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 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 Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index

2013 ◽  
Vol 17 (6) ◽  
pp. 2359-2373 ◽  
Author(s):  
E. Dutra ◽  
F. Di Giuseppe ◽  
F. Wetterhall ◽  
F. Pappenberger
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Seasonal Forecasting ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Blue Nile ◽  
Forecasting System ◽  
The Standardized Precipitation Index

Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.

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 Monitoring and Predicting Drought Based on Multiple Indicators in an Arid Area, China

2020 ◽  
Vol 12 (14) ◽  
pp. 2298
Author(s):  
Yunqian Wang ◽  
Jing Yang ◽  
Yaning Chen ◽  
Zhicheng Su ◽  
Baofu Li ◽  
...  
Keyword(s):  
Lead Time ◽  
Standardized Precipitation Index ◽  
Arid Area ◽  
Lead Times ◽  
Moisture Index ◽  
Predictive Skill ◽  
Drought Prediction ◽  
Spatial Coverage ◽  
Soil Moisture Index ◽  
The Standardized Precipitation Index

Droughts are one of the costliest natural disasters. Reliable drought monitoring and prediction are valuable for drought relief management. This study monitors and predicts droughts in Xinjiang, an arid area in China, based on the three drought indicators, i.e., the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSMI) and the Multivariate Standardized Drought Index (MSDI). Results indicate that although these three indicators could capture severe historical drought events in the study area, the spatial coverage, persistence and severity of the droughts would vary regarding different indicators. The MSDI could best describe the overall drought conditions by incorporating the characteristics of the SPI and SSMI. For the drought prediction, the predictive skill of all indicators gradually decayed with the increasing lead time. Specifically, the SPI only showed the predictive skill at a 1-month lead time, the MSDI performed best in capturing droughts at 1- to 2-month lead times and the SSMI was accurate up to a 3-month lead time owing to its high persistence. These findings might provide scientific support for the local drought management.

scholarly journals Dynamic-Model-Based Seasonal Prediction of Meteorological Drought over the Contiguous United States

2012 ◽  
Vol 13 (2) ◽  
pp. 463-482 ◽  
Author(s):  
Jin-Ho Yoon ◽  
Kingtse Mo ◽  
Eric F. Wood
Keyword(s):  
United States ◽  
Initial Conditions ◽  
Meteorological Drought ◽  
Seasonal Prediction ◽  
Distribution Functions ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Simple Method ◽  
Princeton University

Abstract A simple method was developed to forecast 3- and 6-month standardized precipitation indices (SPIs) for the prediction of meteorological drought over the contiguous United States based on precipitation seasonal forecasts from the NCEP Climate Forecast System (CFS). Before predicting SPI, the precipitation (P) forecasts from the coarse-resolution CFS global model were bias corrected and downscaled to a regional grid of 50 km. The downscaled CFS P forecasts, out to 9 months, were appended to the P analyses to form an extended P dataset. The SPIs were calculated from this new time series. Five downscaling methods were tested: 1) bilinear interpolation; 2) a bias correction and spatial downscaling (BCSD) method based on the probability distribution functions; 3) a conditional probability estimation approach using the mean P ensemble forecasts developed by J. Schaake, 4) a Bayesian approach that bias corrects and downscales P using all ensemble forecast members, as developed by the Princeton University group; and 5) multimethod ensemble as the equally weighted mean of the BCSD, Schaake, and Bayesian forecasts. For initial conditions from April to May, statistical downscaling methods were compared with dynamic downscaling based on the NCEP regional spectral model and forecasts from a high-resolution CFS T382 model. The skill is regionally and seasonally dependent. Overall, the 6-month SPI is skillful out to 3–4 months. For the first 3-month lead times, forecast skill comes from the P analyses prior to the forecast time. After 3 months, the multimethod ensemble has small advantages, but forecast skill may be too low to be useful in practice.

scholarly journals Evaluating skill and robustness of seasonal meteorological and hydrological drought forecasts at the catchment scale – Case Catalonia (Spain)

2020 ◽  
Author(s):  
Henny A.J. Van Lanen ◽  
Theresa C. Van Hateren ◽  
Samuel J. Sutanto
Keyword(s):  
Lead Time ◽  
River Flow ◽  
Standardized Precipitation Index ◽  
Robustness Analysis ◽  
Large Change ◽  
Meteorological Drought ◽  
Hydrological Drought ◽  
Added Value ◽  
Drought Risk ◽  
Catchment Scale

<p>Robust sub-seasonal and seasonal drought forecasts are essential for water managers and stakeholders coping with water scarcity. Many studies have been conducted to evaluate the performance of hydrological forecasts, that is, streamflow. Nevertheless, only few studies evaluated the performance of hydrological drought forecasts, e.g. forecast of deficit volumes in river flow, or duration of deficits. The objective of this study, therefore, was to analyse the skill and robustness of meteorological and hydrological drought forecasts at the catchment scale (the Ter and Llobregat rivers in Catalonia, Spain), rather than at a continental or global scale. Meteorological droughts were forecasted using downscaled (5 km) probabilistic seasonal weather reforecasts (ECMWF-SEAS4). These downscaled data were also used to drive the hydrological model (EFAS-LISFLOOD) to produce hydrological drought forecasts, which were derived from time series of simulated streamflow data. This resulted in seasonal hydro-meteorological reforecasts with a lead time up to 7 months, for the time period 2002-2010. These monthly reforecasts were compared to two datasets: 1) droughts derived from a proxy for observed data, including gridded precipitation data and streamflow simulated by the LISFLOOD model; and 2) droughts derived from in situ observed precipitation and streamflow. Results show that the skill of hydrological drought forecasts is higher than the climatology, up to 3-4 months lead time. On the contrary, meteorological drought forecasts, analysed using the Standardized Precipitation Index (SPI), do not show added value for short accumulation times (SPI1 and SPI3). The robustness analysis show that using either a less extreme or a more extreme threshold leads to a large change in forecasting skill, which points at a rather low robustness of the hydrological drought forecasts. Because the skill found in hydrological drought forecasts is higher than the meteorological ones in this case study, the use of hydrological drought forecasts in Catalonia is highly recommended to improve drought risk management. The results of this study have already been implemented by the Catalonian Water Agency to forecast reservoir volumes of two big reservoirs located in the Ter and Llobregat catchments, which supply the majority of water to the Barcelona metropolitan area.</p>

