Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1851-2025
© 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/gmd-18-1851-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
18 Mar 2025
Methods for assessment of models |
| 18 Mar 2025
| 18 Mar 2025
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
Finnish Meteorological Institute, Helsinki, Finland
Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, Helsinki, Finland
Seppo Pulkkinen
Finnish Meteorological Institute, Helsinki, Finland
Dmitri Moisseev
Finnish Meteorological Institute, Helsinki, Finland
Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, Helsinki, Finland
Daniele Nerini
MeteoSwiss, Locarno-Monti, Switzerland
Related authors
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Loris Foresti, Bernat Puigdomènech Treserras, Daniele Nerini, Aitor Atencia, Marco Gabella, Ioannis V. Sideris, Urs Germann, and Isztar Zawadzki
Nonlin. Processes Geophys., 31, 259–286, https://doi.org/10.5194/npg-31-259-2024, https://doi.org/10.5194/npg-31-259-2024, 2024
Short summary
Short summary
We compared two ways of defining the phase space of low-dimensional attractors describing the evolution of radar precipitation fields. The first defines the phase space by the domain-scale statistics of precipitation fields, such as their mean, spatial and temporal correlations. The second uses principal component analysis to account for the spatial distribution of precipitation. To represent different climates, radar archives over the United States and the Swiss Alpine region were used.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Maxi Boettcher, Andreas Schäfler, Michael Sprenger, Harald Sodemann, Stefan Kaufmann, Christiane Voigt, Hans Schlager, Donato Summa, Paolo Di Girolamo, Daniele Nerini, Urs Germann, and Heini Wernli
Atmos. Chem. Phys., 21, 5477–5498, https://doi.org/10.5194/acp-21-5477-2021, https://doi.org/10.5194/acp-21-5477-2021, 2021
Short summary
Short summary
Warm conveyor belts (WCBs) are important airstreams in extratropical cyclones, often leading to the formation of intense precipitation. We present a case study that involves aircraft, lidar and radar observations of water and clouds in a WCB ascending from western Europe across the Alps towards the Baltic Sea during the field campaigns HyMeX and T-NAWDEX-Falcon in October 2012. A probabilistic trajectory measure and an airborne tracer experiment were used to confirm the long pathway of the WCB.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, Geosci. Model Dev., 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, 2020. a, b, c
Berne, A., Delrieu, G., Creutin, J.-D., and Obled, C.: Temporal and Spatial Resolution of Rainfall Measurements Required for Urban Hydrology, J. Hydrol., 299, 166–179, https://doi.org/10.1016/j.jhydrol.2004.08.002, 2004. a
Beucher, S. and Lantuejoul, C.: Use of Watersheds in Contour Detection, in: International Workshop on Image Processing: Real-time Edge and Motion Detection/Estimation, Rennes, France, https://people.cmm.minesparis.psl.eu/users/beucher/publi/watershed.pdf (last access: 12 March 2025), 1979. a
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Accurate Medium-Range Global Weather Forecasting with 3D Neural Networks, Nature, 619, 533–538, https://doi.org/10.1038/s41586-023-06185-3, 2023. a, b
