Articles | Volume 19, issue 19
https://doi.org/10.5194/amt-19-6193-2026
© 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-19-6193-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of a moist-adiabat cloud-top height retrieval for parallax correction of deep convective clouds across Meteosat generations
Space Research Centre, Polish Academy of Sciences, Bartycka 18a, 00716 Warsaw, Poland
Related authors
Żaneta Nguyen Huu, Andrzej Z. Kotarba, and Agnieszka Wypych
Atmos. Meas. Tech., 18, 3897–3915, https://doi.org/10.5194/amt-18-3897-2025, https://doi.org/10.5194/amt-18-3897-2025, 2025
Short summary
Short summary
Clouds affect Earth's energy balance, with high-altitude cirrus clouds contributing to atmospheric warming. While active satellite sensors are the most accurate for detecting cirrus clouds, they are not ideal for long-term studies. This study compares Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, testing six MODIS methods, one MODIS-based test, and two International Satellite Cloud Climatology Project (ISCCP) tests. The all tests consolidation (ATC) was the most effective, achieving 72.98 % accuracy during daytime and 59.50 % at night, making it relatively accurate for creating a cirrus mask.
Andrzej Z. Kotarba and Izabela Wojciechowska
Atmos. Meas. Tech., 18, 2721–2738, https://doi.org/10.5194/amt-18-2721-2025, https://doi.org/10.5194/amt-18-2721-2025, 2025
Short summary
Short summary
The research investigates methods for detecting deep convective clouds (DCCs) using satellite infrared data, essential for understanding long-term climate trends. By validating three popular detection methods against lidar–radar data, it found moderate accuracy (below 75 %), emphasizing the importance of fine-tuning thresholds regionally. The study shows how small threshold changes significantly affect the climatology of severe storms.
Andrzej Z. Kotarba
Atmos. Meas. Tech., 15, 4307–4322, https://doi.org/10.5194/amt-15-4307-2022, https://doi.org/10.5194/amt-15-4307-2022, 2022
Short summary
Short summary
Space profiling lidars offer a unique insight into cloud properties in Earth’s atmosphere, and are considered the most reliable source of cloud information. However, lidar-based cloud climatologies are infrequently sampled: every 7 to 91 d, and only along the ground track. This study evaluated how accurate are the cloud data from existing (CALIPSO, ICESat-2, Aeolus) and planned (EarthCARE) space lidars, when compared to a cloud climatology obtained with observations taken every day.
Żaneta Nguyen Huu, Andrzej Z. Kotarba, and Agnieszka Wypych
Atmos. Meas. Tech., 18, 3897–3915, https://doi.org/10.5194/amt-18-3897-2025, https://doi.org/10.5194/amt-18-3897-2025, 2025
Short summary
Short summary
Clouds affect Earth's energy balance, with high-altitude cirrus clouds contributing to atmospheric warming. While active satellite sensors are the most accurate for detecting cirrus clouds, they are not ideal for long-term studies. This study compares Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, testing six MODIS methods, one MODIS-based test, and two International Satellite Cloud Climatology Project (ISCCP) tests. The all tests consolidation (ATC) was the most effective, achieving 72.98 % accuracy during daytime and 59.50 % at night, making it relatively accurate for creating a cirrus mask.
Andrzej Z. Kotarba and Izabela Wojciechowska
Atmos. Meas. Tech., 18, 2721–2738, https://doi.org/10.5194/amt-18-2721-2025, https://doi.org/10.5194/amt-18-2721-2025, 2025
Short summary
Short summary
The research investigates methods for detecting deep convective clouds (DCCs) using satellite infrared data, essential for understanding long-term climate trends. By validating three popular detection methods against lidar–radar data, it found moderate accuracy (below 75 %), emphasizing the importance of fine-tuning thresholds regionally. The study shows how small threshold changes significantly affect the climatology of severe storms.
