Articles | Volume 18, issue 3
https://doi.org/10.5194/amt-18-793-2025
© Author(s) 2025. 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-18-793-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Feb 2025
Research article |
| 13 Feb 2025
| 13 Feb 2025
Benchmarking KDP in rainfall: a quantitative assessment of estimation algorithms using C-band weather radar observations
Space Research & Observation Technologies, Finnish Meteorological Institute, Helsinki, Finland
Institute for Atmospheric & Earth system Research, University of Helsinki, Helsinki, Finland
Seppo Pulkkinen
Space Research & Observation Technologies, Finnish Meteorological Institute, Helsinki, Finland
Annakaisa von Lerber
Space Research & Observation Technologies, Finnish Meteorological Institute, Helsinki, Finland
Matthew R. Kumjian
Department of Meteorology & Atmospheric Science, Pennsylvania State University, Penn State University Park, PA, USA
Dmitri Moisseev
Space Research & Observation Technologies, Finnish Meteorological Institute, Helsinki, Finland
Institute for Atmospheric & Earth system Research, University of Helsinki, Helsinki, Finland
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Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Sarah Wugofski, Matthew R. Kumjian, Mariko Oue, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-671, https://doi.org/10.5194/egusphere-2025-671, 2025
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Doppler spectral data inform on how particles of varying vertical velocities contribute to total backscattered power observed. Through examining three case studies, consistent features in radar moment data were found to be characteristic of multi-modal spectra. We quantified how spectrum width and mean Doppler velocity can be used to determine whether or not a layer is multi-modal. The identification criteria and methods are described in Part 1 and assessed in Part 2.
Sarah Wugofski and Matthew R. Kumjian
EGUsphere, https://doi.org/10.5194/egusphere-2025-672, https://doi.org/10.5194/egusphere-2025-672, 2025
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We demonstrate the detection algorithm is successful, with 90.8 % of events verifying. Using this radar moment-based algorithm will save users time and computational expenses of processing large spectral datasets when looking for case studies of processes associated with multi-modal spectra. Storing linear depolarization ratio for detected events can facilitate finding cases specific to ice or drizzle events. Pairing this with temperature helps determine processes that may be active.
Shuai Zhang, Haoran Li, and Dmitri Moisseev
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-194, https://doi.org/10.5194/amt-2024-194, 2025
Revised manuscript accepted for AMT
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The data quality of weather radar near coastlines can be affected by echoes from ships, and this interference is exacerbated when pulse compression technology is used. This study developed a hybrid ship clutter identification algorithm based on artificial intelligence and heuristic criteria, effectively mitigating the issue. The successful reproduction of ship tracks in the Gulf of Finland supports this conclusion.
Zoé Brasseur, Julia Schneider, Janne Lampilahti, Ville Vakkari, Victoria A. Sinclair, Christina J. Williamson, Carlton Xavier, Dmitri Moisseev, Markus Hartmann, Pyry Poutanen, Markus Lampimäki, Markku Kulmala, Tuukka Petäjä, Katrianne Lehtipalo, Erik S. Thomson, Kristina Höhler, Ottmar Möhler, and Jonathan Duplissy
Atmos. Chem. Phys., 24, 11305–11332, https://doi.org/10.5194/acp-24-11305-2024, https://doi.org/10.5194/acp-24-11305-2024, 2024
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Ice-nucleating particles (INPs) strongly influence the formation of clouds by initiating the formation of ice crystals. However, very little is known about the vertical distribution of INPs in the atmosphere. Here, we present aircraft measurements of INP concentrations above the Finnish boreal forest. Results show that near-surface INPs are efficiently transported and mixed within the boundary layer and occasionally reach the free troposphere.
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
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Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.
Maximilian Maahn, Dmitri Moisseev, Isabelle Steinke, Nina Maherndl, and Matthew D. Shupe
Atmos. Meas. Tech., 17, 899–919, https://doi.org/10.5194/amt-17-899-2024, https://doi.org/10.5194/amt-17-899-2024, 2024
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The open-source Video In Situ Snowfall Sensor (VISSS) is a novel instrument for characterizing particle shape, size, and sedimentation velocity in snowfall. It combines a large observation volume with relatively high resolution and a design that limits wind perturbations. The open-source nature of the VISSS hardware and software invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.
