Probabilistic forecast of daily areal precipitation focusing on extreme events

Abstract. A dynamical downscaling scheme is usually used to provide a short range flood forecasting system with high-resolved precipitation fields. Unfortunately, a single forecast of this scheme has a high uncertainty concerning intensity and location especially during extreme events. Alternatively, statistical downscaling techniques like the analogue method can be used which can supply a probabilistic forecasts. However, the performance of the analogue method is affected by the similarity criterion, which is used to identify similar weather situations. To investigate this issue in this work, three different similarity measures are tested: the euclidean distance (1), the Pearson correlation (2) and a combination of both measures (3). The predictor variables are geopotential height at 1000 and 700 hPa-level and specific humidity fluxes at 700 hPa-level derived from the NCEP/NCAR-reanalysis project. The study is performed for three mesoscale catchments located in the Rhine basin in Germany. It is validated by a jackknife method for a period of 44 years (1958–2001). The ranked probability skill score, the Brier Skill score, the Heidke skill score and the confidence interval of the Cramer association coefficient are calculated to evaluate the system for extreme events. The results show that the combined similarity measure yields the best results in predicting extreme events. However, the confidence interval of the Cramer coefficient indicates that this improvement is only significant compared to the Pearson correlation but not for the euclidean distance. Furthermore, the performance of the presented forecasting system is very low during the summer and new predictors have to be tested to overcome this problem.

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The Mediating Role of Sales Performance in the Effect of Antecedents on Sales Force Satisfaction: In the Case of Top Three Chain Retail Stores Operating in Ethiopia

International Journal of Marketing Studies ◽

10.5539/ijms.v10n1p145 ◽

2018 ◽

Vol 10 (1) ◽

pp. 145

Author(s):

Getie Andualem Imiru

Keyword(s):

Job Satisfaction ◽

Confidence Interval ◽

Self Efficacy ◽

Pearson Correlation ◽

Sales Performance ◽

Sales Force ◽

Retail Stores ◽

Mediating Role ◽

Chain Retail ◽

Core Characteristics

The objective of this study is to investigate the effect of antecedent variables on salesforce Job satisfaction mediated by salesforce performance. Data were gathered using a structured questionnaire from top three chain retail stores engaged in retailing business operating in Ethiopia. Although a total of 450 questionnaires were distributed to sales persons of these companies, 380 questionnaires were returned and used at the end of the data collection process, which gave the response rate of 84 per cent. The ability, effort, self-efficacy, and job core characteristics have a significant Pearson correlation of 0.493, 0.105, 0.288, and 0.391 respectively at 0.01 confidence interval with sales performance. On the other hand, five constructs of the study ability, effort, self-efficacy, fixed compensation, and job core characteristics influenced sales performance significantly at 95% confidence interval with a sig. level of 0.000, 0.004, 0.002, 0.000, and 0.000 respectively. The result of the study indicated that six variables which are ability, effort, self-efficacy, fixed compensation, job core characteristics, and sales performance influenced job satisfaction significantly at 95% confidence interval with a sig. level of 0.000 for all variables.

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Comparative verification in complex topography

10.5194/ems2021-246 ◽

2021 ◽

Author(s):

Jonas Bhend ◽

Jean-Christophe Orain ◽

Vera Schönenberger ◽

Christoph Spirig ◽

Lionel Moret ◽

...

Keyword(s):

Weather Forecasting ◽

Absolute Error ◽

Time Of Day ◽

Probabilistic Forecasts ◽

Wide Range ◽

Direct Model ◽

Forecasting System ◽

The Mean ◽

Update Frequency ◽

Core Activity

Verification is a core activity in weather forecasting. Insights from verification are used for monitoring, for reporting, to support and motivate development of the forecasting system, and to allow users to maximize forecast value. Due to the broad range of applications for which verification provides valuable input, the range of questions one would like to answer can be very large. Static analyses and summary verification results are often insufficient to cover this broad range. To this end, we developed an interactive verification platform at MeteoSwiss that allows users to inspect verification results from a wide range of angles to find answers to their specific questions.We present the technical setup to achieve a flexible yet performant interactive platform and two prototype applications: monitoring of direct model output from operational NWP systems and understanding of the capabilities and limitations of our pre-operational postprocessing. We present two innovations that illustrate the user-oriented approach to comparative verification adopted as part of the platform. To facilitate the comparison of a broad range of forecasts issued with varying update frequency, we rely on the concept of time of verification to collocate the most recent available forecasts at the time of day at which the forecasts are used. In addition, we offer a matrix selection to more flexibly select forecast sources and scores for comparison. Doing so, we can for example compare the mean absolute error (MAE) for deterministic forecasts to the MAE and continuous ranked probability scores of probabilistic forecasts to illustrate the benefit of using probabilistic forecasts.

