wingding

Startups of The Year 2023 Winners: North America 

Sometimes when you copy and paste from external sites or platforms, like Microsoft Word or Google Docs, you might have text font or styling that doesn’t work in the HackerNoon editor (no wingdings allowed here). If you simply copy paste and hit the submit button, your piece might be missing headings, bulleted lists, and even line breaks. Our editors will do their best to fix this for you, but if we look at a post and it’s completely broken to the point it’ll take us over 20 minutes to fix it, we will reject it and ask you to edit your story before submitting it. Editing Protocol Index: Editing Protocol Overview Second Human Rule Verified Writers Time to Review Standards of Quality Originality Score 6 Ws Score Objectivity in ranked listicles Unranked listicles Actionable advice Red Flags Subject Matter Subject matter saturation Plagiarism Sources and Citations Formatting is bad or broken Grammar level: gibberish Story is Too Short Backlink Rules & Guidelines Backlink Limits Backlink quality and diversity Diversity of sources Internal linking Changing links Reposting and Canonical Linking Canonical links to company domain Canonical links to blog networks or social networks

Formatting is Broken

El formato está roto

Le formatage est cassé

स्वरूपण टूट गया है

フォーマットが壊れています

A formatação está quebrada

Định dạng bị hỏng

格式化坏了

If you use Microsoft Word and Excel frequently, you might have repeatedly had the question of how to add symbols such as the tick or checkmark in your document or spreadsheet. Keep reading to learn about the various shortcuts you can use to make these routine tasks take less time and effort. In this article we provide you with the handy tips and tricks necessary to add a tick or a cross into your Microsoft Word or Excel project. Copy and Paste Method This technique is possibly the simplest and fastest method of inserting a tick or a cross into your Microsoft document. The only thing you need to do is copy and paste the symbol where desired. To do this on a phone, long tap on whichever symbol below catches your fancy and highlight it. Press the "Copy" option. Then open your Excel sheet and long tap on the cell of choice. Press "Paste". To do this on a computer, drag your mouse from the beginning of your symbol of choice to the end and highlight it. Right-click and select "Copy", or press Ctrl + C on the keyboard. Then open your Excel sheet. Right-click on the cell of choice and select "Paste", or press Ctrl + V on the keyboard. The symbols you can copy and paste are given below: ? Symbol Command Method If the option above isn't for you, then the Symbol Command method is another possible way you might use to achieve the same result. In order for this to be successful, you will need to ensure that you have Microsoft Excel installed to your system. Navigate to the main Excel menu and go to "Insert". Then, go to "Symbol", select the "Font" option, and then opt for "Wingdings". Now you should be able to see that the tick mark you need is located at the bottom of the list. A second way to access this is to go to the "Start” menu. You'll find this situated the "Character Map" portion, within System Tools. Select the "Font" option and choose "Wingdings". After doing this, hover with your mouse over the symbol of choice. You can do this by taking your cursor over to the symbol, without clicking on it. This will make the symbol enlarge, and from here you can select from the range of different versions of the tick mark available. Decide which one you think you want to use, and then copy the icon through the method described above and paste it within your text or sheet wherever required. Tick Symbol Character Code Method A second method of carrying out any of the above is via the use of the character code of the tick symbol. In this technique, you will be required to use the numerical keypad, usually located on the right hand of your keyboard. This will allow you to insert four various check marks. This can be achieved through the following steps: Step 1: The first step is to choose the font called 'Wingdings'. Step 2: The second thing you need to do is to use the numerical keypad on your keyboard. Hold down the 'Alt' key and select one of the numbers shown below to enter: Ticks: • ALT + 0252 • ALT + 0254 Crosses: • ALT + 0251 • ALT + 0253 NOTE: It's important to remember to make sure that you've chosen the 'Wingdings' font in order for the character codes mentioned above to work successfully. Once you are done and have your tick or cross in place, you can go back to the font you were using originally. Using Autocorrect One of the final ways to add a tick mark into your Excel sheet is through the autocorrect function. In fact, it's not just tick marks that you can enter - you can go for almost any symbol you'd like! Autocorrect is a feature of Excel where it can, as the term suggests, automatically correct words that have been misspelled. As an example, you can demonstrate to yourself what happens when you type "bcak" (instead of "back") into Excel. Excel will automatically change the word to its correct spelling, and will change "bcak" into "back". This occurs because there exists in the software a list that has already been prepared of words that people are most likely to spell incorrectly, and thus, Excel automatically checks and corrects these on your behalf. You might want to add a symbol such as the delta symbol into your Excel project. These are the steps you can follow to do so: Step 1: The first step is to click on the File tab. Step 2: The second thing you need to do is to click on Options. Step 3: Then, in the Options dialogue box, choose Proofing. Step 4: After that, click on the button that says 'AutoCorrect Options'. Step 5: Following this, in the AutoCorrect dialogue box, enter the following: Replace: CMARK With: ? (you can copy and paste this) If you would like to enter any other symbol, paste that symbol instead. Below are some symbols that might catch your fancy - though there exists a plethora of these, and a quick Google search should give you what you want. Once you've identified the symbol you want, simply copy it from the site and paste into the "With" box. You can name each symbol as you want in the "Replace" box, for instance a delta symbol might be DELTA, and so on. We've given some suggestions below. What you name each symbol is subjective - it is basically just the text you need to type which will be replaced by your symbol of choice, so it's up to you what you would want to name them. NOTE: However, it’s important you don’t name the symbols with very common words you use frequently. This is discussed in detail in this article; keep reading to find out! Replace: CMARK ; With: ? Replace: XMARK ; With: û Replace: BSTAR ; With: ? Replace: ARROWR ; With: è Replace: ARROWL ; With: ç Replace: FLOWERSYMBOL ; With: ? Replace: CLOCK3 ; With: ¹ Step 6: The final step is to click Add, and then OK. Now, whenever you type the word CMARK into a cell in Excel (in the case that you require a check mark), Excel will automatically change it into a check mark symbol. The following tips are important to note when you use the Autocorrect method. This technique is case sensitive. This means that it matters if you use small or capital letters. So, if you type "cmark" instead of "CMARK" into a cell, it won't convert the text into a check mark symbol. Therefore, make sure that what you type in the cell precisely matches what you had named the symbol, or what you had typed in the "Replace" box. A convenient top tip is that this change gets applied to all your other Microsoft-powered applications! These include MS Word, PowerPoint, and so on. So applying a change in Excel will save you time in your other frequently used apps and you'll get the same result across other Microsoft software.  However, this could go two ways. You might only want, for example, "CMARK" to be replaced by a tick symbol in Excel but not in Word. That's why it's important to be cautious and opt for keywords that you are not very likely to use in other applications. Now, "CMARK" is probably not a very common term you'd use in Word, but "tick" might be one, and so, choose what you name your symbols carefully. If there is any form of text or number before CMARK, CMARK will not get converted to a check mark. For instance, '5%CMARK' will not get autocorrected and converted. A simple remedy is to just put a space after the text or number and then enter the word you'd like to be replaced by the symbol. In this case, '5% CMARK' will change to '5% ?’. This is a highly useful technique if you want a quick and ready reference for the tick mark symbol and you use it very frequently in your work. Hence, by using this method, you no longer need to remember shortcuts or codes, or even to use the symbol dialogue box provided in the system. Instead, all you need to do is remember the short code name you kept and simply type that out as and when required. Plus, the method is so versatile you can use it for virtually any symbol! Also published here .

