Probabilistic forecasts of the onset of the rainy season using global seasonal forecasts

2020 ◽  
Author(s):  
Manuel Rauch ◽  
Jan Bliefernicht ◽  
Patrick Laux ◽  
Seyni Salack ◽  
Moussa Waongo ◽  
...  
Keyword(s):  
West Africa ◽  
Rainy Season ◽  
Late Onset ◽  
Fuzzy Rule ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Economic Sectors ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
The Common

<p>Seasonal forecasts for monsoonal rainfall characteristics like the onset of the rainy season (ORS) are crucial in semi-arid regions to better support decision-making in water resources management, rain-fed agriculture and other socio-economic sectors. However, forecasts for these variables are rarely produced by weather services in a quantitative way. To overcome this problem, we developed an approach for seasonal forecasting of the ORS using global seasonal forecasts. The approach is not computationally intensive and is therefore operational applicable for forecasting centers in developing countries. It consists of a quantile-quantile-transformation for eliminating systematic differences between ensemble forecasts and observations, a fuzzy-rule based method for estimating the ORS date and a graphical method for an improved visualization of probabilistic ORS forecasts, called the onset of the rainy season index (ORSI). The performance of the approach is evaluated from 2000 to 2010 for several climate zones (Sahel, Sudan and Guinean zone) in West Africa, using hindcasts from the Seasonal Forecasting System 4 of ECMWF. Our studies show that seasonal ORS forecasts can be skillful for individual years and specific regions like the Guinean coasts, but also associated with large uncertainties, in particular for longer lead times. The spatial verification of the ORS fields emphasizes the importance of selecting appropriate performance measures to avoid an overestimation of the forecast skill. The ORSI delivers crucial information about an early, mean and late onset of the rainy season and it is much easier to interpret for users compared to the common categorical formats used in seasonal forecasting. Moreover, the new index can be transferred to other seasonal forecast variables, providing an important alternative to the common forecast formats used in seasonal forecasting. In this presentation we show (i) the operational practice of seasonal forecasting of ORS and other monsoonal precipitation characteristics, (ii) the methodology and results of the new ORS approach published in Rauch et al. (2019) and (iii) first results of an advanced statistical algorithm using ECMW-SYS5 hindcasts over a period of 30 years (1981-2010) in combination with an improved observational database.</p><p>Rauch, M., Bliefernicht, J., Laux, P., Salack, S., Waongo, M., & Kunstmann, H. (2019). Seasonal forecasting of the onset of the rainy season in West Africa. Atmosphere, 10(9), 528.</p>

scholarly journals Seasonal Forecasting of the Onset of the Rainy Season in West Africa

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 528 ◽  
Author(s):  
Manuel Rauch ◽  
Jan Bliefernicht ◽  
Patrick Laux ◽  
Seyni Salack ◽  
Moussa Waongo ◽  
...  
Keyword(s):  
West Africa ◽  
Fuzzy Rule ◽  
Seasonal Forecasting ◽  
Ensemble Prediction ◽  
Graphical Methods ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Anomaly Correlation Coefficient ◽  
Computationally Intensive

Seasonal forecasts for monsoonal rainfall characteristics like the onset of the rainy seasons (ORS) are crucial for national weather services in semi-arid regions to better support decision-making in rain-fed agriculture. In this study an approach for seasonal forecasting of the ORS is proposed using precipitation information from a global seasonal ensemble prediction system. It consists of a quantile–quantile-transformation for eliminating systematic differences between ensemble forecasts and observations, a fuzzy-rule based method for estimating the ORS date and graphical methods for an improved visualization of probabilistic ORS forecasts. The performance of the approach is tested for several climate zones (the Sahel, Sudan and Guinean zone) in West Africa for a period of eleven years (2000 to 2010), using hindcasts from the Seasonal Forecasting System 4 of ECMWF. We indicated that seasonal ORS forecasts can be skillful for individual years and specific regions (e.g., the Guinean coasts), but also associated with large uncertainties. A spatial verification of the ORS fields emphasizes the importance of selecting appropriate performance measures (e.g., the anomaly correlation coefficient) to avoid an overestimation of the forecast skill. The graphical methods consist of several common formats used in seasonal forecasting and a new index-based method for a quicker interpretation of probabilistic ORS forecast. The new index can also be applied to other seasonal forecast variables, providing an important alternative to the common forecast formats used in seasonal forecasting. Moreover, the forecasting approach proposed in this study is not computationally intensive and is therefore operational applicable for forecasting centers in tropical and subtropical regions where computing power and bandwidth are often limited.

