Tourists braved a late-spring snowstorm at Mount Rushmore on Friday, as conditions made the presidents hard to see. - Videos from The Weather Channel | weather.com

Mount Rushmore Sees Early May Snowfall - Videos from The Weather Channel

Severe weather possible from Texas to the Midwest. Here&#x27;s what we&#x27;re tracking. - Videos from The Weather Channel | weather.com

Dangerous Storms In Texas Fire Up This Evening - Videos from The Weather Channel

Mesmerizing video from the surface of the sun shows delicate, lace-like patterns of gas that give our star a fluffy look up-close. - Videos from The Weather Channel | weather.com

Solar Orbiter Captures Hairy View Of Sun’s Surface - Videos from The Weather Channel

The weekend is upon us and there are plenty of outdoor events happening around the city and beyond. Unfortunately, the weather is going to be a mixed bag. Here is what you need to know: 5 Boro Bike Tour The 5 Boro Bike Tour is this Sunday. Sadly, it’s going to be the worse half of the weekend ...

NYC weekend weather: forecasts for 5 Boro Bike Tour, Long Island Marathon – NBC New York

A fast-moving cold storm will bring light rain and cool temperatures when it passes to the north of Los Angeles this weekend.

Los Angeles weather forecast includes weekend rain – NBC Los Angeles

Raging floodwaters in southeast Texas swallowed an 18-wheeler, leaving the driver to climb out of the cab in a race against time. - Videos from The Weather Channel | weather.com

Truck Driver Scrambles To Escape Texas Flooding - Videos from The Weather Channel

Parts of southeast Texas were covered by water amid days of heavy rainfall. - Articles from The Weather Channel | weather.com

Houston Weather Brings Flooding, Heavy Rain | Weather.com

NEW YORK (PIX11) — The weekend looks quite gloomy, especially for the latter part. A storm system will approach, bringing some rain on Sunday. Unfortunately, the rainy weather will coincide with the Five Boro Bike Tour and will dampen festivities associated with the event.

New York City weekend weather expected to be chilly with rain

Showers, Storms Could Dampen Part Of Weekend In IL: NWS - Across Illinois, IL - Temperatures could top 80 degrees Saturday, before cooling off a bit on a mostly dry Sunday, according to the National Weather Service.

Showers, Storms Could Dampen Part Of Weekend In IL: NWS | Across Illinois, IL Patch

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Brave New Word: Exploring the Diverse Niches for Decentralized Physical Infrastructure Networks 

5 Devices to Get Started in DePIN

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5 Geräte für den Einstieg in DePIN

5 dispositivos para comenzar en DePIN

5 appareils pour démarrer dans DePIN

DePIN में आरंभ करने के लिए 5 डिवाइस

DePIN を始めるための 5 つのデバイス

DePIN을 시작하기 위한 5가지 장치

5 dispositivos para começar no DePIN

5 устройств, с которых стоит начать работу в DePIN

DePIN'e Başlamak için 5 Cihaz

5 thiết bị để bắt đầu sử dụng DePIN

开始使用 DePIN 的 5 种设备

Startups of The Year 2023 Winners: North America 

Weather Image Recognition with AI: Dataset Labeling, Model Training, and Real-world Usecases

