scholarly journals The Performance of ECMWF sub-seasonal forecasts to predict the Rainy Season Onset Dates in Vietnam

2021 ◽  
Author(s):  
Roderick van der Linden ◽  
Andreas H. Fink
Keyword(s):  
Rainy Season ◽  
Model Performance ◽  
Agricultural Practices ◽  
Absolute Error ◽  
Skill Score ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Weather Forecasts ◽  
Almost All ◽  
Ecmwf Model

Abstract The onset of the rainy season is an important date for the mostly rain-fed agricultural practices in Vietnam. Sub-seasonal to seasonal (S2S) ensemble hindcasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used to evaluate the predictability of the rainy season onset dates (RSODs) over five climatic sub-regions of Vietnam. The results show that the ECMWF model reproduces well the observed inter-annual variability of RSODs, with a high correlation ranging from 0.60 to 0.99 over all sub-regions at all lead times (up to 40 days) using five different RSOD definitions. For increasing lead times, forecasted RSODs tend to be earlier than the observed ones. Positive skill score values for almost all cases examined in all sub-regions indicate that the model outperforms the observed climatology in predicting the RSOD at sub-seasonal lead times (~28–35 days). However, the model is overall more skilful at shorter lead times. The choice of the RSOD criterion should be considered because it can significantly influence the model performance. The result of analysing the highest skill score for each sub-region at each lead time shows that criteria with higher 5-day rainfall thresholds tend to be more suitable for the forecasts at long lead times. However, the values of mean absolute error are approximately the same as the absolute values of the mean error, indicating that the prediction could be improved by a simple bias correction. The present study shows a large potential to use S2S forecasts to provide meaningful predictions of RSODs for farmers.

scholarly journals Seasonal drought prediction for semiarid northeastern Brazil: verification of six hydro-meteorological forecast products

2018 ◽  
Vol 22 (9) ◽  
pp. 5041-5056 ◽  
Author(s):  
José Miguel Delgado ◽  
Sebastian Voss ◽  
Gerd Bürger ◽  
Klaus Vormoor ◽  
Aline Murawski ◽  
...  
Keyword(s):  
Rainy Season ◽  
Meteorological Drought ◽  
Skill Score ◽  
Hydrological Drought ◽  
Northeastern Brazil ◽  
Drought Indices ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Seasonal Drought ◽  
Drought Prediction

Abstract. A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.

scholarly journals Seasonal forecasts of the Saharan heat low characteristics: a multi-model assessment

2021 ◽  
Vol 2 (3) ◽  
pp. 893-912
Author(s):  
Cedric G. Ngoungue Langue ◽  
Christophe Lavaysse ◽  
Mathieu Vrac ◽  
Philippe Peyrillé ◽  
Cyrille Flamant
Keyword(s):  
West African Monsoon ◽  
Seasonal Forecast ◽  
Lead Times ◽  
Model Assessment ◽  
Seasonal Forecasts ◽  
Weather Forecasts ◽  
Modes Of Variability ◽  
Forecast Models ◽  
Sahel Region ◽  
Heat Low

Abstract. The Saharan heat low (SHL) is a key component of the West African Monsoon system at the synoptic scale and a driver of summertime precipitation over the Sahel region. Therefore, accurate seasonal precipitation forecasts rely in part on a proper representation of the SHL characteristics in seasonal forecast models. This is investigated using the latest versions of two seasonal forecast systems namely the SEAS5 and MF7 systems from the European Center of Medium-Range Weather Forecasts (ECMWF) and Météo-France respectively. The SHL characteristics in the seasonal forecast models are assessed based on a comparison with the fifth ECMWF Reanalysis (ERA5) for the period 1993–2016. The analysis of the modes of variability shows that the seasonal forecast models have issues with the timing and the intensity of the SHL pulsations when compared to ERA5. SEAS5 and MF7 show a cool bias centered on the Sahara and a warm bias located in the eastern part of the Sahara respectively. Both models tend to underestimate the interannual variability in the SHL. Large discrepancies are found in the representation of extremes SHL events in the seasonal forecast models. These results are not linked to our choice of ERA5 as a reference, for we show robust coherence and high correlation between ERA5 and the Modern-Era Retrospective analysis for Research and Applications (MERRA). The use of statistical bias correction methods significantly reduces the bias in the seasonal forecast models and improves the yearly distribution of the SHL and the forecast scores. The results highlight the capacity of the models to represent the intraseasonal pulsations (the so-called east–west phases) of the SHL. We notice an overestimation of the occurrence of the SHL east phases in the models (SEAS5, MF7), while the SHL west phases are much better represented in MF7. In spite of an improvement in prediction score, the SHL-related forecast skills of the seasonal forecast models remain weak for specific variations for lead times beyond 1 month, requiring some adaptations. Moreover, the models show predictive skills at an intraseasonal timescale for shorter lead times.

