Articles | Volume 16, issue 17
https://doi.org/10.5194/amt-16-4067-2023
https://doi.org/10.5194/amt-16-4067-2023
Research article
 | 
11 Sep 2023
Research article |  | 11 Sep 2023

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

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

Related authors

Adaptation of RainGaugeQC algorithms for quality control of rain gauge data from professional and non-professional measurement networks
Katarzyna Ośródka, Jan Szturc, Anna Jurczyk, and Agnieszka Kurcz
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-204,https://doi.org/10.5194/amt-2024-204, 2025
Preprint under review for AMT
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
GNSS-RO residual ionospheric error (RIE): a new method and assessment
Dong L. Wu, Valery A. Yudin, Kyu-Myong Kim, Mohar Chattopadhyay, Lawrence Coy, Ruth S. Lieberman, C. C. Jude H. Salinas, Jae N. Lee, Jie Gong, and Guiping Liu
Atmos. Meas. Tech., 18, 843–863, https://doi.org/10.5194/amt-18-843-2025,https://doi.org/10.5194/amt-18-843-2025, 2025
Short summary
Benchmarking KDP in rainfall: a quantitative assessment of estimation algorithms using C-band weather radar observations
Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev
Atmos. Meas. Tech., 18, 793–816, https://doi.org/10.5194/amt-18-793-2025,https://doi.org/10.5194/amt-18-793-2025, 2025
Short summary
Comparative experimental validation of microwave hyperspectral atmospheric soundings in clear-sky conditions
Lei Liu, Natalia Bliankinshtein, Yi Huang, John R. Gyakum, Philip M. Gabriel, Shiqi Xu, and Mengistu Wolde
Atmos. Meas. Tech., 18, 471–485, https://doi.org/10.5194/amt-18-471-2025,https://doi.org/10.5194/amt-18-471-2025, 2025
Short summary
Global Navigation Satellite System (GNSS) radio occultation climatologies mapped by machine learning and Bayesian interpolation
Endrit Shehaj, Stephen Leroy, Kerri Cahoy, Alain Geiger, Laura Crocetti, Gregor Moeller, Benedikt Soja, and Markus Rothacher
Atmos. Meas. Tech., 18, 57–72, https://doi.org/10.5194/amt-18-57-2025,https://doi.org/10.5194/amt-18-57-2025, 2025
Short summary
Determination of low-level temperature profiles from microwave radiometer observations during rain
Andreas Foth, Moritz Lochmann, Pablo Saavedra Garfias, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 7169–7181, https://doi.org/10.5194/amt-17-7169-2024,https://doi.org/10.5194/amt-17-7169-2024, 2024
Short summary

Cited articles

Bonaccorso, B., Brigandì, G., and Aronica, G. T.: Regional sub-hourly extreme rainfall estimates in Sicily under a scale invariance framework, Water Resour. Manage., 34, 4363–4380, https://doi.org/10.1007/s11269-020-02667-5, 2020. 
Burcea, S., Cică, R., and Bojariu, R.: Radar-derived convective storms' climatology for the Prut River basin: 2003–2017, Nat. Hazards Earth Syst. Sci., 19, 1305–1318, https://doi.org/10.5194/nhess-19-1305-2019, 2019. 
Chan, S. C., Kendon, E. J., Roberts, N. M., Fowler, H. J., and Blenkinsop, S.: The characteristics of summer sub-hourly rainfall over the southern UK in a high-resolution convective permitting model, Environ. Res. Lett., 11, 094024, https://doi.org/10.1088/1748-9326/11/9/094024, 2016. 
Fabry, F., Meunier, V., Treserras, B. P., Cournoyer, A., and Nelson, B.: On the Climatological Use of Radar Data Mosaics: Possibilities and Challenges, B. Am. Meteorol. Soc., 98, 2135–2148, https://doi.org/10.1175/BAMS-D-15-00256.1, 2017. 
Hamidi, A., Devineni, N., Booth, J. F., Hosten, A., Ferraro, R. R., and Khanbilvardi, R.: Classifying urban rainfall extremes using weather radar data: An application to the greater New York area, J. Hydrometeorol., 18, 611–623, https://doi.org/10.1175/JHM-D-16-0193.1, 2017. 
Download
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 greater efficiency of the algorithms for data quality control and merging for longer accumulations. Monthly accumulations estimated by RainGRS Clim were found to be significantly more reliable than accumulations generated operationally.
Share