Andrzej Z. Kotarba
Atmos. Meas. Tech., 15, 4307–4322, https://doi.org/10.5194/amt-15-4307-2022, https://doi.org/10.5194/amt-15-4307-2022, 2022
Short summary
Short summary
Space profiling lidars offer a unique insight into cloud properties in Earth’s atmosphere, and are considered the most reliable source of cloud information. However, lidar-based cloud climatologies are infrequently sampled: every 7 to 91 d, and only along the ground track. This study evaluated how accurate are the cloud data from existing (CALIPSO, ICESat-2, Aeolus) and planned (EarthCARE) space lidars, when compared to a cloud climatology obtained with observations taken every day.
Cited articles
Abrams, M., Bailey, B., Tsu, H., and Hato, M.: The ASTER Global DEM, Photogramm. Eng. Rem. S., 76, 344–348, 2010.
Ackerman, S. A.: Global satellite observations of negative brightness temperature differences between 11 and 6.7 µm, J. Atmos. Sci., 53, 2803–2812, https://doi.org/10.1175/1520-0469(1996)053<2803:GSOONB>2.0.CO;2, 1996.
Ai, Y., Li, J., Shi, W., Schmit, T. J., Cao, C., and Li, W.: Deep convective cloud characterizations from both broadband imager and hyperspectral infrared sounder measurements, J. Geophys. Res., 122, 1700–1712, https://doi.org/10.1002/2016JD025408, 2017.
Anzalone, A., Bertaina, M. E., Briz, S., Cassardo, C., Cremonini, R., de Castro, A. J., Ferrarese, S., Isgrò, F., López, F., and Tabone, I.: Methods to Retrieve the Cloud-Top Height in the Frame of the JEM-EUSO Mission, IEEE T. Geosci. Remote, 57, 304–318, https://doi.org/10.1109/TGRS.2018.2854296, 2019.
Apke, J. M., Mecikalski, J. R., Bedka, K., McCaul, E. W., Homeyer, C. R., and Jewett, C. P.: Relationships between Deep Convection Updraft Characteristics and Satellite-Based Super Rapid Scan Mesoscale Atmospheric Motion Vector–Derived Flow, Mon. Weather Rev., 146, 3461–3480, https://doi.org/10.1175/MWR-D-18-0119.1, 2018.
Aumann, H. H. and Ruzmaikin, A.: Frequency of deep convective clouds in the tropical zone from 10 years of AIRS data, Atmos. Chem. Phys., 13, 10795–10806, https://doi.org/10.5194/acp-13-10795-2013, 2013.
Bakhshaii, A. and Stull, R.: Saturated Pseudoadiabats – A Noniterative Approximation, J. Appl. Meteorol. Clim., 52, 5–15, https://doi.org/10.1175/JAMC-D-12-062.1, 2013.
Benas, N., Solodovnik, I., Stengel, M., Hüser, I., Karlsson, K.-G., Håkansson, N., Johansson, E., Eliasson, S., Schröder, M., Hollmann, R., and Meirink, J. F.: CLAAS-3: the third edition of the CM SAF cloud data record based on SEVIRI observations, Earth Syst. Sci. Data, 15, 5153–5170, https://doi.org/10.5194/essd-15-5153-2023, 2023.
Bieliński, T.: A Parallax Shift Effect Correction Based on Cloud Height for Geostationary Satellites and Radar Observations, Remote Sens.-Basel, 12, 365, https://doi.org/10.3390/rs12030365, 2020.
Chae, J. H., Wu, D. L., Read, W. G., and Sherwood, S. C.: The role of tropical deep convective clouds on temperature, water vapor, and dehydration in the tropical tropopause layer (TTL), Atmos. Chem. Phys., 11, 3811–3821, https://doi.org/10.5194/acp-11-3811-2011, 2011.
