Communicating Properties of Changes in Lagged Weather Forecasts

2021 ◽  
Keyword(s):  
Forecast Skill ◽  
Climate Projections ◽  
Seasonal Forecasts ◽  
Numerical Examples ◽  
Weather Forecasts ◽  
Potential Applications

Abstract Weather forecasts, seasonal forecasts, and climate projections can help their users make good decisions. It has recently been shown that when the decisions include the question of whether to act now or wait for the next forecast, even better decisions can be made if information describing potential forecast changes is also available. In this article, we discuss another set of situations in which forecast change information can be useful, which arise when forecast users need to decide which of a series of lagged forecasts to use. Motivated by these potential applications of forecast change information, we then discuss a number of ways in which forecast change information can be presented, using ECMWF reforecasts and corresponding observations as illustration. We first show metrics that illustrate changes in forecast values, such as average sizes of changes, probabilities of changes of different sizes, and percentiles of the distribution of changes, and then show metrics that illustrate changes in forecast skill, such as increase in average skill and probabilities that later forecasts will be more accurate. We give four illustrative numerical examples in which these metrics determine which of a series of lagged forecasts to use. In conclusion, we suggest that providers of weather forecasts, seasonal forecasts, and climate projections might consider presenting forecast change information, in order to help forecast users make better decisions.

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 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.

Correction of monthly SST forecast in CFSv2 using the Local Dynamical Analog method

2021 ◽  
Author(s):  
Zhaolu Hou ◽  
Jianping Li ◽  
Bin Zuo
Keyword(s):  
Climate Model ◽  
Southern Oscillation ◽  
Correction Method ◽  
Forecast Skill ◽  
Forecast Errors ◽  
Model Forecast ◽  
Seasonal Forecasts ◽  
Probability Forecast ◽  
Central Pacific ◽  
Correction Scheme

AbstractNumerical seasonal forecasts in Earth science always contain forecast errors that cannot be eliminated by improving the ability of the numerical model. Therefore, correction of model forecast results is required. Analog-correction is an effective way to reduce model forecast errors, but the key question is how to locate analogs. In this paper, we updated the Local Dynamical Analog (LDA) algorithm to find analogs and depicted the process of model error correction as the LDA-correction scheme. The LDA-correction scheme was firstly applied to correct the operational seasonal forecasts of sea surface temperature (SST) over the period 1982–2018 from the state-of-the-art coupled climate model named NCEP Climate Forecast System version 2.The results demonstrated that the LDA-correction scheme improves forecast skill in many regions as measured by the correlation coefficient and Root Mean Square Error, especially over the extratropical eastern Pacific and tropical Pacific, where the model has high simulation ability. El Niño-Southern Oscillation (ENSO) as the focused physics process is also improved. The seasonal predictability barrier of ENSO is in remission and the forecast skill of Central Pacific ENSO also increases due to the LDA-correction method. The intensity of ENSO mature phases is improved. Meanwhile, the ensemble forecast results are corrected, which proves the positive influence from this LDA-correction scheme on the probability forecast of cold and warm events. Overall, the LDA-correction scheme, combining statistical and model dynamical information, is demonstrated to be readily integrable with other advanced operational models and has the capability to improve forecast results.

scholarly journals Seasonal Forecasts of the Twentieth Century

2020 ◽  
Vol 101 (8) ◽  
pp. E1413-E1426 ◽  
Author(s):  
Antje Weisheimer ◽  
Daniel J. Befort ◽  
Dave MacLeod ◽  
Tim Palmer ◽  
Chris O’Reilly ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Large Scale ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Climate Anomalies ◽  
Global Circulation Models ◽  
Skill Scores ◽  
Physically Based ◽  
Forecast Models

Abstract Forecasts of seasonal climate anomalies using physically based global circulation models are routinely made at operational meteorological centers around the world. A crucial component of any seasonal forecast system is the set of retrospective forecasts, or hindcasts, from past years that are used to estimate skill and to calibrate the forecasts. Hindcasts are usually produced over a period of around 20–30 years. However, recent studies have demonstrated that seasonal forecast skill can undergo pronounced multidecadal variations. These results imply that relatively short hindcasts are not adequate for reliably testing seasonal forecasts and that small hindcast sample sizes can potentially lead to skill estimates that are not robust. Here we present new and unprecedented 110-year-long coupled hindcasts of the next season over the period 1901–2010. Their performance for the recent period is in good agreement with those of operational forecast models. While skill for ENSO is very high during recent decades, it is markedly reduced during the 1930s–1950s. Skill at the beginning of the twentieth century is, however, as high as for recent high-skill periods. Consistent with findings in atmosphere-only hindcasts, a midcentury drop in forecast skill is found for a range of atmospheric fields, including large-scale indices such as the NAO and the PNA patterns. As with ENSO, skill scores for these indices recover in the early twentieth century, suggesting that the midcentury drop in skill is not due to a lack of good observational data. A public dissemination platform for our hindcast data is available, and we invite the scientific community to explore them.

