scholarly journals CSTools: the MEDSCOPE Toolbox for Climate Forecasts postprocessing

2021 ◽  
Author(s):  
Núria Pérez-Zanón ◽  
Louis-Philippe Caron ◽  
Silvia Terzago ◽  
Bert Van Schaeybroeck ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Bias Correction ◽  
Seasonal Forecast ◽  
Ensemble Member ◽  
Forecast Combination ◽  
Precipitation Data ◽  
Grid Cells ◽  
Seasonal Forecasts ◽  
Climate Forecasts ◽  
Daily Precipitation Data ◽  
Stochastic Downscaling

<p>Climate forecasts need to be postprocessed to obtain user-relevant climate information, to develop and implement strategies of adaptation to climate variability and to trigger decisions. Several postprocessing methods are gathered into CSTools (short for Climate Service Tools) for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. </p><p>Besides an overview of the methods and documentation available in CSTools, a practical example is demonstrated. The objective of this practical example is to postprocess a seasonal forecast with a set of CSTools functions in order to obtain the required data to produce forecasts of mountain snow resources. Quantile mapping bias-correction and RainFARM stochastic downscaling methods are applied to raw seasonal forecast daily precipitation data to derive 1 km resolution fields. Bias-adjusted and downscaled precipitation data are then employed to drive a snow model, SNOWPACK, and generate snow depth seasonal forecasts at selected high-elevation sites in North-Western Italian Alps. </p><p>The computational resources required by CSTools to process the forecasts will be discussed. This assessment is relevant given the memory requirements for the use case: while seasonal forecast data occupies ~10MB (8 x 8 grid cells, 215 forecast time steps for 30 different initializations with 25 members each), the data post-processed reaches ~1TB (the RainFARM downscaling requires a refinement factor 100 for the SNOWPACK model increasing the spatial resolution to 800 x 800 grid cells and creating 10 stochastic realizations for each ensemble member). In addition to one strategy using conventional loops, startR is introduced as an efficient alternative. startR is an R package that allows implementing the MapReduce paradigm, i.e. chunking the data and processing them either locally or remotely on high-performance computing systems, leveraging multi-node and multi-core parallelism where possible.</p>

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>

Seasonal forecasts of discharge in the Upper Danube upstream of Vienna

2020 ◽  
Author(s):  
Ignacio Martin Santos ◽  
Mathew Herrnegger ◽  
Hubert Holzmann ◽  
Kristina Fröhlich ◽  
Jennifer Ostermüller
Keyword(s):  
Risk Management ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Meteorological Data ◽  
Seasonal Forecast ◽  
Drought Risk ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Climate Forecasts

<p>In the last years, the demand of reliable seasonal streamflow forecasts has increased with the aim of incorporating them into decision support systems for e.g. river navigation, power plant operation  or drought risk management. Recently, the concept of “climate services” has gained stronger attention in Europe, thereby incorporating useful information derived from climate predictions and projections that support adaptation, mitigation and disaster risk management. In the frame of one of these climate services currently in development, Clim2Power project, a seasonal forecast system for discharge in the Upper Danube upstream Vienna has been established.</p><p>Seasonal forecasts are generated using a dynamical approach running a hydrological model (COSERO) with forecasted climate input provided by DWD (Germany's National Meterological Service). The climate forecasts are based on a large ensemble of predictions, available up to 6 months. After the application of a statistical downscaling method, the climate forecasts have a spatial resolution of 6km. The predictability is related to two main contributions: meteorological forcings (i.e. temperature and precipitation predictability) and initial basin states at the time the forecast is issued.</p><p>The Upper Danube basin with a catchment area of approx. 100.000 km<sup>2</sup> is characterized by complex topography dominated by the Alps, elevations range from about 150 m to slightly under 4000 m. Therefore, the skill of the seasonal forecast is highly influenced by the resolution of the meteorological data, and likewise by the hydrological processes that take place, especially, regarding melting processes. Downscaled hindcasts over the last 20 years, generated with the identical setup as the seasonal forecasts, are used in this contribution to assess the skill of the seasonal forecasts. In addition, some post-processing corrections, based on historical observations, are used to adjust the bias of the forecasts. Nevertheless, remaining non-systematic error patterns do not allow complete bias correction. Apart from the biases, also the correlation patterns show a limited skill. We conclude that the seasonal discharge forecasting is still not sufficient to incorporate the results into water resources decision support systems within the studied Alpine basins.</p>

