devopsy.kinsta.cloud

Tag: development

  • The Rise of Low-Code/No-Code in DevOps

    The Rise of Low-Code/No-Code in DevOps

    The ever-evolving landscape of technology has ushered in a paradigm shift in software development and deployment. One of the prominent trends gaining momentum is the integration of low-code/no-code platforms within the DevOps ecosystem. This article explores how these platforms are revolutionizing the way applications are developed, deployed and managed, fostering collaboration between developers and operations teams.

    1. Democratizing Development:

    Low-code/no-code platforms empower individuals with limited coding experience to actively participate in the application development process. This democratization of development accelerates the delivery of software by allowing business analysts, designers and other non-developers—sometimes referred to as citizen developers—to contribute directly to the creation of applications.

    2. Accelerating Time-to-Market:

    The traditional software development life cycle often involves lengthy coding processes, leading to delayed releases. Low-code/no-code platforms enable rapid prototyping and development through visual interfaces, significantly reducing the time required to bring applications from concept to production. This acceleration aligns with the principles of DevOps, promoting continuous delivery and faster time-to-market.

    3. Enhancing Collaboration:

    DevOps is all about breaking down silos and fostering collaboration between development and operations teams. Low-code/no-code platforms bridge the gap between these traditionally distinct roles by providing a common ground where both technical and non-technical stakeholders can collaborate seamlessly. This collaborative environment promotes better communication, understanding, and alignment of objectives.

    4. Flexibility in Deployment:

    Low-code/no-code solutions are designed to be platform-agnostic, allowing for the deployment of applications across various environments, including on-premises and cloud. This flexibility aligns with the DevOps principle of infrastructure-as-code (IaC), enabling teams to deploy and manage applications consistently across diverse infrastructures.

    5. Reducing Dependency on Specialized Skills:

    In a DevOps environment, reducing bottlenecks and dependencies on specific individuals or roles is crucial. Low-code/no-code platforms enable teams to be more self-sufficient, reducing the reliance on specialized coding skills or DevOps Training. This, in turn, contributes to a more resilient and adaptable DevOps culture.

    6. Challenges and Considerations:

    While the adoption of low-code/no-code in DevOps brings numerous benefits, it also introduces challenges. This section discusses considerations such as security, scalability and the potential need for customization beyond the platform’s capabilities.

    7. Real-World Use Cases:

    Explore real-world examples of organizations successfully incorporating low-code/no-code in their DevOps workflows. Highlight the positive outcomes, lessons learned, and the impact on their overall software development life cycle.

    Conclusion:

    The rise of low-code/no-code platforms in DevOps is reshaping the way software is developed and deployed. By fostering collaboration, accelerating development cycles, and reducing dependencies on specialized skills, these platforms align seamlessly with the principles of DevOps. As organizations continue to embrace this trend, the synergy between low-code/no-code and DevOps is set to redefine the future of application delivery.

  • Survey Sees Cloud Developer Environment Adoption Gaining Momentum

    Survey Sees Cloud Developer Environment Adoption Gaining Momentum

    A global survey found that while 95% of developers and business leaders are familiar with cloud development environments (CDEs), the reasons for adopting one vary widely. The survey polled 223 developers and business leaders working for organizations with more than 2,000 workers and 250 developers.

    Conducted by Coder, a provider of a CDE, the survey, for example, found that while 15% cited optimizing the developer environment setup process as a reason for adopting a CDE, another 14% cited making management tasks such as onboarding or improving remote access easier.

    Coder CEO Rob Whiteley said while CDEs clearly improve the productivity of application development teams and improve security by reducing the amount of source code running on notebook and desktop workstations, organizations looking to adopt CDEs need a significant amount of cultural resistance. Application developers have historically set up their own development environments on their own local machines. That approach, however, means that in addition to making it more challenging to collaborate also requires organizations to spend significant time and effort locking down each endpoint being employed by their developers, noted Whiteley.

    In fact, those issues are why many platform engineering teams formed to centralize the management of DevOps workflows are implementing CDEs, he added. The ultimate goal is to establish a set of guardrails while still enabling developers to self-service their tooling requirements, noted Whiteley.

    In fact, many DevOps teams have already built a homegrown version of a CDE that they currently maintain. Coder and other providers of CDEs are making a case for using a platform supported by a vendor rather than devoting resources to supporting a custom CDE, said Whiteley.

