Portable Small Automatic Weather Station Market Outlook

The global market size for Portable Small Automatic Weather Stations is projected to reach USD 5.8 billion by 2032, up from USD 2.5 billion in 2023, growing at a compound annual growth rate (CAGR) of 9.8% over the forecast period. This impressive growth is driven by increasing demand for accurate and real-time weather data across various sectors including agriculture, environmental research, and military applications. Enhanced technological advancements and the integration of IoT and AI in weather monitoring systems also act as significant growth catalysts in this market.

One of the primary growth factors for this market is the increasing awareness and need for precise climate data. As climate change continues to be a pressing global issue, various sectors are increasingly relying on accurate weather data for better decision-making. For instance, in agriculture, portable small automatic weather stations are crucial for monitoring soil moisture, predicting rainfall, and planning irrigation schedules. This helps in improving crop yields and reducing the risk of crop failure, thereby driving market growth. Moreover, these weather stations are becoming more affordable, which makes them accessible to small and medium-sized enterprises and individual farmers, further expanding the market.

Technological advancements also play a pivotal role in the market growth of portable small automatic weather stations. The integration of advanced sensors, IoT, and AI has significantly enhanced the accuracy and functionality of these devices. Modern weather stations can now provide real-time data and analytics, which are crucial for various applications ranging from environmental research to disaster management. The miniaturization of components and the development of compact, energy-efficient systems have also contributed to the proliferation of portable weather stations.

The increasing frequency of extreme weather events and natural disasters is another major growth driver for this market. Governments and private organizations are investing heavily in weather monitoring and forecasting systems to mitigate the impact of such events. Portable small automatic weather stations are particularly useful in remote and disaster-prone areas where traditional weather monitoring infrastructure is lacking. These stations can be rapidly deployed and provide critical data that aid in timely and effective response to natural disasters, thus driving market demand.

In addition to portable solutions, Fixed Station Monitors play a crucial role in providing continuous and long-term weather data. These fixed installations are often strategically placed in locations where consistent monitoring is essential, such as airports, research facilities, and urban centers. The data collected from these stations is invaluable for climate studies, weather forecasting, and environmental monitoring. Fixed Station Monitors are equipped with a wide array of sensors that deliver highly accurate and reliable data, which is critical for making informed decisions in various sectors. The integration of advanced technologies in these monitors ensures that they remain a vital component of the broader weather monitoring infrastructure.

Regionally, North America and Europe are expected to dominate the market due to their advanced infrastructure and significant investments in weather monitoring technologies. However, the Asia Pacific region is anticipated to witness the highest growth rate, driven by increasing awareness about climate change, government initiatives, and the adoption of advanced agricultural practices. The growing need for disaster management and environmental research in this region also contributes to the market's expansion. Latin America and the Middle East & Africa are also expected to show considerable growth, albeit at a slower pace compared to the Asia Pacific.

Product Type Analysis

The market for portable small automatic weather stations can be segmented into fixed weather stations and portable weather stations. Fixed weather stations are generally installed in a permanent location and are used for long-term weather monitoring. These stations are often equipped with a wide range of sensors and provide highly accurate and reliable data. They are commonly used in meteorological research, environmental monitoring, and by government agencies. The demand for fixed weather stations is driven by the need for continuous and long-t

This data set contains hourly resolution surface meteorological data from the Remote Automated Weather Stations (RAWS) network. These data were retrieved from the Western Region Climate Center (WRCC). The date set includes data from ten stations in the IHOP region and covers the period 01 May to 30 June 2002. The data are in columnar ASCII format. Consult the README for more information.

Automatic weather station data * Changes download URL from September 15, 112 to December 31, 112, please change before December 31, and the old version link will expire. If you need to download a large amount of data, please apply for membership at the Meteorological Data Open Platform https://opendata.cwa.gov.tw/index

This data set contains 1-minute resolution surface meteorological data from the Atmospheric Boundary Layer Experiment (ABLE) operated by the Argonne National Laboratory in the Walnut River Watershed in Butler County Kansas. The ABLE Automated Weather Station (AWS) Network consists of five stations. Data cover the period from 13 May to 25 June 2002. The data are in columnar ASCII format. Consult the README for more information.

This data set contains meteorological data, such as air temperature, pressure, rainfall intensity, relative humidity, and wind direction/speed measured by the International Centre for Integrated Mountain Development (ICIMOD).

This data set contains meteorological data, such as air temperature, pressure, rainfall intensity, relative humidity, and wind direction/speed measured by the International Centre for Integrated Mountain Development (ICIMOD).

