This point layer contains monthly summaries of daily temperatures (means, minimums, and maximums) and precipitation levels (sum, lowest, and highest) for the period January 1981 through December 2010 for weather stations in the Global Historical Climate Network Daily (GHCND). Data in this service were obtained from web services hosted by the Applied Climate Information System ( ACIS). ACIS staff curate the values for the U.S., including correcting erroneous values, reconciling data from stations that have been moved over their history, etc. The data were compiled at Esri from publicly available sources hosted and administered by NOAA. Because the ACIS data is updated and corrected on an ongoing basis, the date of collection for this layer was Jan 23, 2019. The following process was used to produce this dataset:Download the most current list of stations from ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt. Import this into Microsoft Excel and save as CSV. In ArcGIS, import the CSV as a geodatabase table and use the XY Event layer tool to locate each point. Using a detailed U.S. boundary extract the points that fall within the 50 U.S. States, the District of Columbia, and Puerto Rico. Using Python with DA.UpdateCursor and urllib2 access the ACIS Web Services API to determine whether each station had at least 50 monthly values of temperature data for each station. Delete the other stations. Using Python add the necessary field names and acquire all monthly values for the remaining stations. Thus, there are stations that have some missing data. Using Python Add fields and convert the standard values to metric values so both would be present. Thus, there are four sets of monthly data in this dataset: Monthly means, mins, and maxes of daily temperatures - degrees Fahrenheit. Monthly mean of monthly sums of precipitation and the level of precipitation that was the minimum and maximum during the period 1981 to 2010 - mm. Temperatures in 3a. in degrees Celcius. Precipitation levels in 3b in Inches. After initially publishing these data in a different service, it was learned that more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer these most precise coordinates are used. A large subset of the EMSHR metadata is available via EMSHR Stations Locations and Metadata 1738 to Present. If your study area includes areas outside of the U.S., use the World Historical Climate - Monthly Averages for GHCN-D Stations 1981 - 2010 layer. The data in this layer come from the same source archive, however, they are not curated by the ACIS staff and may contain errors. Revision History: Initially Published: 23 Jan 2019 Updated 16 Apr 2019 - We learned more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer the geometry and attributes for 3,222 of 9,636 stations now have more precise coordinates. The schema was updated to include the NCDC station identifier and elevation fields for feet and meters are also included. A large subset of the EMSHR data is available via EMSHR Stations Locations and Metadata 1738 to Present. Cite as: Esri, 2019: U.S. Historical Climate - Monthly Averages for GHCN-D Stations for 1981 - 2010. ArcGIS Online, Accessed

Historical changes of annual temperature and precipitation indices at selected 210 U.S. cities

This dataset provide:

Annual average temperature, total precipitation, and temperature and precipitation extremes calculations for 210 U.S. cities.

Historical rates of changes in annual temperature, precipitation, and the selected temperature and precipitation extreme indices in the 210 U.S. cities.

Estimated thresholds (reference levels) for the calculations of annual extreme indices including warm and cold days, warm and cold nights, and precipitation amount from very wet days in the 210 cities.

Annual average of daily mean temperature, Tmax, and Tmin are included for annual average temperature calculations. Calculations were based on the compiled daily temperature and precipitation records at individual cities.

Temperature and precipitation extreme indices include: warmest daily Tmax and Tmin, coldest daily Tmax and Tmin , warm days and nights, cold days and nights, maximum 1-day precipitation, maximum consecutive 5-day precipitation, precipitation amounts from very wet days.

Number of missing daily Tmax, Tmin, and precipitation values are included for each city.

Rates of change were calculated using linear regression, with some climate indices applied with the Box-Cox transformation prior to the linear regression.

The historical observations from ACIS belong to Global Historical Climatological Network - daily (GHCN-D) datasets. The included stations were based on NRCC’s “ThreadEx” project, which combined daily temperature and precipitation extremes at 255 NOAA Local Climatological Locations, representing all large and medium size cities in U.S. (See Owen et al. (2006) Accessing NOAA Daily Temperature and Precipitation Extremes Based on Combined/Threaded Station Records).

Resources:

See included README file for more information.

