By Miriam Humbe
The Director General and Chief Executive Officer of the Nigerian Meteorological Agency (NiMet), Prof. Charles Anosike, on Tuesday, 18th March 2025, participated in the Anticipatory Action Task Force on Floods (AATF) meeting hosted by the Vice President of Nigeria, Sen. Kashim Shettima.
AATF is Nigeria’s initiative to shift flood response from reactive disaster relief to proactive preparedness.
The initiative aligns with global frameworks such as the Sendai Framework for Disaster Risk Reduction, the Sustainable Development Goals (SDGs), and the Paris Agreement on Climate Change, all of which emphasize proactive disaster preparedness to mitigate the impact of natural disasters and protect vulnerable communities.
Members of the AATF briefed the Vice President on their various agencies’ preparedness and ongoing activities with a view to mitigating the impact of flooding in Nigeria.
Speaking at the meeting, Vice President Shettima said that the human and economic toll of floods in Nigeria has become unbearable.
“We cannot afford to wait for disaster to strike before taking action. We must act now to protect lives and livelihoods by leveraging science, technology, and collaboration”, he said.
Prof. Charles Anosike while speaking thanked the Vice President for his proactive leadership towards the implementation of anticipatory action to enable adequate preparedness and response for climate extremes.
He noted that NiMet’s impact-based forecasts can help facilitate anticipatory action by providing timely alerts before extreme events occur.
“These forecasts enable individuals and vulnerable communities to respond effectively.
“Therefore, by integrating NiMet’s forecasts with other components of early warning systems, we can enhance risk awareness and response capabilities”, he said.
Prof. Anosike highlighted key priority areas for improving NiMet’s forecasts.
These included enhancing NiMet’s weather models, investing in forecast models, expanding the network of stations and improving In-situ observation, improving data assimilation capacity, and increasing application of machine learning techniques to improve predictions.