Bowler, N. E., Pierce, C. E., and Seed, A. W.: STEPS: A Probabilistic Precipitation Forecasting Scheme Which Merges an Extrapolation Nowcast with Downscaled NWP, Q. J. Roy. Meteor. Soc., 132, 2127–2155, https://doi.org/10/fc3234, 2006. a
Browning, K. A. and Collier, C. G.: Nowcasting of Precipitation Systems, Rev. Geophys., 27, 345–370, https://doi.org/10.1029/RG027i003p00345, 1989. a
Clark, A. J., Bullock, R. G., Jensen, T. L., Xue, M., and Kong, F.: Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models, Weather Forecast., 29, 517–542, https://doi.org/10.1175/WAF-D-13-00098.1, 2014. a, b
Crameri, F.: Scientific Colour Maps (8.0.1), Zenodo [data set], https://doi.org/10.5281/zenodo.5501399, 2023. a, b
Crameri, F., Shephard, G. E., and Heron, P. J.: The Misuse of Colour in Science Communication, Nat. Commun., 11, 1–10, https://doi.org/10/ghg5rd, 2020. a
Crouse, D. F.: On Implementing 2D Rectangular Assignment Algorithms, IEEE T. Aero. Elec. Sys., 52, 1679–1696, https://doi.org/10.1109/TAES.2016.140952, 2016. a
Davis, C., Brown, B., and Bullock, R.: Object-Based Verification of Precipitation Forecasts. Part I: Methodology and Application to Mesoscale Rain Areas, Mon. Weather Rev., 134, 1772–1784, https://doi.org/10.1175/MWR3145.1, 2006a. a
Davis, C., Brown, B., and Bullock, R.: Object-Based Verification of Precipitation Forecasts. Part II: Application to Convective Rain Systems, Mon. Weather Rev., 134, 1785–1795, https://doi.org/10.1175/MWR3146.1, 2006b. a
Davis, C. A., Brown, B. G., Bullock, R., and Halley-Gotway, J.: The Method for Object-Based Diagnostic Evaluation (MODE) Applied to Numerical Forecasts from the 2005 NSSL/SPC Spring Program, Weather Forecast., 24, 1252–1267, https://doi.org/10.1175/2009WAF2222241.1, 2009. a
Dixon, M. and Wiener, G.: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting – A Radar-based Methodology, J. Atmos. Ocean. Tech., 10, 785–797, https://doi.org/10/dc5g2t, 1993. a
Ebert, E. E. and McBride, J. L.: Verification of Precipitation in Weather Systems: Determination of Systematic Errors, J. Hydrol., 239, 179–202, https://doi.org/10/ch4m9p, 2000. a
Falcon, W. and The PyTorch Lightning team: PyTorch Lightning, Zenodo [code], https://doi.org/10.5281/zenodo.3828935, 2019. a
Feng, Z., Leung, L. R., Houze Jr., R. A., Hagos, S., Hardin, J., Yang, Q., Han, B., and Fan, J.: Structure and Evolution of Mesoscale Convective Systems: Sensitivity to Cloud Microphysics in Convection-Permitting Simulations Over the United States, J. Adv. Model. Earth Sy., 10, 1470–1494, https://doi.org/10.1029/2018MS001305, 2018. a
Fox, N. I., Micheas, A. C., and Peng, Y.: Applications of Bayesian Procrustes Shape Analysis to Ensemble Radar Reflectivity Nowcast Verification, Atmos. Res., 176–177, 75–86, https://doi.org/10.1016/j.atmosres.2016.02.001, 2016. a
Germann, U. and Zawadzki, I.: Scale-Dependence of the Predictability of Precipitation from Continental Radar Images. Part I: Description of the Methodology, Mon. Wea. Rev., 130, 2859–2873, https://doi.org/10.1175/1520-0493(2002)130<2859:SDOTPO>2.0.CO;2, 2002. a