How To Add A Check Symbol In Excel and Word

Today marks the ten-year anniversary of the death of “Clippy”, Microsoft Word’s helpful and beloved cartoon paper clip. It looks like I’m writing a memorial piece, and dear God, would I like some help. Clippy was born in 1987, the only success in a series of failed experiments to give sentience to various office supplies. He began his life as most paper clips do, working a desk job for little to no money, though soon people began to notice his incredible talent for identifying when people were writing letters, and offering to help. It didn’t take long for Microsoft to notice this up-and-coming talent, and they soon recruited him as an office assistant for Microsoft Word ’97. This was Clippy’s big break, and he began appearing on monitors nationwide, offering keyboard shortcuts and gentle reminders to save your work. Within just a year, he was far more popular than all of the other office assistants, including Albert Einstein, the Smiley Face, and whatever the fuck that cat’s name was. Scribble I think? I don’t know. But when you spend enough time under the spotlight, the light starts to expose the darkest parts of you. Fame became a burden that was simply too much for the young Microsoft Word Office Assistant to bear, and Clippy, like so many paper clips and child stars before him, turned to hard drugs: MDMA, PCP, and even HTML. Despite his incredible popularity, Microsoft was forced to remove Clippy after his addictive habits began to bleed into his work. The computer company received several reports of an intoxicated Clippy lashing out at clients, calling their letters to loved ones “fucking pointless” and making less-than-helpful editorial comments, such as “this is the worst poetry I’ve ever seen”. Clippy’s removal was the final step in his descent into madness, as the former assistant became nothing but a disheveled mess, fodder for the Hollywood tabloids. He wandered the streets of Hollywood, barely coherent, sometimes mumbling completely in Wingdings. He finally died alone, unable to hold a job, a relationship, or even a stack of papers together.   Despite his sad departure from this world, Clippy’s legacy remains as strong as ever. Every time we need help with Microsoft Office’s confusing fucking updates, every time they change the word processor’s entire layout just when we were getting used to the old one, every time we have to type a keyword into a cold, dead help section that doesn’t even have googly eyes — we think of you. is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising &sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

Remembering Clippy: 10 Years Since the Death of Microsoft Word’s Cartoon Paper Clip

