Topographic controls nocturnal cooling extremes in complex terrain revealed by high-frequency GOES-16 observations

Topographic controls nocturnal cooling extremes in complex terrain revealed by high-frequency GOES-16 observations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Topographic controls nocturnal cooling extremes in complex terrain revealed by high-frequency GOES-16 observations Grethel Garcia Bu Bucogen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9484595/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Nocturnal cooling during frost events in complex terrain is governed by interactions between topography and boundary-layer processes, yet these dynamics remain poorly resolved at high temporal resolution. Here, we use high-frequency (10–15 min) thermal infrared observations from GOES-16 to characterise the spatial organisation of nocturnal cooling during a frost event in the Uco Valley (Mendoza, Argentina). We derive brightness temperature minima (BTmin), cooling magnitude (ΔT), and maximum nighttime cooling rates (MNCR), and identify two coherent thermal regimes. Colder and more intense cooling (BTmin = − 5.00°C; ΔT = 6.25°C; MNCR = 2.17°C h⁻¹) occurs over flat terrain, whereas weaker cooling (BTmin = − 2.52°C) is associated with more heterogeneous and sloped areas. Our results show that slope exerts a stronger control than elevation, and minimum temperatures are not systematically associated with topographic low, consistent with enhanced atmospheric decoupling and radiative heat loss over low-slope surfaces. These findings demonstrate that terrain configuration governs the spatial structure of cooling extremes at landscape scale, and that high-frequency geostationary observations provide a new capability to resolve these dynamics. The identified mechanisms have direct implications for frost occurrence in agricultural systems and other cold-air pooling environments. Meteorology Physical Geography GOES-16 thermal remote sensing frost risk cold-air pooling clustering topographic controls Figures Figure 1 Figure 2 Figure 3 1. Introduction Nocturnal cooling in complex terrain plays a central role in the development of frost events, particularly under clear and calm conditions that favour strong surface–atmosphere decoupling. In mountainous regions, spatial variability in minimum temperature is largely governed by terrain-driven processes such as radiative cooling, cold-air drainage, and pooling, which together produce sharp thermal gradients over short distances (Whiteman, 2000 ; Sanderson et al., 2022 ; Fonseca et al., 2024 ). Despite this well-established physical framework, the relative importance of specific topographic controls on cooling intensity at landscape scale remains insufficiently quantified. In particular, while elevation is commonly used as a proxy for temperature variability, it does not fully capture the processes governing nocturnal cooling under stable atmospheric conditions. Instead, terrain configuration—especially slope and its influence on cold-air accumulation and atmospheric decoupling—may exert a stronger control on the intensity and spatial organisation of cooling extremes. However, evaluating these mechanisms requires observations capable of resolving both the spatial structure and temporal evolution of nocturnal cooling at sufficiently high resolution. Meteorological station networks provide detailed point measurements but are often too sparse to capture fine-scale variability in complex terrain. In contrast, recent advances in geostationary remote sensing, such as the Advanced Baseline Imager (ABI) aboard GOES-16, enable continuous monitoring of surface thermal dynamics at sub-hourly scales (Schmit et al., 2017 ; Chen et al., 2021 ). These high-frequency observations offer new opportunities to characterise the evolution of nocturnal cooling and to investigate its controlling factors across heterogeneous landscapes. Here, we use high-frequency (10–15 min) thermal infrared observations from GOES-16 to analyse the spatial organisation of nocturnal cooling during frost conditions in a complex mountain valley (Uco Valley, Argentina). We hypothesise that slope exerts a primary control on nocturnal cooling intensity, exceeding that of elevation, and that minimum temperatures are not necessarily located in topographic lows due to the role of atmospheric decoupling. Our aim is to quantify terrain-controlled cooling regimes at landscape scale and to identify the dominant topographic drivers of cooling extremes. 