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

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.

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

10.5194/egusphere-egu2020-677 ◽

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

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.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.

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

Monthly Weather Review ◽

10.1175/2008mwr2773.1 ◽

2009 ◽

Vol 137 (4) ◽

pp. 1460-1479 ◽

Cited By ~ 45

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.

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A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

Weather and Forecasting ◽

10.1175/2008waf2222140.1 ◽

2009 ◽

Vol 24 (3) ◽

pp. 812-828 ◽

Cited By ~ 33

Author(s):

Young-Mi Min ◽

Vladimir N. Kryjov ◽

Chung-Kyu Park

Keyword(s):

Real Time ◽

Gaussian Approximation ◽

Seasonal Prediction ◽

Asia Pacific ◽

Ensemble Prediction ◽

Prediction System ◽

Sampling Errors ◽

Seasonal Forecasts ◽

Multimodel Ensemble ◽

Ensemble Prediction System

Abstract A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system that combines a large number of models, with individual model ensembles essentially differing in size and model weights in the forecast and hindcast datasets being inconsistent. Justification for the use of a Gaussian approximation of the precipitation probability distribution function for global forecasts is also provided. PMME retrospective and real-time forecasts are assessed. For above normal and below normal categories, temperature forecasts outperform climatology for a large part of the globe. Precipitation forecasts are definitely more skillful than random guessing for the extratropics and climatological forecasts for the tropics. The skill of real-time forecasts lies within the range of the interannual variability of the historical forecasts.

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Forest-Based and Semiparametric Methods for the Postprocessing of Rainfall Ensemble Forecasting

Weather and Forecasting ◽

10.1175/waf-d-18-0149.1 ◽

2019 ◽

Vol 34 (3) ◽

pp. 617-634 ◽

Cited By ~ 8

Author(s):

Maxime Taillardat ◽

Anne-Laure Fougères ◽

Philippe Naveau ◽

Olivier Mestre

Keyword(s):

Heavy Rainfall ◽

Hybrid Methods ◽

Ensemble Prediction ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Wide Range ◽

Selection Step ◽

Heavy Tailed ◽

Ensemble Model Output Statistics ◽

Model Output Statistics

Abstract To satisfy a wide range of end users, rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We introduce local statistical postprocessing methods based on quantile regression forests and gradient forests with a semiparametric extension for heavy-tailed distributions. These hybrid methods make use of the forest-based outputs to fit a parametric distribution that is suitable to model jointly low, medium, and heavy rainfall intensities. Our goal is to improve ensemble quality and value for all rainfall intensities. The proposed methods are applied to daily 51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over France using the Météo-France ensemble prediction system called Prévision d’Ensemble ARPEGE (PEARP). They are verified with a cross-validation strategy and compete favorably with state-of-the-art methods like analog ensemble or ensemble model output statistics. Our methods do not assume any parametric links between the variables to calibrate and possible covariates. They do not require any variable selection step and can make use of more than 60 predictors available such as summary statistics on the raw ensemble, deterministic forecasts of other parameters of interest, or probabilities of convective rainfall. In addition to improvements in overall performance, hybrid forest-based procedures produced the largest skill improvements for forecasting heavy rainfall events.

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Increasing the horizontal resolution of ensemble forecasts at CMC

Nonlinear Processes in Geophysics ◽

10.5194/npg-10-463-2003 ◽

2003 ◽

Vol 10 (6) ◽

pp. 463-468 ◽

Cited By ~ 38

Author(s):

G. Pellerin ◽

L. Lefaivre ◽

P. Houtekamer ◽

C. Girard

Keyword(s):

Operating Characteristic ◽

Horizontal Resolution ◽

Ensemble Prediction ◽

Ensemble Size ◽

Ensemble Forecasts ◽

Relative Operating Characteristic ◽

Sea Level Pressure ◽

Ensemble Prediction System ◽

Probability Of Precipitation ◽

Model Approach

Abstract. Ensemble forecasts are run operationally since February 1998 at the Canadian Meteorological Centre, with outputs up to ten days. The ensemble size was increased from eight to sixteen members in August 1999. The method of producing the perturbed analyses consists of running independent assimilation cycles that use perturbed sets of observations and are driven by eight different models, mainly different in their physical parameterizations. Perturbed analyses are doubled by taking opposite pairs. A multi-model approach is then used to obtain the forecasts. The ensemble output has been used to generate several products. In view of increasing computing facilities, the ensemble prediction system horizontal resolution was increased to TL149 in June 2001. Heights at 500 hPa and mean sea-level pressure maps are regularly used. Charts of precipitation with the probability of precipitation being above various thresholds are also produced at each run. The probabilistic forecast of the 24-h accumulated precipitation has shown skill as demonstrated by the relative operating characteristic (ROC). Verifications of the ensemble forecasts will be presented.