    As DevOps workflows continue to evolve in the age of artificial intelligence (AI), it’s only a matter of time before more organizations embrace CDEs, he added. The sheer volume of data that developers will be working with will make it impractical to continue to use local machines to build applications, said Whiteley.

    Regardless of the motivation for adopting CDEs, significant cultural changes are coming to DevOps workflows that need to evolve as application development continues to become more distributed. In the post-COVID-19 era, it’s now not uncommon for application development teams to consist of IT professionals working in different time zones. That creates a pressing need to leverage cloud services to provide the level of collaboration those teams need to achieve.

    It’s not clear how many CDEs have been deployed, but as the pace at which applications are being built and deployed continues to accelerate, the days when organizations could allow each developer to maintain their own development environment are coming to an end. Organizations need to determine as quickly as possible whether the code being created will actually run in a production environment. The more time spent trying to reconcile differences between development environments and the production environment where the application runs, the longer it will take to complete any given project.

    Unfortunately, time is the one thing that most DevOps teams no longer have the luxury of spending on application development initiatives that, in addition to multiplying, are also becoming much more complex to manage with each passing day.

  • The Great Debate: The Future of Humans and Software in the Age of AI

    The Great Debate: The Future of Humans and Software in the Age of AI

    Listening to the two sides of the debate regarding how much AI will take over software development, delivery and support tasks and its impact on jobs is both exciting for technology and scary for humans. The debate goes something like this:

    AI Advocate: Welcome to the future! With AI’s capabilities today, we’re looking at a world where software applications can practically develop, deliver and support themselves. The need for human involvement is diminishing rapidly.
    AI Antagonist: That’s an overly optimistic view. You’re underestimating the value of human creativity, empathy and ethical judgment. There are areas where AI simply cannot replace the human touch.
    AI Advocate: Consider design and specification. AI can generate prototypes and suggest improvements. It’s about efficiency and leveraging AI to cut down on the grunt work.
    AI Antagonist: But who sets the vision? AI lacks the understanding of user needs and market demands that only humans possess. The strategic decision-making and nuanced understanding of user psychology are irreplaceable.
    AI Advocate: True, but AI’s role in supervision and training cannot be overlooked. It can manage its operations, needing only minimal human oversight to update data and rules.
    AI Antagonist: And yet, who ensures these AI systems are aligned with ethical standards and current practices? Humans are crucial for overseeing these systems, ensuring they operate as intended and do not deviate into unethical territory.
    AI Advocate: I’ll give you that. However, in quality assurance and testing, AI significantly reduces the human workload by automating many processes.
    AI Antagonist: Automation has its place, but human insight is vital for identifying complex issues and understanding user experience in a way AI cannot. There’s a depth to exploratory and usability testing that AI can’t reach.
    AI Advocate: Ethical and legal oversight might be your strongest point. AI does struggle with nuanced decisions in privacy, security and bias. I concede that humans are necessary to navigate these complex fields.
    AI Antagonist: And don’t forget about customer support. AI can handle routine queries, but complex problems and sensitive issues require human intervention. The personal touch is something AI can’t replicate.
    AI Advocate: Innovation is a collaborative effort, I admit. AI assists, but human creativity drives continuous improvement and adapts to market trends.

    What Are the Implications if the AI Advocate is Correct?

    If the AI advocate’s vision comes to fruition, the implications for humanity could be profoundly transformative. First, the automation of routine and complex tasks alike would herald a new era of efficiency and productivity, potentially liberating humans from the drudgery of monotonous work. This shift could lead to an increased focus on creative, strategic and interpersonal tasks that leverage human strengths and foster innovation. Moreover, the widespread adoption of AI in software development could drastically reduce the time and resources needed to bring new technologies to market, accelerating the pace of technological advancement and potentially solving critical challenges in fields like healthcare, education and environmental protection. However, this future also raises concerns about job displacement and the need for societal adjustments to manage the transition, emphasizing the importance of re-skilling and education to prepare the workforce for a new economic landscape dominated by AI and automation.

    What Are the Implications if the AI Antagonist is Correct?

    On the other hand, if the AI antagonist’s perspective holds, the implications underscore the indispensable value of human involvement in technology. This scenario suggests that despite AI’s advancements, the nuanced understanding, ethical judgment and creative problem-solving capabilities of humans remain crucial. It highlights the potential risks of over-reliance on AI, including ethical dilemmas, privacy concerns and the perpetuation of biases if not carefully overseen by human intelligence. The emphasis on human skills and creativity could lead to a more balanced approach to technological development, where AI is used as a tool to augment human capabilities rather than replace them. This future would prioritize the development of technologies that are not only innovative but also ethical, user-centric and socially responsible, ensuring that technological progress benefits all of society.

