CDI-Workflow description of the EOSC Future WP6 Task 3, Science Project 9 'Climate Neutral and Smart Cities' ################################################## Main Sequence of the process CDI-Workflow description of the EOSC Future WP6 Task 3, Science Project 9 'Climate Neutral and Smart Cities' en 8909e85b-2b60-4c6e-a58a-19b54237ef89 int.esseric 1 true true Main Sequence of the process f6eae594-8716-4e79-84d6-92fbf26df99dint.esseric1 9d3db1fd-79cc-4ef8-816d-3a28499a6d0aint.esseric1 9d3db1fd-79cc-4ef8-816d-3334444int.esseric1 SequencePosition 9d3db1fd-79cc-4ef8-816d-3334444 int.esseric 1 true true 1 9d3db1fd-79cc-4ef8-816d-3a28499a6d0aint.esseric1 Arofan Gregory en 77c1f00d-aecc-46d1-9f2f-3f417eaa4e57int.esseric1 Joachim Wackerow en 65a8b222-5a5b-4196-8a39-9c4f96f56f7eint.esseric1 Benjamin Beuster en dcbcb34e-cd30-4b23-8e14-eb920da6a3edint.esseric1 Hilde Orten en dcbcb34e-cd30-4b23-8e14-eb920da6a3edint.esseric1 Eirik B Stavestrand en dcbcb34e-cd30-4b23-8e14-eb920da6a3edint.esseric1 Sikt - Norwegian Agency for Shared Services in Education and Research en dcbcb34e-cd30-4b23-8e14-eb920da6a3edint.esseric1 EOSC project team at Sikt - Norwegian Agency for Shared Services in Education and Research en f6eae594-8716-4e79-84d6-92fbf26df99d int.esseric 1 true true Integrate climate data from ERA5 and air quality data from the EEA with the ESS survey data en dcbcb34e-cd30-4b23-8e14-99998888int.esseric1 dcbcb34e-cd30-4b23-8e14-99998888 int.esseric 1 true true https://ess.sikt.no/en/?tab=overview ESS Dataportal Sikt - Norwegian Agency for Shared Services in Education and Research en dcbcb34e-cd30-4b23-8e14-eb920da6a3ed int.esseric 1 true true Sikt - Norwegian Agency for Shared Services in Education and Research CODATA - Committee on Data of the International Science Council en 77c1f00d-aecc-46d1-9f2f-3f417eaa4e57 int.esseric 1 true true CODATA - Committee on Data of the International Science Council DDI Alliance - The Data Documentation Initiative Alliance en 65a8b222-5a5b-4196-8a39-9c4f96f56f7e int.esseric 1 true true DDI Alliance - The Data Documentation Initiative Alliance Integrate climate data from Copernicus ERA5 and air quality data from the European Environmental Agency (EEA) with data from the European Social Survey (ESS) for Berlin, Oslo, Stockholm, Brussels, London, Paris, Vienna, Prague, Budapest, and Madrid Integrate climate data from Copernicus ERA5 and air quality data from the European Environmental Agency (EEA) with data from the European Social Survey (ESS) for Berlin, Oslo, Stockholm, Brussels, London, Paris, Vienna, Prague, Budapest, and Madrid en 9d3db1fd-79cc-4ef8-816d-3a28499a6d0a int.esseric 1 true true Integrate climate and air quality data with ESS 34be5d18-c1f7-4e3d-82e7-81909e0989f4int.esseric1 a142bdc5-8e35-4de1-94f6-c3a1e298ed79int.esseric1 25c58b1b-c5ed-48fc-8e53-4d2fdd682184int.esseric1 Ingest and prepare data from ERA5 data (Copernicus) Ingest and prepare data from ERA5 data (Copernicus) en ERA5 website https://doi.org/10.24381/cds.adbb2d47 34be5d18-c1f7-4e3d-82e7-81909e0989f4 int.esseric 1 true true ERA5 Data (Copernicus) 70e828d0-4231-4b9b-bb69-7882483fb591int.esseric1 8a364e23-1e2f-4f75-9b61-2ad1ad39fe68int.esseric1 f75ac23d-46b5-4e7f-a52d-638ab34a7b81int.esseric1 The process involves obtaining NUTS - Nomenclature of territorial units for statistics polygons for the relevant regions, followed by calling a public API with GPS coordinates derived from the polygons. A single API call is made per month, resulting in a gridded data response file with a default resolution of 0.1 degree latitude/longitude. Each month and region corresponds to one variable, resulting in over 20.000 files. The performance of this process is relatively slow, taking around a minute per call, and it requires a substantial number of calls to collect the complete dataset. There are over 12.000 raw input files in NetCDF4 format covering the period from 1990 to 2022. The process involves obtaining NUTS - Nomenclature of territorial units for statistics polygons for the relevant regions, followed by calling a public API with GPS coordinates derived from the polygons. A single API call is made per month, resulting in a gridded data response file with a default resolution of 0.1 degree latitude/longitude. Each month and region corresponds to one variable, resulting in over 20.000 files. The performance of this process is relatively slow, taking around a minute per call, and it requires a substantial number of calls to collect the complete dataset. There are over 12.000 raw input files in NetCDF4 format covering the period from 1990 to 2022. en ca. 12.000 NetCDF4 files nuts polygons level 1 https://gisco-services.ec.europa.eu/distribution/v2/nuts/geojson/NUTS_RG_01M_2016_4326_LEVL_1.geojson nuts polygons level 2 https://gisco-services.ec.europa.eu/distribution/v2/nuts/geojson/NUTS_RG_01M_2016_4326_LEVL_2.geojson nuts polygons level 3 https://gisco-services.ec.europa.eu/distribution/v2/nuts/geojson/NUTS_RG_01M_2016_4326_LEVL_3.geojson ERA5 Homepage https://doi.org/10.24381/cds.adbb2d47 70e828d0-4231-4b9b-bb69-7882483fb591 int.esseric 1 true true ERA5 