Commission of Climatology

Transcription

1 Seasonal Forecasting Review Report TANZANIA The districts of Tanzania Content : 0. Introduction 1. Short presentation of the Met Service 2. Seasonal Forecasting (SF) system 2.1 Large Scale information (LSI) 2.2 Regional/Local information (RSI) 2.3 Products to be prepared 3. Operations 4. Methods 4.1 Generality 4.2 Downscaling 4.3 Dataset 4.4 Verification 5. Resources 6. Applications 7. Communication 8. General organisation 9. Additional information 10. Recap of the main points 11. Room for improvements / Key recommendations Annex 1 : Response of TMA to the questionnaire (provided separately) p2 p2 p3 p3 p3 p3 p4 p5 p5 p5 p6 p7 p7 p8 p8 p9 p9 p9 p12 Diffusion list : F. Lucio (GFCS Office), TMA, K. Kolli (CLW Department)), J. Patak (GFCS Office), JP Céron (CCl/ Opace 3 Co-Chair), Page 1/ 12

2 Review of the Seasonal Forecasting System operated at TMA 0. Introduction This review has the main objective to provide the status of the seasonal forecasting system in the country, especially with respect of the provision of associated Climate Services. In addition, one sizes this opportunity to propose some improvements which generally speaking should lead to design and operate a better forecasting system and deliver better associated Climate Services. The seasonal forecast and the use of its products are now quite well understood and consequently are quite often included into operational activities. The underpinning science is starting with the Large Scale Information from the Climate System ; these LSI acting as forcing which are impacting the regional and local Climates. In this respect, the seasonal forecasting review follows this framework. We start with the knowledge of the Large Scale/ Regional / Local climate drivers and their impacts. Then we document the products to be prepared before moving to the operations themselves. More details are collected on the methods used to prepare the different products including the downscaling, the different used dataset and verification as well Then we discuss on the necessary resources, the applications served by the seasonal forecast, the communication related to and the general organisation with respect of the operations and the provision of the information. Last but not least some additional information on the projects related to seasonal forecast and climate services is provided. Last, the main points founded in this review are presented at the end of the document and some recommendations as well. 1. Short presentation of the Met Service To start with, JPC was introduced to the Director of Forecasting and Director of R&D departments, the people involved into the Seasonal Forecast and he benefited of a visit of the building. In the Forecasting Department, they use several models for NWP (Arpege, ECMWF, GFS and Glosea at different resolutions). They also use WRF (and also COSMO) for a dynamical downscaling at 15km resolution. As a matter of evidence, this is the indication of good network capacities (for downloading coupling files). At this stage, there is no verification of the models yet. A daily weather forecasting conference take place inside the building with forecaster and 8 zone representatives. Like for Malawi, the Tropical Cyclones in the South-West of the Indian Ocean are impacting the country. In this respect, the linkage with La Reunion is important to highlight. TMA is involved into the SWFDP project with regional responsibilities for the dissemination of the forecasts to other countries. (to be completed about the parameters) About the Seasonal Forecasting activities, TMA participates to two COFs ; namely the SARCOF and the GHARCOF. The trainings are planned for 1 to 2 weeks prior to the COFs. They are using both statistical approaches and dynamical products. TMA usually accesses to graphic products from GPCLRF or equivalent centres (NOAA, UK, ECMWF, IRI). An In-House seasonal forecast is issued for 12 homogeneous zones for the OND season and for 11 zones for the MAM season. Note that the zones are homogeneous with respect of rainfall. Recently the GFCS project introduced smaller scales (up to the district level), especially thanks to CPT and IRI Capacity Building (thanks to a specific training made in Tanzania). In the phase 1 of the GFCS project, since 3 years, 2 districts (Kondoa and Kiteto) were targeted to benefit with the downscaled information. At the end of phase 1 there are 5 districts on which the downscaled information is provided. Page 2/ 12

