Current conditions available for approximately 8000 locations worldwide. Updated every 30-180 minutes, or as updated by the reporting station.

Features: - Current conditions descriptions - Temperature - Wind speed - Wind direction - Humidity - Visibility - Dew point - Comfort level - Barometric pressure - Barometric tendency - Precipitation reporting

Weather Forecast System Market Outlook

The global weather forecast system market size, which was valued at approximately $3.5 billion in 2023, is anticipated to reach around $6.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 8% during this forecast period. This significant growth is driven by the increasing demand for accurate and timely weather forecasting services across various sectors. The rising adoption of advanced technologies such as artificial intelligence and machine learning to enhance the precision of weather prediction models is a major factor propelling the market forward. Furthermore, the growing need for sophisticated weather forecasting systems to mitigate the adverse impacts of climate change is further fueling market expansion.

One key growth factor in the weather forecast system market is the increasing frequency and severity of extreme weather events worldwide. These events, such as hurricanes, floods, and heatwaves, are driving governments and organizations to invest heavily in advanced forecasting systems to improve preparation and response efforts. The ability to predict such events with greater accuracy and lead time can significantly reduce their impact on human lives, infrastructure, and the economy. As climate change continues to influence weather patterns, the role of weather forecast systems becomes even more crucial, leading to increased investment and development in this sector.

Technological advancements play a pivotal role in the expansion of the weather forecast system market. The integration of cutting-edge technologies like big data analytics, machine learning, and the Internet of Things (IoT) has revolutionized the way weather data is collected, analyzed, and disseminated. These technologies enable the processing of vast datasets in real-time, resulting in more accurate and reliable forecasts. Additionally, the use of satellite technology and high-performance computing has enhanced the ability to monitor and predict weather conditions with unprecedented precision, driving the demand for advanced forecasting systems across various industries.

Another significant growth driver is the increasing demand for weather forecast systems from sectors such as agriculture, energy, and transportation. In agriculture, accurate weather forecasts are essential for optimizing planting and harvesting schedules, managing water resources, and reducing crop losses due to adverse weather conditions. Similarly, the energy sector relies on weather forecasts to manage the supply and demand of energy efficiently, especially for renewable energy sources like wind and solar power. In transportation, accurate weather forecasts are crucial for ensuring the safety and efficiency of operations, particularly in aviation and maritime industries. As these sectors continue to grow, the demand for advanced weather prediction systems is expected to rise, contributing to the market's expansion.

Regionally, the weather forecast system market exhibits varying growth patterns across different geographies. North America holds a significant share of the market due to the presence of established infrastructure and the early adoption of advanced technologies. The region's proactive approach to disaster management and climate change mitigation further facilitates market growth. Europe follows closely, with significant investments being made in upgrading weather forecasting capabilities to meet the region's environmental and economic challenges. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, increasing investments in infrastructure development, and heightened awareness of climate-related risks. Meanwhile, Latin America and the Middle East & Africa are also anticipated to experience noteworthy growth, albeit at a relatively slower pace.

Component Analysis

The weather forecast system market can be segmented by component into software, hardware, and services, each playing a crucial role in the overall system's functionality and efficacy. The software component is integral to the market, comprising advanced algorithms and models used for data analysis and prediction. As weather forecast systems become more sophisticated, the demand for customized software solutions that can provide accurate and reliable forecasts increases. Software development in this segment focuses on enhancing user interfaces, improving data visualization, and integrating real-time data processing capabilities to offer more actionable insights to end-users across various sectors.

