Operational convection-permitting COSMO/ICON ensemble predictions at observation sites (CIENS)

TL;DR

The paper introduces the CIENS dataset, a comprehensive collection of ensemble weather forecasts and observations across Germany, designed for research in forecast verification, post-processing, and climate modeling, with a focus on long-term analysis and model updates.

Contribution

It provides a long-term, detailed ensemble weather forecast dataset with observations, supporting research on model updates, ensemble post-processing, and forecast verification.

Findings

Dataset covers 2010-2023 with 55 variables and observations at 170 locations.

Enables analysis of model updates and forecast accuracy over time.

Illustrates benefits of machine learning in ensemble post-processing.

Abstract

We present the CIENS dataset, which contains ensemble weather forecasts from the operational convection-permitting numerical weather prediction model of the German Weather Service. It comprises forecasts for 55 meteorological variables mapped to the locations of synoptic stations, as well as additional spatially aggregated forecasts from surrounding grid points, available for a subset of these variables. Forecasts are available at hourly lead times from 0 to 21 hours for two daily model runs initialized at 00 and 12 UTC, covering the period from December 2010 to June 2023. Additionally, the dataset provides station observations for six key variables at 170 locations across Germany: pressure, temperature, hourly precipitation accumulation, wind speed, wind direction, and wind gusts. Since the forecast are mapped to the observed locations, the data is delivered in a convenient format for analysis. The CIENS dataset complements the growing collection of benchmark datasets for weather and climate modeling. A key distinguishing feature is its long temporal extent, which encompasses multiple updates to the underlying numerical weather prediction model and thus supports investigations into how forecasting methods can account for such changes. In addition to detailing the design and contents of the CIENS dataset, we outline potential applications in ensemble post-processing, forecast verification, and related research areas. A use case focused on ensemble post-processing illustrates the benefits of incorporating the rich set of available model predictors into machine learning-based forecasting models.