Additional technical details and analyses can be found in: Lai, Y., & Dzombak, D. A. (2019). Use of historical data to assess regional climate change. Journal of climate, 32(14), 4299-4320. https://doi.org/10.1175/JCLI-D-18-0630.1

Other datasets from the same project can be accessed at: https://kilthub.cmu.edu/projects/Use_of_historical_data_to_assess_regional_climate_change/61538

ACIS database for historical observations: http://scacis.rcc-acis.org/

GHCN-D datasets can also be accessed at: https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/

Station information for each city can be accessed at: http://threadex.rcc-acis.org/

• 2024 August updated -

  Annual calculations for 2022 and 2023 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2022 and 2023 data.

  Note that future updates may be infrequent.

• 2022 January updated -

  Annual calculations for 2021 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2021 data.

• 2021 January updated -

  Annual calculations for 2020 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2020 data.

• 2020 January updated -

  Annual calculations for 2019 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2019 data.

  Thresholds for all 210 cities were combined into one single file – Thresholds.csv.

• 2019 June updated -

  Baltimore was updated with the 2018 data (previously version shows NA for 2018) and new ID to reflect the GCHN ID of Baltimore-Washington International AP. city_info file was updated accordingly.

  README file was updated to reflect the use of "wet days" index in this study. The 95% thresholds for calculation of wet days utilized all daily precipitation data from the reference period and can be different from the same index from some other studies, where only days with at least 1 mm of precipitation were utilized to calculate the thresholds. Thus the thresholds in this study can be lower than the ones that would've be calculated from the 95% percentiles from wet days (i.e., with at least 1 mm of precipitation).

Contains global weather station locations with data for monthly means from 1981 through 2010 for: Daily Mean Temperature °C Daily Maximum Temperature °C Daily Minimum Temperature °C Precipitation in mm Highest Daily Temperature °C Lowest Daily Temperature °C Additional monthly fields containing the equivalent values in °F and inches are available at the far right of the attribute table. GHCND stations were included if there were at least fifteen average daily values available in each month for all twelve months of the year, and for at least ten years between 1981 and 2010. 3,197 of the 7,480 stations did not collect or lacked sufficient precipitation data. These data are compiled from archived station values which have not undergone rigorous curation, and thus, there may be unexpected values, particularly in the daily extreme high and low fields. Esri is working to further curate this layer and will make updates as improvements are found. If your area of study is within the United States, we recommend using the U.S. Historical Climate - Monthly Averages for GHCN-D Stations 1981 - 2010 layer because the data in that service were compiled from web services produced by the Applied Climate Information System ( ACIS). ACIS staff curate the values for the U.S., including correcting erroneous values, reconciling data from stations that have been moved over their history, etc., thus the data in the U.S. service is of higher quality. Revision History: Initially Published: 6 Feb 2019 Updated: 12 Feb 2019 - Improved initial extraction algorithm to remove stations with extreme values. This included values higher than the highest temperature ever recorded on Earth, or those with mean values that were considerably different than adjacent neighboring stations.Updated: 18 Feb 2019 - Updated after finding an error in initial processing that excluded a 2,870 stations. Updated 16 Apr 2019 - We learned more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer the geometry and attributes for 635 of 7,452 stations now have more precise coordinates. The schema was updated to include the NCDC station identifier and elevation fields for feet and meters are also included. A large subset of the EMSHR metadata is available via EMSHR Stations Locations and Metadata 1738 to Present. Cite as:

Esri, 2019: World Historical Climate - Monthly Averages for GHCN-D Stations for 1981 - 2010. ArcGIS Online, Accessed April 2019. https://www.arcgis.com/home/item.html?id=ed59d3b4a8c44100914458dd722f054f Source Data: Station locations compiled from: Initially compiled using station locations from ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24 Amended to use the most recent station locations from Russell S. Vose, Shelley McNeill, Kristy Thomas, Ethan Shepherd (2011): Enhanced Master Station History Report of March 2019. NOAA National Climatic Data Center. Access Date: April 10, 2019 doi:10.7289/V5NV9G8D. Station Monthly Means compiled from Daily Data: ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd_all.tar.gz Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24

