scholarly journals Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin

2014 ◽  
Vol 11 (1) ◽  
pp. 861-888 ◽  
Author(s):  
F. Wetterhall ◽  
H. C. Winsemius ◽  
E. Dutra ◽  
M. Werner ◽  
F. Pappenberger
Keyword(s):  
Meteorological Drought ◽  
Spatial Filter ◽  
Reanalysis Data ◽  
Lead Times ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Forecast System ◽  
Dry Spells ◽  
Precipitation Threshold ◽  
Staple Crop

Abstract. The rainfall in Southern Africa has a large interannual variability, which can cause rain-fed agriculture to fail. The staple crop maize is especially sensitive to dry spells during the early growing season. An early prediction of the probability of dry spells and below normal precipitation can potentially mitigate damages through water management. This paper investigates how well ECMWF's seasonal forecasts predict dry spells over the Limpopo basin during the rainy season December–February (DJF) with lead times from 1 to 5 months. The seasonal forecasts were evaluated against ERA-Interim reanalysis data which in turn was corrected with GPCP (EGPCP) to match monthly precipitation totals. The seasonal forecasts were also bias-corrected with the EGPCP using quantile matching as well as post-processed using a precipitation threshold to define a dry day as well as spatial filtering. The results indicate that the forecasts show skill in predicting dry spells in comparison with a "climatological ensemble" based on previous years. Quantile matching in combination with a precipitation threshold improved the skill of the forecast, whereas a spatial filter had no effect. The skill in prediction of dry spells was largest over the most drought-sensitive region. Seasonal forecasts have potential to be used in a probabilistic forecast system for drought-sensitive crops, though these should be used with caution given the large uncertainties.

scholarly journals Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin

2015 ◽  
Vol 19 (6) ◽  
pp. 2577-2586 ◽  
Author(s):  
F. Wetterhall ◽  
H. C. Winsemius ◽  
E. Dutra ◽  
M. Werner ◽  
E. Pappenberger
Keyword(s):  
Meteorological Drought ◽  
Reanalysis Data ◽  
Lead Times ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Forecast System ◽  
Quantile Mapping ◽  
Dry Spells ◽  
Precipitation Threshold ◽  
Staple Crop

Abstract. The rainfall in southern Africa has a large inter-annual variability, which can cause rain-fed agriculture to fail. The staple crop maize is especially sensitive to dry spells during the early growing season. An early prediction of the probability of dry spells and below normal precipitation can potentially mitigate damages through water management. This paper investigates how well ECMWF's seasonal forecasts predict dry spells over the Limpopo basin during the rainy season December–February (DJF) with lead times from 0 to 4 months. The seasonal forecasts were evaluated against ERA-Interim reanalysis data, which in turn were corrected with GPCP (EGPCP) to match monthly precipitation totals. The seasonal forecasts were also bias-corrected with the EGPCP using quantile mapping as well as post-processed using a precipitation threshold to define a dry day. The results indicate that the forecasts show skill in predicting dry spells in comparison with a climatological ensemble based on previous years. Quantile mapping in combination with a precipitation threshold improved the skill of the forecast. The skill in prediction of dry spells was largest over the most drought-sensitive region. Seasonal forecasts have the potential to be used in a probabilistic forecast system for drought-sensitive crops, though these should be used with caution given the large uncertainties.

scholarly journals Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China?

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2010
Author(s):  
Yang Lang ◽  
Lifeng Luo ◽  
Aizhong Ye ◽  
Qingyun Duan
Keyword(s):  
Mainland China ◽  
Meteorological Drought ◽  
Lead Times ◽  
Climate Forecast ◽  
Seasonal Forecasts ◽  
Dynamical Models ◽  
Standard Precipitation Index ◽  
Forecast System ◽  
Climate Forecast System ◽  
Drought Prediction

