1 million-year Synthetic Daily Weather Dataset

# 1 million-year Synthetic Daily Weather Dataset ## Overview This dataset contains **synthetic daily time series of precipitation and mean temperature** generated using a **stochastic, multi-site, multivariate weather generator** as described in Nguyen et al. (2021, 2024). The weather generator was trained on observed weather data from the **Bamberg station** (Station ID 00282, DWD 2022) for the historical period **1961–2010**. ## Data Description - **Variables**: - Daily **precipitation** (mm) - Daily **mean temperature** (°C) - **Length**: 1,000,000 years of daily data (synthetic simulation) - **File structure**: - The dataset is provided as two compressed ZIP archives: - `syn_prec_daily_1000000_years.zip`: Contains synthetic **daily precipitation** data. - `syn_tavg_daily_1000000_years.zip`: Contains synthetic **daily mean temperature** data. - Each ZIP file contains **10 subfolders**, each representing a **100,000-year chunk**. - Each chunk includes **2,000 realizations** of **50-year periods**, all spanning the time frame:- **1 January 1961 to 31 December 2010**. - **Spatial coverage**: Single location: Bamberg; ID= 00282; lon/lat = 10.9206/49.8743 - **Temporal structure**: Daily values with stationary statistical properties ## Methodology The dataset was generated using a **stationary version** of the weather generator by Nguyen et al., which captures both marginal distributions and temporal dependencies. - **Precipitation**: - Modeled using a **monthly-based extended Generalized Pareto Distribution (GPD)**.- **Temperature**: - Modeled using a **normal distribution**, conditioned on precipitation occurrence. The generator accounts for monthly seasonality but assumes stationarity across the entire simulated period. ## Applications This synthetic dataset can be used for:- Hydrological and climate risk modeling- Extreme value analysis (e.g., block maxima, return periods)- Ensemble simulation for stress-testing models- Probabilistic impact assessments ## References DWD: Climate Data Centre: Station ID 00282, DWD [dataset], https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/ (last access: 15 December 2022), 2022 Macdonald, E., Merz, B., Guse, B., Nguyen, V. D., Guan, X., and Vorogushyn, S.: What controls the tail behaviour of flood series: rainfall or runoff generation?, Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, 2024. Nguyen, V. D., Merz, B., Hundecha, Y., Haberlandt, U., and Vorogushyn, S.: Comprehensive evaluation of an improved large-scale multi-site weather generator for Germany, Int. J. Climatol., 41, 4933–4956, https://doi.org/10.1002/joc.7107, 2021. Nguyen, V. D., Vorogushyn, S., Nissen, K., Brunner, L., and Merz, B.: A nonstationary climate-informed weather generator for assessing future flood risks. Advances in Statistical Climatology, Meteorology and Oceanography, 10(2), 195–216, https://doi.org/10.5194/ascmo-10-195-2024, 2024. ## License This dataset is published under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license. You are free to use, share, and adapt the data with appropriate credit. ## Contact For questions or feedback, please contact: **Viet Dung Nguyen (PhD)** Section Hydrology Email: [e-mail] [GFZ Profile](https://www.gfz.de/en/staff/viet.dung.nguyen) [ORCID](https://orcid.org/0000-0002-2649-2520)