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Wave Extremes in the Northeast Atlantic from Ensemble Forecasts

Journal of Climate ◽

10.1175/jcli-d-12-00738.1 ◽

2013 ◽

Vol 26 (19) ◽

pp. 7525-7540 ◽

Cited By ~ 25

Author(s):

Øyvind Breivik ◽

Ole Johan Aarnes ◽

Jean-Raymond Bidlot ◽

Ana Carrasco ◽

Øyvind Saetra

Keyword(s):

Ensemble Prediction ◽

Lead Times ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Northeast Atlantic ◽

The North ◽

Time Period ◽

The North Sea ◽

Mean And Variance ◽

Buoy Data

Abstract A method for estimating return values from ensembles of forecasts at advanced lead times is presented. Return values of significant wave height in the northeast Atlantic, the Norwegian Sea, and the North Sea are computed from archived +240-h forecasts of the ECMWF Ensemble Prediction System (EPS) from 1999 to 2009. Three assumptions are made: First, each forecast is representative of a 6-h interval and collectively the dataset is then comparable to a time period of 226 years. Second, the model climate matches the observed distribution, which is confirmed by comparing with buoy data. Third, the ensemble members are sufficiently uncorrelated to be considered independent realizations of the model climate. Anomaly correlations of 0.20 are found, but peak events (>P97) are entirely uncorrelated. By comparing return values from individual members with return values of subsamples of the dataset it is also found that the estimates follow the same distribution and appear unaffected by correlations in the ensemble. The annual mean and variance over the 11-yr archived period exhibit no significant departures from stationarity compared with a recent reforecast; that is, there is no spurious trend because of model upgrades. The EPS yields significantly higher return values than the 40-yr ECMWF Re-Analysis (ERA-40) and ECMWF Interim Re-Analysis (ERA-Interim) and is in good agreement with the high-resolution 10-km Norwegian Reanalyses (NORA10) hindcast, except in the lee of unresolved islands where EPS overestimates and in enclosed seas where it has low bias. Confidence intervals are half the width of those found for ERA-Interim because of the magnitude of the dataset.

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The Soverato flood in Southern Italy: performance of global and limited-area ensemble forecasts

Nonlinear Processes in Geophysics ◽

10.5194/npg-10-261-2003 ◽

2003 ◽

Vol 10 (3) ◽

pp. 261-274 ◽

Cited By ~ 16

Author(s):

A. Montani ◽

C. Marsigli ◽

F. Nerozzi ◽

T. Paccagnella ◽

S. Tibaldi ◽

...

Keyword(s):

High Resolution ◽

Southern Italy ◽

Ensemble Prediction ◽

Limited Area ◽

Prediction System ◽

Probabilistic Prediction ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Limited Area Model ◽

Area Model

Abstract. The predictability of the flood event affecting Soverato (Southern Italy) in September 2000 is investigated by considering three different configurations of ECMWF ensemble: the operational Ensemble Prediction System (EPS), the targeted EPS and a high-resolution version of EPS. For each configuration, three successive runs of ECMWF ensemble with the same verification time are grouped together so as to generate a highly-populated "super-ensemble". Then, five members are selected from the super-ensemble and used to provide initial and boundary conditions for the integrations with a limited-area model, whose runs generate a Limited-area Ensemble Prediction System (LEPS). The relative impact of targeting the initial perturbations against increasing the horizontal resolution is assessed for the global ensembles as well as for the properties transferred to LEPS integrations, the attention being focussed on the probabilistic prediction of rainfall over a localised area. At the 108, 84 and 60- hour forecast ranges, the overall performance of the global ensembles is not particularly accurate and the best results are obtained by the high-resolution version of EPS. The LEPS performance is very satisfactory in all configurations and the rainfall maps show probability peaks in the correct regions. LEPS products would have been of great assistance to issue flood risk alerts on the basis of limited-area ensemble forecasts. For the 60-hour forecast range, the sensitivity of the results to the LEPS ensemble size is discussed by comparing a 5-member against a 51-member LEPS, where the limited-area model is nested on all EPS members. Little sensitivity is found as concerns the detection of the regions most likely affected by heavy precipitation, the probability peaks being approximately the same in both configurations.

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Predictions of 2010’s Tropical Cyclones Using the GFS and Ensemble-Based Data Assimilation Methods

Monthly Weather Review ◽

10.1175/mwr-d-11-00079.1 ◽

2011 ◽

Vol 139 (10) ◽

pp. 3243-3247 ◽

Cited By ~ 55

Author(s):

Thomas M. Hamill ◽

Jeffrey S. Whitaker ◽

Daryl T. Kleist ◽

Michael Fiorino ◽

Stanley G. Benjamin

Keyword(s):

Data Assimilation ◽

Three Dimensional ◽

Ensemble Prediction ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Weather Forecasts ◽