Get raw input The process involves reading NetCDF files into Panda dataframes, obtaining estimated population data for grids from the Global Human Settlements data based on Eurostat, merging the population data with ERA5 data, and writing the merged data to disk in Parquet format. External experts perform quality checks on the merged data, which could be either a one-off or a regular quality assurance check. The process utilizes over 12.000 NetCDF4 files as input as well as data from the GHSL - Global Human Settlement Layer. The output of the process is a single Parquet file named "Interim data for review" with its corresponding URI. The process involves reading NetCDF files into Panda dataframes, obtaining estimated population data for grids from the Global Human Settlements data based on Eurostat, merging the population data with ERA5 data, and writing the merged data to disk in Parquet format. External experts perform quality checks on the merged data, which could be either a one-off or a regular quality assurance check. The process utilizes over 12.000 NetCDF4 files as input as well as data from the GHSL - Global Human Settlement Layer. The output of the process is a single Parquet file named "Interim data for review" with its corresponding URI. en ERA5 Interim data for review (era5-grids) https://doi.org/10.21338/era5-grids ca. 12.000 NetCDF4 files GHSL - Global Human Settlement Layer https://ghsl.jrc.ec.europa.eu/datasets.php 8a364e23-1e2f-4f75-9b61-2ad1ad39fe68 int.esseric 1 true true ERA5 Marshalling data The process involves creating a date variable from timestamps based on the time zone of each region, considering that the data is recorded hourly. It also addresses unit differences, converting Kelvin to Celsius and meters to millimeters. The data is then grouped by date, variable, and region, and temperature is averaged while also obtaining maximum and minimum values, accumulating precipitation by date, and identifying the maximum wind gust value. Moving averages are calculated for variables using different time windows (7-day, 30-day, 90-day, 365-day). Baseline values for temperature, precipitation, wind gust, and deviations from the baseline (anomalies) are determined based on the period from 1991 to 2020. Data older than 2015 is removed, and a group-by operation is performed, collapsing the data by region using population-weighted averages. It is important to note that the ERA5 data may contain imputed and missing values. In memory, each row corresponds to a region, with mesh-blocks aggregated per day to calculate region-level values by taking the average of all variables weighted by the population of each block. The resulting data is stored to disk in CSV, SAV, or other suitable formats, as the data size remains manageable. The process involves creating a date variable from timestamps based on the time zone of each region, considering that the data is recorded hourly. It also addresses unit differences, converting Kelvin to Celsius and meters to millimeters. The data is then grouped by date, variable, and region, and temperature is averaged while also obtaining maximum and minimum values, accumulating precipitation by date, and identifying the maximum wind gust value. Moving averages are calculated for variables using different time windows (7-day, 30-day, 90-day, 365-day). Baseline values for temperature, precipitation, wind gust, and deviations from the baseline (anomalies) are determined based on the period from 1991 to 2020. Data older than 2015 is removed, and a group-by operation is performed, collapsing the data by region using population-weighted averages. It is important to note that the ERA5 data may contain imputed and missing values. In memory, each row corresponds to a region, with mesh-blocks aggregated per day to calculate region-level values by taking the average of all variables weighted by the population of each block. The resulting data is stored to disk in CSV, SAV, or other suitable formats, as the data size remains manageable. en Integratable datafile for ERA5 (era5-regions) https://doi.org/10.21338/era5-regions ERA5 Interim data for review (era5-grids) https://doi.org/10.21338/era5-grids f75ac23d-46b5-4e7f-a52d-638ab34a7b81 int.esseric 1 true true ERA5 Data Processing 4fd913d0-c3b2-4e95-8a3b-90a5d34a56d3int.esseric1 506c2de5-97d2-4bea-9c8e-dc014ae0e5a2int.esseric1 9de7f212-44d3-4f68-a938-55fbb50d5b7cint.esseric1 056a33e2-0c73-4e1e-9e38-6ac1554f0062int.esseric1 e06e5202-2b1d-4f9b-9d3e-3e864f717ee4int.esseric1 5e9724c6-6205-4846-920c-881fe01ae6d0int.esseric1 770f6e63-8f45-4e8f-a086-c2d2dd774b26int.esseric1 13fb673d-08a1-4ad3-b7c5-282fdd6030dfint.esseric1 51b90bb6-30b0-4e1c-8c0b-19b2a56e49c4int.esseric1 63d9f86f-b32b-4687-b753-662e9f56747fint.esseric1 d25d0d69-51d7-4e18-8eef-d7dc124a2a63int.esseric1 a56e71e4-f9e9-47d7-8c49-6625de7c6804int.esseric1 2c3f42b4-1ed3-4b19-9799-f2e228a66c07int.esseric1 5cfa2f2b-9b94-4e34-b64e-3b808f39e4c7int.esseric1 6a68be34-64d3-4a72-8847-4e0645f474c0int.esseric1 b779a530-0e55-4b61-9c2d-7c9dc2017fb2int.esseric1 7f344be0-8be6-4c1b-91d2-c689bde5f31fint.esseric1 