Changnon, S. A.: Major Damaging Convective Storms in the United States, Phys. Geogr., 32, 286–294, https://doi.org/10.2747/0272-3646.32.3.286, 2011.
de Waard, J., Mohr, T., Diekmann, F. J., Menzel, W., and Schmetz, J.: Meteosat first generation: Laying the foundations of the European geostationary meteorological satellite system, J. Eur. Meteorol. Soc., 3, 100025, https://doi.org/10.1016/j.jemets.2025.100025, 2025.
Di Michele, S., McNally, T., Bauer, P., and Genkova, I.: Quality Assessment of Cloud-Top Height Estimates From Satellite IR Radiances Using the CALIPSO Lidar, IEEE T. Geosci. Remote, 51, 2454–2464, https://doi.org/10.1109/TGRS.2012.2210721, 2013.
Diner, D. J., Beckert, J. C., Reilly, T. H., Bruegge, C. J., Conel, J. E., Kahn, R. A., Martonchik, J. V., Ackerman, T. P., Davies, R., Gerstl, S. A. W., Gordon, H. R., Muller, J.-P., Myneni, R. B., Sellers, P. J., Pinty, B., and Verstraete, M. M.: Multi-angle Imaging SpectroRadiometer (MISR) instrument description and experiment overview, IEEE T. Geosci. Remote, 36, 1072–1087, https://doi.org/10.1109/36.700992, 1998.
Gultepe, I., Sharman, R., Williams, P. D., Zhou, B., Ellrod, G., Minnis, P., Trier, S., Griffin, S., Yum, S. S., Gharabaghi, B., Feltz, W., Temimi, M., Pu, Z., Storer, L. N., Kneringer, P., Weston, M. J., Chuang, H., Thobois, L., Dimri, A. P., Dietz, S. J., França, G. B., Almeida, M. V., and Neto, F. L. A.: A Review of High Impact Weather for Aviation Meteorology, Pure Appl. Geophys., 176, 1869–1921, https://doi.org/10.1007/s00024-019-02168-6, 2019.
Håkansson, N., Adok, C., Thoss, A., Scheirer, R., and Hörnquist, S.: Neural network cloud top pressure and height for MODIS, Atmos. Meas. Tech., 11, 3177–3196, https://doi.org/10.5194/amt-11-3177-2018, 2018.
Hamann, U., Walther, A., Baum, B., Bennartz, R., Bugliaro, L., Derrien, M., Francis, P. N., Heidinger, A., Joro, S., Kniffka, A., Le Gléau, H., Lockhoff, M., Lutz, H.-J., Meirink, J. F., Minnis, P., Palikonda, R., Roebeling, R., Thoss, A., Platnick, S., Watts, P., and Wind, G.: Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms, Atmos. Meas. Tech., 7, 2839–2867, https://doi.org/10.5194/amt-7-2839-2014, 2014.
Hartmann, D. L., Moy, L. A., and Fu, Q.: Tropical convection and the energy balance at the top of the atmosphere, J. Climate, 14, 4495–4511, https://doi.org/10.1175/1520-0442(2001)014<4495:TCATEB>2.0.CO;2, 2001.
Hasler, A. F.: Stereographic Observations from Geosynchronous Satellites: An Important New Tool for the Atmospheric Sciences, B. Am. Meteorol. Soc., 62, 194–212, https://doi.org/10.1175/1520-0477(1981)062<0194:SOFGSA>2.0.CO;2, 1981.
Hawkinson, J. A., Feltz, W., and Ackerman, S. A.: A Comparison of GOES Sounder- and Cloud Lidar- and Radar-Retrieved Cloud-Top Heights, J. Appl. Meteorol., 44, 1234–1242, https://doi.org/10.1175/JAM2269.1, 2005.
Heidinger, A. K. and Pavolonis, M. J.: Gazing at cirrus clouds for 25 years through a split window. Part I: Methodology, J. Appl. Meteorol. Clim., https://doi.org/10.1175/2008JAMC1882.1, 2009.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 Global Reanalysis, Q. J. Roy. Meteor. Soc., https://doi.org/10.1002/qj.3803, 2020.
