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 Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate

2017 ◽  
Vol 21 (11) ◽  
pp. 5747-5762 ◽  
Author(s):  
Rachel Bazile ◽  
Marie-Amélie Boucher ◽  
Luc Perreault ◽  
Robert Leconte
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Meteorological Data ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Hydrological Forecasting ◽  
Northern Climate

Abstract. Hydropower production requires optimal dam and reservoir management to prevent flooding damage and avoid operation losses. 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 Centre for Medium-Range Forecast) 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 (CRPS). 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 forecasts from System 4 (corr-DSP). Simulated streamflow computed using observed meteorological data is used as benchmark. 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 to 5 months depending on the season and watershed. 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 performance for spring varies from one watershed to another. For most of them, the performance is close to the performance of ESP. For longer lead times, the CRPS skill score is mostly in favour of ESP, even if for many watersheds, ESP and corr-DSP have comparable skill. Corr-DSP appears quite reliable but, in some cases, under-dispersion or bias is observed. A more complex bias-correction method should be further investigated to remedy this weakness and take more advantage of the ensemble forecasts produced by the climate model. Overall, in this study, bias-corrected ensemble meteorological forecasts appear to be an interesting source of information for hydrological forecasting for lead times up to 1 month. They could also complement ESP for longer lead times.

scholarly journals The Possible Reasons for the Misrepresented Long-Term Climate Trends in the Seasonal Forecasts of HFP2

2013 ◽  
Vol 141 (9) ◽  
pp. 3154-3169 ◽  
Author(s):  
XiaoJing Jia ◽  
Hai Lin
Keyword(s):  
Initial Conditions ◽  
Warming Trend ◽  
Seasonal Forecast ◽  
Second Phase ◽  
Atmospheric Response ◽  
Seasonal Forecasts ◽  
Climate Trend ◽  
Eurasian Continent ◽  
Sst Anomaly

Abstract The climate trend in a dynamical seasonal forecasting system is examined using 33-yr multimodel ensemble (MME) forecasts from the second phase of the Canadian Historical Forecasting Project (HFP2). It is found that the warming trend of the seasonal forecast in March–May (MAM) over the Eurasian continent is in a good agreement with that in the observations. However, the seasonal forecast failed to reproduce the observed pronounced surface air temperature (SAT) trend in December–February (DJF). The possible reasons responsible for the different behaviors of the HFP2 models in MAM and DJF are investigated. Results show that the initial conditions used for the HFP2 forecast system in MAM have a warming trend over the Eurasian continent, which may come from high-frequency weather systems, whereas the initial conditions for the DJF seasonal forecast do not have such a trend. This trend in the initial condition contributes to the trend of the seasonal forecast in the first month. On the other hand, an examination of the lower boundary SST anomaly forcing shows that the SST trend in MAM has a negative SST anomaly along the central equatorial Pacific, which is favorable for a positive phase of the North Atlantic Oscillation atmospheric response and a warming over the Eurasian continent. The long-term SST trend used for the seasonal forecast in DJF, however, has a negative trend in the tropical eastern Pacific, which is associated with a Pacific–North American pattern–like atmospheric response that has little contribution to a warming in the Eurasian continent.

scholarly journals Sources of Uncertainty in Future Projections of the Carbon Cycle

2016 ◽  
Vol 29 (20) ◽  
pp. 7203-7213 ◽  
Author(s):  
Alan J. Hewitt ◽  
Ben B. B. Booth ◽  
Chris D. Jones ◽  
Eddy S. Robertson ◽  
Andy J. Wiltshire ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Carbon Fluxes ◽  
Global Ocean ◽  
Lead Times ◽  
Carbon Uptake ◽  
The North ◽  
Ocean Carbon

Abstract The inclusion of carbon cycle processes within CMIP5 Earth system models provides the opportunity to explore the relative importance of differences in scenario and climate model representation to future land and ocean carbon fluxes. A two-way analysis of variance (ANOVA) approach was used to quantify the variability owing to differences between scenarios and between climate models at different lead times. For global ocean carbon fluxes, the variance attributed to differences between representative concentration pathway scenarios exceeds the variance attributed to differences between climate models by around 2025, completely dominating by 2100. This contrasts with global land carbon fluxes, where the variance attributed to differences between climate models continues to dominate beyond 2100. This suggests that modeled processes that determine ocean fluxes are currently better constrained than those of land fluxes; thus, one can be more confident in linking different future socioeconomic pathways to consequences of ocean carbon uptake than for land carbon uptake. The contribution of internal variance is negligible for ocean fluxes and small for land fluxes, indicating that there is little dependence on the initial conditions. The apparent agreement in atmosphere–ocean carbon fluxes, globally, masks strong climate model differences at a regional level. The North Atlantic and Southern Ocean are key regions, where differences in modeled processes represent an important source of variability in projected regional fluxes.