The NRCS National Water and Climate Center's Interactive Map displays both current and historic hydrometeorological data in an easy-to-use, visual interface. The information on the map comes from many sources. Natural Resources Conservation Service snowpack and precipitation data are derived from manually-collected snow courses and automated Snow Telemetry (SNOTEL) and Soil Climate Analysis Network (SCAN) stations. Other data sources include precipitation, streamflow, and reservoir data from the U.S. Bureau of Reclamation (BoR), the Applied Climate Information System (ACIS), the U.S. Geological Survey (USGS), and other hydrometeorological monitoring entities. The Interactive Map has two regions: the map display itself, and the map controls which determine both the display mode and the types of data and stations to show on the map: Display Modes; Map Components; Station Conditions Controls; Basin Conditions Controls; Station Inventory Controls. Resources in this dataset:Resource Title: Interactive Map home. File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/wcc/home/quicklinks/predefinedMaps/ The Interactive Map provides spatial visualization of current and historic hydrometeorological data collected by the Natural Resources Conservation Service and other monitoring agencies. The map also provides station inventories based on sensor and geographic filters. This page has links to pre-defined maps organized by data type. After opening a map, users can zoom to area of interest, customize the map, and then bookmark the URL to save the settings.

Solar Tracker Integrated Weather Station Market Outlook

According to our latest research, the global market size for the Solar Tracker Integrated Weather Station Market reached USD 1.32 billion in 2024, driven by growing investments in renewable energy infrastructure and the increasing adoption of smart solar solutions worldwide. The market is projected to expand at a robust CAGR of 9.8% during the forecast period, reaching a value of USD 3.03 billion by 2033. This remarkable growth is primarily attributed to the rising demand for efficient solar power generation, the need for real-time weather monitoring to optimize solar tracker performance, and supportive government policies promoting clean energy adoption.

One of the key growth factors propelling the Solar Tracker Integrated Weather Station Market is the increasing deployment of utility-scale solar power plants across the globe. As solar energy becomes a critical component of national energy strategies, the need for precise weather data to maximize the efficiency of solar trackers has never been more important. Integrated weather stations provide real-time meteorological information, enabling solar trackers to adjust panel orientation for optimal sunlight exposure. This leads to higher energy yields, improved operational efficiency, and lower maintenance costs. Furthermore, the integration of advanced sensors and IoT-enabled data loggers has enhanced the accuracy and reliability of these systems, making them indispensable for modern solar installations.

Another significant driver for this market is the rapid technological advancements in sensor technology and communication modules. With the proliferation of smart grids and the need for seamless connectivity in energy management systems, solar tracker integrated weather stations are increasingly equipped with wireless communication capabilities, advanced data analytics, and cloud-based platforms. These innovations allow for remote monitoring, predictive maintenance, and real-time decision-making, which are crucial for large-scale solar projects. Additionally, the declining cost of sensors and communication modules has made these integrated systems more accessible to commercial and residential users, further expanding the market’s addressable base.

Policy support and incentives from governments worldwide are also playing a pivotal role in the growth of the Solar Tracker Integrated Weather Station Market. Countries in Asia Pacific, North America, and Europe have introduced favorable regulations and financial incentives to accelerate the adoption of solar energy. These policies not only encourage the deployment of solar trackers but also mandate the use of advanced weather monitoring systems to ensure grid stability and maximize renewable energy output. As a result, the integration of weather stations with solar trackers is becoming a standard practice in new solar projects, driving sustained market expansion.

Regionally, Asia Pacific is emerging as the largest and fastest-growing market for solar tracker integrated weather stations, supported by massive investments in solar infrastructure in China, India, and Southeast Asia. North America and Europe are also witnessing significant growth, driven by ambitious renewable energy targets and the modernization of existing solar assets. In contrast, Latin America and the Middle East & Africa are gradually catching up, with increasing solar project pipelines and a growing focus on sustainable energy solutions. The regional landscape is characterized by varying degrees of technology adoption, regulatory frameworks, and investment levels, shaping the overall trajectory of the global market.

Product Type Analysis

The Solar Tracker Integrated Weather Station Market can be segmented by product type into Single-Axis and Dual-Axis Solar Tracker Integrated Weather Stations. Single-axis solar tracker integrated weather stations are widely adopted due to their cost-effectiveness and relatively simpler design, making

Based on studies in the 1980s linking stream water quality (acidification) and atmospheric acid deposition, the GRSM initiated a long-term continuous water quality monitoring program in the early 1990s. The current sampling design consists of two parts: 1) detailed long-term hydrologic and water quality monitoring; and 2) a Park-widestream survey designed to characterize water quality under base-flow conditions throughout the GRSM.&nbsp. Park-wide stream survey (also called Long-term Synoptic Stream Water Quality Monitoring) began in October 1993 to monitor water quality in GRSM streams, and simultaneously assess possible correlations between GRSM water chemistry and atmospheric sources of acid-generating pollutants. Five NDW hydrological stations were installed to monitor the potential effects of long-term acid deposition. These stations include: wet precipitation (open site, OS), throughfall (TF), soil water from lysimeters, and two streamlets (southeast, SE; and northeast, NE sites). This monitoring design provides a means to assess impacts from acidic deposition, both wet and dry deposition (OS, TF sites), effects of soil biogeochemical processes on pollutant fate and transport, and stream acidification response based on levels of atmospheric acid inputs to the watershed.