What is the one thing we all need to ask before we leave the house each morning? “What’s the weather going to be like today?” Luckily, we live in the transformational age of advanced and ever-evolving technology, and we now have the gift of artificial intelligence and machine learning (ML). By looking at years of weather data and pairing it with known weather patterns and outside factors, computers can now predict the weather like never before. For instance, during the Paris Olympics this year, AI tools were used to provide a daily forecast of weather conditions. Recent advances in ML-based weather prediction (MLWP) have produced ML-based models with less forecast error than simpler past models. As noted by the science news magazine EOS, “It took fifty years to build and incrementally improve the sophisticated computer models needed to produce such accurate forecasts. It has taken ML approaches just a couple of years to match, and in some cases surpass, the skill of these traditional models.” Now, Google DeepMind scientists have developed a weather model that largely beats the world’s most accurate modeling system. Researchers believe the model may be able to accurately predict unprecedented events that climate change accentuates. According to a report last month commissioned by the International Chamber of Commerce, “climate-related extreme weather events have cost the global economy more than $2 trillion over the past decade.” According to Maginative, “Google DeepMind’s new high-resolution AI model, GenCast, delivers faster, more accurate weather predictions than traditional methods. AI will fundamentally change how we plan for everyday weather and extreme events.” Part of the reason why this is such a significant development is because GenCast offers weather forecasts that are not only more reliable but also faster. Unlike traditional methods that rely on supercomputers and take hours, GenCast runs on a single TPU chip in just eight minutes, predicting a wide range of possible outcomes rather than a single scenario, as current weather prediction models do. In contrast to traditional models, which make forecasts by solving physics equations, GenCast learns directly from historical weather data. Jacob Mallinder, in CEO Today, outlines some of the top AI innovations that are making a difference in weather forecasting today. He explains that aside from improved data gathering and predictive analytics, AI today allows for insight into climate change, agricultural optimization, improving efficiency in the energy sector, and enhancing aviation safety. MAELSTROM is another large-scale R&D project, aiming to fundamentally improve weather and climate prediction. It aims to combine the powers of high-performance computing (HPC) and ML to cope with the extreme complexity inherent in weather and climate forecasts. In August, NVIDIA Research announced a new generative AI model, dubbed StormCast, for emulating high-fidelity atmospheric dynamics. The model was built to provide reliable weather prediction at the mesoscale — a scale larger than storms but smaller than cyclones — which is critical for disaster planning and mitigation. Quantum ML is another option, but some experts believe it is not the optimal way to go as quantum computing is still in the early stages of development. If it is the case that GenCast and other emerging technologies can predict serious weather events, it will introduce a monumental change. With such abilities, we can warn people ahead of time and prevent traffic accidents. Farmers can plan their farming and prevent crop loss. Energy companies will be able to run more effectively if they can understand sun and wind patterns in advance. And when we walk out the door, we won’t need to wonder what the weather will be like because we’ll already know. What is the one thing we all need to ask before we leave the house each morning? “What’s the weather going to be like today?” Luckily, we live in the transformational age of advanced and ever-evolving technology, and we now have the gift of artificial intelligence and machine learning (ML). By looking at years of weather data and pairing it with known weather patterns and outside factors, computers can now predict the weather like never before. For instance, during the Paris Olympics this year, AI tools were used to provide a daily forecast of weather conditions. Recent advances in ML-based weather prediction (MLWP) have produced ML-based models with less forecast error than simpler past models. less forecast error As noted by the science news magazine EOS , “It took fifty years to build and incrementally improve the sophisticated computer models needed to produce such accurate forecasts. It has taken ML approaches just a couple of years to match, and in some cases surpass, the skill of these traditional models.” noted EOS Now, Google DeepMind scientists have developed a weather model that largely beats the world’s most accurate modeling system. weather model Researchers believe the model may be able to accurately predict unprecedented events that climate change accentuates. According to a report last month commissioned by the International Chamber of Commerce , “climate-related extreme weather events have cost the global economy more than $2 trillion over the past decade.” commissioned by the International Chamber of Commerce According to Maginative , “Google DeepMind’s new high-resolution AI model, GenCast, delivers faster, more accurate weather predictions than traditional methods. AI will fundamentally change how we plan for everyday weather and extreme events.” According to Maginative Part of the reason why this is such a significant development is because GenCast offers weather forecasts that are not only more reliable but also faster. Unlike traditional methods that rely on supercomputers and take hours, GenCast runs on a single TPU chip in just eight minutes, predicting a wide range of possible outcomes rather than a single scenario, as current weather prediction models do. In contrast to traditional models, which make forecasts by solving physics equations, GenCast learns directly from historical weather data. Jacob Mallinder, in CEO Today, outlines some of the top AI innovations that are making a difference in weather forecasting today. outlines some of the top AI innovations He explains that aside from improved data gathering and predictive analytics, AI today allows for insight into climate change, agricultural optimization, improving efficiency in the energy sector, and enhancing aviation safety. MAELSTROM is another large-scale R&D project , aiming to fundamentally improve weather and climate prediction. It aims to combine the powers of high-performance computing (HPC) and ML to cope with the extreme complexity inherent in weather and climate forecasts. large-scale R&D project In August, NVIDIA Research announced a new generative AI model , dubbed StormCast, for emulating high-fidelity atmospheric dynamics. announced a new generative AI model The model was built to provide reliable weather prediction at the mesoscale — a scale larger than storms but smaller than cyclones — which is critical for disaster planning and mitigation. Quantum ML is another option , but some experts believe it is not the optimal way to go as quantum computing is still in the early stages of development. Quantum ML is another option If it is the case that GenCast and other emerging technologies can predict serious weather events, it will introduce a monumental change. With such abilities, we can warn people ahead of time and prevent traffic accidents. Farmers can plan their farming and prevent crop loss. Energy companies will be able to run more effectively if they can understand sun and wind patterns in advance. And when we walk out the door, we won’t need to wonder what the weather will be like because we’ll already know.