2. Methods 2.1 Study area and event selection The study was conducted in the Uco Valley (Mendoza, Argentina), a high-altitude viticultural region characterised by complex terrain (Fig. 1 a). Twelve frost events between 2018 and 2023 were analysed, spanning the grapevine-growing season (September–March). Events were identified using air temperature observations from three INTA meteorological stations (Tupungato, La Consulta, and Pareditas). To ensure spatial representativeness at the valley scale, frost events were required to exhibit simultaneous air temperatures ≤ 0°C at at least two stations under clear-sky conditions. 2.2 Satellite data and nocturnal cooling metrics Nocturnal cooling was characterised using thermal infrared observations from the Advanced Baseline Imager (ABI) onboard GOES-16. Brightness temperature (BT) from Band 13 (10.3 µm) was used as a proxy for surface thermal conditions, retaining only high-quality, cloud-free observations (DQF = 0). Data were acquired at 10–15 min intervals and aggregated into hourly median composites to reduce noise and residual cloud contamination. Satellite-derived BTmin was evaluated against in situ minimum air temperature using standard statistical metrics (bias, RMSE, and correlation coefficient) (Figs. 1 b-e). Three metrics were derived from the nocturnal BT time series at the pixel level: (i) minimum brightness temperature (BTmin), defined as the 10th percentile of nocturnal values; (ii) cooling magnitude (ΔT), calculated as the difference between evening temperature and BTmin; and (iii) maximum night-time cooling rate (MNCR), defined as the largest temperature decrease between consecutive hourly observations (°C h⁻¹). The analysis was restricted to the nocturnal period (sunset to sunrise), excluding pixels with insufficient temporal sampling. 2.3 Spatial analysis and topographic controls Spatial patterns of nocturnal cooling were analysed using k-means clustering applied to BTmin, ΔT, and MNCR. Variables were standardised prior to clustering, and the optimal number of clusters was determined using silhouette analysis. Clustering was used as an exploratory tool to identify coherent thermal regimes. Topographic controls were assessed using a 30 m digital elevation model. Terrain variables included elevation, slope, and Terrain Position Index (TPI), calculated over a ~ 2 km neighbourhood to capture mesoscale variability relevant to cold-air drainage and accumulation. Relationships between terrain and cooling metrics were examined by comparing their distributions across thermal regimes, allowing identification of the dominant topographic drivers of nocturnal cooling. 3. Results and Discussion Satellite-derived minimum brightness temperature (BTmin) shows strong agreement with in situ air temperature observations (R = 0.86–0.91), with RMSE values between 2.4 and 3.4°C (Figs. 1 b-e). A consistent negative bias ( ~ − 2.6°C) is observed, reflecting the expected radiative decoupling between the land surface and near-surface air under stable nocturnal conditions. Despite this offset, the satellite data reliably capture the spatial variability and temporal evolution of nocturnal cooling. Spatial patterns of nocturnal cooling exhibit a clear and consistent organisation across the study area (Fig. 2 ). Two dominant thermal regimes emerge from the clustering analysis, with strong spatial coherence. Colder conditions (BTmin ≈ − 5.0°C), associated with higher cooling magnitude (ΔT ≈ 6.25°C) and stronger cooling rates (MNCR ≈ 2.17°C h⁻¹), are concentrated in the south-eastern sector. In contrast, warmer conditions (BTmin ≈ − 2.5°C) with weaker cooling (ΔT ≈ 4.2°C; MNCR ≈ 1.64°C h⁻¹) dominate central and western areas. The spatial agreement between BTmin (Fig. 2 a) and ΔT (Fig. 2 b) indicates that areas reaching lower minimum temperatures also experience stronger cumulative radiative cooling. However, the partial decoupling between MNCR (Fig. 1 c) and BTmin (Fig. 2 a) suggests that rapid cooling does not necessarily lead to the lowest temperatures, highlighting the importance of cooling persistence throughout the night in controlling frost intensity. The topographic characterisation of the clusters reveals that the observed thermal contrasts are strongly controlled by terrain configuration (Fig. 3 ). While colder conditions (Cluster 2; BTmin = − 5.00°C) are generally associated with higher elevations (median ~ 1340 m a.s.l.) compared to warmer areas (~ 1100 m a.s.l.; Fig. 3 a), elevation alone does