The Snow and Water Interactive Map displays both current and historic hydrometeorological data in an easy-to-use, visual interface. The Snow and Water Interactive Map displays both current and historic hydrometeorological data in an easy-to-use, visual interface. The information on the map comes from many sources. Natural Resources Conservation Service snowpack and precipitation data are derived from manually-collected snow courses and automated Snow Telemetry (SNOTEL) and Soil Climate Analysis Network (SCAN) stations. Other data sources include precipitation, streamflow, and reservoir data from the U.S. Bureau of Reclamation (BoR), the Applied Climate Information System (ACIS), the U.S. Geological Survey (USGS), and other hydrometeorological monitoring entities. Information supplied by the map is updated three times daily.

The Interactive Map has two regions: the map display (on the left) and the map controls (on the right). You use the map controls to determine both the display mode and the types of data and stations to show on the map.

Scandinavian / North European Network of Terrestrial Field Bases (SCANNET) is a network of terrestrial field bases, research stations managers, and user groups that are collaborating to improve comparative observations and access to data and information on environmental change in the North. SCANNET partners provide stability for research and facilitate long term observations in terrestrial and freshwater systems.

Northern landscapes are some of Europe's last wilderness areas and contain specialized and diverse plants and animals as well as large stores of soil carbon. However these regions are experiencing rapid environmental and social changes and are particularly vulnerable to predicted climatic changes. SCANNET seeks to facilitate research into these changes and their implications for the North and for lower latitudes. Scannet is funded by the European Commission, Research DG Key Action Global Change and Biodiversity.

SCANNET includes the following research field sites:

Abisko Scientific Research Station, The Royal Swedish Academy of Sciences, Abisko, Sweden (68 degree 21' N, 18 degree 49' E) [http://www.ans.kiruna.se]

Allt a' Mharcaidh, Cairngorms Mountains, Banchory Research Station, Centre for Ecology and Hydrology (CEH), Scotland (57 degree 07' N, 3 degree 49' W) [http://banchory.ceh.ac.uk/]

Dovrefjell Field Site, Dovrefjell National Park, Dovre, Norway (62 degree 18' N, 9 degree 17' E) [http://www.niva.no/]

Kevo Subarctic Research Institute, University of Turku, Utsjoki, Finland (69 degree 45' N, 27 degree 01' E) [http://www.utu.fi/erill/kevo/]

Kilpisjarvi Biological Station, University of Helsinki, Kilpisjarvi, Finland (69 degree 03' N, 20 degree 50' E) [http://www.helsinki.fi/ml/kilpis]

Litla-Skard Field Station, Icelandic Institute of Natural History, Litla-Skard , Iceland (64 degree 43' N, 21 degree 37' W) [http://www.ni.is/]

Ny-Alesund International Arctic Environmental Research and Monitoring Facility, Spitsbergen, Svalbard Islands, Norway (78 degree 55'N, 11 degree 56'E) [http://www.npolar.no/nyaa-lsf]

Sornfelli Arctic Climate Station, The Faroese Geological Survey, Streymoy Island, Faroe Islands [http://www.jfs.fo/]

Zackenberg Station, Danish Polar Center , Young Sund - Tyrolerfjord, Greenland (74 degree 30' N, 21 degree 00' W) [http://www.zackenberg.dk]

Several science topics are being pursued by SCANNET. These investigations include the following:

(1) Climate Change Scenarios for the SCANNET Region (Completed). This workpackage compiles and presents general, regional and site specific information on climate change scenarios, based on existing General CirculationModel (GCM) simulations, and on downscaling experiments -- both dynamic (through regional climate models), and empirical. The primary selection of GCMs are the five selected by the Arctic Climate Impact Assessment (ACIA). The focus is on temperature and precipitation, with discussions on wind, snow cover, and runoff. The workpackage cooperates with workpackage 5 on snow cover change scenarios.

