0%, 26.4%, and 27.4% at the 15-, 60-, 120-, and 180-min forecast lead times, respectively. In addition, a version of this method configured to predict the irradiance variability predicts irradiance variability more accurately than does a smart persistence technique.
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Iversen, Trond. "Tellus A Special Issue on probabilistic short-range weather forecasting." Tellus A: Dynamic Meteorology and Oceanography 63, no. 3 (January 2011): 371–72. http://dx.doi.org/10.1111/j.1600-0870.2011.00520.x.
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Lewis, John M. "Roots of Ensemble Forecasting." Monthly Weather Review 133, no. 7 (July 1, 2005): 1865–85. http://dx.doi.org/10.1175/mwr2949.1.
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Abstract The generation of a probabilistic view of dynamical weather prediction is traced back to the early 1950s, to that point in time when deterministic short-range numerical weather prediction (NWP) achieved its earliest success. Eric Eady was the first meteorologist to voice concern over strict determinism—that is, a future determined by the initial state without account for uncertainties in that state. By the end of the decade, Philip Thompson and Edward Lorenz explored the predictability limits of deterministic forecasting and set the stage for an alternate view—a stochastic–dynamic view that was enunciated by Edward Epstein. The steps in both operational short-range NWP and extended-range forecasting that justified a coupling between probability and dynamical law are followed. A discussion of the bridge from theory to practice follows, and the study ends with a genealogy of ensemble forecasting as an outgrowth of traditions in the history of science.
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Gouweleeuw, B. T., J. Thielen, G. Franchello, A. P. J. De Roo, and R. Buizza. "Flood forecasting using medium-range probabilistic weather prediction." Hydrology and Earth System Sciences 9, no. 4 (October 7, 2005): 365–80. http://dx.doi.org/10.5194/hess-9-365-2005.
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Abstract. Following the developments in short- and medium-range weather forecasting over the last decade, operational flood forecasting also appears to show a shift from a so-called single solution or 'best guess' deterministic approach towards a probabilistic approach based on ensemble techniques. While this probabilistic approach is now more or less common practice and well established in the meteorological community, operational flood forecasters have only started to look for ways to interpret and mitigate for end-users the prediction products obtained by combining so-called Ensemble Prediction Systems (EPS) of Numerical Weather Prediction (NWP) models with rainfall-runoff models. This paper presents initial results obtained by combining deterministic and EPS hindcasts of the global NWP model of the European Centre for Medium-Range Weather Forecasts (ECMWF) with the large-scale hydrological model LISFLOOD for two historic flood events: the river Meuse flood in January 1995 and the river Odra flood in July 1997. In addition, a possible way to interpret the obtained ensemble based stream flow prediction is proposed.
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Duarte, Roberta, Rodrigo Nemmen, and João Paulo Navarro. "Black hole weather forecasting with deep learning: a pilot study." Monthly Notices of the Royal Astronomical Society 512, no. 4 (March 22, 2022): 5848–61. http://dx.doi.org/10.1093/mnras/stac665.
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ABSTRACT In this pilot study, we investigate the use of a deep learning (DL) model to temporally evolve the dynamics of gas accreting on to a black hole in the form of a radiatively inefficient accretion flow (RIAF). We have trained a convolutional neural network (CNN) on a data set that consists of numerical solutions of the hydrodynamical equations for a range of initial conditions. We find that deep neural networks trained on one simulation seem to learn reasonably well the spatiotemporal distribution of densities and mass continuity of a black hole accretion flow over a duration of 8 × 104GM/c3, comparable to the viscous time-scale at r = 400GM/c2; after that duration, the model drifts from the ground truth suffering from excessive artificial mass injection. Models trained on simulations with different initial conditions show some promise of generalizing to configurations not present in the training set, but also suffer from mass continuity issues. We discuss the caveats behind this method and the potential benefits that DL models offer. For instance, once trained the model evolves an RIAF on a single GPU four orders of magnitude faster than usual fluid dynamics integrators running in parallel on 200 CPU cores. We speculate that a data-driven machine learning approach should be very promising for accelerating simulations of accreting black holes.
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Wandishin, Matthew S., Michael E. Baldwin, Steven L. Mullen, and John V. Cortinas. "Short-Range Ensemble Forecasts of Precipitation Type." Weather and Forecasting 20, no. 4 (August 1, 2005): 609–26. http://dx.doi.org/10.1175/waf871.1.
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Abstract Short-range ensemble forecasting is extended to a critical winter weather problem: forecasting precipitation type. Forecast soundings from the operational NCEP Short-Range Ensemble Forecast system are combined with five precipitation-type algorithms to produce probabilistic forecasts from January through March 2002. Thus the ensemble combines model diversity, initial condition diversity, and postprocessing algorithm diversity. All verification numbers are conditioned on both the ensemble and observations recording some form of precipitation. This separates the forecast of type from the yes–no precipitation forecast. The ensemble is very skillful in forecasting rain and snow but it is only moderately skillful for freezing rain and unskillful for ice pellets. However, even for the unskillful forecasts the ensemble shows some ability to discriminate between the different precipitation types and thus provides some positive value to forecast users. Algorithm diversity is shown to be as important as initial condition diversity in terms of forecast quality, although neither has as big an impact as model diversity. The algorithms have their individual strengths and weaknesses, but no algorithm is clearly better or worse than the others overall.
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JAIN, PANKAJ, ASHOK KUMAR, PARVINDER MAINI, and S. V. SINGH. "Short range SW monsoon rainfall forecasting over India using neural networks." MAUSAM 53, no. 2 (January 18, 2022): 225–32. http://dx.doi.org/10.54302/mausam.v53i2.1637.
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Feedforward Neural Networks are used for daily precipitation forecast using several test stations all over India. The six year European Centre of Medium Range Weather Forecasting (ECMWF) data is used with the training set consisting of the four year data from 1985-1988 and validation set consisting of the data from 1989-1990. Neural networks are used to develop a concurrent relationship between precipitation and other atmospheric variables. No attempt is made to select optimal variables for this study and the inputs are chosen to be same as the ones obtained earlier at National Center for Medium Range Weather Forecasting (NCMRWF) in developing a linear regression model. Neural networks are found to yield results which are atleast as good as linear regression and in several cases yield 10 - 20 % improvement. This is encouraging since the variable selection has so far been optimized for linear regression.
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Lu, Chungu, Huiling Yuan, Barry E. Schwartz, and Stanley G. Benjamin. "Short-Range Numerical Weather Prediction Using Time-Lagged Ensembles." Weather and Forecasting 22, no. 3 (June 1, 2007): 580–95. http://dx.doi.org/10.1175/waf999.1.
