IT-DPC-SRI: A Cloud-Optimized Archive of Italian Radar Precipitation (2010-2025)

We present IRENE (Italian Radar Ensemble Nowcasting Experiment), a deep learning model for probabilistic short-range precipitation nowcasting over the Italian domain at \SI{1}{km} spatial and 5 min temporal resolution. IRENE adopts an encoder--forecaster architecture built on multi-scale Convolutional Gated Recurrent Units (ConvGRUs), trained on the national radar composite produced by the Italian Civil Protection Department (DPC). An importance-sampling scheme focuses training on precipitation-relevant events, while the almost-fair Continuous Ranked Probability Score (afCRPS) is adopted as the primary probabilistic loss function. Two additional training configurations are proposed: an adversarial (GAN) variant, IRENE-GAN, designed to improve the spatial sharpness of the generated forecasts, and a spectrally constrained variant, IRENE-GAN-RAPSD, in which the adversarial objective is complemented by an explicit penalty on the radially averaged power spectral density. The three configurations are evaluated against the stochastic extrapolation method STEPS and the pre-trained deep learning model DGMR. All IRENE configurations attain a lower Continuous Ranked Probability Score than both benchmarks at every lead time and rank histograms closer to uniformity, indicating better probabilistic skill and ensemble calibration. In terms of ensemble-mean mean absolute error the advantage is confined to the first 90 min, beyond which the strongly damped DGMR fields and, to a lesser extent, STEPS become competitive. Spectral analysis shows that the adversarial training removes the progressive loss of small-scale variance exhibited by IRENE, at the cost of an excess of fine-scale power at long lead times that the spectral penalty only partially controls.

Machine learning-based precipitation nowcasting relies on high-fidelity radar reflectivity sequences to model the short-term evolution of convective storms. However, the development of models capable of predicting extreme weather has been constrained by the coarse resolution (1-2 km) of existing public radar datasets, such as SEVIR, HKO-7, and GridRad-Severe, which smooth the fine-scale structures essential for accurate forecasting. To address this gap, we introduce Storm250-L2, a storm-centric radar dataset derived from NEXRAD Level-II and GridRad-Severe data. We algorithmically crop a fixed, high-resolution (250 m) window around GridRad-Severe storm tracks, preserve the native polar geometry, and provide temporally consistent sequences of both per-tilt sweeps and a pseudo-composite reflectivity product. The dataset comprises thousands of storm events across the continental United States, packaged in HDF5 tensors with rich context metadata and reproducible manifests.

This data paper describes METBRA25Y, a harmonized archive of hourly surface meteorological observations from Brazil derived from public historical records of the Instituto Nacional de Meteorologia (INMET). The dataset was designed to support reproducible environmental, climatological, hydrological, agricultural, urban-risk, and machine-learning studies that require station-level meteorological time series with standardized variable names and explicit quality-control metadata. The processing workflow ingests annual INMET archives, parses station metadata from raw file headers, normalizes heterogeneous Portuguese column names into a canonical schema, constructs hourly timestamps, consolidates observations by city and station, and exports compressed CSV files together with station manifests, per-station quality flags, daily precipitation aggregates, variable-level failure summaries, and missing-data audits. The quality-control protocol follows a two-stage strategy: first, physically implausible values are converted to missing values and flagged; second, temporal and cross-variable consistency checks generate diagnostic flags without necessarily overwriting the original measurements. The resulting package covers observations between 2000 and 2025, with stationspecific temporal coverage, and includes key meteorological variables such as precipitation, air temperature, dew point, relative humidity, atmospheric pressure, wind speed, wind gust, wind direction, and global solar radiation. Based on the summary files included in the current release snapshot, the archive contains 616 unique station codes across variable summaries, of which 605 have coordinates within a broad Brazil plausibility envelope. This paper documents the dataset provenance, file organization, harmonized schema, quality-control rules, technical validation outputs, limitations, and recommended usage practices.