    A Balanced Future

    Ultimately, the truth likely lies somewhere in between these two viewpoints, suggesting a future where AI and human capabilities are intertwined. The implications of such a balanced approach would be a collaborative ecosystem where AI enhances human efforts, leading to unprecedented levels of productivity and innovation while also creating new opportunities for human workers. This future emphasizes the importance of adaptability, lifelong learning and ethical considerations as central pillars of technological advancement. It also highlights the need for policies and educational systems that support the workforce in transitioning to roles that leverage human strengths in creativity, empathy and strategic thinking, ensuring that the benefits of AI are distributed equitably across society.

    Image source: https://vecteezy_futuristic-technology-concept-mixed-media-innovations-data_7019122_167-3.jpg

  • Why Startups are Betting Big on Low-Code/No-Code

    Why Startups are Betting Big on Low-Code/No-Code

    It is exhaustive — from infrastructure to app delivery, from data to applications — to modernize your practices, processes and providers to ensure you have the underlying foundation to take advantage of whatever comes next.

    Two or three years ago, apps created through low-code/no-code platforms were not usually as detailed under the surface as software developed from scratch, yet they sufficed for certain purposes. There was even a clear distinction between software developers and everyone else out of necessity because software development was incredibly difficult to master.

    But now, as we head towards more advanced AI, the SaaS-based low-code/no-code (LCNC) platforms empower businesses to create software exponentially faster and cheaper than a code-based approach.

    The future, therefore, of startups in this changing business landscape becomes even more competitive, taking into account the increasing importance of customer-centric UI modeling, data product development and cutting-edge technologies like machine learning and deep learning.

    High-Value Acceleration

    Applications built through traditional software development can take months or even years to launch. If you create a code-based solution, it’s also easy to blame others and there’s a lack of understanding if it’s not well-documented. “Low-code no-code functionalities can change this dynamic,” says Dr. Ingo Mierswa, an industry-veteran computer scientist and founder of Altair RapidMiner. “The visual workflow describes what it’s doing, so people can understand it, buy into it and take ownership of the results.”

    Visual workflows can be understood and created by more people. “They make it easier to maintain and explain why anything, such as a model’s decision or a bug, has happened because the workflows are documenting themselves,” Mierswa articulates. “It would be hard to create such an explanation if you had to sift through 100,000 lines of code, especially if the code had been changed 17 times in the past two weeks.”

    You need to iterate, you will make mistakes, and you don’t want to waste time with coding non-critical things. “My big advice for all starters is to always think about what’s critical for your product. What’s your IP? What do you need to own and customize? And if coding is not that important for your product, I think going low-code/no-code is always the better way,” Mierswa shares. “It’s more important for startups because you have less resources, less time, more competitive pressure and markets are moving very quickly.”

    Everyone is a Citizen Developer

    In the early 1960s and 1970s, there was a clear distinction between software developers and everyone else out of necessity. Software development was incredibly difficult and time-consuming to learn and master. But now with the onset of low-code/no-code solutions, everyone from custodial teams to C-level executives can create and customize applications that make their jobs more efficient.

    Because no-code/low-code platforms are built on the fundamental premise. “People closer to the problem are better equipped to find simpler and effective solutions,” says Kissflow CEO Suresh Sambandam. “It’s a team sport, and accountability, the most important benefit of LCNC solutions, accelerates development and empowers even more people to collaborate the right way.”A big advantage,” Sambandam says, “is that CIOs of large enterprises can solve the IT talent shortage plaguing the industry today by leveraging expert business users who are ‘citizen developers.’ Many companies can’t avoid struggling to recruit and retain tech talent in a highly competitive market, but low-code/no-code solutions exceedingly help bridge that talent gap by freeing up the bandwidth of existing resources.”

    At the highest level, you always need people who understand the business problems that need to be solved. LCNC functionalities also empower adjacent team members — designers, product managers, data scientists and others — to take part in the different stages of development as the product takes shape.