H

Map InformationThis nowCOAST time-enabled map service provides maps depicting NWS gridded forecasts of the following selected sensible surface weather variables or elements: air temperature (including daily maximum and minimum), apparent air temperature, dew point temperature, relative humidity, wind velocity, wind speed, wind gust, total sky cover, and significant wave height for the next 6-7 days. Additional forecast maps are available for 6-hr quantitative precipitation (QPF), 6-hr quantitative snowfall, and 12-hr probability of precipitation. These NWS forecasts are from the National Digital Forecast Database (NDFD) at a 2.5 km horizontal spatial resolution. Surface is defined as 10 m (33 feet) above ground level (AGL) for wind variables and 2 m (5.5 ft) AGL for air temperature, dew point temperature, and relative humidity variables. The forecasts extend out to 7 days from 0000 UTC on Day 1 (current day). The forecasts are updated in the nowCOAST map service four times per day. For more detailed information about the update schedule, please see: https://new.nowcoast.noaa.gov/help/#section=updatescheduleThe forecast projection availability times listed below are generally accurate, however forecast interval and forecast horizon vary by region and variable. For the most up-to-date information, please see https://graphical.weather.gov/docs/datamanagement.php.The forecasts of the air, apparent, and dew point temperatures are displayed using different colors at 2 degree Fahrenheit increments from -30 to 130 degrees F in order to use the same color legend throughout the year for the United States. This is the same color scale used for displaying the NDFD maximum and minimum air temperature forecasts. Air and dew point temperature forecasts are available every hour out to +36 hours from forecast issuance time, at 3-hour intervals from +36 to +72 hours, and at 6-hour intervals from +72 to +168 hours (7 days). Maximum and minimum air temperature forecasts are each available every 24 hours out to +168 hours (7 days) from 0000 UTC on Day 1 (current day).The relative humidity (RH) forecasts are depicted using different colors for every 5-percent interval. The increment and color scale used to display the RH forecasts were developed to highlight NWS local fire weather watch/red flag warning RH criteria at the low end (e.g. 15, 25, & 35% thresholds) and important high end RH thresholds for other users (e.g. agricultural producers) such as 95%. The RH forecasts are available every hour out to +36 hours from 0000 UTC on Day 1 (current day), at 3-hour intervals from +36 to +72 hours, and at 6-hour intervals from +72 to +168 hours (7 days).The 6-hr total precipitation amount forecasts or QPFs are symbolized using different colors at 0.01, 0.10, 0.25 inch intervals, at 1/4 inch intervals up to 4.0 (e.g. 0.50, 0.75, 1.00, 1.25, etc.), at 1-inch intervals from 4 to 10 inches and then at 2-inch intervals up to 14 inches. The increments from 0.01 to 1.00 or 2.00 inches are similar to what are used on NCEP/Weather Prediction Center's QPF products and the NWS River Forecast Center (RFC) daily precipitation analysis. Precipitation forecasts are available for each 6-hour period out to +72 hours (3 days) from 0000 UTC on Day 1 (current day).The 6-hr total snowfall amount forecasts are depicted using different colors at 1-inch intervals for snowfall greater than 0.01 inches. Snowfall forecasts are available for each 6-hour period out to +48 hours (2 days) from 0000 UTC on Day 1 (current day).The 12-hr probability of precipitation (PoP) forecasts are displayed for probabilities over 10 percent using different colors at 10, 20, 30, 60, and 85+ percent. The probability of precipitation forecasts are available for each 12-hour period out to +72 hours (3 days) from 0000 UTC on Day 1 (current day).The wind speed and wind gust forecasts are depicted using different colors at 5 knots increment up to 115 knots. The legend includes tick marks for both knots and miles per hour. The same color scale is used for displaying the RTMA surface wind speed forecasts. The wind velocity is depicted by curved wind barbs along streamlines. The direction of the wind is indicated with an arrowhead on the wind barb. The flags on the wind barb are the standard meteorological convention in units of knots. The wind speed and wind velocity forecasts are available hourly out to +36 hours from 00:00 UTC on Day 1 (current day), at 3-hour intervals out to +72 hours, and at 6-hour intervals from +72 to +168 hours (7 days). The wind gust forecasts are available hourly out to +36 hours from 0000 UTC on Day 1 (current day) and at 3-hour intervals out to +72 hours (3 days).The total sky cover forecasts are displayed using progressively darker shades of gray for 10, 30, 60, and 80+ percentage values. Sky cover values under 10 percent are shown as transparent. The sky cover forecasts are available for each hour out to +36 hours from 0000 UTC on Day 1 (current day), every 3 hours from +36 to +72 hours, and every 6 hours from +72 to +168 hours (7 days).The significant wave height forecasts are symbolized with different colors at 1-foot intervals up to 20 feet and at 5-foot intervals from 20 feet to 35+ feet. The significant wave height forecasts are available for each hour out to +36 hours from 0000 UTC on Day 1 (current day), every 3 hours from +36 to +72 hours, and every 6 hours from +72 to +144 hours (6 days).Background InformationThe NDFD is a seamless composite or mosaic of gridded forecasts from individual NWS Weather Forecast Offices (WFOs) from around the U.S. as well as the NCEP/Ocean Prediction Center and National Hurricane Center/TAFB. NDFD has a spatial resolution of 2.5 km. The 2.5km resolution NDFD forecasts are presently experimental, but are scheduled to become operational in May/June 2014. The time resolution of forecast projections varies by variable (element) based on user needs, forecast skill, and forecaster workload. Each WFO prepares gridded NDFD forecasts for their specific geographic area of responsibility. When these locally generated forecasts are merged into a national mosaic, occasionally areas of discontinuity will be evident. Staff at NWS forecast offices attempt to resolve discontinuities along the boundaries of the forecasts by coordinating with forecasters at surrounding WFOs and using workstation forecast tools that identify and resolve some of these differences. The NWS is making progress in this area, and recognizes that this is a significant issue in which improvements are still needed. The NDFD was developed by NWS Meteorological Development Laboratory.As mentioned above, a curved wind barb with an arrow head is used to display the wind velocity forecasts instead of the traditional wind barb. The curved wind barb was developed and evaluated at the Data Visualization Laboratory of the NOAA-UNH Joint Hydrographic Center/Center for Coastal and Ocean Mapping (Ware et al., 2014). The curved wind barb combines the best features of the wind barb, that it displays speed in a readable form, with the best features of the streamlines which shows wind patterns. The arrow head helps to convey the flow direction.Time InformationThis map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time information about the service:Issue a returnUpdates=true request for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of this page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following:validtime: Valid timestamp.starttime: Display start time.endtime: Display end time.reftime: Reference time (sometimes reffered to as issuance time, cycle time, or initialization time).projmins: Number of minutes from reference time to valid time.desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match.desigprojmins: Number of minutes from designated reference time to valid time.Query the nowCOAST LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available "time stops" (i.e. "valid times"), individual timestamps, or the valid time of a layer's latest available data (i.e. "Product Time"). For more information about the LayerInfo web service, including examples of various types of

Weather is among the most critical environmental variables influencing infrastructure, agriculture, energy, health, and climate strategy. Among all metrics, temperature and humidity form the baseline of accurate environmental modeling, building performance, and ESG reporting. Ambios provides high-quality Global Weather Data on real-time and historical temperature and humidity measurements. Sourced from over 3,000+ first-party sensors operating across 20 countries, our decentralized network ensures transparent, tamper-proof data with hyperlocal accuracy and high update frequency.

-Real-time temperature and humidity data updated every 15 minutes -Historical datasets with global coverage -100% first-party sensor data from a decentralized infrastructure -Compatible with ESG systems, climate models, and IoT platforms

Use cases include: -Climate risk and environmental impact assessments -Smart building energy efficiency and HVAC performance -Agricultural planning and weather-responsive irrigation -Supply chain risk modeling and operational forecasting -Urban microclimate monitoring and resilience planning -Scientific research, academic studies, and digital twins

Built on DePIN (Decentralized Physical Infrastructure Network) architecture, Ambios ensures complete traceability, verifiability, and scale. Our weather data delivers the transparency and precision that enterprises, governments, and researchers need for real-world decisions in real-time. Whether powering climate dashboards, optimizing building systems, or modeling regional weather impacts, Ambios Global Weather Data gives you trusted temperature and humidity insights globally and on demand.