Weather Forecasting Through Machine Learning

My journey from weather app user to developer started after realizing that no apps in my home country had the same level of detail as in the U.S. I noticed that American weather apps often included real-time radar animations, showing precise rainfall zones and even visualizations of precipitation intensity. Intrigued by these features, I realized that while many other countries have weather radars, there is a lack of apps to display precipitation zones. This gap inspired me to create a weather app that could provide detailed radar and real-time precipitation data worldwide. Essential Features of a Competitive Weather App The competitive landscape for weather apps is challenging. In my experience, there are two ways to stand out. First, introduce a unique element—a feature, an engaging animation, or an unconventional way of presenting information. People are drawn to novelty, especially when it enhances the experience. Alternatively, identifying and filling a geographical gap in existing services can be critical. For instance, Rain Viewer filled this niche by bringing radar data to many countries where such technology was scarce or completely out of reach. Breaking into New Markets Expanding the app to other countries required a strategic approach. For example, we quickly gained traction in Saudi Arabia and parts of Asia, where radar data was present but not widely utilized in consumer apps. Interestingly, in India, we saw higher engagement with our web platform rather than the app itself—likely due to local preferences. In Europe and North America, where the technology is more mature, it's harder to differentiate, as users there already have established weather app preferences. It's difficult, but nothing is impossible. Integrating Real-Time Weather Data In the case of our weather app, our core mission is to deliver accurate, real-time weather information worldwide. Radar data, which visualizes real-time precipitation patterns, became our primary focus. The availability of this data has improved dramatically over the past decade, with more countries adopting transparency in their meteorological data policies. Today, much of Europe, North America, and parts of South America openly share radar data, enabling us to enhance our global coverage. However, integration is still a detailed, hands-on process, as formats and standards differ across countries. Choosing the Right Programming Languages Selecting programming languages largely depends on the team's expertise. At Rain Viewer, we use JavaScript and Python for processing information (backend). We rely on Kotlin for Android  and Swift for iOS for mobile development, respectively. Python, in particular, is essential for weather prediction and data science, making it a natural fit for our needs. The Ongoing Challenge of Weather Prediction Accuracy Weather prediction is, by nature, an evolving science, and no solution can eliminate uncertainty. Accurate forecasting requires vast, constantly updated data and collaboration between meteorologists and developers worldwide. Rain Viewer benefits from contributions by researchers globally, but some phenomena—like small, spontaneous tornadoes—remain hard to predict. While our small team continues improving forecast precision, we also recognize that achieving absolute accuracy remains an industry-wide challenge. Prioritizing User Experience and Interface Design Data accuracy is paramount in weather apps. However, user interface (UI) and user experience (UX) design are also critical. Users often come to a weather app for reliable information, but they stay for a clean, intuitive interface that makes data easy to interpret. Our weather app prioritizes user-centric design to help people quickly find the forecasts they need without clutter. This balance between UX/UI and data quality has been essential to our app's success and our users' trust. Overcoming Obstacles in Weather App Development Developing a successful weather app presents its own set of challenges. Choosing the right data providers and APIs can be tricky for new developers. There are numerous weather APIs on the market, each varying in cost and quality. For beginners, high-quality data from established providers may be too expensive. Additionally, deciding on core functionalities—whether to provide radar, forecast, or real-time maps—is critical. Each functionality requires unique resources and planning. It’s important to know what to prioritize based on the target audience. Advice for Aspiring Weather App Developers The weather app market is evolving rapidly, with new apps entering the market each year. If you're considering transitioning from consumer to developer, my advice is simple: "Be persistent." The market may be crowded, but there's always room for innovation, especially with the rise of AI and smart devices. Whether you're creating a major app or a niche tool, stay committed to your vision, make it fun for you and iterate based on user feedback. This will help you build a resonant product. My journey from weather app user to developer started after realizing that no apps in my home country had the same level of detail as in the U.S. I noticed that American weather apps often included real-time radar animations, showing precise rainfall zones and even visualizations of precipitation intensity. Intrigued by these features, I realized that while many other countries have weather radars, there is a lack of apps to display precipitation zones. This gap inspired me to create a weather app that could provide detailed radar and real-time precipitation data worldwide. Essential Features of a Competitive Weather App The competitive landscape for weather apps is challenging. In my experience, there are two ways to stand out. First, introduce a unique element—a feature, an engaging animation, or an unconventional way of presenting information. People are drawn to novelty, especially when it enhances the experience. Alternatively, identifying and filling a geographical gap in existing services can be critical. For instance, Rain Viewer filled this niche by bringing radar data to many countries where such technology was scarce or completely out of reach. Breaking into New Markets Expanding the app to other countries required a strategic approach. For example, we quickly gained traction in Saudi Arabia and parts of Asia, where radar data was present but not widely utilized in consumer apps. Interestingly, in India, we saw higher engagement with our web platform rather than the app itself—likely due to local preferences. In Europe and North America, where the technology is more mature, it's harder to differentiate, as users there already have established weather app preferences. It's difficult, but nothing is impossible. Integrating Real-Time Weather Data In the case of our weather app, our core mission is to deliver accurate, real-time weather information worldwide. Radar data, which visualizes real-time precipitation patterns, became our primary focus. The availability of this data has improved dramatically over the past decade, with more countries adopting transparency in their meteorological data policies. Today, much of Europe, North America, and parts of South America openly share radar data, enabling us to enhance our global coverage. However, integration is still a detailed, hands-on process, as formats and standards differ across countries. Choosing the Right Programming Languages Selecting programming languages largely depends on the team's expertise. At Rain Viewer, we use JavaScript and Python for processing information (backend). We rely on Kotlin for Android  and Swift for iOS for mobile development, respectively. Python, in particular, is essential for weather prediction and data science, making it a natural fit for our needs. The Ongoing Challenge of Weather Prediction Accuracy Weather prediction is, by nature, an evolving science, and no solution can eliminate uncertainty. Accurate forecasting requires vast, constantly updated data and collaboration between meteorologists and developers worldwide. Rain Viewer benefits from contributions by researchers globally, but some phenomena—like small, spontaneous tornadoes—remain hard to predict. While our small team continues improving forecast precision, we also recognize that achieving absolute accuracy remains an industry-wide challenge. Prioritizing User Experience and Interface Design Data accuracy is paramount in weather apps. However, user interface (UI) and user experience (UX) design are also critical. Users often come to a weather app for reliable information, but they stay for a clean, intuitive interface that makes data easy to interpret. Our weather app prioritizes user-centric design to help people quickly find the forecasts they need without clutter. This balance between UX/UI and data quality has been essential to our app's success and our users' trust. Overcoming Obstacles in Weather App Development Developing a successful weather app presents its own set of challenges. Choosing the right data providers and APIs can be tricky for new developers. There are numerous weather APIs on the market, each varying in cost and quality. For beginners, high-quality data from established providers may be too expensive. Additionally, deciding on core functionalities—whether to provide radar, forecast, or real-time maps—is critical. Each functionality requires unique resources and planning. It’s important to know what to prioritize based on the target audience. Advice for Aspiring Weather App Developers The weather app market is evolving rapidly, with new apps entering the market each year. If you're considering transitioning from consumer to developer, my advice is simple: "Be persistent." The market may be crowded, but there's always room for innovation, especially with the rise of AI and smart devices. Whether you're creating a major app or a niche tool, stay committed to your vision, make it fun for you and iterate based on user feedback. This will help you build a resonant product.