    Reducing Technical Debt to Overcome Friction

    In an enterprise scenario, most of the core systems like ERP, CRM, SCM in the front end, as well as in the back end, are bought from standard vendors. There is NO differentiation, as all the top enterprises buy from the same vendor. On the other hand, the bulk of differentiation comes from middle-office processes that are unique to an enterprise. Sambandam points out, “Often these are business processes that require heavy domain knowledge that frequently change to adapt to the evolving business conditions and hence business users are better equipped to build these workflow automations.” He highlights that conventional software development models don’t do justice to the agility required by business users and are too complex for them to use. While business users have the domain knowledge, the system would also require integration touch points with other enterprise systems. These integrations often require deeper technical expertise.

    It is essential to create a cultural change within your organization, where it is not just about having the right tools, but also about investing in your employees and skilling them up, commented DevRev Head of Engineering Ahmed Bashir.
    It still is hard work, and there will be friction. “Overcoming friction requires creating a tighter loop across departments and managers, closing the gap between developers and customers,” he underscores. “And LCNC platforms are designed to act as a system of action, surfacing real-time information that connects work with developers, customers and product.”

    This is exactly where platforms like Kissflow, Blaze and Bubble, which support both no-code and low-code use cases come in and let business users handle the domain portion of the application (90%) and IT teams configure the integration points (10%). This greatly reduces friction between IT and business users, especially when it comes to solutions for long-tail internal workflows.

    Long-tail applications include things like approval management, creative requests, incident management, customer onboarding and IT helpdesk. “We looked at the space and decided to tackle this,” Sambandam shares.

    Long-tail apps and processes are often viewed by IT as a drain on time/resources they don’t have, yet it’s a lack of internal workflow processes automation that gum up digital transformation. “We found as we scaled the company that when you empower business users to quickly build and augment workflows that directly impact their day-to-day by giving them the tools to do so without advanced technical knowledge, they are motivated to make it happen,” he affirmed.

    Security is a Business Enabler

    The main objective of security is to protect organizations and people. Organizations prepare for potential security threats by implementing policies and ensuring they have the tools to mitigate and quickly respond to outside threats.

    However many low-code/no-code adopters do not have security expertise on their team so they rely on the LCNC platform to offer security features readily available for the apps they build. Because creating applications with custom code can lead to a variety of security issues. And such applications are difficult to secure, with the majority of tested applications containing one or more session management vulnerabilities, and others with input validation errors.

    Getting a firm handle on security means considering what to do. That sounds simple enough, but it’s surprising how many startups make blunders because of poor planning and shoddy strategies. “We’ve been looking at a trend. Companies want enterprise-level security like audit logs, SSO and other data security requirements for their apps,” commented Nanxi Liu, Co-CEO of Blaze. “Seeing this strong trend, we worked to design our platform with enterprise security and scalability in mind, since most early-stage companies do not have security expertise on their team and rely on platforms to offer advanced security features for the applications they build.”

    Make, however, no mistake — there is no best security but the best practices. “That’s part of why,” Liu says, “compliance-driven businesses like financial services and healthcare companies that require the highest level of security to build their customer portals, consider enterprise-grade no-code and low-code platforms that provide the required level of security.”

    With low-code/no-code platforms, your applications are secure with industry-standard protocols, data protection practices, and features to block unauthorized access.

    To underscore: LCNC allows non-technical people to build complex apps without needing to be familiar with traditional software logic. You don’t need computational thinking because visual components in low-code/no-code platforms automatically have the logic built-in. “And now with the mind-bending AI breakthroughs,” Liu remarks, “users can tell AI in natural language the workflow or feature they need and see it instantly created.”

  • Improvement Cycles and AI

    Improvement Cycles and AI

    It’s that time of year when pundits start to crank out their predictions for the next twelve months! I’ve been asked to contribute to a private group (and occasionally, Techstrong Group asks me to contribute to their annual list), so I won’t thrill you with my predictions just yet; but I will discuss the cycles and trends that previous predictions have shown.

    Back when I was testing physical servers, we thought of throughput bottlenecks in terms of where the problem was. The circle would go disk->memory-> CPU, and in the case of things like NAS, insert network in there somewhere, too. When one bottleneck was resolved, another would crop up. And users will always seem to be pushed to the next bottleneck.

    We see similar cycles in DevOps. First, Agile made development faster, and that caused us issues throughout the rest of the process. Then, DevOps helped with the build and a little with the testing and deployment steps, but in the early days it was more standardization than speeding up. And since then, we have been in that cycle. We’re seeing GitOps speed up and automate deployment and upgrades—based upon the automation that came before—we’re seeing test and security tools feeding directly into the IDE to speed resolution, we’re seeing container automation cause “cloud-first development” (I hate that name; Kubernetes has cloud-like interfaces, but is not precisely cloud) to be fully automated, and thus faster.