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Abstract A time-lagged ensemble forecast system is developed using a set of hourly initialized Rapid Update Cycle model deterministic forecasts. Both the ensemble-mean and probabilistic forecasts from this time-lagged ensemble system present a promising improvement in the very short-range weather forecasting of 1–3 h, which may be useful for aviation weather prediction and nowcasting applications. Two approaches have been studied to combine deterministic forecasts with different initialization cycles as the ensemble members. The first method uses a set of equally weighted time-lagged forecasts and produces a forecast by taking the ensemble mean. The second method adopts a multilinear regression approach to select a set of weights for different time-lagged forecasts. It is shown that although both methods improve short-range forecasts, the unequally weighted method provides the best results for all forecast variables at all levels. The time-lagged ensembles also provide a sample of statistics, which can be used to construct probabilistic forecasts.
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Holtslag, A. A. M., E. I. F. De Bruijn, and H.-L. Pan. "A High Resolution Air Mass Transformation Model for Short-Range Weather Forecasting." Monthly Weather Review 118, no. 8 (August 1990): 1561–75. http://dx.doi.org/10.1175/1520-0493(1990)118<1561:ahramt>2.0.co;2.
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Peteherych, Steven, William S. Appleby, Peter M. Woiceshyn, John C. Spagnol, and Lawrence Chu. "Application of Seasat Scatterometer Wind Measurements for Operational Short-Range Weather Forecasting." Weather and Forecasting 3, no. 2 (June 1988): 89–103. http://dx.doi.org/10.1175/1520-0434(1988)003<0089:aosswm>2.0.co;2.
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Han, Shasha, and Paulin Coulibaly. "Probabilistic Flood Forecasting Using Hydrologic Uncertainty Processor with Ensemble Weather Forecasts." Journal of Hydrometeorology 20, no. 7 (July 2019): 1379–98. http://dx.doi.org/10.1175/jhm-d-18-0251.1.
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Recent advances in the field of flood forecasting have shown increased interests in probabilistic forecasting as it provides not only the point forecast but also the assessment of associated uncertainty. Here, an investigation of a hydrologic uncertainty processor (HUP) as a postprocessor of ensemble forecasts to generate probabilistic flood forecasts is presented. The main purpose is to quantify dominant uncertainties and enhance flood forecast reliability. HUP is based on Bayes’s theorem and designed to capture hydrologic uncertainty. Ensemble forecasts are forced by ensemble weather forecasts from the Global Ensemble Prediction System (GEPS) that are inherently uncertain, and the input uncertainty propagates through the model chain and integrates with hydrologic uncertainty in HUP. The bias of GEPS was removed using multivariate bias correction, and several scenarios were developed by different combinations of GEPS with HUP. The performance of different forecast horizons for these scenarios was compared using multifaceted evaluation metrics. Results show that HUP is able to improve the performance for both short- and medium-range forecasts; the improvement is significant for short lead times and becomes less obvious with increasing lead time. Overall, the performances for short-range forecasts when using HUP are promising, and the most satisfactory result for the short range is obtained by applying bias correction to each ensemble member plus applying the HUP postprocessor.
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Eckel, F. Anthony, and Clifford F. Mass. "Aspects of Effective Mesoscale, Short-Range Ensemble Forecasting." Weather and Forecasting 20, no. 3 (June 1, 2005): 328–50. http://dx.doi.org/10.1175/waf843.1.
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Abstract This study developed and evaluated a short-range ensemble forecasting (SREF) system with the goal of producing useful, mesoscale forecast probability (FP). Real-time, 0–48-h SREF predictions were produced and analyzed for 129 cases over the Pacific Northwest. Eight analyses from different operational forecast centers were used as initial conditions for running the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). Model error is a large source of forecast uncertainty and must be accounted for to maximize SREF utility, particularly for mesoscale, sensible weather phenomena. Although inclusion of model diversity improved FP skill (both reliability and resolution) and increased dispersion toward statistical consistency, dispersion remained inadequate. Conversely, systematic model errors (i.e., biases) must be removed from an SREF since they contribute to forecast error but not to forecast uncertainty. A grid-based, 2-week, running-mean bias correction was shown to improve FP skill through 1) better reliability by adjusting the ensemble mean toward the mean of the verifying analysis, and 2) better resolution by removing unrepresentative ensemble variance. Comparison of the multimodel (each member uses a unique model) and varied-model (each member uses a unique version of MM5) approaches indicated that the multimodel SREF exhibited greater dispersion and superior performance. It was also found that an ensemble of unequally likely members can be skillful as long as each member occasionally performs well. Finally, smaller grid spacing led to greater ensemble spread as smaller scales of motion were modeled. This study indicates substantial utility in current SREF systems and suggests several avenues for further improvement.
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Casanova, Sophie, and Bodo Ahrens. "On the Weighting of Multimodel Ensembles in Seasonal and Short-Range Weather Forecasting." Monthly Weather Review 137, no. 11 (November 1, 2009): 3811–22. http://dx.doi.org/10.1175/2009mwr2893.1.
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Abstract The performance of multimodel ensemble forecasting depends on the weights given to the different models of the ensemble in the postprocessing of the direct model forecasts. This paper compares the following different weighting methods with or without taking into account the single-model performance: equal weighting of models (EW), simple skill-based weighting (SW), using a simple model performance indicator, and weighting by Bayesian model averaging (BMA). These methods are tested for both short-range weather and seasonal temperature forecasts. The prototype seasonal multimodel ensemble is the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) system, with four different models and nine forecasts per model. The short-range multimodel prototype system is the European Meteorological Services (EUMETNET) Poor-Man’s Ensemble Prediction System (PEPS), with 14 models and one forecast per model. It is shown that despite the different forecast ranges and spatial scales, the impact of weighting is comparable for both forecast systems and is related to the same ensemble characteristics. In both cases the added value of ensemble forecasting over single-model forecasting increases considerably with the decreasing correlation of the models’ forecast errors, with a relation depending only on the number of models. Also, in both cases a larger spread in model performance increases the added value of combining model forecasts using the performance-based SW or BMA weighting instead of EW. Finally, the more complex BMA weighting adds value over SW only if the best model performs better than the ensemble with EW weighting.
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Kazem Al-Samalek, Ahmed Saleh Mahdi, Sonu Lal, P. Sharmila, Abootharmahmoodshakir, Ankita Joshi, and R. Vimala Devi. "Machine Learning Approaches for Short-Range Wind Power Estimation: A Perspective." E3S Web of Conferences 540 (2024): 03005. http://dx.doi.org/10.1051/e3sconf/202454003005.