NEW YORK, United States of America, October 29th, 2024/Chainwire/--Student Coin, an educational crypto project established in 2019, has initiated a structured token redemption process following the decision to wind down its primary operations. This move aims to protect the interests of STC token holders as the team transitions its focus towards new ventures. Originally launched by undergraduates from Kozminski University in Warsaw, Poland, Student Coin began as a student-focused initiative designed to explore blockchain applications in academia. The project quickly expanded, reaching over 15,000 students at more than 500 universities by the end of 2020. Throughout its development, Student Coin introduced various products, including the STC Wallet, STC Terminal, STC Academy, and Coinpaper, which supported blockchain education and fostered community engagement. Following the success of its 2021 STC Launchpad, the project experienced continued growth, culminating in a user base of over 200,000. However, as the crypto market evolved, the team faced challenges ranging from legal obstacles and university resistance to the collapse of major exchanges, all of which impacted its operational outlook. Challenges and Strategic Reassessment Despite significant milestones, Student Coin encountered mounting difficulties in achieving its goals. Efforts to scale the STC Wallet as a multi-functional exchange faced legal risks and the collapse of exchanges like FTX forced a rethink. Attempts to expand the STC Terminal were met with resistance from institutions wary of token-based applications. By 2023, it became evident that scaling $STC to meet its desired utility and market value was increasingly difficult. After careful consideration, the Student Coin team opted to phase out products such as the STC Terminal, STC Academy, and other initiatives. This decision reflects the company’s intention to act responsibly by redeeming $STC tokens instead of continuing with limited prospects for growth. The decision was made to distribute all remaining project funds between token holders, ensuring that through winding down the token, everyone receives fair compensation based on their individual situation. The established redemption prices range between $0.006 and $0.0137 per STC Token and are tailored to individual user profiles. Factors such as purchase price, purchase date, token holdings, and participation in programs like the Premium Program influence the final redemption value. This tiered system rewards those who have been with the project from the start and actively engaged with the Student Coin ecosystem. Image: STC token's historical price index along with the major events affecting its fluctuations. STC Token Redemption Process Student Coin’s token redemption program began on April 9, 2024, with an initial window for STC Wallet users that closed on June 9. An additional deadline for redeeming tokens via redemption request on October 9, 2024, marks the closure of the STC Wallet, while on-chain token holders have time until April 9, 2029, to redeem their tokens. This extended timeline accommodates the shutdown of on-chain support and ensures ample opportunity for token holders to complete the redemption process. On-chain holders can transfer tokens to a designated burn address. Users should expect a processing time of up to three months to receive the USDC back to the address. Keep in mind that USDC can only be sent to the address that burned $STC. Users cannot receive USDC at any other address. Furthermore, it is extremely important to remember that users cannot send $STC to the burn address directly from an exchange, as they would lose all of their funds. Read the detailed on-chain burn procedure. About Student Coin Founded in 2019 by a group of university students in Warsaw, Poland, Student Coin sought to harness blockchain technology for educational and community-building purposes. Initially targeting students and academia, the project grew to encompass a global community of over 200,000 users and introduced products such as the STC Wallet, STC Terminal, STC Academy, and Coinpaper. Focused on accessible blockchain education and tokenized solutions, Student Coin rapidly expanded to more than 500 universities worldwide. As the project winds down, the team remains committed to supporting the community through a comprehensive token redemption program and plans to pursue new initiatives in the crypto media sector. For more information, please visit StudentCoin.org. Contact Coinpaper contact@coinpaper.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here NEW YORK, United States of America, October 29th, 2024/Chainwire/--Student Coin, an educational crypto project established in 2019, has initiated a structured token redemption process following the decision to wind down its primary operations. This move aims to protect the interests of STC token holders as the team transitions its focus towards new ventures. Originally launched by undergraduates from Kozminski University in Warsaw, Poland, Student Coin began as a student-focused initiative designed to explore blockchain applications in academia. The project quickly expanded, reaching over 15,000 students at more than 500 universities by the end of 2020. Throughout its development, Student Coin introduced various products, including the STC Wallet, STC Terminal, STC Academy, and Coinpaper , which supported blockchain education and fostered community engagement. Coinpaper Coinpaper Following the success of its 2021 STC Launchpad, the project experienced continued growth, culminating in a user base of over 200,000. However, as the crypto market evolved, the team faced challenges ranging from legal obstacles and university resistance to the collapse of major exchanges, all of which impacted its operational outlook. Challenges and Strategic Reassessment Despite significant milestones, Student Coin encountered mounting difficulties in achieving its goals. Efforts to scale the STC Wallet as a multi-functional exchange faced legal risks and the collapse of exchanges like FTX forced a rethink. FTX FTX Attempts to expand the STC Terminal were met with resistance from institutions wary of token-based applications. By 2023, it became evident that scaling $STC to meet its desired utility and market value was increasingly difficult. After careful consideration, the Student Coin team opted to phase out products such as the STC Terminal, STC Academy, and other initiatives. This decision reflects the company’s intention to act responsibly by redeeming $STC tokens instead of continuing with limited prospects for growth. The decision was made to distribute all remaining project funds between token holders, ensuring that through winding down the token, everyone receives fair compensation based on their individual situation. The established redemption prices range between $0.006 and $0.0137 per STC Token and are tailored to individual user profiles. Factors such as purchase price, purchase date, token holdings, and participation in programs like the Premium Program influence the final redemption value. This tiered system rewards those who have been with the project from the start and actively engaged with the Student Coin ecosystem. Image: STC token's historical price index along with the major events affecting its fluctuations. STC Token Redemption Process Student Coin’s token redemption program began on April 9, 2024, with an initial window for STC Wallet users that closed on June 9. An additional deadline for redeeming tokens via redemption request on October 9, 2024, marks the closure of the STC Wallet, while on-chain token holders have time until April 9, 2029, to redeem their tokens. This extended timeline accommodates the shutdown of on-chain support and ensures ample opportunity for token holders to complete the redemption process. On-chain holders can transfer tokens to a designated burn address. Users should expect a processing time of up to three months to receive the USDC back to the address. Keep in mind that USDC can only be sent to the address that burned $STC. Users cannot receive USDC at any other address. Furthermore, it is extremely important to remember that users cannot send $STC to the burn address directly from an exchange, as they would lose all of their funds. Read the detailed on-chain burn procedure . on-chain burn procedure on-chain burn procedure About Student Coin Founded in 2019 by a group of university students in Warsaw, Poland, Student Coin sought to harness blockchain technology for educational and community-building purposes. Initially targeting students and academia, the project grew to encompass a global community of over 200,000 users and introduced products such as the STC Wallet, STC Terminal, STC Academy, and Coinpaper. Focused on accessible blockchain education and tokenized solutions, Student Coin rapidly expanded to more than 500 universities worldwide. As the project winds down, the team remains committed to supporting the community through a comprehensive token redemption program and plans to pursue new initiatives in the crypto media sector. For more information, please visit StudentCoin.org . StudentCoin.org StudentCoin.org Contact Coinpaper contact@coinpaper.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Student Coin Announces Comprehensive STC Token Redemption Following Operational Shutdown

Student Coin anuncia un canje integral de tokens STC tras el cierre operativo

Student Coin annonce le rachat complet des jetons STC après l'arrêt opérationnel

Student Coin、運用停止後の包括的なSTCトークン償還を発表

Studentų moneta praneša apie visapusišką STC žetonų išpirkimą po veiklos nutraukimo

Studenta monēta paziņo par visaptverošu STC marķiera izpirkšanu pēc darbības pārtraukšanas

Syiling Pelajar Mengumumkan Penebusan Token STC Komprehensif Selepas Penutupan Operasi

Študentská minca oznamuje komplexné vyplatenie tokenov STC po operačnom odstavení

Studentmynt tillkännager omfattande inlösen av STC-token efter driftstopp

Sarafu ya Mwanafunzi Inatangaza Ukombozi Kabambe wa Tokeni ya STC Kufuatia Kuzimwa kwa Utendaji

Student Coin оголошує про комплексне погашення токенів STC після припинення роботи

Student Coin thông báo về việc đổi toàn bộ token STC sau khi ngừng hoạt động

Uhlamvu Lwemali Lwezitshudeni Lumemezela Ukukhishwa Kwethokheni Ye-STC Okuphelele Ngemva Kokuvala Umsebenzi

Why Laziness is a Superpower

Чаму лянота - гэта звышздольнасць

Por qué la pereza es un superpoder

Miksi laiskuus on supervoima

Zašto je lijenost supermoć

Perché la pigrizia è un superpotere

למה עצלות היא מעצמת על

なぜ怠惰はスーパーパワーなのか

Kāpēc slinkums ir lielvara

Залхуурал яагаад супер хүч вэ?

