qcpcp (qcp²): quality control of precipitation observations

Space-Time Statistical Quality Control of Extreme Precipitation Observation

Goal:

0. Transform data using Box-Cox transormation with suitable parameter (example code 1)
1. Find outlier in precipitation data by cross-validation approach using neighboring observations (example code 2)
2. Identified outliers (a false observation or a single event) should be verified by discharge or radar data.
3. Repeat the procedure over several temporal aggregations (to account for advection)

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Reference paper:

El Hachem, A., Seidel, J., Imbery, F., Junghänel, T., and Bárdossy, A.: Technical Note: Space-Time Statistical Quality Control of Extreme Precipitation Observations, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2022-177, in review, 2022.

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Flowchart - Procedure

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Example data and codes on Github

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Study area

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Box-Cox transformation [Python code 1]

Skewness before and after transformation

Average transformation factor

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Identified outlier [Python code 2]

Time series target and neighbors

Event spatial configuration with Radar image

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Data availability:

The precipitation data and the radar data were made available by the German Weather Service (DWD) [https://opendata.dwd.de/climate_environment/CDC/]. The discharge data were made available by the environmental state of Bavaria and can be requested [https://www.lfu.bayern.de/index.htm]

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Used packages for Github example code:

1. PyKrige: Benjamin Murphy, Roman Yurchak, & Sebastian Müller. (2022). GeoStat-Framework/PyKrige: v1.7.0 (v1.7.0). Zenodo. https://doi.org/10.5281/zenodo.7008206
2. adjusttext: https://adjusttext.readthedocs.io/en/latest/
3. statsmodels: https://www.statsmodels.org/devel/

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Note: this is an exmaple case, in the paper a modified code was used with Variogram estimation and personal kriging code. These are not updated to keep things simple.