Snow Forecasting Model
The Snow Forecasting Model is an innovative, ultra-high resolution snowfall prediction system designed specifically for Greece. With a spatial resolution of 400 meters, this model revolutionizes snowfall forecasting, covering every corner of Greece and even extending beyond its borders.
Leveraging variables such as temperature, humidity, and geopotential heights at different altitudes, as well as precipitation data from numerical models like ICON-EU, GFS and GEM, a sophisticated algorithm for snowfall melting is applied to predict areas in Greece prone to snowfall during approaching cold fronts.
The project's complexity is highlighted by its intricate mapping process. The areas identified for potential snowfall are overlaid onto an ultra-high-resolution topographic grid, using a spatial resolution of 400 x 400 meters. This grid is comprised of approximately 6 million individual points covering Greece and its neighbouring countries. The model's vertical dimension consists of 400 levels, so the resulting three-dimensional grid encompasses a total of 2.5 billion individual points. This way, a cumulative snowfall map is created, as demonstrated in the examples below.
The successful execution of this project is based on careful planning and implementation, as well as substantial computational resources. The fusion of high-resolution forecast data with even finer-grained topographic data helps faithfully replicate the weather patterns of each region, resulting in a very precise outcome.
Since January 2022, the Snow Forecasting Model has been used operationally by Weatherathens. It's essential to acknowledge that the model's performance is influenced by a spectrum of factors, extending beyond its architecture. Temperature and humidity forecasts at various altitudes in the lower troposphere, as well as precipitation forecasts, as generated by numerical weather models, play a pivotal role in shaping the results. Additionally, the calculation of the minimum altitude where snow does not melt, constrained by the vertical resolution of the initial data and abrupt atmospheric changes, along with the inherent approximations of the algorithm, significantly contribute to the final outcome.
It's worth clarifying that the use of the term "model" in this context departs from its conventional association with numerical weather models. Instead, it embodies a comprehensive process of assimilating copious forecast and real-world data to intricately simulate atmospheric conditions. The ultimate goal is to make snowfall predictions accessible and understandable to the public, translating complex meteorological insights into comprehensible information.