Germann, U., Galli, G., Boscacci, M., and Bolliger, M.: Radar Precipitation Measurement in a Mountainous Region, Q. J. Roy. Meteor. Soc., 132, 1669–1692, https://doi.org/10.1256/qj.05.190, 2006. a, b, c, d
Germann, U., Boscacci, M., Clementi, L., Gabella, M., Hering, A., Sartori, M., Sideris, I. V., and Calpini, B.: Weather Radar in Complex Orography, Remote Sens.-Basel, 14, 503, https://doi.org/10.3390/rs14030503, 2022. a
Gerrity, J. P.: A Note on Gandin and Murphy's Equitable Skill Score, Mon. Weather Rev., 120, 2709–2712, https://doi.org/10.1175/1520-0493(1992)120<2709:ANOGAM>2.0.CO;2, 1992. a
Hering, A. M., Morel, C., Galli, G., Senesi, S., Ambrosetti, P., and Boscacci, M.: Nowcasting Thunderstorms in the Alpine Region Using a Radar Based Adaptive Thresholding Scheme, in: Proc. Third European Conf. on Radar Meteorology, ERAD, 6–10 September 2004, Visby, Sweden, 206–211, https://www.copernicus.org/erad/2004/online/ERAD04_P_206.pdf (last access: 12 March 2025), 2004. a, b, c
Hou, J. and Wang, P.: Storm Tracking via Tree Structure Representation of Radar Data, J. Atmos. Ocean. Tech., 34, 729–747, https://doi.org/10/f93gwb, 2017. a
Hu, J., Rosenfeld, D., Zrnic, D., Williams, E., Zhang, P., Snyder, J. C., Ryzhkov, A., Hashimshoni, E., Zhang, R., and Weitz, R.: Tracking and Characterization of Convective Cells through Their Maturation into Stratiform Storm Elements Using Polarimetric Radar and Lightning Detection, Atmos. Res., 226, 192–207, https://doi.org/10/ggk6g7, 2019. a
Ji, L., Zhi, X., Simmer, C., Zhu, S., and Ji, Y.: Multimodel Ensemble Forecasts of Precipitation Based on an Object-Based Diagnostic Evaluation, Mon. Weather Rev., 148, 2591–2606, https://doi.org/10.1175/MWR-D-19-0266.1, 2020. a
Ji, Y., Gong, B., Langguth, M., Mozaffari, A., and Zhi, X.: CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting, Geosci. Model Dev., 16, 2737–2752, https://doi.org/10.5194/gmd-16-2737-2023, 2023. a, b
Joss, J., Schädler, B., Galli, G., Cavalli, R., Boscacci, M., Held, E., Bruna, G. D., Kappenberger, G., Nespor, V., and Spiess, R.: Operational Use of Radar for Precipitation Measurements in Switzerland, vdf Hochschulverlag AG, ETH Zurich, Switzerland, https://www.meteoswiss.admin.ch/dam/jcr:600197d5-fe54-495c-a6f6-5418147f301b/meteoswiss_operational_use_of_radar.pdf (last access: 12 March 2025), 1998. a, b
Kong, D., Zhi, X., Ji, Y., Yang, C., Wang, Y., Tian, Y., Li, G., and Zeng, X.: Precipitation Nowcasting Based on Deep Learning over Guizhou, China, Atmosphere-Basel, 14, 807, https://doi.org/10.3390/atmos14050807, 2023. a, b
Kuhn, H. W.: The Hungarian Method for the Assignment Problem, Nav. Res. Logist. Q., 2, 83–97, https://doi.org/10.1002/nav.3800020109, 1955. a
Leinonen, J., Hamann, U., Nerini, D., Germann, U., and Franch, G.: Latent Diffusion Models for Generative Precipitation Nowcasting with Accurate Uncertainty Quantification, arXiv [preprint], https://doi.org/10.48550/arXiv.2304.12891, 2023. a, b
Li, L., He, Z., Chen, S., Mai, X., Zhang, A., Hu, B., Li, Z., and Tong, X.: Subpixel-Based Precipitation Nowcasting with the Pyramid Lucas–Kanade Optical Flow Technique, Atmosphere-Basel, 9, 260, https://doi.org/10.3390/atmos9070260, 2018. a