not explain the observed cooling patterns. Instead, the most intense cooling systematically occurs over low-slope surfaces (median ~ 1.2°; Fig. 3 b), whereas steeper and more heterogeneous terrain (~ 2.2°) is associated with weaker cooling. This indicates that terrain geometry, rather than absolute elevation, exerts the primary control on nocturnal cooling intensity by modulating atmospheric decoupling and radiative heat loss. In particular, flat or gently sloping surfaces favour air stagnation and reduced mechanical mixing, enhancing cooling persistence under stable conditions. Importantly, the relationship between topography and cooling differs across thermal metrics. Both BTmin and ΔT exhibit consistent spatial patterns, with stronger cooling concentrated in low-slope areas, indicating that cumulative radiative cooling governs the development of minimum temperatures. In contrast, MNCR shows a more spatially variable distribution (Fig. 3 g–i), suggesting that instantaneous cooling rates are less constrained by terrain configuration and more sensitive to transient atmospheric conditions. This distinction highlights that cooling intensity, accumulation, and rate are governed by partially decoupled processes. (a-c) Relationships between elevation, slope, TPI and BTmin, (d–f) ΔT, and (g–i) MNCR. These findings refine the classical conceptual model of cold-air drainage, which assumes that minimum temperatures are primarily associated with topographic lows (Whiteman, 2000 ; Sanderson et al., 2022 ). While cold-air pooling remains an important mechanism, our results show that the lowest temperatures are frequently not located in topographic. Instead, they preferentially occur over low-slope surfaces at intermediate-to-high elevations, where atmospheric decoupling enhances the persistence of radiative cooling. Previous in situ studies have been fundamental in identifying cold-air drainage and pooling processes but are inherently limited in their ability to resolve spatial variability at landscape scale. Remote sensing approaches have characterised broader temperature patterns (Sun and Pinker, 2003 ; Hrisko et al., 2020 ), yet typically lack the temporal resolution required to capture nocturnal cooling dynamics. By contrast, the high-frequency observations used here resolve both the spatial organisation and temporal evolution of cooling processes, allowing direct identification of the mechanisms controlling cooling extremes. 4. Conclusion This study demonstrates that nocturnal cooling extremes in complex terrain are primarily controlled by terrain configuration rather than elevation alone. In particular, low-slope surfaces at intermediate-to-high elevations favour atmospheric decoupling and sustained radiative cooling, leading to lower minimum temperatures than those observed in topographic lows. These results challenge the conventional assumption that frost-prone conditions are systematically associated with valley bottoms, showing instead that terrain geometry governs the spatial distribution of cooling extremes. Furthermore, the contrasting behaviour of BTmin, ΔT, and MNCR indicates that cooling persistence, accumulation, and rate are controlled by partially decoupled processes, with minimum temperatures primarily driven by sustained radiative cooling rather than instantaneous cooling rates. High-frequency GOES-16 observations enable the resolution of both the spatial organisation and temporal evolution of these dynamics at landscape scale, providing new insight into nocturnal boundary-layer processes in complex terrain. These findings have direct implications for frost occurrence in agricultural systems and highlight the importance of considering terrain-driven controls when assessing cooling extremes under stable atmospheric conditions. Declarations Declaration of competing interest s I have nothing to declare Acknowledgements The author wish to CONICET for the postdoctoral fellowship, which supported the development of this research. We also acknowledge the Instituto Nacional de Tecnología Agropecuaria (INTA) for providing the meteorological data used in this study. References Baillet V, Pauthier B, Payen A, Naviaux P, Symoneaux R, Chassaing T, Renaud-Gentié C (2025) Life cycle assessment of active spring frost protection methods in viticulture in the Loire Valley and Champagne French regions. OENO One 59(1):8408. https://doi.org/10.20870/oeno-one.2025.59.1.8408 Bois B, Gavrilescu C, Zito S, Richard Y, Castel T (2023) La incierta evolución de los