Seasonal forecasts from dynamical models are expected to be useful for drought predictions in many regions. This study investigated the usefulness of the Climate Forecast System version 2 (CFSv2) in improving meteorological drought prediction in China based on its 25-year reforecast. The six-month standard precipitation index (SPI6) was used as the drought indicator, and its persistence forecast served as the benchmark against which CFSv2 forecasts were evaluated. The analysis found that the SPI6 persistence forecast shows good skills in all regions at short lead times, and CFSv2 forecast can further improve those skills in most regions. The improvement is particularly pronounced at longer lead times and over the humid regions in the southeast. This study also examined the seasonality and regionality of persistence forecast skills and CFSv2 contributions, and reveals regions where CFSv2 forecast shows no or sometimes even negative contributions.

scholarly journals Seasonal drought prediction for semiarid northeastern Brazil: verification of six hydro-meteorological forecast products

2018 ◽  
Vol 22 (9) ◽  
pp. 5041-5056 ◽  
Author(s):  
José Miguel Delgado ◽  
Sebastian Voss ◽  
Gerd Bürger ◽  
Klaus Vormoor ◽  
Aline Murawski ◽  
...  
Keyword(s):  
Rainy Season ◽  
Meteorological Drought ◽  
Skill Score ◽  
Hydrological Drought ◽  
Northeastern Brazil ◽  
Drought Indices ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Seasonal Drought ◽  
Drought Prediction

Abstract. A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.

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 Drought Prediction for Semiarid Northeast Brazil: Verification of Six Hydro-Meteorological Forecast Products

2017 ◽  
Author(s):  
José Miguel Delgado ◽  
Sebastian Voss ◽  
Gerd Bürger ◽  
Klaus Vormoor ◽  
Aline Murawski ◽  
...  
Keyword(s):  
Rainy Season ◽  
Meteorological Drought ◽  
Skill Score ◽  
Hydrological Drought ◽  
Drought Indices ◽  
Monthly Precipitation ◽  
Northeast Brazil ◽  
Seasonal Forecasts ◽  
Seasonal Drought ◽  
Drought Prediction

Abstract. A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeast Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a generalized linear model to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE) and the relative operating characteristic (ROC) skill score. Forecasts of monthly precipitation had little or no skill considering RMSE. Still, the forecast of extreme events of low monthly precipitation showed skill for the rainy season (ROC skill score of 0.24 to 0.33). A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and significant skill when forecasting drought events of e.g. SPEI01 (ROC skill score of 0.53 to 0.61). Similar results were obtained for low regional reservoir storage forecasts. Regarding the skill in the forecasted months, it was greater for April, when compared to February and March (the remaining months of the rainy season). This work showed that a multimodel ensemble can forecast drought events of time scales relevant to water managers in northeast Brazil with skill. But no or little skill could be found in the forecasts of the whole range of monthly precipitation or drought indices (e.g. forecasting average years). Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeast Brazil.

Seasonal forecasting of tuna habitat for dynamic spatial management

2011 ◽  
Vol 68 (5) ◽  
pp. 898-911 ◽  
Author(s):  
Alistair J. Hobday ◽  
Jason R. Hartog ◽  
Claire M. Spillman ◽  
Oscar Alves
Keyword(s):  
Fishing Effort ◽  
Reanalysis Data ◽  
Habitat Model ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Ocean Reanalysis ◽  
Ocean Atmosphere ◽  
Southern Bluefin Tuna ◽  
Atmosphere Model ◽  
Habitat Boundaries

Capture of the target, bycatch, and protected species in fisheries is often regulated through spatial measures that partition fishing effort, including areal closures. In eastern Australian waters, southern bluefin tuna (SBT, Thunnus maccoyii ) are a quota-limited species in a multispecies longline fishery; minimizing capture by nonquota holders is an important management concern. A habitat preference model (conditioned with electronic tag data) coupled with ocean reanalysis data has been used since 2003 to generate real-time predicted maps of SBT distribution (nowcasts). These maps are used by fishery managers to restrict fisher access to areas with high predicted SBT distribution. Here we use the coupled ocean–atmosphere model, POAMA (predictive ocean atmosphere model for Australia), and a habitat model to forecast SBT distribution at lead times of up to 4 months. These forecasts are comparable with nowcasts derived from the operational system, and show skill in predicting SBT habitat boundaries out to lead-times of 3–4 months. For this fishery, seasonal forecasts can provide managers and fishers with valuable insights into future habitat distributions for the upcoming months, to better inform operational decisions.