scholarly journals Developing Objective Operational Definitions for Monitoring Drought

2009 ◽  
Vol 48 (6) ◽  
pp. 1217-1229 ◽  
Author(s):  
Steven M. Quiring
Keyword(s):  
Water Conservation ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Severity Index ◽  
Decision Makers ◽  
Drought Severity ◽  
Specific Method ◽  
The Standardized Precipitation Index ◽  
Spatially Variant ◽  
Objective Methodology

Abstract Drought is a complex phenomenon that is difficult to accurately describe because its definition is both spatially variant and context dependent. Decision makers in local, state, and federal agencies commonly use operational drought definitions that are based on specific drought index thresholds to trigger water conservation measures and determine levels of drought assistance. Unfortunately, many state drought plans utilize operational drought definitions that are derived subjectively and therefore may not be appropriate for triggering drought responses. This paper presents an objective methodology for establishing operational drought definitions. The advantages of this methodology are demonstrated by calculating meteorological drought thresholds for the Palmer drought severity index, the standardized precipitation index, and percent of normal precipitation using both station and climate division data from Texas. Results indicate that using subjectively derived operational drought definitions may lead to over- or underestimating true drought severity. Therefore, it is more appropriate to use an objective location-specific method for defining operational drought thresholds.

scholarly journals Lagged Ensembles, Forecast Configuration, and Seasonal Predictions

2013 ◽  
Vol 141 (10) ◽  
pp. 3477-3497 ◽  
Author(s):  
Mingyue Chen ◽  
Wanqiu Wang ◽  
Arun Kumar
Keyword(s):  
Lead Time ◽  
Geographical Location ◽  
Seasonal Prediction ◽  
Skill Score ◽  
Negative Influence ◽  
Positive Influence ◽  
Prediction Skill ◽  
Ensemble Size ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts

Abstract An analysis of lagged ensemble seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), is presented. The focus of the analysis is on the construction of lagged ensemble forecasts with increasing lead time (thus allowing use of larger ensemble sizes) and its influence on seasonal prediction skill. Predictions of seasonal means of sea surface temperature (SST), 200-hPa height (z200), precipitation, and 2-m air temperature (T2m) over land are analyzed. Measures of prediction skill include deterministic (anomaly correlation and mean square error) and probabilistic [rank probability skill score (RPSS)]. The results show that for a fixed lead time, and as one would expect, the skill of seasonal forecast improves as the ensemble size increases, while for a fixed ensemble size the forecast skill decreases as the lead time becomes longer. However, when a forecast is based on a lagged ensemble, there exists an optimal lagged ensemble time (OLET) when positive influence of increasing ensemble size and negative influence due to an increasing lead time result in a maximum in seasonal prediction skill. The OLET is shown to depend on the geographical location and variable. For precipitation and T2m, OLET is relatively longer and skill gain is larger than that for SST and tropical z200. OLET is also dependent on the skill measure with RPSS having the longest OLET. Results of this analysis will be useful in providing guidelines on the design and understanding relative merits for different configuration of seasonal prediction systems.

scholarly journals Análisis comparativo de índices de sequía en Andalucía para el periodo 1901-2012

2016 ◽  
Vol 42 (1) ◽  
pp. 67 ◽  
Author(s):  
M. Peña-Gallardo ◽  
S. R. Gámiz-Fortís ◽  
Y. Castro-Diez ◽  
M. J. Esteban-Parra
Keyword(s):  
Time Windows ◽  
Initial Period ◽  
Climatic Variability ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Precipitation Index ◽  
Drought Indices ◽  
Global Evaluation ◽  
Temporal Behaviour ◽  
The Standardized Precipitation Index

The aim of this paper is the analysis of the detection and evolution of droughts occurred in Andalusia for the period 1901-2012, by applying three different drought indices: the Standardized Precipitation Index (SPI), the Standardized Precipitation and Evapotranspiration Index (SPEI) and the Standardized Drought-Precipitation Index (IESP), computed for three time windows from the initial period 1901-2012. This analysis has been carried out after a preliminary study of precipitation trends with the intention of understanding the precipitation behaviour, because this climatic variable is one of the most important in the study of extreme events. The specific objectives of this study are: (1) to investigate and characterize the meteorological drought events, mainly the most important episodes in Andalusia; (2) to provide a global evaluation of the capacities of the three different considered indices in order to characterize the drought in a heterogeneous climatically territory; and (3) to describe the temporal behaviour of precipitation and drought indices series in order to establish the general characteristics of their evolution in Andalusia. The results have shown that not all the indices respond similarly identifying the intensity and duration of dry periods in this kind of region where geographical and climatic variability is one of the main elements to be considered.

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