riesgos de heladas primaverales en el viñedo del siglo XXI. IVES Technical Reviews, vine & wine. https://doi.org/10.20870/IVES-TR.2023.7514 Chen W et al (2021) Land Surface Temperature from GOES-East and GOES-West. J Atmos Ocean Technol 38(4):843–858. https://doi.org/10.1175/JTECH-D-20-0086.1 Fonseca A, Cruz J, Fraga H, Andrade C, Valente J, Alves F, Neto AC, Flores R, Santos JA (2024) Vineyard Microclimatic Zoning as a Tool to Promote Sustainable Viticulture under Climate Change. Sustainability 16(8):3477. https://doi.org/10.3390/su16083477 Hrisko J, Ramamurthy P, Yu Y, Yu P, Melecio-Vázquez D (2020) Urban air temperature model using GOES-16 LST and a diurnal regressive neural network algorithm. Remote Sens Environ 237:111495. https://doi.org/10.1016/j.rse.2019.111495 Jones GV, Webb LB (2010) Climate Change, Viticulture, and Wine: Challenges and Opportunities. J Wine Res 21(2–3):103–106. https://doi.org/10.1080/09571264.2010.530091 Keller M (2015) The Science of Grapevines: Anatomy and Physiology, second edn. Academic, Burlington. https://doi.org/10.1016/C2013-0-13111-3 Mania E, Petrella F, Giovannozzi M, Piazzi M, Wilson A, Guidoni S (2021) Managing vineyard topography and seasonal variability to improve grape quality and vineyard sustainability. Agronomy 11(6):1142. https://doi.org/10.3390/agronomy11061142 Meier M, Fuhrer J, Holzkämper A (2018) Changing risk of spring frost damage in grapevines due to climate change? A case study in the Swiss Rhone Valley. Int J Biometeorol 62(6):991–1002. https://doi.org/10.1007/s00484-018-1501-y Sanderson MG, Teixeira M, Fontes N, Silva S, Graça A (2022) Cold-air pooling in the Upper Douro Valley: An observational study. J Appl Meteorol Climatol 61:1893–1904. https://doi.org/10.1175/JAMC-D-21-0263.1 Schmit TJ, Griffith P, Gunshor MM, Daniels JM, Goodman SJ, Lebair WJ (2017) A Closer Look at the ABI on the GOES-R Series. Bull Amer Meteor Soc 98(4):681–698. https://doi.org/10.1175/BAMS-D-15-00230.1 Snyder RL, de Melo-Abreu JP (2005) Frost protection: Fundamentals, practice and economics, vol 1. Food and Agriculture Organization of the United Nations, Rome. http://home.isa.utl.pt/~jpabreu/Docs/FROST_Volume1.pdf (accessed 29 January 2026) Straffelini E, Carrillo N, Schilardi C, Aguilera R, Estrella Orrego MJ, Tarolli P (2023) Viticulture in Argentina under extreme weather scenarios: Actual challenges, future perspectives. Geogr Sustain 4(2):161–169. https://doi.org/10.1016/j.geosus.2023.03.003 Sun D, Pinker RT (2003) Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES-8). J Geophys Res 108(D11):4326. https://doi.org/10.1029/2002JD002422 Whiteman CD (2000) Mountain Meteorology: Fundamentals and Applications. Oxford University Press, New York Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9484595","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":627074657,"identity":"6dfc5935-148f-4a25-b994-d148fc825791","order_by":0,"name":"Grethel Garcia Bu Bucogen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYJACZgYGCwYDCeYDQLaEDDE6GJuBKoFa2BJAWnhI0cJjAOIR1iIfffj444IKCbvt0j2fX92oseBhYD98dAM+LYbn0hKbZ5yRSN455+w265xjQIfxpKXdwKulh8ewmbdNItngRu424xw2oBYJHjMCWvg/NvP+A2nJeWac848ILfI8PIzNvA0SdkAtzI9z24jQYsDDZjib55hEguWcY2bMuX0SPGyE/CLfw/zgM0+Njb25dPPjzznf6uT42Q8fw2/LAQid2MDAwCYBYrHhUw62pQFC2wMx8wdCqkfBKBgFo2BkAgA3ZET9mbFusgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-6347-7381","institution":"Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales","correspondingAuthor":true,"prefix":"","firstName":"Grethel","middleName":"Garcia Bu","lastName":"Bucogen","suffix":""}],"badges":[],"createdAt":"2026-04-21 13:00:33","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9484595/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9484595/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107491713,"identity":"2f9966f3-bf35-471c-bd35-12398968ad63","added_by":"auto","created_at":"2026-04-22 03:10:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":981627,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area and comparison between satellite-derived and in situ minimum temperatures. (a) Location of the study area. (b–e) Relationship between GOES-16 BTmin and in situ air temperature (Tmin) at INTA stations: (b) La Consulta 1, (c) La Consulta 2, (d) Pareditas, and (e) Tupungato.