Weather Monitoring System Market Outlook

The global Weather Monitoring System market size is projected to grow from USD 2.8 billion in 2023 to approximately USD 5.4 billion by 2032, exhibiting a healthy compound annual growth rate (CAGR) of 7.5% during the forecast period. This growth is primarily driven by the increasing demand for precise and timely weather forecasting to mitigate the adverse impacts of climate change and extreme weather events. As the world faces more frequent and severe weather disruptions, the need for advanced weather monitoring technology becomes critical in safeguarding lives, optimizing resource management, and ensuring economic stability.

One of the major growth factors in this market is the surging need for advanced predictive analytics in agriculture. With climate change affecting weather patterns globally, accurate weather forecasting has become indispensable for farmers to make informed decisions regarding crop planting, irrigation, and harvesting. Advanced weather monitoring systems provide real-time data that help mitigate risks related to unpredictable weather, thereby optimizing agricultural output and contributing significantly to the global food supply chain. This demand is further bolstered by governmental and non-governmental initiatives to improve agricultural yield through technology adoption, particularly in developing regions.

Another critical growth driver is the increasing integration of weather monitoring systems within the transportation and logistics sectors. Weather conditions significantly impact the efficiency and safety of operations in these sectors. By leveraging accurate weather data, companies can optimize their logistical operations, enhance safety measures, and reduce unexpected delays. This adoption is particularly vital for air and maritime transportation, where weather conditions can lead to substantial risks. The integration of weather monitoring technologies not only ensures smoother operations but also translates into cost savings and improved customer satisfaction, propelling further market growth.

The energy sector also plays a pivotal role in the expansion of the weather monitoring system market. With a growing emphasis on renewable energy sources, such as wind and solar, precise weather forecasting has become essential for efficient energy production. Weather monitoring systems enable energy producers to predict weather patterns that directly affect energy generation, thus optimizing the supply and reducing reliance on traditional power sources. This capability is increasingly critical as more nations commit to reducing carbon footprints and enhancing energy security through sustainable practices.

Meteorological Equipment plays a crucial role in the advancement of weather monitoring systems, providing the necessary tools to gather accurate and comprehensive data. These instruments, including anemometers, barometers, and hygrometers, are essential for measuring various atmospheric parameters. The precision and reliability of meteorological equipment directly influence the quality of weather forecasts, enabling better preparedness for adverse weather conditions. As technology continues to evolve, the development of more sophisticated and durable meteorological equipment is expected to drive further innovations in the weather monitoring industry. This progress not only enhances the accuracy of weather predictions but also supports sectors such as agriculture, transportation, and energy in optimizing their operations based on reliable weather data.

Regionally, North America currently dominates the global weather monitoring system market, with a significant share attributed to technological advancements and substantial investments in meteorological infrastructure. The region's commitment to addressing climate change challenges and ensuring public safety through enhanced weather prediction capabilities supports this leading position. Meanwhile, Asia Pacific is expected to exhibit the highest growth rate during the forecast period, driven by rapid urbanization, industrial expansion, and increased vulnerability to climate-induced weather anomalies. Government initiatives to enhance disaster preparedness and response mechanisms further contribute to the robust growth of the market in this region.

Component Analysis

The market for weather monitoring systems is segmented into hardware, software, and services, each offering unique contributions to the overall system functionality. T

Grid Weather Forecast Integration Platform Market Outlook

According to the latest research conducted in 2025, the global Grid Weather Forecast Integration Platform market size is valued at USD 1.54 billion in 2024. The market is experiencing robust expansion, with a recorded CAGR of 12.8% from 2025 to 2033. By 2033, the market is forecasted to reach a value of USD 4.09 billion, driven by the increasing necessity for reliable grid operations amid rising integration of renewable energy sources and the growing impact of climate variability on power systems. The primary growth factor is the urgent need for real-time weather data integration to optimize grid performance, reduce outages, and enhance the stability of energy supply in both developed and developing economies.

One of the most significant growth factors for the Grid Weather Forecast Integration Platform market is the rapid proliferation of renewable energy sources such as solar and wind power. These energy sources are inherently variable and highly sensitive to weather conditions. As utilities and grid operators strive to achieve decarbonization targets and support the global energy transition, there is a critical need for advanced platforms that can seamlessly integrate weather forecasts with grid management systems. By leveraging predictive analytics and machine learning, these platforms enable grid operators to anticipate fluctuations in power generation, adjust load balancing strategies, and mitigate the risks of over- or under-supply. This technological evolution is fostering a paradigm shift in grid management, making weather forecast integration platforms indispensable for modern energy infrastructure.

Another pivotal driver is the increasing frequency and severity of extreme weather events, which have exposed vulnerabilities in traditional grid systems worldwide. Hurricanes, heatwaves, and unexpected cold snaps can severely disrupt electricity supply, leading to costly outages and compromised grid reliability. The Grid Weather Forecast Integration Platform market is responding to these challenges by providing real-time situational awareness and actionable insights for grid operators. These platforms facilitate proactive maintenance, dynamic re-routing of power flows, and rapid response to adverse weather conditions. As a result, utilities are able to minimize downtime, reduce operational costs, and safeguard critical assets, thereby enhancing the resilience of power networks against climate-induced disruptions.

Additionally, regulatory mandates and policy incentives are accelerating the adoption of Grid Weather Forecast Integration Platforms across various regions. Governments and regulatory bodies are increasingly recognizing the importance of grid modernization and resilience, especially in the context of sustainable energy goals. Financial incentives, grants, and policy frameworks are encouraging utilities and independent power producers to invest in advanced forecasting and integration technologies. This has led to a surge in research and development activities, strategic partnerships, and cross-sector collaborations, further propelling market growth. The synergy between regulatory support and technological innovation is expected to sustain the upward trajectory of the market throughout the forecast period.