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The evolution of wind energy production, especially in near and offshore farms, has seen significant advancements due to the integration of novel technologies and the reduction in economic costs. This paper reviews the work in the domain of wind power estimation, emphasizing the innovative approaches leveraging satellite data and artificial intelligence (AI) methodologies. A notable method integrates Sentinel satellite imagery analysis in a two-phased approach, combined with machine learning techniques, to forecast wind speed. This method utilizes sentinel-1 and sentinel-2 satellite images for wind speed and bathymetry analysis, respectively. Furthermore, a hybrid forecasting model, comprising the generalized regression neural network (GRNN) and the whale optimization algorithm (WOA), has been introduced. Another pivotal advancement comes from the National Center for Atmospheric Research (NCAR), which has revamped its wind power forecasting system. This enhancement focuses on short-term forecasting, uncertainty quantification in wind speed prediction, and the prediction of extreme events like icing. The integration of numerical weather prediction with machine-learning methods, such as the fuzzy logic artificial intelligence system, has further elevated the accuracy and efficiency of these forecasting models. Collectively, these advancements offer a comprehensive perspective on the future of shortrange wind power estimation.
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Ibrahim, Salem, and Gamal Afandi. "Short-range Rainfall Prediction over Egypt using the Weather Research and Forecasting Model." Open Journal of Renewable Energy and Sustainable Development 2014, no. 2 (July 31, 2014): 56–70. http://dx.doi.org/10.15764/resd.2014.02006.
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Nasooh Ismail. "Mini-review of Pysteps: An open-source python library for precipitation nowcasting." World Journal of Advanced Engineering Technology and Sciences 11, no. 2 (April 30, 2024): 289–95. http://dx.doi.org/10.30574/wjaets.2024.11.2.0114.
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Weather forecasting, particularly in short timeframes has been a longstanding challenge in meteorology, addressed in part by nowcasting methods. Leveraging radar data and innovative methodologies, nowcasting tools have evolved significantly, with open-source python platforms like Pysteps making is accessible to researchers to try advanced techniques. This review focus on Pysteps, a modular and user-friendly framework, offering optical flow based deterministic nowcasts and Short-Term Ensemble Prediction System (STEPS) ensemble nowcasts. Recent studies highlights its efficacy, including blending with Numerical Weather Prediction (NWP) models for improved performance beyond the nowcasting timeframe. Pysteps emerges as a versatile solution, facilitating both research innovation and operational forecasting needs, with wide range of input data and modularity.
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Younis, J., S. Anquetin, and J. Thielen. "The benefit of high-resolution operational weather forecasts for flash flood warning." Hydrology and Earth System Sciences 12, no. 4 (July 30, 2008): 1039–51. http://dx.doi.org/10.5194/hess-12-1039-2008.
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Abstract. In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of loss of human life and infrastructures. Over the last two decades, flash floods have caused damage costing a billion Euros in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available short-range numerical weather forecasts together with a rainfall-runoff model can be used for early indication of the occurrence of flash floods. One of the challenges in flash flood forecasting is that the watersheds are typically small, and good observational networks of both rainfall and discharge are rare. Therefore, hydrological models are difficult to calibrate and the simulated river discharges cannot always be compared with ground measurements. The lack of observations in most flash flood prone basins, therefore, necessitates the development of a method where the excess of the simulated discharge above a critical threshold can provide the forecaster with an indication of potential flood hazard in the area, with lead times of the order of weather forecasts. This study is focused on the Cévennes-Vivarais region in the Southeast of the Massif Central in France, a region known for devastating flash floods. This paper describes the main aspects of using numerical weather forecasting for flash flood forecasting, together with a threshold – exceedance. As a case study the severe flash flood event which took place on 8–9 September 2002 has been chosen. Short-range weather forecasts, from the Lokalmodell of the German national weather service, are used as input for the LISFLOOD model, a hybrid between a conceptual and physically based rainfall-runoff model. Results of the study indicate that high resolution operational weather forecasting combined with a rainfall-runoff model could be useful to determine flash floods more than 24 h in advance.
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DROFA, A. S., S. V. KOZLOV, and A. A. SPRYGIN. "FORECAST OF RESOURCE CONVECTIVE CLOUDS FOR WEATHER MODIFICATION." Meteorologiya i Gidrologiya, no. 7 (July 2022): 51–60. http://dx.doi.org/10.52002/0130-2906-2022-7-51-60.
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A technique is presented for monitoring and short-range forecasting of resource convective clouds suitable for obtaining additional precipitation by seeding with hygroscopic reagents. The short-range forecast of resource clouds is based on the analysis of a set of favorable conditions (a system of certain predictors). The predictors are computed with the global GFS model and are visualized as forecast maps. Predictor ratios are established empirically based on the analysis of meteorological conditions in a current season for the study region. The developed technique is tested using the data of convective clouds monitoring in the Stavropol region. The forecast results with a one-day lead time are in satisfactory agreement with the observed parameters of convective clouds.
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Arribas, A., K. B. Robertson, and K. R. Mylne. "Test of a Poor Man’s Ensemble Prediction System for Short-Range Probability Forecasting." Monthly Weather Review 133, no. 7 (July 1, 2005): 1825–39. http://dx.doi.org/10.1175/mwr2911.1.
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Abstract Current operational ensemble prediction systems (EPSs) are designed specifically for medium-range forecasting, but there is also considerable interest in predictability in the short range, particularly for potential severe-weather developments. A possible option is to use a poor man’s ensemble prediction system (PEPS) comprising output from different numerical weather prediction (NWP) centers. By making use of a range of different models and independent analyses, a PEPS provides essentially a random sampling of both the initial condition and model evolution errors. In this paper the authors investigate the ability of a PEPS using up to 14 models from nine operational NWP centers. The ensemble forecasts are verified for a 101-day period and five variables: mean sea level pressure, 500-hPa geopotential height, temperature at 850 hPa, 2-m temperature, and 10-m wind speed. Results are compared with the operational ECMWF EPS, using the ECMWF analysis as the verifying “truth.” It is shown that, despite its smaller size, PEPS is an efficient way of producing ensemble forecasts and can provide competitive performance in the short range. The best relative performance is found to come from hybrid configurations combining output from a small subset of the ECMWF EPS with other different NWP models.