Proactive Drought and Extreme Event Preparedness: Seasonal Climate Forecasts offer Benefit for Decision Making in Water Management in Semi-arid Regions

2020 ◽  
Author(s):  
Tanja Portele ◽  
Christof Lorenz ◽  
Patrick Laux ◽  
Harald Kunstmann
Keyword(s):  
Water Management ◽  
Arid Regions ◽  
Extreme Event ◽  
Seasonal Forecast ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Early Action ◽  
Climate Forecasts ◽  
Semi Arid ◽  
Seasonal Climate Forecasts

<p>Semi-arid regions are the regions mostly affected by drought. In these climatically sensitive regions, the frequency and intensity of drought and hot extremes is projected to increase. With increasing precipitation variability in semi-arid regions, sustainable water management is required. Proactive drought and extreme event preparedness, as well as damage mitigation could be provided by the use of seasonal climate forecasts. However, their probabilistic nature, the lack of clear action derivations and institutional conservatism impedes their application in decision making of the water management sector. Using the latest global seasonal climate forecast product (SEAS5) at 35 km resolution and 7 months forecast horizon of the European Centre for Medium-Range Weather Forecasts, we show that seasonal-forecast-based actions offer potential economic benefit and allow for climate proofing in semi-arid regions in the case of drought and extreme events. Our analysis includes 7 semi-arid, in parts highly managed river basins with extents from tens of thousands to millions of square kilometers in Africa, Asia and South America. The value of the forecast-based action is derived from the skill measures of hit (worthy action) and false alarm (action in vain) rate and is related to economic expenses through ratios of associated costs and losses of an early action. For water management policies, forecast probability triggers for early action plans can be offered based on expense minimization and event maximization criteria. Our results show that even high lead times and long accumulation periods attain value for a range of users and cost-loss situations. For example, in the case of extreme wet conditions (monthly precipitation above 90<sup>th</sup> percentile), seasonal-forecast-based action in 5 out of 7 regions can still achieve more than 50 % of saved expenses of a perfect forecast at 6 months in advance. The utility of seasonal forecasts strongly depends on the user, the cost-loss situation, the region and the concrete application. In general, seasonal forecasts allow decision makers to save expenses, and to adapt to and mitigate damages of extreme events related to climate change.</p>

scholarly journals The CSTools (v4.0) Toolbox: from Climate Forecasts to Climate Forecast Information

2021 ◽  
Author(s):  
Núria Pérez-Zanón ◽  
Louis-Philippe Caron ◽  
Silvia Terzago ◽  
Bert Van Schaeybroeck ◽  
Llorenç Lledó ◽  
...  
Keyword(s):  
R Package ◽  
Climate Forecast ◽  
Forecast Combination ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Climate Forecasts ◽  
Extreme Wind ◽  
Forecast Data ◽  
Extreme Wind Speed

Abstract. Despite the wealth of existing climate forecast data, only a small part is effectively exploited for sectoral applications. A major cause of this is the lack of integrated tools that allow the translation of data into useful and skilful climate information. This barrier is addressed through the development of an R package. CSTools is an easy-to-use toolbox designed and built to assess and improve the quality of climate forecasts for seasonal to multi–annual scales. The package contains process-based state-of-the-art methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. Due to the design of the toolbox in individual functions, the users can develop their own post-processing chain of functions as shown in the use cases presented in this manuscript: the analysis of an extreme wind speed event, the generation of seasonal forecasts of snow depth based on the SNOWPACK model and the post-processing of data to be used as input for the SCHEME hydrological model.

