Spring 2025 Lecture in Climate Data Science: BERNARD MINOUNGOU

Tue Jan 14 2025 at 12:00 pm to 01:30 pm UTC-05:00

Columbia Engineering Innovation Hub | New York

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Spring 2025 Lecture in Climate Data Science: BERNARD MINOUNGOU
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TITLE: "NEXT-GEN APPROACH TO HYDROLOGICAL FORECASTING: ADAPTING PyCPT TOOL FOR HYDROLOGICAL FORECASTING"
Speaker: Bernard Minoungou (Centre Régional AGRHYMET, Niger)
Date: January 14, 2025
Time: 12:00 p.m.
Format: Hybrid
Virtual: Zoom link provided upon registration
In-person: Columbia Innovation Hub, 2276 12th Avenue, Second Floor, Hirsh Conference Room, New York, NY 10027


*Please note that in-person space is limited.*

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Abstract: The NextGen forecasting system helps forecasters to evaluate the performance of different global climate models, which helps determine how best to correct and combine them. AGRHYMET Regional Climate Centre has been capacitating National Meteorological and Hydrological Services (NMHSs) in West Africa and the Sahel on NextGen seasonal forecasting systems. The capacity development efforts focus mainly on Python interface to the Climate Predictability Tool (CPT) or PyCPT, a tool developed by the International Research Institute for Climate and Society (IRI) to implement the NextGen approach to climate forecasting. While hydrological forecasts of water availability from watersheds in major river basins are essential to support operational planning and management, the latest version of PyCPT developed by IRI does not take into account the seasonal forecast of hydrological variables. To address this challenge and respond to the needs of NMHSs in the region in charge of hydrological monitoring, AGRHYMET has adapted and improving the PyCPT tool for seasonal hydrological forecasts. The primary hydrological data used to produce seasonal forecasts are the daily flows recorded at the main river stations in the region. These data are distributed across the main river basins. Forecasts are based on correlations between ocean surface temperature, precipitation, wind speed, and hydrological variables such as river flows for the high-water period. Principal component regression (PCR) is the most widely considered method. A variety of forecast performance scores consisting of scores based on continuous measurements, those on observed measurements, and, in some cases, forecasts are used as well. The work carried out by the AGRHYMET Regional Center, in response to the needs expressed by players in the sub-region, has resulted in the enhancement of PyCPT for hydrological applications. The current development has been tested through case studies in West Africa, and the results obtained are promising.

Bio: Bernard Minoungou is a hydrologist with expertise in hydrological modeling at the AGRHYMET Regional Center. His activities include the development of decision-support approaches, tools and products on water resources, climate and food security. It also conducts capacity-building activities for national meteorological and hydrological services in 17 CILSS-ECOWAS (Comité inter-État de lutte contre la sécheresse au Sahel- Communauté économique des États de l'Afrique de l'Ouest) countries. It contributes to the adaptation of Enhancing National Climate Services initiative tools (ENACTS) initiative and their appropriation by national staffs in order to improve climate services.


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BERNARD MINOUNGOU
Centre Régional AGRHYMET

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Columbia Engineering Innovation Hub, 2276 12th Avenue, New York, United States

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