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Seasonnal Forecasting in Africa J.P. Céron – Direction de la Climatologie.

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Presentation on theme: "Seasonnal Forecasting in Africa J.P. Céron – Direction de la Climatologie."— Presentation transcript:

1 Seasonnal Forecasting in Africa J.P. Céron – Direction de la Climatologie

2 The Oceanic Forcing (ENSO) Planetary influence of El Niño (on left) and La Niña (on right)

3 The RCOFs Processes Main Objectives To reduce the socio-economic vulnerabilty of countries to the impacts of Climate events, To strengthen the capacity of NMHS and their users in the domain of Long Range Forecasts and their use, To provide usefull and comprehensible products to the benefit of end-users (from making decision domain, National Authorities, Agriculture, hydrology, health domain, …).

4 Seasonnal Forecasting Process in Africa - RCOF (1) Preforum (typically a few weeks) Presentation of key points for the next rainy season, Preparation of national statistical forecasts, Capacity building activity to the benefit of NMHS and users in relationship with the general topics of Fora Sharing of experience in creating new products or improving exixting material, Forum (typically a few days) Presentation of the last informations on the climate system and its evolution, Elaboration of consensual and regional products forecasting the quality of the next rainy season (+ hydrological caracteristics), Presentation and discussion about specific topics (Agriculture, Climate forecasting, Hydrology, Climate and Health, Communication, …). Discussion on expected, desirable and/or realistic developpments,

5 Seasonnal Forecasting Process in Africa - RCOF (1) Dissemination Dissemination of products by the NMHS, and interpretation to the benefit of users including national adaptation of regional products. Update of the forecasts (Monthly base) : Continuous adaptation of the forecasts to the last available information on the climate system and its evolutions (notably update of the SST). Evaluation of Forecasts : Quality of rainfall forecasts (technical evaluation) and use of the Forecasts (Users point of view evaluation – interest and value).

6 The RCOF processes (2) Process evaluation : Pretoria meeting (16 – 20 October 2000) Sarcof (2000 – DMC Harare) Presao 5 (June Niamey)

7 The RCOF processes (3) Targetted Zones : West Africa (5 PRESAO) Central Africa (1 PRESAC) East Africa (10 GHACOF) South Africa (6 SARCOF) North Africa (PRESANOR)

8 The forecasting method Oceanic key zones Central/East Pacific (Niño 3 & 3.4 boxes), Atlantic (including the Atlantic dipole), Guinean Gulf Indian Ocean Regions depending of the features of the rainy season in Africa. Use of information coming from SST Countries divided by Zones, One model for each zone, Multiple Regression models and setpwise predictors selection, Transformation of quantitative forecast into qualitative forecast (building 3 categories using the terciles of the anomaliesdistribution), Evaluation of the quality of each model using cross validation and contengency tables (Observed categories vs Forecasted categories)

9 Africa and ENSO in summer Interannual variability

10 The Sahel and the Atlantic dipole Climatic variability Warm=wet Sahel Cool=dry Sahel Cool=wet Sahel warm=dry Sahel

11 The statistical models An exemple of National Forecast : The Congo Brazzaville

12 ZONING Iso-correlation Factor1 Brazzaville Djambala Dolisie Gamboma Impfondo Makoua Mouyondzi Makabana Mpouya Ouesso Pointe_noire Sibiti Souanke Kelle ZONE 4 ZONE 3 ZONE 2 ZONE 1 Iso-correlation Factor3

13 3 models are calibrated for the September – October – November season Statistical models Zone 1(id) = – PAC (Mai) R = R2 = 0.394Performance of the model : SKILL(3) = Zone 2(id) = – ALT (Mai) R = R2 = 0.296Performance of the model : SKILL(3) = R = 0.54 R2 = 0.292Performance of the model : SKILL(3) = 0.44 Zone 3(id) = – – ALT – EOF 07 (Août)


15 The Consensual Forecast To put together forecasts coming from each country, To take into account the complementary information coming from Numerical Models (coupled and forced), To adapt the different forecasts to the expected evolution of the climate system, to the Climate expertise of wellknown experts, …. The Result : Regional forecast (AO, AC, GH, SA) expressed as a probabilistic forecast for the 3 categories previously presented (Dry, Normal and Wet)

16 Concensual Forecast Rainfall

17 Consensual Forecast Hydrology

18 Seasonnal Forecast MAM 2002 GH

19 Seasonnal Forecast 2002 AO

20 Seasonnal Forecast SOND 2002 AC

21 Seasonnal Forecast SOND 2002 GH

22 Seasonnal Forecast OND 2002 SA

23 Seasonnal Forecast JFM 2003 SA

24 Forecast Verification A National exemple : The Niger






30 Forecast Verification A Regional exemple : South Africa

31 Forecast Verification A Regional exemple : South Africa

32 Conclusions on RCOFs A quite common way for seasonnal forecasting in Tropical regions (WMO/Clips), Processes presently evaluated and recognized as a very usefull Processes, Expected improvments for the future : Evaluation of use and value of the forecasts, Elaboration of new products usersoriented and adapted (notably but not only in term of downscaling), Consolidation and improvment of existing material (like statistical models, new predictors, mixing of informations coming from different models, … ), Strengthenning the communication toward users. …..

