Files: ‘zip.temp.data_[year].rds’, where [year] is between 2000-2017 Data frame with arithmetic (.Mean) and population-weighted (.Wght) averages of mean/max/min temperature, dew point, relative humidity, and apparent temperature for 9,917 ZIP codes located in the urban cores of 120 metropolitan areas in the contiguous United States for 01/01/2000 to 12/31/2017. A data dictionary describing all variables included in the dataset can be found in: 'Data Dictionary.docx'

These datasets are associated with the manuscript "Urban Heat Island Impacts on Heat-Related Cardiovascular Morbidity: A Time Series Analysis of Older Adults in US Metropolitan Areas." The datasets include (1) ZIP code-level daily average temperature for 2000-2017, (2) ZIP code-level daily counts of Medicare hospitalizations for cardiovascular disease for 2000-2017, and (3) ZIP code-level population-weighted urban heat island intensity (UHII). There are 9,917 ZIP codes included in the datasets, which are located in the urban cores of 120 metropolitan statistical areas across the contiguous United States.

(1) The ZIP code-level daily temperature data is publicly available at: https://doi.org/10.15139/S3/ZL4UF9. A data dictionary is also available at this link.

(2) The ZIP code-level daily counts of Medicare hospitalizations cannot be uploaded to ScienceHub because of privacy requirements in the data use agreement with Medicare.

(3) The ZIP code-level UHII data is attached, along with a data dictionary describing the dataset. Portions of this dataset are inaccessible because: The ZIP code-level daily counts of Medicare cardiovascular disease hospitalizations cannot be uploaded to ScienceHub due to privacy requirements in data use agreements with Medicare. They can be accessed through the following means: The Medicare data can only be accessed internally at EPA with the correct permissions. Format: The Medicare data includes counts of the number of cardiovascular disease hospitalizations in each ZIP code on each day between 2000-2017.

This dataset is associated with the following publication: Cleland, S., W. Steinhardt, L. Neas, J. West, and A. Rappold. Urban Heat Island Impacts on Heat-Related Cardiovascular Morbidity: A Time Series Analysis of Older Adults in US Metropolitan Areas. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 178(108005): 1, (2023).

This interactive mapping application easily searches and displays global tropical cyclone data. Users are able to query storms by the storm name, geographic region, or latitude/longitude coordinates. Custom queries can track storms of interest and allow for data extraction and download.

Hourly Precipitation Data (HPD) is digital data set DSI-3240, archived at the National Climatic Data Center (NCDC). The primary source of data for this file is approximately 5,500 US National Weather Service (NWS), Federal Aviation Administration (FAA), and cooperative observer stations in the United States of America, Puerto Rico, the US Virgin Islands, and various Pacific Islands. The earliest data dates vary considerably by state and region: Maine, Pennsylvania, and Texas have data since 1900. The western Pacific region that includes Guam, American Samoa, Marshall Islands, Micronesia, and Palau have data since 1978. Other states and regions have earliest dates between those extremes. The latest data in all states and regions is from the present day. The major parameter in DSI-3240 is precipitation amounts, which are measurements of hourly or daily precipitation accumulation. Accumulation was for longer periods of time if for any reason the rain gauge was out of service or no observer was present. DSI 3240_01 contains data grouped by state; DSI 3240_02 contains data grouped by year.

OnPoint Weather is a global weather dataset for business available for any lat/lon point and geographic area such as ZIP codes. OnPoint Weather provides a continuum of hourly and daily weather from the year 2000 to current time and a forward forecast of 45 days. OnPoint Climatology provides hourly and daily weather statistics which can be used to determine ‘departures from normal’ and to provide climatological guidance of expected weather for any location at any point in time. The OnPoint Climatology provides weather statistics such as means, standard deviations and frequency of occurrence. Weather has a significant impact on businesses and accounts for hundreds of billions in lost revenue annually. OnPoint Weather allows businesses to quantify weather impacts and develop strategies to optimize for weather to improve business performance. Examples of Usage Quantify the impact of weather on sales across diverse locations and times of the year Understand how supply chains are impacted by weather Understand how employee’s attendance and performance are impacted by weather Understand how weather influences foot traffic at malls, stores and restaurants OnPoint Weather is available through Google Cloud Platform’s Commercial Dataset Program and can be easily integrated with other Google Cloud Platform Services to quickly reveal and quantify weather impacts on business. Weather Source provides a full range of support services from answering quick questions to consulting and building custom solutions. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery 瞭解詳情

Nowcast is a representation of present conditions for any location at any point in time. OnPoint Weather is described exactly as it sounds, weather data for any location at any point in time. Unlike other providers who rely on singular inputs that in many instances may be many miles away from your location of interest to be meaningful or actionable, Weather Source ingests all of the best weather sensing inputs available including:

Airport observation stations NOAA & NWS data Satellites Radar IoT Devices and other sensor information Telematics Weather analyses and model outputs

Weather Source unifies and homogenizes the inputs on our high resolution global grid. The globally consistent OnPoint Grid covers every land mass in the world and up to 200 miles offshore. Each grid point - millions in total - represents a “virtual” weather station with a unique OnPoint ID from which weather data can be mapped to any lat/lon coordinates or specified geographically bounded areas such as:

Census tract/block County/parish or state Designated Market Area (DMA) ZIP/Postal Code

All Weather Source data is available in hourly or daily format. Daily format includes minimum and maximum values as well as daily averages for each supported weather parameter. * Purchasing a subscription through Mobito will provide instant access to all Weather Source tiles and resources listed in the Mobito Marketplace including historical, forecast and climatology subject to the subscription tier purchased.

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information If you have questions about this underlying data, contact the CDC ATSDR Place and Health - Geospatial Research, Analysis, and Services Program (GRASP) at https://www.cdc.gov/cdc-info/forms/contact-us.html "The Heat and Health Index (HHI) is a national tool that incorporates historical temperature, heat-related illness, and community characteristics data at the ZIP code level to identify areas most likely to experience negative health outcomes from heat and help communities prepare for heat in a changing climate." [Quote from https://www.atsdr.cdc.gov/place-health/php/hhi/index.html]

This dataset includes climate change hazard projections and combined climate hazard index values for 42,786 drinking water utilities accross the continental United States (US). The projections are compiled from multiple sources, including the Climate Mapping for Resilience and Adaptation tool (CMRA) and Climate Risk and Resilience Portal (ClimRR), and use mid-century (2050) Representative Concentration Pathway 4.5 CMIP5 Localized Constructed Analogs (LOCA) CMIP5 Projections for North America. The included climate hazards are extreme heat, energy demand, freeze-thaw cycles, extreme precipitation, wildfires, water supply stress, and sea level rise. Each row of the dataset corresponds to a different community water system within the contiguous US, each identified using their assigned Public Water System Identification number More details about the data sources and modeled combined climate hazard index can be found in the publication: Lyle et al 2025, Environ. Res.: Climate, https://doi.org/10.1088/2752-5295/adab10. Code can be found here: https://github.com/zialyle/DW-climate-change-hazard-index

The columns in the database are as follows:

pwsid: Public Water System Identification Number

primacy_agency_code: Two character postal code for the state or territory having regulatory oversight for the water system.

pws_name: Name of the water system

State: State in which water system is located

city_served: City in which water system is located

County: County in which water system is located

population_served_count: Number of customers served by water system

service_connections_count: Number of service connections maintained by water system

service_area_type_code: Service area type code

owner_type_code: Code that dentifies the ownership category of the water system consisting of: F (Federal Government), L (Local Government), M (Public/Private), N (Native American), P (Private), or S (State Government)

is_wholesaler_ind: Indicates whether the system is a wholesaler of water

primacy_type: Code that indicates if the water system is regulated by a state, tribal, or territorial primacy program. Note that EPA direct implementation programs, except for Wyoming, are tribal primacy programs

primary_source_code: The code showing the differentiation between the sources of water: ground water (GW),groundwater purchased (GWP), surface water (SW), surface water purchased (SWP), groundwater under influence of surface water (GU), or purchased ground water under influence of surface water source (GUP)

centroid_lat: Latitude ocation of water system

centroid_lon: Longitude ocation of water system

NOAA.Region: NOAA Climate Region in which water system is located

heat_index: Extreme heat index value

historic_mean_maxtemp_5d: Annual highest maximum temperature averaged over a 5-day period [degF], historical mean

RCP4.5_mid_mean_maxtemp_5d: Annual highest maximum temperature averaged over a 5-day period [degF], RCP 4.5 mid-century

RC_maxtemp_5d: Relative change in annual highest maximum temperature averaged over a 5-day period [degF] from historical to RCP 4.5 mid-century

Diff_maxtemp_5d: Absolute change in annual highest maximum temperature averaged over a 5-day period [degF] from historical to RCP 4.5 mid-century

extremeprecip_index: Extreme precipitation index value

historic_mean_highest_precip_5d: Annual highest precipitation total over a 5-day period [inches] , historical mean

RCP4.5_mid_mean_highest_precip_5d: Annual highest precipitation total over a 5-day period [inches] , RCP 4.5 mid-century

RC_highest_precip_5d: Relative change in annual highest precipitation total over a 5-day period [inches] from historical to RCP 4.5 mid-century

Diff_highest_precip_5d: Absolute change in annual highest precipitation total over a 5-day period [inches] from historical to RCP 4.5 mid-century

