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	<title>Integrated Projects Archives - climateservices.it CNR-IBE</title>
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	<title>Integrated Projects Archives - climateservices.it CNR-IBE</title>
	<link>https://climateservices.it/categoria-progetti/progetti-integrati/</link>
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		<title>Linking Earth Observation and Precipitation In-Situ Data in the Sirba River Basin in West Africa</title>
		<link>https://climateservices.it/publication/linking-earth-observation-and-precipitation-in-situ-data-in-the-sirba-river-basin-in-west-africa/</link>
		
		<dc:creator><![CDATA[Vieri Tarchiani]]></dc:creator>
		<pubDate>Tue, 30 Jun 2026 09:50:54 +0000</pubDate>
				<guid isPermaLink="false">https://climateservices.it/?post_type=publication&#038;p=16255</guid>

					<description><![CDATA[<p>Floods have caused substantial loss of life and economic damages in West Africa, with a marked increase of extremes over the past decades. These flood events are largely driven by frequent extreme local rainfall. The sparse meteorological stations in Burkina Faso contain data gaps that limit effective monitoring of rainfall. This study fills the gap [&#8230;]</p>
<p>The post <a href="https://climateservices.it/publication/linking-earth-observation-and-precipitation-in-situ-data-in-the-sirba-river-basin-in-west-africa/">Linking Earth Observation and Precipitation In-Situ Data in the Sirba River Basin in West Africa</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
]]></description>
										<content:encoded><![CDATA[<p>Floods have caused substantial loss of life and economic damages in West Africa, with a marked increase of extremes over the past decades. These flood events are largely driven by frequent extreme local rainfall. The sparse meteorological stations in Burkina Faso contain data gaps that limit effective monitoring of rainfall. This study fills the gap by integrating satellite data with in-situ precipitation measurements. This study assesses spatio-temporal correlations between cloud coverage and reservoir surface water areas extracted from Sentinel-2 to in-situ rainfall historical data complemented with spatially explicit gridded meteorological products, including ERA5 and CHIRPS. In-situ precipitation shows a strong correlation with cloud coverage (Mean r = 0.65), better agreements with ERA5 (r = 0.83) and CHIRPS (r = 0.91), and a timelagged correlation with surface water areas. A developed open-access app (Abraiz, 2026) provides insights for realtime dynamics of the cloud coverage, precipitation, and surface water area of reservoirs in the Sirba River basin with potential applicability in other data-scarce regions.</p>
<p>The post <a href="https://climateservices.it/publication/linking-earth-observation-and-precipitation-in-situ-data-in-the-sirba-river-basin-in-west-africa/">Linking Earth Observation and Precipitation In-Situ Data in the Sirba River Basin in West Africa</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
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		<title>A streamlined flood-specific evaluation framework: Assessing African riverine early warning systems</title>
		<link>https://climateservices.it/publication/a-streamlined-flood-specific-evaluation-framework-assessing-african-riverine-early-warning-systems/</link>
		
		<dc:creator><![CDATA[Vieri Tarchiani]]></dc:creator>
		<pubDate>Fri, 26 Jun 2026 08:38:28 +0000</pubDate>
				<guid isPermaLink="false">https://climateservices.it/?post_type=publication&#038;p=16207</guid>

					<description><![CDATA[<p>Intense hydrological events are rising across Africa, increasing flood risks. Flood Early Warning Systems (FEWS) are internationally acknowledged as fundamental tools for risk reduction and management. Their effectiveness depends on the integrated and balanced development of all essential components -risk knowledge, monitoring and forecasting, dissemination and communication, preparedness and response-yet their integration across operational systems [&#8230;]</p>
<p>The post <a href="https://climateservices.it/publication/a-streamlined-flood-specific-evaluation-framework-assessing-african-riverine-early-warning-systems/">A streamlined flood-specific evaluation framework: Assessing African riverine early warning systems</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
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										<content:encoded><![CDATA[<div id="abspara0010" class="u-margin-s-bottom">Intense hydrological events are rising across Africa, increasing flood risks. Flood Early Warning Systems (FEWS) are internationally acknowledged as fundamental tools for risk reduction and management. Their effectiveness depends on the integrated and balanced development of all essential components -risk knowledge, monitoring and forecasting, dissemination and communication, preparedness and response-yet their integration across operational systems is poorly documented.</div>
<div id="abspara0015" class="u-margin-s-bottom">This study develops a flood-specific evaluation framework adapted from the UNDRR-WMO Multi-Hazard Early Warning System Custom Indicators (MHEWS-CI), reducing its 53 indicators to 25 through thematic consolidation and flood-specific adaptation. The framework is applied to assess 19 African riverine FEWS using publicly available documentation, as a proof-of-concept. Each indicator is assessed through a recognition-based approach: documented system functionalities are matched against three predefined development stages (absent, basic, advanced), lightning the reporting burden of MHEWS-CI. Scores are compared to illustrate relationships between system characteristics and development of components.</div>
<div id="abspara0020" class="u-margin-s-bottom">Results reveal imbalances: 74% of systems demonstrate advanced monitoring and forecasting, but only 5% achieve advanced response capabilities. Web architectures and hydrological models favour monitoring and forecasting but show no corresponding advantage in response integration. Hybrid systems combining model forecasting with community engagement achieve the highest overall scores, suggesting that optimal development requires balancing technical sophistication with a participatory approach.</div>
<div id="abspara0025" class="u-margin-s-bottom">These patterns may reflect institutional factors -governance fragmentation and insufficient operational integration-rather than causal effects of system characteristics. The framework delivers a streamlined, replicable instrument for insightful FEWS assessment, successfully corroborating prior research and supporting evidence-based identification of operational gaps and investment prioritisation.</div>
<p>The post <a href="https://climateservices.it/publication/a-streamlined-flood-specific-evaluation-framework-assessing-african-riverine-early-warning-systems/">A streamlined flood-specific evaluation framework: Assessing African riverine early warning systems</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
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		<title>Slapis Sahel Co-producing Competence</title>
		<link>https://climateservices.it/progetto/slapis-sahel-training-in-numerical-weather-prediction/</link>
		
		<dc:creator><![CDATA[Vieri Tarchiani]]></dc:creator>
		<pubDate>Thu, 11 Jun 2026 10:23:39 +0000</pubDate>
				<guid isPermaLink="false">https://climateservices.it/?post_type=progetto&#038;p=16185</guid>