scholarly journals Global meteorological drought – Part 2: Seasonal forecasts

2014 ◽  
Vol 18 (7) ◽  
pp. 2669-2678 ◽  
Author(s):  
E. Dutra ◽  
W. Pozzi ◽  
F. Wetterhall ◽  
F. Di Giuseppe ◽  
L. Magnusson ◽  
...  
Keyword(s):  
Lead Time ◽  
Initial Conditions ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Added Value ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Global Precipitation Climatology Centre ◽  
The World

Abstract. Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two data sets as initial conditions: the Global Precipitation Climatology Centre (GPCC) and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation, the most recent ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast evaluation focuses on the periods where precipitation deficits are likely to have higher drought impacts, and the results were summarized over different regions in the world. The verification of the forecasts with lead time indicated that generally for all regions the least reduction on skill was found for (i) long lead times using ERAI or GPCC for monitoring and (ii) short lead times using ECMWF or climatological seasonal forecasts. The memory effect of initial conditions was found to be 1 month of lead time for the SPI-3, 4 months for the SPI-6 and 6 (or more) months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value with skills at least equal to and often above that of climatological forecasts. Furthermore, it is very difficult to improve on the use of climatological forecasts for long lead times. Our results also support recent questions of whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

scholarly journals Global meteorological drought – Part 2: Seasonal forecasts

2014 ◽  
Vol 11 (1) ◽  
pp. 919-944 ◽  
Author(s):  
E. Dutra ◽  
W. Pozzi ◽  
F. Wetterhall ◽  
F. Di Giuseppe ◽  
L. Magnusson ◽  
...  
Keyword(s):  
Lead Time ◽  
Initial Conditions ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Added Value ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
The World ◽  
The Standardized Precipitation Index

Abstract. Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two datasets as initial conditions: the Global Precipitation Climatology Center (GPCC) and the ECMWF ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation: the most current ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast skill is concentrated on verification months where precipitation deficits are likely to have higher drought impacts and grouped over different regions in the world. Verification of the forecasts as a function of lead time revealed a reduced impact on skill for: (i) long lead times using different initial conditions, and (ii) short lead times using different precipitation forecasts. The memory effect of initial conditions was found to be 1 month lead time for the SPI-3, 3 to 4 months for the SPI-6 and 5 months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value, a skill similar or better than climatological forecasts. In some cases, particularly for long SPI time scales, it is very difficult to improve on the use of climatological forecasts. Our results also support recent questions whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

scholarly journals Sub-seasonal forecasts of demand, wind power and solar power generation for 28 European Countries

2021 ◽  
Author(s):  
Hannah C. Bloomfield ◽  
David J. Brayshaw ◽  
Paula L. M. Gonzalez ◽  
Andrew Charlton-Perez
Keyword(s):  
Power Systems ◽  
Power System ◽  
Wind Power ◽  
Solar Power ◽  
Meteorological Data ◽  
Winter Season ◽  
European Countries ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Climate Services

Abstract. Electricity systems are becoming increasingly exposed to weather. The need for high-quality meteorological forecasts for managing risk across all timescales has therefore never been greater. This paper seeks to extend the uptake of meteorological data in the power systems modelling community to include probabilistic meteorological forecasts at sub-seasonal lead-times. Such forecasts are growing in skill and are receiving considerable attention in power system risk management and energy trading. Despite this interest, these forecasts are rarely evaluated in power system terms and technical barriers frequently prohibit use by non-meteorological specialists. This paper therefore presents data produced through a new EU climate services program Subseasonal-to-seasonal forecasting for Energy (S2S4E). The data corresponds to a suite of well-documented, easy-to-use, self-consistent daily- and nationally-aggregated time-series for wind power, solar power and electricity demand across 28 European countries. The DOI http://dx.doi.org/10.17864/1947.275 will be activated after the paper has been accepted for publication. In the meantime, the data is accessible via https://researchdata.reading.ac.uk/275/, (Gonzalez et al., 2020). The data includes a set of daily ensemble reforecasts from two leading forecast systems spanning 20-years (ECMWF, 1996–2016) and 11-years (NCEP, 1999–2010). The reforecasts containing multiple plausible realisations of daily-weather and power data for up to 6 weeks in the future. To the authors' knowledge, this is the first time fully calibrated and post-processed daily power system forecast set has been published, and this is the primary purpose of this paper. A brief review of forecast skill in each of the individual primary power system properties and the composite property demand-net-renewables is presented, focusing on the winter season. The forecast systems contain additional skill over climatological expectation for weekly-average forecasts at extended lead-times, though this skill depends on the nature of the forecast metric considered. This highlights the need for greater collaboration between the energy- and meteorological research communities to develop applications, and it is hoped that publishing these data and tools will support this.