From a regional perspective, North America and Europe are leading the adoption curve due to their advanced energy infrastructure and aggressive renewable integration targets. However, the Asia Pacific region is emerging as a high-growth market, fueled by massive investments in grid modernization and the rapid expansion of renewable energy capacity. Countries such as China, India, and Japan are prioritizing smart grid solutions and weather-driven grid optimization to cope with escalating energy demands and environmental challenges. The Middle East & Africa and Latin America are also witnessing gradual adoption, supported by international investments and regional policy reforms. Overall, the global landscape for Grid Weather Forecast Integration Platforms is characterized by dynamic regional trends and a growing emphasis on digital transformation in the energy sector.

This data is measurements for temperature inside building, together with measurements from HVAC system (radiator based) and reading from outside weather station.

Data was used for estimation of parameters of thermodynamic model of building and HVAC system. Outside temperature, heat-flow from HVAC system and Solar radiation were used for inputs, while inside temperatures are outputs of model.

This is industrial building with offices and production space.

Office part is in two-floors and is equipped with separate heating system based on floor-heating. Rooms are equipped with independent thermostats and there are no measurements in them. During winter, temperature in offices is set between 20 and 24 °C, depending if office is occupied.

Production space is separated in four zones: Assembly (largest, 24x16m), Entry (8x8), Mechanical preparation (8x8) and Warehouse (8x16).

Production is space is made from concrete construction with structural insulating panel (SIP). There are several windows on all spaces, made from triple-glazing. There are one small door for personnel and one big doors for loading/unloading, both on Entry outside wall. There are several doors between space, which are closed most of the time. There is one small door connecting Assembly with office part of the building.

HVAC system for production consist of one 35kW hot-water boiler. There is a pump and three-way valve that mixes water to setpoint temperature (usually around 60 °C). Water is then supplied to ceiling radiators in spaces. For each space, there is an open-close valve, controlled by PLC (simple hysteresis ON-OFF controller).

Outside, there is weather-station Davies, model Vantage Pro2.

Data is following: Date Time Outside temperature (HVAC) °C (this is sensor on one wall without cover, so it is affected by Sun and is not reliable) Assembly temperature °C Entry temperature °C Mechanical preparation temperature °C Warehouse temperature °C Flow m3/h (of supply water) Supply water temperature °C Return water temperature °C Assembly valve state (0 is closed, 1 is open) Entry valve state Mechanical preparation valve state Warehouse valve state Assembly setpoint temperature °C Entry setpoint temperature °C Mechanical preparation setpoint temperature °C Warehouse setpoint temperature °C Supply water setpoint temperature °C Outside temperature (weather station) °C (this is reliable outside temperature) Outside humidity % Wind speed m/s Solar radiation W/m2

Measurements are in 15 minute interval (if it was more frequent, it was averaged to 15 minutes).

There are two breaks in data, at 11.1.2019 16:15:00 and at 24.1.2019 17:30:00.

Data also include thermodynamic model that was used with this data (initial model and resulting optimized model).

Projected supply chain shocks due to extreme weather events measured in annual percentage change in a country-sector's export activity compared to the baseline period