Ke ka baka la’ng Botswa e le Matla a Magolo

Imaraykutaq Qella kayqa Superpoder

Waa maxay sababta caajisnimadu u tahay awood sare

The Unexpected Journey: From Code Sprints to Mindful Tech Leadership

Authors:
(1) Toshit Jain, Indian Institute of Science Bangalore, India;
(2) Varun Singh, Indian Institute of Science Bangalore, India;
(3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India;
(4) Upkar Singh, Indian Institute of Science Bangalore, India;
(5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden;
(6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India;
(7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Table of Links Abstract and Intro
Ocean data
pyParaOcean: Architecture
pyParaOcean: Functionalities
Case study: Bay of Bengal
Conclusion
Acknowledgments and References Acknowledgments This research was funded by a grant from SERB, Govt. of India (CRG/2021/005278), partial support from National Supercomputing Mission, DST, the J. C. Bose Fellowship awarded by the SERB, DST, Govt. of India, the Dr. Ram Kumar IISc Distinguished Visiting Chair Professorship in EECS, and a scholarship from MoE, Govt. of India. Part of this work was carried out towards partial fulfilment of a thesis requirement at BITS Pilani. References [AGL05] AHRENS J., GEVECI B., LAW C.: Paraview: An end-user tool for large data visualization. The visualization handbook 717 (2005). 2 [AGT∗19] AFZAL S., GHANI S., TISSINGTON G., LANGODAN S., [AGT∗19] AFZAL S., GHANI S., TISSINGTON G., LANGODAN S.,DASARI H. P., RAITSOS D. E., GITTINGS J. A., JAMIL T., SRINIVASAN M., HOTEIT I.: RedSeaAtlas: A visual analytics tool for spatiotemporal multivariate data of the red sea. In EnvirVis: Workshop on Visualization in Environmental Sciences (EnvirVis2019) (2019), pp. 25–32. 1 [AHG∗19] AFZAL S., HITTAWE M. M., GHANI S., JAMIL T., KNIO O., HADWIGER M., HOTEIT I.: The state of the art in visual analysis approaches for ocean and atmospheric datasets. Computer Graphics Forum 38, 3 (2019), 881–907. 1, 4 [AMM17] AMORES A., MELNICHENKO O., MAXIMENKO N.: Coherent mesoscale eddies in the North Atlantic subtropical gyre: 3-D structure and transport with application to the salinity maximum. Journal of Geophysical Research: Oceans 122, 1 (2017), 23–41. 5 [BNBD∗07] BENITEZ-NELSON C. R., BIDIGARE R. R., DICKEY T. D., LANDRY M. R., LEONARD C. L., BROWN S. L., NENCIOLI F., RII Y. M., MAITI K., BECKER J. W., ET AL.: Mesoscale eddies drive increased silica export in the subtropical pacific ocean. Science 316, 5827 (2007), 1017–1021. 1 [DAN12] DINESHA V., ADABALA N., NATARAJAN V.: Uncertainty visualization using HDR volume rendering. The Visual Computer 28 (2012), 265–278. 1 [FD06] FRASER S., DICKSON B.: Data mining geoscientific data sets using self organizing maps. Mastering the Data Explosion in the Earth and Environmental Sciences, Extended Abstracts (2006), 5–7. 1 [Fer23] Ferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]. 1 [FFH21] FRIEDERICI A., FALK M., HOTZ I.: A winding angle framework for tracking and exploring eddy transport in oceanic ensemble simulations. In EnvirVis: Workshop [GEP04] GUO D., EVANGELINOS C., PATRIKALAKIS N.: Flow feature extraction in oceanographic visualization. In Proceedings of Computer Graphics International Conference (07 2004), pp. 162–173. doi:10. 1109/CGI.2004.1309207. 5 [GSK∗08] GROCHOW K., STOERMER M., KELLEY D., DELANEY J., LAZOWSKA E.: COVE: A visual environment for ocean observatory design. Journal of Physics: Conference Series 125, 1 (2008), 012092. 1 [KNR∗07] KUMAR S. P., NUNCIO M., RAMAIAH N., SARDESAI S., NARVEKAR J., FERNANDES V., PAUL J. T.: Eddy-mediated biological productivity in the Bay of Bengal during fall and spring intermonsoons. Deep Sea Research Part I: Oceanographic Research Papers 54, 9 (2007), 1619–1640. 4 [LJP∗19] LI S., JAROSZYNSKI S., PEARSE S., ORF L., CLYNE J.: Vapor: A visualization package tailored to analyze simulation data in earth system science. Atmosphere 10, 9 (2019), 488. 1 [Mad08] MADEC G.: NEMO ocean engine. Note du Pôle de modélisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008. 2 [MAIS16] MATSUOKA D., ARAKI F., INOUE Y., SASAKI H.: A new approach to ocean eddy detection, tracking, and event visualization– application to the northwest pacific ocean. Procedia Computer Science 80 (2016), 1601–1611. 5 [McW90] MCWILLIAMS J. C.: The vortices of two-dimensional turbulence. Journal of Fluid mechanics 219 (1990), 361–385. 4 [McW08] MCWILLIAMS J. C.: The nature and consequences of oceanic eddies. Ocean modeling in an eddying regime 177 (2008), 5–15. 1 [MJD∗99] MCNEIL J., JANNASCH H., DICKEY T., MCGILLICUDDY D., BRZEZINSKI M., SAKAMOTO C.: New chemical, bio-optical and physical observations of upper ocean response to the passage of a mesoscale eddy off bermuda. Journal of Geophysical Research: Oceans 104, C7 (1999), 15537–15548. 1 [myO23] Copernicus myOcean. https://marine.copernicus. eu/access-data/ocean-visualisation-tools, 2023. [Online; accessed 28-April-2023]. 1 [NL15] NOBRE C., LEX A.: OceanPaths: Visualizing multivariate oceanography data. In Proceedings of the Eurographics Conference on Visualization (EuroVis 2015) - Short Papers (2015), The Eurographics Association. doi:10.2312/eurovisshort.20151124. 