					<description><![CDATA[<p>Building operational forecasting capacity through long-term institutional collaboration Early warning systems are based on weather forecasts, numerical models, data platforms and technological innovation. Yet experience shows that technology alone is not enough. The effectiveness and sustainability of early warning services ultimately depend on the ability of institutions to operate, assess, adapt and maintain forecasting systems [&#8230;]</p>
<p>The post <a href="https://climateservices.it/progetto/slapis-sahel-training-in-numerical-weather-prediction/">Slapis Sahel Co-producing Competence</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
]]></description>
										<content:encoded><![CDATA[		<div data-elementor-type="wp-post" data-elementor-id="16185" class="elementor elementor-16185" data-elementor-post-type="progetto">
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									<img fetchpriority="high" decoding="async" class="wp-image-16186 aligncenter" src="https://climateservices.it/wp-content/uploads/2026/06/Visual-abstract-300x200.png" alt="" width="794" height="530" srcset="https://climateservices.it/wp-content/uploads/2026/06/Visual-abstract-300x200.png 300w, https://climateservices.it/wp-content/uploads/2026/06/Visual-abstract-1024x683.png 1024w, https://climateservices.it/wp-content/uploads/2026/06/Visual-abstract-768x512.png 768w, https://climateservices.it/wp-content/uploads/2026/06/Visual-abstract.png 1536w" sizes="(max-width: 794px) 100vw, 794px" />
<h2>Building operational forecasting capacity through long-term institutional collaboration</h2>
<p class="isSelectedEnd">Early warning systems are based on weather forecasts, numerical models, data platforms and technological innovation. Yet experience shows that technology alone is not enough. The effectiveness and sustainability of early warning services ultimately depend on the ability of institutions to operate, assess, adapt and maintain forecasting systems within their daily activities.</p>
<p class="isSelectedEnd">Recognising this challenge, the SLAPIS Sahel initiative promoted an innovative capacity-development pathway involving the Direction Nationale de la Météorologie (DNM) of Niger, the Agence Nationale de la Météorologie (ANAM) of Burkina Faso, the <a href="https://www.lamma.toscana.it/">Tuscany Regional Meteorological Service (LaMMA)</a>, the Institute for BioEconomy of the Italian National Research Council (IBE-CNR), and the <a href="https://climateservices.it/rtc-italy/">WMO Regional Training Centre in Italy</a>.</p>
<p class="isSelectedEnd">The objective was not simply to transfer technical knowledge, but to strengthen operational competence through a process of co-production. Building on more than a decade of collaboration between the institutions involved, forecasting experts, modelers, IT specialists, managers and trainers worked together to identify operational needs, define priorities, develop solutions and progressively integrate them into institutional workflows. The process started long before the training activities themselves, through continuous dialogue and joint reflection on how to strengthen forecasting capabilities in support of flood early warning services.</p>
<p class="isSelectedEnd">A cornerstone of the experience was the long-term embedding of African modelers within the operational forecasting team of LaMMA. Rather than attending isolated training courses, trainees became part of a real operational environment where they participated in the installation, configuration, operation, verification and interpretation of numerical weather prediction systems. Working side-by-side with forecasters and model developers enabled learning to be directly connected to real operational challenges and decision-making processes.</p>
<p class="isSelectedEnd">The approach combined on-the-job learning, training-of-trainers activities, routine forecast verification and continuous interaction between teams in Niger, Burkina Faso and Italy. Through this process, participants progressively acquired greater autonomy in managing forecasting systems, while institutions strengthened their capacity to maintain, evaluate and further develop operational services.</p>
<p class="isSelectedEnd">An important outcome of the initiative was the strengthening and institutionalisation of long-term partnerships. During the project, LaMMA signed cooperation agreements with both DNM Niger and ANAM Burkina Faso, providing a framework for continued technical collaboration, staff exchanges, joint innovation activities and mutual support in the development of forecasting and early warning services. These agreements represent a concrete step towards ensuring that capacity development extends beyond the duration of individual projects.<img decoding="async" class=" wp-image-16191 alignright" src="https://climateservices.it/wp-content/uploads/2026/06/20251104_112546-300x225.jpg" alt="" width="249" height="187" srcset="https://climateservices.it/wp-content/uploads/2026/06/20251104_112546-300x225.jpg 300w, https://climateservices.it/wp-content/uploads/2026/06/20251104_112546-1024x768.jpg 1024w, https://climateservices.it/wp-content/uploads/2026/06/20251104_112546-768x576.jpg 768w, https://climateservices.it/wp-content/uploads/2026/06/20251104_112546-1536x1152.jpg 1536w, https://climateservices.it/wp-content/uploads/2026/06/20251104_112546-2048x1536.jpg 2048w" sizes="(max-width: 249px) 100vw, 249px" /></p>
<p class="isSelectedEnd">Beyond the technical achievements, the experience fostered trust, peer-to-peer collaboration and the emergence of a regional community of practice connecting meteorological services across national borders. These elements are particularly important in transboundary river basins such as the Sirba, where effective flood early warning depends on coordinated interpretation of forecasts and shared operational procedures among neighbouring countries.</p>
<p class="isSelectedEnd">The experience demonstrated that sustainable early warning systems are built not only through technology, but also through competent people, collaborative processes and strong institutions. Operational competence emerged as a key ingredient for transforming weather forecasts into actionable information for disaster risk reduction and climate resilience.<img decoding="async" class=" wp-image-16188 alignright" src="https://climateservices.it/wp-content/uploads/2026/06/MG_8714conv-300x200.jpg" alt="" width="250" height="167" srcset="https://climateservices.it/wp-content/uploads/2026/06/MG_8714conv-300x200.jpg 300w, https://climateservices.it/wp-content/uploads/2026/06/MG_8714conv-1024x683.jpg 1024w, https://climateservices.it/wp-content/uploads/2026/06/MG_8714conv-768x512.jpg 768w, https://climateservices.it/wp-content/uploads/2026/06/MG_8714conv-1536x1024.jpg 1536w, https://climateservices.it/wp-content/uploads/2026/06/MG_8714conv-2048x1365.jpg 2048w" sizes="(max-width: 250px) 100vw, 250px" /></p>
<p class="isSelectedEnd">The relevance of this experience was recognised through several international dissemination and knowledge-sharing initiatives. In October 2025, the SLAPIS Sahel project organised the international conference <a href="https://wmo.int/media/news-from-members/italian-african-cooperation-strengthen-early-warnings-sahel#:~:text=The%20event%20%E2%80%9CInternational%20Cooperation%20in,Italy%2C%20on%2014%20October%202025.&amp;text=Climate%20change%20is%20amplifying%20the,on%20societies%2C%20infrastructures%20and%20economies."><em>International Cooperation in Applied Meteorology for Reducing Hydroclimatic Risks</em></a>, bringing together meteorological services, researchers, development agencies and practitioners from Africa and Europe to discuss operational forecasting, early warning systems and capacity development. The conference provided an opportunity to share lessons learned and reflect on the institutional and human dimensions required to sustain forecasting services in resource-constrained contexts.</p>
<img loading="lazy" decoding="async" class=" wp-image-16187 alignright" src="https://climateservices.it/wp-content/uploads/2026/06/Immagine-copertina-300x225.jpeg" alt="" width="250" height="188" srcset="https://climateservices.it/wp-content/uploads/2026/06/Immagine-copertina-300x225.jpeg 300w, https://climateservices.it/wp-content/uploads/2026/06/Immagine-copertina-1024x768.jpeg 1024w, https://climateservices.it/wp-content/uploads/2026/06/Immagine-copertina-768x576.jpeg 768w, https://climateservices.it/wp-content/uploads/2026/06/Immagine-copertina-1536x1152.jpeg 1536w, https://climateservices.it/wp-content/uploads/2026/06/Immagine-copertina.jpeg 2040w" sizes="(max-width: 250px) 100vw, 250px" />
<p class="isSelectedEnd">The experience was subsequently selected as the subject of a dedicated panel session during the <a href="http://www.calmet.org/p/joint-calmet-xvi-conect-3-third.html">Joint CALMET XVI and CONECT-3 Conference</a>, where trainees, tutors and institutional representatives discussed the challenges, successes and lessons learned from the collaboration. The reflections emerging from this experience were later consolidated in a <a href="https://climateservices.it/publication/from-training-to-transformation-co-producing-sustainable-early-warning-competence-in-west-africa/">scientific paper</a> published in the journal <em>Climate Services</em>, contributing to the international debate on how co-production can support sustainable capacity development for weather and climate services.</p>
<strong>For SLAPIS Sahel, strengthening early warning systems means not only improving forecasts, but also investing in the people, partnerships and institutions that transform forecasts into action.</strong>								</div>
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					<h2 class="elementor-heading-title elementor-size-default"><br>Training activities</h2>				</div>
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				<a href="https://climateservices.it/wp-content/uploads/2026/06/SLAPIS_Rapport-F0_022023_Niamey.pdf" target="_blank" class="elementor-icon" tabindex="-1" aria-label="03/03/2023 | Niamey, Formation Prévision Numérique du Temps - WRF 1">
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							03/03/2023 | Niamey, Formation Prévision Numérique du Temps - WRF 1						</a>
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						Opérationnalisation du modèle Weather Research and Forecasting (WRF-Niger) et renforcement des capacités du personnel de la Direction de la Météorologie Nationale en Prévision Numérique du Temps (PNT): les travaux de cette Formation Action se sont déroulés du 27 février au 3 mars 2023 à la DMN de Niamey, Niger.  5 jours, 15 participants du Niger, 3 formateurs d'Italie. 					</p>
				
