scholarly journals Temperature and precipitation seasonal forecasts over the Mediterranean region: added value compared to simple forecasting methods

2021 ◽  
Author(s):  
Filippo Calì Quaglia ◽  
Silvia Terzago ◽  
Jost von Hardenberg
Keyword(s):  
Lead Time ◽  
Mediterranean Region ◽  
Added Value ◽  
Lead Times ◽  
Model Ensemble ◽  
Seasonal Forecasts ◽  
Anomaly Correlation ◽  
Temperature And Precipitation ◽  
Precipitation Anomalies ◽  
The Mediterranean

AbstractThis study considers a set of state-of-the-art seasonal forecasting systems (ECMWF, MF, UKMO, CMCC, DWD and the corresponding multi-model ensemble) and quantifies their added value (if any) in predicting seasonal and monthly temperature and precipitation anomalies over the Mediterranean region compared to a simple forecasting method based on the ERA5 climatology (CTRL) or the persistence of the ERA5 anomaly (PERS). This analysis considers two starting dates, May 1st and November 1st and the forecasts at lead times up to 6 months for each year in the period 1993–2014. Both deterministic and probabilistic metrics are employed to derive comprehensive information on the forecast quality in terms of association, reliability/resolution, discrimination, accuracy and sharpness. We find that temperature anomalies are better reproduced than precipitation anomalies with varying spatial patterns across different forecast systems. The Multi-Model Ensemble (MME) shows the best agreement in terms of anomaly correlation with ERA5 precipitation, while PERS provides the best results in terms of anomaly correlation with ERA5 temperature. Individual forecast systems and MME outperform CTRL in terms of accuracy of tercile-based forecasts up to lead time 5 months and in terms of discrimination up to lead time 2 months. All seasonal forecast systems also outperform elementary forecasts based on persistence in terms of accuracy and sharpness.

scholarly journals Assessment of seasonal forecast skills of temperature and precipitation: a comparison of 5 different models over the Mediterranean region

2021 ◽  
Author(s):  
Filippo Calì Quaglia ◽  
Silvia Terzago ◽  
Jost von Hardenberg
Keyword(s):  
Characteristic Curve ◽  
Seasonal Forecast ◽  
Added Value ◽  
Sources Of Information ◽  
Seasonal Forecasts ◽  
Anomaly Correlation ◽  
Probability Score ◽  
Temperature And Precipitation ◽  
Skill Scores ◽  
The Mediterranean

<p>Seasonal forecasts are increasingly employed as sources of information on the expected evolution of climate in the few months ahead by various end-users. This study provides an overall assessment of the skills of the main seasonal forecast systems available in the Copernicus Climate Data Store (C3S) in representing temperature and precipitation anomalies at the monthly time scale. The focus area is the Mediterranean, a densely populated region identified as a hotspot for climate change, where seasonal forecasts could be useful to a variety of economic sectors, including water management, hydropower production, agriculture.</p><p>In this study, seasonal forecast systems issued by 5 European institutions (ECMWF, Météo-France, UKMO, DWD, CMCC), together with two different Multi-Model Ensembles (MME) derived from them, have been analysed. The added value of these forecast systems with respect to simpler forecast approaches based on climatology and persistence has been investigated.</p><p>Different deterministic (Anomaly Correlation Coefficient) and probabilistic scores (Ranked Probability Score, Continuous Ranked Probability Score and Receiver Operating Characteristic Curve) have been employed to obtain an overall assessment of the quality of the forecasts (as of Murphy, 1993 and WMO, 2018), using ERA5 dataset as a reference. We performed the analysis using 6-month forecasts starting in May and November to reproduce the following summer and the winter seasons.</p><p>In general, temperature patterns and respective skill scores are better reproduced than those regarding precipitation. The anomaly correlation coefficients for MME reach the best agreement values for each season and variable except for winter temperature. Different behaviours are found for the different skill scores; their high spatial variability suggests that smaller regions could perform better for a single variable or starting date. Seasonal forecast systems, despite some limitations, show an added value with respect to simple forecast approaches based on the climatology or persistence.</p>

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.

NMME-based Assessment of Prediction Skills of US Summertime Droughts and Pluvials: Role of Near-surface Temperature Prediction Skill

2021 ◽  
Author(s):  
Jonghun Kam ◽  
Sungyoon Kim ◽  
Joshua Roundy
Keyword(s):  
Surface Temperature ◽  
Climate Extremes ◽  
Prediction Skill ◽  
Seasonal Forecasts ◽  
Near Surface ◽  
Anomaly Correlation ◽  
The North ◽  
Anomaly Correlation Coefficient ◽  
Precipitation Anomalies