Roberto Cremonini, Tanel Voormansik, Piia Post, and Dmitri Moisseev
Atmos. Meas. Tech., 16, 2943–2956, https://doi.org/10.5194/amt-16-2943-2023, https://doi.org/10.5194/amt-16-2943-2023, 2023
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Extreme rainfall for a specific location is commonly evaluated when designing stormwater management systems. This study investigates the use of quantitative precipitation estimations (QPEs) based on polarimetric weather radar data, without rain gauge corrections, to estimate 1 h rainfall total maxima in Italy and Estonia. We show that dual-polarization weather radar provides reliable QPEs and effective estimations of return periods for extreme rainfall in climatologically homogeneous regions.
Haoran Li, Dmitri Moisseev, Yali Luo, Liping Liu, Zheng Ruan, Liman Cui, and Xinghua Bao
Hydrol. Earth Syst. Sci., 27, 1033–1046, https://doi.org/10.5194/hess-27-1033-2023, https://doi.org/10.5194/hess-27-1033-2023, 2023
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A rainfall event that occurred at Zhengzhou on 20 July 2021 caused tremendous loss of life and property. This study compares different KDP estimation methods as well as the resulting QPE outcomes. The results show that the selection of the KDP estimation method has minimal impact on QPE, whereas the inadequate assumption of rain microphysics and unquantified vertical air motion may explain the underestimated 201.9 mm h−1 record.
Joshua S. Soderholm and Matthew R. Kumjian
Atmos. Meas. Tech., 16, 695–706, https://doi.org/10.5194/amt-16-695-2023, https://doi.org/10.5194/amt-16-695-2023, 2023
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Hailstones often exhibit opaque and clear ice layers that have an onion-like appearance. These layers are record of the conditions during growth and can be simulated by hail growth models. A new technique for automating the measurement of these layers from hail cross sections is demonstrated. This technique is applied to a collection of hailstones from Melbourne, Australia, to understand their growth evolution, and a first look at evaluating a hail growth model is demonstrated.
Jenna Ritvanen, Ewan O'Connor, Dmitri Moisseev, Raisa Lehtinen, Jani Tyynelä, and Ludovic Thobois
Atmos. Meas. Tech., 15, 6507–6519, https://doi.org/10.5194/amt-15-6507-2022, https://doi.org/10.5194/amt-15-6507-2022, 2022
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Doppler lidars and weather radars provide accurate wind measurements, with Doppler lidar usually performing better in dry weather conditions and weather radar performing better when there is precipitation. Operating both instruments together should therefore improve the overall performance. We investigate how well a co-located Doppler lidar and X-band radar perform with respect to various weather conditions, including changes in horizontal visibility, cloud altitude, and precipitation.
Silvia M. Calderón, Juha Tonttila, Angela Buchholz, Jorma Joutsensaari, Mika Komppula, Ari Leskinen, Liqing Hao, Dmitri Moisseev, Iida Pullinen, Petri Tiitta, Jian Xu, Annele Virtanen, Harri Kokkola, and Sami Romakkaniemi
Atmos. Chem. Phys., 22, 12417–12441, https://doi.org/10.5194/acp-22-12417-2022, https://doi.org/10.5194/acp-22-12417-2022, 2022
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The spatial and temporal restrictions of observations and oversimplified aerosol representation in large eddy simulations (LES) limit our understanding of aerosol–stratocumulus interactions. In this closure study of in situ and remote sensing observations and outputs from UCLALES–SALSA, we have assessed the role of convective overturning and aerosol effects in two cloud events observed at the Puijo SMEAR IV station, Finland, a diurnal-high aerosol case and a nocturnal-low aerosol case.
Annakaisa von Lerber, Mario Mech, Annette Rinke, Damao Zhang, Melanie Lauer, Ana Radovan, Irina Gorodetskaya, and Susanne Crewell
Atmos. Chem. Phys., 22, 7287–7317, https://doi.org/10.5194/acp-22-7287-2022, https://doi.org/10.5194/acp-22-7287-2022, 2022
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Snowfall is an important climate indicator. However, microphysical snowfall processes are challenging for atmospheric models. In this study, the performance of a regional climate model is evaluated in modeling the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations. Excellent agreement in averaged annual snowfall rates is found, and the shown methodology offers a promising diagnostic tool to investigate the shown differences further.