scholarly journals REVIEW OF SEASONAL CLIMATE FORECASTING FOR AGRICULTURE IN SUB-SAHARAN AFRICA

2011 ◽  
Vol 47 (2) ◽  
pp. 205-240 ◽  
Author(s):  
JAMES W. HANSEN ◽  
SIMON J. MASON ◽  
LIQIANG SUN ◽  
ARAME TALL
Keyword(s):  
Smallholder Farmers ◽  
Seasonal Forecast ◽  
Sub Saharan Africa ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Seasonal Climate ◽  
Climate Forecasting ◽  
Probabilistic Forecasts ◽  
Sub Saharan

SUMMARYWe review the use and value of seasonal climate forecasting for agriculture in sub-Saharan Africa (SSA), with a view to understanding and exploiting opportunities to realize more of its potential benefits. Interaction between the atmosphere and underlying oceans provides the basis for probabilistic forecasts of climate conditions at a seasonal lead-time, including during cropping seasons in parts of SSA. Regional climate outlook forums (RCOF) and national meteorological services (NMS) have been at the forefront of efforts to provide forecast information for agriculture. A survey showed that African NMS often go well beyond the RCOF process to improve seasonal forecast information and disseminate it to the agricultural sector. Evidence from a combination of understanding of how climatic uncertainty impacts agriculture, model-based ex-ante analyses, subjective expressions of demand or value, and the few well-documented evaluations of actual use and resulting benefit suggests that seasonal forecasts may have considerable potential to improve agricultural management and rural livelihoods. However, constraints related to legitimacy, salience, access, understanding, capacity to respond and data scarcity have so far limited the widespread use and benefit from seasonal prediction among smallholder farmers. Those constraints that reflect inadequate information products, policies or institutional process can potentially be overcome. Additional opportunities to benefit rural communities come from expanding the use of seasonal forecast information for coordinating input and credit supply, food crisis management, trade and agricultural insurance. The surge of activity surrounding seasonal forecasting in SSA following the 1997/98 El Niño has waned in recent years, but emerging initiatives, such as the Global Framework for Climate Services and ClimDev-Africa, are poised to reinvigorate support for seasonal forecast information services for agriculture. We conclude with a discussion of institutional and policy changes that we believe will greatly enhance the benefits of seasonal forecasting to agriculture in SSA.

Using seasonal forecast information to strengthen resilience and improve food security in Niger River Basin

2020 ◽  
Author(s):  
Bernard Minoungou ◽  
Jafet Andersson ◽  
Abdou Ali ◽  
Mohamed Hamatan
Keyword(s):  
Food Security ◽  
West Africa ◽  
River Basin ◽  
Rainy Season ◽  
Hydrological Model ◽  
Seasonal Forecast ◽  
Reference Period ◽  
Seasonal Forecasts ◽  
Niger River

<p>The rainy season occupies a central place in socio-economic activities in the Sahelian regions, as more than 80% of the population lives on agriculture and livestock. However, extreme hydroclimatic events such as droughts and floods affect these activities. Efforts made in recent years in the production of hydroclimatic information to enhance the resilience of populations have become insufficient, given the variability and climate change.</p><p>In this context, we have conducted a study on improving the quality of seasonal forecast information to strengthen the resilience and improve the food security in West Africa, especially on the Niger River Basin. We used seasonal climate forecasts and the HYPE hydrological model to predict some characteristics of the rainy season in West Africa. The ECMWF seasonal forecast ensemble (system 5) from 1993 to 2015 (hindcast) and 2018 (forecast), available in the Climate Data Store (CDS) catalogue were used. The climatic variables considered are daily precipitation, mean and extreme temperatures (minimum and maximum) at the seasonal scale. The main objective was to assess the ability of the HYPE hydrological model, developed by Swedish Meteorological and Hydrological Institue, to predict runoff over the historical period and to produce hydrological seasonal forecasts for next years.</p><p>The main season’s characteristics produced are: (i) cumulative rainfall map for the rainy season (May to November), (ii) the rainfall situation of the season (above, near or below normal considering 1993-2015 as reference period), (iii) hydrological situation of the season (above, near or below normal considering 1993-2015 as reference period), (v) graph of the mean seasonal streamflow over the Niger Basin compared to the reference period (1993-2015).</p><p>The predictability of 2018 hydrological seasonal products were assessed and the results are promising. The main challenges we faced were the initialisation of the model, the bias correction (the reference data to be considered and the appropriate method). Further research on these topics should continue to improve the quality of results.</p>