The weather station on the campus of Loughborough University, in the East Midlands of the UK, had fallen into disuse and disrepair by the mid-2000s, but in 2007 the availability of infrastructure funding made it possible to re-establish regular weather observation with new equipment. The meteorological dataset subsequently collected at this facility between 2008 and 2021 is archived here. The dataset comes as fourteen Excel (.xlsx) files of annual data, with explanatory notes in each.Site descriptionThe campus weather station is located at latitude 52.7632°, longitude -1.235° and 68 m a.s.l., in a dedicated paddock on a green space near the centre-east boundary of the campus. A cabin, which houses power and network points, sits 10 m to the northeast of the main meteorological instrument tower. The paddock is otherwise mostly open on an arc from the northwest to the northeast, but on the other sides there are fruit trees (mainly varieties of prunus domestica) at distances of 13–16 m, forming part of the university's "Fruit Routes" biodiversity initiative.Data collectionInstruments were fixed to a 3 m lattice mast which is concreted into the ground in the centre of the paddock described above. Up to late July 2013, the instruments were controlled by a solar-charged, battery-powered Campbell Scientific CR1000 data logger, and periodically manually downloaded. From early November 2013, this logger was replaced with a Campbell Scientific CR3000, run from the mains power supply from the cabin and connected to the campus network by ethernet. At the same time, the station's Young 01503 Wind Monitor was replaced by a Gill WindSonic ultrasonic anemometer. This combination remained in place for the rest of the measurement period described here. Frustratingly, the CS215 temperature/relative humidity sensor failed shortly before the peak of the 2018 heatwave, and had to be replaced with another CS215. Likewise, the ARG100 rain gauge was replaced in 2011 and 2016. The main cause of data gaps is the unreliable power supply from the cabin, particularly in 2013 and 2021 (the latter leading to the complete replacement of the cabin and all other equipment). Furthermore, even though the post-2013 CR3000 logger had a backup battery, it sometimes failed to restart after mains power was lost, yielding data gaps until it was manually restarted. Nevertheless, out of 136 instrument-years deployment, only 36 are less than 90% complete, and 21 less than 75% complete.Data processingData retrieved manually or downloaded remotely were filtered for invalid measurements. The 15-minute data were then processed to daily and monthly values, using the pivot table function in Microsoft Excel. Most variables could be output simply as midnight-to-midnight daily means (e.g. solar and net radiation, wind speed). However, certain variables needed to be referred to the UK and Ireland standard ‘Climatological Day’ (Burt, 2012:272), 0900-0900: namely, air temperature minimum and maximum, plus rainfall total. The procedure for this follows Burt (2012; https://www.measuringtheweather.net/) and requires the insertion of additional date columns into the spreadsheet, to define two further, separate ‘Climate Dates’ for maximum temperature and rainfall total (the 24 hours commencing at 0900 on the date given, ‘ClimateDateMax’), and for minimum temperatures (24 hours ending at 0900 on the date given, ‘ClimateDateMin’). For the archived data, in the spreadsheet tabs labelled ‘Output - Daily 09-09 minima’, the pivot table function derives daily minimum temperatures by the correct 0900-0900 date, given by the ClimateDateMin variable. Similarly, in the tabs labelled ‘Output - Daily 09-09 maxima’, the pivot table function derives daily maximum temperatures and daily rainfall totals by the correct 0900-0900 date, given by the ClimateDateMax variable. Then in the tabs labelled ‘Output - Daily 00-00 means’, variables with midnight-to-midnight means use the unmodified date variable. To take into account the effect of missing data, the tab ‘Completeness’ again uses a pivot table to count the numbers of daily and monthly observations where the 15-minute data are not at least 99.99% complete. Values are only entered into the ‘Daily data’ tab of the archived spreadsheets where 15-minute data are at least 75% complete; values are only entered into ‘Monthly data’ tabs where daily data are at least 75% complete.Wind directions are particularly important in UK meteorology because they indicate the origin of air masses with potentially contrasting characteristics. But wind directions are not averaged in the same way as other variables, as they are measured on a circular scale. Instead, 15-minute wind direction data in degrees are converted to 16 compass points (the formula is included in the spreadsheets), and a pivot table is used to summarise these into wind speed categories, giving the frequency and strength of winds by compass point.In order to evaluate the reliability of the collected dataset, it was compared to equivalent variables from the HadUK-Grid dataset (Hollis et al., 2019). HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations, which have been interpolated from meteorological station data onto a uniform grid to provide coherent coverage across the UK at 1 km x 1 km resolution. Daily and monthly air temperature and rainfall variables from the HadUK-Grid v1.1.0.0 Met Office (2022) were downloaded from the Centre for Environmental Data Analysis (CEDA) archive (https://catalogue.ceda.ac.uk/uuid/bbca3267dc7d4219af484976734c9527/). Then the grid square containing the campus weather station was identified using the Point Subset Tool of the NOAA Weather and Climate Toolkit (https://www.ncdc.noaa.gov/wct/index.php) in order to retrieve data from that specific location. Daily and monthly HadUK-grid data are included in the spreadsheets for convenience.Campus temperatures are slightly, but consistently, higher than those indicated by HadUK-grid, while HadUK-Grid rainfall is on average almost 10% higher than that recorded on the campus. Trend-free statistical relationships between campus and HadUK-grid data implies that there is unlikely to be any significant temporal bias in the campus dataset.ReferencesBurt, S. (2012). The Weather Observer's Handbook. Cambridge University Press, https://doi.org/10.1017/CBO9781139152167.Hollis, D, McCarthy, M, Kendon, M., Legg, T., Simpson, I. (2019). HadUK‐Grid—A new UK dataset of gridded climate observations. Geoscience Data Journal 6, 151–159, https://doi.org/10.1002/gdj3.78.Met Office; Hollis, D.; McCarthy, M.; Kendon, M.; Legg, T. (2022). HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.1.0.0 (1836-2021). NERC EDS Centre for Environmental Data Analysis, https://dx.doi.org/10.5285/bbca3267dc7d4219af484976734c9527.

The rain weather station market is experiencing robust growth, driven by increasing demand for accurate weather forecasting in agriculture, urban planning, and environmental monitoring. The market size in 2025 is estimated at $500 million, demonstrating significant expansion since 2019. This growth trajectory is projected to continue at a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, propelled by technological advancements leading to more sophisticated and cost-effective rain gauge technologies. Factors like the rising frequency of extreme weather events and the increasing need for precise water resource management are further contributing to market expansion. The integration of IoT (Internet of Things) capabilities and advanced data analytics within rain weather stations is transforming the industry, enabling real-time data acquisition and improved decision-making. This technological shift is appealing to a broader range of users, from individual farmers to large meteorological agencies. The market is segmented based on various factors, including the type of technology used (e.g., tipping bucket, ultrasonic, weighing), application (e.g., agriculture, hydrology, meteorology), and end-user (e.g., government agencies, research institutions, private companies). Key players in the market such as RM Young, Onset Computer, and Campbell Scientific are actively involved in developing and deploying innovative solutions, further intensifying competition and driving market growth. Geographic expansion into developing regions with burgeoning agricultural sectors and increasing infrastructure development presents significant opportunities. However, challenges remain, including the need for reliable power supply in remote locations and the potential impact of adverse weather conditions on the functionality of certain types of rain gauges. Despite these hurdles, the market's long-term growth outlook remains positive due to the essential role of accurate rain data in various sectors.

This dataset is about books. It has 1 row and is filtered where the book is Climate change risks and supply chain responsibility : how should companies respond when extreme weather affects small-scale producers in their supply chain?. It features 7 columns including author, publication date, language, and book publisher.

Weather Service Market Outlook

The global weather service market size was valued at approximately USD 2.5 billion in 2023 and is anticipated to reach around USD 5.2 billion by 2032, growing at a CAGR of 8.5% from 2024 to 2032. Growth factors driving this market include increasing climate change awareness, technological advancements in data analytics, and the rising demand for accurate weather forecasting across various industries. The integration of artificial intelligence (AI) and machine learning (ML) algorithms in weather prediction models is also contributing significantly to market expansion.

One of the primary growth factors in the weather service market is the increasing awareness and urgency surrounding climate change. As environmental concerns become more pronounced, industries and governments are investing extensively in weather services to better understand and mitigate the impacts of extreme weather events. This is particularly critical in sectors such as agriculture, where weather conditions directly influence crop yields, and in energy, where weather data is vital for optimizing renewable energy sources like wind and solar power. Moreover, the frequency of extreme weather events has necessitated more accurate and real-time weather forecasts, further driving market growth.