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

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

<p>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. 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.</p><p>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. 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.</p><p>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.</p><p>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. 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).</p><p>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. Forecast performance has been assessed using ERA 5 reanalysis as pseudo-observations. </p><p>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.</p>

scholarly journals Assessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UK

2020 ◽  
Author(s):  
Andres Peñuela ◽  
Christopher Hutton ◽  
Francesca Pianosi
Keyword(s):  
Decision Maker ◽  
Water Resource ◽  
Water Resource Management ◽  
Skill Score ◽  
Forecast Skill ◽  
Weather Data ◽  
Seasonal Forecasts ◽  
Weather Forecasts ◽  
The Common ◽  
The Uk

Abstract. Improved skill of long-range weather forecasts has motivated an increasing effort towards developing seasonal hydrological forecasting systems across Europe. Among other purposes, such forecasting systems are expected to support better water management decisions. In this paper we evaluate the potential use of a real-time optimisation system (RTOS) informed by seasonal forecasts in a water supply system in the UK. For this purpose, we simulate the performances of the RTOS fed by ECMWF seasonal forecasting systems (SEAS5) over the past ten years, and we compare them to a benchmark operation that mimics the common practices for reservoir operation in the UK. We also attempt to link the improvement of system performances, i.e. the forecast value, to the forecast skill (measured by the mean error and the Continuous Ranked Probability Skill Score) as well as other factors such as bias correction, the decision maker priorities, hydrological conditions and level of uncertainty consideration. We find that some of these factors control the forecast value much more strongly than the forecast skill. For the (realistic) scenario where the decision-maker prioritises water resource availability over energy cost reductions, we identify clear operational benefits from using seasonal forecasts, provided that forecast uncertainty is explicitly considered. However, when comparing the use of ECMWF-SEAS5 products to ensemble streamflow predictions (ESP), which are more easily derived from historical weather data, we find that ESP remains a hard-to-beat reference not only in terms of skill but also in terms of value.

scholarly journals Seasonal Drought Prediction for Semiarid Northeast Brazil: Verification of Six Hydro-Meteorological Forecast Products

2017 ◽  
Author(s):  
José Miguel Delgado ◽  
Sebastian Voss ◽  
Gerd Bürger ◽  
Klaus Vormoor ◽  
Aline Murawski ◽  
...  
Keyword(s):  
Rainy Season ◽  
Meteorological Drought ◽  
Skill Score ◽  
Hydrological Drought ◽  
Drought Indices ◽  
Monthly Precipitation ◽  
Northeast Brazil ◽  
Seasonal Forecasts ◽  
Seasonal Drought ◽  
Drought Prediction

Abstract. A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeast Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a generalized linear model to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE) and the relative operating characteristic (ROC) skill score. Forecasts of monthly precipitation had little or no skill considering RMSE. Still, the forecast of extreme events of low monthly precipitation showed skill for the rainy season (ROC skill score of 0.24 to 0.33). A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and significant skill when forecasting drought events of e.g. SPEI01 (ROC skill score of 0.53 to 0.61). Similar results were obtained for low regional reservoir storage forecasts. Regarding the skill in the forecasted months, it was greater for April, when compared to February and March (the remaining months of the rainy season). This work showed that a multimodel ensemble can forecast drought events of time scales relevant to water managers in northeast Brazil with skill. But no or little skill could be found in the forecasts of the whole range of monthly precipitation or drought indices (e.g. forecasting average years). Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeast Brazil.

scholarly journals Evaluation and Error Correction of the ECMWF Subseasonal Precipitation Forecast over Eastern China during Summer

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Huanran He ◽  
Suxiang Yao ◽  
Anning Huang ◽  
Kejian Gong
Keyword(s):  
Error Correction ◽  
Forecast Error ◽  
Eastern China ◽  
Model Performance ◽  
Probability Of Detection ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Ecmwf Model ◽  
Mean Square Errors

