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Introduction to the special issue on “25 years of ensemble forecasting”
Full text linkTwenty-five years ago the first operational, ensemble forecasts were issued by the European Centre for Medium-Range Weather Forecasts and the National Centers for Environmental Prediction. These centres were followed in 1996 by the Meteorological Service of Canada, and in the subsequent years by many others. Operational ensemble-based, probabilistic forecasts signed a paradigm shift in weather prediction: for the first time, forecasters and users could have reliable and accurate estimates of the range of possible future scenarios, and not just a single realization of the future. Today, ensembles are used not only to provide reliable and accurate forecasts for the short and medium range, the monthly and seasonal time-scale, but also to provide estimates of the initial state of the atmosphere, and to generate future climate projections. This article provides an overview on how we developed the early ensembles, illustrates the key characteristics of the seven operational, global, medium-range ensembles, and discusses ongoing trends to further improve ensemble performance
The impact of horizontal resolution and ensemble size on probabilistic forecasts of precipitation by the ECMWF ensemble prediction system
No full textThe effect of horizontal resolution and ensemble size on the ECMWF Ensemble Prediction System (EPS) is assessed for probabilistic forecasts of 24-h accumulated precipitation. Two sets of experiments are analyzed. The primary experiment compares two spectral truncations (total wavenumbers 159 and 255) for 30 summer and 57 winter dates. An auxiliary experiment compares three truncations (total wavenumbers 159, 255, and 319) for 16 initial dates (8 cool- and 8 warm-season events) during which heavy precipitation (>50 mm) occurred over the eastern United States at day 5 of the forecast. Rain gauge data from the River Forecast Centers of NOAA are used for verification. Skill is measured relative to long-term climatic frequencies, and the statistical significance of differences in the accuracy among the forecasts is estimated. Finer model resolution produces statistically significant improvements in EPS performance for ensemble configurations with the same number of members, especially for lighter thresholds (1 and 10 mm day-1). Performance changes somewhat when ensemble configurations with different resolutions and ensemble sizes, but equivalent computational costs, are compared for the heavier amounts (20 and 50 mm day-1). Coarser-resolution, larger-member ensembles can outperform higher-resolution, smaller-member ensembles in terms of ability to predict rare (in terms of climatic frequency of occurrence) precipitation events. The overall conclusion is that probabilistic forecasts of precipitation from large ensemble sizes at lower resolution can be more valuable to users and decision makers than probabilistic forecasts from smaller ensemble sizes at higher resolution, particularly when heavy precipitation occurs
Reforecasting the Flooding of Florence of 4 November 1966 With Global and Regional Ensembles
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How can we weather a virus storm? Health prediction inspired by meteorology could be the answer
No full textAtmospheric drivers affect crop yields in Mozambique
Full text linkClimate change has been inducing variations in the statistics of both the large-scale weather patterns and the local weather in many regions of the world, and these variations have been affecting several human activities, including agriculture. In this study, we look at the links between large-scale weather patterns and local weather as well as agriculture, with a specific regional focus on Mozambique between 1981 and 2019. First, we investigated linear trends and links between large-scale weather patterns and local weather in the region using the ERA5 dataset. We used the same data to investigate how climate change has been affecting the statistics of large-scale weather patterns. Then, we derived Mozambique country-level cereal yield data from FAO and linked it up with climate and weather data to assess what is the relationship between large-scale patterns and local agronomic outputs using a multiple linear regression (MLR) model with crop yield as the response variable and climate drivers as predictors. The results indicate that in Mozambique, the crop season warmed substantially and consistently with climate change-induced global warming, and the rainy season had become drier and shorter, with precipitation concentrated in fewer, more intense events. These changes in the local weather have been linked to variations in the statistics of large-scale weather patterns that characterize the (large-scale) atmospheric flow over the region. Our results indicate a negative impact on yield associated with climate change, with average yield losses of 20% for rice and 8% for maize over the analyzed period (1981–2019). This negative impact suggests that, at the country scale, further future warming during the growing season may offset some of the cereal yield gains from technological advances
Wind Power Density Forecasting Using Wind Ensemble Predictions and Time Series Models
No full textWind power is an increasingly used form of renewable energy. The uncertainty in wind generation is very large due to the inherent variability in wind speed, and this needs to be understood by operators of power systems and wind farms. To assist with the management of this risk, this paper investigates methods for predicting the probability density function of generated wind power from one to ten days ahead at five U.K. wind farm locations. These density forecasts provide a description of the expected future value and the associated uncertainty. We construct density forecasts from weather ensemble predictions, which are a relatively new type of weather forecast generated from atmospheric models. We also consider density forecasting from statistical time series models. The best results for wind power density prediction and point forecasting were produced by an approach that involves calibration and smoothing of the ensemble-based wind power density
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