An object-based approach to Forecasting and Verification of heat waves over India

An object-based approach to Forecasting and Verification of heat waves over India | 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 Research Article An object-based approach to Forecasting and Verification of heat waves over India Harvir Singh, Anumeha Dube, Raghavendra Ashrit, Prashant K Srivastava, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3916379/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 Heat waves are one of the most dangerous natural hazards in the world. Higher daily peak temperatures as well as duration, intensity and frequency of heat waves are increasing globally due to climate change. In India, the instances of heat waves have increased in recent years along with their intensity which has resulted in the increased number of casualties. For the purpose of disaster mitigation and reduction of losses due to heat waves, a timely and accurate forecast of these events is required. However, traditional verification methods (which rely on grid-wise comparisons) used for verification of forecasts from high resolution models have a limited utility. In order to assess the utility of these forecasts, spatial verification techniques that can differentiate between forecast and observed features are needed. In this study, we have tried to demonstrate the ability of the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model (NCUM) in predicting maximum 2m temperature (Tmax), over the heat wave-prone zones of India. The state-of-art Object-Based Diagnostic Evaluation (MODE) was used for verification of the heat waves. Additionally, applying fuzzy logic enhances the discernment of similarities between forecasted and observed objects, offering a valuable tool for adapting to varying object types and leveraging human knowledge. For instance, in certain scenarios, emphasizing the matching of area sizes may be crucial, while in others, aligning object locations could be more pertinent. In both cases, achieving the desired outcome involves adjusting weights or revising interest maps. This study shows that NCUM forecasts have a southwesterly bias in the location of Tmax objects for values exceeding 43 and 45°C, indicating a potential lag in system propagation. When verified over a season, it is seen that the performance of the model in predicting forecast attributes is rather very good in terms of smaller variation in the median of centroid distance (~150-200 km upto 120 hr lead time), an 83% match in the internal structure of the forecasts with a near perfect curvature ratio (95-97%), a near perfect 50th percentile intensity ratio (98-99%) and the symmetric difference which is small enough to coincide with the observed heat wave zones. In summary, NCUM forecasts demonstrate accurate heat wave predictions in terms of geographical area, structure, shape, and size up to a 5-day lead time, showcasing their potential for effective disaster preparedness. METplus MODE heat waves centroid distance area ratio total interest Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Full Text 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3916379","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271862939,"identity":"605e590c-d20b-4e29-90eb-9dcba72575fc","order_by":0,"name":"Harvir Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACAxCRAONVVAAJZuYGErScOQPSwkiEFjg42wYiCWgxZz/7TOLhHoY8/mlAxsF5tdH87UAtPyq24dRi2ZNuJpHwjKFY4jaQcXDb8dwZhxkbGHvO3MbtsANpzAYJBxgSN0insd3+uO1YbgNQCzNjGx4t558htNw4OOdY7nyCWm6kMT5AaGmoyd1AWMszkBaJxBm309h/HDh2IHcjUMtBvH45n8Zw8McBm8T+2UBPHaipy513/vDBBz8qcGuBAgkY4zCYPEBIPTKoI0XxKBgFo2AUjBAAALZpX7xiLhpyAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1355-7628","institution":"National Centre for Medium Range Weather Forecasting","correspondingAuthor":true,"prefix":"","firstName":"Harvir","middleName":"","lastName":"Singh","suffix":""},{"id":271862940,"identity":"9348b973-b518-48af-928d-3a1f15f2d85c","order_by":1,"name":"Anumeha Dube","email":"","orcid":"","institution":"National Centre for Medium Range Weather Forecasting","correspondingAuthor":false,"prefix":"","firstName":"Anumeha","middleName":"","lastName":"Dube","suffix":""},{"id":271862941,"identity":"6d10a205-62c3-492c-b9cf-b3ddfd4f1467","order_by":2,"name":"Raghavendra Ashrit","email":"","orcid":"","institution":"National Centre for Medium Range Weather Forecasting","correspondingAuthor":false,"prefix":"","firstName":"Raghavendra","middleName":"","lastName":"Ashrit","suffix":""},{"id":271862942,"identity":"dd3765fb-f0ea-41ce-ae39-9cb0b4b86c09","order_by":3,"name":"Prashant K Srivastava","email":"","orcid":"","institution":"Banaras Hindu University Institute of Environment \u0026 Sustainable Development","correspondingAuthor":false,"prefix":"","firstName":"Prashant","middleName":"K","lastName":"Srivastava","suffix":""},{"id":271862943,"identity":"37cc476f-b268-4340-8c15-d53a722290bc","order_by":4,"name":"VS Prasad","email":"","orcid":"","institution":"National Centre for Medium Range Weather Forecasting","correspondingAuthor":false,"prefix":"","firstName":"VS","middleName":"","lastName":"Prasad","suffix":""}],"badges":[],"createdAt":"2024-02-01 07:06:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3916379/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3916379/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51018734,"identity":"3dfd6258-4785-46ac-ae74-544248d7b999","added_by":"auto","created_at":"2024-02-12 19:23:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1177540,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Mean Maximum Temperature observed over India during 1981–2010 along with the average frequency of days with Tmax (b) \u0026gt; 40 ° C (d) \u0026gt; 45 ° C and (d) \u0026gt; 47 ° C.