    And we’re looking at that cycle continuing. No doubt you will hear a lot of predictions that start with “AI …” For the most part, AI is a tool and/or technique. Listen for what it is improving because it is doing very little that’s actually new. (Delete list of things that sounded alarmingly like those predictions I said I wouldn’t make.)

    All in all, we are still living in a very cool technological time. We can have a tool generate code snippets of the dull work for us so we can focus on the important bits. Some shy away from this use of “AI,” but honestly, we already use libraries to do the grunt work—I haven’t written to a screen buffer in forever—because we don’t have to. Same story—use the tools that make you productive. Some of the more obscure bits of Linux command lines are far better dealt with by asking an AI to give me examples than searching through tons of docs across several platforms. Never was much of a fan of man output for complex commands, this makes it easier. The nice bit is that bash and UNIX utilities don’t change much over time, so answers can be a bit dated and perfectly accurate. That’s not true for much of IT.

    The environment has consistently gotten more complex, while the building blocks have gotten easier to use. This allows things to keep moving and changing while it offers us better ways to serve the organization’s needs. This does change at some point—infinite change is indeed a thing, but any technology does have its limits—but I honestly can’t see the point at which we are content with our IT tools and stop looking for more. Oh, most organizations go through those cycles—organizationally, once an infrastructure and process works correctly, it is likely to hang around until better is found—but the industry will keep changing.

    And you’ll keep learning and changing. That’s one prediction I will make for next year. Teams and IT staff will grow and learn new things, because at its heart, that is what we do. We learn new things and keep the lights on. In good years, we offer sweet new tools and functionality to the company or our customers. But either way, we keep things online. Year after year. You all rock, so that’s an easy prediction.

  • The API Life Cycle Mixer’s Manual: What Goes Into a Single Source of Truth?

    The API Life Cycle Mixer’s Manual: What Goes Into a Single Source of Truth?

    In bartending, the mixer’s manual is a trusted source of information. These books can contain hundreds of recipes for classic cocktails, some of which might be 100 years old, while others might be for drinks that only recently arrived on the scene. Mixer’s manuals also can be great sources of inspiration for new, yet-to-be-tested recipes for exciting additions to a bar’s or restaurant’s offerings. This balancing act of preserving the old while encouraging exploration into the new can provide a valuable lesson for API development in the software world. An API’s “single source of truth” is, in many ways, the software organization’s own mixer’s manual. This is especially true when the right approach is taken to ensure that software remains functional and performant and serves up experiences that delight customers and keep them coming back for more.

    The Rise of APIs and Mixology

    While we might think of mixology and the mixologists who practice this craft as relatively new terms, the first recorded use of the word “mixologist” dates back to 1852 and “mixology” followed shortly after.

    Just how similar are the work, mindsets and passions of mixologists and API developers and designers? Check out this definition of a mixologist and count the similarities for yourself:

    “The term ‘mixologist’ refers to someone who studies the history of mixed drinks, has a rich appreciation of the ingredients and techniques used and regularly creates new and innovative mixed drinks … their job title implies that they do a significant portion of their job behind the scenes, creating new craft cocktails and putting their signature twist on existing favorites.”

    The best API developers, designers and architects of the world should recognize a lot of the above in their own work. They have deep respect for their craft and are always honing it further. They seek to understand the tastes and palates of their consumers. They factor in other stakeholders’ inputs, they test, iterate and test their creations again before “serving” those APIs to production.

    Like mixologists and bartenders (if you see them as two different roles), every professional along the API life cycle should understand the needs of consumers, customers and the business–and then design and develop their APIs accordingly. An essential part of this process is continuous validation and adaptation to market requirements.

    Maintaining Confidence in Ever-Changing Recipes

    Changes to APIs, even minor ones, can have significant downstream effects. And the same can be said for tweaks made to an age-old cocktail recipe, where adding or removing a single dash of this or that can completely change the flavor of a drink … as well as people’s interest in it.

    While there’s no “standard” rate of change to APIs to plan for, you can expect changes to be needed with each new software release. This could mean changes are being made (and need to be tested against and validated) every few weeks, every month or even every day, depending on your release cadence. There also can be as-needed, ad-hoc changes.