2d3eb36e-15b7-45e6-8f42-824956c550c2int.esseric1 f047699d-0c01-4289-89fd-bf8d01efb9b0int.esseric1 51b3843d-4f7b-4f2d-b2be-9e9a50c3e7b2int.esseric1 e2decc61-8a29-49eb-96fc-c255b48598b0int.esseric1 032365cc-6b7f-49df-a8c6-2207f9475032int.esseric1 c06776b1-81ea-4e7a-bc3b-b168a98b4984int.esseric1 0c31e2ac-b408-4b4f-80e5-8e2e813ce25cint.esseric1 52dcbefa-0633-44da-8cc2-ee42b535f3fdint.esseric1 91e18b23-0e5e-42f5-9231-238c22f40274int.esseric1 7b9e29e4-4a25-496d-9f28-8dca7c3e94d2int.esseric1 Ingest and prepare data from EEA Air Quality Ingest and prepare data from EEA Air Quality en website https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm a142bdc5-8e35-4de1-94f6-c3a1e298ed79 int.esseric 1 true true EEA Air Quality 6a8934ad-b0c7-4a2c-8899-129ce9e9b4a3int.esseric1 d79858ab-3d27-48d2-ba6a-64f37b7dd06dint.esseric1 c6b1e1d8-2e41-441c-83a7-3e809d7a07dbint.esseric1 The process involves obtaining a list of stations with GPS coordinates from a pan-European metadata CSV file and selecting specific stations based on their GPS coordinates and ID. Only background stations are selected. The NUTS regions, defined by their GPS coordinates for polygons, are taken into account when the data is collected. The process involves obtaining a list of stations with GPS coordinates from a pan-European metadata CSV file and selecting specific stations based on their GPS coordinates and ID. Only background stations are selected. The NUTS regions, defined by their GPS coordinates for polygons, are taken into account when the data is collected. en ca. 5.000 CSV files nuts polygons level 1 https://gisco-services.ec.europa.eu/distribution/v2/nuts/geojson/NUTS_RG_01M_2016_4326_LEVL_1.geojson nuts polygons level 2 https://gisco-services.ec.europa.eu/distribution/v2/nuts/geojson/NUTS_RG_01M_2016_4326_LEVL_2.geojson nuts polygons level 3 https://gisco-services.ec.europa.eu/distribution/v2/nuts/geojson/NUTS_RG_01M_2016_4326_LEVL_3.geojson nuts GPS coordinates (CSV) https://discomap.eea.europa.eu/map/fme/metadata/PanEuropean_metadata.csv 6a8934ad-b0c7-4a2c-8899-129ce9e9b4a3 int.esseric 1 true true EEA Get raw input All the collected data, including pollutant-by-station-by-hour information, is consolidated into a single file named "eea-stations," with each row representing a specific pollutant, station, and hour. The merged data is stored in Parquet format, serving as a checkpoint for external reviewers to validate the analysis. All the collected data, including pollutant-by-station-by-hour information, is consolidated into a single file named "eea-stations," with each row representing a specific pollutant, station, and hour. The merged data is stored in Parquet format, serving as a checkpoint for external reviewers to validate the analysis. en EEA Interim data for review (eea-stations) https://doi.org/10.21338/eea-stations ca. 5.000 CSV files d79858ab-3d27-48d2-ba6a-64f37b7dd06d int.esseric 1 true true EEA Marshalling data The process involves collapsing the data on time to group it by day, using maximum values. It is then collapsed by region, also using the maximum value for each region. Index variables are calculated based on the concentrations provided by the EEA. These index variables help create a classification of air quality (e.g., Good, Fair, Moderate) are based on the European Air Quality Index as of August 2023. Derived variables are computed to determine the worst quality and the number of days with poor quality for different time periods, with specific dates serving as reference points. The resulting data is stored as a file, which can be of a suitable format based on the size requirements. The process involves collapsing the data on time to group it by day, using maximum values. It is then collapsed by region, also using the maximum value for each region. Index variables are calculated based on the concentrations provided by the EEA. These index variables help create a classification of air quality (e.g., Good, Fair, Moderate) are based on the European Air Quality Index as of August 2023. Derived variables are computed to determine the worst quality and the number of days with poor quality for different time periods, with specific dates serving as reference points. The resulting data is stored as a file, which can be of a suitable format based on the size requirements. en Integratable datafile for EEA (eea-regions) https://doi.org/10.21338/eea-regions EEA Interim data for review (eea-stations) https://doi.org/10.21338/eea-stations c6b1e1d8-2e41-441c-83a7-3e809d7a07db int.esseric 1 true true EEA Data Processing 70b92d2b-cc0a-44a7-9b1b-1e84084f4c50int.esseric1 e5719aa7-5c9f-4e8c-b8b2-ff3b6bb1e6c2int.esseric1 8e76387e-0a01-4282-89bc-9db776546f98int.esseric1 920f3b9b-6aa2-4a33-aa15-fa41b3c45ee3int.esseric1 5d4a2d9d-54f8-419e-a4b5-4ef2223dceaaint.esseric1 03e16b5f-7876-4d78-bc64-23e16cfd4500int.esseric1 cdb34f49-1977-4a9f-bc6b-2db89292442dint.esseric1 1b3b0f21-2c18-4edc-82f2-58e50b0e40c9int.esseric1 e21b1e54-18a0-4296-af9d-0b0eb9762cc7int.esseric1 74190ce2-23d5-4c2b-a9e3-5017f5a2b9e7int.esseric1 d8885706-02d6-4e0a-8a37-dc5732c0c79fint.esseric1 2b702183-45de-45d5-80a9-5ebc1be9fe1cint.esseric1 