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Saxen, Thomas R., Cynthia K. Mueller, Thomas T. Warner, Matthias Steiner, Edward E. Ellison, Eric W. Hatfield, Terri L. Betancourt, Susan M. Dettling, and Niles A. Oien. "The Operational Mesogamma-Scale Analysis and Forecast System of the U.S. Army Test and Evaluation Command. Part IV: The White Sands Missile Range Auto-Nowcast System." Journal of Applied Meteorology and Climatology 47, no. 4 (April 1, 2008): 1123–39. http://dx.doi.org/10.1175/2007jamc1656.1.
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Abstract During the summer months at the U.S. Army Test and Evaluation Command’s (ATEC) White Sands Missile Range (WSMR), forecasting thunderstorm activity is one of the primary duties of the range forecasters. The safety of personnel working on the range and the protection of expensive test equipment depend critically on the quality of forecasts of thunderstorms and associated hazards, including cloud-to-ground lightning, hail, strong winds, heavy rainfall, flash flooding, and tornadoes. The National Center for Atmospheric Research (NCAR) Auto-Nowcast (ANC) system is one of the key forecast tools in the ATEC Four-Dimensional Weather System (4DWX) at WSMR, where its purpose is to aid WSMR meteorologists in their mission of very short term thunderstorm forecasting. Besides monitoring the weather activity throughout the region and warning personnel of potentially hazardous thunderstorms, forecasters play a key role in assisting with the day-to-day planning of test operations on the range by providing guidance with regard to weather conditions favorable to testing. Moreover, based on climatological information about the local weather conditions, forecasters advise their range customers about scheduling tests at WSMR months in advance. This paper reviews the NCAR ANC system, provides examples of the ANC system’s use in thunderstorm forecasting, and describes climatological analyses of WSMR summertime thunderstorm activity relevant for long-range planning of tests. The climatological analysis illustrates that radar-detected convective cells with reflectivity of ≥35 dBZ at WSMR are 1) short lived, with 76% having lifetimes of less than 30 min; 2) small, with 67% occupying areas of less than 25 km2; 3) slow moving, with 79% exhibiting speeds of less than 4 m s−1; 4) moderately intense, with 80% showing reflectivities in excess of 40 dBZ; and 5) deep, with 80% of the storms reaching far enough above the freezing level to be capable of generating lightning.
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Benjamin, Stanley G., Barry E. Schwartz, Edward J. Szoke, and Steven E. Koch. "The Value of Wind Profiler Data in U.S. Weather Forecasting." Bulletin of the American Meteorological Society 85, no. 12 (December 1, 2004): 1871–86. http://dx.doi.org/10.1175/bams-85-12-1871.
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An assessment of the value of data from the NOAA Profiler Network (NPN) on weather forecasting is presented. A series of experiments was conducted using the Rapid Update Cycle (RUC) model/assimilation system in which various data sources were denied in order to assess the relative importance of the profiler data for short-range wind forecasts. Average verification statistics from a 13-day cold-season test period indicate that the profiler data have a positive impact on short-range (3–12 h) forecasts over the RUC domain containing the lower 48 United States, which are strongest at the 3-h projection over a central U.S. subdomain that includes most of the profiler sites, as well as downwind of the profiler observations over the eastern United States. Overall, profiler data reduce wind forecast errors at all levels from 850 to 150 hPa, especially below 300 hPa where there are relatively few automated aircraft observations. At night when fewer commercial aircraft are flying, profiler data also contribute strongly to more accurate 3-h forecasts, including near-tropopause maximum wind levels. For the test period, the profiler data contributed up to 20%–30% (at 700 hPa) of the overall reduction of 3-h wind forecast error by all data sources combined. Inclusion of wind profiler data also reduced 3-h errors for height, relative humidity, and temperature by 5%-15%, averaged over different vertical levels. Time series and statistics from large-error events demonstrate that the impact of profiler data may be much larger in peak error situations. Three data assimilation case studies from cold and warm seasons are presented that illustrate the value of the profiler observations for improving weather forecasts. The first case study indicates that inclusion of profiler data in the RUC model runs for the 3 May 1999 Oklahoma tornado outbreak improved model guidance of convective available potential energy (CAPE), 300-hPa wind, and precipitation in southwestern Oklahoma at the onset of the event. In the second case study, inclusion of profiler data led to better RUC precipitation forecasts associated with a severe snow and ice storm that occurred over the central plains of the United States in February 2001. A third case study describes the effect of profiler data for a tornado event in Oklahoma on 8 May 2003. Summaries of National Weather Service (NWS) forecaster use of profiler data in daily operations, although subjective, support the results from these case studies and the statistical forecast model impact study in the broad sense that profiler data contribute significantly to improved short-range forecasts over the central United States where these observations currently exist.
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Sen Roy, Soma, Pradeep Sharma, Bikram Sen, K. Sathi Devi, S. Sunitha Devi, Neetha K. Gopal, Naresh Kumar, et al. "A new paradigm for short-range forecasting of severe weather over the Indian region." Meteorology and Atmospheric Physics 133, no. 4 (March 28, 2021): 989–1008. http://dx.doi.org/10.1007/s00703-021-00788-z.
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A.V.M. SUBBA RAO, T. SATYANARAYANA, G.G.S.N. RAO, V.U.M. RAO, V. BHASKARA RAO, N. MANIKANDAN, P. SANTHI BHUSAN CHOWDARY, V. RAVIKUMAR, and Y.S. RAMAKRISHNA. "Utilization of high resolution short range weather forecast for agro advisory services." Journal of Agrometeorology 12, no. 2 (December 1, 2010): 229–33. http://dx.doi.org/10.54386/jam.v12i2.1312.
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Agriculture is heavily weather dependent in India. The economy of the country is largely synchronized to the success of agriculture every year, as it contributes nearly 25% to gross domestic product (GDP). Farming community, planners and line department people always look forward to the accurate forecast from the weather men for decision making process in the event of natural disturbances. The district level agro advisories provided by IMD using medium range forecast (3-10 days) are good but covering large area. The advent of mesoscale model (Mesoscale Model (MM5) & Weather Research and Forecasting (WRF) etc) with high resolution (available at 9×9 sq km or below) enable the weathermen to give forecasts at village level and also location specific. This requires verification before value addition to it. Mesoscale model forecast data at 9×9 sq km grid have been generated by Department of meteorology and oceanography, Andhra University was collected for verification over Andhra Padesh.The verification skill scores, ratio scores, correlation coefficient and RMSE have been analyzed. Majority of the stations are showing good correlation for maximum and minimum temperature and relative humidity, where as rainfall forecast have desirable skill scores for most of the stations.