SLR_index: Sea level rise index value

SLR_indicator: Sea level rise indicator, where 0 indicates utility is not in a county expecting some amount of sea level rise by 2100 and 1 indicates utility is in a county expecting some amount of sea level rise by 2100.

wildfirerisk_index: Wildfire index value

RC_avg_wildfire: Relative change in Fire Weather Index from historical to RCP 4.5 mid-century

D_avg_wildfire: Absolute change in Fire Weather Index from historical to RCP 4.5 mid-century

FT_index: Freeze-Thaw cycle index value

RCP_mid_mean_FT: Number of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC), RCP 4.5

historical_mean_FT: Number of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC), historical mean

RC_FT: Relative change in the umber of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC) from historical to RCP 4.5 mid-century

Diff_FT: Absolute change in the umber of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC) from historical to RCP 4.5 mid-century

waterrisk_index: Water stress index value, using (Dickson & Dzombak, 2019)

water_stress: Change in water supply stress from historical to RCP 4.5 mid-century, using Water Supply Stress Index from (Dickson & Dzombak, 2019)

energydemand_index: Energy demand index value, using regression model developed by (Sowby & Burian, 2022)

energy_demand: Change in energy demand by mid-century under RCP 4.5 scenarios, using utility energy use model from (Sowby & Hales, 2022).

historic_mean_avg_temp: Daily average temperature [degF] , historical mean

RCP4.5_mid_mean_avg_temp: Daily average temperature [degF] , RCP 4.5 mid-century

RC_avg_temp: Relative change in daily average temperature [degF] from historical to RCP 4.5 mid-century

Diff_avg_temp: Absolute change in daily average temperature [degF] from historical to RCP 4.5 mid-century

historic_mean_avg_precip: Daily average precipitation [inches] , historical mean

RCP4.5_mid_mean_avg_precip: Daily average precipitation [inches] , RCP 4.5 mid-century

RC_avg_precip: Relative change in daily average precipitation [inches] from historical to RCP 4.5 mid-century

Diff_avg_precip: Absolute change in daily average precipitation [inches] from historical to RCP 4.5 mid-century

hazard_index: Combined climate change hazard index value, normalized from 0 to 1

hazard_index_group: Classification group for combined climate change hazard index value (minimal, low, moderate, high)

heat_threshold: Binary value indicating whether PWS exceeded risk threshold level for extreme heat (0 indicating no, 1 indicating yes)

precip_threshold: Binary value indicating whether PWS exceeded risk threshold level for extreme precipitation (0 indicating no, 1 indicating yes)

SLR_threshold: Binary value indicating whether PWS exceeded risk threshold level for sea level rise (0 indicating no, 1 indicating yes)

wildfire_threshold: Binary value indicating whether PWS exceeded risk threshold level for wildfires (0 indicating no, 1 indicating yes)

FT_threshold: Binary value indicating whether PWS exceeded risk threshold level for freeze-thaw cycles (0 indicating no, 1 indicating yes)

waterstress_threshold: Binary value indicating whether PWS exceeded risk threshold level for water stress (0 indicating no, 1 indicating yes)

energydemand_threshold: Binary value indicating whether PWS exceeded risk threshold level for enegery demand (0 indicating no, 1 indicating yes)

sum: Total number of climate hazard risk threshold values exceeded

exposure: Product of combined climate change hazard index value and population served

This repository contains the resulting networks (.nc) and derived metrics (.csv) files from (1) a join capacity and dispatch optimization with 62 different weather years (design years) from 1960 to 2021 as input, and (2) a dispatch optimization of the 62 capacity layouts using weather years (operational years) different from the design year. All results from (1) are found in "Capacity_optimization.zip" and results from (2) are found in "Dispatch_optimization.zip".

We also provide the Python code used to derive the metrics and to create the visualizations included in the paper. This is located in "Jupyter_notebooks". The Jupyter notebooks refer to Python scripts located here: https://github.com/ebbekyhl/multi-weather-year-assessment

A complete description of the dataset is given by Jones et al. (2023). Key information is provided below.

Background

A dataset describing the global warming response to national emissions CO₂, CH₄ and N₂O from fossil and land use sources during 1851-2021.

National CO₂ emissions data are collated from the Global Carbon Project (Andrew and Peters, 2024; Friedlingstein et al., 2024).

National CH₄ and N₂O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2024).

We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH₄ and N₂O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021).

Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST).

The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO₂, CH₄, N₂O or 3-GHG total) and source (fossil emissions, land use emissions or the total).

Data records: overview

The data records include three comma separated values (.csv) files as described below.

All files are in ‘long’ format with one value provided in the Data column for each combination of the categorical variables Year, Country Name, Country ISO3 code, Gas, and Component columns.

Component specifies fossil emissions, LULUCF emissions or total emissions of the gas.