3 2. Seasonal Forecasting (SF) system 2.1 Large Scale information (LSI) The LSI are quite well documented (see ICPAC, PhD works, ) ; the ENSO and the IOD are the most important climate drivers. However, IOD seems not to be used inside the downscaling models. It is also made use of the QBO while there is no knowledge of the TBO. The MJO is more used on shorter time scales (especially via the EPS). In spite of some quite advanced knowledge in seasonal forecasting, the monthly forecast is more or less unknown and so not used. This is a pity as the monthly forecast can be a very good complement to the seasonal forecast in terms of both, forecast itself and products useful for application sectors and their decisions. The problem seems at least partly related to the access to the relevant products as TMA is already accessing to centres which are providing such monthly forecasts like ECMWF or NCEP. Regarding the impact of LSI, likely some more investigations could be done (especially with respect of the atmospheric circulation) leading to potential new predictors. But in this respect, there is a need to access to reanalysis dataset (see data section). The large scale information is mostly provided via the IRI web site (data library, Map room, Global Climate Models forecasts, ). Also some information is coming from ECMWF, UK Met Office and APECC. To be noted that TMA has a login and password for the LC-LRFMME website but it seems that it is not working properly. This should be investigated quite rapidly as it should be fixed quite easily. Last, it seems that UK Met Office forecasts are not provided always timely (especially digital data). No major gaps in the LSI knowledge, especially the SSTs. Investigation of the impact of the atmospheric circulation Consolidation of the timely access to the relevant dataset Setting the concern with the login and password at the LC-LRFMME 2.2 Regional/Local information (RLSI) The RSI are mostly provided by the COFs. An In-House Seasonal Forecast for rainfall and temperature is also used ; it is based on both statistical and input from LSI/Global Circulation Models. The COFs are the main data providers especially via appropriate websites (available at TMA). In addition a specific web site was developed in the frame of the SCIPEA project (funded by DFID from UK) allowing to access to most of the necessary information. Regarding the local data, it is interesting to highlight that TMA has made a huge effort in using some gridded dataset over the region, especially for the rainfall. It is quite interesting with respect of the downscaling work and provision of information at a finer scale across all the country. No major gaps; all the relevant ingredients are in place Consolidation of the timely access to the relevant dataset 2.3 Products to be prepared Not surprisingly, the relevant information is about rainfall but sometime also temperature (for the public). For rainfall the target is the total amount of rainfall. The products are presented in maps and bulletin. The digital form can be provided on demand if necessary. The associated Lead Time is at least one month (for Seasonal Forecast) but sub-seasonal information is also expected. This later comment strengthen the comment on the monthly forecast. The onset and cessation dates are requested (a statistical model is existing). Again, it could be relevant to explore the potentiality of monthly forecasts for these products (e.g. ECMWF). In addition some GPCLRF (like Exeter) are issuing such forecasts. A TMA investigation of the Page 3/ 12

4 interest and skill of such products could be likely beneficial to the application sectors. In any case, the most relevant forecast range to be used depends on the decision made and the related decisional calendar. Their predictability (with respect of the different forecast ranges and the characteristic Lead-Time corresponding to the decisions) should be investigated. The quantities which are processed are monthly and seasonal but it should be very relevant to investigate the use of daily data. Especially the Dry Spells information is frequently asked for. Very likely this parameter should be better addressed using the EPS and the monthly forecast. Nevertheless, the number of dry or rainy days could be investigated at the Seasonal scale and could lead to some benefits for the stakeholders and their decisions at seasonal scales. For the wind the use of ERA-40 limits the possibilities. It should be better to use a more recent set of reanalysis like the ERA-Interim dataset. In this respect, there is likely some coordination to have at the regional level and such update could be supported at this regional level to the benefit of all the countries. However, one have to recognize that some difficulties to access to this information is evident including the understanding of their content (e.g. convective rainfall vs total rainfall ; ). Then if the direct forecast of wind characteristics at seasonal scales is very likely too ambitious, the large scale conditions favourable to specific wind conditions could be explored with some benefits e.g. via pressure gradient, Obviously these benefits depend a lot of the potential of use in terms of decisions and actions on the user side. Interestingly, TMA is checking the quality of the products even if it is in a subjective/qualitative approach (in relationship with the provided supports in form of maps and bulletins) No major gaps at seasonal scales Access to sub-seasonal products, especially the monthly forecasts Consolidation of the knowledge on reanalysis Investigation of the post processing of daily data (nb of dry or rainy days, ) and new categories tailored to user needs (e.g. outer quintiles, ). Investigation of the interest of monthly forecast products 3. Operations The Seasonal Forecast is operated routinely four time a year (OND, JFM, AMF and JAS). The date of issuance is after mid-month preceding the targeted season. The preparation and issuance of the seasonal forecast takes approximatively one month (including the COFs). Because of the use of the software (see method section), the system is operated manually. At this stage, one has to note that the use of software coded in «R» (like a CPT-like version in use at SWIOCOF) could facilitate the automation of at least some of the operational processes. Interestingly, the operations are described at least partly in some internal documents. This contribute to the sustainability of the operations. They are ensured by making seasonal weather forecasting capacity building every time a forecast is prepared and by making sure that the computers and packages are in place and well maintained. A Mac machine was recently procured specifically for seasonal forecasting. To be highlighted that the seasonal forecast is operated separately of the weather forecast but within the same department. And this is a very positive point in term of sustainability of the operations. Last to be noted that, without any consideration on the resources (and associated competencies), the use of the monthly forecasts could be quite easily implemented within the existing operational structure. No gaps in the current operations. Investigation of the R software and packages which could be relevant for automation of the processes Page 4/ 12

