{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T14:20:06Z","timestamp":1762784406657,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:00:00Z","timestamp":1762560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The Dvorak technique has represented a fundamental tool for understanding the power of tropical cyclones based on their shape and geometric evolution. However, it should be noted that the Dvorak technique is purely morphological in nature and was developed for wind, not precipitation. The role of shape methods in precipitation prediction remains uncertain, particularly in the context of modern multi-sensor capabilities. This uncertainty forms the motivation for the present study. In an attempt to enrich Dvorak\u2019s technique, this study proposes a novel hypothesis. This study tests the hypothesis that higher precipitation intensity is associated with more organized cloud-system morphology, as captured by simple geometric descriptors and indicative of dynamically coherent convection. A total of 3419 cloud-system objects (after size filter) were utilized to establish geometric relationships in each of them. For the case study of Hurricane Patricia over the Mexican coast in 2015, 3858 geometric shapes were processed. The cloud-system morphology was derived from geostationary imagery (GOES-13) and collocated with satellite precipitation estimates in order to isolate intense-rainfall objects (&gt;50 mm\/h). For each object, simple geometric descriptors were computed, and shape variability was summarised via Principal Component Analysis (PCA). The present study sought to evaluate the associations with rain-rate metrics (mean, mode, maximum) using rank correlations and k-means clustering. Furthermore, sensitivity analyses were conducted on the rain threshold and minimum object size. A Shape Descriptor: ratio between perimeter and diameter was identified as a promising tool to enhance early prediction models of extreme rainfall, contributing to enhanced meteorological risk management. The study indicates that cloud shape can serve as a valuable indicator in the classification and forecasting of intense cloud systems.<\/jats:p>","DOI":"10.3390\/ijgi14110443","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T13:51:08Z","timestamp":1762782668000},"page":"443","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hurricane Precipitation Intensity as a Function of Geometric Shape: The Evolution of Dvorak Geometries"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6331-5761","authenticated-orcid":false,"given":"Ivan Gonzalez","family":"Garcia","sequence":"first","affiliation":[{"name":"Facultad de Informatica, Autonomous University of Queretaro, Juriquilla, Santiago de Quer\u00e9taro 76230, Queretaro, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2770-8642","authenticated-orcid":false,"given":"Alfonso","family":"Gutierrez-Lopez","sequence":"additional","affiliation":[{"name":"Water Research Center, International Flood Initiative, Latin-American and the Caribbean Region (IFI-LAC), Intergovernmental Hydrological Programme (IHP), Autonomous University of Queretaro, Santiago de Quer\u00e9taro 76010, Queretaro, Mexico"},{"name":"Mexican Institute of Water Technology (IMTA), Water Security Head Office, Paseo Cuauhnahuac 8532, Col. Progreso, Jiutepec 62550, Morelos, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7711-9585","authenticated-orcid":false,"given":"Ana Marcela Herrera","family":"Navarro","sequence":"additional","affiliation":[{"name":"Facultad de Informatica, Autonomous University of Queretaro, Juriquilla, Santiago de Quer\u00e9taro 76230, Queretaro, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0827-6645","authenticated-orcid":false,"given":"Hugo","family":"Jimenez-Hernandez","sequence":"additional","affiliation":[{"name":"Facultad de Informatica, Autonomous University of Queretaro, Juriquilla, Santiago de Quer\u00e9taro 76230, Queretaro, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,8]]},"reference":[{"key":"ref_1","unstructured":"Dvorak, V.F. (1973). A Technique for the Analysis and Forecasting of Tropical Cyclone Intensities from Satellite Pictures, NOAA technical memorandum NESS."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1175\/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2","article-title":"Tropical Cyclone Intensity Analysis and Forecasting from Satellite Imagery","volume":"103","author":"Dvorak","year":"1975","journal-title":"Mon. Weather. Rev."},{"key":"ref_3","unstructured":"Dvorak, V.F. (1984). Tropical Cyclone Intensity Analysis Using Satellite Data."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1175\/BAMS-87-9-1195","article-title":"The Dvorak Tropical Cyclone Intensity Estimation Technique: A Satellite-Based Method That Has Endured for over 30 Years","volume":"87","author":"Velden","year":"2006","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1080\/15481603.2021.1908675","article-title":"Regionalization of Precipitation Associated with Tropical Cyclones Using Spatial Metrics and Satellite Precipitation","volume":"58","author":"Zhou","year":"2021","journal-title":"GIScience Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2004RG000150","article-title":"Mesoscale Convective Systems","volume":"42","author":"Houze","year":"2004","journal-title":"Rev. Geophys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2070","DOI":"10.1175\/1520-0493(1995)123<2070:TMSOSP>2.0.CO;2","article-title":"The Mesoscale Structure of Severe Precipitation Systems in Switzerland","volume":"123","author":"Schiesser","year":"1995","journal-title":"Mon. Weather. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1175\/1520-0434(1998)013<0172:DOAOST>2.0.CO;2","article-title":"Development of an Objective Scheme to Estimate Tropical Cyclone Intensity from Digital Geostationary Satellite Infrared Imagery","volume":"13","author":"Velden","year":"1998","journal-title":"Weather. Forecast."