Articles | Volume 19, issue 2
https://doi.org/10.5194/amt-19-461-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-461-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Jan 2026
Research article |
| 21 Jan 2026
| 21 Jan 2026
Have you ever seen the rain? Observing a record convective rainfall with national and local monitoring networks and opportunistic sensors
Louise Petersson Wårdh
CORRESPONDING AUTHOR
Division of Water Resources Engineering, Faculty of Engineering, Lund University, P.O. Box 118, 22100 Lund, Sweden
Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 601 76 Norrköping, Sweden
Hasan Hosseini
Division of Water Resources Engineering, Faculty of Engineering, Lund University, P.O. Box 118, 22100 Lund, Sweden
Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 601 76 Norrköping, Sweden
Remco van de Beek
Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 601 76 Norrköping, Sweden
Jafet C. M. Andersson
Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 601 76 Norrköping, Sweden
Hossein Hashemi
Division of Water Resources Engineering, Faculty of Engineering, Lund University, P.O. Box 118, 22100 Lund, Sweden
Jonas Olsson
Division of Water Resources Engineering, Faculty of Engineering, Lund University, P.O. Box 118, 22100 Lund, Sweden
Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 601 76 Norrköping, Sweden
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We present a novel method for classifying rain and snow by combining data from commercial microwave links (CMLs) with weather radar. We compare this to a reference method using dew point temperature for precipitation type classification. Evaluations with nearby disdrometers show that CMLs improve the classification of dry snow and rainfall, outperforming the reference method.
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This article presents data from three types of sensors for rain measurement, i.e. commercial microwave links (CMLs), gauges, and weather radar. Access to CML data is typically restricted, which limits research and applications. We openly share a large CML database (364 CMLs at 10 s resolution with true coordinates), along with 11 gauges and one radar composite. This opens up new opportunities to study CMLs, to benchmark algorithms, and to investigate how multiple sensors can best be combined.
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Many western Africans encounter serious floods every year. The FANFAR project co-designed a pre-operational flood forecasting system (FEWS) with 50 key western African stakeholders. Participatory multi-criteria decision analysis (MCDA) helped prioritize a FEWS that meets their needs: it should provide accurate, clear, and timely flood risk information and work reliably in tough conditions. As a theoretical contribution, we propose an assessment framework for transdisciplinary hydrology research.
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We evaluate the skill of a regional climate model, HARMONIE-Climate, to capture the present-day characteristics of heavy precipitation in the Nordic region and investigate the added value provided by a convection-permitting model version. The higher model resolution improves the representation of hourly heavy- and extreme-precipitation events and their diurnal cycle. The results indicate the benefits of convection-permitting models for constructing climate change projections over the region.
Jonas Olsson, Peter Berg, and Remco van de Beek
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We have developed a tool to visualize rainfall observations, based on a combination of meteorological stations and weather radars, over Sweden in near real-time. By accumulating the rainfall in time (1–12 h) and space (hydrological basins), the tool is designed mainly for hydrological applications, e.g. to support flood forecasters and to facilitate post-event analyses. Despite evident uncertainties, different users have confirmed an added value of the tool in case studies.
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-177, https://doi.org/10.5194/hess-2021-177, 2021
Manuscript not accepted for further review
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West Africa faces serious floods, affecting millions of people every year. The FANFAR project co-designed a flood forecasting and warning system at lively workshops together with 50–60 key West African stakeholders. We prioritized FANFAR system configurations that best meet stakeholders’ needs and expectations. Stakeholders preferred a system producing accurate, clear, and accessible flood risk information, which works reliably under difficult West African conditions.
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Short summary
Extreme rainfall can cause severe damage, especially in cities. However, national meteorological institutes have difficulties to observe such events. In this study we show that rainfall observations collected by local actors, such as municipalities and even citizens, can contribute to better rainfall observations. Sweden’s official monitoring network could not capture the event under study, whereas the complementary sensors contributed to a better understanding of the magnitude of the event.
Extreme rainfall can cause severe damage, especially in cities. However, national meteorological...