5 Investigation of the implementation of the use of the monthly forecasts within the operations. 4. Methods 4.1 Generality The used methods are both statistical and hybrid. There are two statistical methods. The first one is using lagged correlation (regression method) between the SSTs and the rainfall. The second one is using analogue years. The choice of analogue years includes criteria related to rainfall in the 4 preceding months (patterns and quantities).12 stations are used (for 12 zones). This lead to the selection of around 8 analogues for the forecast. The number of selected analogues seems correct with respect of the sampling of the uncertainty. However, it's important to remind that one has to be cautious when using them for impact interpretation. The hybrid method uses the information from 4 different models (2 from CMC and 2 from NCEP) and the CCA to build the relationship between the predicted SSTs and the rainfall (thanks to CPT). One important point to note is that different rainfall dataset are used depending of the method ; namely ERA-40, GPCP or Fewsnet dataset. This complicates the comparison of the skills between the different methods. It could be very beneficial to focus on one dataset after some crosscomparisons of the results between the different methods and the different dataset. In addition, some expert judgement are used especially for the meso-scale knowledge at the district level. The tools which are used are Systat, GeoCOF, CPT and Grads. The expertise is sometime limited for some packages (e.g. GeoCOF) and for some of the used methodologies as well. It's sometime difficult to ensure the follow-up (and consequently the update) of the software likely in relationship with Man-Power. No gaps; all the relevant elements are in place, especially the use of hybrid methods. Consolidation of the expertise with the different software, their evolution a,d the methods. Investigation of new predictors and new categories. 4.2 Downscaling The downscaled information is obtained thanks to the different gridded dataset ; namely ECMWF reanalysis (ERA-40), GPCP and Fewsnet. It is issued through empirical methods (see 4.1). The finest resolution is obtained using the Fewsnet dataset (0 1 degree of resolution). This allows to provide information even over zones where no observations are available. However, no studies demonstrated that this dataset give better results (in term of skill) than the others. Indeed, it could be possibly just seen as a spatial interpolation. However, TMA conducts a very interesting study comparing the Fewsnet dataset and the observations from the ground network. It appears that there is sometime some quite large differences. But a correction of the Fewsnet dataset (using Multiple Linear Regression) was studied and seems to give quite good results for the grid points close to the stations. The extension to all the grid points has to be introduced and could be a good solution leading to use this corrected fewsnet dataset for all the Seasonal Forecasting activities. There are different models for downscaling (see also method section), using predictors from observed SSTs or predicted informations. For observed SSTs it is made use of ECMWF information (including gridded rainfall dataset) and for predicted information, it is made use of CMC and CFS models (4 models) which are part of the GPCLRF community. In the phase 1 of the GFCS project, 2 districts (Kondoa and Kiteto) were targeted to benefit with the downscaled information. At the end of phase 1 there are currently 5 districts on which the Page 5/ 12