scholarly journals Seasonal Ensemble Forecasts: Are Recalibrated Single Models Better than Multimodels?

2009 ◽  
Vol 137 (4) ◽  
pp. 1460-1479 ◽  
Author(s):  
Andreas P. Weigel ◽  
Mark A. Liniger ◽  
Christof Appenzeller
Keyword(s):  
Ensemble Prediction ◽  
Model Errors ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Near Surface ◽  
Temperature And Precipitation ◽  
Probabilistic Forecasts ◽  
The Difference

Abstract Multimodel ensemble combination (MMEC) has become an accepted technique to improve probabilistic forecasts from short- to long-range time scales. MMEC techniques typically widen ensemble spread, thus improving the dispersion characteristics and the reliability of the forecasts. This raises the question as to whether the same effect could be achieved in a potentially cheaper way by rescaling single model ensemble forecasts a posteriori such that they become reliable. In this study a climate conserving recalibration (CCR) technique is derived and compared with MMEC. With a simple stochastic toy model it is shown that both CCR and MMEC successfully improve forecast reliability. The difference between these two methods is that CCR conserves resolution but inevitably dilutes the potentially predictable signal while MMEC is in the ideal case able to fully retain the predictable signal and to improve resolution. Therefore, MMEC is conceptually to be preferred, particularly since the effect of CCR depends on the length of the data record and on distributional assumptions. In reality, however, multimodels consist only of a finite number of participating single models, and the model errors are often correlated. Under such conditions, and depending on the skill metric applied, CCR-corrected single models can on average have comparable skill as multimodel ensembles, particularly when the potential model predictability is low. Using seasonal near-surface temperature and precipitation forecasts of three models of the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) dataset, it is shown that the conclusions drawn from the toy-model experiments hold equally in a real multimodel ensemble prediction system. All in all, it is not possible to make a general statement on whether CCR or MMEC is the better method. Rather it seems that optimum forecasts can be obtained by a combination of both methods, but only if first MMEC and then CCR is applied. The opposite order—first CCR, then MMEC—is shown to be of only little effect, at least in the context of seasonal forecasts.

scholarly journals Estimating the Potential Economic Value of Seasonal Forecasts in West Africa: A Long-Term Ex-Ante Assessment in Senegal

2010 ◽  
Vol 2 (1) ◽  
pp. 69-87 ◽  
Author(s):  
Benjamin Sultan ◽  
Bruno Barbier ◽  
Jeanne Fortilus ◽  
Serigne Modou Mbaye ◽  
Grégoire Leclerc
Keyword(s):  
West Africa ◽  
Rainy Season ◽  
Dynamic Models ◽  
Economic Benefits ◽  
Semiarid Region ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Rainfall Prediction ◽  
Smallholder Farm ◽  
Potential Benefits