A novel bias correction framework of TMPA 3B42 daily precipitation data using similarity matrix/homogeneous conditions

2019 ◽  
Vol 694 ◽  
pp. 133680 ◽  
Author(s):  
Bahram Choubin ◽  
Shahram Khalighi-Sigaroodi ◽  
Ashok Mishra ◽  
Massoud Goodarzi ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Bias Correction ◽  
Daily Precipitation ◽  
Precipitation Data ◽  
Similarity Matrix ◽  
Daily Precipitation Data

scholarly journals A novel way of correcting between-ensemble-member biases in a lagged S2S ensemble

2021 ◽  
Author(s):  
Marion Mittermaier ◽  
Seshagiri Rao Kolusu ◽  
Joanne Robbins
Keyword(s):  
Lead Time ◽  
Monsoon Season ◽  
Seasonal Forecast ◽  
Ensemble Forecast ◽  
Ensemble Member ◽  
Evaluation Methodology ◽  
Seasonal Forecasts ◽  
Forecast System ◽  
Forecast Horizon ◽  
The Impact

<p>The UK Met Office seasonal forecast system, Global Seasonal Forecast System version 5 (GloSea5), is an ensemble forecast prediction system providing sub-seasonal and seasonal forecasts over the globe with ~60 km resolution in the mid-latitudes. GloSea5 also produces hindcasts or historical re-forecasts. The system produces 4 members a day, initialised at 00UTC. Two members run out to 64 days and two run out to 216 days. We use these four members to generate a 40-member lagged ensemble with 10 days of lag time, i.e. for any forecast horizon the oldest members are always 10 days older. Due to this lag and the way these ensemble members are initialised, there is a considerable within-ensemble bias, even for a nominal “day 1” forecast. This within-ensemble bias evolves with increasing lead time horizon.</p><p>Traditionally hindcasts are used to correct for the so-called model drift. In this work the idea of using a distribution of daily rainfall amounts from short-lead time forecasts is used using the 2019 Indian monsoon season. Quantile mapping is trialled as a means of removing the “within-ensemble-member” bias to ensure that all ensemble members are drawn from a more consistent underlying distribution. Achieving this would enable the members to be used to drive downstream applications such as hazard or impact models, as such models require individual ensemble members.</p><p>The presentation will demonstrate the methodology and the impact it has on ensemble forecast skill, complementing the presentation by Kolusu et al. (same session in conference) which presents an evaluation methodology focusing on patterns for different accumulation lengths and forecast horizons.</p>

scholarly journals Yearly evolution of Euro-Atlantic weather regimes and of their sub-seasonal predictability

2021 ◽  
Author(s):  
Nicola Cortesi ◽  
Verónica Torralba ◽  
Llorenó Lledó ◽  
Andrea Manrique-Suñén ◽  
Nube Gonzalez-Reviriego ◽  
...  
Keyword(s):  
Annual Cycle ◽  
Winter Season ◽  
Skill Score ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Sea Level Pressure ◽  
Weather Regimes ◽  
Start Dates ◽  
Weekly Frequency

AbstractIt is often assumed that weather regimes adequately characterize atmospheric circulation variability. However, regime classifications spanning many months and with a low number of regimes may not satisfy this assumption. The first aim of this study is to test such hypothesis for the Euro-Atlantic region. The second one is to extend the assessment of sub-seasonal forecast skill in predicting the frequencies of occurrence of the regimes beyond the winter season. Two regime classifications of four regimes each were obtained from sea level pressure anomalies clustered from October to March and from April to September respectively. Their spatial patterns were compared with those representing the annual cycle. Results highlight that the two regime classifications are able to reproduce most part of the patterns of the annual cycle, except during the transition weeks between the two periods, when patterns of the annual cycle resembling Atlantic Low regime are not also observed in any of the two classifications. Forecast skill of Atlantic Low was found to be similar to that of NAO+, the regime replacing Atlantic Low in the two classifications. Thus, although clustering yearly circulation data in two periods of 6 months each introduces a few deviations from the annual cycle of the regime patterns, it does not negatively affect sub-seasonal forecast skill. Beyond the winter season and the first ten forecast days, sub-seasonal forecasts of ECMWF are still able to achieve weekly frequency correlations of r = 0.5 for some regimes and start dates, including summer ones. ECMWF forecasts beat climatological forecasts in case of long-lasting regime events, and when measured by the fair continuous ranked probability skill score, but not when measured by the Brier skill score. Thus, more efforts have to be done yet in order to achieve minimum skill necessary to develop forecast products based on weather regimes outside winter season.

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