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							16/02/2024 | Niamey, Formation Prévision Numérique du Temps - WRF 2						</a>
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						Séminaire formation-action sur la performance et réglage du modèle Weather Research and Forecasting (WRF) dans le cadre du Projet SLAPIS Sahel : les travaux de cette Formation Action se sont déroulés du 13 au 16 février 2024 à la DMN de Niamey, Niger.  4 jours, 24 participants du Niger et du Burkina Faso, 3 formateurs d'Italie.					</p>
				
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							23/02/2024 | Ouagadougou, Formation Alerte Précoce Inondations - Burkina Faso						</a>
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						Atelier de Formation-action sur le Cadrage du système d’alerte précoce inondations au Burkina  Faso: l'atelier a été organisé à Ouagadougou du 20 au 23 février 2024.  4 jours, 33 participants du Burkina Faso, 3 formateurs d'Italie et 2 du Niger.					</p>
				
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							07/06/2024 | Turin, Formation Modélisation Hydrologique						</a>
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						L'atelier de Formation en « Modélisation Hydrologique » à été organisé à Turin, Italie, du 27 mai au 7 juin 2024 auprès du Polythecnique de Turin. Y ont participé 4 experts hydrologues de la DGRE du Burkina et de la DRE du Niger, encadrés par Daniele GANORA et Stefania TAMEA du Polytechnic.					</p>
				
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							31/10/2024 | Niamey, Formation Réduction Risque Inondation						</a>
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						L’atelier de formation-action sur « la réduction du risque d’inondation dans les communes de Namaro et de Karma au Niger » a été organisé à Niamey, Niger le 30 et 31 octobre 2024. Y ont pris part 50 participants du Niger e 4 encadreurs d'Italie					</p>
				
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							23/11/2024 | Turin, Formation Géomatique						</a>
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						L’atelier de formation en « géomatique et modélisation hydraulique » a été eorganisé à Turin, Italie, du 8 au 23 novembre 2024 auprès du Polythecnique de Turin. Y ont participé 6 experts du Burkina et du Niger, encadrés par Marco Piras et Riccardo Vesipa du Polythecnique.					</p>
				
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							07/11/2025 | Ouagadougou, Formation MOLOCH - Burkina Faso						</a>
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						Le séminaire de formation a été organisé du 03 au 07 novembre 2025 à Ouagadougou, Burkina Faso, à bénéfice des experts en prévision météorologique et en modélisation de l’ANAM du Burkina Faso. Cette formation visait à assurer un transfert de compétences entre les différentes parties prenantes (Burkina Faso, Niger, Italie) pour la maîtrise et l’exploitation du modèle MOLOCH au Burkina Faso. 5 jours, 15 participants Burkina, 3 formateurs Italie, 1 Burkina et 1 Niger. 5 jours, 15 participants Burkina, 3 formateurs Italie, 1 Burkina et 1 Niger					</p>
				
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				<a href="https://climateservices.it/wp-content/uploads/2026/06/SLAPIS_Rapport_Stage_032026_Florence.pdf" target="_blank" class="elementor-icon" tabindex="-1" aria-label="06/03/2026 | Florence, Stage Formation PNT - Burkina Faso et Niger">
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						<a href="https://climateservices.it/wp-content/uploads/2026/06/SLAPIS_Rapport_Stage_032026_Florence.pdf" target="_blank" >
							06/03/2026 | Florence, Stage Formation PNT - Burkina Faso et Niger						</a>
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									<p class="elementor-icon-box-description">
						Ce rapport synthétise les travaux réalisés dans le cadre du stage de formation de longue durée sur la « Prévision Numérique du Temps pour l’alerte précoce contre les inondations » d’une durée de 16 mois (novembre 2024 – mars 2026), effectuée à l’Institut de Bioéconomie (IBE - CNR) de Florence par Younoussa Adamou Sayri (DNM Niger) et Rakiswende Thomas Bere (ANAM Burkina Faso).  L’enjeu central est l’opérationnalisation du modèle régional WRF, ainsi que du modèle MOLOCH, afin d’alimenter le système d’alerte précoce contre les crues éclair sur les bassins du fleuve Niger et de la Sirba. Les activités menées durant cette période se sont articulées autour des axes stratégiques suivants : • Maîtrise technique et configuration des modèles WRF et MOLOCH ; • Réalisation de simulations a posteriori pour l'analyse d'événements passés ; • Évaluation des performances et calibration des paramètres physiques ; • Contribution à l’opérationnalisation du système et rédaction de la documentation technique (manuel d’exploitation) 					</p>
				
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							22/05/2026 | Niamey, Formation MOLOCH - Niger						</a>
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						Le séminaire de formation a été organisé du 18 au 22 mai 2026 à Niamey, Niger, à bénéfice des experts en prévision météorologique et en modélisation de la DNM du Niger. Cette formation visait à assurer un transfert de compétences entre les différentes parties prenantes (Burkina Faso, Niger, Italie) pour la maîtrise et l’exploitation du modèle MOLOCH au Niger et pour l'évaluation de ses produits.  5 jours, 15 participants Niger, 1 formateur  Burkina et 1 Niger. 					</p>
				
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		<p>The post <a href="https://climateservices.it/progetto/slapis-sahel-training-in-numerical-weather-prediction/">Slapis Sahel Co-producing Competence</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
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		<title>Dataset on Flood Risk Along the Niger River Upstream of Niamey</title>
		<link>https://climateservices.it/publication/dataset-on-flood-risk-along-the-niger-river-upstream-of-niamey/</link>
		
		<dc:creator><![CDATA[Vieri Tarchiani]]></dc:creator>
		<pubDate>Wed, 10 Jun 2026 14:31:38 +0000</pubDate>
				<guid isPermaLink="false">https://climateservices.it/?post_type=publication&#038;p=16180</guid>

					<description><![CDATA[<p>Knowledge of river flood risk in semiarid rural areas is often based on outdated, low-resolution geoinformation. Consequently, identification of exposed settlements, assets and risk-reduction measures remains challenging. This dataset provides up-to-date, fine-grained information for a rural area spanning 931 km2 that is exposed to flooding from the Niger River and the Karma Wadi. The dataset includes [&#8230;]</p>
<p>The post <a href="https://climateservices.it/publication/dataset-on-flood-risk-along-the-niger-river-upstream-of-niamey/">Dataset on Flood Risk Along the Niger River Upstream of Niamey</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
]]></description>
										<content:encoded><![CDATA[<p>Knowledge of river flood risk in semiarid rural areas is often based on outdated, low-resolution geoinformation. Consequently, identification of exposed settlements, assets and risk-reduction measures remains challenging. This dataset provides up-to-date, fine-grained information for a rural area spanning 931 km<sup>2</sup> that is exposed to flooding from the Niger River and the Karma Wadi. The dataset includes information on (i) areas exposed to the two flood types that characterise the river’s hydrological regime and flash floods from the wadi, (ii) flood-prone crops, buildings and (iii) measures for risk treatment. Discharge data, a 4 m horizontal-resolution digital elevation model, and two-dimensional hydraulic modelling with BASEMENT were used to identify flood-prone areas. Visual interpretation of high-resolution satellite imagery in Google Earth, together with field inspections, enabled the identification of exposed assets. The Information System on Rural Markets of Niger and house compensation values recognised during resettlement-related works enabled asset valuation. Risk was expressed in monetary terms as the product of flood probability and expected damage. Risk-reduction measures were identified with stakeholders through a SWOT analysis and prioritised using eight criteria. The dataset can support emergency plans, flood early warning systems, rescue and recovery operations and flood risk management.</p>
<p>The post <a href="https://climateservices.it/publication/dataset-on-flood-risk-along-the-niger-river-upstream-of-niamey/">Dataset on Flood Risk Along the Niger River Upstream of Niamey</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
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		<title>Mapping precipitation extremes for pluvial flood risk management in the Sirba river basin, Burkina Faso</title>
		<link>https://climateservices.it/publication/mapping-precipitation-extremes-for-pluvial-flood-risk-management-in-the-sirba-river-basin-burkina-faso/</link>
		