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Tóth, Boglárka, and István Ihász. "Validation of subgrid scale ensemble precipitation forecasts based on the ECMWF’s ecPoint Rainfall project." Időjárás 125, no. 3 (2021): 397–418. http://dx.doi.org/10.28974/idojaras.2021.3.2.
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Nowadays, state-of-the-art numerical weather prediction models successfully predict the general weather characteristics several days ahead, but forecasting extreme precipitation is quite challenging even in the short time range. In the framework of the ecPoint Project, the European Centre for Medium-Range Weather Forecasts (ECMWF) developed a new innovative probabilistic post-processing tool which produces 4-day precipitation forecast as accurate as the raw ensemble forecast at day 1. In the framework of the scientific co-operation between ECMWF and the Hungarian Meteorological Service (OMSZ), we were invited to participate in the validation of the experimental products. Quasi operational post-processed products have been available since July 1, 2018. During our work, besides using different verification technics, a new ensemble meteogram was also developed which can support operational forecasters during extreme precipitation events. As a result of our work, products of the ecPoint Project have been included in the operational forecasting activity.
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Homar, Victor, David J. Stensrud, Jason J. Levit, and David R. Bright. "Value of Human-Generated Perturbations in Short-Range Ensemble Forecasts of Severe Weather." Weather and Forecasting 21, no. 3 (June 1, 2006): 347–63. http://dx.doi.org/10.1175/waf920.1.
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Abstract During the spring of 2003, the Storm Prediction Center, in partnership with the National Severe Storms Laboratory, conducted an experiment to explore the value of having operational severe weather forecasters involved in the generation of a short-range ensemble forecasting system. The idea was to create a customized ensemble to provide guidance on the severe weather threat over the following 48 h. The forecaster was asked to highlight structures of interest in the control run and, using an adjoint model, a set of perturbations was obtained and used to generate a 32-member fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) ensemble. The performance of this experimental ensemble is objectively evaluated and compared with other available forecasts (both deterministic and ensemble) using real-time severe weather reports and precipitation in the central and eastern parts of the continental United States. The experimental ensemble outperforms the operational forecasts considered in the study for episodes with moderate-to-high probability of severe weather occurrence and those with moderate probability of heavy precipitation. On the other hand, the experimental ensemble forecasts of low-probability severe weather and low precipitation amounts have less skill than the operational models, arguably due to the lack of global dispersion in a system designed to target the spread over specific areas of concern for severe weather. Results from an additional test ensemble constructed by combining automatic and manually perturbed members show the best results for numerical forecasts of severe weather for all probability values. While the value of human contribution in the numerical forecast is demonstrated, further research is needed to determine how to better use the skill and experience of the forecaster in the construction of short-range ensembles.
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BHOWMIK, S. K. ROY, ANUPAM KUMAR, and ANANDA K.DAS. "Real-time mesoscale modeling for short range prediction of weather over Maitri region in Antarctica." MAUSAM 62, no. 4 (December 16, 2021): 535–46. http://dx.doi.org/10.54302/mausam.v62i4.339.
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The main objective of this paper is to implement Polar WRF model for the Maitri (Lat. 70° 45 S, Long. 11° 44 E) region at the horizontal resolution of 15 km using initial and boundary conditions of the Global Forecast System T-382 operational at the India Meteorological Department (IMD). The study evaluates the performance of the model using the conventional approach of case studies. The results of the case studies illustrated in this paper reveal that the model is capable of capturing synoptic and meso-scale weather systems. Forecast fields are consistent with the corresponding analysis fields. Synoptic charts of mean sea level pressure prepared by the Weather Service of South Africa at Pretoria are used for the model validation. The model derived meteograms of mean sea level pressure are compared against the corresponding observations. The study demonstrates the usefulness of the forecast products for short range forecasting of weather over the Maitri region. The forecast outputs are made available in the real-time mode in the national web site of IMD www.imd.gov.in. The study is expected to benefit weather forecasters at Maitri.
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Younis, J., S. Anquetin, and J. Thielen. "The benefit of high-resolution operational weather forecasts for flash flood warning." Hydrology and Earth System Sciences Discussions 5, no. 1 (February 12, 2008): 345–77. http://dx.doi.org/10.5194/hessd-5-345-2008.
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Abstract. In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of human life loss and infrastructures. Over the last two decades, flash floods brought losses of a billion Euros of damage in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available shortrange numerical weather forecasts together with a rainfall-runoff model can be used as early indication for the occurrence of flash floods. One of the challenges in flash flood forecasting is that the watersheds are typically small and good observational networks of both rainfall and discharge are rare. Therefore, hydrological models are difficult to calibrate and the simulated river discharges cannot always be compared with ground "truth". The lack of observations in most flash flood prone basins, therefore, lead to develop a method where the excess of the simulated discharge above a critical threshold can provide the forecaster with an indication of potential flood hazard in the area with leadtimes of the order of the weather forecasts. This study is focused on the Cévennes-Vivarais region in the Southeast of the Massif Central in France, a region known for devastating flash floods. The critical aspects of using numerical weather forecasting for flash flood forecasting are being described together with a threshold – exceedance. As case study the severe flash flood event which took place on 8–9 September 2002 has been chosen. The short-range weather forecasts, from the Lokalmodell of the German national weather service, are driving the LISFLOOD model, a hybrid between conceptual and physically based rainfall-runoff model. Results of the study indicate that high resolution operational weather forecasting combined with a rainfall-runoff model could be useful to determine flash floods more than 24 hours in advance.
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Kosovic, Branko, Sue Ellen Haupt, Daniel Adriaansen, Stefano Alessandrini, Gerry Wiener, Luca Delle Monache, Yubao Liu, et al. "A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction." Energies 13, no. 6 (March 16, 2020): 1372. http://dx.doi.org/10.3390/en13061372.
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The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numerical weather prediction and machine-learning methods. While the original system was designed with the primary focus on day-ahead power prediction in support of power trading, the enhanced system provides short-term forecasting for unit commitment and economic dispatch, uncertainty quantification in wind speed prediction with probabilistic forecasting, and prediction of extreme events such as icing. Furthermore, the empirical power conversion machine-learning algorithms now use a quantile approach to data quality control that has improved the accuracy of the methods. Forecast uncertainty is quantified using an analog ensemble approach. Two methods of providing short-range ramp forecasts are blended: the variational doppler radar analysis system and an observation-based expert system. Extreme events, specifically changes in wind power due to high winds and icing, are now forecasted by combining numerical weather prediction and a fuzzy logic artificial intelligence system. These systems and their recent advances are described and assessed.