    What’s crucial for teams to ensure is that these changes do not disrupt an API’s functionality or performance. To make this possible, teams are increasingly leveraging API contract testing to provide a safety net of sorts. This comes in the form of testing that validates changes against a “contract” that details an API’s original, agreed upon and, therefore, required functionality.

    The ability to foresee the impacts of changes to APIs can depend on the maturity of an engineering team and its ability to collaborate across departments, time zones and varying levels of technical expertise. Tools and guidelines that promote standardization and collaboration can prevent mishaps from overlooking or being blind to negative downstream effects.

    Documentation is Key

    Whether we’re talking about being able to trust a single source of truth for the most up-to-date version of a published API or the latest edition of a mixer’s manual for tried-and-true drink recipes, clear and concise documentation is key. But it’s also not enough for this documentation to simply exist.

    It seems like common sense—although you’ve probably worked somewhere where it wasn’t—that your API documentation also needs to be easily accessible, understandable and usable. Ensuring all of these things leads to smoother onboarding and a positive developer experience and, ultimately, to wider adoption of a functional, performant API that meets or even exceeds expectations.

    API documentation is often the first point of contact a developer has with an API. It serves as a guide, walking developers through the API’s features, endpoints, request/response models and error messages. It also provides insight into the underlying business logic and unique characteristics of an API.

    Good API documentation follows the principle of a single source of truth, meaning that it should be comprehensive, up-to-date, and consistent. It should always mirror the actual behavior of the API, reducing the guesswork for developers and minimizing the potential for misunderstandings and errors.

    Just as a mixologist relies on a detailed and accurate cocktail recipe to deliver a pleasing experience to a patron, API developers depend on good documentation to create and deliver useful, value-add APIs and powerful applications.

    Continuous Improvement = Continuous Education

    Like a mixologist who meticulously adjusts the ingredients of a cocktail or who creates new cocktails to meet the evolving tastes of customers, developers must constantly adapt their APIs to meet changing market demands, business needs and industry standards.

    This is achieved through a potent blend of market research, collaboration, adherence to standards and governance and meticulous testing. However, as software development, testing and deployment technologies and processes continue to evolve, the tech industry must continue educating itself and refining its practices.

    With a mindset focused on constant learning and continuous improvement and a dependable single source of truth as their guide, development teams can confidently serve up exceptional API experiences in every release. In the end, the aim is to create APIs that are reliable and easy to consume and that meet the unique needs of the moment–much like a mixologist’s perfect beverage.

  • Technological Breakthroughs in Hurricane Forecasting

    Technological Breakthroughs in Hurricane Forecasting

    The 2023 Atlantic hurricane season marks a transformative era in tropical forecasting, driven by the remarkable progress in machine learning and big data analysis. It’s a rapidly evolving field, where technology and meteorology intersect to better prepare the public for the formidable forces of Mother Nature.

    NOAA’s 2023 Atlantic Hurricane Season Forecast

    The National Oceanic and Atmospheric Administration (NOAA) projects that this year’s Atlantic hurricane season will produce 12 to 17 named storms, with five to nine major (Category 3+) hurricanes. NOAA expresses 70% confidence in these ranges, bolstered by its evolving technology.

    Leveraging Technological Innovations for Advanced Hurricane Forecasting and Resilience
    A summary infographic showing hurricane season probability and numbers of named storms predicted from NOAA’s 2023 Atlantic Hurricane Season Outlook. (Image credit: NOAA)

    Upcoming Upgrades: Supercomputing System Expansion

    The recent implementation of the Probabilistic Storm Surge model improves storm surge forecasting for the U.S. mainland, Puerto Rico and the U.S. Virgin Islands. The model provides forecasters with a range of potential flooding scenarios, enhancing community preparedness. 

    NOAA plans to implement several more upgrades this summer, including a 20% expansion of its operational supercomputing system’s capacity. This enhancement will enable NOAA to run more complex forecast models, leading to more accurate predictions.

    In late June, the Hurricane Analysis and Forecast System (HAFS) becomes operational. Running in tandem with the current Hurricane Weather Research and Forecast Model System, HAFS is projected to show a 10-15% improvement in track forecasts over existing models.

    HAFS represents a significant leap forward in the integration of machine learning algorithms and sophisticated data analysis techniques. By leveraging state-of-the-art computational models and assimilating an extensive array of meteorological data, HAFS has the potential to unlock new insights into the behavior and trajectory of tropical cyclones. This fusion of technology and meteorology is projected to surpass the capabilities of existing models.

