{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T14:08:40Z","timestamp":1777385320764,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T00:00:00Z","timestamp":1657670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Thunderstorms are among the most common and most dangerous meteorological hazards in the world. They cause lightning and can lead to strong wind gusts, squall lines, hail and heavy precipitation combined with flooding, and therefore pose a threat to health and life, can cause enormous property damage and also endanger flight safety. Monitoring and forecast of thunderstorms are, therefore, important topics. In this work, a novel method for the detection and forecast of thunderstorms and strong convection is presented. The detection is based on the global GLD360 lightning data in combination with satellite information from the satellite series Meteosat, HIMAWARI and GOES, covering the complete geostationary ring. Three severity levels are defined depending on the occurrence of lightning and the brightness temperature difference of the water vapour channels and the infrared window channel (\u223c10.8 \u03bcm). The detection of thunderstorms and strong convection is the basis for the nowcasting up to 2 h, which is performed with the optical flow method TV-L1. This method provides the needed atmospheric motion vectors for the extrapolation of the thunderstorm movement. Both, the validation results as well as the feedback of the customers show the great value of the new NowCastSat-Aviation (NCS-A) method. For example, the Critical Success Index (CSI) is, with 0.64, still quite high for the 60 min forecast of severe thunderstorms. The method is operated 24\/7 by the German Weather Service (DWD), and is used to provide thunderstorm information to aviation customers and the central weather forecast unit of DWD.<\/jats:p>","DOI":"10.3390\/rs14143372","type":"journal-article","created":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:12:40Z","timestamp":1657757560000},"page":"3372","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Novel Approach for the Global Detection and Nowcasting of Deep Convection and Thunderstorms"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0376-0393","authenticated-orcid":false,"given":"Richard","family":"M\u00fcller","sequence":"first","affiliation":[{"name":"Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5247-816X","authenticated-orcid":false,"given":"Axel","family":"Barleben","sequence":"additional","affiliation":[{"name":"Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"St\u00e9phane","family":"Haussler","sequence":"additional","affiliation":[{"name":"Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthias","family":"Jerg","sequence":"additional","affiliation":[{"name":"Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.5194\/adgeo-17-55-2008","article-title":"Cell-tracking with lightning data from LINET","volume":"17","author":"Betz","year":"2008","journal-title":"Adv. Geosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.atmosres.2012.10.019","article-title":"The comparison of GLD360 and EUCLID lightning location systems in Europe","volume":"123","author":"Pohjola","year":"2013","journal-title":"Atmos. Res."},{"key":"ref_3","unstructured":"VAISALA (2022). VAISALA GLD360 Global Dataset\u2014Understanding the GLD360 Dataset, Vaisala Corporation, Head Office."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6905","DOI":"10.1002\/jgrd.50508","article-title":"Highly intense lightning over the oceans: Estimated peak currents from global GLD360 observations","volume":"118","author":"Said","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_5","unstructured":"Said, R., and Murphy, M. (2016, January 18\u201321). GLD360 Upgrade: Performance Analysis and Applications. Proceedings of the 24th International Lightning Detection Conference and 6th International Lightning Meteorology Conference, San Diego, CA, USA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1175\/2008JTECHA1132.1","article-title":"Development of a Long-Range Lightning Detection Network for the Pacific: Construction, Calibration, and Performance","volume":"26","author":"Pessi","year":"2009","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_7","unstructured":"Autones, F. (2013). Algorithm Theoretical Basis Document for Rapid Development Thunderstorms, NWC-SAF. Technical Report."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"M\u00fcller, R., Haussler, S., and Jerg, M. (2018). The Role of NWP Filter for the Satellite Based Detection of Cumulonimbus Clouds. Remote Sens., 10.","DOI":"10.3390\/rs10030386"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Urbich, I., Benidx, J., and M\u00fcller, R. (2018). A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo. Remote Sens., 10.","DOI":"10.20944\/preprints201804.0367.v1"},{"key":"ref_10","unstructured":"Zach, C., Pock, T., and Bischof, H. (2007, January 12\u201314). A duality based approach for realtime TV-L1 optical flow. Proceedings of the Joint Pattern Recognition Symposium, Heidelberg, Germany."