A global dataset of meteorological forcings has been developed that can be used to drive models of land surface hydrology. The dataset is constructed by combining a suite of global observation-based datasets with the NCEP/NCAR reanalysis. Known biases in the reanalysis precipitation and near-surface meteorology have been shown to exert an erroneous effect on modeled land surface water and energy budgets and are thus corrected using observation-based datasets of precipitation, air temperature and radiation. Corrections are also made to the rain day statistics of the reanalysis precipitation which have been found to exhibit a spurious wave-like pattern in high-latitude wintertime. Wind-induced low measurement of solid precipitation is removed using the results from the World Meteorological Organization (WMO) Solid Precipitation Measurement Intercomparison. Precipitation is disaggregated in space to 1.0 degree and 0.25 degree by statistical downscaling using relationships developed with the Global Precipitation Climatology Project (GPCP) daily product. Disaggregation in time from daily to 3-hourly is accomplished similarly, using the Tropical Rainfall Measuring Mission (TRMM) 3-hourly real-time dataset. Other meteorological variables (downward short- and longwave, specific humidity, surface air pressure and wind speed) are downscaled in space with account for changes in elevation. The dataset is evaluated against the bias-corrected forcing dataset of the second Global Soil Wetness Project. The final product provides a long-term, globally-consistent dataset of near-surface meteorological variables that can be used to drive models of the terrestrial hydrologic and ecological processes for the study of seasonal and interannual variability and for the evaluation of coupled models and other land surface prediction schemes.

The Gridded Surface Meteorological dataset provides high spatial resolution (~4-km) daily surface fields of temperature, precipitation, winds, humidity and radiation across the contiguous United States from 1979. The dataset blends the high resolution spatial data from PRISM with the high temporal resolution data from the National Land Data Assimilation System (NLDAS) to produce spatially and temporally continuous fields that lend themselves to additional land surface modeling. This dataset contains provisional products that are replaced with updated versions when the complete source data become available. Products can be distinguished by the value of the 'status' property. At first, assets are ingested with status='early'. After several days, they are replaced by assets with status='provisional'. After about 2 months, they are replaced by the final assets with status='permanent'.

This data release is the update of the U.S. Geological Survey - ScienceBase data release by Bera (2020), with the processed data through September 30, 2020. The primary data for water year 2020 (a water year is the 12-month period, October 1 through September 30, in which it ends) is downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2020) and is processed following the guidelines documented in Over and others (2010). Daily potential evapotranspiration (PET) in thousandths of an inch is computed from average daily air temperature in degrees Fahrenheit (°F), average daily dewpoint temperature in degrees Fahrenheit (°F), daily total wind movement in miles (mi), and daily total solar radiation in Langleys per day (Lg/d) and disaggregated to hourly PET in thousandths of an inch using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby stations used as "backup". Temporal variations in the statistical properties of the data resulting from changes in measurement and data storage methodologies were adjusted to match the statistical properties resulting from the data collection procedures that have been in place since January 1, 1989 (Over and others, 2010). The adjustments were computed based on the regressions between the primary data series from ANL and the backup series using data obtained during common periods; the statistical properties of the regressions were used to assign estimated standard errors to values that were adjusted or filled from other series. The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2020) station at St. Charles, Illinois is used as "backup" for the air temperature, solar radiation and wind speed data. The Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2020) provided the hourly dewpoint temperature and wind speed data collected by the National Weather Service from the station at O'Hare International Airport and used as "backup". Each data source flag is of the form "xyz" that allows the user to determine its source and the methods used to process the data (Over and others, 2010). References Cited: Argonne National Laboratory, 2020, Meteorological data, accessed on November 17, 2020, at http://gonzalo.er.anl.gov/ANLMET/. Bera, M., 2020, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9X0P4HZ. Midwestern Regional Climate Center, 2020, Meteorological data, accessed on November 3, 2020, at https://mrcc.illinois.edu/CLIMATE/. Murphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program. Illinois Climate Network, 2020. Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820-7495. Data accessed on November 9, 2020, at http://dx.doi.org/10.13012/J8MW2F2Q.