Successful Testing ◽

Gfs Model ◽

Environmental Prediction ◽

Statistical Interpolation

Abstract Experimental ensemble predictions of tropical cyclone (TC) tracks from the ensemble Kalman filter (EnKF) using the Global Forecast System (GFS) model were recently validated for the 2009 Northern Hemisphere hurricane season by Hamill et al. A similar suite of tests is described here for the 2010 season. Two major changes were made this season: 1) a reduction in the resolution of the GFS model, from 2009’s T384L64 (~31 km at 25°N) to 2010’s T254L64 (~47 km at 25°N), and some changes in model physics; and 2) the addition of a limited test of deterministic forecasts initialized from a hybrid three-dimensional variational data assimilation (3D-Var)/EnKF method. The GFS/EnKF ensembles continued to produce reduced track errors relative to operational ensemble forecasts created by the National Centers for Environmental Prediction (NCEP), the Met Office (UKMO), and the Canadian Meteorological Centre (CMC). The GFS/EnKF was not uniformly as skillful as the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system. GFS/EnKF track forecasts had slightly higher error than ECMWF at longer leads, especially in the western North Pacific, and exhibited poorer calibration between spread and error than in 2009, perhaps in part because of lower model resolution. Deterministic forecasts from the hybrid were competitive with deterministic EnKF ensemble-mean forecasts and superior in track error to those initialized from the operational variational algorithm, the Gridpoint Statistical Interpolation (GSI). Pending further successful testing, the National Oceanic and Atmospheric Administration (NOAA) intends to implement the global hybrid system operationally for data assimilation.

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The Dynamics of an Extreme Precipitation Event in Northeastern Vietnam in 2015 and Its Predictability in the ECMWF Ensemble Prediction System

Weather and Forecasting ◽

10.1175/waf-d-16-0142.1 ◽

2017 ◽

Vol 32 (3) ◽

pp. 1041-1056 ◽

Cited By ~ 5

Author(s):

Roderick van der Linden ◽

Andreas H. Fink ◽

Joaquim G. Pinto ◽

Tan Phan-Van

Keyword(s):

Large Scale ◽

Extreme Event ◽

Coastal Region ◽

Economic Loss ◽

Precipitation Event ◽

Ensemble Prediction ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Upper Level ◽

Quang Ninh

Abstract A record-breaking rainfall event occurred in northeastern Vietnam in late July–early August 2015. The coastal region in Quang Ninh Province was hit severely, with station rainfall sums in the range of 1000–1500 mm. The heavy rainfall led to flooding and landslides, which resulted in an estimated economic loss of $108 million (U.S. dollars) and 32 fatalities. Using a multitude of data sources and ECMWF ensemble forecasts, the synoptic–dynamic development and practical predictability of the event is investigated in detail for the 4-day period from 1200 UTC 25 July to 1200 UTC 29 July 2015, during which the major portion of the rainfall was observed. A slowly moving upper-level subtropical trough and the associated surface low in the northern Gulf of Tonkin promoted sustained moisture convergence and convection over northeastern Vietnam. The humidity was advected in a moisture transport band lying across the Indochina Peninsula and emanating from a tropical storm over the Bay of Bengal. Analyses of the ECMWF ensemble forecasts clearly showed a sudden emergence of the predictability of the extreme event at lead times of 3 days that was associated with the correct forecasts of the intensity and location of the subtropical trough in the 51 ensemble members. Thus, the Quang Ninh event is a good example in which the predictability of tropical convection arises from large-scale synoptic forcing; in the present case it was due to a tropical–extratropical interaction that has not been documented before for the region and season.

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Sub-seasonal Monsoon Onset forecasting over West Africa

10.5194/egusphere-egu2020-542 ◽

2020 ◽

Author(s):

Elisabeth Thompson ◽

Caroline Wainwright ◽

Linda Hirons ◽

Felipe Marques de Andrade ◽

Steven Woolnough

Keyword(s):

West Africa ◽

Lead Time ◽

Monsoon Onset ◽

Ensemble Prediction ◽

Spatial Averaging ◽

Seasonal Forecasts ◽

Research And Innovation ◽

Prediction Systems ◽

Operational Methods ◽

Programme Grant

Skilful onset forecasts are highly sought after in West Africa, due to the importance of monsoon onset for agriculture, disease prevalence and energy provision. With research on the sub-seasonal timescale bridging the gap between weather and seasonal forecasts, sub-seasonal forecasts may provide useful information in the period preceding monsoon onset. This study explores sub-seasonal monsoon onset forecasts over West Africa using three operational ensemble prediction systems (ECMWF, UKMO, and NCEP) from the Sub-seasonal to Seasonal (S2S) prediction project database in order to determine the spatial scale and lead time at which sub-seasonal forecasts can provide useful monsoon onset information. Current research and operational methods of determining onset are identified and compared. The effect of spatial averaging on onset forecasting and skill is explored by comparing regional [Coast, Forest, Transition and North] and local forecasts at 4 major cities over Ghana.&#160;&#8216;This work was supported by UK Research and Innovation as part of the Global Challenges Research Fund, African SWIFT programme, grant number NE/P021077/1&#8217;

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