Gas specifies CO₂, CH₄, N₂O or the three-gas total (labelled 3-GHG).

Country ISO3 codes are specifically the unique ISO 3166-1 alpha-3 codes of each country.

Data records: specifics

Data are provided relative to 2 reference years (denoted ref_year below): 1850 and 1991. 1850 is a mutual first year of data spanning all input datasets. 1991 is relevant because the United Nations Framework Convention on Climate Change was operationalised in 1992.

EMISSIONS_ANNUAL_{ref_year-20}-2023.csv: Data includes annual emissions of CO₂ (Pg CO₂ year^-1), CH₄ (Tg CH₄ year^-1) and N₂O (Tg N₂O year^-1) during the period ref_year-20 to 2023. The Data column provides values for every combination of the categorical variables. Data are provided from ref_year-20 because these data are required to calculate GWP* for CH₄.

EMISSIONS_CUMULATIVE_CO2e100_{ref_year+1}-2023.csv: Data includes the cumulative CO₂ equivalent emissions in units Pg CO₂-e₁₀₀ during the period ref_year+1 to 2023 (i.e. since the reference year). The Data column provides values for every combination of the categorical variables.

GMST_response_{ref_year+1}-2023.csv: Data includes the change in global mean surface temperature (GMST) due to emissions of the three gases in units °C during the period ref_year+1 to 2023 (i.e. since the reference year). The Data column provides values for every combination of the categorical variables.

Accompanying Code

Code is available at: https://github.com/jonesmattw/National_Warming_Contributions .

The code requires Input.zip to run (see README at the GitHub link).

Further info: Country Groupings

We also provide estimates of the contributions of various country groupings as defined by the UNFCCC:

• Annex I countries (number of countries, n = 42)
• Annex II countries (n = 23)
• economies in transition (EITs; n = 15)
• the least developed countries (LDCs; n = 47)
• the like-minded developing countries (LMDC; n = 24).

And other country groupings:

• the organisation for economic co-operation and development (OECD; n = 38)
• the European Union (EU27 post-Brexit)
• the Brazil, South Africa, India and China (BASIC) group.

See COUNTRY_GROUPINGS.xlsx for the lists of countries in each group.

This item contains data and code used in experiments that produced the results for Sadler et. al (2022) (see below for full reference). We ran five experiments for the analysis, Experiment A, Experiment B, Experiment C, Experiment D, and Experiment AuxIn. Experiment A tested multi-task learning for predicting streamflow with 25 years of training data and using a different model for each of 101 sites. Experiment B tested multi-task learning for predicting streamflow with 25 years of training data and using a single model for all 101 sites. Experiment C tested multi-task learning for predicting streamflow with just 2 years of training data. Experiment D tested multi-task learning for predicting water temperature with over 25 years of training data. Experiment AuxIn used water temperature as an input variable for predicting streamflow. These experiments and their results are described in detail in the WRR paper. Data from a total of 101 sites across the US was used for the experiments. The model input data and streamflow data were from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset (Newman et. al 2014, Addor et. al 2017). The water temperature data were gathered from the National Water Information System (NWIS) (U.S. Geological Survey, 2016). The contents of this item are broken into 13 files or groups of files aggregated into zip files:

1. input_data_processing.zip: A zip file containing the scripts used to collate the observations, input weather drivers, and catchment attributes for the multi-task modeling experiments
2. flow_observations.zip: A zip file containing collated daily streamflow data for the sites used in multi-task modeling experiments. The streamflow data were originally accessed from the CAMELs dataset. The data are stored in csv and Zarr formats.
3. temperature_observations.zip: A zip file containing collated daily water temperature data for the sites used in multi-task modeling experiments. The data were originally accessed via NWIS. The data are stored in csv and Zarr formats.
4. temperature_sites.geojson: Geojson file of the locations of the water temperature and streamflow sites used in the analysis.
5. model_drivers.zip: A zip file containing the daily input weather driver data for the multi-task deep learning models. These data are from the Daymet drivers and were collated from the CAMELS dataset. The data are stored in csv and Zarr formats.
6. catchment_attrs.csv: Catchment attributes collatted from the CAMELS dataset. These data are used for the Random Forest modeling. For full metadata regarding these data see CAMELS dataset.
7. experiment_workflow_files.zip: A zip file containing workflow definitions used to run multi-task deep learning experiments. These are Snakemake workflows. To run a given experiment, one would run (for experiment A) 'snakemake -s expA_Snakefile --configfile expA_config.yml'
8. river-dl-paper_v0.zip: A zip file containing python code used to run multi-task deep learning experiments. This code was called by the Snakemake workflows contained in 'experiment_workflow_files.zip'.
9. random_forest_scripts.zip: A zip file containing Python code and a Python Jupyter Notebook used to prepare data for, train, and visualize feature importance of a Random Forest model.
10. plotting_code.zip: A zip file containing python code and Snakemake workflow used to produce figures showing the results of multi-task deep learning experiments.
11. results.zip: A zip file containing results of multi-task deep learning experiments. The results are stored in csv and netcdf formats. The netcdf files were used by the plotting libraries in 'plotting_code.zip'. These files are for five experiments, 'A', 'B', 'C', 'D', and 'AuxIn'. These experiment names are shown in the file name.
12. sample_scripts.zip: A zip file containing scripts for creating sample output to demonstrate how the modeling workflow was executed.
13. sample_output.zip: A zip file containing sample output data. Similar files are created by running the sample scripts provided.