		<dc:creator><![CDATA[Vieri Tarchiani]]></dc:creator>
		<pubDate>Fri, 22 May 2026 10:11:30 +0000</pubDate>
				<guid isPermaLink="false">https://climateservices.it/?post_type=publication&#038;p=16164</guid>

					<description><![CDATA[<p>Sahelian Africa is increasingly exposed to extreme hydrological events. Both fluvial and pluvial floods are becoming more severe and frequent, posing significant new threats to the livelihoods of local communities. To enhance resilience to floods, the development of effective operational tools for assessing risk and supporting decision-making is crucial. When it comes to pluvial floods, [&#8230;]</p>
<p>The post <a href="https://climateservices.it/publication/mapping-precipitation-extremes-for-pluvial-flood-risk-management-in-the-sirba-river-basin-burkina-faso/">Mapping precipitation extremes for pluvial flood risk management in the Sirba river basin, Burkina Faso</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
]]></description>
										<content:encoded><![CDATA[<p>Sahelian Africa is increasingly exposed to extreme hydrological events. Both fluvial and pluvial floods are becoming more severe and frequent, posing significant new threats to the livelihoods of local communities. To enhance resilience to floods, the development of effective operational tools for assessing risk and supporting decision-making is crucial. When it comes to pluvial floods, the first step towards this goal is to improve the understanding of extreme daily and sub-daily precipitation events and their spatial patterns in the target areas. Within the SLAPIS Project framework, this work does so for the Sirba river basin (Burkina Faso and Niger) proposing a methodology to address the challenges posed by the scarcity of hydrological data typical of the Sahel region. First, it was assessed how well gridded precipitation products (ERA5, TRMM, TAMSAT) match observed rainfall records. Then, bias correction of selected datasets was performed and tested to evaluate its reliability when spatially interpolated through the whole basin. The Metastatistical Extreme Value Distribution was finally applied to the corrected datasets to investigate the precipitation extremes exploiting the bulk of the available data, unlike classical extreme value analysis, which relies on only a small subset of the data. This procedure resulted in the production of extreme daily and sub-daily precipitation maps with enhanced accuracy and robustness, providing novel information on events that can cause pluvial flooding at the settlement scale. The methodology adopted in this study could be applied to other Sahelian basins where enhanced knowledge of extreme precipitation magnitudes and patterns is needed.</p>
<p>The post <a href="https://climateservices.it/publication/mapping-precipitation-extremes-for-pluvial-flood-risk-management-in-the-sirba-river-basin-burkina-faso/">Mapping precipitation extremes for pluvial flood risk management in the Sirba river basin, Burkina Faso</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
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		<title>From training to transformation: co-producing sustainable early warning competence in West Africa</title>
		<link>https://climateservices.it/publication/from-training-to-transformation-co-producing-sustainable-early-warning-competence-in-west-africa/</link>
		
		<dc:creator><![CDATA[Vieri Tarchiani]]></dc:creator>
		<pubDate>Mon, 13 Apr 2026 09:51:45 +0000</pubDate>
				<guid isPermaLink="false">https://climateservices.it/?post_type=publication&#038;p=16100</guid>

					<description><![CDATA[<p>Operational numerical weather prediction in Sub-Saharan Africa remains fragile despite increasing availability of models and data. A key limiting factor is the limited operational and institutional capacity of National Meteorological Services to operate, assess, adapt, and sustain forecasting chains within routine operations for early warning systems. Short-term, tool-oriented training initiatives often fail to address this [&#8230;]</p>
<p>The post <a href="https://climateservices.it/publication/from-training-to-transformation-co-producing-sustainable-early-warning-competence-in-west-africa/">From training to transformation: co-producing sustainable early warning competence in West Africa</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
]]></description>
										<content:encoded><![CDATA[<div id="sp0010" class="u-margin-s-bottom">Operational numerical weather prediction in Sub-Saharan Africa remains fragile despite increasing availability of models and data. A key limiting factor is the limited operational and institutional capacity of National Meteorological Services to operate, assess, adapt, and sustain forecasting chains within routine operations for early warning systems. Short-term, tool-oriented training initiatives often fail to address this gap, as learning remains weakly embedded in daily service delivery and rarely translates into sustained operational competence.</div>
<div id="sp0015" class="u-margin-s-bottom">This paper presents a qualitative case study examining how co-production principles can be operationalized to support the development of operational competence for flood early warning. The study draws on a long-term capacity-development experience involving the National Meteorological Services of Niger and Burkina Faso and the Tuscany Regional Meteorological Service in Italy. The approach combined long-term embedding of trainees within an operational forecasting team, training-of-trainers, and peer-to-peer institutional collaboration, linking learning to real operational workflows.</div>
<div id="sp0020" class="u-margin-s-bottom">Results are analysed across methodological, operational, and institutional dimensions. Methodological findings show how co-production principles can structure competence-oriented training processes by integrating instructional design, operational practice, and iterative evaluation. Operational results highlight the importance of sustained practice and routine verification, while institutional results point to the role of training of trainers and public institutional collaboration supporting sustainability of competence beyond individual skills and knowledge.</div>
<div id="sp0025" class="u-margin-s-bottom">By reframing competence as a foundational component of climate services, the study offers transferable insights for capacity development in resource-constrained and transboundary contexts.</div>
<p>The post <a href="https://climateservices.it/publication/from-training-to-transformation-co-producing-sustainable-early-warning-competence-in-west-africa/">From training to transformation: co-producing sustainable early warning competence in West Africa</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
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		<title>Building Local Capacity through Cascading Agrometeorological Training in Niger A scalable model from the PRIMESA initiative</title>
		<link>https://climateservices.it/poster/building-local-capacity-through-cascading-agrometeorological-training-in-niger-a-scalable-model-from-the-primesa-initiative/</link>
		
		<dc:creator><![CDATA[Vieri Tarchiani]]></dc:creator>
		<pubDate>Mon, 17 Nov 2025 10:47:06 +0000</pubDate>
				<guid isPermaLink="false">https://climateservices.it/?post_type=poster&#038;p=16047</guid>

					<description><![CDATA[<p>Within the framework of the PRIMESA initiative, funded by the Italian Agency for Development Cooperation (AICS), a structured multi-scale training program is being<br />
implemented in Niger to enhance the use of agrometeorological information for climate-resilient agriculture. In collaboration with the National Directorate of<br />
Meteorology (DMN) and the Ministry of Agriculture of Niger, this training scheme builds on lessons learned from the earlier ANADIA project and aims to reach farmers through a cascading training model.<br />
The program begins at the national level, where international experts train technical staff from the DMN and the Ministry of Agriculture on key topics such as the use<br />
of low-cost rain gauges (pluviomètres paysans), seasonal forecasts, and 10-day agrometeorological bulletins to support decision-making in agronomic crop<br />
management. These national trainers then deliver training at the regional level to agricultural extension officers, who subsequently work directly with farmers in the<br />
field.<br />
The approach combines in-person sessions, practical demonstrations, printed and digital materials, and communication via rural radio and WhatsApp groups during<br />
the growing season. Preliminary evidence shows that trained farmers not only better understand weather and climate information but also apply it more effectively<br />
leading to improved yields.<br />
This experience underscores the importance of co-developing training content with local actors and creating robust, multi-level partnerships to ensure that<br />
meteorological information is not only disseminated but understood and used effectively at the community level. The model developed in Niger offers a replicable<br />
approach for other regions facing similar challenges in agricultural adaptation and training dissemination.</p>
<p>The post <a href="https://climateservices.it/poster/building-local-capacity-through-cascading-agrometeorological-training-in-niger-a-scalable-model-from-the-primesa-initiative/">Building Local Capacity through Cascading Agrometeorological Training in Niger A scalable model from the PRIMESA initiative</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
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										<content:encoded><![CDATA[<p>The post <a href="https://climateservices.it/poster/building-local-capacity-through-cascading-agrometeorological-training-in-niger-a-scalable-model-from-the-primesa-initiative/">Building Local Capacity through Cascading Agrometeorological Training in Niger A scalable model from the PRIMESA initiative</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
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		<title>From Training to Transformation: Co-Developing Forecasting Capacities for Early Warning in West Africa</title>
		<link>https://climateservices.it/poster/from-training-to-transformation-co-developing-forecasting-capacities-for-early-warning-in-west-africa/</link>
		