Li, L., Li, Y., and Li, Z.: Object-Based Tracking of Precipitation Systems in Western Canada: The Importance of Temporal Resolution of Source Data, Clim. Dynam., 55, 2421–2437, https://doi.org/10.1007/s00382-020-05388-y, 2020. a, b
Limpert, G., Houston, A., and Lock, N.: The Advanced Algorithm for Tracking Objects (AALTO): Advanced Algorithm for Tracking Objects, Meteorol. Appl., 22, 694–704, https://doi.org/10/f7z6jx, 2015. a
Lu, M., Li, Y., Yu, M., Zhang, Q., Zhang, Y., Liu, B., and Wang, M.: Spatiotemporal Prediction of Radar Echoes Based on ConvLSTM and Multisource Data, Remote Sens.-Basel, 15, 1279, https://doi.org/10.3390/rs15051279, 2023. a
Lucas, B. D. and Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision, in: Proceedings of the 1981 DARPA Imaging Understanding Workshop, 121–130, https://www.ri.cmu.edu/pub_files/pub3/lucas_bruce_d_1981_2/lucas_bruce_d_1981_2.pdf (last access: 12 March 2025), 1981. a, b
Marzban, C., Sandgathe, S., Lyons, H., and Lederer, N.: Three Spatial Verification Techniques: Cluster Analysis, Variogram, and Optical Flow, Weather Forecast., 24, 1457–1471, https://doi.org/10/cgxkbc, 2009. a
Micheas, A. C., Fox, N. I., Lack, S. A., and Wikle, C. K.: Cell Identification and Verification of QPF Ensembles Using Shape Analysis Techniques, J. Hydrol., 343, 105–116, https://doi.org/10/b93xmm, 2007. a
Mittermaier, M. P. and Bullock, R.: Using MODE to Explore the Spatial and Temporal Characteristics of Cloud Cover Forecasts from High-Resolution NWP Models, Meteorol. Appl., 20, 187–196, https://doi.org/10.1002/met.1393, 2013. a, b
Nerini, D., Pulkkinen, S., Hortal, A. P., Velasco, C., Foresti, L., Imhoff, R., Pulkkinen, S., Feldmann, M., Buekenhout, D., Ghaemi, E., Karsisto, P., chiara-arpae, Badger, C., Fangyh09, Ritvanen, J., Joep1999, Cruz, L. D., Rombeek, N., Karsisto, P., aitaten, Carpentieri, A., and mpvginde: pySTEPS/Pysteps: Pysteps v1.8.0, Zenodo [code], https://doi.org/10.5281/zenodo.10411141, 2023. a, b, c
Nerini, D., Pulkkinen, S., Hortal, A. P., Velasco, C., Foresti, L., Imhoff, R., Pulkkinen, S., EsmailGhaemi, Feldmann, M., mpvginde, chiara-arpae, Karsisto, P., Buekenhout, D., Badger, C., Fangyh09, Ritvanen, J., Joep1999, Cruz, L. D., NathalieRombeek, Karsisto, P., aitaten, and Carpentieri, A.: ritvje/pysteps: T-DaTing with splits & merges, Zenodo [code], https://doi.org/10.5281/zenodo.11242613, 2024. a, b
Newman, K. M., Brown, B., Gotway, J. H., Bernardet, L., Biswas, M., Jensen, T., and Nance, L.: Advancing Tropical Cyclone Precipitation Forecast Verification Methods and Tools, Weather Forecast., 38, 1589–1603, https://doi.org/10.1175/WAF-D-23-0001.1, 2023. a, b
Pan, X., Lu, Y., Zhao, K., Huang, H., Wang, M., and Chen, H.: Improving Nowcasting of Convective Development by Incorporating Polarimetric Radar Variables Into a Deep-Learning Model, Geophys. Res. Lett., 48, e2021GL095302, https://doi.org/10/gpbg5d, 2021. a, b
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S.: PyTorch: An Imperative Style, High-Performance Deep Learning Library, Adv. Neur. In., Vol. 32, https://proceedings.neurips.cc/paper_files/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf (last access: 12 March 2025), 2019. a