Technological advancements in data analytics and the proliferation of IoT (Internet of Things) devices have revolutionized the weather service market. High-precision sensors and satellites are now capable of collecting extensive and detailed meteorological data. This data, when analyzed using advanced algorithms and AI, provides highly accurate and timely weather forecasts. Additionally, cloud computing has enabled the processing and storage of massive amounts of meteorological data, making it accessible to various stakeholders globally. These technological enhancements are significantly boosting the effectiveness and reliability of weather services, thereby attracting more users.

Another significant growth factor is the increasing demand for weather forecasting services across various industries. Sectors such as aviation, marine, and transportation rely heavily on accurate weather forecasts for operational safety and efficiency. For instance, airlines use weather data to plan safer and more fuel-efficient flight routes, while maritime companies depend on weather services to ensure safe navigation. Furthermore, the energy sector utilizes weather forecasts to manage the supply and demand of electricity, particularly with the growing reliance on renewable energy sources. The expanding application of weather services across these industries is contributing substantially to market growth.

Weather Forecasting Technology has become a cornerstone in enhancing the accuracy and reliability of weather predictions. The integration of cutting-edge technologies such as AI, ML, and big data analytics has revolutionized the way meteorological data is processed and interpreted. These advancements enable the creation of sophisticated models that can predict weather patterns with greater precision, thus providing critical insights for industries reliant on weather data. As these technologies continue to evolve, they are expected to further improve the timeliness and accuracy of weather forecasts, making them indispensable tools for sectors like agriculture, aviation, and energy.

From a regional perspective, North America holds a dominant position in the weather service market due to its advanced technological infrastructure and significant investments in meteorological research. Europe follows closely, driven by stringent environmental regulations and a strong focus on renewable energy. The Asia Pacific region is expected to witness the highest growth during the forecast period, fueled by rapid industrialization, urbanization, and increasing government initiatives for disaster management and climate change mitigation. Meanwhile, the markets in Latin America and the Middle East & Africa are also growing steadily, driven by the agricultural sector and the need for efficient weather management systems.

Service Type Analysis

The weather service market can be segmented by service type into forecasting, data services, consulting, and others. Forecasting services form the backbone of the weather service market, providing essential informat

Weather Sensors Market Outlook

According to our latest research, the global weather sensors market size reached USD 2.85 billion in 2024, reflecting robust demand across multiple industries. The market is projected to grow at a CAGR of 6.1% from 2025 to 2033, with the total market value expected to reach USD 4.83 billion by 2033. This growth is primarily driven by increasing climate variability, heightened demand for precision agriculture, and the rising need for advanced weather monitoring systems in transportation and industrial sectors.

One of the primary growth factors propelling the weather sensors market is the growing impact of climate change, which has intensified the need for accurate and real-time weather data. Governments and private organizations worldwide are investing heavily in infrastructure to monitor and predict weather patterns, aiming to mitigate the risks associated with extreme weather events. This surge in investment is particularly evident in sectors like agriculture, where weather sensors are essential for optimizing irrigation, predicting crop yields, and ensuring food security. Furthermore, the integration of weather sensors with IoT and cloud-based platforms is enhancing the ability to collect, analyze, and disseminate weather data, making these technologies indispensable for both public and private stakeholders.

Another significant driver is the rapid expansion of smart city initiatives and the increasing adoption of weather sensors in transportation systems. Urban planners and municipal authorities are leveraging advanced weather monitoring solutions to manage traffic, reduce pollution, and improve public safety. For instance, real-time weather data can be used to optimize traffic flow, alert commuters about hazardous conditions, and support emergency response operations. The proliferation of connected vehicles and autonomous transportation systems further underscores the importance of accurate environmental sensing, as these technologies rely on up-to-the-minute weather information to operate safely and efficiently. As a result, the demand for weather sensors in the transportation sector is expected to see sustained growth through the forecast period.

Industrial automation and the aerospace & defense sectors are also contributing significantly to the expansion of the weather sensors market. In manufacturing and energy production, weather sensors help optimize operational efficiency by predicting adverse weather that could impact production schedules or energy consumption. In aerospace and defense, accurate weather data is critical for flight safety, mission planning, and operational readiness. The increasing frequency of extreme weather events, combined with the need for resilient supply chains and infrastructure, is prompting organizations to adopt advanced weather monitoring solutions. This broadening scope of application is fostering innovation and driving the development of more sophisticated and reliable weather sensors.

From a regional perspective, North America and Europe continue to lead the market, driven by strong government initiatives and the presence of leading technology providers. However, the Asia Pacific region is emerging as a high-growth market due to rapid industrialization, expanding agricultural activities, and increased infrastructure investments. Countries such as China, India, and Japan are investing in modernizing their weather monitoring networks, which is expected to significantly boost demand for weather sensors. Latin America and the Middle East & Africa are also witnessing growing adoption, particularly in agriculture and disaster management applications, as these regions face increasing challenges related to climate change and extreme weather conditions.

Product Type Analysis

The weather sensors market is segmented by product type into temperature sensors, humidity sensors, pressure sensors, wind sensors, rain sensors, and others. Temperature sensors hold the largest share of the market, owing to their widespread use across almost every w

Weather Source offers European Centre for Medium-Range Weather Forecast (ECMWF) including the ECMWF Short / Mid historical forecasts going back to July 1, 2017.

Weather Source is known worldwide for its industry leading data and novel weather solutions. Our data products and solutions are the most reliable on the market and provide businesses with properly collocated and actionable data enabling them to identify and quantify the impact of weather on any KPI at their locations of interest. Our curated continuum of weather data was built for analytics and machine learning. Weather Source data is delivered via its high-resolution OnPoint grid, which ensures your location of interest is never more than 2.2 miles away from a grid point.

Utilize Weather Source weather and climate data to reveal meaningful observations for a variety of industries. By leveraging hyper-local weather & climate information, businesses are able to create business intelligence and models around sales and footfall traffic forecasts, advertising and marketing, logistics and supply chain, inventory, staffing, management, and more.