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 Seasonal Rainfall Forecasts for the Yangtze River Basin in the Extreme Summer of 2020

2021 ◽  
Author(s):  
Philip E. Bett ◽  
Gill M. Martin ◽  
Nick Dunstone ◽  
Adam A. Scaife ◽  
Hazel E. Thornton ◽  
...  
Keyword(s):  
Linear Regression ◽  
River Basin ◽  
Yangtze River ◽  
Asian Summer Monsoon ◽  
Yangtze River Basin ◽  
Early Summer ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
June July ◽  
The Impact

AbstractSeasonal forecasts for Yangtze River basin rainfall in June, May–June–July (MJJ), and June–July–August (JJA) 2020 are presented, based on the Met Office GloSea5 system. The three-month forecasts are based on dynamical predictions of an East Asian Summer Monsoon (EASM) index, which is transformed into regional-mean rainfall through linear regression. The June rainfall forecasts for the middle/lower Yangtze River basin are based on linear regression of precipitation. The forecasts verify well in terms of giving strong, consistent predictions of above-average rainfall at lead times of at least three months. However, the Yangtze region was subject to exceptionally heavy rainfall throughout the summer period, leading to observed values that lie outside the 95% prediction intervals of the three-month forecasts. The forecasts presented here are consistent with other studies of the 2020 EASM rainfall, whereby the enhanced mei-yu front in early summer is skillfully forecast, but the impact of midlatitude drivers enhancing the rainfall in later summer is not captured. This case study demonstrates both the utility of probabilistic seasonal forecasts for the Yangtze region and the potential limitations in anticipating complex extreme events driven by a combination of coincident factors.

Evaluation of Precipitation Forecast from Global Forecast System Over Transboundary Rivers in Africa

2021 ◽  
Author(s):  
Haowen Yue ◽  
Mekonnen Gebremichael
Keyword(s):  
Correlation Coefficients ◽  
Precipitation Forecast ◽  
Nile River ◽  
Lead Times ◽  
General Tendency ◽  
Global Forecast System ◽  
Forecast System ◽  
Transboundary River Basins ◽  
Unbiased Estimates ◽  
Good Agreement

<p>This study evaluates the short-to-medium range precipitation forecasts from Global Forecast System for 14 major transboundary river basins in Africa against GPM IMERG “Early”, IMERG “Final”, and CHIRPSv2 products. Daily precipitation forecasts with lead times of 1-day, 5-day, 10-day, and 15-day and accumulated precipitation forecasts with periods of 1-day, 5-day, 10-day, and 15-day are investigated. The 14 selected basins are (1) Senegal; (2) Volta; (3) Niger; (4) Chad; (5) Nile; (6) Awash; (7) Congo; (8) Omo Gibe; (9) Tana; (10) Pangani; (11) Zambezi; (12) Okavango; (13) Limpopo and (14) Orange. For each basin, several sub-basins are defined by the major dams in the basin. Our preliminary results in the Nile river basin show that in terms of temporal variability, there was a good agreement between the forecasted and observed accumulated precipitation on a 15-day basis. When compared to IMERG “Final”, the correlation coefficients of accumulated GFS forecasts scored as high as 0.75. Thus, GFS products provide relatively reliable accumulated precipitation forecasts. However, the precipitation forecasts were mostly biased: they tend to overpredict rainfall for the eastern part of the Nile river, underestimate rainfall for the northern part of the Nile river and produce almost unbiased estimates for the southern part of the river. Additionally, GFS forecasts have a general tendency to underpredict the area of precipitation across the Nile basin. Although the performance of GFS varies at different locations, the GFS precipitation forecasts can be a good reference to dam operators in Africa. </p>

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