		<dc:creator><![CDATA[Vieri Tarchiani]]></dc:creator>
		<pubDate>Mon, 17 Nov 2025 10:34:04 +0000</pubDate>
				<guid isPermaLink="false">https://climateservices.it/?post_type=poster&#038;p=16039</guid>

					<description><![CDATA[<p>In Sub-Saharan Africa, the operational implementation of regional Numerical Weather Prediction (NWP) models remains a significant challenge due to limited financial and technical resources, insufficient understanding of tropical weather systems, and the inadequate performance of global models in simulating rainfall over the region. Although recent developments—such as cloud computing and convection-permitting models—offer new opportunities, forecasting intense rainfall events, often linked to mesoscale convective systems, continues to be a major limitation for effective early warning systems.<br />
Within the framework of the SLAPIS Sahel project, a development cooperation initiative, the Regional Training Center in Italy designed and implemented a blended and integrated training programme aimed at enhancing the capacities of the National Meteorological Services (NMSs) of Niger and Burkina Faso. The objective was to enable local forecasters, modelers, and IT personnel to operationalize regional NWP chains in support of hydrometeorological early warning systems.</p>
<p>The post <a href="https://climateservices.it/poster/from-training-to-transformation-co-developing-forecasting-capacities-for-early-warning-in-west-africa/">From Training to Transformation: Co-Developing Forecasting Capacities for Early Warning in West Africa</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
]]></description>
										<content:encoded><![CDATA[<p>The post <a href="https://climateservices.it/poster/from-training-to-transformation-co-developing-forecasting-capacities-for-early-warning-in-west-africa/">From Training to Transformation: Co-Developing Forecasting Capacities for Early Warning in West Africa</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
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		<title>Slapis Sahel Numerical Weather Prediction</title>
		<link>https://climateservices.it/progetto/slapis-sahel-numerical-weather-prediction/</link>
		
		<dc:creator><![CDATA[Web Editor]]></dc:creator>
		<pubDate>Wed, 18 Jun 2025 10:19:06 +0000</pubDate>
				<guid isPermaLink="false">https://climateservices.it/?post_type=progetto&#038;p=15216</guid>

					<description><![CDATA[<p>SLAPIS SAHELProject Numerical Weather Predictionin SLAPIS Sahel One of the key objectives of the SLAPIS Sahel project is to strengthen national capacities in Numerical Weather Prediction (NWP) as a support for hydrological early warning, particularly for flash floods and flooding events along the Sirba River and the Niger River. The national meteorological agencies of Niger [&#8230;]</p>
<p>The post <a href="https://climateservices.it/progetto/slapis-sahel-numerical-weather-prediction/">Slapis Sahel Numerical Weather Prediction</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
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					<h1 class="elementor-heading-title elementor-size-default"><span>SLAPIS </span></b>SAHEL<br>Project</h1>				</div>
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					<h3 class="elementor-heading-title elementor-size-default">Numerical Weather Prediction<br>in SLAPIS Sahel</h3>				</div>
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									<p>One of the key objectives of the SLAPIS Sahel project is to strengthen national capacities in Numerical Weather Prediction (NWP) as a support for hydrological early warning, particularly for flash floods and flooding events along the Sirba River and the Niger River.</p>								</div>
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									<p>The national meteorological agencies of Niger (DMN) and Burkina Faso (ANAM) strategically decided to develop NWP chains based on the <a href="https://www.mmm.ucar.edu/models/wrf" target="_blank" rel="noopener">Weather Research and Forecasting (WRF) model</a>. WRF is a widely used regional model for both operational weather forecasting and research, developed primarily by the <a href="https://ncar.ucar.edu/" target="_blank" rel="noopener">National Center for Atmospheric Research (NCAR)</a> in the United States.</p>								</div>
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									<p>To this end, Niger and Burkina Faso receive support from IBE-CNR and, through it, from the <a href="http://www.lamma.toscana.it/">LaMMA Consortium</a> — joint initiative between CNR and the Tuscany Region, which provides regional meteorological services in Italy.</p>								</div>
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					<h3 class="elementor-heading-title elementor-size-default">Capacity Building Timeline</h3>				</div>
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									<p>In the framework of SLAPIS Sahel project, a series of capacity building actions were carried out  to build local expertise on WRF and numerical forecasting.</p>								</div>
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			27 Feb – 3 Mar 2023		</div><div class="timeline-item__content__wysiwyg">
				<h2>Niamey (Niger)</h2><p>Capacity building for the staff of the Direction de la Météorologie Nationale in Numerical Weather Prediction.</p><p><em>Duration: 5 days — Participants: 15 (Niger)</em></p>			</div>
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			27 Feb – 3 Mar 2023		</div></div>
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			13–16 Feb 2024		</div><div class="timeline-item__content__wysiwyg">
				<h2>Niamey (Niger)</h2><p>Action-based training: Performance and tuning of the Weather Research and Forecasting (WRF) model.</p><p><em>Duration: 4 days — Participants: 24 (Niger and Burkina Faso).</em></p>			</div>
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			13–16 Feb 2024		</div></div>
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			13 Nov 2024 – 12 Nov 2025		</div><div class="timeline-item__content__wysiwyg">
				<h2>Florence (Italy)</h2><p>Long-term training on Numerical Weather Prediction for early flood warning.</p><p><em>Duration: 12 months — Participants: 2 (Niger and Burkina Faso)</em></p>			</div>
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			13 Nov 2024 – 12 Nov 2025		</div></div>
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			16 April 2025		</div><div class="timeline-item__content__wysiwyg">
				<h2>Ouagadougou (Burkina Faso)</h2><p>WRF product validation workshop.</p><p><em>Duration: 1 day — Participants: 12 (Burkina Faso)</em></p>			</div>
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			16 April 2025		</div></div>
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			17 April 2025		</div><div class="timeline-item__content__wysiwyg">
				<h2>Niamey (Niger)</h2><p>WRF product validation workshop.</p><p><em>Duration: 1 day — Participants: 18 (Niger)</em></p>			</div>
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			17 April 2025		</div></div>
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			23 April 2025		</div><div class="timeline-item__content__wysiwyg">
				<h2>Niamey (Niger)</h2>
Rainfall estimation merging and operational integration of WRF Workshop.