As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY) methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) files that can be used for building simulation to estimate the impact of climate scenarios on the built environment.

This dataset contains fTMY files for 18 cities in the continental United States. The locations are representative cities for each climate zone. The data for each city is derived from six different global climate models (GCMs) from the 6^th Phase of Coupled Models Intercomparison Project CMIP6- ACCESS-CM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2059 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 5 and the representative concentration pathway (RCP) used was RCP 8.5. More information about SSP and RCP can be referred to O’Neill et al. (2020).

More information about the six selected CMIP6 GCMs:

ACCESS-CM2 - http://dx.doi.org/10.1071/ES19040

BCC-CSM2-MR - https://doi.org/10.5194/gmd-14-2977-2021

CNRM-ESM2-1- https://doi.org/10.1029/2019MS001791

MPI-ESM1-2-HR - https://doi.org/10.5194/gmd-12-3241-2019

MRI-ESM2-0 - https://doi.org/10.2151/jmsj.2019-051

NorESM2-MM - https://doi.org/10.5194/gmd-13-6165-2020

Additional references:

O’Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework. Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0

Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734

Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.

Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest)." ORNL internal Scientific and Technical Information (STI) report, doi:10.5281/zenodo.8338549, Sept 2023. [Data]

Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South)." ORNL internal Scientific and Technical Information (STI) report, doi:10.5281/zenodo.8335815, Sept 2023. [Data]

Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [Data]

Context

The dataset featured below was created by reconciling measurements from requests of individual weather attributes provided by the European Climate Assessment (ECA). The measurements of this particular dataset were recorded by a weather station near Heathrow airport in London, UK.

-> This weather dataset is a great addition to this London Energy Dataset. You can join both datasets on the 'date' attribute, after some preprocessing, and perform some interesting data analytics regarding how energy consumption was impacted by the weather in London.

Content

The size for the file featured within this Kaggle dataset is shown below — along with a list of attributes and their description summaries: - london_weather.csv - 15341 observations x 10 attributes

1. date - recorded date of measurement - (int)
2. cloud_cover - cloud cover measurement in oktas - (float)
3. sunshine - sunshine measurement in hours (hrs) - (float)
4. global_radiation - irradiance measurement in Watt per square meter (W/m2) - (float)
5. max_temp - maximum temperature recorded in degrees Celsius (°C) - (float)
6. mean_temp - mean temperature in degrees Celsius (°C) - (float)
7. min_temp - minimum temperature recorded in degrees Celsius (°C) - (float)
8. precipitation - precipitation measurement in millimeters (mm) - (float)
9. pressure - pressure measurement in Pascals (Pa) - (float)
10. snow_depth - snow depth measurement in centimeters (cm) - (float)

Source

Weather Data - https://www.ecad.eu/dailydata/index.php

description: This archive contains daily surface meteorological (METDATA) data for the Continental United States at 4-km (1/24-deg) resolution. The meteorological variables are maximum/minimum temperature, precipitation amount and duration, maximum/minimum relative humidity,downward shortwave solar radiation, wind speed and direction, and specific humidity. The method utilized here combines desirable spatial attributes of gridded climate data from PRISM and desirable temporal attributes of regional-scale reanalysis and daily gauge-based precipitation from NLDAS-2 to derive a spatially and temporally complete high resolution gridded dataset of surface meteorological variables for the continental US for 1979-present. Validation of this data suggests that it can serve as a suitable surrogate for landscape-scale ecological modeling across vast unmonitored areas of the US. For more information visit: http://metdata.northwestknowledge.net/; abstract: This archive contains daily surface meteorological (METDATA) data for the Continental United States at 4-km (1/24-deg) resolution. The meteorological variables are maximum/minimum temperature, precipitation amount and duration, maximum/minimum relative humidity,downward shortwave solar radiation, wind speed and direction, and specific humidity. The method utilized here combines desirable spatial attributes of gridded climate data from PRISM and desirable temporal attributes of regional-scale reanalysis and daily gauge-based precipitation from NLDAS-2 to derive a spatially and temporally complete high resolution gridded dataset of surface meteorological variables for the continental US for 1979-present. Validation of this data suggests that it can serve as a suitable surrogate for landscape-scale ecological modeling across vast unmonitored areas of the US. For more information visit: http://metdata.northwestknowledge.net/