From Consumer to Developer: How to Build Your Own Weather App

Meteorology is rapidly evolving due to the development of new technology. Traditional forecasting methods, which relied heavily on physical instruments and historical data, are now being reshaped by integrating artificial intelligence (AI) and machine learning. This shift is expected to improve the accuracy and granularity of forecasts and fundamentally change how we interact with weather information. Bridging the Gap Between Short-Term and Long-Term Forecasts One of the most intriguing advancements in meteorology is the use of AI to bridge the gap between short-term and long-term forecasting. Traditionally, predicting weather events a few minutes to 24 hours ahead has been a challenge. Machine learning models are now being trained on vast datasets to predict weather patterns with unprecedented precision. These models can integrate real-time data, such as satellite imagery and user-generated reports, to produce hyperlocal forecasts. Being able to predict severe weather, like sudden storms or heavy rain, more quickly and accurately could be a big breakthrough soon. Enhancing User Experience Improvements in user experience must complement technological advancements. Integrating these new technologies in a way that is accessible and useful to the average user is one of the biggest challenges. Weather apps have become a primary source of information for many and must evolve to present complex data in a simple and actionable manner. The key to success lies in user-centric design. People need information quickly and in a format that is easy to understand, whether it’s for a sporting event, a morning commute, or planning a trip. The challenge is to personalize this information without overwhelming the user. Towards a More Adaptive Forecasting System Personalization is the future of weather apps. Imagine a weather app that knows you’re about to go for a run and gives you a tailored forecast for your route and time of day. Or one that alerts you to potential weather disruptions based on your calendar events. This level of personalization, however, raises concerns about data privacy and user consent. It’s crucial for companies to be transparent about how they use data, and users should have control over what they share. With responsible practices, personalized experiences can enhance usability without compromising privacy. The Role of AI in Meteorology The pioneers in using AI assistants in weather apps were Pixel phones (which analyze forecast data and offer summaries). Soon, AI hints appeared in the Rain Viewer weather app. I am sure that this is just the beginning of the trend, and soon, we will see the widespread use of AI in weather apps. Predicting the weather for the next decade is challenging due to the fast-paced technological advancements. AI is playing a crucial role in this evolution. The focus has shifted from hardware advancements, like new sensors, to developing sophisticated software that enhances weather prediction algorithms. By using big data, AI refines both short-term and long-term forecasts. Even though physical instruments might not change much, new software will soon make weather forecasts more accurate and reliable. Current forecasting models cannot process all the available data effectively, especially satellite information. There are five operational meteorological satellites, yet we can’t fully utilize their data. Most of it just gets stored as images without being immediately analyzed. The challenge is in processing all the available massive amounts of data from modern meteorological equipment in real time to provide hyperlocal forecasts. AI and machine learning can help fill this gap by automating data analysis and improving forecast accuracy. The breakthrough will be predicting exactly which houses a tornado will hit so that people can evacuate in time (now the Tornado Warning issues for large territories, and everyone knows that “there will be no tornado” and ignore it due to lack of such hyperlocal predictions). Simplifying User Experience Through Integration The future of weather forecasting is moving towards simplifying how information is delivered and processed. In the near future, you may walk past a digital billboard that shows you personalized weather information—like a forecast for your specific location—without needing to pull out your phone. The goal is to integrate weather data seamlessly into our daily environments. Dedicated weather apps may not be necessary, as the information will be readily accessible on any screen and personalized to our needs, current location, and activity. Weather Apps as Part of a Broader Ecosystem As standalone entities, weather apps may become redundant. People don’t need weather apps just to know the weather. They need them to make decisions, like whether to take an umbrella, call a taxi, or plan a vacation. Weather data is already being incorporated into various ecosystems. Modern cars can replan you driving route because of severe weather events, and smart home thermostats can adjust temperature settings based on forecasts, smart irrigation systems check not only how soil is wet, but also how much precipitation will come in the next 24 hours. Weather information is everywhere—cars, fridges, calendars, and even when buying tickets for a trip. This trend will continue, making weather forecasting an integral part of our everyday decisions rather than a separate service. Back to Basics: The Future of Weather Apps Interestingly, the future of weather apps might involve a return to simplicity. Over time, as features and complexities were added, many apps lost their initial value. The essence of a good weather app lies in its ease of use and clarity of information. The focus should be on creating an intuitive, straightforward experience where users can access relevant information without hassle. A More Integrated, User-Friendly Future As weather forecasting technology advances, it will become more important in our daily decisions. The focus will be on delivering relevant, timely, and personalized information through various digital platforms and devices. The future of weather forecasting is about making predictions a natural part of our daily lives, helping us make better decisions and simplifying our routines. It’s an exciting time for meteorology, and we are only beginning to scratch the surface of what is possible. The power of weather forecasting is in how it helps make our lives safer and easier. As technology advances, we must harness this potential to create a future where we are always ready for the