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Rozante, J. R., D. S. Moreira, R. C. M. Godoy, and A. A. Fernandes. "Multi-model ensemble: technique and validation." Geoscientific Model Development 7, no. 5 (October 14, 2014): 2333–43. http://dx.doi.org/10.5194/gmd-7-2333-2014.
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Abstract. In this study, a method of numerical weather prediction by ensemble for the South American region is proposed. This method takes into account combinations of the numerical predictions of various models, assigning greater weight to models that exhibit the best performance. Nine operational numerical models were used to perform this study. The main objective of the study is to obtain a weather forecasting product (short-to-medium range) that combines what is best in each of the nine models used in the study, thus producing more reliable predictions. The proposed method was evaluated during austral summer (December 2012, and January and February 2013) and winter (June, July and August 2013). The results show that the proposed method can significantly improve the results provided by the numerical models and consequently has promising potential for operational applications in any weather forecasting center.
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Rozante, J. R., D. S. Moreira, R. C. M. Godoy, and A. A. Fernandes. "Multi-model ensemble: technique and validation." Geoscientific Model Development Discussions 7, no. 3 (May 6, 2014): 2933–59. http://dx.doi.org/10.5194/gmdd-7-2933-2014.
Повний текст джерелаАнотація:
Abstract. In this study, a method of numerical weather prediction by ensemble for the South American region is proposed. This method takes into account combinations of the numerical predictions of various models, assigning greater weight to models that exhibit the best performance. Nine operational numerical models were used to perform this study. The main objective of the study is to obtain a weather forecasting product (short-to-medium range) that combines what is best in each of the nine models used in the study, thus producing more reliable predictions. The proposed method was evaluated during austral summer (December 2012, and January and February 2013) and winter (June, July and August 2013). The results show that the proposed method can significantly improve the results provided by the numerical models, and consequently has promising potential for operational applications in any weather forecasting center.
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Müller, Malte, Yurii Batrak, Jørn Kristiansen, Morten A. Ø. Køltzow, Gunnar Noer, and Anton Korosov. "Characteristics of a Convective-Scale Weather Forecasting System for the European Arctic." Monthly Weather Review 145, no. 12 (December 2017): 4771–87. http://dx.doi.org/10.1175/mwr-d-17-0194.1.
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In this study a 1-yr dataset of a convective-scale atmospheric prediction system of the European Arctic (AROME-Arctic) is compared with the ECMWF’s medium-range forecasting, ensemble forecasting, and reanalysis systems, by using surface and radiosonde observations of wind and temperature. The focus is on the characteristics of the model systems in the very short-term forecast range (6–15 h), but without a specific focus on lead-time dependencies. In general, AROME-Arctic adds value to the representation of the surface characteristics. The atmospheric boundary layer thickness, during stable conditions, is overestimated in the global models, presumably because of a too diffusive turbulence scheme. Instead, AROME-Arctic shows a realistic mean thickness compared to the radiosonde observations. All models behave similarly for the upper-air verification and surprisingly, as well, in forecasting the location of a polar low in the short-range forecasts. However, when comparing with the largest wind speeds from ocean surface winds and at coastal synoptic weather stations during landfall of a polar low, AROME-Arctic shows the most realistic values. In addition to the model intercomparison, the limitation of the representation of sea ice and ocean surface characteristics on kilometer scales are discussed in detail. This major challenge is illustrated by showing the rapid drift and development of sea ice leads during a cold-air outbreak. As well, the available sea surface temperature products and a high-resolution ocean model result are compared qualitatively. New developments of satellite products, ocean–sea ice prediction models, or parameterizations, tailored toward high-resolution atmospheric Arctic prediction, are necessary to overcome this limitation.
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Kann, A., T. Haiden, and C. Wittmann. "Combining 2-m temperature nowcasting and short range ensemble forecasting." Nonlinear Processes in Geophysics 18, no. 6 (December 2, 2011): 903–10. http://dx.doi.org/10.5194/npg-18-903-2011.
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Abstract. During recent years, numerical ensemble prediction systems have become an important tool for estimating the uncertainties of dynamical and physical processes as represented in numerical weather models. The latest generation of limited area ensemble prediction systems (LAM-EPSs) allows for probabilistic forecasts at high resolution in both space and time. However, these systems still suffer from systematic deficiencies. Especially for nowcasting (0–6 h) applications the ensemble spread is smaller than the actual forecast error. This paper tries to generate probabilistic short range 2-m temperature forecasts by combining a state-of-the-art nowcasting method and a limited area ensemble system, and compares the results with statistical methods. The Integrated Nowcasting Through Comprehensive Analysis (INCA) system, which has been in operation at the Central Institute for Meteorology and Geodynamics (ZAMG) since 2006 (Haiden et al., 2011), provides short range deterministic forecasts at high temporal (15 min–60 min) and spatial (1 km) resolution. An INCA Ensemble (INCA-EPS) of 2-m temperature forecasts is constructed by applying a dynamical approach, a statistical approach, and a combined dynamic-statistical method. The dynamical method takes uncertainty information (i.e. ensemble variance) from the operational limited area ensemble system ALADIN-LAEF (Aire Limitée Adaptation Dynamique Développement InterNational Limited Area Ensemble Forecasting) which is running operationally at ZAMG (Wang et al., 2011). The purely statistical method assumes a well-calibrated spread-skill relation and applies ensemble spread according to the skill of the INCA forecast of the most recent past. The combined dynamic-statistical approach adapts the ensemble variance gained from ALADIN-LAEF with non-homogeneous Gaussian regression (NGR) which yields a statistical \\mbox{correction} of the first and second moment (mean bias and dispersion) for Gaussian distributed continuous variables. Validation results indicate that all three methods produce sharp and reliable probabilistic 2-m temperature forecasts. However, the statistical and combined dynamic-statistical methods slightly outperform the pure dynamical approach, mainly due to the under-dispersive behavior of ALADIN-LAEF outside the nowcasting range. The training length does not have a pronounced impact on forecast skill, but a spread re-scaling improves the forecast skill substantially. Refinements of the statistical methods yield a slight further improvement.
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Wang, Ying, Bo Feng, Qing-Song Hua, and Li Sun. "Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method." Sustainability 13, no. 7 (March 25, 2021): 3665. http://dx.doi.org/10.3390/su13073665.