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Probabilistic forecasting of wind power production losses in cold climates: a case study

Wind Energy Science ◽

10.5194/wes-3-667-2018 ◽

2018 ◽

Vol 3 (2) ◽

pp. 667-680 ◽

Cited By ~ 7

Author(s):

Jennie Molinder ◽

Heiner Körnich ◽

Esbjörn Olsson ◽

Hans Bergström ◽

Anna Sjöblom

Keyword(s):

Wind Power ◽

Energy Production ◽

Spatial Representation ◽

Forecast Skill ◽

Cold Climates ◽

Probabilistic Forecasting ◽

Probabilistic Forecasts ◽

Forecasting System ◽

Production Losses

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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Probabilistic forecasting of wind power production losses in cold climates: A case study

10.5194/wes-2017-28 ◽

2017 ◽

Cited By ~ 1

Author(s):

Jennie P. Söderman ◽

Heiner Körnich ◽

Esbjörn Olsson ◽

Hans Bergström ◽

Anna Sjöblom

Keyword(s):

Wind Power ◽

Energy Production ◽

Cold Climate ◽

Forecast Skill ◽

Cold Climates ◽

Probabilistic Forecasting ◽

Probabilistic Forecasts ◽

Forecasting System ◽

Production Losses

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 two- 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 climate can be valuable when planning next-day energy production, in the usage of de-icing systems, and for site safety.

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Constrained K-Means Classification

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.2149 ◽

2018 ◽

Vol 8 (4) ◽

pp. 3203-3208

Author(s):

P. N. Smyrlis ◽

D. C. Tsouros ◽

M. G. Tsipouras

Keyword(s):

Euclidean Distance ◽

Similarity Criterion ◽

Experimental Results ◽

Classification Tasks

Classification-via-clustering (CvC) is a widely used method, using a clustering procedure to perform classification tasks. In this paper, a novel K-Means-based CvC algorithm is presented, analysed and evaluated. Two additional techniques are employed to reduce the effects of the limitations of K-Means. A hypercube of constraints is defined for each centroid and weights are acquired for each attribute of each class, for the use of a weighted Euclidean distance as a similarity criterion in the clustering procedure. Experiments are made with 42 well–known classification datasets. The experimental results demonstrate that the proposed algorithm outperforms CvC with simple K-Means.

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Determination of seasonal forecast skill in identifying extreme events of temperature, wind speed, and SPI

10.5194/egusphere-egu21-9563 ◽

2021 ◽

Author(s):

Massimiliano Palma ◽

Franco Catalano ◽

Irene Cionni ◽

Marcello Petitta

Keyword(s):