Victoria Anne Sinclair, Jenna Ritvanen, Gabin Urbancic, Irene Erner, Yurii Batrak, Dmitri Moisseev, and Mona Kurppa
Atmos. Meas. Tech., 15, 3075–3103, https://doi.org/10.5194/amt-15-3075-2022, https://doi.org/10.5194/amt-15-3075-2022, 2022
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We investigate the boundary-layer (BL) height and surface stability in southern Finland using radiosondes, a microwave radiometer and ERA5 reanalysis. Accurately quantifying the BL height is challenging, and the diagnosed BL height can depend strongly on the method used. Microwave radiometers provide reliable estimates of the BL height but only in unstable conditions. ERA5 captures the BL height well except under very stable conditions, which occur most commonly at night during the warm season.
Zoé Brasseur, Dimitri Castarède, Erik S. Thomson, Michael P. Adams, Saskia Drossaart van Dusseldorp, Paavo Heikkilä, Kimmo Korhonen, Janne Lampilahti, Mikhail Paramonov, Julia Schneider, Franziska Vogel, Yusheng Wu, Jonathan P. D. Abbatt, Nina S. Atanasova, Dennis H. Bamford, Barbara Bertozzi, Matthew Boyer, David Brus, Martin I. Daily, Romy Fösig, Ellen Gute, Alexander D. Harrison, Paula Hietala, Kristina Höhler, Zamin A. Kanji, Jorma Keskinen, Larissa Lacher, Markus Lampimäki, Janne Levula, Antti Manninen, Jens Nadolny, Maija Peltola, Grace C. E. Porter, Pyry Poutanen, Ulrike Proske, Tobias Schorr, Nsikanabasi Silas Umo, János Stenszky, Annele Virtanen, Dmitri Moisseev, Markku Kulmala, Benjamin J. Murray, Tuukka Petäjä, Ottmar Möhler, and Jonathan Duplissy
Atmos. Chem. Phys., 22, 5117–5145, https://doi.org/10.5194/acp-22-5117-2022, https://doi.org/10.5194/acp-22-5117-2022, 2022
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The present measurement report introduces the ice nucleation campaign organized in Hyytiälä, Finland, in 2018 (HyICE-2018). We provide an overview of the campaign settings, and we describe the measurement infrastructure and operating procedures used. In addition, we use results from ice nucleation instrument inter-comparison to show that the suite of these instruments deployed during the campaign reports consistent results.
Teresa Vogl, Maximilian Maahn, Stefan Kneifel, Willi Schimmel, Dmitri Moisseev, and Heike Kalesse-Los
Atmos. Meas. Tech., 15, 365–381, https://doi.org/10.5194/amt-15-365-2022, https://doi.org/10.5194/amt-15-365-2022, 2022
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We are using machine learning techniques, a type of artificial intelligence, to detect graupel formation in clouds. The measurements used as input to the machine learning framework were performed by cloud radars. Cloud radars are instruments located at the ground, emitting radiation with wavelenghts of a few millimeters vertically into the cloud and measuring the back-scattered signal. Our novel technique can be applied to different radar systems and different weather conditions.
Anna Franck, Dmitri Moisseev, Ville Vakkari, Matti Leskinen, Janne Lampilahti, Veli-Matti Kerminen, and Ewan O'Connor
Atmos. Meas. Tech., 14, 7341–7353, https://doi.org/10.5194/amt-14-7341-2021, https://doi.org/10.5194/amt-14-7341-2021, 2021
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We proposed a method to derive a convective boundary layer height, using insects in radar observations, and we investigated the consistency of these retrievals among different radar frequencies (5, 35 and 94 GHz). This method can be applied to radars at other measurement stations and serve as additional way to estimate the boundary layer height during summer. The entrainment zone was also observed by the 5 GHz radar above the boundary layer in the form of a Bragg scatter layer.