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 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.

scholarly journals Sub-seasonal forecasts of demand and wind power and solar power generation for 28 European countries

2021 ◽  
Vol 13 (5) ◽  
pp. 2259-2274
Author(s):  
Hannah C. Bloomfield ◽  
David J. Brayshaw ◽  
Paula L. M. Gonzalez ◽  
Andrew Charlton-Perez
Keyword(s):  
Power Systems ◽  
Power System ◽  
Wind Power ◽  
Solar Power ◽  
Meteorological Data ◽  
Winter Season ◽  
European Countries ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Climate Services

Abstract. Electricity systems are becoming increasingly exposed to weather. The need for high-quality meteorological forecasts for managing risk across all timescales has therefore never been greater. This paper seeks to extend the uptake of meteorological data in the power systems modelling community to include probabilistic meteorological forecasts at sub-seasonal lead times. Such forecasts are growing in skill and are receiving considerable attention in power system risk management and energy trading. Despite this interest, these forecasts are rarely evaluated in power system terms, and technical barriers frequently prohibit use by non-meteorological specialists. This paper therefore presents data produced through a new EU climate services programme Subseasonal-to-seasonal forecasting for Energy (S2S4E). The data correspond to a suite of well-documented, easy-to-use, self-consistent daily and nationally aggregated time series for wind power, solar power and electricity demand across 28 European countries. The data are accessible from https://doi.org/10.17864/1947.275 (Gonzalez et al., 2020). The data include a set of daily ensemble reforecasts from two leading forecast systems spanning 20 years (ECMWF, an 11-member ensemble, with twice-weekly starts for 1996–2016, totalling 22 880 forecasts) and 11 years (NCEP, a 12-member lagged-ensemble, constructed to match the start dates from the ECMWF forecast from 1999–2010, totalling 14 976 forecasts). The reforecasts contain multiple plausible realisations of daily weather and power data for up to 6 weeks in the future. To the authors’ knowledge, this is the first time a fully calibrated and post-processed daily power system forecast set has been published, and this is the primary purpose of this paper. A brief review of forecast skill in each of the individual primary power system properties and a composite property is presented, focusing on the winter season. The forecast systems contain additional skill over climatological expectation for weekly-average forecasts at extended lead times, though this skill depends on the nature of the forecast metric considered. This highlights the need for greater collaboration between the energy and meteorological research communities to develop applications, and it is hoped that publishing these data and tools will support this.

scholarly journals Evaluation of subseasonal to seasonal forecasts over India for renewable energy applications

2021 ◽  
Vol 56 ◽  
pp. 89-96
Author(s):  
Aheli Das ◽  
Somnath Baidya Roy
Keyword(s):  
Renewable Energy ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Numerical Models ◽  
Supply And Demand ◽  
Skill Score ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Coupled Models ◽  
Surface Solar Radiation

Abstract. This study evaluates subseasonal to seasonal scale (S2S) forecasts of meteorological variables relevant for the renewable energy (RE) sector of India from six ocean-atmosphere coupled models: ECMWF SEAS5, DWD GCFS 2.0, Météo-France's System 6, NCEP CFSv2, UKMO GloSea5 GC2-LI, and CMCC SPS3. The variables include 10 m wind speed, incoming solar radiation, 2 m temperature, and 2 m relative humidity because they are critical for estimating the supply and demand of renewable energy. The study is conducted over seven homogenous regions of India for 1994–2016. The target months are April and May when the electricity demand is the highest and June–September when the renewable resources outstrip the demand. The evaluation is done by comparing the forecasts at 1, 2, 3, 4, and 5-months lead-times with the ERA5 reanalysis spatially averaged over each region. The fair continuous ranked probability skill score (FCRPSS) is used to quantitatively assess the forecast skill. Results show that incoming surface solar radiation predictions are the best, while 2 m relative humidity is the worst. Overall SEAS5 is the best performing model for all variables, for all target months in all regions at all lead times while GCFS 2.0 performs the worst. Predictability is higher over the southern regions of the country compared to the north and north-eastern parts. Overall, the quality of the raw S2S forecasts from numerical models over India are not good. These forecasts require calibration for further skill improvement before being deployed for applications in the RE sector.

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