Data and coding scripts for Seddon et al. (2016) Nature (DOI 10.1038/nature16986). We derived monthly time-series of four key terrestrial ecosystem variables at 0.05 degree (~5km) resolution from observations by the MODIS sensor on Terra (AM) for the period February 2010-December 2013 inclusive, and developed a method to identify vegetation sensitivity to climate variability over this period (see Methods in main paper).

This ORA item contains all data and files required to run the analysis described in the paper. Data required to run the script are provided in six zip files evi.zip, temp.zip, aetpet.zip, cld.zip, stdev.zip, numpxl.zip, each containing 167 text files, one per month of available data, in addition to a supporting files folder. Details are as follows.

supporting_files.zip : This directory includes computer code and additional supporting files. Please see the 'read me.txt' file within this directory for more information.

evi.zip: ENHANCED VEGETATION INDEX (EVI). We used the MOD13C2 product (Huete et al 2002) which comprises monthly, global EVI at 0.05 degree resolution. In some cases where no clear-sky observations are available, the MOD13C2 version 5 product replaces no-data values with climatological monthly means, so we removed these values where appropriate.

EVI format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = dimensionless scale factor = 10000 (divide the value by 10000 to get EVI) filenames = yyyymmevi.txt

numpxl.zip - COUNTS OF THE NUMBER OF PIXELS USED IN EVI CALCULATION. The MOD13C2 product is the result of a spatially and temporally averaged mosaic of higher resolution (1km pixels). Data in this directory represent the number of 1km observations used to calculate the MODIS EVI product. See the online documentation for more details (Solano et al. 2010).

numpxl format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = counts filenames = yyyy_mm_numpxl_pt05deg.txt

stdev.zip - STANDARD DEVIATION OF EVI VALUES. Standard deviation of the monthly EVI observations. See discussion in numpxl.zip item (above) and the online documentation for more details (Solano et al. 2010).

stdev format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = dimensionless scale factor = 10000 (divide the value by 10000 to get EVI) filenames = yyyy_mm_stdev_pt05deg.txt

temp.zip: AIR TEMPERATURE. We used the MOD07_L2 Atmospheric Profile product (Seeman et al 2006) as a measure of air temperature. Five-minute swaths of Retrieved Temperature Profile were projected to geographic co-ordinates. Pixels from the highest available pressure level, corresponding to the temperature closest to the Earth's surface, were selected from each swath. Swaths were then mean-mosaicked into global daily grids, and the daily global grids were mean-composited to monthly grids of air temperature.

Air temperature format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = degrees C scale factor = 1 (divide the value by 1 to get Air temperature) filenames = yyyymmtemp.txt

aetpet.zip: WATER AVAILABILITY. We used the MOD16 Global Evapotranspiration product (Mu et al 2011) to calculate the monthly 0.05 degree ratio of Actual to Potential Evapotranspiration (AET/PET).

AET/PET format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = dimensionless scale factor = 10000 (divide the value by 10000 to get AET/PET) filenames = yyyymmaetpet.txt

cld.zip - CLOUDINESS. We used the MOD35_L2 Cloud Mask product (Ackerman et al 2010). This product provides daily records on the presence of cloudy vs cloudless skies, and we used this to make an index of the proportion of of cloudy to clear days in a given pixel. After conversion to geographic co-ordinates, five-minute swaths at 1-km resolution were reclassed as clear sky or cloudy, and these daily swaths were mean-mosaicked to global coverages, mean composited from daily to monthly, and mean-aggregated from 1km to 0.05 degree.

cld format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = percentage of days in the month which were cloudy scale factor = 100 (divide the value by 100 to get percentage cloudy days) filenames = yyyymmcld.txt

References

Ackerman, S. et al. (2010) Discriminating clear-sky from cloud with MODIS: Algorithm Theoretical Basis Document (MOD35), Version 6.1. (URL: ttp://modis- atmos.gsfc.nasa.gov/_docs/MOD35_A TBD_Collection6.pdf)

Huete, A. et al. (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83, 195–213.