<em>Duration: 1 day — Participants: 7 (Niger)</em>			</div>
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			23 April 2025		</div></div>
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			19 May 2025		</div><div class="timeline-item__content__wysiwyg">
				<h2>Webinar</h2><p>Action-oriented seminar on the MOLOCH operational model.</p><p><em>Duration: 1 day — Participants: 14 (Niger and Burkina Faso)</em></p>			</div>
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			19 May 2025		</div></div>
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			11 June 2025		</div><div class="timeline-item__content__wysiwyg">
				<h2>Webinar</h2><p>Presentation of the web interface for accessing operational MOLOCH and WRF forecasts.</p><p><em>Duration: 1 day — Participants: 7 (Niger and Burkina Faso)</em></p>			</div>
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			11 June 2025		</div></div>
			</div></section>				</div>
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								<div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="1 of 3"><a data-elementor-open-lightbox="yes" data-elementor-lightbox-slideshow="ef40e55" data-elementor-lightbox-title="Untitled-1" data-e-action-hash="#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6MTUzODAsInVybCI6Imh0dHBzOlwvXC9jbGltYXRlc2VydmljZXMuaXRcL3dwLWNvbnRlbnRcL3VwbG9hZHNcLzIwMjVcLzA2XC9VbnRpdGxlZC0xLmpwZyIsInNsaWRlc2hvdyI6ImVmNDBlNTUifQ%3D%3D" href="https://climateservices.it/wp-content/uploads/2025/06/Untitled-1.jpg"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://climateservices.it/wp-content/uploads/2025/06/Untitled-1.jpg" alt="Slapis Sahel - training in Florence" /></figure></a></div><div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="2 of 3"><a data-elementor-open-lightbox="yes" data-elementor-lightbox-slideshow="ef40e55" data-elementor-lightbox-title="Untitled-2" data-e-action-hash="#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6MTUzODEsInVybCI6Imh0dHBzOlwvXC9jbGltYXRlc2VydmljZXMuaXRcL3dwLWNvbnRlbnRcL3VwbG9hZHNcLzIwMjVcLzA2XC9VbnRpdGxlZC0yLmpwZyIsInNsaWRlc2hvdyI6ImVmNDBlNTUifQ%3D%3D" href="https://climateservices.it/wp-content/uploads/2025/06/Untitled-2.jpg"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://climateservices.it/wp-content/uploads/2025/06/Untitled-2.jpg" alt="Slapis Sahel - training in Florence" /></figure></a></div><div class="swiper-slide" role="group" aria-roledescription="slide" aria-label="3 of 3"><a data-elementor-open-lightbox="yes" data-elementor-lightbox-slideshow="ef40e55" data-elementor-lightbox-title="Untitled-3" data-e-action-hash="#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6MTUzODIsInVybCI6Imh0dHBzOlwvXC9jbGltYXRlc2VydmljZXMuaXRcL3dwLWNvbnRlbnRcL3VwbG9hZHNcLzIwMjVcLzA2XC9VbnRpdGxlZC0zLmpwZyIsInNsaWRlc2hvdyI6ImVmNDBlNTUifQ%3D%3D" href="https://climateservices.it/wp-content/uploads/2025/06/Untitled-3.jpg"><figure class="swiper-slide-inner"><img decoding="async" class="swiper-slide-image" src="https://climateservices.it/wp-content/uploads/2025/06/Untitled-3.jpg" alt="Slapis Sahel Project - Photo of the group" /></figure></a></div>			</div>
							