6 downscaled information is provided. At the district level, the probabilities are mostly given by CPT and its statistical approach. The non use of the information from the ensemble forecasts given by GPCLRF lead likely to a lost of information on the uncertainty. Some multi models approach is done by averaging the probabilities across the 4 used models downstream to CPT. Last, one founds some quite fine spatial details which are given thanks to the resolution of the gridded dataset (especially Fewsnet). Obviously, the calibration of the downscaled forecasts with respect of the application side should be investigated in the next. Such a work could be conducted in the perspective of future climate services. However, it depends on the availability of relevant dataset from the user side. One thing which remain still unclear is the reason for not extending to all districts the downscaled forecasts. Indeed, once you get the predictors timely, one doesn't need the observation for the forecast. The observation is only useful for the verification. So this limitation should be documented. At least one of the concerns (available format of the data) should be quite easy to address (transformation from a netcdf format to a CPT one or other). To be noted that TMA expresses some concerns about the combination of the information provided by different models (subjective vs objective combination). Some work started at the Commission of Climatology and Secretariat level could help for answering more clearly to this important question. One of the difficulty is that the combination depends on the predictability, the ensemble used and the probabilities which are not comparable between the dynamical climate global models and the Statistical models. However, one could work on methods allowing ti have more consistent approaches between the different methods. The question of the upscaling has not been addressed as the target is to provide the most refined information (especially in term of spatial scale) and the solution is provided by the resolution of the used gridded dataset. So there is no integrated information at the districts or at the zones level No major gaps in the downscaling process to the exception of the objective combination of the information provided by different models lacking of use of the ensemble forecasts provided by the GPCLRF Assessment of the skill of the different methods versus the different dataset and selection of the most relevant dataset Setting the concern with the use of the Fewsnet dataset (if selected) for the current forecast Provision of downscaled information to all the districts 4.3 Dataset The reference period used for SF is corresponding to the one given by the GPCLRF so All local data are quality controlled and are homogeneous with respect of the period. The Data collection (local data) is still not optimal and consequently is resource consuming. The data section is working on the estimate of missing data. There is still some questions regarding the network density with respect of the expected resolution for the downscaled information (district level some having no observations). Nevertheless, the use of a gridded dataset seems to be a good and relevant solution to overcome such a problem. Interestingly, the Fewsnet dataset is a very high density gridded dataset (0 1 resolution). But one concern is that the use of the gridded rainfall dataset for the downscaling depends on the method ; ECMWF reanalysis (ERA-40), Fewsnet, GPCP with respect of Statistical or hybrid methods. The access to the information is mostly done via the IRI web site (data library, Map room, GCM forecasts, ). But there is some lack of expertise and information for using IRI facilities. In general, the data access in digital format is still a challenge for the operations. Surprisingly, the UK Met Office forecasts are not provided timely while they have access to the SCIPEA web site. The access to the GPCLRF forecasts is mostly done via the COFs (during the training phase and/or via their dedicated web sites). In general, the access to individual GPCLRF web site is not really Page 6/ 12

7 used and is perceived as more difficult (especially though not only for digital data). Thanks to the structural organisation, the R&D is working in close relation with the application and the operations so that the access to the different dataset is common to all; they are also participating to the SF process. No major gaps in the used dataset Consolidation of the timely access to the relevant information ; especially the GPCLRF forecasts thought not only. Changing the used reanalysis dataset for a more recent one (ERA-Interim suggested). 4.4 Verification Verification on the hindcast is done thanks to CPT. So in this respect, one can assume that the scores are consistent with the SVS and that even more scores are available. The verification of the current forecast is done routinely using the PoD (with scores close to 80 % on the recent years) in the modelling lab. One concern is the natural bias of such metric with respect of the median category and the fact that it favors hedging (or cheating). So some additional metric like the RPSS could be used in order to complement this process. Note that the use of different rainfall dataset for the calibration of the models complicate the verification process (see dataset section). Regarding the verification, on the scientific side, the role of the predictability is still unclear. For the practical aspects, the use of «R» packages would be very welcome. In this respect some trainings using «R» packages (like for the SWIOCOF or the RA VI region) could be very helpful in consolidating the verification part. Last TMA is using CPT but with the limitation that it is not able to map observations in a similar way than the forecasts (for the verification of the current forecasts). Such specification could be kept in mind for the further CST developments. No gaps on the verification on the hindcast Improvement of the verification of the current forecast, especially by introducing criteria relevant for the probabilistic aspects (e.g. RPSS, ) Investigation of the possibility to replicate a training on verification, especially using R packages 5. Resources There is close to 20 people who are involved into the operations at TMA. As there is no separation between the weather and seasonal forecasts, more or less all the concerned staff is informed about the Seasonal Forecasting operations. In addition the issuance of the forecasts is conducted jointly with the weather forecasting section and the R&D section which is a very good point for the sustainability of the processes but also for the developments of the Climate Services which are undoubtedly closely related to the R&D activities within the country. The computing resources are constituted of the PCs, MLC Desktop and laptops present in the weather forecasting section. This ensure some sustainability of operations even in case of concern with a specific computing resource. However, when some computing concerns are faced, it's always take time to fix them. For human resources, the landscape is quite good especially thanks to the organisation which facilitate the maintenance of competencies and the work. No gaps at this stage Page 7/ 12