Mu, Q., Zhao, M., Running, S.R. (2011) Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment 115, 1781-1800

Seeman, S. W., Borbas, E. E., Li, J., Menzel, W. P. & Gumley, L. E. (2006) MODIS Atmospheric Profile Retrieval Algorithm Theoretical Basis Document, Version 6 (URL: http://modis-atmos.gsfc.nasa.gov/_docs/MOD07_atbd_v7_April2011.pdf)

Solano, R. et al. (2010) MODIS Vegetation Index User’s Guide (MOD13 Series) Version 2.00, May 2010 (Collection 5) (URL: http://vip.arizona.edu/documents/MODIS/MODIS_VI_UsersGuide_01_2012.pdf) Seddon et al. (2016) Nature (DOI 10.1038/nature16986) ABSTRACT: Identification of properties that contribute to the persistence and resilience of ecosystems despite climate change constitutes a research priority of global significance. Here, we present a novel, empirical approach to assess the relative sensitivity of ecosystems to climate variability, one property of resilience that builds on theoretical modelling work recognising that systems closer to critical thresholds respond more sensitively to external perturbations. We develop a new metric, the Vegetation Sensitivity Index (VSI) which identifies areas sensitive to climate variability over the past 14 years. The metric uses time-series data of MODIS derived Enhanced Vegetation Index (EVI) and three climatic variables that drive vegetation productivity (air temperature, water availability and cloudiness). Underlying the analysis is an autoregressive modelling approach used to identify regions with memory effects and reduced response rates to external forcing. We find ecologically sensitive regions with amplified responses to climate variability in the arctic tundra, parts of the boreal forest belt, the tropical rainforest, alpine regions worldwide, steppe and prairie regions of central Asia and North and South America, the Caatinga deciduous forest in eastern South America, and eastern areas of Australia. Our study provides a quantitative methodology for assessing the relative response rate of ecosystems – be they natural or with a strong anthropogenic signature – to environmental variability, which is the first step to address why some regions appear to be more sensitive than others and what impact this has upon the resilience of ecosystem service provision and human wellbeing.

Residential Home Energy Efficiency

Evaluated Meter Project Data 2007-2012

By State of New York [source]

About this dataset

This dataset provides energy efficiency meter evaluated data from 2007-2012 for residential existing homes (one to four units) in New York State. It includes the following data points: Project County, Project City, Project ZIP, Climate Zone, Weather Station, Weather Station-Normalization, Project Completion Date, Customer Type, Size of Home, Volume of Home, Number of Units .Year Home Built , Total Project Cost , Contractor Incentive , Total Incentives , Amount Financed through Program , Estimated Annual Electric Savings (kWh), Estimated Annual Gas Savings (MMBtu), Estimated First Year Energy Bill Savings ($) Baseline Electric (kWh), Baseline Gas (MMBtu), Reporting Electric (kWh), Reporting Gas (MMBtu ),Evaluated Annual Electric Savings( kWh ), Evaluated Annual gas Savings( MMBTU )Central Hudson LIPA National Fuel gas NYSEG Orange and Rockland Rochester Gas and electric Location 1. This dataset backcasts estimated modeled savings for a subset of 2007 -2012 completed projects in the Home Performance with ENERGY STARprogram against normalized savings calculated by an open source energy efficiency meter. The open source code uses utility grade metered consumption to weather normalize the pre -and post consumption data using standard methods with no discretionary independent variables. It is intended to lay a foundation for future innovation and deployment of the open source energy efficiency meter across the residential energy sector and help inform stakeholders interested in Pay For Performance programs where providers are paid for realizing measurable weather normalized results. Please make sure you read the Disclaimer included before using this data; it contains important information about evaluating savings from contractor reported modeling estimates as well as evaluating Normalized Savigns using Open Source OEE meter

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• Last updated information: The last update for this dataset was 2019-11-15

• Data Elements Overview:This dataset includes a variety of data points that provide valuable insights into residential energy efficiency projects undertaken between 2007 TO 2012 in New York State; including project ID, county, city zip code, climate zone, weather station used for normalization methods, completion date customer type size and volume of home number of units year home was built total project cost contractor incentive total incentives amount financed through program etc.