weather, not just reacting to it. Meteorology is rapidly evolving due to the development of new technology. Traditional forecasting methods, which relied heavily on physical instruments and historical data, are now being reshaped by integrating artificial intelligence (AI) and machine learning. This shift is expected to improve the accuracy and granularity of forecasts and fundamentally change how we interact with weather information. Bridging the Gap Between Short-Term and Long-Term Forecasts One of the most intriguing advancements in meteorology is the use of AI to bridge the gap between short-term and long-term forecasting. Traditionally, predicting weather events a few minutes to 24 hours ahead has been a challenge. Machine learning models are now being trained on vast datasets to predict weather patterns with unprecedented precision. These models can integrate real-time data, such as satellite imagery and user-generated reports, to produce hyperlocal forecasts. Being able to predict severe weather, like sudden storms or heavy rain, more quickly and accurately could be a big breakthrough soon. Enhancing User Experience Improvements in user experience must complement technological advancements. Integrating these new technologies in a way that is accessible and useful to the average user is one of the biggest challenges. Weather apps have become a primary source of information for many and must evolve to present complex data in a simple and actionable manner. The key to success lies in user-centric design. People need information quickly and in a format that is easy to understand, whether it’s for a sporting event, a morning commute, or planning a trip. The challenge is to personalize this information without overwhelming the user. Towards a More Adaptive Forecasting System Personalization is the future of weather apps. Imagine a weather app that knows you’re about to go for a run and gives you a tailored forecast for your route and time of day. Or one that alerts you to potential weather disruptions based on your calendar events. This level of personalization, however, raises concerns about data privacy and user consent. It’s crucial for companies to be transparent about how they use data, and users should have control over what they share. With responsible practices, personalized experiences can enhance usability without compromising privacy. The Role of AI in Meteorology The pioneers in using AI assistants in weather apps were Pixel phones (which analyze forecast data and offer summaries). Soon, AI hints appeared in the Rain Viewer weather app. I am sure that this is just the beginning of the trend, and soon, we will see the widespread use of AI in weather apps. Predicting the weather for the next decade is challenging due to the fast-paced technological advancements. AI is playing a crucial role in this evolution. The focus has shifted from hardware advancements, like new sensors, to developing sophisticated software that enhances weather prediction algorithms. By using big data, AI refines both short-term and long-term forecasts. Even though physical instruments might not change much, new software will soon make weather forecasts more accurate and reliable. Current forecasting models cannot process all the available data effectively, especially satellite information. There are five operational meteorological satellites, yet we can’t fully utilize their data. Most of it just gets stored as images without being immediately analyzed. The challenge is in processing all the available massive amounts of data from modern meteorological equipment in real time to provide hyperlocal forecasts. AI and machine learning can help fill this gap by automating data analysis and improving forecast accuracy. The breakthrough will be predicting exactly which houses a tornado will hit so that people can evacuate in time (now the Tornado Warning issues for large territories, and everyone knows that “there will be no tornado” and ignore it due to lack of such hyperlocal predictions). Simplifying User Experience Through Integration The future of weather forecasting is moving towards simplifying how information is delivered and processed. In the near future, you may walk past a digital billboard that shows you personalized weather information—like a forecast for your specific location—without needing to pull out your phone. The goal is to integrate weather data seamlessly into our daily environments. Dedicated weather apps may not be necessary, as the information will be readily accessible on any screen and personalized to our needs, current location, and activity. Weather Apps as Part of a Broader Ecosystem As standalone entities, weather apps may become redundant. People don’t need weather apps just to know the weather. They need them to make decisions, like whether to take an umbrella, call a taxi, or plan a vacation. Weather data is already being incorporated into various ecosystems. Modern cars can replan you driving route because of severe weather events, and smart home thermostats can adjust temperature settings based on forecasts, smart irrigation systems check not only how soil is wet, but also how much precipitation will come in the next 24 hours. Weather information is everywhere—cars, fridges, calendars, and even when buying tickets for a trip. This trend will continue, making weather forecasting an integral part of our everyday decisions rather than a separate service. Back to Basics: The Future of Weather Apps Interestingly, the future of weather apps might involve a return to simplicity. Over time, as features and complexities were added, many apps lost their initial value. The essence of a good weather app lies in its ease of use and clarity of information. The focus should be on creating an intuitive, straightforward experience where users can access relevant information without hassle. A More Integrated, User-Friendly Future As weather forecasting technology advances, it will become more important in our daily decisions. The focus will be on delivering relevant, timely, and personalized information through various digital platforms and devices. The future of weather forecasting is about making predictions a natural part of our daily lives, helping us make better decisions and simplifying our routines. It’s an exciting time for meteorology, and we are only beginning to scratch the surface of what is possible. The power of weather forecasting is in how it helps make our lives safer and easier. As technology advances, we must harness this potential to create a future where we are always ready for the weather, not just reacting to it.