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"137","DOI":"10.5201\/ipol.2013.26","article-title":"TV-L1 optical flow estimation","volume":"3","author":"Facciolo","year":"2013","journal-title":"Image Process. Online"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Barleben, A., Haussler, S., M\u00fcller, R., and Jerg, M. (2020). A Novel Approach for Satellite-Based Turbulence Nowcasting for Aviation. Remote Sens., 12.","DOI":"10.3390\/rs12142255"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.5194\/gmd-12-1387-2019","article-title":"Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)","volume":"12","author":"Ayzel","year":"2019","journal-title":"Geosci. Model Dev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1175\/WAF-D-18-0038.1","article-title":"NowCastMIX: Automatic Integrated Warnings for Severe Convection on Nowcasting Time Scales at the German Weather Service, Weather and Forecasting","volume":"33","author":"James","year":"2018","journal-title":"Weather. Forecast."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1175\/BAMS-83-7-Schmetz-2","article-title":"An introduction to Meteosat Second Generation (MSG)","volume":"83","author":"Schmetz","year":"2002","journal-title":"Bull. Am. Met. Soc."},{"key":"ref_16","unstructured":"GOES (2019). GOES-R Series Data Book, GOES-R Series Program Office Goddard Space Flight Center."},{"key":"ref_17","unstructured":"Himawari (2017). Himawari-89\u2014Himawari Standard Data User\u2019s Guide, Japan Meteorological Agency. Version 1.3."},{"key":"ref_18","unstructured":"Schneider, M., and Barleben, A. User Feedback Loop with M. Schneider from MUAC Starting April 2019 and Finalized 31 March 2022. Personal communication."},{"key":"ref_19","unstructured":"Mosher, F. (2002, January 13\u201316). Detection of deep convection around the globe. Proceedings of the 10th Conference on Aviation, Range and Aerospace Meteorology, Portland, OR, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1175\/2009JAMC2286.1","article-title":"Objective satellite-based detection of overshooting tops using infrared window channel brightness temperature gradients","volume":"49","author":"Bedka","year":"2010","journal-title":"Appl. Meteorol. Climatol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/S0273-1177(97)00051-3","article-title":"Monitoring deep convection and convective overshooting with Meteosat","volume":"19","author":"Schmetz","year":"1997","journal-title":"Adv. Space Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2009.2028334","article-title":"Sensitive Broadband ELF\/VLF Radio Reception With the AWESOME Instrument","volume":"48","author":"Cohen","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1002\/qj.2378","article-title":"The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core","volume":"141","author":"Reinert","year":"2015","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1175\/JAMC-D-15-0170.1","article-title":"A Method for Calculating the Height of Overshooting Convective Cloud Tops Using Satellite-Based IR Imager and CloudSat Cloud Profiling Radar Observations","volume":"55","author":"Griffin","year":"2016","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_25","unstructured":"OpenCV (2022, April 11). OpenCV Homepage. OpenCV Is an Open-Source, Computer-Vision Library for Extracting and Processing Meaningful Data from Images, Available online: http:\/\/opencv.org\/."},{"key":"ref_26","first-page":"122","article-title":"The OpenCV Library","volume":"120","author":"Bradski","year":"2000","journal-title":"Dr. Dobb\u2019s J. Softw. Tools"},{"key":"ref_27","unstructured":"Joe, P., Koppert, H.J., Heizenreder, D., Erbshaeusser, B., Raatz, W., Reichert, B., and Rohn, M. (2005, January 5\u20139). Severe weather forecasting tools in the NinJo workstation. Proceedings of the World Weather Research Program Symposium on Nowcasting and Very Short Range Forecasting, Toulouse, France."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.5194\/amt-6-1567-2013","article-title":"Validation of the Meteosat storm detection and nowcasting system Cb-TRAM with lightning network data\u2014Europe and South Africa","volume":"6","author":"Zinner","year":"2013","journal-title":"Atmos. Meas. Tech."},{"key":"ref_29","unstructured":"Wilks, D.S. (2006). Statistical Methods in the Atmospheric Sciences, Elsevier."},{"key":"ref_30","unstructured":"Gatzen, C., Pucik, T., and Groenemeijer, P. (2021). Report on the Evaluation of DWD Nowcast and Warning Products at the ESSL Testbed 2021, European Severe Storms Laboratory. Technical Report DWD internal 3053581 19-KAP."},{"key":"ref_31","unstructured":"Reinert, D., Prill, F., Frank, H., Denhard, M., Baldauf, M., Schraff, C., Gebhardt, C., Marsigli, C., and Z\u00e4ngl, G. (2021). DWD Database Reference for the Global and Regional ICON and ICON-EPS Forecasting System, DWD. Technical Report Version 2.1.7."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Urbich, I., Bendix, J., and M\u00fcller, R. (2020). Development of a Seamless Forecast for Solar Radiation Using ANAKLIM++. Remote Sens., 12.","DOI":"10.3390\/rs12213672"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Brodehl, S., M\u00fcller, R., Sch\u00f6mer, E., Spichtinger, P., and Wand, M. (2022). End-to-End Prediction of Lightning Events from Geostationary Satellite Images. Preprints.","DOI":"10.20944\/preprints202206.0238.v1"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3372\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:49:40Z","timestamp":1760140180000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3372"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,13]]},"references-count":33,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14143372"],"URL":"https:\/\/doi.org\/10.3390\/rs14143372","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202206.0008.v1","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,13]]}}}