The AI Nowcasting Pilot Project (AINPP) is a global initiative led by the World Meteorological Organization (WMO) to transform severe weather forecasting through the power of Artificial Intelligence (AI). With a special focus on developing countries, the project aims to accelerate the adoption of effective nowcasting systems that deliver timely early warnings, helping to save lives and reduce socio-economic losses.
AINPP brings together a global network of national meteorological services, academic institutions, and industry leaders to develop, test, and implement AI-based nowcasting solutions.
Python library for Forecasting and Tracking the Evolution of Configurable Clusters (pyForTraCC).
Satellite Precipitation Validation System.
A nowcasting system leverages real-time radar, satellite imagery, and machine learning algorithms to deliver short-term forecasts of imminent weather events.
Python library for Forecasting and Tracking the Evolution of Configurable Clusters (pyForTraCC) is a Python library developed for identifying, tracking, and forecasting clusters in diverse datasets. Its modular structure enables flexible integration, supporting user-defined configurations and compatibility with multiple input formats.
>> Learn more about the pyForTraCC
Precipitation detection performance is evaluated by comparing satellite product (e.g. GSMaP) estimates with ground reference data (RADAR and/or Raingauges) when available.
Satellite Precipitation Validation System.
Learn more about the ValidationA nowcasting system leverages near real time satellite products and machine learning algorithms to deliver short-term forecasts of imminent weather events.
Forecast.
Learn more about the ForecastNational Meteorological and Hydrometeorological Services (NMHS):
Academia and private sectors:
