Saudi Arabia hourly climate integrated surface data with the below data observations, WindSky conditionVisibilityAir temperatureDewSea level pressureNote: The dataset will contain the last 5 years hourly data, however, check the attachments section in this dataset if you need historical data.

DATA SOURCE: National Institute for Water and Atmospheric Research (NIWA) [Technical report available at https://www.mfe.govt.nz/publications/environmental-reporting/ministry-environment-atmosphere-and-climate-report-2020-updated]

Adapted by Ministry for the Environment and Statistics New Zealand to provide for environmental reporting transparency

This lowest aggregation dataset, was used to develop three ‘Our Atmosphere and Climate’ indicators. See Statistics New Zealand indicator links for specific methodologies and state/trend datasets (see ‘Shiny App’ downloads). 1) Temperature (https://www.stats.govt.nz/ndicators/temperature) 2) First and last frost days (https://www.stats.govt.nz/ndicators/frost-and-warm-days) 3) Growing degree days (https://www.stats.govt.nz/ndicators/growing-degree-days)

IMPORTANT INFORMATION Due to the size of this dataset (111 MB), a 32-bit version of Microsoft Excel will only display/download ~ 1 million rows. A DBMS, statistical or GIS application is needed to view the entire dataset.

This dataset shows two measures of temperature change in New Zealand: New Zealand’s national temperature from NIWA’s ‘seven-station’ temperature series from 1909 to 2019, and temperature at 30 sites around the country from at least 1972 to 2019. For national temperature, we report daily average, minimum and maximum temperatures. We also present New Zealand national and global temperature anomalies.

More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

Grass-Cast: Experimental Grassland Productivity Forecast for the Great Plains Grass-Cast uses almost 40 years of historical data on weather and vegetation growth in order to project grassland productivity in the Western U.S. More details on the projection model and method can be found at https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.3280. Every spring, ranchers in the drought‐prone U.S. Great Plains face the same difficult challenge—trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass‐Cast, to provide science‐informed estimates of growing season aboveground net primary production (ANPP). Grass‐Cast uses over 30 yr of historical data including weather and the satellite‐derived normalized vegetation difference index (NDVI)—combined with ecosystem modeling and seasonal precipitation forecasts—to predict if rangelands in individual counties are likely to produce below‐normal, near‐normal, or above‐normal amounts of grass biomass (lbs/ac). Grass‐Cast also provides a view of rangeland productivity in the broader region, to assist in larger‐scale decision‐making—such as where forage resources for grazing might be more plentiful if a rancher’s own region is at risk of drought. Grass‐Cast is updated approximately every two weeks from April through July. Each Grass‐Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real‐time 8‐d NDVI can be used to supplement Grass‐Cast in predicting cumulative growing season NDVI and ANPP starting in mid‐April for the Southern Great Plains and mid‐May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass‐Cast along with the county‐level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end‐of‐growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass‐Cast end‐of‐growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20‐yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass‐Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production. This new experimental grassland forecast is the result of a collaboration between Colorado State University, U.S. Department of Agriculture (USDA), National Drought Mitigation Center, and the University of Arizona. Funding for this project was provided by the USDA Natural Resources Conservation Service (NRCS), USDA Agricultural Research Service (ARS), and the National Drought Mitigation Center. Watch for updates on the Grass-Cast website or on Twitter (@PeckAgEc). Project Contact: Dannele Peck, Director of the USDA Northern Plains Climate Hub, at dannele.peck@ars.usda.gov or 970-744-9043. Resources in this dataset:Resource Title: Cattle weight gain. File Name: Cattle_weight_gains.xlsxResource Description: Cattle weight gain data for Grass-Cast Database. Resource Title: NDVI. File Name: NDVI.xlsxResource Description: Annual NDVI growing season values for Grass-Cast sites. See readme for more information and NDVI_raw for the raw values. Resource Title: NDVI_raw . File Name: NDVI_raw.xlsxResource Description: Raw bimonthly NDVI values for Grass-Cast sites. Resource Title: ANPP. File Name: ANPP.xlsxResource Description: Dataset for annual aboveground net primary productivity (ANPP). Excel sheet is broken into two tabs, 1) 'readme' describing the data, 2) 'ANPP' with the actual data. Resource Title: Grass-Cast_sitelist . File Name: Grass-Cast_sitelist.xlsxResource Description: This provides a list of sites-studies that are currently incorporated into the Database as well as meta-data and contact info associated with the data sets. Includes a 'readme' tab and 'sitelist' tab. Resource Title: Grass-Cast_AgDataCommons_overview. File Name: Grass-Cast_AgDataCommons_download.htmlResource Description: Html document that shows database overview information. This document provides a glimpse of the data tables available within the data resource as well as respective meta-data tables. The R script (R markdown, .Rmd format) that generates the html file, and can be used to upload the Grass-Cast associated Ag Data Commons data files can be downloaded at the 'Grass-Cast R script' zip folder. The Grass-Cast files still need to be locally downloaded before use, but we are looking to make a download automated. Resource Title: Grass-Cast R script . File Name: R_access_script.zipResource Description: R script (in Rmarkdown [Rmd] format) for uploading and looking at Grass-Cast data.

This record links to Bureau of Meteorology "Precis forecast" information for Western Australia, available through an ftp download. The Bureau of Meteorology's "Precis forecast - Western Australia" …Show full descriptionThis record links to Bureau of Meteorology "Precis forecast" information for Western Australia, available through an ftp download. The Bureau of Meteorology's "Precis forecast - Western Australia" product contains a 7 day forecast, per location across Western Australia, with daily projected values for temperature, rainfall and weather conditions. Data (7-day precis forecast data, for Western Australia) is available in XML format. (The plain text and html formats were withdrawn in Feb 2016). Place Names in the xml are the same as those that were used in the plain text and html format files. The XML file uses the AAC location code (and location name), rather than the StationID code. The coordinates related to each AAC code/ location name, in the XML formatted file, are listed in the PointPlaces [IDM00013.*] data files, available from ftp://ftp.bom.gov.au/anon/home/adfd/spatial/IDM00013.dbf [open the dbf file, using Excel]. Note that the precis forecasts relate to an area surrounding the nominated location, the coordinates of which are intended to be the "centre of town" for that location ( as derived from Geoscience Australia's placename Gazetteer)". Data content As well as forecast values [per day, across 7 days] for minimum and maximum temperature, rainfall (range and probability), and a precis of expected weather conditions for locations in Western Australia, the dataset (latest forecast only) also contains information on when the file was created, and the timespan that a value applies to.

Metadata record for data from ASAC Project 2386 See the link below for public details on this project.

---- Public Summary from Project ---- 'Frozen dunes: An indicator of climate variability, McMurdo Dry Valleys, Antarctic'. This is a two year study involving scientists from Australia and New Zealand, which aims to use the internal structure of frozen sand dunes to identify climate change in the unique hyper-arid region of the McMurdo Dry Valleys.

This metadata record describes data collected from an automatic weather station (AWS) situated in the McMurdo Dry Valleys of Antarctica.

The download file contains an excel spreadsheet, which provides some general site information about the location of the AWS, as well as ten minute observations from the end of November 2004 to early December 2004.

The fields in this dataset are:

Date Time Pressure (mbar) Wind Speed (m/s) Gust Speed (m/s) Temperature (degrees C) Wind Direction (degrees) Solar Radiation (W/m^2) Dew Point (degrees C) Relative Humidity (%)

This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This report describes the data collected for ASAC project 2363 (a continuation of ASAC 1158). A full report of the data collected and the work completed is available for download with the dataset.

The download file contains data in the following areas:

Ablation Chemistry DEM - Digital Elevation Model Lagoon Bathymetry Meltwater Photos Report RES - Radio Echo Sounder Surveys Weather Observations

This CD contains data collected by the Heard Island glaciology team during the 2003-04 Australian Antarctic Division expedition, as well as some data from the previous expedition in November 2000. The data report associated with these files is provided as a PDF in the Report folder.

Description of data files available on CD

Ablation folder survey_canes_ablation.xls Excel file with the measured height of each survey wand above snow/ice surface for the field season. BG35_pinger.xls Excel file with sonic ranger data for BG35. BG50_pinger.xls Excel file with sonic ranger data for BG50.

Chemistry folder Ion_Chemistry.xls Excel file with analyses of chloride, sulphate, nitrate, Mg, Ca, Na of samples collected from crevasses and cores. Oxygen_isotopes.xls Excel file with dO18 analyses of samples collected from crevasses and cores.

DEM folder dem_2003.grd ASCII file with the 2003 DEM grid, generated using Golden Software Surfer v7.0. Header file format is:

id The identification string DSAA that identifies the file as an ASCII grid file.

nx nynx is the integer number of grid lines along the X axis (columns) ny is the integer number of grid lines along the Y axis (rows)

xlo xhi xlo is the minimum X value of the grid xhi is the maximum X value of the grid

ylo yhi ylo is the minimum Y value of the grid yhi is the maximum Y value of the grid

zlo zhi zlo is the minimum Z value of the grid zhi is the maximum Z value of the grid

grid row 1 grid row 2 grid row 3 -these are the rows of Z values of the grid, organized in row order. Each row has a constant Y coordinate. Grid row 1 corresponds to ylo and the last grid row corresponds to yhi. Within each row, the Z values are arranged from xlo to xhi. Blanked grid nodes are given a Z value of 1.070141E+038. Rows are 39.855 m apart, Columns are 40 m apart.

dem_11.xls Excel file with all points used to calculate the dem_2003.xls grid (refer to A2). The folder also contains high resolution jpeg images of Fig. 16 and the data distribution figure (A2).

Lagoon bathymetry folder Folder containing Excel files with Easting, Northing (acquired using Garmin GPS V; WGS84, UTM zone 43) and depth (acquired using Garmin 'Fishfinder' depth sounder) for each lagoon surveyed. Also high resolution jpeg images of bathymetric maps reproduced in appendix A3.

Meltwater folder Contains an excel file with stream profiles and flux calculations, and repeat measurements of Brown Lagoon outflow stream. Also contains jpeg photos of three of the inflow streams, and an image showing their location using the Quickbird satellite image for reference.

Photos folder Contains jpeg digital photos used in this report.

Report folder HI_data_report_screen.pdf HI_data_report_print.pdf This data report is reproduced as both a low and high resolution Adobe Acrobat PDF file, for on-screen viewing and printing respectively.

RES folder BG_35_2000.xls Excel file with RES data for the BG35 profile, 2000 field season. RES.xls Excel file with RES data for the BG25 and BG20 profiles, 2003-04 field season.

Surveys folder all_survey_points.xls Excel file with the position of the survey markers and additional points. daily_position_BG50.xls Excel file with daily (occasionally more frequent) DGPS position near BG50 kinematic_2000.xls Excel file with all DGPS kinematic surveys conducted during the 2000 field season. kinematic_surveys.xls Excel file with all DGPS kinematic surveys conducted during the 2003-04 field season. surface_site_surveys.xls Excel file with the DGPS repeat survey positions of each survey site, for the 2000 and 2003-04 field seasons, and velocity calculations for each epoch. terminus_surveys.xls Excel file with the DGPS surveys of the position of the glacier terminus.

Weather observations folder AANDERAA_data.xls Excel file with data recorded by the automatic weather station at 550 m a.s.l. all_data_comparison.xls Excel file with compilation and graphs of all data from each of the Brown Glacier AWS. MAWS1_data.xls Excel file with data recorded by the automatic weather station at 115 m a.s.l. MAWS2_data.xls Excel file with data recorded by the automatic weather station at 920 m a.s.l. precipitation_record.xls Excel file with rain gauge records from Jacka Valley, Brown Hut, Spit Bay and Capsize Beach. ttec_T_RH_data.xls Excel file containing temperature and relative humidity data from the three T-TEC loggers, deployed at Jacka Valley, Capsize Beach, and Doppler Hill. wx station photos folder Folder containing jpeg photos of each of the weather stations, as well as the field camp obs...

This is data on the Rainfall(mm) and Temperature(Celsius) of Kenya between the years 1991 to 2016. This data was collected by the climate knowledge portal by the World Bank.

This record links to Bureau of Meteorology metadata for each State's latest "Precis forecast" information, available through an ftp download.

The Bureau of Meteorology's "Precis forecast" product (per State) contains the latest 7 day forecast, per location across that State, with daily projected values for temperature, rainfall and weather conditions.

Data (7-day precis forecast data, for {{State}}) is available in XML format. (The plain text and html formats were withdrawn in Feb 2016)

Place Names are the same, in the plain text, html and xml format files.

The XML file uses the AAC location code (and location name), rather than the StationID code. The coordinates related to each AAC code/ location name, in the XML formatted file, are listed in the PointPlaces [IDM00013.*] data files, available from ftp://ftp.bom.gov.au/anon/home/adfd/spatial/IDM00013.dbf [open the dbf file, using Excel].

Note that the precis forecasts relate to an area surrounding the nominated location, the coordinates of which are intended to be the "centre of town" for that location ( as derived from Geoscience Australia's placename Gazetteer)".

Data content

As well as forecast values [per day, across 7 days] for minimum and maximum temperature, rainfall (range and probability), and a precis of expected weather conditions, each file contains information on when the file was created, and the timespan that a value applies to.

User /Licence information

• The Bureau does not guarantee the availability of information on the ftp site.
• Please read how to access the Bureau ftp site and its structure.
• The Service Announcements page details changes to Bureau services.

• Use of data should be in accordance with the copyright notice and disclaimer.

• Secondary distribution of Bureau of Meteorology information currently freely available on the Bureau website and ftp sites requires formal permission.

• Correct attribution of the Australian Bureau of Meteorology as the source of Bureau information is an important component of any secondary distribution permission that may be granted. Where Bureau information is to be used on a website, permission for use of that information should be applied for by the website owner.

Abstract copyright UK Data Service and data collection copyright owner.

The heat pump monitoring datasets are a key output of the Electrification of Heat Demonstration (EoH) project, a government-funded heat pump trial assessing the feasibility of heat pumps across the UK’s diverse housing stock. These datasets are provided in both cleansed and raw form and allow analysis of the initial performance of the heat pumps installed in the trial. From the datasets, insights such as heat pump seasonal performance factor (a measure of the heat pump's efficiency), heat pump performance during the coldest day of the year, and half-hourly performance to inform peak demand can be gleaned.

For the second edition (December 2024), the data were updated to include cleaned performance data collected between November 2020 and September 2023. The only documentation currently available with the study is the Excel data dictionary. Reports and other contextual information can be found on the Energy Systems Catapult website.

The EoH project was funded by the Department of Business, Energy and Industrial Strategy. From 2023, it is covered by the new Department for Energy Security and Net Zero.

Data availability

This study comprises the open-access cleansed data from the EoH project and a summary dataset, available in four zipped files (see the 'Access Data' tab). Users must download all four zip files to obtain the full set of cleansed data and accompanying documentation.

When unzipped, the full cleansed data comprises 742 CSV files. Most of the individual CSV files are too large to open in Excel. Users should ensure they have sufficient computing facilities to analyse the data.

The UKDS also holds an accompanying study, SN 9049 Electrification of Heat Demonstration Project: Heat Pump Performance Raw Data, 2020-2023, which is available only to registered UKDS users. This contains the raw data from the EoH project. Since the data are very large, only the summary dataset is available to download; an order must be placed for FTP delivery of the remaining raw data. Other studies in the set include SN 9209, which comprises 30-minute interval heat pump performance data, and SN 9210, which includes daily heat pump performance data.

The Python code used to cleanse the raw data and then perform the analysis is accessible via the Energy Systems Catapult Github

Heat Pump Performance across the BEIS funded heat pump trial, The Electrification of Heat (EoH) Demonstration Project. See the documentation for data contents.

Since 2007, the Coral Reef Research Foundation (CRRF) has operated a Campbell Scientific automatic weather station (AWS) in Palau designed to measure meteorological/atmospheric conditions relevant to Koror State's Rock Islands Southern Lagoon, a World Heritage Site. With little flat land in the Rock Islands, the weather station is located on a 40-ft tower situated on a karst ridge on Ngeanges Island at 100 ft elevation, about 5.4 km (3.5 mi) from CRRF's study site at Jellyfish Lake. It measures a suite of atmospheric conditions for comparison with CRRF's temporary, floating weather station located on a tripod in Jellyfish Lake, and provides vital data for studying how local weather conditions and ENSO events affect the marine lake environment. _NCProperties=version=2,netcdf=4.6.3,hdf5=1.10.2 acknowledgement=This weather station is owned and operated by the Coral Reef Research Foundation (CRRF). The original deployment and maintenance of the station was funded by the David and Lucille Packard Foundation for 8 years, with government permission by Koror State. The Pacific Islands Ocean Observing System (PacIOOS) is funded through the National Oceanic and Atmospheric Administration (NOAA) as a Regional Association within the U.S. Integrated Ocean Observing System (IOOS). PacIOOS is coordinated by the University of Hawaii School of Ocean and Earth Science and Technology (SOEST). cdm_data_type=TimeSeries cdm_timeseries_variables=station_name, longitude, latitude, altitude citation=Citation to be used in publications should follow the form: "PacIOOS. [year-of-data-download], [Title], [Data access URL], accessed [date-of-access]." comment=Data produced by Sharon Patris (crrfpalau@gmail.com). contributor_email=crrfpalau@gmail.com contributor_institution=Coral Reef Research Foundation (CRRF) contributor_role=originator contributor_type=institution contributor_url=https://coralreefpalau.org Conventions=CF-1.6, ACDD-1.3, IOOS-1.2 data_center=Pacific Islands Ocean Observing System (PacIOOS) data_center_email=info@pacioos.org date_metadata_modified=2022-11-14 defaultDataQuery=time,air_temperature,buoy_air_temperature,photosynthetic_radiation,rainfall_amount,relative_humidity_max,shortwave_radiation,wind_from_direction,wind_speed,&time>=max(time)-3days defaultGraphQuery=time,air_temperature&time>=max(time)-3days&.draw=lines distribution_statement=PacIOOS data may be re-used, provided that related metadata explaining the data have been reviewed by the user, and that the data are appropriately acknowledged. Data, products and services from PacIOOS are provided "as is" without and warranty as to fitness for a particular purpose. Easternmost_Easting=132.371 featureType=TimeSeries geospatial_bounds=POINT Z (7.20915 132.371 42.7) geospatial_bounds_crs=EPSG:4326 geospatial_bounds_vertical_crs=EPSG:5829 geospatial_lat_max=7.20915 geospatial_lat_min=7.20915 geospatial_lat_resolution=0.0 geospatial_lat_units=degrees_north geospatial_lon_max=132.371 geospatial_lon_min=132.371 geospatial_lon_resolution=0.0 geospatial_lon_units=degrees_east geospatial_vertical_max=42.7 geospatial_vertical_min=42.7 geospatial_vertical_positive=up geospatial_vertical_resolution=0.0 geospatial_vertical_units=m grid_mapping_epsg_code=EPSG:4326 grid_mapping_inverse_flattening=298.25723 grid_mapping_long_name=coordinate reference system grid_mapping_name=latitude_longitude grid_mapping_semi_major_axis=6378137.0 gts_ingest=false history=2015-03-01T00:00:00Z CRRF (L. Colin) supplied 2007-2015 data to PacIOOS as Excel and PacIOOS converted to NetCDF. 2015-08-13T00:00:00Z NetCDF format converted to CF-1.6 discrete geometry (DSG). 2020-01-03T00:00:00Z NetCDF format modified to match NCEI_NetCDF_TimeSeries_Incomplete_Template_v2.0. 2021-03-08T00:00:00Z NetCDF variables station_name, platform1, instrument1, and crs converted from int to string and now contain respective data values. 2022-11-14T12:16:00Z Dataset identifier modified from "AWS-CRRF" to "aws_crrf" to satisfy future ERDDAP requirements. id=aws_crrf infoUrl=https://www.pacioos.hawaii.edu/weather/obs-koror/ institution=Coral Reef Research Foundation (CRRF) instrument=Earth Remote Sensing Instruments > Passive Remote Sensing > Thermal/Radiation Detectors > > Pyranometers, In Situ/Laboratory Instruments > Current/Wind Meters > > Anemometers, In Situ/Laboratory Instruments > Gauges > > Rain Gauges, In Situ/Laboratory Instruments > Radiation Sensors > > > Licor Quantum Sensor, In Situ/Laboratory Instruments > Temperature/Humidity Sensors > > > Humidity Sensors, In Situ/Laboratory Instruments > Temperature/Humidity Sensors > > > Temperature Sensors instrument_vocabulary=GCMD Instrument Keywords ioos_ingest=true ISO_Topic_Categories=climatologyMeteorologyAtmosphere keywords_vocabulary=GCMD Science Keywords local_time_zone=9 locations=Ocean > Pacific Ocean > Western Pacific Ocean > Palau > Koror, Ocean > Pacific Ocean > Western Pacific Ocean > Palau > Mecherchar, Ocean > Pacific Ocean > Western Pacific Ocean > Palau > Ngeanges, Ocean > Pacific Ocean > Western Pacific Ocean > Palau > Rock Islands locations_vocabulary=GCMD Location Keywords metadata_link=https://www.pacioos.hawaii.edu/metadata/aws_crrf.html naming_authority=org.pacioos ncei_template_version=NCEI_NetCDF_TimeSeries_Incomplete_Template_v2.0 Northernmost_Northing=7.20915 platform=In Situ Land-based Platforms > Weather Stations/Networks > Weather Stations platform_code=aws_crrf platform_vocabulary=GCMD Platform Keywords processing_level=near real-time (nrt) and possibly delayed mode (dm) program=Pacific Islands Ocean Observing System (PacIOOS) project=Pacific Islands Ocean Observing System (PacIOOS) references=https://coralreefpalau.org/research/oceanographyweather/meteorological-monitoring/ sea_name=Northwest Pacific Ocean (limit-180) source=automatic weather station (AWS) sourceUrl=https://coralreefpalau.org/research/oceanographyweather/meteorological-monitoring/ Southernmost_Northing=7.20915 standard_name_vocabulary=CF Standard Name Table v71 time_coverage_end=2015-02-28T14:00:00Z time_coverage_resolution=PT1H time_coverage_start=2007-03-01T06:00:00Z uuid=org.pacioos.aws_crrf Westernmost_Easting=132.371

The data set consists of an Excel file containing the supporting data for the following publication:

Lambert, A., Huang, J., Courtier, N., & Pavlic, G. (2020). Constraints on secular geocenter velocity from absolute gravity observations in central North America: Implications for global melting rates. J. Geophys. Res., in prep.

The Excel data file comprises four sheets:

Sheet 1: Annual absolute gravity observations at six sites (1995-2010) and two sites (2002-2010) showing site names, observation time in decimal years, gravity values and standard errors in microGal (1 microGal = 10 nm/s2), site reference gravity values, instruments used, observers names and site co-ordinates.

Sheet 2: Daily GPS heights at five sites (1996/7-2010) and one site (2003-2010) showing site names, observation time in decimal years, heights and standard deviations in meters, and site co-ordinates.

Sheet 3: Daily GPS heights at ten sites (2002-2010) used with data from two sites in sheet 2 to calculate vertical velocities at two absolute gravity sites (sheet 4) where no continuous GPS was available. Site co-ordinates are given in sheet 4.

Sheet 4: Long-term height trends (vertical velocities) are estimated for the two sites lacking continuous GPS by using a 2-D adaptive Gaussian interpolation function, with a half-width defined as the distance to the nearest GPS site. The absolute gravity drop data were processed using the Micro-g LaCoste "g8" software.

The GPS data were processed with the NRCan Precise Point Positioning PPP 1.05 software (Héroux and Kouba, 2001). For each site, daily positions were computed using ionosphere-free combinations of un-differenced pseudo-range and phase observations, with satellite orbits and clocks fixed to the International GNSS Service (IGS) precise products, absolute phase-center calibrations for the GPS and satellite antennas (Schmid et al., 2007), gridded Vienna Mapping Functions (VMF1, Boehm et al., 2006) for the troposphere model, and solid earth and ocean tide corrections. The GPS post-processing was originally carried out in support of Mazzotti et al. (2011).References:

Boehm, J., Werl, B., & Schuh, H. (2006). Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Median-Range Weather Forecast operational analysis data. J. Geophys. Res., 111, B02406. https://doi.org/10.1029/2005JB003629

Héroux, P., & Kouba, J. (2001). GPS Precise Point Positioning using IGS orbit products. Phys. Chem. Earth (A), 26, 573-578. https://doi.org/10.1016/S1464-1895(01)00103-X

Mazzotti, S., Lambert, A., Henton, J., James, T.S., & Courtier, N. (2011). Absolute gravity calibration of GPS velocities and glacial isostatic adjustment in mid-continent North America. Geophys. Res. Lett., 38, L24311. https://doi.org/10.1029/2011GL049846

Schmid, R., Steigenberger, P., Gendt, G., Ge, M., & Rothacher, M. (2007). Generation of a consistent absolute phase center correction model for GPS receiver and satellite antennas. J. Geod., 81 (12) 781-798. https://doi.org/10.1007/s00190-007-0148-yData Sources and Open Data Policy

Absolute gravity data source: Geological Survey of Canada.

GPS data sources: Canadian Active Control System (CACS) data from Canadian Geodetic Survey’s Geodetic Data Products web site, NASA Crustal Dynamics Data Information System (CDDIS), and U.S. National Geodetic Survey, Continually Operating Reference Stations (CORS) data download site.