Subseasonal-to-seasonal (S2S) prediction is a highly regarded skill around the world. To improve the S2S forecast skill, an S2S prediction project and an extensive database have been established. In this study, the European Center for Medium-Range Weather Forecasts (ECMWF) model hindcast, which participates in the S2S prediction project, is systematically assessed by focusing on the hindcast quality for the summer accumulated ten-day precipitation at lead times of 0–30 days during 1995–2014 in eastern China. Additionally, the hindcast error is corrected by utilizing the preceding sea surface temperature (SST). The metrics employed to measure the ECMWF hindcast performance indicate that the ECMWF model performance drops as the lead time increases and exhibits strong interannual differences among the five subregions of eastern China. In addition, the precipitation forecast skill of the ECMWF hindcast is best at approximately 15 days in some areas of Southeast China; after correcting the forecast error, the forecast skill is increased to 30 days. At lead times of 0–30 days, regardless of whether the forecast error is corrected, the root mean square errors are lowest in Northeast China. After correcting the forecast error, the performance of the ECMWF hindcast shows better improvement in depicting the quantity and temporal and spatial variation of precipitation at lead times of 0–30 days in eastern China. The false alarm ratio (FAR), probability of detection (POD), and equitable threat score (ETS) reveal that the ECMWF model has a preferable performance at forecasting accumulated ten-day precipitation rates of approximately 20∼50 mm and indicates an improved hindcast quality after the forecast error correction. In short, adopting the preceding SST to correct the summer subseasonal precipitation of the ECMWF hindcast is preferable.

scholarly journals Subseasonal Predictions of Tropical Cyclone Occurrence and ACE in the S2S Dataset

2020 ◽  
Vol 35 (3) ◽  
pp. 921-938 ◽  
Author(s):  
Chia-Ying Lee ◽  
Suzana J. Camargo ◽  
Fréderic Vitart ◽  
Adam H. Sobel ◽  
Joanne Camp ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Linear Regression ◽  
Skill Score ◽  
Regression Method ◽  
Prediction Skill ◽  
Lead Times ◽  
Linear Regression Method ◽  
Weather Forecasts ◽  
Out Of Sample ◽  
Calibration Techniques

Abstract Probabilistic tropical cyclone (TC) occurrence, at lead times of week 1–4, in the Subseasonal to Seasonal (S2S) dataset are examined here. Forecasts are defined over 15° in latitude × 20° in longitude regions, and the prediction skill is measured using the Brier skill score with reference to climatological reference forecasts. Two types of reference forecasts are used: a seasonally constant one and a seasonally varying one, with the latter used for forecasts of anomalies from the seasonal climatology. Models from the European Centre for Medium-Range Weather Forecasts (ECMWF), Australian Bureau of Meteorology, and Météo-France/Centre National de Recherche Météorologiques have skill in predicting TC occurrence four weeks in advance. In contrast, only the ECMWF model is skillful in predicting the anomaly of TC occurrence beyond one week. Errors in genesis prediction largely limit models’ skill in predicting TC occurrence. Three calibration techniques, removing the mean genesis and occurrence forecast biases, and a linear regression method, are explored here. The linear regression method performs the best and guarantees a higher skill score when applied to the in-sample dataset. However, when applied to the out-of-sample data, especially in areas where the TC sample size is small, it may reduce the models’ prediction skill. Generally speaking, the S2S models are more skillful in predicting TC occurrence during favorable Madden–Julian oscillation phases. Last, we also report accumulated cyclone energy predictions skill using the ranked probability skill score.

scholarly journals Seasonal forecasting of mountain snow resources: evaluation of a climate service prototype

2021 ◽  
Author(s):  
Silvia Terzago ◽  
Giulio Bongiovanni ◽  
Jost von Hardenberg
Keyword(s):  
Climate Change ◽  
Snow Depth ◽  
Skill Score ◽  
Small Scale ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Current Season ◽  
Near Surface ◽  
Forecast Quality

<p>Warming trends in the past decades in mountain regions have resulted in glacier shrinking, seasonal snow cover reduction, changes in the amount and seasonality of meltwater runoff (IPCC, 2019), and we expect droughts to become more severe in the future (Haslinger et al., 2014) with consequences for both mountain and downstream economies. Effective adaptation strategies to address and reduce negative climate change impacts involve multiple time scales, from the long-term support of mountain water resource management and the diversification of mountain tourism activities, to the seasonal scale, for the optimization of the available snow resources. </p><p>In the frame of the MEDSCOPE project we developed a prototype to generate seasonal forecasts of mountain snow resources, in order to estimate the temporal evolution of the depth and the water content of the snowpack with lead times of several months. The prototype has been tailored on the needs of water and hydropower plant managers and of mountain ski resorts managers. We present the modelling chain, based on the seasonal forecasts of ECMWF and Météo-France seasonal prediction systems, made available through the Copernicus Climate Change Service (C3S). Seasonal forecasts of precipitation, near-surface air temperature, radiative fluxes, wind and humidity are bias-corrected and downscaled to the site of Bocchetta delle Pisse 2410 m a.s.l. in the North-Western Italian Alps, and finally used as input for a physically-based multi-layer snow model (SNOWPACK, Bartelt and Lehning, 2002). The RainFARM stochastic downscaling procedure (Terzago et al., 2018) is used for precipitation data in order to allow an estimate of uncertainties linked to small-scale variability in the forcing.</p><p>The skills of the prototype in predicting the snow depth evolution from November 1st to May 31st in each season of the hindcast period 1995-2015 are demonstrated using station measurements as a reference. We show the correlation between forecast and observed snow depth anomalies and we quantify the forecast quality in terms of reliability, resolution, discrimination and sharpness using a set of probabilistic measures (Brier Skill Score, the Area Under the ROC Curve Skill Score and the Continuous Ranked Probability Skill Score). Implications of the forecast quality at different lead times on climate services are discussed. </p><p>Real-time snow forecasts for the current season (2020-2021) are available at this link: http://wilma.to.isac.cnr.it/diss/snowpack/snowseas-eng.html</p>

