Numerical Weather Prediction (NWP) plays a critical role in meteorology, providing essential data for accurate weather forecasting and supporting decision-making processes in sectors such as agriculture, disaster management, transportation, and public safety. By enabling the prediction of weather phenomena with increasing precision, NWP models contribute significantly to safeguarding lives, protecting property, and enhancing the socio-economic prosperity of communities around the world.

As technological advancements and artificial intelligence (AI) continue to enhance the capabilities of NWP systems, there is a growing need for meteorologists and practitioners to build their capacity to effectively utilize these tools. This includes mastering advanced models, such as the Weather Research and Forecasting (WRF) model, and employing post-processing techniques to refine forecasts.

Additionally, participants will be introduced to the PN-Tool, a specialized application designed to facilitate operational meteorology tasks. This tool provides a streamlined interface for utilizing NWP data, making it a valuable resource for improving forecast accuracy and efficiency in real-world scenarios.

In response to these needs, the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) as one of the Regional Training Centers of WMO in the Regional Association V (Southwest Pacific) will organize an online training course on NWP. This program aims to equip participants with the fundamental and practical knowledge necessary to utilize the full potential of NWP for operational meteorology, contributing to a resilient and prosperous society. 

The purpose of Online Training on Numerical Weather Prediction (NWP) is to enhance a comprehensive understanding of a scientific method of weather forecasting using mathematical models that simulate the earth’s atmosphere, oceans, and other environmental factors. This training also aims to equip participants with the skills to understand and effectively use NWP models in operational and research contexts, as well as to perform model verification to assess the accuracy and reliability of predictions.