1a6476be-23a9-4e0f-9a48-f70307579001int.esseric1 4a51ff8e-0250-45c0-aedc-7433e1d2db87int.esseric1 6a2f8d7e-5fc9-44e5-bf60-71a91c3614c6int.esseric1 a7d2eaaa-3c8e-4f8e-9f8a-c12c447e02d0int.esseric1 d06dfe8a-7685-4ed5-b54d-5e240de6a8efint.esseric1 9267e50e-8f2a-4321-9ce4-014b98899f4cint.esseric1 3cf520d5-1d16-40a2-8a32-02eef2f9784bint.esseric1 9d0f2b2e-bfd5-4d9b-b6f3-b0e7c7a85c8eint.esseric1 57cc0d7f-3df3-4c61-b0ce-4a3454a495ccint.esseric1 a8e2f5e3-95af-48b1-8770-9ae8d4b840d9int.esseric1 8f85e449-0f80-41b7-8921-4db59106e23bint.esseric1 b6c22f5e-4985-4a87-a5ff-3353b54a2096int.esseric1 4c6e7249-9c49-4140-a0d6-88bde940012eint.esseric1 2b97a7f9-c5fe-4bc4-8e9a-dc2c93d406acint.esseric1 12b1349d-eb60-46b6-9d5f-b7ee92e4e7afint.esseric1 c8a132ef-57e1-40a4-ba11-82690a893f61int.esseric1 bbea52b0-7e47-4942-bb8c-bc16da36a4d3int.esseric1 3d2b0c76-491c-46a4-b57f-13b7b90e38d9int.esseric1 7078715e-59f5-43e6-9c0c-ef5be032d9eeint.esseric1 Merging of ERA5, EEA and ESS data Merging of ERA5, EEA and ESS data en 25c58b1b-c5ed-48fc-8e53-4d2fdd682184 int.esseric 1 true true Merging of ERA5, EEA and ESS data 970e8311-50e6-480f-b227-749354e5f879int.esseric1 b9e2a47a-1391-4169-a207-bd3a53a50dd2int.esseric1 213886c1-9e5d-4d9d-8d3e-96b017181769int.esseric1 13e9d99b-d48f-4a8c-934e-2b9a8d942e4eint.esseric1 The process involves merging ERA5, EEA, and ESS8 data, selecting only the observations relevant to the specified regions based on coverage, and saving the integrated data as a single file for ESS Round 8. The process involves merging ERA5, EEA, and ESS8 data, selecting only the observations relevant to the specified regions based on coverage, and saving the integrated data as a single file for ESS Round 8. en Integrated datafile for ERA5, EEA and ESS8 https://doi.org/10.21338/merged-EOSC-ESS8e02_2 970e8311-50e6-480f-b227-749354e5f879 int.esseric 1 true true Merging of ERA5, EEA and ESS8 data The process involves merging ERA5, EEA, and ESS9 data, selecting only the observations relevant to the specified regions based on coverage, and saving the integrated data as a single file for ESS Round 9. The process involves merging ERA5, EEA, and ESS9 data, selecting only the observations relevant to the specified regions based on coverage, and saving the integrated data as a single file for ESS Round 9. en Integrated datafile for ERA5, EEA and ESS9 https://doi.org/10.21338/merged-EOSC-ESS9e03_1 b9e2a47a-1391-4169-a207-bd3a53a50dd2 int.esseric 1 true true Merging of ERA5, EEA and ESS9 data The process involves merging ERA5, EEA, and ESS10 face-to-face data, selecting only the observations relevant to the specified regions based on coverage, and saving the integrated data as a single file for ESS Round 10. The process involves merging ERA5, EEA, and ESS10 face-to-face data, selecting only the observations relevant to the specified regions based on coverage, and saving the integrated data as a single file for ESS Round 10. en Integrated datafile for ERA5, EEA and ESS10 https://doi.org/10.21338/merged-EOSC-ESS10 213886c1-9e5d-4d9d-8d3e-96b017181769 int.esseric 1 true true Merging of ERA5, EEA and ESS10 data The process involves merging ERA5, EEA, and ESS10 Self-completion data, selecting only the observations relevant to the specified regions based on coverage, and saving the integrated data as a single file for ESS Round 10 (Self-completion). The process involves merging ERA5, EEA, and ESS10 Self-completion data, selecting only the observations relevant to the specified regions based on coverage, and saving the integrated data as a single file for ESS Round 10 (Self-completion). en Integrated datafile for ERA5, EEA and ESS10 Self-completion https://doi.org/10.21338/merged-EOSC-ESS10SC 13e9d99b-d48f-4a8c-934e-2b9a8d942e4e int.esseric 1 true true Merging of ERA5, EEA and ESS10 Self-completion data Use variables 'DatetimeBegin' and 'DatetimeEnd' to create variable 'date'. 70b92d2b-cc0a-44a7-9b1b-1e84084f4c50 int.esseric 1 true true Create variable date https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L171 Python3 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 15f93862-58a3-4f13-aef2-0a5c7f250a8eint.esseric1 64f584c5-d271-4e60-89a2-7ff6c5a0ed1aint.esseric1 Compute target variable 'aqiwdpm10' starting from variables 'Concentration' and 'AirPollutant = PM10' for each background station. Compute where 'AirQualityStation' has values for the pollutant. Find 'Concentration' value at the 99th percentile for the pollutant. Create EEA Air Quality Index where PM10: 0 to 20 eq 'Good' represented by value '0'; 20 to 40 eq 'Fair' represented by value '1'; 40 to 50 eq 'Moderate' represented by value '2'; 50 to 100 eq 'Poor' represented by value '3'; 100 to 150 eq 'Very Poor' represented by value '4'; 150 to 1200 eq 'Extremely poor' represented by value '5'. The worst air quality level measured on a specific date on one of the background stations in the region provides values to the variable. e5719aa7-5c9f-4e8c-b8b2-ff3b6bb1e6c2 int.esseric 1 true true Create variable aqiwdpm10 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L71 Python3 