\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9484595/v1/380dc4814aa0aa7ad186370b.jpeg"},{"id":107491717,"identity":"992ffd4a-0a06-4605-87a1-88dcc54d9982","added_by":"auto","created_at":"2026-04-22 03:10:29","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":884434,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of clustered nocturnal cooling metrics derived from GOES-16. (a) BTmin, (b) ΔT, and (c) MNCR. Colours represent k-means cluster membership (K = 2), distinguishing areas with weaker (Cluster 1, blue) and stronger (Cluster 2, yellow) nocturnal cooling.\u003c/p\u003e","description":"","filename":"Figure2.Clusters.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9484595/v1/89754430d3489782e8b075af.jpeg"},{"id":107491860,"identity":"1aef3668-21e2-4035-b6e4-906470f14191","added_by":"auto","created_at":"2026-04-22 03:10:53","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":821903,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTopographic controls on clustered nocturnal cooling metrics.\u003c/strong\u003e\u003cbr\u003e\n(a-c) Relationships between elevation, slope, TPI and BTmin, (d–f) ΔT, and (g–i) MNCR\u003c/p\u003e","description":"","filename":"Figure3topovariables.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9484595/v1/90c6f7ac1daedfae7902207d.jpeg"},{"id":107705635,"identity":"e77876f2-3a60-4167-a87b-ca81208e23e8","added_by":"auto","created_at":"2026-04-24 09:14:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2821609,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9484595/v1/3f86ed57-5e1b-4506-ac5b-558b980edf97.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTopographic controls nocturnal cooling extremes in complex terrain revealed by high-frequency GOES-16 observations\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNocturnal cooling in complex terrain plays a central role in the development of frost events, particularly under clear and calm conditions that favour strong surface\u0026ndash;atmosphere decoupling. In mountainous regions, spatial variability in minimum temperature is largely governed by terrain-driven processes such as radiative cooling, cold-air drainage, and pooling, which together produce sharp thermal gradients over short distances (Whiteman, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Sanderson et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fonseca et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite this well-established physical framework, the relative importance of specific topographic controls on cooling intensity at landscape scale remains insufficiently quantified.\u003c/p\u003e \u003cp\u003eIn particular, while elevation is commonly used as a proxy for temperature variability, it does not fully capture the processes governing nocturnal cooling under stable atmospheric conditions. Instead, terrain configuration\u0026mdash;especially slope and its influence on cold-air accumulation and atmospheric decoupling\u0026mdash;may exert a stronger control on the intensity and spatial organisation of cooling extremes. However, evaluating these mechanisms requires observations capable of resolving both the spatial structure and temporal evolution of nocturnal cooling at sufficiently high resolution.\u003c/p\u003e \u003cp\u003eMeteorological station networks provide detailed point measurements but are often too sparse to capture fine-scale variability in complex terrain. In contrast, recent advances in geostationary remote sensing, such as the Advanced Baseline Imager (ABI) aboard GOES-16, enable continuous monitoring of surface thermal dynamics at sub-hourly scales (Schmit et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These high-frequency observations offer new opportunities to characterise the evolution of nocturnal cooling and to investigate its controlling factors across heterogeneous landscapes. Here, we use high-frequency (10\u0026ndash;15 min) thermal infrared observations from GOES-16 to analyse the spatial organisation of nocturnal cooling during frost conditions in a complex mountain valley (Uco Valley, Argentina). We hypothesise that slope exerts a primary control on nocturnal cooling intensity, exceeding that of elevation, and that minimum temperatures are not necessarily located in topographic lows due to the role of atmospheric decoupling. Our aim is to quantify terrain-controlled cooling regimes at landscape scale and to identify the dominant topographic drivers of cooling extremes.