What Could the Future of Weather Forecasting Look Like?

Modern mobile weather apps for iOS and Android keep you up-to-date with rain and extreme heat, and can be life-saving during hurricane season. In this article, I'll analyze how these apps have evolved and explore their role for people living in a country that suffers from the threat of tropical storms and their consequences every year. How did we live without weather apps, or where did it all start? Immediately after the launch of the App Store in July 2008, the first iOS weather apps began to appear on this platform. They provided basic weather information, such as temperature, forecasts, and radar images. The first weather apps were basic with elementary features. They did not have interactive maps in their functionality; the apps duplicated the weather forecast from national weather agencies. At that time, weather maps were just static images. Over time, interactive maps began to appear. This was a significant step in the evolution of weather mobile apps, transforming the category and setting a new standard. Gradually, indie developers, and then weather-related TV channels in the US began to create their weather apps using interactive maps. These maps allowed the user to navigate, zoom in and out, and see various indicators. On the App Store, software developers started experimenting with new devices and weather maps. That's how Dark Sky appeared (and was among the leaders in this category in the App Store until Apple acquired it). The second important point in the evolution of mobile weather apps is the use of radar data with interactive maps. The third important point in the development of weather apps is an opportunity to use the location of the device and adjust the forecast exactly to the user's request. That is, to receive alerts about a certain intensity of precipitation, high temperature, wind speed, etc. Such data personalization in weather apps and selecting information important to the user has made them more convenient for each target audience. How weather apps changed the game during hurricane seasons? Mobile weather apps have evolved a lot, raising awareness and allowing people to plan their time with the weather in mind. Apps, as a whole, have changed our world and our perception. This also applies to tracking the movement of hurricanes because now we have the weather forecast in our pocket. Of course, this includes the significant role of TV in weather forecasts and a well-established system of notifications from weather agencies. However, in contrast, mobile weather apps make information consumption more personalized and accessible. You can now track the weather in a few seconds and at a convenient time. Weather apps are the best trackers for natural phenomena like tornadoes (which occur very quickly and are less predictable). For example, citizens of Puerto Rico are the first to "welcome" tropical storms from the Atlantic Ocean due to their geographical location. For at least six months of the year, weather apps are the most viewed apps in this country because life can depend on them. A hurricane can do much more damage if no one knows about it. Ignorance, not the hurricane itself, is the biggest problem for coastal Americans. The evolution of weather apps Back in 2013, I was interested in developing a weather mobile app that offered users more than just a standard forecast from a weather station. Living in Florida, I know firsthand the importance of providing users with accurate long-term weather forecasts (up to 48 hours). It's crucial to warn users about tropical storms and hurricanes, even if these storms only slightly affect their area. For example, the Rain Viewer weather app my team and I created has detailed rainfall maps, and tracks storms in different regions. Of course, this is not a panacea for the user, but such data is a valuable tool during hurricane season. Last year over 700 weather apps appeared in the App Store, and three times as many in Google Play. The qualitative characteristics of forecasts include not only visualization of maps but also accurate data. For this, the app must have extensive radar coverage (today, most mobile weather apps use radar data). The bad news is that climate change makes yearly tropical storms and hurricanes more destructive. The good news is that modern mobile apps are rapidly evolving. Now they allow you to predict the effects of hurricanes, such as strong winds, intense precipitation, and extreme heat. Weather apps designed for weather enthusiasts, outdoor workers, commuters, vacation planners, and those who like to keep everything under control, including the weather, offer the following hurricane tracking tools: Hurricane path tracking considering the change in its strength (categories),
Description of the hurricane’s characteristics at its location,
Detailed hurricane timeline,
Alerts about the formation of a hurricane and its approach to the current location,
Current precipitation on the radar map and its forecast for the next 24-48 hours,
Wind speed forecast map or hourly wind speed readings to track hurricane force winds. Add to that the ability to: Track temperature on the map,
View weather forecast on home screen widgets without launching the app,
Get customizable push notifications about upcoming precipitation (you can set up the intensity of rainfall to be notified about),
Visualize precipitation and cloud cover in real time using radar and satellite maps. While weather apps still use core data from NOAA (National Oceanic and Atmospheric Administration) and NHC (National Hurricane Center), they have completely personalized weather alerts and made tropical storm tracking as quick and easy as possible. Radar data and map visualization have also brought the use of these apps to a new and more interactive level. Many users rely on weather apps for evacuation alerts during approaching hurricanes, even though this information is primarily provided by weather agencies. Modern mobile weather apps for iOS and Android keep you up-to-date with rain and extreme heat, and can be life-saving during hurricane season. In this article, I'll analyze how these apps have evolved and explore their role for people living in a country that suffers from the threat of tropical storms and their consequences every year. How did we live without weather apps, or where did it all start? Immediately after the launch of the App Store in July 2008, the first iOS weather apps began to appear on this platform. They provided basic weather information, such as temperature, forecasts, and radar images. The first weather apps were basic with elementary features. They did not have interactive maps in their functionality; the apps duplicated the weather forecast from national weather agencies. At that time, weather maps were just static images. Over time, interactive maps began to appear. This was a significant step in the evolution of weather mobile apps, transforming the category and setting a new standard. Gradually, indie developers, and then weather-related TV channels in the US began to create their weather apps using interactive maps. These maps allowed the user to navigate, zoom in and out, and see various indicators. This was a significant step in the evolution of weather mobile apps, transforming the category and setting a new standard. On the App Store, software developers started experimenting with new devices and weather maps. That's how Dark Sky appeared (and was among the leaders in this category in the App Store until Apple acquired it). The second important point in the evolution of mobile weather apps is the use of radar data with interactive maps. The second important point in the evolution of mobile weather apps is the use of radar data with interactive maps. The third important point in the development of weather apps is an opportunity to use the location of the device and adjust the forecast exactly to the user's request. That is, to receive alerts about a certain intensity of precipitation, high temperature, wind speed, etc. Such data personalization in weather apps and selecting information important to the user has made them more convenient for each target audience. How weather apps changed the game during hurricane seasons? Mobile weather apps have evolved a lot, raising awareness and allowing people to plan their time with the weather in mind. Apps, as a whole, have changed our world and our perception. This also applies to tracking the movement of hurricanes because now we have the weather forecast in our pocket. Of course, this includes the significant role of TV in weather forecasts and a well-established system of notifications from weather agencies. However, in contrast, mobile weather apps make information consumption more personalized and accessible. You can now track the weather in a few seconds and at a convenient time. Weather apps are the best trackers for natural phenomena like tornadoes (which occur very quickly and are less predictable). For example, citizens of Puerto Rico are the first to "welcome" tropical storms from the Atlantic Ocean due to their geographical location. For at least six months of the year, weather apps are the most viewed apps in this country because life can depend on them. A hurricane can do much more damage if no one knows about it. Ignorance, not the hurricane itself, is the biggest problem for coastal Americans. Ignorance, not the hurricane itself, is the biggest problem for coastal Americans. The evolution of weather apps Back in 2013, I was interested in developing a weather mobile app that offered users more than just a standard forecast from a weather station. Living in Florida, I know firsthand the importance of providing users with accurate long-term weather forecasts (up to 48 hours). It's crucial to warn users about tropical storms and hurricanes, even if these storms only slightly affect their area. For example, the Rain Viewer weather app my team and I created has detailed rainfall maps, and tracks storms in different regions. Of course, this is not a panacea for the user, but such data is a valuable tool during hurricane season. Last year over 700 weather apps appeared in the App Store, and three times as many in Google Play. The qualitative characteristics of forecasts include not only visualization of maps but also accurate data. For this, the app must have extensive radar coverage (today, most mobile weather apps use radar data). The bad news is that climate change makes yearly tropical storms and hurricanes more destructive. The good news is that modern mobile apps are rapidly evolving. Now they allow you to predict the effects of hurricanes, such as strong winds, intense precipitation, and extreme heat. Weather apps designed for weather enthusiasts, outdoor workers, commuters, vacation planners, and those who like to keep everything under control, including the weather, offer the following hurricane tracking tools: Hurricane path tracking considering the change in its strength (categories), Description of the hurricane’s characteristics at its location, Detailed hurricane timeline, Alerts about the formation of a hurricane and its approach to the current location, Current precipitation on the radar map and its forecast for the next 24-48 hours, Wind speed forecast map or hourly wind speed readings to track hurricane force winds. Hurricane path tracking considering the change in its strength (categories), Description of the hurricane’s characteristics at its location, Detailed hurricane timeline, Alerts about the formation of a hurricane and its approach to the current location, Current precipitation on the radar map and its forecast for the next 24-48 hours, Wind speed forecast map or hourly wind speed readings to track hurricane force winds. Add to that the ability to: Track temperature on the map, View weather forecast on home screen widgets without launching the app, Get customizable push notifications about upcoming precipitation (you can set up the intensity of rainfall to be notified about), Visualize precipitation and cloud cover in real time using radar and satellite maps. Track temperature on the map, View weather forecast on home screen widgets without launching the app, Get customizable push notifications about upcoming precipitation (you can set up the intensity of rainfall to be notified about), Visualize precipitation and cloud cover in real time using radar and satellite maps. While weather apps still use core data from NOAA (National Oceanic and Atmospheric Administration) and NHC (National Hurricane Center), they have completely personalized weather alerts and made tropical storm tracking as quick and easy as possible. Radar data and map visualization have also brought the use of these apps to a new and more interactive level. Many users rely on weather apps for evacuation alerts during approaching hurricanes, even though this information is primarily provided by weather agencies.