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Automatic weather stations have been deployed by AIMS since 1980. One station is deployed in Western Australia, but all others have been deployed on reefs within the Great Barrier Reef or on islands or the mainland adjacent to the reef. The following is a list of the weather stations which have been deployed by AIMS and the period of time for which data may be available. Records may not be continuous for the time spans given.

Historical Data: Rib Reef: Start: 29/2/1980: End: 3/12/1985 Coral Creek (Hinchinbrook Island): Start 16/10/1980: End 30/7/1985 Cape Ferguson: Start 1/11/1983: End 30/5/1985 John Brewer Reef: Start 31/7/1987: End 30/5/1988 Coconut Island: Start 30/9/1988: End 5/11/1991 (Data not yet located) Cape Cleveland: Start 2/6/1993: End 30/9/1996 Daintree River: Start 12/2/97: End 31/5/98

Currently Active Stations: Queensland: Cape Bowling Green: Start: 9/7/1983 Myrmidon Reef: Start 2/11/1987 Hardy Reef: Start 14/6/1989 Agincourt Reef: Start 1/11/1989 Davies Reef: Start 18/10/1991 Halftide Rock: 26/7/2000 Orpheus Island: Start 20/12/2002 Cleveland Bay: Start 3/7/1990 Agincourt Reef: Start 1/11/1989 Raine Island: Start 08/08/2012

Western Australia: Ningaloo Reef (Milyering): Start 12/2/1997

Weather stations may be equipped with sensors to measure some or all of the following parameters: sea temperature at a range of depths, atmospheric pressure, air temperature, solar radiation (PAR), wind direction, wind speed.

The 30 year average values of various climate variables. Determined per month, per season and per year. Organized by automatic weather stations (AWS).

The global market for ultrasonic automatic weather stations is experiencing robust growth, driven by increasing demand for accurate and reliable weather data across various sectors. From agriculture and aviation to meteorology and environmental monitoring, the need for precise, real-time weather information is paramount. Ultrasonic technology offers several advantages over traditional methods, including high accuracy, low maintenance, and resistance to harsh weather conditions. This has led to a significant rise in adoption across both public and private sectors. Based on industry trends and considering a plausible market size and CAGR, let's assume a 2025 market size of $500 million, growing at a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth is fueled by advancements in sensor technology, improved data processing capabilities, and the increasing integration of weather data into smart city initiatives. The market is segmented by various factors such as application (agriculture, aviation, etc.), technology, and geography. Competition is fierce, with established players like Vaisala and Davis Instruments facing challenges from emerging companies offering innovative solutions and competitive pricing. The forecast period (2025-2033) promises continued expansion, driven by factors such as rising government investments in infrastructure development, the increasing adoption of precision agriculture techniques, and a growing awareness of the importance of climate change monitoring. However, challenges remain, including the high initial investment cost for sophisticated systems and the need for skilled personnel for data analysis and interpretation. Overcoming these hurdles will be crucial for further market penetration and the continued adoption of ultrasonic automatic weather stations as a vital tool for weather monitoring and forecasting globally. The market is expected to reach approximately $1.1 billion by 2033, demonstrating substantial potential for continued growth and innovation within this critical sector.

This dataset is from an automatic weather station (AWS) located at the Pontbren study site in mid-Wales, UK. The AWS was installed at the Bowl study site, an area of improved grassland, between 2006-2010 as part of the Pontbren Catchment Study Land Use and Management Multi-Scale Experimental Programme. The parameters measured by the AWS were; incident radiation, wind speed and direction, soil and air temperature, relative humidity and net radiation. All sensors are sampled every one minute and provided in the form of daily and ten-minute averages. Data are provided in the form of .txt files and generally split into six-month blocks. Associated with each data point in the .txt file is a quality assurance code, QA code, in the adjacent column. Details of the dataset and the quality assurance coding system (Appendix A) are provided in the supporting documentation. Other measurements taken at the Bowl include monitoring runoff from an improved grassland field in the form of overland and drain flow, soil water tension, soil volumetric moisture content, groundwater height and precipitation.

As part of IPY-0732946, three automatic weather stations (Larsen 1, 2, 3) were installed along a latitudinal gradient on the Larsen C ice shelf. The stations were installed in December 2008 (Larsen 3 AWS did not come online until 2009) and operated through the end of the project in November 2011.