83ef6dcf-2d56-4fd6-94e1-8cbbdca65f55int.esseric1 1b87f6c7-d1ce-4321-8e76-f94a16e780b6int.esseric1 2eddc4ae-156e-4b85-8449-09487a26c0a0int.esseric1 1c576b4d-0eaa-4542-8c17-4b74ff1a854bint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'aqiwdpm2_5' starting from variables 'Concentration' and 'AirPollutant = PM2.5' for each background station. Compute where 'AirQualityStation' has values for the pollutant. Find 'Concentration' value at the 99th percentile for the pollutant. Create EEA Air Quality Index where PM2.5: 0 to 10 eq 'Good' represented by value '0'; 10 to 20 eq 'Fair' represented by value '1'; 20 to 25 eq 'Moderate' represented by value '2'; 25 to 50 eq 'Poor' represented by value '3'; 50 to 75 eq 'Very Poor' represented by value '4'; 75 to 800 eq 'Extremely poor' represented by value '5'. The worst air quality level measured on a specific day on one of the background stations in the region provides values to the variable. 8e76387e-0a01-4282-89bc-9db776546f98 int.esseric 1 true true Create variable aqiwdpm2_5 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L72 Python3 046a6012-3f1e-4945-8e57-7d24b4b9a73fint.esseric1 1b87f6c7-d1ce-4321-8e76-f94a16e780b6int.esseric1 2eddc4ae-156e-4b85-8449-09487a26c0a0int.esseric1 1c576b4d-0eaa-4542-8c17-4b74ff1a854bint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'aqiwdso2' starting from variables 'Concentration' and 'AirPollutant = SO2' for each background station. Compute where 'AirQualityStation' has values for the pollutant. Find 'Concentration' value at the 99th percentile for the pollutant. Create EEA Air Quality Index where SO2: 0 to 100 eq 'Good' represented by value '0'; 100 to 200 eq 'Fair' represented by value '1'; 200 to 350 eq 'Moderate' represented by value '2'; 350 to 500 eq 'Poor' represented by value '3'; 500 to 750 eq 'Very Poor' represented by value '4'; 750 to 1250 eq 'Extremely poor' represented by value '5'. The worst air quality level measured on a specific day on one of the background stations in the region provides values to the variable. 920f3b9b-6aa2-4a33-aa15-fa41b3c45ee3 int.esseric 1 true true Create variable aqiwdso2 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L73 Python3 5d1db345-d4d5-4d64-a3dd-fd2e7d8c8e50int.esseric1 1b87f6c7-d1ce-4321-8e76-f94a16e780b6int.esseric1 2eddc4ae-156e-4b85-8449-09487a26c0a0int.esseric1 1c576b4d-0eaa-4542-8c17-4b74ff1a854bint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'aqiwdno2' starting from variables 'Concentration' and 'AirPollutant = NO2' for each background station. Compute where 'AirQualityStation' has values for the pollutant. Find 'Concentration' value at the 99th percentile for the pollutant. Create EEA Air Quality Index where NO2: 0 to 40 eq 'Good' represented by value '0'; 40 to 90 eq 'Fair' represented by value '1'; 90 to 120 eq 'Moderate' represented by value '2'; 120 to 230 eq 'Poor' represented by value '3'; 230 to 340 eq 'Very Poor' represented by value '4'; 340 to 1000 eq 'Extremely Poor' represented by value '5'. The worst air quality level measured on a specific day on one of the background stations in the region provides values to the variable. 5d4a2d9d-54f8-419e-a4b5-4ef2223dceaa int.esseric 1 true true Create variable aqiwdno2 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L74 Python3 0cb5b141-7c62-4d67-84a5-9c4a6e3cb8d1int.esseric1 1b87f6c7-d1ce-4321-8e76-f94a16e780b6int.esseric1 2eddc4ae-156e-4b85-8449-09487a26c0a0int.esseric1 1c576b4d-0eaa-4542-8c17-4b74ff1a854bint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'aqiwdo3' starting from variables 'Concentration' and 'AirPollutant = O3' for each background station. Compute where 'AirQualityStation' has values for the pollutant. Find 'Concentration' value at the 99th percentile for the pollutant. Create EEA Air Quality Index where O3: 0 to 50 eq 'Good' represented by value '0', 50 to 100 eq 'Fair' represented by value '1'; 100 to 130 eq 'Moderate' represented by value '2'; 130 to 240 eq 'Poor' represented by value '3'; 2040 to 380 eq 'Very poor' represented by value '4'; 380 to 800 eq 'Extremely poor' represented by value '5'. The average of the worst air quality level measured on a specific day on each of the background stations in the region provides values to the variable. 03e16b5f-7876-4d78-bc64-23e16cfd4500 int.esseric 1 true true Create variable aqiwdo3 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L75 Python3 b5ee4a8f-84ea-4b17-8e3a-0f7146cc6f6aint.esseric1 1b87f6c7-d1ce-4321-8e76-f94a16e780b6int.esseric1 2eddc4ae-156e-4b85-8449-09487a26c0a0int.esseric1 1c576b4d-0eaa-4542-8c17-4b74ff1a854bint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'aqiwd' based on 'aqiwdpm10'; 'aqiwdpm2_5'; 'aqiwdso2'; 'aqiwdno2; 'aqiwdo3' by date and region. The maximum value across the source variables becomes the value of the target variable cdb34f49-1977-4a9f-bc6b-2db89292442d int.esseric 1 true true Create variable aqiwd https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L80 Python3 977a1c8e-2fe5-4a2f-bba9-7b7f2a663ee8int.esseric1 83ef6dcf-2d56-4fd6-94e1-8cbbdca65f55int.esseric1 046a6012-3f1e-4945-8e57-7d24b4b9a73fint.esseric1 5d1db345-d4d5-4d64-a3dd-fd2e7d8c8e50int.esseric1 0cb5b141-7c62-4d67-84a5-9c4a6e3cb8d1int.esseric1 b5ee4a8f-84ea-4b17-8e3a-0f7146cc6f6aint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'aqiw2dpm10' based on 'aqiwdpm10' 'region' and 'date'. The worst value for the pollutant across a date and the date before becomes the value of the target variable 1b3b0f21-2c18-4edc-82f2-58e50b0e40c9 int.esseric 1 true true Create variable aqiw2dpm10 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L83 Python3 0f79a5f7-d011-441b-a6fe-5c8a34e7bc3eint.esseric1 83ef6dcf-2d56-4fd6-94e1-8cbbdca65f55int.