1 [Oku70] OKUBO A.: Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences. Deep sea research and oceanographic abstracts 17, 3 (1970), 445–454. 4 [PBI04] PARK S., BAJAJ C., IHM I.: Visualization of very large oceanography time-varying volume datasets. In Computational Science-ICCS 2004: 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part II 4 (2004), Springer, pp. 419–426. 1 [pyF23] pyferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]. 1 [Ros89] ROSENBLUM L. J.: Visualizing oceanographic data. IEEE computer graphics and applications 9, 3 (1989), 14–19. 1 [RR10] ROBINSON I. S., ROBINSON I. S.: Mesoscale ocean features: Eddies. Discovering the Ocean from Space: The unique applications of satellite oceanography (2010), 69–114. 1 [Sar13] SARMIENTO J. L.: Ocean biogeochemical dynamics. In Ocean Biogeochemical Dynamics. Princeton university press, 2013. 4 [SDVN22] SINGH U., DHIPU T. M., VINAYACHANDRAN P. N., NATARAJAN V.: Front and skeleton features based methods for tracking salinity propagation in the ocean. Computers & Geosciences 159 (2022), 104993. doi:https://doi.org/10.1016/j.cageo. 2021.104993. 5 [TFL∗17] TIERNY J., FAVELIER G., LEVINE J. A., GUEUNET C., MICHAUX M.: The Topology Toolkit. IEEE Transactions on Visualization and Computer Graphics 24, 1 (2017), 832–842. 5 [TZG∗17] TOYE H., ZHAN P., GOPALAKRISHNAN G., KARTADIKARIA A. R., HUANG H., KNIO O., HOTEIT I.: Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing. Ocean Dynamics 67, 7 (Jul 2017), 915–933. doi:10.1007/ s10236-017-1064-1. 2 [VCMN04] VINAYACHANDRAN P. N., CHAUHAN P., MOHAN M., NAYAK S.: Biological response of the sea around Sri Lanka to summer monsoon. Geophysical Research Letters 31, 1 (2004). 6 [VY98] VINAYACHANDRAN P. N., YAMAGATA T.: Monsoon response of the sea around Sri Lanka: generation of thermal domes and anticyclonic vortices. Journal of Physical Oceanography 28, 10 (1998), 1946– 1960. 5, 6 [WHP∗11] WILLIAMS S., HECHT M., PETERSEN M., STRELITZ R., MALTRUD M., AHRENS J., HLAWITSCHKA M., HAMANN B.: Visualization and analysis of eddies in a global ocean simulation. Computer graphics forum 30, 3 (2011), 991–1000. 5 [XLWD19] XIE C., LI M., WANG H., DONG J.: A survey on visual analysis of ocean data. Visual Informatics 3, 3 (2019), 113–128. 1, 2 This paper is available on arxiv under CC 4.0 license. Authors: (1) Toshit Jain, Indian Institute of Science Bangalore, India; (2) Varun Singh, Indian Institute of Science Bangalore, India; (3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India; (4) Upkar Singh, Indian Institute of Science Bangalore, India; (5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden; (6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India; (7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Authors: Authors: (1) Toshit Jain, Indian Institute of Science Bangalore, India; (2) Varun Singh, Indian Institute of Science Bangalore, India; (3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India; (4) Upkar Singh, Indian Institute of Science Bangalore, India; (5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden; (6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India; (7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Table of Links Abstract and Intro Ocean data pyParaOcean: Architecture pyParaOcean: Functionalities Case study: Bay of Bengal Conclusion Acknowledgments and References Abstract and Intro Abstract and Intro Ocean data Ocean data pyParaOcean: Architecture pyParaOcean: Architecture pyParaOcean: Functionalities pyParaOcean: Functionalities Case study: Bay of Bengal Case study: Bay of Bengal Conclusion Conclusion Acknowledgments and References Acknowledgments and References Acknowledgments This research was funded by a grant from SERB, Govt. of India (CRG/2021/005278), partial support from National Supercomputing Mission, DST, the J. C. Bose Fellowship awarded by the SERB, DST, Govt. of India, the Dr. Ram Kumar IISc Distinguished Visiting Chair Professorship in EECS, and a scholarship from MoE, Govt. of India. Part of this work was carried out towards partial fulfilment of a thesis requirement at BITS Pilani. References [AGL05] AHRENS J., GEVECI B., LAW C.: Paraview: An end-user tool for large data visualization. The visualization handbook 717 (2005). 2 [AGT∗19] AFZAL S., GHANI S., TISSINGTON G., LANGODAN S., [AGT∗19] AFZAL S., GHANI S., TISSINGTON G., LANGODAN S.,DASARI H. P., RAITSOS D. E., GITTINGS J. A., JAMIL T., SRINIVASAN M., HOTEIT I.: RedSeaAtlas: A visual analytics tool for spatiotemporal multivariate data of the red sea. In EnvirVis: Workshop on Visualization in Environmental Sciences (EnvirVis2019) (2019), pp. 25–32. 1 [AHG∗19] AFZAL S., HITTAWE M. M., GHANI S., JAMIL T., KNIO O., HADWIGER M., HOTEIT I.: The state of the art in visual analysis approaches for ocean and atmospheric datasets. Computer Graphics Forum 38, 3 (2019), 881–907. 1, 4 [AMM17] AMORES A., MELNICHENKO O., MAXIMENKO N.: Coherent mesoscale eddies in the North Atlantic subtropical gyre: 3-D structure and transport with application to the salinity maximum. Journal of Geophysical Research: Oceans 122, 1 (2017), 23–41. 