Holmlund, K., Grandell, J., Schmetz, J., Stuhlmann, R., Bojkov, B., Munro, R., Lekouara, M., Coppens, D., Viticchie, B., August, T., Theodore, B., Watts, P., Dobber, M., Fowler, G., Bojinski, S., Schmid, A., Salonen, K., Tjemkes, S., Aminou, D., and Blythe, P.: Meteosat Third Generation (MTG): Continuation and Innovation of Observations from Geostationary Orbit, B. Am. Meteorol. Soc., 102, E990–E1015, https://doi.org/10.1175/BAMS-D-19-0304.1, 2021.
Holz, R. E., Ackerman, S. A., Nagle, F. W., Frey, R., Dutcher, S., Kuehn, R. E., Vaughan, M. A., and Baum, B.: Global Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection and height evaluation using CALIOP, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2008JD009837, 2008.
Hong, Y., Nesbitt, S. W., Trapp, R. J., and Di Girolamo, L.: Near-global distributions of overshooting tops derived from Terra and Aqua MODIS observations, Atmos. Meas. Tech., 16, 1391–1406, https://doi.org/10.5194/amt-16-1391-2023, 2023.
Huang, Y., Siems, S., Manton, M., Protat, A., Majewski, L., and Nguyen, H.: Evaluating Himawari-8 Cloud Products Using Shipborne and CALIPSO Observations: Cloud-Top Height and Cloud-Top Temperature, J. Atmos. Ocean. Tech., 36, 2327–2347, https://doi.org/10.1175/JTECH-D-18-0231.1, 2019.
Koffler, R., DeCotiis, A. G., and Krishna Rao, P.: A Procedure for Estimating Cloud Amount and Height From Satellite Infrared Radiation Data, Mon. Weather Rev., 101, 240–243, https://doi.org/10.1175/1520-0493(1973)101<0240:APFECA>2.3.CO;2, 1973.
Kotarba, A. Z.: Impact of the revisit frequency on cloud climatology for CALIPSO, EarthCARE, Aeolus, and ICESat-2 satellite lidar missions, Atmos. Meas. Tech., 15, 4307–4322, https://doi.org/10.5194/amt-15-4307-2022, 2022.
Kotarba, A. Z. and Wojciechowska, I.: Satellite-based detection of deep-convective clouds: the sensitivity of infrared methods and implications for cloud climatology, Atmos. Meas. Tech., 18, 2721–2738, https://doi.org/10.5194/amt-18-2721-2025, 2025.
Lancaster, R. S., Spinhirne, J. D., and Manizade, K. F.: Combined Infrared Stereo and Laser Ranging Cloud Measurements from Shuttle Mission STS-85, J. Atmos. Ocean. Tech., 20, 67–78, https://doi.org/10.1175/1520-0426(2003)020<0067:CISALR>2.0.CO;2, 2003.
Li, Y., Baum, B. A., Heidinger, A. K., Menzel, W. P., and Weisz, E.: Improvement in cloud retrievals from VIIRS through the use of infrared absorption channels constructed from VIIRS+CrIS data fusion, Atmos. Meas. Tech., 13, 4035–4049, https://doi.org/10.5194/amt-13-4035-2020, 2020.
Liu, Q., Hao, X., Zou, C.-Z., Wang, L., Qu, J. J., and Yan, B.: A Preliminary Assessment of the VIIRS Cloud Top and Base Height Environmental Data Record Reprocessing, Remote Sens.-Basel, 17, 1036, https://doi.org/10.3390/rs17061036, 2025.
Mace, G. G. and Zhang, Q.: The CloudSat radar-lidar geometrical profile product (RL-GeoProf): Updates, improvements, and selected results, J. Geophys. Res., 119, 9441–9462, https://doi.org/10.1002/2013JD021374, 2014.