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Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should meet requirements proscribed by environmental law and safety standards applied for consumer protection. First, time-series-based solar power forecasting (SPF) model is developed with the time element and predicted weather information from the local meteorological station. Considering the data correlation, long short-term memory (LSTM) algorithm is utilized for short-term SPF. However, the point prediction provided by LSTM fails in revealing the underlying uncertainty range of the solar power output, which is generally needed in some stochastic optimization frameworks. A novel hybrid strategy combining LSTM and Gaussian process regression (GPR), namely LSTM-GPR, is proposed to obtain a highly accurate point prediction with a reliable interval estimation. The hybrid model is evaluated in comparison with other algorithms in terms of two aspects: Point prediction accuracy and interval forecasting reliability. Numerical investigations confirm the superiority of LSTM algorithm over the conventional neural networks. Furthermore, the performance of the proposed hybrid model is demonstrated to be slightly better than the individual LSTM model and significantly superior to the individual GPR model in both point prediction and interval forecasting, indicating a promising prospect for future SPF applications.
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Jung, Thomas, and Frederic Vitart. "Short-Range and Medium-Range Weather Forecasting in the Extratropics during Wintertime with and without an Interactive Ocean." Monthly Weather Review 134, no. 7 (July 1, 2006): 1972–86. http://dx.doi.org/10.1175/mwr3206.1.
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Abstract The ECMWF monthly forecasting system is used to investigate the impact that an interactive ocean has on short-range and medium-range weather predictions in the Northern Hemisphere extratropics during wintertime. On a hemispheric scale the predictive skill for mean sea level pressure (MSLP) with and without an interactive ocean is comparable. This can be explained by the relatively small impact that coupling has on MSLP forecasts. In fact, deterministic and ensemble integrations reveal that the magnitude of forecast error and the perturbation growth due to analysis uncertainties, respectively, by far outweigh MSLP differences between coupled and uncoupled integrations. Furthermore, no significant difference of the ensemble spread between the uncoupled and coupled system is found. The authors’ conclusions apply equally for a number of cases of rapidly intensifying extratropical cyclones in the North Atlantic region. Further experimentation with different atmospheric model versions, different horizontal atmospheric resolutions, and different ocean model formulation reveals the robustness of the findings. The results suggest that (for the cases, resolutions, and model complexities considered is this study) the benefit of using coupled atmosphere–ocean models to carry out 1–10-day MSLP forecasts is relatively small, at least in the Northern Hemisphere extratropics during wintertime.
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Sonechkin, D. M. "Observability of planetary waves and their predictability in the ECMWF H500 forecasts." Advances in Science and Research 4, no. 1 (February 19, 2010): 5–7. http://dx.doi.org/10.5194/asr-4-5-2010.
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Abstract. The problem of a hard contradiction between limited possibilities of meteorological observations and uncontrolled complication of the present-day weather forecasting models, and its consequences for the planetary wave predictability are considered with examples from the ECMWF short- and medium-range H500 forecasts for the Northern Hemisphere. A supposition is voiced that this problem adds difficulties to the weekly predictability limit overcoming.
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Megann, A., D. Storkey, Y. Aksenov, S. Alderson, D. Calvert, T. Graham, P. Hyder, J. Siddorn, and B. Sinha. "GO5.0: the joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications." Geoscientific Model Development 7, no. 3 (June 6, 2014): 1069–92. http://dx.doi.org/10.5194/gmd-7-1069-2014.
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Abstract. We describe a new Global Ocean standard configuration (GO5.0) at eddy-permitting resolution, developed jointly between the National Oceanography Centre and the Met Office as part of the Joint Ocean Modelling Programme (JOMP), a working group of the UK's National Centre for Ocean Forecasting (NCOF) and part of the Joint Weather and Climate Research Programme (JWCRP). The configuration has been developed with the seamless approach to modelling in mind for ocean modelling across timescales and for a range of applications, from short-range ocean forecasting through seasonal forecasting to climate predictions as well as research use. The configuration has been coupled with sea ice (GSI5.0), atmosphere (GA5.0), and land-surface (GL5.0) configurations to form a standard coupled global model (GC1). The GO5.0 model will become the basis for the ocean model component of the Forecasting Ocean Assimilation Model, which provides forced short-range forecasting services. The GC1 or future releases of it will be used in coupled short-range ocean forecasting, seasonal forecasting, decadal prediction and for climate prediction as part of the UK Earth System Model. A 30-year integration of GO5.0, run with CORE2 (Common Ocean-ice Reference Experiments) surface forcing from 1976 to 2005, is described, and the performance of the model in the final 10 years of the integration is evaluated against observations and against a comparable integration of an existing standard configuration, GO1. An additional set of 10-year sensitivity studies, carried out to attribute changes in the model performance to individual changes in the model physics, is also analysed. GO5.0 is found to have substantially reduced subsurface drift above the depth of the thermocline relative to GO1, and also shows a significant improvement in the representation of the annual cycle of surface temperature and mixed layer depth.
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Megann, A., D. Storkey, Y. Aksenov, S. Alderson, D. Calvert, T. Graham, P. Hyder, J. Siddorn, and B. Sinha. "GO5.0: The joint NERC-Met Office NEMO global ocean model for use in coupled and forced applications." Geoscientific Model Development Discussions 6, no. 4 (November 26, 2013): 5747–99. http://dx.doi.org/10.5194/gmdd-6-5747-2013.
Повний текст джерелаАнотація:
Abstract. We describe a new Global Ocean standard configuration (GO5.0) at eddy-permitting resolution, developed jointly between the National Oceanography Centre and the Met Office as part of the Joint Ocean Modelling Programme (JOMP). This programme is a working group of the UK's National Centre for Ocean Forecasting (NCOF) and part of the Joint Weather and Climate Research Programme (JWCRP). The configuration has been developed with the seamless approach to modelling in mind for ocean modelling across timescales and for a range of applications, from short-range ocean forecasting through seasonal forecasting to climate predictions as well as research use. The GO5.0 configuration has been coupled with sea-ice (GSI5.0), atmosphere (GA5.0) and land-surface (GL5.0) configurations to form a standard coupled global model (GC1). The GO5.0 model will become the basis for the ocean model component of the Forecasting Ocean Assimilation Model, which provides forced short-range forecasting services. The global coupled model (GC1) or future releases of it will be used in coupled short-range ocean forecasting, seasonal forecasting, decadal prediction and for climate prediction as part of the UK Earth System Model. A 30 yr integration of GO5.0, run with CORE2 surface forcing from 1976 to 2005, is described, and the performance of the model in the final ten years of the integration is evaluated against observations and against a comparable integration of an earlier configuration, GO1. An additional set of 10 yr sensitivity studies, carried out to attribute changes in the model performance to individual changes in the model physics, is also analysed. GO5.0 is found to have substantially reduced subsurface drift above the depth of the thermocline relative to GO1, and also shows a significant improvement in the representation of the annual cycle of surface temperature and mixed-layer depth.