Pulkkinen, S., Chandrasekar, V., and Harri, A.-M.: Nowcasting of Precipitation in the High-Resolution Dallas–Fort Worth (DFW) Urban Radar Remote Sensing Network, IEEE J. Sel. Top. Appl., 11, 2773–2787, https://doi.org/10.1109/JSTARS.2018.2840491, 2018. a
Pulkkinen, S., Chandrasekar, V., and Harri, A.-M.: Fully Spectral Method for Radar-Based Precipitation Nowcasting, IEEE J. Sel. Top. Appl., 12, 1369–1382, https://doi.org/10.1109/JSTARS.2019.2908974, 2019a. a, b, c
Pulkkinen, S., Chandrasekar, V., von Lerber, A., and Harri, A.-M.: Nowcasting of Convective Rainfall Using Volumetric Radar Observations, IEEE T. Geosci. Remote, 58, 7845–7859, https://doi.org/10/ggsw7s, 2020. a
pySTEPS developers: pySTEPS/Pysteps: Python Framework for Short-Term Ensemble Prediction Systems [code], GitHub [code], https://github.com/pySTEPS/pysteps (last access: 12 March 2025), 2023. a
Ravuri, S. V., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., Prudden, R., Mandhane, A., Clark, A., Brock, A., Simonyan, K., Hadsell, R., Robinson, N. H., Clancy, E., Arribas, A., and Mohamed, S.: Skilful Precipitation Nowcasting Using Deep Generative Models of Radar, Nature, 597, 672–677, https://doi.org/10/gmx6dc, 2021. a, b, c
Raynaud, L., Pechin, I., Arbogast, P., Rottner, L., and Destouches, M.: Object-Based Verification Metrics Applied to the Evaluation and Weighting of Convective-Scale Precipitation Forecasts, Q. J. Roy. Meteor. Soc., 145, 1992–2008, https://doi.org/10.1002/qj.3540, 2019. a
Ritvanen, J.: fmidev/lagrangian-convolutional-neural-network: L-CNN model with Swiss data, Zenodo [code], https://doi.org/10.5281/zenodo.11242483, 2024a. a, b
Ritvanen, J.: fmidev/nowcast-verification-cell-tracking: Cell tracking-based verification framework for nowcasts, Zenodo [code], https://doi.org/10.5281/zenodo.14227567, 2024b. a
Ritvanen, J., Pulkkinen, S., Moisseev, D., and Nerini, D.: Data for the manuscript “Cell tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall” by Ritvanen et al., b2share [data set], https://doi.org/10.57707/fmi-b2share.627e6133c2594dc3945d14fe0ef9c922, 2024a. a
Ritvanen, J., Pulkkinen, S., Moisseev, D., and Nerini, D.: Results for the manuscript “Cell tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall” by Ritvanen et al., b2share [data set], https://doi.org/10.57707/fmi-b2share.e1897cfb9a9d4466bb9d7235882bc511, 2024b. a
Roberts, N. M. and Lean, H. W.: Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events, Mon. Weather Rev., 136, 78–97, https://doi.org/10/fn58w4, 2008. a
Rosenfeld, D.: Objective Method for Analysis and Tracking of Convective Cells as Seen by Radar, J. Atmos. Ocean. Tech., 4, 422–434, https://doi.org/10.1175/1520-0426(1987)004<0422:OMFAAT>2.0.CO;2, 1987. a
Schaefer, J. T.: The Critical Success Index as an Indicator of Warning Skill, Weather Forecast., 5, 570–575, https://doi.org/10.1175/1520-0434(1990)005<0570:TCSIAA>2.0.CO;2, 1990. a, b
Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., and Woo, W. C.: Convolutional LSTM network: A machine learning approach for precipitation nowcasting, Adv. Neur. In., 28, 802–810, 2015. a
Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W.-C.: Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model, Adv. Neur. In., 30, https://proceedings.neurips.cc/paper_files/paper/2017/file/a6db4ed04f1621a119799fd3d7545d3d-Paper.pdf (last access: 12 March 2025), 2017. a