Use of Canadian Geodetic Survey products and data is subject to the

Open Government Licence - Canada

© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2020

ENTSO-E Pan-European Climatic Database (PECD 2021.3) in Parquet format

TL;DR: this is a tidy and friendly version of a subset of the PECD 2021.3 data by ENTSO-E: hourly capacity factors for wind onshore, offshore, solar PV, hourly electricity demand, weekly inflow for reservoir and pumping and daily generation for run-of-river. All the data is provided for >30 climatic years (1982-2019 for wind and solar, 1982-2016 for demand, 1982-2017 for hydropoer) and at national and sub-national (>140 zones) level.

ENTSO-E has released with the latest European Resource Adequacy Assessment (ERAA 2021) all the inputs used in the study.
Those inputs include:
- Demand dataset: https://eepublicdownloads.azureedge.net/clean-documents/sdc-documents/ERAA/Demand%20Dataset.7z
- Climate data: https://eepublicdownloads.entsoe.eu/clean-documents/sdc-documents/ERAA/Climate%20Data.7z

The data files and the methodology are available on the official webpage.

As done for the previous releases (see https://zenodo.org/record/3702418#.YbmhR23MKMo and https://zenodo.org/record/3985078#.Ybmhem3MKMo), the original data - stored in large Excel spreadsheets - have been tidied and formatted in open and friendly formats (CSV for the small tables and Parquet for the large files)

Furthermore, we have carried out a simple country-aggregation for the original data - that uses instead >140 zones.

DISCLAIMER: the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies.

Description

This dataset includes the following files:

- capacities-national-estimates.csv: installed capacity in MW per zone, technology and the two scenarios (2025 and 2030). The files include also the total capacity for each technology per country (sum of all the zones within a country)
- PECD-2021.3-wide-LFSolarPV-2025 and PECD-2021.3-wide-LFSolarPV-2030: tables in Parquet format storing in each row the capacity factor for solar PV for a hour of the year and all the climatic years (1982-2019) for a specific zone. The two files contain the capacity factors for the scenarios "National Estimates 2025" and "National Estimates 2030"
- PECD-2021.3-wide-Onshore-2025 and PECD-2021.3-wide-Onshore-2030: same as above but for wind onshore
- PECD-2021.3-wide-Offshore-2025 and PECD-2021.3-wide-Offshore-2030: same as above but for wind offshore
- PECD-wide-demand_national_estimates-2025 and PECD-wide-demand_national_estimates-2030: hourly electricity demand for all the climatic years for a specific zone. The two files contain the load for the scenarios "National Estimates 2025" and "National Estimates 2030"
- PECD-2021.3-country-LFSolarPV-2025 and PECD-2021.3-country-LFSolarPV-2030: tables in Parquet format storing in each row the capacity factor for country/climatic year and hour of the year. The two files contain the capacity factors for the scenarios "National Estimates 2025" and "National Estimates 2030"
- PECD-2021.3-country-Onshore-2025 and PECD-2021.3-country-Onshore-2030: same as above but for wind onshore
- PECD-2021.3-country-Offshore-2025 and PECD-2021.3-country-Offshore-2030: same as above but for wind offshore
- PECD-country-demand_national_estimates-2025 and PECD-country-demand_national_estimates-2030: same as above but for electricity demand
- PECD_EERA2021_reservoir_pumping.zip: archive with four files per each scenario: 1. table.csv with generation and storage capacities per zone/technology, 2. zone weekly inflow (GWh), 3. table.csv with generation and storage per country/technology and 4. country weekly inflow (GWh)
- PECD_EERA2021_ROR.zip: as for the previous file but the inflow is daily
- plots.zip: archive with 182 png figures with the weekly climatology for all the variables (daily for the electricity demand)

Note

I would like to thank Laurens Stoop for sharing the onshore wind data for the scenario 2030, that was corrupted in the original archive.

Dataset of marine mammal observations made in the Southern Ocean from late 1998 to early 2000.

Further information about the data are included in a word document in the download.

The data are held in excel spreadsheets. The word document mentioned above lists the column headings for the excel spreadsheets.

The fields in this dataset are:

date time species Number of animals Distance Bearing Heading Initial Cue Behaviour Latitude Longitude Effort status Notes Wind speed Wind direction Actual wind speed Actual wind direction Sea State Cloud cover Visibility Boat speed Boat course Speed made good Course made good Temperature Wave Height Weather Depth Swell height More notes