(2) Climate Trends (In Progress). The aim of this work package is to perform a regional assessment of climatic variability from different sources at each of the SCANNET sites. Investigators will identify and analyze detailed climate parameters (and proxies) other than long term means, relevant to various environmental change impacts such as phenology, winter damage to vegetation, etc. Contact Jack Kohler Jack@npolar.no for more information.

(3) Biodiversity (In Progress). The aim of this work package is to compile and present regional and site specific information on biodiversity at different levels. Baseline information on habitat and species diversity is assessed and presented at different spatial levels from sites to natural historic provinces. The often low species diversity at SCANNET sites is due to climatic severity, spatial isolation and recent deglaciation. However, some taxa (e.g., waders, sawflies) show relatively high species diversity in northern areas. Intraspecific variability and active speciation are frequent phenomena in the area. Assessment of the diverse baseline information on habitat richness and species diversity will preceed improvement in standardisation and comparability of observations. Check lists for major groups (birds, vascular plants, lichens) have been compiled and selected invertebrate groups are now addressed (Lepidoptera, Carabidae, Aquatic insects). Contact Seppo Neuvonen sepne@utu.fi for more information.

(4) Phenology and Species Performance (In Progress). The main aim is the construction of a meta database of monitoring data from field stations and adjacent sites. By compiling information from a large number of studies, investigators will produce a unique review of existing long-term data series on terrestrial species. Th... Visit https://dataone.org/datasets/Scandinavian_North_European_Network_of_Terrestrial_Field_Bases_(SCANNET).xml for complete metadata about this dataset.

This layer contains present and historic locations and metadata for weather stations from the Enhanced Master Station History Report (EMSHR) managed by NOAA NCEI. Use this layer to join weather or climate information from the station records available from the various observation programs. For instance: Global Historical Climatology Network - Daily (GHCN-Daily) World Meteorological Organization Standard Normals Applied Climate Imformation System (ACIS) (U.S. Only) Cooperative Observer Network (COOP) Weather stations are moved for a variety of reasons, sometimes several kilometers from their previous location. Thus, it is often important use the exact location of the station and its context when conducting analysis of the values observed at that location. This layer contains one point for each location and date range where a given weather station has been or is currently located.Below are the field names and description of the data in each field:NCDC - NCEI Unique ID: Unique identifier of source NCEI system. GHCN-D ID: Populated if station is included in GHCN-Daily dataset. ICAO ID: Used for geographical locations throughout the world, managed by the International Civil Aviation Organization. WBAN ID: Assigned by NCEI, used for digital data storage and general station identification purposes. FAA ID: Alpha-numeric, managed by USDT Federal Aviation Administration used for site identification of sites vital to navigation. Commonly referred to as "Call Sign". NWSLI ID: Alpha-numeric, location identifier assigned by the National Weather Service for use in real-time data transmissions and forecasts. WMO ID: Assigned by World Meteorological Organization, used for international weather data exchange and station documentation. COOP ID: Assigned by NCEI, first 2 digits represent state, last 4 digits are assigned numerically by alphabetical ordering of the station name. Transmittal ID: Holds miscellaneous IDs that do not fall into an officially sourced ID category that are needed in support of NCEI data datasets and ingests. Principal Station Name: Name of station, upper case may contain characters, numbers or symbols. Begin Date: Beginning date of record, set to 10101 if date is unknown. End Date: Ending date of record, set to 99991231 if station is currently open. Latitude (dd): Decimal degree latitude, "-" indicates South. Longitude (dd): Decimal longitude, "-" indicates West. Ground Elev. (ft): Ground elevation. For Coop network, average elevation of the ground in a 20-m (60-ft) circle around the primary rain gauge. For 1st and 2nd Order stations, elevation of the official temperature sensor for the station. Ground Elev. (m): Ground Elev. (ft) converted to meters. U.S. Climate Division: Usually contains a number between 1 and 12 indicating climate division as determined by master divisional reference maps. Assigned by NCEI. Relocation Note: Distance and direction of station relocation expressed as a distance value (1-4 characters), space, distance units (2 character abbreviation), space, and direction (1-3 character 16-point cardinal direction). Date of relocation indicated by begin date of record. Location Precision: Indicates precision of source lat and lon using the below codes: DD Whole DegreesDDMM Degrees, Whole MinutesDDMMSS Degrees, Whole Minutes, Whole SecondsDDd Decimal Degrees, to TenthsDDdd Decimal Degrees, to HundredthsDDddd Decimal Degrees, to ThousandthsDDdddd Decimal Degrees, to Ten ThousandthsDDddddd Decimal Degrees, to Hundred ThousandthsDDMMm Degrees, Decimal Minutes to TenthsDDMMmm Degrees, Decimal Minutes to HundredthsDDMMmmm Degrees, Decimal Minutes to ThousandthsDDMMSSs Degrees, Minutes, Decimal Seconds to TenthsDDMMSSss Degrees, Minutes, Decimal Seconds to HundredthsDDMMSSss Degrees, Minutes, Decimal Seconds to HundredthsGHCN-D IDs: This was added by Esri as an aid for database management.1 = NCDC ID has two or more GHCN-D IDs. 0 = NCDC ID has 0 or 1 GHCN-D ID. Cite as:

Esri, 2019: EMSHR Station Locations and Metadata 1738 to Present. ArcGIS Online, Accessed

Russell S. Vose, Shelley McNeill, Kristy Thomas, Ethan Shepherd (2011): Enhanced Master Station History Report of March 2019. NOAA National Climatic Data Center. April 10, 2019. doi:10.7289/V5NV9G8D.

This data set provides atmospheric deposition data for Walker Branch Watershed. The data were collected during the period 1980 - 2000 as part of the National Atmospheric Deposition Program /National Trends Network.

The National Atmospheric Deposition Program/National Trends Network (NADP/NTN) is a nationwide network of precipitation monitoring sites. The network is a cooperative effort between many different groups, including the State Agricultural Experiment Stations, U.S. Geological Survey, U.S. Department of Agriculture, and numerous other governmental and private entities.

The purpose of the network is to collect data on the chemistry of precipitation for monitoring of geographical and temporal long-term trends. The precipitation at each station is collected weekly according to strict clean-handling procedures. It is then sent to the Central Analytical Laboratory where it is analyzed for hydrogen (acidity as pH), sulfate, nitrate, ammonium, chloride, and base cations (such as calcium, magnesium, potassium and sodium).

Metadata record for data from ASAC Project 1172 See the link below for public details on this project. ---- Public Summary from Project ---- The aim of ice core drilling is to extract information which aids in the reconstruction of past climatic conditions. There has been limited research, however, towards calibrating records preserved in the ice sheet with atmospheric conditions. The intention of this project is to conduct a detailed study of individual snowfall events in the upper few meters of snow and their relationship with observed meteorological conditions during the events.

Na, Ca, Cl, K, Mg are all sea salt indicator species. NO3 is a summer marker (debate exists on the primary source of this species).

Work has been carried out over three field seasons to date. 1999/2000, 2001/2002, 2004/2005.

The data are held as a series of excel spreadsheets (.xls). The files relate to snow pit and PICO ice core data collected over 2 summers at Law Dome for ASAC 1172. Below is a summary of the spreadsheets.

Summer 99/00 PICO cores between 3-10m: 1181_99b MPC N4k S0k S0p1k S0p3k S0p7k S1p7k S3p7k S7p7k

Snow pits to 3m: Rama Paddy Karioke

Summer 01/02 PICO cores between 3-10m: CC1 CC2 DSS0102

Snow pit to 2.25m: Matilda

The fields in this dataset are:

Depth Concentrations of MSA (Methansulphonic acid) Concentrations of Cl Concentrations of NO3 Concentrations of SO4 Concentrations of nss SO4 (non-seasalt sulphate) Concentrations of Na Concentrations of K Concentrations of Mg Concentrations of Ca

View the locations of hydrologic data stations along with interpolated hydrologic data sets.The Drought Risk Atlas provides historic data about drought through 2012 for weather stations across the United States that have at least 40 years of records. Users can select a station and view data for several drought indices over time, frequency statistics for drought thresholds, drought period information, and index comparisons. Where do these data come from?The Drought Risk Atlas uses precipitation records from the National Weather Service Cooperative data (COOP) that is archived by the Regional Climate Centers (RCC) in their Applied Climate Information System (ACIS).

The Mohonk Preserve has been collecting data on acid rain precipitation at Mohonk Lake since January 1976. The Level1_MohonkPrecipData includes precipitation levels recorded either during an acid rain precipitation event or at the conclusion of the event. This dataset additionally includes the pH for select precipitation events. The Level2_MohonkPrecipData is comprised of precipitation levels recorded following an acid rain precipitation event and their respective pH. This dataset defines a storm as all precipitation events within a 24-hour window.The rain is collected via a National Weather Service Rain Gauge by members of the Mohonk Preserve. The pH is then measured by the members at a Cooperative Weather Service Station at Mohonk Lake.

