Hail risk assessment is necessary to estimate and reduce damage to crops,
orchards, and infrastructure. Also, it helps to estimate and reduce consequent
losses for businesses and, particularly, insurance companies. But hail
forecasting is challenging. Data used for designing models for this purpose are
tree-dimensional geospatial time series. Hail is a very local event with
respect to the resolution of available datasets. Also, hail events are rare -
only 1% of targets in observations are marked as "hail". Models for nowcasting
and short-term hail forecasts are improving. Introducing machine learning
models to the meteorology field is not new. There are also various climate
models reflecting possible scenarios of climate change in the future. But there
are no machine learning models for data-driven forecasting of changes in hail
frequency for a given area.
The first possible approach for the latter task is to ignore spatial and
temporal structure and develop a model capable of classifying a given vertical
profile of meteorological variables as favorable to hail formation or not.
Although such an approach certainly neglects important information, it is very
light weighted and easily scalable because it treats observations as
independent from each other. The more advanced approach is to design a neural
network capable to process geospatial data. Our idea here is to combine
convolutional layers responsible for the processing of spatial data with
recurrent neural network blocks capable to work with temporal structure.
This study compares two approaches and introduces a model suitable for the
task of forecasting changes in hail frequency for ongoing decades