Menzel, P. W., Frey, R. A., Zhang, H., Wylie, D. P., Moeller, C. C., Holz, R. E., Maddux, B., Baum, B. A., Strabala, K. I., and Gumley, L. E.: MODIS global cloud-top pressure and amount estimation: Algorithm description and results, J. Appl. Meteorol. Clim., 47, 1175–1198, https://doi.org/10.1175/2007JAMC1705.1, 2008.
Min, M., Li, J., Wang, F., Liu, Z., and Menzel, W. P.: Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms, Remote Sens. Environ., 239, 111616, https://doi.org/10.1016/j.rse.2019.111616, 2020.
Moisseeva, N. and Stull, R.: Technical note: A noniterative approach to modelling moist thermodynamics, Atmos. Chem. Phys., 17, 15037–15043, https://doi.org/10.5194/acp-17-15037-2017, 2017.
Moroney, C., Davies, R., and Muller, J. P.: Operational retrieval of cloud-top heights using MISR data, IEEE T. Geosci. Remote, 40, 1532–1540, https://doi.org/10.1109/TGRS.2002.801150, 2002.
Muller, J.-P., Denis, M.-A., Dundas, R. D., Mitchell, K. L., Naud, C., and Mannstein, H.: Stereo cloud-top heights and cloud fraction retrieval from ATSR-2, Int. J. Remote Sens., 28, 1921–1938, https://doi.org/10.1080/01431160601030975, 2007.
Musil, D. J., Christopher, S. A., Deola, R. A., and Smith, P. L.: Some Interior Observations of Southeastern Montana Hailstorms, J. Appl. Meteorol. Clim., 30, 1596–1612, https://doi.org/10.1175/1520-0450(1991)030<1596:SIOOSM>2.0.CO;2, 1991.
Pfister, L., Ueyama, R., Jensen, E. J., and Schoeberl, M. R.: Deep Convective Cloud Top Altitudes at High Temporal and Spatial Resolution, Earth Sp. Sci., 9, e2022EA002475, https://doi.org/10.1029/2022EA002475, 2022.
Pfreundschuh, S., Eriksson, P., Duncan, D., Rydberg, B., Håkansson, N., and Thoss, A.: A neural network approach to estimating a posteriori distributions of Bayesian retrieval problems, Atmos. Meas. Tech., 11, 4627–4643, https://doi.org/10.5194/amt-11-4627-2018, 2018.
Pilorz, W., Laskowski, I., Surowiecki, A., and Łupikasza, E.: Fatalities related to sudden meteorological events across Central Europe from 2010 to 2020, Int. J. Disast. Risk Re., 88, 103622, https://doi.org/10.1016/j.ijdrr.2023.103622, 2023.
Raspaud, M., Hoese, D., Dybbroe, A., Lahtinen, P., Devasthale, A., Itkin, M., Hamann, U., Rasmussen, L. Ø., Nielsen, E. S., Leppelt, T., Maul, A., Kliche, C., and Thorsteinsson, H.: PyTroll: An Open-Source, Community-Driven Python Framework to Process Earth Observation Satellite Data, B. Am. Meteorol. Soc., 99, 1329–1336, https://doi.org/10.1175/BAMS-D-17-0277.1, 2018.
Rodgers, C. D.: Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation, Rev. Geophys., 14, 609–624, https://doi.org/10.1029/RG014i004p00609, 1976.
Rossow, W. B. and Schiffer, R. A.: Advances in Understanding Clouds from ISCCP, B. Am. Meteorol. Soc., 80, 2261–2287, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2, 1999.
Sauer, M., Steiner, M., Sharman, R. D., Pinto, J. O., and Deierling, W. K.: Tradeoffs for routing flights in view of multiple weather hazards, J. Air Transp., 27, 70–80, https://doi.org/10.2514/1.D0124, 2019.
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and Ratier, A.: An Introduction to Meteosat Second Generation (MSG), B. Am. Meteorol. Soc., 83, 977–992, https://doi.org/10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2, 2002.
