Global meteorological drought – Part 1: Probabilistic monitoring

Dutra, Emanuel; Wetterhall, F.; Giuseppe, Francesca Di; Naumann, Gustavo; Barbosa, Paulo; Vogt, J.; Pozzi, W.; Pappenberger, Florian

doi:10.5194/hess-18-2657-2014

Cited by 41 publications

(20 citation statements)

References 28 publications

(38 reference statements)

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“…The forecasting component of their system relies on a statistical approach based on an ensemble streamflow prediction (ESP) methodology. Dutra et al [18,19] generated global forecasts of 3-, 6-, and 12-month SPI by combining seasonal precipitation reforecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 (S4) with precipitation observations from the Global Precipitation Climatology Centre (GPCC) and, alternatively, the ECMWF Interim Reanalysis. They reported on several verification metrics for the SPI forecasts for 18 regions around the globe.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we build on the work of [18,19] by considering the ECMWF S4 ensemble framework to generate seasonal forecasts of the SPI, and perform their verification against corresponding SPI from precipitation observations of the GPCC over Latin America. Drought is viewed from a meteorological perspective, and seasonal forecasts of the 3-and 6-month SPI (SPI3 and SPI6) are generated and verified on a monthly basis for the hindcast period of 1981-2010.…”

Section: Introductionmentioning

confidence: 99%

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Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

Carrão

Naumann

Dutra

et al. 2018

Climate

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Meaningful seasonal prediction of drought conditions is key information for end-users and water managers, particularly in Latin America where crop and livestock production are key for many regional economies. However, there are still not many studies of the feasibility of such a forecasts at continental level in the region. In this study, precipitation predictions from the European Centre for Medium Range Weather (ECMWF) seasonal forecast system S4 are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI) for Latin America, and their skill is evaluated over the hindcast period 1981-2010. The value-added utility in using the ensemble S4 forecast to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on their climatological characteristics. As expected, skill of the S4-generated SPI forecasts depends on the season, location, and the specific aggregation period considered (the 3-and 6-month SPI were evaluated). Added skill from the S4 for lead times equaling the SPI accumulation periods is primarily present in regions with high intra-annual precipitation variability, and is found mostly for the months at the end of the dry seasons for 3-month SPI, and half-yearly periods for 6-month SPI. The ECMWF forecast system behaves better than the climatology for clustered grid points in the North of South America, the Northeast of Argentina, Uruguay, southern Brazil and Mexico. The skillful regions are similar for the SPI3 and -6, but become reduced in extent for the severest SPI categories. Forecasting different magnitudes of meteorological drought intensity on a seasonal time scale still remains a challenge. However, the ECMWF S4 forecasting system does capture the occurrence of drought events for the aforementioned regions and seasons reasonably well. In the near term, the largest advances in the prediction of meteorological drought for Latin America are obtainable from improvements in near-real-time precipitation observations for the region. In the longer term, improvements in precipitation forecast skill from dynamical models, like the fifth generation of the ECMWF seasonal forecasting system, will be essential in this effort.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

Carrão

Naumann

Dutra

et al. 2018

Climate

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“…Wilhite et al, 2000;Dutra et al, 2014;Mwangi et al, 2014;Wetterhall et al, 2015). Extended-range forecasting systems can be valuable to help decision makers in planning long-term strategies for water storage (Crochemore et al, 2016) and to support adaptation to climate change (Winsemius et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

Crochemore

Ramos²,

Pappenberger

2016

Hydrol. Earth Syst. Sci.

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189

167

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Abstract. Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias-corrected precipitation and streamflow ensemble forecasts in 16 catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e. ESP method) is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contributes mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping of daily values is successful in improving forecast reliability.

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“…Most of them indicate that those products have great potential in supplying excellent spatial and temporal data for hydrometeorological applications such as hydrological modeling and drought monitoring (e.g., [19][20][21]). For example, Su et al [22] and Wagner et al [23] evaluated the robustness of TRMM 3B42V6 by comparing the precipitation data with available gauged data and applied it as inputs for hydrological modeling in the La Plata basin in South America and the White Volta catchment in West Africa, respectively.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of TRMM 3B43 Precipitation Data for Drought Monitoring in Jiangsu Province, China

Tao

Fischer

Zeng

et al. 2016

Water

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Satellite-based precipitation monitoring at high spatial resolution is crucial for assessing the water and energy cycles at the global and regional scale. Based on the recently released 7th version of the Multi-satellite Precipitation Analysis (TMPA) product of the Tropical Rainfall Measuring Mission (TRMM), and the monthly precipitation data (3B43) are evaluated using observed monthly precipitation from 65 meteorological stations in Jiangsu Province, China, for the period 1998-2014. Additionally, the standardized precipitation index (SPI), which is derived by a nonparametric approach, is employed to investigate the suitability of the TRMM 3B43 precipitation data for drought monitoring in Jiangsu Province. The temporal correlations between observations and the TRMM 3B43 precipitation data show, in general, reasonable agreement for different time scales. However, in summer, only 50% of the stations present correlation coefficients that are statistically significant at the 95% confidence interval. The overall best agreement of TRMM 3B43 precipitation data at seasonal scale tends to occur in autumn (SON). The comparative analysis of the calculated SPI time series suggests that the accuracy of TRMM3B43 decreases with increasing time scale. Stations with significant correlation coefficients also become less spatially homogeneous with increasing time scale. In summary, the findings demonstrate that TRMM 3B43 precipitation data can be used for reliable short-term drought monitoring in Jiangsu province, while temporal-spatial limitations exist for longer time scales.

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Global meteorological drought – Part 1: Probabilistic monitoring

Cited by 41 publications

References 28 publications

Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

Seasonal Drought Forecasting for Latin America Using the ECMWF S4 Forecast System

Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

Evaluation of TRMM 3B43 Precipitation Data for Drought Monitoring in Jiangsu Province, China

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