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area and event selection\u003c/h2\u003e \u003cp\u003eThe study was conducted in the Uco Valley (Mendoza, Argentina), a high-altitude viticultural region characterised by complex terrain (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Twelve frost events between 2018 and 2023 were analysed, spanning the grapevine-growing season (September\u0026ndash;March). Events were identified using air temperature observations from three INTA meteorological stations (Tupungato, La Consulta, and Pareditas). To ensure spatial representativeness at the valley scale, frost events were required to exhibit simultaneous air temperatures\u0026thinsp;\u0026le;\u0026thinsp;0\u0026deg;C at at least two stations under clear-sky conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Satellite data and nocturnal cooling metrics\u003c/h2\u003e \u003cp\u003eNocturnal cooling was characterised using thermal infrared observations from the Advanced Baseline Imager (ABI) onboard GOES-16. Brightness temperature (BT) from Band 13 (10.3 \u0026micro;m) was used as a proxy for surface thermal conditions, retaining only high-quality, cloud-free observations (DQF\u0026thinsp;=\u0026thinsp;0). Data were acquired at 10\u0026ndash;15 min intervals and aggregated into hourly median composites to reduce noise and residual cloud contamination. Satellite-derived BTmin was evaluated against in situ minimum air temperature using standard statistical metrics (bias, RMSE, and correlation coefficient) (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-e).\u003c/p\u003e \u003cp\u003eThree metrics were derived from the nocturnal BT time series at the pixel level: (i) minimum brightness temperature (BTmin), defined as the 10th percentile of nocturnal values; (ii) cooling magnitude (ΔT), calculated as the difference between evening temperature and BTmin; and (iii) maximum night-time cooling rate (MNCR), defined as the largest temperature decrease between consecutive hourly observations (\u0026deg;C h⁻\u0026sup1;). The analysis was restricted to the nocturnal period (sunset to sunrise), excluding pixels with insufficient temporal sampling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Spatial analysis and topographic controls\u003c/h2\u003e \u003cp\u003eSpatial patterns of nocturnal cooling were analysed using k-means clustering applied to BTmin, ΔT, and MNCR. Variables were standardised prior to clustering, and the optimal number of clusters was determined using silhouette analysis. Clustering was used as an exploratory tool to identify coherent thermal regimes.\u003c/p\u003e \u003cp\u003eTopographic controls were assessed using a 30 m digital elevation model. Terrain variables included elevation, slope, and Terrain Position Index (TPI), calculated over a\u0026thinsp;~\u0026thinsp;2 km neighbourhood to capture mesoscale variability relevant to cold-air drainage and accumulation. Relationships between terrain and cooling metrics were examined by comparing their distributions across thermal regimes, allowing identification of the dominant topographic drivers of nocturnal cooling.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003eSatellite-derived minimum brightness temperature (BTmin) shows strong agreement with in situ air temperature observations (R\u0026thinsp;=\u0026thinsp;0.86\u0026ndash;0.91), with RMSE values between 2.4 and 3.4\u0026deg;C (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-e). A consistent negative bias (\u0026thinsp;~\u0026thinsp;\u0026minus;\u0026thinsp;2.6\u0026deg;C) is observed, reflecting the expected radiative decoupling between the land surface and near-surface air under stable nocturnal conditions. Despite this offset, the satellite data reliably capture the spatial variability and temporal evolution of nocturnal cooling.\u003c/p\u003e \u003cp\u003eSpatial patterns of nocturnal cooling exhibit a clear and consistent organisation across the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Two dominant thermal regimes emerge from the clustering analysis, with strong spatial coherence. Colder conditions (BTmin\u0026thinsp;\u0026asymp;\u0026thinsp;\u0026minus;\u0026thinsp;5.0\u0026deg;C), associated with higher cooling magnitude (ΔT\u0026thinsp;\u0026asymp;\u0026thinsp;6.25\u0026deg;C) and stronger cooling rates (MNCR\u0026thinsp;\u0026asymp;\u0026thinsp;2.17\u0026deg;C h⁻\u0026sup1;), are concentrated in the south-eastern sector. In contrast, warmer conditions (BTmin\u0026thinsp;\u0026asymp;\u0026thinsp;\u0026minus;\u0026thinsp;2.5\u0026deg;C) with weaker cooling (ΔT\u0026thinsp;\u0026asymp;\u0026thinsp;4.2\u0026deg;C; MNCR\u0026thinsp;\u0026asymp;\u0026thinsp;1.64\u0026deg;C h⁻\u0026sup1;) dominate central and western areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe spatial agreement between BTmin (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) and ΔT (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) indicates that areas reaching lower minimum temperatures also experience stronger cumulative radiative cooling. However, the partial decoupling between MNCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) and BTmin (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) suggests that rapid cooling does not necessarily lead to the lowest temperatures, highlighting the importance of cooling persistence throughout the night in controlling frost intensity.