The SBU Meteorological Station IMPACTS dataset consists of weather station data collected at two Stony Brook University (SBU) weather stations (1 mobile radar truck and 1 stationary site in Manhattan, New York City, New York) during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The surface meteorological data variables include temperature, dew point, relative humidity, absolute humidity, mixing ratio, air pressure, windspeed, and wind direction. The dataset files are available from January 1, 2020, through January 25, 2023, in netCDF-4 and ASCII-CSV formats.

The dataset with 2.5 arcminutes (~4 km) was spatially interpolated based on 699 weather stations in China using thin-plate spline and random forest methods with six covariates (e.g., location, terrain, and reanalysis data), including daily 2m maximum temperature (maxtmp), maximum temperature (mintmp), mean temperature (meantmp), total precipitation (pre), skin temperature (gst), 10m wind speed (win), relative humidity (rhu), surface pressure (prs), and sunshine duration (ssd). The dataset covers the mainland area of China and ranges from 1 January 2000 to 31 December 2020. The dataset was well evaluated by independent weather stations and reliable for the study related to climate change across China.

Zhang Jielin, Liu Bo, Ren Siqing, Han Wenqi, Ding Yongxia, Peng Shouzhang. A 4 km daily gridded meteorological dataset for China from 2000 to 2020. Scientific Data, 2024, 11, 1230. https://doi.org/10.1038/s41597-024-04029-x

The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set includes measurements of wind speed and direction; air temperature; relative humidity; and canopy, trunk, and snow surface temperatures within three forest types. The data were collected in the SSA-OJP (1994) and SSA-OBS and SSA-OA (1996). Measurements were taken for 3 days in 1994 and 4 days at each site in 1996. These measurements were intended to be short term to allow the relationship between subcanopy measurements and those collected above the forest canopy to be determined. The subcanopy estimates of wind speed were used in a snow melt model to help predict the timing of snow ablation.

This data release is the update of the U.S. Geological Survey - ScienceBase data release by Bera and Over (2017), with the processed data through September 30, 2017. The primary data for each year is downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2017) and is processed following the guidelines documented in Over and others (2010). Daily potential evapotranspiration (PET) in thousandths of an inch is computed from average daily air temperature in degrees Fahrenheit (°F), average daily dewpoint temperature in degrees Fahrenheit (°F), daily total wind movement in miles (mi), and daily total solar radiation in Langleys per day (Lg/d) and disaggregated to hourly PET in thousandths of an inch using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby stations used as “backup”. Temporal variations in the statistical properties of the data resulting from changes in measurement and data storage methodologies were adjusted to match the statistical properties resulting from the data collection procedures that have been in place since January 1, 1989 (Over and others, 2010). The adjustments were computed based on the regressions between the primary data series from ANL and the backup series using data obtained during common periods; the statistical properties of the regressions were used to assign estimated standard errors to values that were adjusted or filled from other series. Each hourly value is assigned a corresponding data source flag that indicates the source of the value and its transformations. The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2015) station at St. Charles, Illinois is used as "backup" for the air temperature, solar radiation and wind speed data. Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2017) station at Chicago O'Hare International Airport is used as "backup" for the dewpoint temperature and wind speed data. Each data source flag is of the form "xyz" that allows the user to determine its source and the methods used to process the data (Over and others, 2010). References Cited: Argonne National Laboratory, 2017, Meteorological data, accessed on October 25, 2017, at URL http://gonzalo.er.anl.gov/ANLMET/. Bera, M., and Over, T. M., 2017, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2016: U.S. Geological Survey data release, https://doi.org/10.5066/F7SJ1HS5. Midwestern Regional Climate Center, 2017, Meteorological data, accessed on December 5, 2017, at URL http://mrcc.isws.illinois.edu/CLIMATE/welcome.jsp. Murphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program, 2015, Illinois Climate Network: Champaign, Ill., Illinois State Water Survey, accessed on December 5, 2017, at http://dx.doi.org/10.13012/J8MW2F2Q.

As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.

This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 5 and the representative concentration pathway (RCP) used was RCP 8.5. More information about SSP and RCP can be referred to O'Neill et al. (2020).

Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.

More information about the six selected CMIP6 GCMs:

ACCESS-CM2 -
http://dx.doi.org/10.1071/ES19040
BCC-CSM2-MR -
https://doi.org/10.5194/gmd-14-2977-2021
CNRM-ESM2-1-
https://doi.org/10.1029/2019MS001791
MPI-ESM1-2-HR -
https://doi.org/10.5194/gmd-12-3241-2019
MRI-ESM2-0 -
https://doi.org/10.2151/jmsj.2019-051
NorESM2-MM -
https://doi.org/10.5194/gmd-13-6165-2020

Additional references:
O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.
Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0
Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.

Please cite the following if this data is used in any research or project:

Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: 10.1145/3600100.3626637

Cross-Model Version:

Representative Cities Version:

Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a

The meteorology data (air temperature, wet bulb temperature, cloud cover, atmospheric pressure, wind speed) was assembled from NOAA and CIMIS; data from CIMIS did not have wet bulb data, so it was calculated using relative humidity and air temperature

The High-resolution Urban Meteorology for Impacts Dataset, HUMID, will be useful for studies examining spatial variability of near surface meteorology and the impacts of urban heat islands across many disciplines including epidemiology, ecology, and climatology. We have explicitly included representation of spatial meteorological variability over urban areas in the contiguous United States (CONUS) as compared to other observation-only gridded meteorology products by employing the High-Resolution Land Data Assimilation System (HRLDAS), which accounts for the fine-scale impacts of spatiotemporally varying land surfaces on weather. Further, we include in situ meteorological observations such as local mesonets to bias correct the HRLDAS output, creating a model-observation fusion product. The data spans 1 January 1981 to 31 December 2018, covering all of CONUS at 1 km grid spacing. The dataset includes daily maximum, minimum, and mean values for a variety of temperature estimates such as 2 m temperature, skin temperature, urban temperatures, as well as specific humidity and surface energy budget terms. The full variable list with corresponding file and variable metadata is in this file [https://rda.ucar.edu/OS/web/datasets/d314008/docs/humid_dataset_readme.pdf].

The metadata record summarizes information for time series of hourly weather measurements from the 18 meteo stations of AGE. The hourly data is shown in local wintertime (GMT+1). The dataset goes back one year in time, starting at the foundation of each station. The kind of measurements differ from station to station. This dataset is available via the WMS Time (https://wms.inspire.geoportail.lu/geoserver/mf/wms?service=WMS&version=1.3.0&request=GetCapabilities) and WFS (https://wms.inspire.geoportail.lu/geoserver/mf/wfs?service=WFS&version=2.0.0&request=GetCapabilities) API protocols. See for example the following sample requests: WFS: https://wms.inspire.geoportail.lu/geoserver/mf/wfs?SERVICE=wfs&VERSION=2.0.0&REQUEST=GetFeature&TRANSPARENT=true&TYPENAME=MF.PointTimeSeriesObservation_Hourly_AGE_sum_nn050&srsName=EPSG:3857&OUTPUTFORMAT=application/json&CQL_FILTER=datetime%20BEFORE%202020-05-31T10:00:00Z%20%20AND%20datetime%20AFTER%202020-04-01T10:00:00Z%20AND%20name_descr=%20%27Bigonville%27 WMS Time: https://wms.inspire.geoportail.lu/geoserver/mf/wms?SERVICE=WMS&VERSION=1.3.0&REQUEST=GetMap&FORMAT=image%2Fpng&TRANSPARENT=true&STYLES&LAYERS=mf%3AMF.PointTimeSeriesObservation_Hourly_AGE_sum_nn050&CRS=EPSG%3A3035&WIDTH=474&HEIGHT=769&BBOX=2914640.6691353433%2C4006619.263203916%2C3031916.5131074684%2C4079000.448155462&TIME=2020-06-02T14:00:00.000Z Data is transformed into INSPIRE data model. Description copied from catalog.inspire.geoportail.lu.