8 No specific needs to the exception of the maintenance and consolidation of the existing resources. 6. Applications The main sectors which are targeted are first the Agriculture and the Food Security, also Nutrition and Health and DRR. The products are both probabilistic and deterministic (especially with respect of the end users). A large range of stakeholders are into the landscape, from ministries, institutional organisations and also private users. The Decision Making Processes of the different stakeholders are not always well identified and known and the feedback processes are still limited.. However, it exists a NCOF where all these sectors are participating and where it is discussed of the impact of the forecasts. In general, when there is some feedback, it is positive ; the forecasts is useful for the users. While there is already some good base for addressing the users liaison and their engagement on the Climate Services topic, the liaison mechanism with the stakeholders could be improved. Indeed, it remains some questions on how to collect the evolution of needs and about a help-desk like organisation. Tropical Cyclone activity is also an interesting information especially when a high activity is predicted ; in this respect the linkage with La Reunion is important to ensure (see Seasonal Forecasting products prepared by La Reunion in the frame of the SWIOCOF). Consolidation of the NCOF, especially with respect of the feedback from the users, the follow-up of the need evolution and the understanding of the decisions made. Investigation of new products at seasonal scales (e.g. onset and cessation dates, wind, TC activity, ). Exploration of the potential of use of monthly forecast products 7. Communication For each SF, a statement is prepared and disseminated to the agriculture, extension officers, ministries, All available Media are used like Press Release, , Social Media (Twitter, Facebook) without forgetting the TV and Radio. As usual, there is some concern in the communication of the key messages with the local langages and the vocabulary. The question of the translation fo the products has still to be investigated. In this respect, the training of the intermediaries is a good and efficient solution. Indeed, intermediaries play a crucial role in explaining the information to the farmers, contributing to the update and providing some support for the communication. As expected not all users get the message or get it timely. The main reasons are the access to relevant Media like Social Media, TV or Radio. There is no real user facilities to help them into the use of the information. Nevertheless, prior to each release of the SF, the media is trained to convey the message in the best possible way. Also when possibilities to reach the users are limited, some mobile application can be used to convey the message up to the farmers There is No direct feedback mechanism, but social media is used to get some information. Some interactions with the users are in place. Nevertheless, there is no help desk like functions, FAQ, user forums, Some electronic support should be developed. Page 8/ 12

9 No help desk like functionality Consolidation of the feedback processes with the stakeholders Development of some electronic support for the stakeholders 8. General organisation In term of Mandates, TMA is the sole entity which is mandated to issue weather and climate information to the public. Consistently the mandate is extended to the provision of Climate Services. The general organisation of TMA is quite well adapted to the provision of Climate Services. Especially, one has to highlight the focus of operations into one section (Weather Forecasting) and the strong linkage with the Research and Application section. The GFCS project and the SCIPEA one are specifically dedicated to the Climate Services provision. At least in the frame of the GFCS project, a steering committee has been set up and is currently working to ensure the follow-up of the developements in Climate Services delivery. Last, a National Climate Services Forum is in place thanks to the GFCS project. So in terms of organisation, it seems that all the relevant ingredients are in place and demonstrated some capacities to provide the expected services. And some (usual) consolidation of the structure and the processes should be sufficient to stay confident for the coming years. No specific gaps in the current organisation. Consolidation of the structure and the processes (both in R&D and operations). 9. Additional information The GFCS project : Started in 2014, its Phase 1 is ending at spring 2017 and its phase 2 will start at fall this year. It has been made a huge effort in Capacity Building and trainings, including intermediaries who are implemented at the village level and who play a crucial role (cf. communication section). Also several visits on the terrain was conducted for building awareness. Trainings of key people from the stakeholder side were given. For phase 1 two districts (Kondoa and Kiteto) were pilot and the objectives were reached. The linkage with key people at the government level is in place and there is right now a good environment for Decision Making and about capacities. A National Climate Service Forum is in place. A steering committee is also working. For phase 2, it's time to start the implementation. Still some Capacity Building should be conducted, The consolidation of the results and extension to 10 districts will be the main objectives to target.. The sustainability should be reached. Note that in term of work plan that it should be relevant to start first the decided actions for improvements of the system before moving to the extension phase. Also to be noted the SCIPEA project (funded by DFID from UK) which complement the GFCS one. 10. Recap of the main points The first and very positive point is that there is very motivated staff in all sections of TMA. This is a key element for future developments. Then regarding the impact of LSI, some more investigations could be conducted (especially with respect of the atmospheric circulation). These studies could lead to a potential set of new predictors. But in this respect, one have to quote the access to a reanalysis dataset. And consequently it is recommended to use a more recent one like ERA-Interim. Interestingly, the consolidation of the knowledge on reanalysis will help also for the RSI and the products to prepare. Last the access to Page 9/ 12