• Definitions Overview: There are several acronyms included in this datasets such as Central Hudson (a utility company), LIPA (the Long Island Power Authority), National Fuel Gas (National Fuel Gas Utility Company), NYSEG (New York State Electric & Gas Utility Company) and Rochester Gas & Electric (Rochester Gas & Electric Utility Company). Additionally “Climate Zone” are numbered 1 through 5 representing regions from coolest north/northwest regions to warmest south/southeast regions across New York; these correspond with Warm-Humid, Marine VBZc&De2VBladium Marine Subtropical HotSummer ColdWinter ColdSummer Moderate Winter regions respectively. A Weather Station is used for normalizing Savings Data which a location like described Niagara Falls International Airport that obtains historical average temperature values from various temperatures sources . Weather Stations Normalization compares day-of vs seasonal temperature difference outside homes against model prediction retrofit reduction predictions inside home without weather normalizing watt reduction products can be over or under estimated depending on current season vs expected seasons which this model accounts The estimated annual electric savings are calculated using factors such as pre-retrofit baseline electric kWh post-retrofit usage electric kWh evaluated annual electric savings calculated by open source library software installed by customers neighborhood ? measured GHG emission reductions determined with assumptions provided input device SDK so on life cycle greenhouse gas emission reductions also tracked documented impact studies have been conducted verify conclusion accuracy projected values reported nyserda industry rebate programs benchmarking standardized meter data allowing future compare patterns? measurements document capture utilities grid management initiated demand response events companies target focus market . Moving forward Total Project Cost is figure analyzed depending estimates provided

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**Description of GlobalRx Dataset:

198,890 records of prescribed burns in 16 countries. In the information below, the name of the variable's column within the dataset is given in parentheses () in code font. For example, the column with the Drought Code data is titled DC.

For each record, the following general information (derived from the original burn records sources) is included, where available:

• Latitude (Latitude)
• Longitude (Longitude)
• Year (Year)
• Month (Month)
• Day (Day)
• Time* (Time)
• DOY (DOY)
• Country (Country)
• State/Province (State/Province)
• Agency/Organisation (Agency/Organisation)
• Burn Objective* (Burn Objective)
• Area Burned (Ha)* (Area Burned (Ha))
• Data Repository (Data Repository)
• Citation (Citation)

* Not available for every record

For each record, the following meteorological information (derived from the ERA5 single levels reanalysis product) is also included:

• Daily total accumulated precipitation (PPT_tot)
• Daily minimum and mean relative humidity (RH_min, RH_mean)*
• Daily maximum 2-meter temperature (T_max)
• Daily maximum and mean 10-meter wind speed (Wind_max, Wind_mean)
• Daily minimum boundary layer height (BLH_min)
• C-Haines Index (CHI)*
• Vapor pressure deficit (VPD)*

* Computed from other ERA5 meteorological variables.

For each record, the following fire weather indices and components (derived from ERA5 fire weather reanalysis product) are also included:

• Canadian fire weather index (FWI)
• Fine fuel moisture code (FFMC)
• Drought moisture code (DMC)
• Drought code (DC)
• McArthur forest fire danger index (FFDI)
• Keetch-Byram drought index (KBDI)
• US burning index (USBI)

For each record, the following environmental information (derived from various sources, see paper for more information) is also included:

• Ecoregion (Olson et al. 2001) (Ecoregion (Olson))
• Biome (Olson et al. 2001) (Biome (Olson))
• Koppen Climate (Beck et al. 2023) (Koppen Climate)
• Topography (Danielson and Gesch 2011) (Topography)
• Fuelbed Classification (GFD-FCCS) (Pettinari and Chuvieco 2016) (Fuelbed Classification (GFD-FCCS))
• WDPA Name (WDPA 2024) (WDPA Name)
• WDPA Governance (WDPA 2024) (WDPA Governance)
• WDPA Ownership (WDPA 2024) (WDPA Ownership)
• WDPA Designation (WDPA 2024) (WDPA Designation)
• WDPA IUCN Category (WDPA 2024) (WDPA IUCN Category)

You can enrich and improve your website, your analysis, your Data Science project, or trigger any event based on high accurate weather forecast. Forecast data includes pollen data for the next 5 days on daily level for a given ZIP code. In comparison to the full data set, this data sample provides information for one ZIP code.

The data can be found here: "PUBLIC"."FORECAST_POLLEN_VIEW_EXAMPLE”

The dataset has the following fields:

• date_forecast: Date on which the forecast was created
• country: country of the zip_code.
• zip_code: Zip code for which the index was calculated for a pollen type
• date: date for which the index was calculated
• pollen_index: Index for the pollen severity on a given date. The index ranges from 0 to 3.