    Harnessing Machine Learning for Advanced Forecasting

    The core strength of HAFS lies in its ability to process vast amounts of atmospheric and oceanic data, utilizing advanced machine learning algorithms to identify patterns and correlations. These algorithms can discern intricate relationships between various meteorological variables, such as sea surface temperatures, atmospheric pressure, and wind patterns, enabling more precise forecasting. By comprehensively analyzing historical storm data and incorporating real-time observations, HAFS can generate forecasts with higher accuracy and longer lead times.

    HAFS also harnesses the power of high-performance computing, allowing for faster and more complex simulations. This computational prowess enables the system to simulate a multitude of possible scenarios, accounting for various atmospheric conditions and factors that influence hurricane behavior. The ability to run multiple simulations and generate ensemble forecasts enhances the robustness of predictions and provides forecasters with a comprehensive range of possibilities.

    Innovative Technologies: Drones, Saildrones, Underwater Gliders, and Sounding Balloons

    Innovative technologies like small aircraft drone systems, additional Saildrones, underwater gliders, and WindBorne global sounding balloons will contribute to advancing knowledge and improving hurricane forecast accuracy. 

    Small aircraft drone systems offer a unique vantage point for data collection in the lower atmosphere, enabling meteorologists to gather critical information on temperature, humidity, wind patterns, and other atmospheric variables. These drones are equipped with advanced sensors and instruments, allowing them to navigate through hurricane environments and provide valuable real-time data that can significantly improve forecast models.

    Saildrones, alternatively, are autonomous surface vehicles equipped with an array of meteorological and oceanographic sensors. They harness wind and solar energy for propulsion, enabling them to traverse vast oceanic regions and collect crucial data on sea surface temperatures, ocean currents, and other parameters relevant to hurricane formation and intensification. By expanding the fleet of Saildrones, scientists can enhance their observational capabilities and obtain a more comprehensive understanding of the complex interactions between the ocean and the atmosphere during hurricane events.

    Underwater gliders, equipped with sensors and instruments, offer a unique perspective beneath the ocean’s surface. These autonomous vehicles can collect data on temperature, salinity, and ocean currents, providing valuable insights into the oceanic conditions that influence hurricane development and intensity. By combining data from underwater gliders with other observational platforms, scientists can gain a more holistic understanding of the underlying processes that fuel tropical cyclones.

    WindBorne global sounding balloons ascend to the upper levels of the atmosphere, collecting data on temperature, humidity, and wind patterns. This information is vital for initializing and validating numerical models used in hurricane forecasting, enhancing the accuracy of predictions.

    Leveraging Technological Innovations for Advanced Hurricane Forecasting and Resilience
    NOAA GOES satellite captures Hurricane Ian as it made landfall on the barrier island of Cayo Costa in southwest Florida on September 28, 2022.

    Improving El Niño & La Niña Forecasts

    The Tropical Atmosphere Ocean buoy array is undergoing modernization and upgrades, bolstering the accuracy of El Niño and La Niña forecasts. These climate patterns have significant impacts on hurricane activity, and accurate predictions of their behavior are crucial for understanding the overall climate system. 

    By modernizing and enhancing the buoy array, scientists can gather more precise data on oceanic and atmospheric conditions associated with El Niño and La Niña, leading to more accurate long-range hurricane forecasts.

    New tools and upgrades have also been introduced for more precise hurricane analysis and forecasting. The National Hurricane Center has expanded its Tropical Weather Outlook graphic forecast range to seven days from five. To give communities more time to prepare, the Weather Prediction Center has also extended the Excessive Rainfall Outlook by two days.

    Leveraging Technological Innovations for Advanced Hurricane Forecasting and Resilience
    A summary graphic showing an alphabetical list of the 2023 Atlantic tropical cyclone names as selected by the World Meteorological Organization. The official start of the Atlantic hurricane season is June 1 and runs through November 30. (Image credit: NOAA)

    Bolstering Resilience Against Future Hurricanes

    The integration of these innovative technologies and the modernization of existing observational systems expands observational capabilities both in the atmosphere and the ocean to track and monitor storms. The evolution of forecasting techniques holds promise for even greater resilience against future hurricanes, showcasing the remarkable impact of technological advancements in safeguarding coastal communities.