10 the LC-LRFMME could be easily consolidated (if needed JPC can take an action for that). The extension of the previous studies on the circulation and climate drivers to the regional scale is very obvious. This could lead also to some new predictors and products to prepare. Regarding the products to prepare, a first work should be to investigate new categories (instead only terciles) in close liaison with the users for the relevant boundaries of the categories (which will be a compromise between the user needs and the predictability). Also the investigation of the post processing of daily data (nb of dry or rainy days, ) should allow to improve the nature of the products provided at seasonal scales to the users impacted by some specific events such as Dry Spells. The interest of monthly forecast products should be documented and could bring some additional and relevant support to the users and their decision processes, especially agriculture and the request of Onset and Cessation dates. For the wind some complementary studies are needed to demonstrate some predictability and consequently usefulness of such products (like the conditions favourable to strong wind events). For the TC activity, the Mozambique channel is one of the most difficult region for such forecast at seasonal range. So some regular access to the products prepared by La Reunion could be a reasonable start especially to demonstrate the real usefulness of such products at the seasonal scales. The operation side is quite well designed and robust. Especially the R&D and its linkage with the operations and the focus of all the operations into one single department. To get more available resources for the development of Climate Services, one way is to investigate relevant software (like R software and packages) which could be relevant for automation of the processes. Obviously, regarding the monthly forecast and the use of its products, the implementation within the operations should not be a big issue ; the access to such forecasts being the main limitation. Regarding the methods, all the relevant elements are in place, especially, thought not only, the use of hybrid methods. Nevertheless some investigation toward new predictors and the design of new categories better tailored to user needs (instead only terciles) has been already pointed out and consequently should be strongly encouraged. Complementary to the methods themselves, it is important to maintain the expertise related to the different used software and consequently to ensure the follow-up of their evolution. For the downscaling one can assume that there are no major gaps in the downscaling process to the exception of the objective combination of the information provided by different models. This could be addressed in the next once some clear guidance will be provided by the Commission of Climatology which is working on this topic. Nevertheless, the use of several dataset in association with the different used methods does not allow to compare and choose the best model(s). So an action toward the assessment of the skill of the different methods versus the different dataset and the selection of the most relevant dataset should be beneficial to the processes, the mobilized resources at TMA and also to the provision of downscaled information to all the districts. In conjunction with such work, it should be quite easy to fix the concern with the use of the Fewsnet dataset for the current forecast. To improve the assessment of the uncertainty, the downscaled forecasts could take benefit of the ensemble forecasts provided by the GPCLRF. This need further investigation, especially in conjunction with the use of CPT which provide a statistical estimate of the uncertainty range. For the dataset, in addition to the selection of one relevant dataset for downscaling, one can point out the consolidation of the timely access to the relevant information ; especially the GPCLRF forecasts thought not only. Also, if the use of reanalysis at TMA is a good point, the change for a more recent dataset (ERA-Interim suggested) would be a clear improvement. Likely, this change could be implemented in coordination with the regional level. Regarding the verification there is no gaps for the verification on the hindcast as the used software is providing all the SVS scores and even more. Nevertheless, the verification of the current forecast, could be improved, especially by introducing criteria relevant for the probabilistic aspects (e.g. RPSS, ). There is also a clear need of consolidation of the knowledge about the scores and their interpretations and uses. This could be done with the investigation of the possible trainings already Page 10/ 12