5 [BNBD∗07] BENITEZ-NELSON C. R., BIDIGARE R. R., DICKEY T. D., LANDRY M. R., LEONARD C. L., BROWN S. L., NENCIOLI F., RII Y. M., MAITI K., BECKER J. W., ET AL.: Mesoscale eddies drive increased silica export in the subtropical pacific ocean. Science 316, 5827 (2007), 1017–1021. 1 [DAN12] DINESHA V., ADABALA N., NATARAJAN V.: Uncertainty visualization using HDR volume rendering. The Visual Computer 28 (2012), 265–278. 1 [FD06] FRASER S., DICKSON B.: Data mining geoscientific data sets using self organizing maps. Mastering the Data Explosion in the Earth and Environmental Sciences, Extended Abstracts (2006), 5–7. 1 [Fer23] Ferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]. 1 [FFH21] FRIEDERICI A., FALK M., HOTZ I.: A winding angle framework for tracking and exploring eddy transport in oceanic ensemble simulations. In EnvirVis: Workshop [GEP04] GUO D., EVANGELINOS C., PATRIKALAKIS N.: Flow feature extraction in oceanographic visualization. In Proceedings of Computer Graphics International Conference (07 2004), pp. 162–173. doi:10. 1109/CGI.2004.1309207. 5 [GSK∗08] GROCHOW K., STOERMER M., KELLEY D., DELANEY J., LAZOWSKA E.: COVE: A visual environment for ocean observatory design. Journal of Physics: Conference Series 125, 1 (2008), 012092. 1 [KNR∗07] KUMAR S. P., NUNCIO M., RAMAIAH N., SARDESAI S., NARVEKAR J., FERNANDES V., PAUL J. T.: Eddy-mediated biological productivity in the Bay of Bengal during fall and spring intermonsoons. Deep Sea Research Part I: Oceanographic Research Papers 54, 9 (2007), 1619–1640. 4 [LJP∗19] LI S., JAROSZYNSKI S., PEARSE S., ORF L., CLYNE J.: Vapor: A visualization package tailored to analyze simulation data in earth system science. Atmosphere 10, 9 (2019), 488. 1 [Mad08] MADEC G.: NEMO ocean engine. Note du Pôle de modélisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008. 2 [MAIS16] MATSUOKA D., ARAKI F., INOUE Y., SASAKI H.: A new approach to ocean eddy detection, tracking, and event visualization– application to the northwest pacific ocean. Procedia Computer Science 80 (2016), 1601–1611. 5 [McW90] MCWILLIAMS J. C.: The vortices of two-dimensional turbulence. Journal of Fluid mechanics 219 (1990), 361–385. 4 [McW08] MCWILLIAMS J. C.: The nature and consequences of oceanic eddies. Ocean modeling in an eddying regime 177 (2008), 5–15. 1 [MJD∗99] MCNEIL J., JANNASCH H., DICKEY T., MCGILLICUDDY D., BRZEZINSKI M., SAKAMOTO C.: New chemical, bio-optical and physical observations of upper ocean response to the passage of a mesoscale eddy off bermuda. Journal of Geophysical Research: Oceans 104, C7 (1999), 15537–15548. 1 [myO23] Copernicus myOcean. https://marine.copernicus. eu/access-data/ocean-visualisation-tools, 2023. [Online; accessed 28-April-2023]. 1 [NL15] NOBRE C., LEX A.: OceanPaths: Visualizing multivariate oceanography data. In Proceedings of the Eurographics Conference on Visualization (EuroVis 2015) - Short Papers (2015), The Eurographics Association. doi:10.2312/eurovisshort.20151124. 1 [Oku70] OKUBO A.: Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences. Deep sea research and oceanographic abstracts 17, 3 (1970), 445–454. 4 [PBI04] PARK S., BAJAJ C., IHM I.: Visualization of very large oceanography time-varying volume datasets. In Computational Science-ICCS 2004: 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part II 4 (2004), Springer, pp. 419–426. 1 [pyF23] pyferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]. 1 [Ros89] ROSENBLUM L. J.: Visualizing oceanographic data. IEEE computer graphics and applications 9, 3 (1989), 14–19. 1 [RR10] ROBINSON I. S., ROBINSON I. S.: Mesoscale ocean features: Eddies. Discovering the Ocean from Space: The unique applications of satellite oceanography (2010), 69–114. 1 [Sar13] SARMIENTO J. L.: Ocean biogeochemical dynamics. In Ocean Biogeochemical Dynamics. Princeton university press, 2013. 4 [SDVN22] SINGH U., DHIPU T. M., VINAYACHANDRAN P. N., NATARAJAN V.: Front and skeleton features based methods for tracking salinity propagation in the ocean. Computers & Geosciences 159 (2022), 104993. doi:https://doi.org/10.1016/j.cageo. 2021.104993. 5 [TFL∗17] TIERNY J., FAVELIER G., LEVINE J. A., GUEUNET C., MICHAUX M.: The Topology Toolkit. IEEE Transactions on Visualization and Computer Graphics 24, 1 (2017), 832–842. 5 [TZG∗17] TOYE H., ZHAN P., GOPALAKRISHNAN G., KARTADIKARIA A. R., HUANG H., KNIO O., HOTEIT I.: Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing. Ocean Dynamics 67, 7 (Jul 2017), 915–933. doi:10.1007/ s10236-017-1064-1. 2 [VCMN04] VINAYACHANDRAN P. N., CHAUHAN P., MOHAN M., NAYAK S.: Biological response of the sea around Sri Lanka to summer monsoon. Geophysical Research Letters 31, 1 (2004). 6 [VY98] VINAYACHANDRAN P. N., YAMAGATA T.: Monsoon response of the sea around Sri Lanka: generation of thermal domes and anticyclonic vortices. Journal of Physical Oceanography 28, 10 (1998), 1946– 1960. 5, 6 [WHP∗11] WILLIAMS S., HECHT M., PETERSEN M., STRELITZ R., MALTRUD M., AHRENS J., HLAWITSCHKA M., HAMANN B.: Visualization and analysis of eddies in a global ocean simulation. Computer graphics forum 30, 3 (2011), 991–1000. 5 [XLWD19] XIE C., LI M., WANG H., DONG J.: A survey on visual analysis of ocean data. Visual Informatics 3, 3 (2019), 113–128. 1, 2 This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