The definition of the pollen index is: 0: no impact for this pollen type 1: low impact 2: medium impact 3: high impact

We offer the index for the following pollen types:

• Abies
• Acer
• Aesculus
• Ambrosia
• Artemisia
• Asteraceae
• Betula
• Carpinus
• Castanea
• Corylus
• Cruciferae
• Cyperaceae
• Fraxinus
• Fungus
• Galium
• Humulus
• Larix
• Pinaceae
• Pinus
• Plantago
• Populus
• Quercus ilex
• Rumex
• Salix
• Secale
• Chenopodium
• Tilia
• Urtica
• Alnus
• Erica
• Fagus
• Impatiens
• Juglans
• Poaceae
• Quercus
• Sambucus
• Picea
• Ulmus
• Varia
• Platanus
• Taxus

CIMIS data is available to the public free of charge via a web Application Programming Interface (API). The CIMIS Web API delivers data over the REST protocol from an enterprise production platform. The system provides reference evapotranspiration (ETo) and weather data from the CIMIS Weather Station Network and the Spatial CIMIS System. Spatial CIMIS provides daily maps of ETo and solar radiation (Rs) data at 2-km grid by coupling remotely sensed satellite data with point measurements from the CIMIS weather stations. In summary, the data provided through the CIMIS Web API is comprised by a) Weather and ETo data registered at the CIMIS Weather Station Network (more than 150 stations located throughout the state of California and b) Spatial CIMIS System data that provides statewide ETo and solar radiation (Rs) data as well as aeraged ETo by zip-codes. The RESTful HTTP services reach a broader range of clients; including Wi-Fi aware irrigation smart controllers as well as browser and mobile applications, all while expanding the delivery options by providing data in either JSON or XML formats.

Dataset Description This dataset contains aggregated meteorological variables for U.S. counties and ZIP Code Tabulation Areas (ZCTAs) derived from the gridMET dataset. The gridMET product combines high-resolution spatial climate data (e.g., temperature, precipitation, humidity) from the PRISM Climate Group with daily temporal attributes and additional meteorological variables from the NLDAS-2 regional reanalysis dataset. The final product includes daily meteorological data at approximately 4km x 4km spatial resolution across the contiguous United States. This dataset has been processed to provide spatial (ZCTA, County) and temporal (daily, yearly) aggregations for broader climate analysis. This dataset was created to support climate and public health research by providing ready-to-use, high-resolution meteorological data aggregated at county and ZCTA levels. This allows for efficient linking with health and socio-demographic data to explore the impacts of climate on public health. Contributors: Harvard T.H. Chan School of Public Health, NSAPH (National Studies on Air Pollution and Health) The data is organized by geographic unit (County and ZCTA) and temporal scale (daily, yearly). The dataset is structured to facilitate the computation of climate exposure variables for health impact studies. A data processing pipeline was used to generate this dataset.

Each annual file contains 21 metrics developed by the CANUE Weather and Climate Team, and calculated by CANUE staff using base data provided by the Canadian Forest Service of Natural Resources Canada.The base data consist of interpolated daily maximum temperature, minimum temperature and total precipitation for all unique DMTI Spatial Inc. postal code locations in use at any time between 1983 and 2015. These were generated using thin-plate smoothing splines, as implemented in the ANUSPLIN climate modeling software. The earliest applications of thin-plate smoothing splines were described by Wahba and Wendelberger (1980) and Hutchinson and Bischof (1983), but the methodology has been further developed into an operational climate mapping tool at the ANU over the last 20 years. ANUSPLIN has become one of the leading technologies in the development of climate models and maps, and has been applied in North America and many regions around the world. ANUSPLIN is essentially a multidimensional “nonparametric” surface fitting method that has been found particularly well suited to the interpolation of various climate parameters, including daily maximum and minimum temperature, precipitation, and solar radiation.The water balance model was developed by Pei-Ling Wang and Dr. Johannes Feddema at the University of Victoria, Geography Department, and implemented by CANUE staff Mahdi Shooshtari. (THESE DATA ARE ALSO AVAILABLE AS MONTHLY METRICS).

This data release collates stream water temperature observations from across the United States from four data sources: The U.S. Geological Survey's National Water Information System (NWIS), Water Quality Portal (WQP), Spatial Hydro-Ecological Decision Systems temperature database (EcoSHEDS), and the U.S. Fish and Wildlife's NorWeST stream temperature database. These data were compiled for use in broad scale water temperature models. Observations are included from the contiguous continental US, as well as Alaska, Hawaii, and territories. Temperature monitoring sites were paired to stream segments from the Geospatial Fabric for the National Hydrologic Model. Continuous and discrete data were reduced to daily mean, minimum, and maximum temperatures when more than one measurement was made per site-day. Various quality control checks were conducted including inspecting and converting units, eliminating some duplicate entries, interpreting flags and removing low quality observations, fixing date issues from the WQP, and filtering to expected water temperature ranges. However, we expect data quality issues persist and users should conduct further data quality checks that match the intended use of the data. This data release contains four core files: - site_metadata.csv contains information about each site at which water temperature observations are reported in this dataset. - national_stream_temp_code.zip contains the R code used to derive the data in this data release. - daily_stream_temperature.zip is a compressed comma separated file of observed water temperatures. - spatial.zip contains the geographic information about each site at which water temperature observations are reported in this dataset.