Our company, founded in Serbia in 2002, initially focused on various projects, with publishing weather information as a secondary activity. A few years later, a severe rainfall and subsequent flood marked a turning point. The flood destroyed our offices and, most devastatingly, all personal photos, effectively erasing my personal history.
This event led to an obsession to provide timely, easy-to-understand, accurate, and reliable weather information and assist in preparing for the meteorological curveballs that Mother Earth daily throws at us.
Over 20 years later, our company operates internationally, and the battle with the gods of the skies continues. And each time we save someone's holiday, wedding, or BBQ from being spoiled, or keep one more person from getting caught in the rain without an umbrella, we consider it another divine victory. Take that, Zeus!
For details about our team and headquarters, please visit the contact page.
We are providing a free weather WordPress widget, designed for webmasters to effortlessly integrate weather forecasts into their websites.
The Weather Atlas Widget is the leading weather widget on WordPress.org, with over 10,000 active installations. Covering more than 700,000 locations across 238 countries, and over 50,000 locations in the U.S., the widget allows websites to stylishly display the most accurate and relevant numerical and textual weather information to their visitors.
Weather forecasting & Climate
Daily, around the globe, people depend on weather forecasts to make decisions that affect their comfort, wellness, and security. How hot will it be today? What is the forecast for tomorrow? Will it rain over the weekend? What time will the thunderstorm hit? How long will the heatwave last? Is there a cold front on the way? Are we in for a dry spell? Will the weather be sunny for the upcoming outdoor event? Is it safe to go boating in this weather? Will it rain on my wedding day in September? What is the skiing weather forecast for December?
The prediction of future atmospheric conditions, termed as a weather forecasts, hinges on thorough analysis of meteorological data, encompassing elements including temperature, humidity, velocity of wind, and potential precipitation. Due to the chaotic nature of the atmosphere and the constraints of observational data and numerical models, weather forecasting inherently involves a degree of uncertainty. Consequently, as the lead time extends, forecast accuracy tends to wane.
Short-Term Forecasts: These prognostications target a temporal range of a few hours up to two days. The nature of short-term forecasts ensures relatively high accuracy, offering a detailed view of imminent local weather patterns. Medium-Range Forecasts: With a scope extending from three to ten days into the future, medium-range forecasts deliver a broader perspective on anticipated weather formations. However, the accuracy of these predictions tends to diminish as the forecast timeline elongates. Long-Range Forecasts: Projecting atmospheric conditions spanning several weeks to months, long-range forecasts sacrifice precision for the benefit of discerning overarching trends and seasonal weather variations.
Simply put, weather forecasting is uncertain because of the chaotic atmosphere and limitations of data and models. As predictions cover longer periods, their accuracy decreases.
Generally, use this as a guideline:
1-2 days (24-48 hours) - Weather forecasts are highly accurate within this short-term period, with a certainty level of about 85-90%. Most meteorological models can predict weather patterns reliably during this time frame.
3-5 days - Forecasts remain fairly reliable, but their certainty begins to decline slightly. Accuracy typically falls around 70-80% for this period.
6-10 days - Forecast accuracy decreases further, often dropping to 50-60%. Predictions for this period become less dependable due to the increasing complexity and variability of atmospheric conditions.
10 days and beyond - Forecasts for this time frame are generally much less reliable, with accuracy frequently below 50%. Long-term weather predictions are challenging because of the chaotic nature of the atmosphere and the limitations of current meteorological models.
Climate refers to the consistent patterns of atmospheric elements including temperature, humidity, wind, and precipitation in a specific region. It signifies the averaged weather parameters over a significant span, typically 30 years, molded by factors involving latitude, elevation, proximity to large water bodies, and currents within these bodies.
Simplified essence: Weather forecasts and climate are related but distinct. Weather forecasts predict short-term atmospheric conditions (hours to weeks), while climate represents the long-term average of weather patterns over extended periods.
Sources
What is Weather Data?
Weather data involves the collection and analysis of meteorological elements, providing critical information about the current state and future changes in weather patterns. Timely provided and accurate data is essential for meteorologists, researchers, and organizations to understand and forecast weather conditions, aiding in better planning for daily tasks and decision-making across sectors such as disaster management, agriculture and transportation.
How is Data Collected and Measured?