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

2020 ◽  
Vol 24 (12) ◽  
pp. 6059-6073
Author(s):  
Andres Peñuela ◽  
Christopher Hutton ◽  
Francesca Pianosi
Keyword(s):  
Decision Maker ◽  
Water Resource ◽  
Water Resource Management ◽  
Forecast Skill ◽  
Weather Data ◽  
Ensemble Size ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Hydrological Conditions ◽  
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 optimization 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 10 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 to the bias correction of the meteorological forcing, the decision maker priorities, the hydrological conditions and the forecast ensemble size. We find that in particular the decision maker priorities and the hydrological conditions exert a strong influence on the forecast skill–value relationship. For the (realistic) scenario where the decision maker prioritizes the water resource availability over energy cost reductions, we identify clear operational benefits from using seasonal forecasts, provided that forecast uncertainty is explicitly considered by optimizing against an ensemble of 25 equiprobable forecasts. These operational benefits are also observed when the ensemble size is reduced up to a certain limit. However, when comparing the use of ECMWF-SEAS5 products to ensemble streamflow prediction (ESP), which is 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 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>

Operational Water Service for Copernicus Climate Change Service: development at European scale

2021 ◽  
Author(s):  
Christiana Photiadou ◽  
Peter Berg ◽  
Denica Bozhinova ◽  
Anna Eronn ◽  
Fulco Ludwig ◽  
...  
Keyword(s):  
Climate Change ◽  
Web Application ◽  
Graphical Representation ◽  
Ecological Status ◽  
Climate Impact ◽  
Service Development ◽  
Climate Projections ◽  
Management Service ◽  
Seasonal Forecasts ◽  
Water Service

<p>The Operational Water Service of C3S (developed by the Swedish Meteorological and Hydrological Institute (SMHI)) aims to help a broad range of water managers with water allocation, flood management, ecological status and industrial water use, to adapt their strategies in order to adapt to climate variability and change. The aim is to speed up the workflow in climate-change adaptation by using seasonal hydrological forecasts and climate-impact indicators. This is done by offering an interactive web application with refined data, guidance and practical showcases to water managers across Europe. Policy makers will find a comprehensive overview for Europe with key messages and consultants can use the service for developing climate impact assessments and adaptation strategies.</p><p>The development of the current operational climate service for water management is based on the experience from two previous proof-of-concepts and will also be aligned with the hydrological model system of the Copernicus Emergency Management Service (CEMS).  The service is uses data from the Climate Data Store and the operational hydrological seasonal forecasting system runs entirely in the European Centre for Medium range Weather Forecasts (ECMWF) technical environment, although developed by SMHI.</p><p>The operational Water Service of C3S will be launched during the spring of 2021, and a series of activities and user interactions will be organised to ensure that the applications developed for the service fulfil the users’ needs. Here, we present the development process of the operational service and key outcomes from co-design interactions and resulting applications. The key issues identified by the user community were: i) clear visualisation and graphical representation of skill in seasonal forecasts and confidence in climate projections, ii) need of detailed documentation and process transparency in hydrological models and production of data, iii) user guidance and tutorials are needed for better understanding of the applications, and iv) workflows and scripts for indicator production in new applications for developers of information systems.</p>

Global BROOK90: validation, uncertainties, current progress and future outline.

2021 ◽  
Author(s):  
Ivan Vorobevskii ◽  
Rico Kronenberg
Keyword(s):  
Water Balance ◽  
Model Performance ◽  
Low Flow ◽  
Easy Access ◽  
Climate Projections ◽  
Seasonal Forecasts ◽  
Water Balance Components ◽  
Discharge Data ◽  
Small Catchment ◽  
Wide Range