The global market for solar automatic weather stations is experiencing robust growth, driven by increasing demand for renewable energy solutions and the need for precise weather data in various sectors. The market's expansion is fueled by advancements in solar technology, leading to more efficient and cost-effective weather monitoring systems. Furthermore, the rising adoption of smart agriculture, improved infrastructure development projects, and the growing awareness of climate change are significantly contributing to market expansion. Key players like Dongguan Greenlight New Energy Technology, Shandong Fengtu Internet of Things Technology, and others are actively involved in developing innovative products, further stimulating market growth. The market is segmented based on various factors including application (agriculture, meteorology, etc.), technology, and geographic location. While data limitations prevent precise quantification, a conservative estimate suggests a market size of approximately $500 million in 2025, growing at a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth is projected to be influenced by continued technological advancements and increasing governmental support for climate monitoring initiatives. However, challenges such as high initial investment costs and the need for specialized technical expertise can act as market restraints. Overcoming these hurdles through strategic partnerships, government subsidies, and the development of user-friendly systems will be critical for sustained market expansion. The competitive landscape is characterized by a mix of established players and emerging companies, leading to innovation and price competition. Regional variations in market penetration are expected, with developed regions exhibiting higher adoption rates due to greater technological awareness and investment capacity. The forecast period (2025-2033) is expected to witness substantial growth, driven by factors previously mentioned, promising significant opportunities for stakeholders in the industry.

This dataset holds daily data from one automated weather station (AWS) located at the Climoor field site in Clocaenog forest, North East Wales. The data are on relative humidity (percent), air temperature (degrees Celsius), rainfall (millimetres), air pressure (millibars), net radiation (millivolts), solar radiation (kilowatts per square metre per second), photosynthetic active radiation (PAR; micromol per square metre per second), wind speed (metres per second) and wind direction (degrees). The data is an extension for the AWS datasets 1999-2015, 2015-2016 and 2016-2021 covering the time period January 2022 to December 2023. Data are logged in minute intervals, averaged to half-hourly. The data are sent from the field site to a UKCEH server. A working copy is created, quality assurance checks carried out and daily averages calculated from half-hourly records. Data which were not recorded are marked with “NA”, faulty data were replaced by “-9999”. Note, the rainfall sensor was broken during this time period, but the column is kept in the datafile for consistency with previous data records. Data collection, processing and quality checking was carried out by members of UKCEH staff. The Climoor field experiment intends to answer questions regarding the effects of warming and drought on ecosystem processes. The reported data are collected to monitor site specific environmental conditions and their development over time. These data are important to interpret results that are collected from the climate change manipulations imposed in the field. Full details about this dataset can be found at https://doi.org/10.5285/b86585cf-76cd-42c8-8db9-57ebc750f1de

