The advances in Numerical Weather Prediction (NWP) in the last decades have been tremendous thanks to more, and better assimilated, observations, higher computing power and progress in our understanding of dynamics and physics. These advances, which have led to increasingly skilful weather forecasting, will become even more relevant in the future. Consequently, the emphasis in operational meteorology, hydrology, oceanography and climatology has shifted towards the implementation of increasingly sophisticated and diverse numerical models and applications in order to serve an ever-increasing variety of users. Operational Numerical Weather Prediction systems generally provide an accurate indication of developing weather events from hours to days ahead. They are, therefore, one of the most relevant components of routine and severe weather forecasting and warnings at National Meteorological and Hydrological Services. However, the weather forecasting capability among these National Services in varies enormously. The more advanced are making use of the progress in Numerical Weather Prediction, but those in the developing and least developed countries have seen little advancement due to limited budgets and reduced capabilities. And the gap is increasing.
The Global Data-Processing and Forecasting System (GDPFS) encompasses all systems operated by Members (including those jointly coordinated with other international organizations such as ICAO) and enables them to make use of the advances in Numerical Weather Prediction by providing a framework for sharing data related to operational meteorology, hydrology, oceanography and climatology. The main support for the exchange and delivery of these data is the WMO Information System (WIS). One of the key benefits of the WMO Information System is the expansion of the range of centres that can connect to the system, increasing the range of Global Data-Processing and Forecasting System applications.
The cascading forecasting process
Owing to the high computational cost of global and limited-area Numerical Weather Prediction models, including Ensemble Prediction Systems using multiple model runs, few Members have the operational capacity to implement such systems. Many of the latest advances in Numerical Weather Prediction systems, such as so-called “convection-permitting” models, are particularly suitable for severe weather forecasting in tropical and sub-tropical regions; however, as they are extremely computationally intensive, they are supported only by the leading Meteorological Services. The Severe Weather Forecasting Demonstration Project of the Global Data-Processing and Forecasting System makes products from Numerical Weather Prediction models, including Ensemble Prediction Systems, of the most advanced Members available to all using a Cascading Forecasting Process.
The Severe Weather Forecasting Demonstration Project contributes to capacity building by helping developing countries to access and make use of existing Numerical Weather Prediction products for improving hazardous weather warnings. It encourages operational forecasters to use relevant standards and newly developed products and procedures. The Project outcomes:
- Enhanced capability for National Meteorological and Hydrological Services to forecast severe weather and issue warnings at the national level, including improved accuracy and longer lead-times;
- Establishment of processes for multi-hazard early warnings with national disaster management and civil protection authorities, with planned responses for protection of lives and property;
- Established forecast processes and Quality Management Systems (QMS), and strengthened forecast capabilities in support of other socio-economic (such as agriculture and food security, aviation, marine safety and transportation, etc.) at the national level;
- Raised awareness of the value of National Meteorological and Hydrological Services with national governments and their agencies, leading in the longterm to greater national support and investment…leading, in turn, to improved supply of observations and feedback into the Global Data-Processing and Forecasting System ; and
- Reduced loss of life and damage to property and contributions to the 2030 Development Agenda and the Sendai Framework for Disaster Risk Reduction.