Climate Change ◽

Wind Speed ◽

Climate Variability ◽

Extreme Events ◽

Bias Correction ◽

Skill Score ◽

Seasonal Forecast ◽

Energy Sector ◽

Seasonal Forecasts ◽

Weather Forecasts

Renewable energy is the fastest-growing source of electricity globally, but climate variability and impacting events affecting the potential productivity of plants are obstacles to its integration and planning.&#160;Knowing a few months in advance the productivity of plants and the impact of extreme events on productivity and infrastructure can help operators and policymakers make the energy sector more resilient to climate variability, promoting the deployment of renewable energy while maintaining energy security.The energy sector already uses weather forecasts up to 15 days for plant management; beyond this time horizon, climatologies are routinely used. This approach has inherent weaknesses, including the inability to predict extreme events, the prediction of which is extremely useful to decision-makers. Information on seasonal climate variability obtained through climate forecasts can be of considerable benefit in decision-making processes.&#160;The Climate Data Store of the Copernicus Climate Change Service (C3S) provides seasonal forecasts and a common period of retrospective simulations (hindcasts) with equal spatial temporal resolution for simulations from 5 European forecast centres (European Centre for Medium-Range Weather Forecasts (ECMWF), Deutscher Wetterdienst (DWD), Meteo France (MF), UK Met Office (UKMO) and Euro-Mediterranean Centre on Climate Change (CMCC)), one US forecasting centre (NCEP) plus the Japan Meteorological Agency (JMA) model.In this work, we analyse the skill and the accuracy of a subset of the operational seasonal forecasts provided by Copernicus C3S, focusing on three relevant essential climate variables for the energy sector: temperature (t2m), wind speed (sfcWind, relevant to the wind energy production), and precipitation. The latter has been analysed by taking the Standard Precipitation Index (SPI) into account.First, the methodologies for bias correction have been defined. Subsequently, the reliability of the forecasts has been assessed using appropriate reliability indicators based on comparison with ERA5 reanalysis dataset.&#160;The hindcasts cover the period 1993-2017. For each of the variables considered, we evaluated the seasonal averages based on monthly means for two seasons: winter (DJF) and summer (JJA). Data have been bias corrected following two methodologies, one based on the application of a variance inflation technique to ensure the correction of the bias and the correspondence of variance between forecast and observation; the other based on the correction of the bias, the overall forecast variance and the ensemble spread as described in Doblas-Reyes et al. (2005).Predictive ability has been assessed by calculating binary (Brier Skill Score, BSS hereafter, and Ranked Probability Skill Score, RPSS hereafter) and continuous (Continuous Ranked Probability Skill Score, CRPSS hereafter) scores.&#160;Forecast performance has been assessed using ERA 5 reanalysis as pseudo-observations.&#160;In this work we discuss the results obtained with different bias correction techniques highlighting the outcomes obtained analyzing the BSS for the first and the last terciles and the first and the last percentiles (10th and 90th). This analysis has the goal to identify the regions in which the seasonal forecast can be used to identify potential extreme events.

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Extreme Quantiles Dynamic Line Rating Forecasts and Application on Network Operation

Energies ◽

10.3390/en13123090 ◽

2020 ◽

Vol 13 (12) ◽

pp. 3090

Author(s):

Romain Dupin ◽

Laura Cavalcante ◽

Ricardo J. Bessa ◽

Georges Kariniotakis ◽

Andrea Michiorri

Keyword(s):

Extreme Events ◽

Parametric Model ◽

Safe Operation ◽

Extreme Quantiles ◽

Application Method ◽

Probabilistic Forecasts ◽

Network Operation ◽

New Distribution ◽

Non Parametric

This paper presents a study on dynamic line rating (DLR) forecasting procedure aimed at developing a new methodology able to forecast future ampacity values for rare and extreme events. This is motivated by the belief that to apply DLR network operators must be able to forecast their values and this must be based on conservative approaches able to guarantee the safe operation of the network. The proposed methodology can be summarised as follows: firstly, probabilistic forecasts of conductors’ ampacity are calculated with a non-parametric model, secondly, the lower part of the distribution is replaced with a new distribution calculated with a parametric model. The paper presents also an evaluation of the proposed methodology in network operation, suggesting an application method and highlighting the advantages. The proposed forecasting methodology delivers a high improvement of the lowest quantiles’ reliability, allowing perfect reliability for the 1% quantile and a reduction of roughly 75% in overconfidence for the 0.1% quantile.

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The Potential Impact of Using Persistence as a Reference Forecast on Perceived Forecast Skill

Weather and Forecasting ◽

10.1175/2008waf2007037.1 ◽

2008 ◽

Vol 23 (5) ◽

pp. 1022-1031 ◽

Cited By ~ 15

Author(s):

Marion P. Mittermaier

Keyword(s):