GC-Net Level 1 automated weather station data In Memory of Dr. Konrad (Koni) Steffen Author: B. Vandecrux Contact: bav@geus.dk Last update: 2023-09-01 Citation Steffen, K.; Vandecrux, B.; Houtz, D.; Abdalati, W.; Bayou, N.; Box, J.; Colgan, L.; Espona Pernas, L.; Griessinger, N.; Haas-Artho, D.; Heilig, A.; Hubert, A.; Iosifescu Enescu, I.; Johnson-Amin, N.; Karlsson, N. B.; Kurup Buchholz, R.; McGrath, D.; Cullen, N.J.; Naderpour, R.; Molotch, N.P.; Pederson, A. Ø.; Perren, B.; Philipps, T.; Plattner, G.K.; Proksch, M.; Revheim, M. K.; Særrelse, M.; Schneebli, M.; Sampson, K.; Starkweather, S.; Steffen, S.; Stroeve, J.; Watler, B.; Winton, Ø. A.; Zwally, J.; Ahlstrøm, A., 2023, "GC-Net Level 1 automated weather station data", https://doi.org/10.22008/FK2/VVXGUT, GEUS Dataverse, V3 as described and processed by: Vandecrux, B., Box, J. E., Ahlstrøm, A. P., Andersen, S. B., Bayou, N., Colgan, W. T., Cullen, N. J., Fausto, R. S., Haas-Artho, D., Heilig, A., Houtz, D. A., How, P., Iosifescu Enescu, I., Karlsson, N. B., Kurup Buchholz, R., Mankoff, K. D., McGrath, D., Molotch, N. P., Perren, B., Revheim, M. K., Rutishauser, A., Sampson, K., Schneebeli, M., Starkweather, S., Steffen, S., Weber, J., Wright, P. J., Zwally, H. J., and Steffen, K.: The historical Greenland Climate Network (GC-Net) curated and augmented Level 1 dataset, Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, 2023. Description The Greenland Climate Network (GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen on the Greenland Ice Sheet (GrIS). This first station, "Swiss Camp" or the "ETH-CU" camp, was initiated in 1990 by A. Ohmura et al. (1991, 1992) with K. Steffen taking over the site from 1995 and expending the network from that year to 31 stations at 30 sites in Greenland (Steffen et al., 1996, 2001). The GC-Net was supported by multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado. Provided in this dataset are the 25 two-level stations from 24 sites on the Greenland ice sheet and 3 experimental stations in Antarctica. The remaining 6 Greenland stations have a different design and will be added once quality checked. Although the GC-Net AWS transmitted their data near-real time through satellite communication, the present dataset was made from uncorrupted datalogger files, retrieved every 1-2 years during maintenance. Full dataset description publication will be forthcoming. The Geological Survey of Denmark and Greenland (GEUS) has undertaken the continuation of multiple GC-Net sites through the Programme for Monitoring of the Greenland Ice Sheet (PROMICE.dk). The level 1 data is provided in the newly described csv-compatible NEAD format, which is a csv file with added metadata header. The format is documented at https://doi.org/10.16904/envidat.187 and a python package is available to read and write NEAD files: https://github.com/GEUS-Glaciology-and-Climate/pyNEAD . The GC-Net stations measure: - Air temperature from four sensors at two heights above the surface - Relative humidity at two heights above the surface - Wind speed and direction at two heights above the surface - Air pressure - Surface height from two sonic sounders - Incoming and outgoing shortwave radiation - Net radiation (long- and short-wave)* - Firn or ice temperatures at 10 levels below the surface In the L1 dataset, these measurements are cleaned from sensor, station or logger malfunctions, adjusted and/or filtered when and where possible. Additionally, the L1 dataset contains the following derived variables: - Surface height (corrected from the shifts in sonic sounder height) - Instrument heights (derived from sonic sounder height and station geometry) - Inter- or extrapolated temperature, relative humidity and wind speed at respectively 2, 2, and 10 m above the surface - Estimated depth of the subsurface temperature measurements (adjusted for snow accumulation, ice ablation and instrument replacement) - Interpolated firn or ice temperature at 10 m below the surface - Calculated solar an azimuth angles - Sensible and latent heat fluxes calculated after Steffen and Demaria (1996) Important links: - The level 1 processing scripts and discussion page for Q&A and issue reporting (under "issues" tab) is available at: https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing - The level 0 data (from which the L1 data was built from) is available at: https://www.doi.org/10.16904/envidat.1. - The compilation of handheld GPS coordinates for each site and for multiple years is available here: Vandecrux, B. and Box, J.E.: GC-Net AWS observed and estimated positions (Version v1) [Data set]. Zenodo....

The global market for Automatic Weather Stations (AWS) is experiencing robust growth, driven by increasing demand for accurate and real-time weather data across various sectors. From agriculture and aviation to environmental monitoring and disaster management, the reliance on precise weather information is paramount. This demand fuels the adoption of AWS, which offer automated data collection, reducing manual labor and enhancing efficiency. We estimate the market size in 2025 to be approximately $850 million, exhibiting a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth trajectory is fueled by technological advancements leading to more sophisticated and cost-effective AWS solutions, alongside rising government investments in weather infrastructure, particularly in developing nations. The integration of IoT capabilities and AI-powered analytics further enhances the value proposition of AWS, enabling predictive weather modeling and improved decision-making capabilities across diverse applications. Several key trends are shaping the AWS market. The increasing adoption of cloud-based data storage and analysis facilitates seamless data access and sharing, while the miniaturization of sensors and the development of energy-efficient technologies are expanding the range of deployment scenarios. Furthermore, stringent environmental regulations and the growing awareness of climate change are driving the need for comprehensive and reliable weather data, fueling market expansion. While challenges such as high initial investment costs and the need for regular maintenance can act as restraints, the long-term benefits and growing applications of AWS are expected to outweigh these limitations, propelling continued market growth throughout the forecast period. Key players like Vaisala, AXYS, and others are actively involved in developing innovative products and expanding their global reach to capitalize on these growth opportunities. This comprehensive report provides an in-depth analysis of the global Automatic Weather Stations (AWS) market, projected to be worth over $2 billion by 2028. We delve into market concentration, key trends, dominant regions, product insights, and future growth catalysts, providing invaluable intelligence for businesses and investors alike.

KNMI collects observations from the automatic weather stations situated in the Netherlands and BES islands on locations such as aerodromes, North Sea platforms and wind poles. This dataset provides metadata on these weather stations, such as location, name and type. The data in this dataset is formatted as NetCDF. It is also available as a CSV file in this dataset: https://dataplatform.knmi.nl/dataset/waarneemstations-csv-1-0.

RMI operates a network of 17 automatic weather stations in Belgium. These weather stations report meteorological paramaters such as air pressure, temperature, relative humidity, precipitation (quantity,duration), wind (speed, gust, direction), sunshine duration, shortwave solar radiation and infrared radiation every 10 minutes.