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					<h3 class="elementor-heading-title elementor-size-default">Operational Forecasting Chains</h3>				</div>
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									<p>The establishment of operational NWP systems in Niger and Burkina Faso stands as a concrete result of SLAPIS Sahel. Tailored to national needs, the two chains strengthen local forecasting capabilities and support early warning efforts.</p>								</div>
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															<img loading="lazy" decoding="async" width="1" height="1" src="https://climateservices.it/wp-content/uploads/2025/06/Flag_of_Niger.svg" class="attachment-large size-large wp-image-15245" alt="Flag of Niger" />															</div>
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									With support from SLAPIS Sahel, DMN Niger implemented an operational WRF chain producing
two daily runs (00 and 12 UTC)with a 4 km spatial resolution. Boundary conditions are derived from the <a href="https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast" target="_blank" rel="noopener">GFS global model </a>— of the National Centre for Environmental Prediction (<a href="https://www.weather.gov/ncep/">NCEP</a>) at 0.25-degree resolution.								</div>
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															<img loading="lazy" decoding="async" width="1" height="1" src="https://climateservices.it/wp-content/uploads/2025/06/Flag_of_Burkina_Faso.svg" class="attachment-large size-large wp-image-15246" alt="Flag of Burkina Faso" />															</div>
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									<p class="p1">Since 2018, ANAM has been developing its WRF-based forecast chain through initiatives such as the <span class="s1" style="">HYDROMET Project</span> (World Bank) and <span class="s1" style="">CREWS Project</span> (WMO). Since June 2023, WRF (ARW) has been operational in Burkina Faso with <span class="s1" style="">two runs per day</span> at <span class="s1" style="">9 km and 3 km resolutions</span>.</p>								</div>
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					<h3 class="elementor-heading-title elementor-size-default">Model Evaluation &amp; Verification</h3>				</div>
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					<h3 class="elementor-heading-title elementor-size-default">Methodology</h3>				</div>
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									<p>Due to different configurations and the absence of systematic evaluation, a verification process was initiated in collaboration with LaMMA and supported through two research fellowships in Florence. </p>								</div>
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									<p>Historical forecasts (hindcasts) were generated for July–August–September 2023 and 2024, comparing WRF-Niger, WRF-Burkina, and GFS.</p><p>Simulations were run on an 8-node Intel Xeon Gold 6130 cluster (256 cores total) with the latest WRF version. The simulation domain covered Niger, Burkina Faso, extended southward to the Gulf of Guinea and eastward to Chad. Initialization data were sourced from the GFS global model, and 36-hour simulations were launched from 18 UTC. The initial 6-hour spin-up period was excluded from the analysis. Comparisons were performed using both satellite estimates (00h–00h accumulation) and station data (06h–06h accumulation).</p>								</div>
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					<h3 class="elementor-heading-title elementor-size-default">Results</h3>				</div>
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					<h3 class="elementor-heading-title elementor-size-default">Deterministic Metrics (ME, MAE, Correlation)</h3>				</div>
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									Deterministic metrics — including Mean Error (ME), Mean Absolute Error (MAE), and correlation — indicate that WRF-Niger tended to perform better for low rainfall (≤5 mm/day), while WRF-Burkina showed higher scores for heavier precipitation. 								</div>
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																<a href="https://climateservices.it/wp-content/uploads/2025/06/fig_1.jpg" data-elementor-open-lightbox="yes" data-elementor-lightbox-title="fig_1" data-e-action-hash="#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6MTUyNTksInVybCI6Imh0dHBzOlwvXC9jbGltYXRlc2VydmljZXMuaXRcL3dwLWNvbnRlbnRcL3VwbG9hZHNcLzIwMjVcLzA2XC9maWdfMS5qcGcifQ%3D%3D">
							<img loading="lazy" decoding="async" width="800" height="298" src="https://climateservices.it/wp-content/uploads/2025/06/fig_1-1024x381.jpg" class="attachment-large size-large wp-image-15259" alt="Deterministic Scores - Project Slapis Sahel" srcset="https://climateservices.it/wp-content/uploads/2025/06/fig_1-1024x381.jpg 1024w, https://climateservices.it/wp-content/uploads/2025/06/fig_1-300x112.jpg 300w, https://climateservices.it/wp-content/uploads/2025/06/fig_1-768x286.jpg 768w, https://climateservices.it/wp-content/uploads/2025/06/fig_1.jpg 1200w" sizes="(max-width: 800px) 100vw, 800px" />								</a>
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																<a href="https://climateservices.it/wp-content/uploads/2025/06/WRF_1Artboard-2@2x.webp" data-elementor-open-lightbox="yes" data-elementor-lightbox-title="WRF_1Artboard 2@2x" data-e-action-hash="#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6MTUzMDEsInVybCI6Imh0dHBzOlwvXC9jbGltYXRlc2VydmljZXMuaXRcL3dwLWNvbnRlbnRcL3VwbG9hZHNcLzIwMjVcLzA2XC9XUkZfMUFydGJvYXJkLTJAMngud2VicCJ9">
							<img loading="lazy" decoding="async" width="800" height="402" src="https://climateservices.it/wp-content/uploads/2025/06/WRF_1Artboard-2@2x-1024x514.webp" class="attachment-large size-large wp-image-15301" alt="Infograph WRF Model performance by rainfall intensity" srcset="https://climateservices.it/wp-content/uploads/2025/06/WRF_1Artboard-2@2x-1024x514.webp 1024w, https://climateservices.it/wp-content/uploads/2025/06/WRF_1Artboard-2@2x-300x151.webp 300w, https://climateservices.it/wp-content/uploads/2025/06/WRF_1Artboard-2@2x-768x385.webp 768w, https://climateservices.it/wp-content/uploads/2025/06/WRF_1Artboard-2@2x.webp 1152w" sizes="(max-width: 800px) 100vw, 800px" />								</a>
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					<h3 class="elementor-heading-title elementor-size-default">Fraction Skill Score (FSS) </h3>				</div>
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									Fractional Skill Score (FSS): WRF-Burkina showed better skill in producing significant forecasts at smaller spatial scales for high rainfall accumulations. (Figure 2)								</div>
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																<a href="https://climateservices.it/wp-content/uploads/2025/06/fig_2_A.jpg" data-elementor-open-lightbox="yes" data-elementor-lightbox-title="fig_2_A" data-e-action-hash="#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6MTUyNjAsInVybCI6Imh0dHBzOlwvXC9jbGltYXRlc2VydmljZXMuaXRcL3dwLWNvbnRlbnRcL3VwbG9hZHNcLzIwMjVcLzA2XC9maWdfMl9BLmpwZyJ9">
							<img loading="lazy" decoding="async" width="800" height="263" src="https://climateservices.it/wp-content/uploads/2025/06/fig_2_A-1024x336.jpg" class="attachment-large size-large wp-image-15260" alt="Fractional Skill Scores - Project Slapis Sahel" srcset="https://climateservices.it/wp-content/uploads/2025/06/fig_2_A-1024x336.jpg 1024w, https://climateservices.it/wp-content/uploads/2025/06/fig_2_A-300x99.jpg 300w, https://climateservices.it/wp-content/uploads/2025/06/fig_2_A-768x252.jpg 768w, https://climateservices.it/wp-content/uploads/2025/06/fig_2_A.jpg 1129w" sizes="(max-width: 800px) 100vw, 800px" />								</a>
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																<a href="https://climateservices.it/wp-content/uploads/2025/06/fig_2_b.jpg" data-elementor-open-lightbox="yes" data-elementor-lightbox-title="fig_2_b" data-e-action-hash="#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6MTUyNjEsInVybCI6Imh0dHBzOlwvXC9jbGltYXRlc2VydmljZXMuaXRcL3dwLWNvbnRlbnRcL3VwbG9hZHNcLzIwMjVcLzA2XC9maWdfMl9iLmpwZyJ9">
							<img loading="lazy" decoding="async" width="800" height="277" src="https://climateservices.it/wp-content/uploads/2025/06/fig_2_b-1024x354.jpg" class="attachment-large size-large wp-image-15261" alt="Fractional Skill Scores - Project Slapis Sahel" srcset="https://climateservices.it/wp-content/uploads/2025/06/fig_2_b-1024x354.jpg 1024w, https://climateservices.it/wp-content/uploads/2025/06/fig_2_b-300x104.jpg 300w, https://climateservices.it/wp-content/uploads/2025/06/fig_2_b-768x265.jpg 768w, https://climateservices.it/wp-content/uploads/2025/06/fig_2_b.jpg 1129w" sizes="(max-width: 800px) 100vw, 800px" />								</a>
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					<h3 class="elementor-heading-title elementor-size-default">Case Study Analysis</h3>				</div>
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									<p>The verification included specific case studies — such as the events of 20 and 30 August 2024 — to illustrate how the models performed under particular meteorological conditions. These cases (Figure 3) were used to assess the models’ ability to capture the spatial and temporal characteristics of precipitation, with a focus on convective rainfall.</p>								</div>
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																<a href="https://climateservices.it/wp-content/uploads/2025/06/fig_3.jpg" data-elementor-open-lightbox="yes" data-elementor-lightbox-title="fig_3" data-e-action-hash="#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6MTUyNjIsInVybCI6Imh0dHBzOlwvXC9jbGltYXRlc2VydmljZXMuaXRcL3dwLWNvbnRlbnRcL3VwbG9hZHNcLzIwMjVcLzA2XC9maWdfMy5qcGcifQ%3D%3D">
							<img loading="lazy" decoding="async" width="800" height="448" src="https://climateservices.it/wp-content/uploads/2025/06/fig_3-1024x574.jpg" class="attachment-large size-large wp-image-15262" alt="Precipitation comparison 20/08/2024 - Project Slapis Sahel" srcset="https://climateservices.it/wp-content/uploads/2025/06/fig_3-1024x574.jpg 1024w, https://climateservices.it/wp-content/uploads/2025/06/fig_3-300x168.jpg 300w, https://climateservices.it/wp-content/uploads/2025/06/fig_3-768x430.jpg 768w, https://climateservices.it/wp-content/uploads/2025/06/fig_3-1536x861.jpg 1536w, https://climateservices.it/wp-content/uploads/2025/06/fig_3.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" />								</a>
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					<h3 class="elementor-heading-title elementor-size-default">General Comparison</h3>				</div>
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									Overall, both WRF models (Burkina Faso and Niger) demonstrated better performance than the global GFS model.								</div>
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					<h3 class="elementor-heading-title elementor-size-default">Future Perspectives</h3>				</div>
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									As a perspective for improvement, future tests will aim to run both WRF models using open-access data from the European Centre for Medium-Range Weather Forecasts (ECMWF).								</div>
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					<h3 class="elementor-heading-title elementor-size-default">Parallel Backup Chain and MOLOCH</h3>				</div>
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									<p>In order to ensure a backup forecasting chain in case of outages affecting the operational systems of Niger and Burkina Faso during the rainy season, a parallel WRF chain was implemented on LaMMA’s cluster, using the same configuration adopted by DMN.</p>								</div>
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					<h3 class="elementor-heading-title elementor-size-default">What is Moloch?</h3>				</div>
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									<p>MOLOCH (<a href="http://www.isac.cnr.it/~dinamica/moloch/index.html">MOdel LOCal H-coordinates</a>) is a non-hydrostatic, convection-resolving numerical weather prediction model developed by the Institute of Atmospheric Sciences and Climate (<a href="https://www.isac.cnr.it/">CNR-ISAC</a>) and used by the LaMMA Consortium. It features an Arakawa C-grid, a hybrid terrain-following coordinate system, and updated radiation and soil schemes. The microphysics scheme includes five hydrometeors and does not use graupel parametrization, unlike some WRF schemes.</p>								</div>
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									<p>&#8211; <strong>CORE</strong>: Non-hydrostatic, fully compressible, convection-resolving<br />&#8211; <strong>GRID</strong>: Arakawa C-grid, latitude-longitude, hybrid terrain-following<br />&#8211; <strong>RADIATION</strong>: ECMWF scheme (updated 2024)<br />&#8211; <strong>TURBULENCE</strong>: E–L Kinetic Energy, 1.5-order closure<br />&#8211; <strong>SOIL</strong>: 6-level DROPA scheme<br />&#8211; <strong>CUMULUS SCHEME</strong>: Kain-Fritsch</p>								</div>
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									<p>MOLOCH is widely used for temperature and precipitation forecasts in Italy, Spain, and Greece. The latest version is available on GitLab:<br /><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/1f517.png" alt="🔗" class="wp-smiley" style="height: 1em; max-height: 1em;" /> <a href="https://gitlab.com/isac-meteo/globo-bolam-moloch">https://gitlab.com/isac-meteo/globo-bolam-moloch</a></p>								</div>
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									<p>In addition, an operational MOLOCH chain (what is MOLOCH? see box above) was also deployed to provide numerical forecasts for West Africa.  As a dedicated test comparing MOLOCH and WRF in the region showed that MOLOCH is two to three times faster than WRF.</p>								</div>
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					<h3 class="elementor-heading-title elementor-size-default">Moloch as Innovative Solution</h3>				</div>
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									<p><span style="font-size: 1.1rem;">The deployment of MOLOCH in the Sahel is one of the innovative aspects of this study: the model has traditionally been used in mid-latitude regions (Davolio et al., 2020), and very few attempts had previously been made to run it in tropical environments using explicit convection schemes.</span></p><p>Thanks to its faster runtime, MOLOCH was able to simulate a wider domain covering all of West Africa at a spatial resolution of 3 km.</p>								</div>
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																<a href="https://climateservices.it/wp-content/uploads/2025/06/MOL_WRFArtboard-1@2x.webp" data-elementor-open-lightbox="yes" data-elementor-lightbox-title="MOL_WRFArtboard 1@2x" data-e-action-hash="#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6MTUzNzAsInVybCI6Imh0dHBzOlwvXC9jbGltYXRlc2VydmljZXMuaXRcL3dwLWNvbnRlbnRcL3VwbG9hZHNcLzIwMjVcLzA2XC9NT0xfV1JGQXJ0Ym9hcmQtMUAyeC53ZWJwIn0%3D">
							<img loading="lazy" decoding="async" width="800" height="595" src="https://climateservices.it/wp-content/uploads/2025/06/MOL_WRFArtboard-1@2x-1024x762.webp" class="attachment-large size-large wp-image-15370" alt="Image comparing two models Moloch and WRF" srcset="https://climateservices.it/wp-content/uploads/2025/06/MOL_WRFArtboard-1@2x-1024x762.webp 1024w, https://climateservices.it/wp-content/uploads/2025/06/MOL_WRFArtboard-1@2x-300x223.webp 300w, https://climateservices.it/wp-content/uploads/2025/06/MOL_WRFArtboard-1@2x-768x572.webp 768w, https://climateservices.it/wp-content/uploads/2025/06/MOL_WRFArtboard-1@2x-1536x1143.webp 1536w, https://climateservices.it/wp-content/uploads/2025/06/MOL_WRFArtboard-1@2x-2048x1524.webp 2048w" sizes="(max-width: 800px) 100vw, 800px" />								</a>
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					<h3 class="elementor-heading-title elementor-size-default">Forecast Products &amp; Visualization Tools</h3>				</div>
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									<p>A web interface was developed to visualize outputs from both WRF and MOLOCH. This tool is available to DMN and ANAM forecasters to support daily operations and gather feedback on content and format improvements.</p>								</div>
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									<ol><li><strong>Model Output Viewe</strong>r<br />This section provides access to the outputs of the WRF and MOLOCH models.<br />Currently, both models are initialised using data from the GFS (Global Forecast System). Two forecast runs are available per day: one at 00 UTC and one at 12 UTC. <br /><em>In the near future, additional forecast chains based on ECMWF Open Data are planned to be implemented.</em></li><li><strong>Archive Access</strong><br />Archived model outputs are available both as images and in GRIB format. These resources are intended to support daily bulletins as well as verification analyses and retrospective studies.</li><li><strong>Model Evaluation</strong><br />This section will collect performance assessments of WRF and MOLOCH, comparing model outputs with observed data from in-situ stations or satellite sources.</li></ol><p>At the bottom of the page, a table summarises key characteristics of the two models, including resolution, microphysics schemes, convection treatment, domain, and input data sources.</p>								</div>
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<a data-elementor-open-lightbox="yes" data-elementor-lightbox-slideshow="b2ad7fe" data-elementor-lightbox-title="screen_2" data-e-action-hash="#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6MTUzMTEsInVybCI6Imh0dHBzOlwvXC9jbGltYXRlc2VydmljZXMuaXRcL3dwLWNvbnRlbnRcL3VwbG9hZHNcLzIwMjVcLzA2XC9zY3JlZW5fMi5qcGciLCJzbGlkZXNob3ciOiJiMmFkN2ZlIn0%3D" href='https://climateservices.it/wp-content/uploads/2025/06/screen_2.jpg'><img loading="lazy" decoding="async" width="1920" height="1247" src="https://climateservices.it/wp-content/uploads/2025/06/screen_2.jpg" class="attachment-full size-full" alt="Screenshot of the application developed in the project Slapis Sahel - Wind map" srcset="https://climateservices.it/wp-content/uploads/2025/06/screen_2.jpg 1920w, https://climateservices.it/wp-content/uploads/2025/06/screen_2-300x195.jpg 300w, https://climateservices.it/wp-content/uploads/2025/06/screen_2-1024x665.jpg 1024w, https://climateservices.it/wp-content/uploads/2025/06/screen_2-768x499.jpg 768w, https://climateservices.it/wp-content/uploads/2025/06/screen_2-1536x998.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></a>
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					<h4 class="elementor-heading-title elementor-size-default">MOLOCH model hourly mean 2 meters temperature °C (6 km resolution), initialized with ECMWF IFS data
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		<p>The post <a href="https://climateservices.it/progetto/slapis-sahel-numerical-weather-prediction/">Slapis Sahel Numerical Weather Prediction</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
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		<title>DEM Generation from Multi-View Satellite Images in Sub-Sahel Region</title>
		<link>https://climateservices.it/publication/dem-generation-from-multi-view-satellite-images-in-sub-sahel-region/</link>
		