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'aqiw2dpm2_5' based on 'aqiwdpm2_5', 'region' and 'date'. The worst value for the pollutant across a date and the date before becomes the value of the target variable e21b1e54-18a0-4296-af9d-0b0eb9762cc7 int.esseric 1 true true Create variable aqiw2dpm2_5 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L83 Python3 1198b158-2a18-44b6-b6b3-739f7f7f53a4int.esseric1 046a6012-3f1e-4945-8e57-7d24b4b9a73fint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'aqiw2dso2' based on 'aqiwdso2', 'region' and 'date'. The worst value for the pollutant across a date and the date before becomes the value of the target variable 74190ce2-23d5-4c2b-a9e3-5017f5a2b9e7 int.esseric 1 true true Create variable aqiw2dso2 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L83 Python3 3e5a1e7a-0c99-40f3-a8e2-935fbaa3be42int.esseric1 5d1db345-d4d5-4d64-a3dd-fd2e7d8c8e50int.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'aqiw2dno2' based on 'aqiwdno2', 'region' and 'date'. The worst value for the pollutant across a date and the date before becomes the value of the target variable d8885706-02d6-4e0a-8a37-dc5732c0c79f int.esseric 1 true true Create variable aqiw2dno2 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L83 Python3 54b03ec0-08d3-449f-8a6b-2a51370d7c6fint.esseric1 0cb5b141-7c62-4d67-84a5-9c4a6e3cb8d1int.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'aqiw2do3' based on 'aqiwdo3', 'region' and 'date'. The worst value for the pollutant across a date and the date before becomes the value of the target variable 2b702183-45de-45d5-80a9-5ebc1be9fe1c int.esseric 1 true true Create variable aqiw2do3 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L83 Python3 1726b265-7d4d-4e11-bf14-d6dd4e8a8fa1int.esseric1 b5ee4a8f-84ea-4b17-8e3a-0f7146cc6f6aint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'aqiw2d ' based on 'aqiw2dpm10'; 'aqiw2dpm2_5'; 'aqiw2dso2'; 'aqiwdno2'; 'aqiw2do3', 'region' and 'date'. The maximum value across the source variables becomes the value of the target variable 1a6476be-23a9-4e0f-9a48-f70307579001 int.esseric 1 true true Create variable aqiw2d https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L83 Python3 4cddfb70-6f52-4844-8c1a-df5a27e0c8cdint.esseric1 0f79a5f7-d011-441b-a6fe-5c8a34e7bc3eint.esseric1 1198b158-2a18-44b6-b6b3-739f7f7f53a4int.esseric1 3e5a1e7a-0c99-40f3-a8e2-935fbaa3be42int.esseric1 54b03ec0-08d3-449f-8a6b-2a51370d7c6fint.esseric1 1726b265-7d4d-4e11-bf14-d6dd4e8a8fa1int.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndyprwpm10' based on 'aqiwdpm10', 'region' and 'date'. The number of days during the last seven days from date, date included, where 'aqiwdpm10' = '3' 'Poor' or 'aqiwdpm10' = '4' 'Very poor' or 'aqiwdpm10' = '5' 'Extremely poor' becomes the value of the target variable 4a51ff8e-0250-45c0-aedc-7433e1d2db87 int.esseric 1 true true Create variable ndyprwpm10 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L92 Python3 8e8c7a55-682d-4eae-bb81-4e120dfb92f5int.esseric1 83ef6dcf-2d56-4fd6-94e1-8cbbdca65f55int.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndyprwpm2_5' based on 'aqiwdpm2_5', 'region' and 'date'. The number of days during the last seven days from date, date included, where 'aqiwdpm2_5' = '3' 'Poor' or 'aqiwdpm2_5' = '4' 'Very poor' or 'aqiwdpm2_5' = '5' 'Extremely poor' becomes the value of the target variable 6a2f8d7e-5fc9-44e5-bf60-71a91c3614c6 int.esseric 1 true true Create variable ndyprwpm2_5 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L92 Python3 618a1449-0c2b-43c4-a1c0-ceddb988fb06int.esseric1 046a6012-3f1e-4945-8e57-7d24b4b9a73fint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndyprwso2' based on 'aqiwdso2', 'region' and 'date'. The number of days during the last seven days from date, date included, where 'aqiwdso2' = '3' 'Poor' or 'aqiwdso2' = '4' 'Very poor' or 'aqiwdso2' = '5' 'Extremely poor' becomes the value of the target variable a7d2eaaa-3c8e-4f8e-9f8a-c12c447e02d0 int.esseric 1 true true Create variable ndyprwso2 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L92 Python3 9e9826c5-2e42-4e29-ba01-f4e2f45553c4int.esseric1 5d1db345-d4d5-4d64-a3dd-fd2e7d8c8e50int.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndyprwno2' based on 'aqiwdno2', 'region'and 'date'. The number of days during the last seven days from date, date included, where 'aqiwdno2r' = '3' 'Poor' or 'aqiwdno2r' = '4' 'Very poor' or 'aqiwdno2r' = '5' 'Extremely poor' becomes the value of the target variable d06dfe8a-7685-4ed5-b54d-5e240de6a8ef int.esseric 1 true true Create variable ndyprwno2 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L92 Python3 0f2db12e-4c23-48b0-bbb3-fcb38afec775int.esseric1 0cb5b141-7c62-4d67-84a5-9c4a6e3cb8d1int.