scholarly journals Verification of ECMWF System4 for seasonal hydrological forecasting in a northern climate

2017 ◽  
Author(s):  
Rachel Bazile ◽  
Marie-Amélie Boucher ◽  
Luc Perreault ◽  
Robert Leconte
Keyword(s):  
Climate Models ◽  
Initial Conditions ◽  
Meteorological Data ◽  
Skill Score ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Hydro Power ◽  
Hydrological Forecasting ◽  
Northern Climate

Abstract. Hydro-power production requires optimal dam management. In a northern climate, where spring freshet constitutes the main inflow volume, seasonal forecasts can help to establish a yearly strategy. Long-term hydrological forecasts often rely on past observations of streamflow or meteorological data. Another alternative is to use ensemble meteorological forecasts produced by climate models. In this paper, those produced by the ECMWF (European Center for Medium-Range Forecast)'s System 4 are examined and bias is characterized. Bias correction, through the linear scaling method, improves the performance of the raw ensemble meteorological forecasts in terms of Continuous Ranked Probability Score. Then, three seasonal ensemble hydrological forecasting systems are compared: 1) the climatology of simulated streamflow, 2) the ensemble hydrological forecasts based on climatology (ESP) and 3) the hydrological forecasts based on bias-corrected ensemble meteorological fore- casts from System4 (corr-DSP). Simulated streamflows are used as observations. Accounting for initial conditions is valuable even for long-term forecasts. ESP and corr-DSP both outperform the climatology of simulated streamflow for lead-times from 1-month to 5-month depending on the season and watershed. Corr-DSP appears quite reliable but sometimes suffer from under- dispersion. Integrating information about future meteorological conditions also improves monthly volume forecasts. For the 1-month lead-time, a gain exists for almost all watersheds during winter, summer and fall. However, volume forecasts per- formance for spring is close to the performance of ESP. For longer lead-times, results are mixed and the CRPS skill score is close to 0 in most cases. Bias-corrected ensemble meteorological forecasts appear to be an interesting source of information for hydrological forecasting.

scholarly journals A Comparative Verification of Raw and Bias-Corrected ECMWF Seasonal Ensemble Precipitation Reforecasts in Java (Indonesia)

2019 ◽  
Vol 58 (8) ◽  
pp. 1709-1723 ◽  
Author(s):  
Dian Nur Ratri ◽  
Kirien Whan ◽  
Maurice Schmeits
Keyword(s):  
Lead Time ◽  
Economic Value ◽  
Daily Rainfall ◽  
Skill Score ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Good Reliability ◽  
Loss Analysis ◽  
Skill Scores ◽  
Accumulated Rainfall

AbstractDynamical seasonal forecasts are afflicted with biases, including seasonal ensemble precipitation forecasts from the new ECMWF seasonal forecast system 5 (SEAS5). In this study, biases have been corrected using empirical quantile mapping (EQM) bias correction (BC). We bias correct SEAS5 24-h rainfall accumulations at seven monthly lead times over the period 1981–2010 in Java, Indonesia. For the observations, we have used a new high-resolution (0.25°) land-only gridded rainfall dataset [Southeast Asia observations (SA-OBS)]. A comparative verification of both raw and bias-corrected reforecasts is performed using several verification metrics. In this verification, the daily rainfall data were aggregated to monthly accumulated rainfall. We focus on July, August, and September because these are agriculturally important months; if the rainfall accumulation exceeds 100 mm, farmers may decide to grow a third rice crop. For these months, the first 2-month lead times show improved and mostly positive continuous ranked probability skill scores after BC. According to the Brier skill score (BSS), the BC reforecasts improve upon the raw reforecasts for the lower precipitation thresholds at the 1-month lead time. Reliability diagrams show that the BC reforecasts have good reliability for events exceeding the agriculturally relevant 100-mm threshold. A cost/loss analysis, comparing the potential economic value of the raw and BC reforecasts for this same threshold, shows that the value of the BC reforecasts is larger than that of the raw ones, and that the BC reforecasts have value for a wider range of users at 1- to 7-month lead times.

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