		<dc:creator><![CDATA[Web Editor]]></dc:creator>
		<pubDate>Fri, 13 Jun 2025 07:37:12 +0000</pubDate>
				<guid isPermaLink="false">https://climateservices.it/?post_type=publication&#038;p=15194</guid>

					<description><![CDATA[<p>Floods are causing a significant loss of human lives and valuable resources in West Africa. In particular, Niger and Burkina Faso were highly affected areas in past years. In order to predict flood, an accurate Digital elevation model (DEM) is required for flood mapping. At the studied area in Niger, up to this date, the [&#8230;]</p>
<p>The post <a href="https://climateservices.it/publication/dem-generation-from-multi-view-satellite-images-in-sub-sahel-region/">DEM Generation from Multi-View Satellite Images in Sub-Sahel Region</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
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										<content:encoded><![CDATA[<p>Floods are causing a significant loss of human lives and valuable resources in West Africa. In particular, Niger and Burkina Faso were highly affected areas in past years. In order to predict flood, an accurate Digital elevation model (DEM) is required for flood mapping. At the studied area in Niger, up to this date, the LiDAR DEMs are scarcely available, and the only available DEMs are global DEMs like global SRTM DEMs with a resolution of 10m. These global DEMs are not accurate enough to be used for flood mapping. So, in this context, this study investigates the potential of multidate, multi-view stereo pairs PlanetScope images for the generation of DEM. Three DEMs were generated from images with slightly different view angles to see the effect of view angles of images on 3D modelling. One of the DEM generated by PlanetScope images was compared with DEM generated by high-resolution drone imagery and shows the normalized Median of Absolute Deviation (NMAD) of the elevation differences of 10m. Results show that planetScope images are useful assets for generating multiple DEMs due to their high temporal resolution. Such DEMs could be extremely useful for studying dynamic phenomena or monitoring disaster events like floods.</p>
<p><strong>Keywords</strong>: Remote Sensing, DEM Generation, Cubesat Satellites, Stereo-Modelling, 3D Modelling</p>
<p>The post <a href="https://climateservices.it/publication/dem-generation-from-multi-view-satellite-images-in-sub-sahel-region/">DEM Generation from Multi-View Satellite Images in Sub-Sahel Region</a> appeared first on <a href="https://climateservices.it">climateservices.it CNR-IBE</a>.</p>
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