33 The Evaluation of RCOFs Processes What is well adressed Strengthenning of capacity building of NMHs Trainning of Climate forecasters, Forecast of the quality of the next rainy season and the discharge of the main rivers (not everywhere). What is less adressed : The use of the forecasted products (notably the improvement of products and the demonstration of the real value of the SIF), Involvement of all the partners (including national levels and financial supports), Some Scheduling problems (dates, welfare problems, …)

34 The Preforum Major problems : Some organisation problems, Use of the experience of participants (new vs experienced people), Lack of expertise for users categories, Positive points : Satistical software quite well appreciated (despite some problems for graphical aspects), Satistical methods seen as good starting base (despite the SSTs limitations),

35 The Forum Major Problems : Organisation problems (not everywhere), Lack of confidence of users and Authorities in Forums products, Positive Points : To be preserve and improve

36 The dissemination Major problems : Comprehension for, usefulness of and confidence in disseminated products (partly related to the probabilistic formulation), A few technical problems to get efficient tools for dissemination, Dissemination (sometime) only toward the National Authorities,

37 Update of the Forecasts Major problems : A few difficulties with internet capabilities, A very few feedbacks to the update from users,

38 The Evaluation Major Problems : Scheduling of evaluations (depending of the products : e.g. rain vs river discharges), A few feedbacks from users (organisation?), No evaluation on the reason why the forecast is right or wrong, Lack of feedbacks toward all the participants (e.g. Global Numerical Products Centres). No evaluation of the use and the value of Seasonal to Interannual products.

39 Conclusions and Suggestions Suggestions (for Presao) : Fix the process in a sustainable way particularly in term of resources (human, material and finances), Get, in a contractual form, the commitment of the different participants, Create a committee in charge of the follow-up of the organisation (including Planification and organisation of PRESAO sufficiently early - at least 6 month in advance), its progress report and the reporting of decided actions in the frame of Presao, Reorganize the different component of the process (notably in term of calendar and delocalisation), Install pilot studies at the national level that use seasonal forecasting, particularly but not only, in order to establish the value and the usefulness of the products,

40 Conclusions and Suggestions Suggestions (for Presao) : To organise the sharing of experiences at the regional or sub-regional level, To strengthen the users linkage (particularly to take into account present and future needs) and to insist on training of the dissemination chain of information, To write a PRESAO guide where should be explained, in a step by step way, the methods and described inputs and outputs, (without forgetting the update of the guide!), To organise an inventory at the country level devoted to the evaluation of all encountered problems in each country.

41 Some other available products (1) Numerical products from big numerical seasonnal forecasting centres (ECMWF, IRI, NCEP, MF, UKMO, JMA, …) Specific statistical products built out off Africa (UKMO, African Desk, …) Other Products Beginning and end of the rainy season (AO - Omotosho) Intraseasonnal evolution

42 Some other available products (2) Numerical products from big seasonnal numerical forecast centres (ECMWF, IRI, NCEP, MF, UKMO, JMA, …)

43 Elaboration of numerical products Direct Methods (deterministic and probabilistic products) formulation as Indices or Anomalies Model forecats compared to its own climatology Adaptation to « local » observation properties

44 Numerical products



47 Last Forecasts JFM

48 Butterfly effect (JFM Forecast)

49 Statistical products

50 Statistical products

51 Other Products Beginning of the rainy season Omotosho method using the wind shear int the lower and middle troposphere, Low-frequency evolution of rainfall, Numerical Products (Céron & Guérémy) Statistical adaptation of GCM (Mainguy, Guérémy & Céron for Kenya)

52 Some complementary products Forecasts at smaller time scales (Monthly/10 days monitoring and forecasts) Exemple of DMC (East Africa) Exemple of ACMAD (Experimental product - Kamga – West Africa)

53 Some complementary products Monthly bulletins ( DMC) Drought Severity – October 2002Rainfall anomaly – ASO

54 Some complementary products 10 days follow-up and forecasts (DMC) Drought Severity – Decade 31Forecast – Decade 33

55 Some complementary products 10 days bulletins and associated forecasts ( ACMAD)


57 Seasonnal Forecasting in Africa (additionnal) J.P. Céron – Direction de la Climatologie

58 East Africa and the SST signal

59 Southern Africa and the Indian Ocean signal Summary: For the Indian Ocean, Negative correlations (up to –0.6) exist between area-averaged rainfall in southern Africa and central equatorial Indian Ocean SSTs (Makarau, Rocha, Jury, Pathack, Mason, Zhakata, Landman). The window comprising the equator-10°S and ° E offers useful forecast guidance at 3-6 months prior to austral summer. At –9 months, rainfall is positively correlated with SSTs in the South Indian Ocean (r=+0.42 near 35 ° S, 65 ° E)

60 Southern Africa and the Atlantic signal Summary: The Atlantic Ocean correlations between Atlantic Ocean SSTs and area-averaged rainfall in Southern Africa have been relatively weak for operational usage (e.g., Walker, Pathack, Landman, Rocha, Mason, Makarau, Zhakata).Central Atlantic SST are however positively correlated with early summer rainfall over western South Africa, Namibia, Angola and DRC

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