Preprints
https://doi.org/10.5194/amt-2023-98
https://doi.org/10.5194/amt-2023-98
15 May 2023
 | 15 May 2023
Status: this preprint is currently under review for the journal AMT.

Long-term multi-source precipitation estimation with high resolution (RainGRS Clim)

Anna Jurczyk, Katarzyna Ośródka, Jan Szturc, Magdalena Pasierb, and Agnieszka Kurcz

Abstract. This paper explores the possibility of using multi-source precipitation estimates for climatological applications. A data processing algorithm (RainGRS Clim) has been developed to work on precipitation accumulations such as daily or monthly totals, which are significantly longer than operational accumulations (generally between 5 min and 1 h). The algorithm makes the most of additional opportunities, such as the possibility to complement with delayed data, access to high-quality data that are not operationally available, and the greater efficiency of the algorithms for data quality control and merging on longer accumulations. Verification of the developed algorithms was carried out on monthly accumulations through comparison with precipitation from manual rain gauges. As a result, monthly accumulations estimated by RainGRS Clim were found to be significantly more reliable than accumulations generated operationally. This improvement is particularly noticeable for the winter months, when precipitation estimation is much more difficult due to less reliable radar estimates.

Anna Jurczyk et al.

Status: open (until 05 Jul 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Anna Jurczyk et al.

Anna Jurczyk et al.

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Short summary
A data processing algorithm RainGRS Clim has been developed to work on precipitation accumulations such as daily or monthly totals. The algorithm makes the most of additional opportunities: access to high-quality data that are not operationally available and the greater efficiency of the algorithms for data quality control and merging on longer accumulations. Monthly accumulations estimated by RainGRS Clim were found to be significantly more reliable than accumulations generated operationally.