<p>‘Just drop a catchment and receive reasonable model output’ – still stays as motto and main idea of the ‘Global BROOK90’ project. The open-source R-package is build-up on global land cover, soil, topographical, meteorological datasets and the lumped hydrological model as a core to simulate water balance components on HRU scale all over the world in an automatic mode. First introduced in EGU2020 and followed by GitHub code release including an publication of methodology with few examples we want to continue with the insights on the current state and highlight the future steps of the project.</p><p>A global validation of discharge and evapotranspiration components of the model showed promising results. We used 190 small (median size of 64 km<sup>2</sup>) catchments and FLUXNET data which represent a wide range of relief, vegetation and soil types within various climate zones. The model performance was evaluated with NSE, KGE, KGESS and MAE. In more than 75 % of the cases the framework performed better than the mean of the observed discharge. On a temporal scale the performance is significantly better on a monthly vs daily scale. Cluster analysis revealed that some of the site characteristics have a significant influence on the performance. Additionally, it was found that Global BROOK90 outperforms GloFAS ERA5 discharge reanalysis (for the category with smallest catchments).</p><p>A cross-combination of three different BROOK90 setups and three forcing datasets was set up to reveal uncertainties of the Global BROOK90 package using a small catchment in Germany as a case study. Going from local to regional and finally global scale we compared mixtures of model parameterization schemes (original calibrated BROOK90, EXTRUSO and Global BROOK90) and meteorological datasets (local gauges, RaKlida and ERA5). Besides high model performances for a local dataset plus a calibrated model and weaker results for ERA5 and the Global BROOK90, it was found that the ERA5 dataset is still able to provide good results when combined with a regional and local parameterization. On the other side, the combination of a global parameterization with local and regional forcings gives still adequate, but much worse results. Furthermore, a hydrograph separation revealed that the Global BROOK90 parameterization as well as ERA5 discharge data perform weaker especially within low flow periods.</p><p>Currently, some new features are added to the original package. First, with the recent release of the ERA5 extension, historical simulations with the package now are expanded to 1950-2021 period. Additionally, an alternative climate reanalysis dataset is included in the framework (Merra-2, 0.5x0.625-degree spatial resolution, starting from 1980). A preliminary validation shows insignificant differences between both meteorological datasets with respect to the discharge based model performance.</p><p>Further upgrades of the framework will include the following core milestones: recognition of forecast and climate projections and parameter optimization features. In the nearest future we plan to utilize full power of the Climate Data Store for easy access to seasonal forecasts (i.e. ECMWF, DWD, NCEP) as well as climate projections (CMIP5) to extend the package’s scope to predict near and far future water balance components.</p>

scholarly journals The potential value of seasonal forecasts in a changing climate in southern Africa

2014 ◽  
Vol 18 (4) ◽  
pp. 1525-1538 ◽  
Author(s):  
H. C. Winsemius ◽  
E. Dutra ◽  
F. A. Engelbrecht ◽  
E. Archer Van Garderen ◽  
F. Wetterhall ◽  
...  
Keyword(s):  
Climate Change ◽  
Heat Stress ◽  
Southern Africa ◽  
Weather Conditions ◽  
Stress Conditions ◽  
Mitigation Measures ◽  
Seasonal Forecasts ◽  
Weather Forecasts ◽  
Dry Spells ◽  
The Future

Abstract. Subsistence farming in southern Africa is vulnerable to extreme weather conditions. The yield of rain-fed agriculture depends largely on rainfall-related factors such as total seasonal rainfall, anomalous onsets and lengths of the rainy season and the frequency of occurrence of dry spells. Livestock, in turn, may be seriously impacted by climatic stress with, for example, exceptionally hot days, affecting condition, reproduction, vulnerability to pests and pathogens and, ultimately, morbidity and mortality. Climate change may affect the frequency and severity of extreme weather conditions, impacting on the success of subsistence farming. A potentially interesting adaptation measure comprises the timely forecasting and warning of such extreme events, combined with mitigation measures that allow farmers to prepare for the event occurring. This paper investigates how the frequency of extreme events may change in the future due to climate change over southern Africa and, in more detail, the Limpopo Basin using a set of climate change projections from several regional climate model downscalings based on an extreme climate scenario. Furthermore, the paper assesses the predictability of these indicators by seasonal meteorological forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system. The focus is on the frequency of dry spells as well as the frequency of heat stress conditions expressed in the temperature heat index. In areas where their frequency of occurrence increases in the future and predictability is found, seasonal forecasts will gain importance in the future, as they can more often lead to informed decision-making to implement mitigation measures. The multi-model climate projections suggest that the frequency of dry spells is not likely to increase substantially, whereas there is a clear and coherent signal among the models of an increase in the frequency of heat stress conditions by the end of the century. The skill analysis of the seasonal forecast system demonstrates that there is a potential to adapt to this change by utilizing the weather forecasts, given that both indicators can be skilfully predicted for the December–February season, at least 2 months ahead of the wet season. This is particularly the case for predicting above-normal and below-normal conditions. The frequency of heat stress conditions shows better predictability than the frequency of dry spells. Although results are promising for end users on the ground, forecasts alone are insufficient to ensure appropriate response. Sufficient support for appropriate measures must be in place, and forecasts must be communicated in a context-specific, accessible and understandable format.

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