RuDar: Weather Radar Dataset for Precipitation Nowcasting with Geographical and Seasonal Variability

Petr Vytovtov, Eugenia Elistratova, Evgenii Tsymbalov, Alexander Ganshin, Yuri Pavlyukov

Keywords: precipitation nowcasting, weather forecasting, weather radar, benchmark

TL;DR: Weather radar dataset with benchmarks for nowcasting (next frame prediction) tasks with seasonal and geographical dependencies

Abstract:

Precipitation nowcasting, a short-term (up to six hours) rain prediction, is arguably one of the most demanding weather forecasting tasks.
To achieve accurate predictions, a forecasting model should consider miscellaneous meteorological and geographical data sources.
Currently available datasets provide information only about precipitation intensity, vertically integrated liquid (VIL), or maximum reflectivity on the vertical section.
Such single-level or aggregated data lacks description of the reflectivity change in vertical dimension, simplifying or distorting the corresponding models.

To fill this gap, we introduce an additional dimension of the precipitation measurements in the RuDar dataset that incorporates 3D radar echo observations.
Measurements are collected from 30 weather radars located mostly in the European part of Russia, covering multiple climate zones.
Radar product updates every 10 minutes with a 2 km spatial resolution.
The measurements include precipitation intensity (mm/h) at an altitude of 600 m, reflectivity (dBZ) and radial velocity (m/s) at 10 altitude levels from 1 km to 10 km with 1 km step.
We also add the orography information as it affects the intensity and distribution of precipitation.
The dataset includes over 50 000 timestamps over a two-year period from 2019 to 2021, totalling in roughly 100 GB of data.

We evaluate several baselines, including optical flow and neural network models, for precipitation nowcasting on the proposed data. We also evaluate the uncertainty quantification for the ensemble scenario and show that the corresponding estimates do correlate with the ensemble errors on different sections of data.
We believe that RuDar dataset will become a reliable benchmark for precipitation nowcasting models and also will be used in other machine learning tasks, e.g., in data shift studying, anomaly detection, or uncertainty estimation.
Both dataset and code for data processing and model preparation are publicly available.

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