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndyprwo3' based on 'aqiwdo3', 'region' and 'date'. The number of days during the last seven days from date, date included, where 'aqiwdo3r' = '3' 'Poor' or 'aqiwdo3r' = '4' 'Very poor' or 'aqiwdo3r' = '5' 'Extremely poor' becomes the value of the target variable 9267e50e-8f2a-4321-9ce4-014b98899f4c int.esseric 1 true true Create variable ndyprwo3 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L92 Python3 3764a9e4-b82b-4087-9384-46037772f3dcint.esseric1 b5ee4a8f-84ea-4b17-8e3a-0f7146cc6f6aint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndyprw' based on 'aqiwdpm10', 'aqiwdpm2_5', 'aqiwdso2', 'aqiwdno2', 'aqiwdo3', 'region' and 'date'. The number of days during the last seven days from date, date included, where one or more of variables 'aqiwdpm10', 'aqiwdpm2_5', 'aqiwdso2r', 'aqiwdno2', 'aqiwdo3' has value '3' 'Poor' or '4' 'Very poor' or '5' 'Extremely poor' becomes the value of the target variable 3cf520d5-1d16-40a2-8a32-02eef2f9784b int.esseric 1 true true Create variable ndyprw https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L92 Python3 50b171a3-ef1a-4a25-8707-0451d71d69b5int.esseric1 83ef6dcf-2d56-4fd6-94e1-8cbbdca65f55int.esseric1 046a6012-3f1e-4945-8e57-7d24b4b9a73fint.esseric1 5d1db345-d4d5-4d64-a3dd-fd2e7d8c8e50int.esseric1 0cb5b141-7c62-4d67-84a5-9c4a6e3cb8d1int.esseric1 b5ee4a8f-84ea-4b17-8e3a-0f7146cc6f6aint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndyprmpm10' based on 'aqiwdpm10', 'region' and 'date'. The number of days during the last thirty days from date, date included, where 'aqiwdpm10' = '3' 'Poor' or 'aqiwdpm10' = '4' 'Very poor' or 'aqiwdpm10' = '5' 'Extremely poor' becomes the value of the target variable 9d0f2b2e-bfd5-4d9b-b6f3-b0e7c7a85c8e int.esseric 1 true true Create variable ndyprmpm10 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L95 Python3 08c32e0a-9d9a-4b1f-8813-4a61a97f5eb5int.esseric1 83ef6dcf-2d56-4fd6-94e1-8cbbdca65f55int.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndyprmpm2_5' based on ' aqiwdpm2_5 ' , 'region' and 'date'. The number of days during the last thirty days from date, date included, where 'aqiwdpm2_5' = '3' 'Poor' or 'aqiwdpm2_5' = '4' 'Very poor' or 'aqiwdpm2_5' = '5' 'Extremely poor' becomes the value of the target variable 57cc0d7f-3df3-4c61-b0ce-4a3454a495cc int.esseric 1 true true Create variable ndyprmpm2_5 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L95 Python3 06f845d3-6eef-4c86-8f08-2ff09cb8f336int.esseric1 046a6012-3f1e-4945-8e57-7d24b4b9a73fint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable ' ndyprmso2' based on 'aqiwdso2', 'region' and 'date'. The number of days during the last thirty days from date, date included, where 'aqiwdso2' = '3' 'Poor' or 'aqiwdso2' = '4' 'Very poor' or 'aqiwdso2' = '5' 'Extremely poor' becomes the value of the target variable a8e2f5e3-95af-48b1-8770-9ae8d4b840d9 int.esseric 1 true true Create variable ndyprmso2 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L95 Python3 a0f03669-7d69-456a-b0a2-5dc2f5ceee0eint.esseric1 5d1db345-d4d5-4d64-a3dd-fd2e7d8c8e50int.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndyprmno2' based on 'aqiwdno2', 'region' and 'date'. The number of days during the last thirty days from date, date included, where 'aqiwdno2' = '3' 'Poor' or 'aqiwdno2' = '4' 'Very poor' or 'aqiwdno2' = '5' 'Extremely poor' becomes the value of the target variable 8f85e449-0f80-41b7-8921-4db59106e23b int.esseric 1 true true Create variable ndyprmno2 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L95 Python3 48ad7d75-3a71-4960-b234-faf6b43eb0d0int.esseric1 0cb5b141-7c62-4d67-84a5-9c4a6e3cb8d1int.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndyprmo3' based on 'aqiwdo3', 'region' and 'date'. The number of days during the last thirty days from date, date included, where 'aqiwdo3' = '3' 'Poor' or 'aqiwdo3' = '4' 'Very poor' or 'aqiwdo3' = '5' 'Extremely poor' becomes the value of the target variable b6c22f5e-4985-4a87-a5ff-3353b54a2096 int.esseric 1 true true Create variable ndyprmo3 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L95 Python3 94f04e4e-9a9c-4c1a-8bf5-ba09d7087c7cint.esseric1 b5ee4a8f-84ea-4b17-8e3a-0f7146cc6f6aint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndyprm' based on 'aqiwdpm10', 'aqiwdpm2_5', 'aqiwdso2', 'aqiwdno2', 'aqiwdo3', 'region' and 'date'. The number of days during the last thirty days from date, date included, where one or more of variables 'aqiwdpm10r', 'aqiwdpm2_5r', 'aqiwdso2r', 'aqiwdno2r', 'aqiwdo3r' has value '3' 'Poor' or '4' 'Very poor' or '5' 'Extremely poor' becomes the value of the target variable 4c6e7249-9c49-4140-a0d6-88bde940012e int.esseric 1 true true Create variable ndyprm https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L95 Python3 53a7b2b0-6e47-4ae3-939a-3e4dbb87a5e1int.esseric1 83ef6dcf-2d56-4fd6-94e1-8cbbdca65f55int.esseric1 