\u003c/p\u003e \u003cp\u003eThe topographic characterisation of the clusters reveals that the observed thermal contrasts are strongly controlled by terrain configuration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). While colder conditions (Cluster 2; BTmin\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.00\u0026deg;C) are generally associated with higher elevations (median\u0026thinsp;~\u0026thinsp;1340 m a.s.l.) compared to warmer areas (~\u0026thinsp;1100 m a.s.l.; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), elevation alone does not explain the observed cooling patterns. Instead, the most intense cooling systematically occurs over low-slope surfaces (median\u0026thinsp;~\u0026thinsp;1.2\u0026deg;; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), whereas steeper and more heterogeneous terrain (~\u0026thinsp;2.2\u0026deg;) is associated with weaker cooling.\u003c/p\u003e \u003cp\u003eThis indicates that terrain geometry, rather than absolute elevation, exerts the primary control on nocturnal cooling intensity by modulating atmospheric decoupling and radiative heat loss. In particular, flat or gently sloping surfaces favour air stagnation and reduced mechanical mixing, enhancing cooling persistence under stable conditions.\u003c/p\u003e \u003cp\u003eImportantly, the relationship between topography and cooling differs across thermal metrics. Both BTmin and ΔT exhibit consistent spatial patterns, with stronger cooling concentrated in low-slope areas, indicating that cumulative radiative cooling governs the development of minimum temperatures. In contrast, MNCR shows a more spatially variable distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg\u0026ndash;i), suggesting that instantaneous cooling rates are less constrained by terrain configuration and more sensitive to transient atmospheric conditions. This distinction highlights that cooling intensity, accumulation, and rate are governed by partially decoupled processes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(a-c) Relationships between elevation, slope, TPI and BTmin, (d\u0026ndash;f) ΔT, and (g\u0026ndash;i) MNCR.\u003c/p\u003e \u003cp\u003eThese findings refine the classical conceptual model of cold-air drainage, which assumes that minimum temperatures are primarily associated with topographic lows (Whiteman, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Sanderson et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While cold-air pooling remains an important mechanism, our results show that the lowest temperatures are frequently not located in topographic. Instead, they preferentially occur over low-slope surfaces at intermediate-to-high elevations, where atmospheric decoupling enhances the persistence of radiative cooling.\u003c/p\u003e \u003cp\u003ePrevious in situ studies have been fundamental in identifying cold-air drainage and pooling processes but are inherently limited in their ability to resolve spatial variability at landscape scale. Remote sensing approaches have characterised broader temperature patterns (Sun and Pinker, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Hrisko et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), yet typically lack the temporal resolution required to capture nocturnal cooling dynamics. By contrast, the high-frequency observations used here resolve both the spatial organisation and temporal evolution of cooling processes, allowing direct identification of the mechanisms controlling cooling extremes.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study demonstrates that nocturnal cooling extremes in complex terrain are primarily controlled by terrain configuration rather than elevation alone. In particular, low-slope surfaces at intermediate-to-high elevations favour atmospheric decoupling and sustained radiative cooling, leading to lower minimum temperatures than those observed in topographic lows.