Haoran Li, Ottmar Möhler, Tuukka Petäjä, and Dmitri Moisseev
Atmos. Chem. Phys., 21, 14671–14686, https://doi.org/10.5194/acp-21-14671-2021, https://doi.org/10.5194/acp-21-14671-2021, 2021
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In natural clouds, ice-nucleating particles are expected to be rare above –10 °C. In the current paper, we found that the formation of ice columns is frequent in stratiform clouds and is associated with increased precipitation intensity and liquid water path. In single-layer shallow clouds, the production of ice columns was attributed to secondary ice production, despite the rime-splintering process not being expected to take place in such clouds.
Haoran Li, Alexei Korolev, and Dmitri Moisseev
Atmos. Chem. Phys., 21, 13593–13608, https://doi.org/10.5194/acp-21-13593-2021, https://doi.org/10.5194/acp-21-13593-2021, 2021
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Kelvin–Helmholtz (K–H) clouds embedded in a stratiform precipitation event were uncovered via radar Doppler spectral analysis. Given the unprecedented detail of the observations, we show that multiple populations of secondary ice columns were generated in the pockets where larger cloud droplets are formed and not at some constant level within the cloud. Our results highlight that the K–H instability is favorable for liquid droplet growth and secondary ice formation.
Julia Schneider, Kristina Höhler, Paavo Heikkilä, Jorma Keskinen, Barbara Bertozzi, Pia Bogert, Tobias Schorr, Nsikanabasi Silas Umo, Franziska Vogel, Zoé Brasseur, Yusheng Wu, Simo Hakala, Jonathan Duplissy, Dmitri Moisseev, Markku Kulmala, Michael P. Adams, Benjamin J. Murray, Kimmo Korhonen, Liqing Hao, Erik S. Thomson, Dimitri Castarède, Thomas Leisner, Tuukka Petäjä, and Ottmar Möhler
Atmos. Chem. Phys., 21, 3899–3918, https://doi.org/10.5194/acp-21-3899-2021, https://doi.org/10.5194/acp-21-3899-2021, 2021
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By triggering the formation of ice crystals, ice-nucleating particles (INP) strongly influence cloud formation. Continuous, long-term measurements are needed to characterize the atmospheric INP variability. Here, a first long-term time series of INP spectra measured in the boreal forest for more than 1 year is presented, showing a clear seasonal cycle. It is shown that the seasonal dependency of INP concentrations and prevalent INP types is driven by the abundance of biogenic aerosol.
Tanel Voormansik, Roberto Cremonini, Piia Post, and Dmitri Moisseev
Hydrol. Earth Syst. Sci., 25, 1245–1258, https://doi.org/10.5194/hess-25-1245-2021, https://doi.org/10.5194/hess-25-1245-2021, 2021
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A long set of operational polarimetric weather radar rainfall accumulations from Estonia and Italy are generated and investigated. Results show that the combined product of specific differential phase and horizontal reflectivity yields the best results when compared to rain gauge measurements. The specific differential-phase-based product overestimates weak precipitation, and the horizontal-reflectivity-based product underestimates heavy rainfall in all analysed accumulation periods.
Michael Fehlmann, Mario Rohrer, Annakaisa von Lerber, and Markus Stoffel
Atmos. Meas. Tech., 13, 4683–4698, https://doi.org/10.5194/amt-13-4683-2020, https://doi.org/10.5194/amt-13-4683-2020, 2020
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The Thies disdrometer is used to monitor precipitation intensity and its phase and thus may provide valuable information for the management of meteorological and hydrological risks. In this study, we characterize biases of this instrument using common reference instruments at a pre-alpine study site in Switzerland. We find a systematic underestimation of liquid precipitation amounts and suggest possible reasons for and corrections to this bias and relate these findings to other study sites.
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Short summary
Accurate KDP estimates are crucial in radar-based applications. We quantify the uncertainties of several publicly available KDP estimation methods for multiple rainfall intensities. We use C-band weather radar observations and employed a self-consistency KDP, estimated from reflectivity and differential reflectivity, as a framework for the examination. Our study provides guidance for the performance, uncertainties, and optimisation of the methods, focusing mainly on accuracy and robustness.
Accurate KDP estimates are crucial in radar-based applications. We quantify the uncertainties of...