Weather data is gathered and measured using a variety of modern and traditional methods, leveraging advanced technology for accuracy.
Common collection methods include:
Thermometers for temperature
Barometers for atmospheric pressure
Hygrometers for humidity
Wind vanes and anemometers for wind speed and direction
Radar systems to track rain cloud movement
Transmissometers for atmospheric visibility
Weather satellites to identify clouds, snow cover, wildfires, ocean temperatures, and tidal patterns
Radiosondes (balloons) to measure atmospheric characteristics as they move through the air
The data is constantly updated from sources like satellites, airport observation stations, and drone sensors. Sophisticated technology and weather models ensure increasingly accurate and detailed information.
What Are the Types of Weather Data?
Historical Weather Data vs. Real-time Weather Data Historical weather data offers insights into weather patterns and conditions from previous days, months, years, or decades. Real-time weather data provides the most current information on weather conditions as they occur, allowing for tracking hour-to-hour or day-to-day changes.
Global Weather Data vs. Local Weather Data Global weather data covers weather and climate patterns for entire continents or the planet. Examples include tracking global temperatures or measuring wind patterns between continents. Local weather data provides information about weather conditions in a specific localized area, such as a city. Checking your city's daily forecast is an example of using local weather data.
What Types of Weather Attributes Can Be Presented?
Temperature: Average, median, minimum/maximum temperature, in degrees Celsius or Fahrenheit.
Air Pressure: Millibars or mm/inches of mercury, air current.
Solar Radiation: Typically measured in watts per square meter (w/m²).
Severe Weather Risks and Events:Hurricanes, tornadoes, storms, floods, wildfires, and other extreme weather phenomena.
What Are Well-Known Sources of Weather Data?
National Oceanic and Atmospheric Administration (NOAA): A primary source of weather data in the United States.
European Centre for Medium-Range Weather Forecasts (ECMWF): Provides comprehensive weather data and forecasts for Europe and beyond.
Private Weather Companies: Companies such as The Weather Channel and AccuWeather offer detailed weather information and forecasts.
Crowd-Sourced Weather Data: Projects that collect weather data from the public.
πάντα ῥεῖ / Panta Rhei / Everything flows
Weather Forecasting Accuracy
Numerous scientists work tirelessly to create the most reliable weather forecasting models. These predictions are not mere guesswork; they are based on extensive analysis of vast amounts of weather data Organizations like the National Weather Service (NWS), AccuWeather, and The Weather Channel utilize advanced models that simulate atmospheric conditions using billions of data points. Processing this data to generate accurate forecasts requires massive computing power.
Different prediction models produce different forecasts, and there will always be discrepancies between predictions and actual outcomes. The primary objective in weather forecasting is to achieve the highest accuracy (lowest error rate). By comparing predictions with actual outcomes, the precision of these weather models can be assessed.
An independent Global and Regional Weather Forecast Accuracy Overview, 2017-2022 study by ForecastWatch identified The Weather Channel as the world's most accurate forecaster overall. The study evaluated 250 million forecasts over six years, covering 2,182 global locations across eight regions, 23 different weather providers, and 84 accuracy metrics for precipitation, temperature, cloud cover, and wind variables.
Key findings of the report include:
The Weather Company is the world's most accurate weather forecaster overall among the providers studied.
The Weather Company was over three times more likely to be the most accurate forecaster than any other provider studied.
The accuracy gap between The Weather Channel and the next best provider has increased, from being twice as likely to be the most accurate in 2017 to over three times more likely in 2022.
The Weather Channel was the most accurate provider in each region studied, including the U.S., Canada, Central America, South America, Europe, Africa, the Middle East, and Asia-Pacific.
Where Does Weather U.S. Get Its Data From?
Are we one of the major providers of weather data? Unfortunately, we are not one of the "big guys". Nevertheless, the ability to selectively choose and cross-reference multiple sources of weather data simultaneously certainly has its advantages.
Current weather - for hyperlocal forecasting (depending on availability for a location)
Monthly (Historical) Weather Data The process for retrieving historical weather data involves sourcing information from multiple historical weather data providers, preferably from national weather services (list below). As a fallback historical weather data comes from the U.S. NCEI's Climate Data Online. The minimum required span for historical data is 10 years, though most data covers at least 30 years. Efforts are ongoing year-by-year to retrieve and update historical data with the latest information.