Abstract Recent improvements in the capability of statistical or dynamic models to predict climate fluctuations several months in advance may be an opportunity to improve the management of climatic risk in rain-fed agriculture. The aim of this paper is to evaluate the potential benefits that seasonal climate predictions can bring to farmers in West Africa. The authors have developed an archetypal bioeconomic model of a smallholder farm in Nioro du Rip, a semiarid region of Senegal. The model is used to simulate the decisions of farmers who have access to a priori information on the quality of the next rainy season. First, the potential economic benefits of a perfect rainfall prediction scheme are evaluated, showing how these benefits are affected by forecast accuracy. Then, the potential benefits of several widely used rainfall prediction schemes are evaluated: one group of schemes based on the statistical relationship between rainfall and sea surface temperatures, and one group based on the predictions of coupled ocean–atmosphere models. The results show that forecasting a dryer than average rainy season would be the most useful to Nioro du Rip farmers if they interpret forecasts as deterministic. Indeed, because forecasts are imperfect, predicting a wetter than average rainy season exposes the farmers to a high risk of failure by favoring cash crops such as maize and peanut that are highly vulnerable to drought. On the other hand, the farmers’ response to a forecast of a dryer than average rainy season minimizes the climate risk by favoring robust crops such as millet and sorghum, which will tolerate higher rainfall in case the forecast is wrong. When either statistical or dynamic climate models are used for forecasting under the same lead time and the same 31-yr hindcast period (i.e., 1970–2000), similar skill and economic values at farm level are found. When a dryer than average rainy season is predicted, both methods yield an increase of the farmers’ income—13.8% for the statistical model and 9.6% for the bias-corrected Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) multimodel ensemble mean.

Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables Using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar Data Assimilation

2017 ◽  
Vol 145 (6) ◽  
pp. 2257-2279 ◽  
Author(s):  
Bryan J. Putnam ◽  
Ming Xue ◽  
Youngsun Jung ◽  
Nathan A. Snook ◽  
Guifu Zhang
Keyword(s):  
Mesoscale Convective System ◽  
Radar Data ◽  
Convective Precipitation ◽  
Convective System ◽  
Ensemble Forecasts ◽  
Verification Methods ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective ◽  
Single Moment ◽  
Double Moment

Abstract Ensemble-based probabilistic forecasts are performed for a mesoscale convective system (MCS) that occurred over Oklahoma on 8–9 May 2007, initialized from ensemble Kalman filter analyses using multinetwork radar data and different microphysics schemes. Two experiments are conducted, using either a single-moment or double-moment microphysics scheme during the 1-h-long assimilation period and in subsequent 3-h ensemble forecasts. Qualitative and quantitative verifications are performed on the ensemble forecasts, including probabilistic skill scores. The predicted dual-polarization (dual-pol) radar variables and their probabilistic forecasts are also evaluated against available dual-pol radar observations, and discussed in relation to predicted microphysical states and structures. Evaluation of predicted reflectivity (Z) fields shows that the double-moment ensemble predicts the precipitation coverage of the leading convective line and stratiform precipitation regions of the MCS with higher probabilities throughout the forecast period compared to the single-moment ensemble. In terms of the simulated differential reflectivity (ZDR) and specific differential phase (KDP) fields, the double-moment ensemble compares more realistically to the observations and better distinguishes the stratiform and convective precipitation regions. The ZDR from individual ensemble members indicates better raindrop size sorting along the leading convective line in the double-moment ensemble. Various commonly used ensemble forecast verification methods are examined for the prediction of dual-pol variables. The results demonstrate the challenges associated with verifying predicted dual-pol fields that can vary significantly in value over small distances. Several microphysics biases are noted with the help of simulated dual-pol variables, such as substantial overprediction of KDP values in the single-moment ensemble.

Can Farmers of Burkina Faso Use Seasonal Rainfall Forecasts?

2000 ◽  
Vol 22 (4) ◽  
pp. 24-28 ◽  
Author(s):  
Carla Roncoli ◽  
Keith Ingram ◽  
Paul Kirshen
Keyword(s):  
West Africa ◽  
Burkina Faso ◽  
Agricultural Production ◽  
Scientific Information ◽  
Seasonal Rainfall ◽  
Seasonal Forecasts ◽  
Recent Event ◽  
Livelihood Security ◽  
Research Findings ◽  
Seasonal Rainfall Forecasts

In this article we bring anthropological reflections to bear on a recent event we participated in, whereby farmers and scientists came together to discuss the possibility of applying rainfall seasonal forecasts to improve agricultural production and livelihood security in West Africa. In so doing, We also report on the research findings from the project that organized this encounter and that we have been working with for the last three years. Our intent is to highlight the complexities and challenges inherent in this process of integrating scientific information and farmers' production decisions, while also pointing to practical issues to be considered in implementing such initiatives.

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 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 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.

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