11 existing (like the SWIOCOF or likely SARCOF and GHARCOF) or replicating some already existing trainings (like in RA VI), without forgetting the possibility to use R packages. There are very likely several countries in the sub-region which should be interested in. The organisation and management of the resources seem to be fine. At this stage there is no specific needs to the exception of the obvious maintenance and consolidation of the existing resources. On the application side, the presence of a NCOF is very positive. However, this structure should be consolidated, especially with respect of the feedback from the users, the follow-up of the need evolution and the understanding of the decisions made. Then, some investigation of new products at seasonal scales (e.g. onset and cessation dates, wind, TC activity, ) should be conducted, leading to a better offer of Climate Services. In the same idea, the potential of use of monthly forecast products should be explored in close liaison with the stakeholders and their decision making processes. This should improve significantly the efficiency of the provided climate services and the benefit on the stakeholder side, especially in the perspective of the seamless provision of services associated to their different decisions. For the communication, there is a lack of a help desk like functionality which could significantly improve the use of the products and also the feedback process with the stakeholders which should be consolidated. So, the development of some electronic support for the stakeholders would be very welcome as a concrete support to the communication and the feedback mechanism. To end this recap, in term of organisation it's difficult to identify some specific gaps in the current organisation. The only obvious recommendation could be to continue the consolidation of the structure and the processes (both in R&D and operations). The question of the necessary/needed trainings has to be addressed quite soon as they should be proposed prior to any perspective of improvements. In this respect, it is interesting to note that the SWIO region is proposing a training prior to the SWIOCOF which could help a lot to reach some of the objectives of improvements. Namely the basic knowledge, the use of GPCLRF forecasts, the use of R software or packages, the verification. But it is important that such training could be conducted in a close liaison with the regional entity involved into the relevant COFs (SARCOF) which is the SADC. To conclude TMA showed some spectacular and relevant progresses in the frame of Seasonal Forecast and the provision of associated Climate Services. The foundations appear to be really very good and robust and consequently one can schedule the future developments with very good chances of success. The question of the necessary/needed trainings has to be addressed quite soon as they should be proposed prior to any perspective of improvements. In this respect, it is interesting to note that the SWIO region is proposing a training prior to the SWIOCOF which could help a lot to reach some of the objectives of improvements. Namely consolidating the basic knowledge, the use of GPCLRF forecasts, investigating the use of R software or packages, the verification. But it is important that such training could be conducted in a close liaison with the regional entity involved into the relevant COF (SARCOF) which is the SADC. Obviously as usual, there are some rooms for improvements (see next section) which should be discussed and prioritized with respect of specific TMA resources and priorities, without forgetting GFCS Office objectives and the necessary regional coordination. So in the next section, a highlight of proposed actions and a first draft on the priorities is proposed helping to start the virtuous wheel of continuous improvement which is the aim of a service provision perspective. The P0 point out the actions of major importance to put in place, P1 indicates some very important actions to conduct while P2 indicates the important actions which could take place likely after the two first one categories. Page 11/ 12

12 11. Room for improvements / Key recommendations Domain Topic Downscaling Improve the downscaling processes (especially using an appropriate gridded dataset) and extend the provided information to all the districts P1 Verification Improve and /or consolidate the knowledge on the verification via appropriate training P2 Verification Improve the verification of the current forecast by introducing complementary metric(s) dedicated to the probabilistic forecast P1 Dataset Consolidate the use of the information provided at global and regional levels (including recent reanalysis, access to hindcast and timely provision of GPCLRF forecasts) P2 Products Investigate new predictors and other categories (instead only tercile). P1 Products Investigate new tailored products (e.g. number of dry and rainy days, onset dates, agromet parameters, ) in liaison with the R&D activities P2 Products Investigate the potential use of monthly forecast products P2 Application User support Develop some help desk like functions, FAQ, user forums, Some electronic support should be developed. P2 Application Organisation Extend to the other districts : still a challenge. P2 Recommendation Page 12/ 12 Priority