\u003c/p\u003e \u003cp\u003eThese results challenge the conventional assumption that frost-prone conditions are systematically associated with valley bottoms, showing instead that terrain geometry governs the spatial distribution of cooling extremes. Furthermore, the contrasting behaviour of BTmin, ΔT, and MNCR indicates that cooling persistence, accumulation, and rate are controlled by partially decoupled processes, with minimum temperatures primarily driven by sustained radiative cooling rather than instantaneous cooling rates.\u003c/p\u003e \u003cp\u003eHigh-frequency GOES-16 observations enable the resolution of both the spatial organisation and temporal evolution of these dynamics at landscape scale, providing new insight into nocturnal boundary-layer processes in complex terrain. These findings have direct implications for frost occurrence in agricultural systems and highlight the importance of considering terrain-driven controls when assessing cooling extremes under stable atmospheric conditions.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI have nothing to declare\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe author wish to CONICET for the postdoctoral fellowship, which supported the development of this research. We also acknowledge the Instituto Nacional de Tecnolog\u0026iacute;a Agropecuaria (INTA) for providing the meteorological data used in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaillet V, Pauthier B, Payen A, Naviaux P, Symoneaux R, Chassaing T, Renaud-Genti\u0026eacute; C (2025) Life cycle assessment of active spring frost protection methods in viticulture in the Loire Valley and Champagne French regions. 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Oxford University Press, New York\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales ","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GOES-16, thermal remote sensing, frost risk, cold-air pooling, clustering, topographic controls","lastPublishedDoi":"10.21203/rs.3.rs-9484595/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9484595/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNocturnal cooling during frost events in complex terrain is governed by interactions between topography and boundary-layer processes, yet these dynamics remain poorly resolved at high temporal resolution. Here, we use high-frequency (10\u0026ndash;15 min) thermal infrared observations from GOES-16 to characterise the spatial organisation of nocturnal cooling during a frost event in the Uco Valley (Mendoza, Argentina). We derive brightness temperature minima (BTmin), cooling magnitude (ΔT), and maximum nighttime cooling rates (MNCR), and identify two coherent thermal regimes. Colder and more intense cooling (BTmin\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.00\u0026deg;C; ΔT\u0026thinsp;=\u0026thinsp;6.25\u0026deg;C; MNCR\u0026thinsp;=\u0026thinsp;2.17\u0026deg;C h⁻\u0026sup1;) occurs over flat terrain, whereas weaker cooling (BTmin\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.52\u0026deg;C) is associated with more heterogeneous and sloped areas. Our results show that slope exerts a stronger control than elevation, and minimum temperatures are not systematically associated with topographic low, consistent with enhanced atmospheric decoupling and radiative heat loss over low-slope surfaces. These findings demonstrate that terrain configuration governs the spatial structure of cooling extremes at landscape scale, and that high-frequency geostationary observations provide a new capability to resolve these dynamics. The identified mechanisms have direct implications for frost occurrence in agricultural systems and other cold-air pooling environments.\u003c/p\u003e","manuscriptTitle":"Topographic controls nocturnal cooling extremes in complex terrain revealed by high-frequency GOES-16 observations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 03:09:40","doi":"10.21203/rs.3.rs-9484595/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a2c6267-7143-49b2-aac8-20c7db041d79","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66740150,"name":"Meteorology"},{"id":66740151,"name":"Physical Geography"}],"tags":[],"updatedAt":"2026-04-22T03:09:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 03:09:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9484595","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9484595","identity":"rs-9484595","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}