How Mobile Weather Apps Help You Survive the Hurricane Season

Weather tracking is a common requirement for many web applications, ranging from personal projects to commercial services. PHP, a server-side scripting language, can be a powerful tool for retrieving and displaying weather information on websites. In this beginner's guide, we'll explore how you can use PHP to track weather data and integrate it into your web applications. Getting Started with Weather APIs To track Weather using PHP, we'll need access to weather data from a reliable source. Fortunately, there are several weather APIs available that provide developers with access to real-time and forecast weather information. One popular weather API is OpenWeatherMap. It offers a wide range of weather data, including current weather conditions, forecasts, and historical weather data. To get started, you'll need to sign up for an API key, which you can obtain by registering on the OpenWeatherMap website. Retrieving Weather Data with PHP Once you have obtained your API key, you can use PHP to retrieve weather data from the OpenWeatherMap API. Here's a simple example of how you can make a request to the API and display the current weather conditions: phpCopy code<?php

// Replace 'YOUR_API_KEY' with your actual OpenWeatherMap API key
$apiKey = 'YOUR_API_KEY';

// City and country code for the location you want to retrieve weather data for
$city = 'London';
$countryCode = 'UK';

// API endpoint URL
$url = "http://api.openweathermap.org/data/2.5/weather?q={$city},{$countryCode}&appid={$apiKey}";

// Make a request to the API
$response = file_get_contents($url);

// Decode the JSON response
$data = json_decode($response, true);

// Check if the request was successful
if ($data && $data['cod'] === 200) {
    // Extract relevant weather information
    $weatherDescription = $data['weather'][0]['description'];
    $temperature = round($data['main']['temp'] - 273.15, 1); // Convert temperature from Kelvin to Celsius

    // Display weather information
    echo "<h2>Current Weather in {$city}, {$countryCode}</h2>";
    echo "<p><strong>Temperature:</strong> {$temperature} °C</p>";
    echo "<p><strong>Description:</strong> {$weatherDescription}</p>";
} else {
    // Display error message if request fails
    echo 'Failed to retrieve weather data.';
}

?> In this example, we construct a URL with the desired city and country code, along with our API key. We then make a request to the OpenWeatherMap API using and decode the JSON response using . Finally, we extract relevant from the response and display it on the webpage. file_get_contents() json_decode() weather information Enhancements and Considerations : It's important to implement error handling to gracefully handle situations where the API request fails or returns unexpected data. Error Handling : Consider implementing caching mechanisms to reduce the number of API requests and improve performance. Caching : You can enhance the display of weather information by incorporating CSS styling and additional details such as wind speed, humidity, and atmospheric pressure. Display Formatting : Make your weather-tracking application accessible to users worldwide by supporting multiple languages and units of measurement. Localization : Keep your API key secure by avoiding hardcoding it directly into your PHP files. Consider using environment variables or configuration files to store sensitive information. Security Conclusion Tracking weather using PHP can be a valuable addition to your web applications, providing users with up-to-date weather information for their desired locations. By leveraging weather APIs such as OpenWeatherMap and incorporating PHP to retrieve and display weather data, you can create dynamic and engaging experiences for your website visitors. With the foundational knowledge provided in this guide, you can explore further customization and integration possibilities to meet the specific needs of your projects.