<p>This study used the North American Multi-Model Ensemble (NMME) system to understand the role of near surface temperature in the prediction skill for US climate extremes. In this study, the forecasting skill was measured by anomaly correlation coefficient (ACC) between the observed and forecasted precipitation (PREC) or 2-meter air temperature (T2m) over the contiguous United States (CONUS) during 1982–2012. The strength of the PREC-T2m coupling was measured by ACC between observed PREC and T2m or forecasted PREC and T2m over the CONUS. This study also assessed the NMME forecasting skill for the summers of 2004 (spatial anomaly correlation between PREC and T2m: 0.05), 2011 (-0.65), and 2012 (-0.60) when the PREC-T2m coupling is weaker or stronger than the 1982–2012 climatology (ACC:-0.34). This study found that most of the NMME models show stronger (negative) PREC-T2m coupling than the observed coupling, indicating that they fail to reproduce interannual variability of the observed PREC-T2m coupling. Some NMME models with skillful prediction for T2m show the skillful prediction of the precipitation anomalies and US droughts in 2011 and 2012 via strong PREC-T2m coupling despite the fact that the forecasting skill is year-dependent and model-dependent. Lastly, we explored how the forecasting skill for SSTs over north Pacific and Atlantic Oceans affects the forecasting skill for T2m and PREC over the US. The findings of this study suggest a need for the selective use of the current NMME seasonal forecasts for US droughts and pluvials.</p>

scholarly journals Information Content of Seasonal Forecasts in a Changing Climate

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Nir Y. Krakauer ◽  
Michael D. Grossberg ◽  
Irina Gladkova ◽  
Hannah Aizenman
Keyword(s):  
Information Gain ◽  
Seasonal Forecasts ◽  
Temperature And Precipitation ◽  
Skill Scores ◽  
The Usa ◽  
Precipitation Anomalies ◽  
The Mean ◽  
Climate Prediction Center

We study the potential value to stakeholders of probabilistic long-term forecasts, as quantified by the mean information gain of the forecast compared to climatology. We use as a case study the USA Climate Prediction Center (CPC) forecasts of 3-month temperature and precipitation anomalies made at 0.5-month lead time since 1995. Mean information gain was positive but low (about 2% and 0.5% of the maximum possible for temperature and precipitation forecasts, resp.) and has not increased over time. Information-based skill scores showed similar patterns to other, non-information-based, skill scores commonly used for evaluating seasonal forecasts but tended to be smaller, suggesting that information gain is a particularly stringent measure of forecast quality. We also present a new decomposition of forecast information gain into Confidence, Forecast Miscalibration, and Climatology Miscalibration components. Based on this decomposition, the CPC forecasts for temperature are on average underconfident while the precipitation forecasts are overconfident. We apply a probabilistic trend extrapolation method to provide an improved reference seasonal forecast, compared to the current CPC procedure which uses climatology from a recent 30-year period. We show that combining the CPC forecast with the probabilistic trend extrapolation more than doubles the mean information gain, providing one simple avenue for increasing forecast skill.

scholarly journals The benefits of pre- and postprocessing streamflow forecasts for an operational flood-forecasting system of 119 Norwegian catchments

2021 ◽  
Author(s):  
Trine J. Hegdahl ◽  
Kolbjørn Engeland ◽  
Ingelin Steinsland ◽  
Andrew Singleton
Keyword(s):  
Lead Time ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Flood Forecasting ◽  
Added Value ◽  
Flood Events ◽  
Ensemble Forecasts ◽  
Processing Scheme ◽  
Temperature And Precipitation ◽  
Streamflow Forecasts

Abstract. The novelty of this study is to evaluate the univariate and the combined effects of including both precipitation and temperature forecasts in the preprocessing together with the postprocessing of streamflow for forecasting of floods as well as all streamflow values for a large sample of catchments. A hydrometeorological forecasting chain in an operational flood forecasting setting with 119 Norwegian catchments was used. This study evaluates the added value of pre- and postprocessing methods for ensemble forecasts in a hydrometeorological forecasting chain in an operational flood forecasting setting with 119 Norwegian catchments. Two years of ECMWF ensemble forecasts of temperature (T) and precipitation (P) with a lead-time up to 9 days were used to force the operational hydrological HBV model to establish streamflow forecasts. Two approaches to preprocess the temperature and precipitation forecasts were tested. 1) An existing approach applied to the gridded forecasts using quantile mapping for temperature and a Bernoulli-gamma distribution for precipitation. 2) Bayesian model averaging (BMA) applied to catchment average values of temperature and precipitation. BMA was also used for postprocessing catchment streamflow forecasts. Ensemble forecasts of streamflow were generated for a total of fourteen schemes based on combinations of raw, preprocessed, and postprocessed forecasts in the hydrometeorological forecasting chain. The aim of this study is to assess which pre- and postprocessing approaches should be used to improve streamflow and flood forecasts and look for regional or seasonal patterns in preferred approaches. The forecasts were evaluated for two datasets: i) all streamflows and ii) flood events with streamflow above mean annual flood. Evaluations were based on reliability, continuous ranked probability score (CRPS) and -skill score (CRPSS). For the flood dataset, the critical success index (CSI) was used. Evaluations based on all streamflow data showed that postprocessing improved the forecasts only up to a lead-time of two to three days, whereas preprocessing T and P using BMA improved the forecasts for 50 %–90 % of the catchments beyond three days lead-time. However, for flood events, the added value of pre- and postprocessing is smaller. Preprocessing of P and T gave better CRPS for marginally more catchments compared to the other schemes. Based on CSI, we found that many of the forecast schemes perform equally well. Further, we found large differences in the ability to issue warnings between spring and autumn floods. There was almost no ability to predict autumn floods beyond 3 days, whereas the spring floods had predictability up to 9 days for many events and catchments. The results indicate that the ensemble forecasts have problems in predicting correct autumn precipitation, and the uncertainty is larger for heavy autumn precipitation compared to spring events when temperature driven snow melt is important. To summarize we find that the flood forecasts benefit from most pre-and postprocessing schemes, although the best processing approaches depend on region, catchment, and season, and that the processing scheme should be tailored to each catchment, lead time, season and the purpose of the forecasting.