About San Francisco San Francisco is a vibrant and dynamic city located on the west coast of the United States, in the state of California. Known for its hilly terrain, diverse neighborhoods, and iconic landmarks such as the Golden Gate Bridge and Alcatraz Island, San Francisco is a hub of culture, creativity, and innovation. The city is renowned for its world-class restaurants, thriving arts scene, and historic architecture, and is home to many tech companies and startups. With its mild climate, stunning views, and rich history, San Francisco is a must-visit destination for travelers from around the world.

About Dataset This dataset contains daily weather observations for San Francisco, USA from January 1, 1993 to January 1, 2023. The data is collected from Meteostat. The dataset contains 10 columns with 10958 rows.

The LTAR network maintains stations for standard meteorological measurements including, generally, air temperature and humidity, shortwave (solar) irradiance, longwave (thermal) radiation, wind speed and direction, barometric pressure, and precipitation. Many sites also have extensive comparable legacy datasets. The LTAR scientific community decided that these needed to be made available to the public using a single web source in a consistent manner. To that purpose, each site sent data on a regular schedule, as frequently as hourly, to the National Agricultural Library, which has developed a web service to provide the data to the public in tabular or graphical form. This archive of the LTAR legacy database exports contains meteorological data through April 30, 2021. For current meteorological data, visit the GeoEvent Meteorology Resources page, which provides tools and dashboards to view and access data from the 18 LTAR sites across the United States. Resources in this dataset:Resource Title: Meteorological data. File Name: ltar_archive_DB.zipResource Description: This is an export of the meteorological data collected by LTAR sites and ingested by the NAL LTAR application. This export consists of an SQL schema definition file for creating database tables and the data itself. The data is provided in two formats: SQL insert statements (.sql) and CSV files (.csv). Please use the format most convenient for you. Note that the SQL insert statements take much longer to run since each row is an individual insert. Description of zip files The ltararchive*.zip files contain database exports. The schema is a .sql file; the data is exported as both SQL inserts and CSV for convenience. There is a README in markdown and PDF in the zips. Contains the database export of the schema and data for the site, site_station, and met tables as SQL insert statements. ltar_archive_db_sql_export_20201231.zip --> has data until 2020-12-31 ltar_archive_db_sql_export_20210430.zip --> has data until 2021-04-30 Contains the database export of the schema and data for the site, site_station, and met tables as CSV. ltar_archive_db_csv_export_20201231.zip --> has data until 2020-12-31 ltar_archive_db_csv_export_20210430.zip --> has data until 2021-04-30 Contains the raw CSV files that were sent to NAL from the LTAR sites/stations. ltar_rawcsv_archive.zip --> has data until 2021-04-30

The Canadian Atmospheric Environment Service (AES) provided BOREAS with hourly and daily surface meteorological data from 23 of the AES meteorological stations located across Canada and upper air data from 1 station at The Pas, Manitoba. Due to copyright restrictions on the full resolution surface meteorological data, this data set contains 5-day average values for the surface parameters. The upper air data are provided in their full resolution form. The 5-day averaging was performed in order to create a data set that could be publicly distributed at no cost. Temporally, the surface meteorological data cover the period of January 1975 to December 1996 and the upper air data cover the period of January 1961 to November 1996.

This data set contains surface meteorological data collected coincident with the MIPS profiling system during the PLOWS field seasons. Two different instrumentation setups were utilized as described in the readme file. The temporal resolution is 1 second for IOP10, IOP14, IOP17 and IOP18 and 5 second for IOP 1,2,4,5,7,8,9,12,13,19,21,23 and 24. The data were provided by the University of Alabama-Huntsville. Both systems are in a comma-delimited ascii format, but the parameters included differ.

These meteorological data were collected at Pitilla Bilogical Station, in the Guanacaste Conservation Area, Guanacaste, Costa Rica. The data were collected either manually ("manual" in variable name) or by a Campbell automated weather station ("stn" in variable name). The manual data was recorded manually each morning at approximately 0900 h with basic instruments. Manual temperature was measured with a battery operated max-min thermometer, and gives the maximum and minimum temperature over the preceding 24 hours. Manual precipitation (rainfall) data was physically collected in a rain gauge over the 24 hours preceding the recording of this data. Automated temperature data was collected by the Campbell station each minute with the MetSENS500-DS-17-PT sensor and communicated with a CR300 wireless data logger. The station outputs calculations of maximum and minimum temperature based on by the temperature data collected each minute from midnight (00:00) to midnight (24:00) on “Date”. The automated precipitation data was calculated from 06:01 the previous day to 06:00 on the day corresponding to “Date”, and utilized an automatically-emptying rain gauge. This rain gauge was found in October 2023 to have been colonized by ants, and the measures leading up to this period would have been affected by this. In general, automatic rain gauges may not be reliable for the amount of rain experienced during some tropical rain storms. We re-calculated the maximum and minimum temperatures to correspond to the same time window as the manual temperature data; specifically, starting the previous day at 09:01 h and ending the day corresponding to “Date” at 09:00 h. This staggering procedure can only be done for temperature, not rainfall, because rainfall is only reported by Campbell for 6 hour blocks. Staggered measures of temperature include "stag" in the variable name. All time measures in Central Standard Time (GMT-6). Variable names: "Date": yyyy-mm-dd format date of measurement "AirTemp_max_stag_stn": maximum air temperature from station, 09:01 previous day-0:900 "AirTemp_min_stag_stn" : minimum air temperature from station, 09:01 previous day-0:900 "Rain_Tot_stn": precipitation automatically measured by station, unreliable, 06:01 the previous day to 06:00 "AirTemp_min_raw_stn": minimum air temperature from station, 00:00 to 24:00 "AirTemp_max_raw_stn": maximum air temperature from station, 00:00 to 24:00 "AirTemp_min_manual": minimum air temperature measured manually, 09:01 previous day-0:900 "AirTemp_max_manual": maximum air temperature measured manually, 09:01 previous day-0:900 "Rain_Tot_manual": total precipitation measured manually, 09:01 previous day-0:900

Access to continuous, quality assessed meteorological data is critical for understanding the climatology and atmospheric dynamics of a region. Research facilities like Oak Ridge National Laboratory (ORNL) rely on such data to assess site-specific climatology, model potential emissions, establish safety baselines, and prepare for emergency scenarios. To meet these needs, on-site towers at ORNL collect meteorological data at 15-minute and hourly intervals. However, data measurements from meteorological towers are affected by sensor sensitivity, degradation, lightning strikes, power fluctuations, glitching, and sensor failures, all of which can affect data quality. To address these challenges, we conducted a comprehensive quality assessment and processing of five years of meteorological data collected from ORNL at 15-minute intervals, including measurements of temperature, pressure, humidity, wind, and solar radiation. The time series of each variable was pre-processed and gap-filled using established meteorological data collection and cleaning techniques, i.e., the time series were subjected to structural standardization, data integrity testing, automated and manual outlier detection, and gap-filling. The data product and highly generalizable processing workflow developed in Python Jupyter notebooks are publicly accessible online. As a key contribution of this study, the evaluated 5-year data will be used to train atmospheric dispersion models that simulate dispersion dynamics across the complex ridge-and-valley topography of the Oak Ridge Reservation in East Tennessee.