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3916379/v1/870ea73e676ee6fc50f74fa7.png"},{"id":51018732,"identity":"b3654f3b-b1b2-4bd2-9adc-9c92b4634dcd","added_by":"auto","created_at":"2024-02-12 19:23:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1187701,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Mean Maximum Temperature observed over India during 2022 along with the average frequency of days with Tmax (b) \u0026gt; 40 ° C (d) \u0026gt; 45 ° C and (d) \u0026gt; 47 ° C.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3916379/v1/4d2470231b6b5aa285c43b38.png"},{"id":51018733,"identity":"57872791-4f25-49da-b27d-2796b73b5405","added_by":"auto","created_at":"2024-02-12 19:23:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":147781,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Heat waves days from 2010 to 2022 (b) State-wise distribution of 280 heatwave days\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3916379/v1/9131d63b204b175d0262d1ab.png"},{"id":51018730,"identity":"d2f0261c-d0ab-4c35-83aa-b41d519f8633","added_by":"auto","created_at":"2024-02-12 19:23:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":953897,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of IMD Tmax heat wave episodes over India during 11 th May 2022 to 17 th May 2022.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3916379/v1/932cfe0b2b3d548e22fd2626.png"},{"id":51018735,"identity":"1681bc62-c27f-41ea-a696-56791bd6fd2a","added_by":"auto","created_at":"2024-02-12 19:23:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1175882,"visible":true,"origin":"","legend":"\u003cp\u003eThe synoptic features for heat wave, incursion of dry air from the northwest India, (a) NCUM analysis for 10m winds at Tmax (°C) scale and 2m Relative Humidity (in shades) for 12Z14May 2022 and (b) 12Z15May 2022.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3916379/v1/99b61b644482097bd3cf8aeb.png"},{"id":51018736,"identity":"db4a45af-c892-4e70-a15e-14cb8db0de65","added_by":"auto","created_at":"2024-02-12 19:23:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1118079,"visible":true,"origin":"","legend":"\u003cp\u003eHeat waves object-area identification with Convolution Radius=4 grid size and CT ≥ 43°C by spatial-verification of NCUM 24hr Tmax forecast with IMD observations valid for 15 May 2022\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3916379/v1/6a20423e37388d2b5c4f8594.png"},{"id":51018731,"identity":"9924cabf-2047-4c4f-a666-14cf2014a636","added_by":"auto","created_at":"2024-02-12 19:23:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":513829,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial verification of heat wave event as Observation Object with forecast outline for convolution threshold CT ≥ 41°C, CT ≥ 43°C, and CT ≥ 45°C, from day 1 to day 5 valid for 15 May 2022.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3916379/v1/e562c3c45f359969c566472f.png"},{"id":51018729,"identity":"4aba9c3b-3bba-4e57-ad20-fc02eff92b0d","added_by":"auto","created_at":"2024-02-12 19:23:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":628787,"visible":true,"origin":"","legend":"\u003cp\u003eVerification scores obtained from MODE valid for 15th May 2022, for convolution threshold CT ≥ 41°C, CT ≥ 43°C,and CT ≥ 45°C, from 24 hr to 120 hr lead time (a) Centroid distance (km) in upper left panel (b) The 50 th percentile intensity ratio (c) The Area Ratio (d) The intersection of area (e) the complexity ratio and (f) the total Interest or simple interest\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3916379/v1/c016168cbe8f5106062a4820.png"},{"id":51018737,"identity":"9464a7aa-eda2-46dc-a110-602c2b970b7c","added_by":"auto","created_at":"2024-02-12 19:23:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":418746,"visible":true,"origin":"","legend":"\u003cp\u003eVerification scores obtained from MODE for the summer season ie. MAM 2022, for convolution threshold CT ≥ 41°C, CT ≥ 43°C,and CT ≥ 45°C, from 24 hr to 120 hr lead time (a) the centroid distance (in Km) and (b) Intersection area (in Km 2 )\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3916379/v1/a285558bbbccc177a9a94dba.png"},{"id":51018738,"identity":"9ad01d9b-6cce-437b-8d75-a9f4e75c5178","added_by":"auto","created_at":"2024-02-12 19:23:08","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":308081,"visible":true,"origin":"","legend":"\u003cp\u003eVerification scores obtained from MODE for the summer season ie. MAM 2022, for convolution threshold CT ≥ 41°C, CT ≥ 43°C,and CT ≥ 45°C, from 24 hr to 120 hr lead time (a) The complecity Ratio and (b) the curvature