pyParaOcean, A System for Visual Analysis of Ocean Data: Acknowledgments and References

pyParaOcean, একটি সিস্টেম ফর ভিজ্যুয়াল অ্যানালাইসিস অফ ওশান ডেটা: স্বীকৃতি এবং রেফারেন্স

pyParaOcean, ein System zur visuellen Analyse von Ozeandaten: Danksagungen und Referenzen

pyParaOcean, un sistema de análisis visual de datos oceánicos: reconocimientos y referencias

pyParaOcean, un système d'analyse visuelle des données océaniques : remerciements et références

pyParaOcean, महासागर डेटा के दृश्य विश्लेषण के लिए एक प्रणाली: आभार और संदर्भ

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pyParaOcean, um sistema para análise visual de dados oceânicos: agradecimentos e referências

pyParaOcean, Система визуального анализа океанических данных: благодарности и ссылки

pyParaOcean, Okyanus Verilerinin Görsel Analizi İçin Bir Sistem: Teşekkür ve Referanslar

pyParaOcean, Hệ thống phân tích trực quan dữ liệu đại dương: Lời cảm ơn và tài liệu tham khảo

pyParaOcean，海洋数据可视化分析系统：致谢和参考文献

Authors:
(1) Toshit Jain, Indian Institute of Science Bangalore, India;
(2) Varun Singh, Indian Institute of Science Bangalore, India;
(3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India;
(4) Upkar Singh, Indian Institute of Science Bangalore, India;
(5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden;
(6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India;
(7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Table of Links Abstract and Intro
Ocean data
pyParaOcean: Architecture
pyParaOcean: Functionalities
Case study: Bay of Bengal
Conclusion
Acknowledgments and References 4. pyParaOcean: Functionalities We now list and describe the various functions that are implemented in the pyParaOcean plugin and made available as Paraview filters. Figure 2 and the video in the supplementary material show the different filters in pyParaOcean and the user interface. 4.1. Isovolume visualization Volume rendering is a natural choice for visualizing the 3D scalar fields in the ocean data because it provides a quick overview of the distribution (Figure 2(D)). Animation with a fixed transfer function allows visualization of the scalar field over time. The volume rendering filter within Paraview can be tuned to visualize subvolumes of interest by choosing a interval within the range of the scalar field. Specifically, an isovolume containing the mean salinity/temperature value within the spatial region of interest or an isovolume that captures high salinity water provide a good overview of the 3D field. 4.2. Seed placement and field lines Field lines, including streamlines and pathlines, provide a good overview of a 3D vector field. pyParaOcean provides a filter that implements multiple seeding strategies for initiating the computation of streamlines and pathlines, and allows the user to choose one. Seeds generated using this filter are fed as input to the custom source streamline integrator or particle tracer in Paraview (Figure 2(G,H)). Streamlines are a set of integral curves tangent to the velocity at every point in space. They portray instantaneous lines of flow that characterize important oceanographic phenomena such as eddies, currents, and filaments. Pathlines are tangential to the velocity as it evolves over time. A pathline describes the path a massless virtual particle would take beginning from the seed positioned at a particular timestep. Pathlines are useful for understanding transport, such as salinity advection and debris collection. They are more compute-intensive than streamlines. The seeding filter controls the number of seeds and how the domain is sampled for seed placement, see Figure 2 (C)). Sampling can be (a) uniform, (b) weighted by flow speed, curl, vorticity, or the Okubo-Weiss criterion [Oku70], or (c) weighted by user-defined scalar fields that are computed earlier in the pipeline. A user may tune the line integration parameters and the sampling options to reduce visual clutter, focus the computation to regions of interest, maximize coverage of the domain, and highlight interesting flow features. For example, rendering wispy streamlines in regions of high vorticity produces closed loops around eddies with some temporal coherence across frames (Figure 7). Additionally, each component of the vector field to which the seeding filter is applied can be specified as a separate scalar field. This makes it easier to perform other downstream operations like ignoring the vertical velocity component or adjusting the scale along each axis. 4.3. Interactive particle paths This filter facilitates heat and mass transport queries on ocean data with an interactive seeding extension to the particle tracer from Paraview. It displays a linked parallel coordinates plot, where the user can brush to select ranges of scalars like temperature and salinity and hence restrict the seeding to isovolumes. Points sampled from these subvolumes serve as seeds for pathline computation (Figure 2(H)). 4.4. Depth profile view This filter enables the user to inspect a vertical column of the ocean, specified by a longitude and latitude pair. It drops a “needle” into the ocean and samples points along this line at different values of depth (Figure 2(D)). It displays a linked parallel coordinates plot that gives a depth profile of all scalars sampled along the vertical column. A line plot view of the chosen scalar against depth (Figure 2(E)) is displayed. Optionally, the scalar field mapped to a vertical slice at the chosen longitude is shown in the volume render window. The user can select and highlight a subset of the points in the vertical column from the parallel coordinates plot (Figure 2(F)), and track them across time in all views. This is useful for studying vertical mass transport, especially upwelling or downwelling via Ekman transport [Sar13] in eddy centres, and to study the depression of isotherms indicating redistribution of heat [KNR∗ 07]. Studying these changes is important to understand marine life as they drive phytoplankton bloom and nutrient transport 4.5. Eddy identification and visualization Several algorithms have been devised for reliable and automatic identification of eddies [AHG∗ 19]. McWilliam [McW90] developed a 2D method using vorticity ω as a physical parameter, whose local minima and maxima locate the centres of potential eddies and the values of vorticity in the neighborhood relative to the centre helps determine the eddy boundary. Okubo [Oku70] uses a special Okubo-Weiss parameter based on shear and strain deformation and the vertical component of vorticity to measure rotation and hence identify potential eddies. A circularity criterion may be applied after the Okubo-Weiss criterion to improve the results [WHP∗ 11]. Sea surface height and velocity profile have also been used for eddy detection [MAIS16]. The winding angle criterion, together with a streamline clustering, helps identify eddies in 3D [FFH21]. The eddy identification filter in pyParaOcean focuses on mesoscale eddies [AMM17] . It uses only the velocity field in individual time steps and does not compute any derived fields. This 3D detection scheme can be applied in parallel across timesteps and across depth slices since the vertical velocity is not used. Flow speed of swirling fluid decreases radially inwards towards the centre of rotation. The filter inspects local minima of the flow speed to identify potential eddy centres. Vertical velocity is ignored to discount the motion of upwelling or downwelling in vortex cores, thus enhancing the corresponding flow minima. Noise and less significant minima are removed by applying topological simplification directed by the notion of persistence [TFL∗ 17]. Next, the method employs an approximation of the winding angle criterion [FFH21] by checking if the streamline crosses into all four quadrants of an XY plane centred at the minimum [GEP04]. This method is more effective in regions with relatively stationary eddy centres like the Red Sea and Gulf of Aden. Figure 3 shows the set of potential eddy centres identified in the Red Sea using this filter. Streamlines seeded near the core of an eddy form spirals or closed loops. The boundary of an eddy is determined using a binary search along the radial axes. The search helps locate the seed that is furthest from the eddy centre but results in a spiral or nearly closed loop streamline. The filter displays all streamlines originating near the detected vortex core and hence presents a 3D profile of the eddy (Figure 2(I)). It may be extended to support other eddy detection methods [MAIS16, FFH21] that may be selected via the interface. 