Skill Score ◽

Skill Assessment ◽

Added Value ◽

Forecast Performance ◽

Additional Degree ◽

Perceived Performance ◽

Precipitation Total ◽

Forecasting System ◽

Potential Impact ◽

The Impact

Abstract Skill is defined as actual forecast performance relative to the performance of a reference forecast. It is shown that the choice of reference (e.g., random or persistence) can affect the perceived performance of the forecast system. Two scores, the equitable threat score (ETS) and the odds ratio benefit skill score (ORBSS), were chosen to show the impact of using a persistence forecast, first using some simple hypothetical scenarios and second for actual forecasts from the Met Office Unified Model (UM) of precipitation, total cloud cover, and visibility during 2006. Overall persistence offers a sterner test of true forecast added value and accuracy, but using a more realistic reference may come at a cost. Using persistence introduces an additional degree of freedom to the skill assessment, which may be rather variable for “weather parameters.” Ultimately, the aim of any forecasting system should be to achieve a substantive separation between the inherent skill of the reference (which represents basic predictability) and the actual forecast.

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Tracking bits of information through forecasting systems: from source to decision

10.5194/egusphere-egu2020-13223 ◽

2020 ◽

Author(s):

Steven Weijs ◽

Hossein Foroozand

Keyword(s):

Information Theory ◽

Monitoring Network ◽

Sources Of Information ◽

Probabilistic Forecasting ◽

Good Decision ◽

Monitoring Network Design ◽

Information Monitoring ◽

Maximum Value ◽

Probabilistic Forecasts ◽

Forecasting System

Probabilistic forecasts are essential for good decision making, because they communicate the forecaster's best attempt at representation of both information available and the remaining uncertainty of a variable of interest. The amount of information provided, which can be measured in bits using information theory, would then be a natural measure of success for the forecast in a verification exercise. On the other hand, it may seem rational to tune the forecasting system to provide maximum value to users. Somewhat counter-intuitively, there are arguments against tuning for maximum value. When the design of the forecasting system also includes the choice of the sources of information, monitoring network optimization becomes part of the problem to solve. &#160; In this presentation, we give a brief overview of the different roles information theory can have in these different aspects of probabilistic forecasting. These roles range from analysis of predictability, model selection, forecast verification, monitoring network design, and data assimilation by ensemble weighting. Using the same theoretical framework for all these aspects has the advantage that some connections can be made that may eventually lead to a more unified perspective on forecasting.&#160;

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Evaluation of the IRI'S “Net Assessment” Seasonal Climate Forecasts: 1997–2001

Bulletin of the American Meteorological Society ◽

10.1175/bams-84-12-1761 ◽

2003 ◽

Vol 84 (12) ◽

pp. 1761-1782 ◽

Cited By ~ 111

Author(s):

L. Goddard ◽

A. G. Barnston ◽

S. J. Mason

Keyword(s):

Skill Score ◽

Forecast Skill ◽

Seasonal Temperature ◽

Seasonal Climate ◽

Prediction Tools ◽

Temperature And Precipitation ◽

Climate Forecasts ◽

Probabilistic Forecasts ◽

International Research Institute ◽

The Individual

The International Research Institute for Climate Prediction (IRI) net assessment seasonal temperature and precipitation forecasts are evaluated for the 4-yr period from October–December 1997 to October–December 2001. These probabilistic forecasts represent the human distillation of seasonal climate predictions from various sources. The ranked probability skill score (RPSS) serves as the verification measure. The evaluation is offered as time-averaged spatial maps of the RPSS as well as area-averaged time series. A key element of this evaluation is the examination of the extent to which the consolidation of several predictions, accomplished here subjectively by the forecasters, contributes to or detracts from the forecast skill possible from any individual prediction tool. Overall, the skills of the net assessment forecasts for both temperature and precipitation are positive throughout the 1997–2001 period. The skill may have been enhanced during the peak of the 1997/98 El Niño, particularly for tropical precipitation, although widespread positive skill exists even at times of weak forcing from the tropical Pacific. The temporally averaged RPSS for the net assessment temperature forecasts appears lower than that for the AGCMs. Over time, however, the IRI forecast skill is more consistently positive than that of the AGCMs. The IRI precipitation forecasts generally have lower skill than the temperature forecasts, but the forecast probabilities for precipitation are found to be appropriate to the frequency of the observed outcomes, and thus reliable. Over many regions where the precipitation variability is known to be potentially predictable, the net assessment precipitation forecasts exhibit more spatially coherent areas of positive skill than most, if not all, prediction tools. On average, the IRI net assessment forecasts appear to perform better than any of the individual objective prediction tools.

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