How to Integrate Weather Tracking into Web Applications with PHP

As climate change plays havoc with weather forecasting across the globe, one company's long-term goal is to save thousands of lives by ensuring early and timely warnings of these severe weather events are reaching people across the globe. Rainbow Weather’s Mission Using artificial intelligence, is on a mission to provide meteorological forecasts at this crucial time when severe weather events cause billions of dollars in damages each year, as reported in USA Today. Rainbow Weather The planet was plagued by $42 billion worth of weather disasters in 2022 alone, and the all-time total damage wrought by weather disasters sits at $360 billion, with 39% of that total being insured damages, according to insurance broker Gallagher Re in its annual report. The global Weather Forecasts market was valued at $2.8 billion in 2022 and is projected to reach $4.6 billion by 2029, at a CAGR of 7.3% according to Stats Market Research. The World Meteorological Organization reports that "just 24 hours’ notice prior to an impending weather hazard can cut the ensuing damage by 30 percent, calling early warnings the “low-hanging fruit” of climate change adaptation because of their tenfold return on investment. " That is why investors have ploughed almost $1.5 million of funding into Rainbow Weather to date, to take on more established apps and forecasters who often fall short of accurate forecasts. As reported in Vox.com, Apple’s weather app is ‘pretty much useless’ as it cannot distinguish between ‘light rain’ and ‘rain’, that the percentages it spits out feel ‘bogus’, and to never trust it when it tells you what time the rain will stop. Apple bought the weather forecasting app Dark Sky three years ago, and it seems that the tech giant’s investment is not paying off yet. However, investment in Rainbow Weather is paying off. The first round included private investors as well as Venture Capitalist firms Kolos Ventures, Verras Capital, and Melnichek Investments. Furthermore, the company which trades as Rainbow Meteo Technologies and is based in Poland, has had further financial backing from the European Bank for Reconstruction and Development. What sets Rainbow Weather apart from Apple and other weather apps is its advanced AI modeling which delivers superior weather predictions based on a wide range of data sources (from satellites and meteorology stations to user reports and shared barometer data), beating the competition in both speed and precision. According to the company's internal analysis, Rainbow Weather has already achieved 3.5 percentage points of rain prediction precision over Apple Weather in the US (where Apple has the best coverage) and 9.5 percentage points over AccuWeather worldwide. Precision was calculated based on the share of rain accurately predicted within a 30-minute forecast window. To put these numbers into perspective, that means for every million weather app users, 35,000 to 95,000 more people will get correctly notified about an upcoming shower if they opt for Rainbow Weather. Words From the CEO Rainbow Weather CEO Alexander Matveenko shares the story of how the company was born: "I was swimming in Sri Lanka when suddenly it began to rain quite heavily. The downpour started to overwhelm me, and I wasn't sure I was going to make it to the shore. This experience made me realize how critical timely weather warnings can be. My co-founder Yuri Melnichek had a story of his own. He almost had his passport destroyed while on a hike when all weather apps failed to predict a sudden rain. After discussing both of these incidents we decided to create a new weather forecast tech based on machine learning algorithms that would be able to better predict highly variable local weather events." Their first product on the market is the mobile app for iOS and Android which provides super-accurate hyperlocal forecasts notifying users about upcoming rain and snowfalls, storms and hurricanes, and their movements on the map. The app is picking up steam with half a million downloads and great reviews from both users and the press. However, the most stellar review comes from Tatsiana Khakhelka, Associate for SME Finance & Development at the European Bank for Reconstruction and Development: “The EBRD’s Star Venture program targets promising start-ups to unleash their full potential. The EBRD supports them with world-class business advice and access to finance to strengthen their performance and accelerate their life cycle. Star Venture assigns the start-ups advisory projects tailored to their specific competencies and needs. Across 23 countries, Star Venture has nurtured 550+ early-stage companies, spanning diverse regions from Eastern Europe to the Western Balkans. One of these rising stars is Rainbow Meteo Technologies. After a rigorous selection process, this start-up company was shortlisted as a finalist in the EBRD’s Star Venture program supported by the European Union, and the Bank continues to provide advisory assistance. I am personally inspired by Rainbow’s product and the team’s professionalism and look forward to their future endeavors.” Disclaimer: Parts of this article have previously appeared on other news media platforms Also published here

Rainbow Weather, a Deep AI Driven Tech Startup, Aims to Take on the Tech Giants With Only $1.5m

রেইনবো ওয়েদার, একটি গভীর AI চালিত টেক স্টার্টআপ, মাত্র $1.5 মিলিয়ন দিয়ে টেক জায়ান্টদের সাথে লড়াই করার লক্ষ্য

Rainbow Weather, ein tiefgreifendes KI-gesteuertes Technologie-Startup, will es mit nur 1,5 Millionen US-Dollar mit den Technologiegiganten aufnehmen

Rainbow Weather, una startup tecnológica impulsada por la IA, tiene como objetivo enfrentarse a los gigantes tecnológicos con solo 1,5 millones de dólares

Rainbow Weather, une startup technologique basée sur l'IA profonde, vise à affronter les géants de la technologie avec seulement 1,5 million de dollars

रेनबो वेदर, एक डीप एआई संचालित टेक स्टार्टअप, जिसका लक्ष्य केवल $1.5m के साथ टेक दिग्गजों से मुकाबला करना है

ディープ AI 主導のテクノロジースタートアップ、Rainbow Weather がわずか 150 万ドルでテクノロジー巨人に対抗することを目指す

딥 AI 기반 기술 스타트업 Rainbow Weather, 단 150만 달러로 거대 기술 기업을 상대하는 것을 목표로 함

Rainbow Weather, uma startup de tecnologia baseada em IA profunda, pretende enfrentar os gigantes da tecnologia com apenas US$ 1,5 milhão

Rainbow Weather, технологический стартап, основанный на искусственном интеллекте, стремится бросить вызов технологическим гигантам всего за 1,5 миллиона долларов

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