4.6. Surface front tracking and salinity visualization Oceanographers are often interested in water parcels that transport mass or heat. These are moving volumes of water with distinctive temperature or salinity characteristics. The surface front tracking filter computes connected components of the boundary of a scalarfield isovolume (called the surface front), tracks them over time, and generates a track graph that summarizes movement of all surface fronts. A subset of tracks extracted from this graph are rendered for visual analysis. Surface fronts have been shown to be a good representative of high salinity water masses [SDVN22]. It has been used to trace the path of the high salinity core (HSC) entering into the Bay of Bengal from the Arabian Sea (Figure 2(J)). This paper is available on arxiv under CC 4.0 license. Authors: (1) Toshit Jain, Indian Institute of Science Bangalore, India; (2) Varun Singh, Indian Institute of Science Bangalore, India; (3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India; (4) Upkar Singh, Indian Institute of Science Bangalore, India; (5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden; (6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India; (7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Authors: Authors: (1) Toshit Jain, Indian Institute of Science Bangalore, India; (2) Varun Singh, Indian Institute of Science Bangalore, India; (3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India; (4) Upkar Singh, Indian Institute of Science Bangalore, India; (5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden; (6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India; (7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Table of Links Abstract and Intro Ocean data pyParaOcean: Architecture pyParaOcean: Functionalities Case study: Bay of Bengal Conclusion Acknowledgments and References Abstract and Intro Abstract and Intro Ocean data Ocean data pyParaOcean: Architecture pyParaOcean: Architecture pyParaOcean: Functionalities pyParaOcean: Functionalities Case study: Bay of Bengal Case study: Bay of Bengal Conclusion Conclusion Acknowledgments and References Acknowledgments and References 4. pyParaOcean: Functionalities We now list and describe the various functions that are implemented in the pyParaOcean plugin and made available as Paraview filters. Figure 2 and the video in the supplementary material show the different filters in pyParaOcean and the user interface. 4.1. Isovolume visualization Volume rendering is a natural choice for visualizing the 3D scalar fields in the ocean data because it provides a quick overview of the distribution (Figure 2(D)). Animation with a fixed transfer function allows visualization of the scalar field over time. The volume rendering filter within Paraview can be tuned to visualize subvolumes of interest by choosing a interval within the range of the scalar field. Specifically, an isovolume containing the mean salinity/temperature value within the spatial region of interest or an isovolume that captures high salinity water provide a good overview of the 3D field. 4.2. Seed placement and field lines Field lines, including streamlines and pathlines, provide a good overview of a 3D vector field. pyParaOcean provides a filter that implements multiple seeding strategies for initiating the computation of streamlines and pathlines, and allows the user to choose one. Seeds generated using this filter are fed as input to the custom source streamline integrator or particle tracer in Paraview (Figure 2(G,H)). Streamlines are a set of integral curves tangent to the velocity at every point in space. They portray instantaneous lines of flow that characterize important oceanographic phenomena such as eddies, currents, and filaments. Pathlines are tangential to the velocity as it evolves over time. A pathline describes the path a massless virtual particle would take beginning from the seed positioned at a particular timestep. Pathlines are useful for understanding transport, such as salinity advection and debris collection. They are more compute-intensive than streamlines. The seeding filter controls the number of seeds and how the domain is sampled for seed placement, see Figure 2 (C)). Sampling can be (a) uniform, (b) weighted by flow speed, curl, vorticity, or the Okubo-Weiss criterion [Oku70], or (c) weighted by user-defined scalar fields that are computed earlier in the pipeline. A user may tune the line integration parameters and the sampling options to reduce visual clutter, focus the computation to regions of interest, maximize coverage of the domain, and highlight interesting flow features. For example, rendering wispy streamlines in regions of high vorticity produces closed loops around eddies with some temporal coherence across frames (Figure 7). Additionally, each component of the vector field to which the seeding filter is applied can be specified as a separate scalar field. This makes it easier to perform other downstream operations like ignoring the vertical velocity component or adjusting the scale along each axis. 4.3. Interactive particle paths This filter facilitates heat and mass transport queries on ocean data with an interactive seeding extension to the particle tracer from Paraview. It displays a linked parallel coordinates plot, where the user can brush to select ranges of scalars like temperature and salinity and hence restrict the seeding to isovolumes. Points sampled from these subvolumes serve as seeds for pathline computation (Figure 2(H)). 4.4. Depth profile view This filter enables the user to inspect a vertical column of the ocean, specified by a longitude and latitude pair. It drops a “needle” into the ocean and samples points along this line at different values of depth (Figure 2(D)). It displays a linked parallel coordinates plot that gives a depth profile of all scalars sampled along the vertical column. A line plot view of the chosen scalar against depth (Figure 2(E)) is displayed. Optionally, the scalar field mapped to a vertical slice at the chosen longitude is shown in the volume render window. The user can select and highlight a subset of the points in the vertical column from the parallel coordinates plot (Figure 2(F)), and track them across time in all views. This is useful for studying vertical mass transport, especially upwelling or downwelling via Ekman transport [Sar13] in eddy centres, and to study the depression of isotherms indicating redistribution of heat [KNR∗ 07]. Studying these changes is important to understand marine life as they drive phytoplankton bloom and nutrient transport 4.5. Eddy identification and visualization Several algorithms have been devised for reliable and automatic identification of eddies [AHG∗ 19]. McWilliam [McW90] developed a 2D method using vorticity ω as a physical parameter, whose local minima and maxima locate the centres of potential eddies and the values of vorticity in the neighborhood relative to the centre helps determine the eddy boundary. Okubo [Oku70] uses a special Okubo-Weiss parameter based on shear and strain deformation and the vertical component of vorticity to measure rotation and hence identify potential eddies. A circularity criterion may be applied after the Okubo-Weiss criterion to improve the results [WHP∗ 11]. Sea surface height and velocity profile have also been used for eddy detection [MAIS16]. The winding angle criterion, together with a streamline clustering, helps identify eddies in 3D [FFH21]. The eddy identification filter in pyParaOcean focuses on mesoscale eddies [AMM17] . It uses only the velocity field in individual time steps and does not compute any derived fields. This 3D detection scheme can be applied in parallel across timesteps and across depth slices since the vertical velocity is not used. Flow speed of swirling fluid decreases radially inwards towards the centre of rotation. The filter inspects local minima of the flow speed to identify potential eddy centres. Vertical velocity is ignored to discount the motion of upwelling or downwelling in vortex cores, thus enhancing the corresponding flow minima. Noise and less significant minima are removed by applying topological simplification directed by the notion of persistence [TFL∗ 17]. Next, the method employs an approximation of the winding angle criterion [FFH21] by checking if the streamline crosses into all four quadrants of an XY plane centred at the minimum [GEP04]. This method is more effective in regions with relatively stationary eddy centres like the Red Sea and Gulf of Aden. Figure 3 shows the set of potential eddy centres identified in the Red Sea using this filter. Streamlines seeded near the core of an eddy form spirals or closed loops. The boundary of an eddy is determined using a binary search along the radial axes. The search helps locate the seed that is furthest from the eddy centre but results in a spiral or nearly closed loop streamline. The filter displays all streamlines originating near the detected vortex core and hence presents a 3D profile of the eddy (Figure 2(I)). It may be extended to support other eddy detection methods [MAIS16, FFH21] that may be selected via the interface. 4.6. Surface front tracking and salinity visualization Oceanographers are often interested in water parcels that transport mass or heat. These are moving volumes of water with distinctive temperature or salinity characteristics. The surface front tracking filter computes connected components of the boundary of a scalarfield isovolume (called the surface front), tracks them over time, and generates a track graph that summarizes movement of all surface fronts. A subset of tracks extracted from this graph are rendered for visual analysis. Surface fronts have been shown to be a good representative of high salinity water masses [SDVN22]. It has been used to trace the path of the high salinity core (HSC) entering into the Bay of Bengal from the Arabian Sea (Figure 2(J)). This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

pyParaOcean, A System for Visual Analysis of Ocean Data: pyParaOcean: Functionalities

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