Sensitivity of soil moisture to climate variability in the Mediterranean region

2020 ◽  
Author(s):  
Louise Mimeau ◽  
Yves Tramblay ◽  
Luca Brocca ◽  
Christian Massari ◽  
Stefania Camici ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Mediterranean Region ◽  
Rectangular Pulse ◽  
Precipitation Intensity ◽  
Runoff Generation ◽  
Time Step ◽  
Temperature And Precipitation ◽  
The Mediterranean ◽  
Good Proxy ◽  
Response To Temperature

<p>Studies on future precipitation trends in the Mediterranean region show a possible decrease in annual precipitation amounts with an intensification of extreme events in the coming years. A major challenge in this region is to evaluate the impacts of changing precipitation patterns on extreme hydrological events such as droughts and floods. For this, it is important to understand the effects of changing temperature and precipitation on soil moisture since it is a good proxy for drought monitoring and it plays a key role on flood runoff generation. This study focuses on 11 sites located in the South of France, with soil moisture, temperature, and precipitation observations over a 10 year time period. Soil moisture is simulated at the hourly time step for each site using a soil moisture model based on the Green-Ampt infiltration scheme. The elasticity of the simulated soil moisture to different changes in precipitation and temperature is analyzed by simulating the soil moisture response to temperature and precipitation changes, generated using a delta change method for temperature and a stochastic model (Neyman-Scott rectangular pulse model) for precipitation. Results show that soil moisture is more impacted by changes in precipitation intermittence than precipitation intensity and temperature. Although there is variability in the soil moisture response to the considered forcing scenarios, increased temperature combined to increased precipitation intensity and intermittency leads to decreased median soil moisture and an increased number of dry days.</p>

Weighted multi-model ensemble projection of extreme precipitation in the Mediterranean region using statistical downscaling

2019 ◽  
Vol 138 (3-4) ◽  
pp. 1269-1295
Author(s):  
Luzia Keupp ◽  
Elke Hertig ◽  
Irena Kaspar-Ott ◽  
Felix Pollinger ◽  
Christoph Ring ◽  
...  
Keyword(s):  
Statistical Downscaling ◽  
Extreme Precipitation ◽  
Mediterranean Region ◽  
Model Ensemble ◽  
The Mediterranean

Seasonal forecast of water resources over the Euro-Mediterranean region

2020 ◽  
Author(s):  
Gildas Dayon ◽  
François Besson ◽  
Jean-Michel Soubeyroux ◽  
Chrisitian Viel ◽  
Paola Marson
Keyword(s):  
Mediterranean Region ◽  
Snow Water Equivalent ◽  
Seasonal Forecast ◽  
Added Value ◽  
Seasonal Forecasts ◽  
Forecast System ◽  
Physically Based Model ◽  
Physically Based ◽  
Snow Water

<p><span>I</span><span>n the </span><span>framework</span><span> of the MEDSCOPE project, a </span><span>forecasting</span><span> chain is developed at Météo-France </span><span>for hydrological long term predictions over </span><span>the Euro-Mediterranean region</span><span>, from one month up to seven months. </span><span>This new prototype </span><span>is based on the Météo-France System 6 global seasonal forecast system</span><span>. </span><span>Atmospheric forecasts are</span> <span>interpolated </span><span>to 5.5 km </span><span>and corrected by</span><span> the statistical method ADAMONT </span><span>using </span><span>the </span><span>UERRA regional </span><span>atmospheric</span><span> reanalysis as reference</span><span>. </span><span>These h</span><span>igh resolution forecast</span><span>s</span><span> driv</span><span>e</span><span> the physically-based model SURFEX coupled to CTRIP </span><span>providing seasonal forecasts of surface variables : river discharges, soil wetness indices, snow water equivalent</span><span>.</span></p><p>A forecast using the climatology (ESP approach) has been produced on the period 1993-2016. It is use to explore the sources of predictability in the different watersheds (Ebro, Po, Rhône). Predictability is mostly coming from the snow pack built during the winter and the soil moisture evolution in spring and summer. A hindcast on the period 1993-2016 is produced to assess the added value of the seasonal forecast compared to the climatology for the end-users in agriculture and energy.</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.

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