Seiz, G., Tjemkes, S., and Watts, P.: Multiview Cloud-Top Height and Wind Retrieval with Photogrammetric Methods: Application to Meteosat-8 HRV Observations, J. Appl. Meteorol. Clim., 46, 1182–1195, https://doi.org/10.1175/JAM2532.1, 2007.
Šoljan, V., Jurković, J., and Babić, N.: Fast approximation for calculating deep convection cloud top heights from satellite brightness temperature, in: EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-861, https://doi.org/10.5194/ems2024-861, 2024.
Stephens, G., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C., L'Ecuyer, T., and Lebsock, M.: Cloudsat and calipso within the a-train: Ten years of actively observing the earth system, B. Am. Meteorol. Soc., 99, 569–581, https://doi.org/10.1175/BAMS-D-16-0324.1, 2018.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., and Mitrescu, C.: The cloudsat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation, B. Am. Meteorol. Soc., 83, 1771–1790, https://doi.org/10.1175/BAMS-83-12-1771, 2002.
Tan, Z., Ma, S., Zhao, X., Yan, W., and Lu, W.: Evaluation of Cloud Top Height Retrievals from China's Next-Generation Geostationary Meteorological Satellite FY-4A, J. Meteorol. Res.-PRC, 33, 553–562, https://doi.org/10.1007/s13351-019-8123-0, 2019.
Tan, Z., Liu, C., Ma, S., Wang, X., Shang, J., Wang, J., Ai, W., and Yan, W.: Detecting Multilayer Clouds From the Geostationary Advanced Himawari Imager Using Machine Learning Techniques, IEEE T. Geosci. Remote, 60, 1–12, https://doi.org/10.1109/TGRS.2021.3087714, 2022.
Vicente, G. A., Davenport, J. C., and Scofield, R. A.: The role of orographic and parallax corrections on real time high resolution satellite rainfall rate distribution, Int. J. Remote Sens., 23, 221–230, https://doi.org/10.1080/01431160010006935, 2002.
Wang, T., Fetzer, E. J., Wong, S., Kahn, B. H., and Yue, Q.: Validation of MODIS cloud mask and multilayer flag using CloudSat-CALIPSO cloud profiles and a cross-reference of their cloud classifications, J. Geophys. Res., 121, 11620–11635, https://doi.org/10.1002/2016JD025239, 2016.
White, C. H., Heidinger, A. K., and Ackerman, S. A.: Probing the Explainability of Neural Network Cloud-Top Pressure Models for LEO and GEO Imagers, Artif. Intell. Earth Syst., 1, 210001, https://doi.org/10.1175/AIES-D-21-0001.1, 2022.
Winker, D., Vaughan, M., and Hunt, B.: The CALIPSO mission and initial results from CALIOP, in: Lidar Remote Sensing for Environmental Monitoring VII, vol. 6409, 640902, https://doi.org/10.1117/12.698003, 2006.
Wu, Q., Wang, H.-Q., Zhuang, Y.-Z., Lin, Y.-J., Zhang, Y., and Ding, S.-S.: Correlations of Multispectral Infrared Indicators and Applications in the Analysis of Developing Convective Clouds, J. Appl. Meteorol. Clim., 55, 945–960, https://doi.org/10.1175/JAMC-D-15-0081.1, 2016.
Yang, K., Wang, Z., Deng, M., and Dettmann, B.: Improved tropical deep convective cloud detection using MODIS observations with an active sensor trained machine learning algorithm, Remote Sens. Environ., 297, 113762, https://doi.org/10.1016/j.rse.2023.113762, 2023.
Short summary
Deep convective clouds are difficult to locate precisely in weather satellite images due to viewing-angle distortions. We tested a method that estimates cloud-top heights from infrared temperature data alone — available on weather satellites for over 40 years without need for advanced sensors. The method proved accurate enough to correct these distortions, enabling consistent long-term storm cloud records across all generations of European weather satellites using a single, uniform approach.
Deep convective clouds are difficult to locate precisely in weather satellite images due to...