Sønderby, C. K., Espeholt, L., Heek, J., Dehghani, M., Oliver, A., Salimans, T., Agrawal, S., Hickey, J., and Kalchbrenner, N.: MetNet: A Neural Weather Model for Precipitation Forecasting, arXiv [preprint], https://doi.org/10.48550/arXiv.2003.12140, 2020. a
Trebing, K., Staǹczyk, T., and Mehrkanoon, S.: SmaAt-UNet: Precipitation Nowcasting Using a Small Attention-UNet Architecture, Pattern Recogn. Lett., 145, 178–186, https://doi.org/10.1016/j.patrec.2021.01.036, 2021. a
van der Walt, S. J., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., and Yu, T.: Scikit-Image: Image Processing in Python, PeerJ, 2, e453, https://doi.org/10.7717/peerj.453, 2014. a, b
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., and van Mulbregt, P.: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Wen, Y., Zhang, J., Wang, D., Wang, C., and Wang, P.: Research on Radar Echo Extrapolation Method by Fusing Environment Grid Point Field Information, Atmosphere-Basel, 14, 980, https://doi.org/10.3390/atmos14060980, 2023. a, b, c, d
Wernli, H., Paulat, M., Hagen, M., and Frei, C.: SAL – A Novel Quality Measure for the Verification of Quantitative Precipitation Forecasts, Mon. Weather Rev., 136, 4470–4487, https://doi.org/10/fprx82, 2008. a, b
World Meteorological Organization: Guidelines for Nowcasting Techniques, Tech. Rep. WMO-No.1198, World Meteorological Organization, Geneva, Switzerland, ISBN 978-92-63-11198-2, https://library.wmo.int/idurl/4/55666 (last access: 12 March 2025), 2017. a
Zahraei, A., Hsu, K.-L., Sorooshian, S., Gourley, J. J., Lakshmanan, V., Hong, Y., and Bellerby, T.: Quantitative Precipitation Nowcasting: A Lagrangian Pixel-Based Approach, Atmos. Res., 118, 418–434, https://doi.org/10.1016/j.atmosres.2012.07.001, 2012. a
Zan, B., Yu, Y., Li, J., Zhao, G., Zhang, T., and Ge, J.: Solving the Storm Split-Merge Problem – A Combined Storm Identification, Tracking Algorithm, Atmos. Res., 218, 335–346, https://doi.org/10/gf52zh, 2019. a, b
Zhang, F., Wang, X., and Guan, J.: A Novel Multi-Input Multi-Output Recurrent Neural Network Based on Multimodal Fusion and Spatiotemporal Prediction for 0–4 Hour Precipitation Nowcasting, Atmosphere-Basel, 12, 1596, https://doi.org/10/gpbppc, 2021. a, b
Zhang, Y., Long, M., Chen, K., Xing, L., Jin, R., Jordan, M. I., and Wang, J.: Skilful Nowcasting of Extreme Precipitation with NowcastNet, Nature, 619, 526–532, https://doi.org/10.1038/s41586-023-06184-4, 2023. a, b, c
Zheng, K., Liu, Y., Zhang, J., Luo, C., Tang, S., Ruan, H., Tan, Q., Yi, Y., and Ran, X.: GAN–argcPredNet v1.0: a generative adversarial model for radar echo extrapolation based on convolutional recurrent units, Geosci. Model Dev., 15, 1467–1475, https://doi.org/10.5194/gmd-15-1467-2022, 2022. a
Zhu, K., Chen, H., and Han, L.: MCT U-net: A Deep Learning Nowcasting Method Using Dual-polarization Radar Observations, in: IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, 17–22 July 2022, Kuala Lumpur, Malaysia, 4665–4668, https://doi.org/10.1109/IGARSS46834.2022.9884871, 2022. a
Short summary
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.
Nowcasting models struggle with the rapid evolution of heavy rain, and common verification...
