Using MATLAB to Empower Modern Numerical Weather Forecasts

The availability of quality weather data has improved dramatically over the past decade. At the same time, the number of big data analytics businesses delivering sector-specific solutions and business insights has also grown accordingly. However, timely access to such quality weather data, suited to specific business requirements and delivered in formats that users can apply seamlessly to in-house systems, has remained a challenge.

Meteomatics is a commercial weather data provider that is working collaboratively with national meteorological services and scientific communities. The company brings together historical, nowcast, and forecast weather data from global models such as the UK Met Office, ECMWF IFS model, satellite operations, station data, and their in-house developed Meteodrone system. By applying hyper-local modelling and downscaling capabilities, Meteomatics is able to deliver weather data for any location and time period for use in third-party models via an industrial-scale robust weather API. In this talk, the API is introduced and demonstrated.

Weather API data enables insights that are relevant across all sectors, both public and private. Energy companies, both in the traditional and renewable sectors, are extensively using these solutions to forecast demand and power output, inform energy trading, protect themselves against unfavorable seasons, and safeguard revenues. Also, water utilities are enhancing demand and leakage models, better management of system capacity, and regulatory compliance through weather-enabled automation of alarms, catchment modelling, and enhanced workforce management.

In summary, simple API access to quality weather data is extending the understanding of weather risk for a broad range of sectors. The speed of development of new products and services underpinned by quality weather data, indices, benchmarks, and parametric triggers is growing rapidly.