Weather Sensors Market Outlook

The global market size for weather sensors was valued at approximately $1.8 billion in 2023, and it is expected to reach around $3.5 billion by 2032, reflecting a compound annual growth rate (CAGR) of 7.9%. The increasing need for accurate and reliable weather forecasting systems, driven by climate change and the necessity for data-driven decision-making in various industries, is a significant growth factor for this market. Additionally, technological advancements in sensor technology have made weather sensors more affordable and accessible, further fueling their adoption across different sectors.

One of the primary growth factors for the weather sensors market is the increasing impact of climate change, prompting a higher demand for precise weather forecasting tools. As global weather patterns become more unpredictable and extreme, the need for accurate weather data becomes imperative for governments, businesses, and individual users alike. This demand is particularly pronounced in sectors such as agriculture, where weather conditions directly affect crop yields and food supply chains. Consequently, investment in advanced weather sensors that can offer real-time data and predictive analytics is on the rise, providing significant opportunities for market expansion.

Another key growth driver is the rapid advancement in sensor technologies, which has made weather sensors more sophisticated, energy-efficient, and cost-effective. New materials and manufacturing techniques have resulted in sensors that are not only smaller and cheaper but also more accurate and durable. This technological progression has made it feasible to deploy weather sensors in a wider range of applications, from personal weather stations to large-scale meteorological networks. Furthermore, the integration of IoT and AI technologies has enhanced the functionality of weather sensors, enabling real-time data processing and improved prediction capabilities.

Government initiatives and regulatory frameworks are also playing a crucial role in the growth of the weather sensors market. Many countries are investing heavily in weather monitoring infrastructures to mitigate the adverse effects of climate change and natural disasters. Government bodies are collaborating with private enterprises to develop and deploy advanced weather monitoring systems, resulting in a surge in demand for high-quality weather sensors. Additionally, regulatory mandates for environmental monitoring and disaster management are compelling industries to adopt sophisticated weather sensors, further contributing to market growth.

Regionally, North America and Europe are at the forefront of the weather sensors market, with significant investments in technology and infrastructure. North America, in particular, benefits from a robust technological ecosystem and substantial funding for research and development in weather monitoring solutions. Europe’s focus on environmental sustainability and climate change mitigation initiatives is driving the demand for advanced weather sensors. Meanwhile, the Asia-Pacific region is witnessing rapid growth, driven by emerging economies like China and India, which are increasingly investing in weather monitoring systems to support their agricultural sectors and industrial growth.

Product Type Analysis

The weather sensors market is segmented into various product types, including temperature sensors, humidity sensors, pressure sensors, wind sensors, rain sensors, and others. Temperature sensors are one of the most widely used types of weather sensors, playing a critical role in diverse applications such as meteorology, agriculture, and industrial processes. These sensors provide crucial data that help in the accurate prediction of weather conditions and climate patterns. The increasing demand for advanced temperature sensors with higher accuracy and reliability is driving innovation and growth within this segment. Furthermore, advancements in technology have led to the development of smart temperature sensors that can transmit data wirelessly, offering real-time monitoring capabilities.

Humidity sensors are another essential component of the weather sensors market, serving critical functions in both agricultural and meteorological applications. These sensors measure the amount of moisture in the air, providing valuable information for weather predictions and agricultural planning. The adoption of humidity sensors is gaining traction due to the growing emphasis on precision agriculture and the need for optimized crop management solutions. Moreover, the integration of humidit

Automated Water Supply Model (AWSM) was developed at the USDA Agricultural Research Service (ARS) in Boise, ID. AWSM was designed to streamline the work flow used by the ARS to forecast the water supply of multiple water basins. AWSM standardizes the steps needed to distribute weather station data with SMRF, run an energy and mass balance with iSnobal, and process the results, while maintaining the flexibility of each program. Resources in this dataset:Resource Title: AWSM GitHub Repository. File Name: Web Page, url: https://github.com/USDA-ARS-NWRC/awsm Automated Water Supply Model (AWSM) was developed at the USDA Agricultural Research Service. AWSM was designed to streamline the workflow used to forecast the water supply of multiple water basins.

The portable small automatic weather station market is experiencing robust growth, driven by increasing demand for precise, localized weather data across various sectors. The market's expansion is fueled by advancements in sensor technology, leading to smaller, more affordable, and energy-efficient devices. These stations are increasingly adopted by amateur meteorologists, agricultural businesses for precision farming, educational institutions for research and teaching, and even individual homeowners interested in monitoring local weather conditions. The rising adoption of smart home technologies and the Internet of Things (IoT) further contributes to market growth, enabling seamless data integration and remote monitoring capabilities. We estimate the market size in 2025 to be approximately $250 million, considering a reasonable CAGR (let's assume 8%) and the current market trends. This suggests a considerable expansion from previous years. While challenges exist, such as the need for robust data security measures and overcoming potential supply chain constraints, the long-term outlook for the portable small automatic weather station market remains positive. We project continued growth driven by technological advancements, broader applications, and rising consumer awareness of the value of hyperlocal weather information. The competitive landscape is characterized by a mix of established players and emerging companies. Established players like Ecowitt Weather, Campbell Scientific, and others leverage their brand reputation and extensive product portfolios. Newer entrants, many focusing on innovative sensor technology or specific niche applications, are challenging the status quo. Geographical distribution is likely skewed towards developed regions, initially, but increasing adoption in developing economies, particularly in agricultural sectors, is expected to drive future expansion. Key segments include consumer/home use, professional/research use, and industrial applications. The market is likely to witness consolidation in the coming years with larger players acquiring smaller companies to broaden their product offerings and expand their market reach. This expansion will be fueled by the increasing availability of affordable yet high-quality sensors, improved battery life, and more user-friendly data analysis software.

This dataset contains gridded meteorological data associated with challenging periods of weather for highly-renewable UK and European electricity systems of the future collected during the Adverse Weather Scenarios for Future Electricity Systems project. This project is a collaboration between the Met Office, the National Infrastructure Commission and the Climate Change Committee. More details about the project can be found in the associated documentation.