ratio\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3916379/v1/2d288ea51f01699f99419f2d.png"},{"id":51018739,"identity":"d5c24c55-d250-420c-8da0-12892307dc47","added_by":"auto","created_at":"2024-02-12 19:23:08","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":409889,"visible":true,"origin":"","legend":"\u003cp\u003eVerification scores obtained from MODE for the summer season ie. MAM 2022, for convolution threshold CT ≥ 41°C, CT ≥ 43°C,and CT ≥ 45°C, from 24 hr to 120 hr lead time (a) The 50 th percentile intensity ratio and (b) The Symmetric difference\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-3916379/v1/6c12bc10a31e72b0b21e8d98.png"},{"id":51018741,"identity":"53e1e25e-6e32-4f53-87d8-8f4979cd508b","added_by":"auto","created_at":"2024-02-12 19:23:09","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":326683,"visible":true,"origin":"","legend":"\u003cp\u003eVerification seasonal scores obtained from MODE for the summer season ie. MAM 2022, for convolution threshold CT ≥ 41°C, CT ≥ 43°C,and CT ≥ 45°C, from 24 hr to 120 hr lead time (a) The Area Ratio and (b) Interest\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-3916379/v1/6e32aaaad2e426165235dd4f.png"},{"id":56335088,"identity":"b95bfef9-6d52-4018-afe3-4a00e2b3964d","added_by":"auto","created_at":"2024-05-12 15:44:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1741784,"visible":true,"origin":"","legend":"","description":"","filename":"AnobjectbasedapproachtoForecastingofHW2022.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3916379/v1_covered_d975ddd8-bdbe-417a-bf9e-8dc51e7e0edc.pdf"}],"financialInterests":"","formattedTitle":"An object-based approach to Forecasting and Verification of heat waves over India","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"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":"METplus, MODE, heat waves, centroid distance , area ratio, total interest","lastPublishedDoi":"10.21203/rs.3.rs-3916379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3916379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Heat waves are one of the most dangerous natural hazards in the world. Higher daily peak temperatures as well as duration, intensity and frequency of heat waves are increasing globally due to climate change. In India, the instances of heat waves have increased in recent years along with their intensity which has resulted in the increased number of casualties. For the purpose of disaster mitigation and reduction of losses due to heat waves, a timely and accurate forecast of these events is required. However, traditional verification methods (which rely on grid-wise comparisons) used for verification of forecasts from high resolution models have a limited utility. In order to assess the utility of these forecasts, spatial verification techniques that can differentiate between forecast and observed features are needed. In this study, we have tried to demonstrate the ability of the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model (NCUM) in predicting maximum 2m temperature (Tmax), over the heat wave-prone zones of India. The state-of-art Object-Based Diagnostic Evaluation (MODE) was used for verification of the heat waves. Additionally, applying fuzzy logic enhances the discernment of similarities between forecasted and observed objects, offering a valuable tool for adapting to varying object types and leveraging human knowledge. For instance, in certain scenarios, emphasizing the matching of area sizes may be crucial, while in others, aligning object locations could be more pertinent. In both cases, achieving the desired outcome involves adjusting weights or revising interest maps. This study shows that NCUM forecasts have a southwesterly bias in the location of Tmax objects for values exceeding 43 and 45°C, indicating a potential lag in system propagation. When verified over a season, it is seen that the performance of the model in predicting forecast attributes is rather very good in terms of smaller variation in the median of centroid distance (~150-200 km upto 120 hr lead time), an 83% match in the internal structure of the forecasts with a near perfect curvature ratio (95-97%), a near perfect 50th percentile intensity ratio (98-99%) and the symmetric difference which is small enough to coincide with the observed heat wave zones. In summary, NCUM forecasts demonstrate accurate heat wave predictions in terms of geographical area, structure, shape, and size up to a 5-day lead time, showcasing their potential for effective disaster preparedness.","manuscriptTitle":"An object-based approach to Forecasting and Verification of heat waves over India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-12 19:23:00","doi":"10.21203/rs.3.rs-3916379/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":"6156b577-c631-40d3-94be-f36e4440b9c6","owner":[],"postedDate":"February 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-12T15:36:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-12 19:23:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3916379","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3916379","identity":"rs-3916379","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}