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Cluckie, I. D., Y. Xuan, and Y. Wang. "Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction." Hydrology and Earth System Sciences Discussions 3, no. 5 (October 19, 2006): 3211–37. http://dx.doi.org/10.5194/hessd-3-3211-2006.
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Abstract. Advances in meso-scale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1) The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2) The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3) the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.
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Xuan, Y., I. D. Cluckie, and Y. Wang. "Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction." Hydrology and Earth System Sciences 13, no. 3 (March 6, 2009): 293–303. http://dx.doi.org/10.5194/hess-13-293-2009.
Повний текст джерелаАнотація:
Abstract. Advances in mesoscale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1) The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2) The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3) the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.
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Vil’fand, R. M., G. S. Rivin, and I. A. Rozinkina. "Mesoscale weather short-range forecasting at the Hydrometcenter of Russia, on the example of COSMO-RU." Russian Meteorology and Hydrology 35, no. 1 (January 2010): 1–9. http://dx.doi.org/10.3103/s1068373910010012.
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Kann, A. "On the skill of various ensemble spread estimators for probabilistic short range wind forecasting." Advances in Science and Research 8, no. 1 (May 15, 2012): 115–20. http://dx.doi.org/10.5194/asr-8-115-2012.
Повний текст джерелаАнотація:
Abstract. A variety of applications ranging from civil protection associated with severe weather to economical interests are heavily dependent on meteorological information. For example, a precise planning of the energy supply with a high share of renewables requires detailed meteorological information on high temporal and spatial resolution. With respect to wind power, detailed analyses and forecasts of wind speed are of crucial interest for the energy management. Although the applicability and the current skill of state-of-the-art probabilistic short range forecasts has increased during the last years, ensemble systems still show systematic deficiencies which limit its practical use. This paper presents methods to improve the ensemble skill of 10-m wind speed forecasts by combining deterministic information from a nowcasting system on very high horizontal resolution with uncertainty estimates from a limited area ensemble system. It is shown for a one month validation period that a statistical post-processing procedure (a modified non-homogeneous Gaussian regression) adds further skill to the probabilistic forecasts, especially beyond the nowcasting range after +6 h.
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Karvelis, Petros, Daniele Mazzei, Matteo Biviano, and Chrysostomos Stylios. "PortWeather: A Lightweight Onboard Solution for Real-Time Weather Prediction." Sensors 20, no. 11 (June 3, 2020): 3181. http://dx.doi.org/10.3390/s20113181.
Повний текст джерелаАнотація:
Maritime journeys significantly depend on weather conditions, and so meteorology has always had a key role in maritime businesses. Nowadays, the new era of innovative machine learning approaches along with the availability of a wide range of sensors and microcontrollers creates increasing perspectives for providing on-board reliable short-range forecasting of main meteorological variables. The main goal of this study is to propose a lightweight on-board solution for real-time weather prediction. The system is composed of a commercial weather station integrated with an industrial IOT-edge data processing module that computes the wind direction and speed forecasts without the need of an Internet connection. A regression machine learning algorithm was chosen so as to require the smallest amount of resources (memory, CPU) and be able to run in a microcontroller. The algorithm has been designed and coded following specific conditions and specifications. The system has been tested on real weather data gathered from static weather stations and onboard during a test trip. The efficiency of the system has been proven through various error metrics.
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Pan, Sijie, Jidong Gao, Thomas A. Jones, Yunheng Wang, Xuguang Wang, and Jun Li. "The Impact of Assimilating Satellite-Derived Layered Precipitable Water, Cloud Water Path, and Radar Data on Short-Range Thunderstorm Forecasts." Monthly Weather Review 149, no. 5 (May 2021): 1359–80. http://dx.doi.org/10.1175/mwr-d-20-0040.1.
Повний текст джерелаАнотація:
AbstractWith the launch of GOES-16 in November 2016, effective utilization of its data in convective-scale numerical weather prediction (NWP) has the potential to improve high-impact weather (HIWeather) forecasts. In this study, the impact of satellite-derived layered precipitable water (LPW) and cloud water path (CWP) in addition to NEXRAD observations on short-term convective-scale NWP forecasts are examined using three severe weather cases that occurred in May 2017. In each case, satellite-derived CWP and LPW products and radar observations are assimilated into the Advanced Research Weather Research and Forecasting (WRF-ARW) Model using the NSSL hybrid Warn-on-Forecast (WoF) analysis and forecast system. The system includes two components: the GSI-EnKF system and a deterministic 3DEnVAR system. This study examines deterministic 0–6-h forecasts launched from the hybrid 3DEnVAR analyses for the three severe weather events. Three types of experiments are conducted and compared: (i) the control experiment (CTRL) without assimilating any data, (ii) the radar experiment (RAD) with the assimilation of radar and surface observations, and (iii) the satellite experiment (RADSAT) with the assimilation of all observations including surface-, radar-, and satellite-derived CWP and LPW. The results show that assimilating additional GOES products improves short-range forecasts by providing more accurate initial conditions, especially for moisture and temperature variables.
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Kiktev, Dmitry, Paul Joe, George A. Isaac, Andrea Montani, Inger-Lise Frogner, Pertti Nurmi, Benedikt Bica, et al. "FROST-2014: The Sochi Winter Olympics International Project." Bulletin of the American Meteorological Society 98, no. 9 (September 1, 2017): 1908–29. http://dx.doi.org/10.1175/bams-d-15-00307.1.
Повний текст джерелаАнотація:
Abstract The World Meteorological Organization (WMO) World Weather Research Programme’s (WWRP) Forecast and Research in the Olympic Sochi Testbed program (FROST-2014) was aimed at the advancement and demonstration of state-of-the-art nowcasting and short-range forecasting systems for winter conditions in mountainous terrain. The project field campaign was held during the 2014 XXII Olympic and XI Paralympic Winter Games and preceding test events in Sochi, Russia. An enhanced network of in situ and remote sensing observations supported weather predictions and their verification. Six nowcasting systems (model based, radar tracking, and combined nowcasting systems), nine deterministic mesoscale numerical weather prediction models (with grid spacings down to 250 m), and six ensemble prediction systems (including two with explicitly simulated deep convection) participated in FROST-2014. The project provided forecast input for the meteorological support of the Sochi Olympic Games. The FROST-2014 archive of winter weather observations and forecasts is a valuable information resource for mesoscale predictability studies as well as for the development and validation of nowcasting and forecasting systems in complex terrain. The resulting innovative technologies, exchange of experience, and professional developments contributed to the success of the Olympics and left a post-Olympic legacy.