Two categories of challenging weather conditions; long duration adverse events and short duration wind ramping events, are provided.

Long duration events

The long duration event component of the dataset provides daily time series at 60 x 60 km spatial resolution, covering a European domain, for surface temperature, 100 m wind speed and net surface solar radiation data, representative of a selection of adverse weather scenarios. Each adverse weather scenario is contained within a time slice of data. For summer-time events, one calendar year (January - December) of data is provided, with the summer-time event occurring in the summer of that year. For winter-time events, two calendar years of data are provided, with the winter-time event occurring in the winter (October-March) intersecting the two calendar years. In all cases, the start date, duration and severity of the adverse weather event, contained within the time slice of data, are given in the NetCDF global ttributes.

Three types of long-duration adverse weather scenarios are represented: winter-time wind-drought-peak-demand events, summer-time wind-drought-peak-demand events, and summer-time surplus generation events. These are provided at various extreme levels (1 in 2, 5, 10, 20 ,50 and 100-year events); and for a range of current and nominal future climate change warming levels (1.2 [current day, early 2020s], 1.5, 2, 3, and 4 degrees Celsius above pre-industrial level), representative of events impacting either just the UK, or Europe as a whole.

The data provided are derived from the Met Office decadal prediction system hindcast (https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/climate-models/depresys), according to the climate change impacts identified from UKCP18 (https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/index).

Short duration events The short duration event component of the dataset provides hourly time series at 4 x 4 km spatial resolution, covering a UK and surrounding offshore area domain, for 100 m wind speed, representative of a selection of wind generation ramping events. Each adverse weather scenario is contained within a time slice of data with up to one week before and one week after the day on which the event occurs (up to 15 days in total) provided. For the majority of events provided, the full 15 days are available, however for a small number of events which occur less than one week from the beginning or end of the underlying data used to derive this dataset, this is not possibly to supply, and these events are listed below. The start date and time along with the direction and magnitude of the ramp (change in wind capacity factor) contained within the time slice of data, are given in the NetCDF global attributes.

The short duration wind generation ramping events are representative of events impacting five separate regions of Great Britain and surrounding offshore areas, as defined in the accompanying documentation. These regions are Scotland, the East England, West England and Wales offshore North and offshore South. The events are defined by changes in wind capacity factors occurring over different length time windows (1-hour, 3-hour, 6-hour, 12-hour and 24-hour windows). These are provided at various extreme levels (1 in 2, 5, 10, 20 ,50 and 100-year events) for the 1.2 degrees Celsius above pre-industrial level (I.e. representative of early 2020s climate) and through the analysis outlined in the accompanying documentation are though to also be representative of the 2, 3, and 4 degrees Celsius above pre-industrial level nominal future climate change warming levels.

The data provided are derived from the UKCP18 local projections (https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/index).

The methods developed for characterising and representing these adverse weather scenarios, and the approach used to compile the final dataset are presented in the accompanying documentation.

Use of this data is subject to the terms of the Open Government Licence (http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/). The following acknowledgment must be given when using the data: © Crown Copyright 2021, Met Office, funded by the National Infrastructure Commission.

According to Cognitive Market Research, the global Weather Forecasting Service market size will be USD 1621.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 8.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 648.60 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 486.45 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 372.95 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.5% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 81.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 32.43 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.2% from 2024 to 2031.
The advanced analytics and technology-driven solutions category is the fastest growing segment of the Weather Forecasting Service industry

Market Dynamics of Weather Forecasting Service Market

Key Drivers for Weather Forecasting Service Market

Improved forecasting accuracy through AI and IoT to Boost Market Growth

The key drivers and restraints of the market where improved forecasting accuracy through AI and IoT enable businesses to predict trends, inventory needs, and market demands more accurately, reducing overproduction and wastage. Real-time data from IoT devices ensures up-to-date insights for decision-making. Implementing AI and IoT systems requires significant upfront costs, including hardware, software, and skilled personnel. Small and medium-sized enterprises (SMEs) may find it challenging to afford these investments. By addressing these drivers and restraints, businesses can leverage AI and IoT to revolutionize forecasting and achieve substantial market growth

Rising demand for precise weather prediction services

Growing climatic unpredictability brought on by global warming, which increases the frequency and severity of extreme weather events, is one of the main factors driving the growing demand for accurate weather prediction services. This increases the need for precise forecasts to reduce risks and maximize operations in sectors including insurance, energy, transportation, and agriculture. Developments improve prediction accuracy in data analytics, artificial intelligence, and satellite technology. Additionally, investments in improved weather forecasting technology are driven by government disaster preparedness activities and a rising reliance on renewable energy sources.

Restraint Factor for the Weather Forecasting Service Market

Advanced forecasting tools require significant financial investment

The high upfront costs of software, infrastructure, and deployment are among the major financial barriers preventing the widespread use of advanced forecasting techniques. Budgets may also be strained by continuing costs for upkeep, upgrades, and qualified staff, especially for small and medium-sized businesses (SMEs). These tools' intricacy may also call for intensive training, which would raise expenses even more. Even though advanced forecasting technologies have the potential to enhance operational efficiency and decision-making, these factors frequently discourage firms from implementing them fully.

Impact of Covid-19 on the Weather Forecasting Service Market

The COVID-19 pandemic significantly impacted the Weather Forecasting Service Market by disrupting supply chains, delaying data collection, and reducing demand from sectors like aviation and energy. Remote work and increased reliance on online services also accelerated the adoption of digital forecasting technologies. While the immediate financial impact was negative, the crisis highlighted the importance of accurate weather predictions for risk management and resilience, leading to increased investments in advanced forecasting systems post-pandemic. Introduction of the Weather Forecasting Service Market

The Weather Forecasting Service Market encompasses a range of solutions and technologies used to predict atmospheric conditions, such as weather ...