This study was undertaken to assess the current acid deposition monitoring network in Alberta, to rank the importance of the current network's nine stations, and to identify the locations of future optimal monitoring stations for Alberta.

Flexibility in activity timing may enable organisms to quickly adapt to environmental changes. Under global warming, diurnally adapted endotherms may achieve a better energy balance by shifting their activity towards cooler nocturnal hours. However, this shift may expose animals to new or increased environmental challenges (e.g., increased predation risk, reduced foraging efficiency). We analysed a large dataset of activity data from 47 ibex (Capra ibex) in two protected areas, characterized by varying levels of predation risk (presence vs absence of the wolf – Canis lupus). We found that ibex increased nocturnal activity following warmer days and during brighter nights. Despite the considerable sexual dimorphism typical of this species and the consequent different predation-risk perception, males and females demonstrated consistent responses to heat in both predator-present and predator-absent areas. This supports the hypothesis that shifting activity towards nighttime may be a common strategy adopted by diurnal endotherms in response to global warming. As nowadays different pressures are pushing mammals towards nocturnality, our findings emphasize the urgent need to integrate knowledge of temporal behavioural modifications into management and conservation planning. Methods Activity data logging: The activity data of individual ibex (18 males and 9 females in PNGP, 12 males and 8 females in SNP, more details in the Supporting Information Table S1 and Table S2) were recorded by means of a dual-axis motion sensor (i.e., accelerometer) fitted on the collars. The accelerometer simultaneously measures along two orthogonal directions the changes in acceleration associated with the actual motion experienced by the collar. On the X-axis, the accelerometer was sensitive to acceleration events with forward/backward direction/axes, while on the Y-axis, it recorded acceleration events with a sideways and rotary direction. The accelerometer had a dynamic range of ±2g and measured activity as the change of static acceleration (gravity) and dynamic acceleration (collar) with a frequency of 4 Hz. The motion data from accelerometers, i.e., activity values, were calculated as the difference between consecutive measurements, averaged over a time interval of 4 or 5 minutes and given within a relative range between 0 (no difference between consecutive data) and 255 (difference of -2 g/+2 g), with the associated date and time. The activity data recorded were downloaded by means of a handheld terminal (Vectronic Aerospace GmbH, Berlin) and Yagi antenna. Weather and Astronomical Data: Weather data such as hourly air temperature (°C) and hourly precipitation (i.e., the amount of rain expressed in millimetres of water) were provided by Meteorological Service of Regione Autonoma Valle d’Aosta (weather station of Pont, 45° 31′ N, 7° 12′ E; 1951 m a.s.l.) and by the Federal Office for Meteorology and Climatology (weather station of Samedan, 46° 31′ N; 9° 52′ E; 1710 m a.s.l.), for the GPNP and SNP study areas, respectively. We a priori chose to use temperature rather than radiation (which are highly correlated) because previous research suggested that air temperature was the main driver affecting ibex spatial choices (Brivio et al. 2019). Moon illumination was calculated using the suncalc package in R (Thieurmel and Elmarhraoui 2019) and was expressed as the illuminated fraction of the moon, which ranged from 0.0 (new moon) to 1.0 (full moon). Cloud cover estimates were downloaded from the NCEP/NCAR data set (Kalnay et al. 1996) by using the interpolation method “Inverse Distance Weighting” (Shepard 1968) by means of the NCEP.interp function in the RNCEP package in R (Kemp et al. 2012). Cloud cover data was expressed as the percentage of sky covered by clouds and had a spatial and temporal gridded resolution of 2.5° and 6 hours, respectively. In our analyses, only cloud cover data recorded at 00.00 AM were used.

We, the staff, volunteers and associates of the Mohonk Preserve, have been collecting precipitation data at Mohonk Lake since January 1976. The Level1_MohonkPrecipData includes precipitation depths recorded either during or at the conclusion of every precipitation event. This dataset additionally includes pH measurements for most precipitation collections.The Level2_MohonkPrecipData summarizes data collections into precipitation events where the precipitation depth is cumulative and the pH is averaged for all data collections in that event. We measured precipitation via a National Weather Service Rain Gauge and the pH was measured at a Cooperative Weather Service Station at Mohonk Lake.