During the 77th UN General Assembly Science Summit side event in September 2022, I had the honor of chairing a finance working group focused on advocating for timely, reliable, and standardized statistical climate and weather data across Sub-Saharan Africa to effectively mitigate climate risks. The working group comprised representatives from esteemed institutions including the African Development Bank, the banking sector, private enterprises, and civil society organizations.
The imperative for such advocacy stems from the significant challenges encountered by statisticians both nationally and regionally within Sub-Saharan Africa over the past decade. These challenges have hindered their ability to report accurately on weather, water, and climate phenomena. Factors such as the inadequate availability of meteorological observation stations, compounded by limited financial and human resources, have exacerbated this issue. Additionally, statisticians lack access to refined climate scenarios and detailed global climate models necessary for comprehensive understanding and analysis of large-scale climate variables. The output from existing climate models in the region is often insufficient for informing critical processes such as environmental impact assessments, climate adaptation planning, and local or regional decision-making.
The absence of robust climate data severely hampers the ability of African nations to address pressing issues like droughts and floods, which disproportionately affect vulnerable communities, including small, medium, and large enterprises reliant on natural resources such as agriculture. Existing satellite data, while valuable for mapping and short-term climate predictions, does not fully address the need for comprehensive climate information systems and expanded meteorological monitoring infrastructure in Sub-Saharan Africa.
At the summit, I had the opportunity to showcase how IBM's advanced technology could effectively bridge the existing gaps in weather and climate data, particularly in Sub-Saharan Africa. IBM's collaboration with the National Oceanic and Atmospheric Administration (NOAA) represents a significant stride towards enhancing weather forecasting accuracy and accessibility.
IBM provides open-source cloud platforms like the IBM Cloud Open Data Platform and the IBM Watson Developer Cloud, which facilitate the sharing and access of weather data. Leveraging IBM's expertise in artificial intelligence (AI) and machine learning, vast amounts of weather data can be analyzed to discern intricate patterns, thereby contributing to the development of more sophisticated forecast models.
Meanwhile, NOAA's extensive network of weather stations, satellites, and other instruments ensures the continuous collection of high-quality weather data, adhering to rigorous standards and implementing robust quality control measures.
The collaboration between IBM and NOAA is poised to enhance weather forecasting accuracy, thereby bolstering preparedness for severe weather events, reducing fatalities, and mitigating property damage. Timelier and more precise forecasts empower individuals, businesses, and governments to make informed decisions, optimize resource allocation, and plan effectively.
Open weather data not only aids in studying climate change patterns but also serves as a catalyst for developing innovative applications across various sectors such as agriculture, transportation, and renewable energy.
Nevertheless, challenges persist, including the need to ensure the privacy and security of sensitive weather data while maintaining openness. Consistency in data quality and adherence to standards across different sources are imperative for accurate analysis. Additionally, open data platforms must be user-friendly and accessible to stakeholders with varying levels of technical expertise.
In conclusion, the collaboration between IBM and NOAA holds immense potential to revolutionize weather forecasting and its applications. By addressing challenges and leveraging opportunities, this collaboration can usher in a safer, more resilient, and data-driven future.
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[i] Solomon H. Gebrechorkos, Stephan Hülsmann & Christian Bernhofer (2019), Statistically downscaled climate dataset for East Africa.