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.tcrr.2021.11.003","article-title":"An Evaluation of the Advanced Dvorak Technique (9.0) for the Topical Cyclones over the North Indian Ocean","volume":"10","author":"Ahmed","year":"2021","journal-title":"Trop. Cyclone Res. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1038\/nature13278","article-title":"The Poleward Migration of the Location of Tropical Cyclone Maximum Intensity","volume":"509","author":"Kossin","year":"2014","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"435","DOI":"10.2151\/jmsj.2023-025","article-title":"The 30-Year (1987\u20132016) Trend of Strong Typhoons and Genesis Locations Found in the Japan Meteorological Agency\u2019s Dvorak Reanalysis Data. Kisho shushi","volume":"101","author":"Kawabata","year":"2023","journal-title":"J. Meteorol. Soc. Jpn."},{"key":"ref_12","unstructured":"Brown, D., and Franklin, J. (2004, January 3\u20137). Dvorak Tropical Cyclone Wind Speed Biases Determined from Reconnaissance-Based \u201cBest Track\u201d Data (1997\u20132003). Proceedings of the 26th Conference on Hurricanes and Tropical Meteorology, Miami, FL, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1175\/WAF-D-19-0186.1","article-title":"Constraints in Dvorak Wind Speed Estimates: How Quickly Can Hurricanes Intensify?","volume":"35","author":"Sangster","year":"2020","journal-title":"Weather Forecast."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"33","DOI":"10.2151\/sola.2009-009","article-title":"Intercomparison of Dvorak Parameters in the Tropical Cyclone Datasets over the Western North Pacific","volume":"5","author":"Nakazawa","year":"2009","journal-title":"Sola"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1177\/03091333221133098","article-title":"Anatomy of a Storm: A Review of Shape Analysis Research That Fuses Form and Function in Weather Forecasting and Analysis","volume":"47","author":"Zick","year":"2022","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_16","unstructured":"Aguilar, J. (2019). Severidad por Intensidad de Tormenta Asociada a su Evoluci\u00f3n Geom\u00e9trica Utilizando Redes Neuronales Artificiales, Universidad Aut\u00f3noma de Quer\u00e9taro, Facultad de Ingenier\u00eda. Available online: https:\/\/ri-ng.uaq.mx\/handle\/123456789\/1762?mode=full."},{"key":"ref_17","unstructured":"Servicio Meteorol\u00f3gico Nacional (2025, May 23). Portal Oficial del Servicio Meteorol\u00f3gico Nacional. Available online: https:\/\/smn.conagua.gob.mx\/es\/."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gutierrez-Lopez, A. (2022). Methodological Guide to Forensic Hydrology. Water, 14.","DOI":"10.2139\/ssrn.4184688"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3390\/forecast2020005","article-title":"Goes-13 IR Images for Rainfall Forecasting in Hurricane Storms","volume":"2","year":"2020","journal-title":"Forecasting"},{"key":"ref_20","first-page":"179","article-title":"A Method of Assigning Numerical and Percentage Values to the Degree of Roundness of Sand Grains","volume":"1","author":"Cox","year":"1927","journal-title":"J. Paleontol."},{"key":"ref_21","unstructured":"Pentland, A. (1927). A method of measuring the angularity of sands. Acta Eng. Dom. Trans. R. Soc. Can., 21."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s10851-009-0158-x","article-title":"New Resolution Independent Measures of Circularity","volume":"35","author":"Ritter","year":"2009","journal-title":"J. Math. Imaging Vis."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1109\/TPAMI.2004.19","article-title":"A New Convexity Measure for Polygons","volume":"26","author":"Zunic","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","unstructured":"Yaglom, I.M., and Boltyanskii, V.G. (1951). Convex Figures, State Publishing House for Technical and Theoretical Literature. (In Russian)."},{"key":"ref_25","first-page":"278","article-title":"Eine Extremaleigenschaft Der Kurven Konstanter Breite","volume":"25","author":"Szasz","year":"2025","journal-title":"Jahresber. Der Dtsch. Math. Ver."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1080\/17421772.2023.2176539","article-title":"Katarzyna Kopczewska Akaike Information Criterion in Choosing the Optimal K-Nearest Neighbours of the Spatial Weight Matrix","volume":"19","author":"Kubara","year":"2023","journal-title":"Spat. Econ. Anal."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ryu, S., Hong, S.-E., Park, J.-D., and Hong, S. (2020). An Improved Conversion Relationship between Tropical Cyclone Intensity Index and Maximum Wind Speed for the Advanced Dvorak Technique in the Northwestern Pacific Ocean Using SMAP Data. Remote Sens., 12.","DOI":"10.3390\/rs12162580"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1175\/1520-0493(1979)107<0575:TBOUSI>2.0.CO;2","article-title":"The Benefits of Using Short-Interval Satellite Images to Derive Winds for Tropical Cyclones","volume":"107","author":"Rodgers","year":"1979","journal-title":"Mon. Weather. Rev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1007\/s13351-024-3186-y","article-title":"Enhancing Tropical Cyclone Intensity Estimation from Satellite Imagery through Deep Learning Techniques","volume":"38","author":"Yang","year":"2024","journal-title":"J. Meteorol. Res."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/11\/443\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T14:17:56Z","timestamp":1762784276000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/11\/443"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,8]]},"references-count":29,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["ijgi14110443"],"URL":"https:\/\/doi.org\/10.3390\/ijgi14110443","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,8]]}}}