046a6012-3f1e-4945-8e57-7d24b4b9a73fint.esseric1 5d1db345-d4d5-4d64-a3dd-fd2e7d8c8e50int.esseric1 0cb5b141-7c62-4d67-84a5-9c4a6e3cb8d1int.esseric1 b5ee4a8f-84ea-4b17-8e3a-0f7146cc6f6aint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndyprypm10' based on 'aqiwdpm10', 'region' and 'date'. The number of days during the last three hundred and sixty five days from date, date included, where 'aqiwdpm10' = '3' 'Poor' or 'aqiwdpm10' = '4' 'Very poor' or 'aqiwdpm10' = '5' 'Extremely poor' becomes the value of the target variable 2b97a7f9-c5fe-4bc4-8e9a-dc2c93d406ac int.esseric 1 true true Create variable ndyprypm10 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L98 Python3 28e2e3f7-5d32-43d5-9967-dc8c2b52a812int.esseric1 83ef6dcf-2d56-4fd6-94e1-8cbbdca65f55int.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndyprypm2_5' based on 'aqiwdpm2_5', 'region' and 'date'. The number of days during the last three hundred and sixty five days from date, date included, where 'aqiwdpm2_5' = '3' 'Poor' or 'aqiwdpm2_5' = '4' 'Very poor' or 'aqiwdpm2_5' = '5' 'Extremely poor' becomes the value of the target variable 12b1349d-eb60-46b6-9d5f-b7ee92e4e7af int.esseric 1 true true Create variable ndyprypm2_5 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L98 Python3 63c688b1-7eac-4467-ae78-9ffdcf61a7e0int.esseric1 046a6012-3f1e-4945-8e57-7d24b4b9a73fint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndypryso2' based on 'aqiwdso2', 'region' and 'date'. The number of days during the last three hundred and sixty five days from date, date included, where 'aqiwdso2' = '3' 'Poor' or 'aqiwdso2' = '4' 'Very poor' or 'aqiwdso2' = '5' 'Extremely poor' becomes the value of the target variable c8a132ef-57e1-40a4-ba11-82690a893f61 int.esseric 1 true true Create variable ndypryso2 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L98 Python3 e126afbf-d4d0-4489-a1ff-68d24d6b2739int.esseric1 5d1db345-d4d5-4d64-a3dd-fd2e7d8c8e50int.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndypryno2' based on 'aqiwdno2', 'region' and 'date'. The number of days during the last three hundred and sixty five days from date, date included, where 'aqiwdno2' = '3' 'Poor' or 'aqiwdno2' = '4' 'Very poor' or 'aqiwdno2' = '5' 'Extremely poor' becomes the value of the target variable bbea52b0-7e47-4942-bb8c-bc16da36a4d3 int.esseric 1 true true Create variable ndypryno2 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L98 Python3 8200aede-3eaa-45b9-ae3f-2252e5fe9a6bint.esseric1 0cb5b141-7c62-4d67-84a5-9c4a6e3cb8d1int.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable ' ndypryo3' based on 'aqiwdo3', 'region' and 'date'. The number of days during the last three hundred and sixty five days from date, date included, where 'aqiwdo3' = '3' 'Poor' or 'aqiwdo3' = '4' 'Very poor' or 'aqiwdo3' = '5' 'Extremely poor' becomes the value of the target variable 3d2b0c76-491c-46a4-b57f-13b7b90e38d9 int.esseric 1 true true Create variable ndypryo3 https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L98 Python3 b23dadd2-c0e7-450d-a5e2-95ff625cbb46int.esseric1 b5ee4a8f-84ea-4b17-8e3a-0f7146cc6f6aint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'ndypry ' based on 'aqiwdpm10', 'aqiwdpm2_5', 'aqiwdso2', 'aqiwdno2', 'aqiwdo3', 'region' and 'date'. The number of days during the last three hundred and sixty five days from date, date included, where one or more of variables 'aqiwdpm10', 'aqiwdpm2_5', 'aqiwdso2', 'aqiwdno2', 'aqiwdo3' has value '3' 'Poor' or '4' 'Very poor' or '5' 'Extremely poor' becomes the value of the target variable 7078715e-59f5-43e6-9c0c-ef5be032d9ee int.esseric 1 true true Create variable ndypry https://github.com/sikt-no/ess-labs-data-sp9/blob/master/eea-prepare.py#L98 Python3 4a68ab66-0c79-42a7-bb1b-d69be74eef1dint.esseric1 83ef6dcf-2d56-4fd6-94e1-8cbbdca65f55int.esseric1 046a6012-3f1e-4945-8e57-7d24b4b9a73fint.esseric1 5d1db345-d4d5-4d64-a3dd-fd2e7d8c8e50int.esseric1 0cb5b141-7c62-4d67-84a5-9c4a6e3cb8d1int.esseric1 b5ee4a8f-84ea-4b17-8e3a-0f7146cc6f6aint.esseric1 7d6e0e95-572c-40a5-82e4-bc51a2a68c77int.esseric1 87419b24-5963-48e7-8db9-0c6edf44bce6int.esseric1 Compute target variable 'date' based on variable 'time' and 'region' to convert from UTC to local time zones 4fd913d0-c3b2-4e95-8a3b-90a5d34a56d3 int.esseric 1 true true Create variable date https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L14 Python3 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 fa27c73e-7e8c-4b9a-b2cd-2de28f8352b2int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'tmpdca' based on variable 'tmpdc', 'region_id', 'date' and 'pop. Calculate average temperature by date for each grid cell to be included in the data. Temperature measured in Kelvin is converted to degrees Celsius (°C) by subtracting 273.15. Calculate temperature averages for each region, weighted by variable 'pop' that is based on global human settlements statistics. 506c2de5-97d2-4bea-9c8e-dc014ae0e5a2 int.esseric 1 true true Create variable tmpdca https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L41 Python3 d570cff6-39ae-4383-a0d6-8e2e502cd55fint.esseric1 b811f20c-498d-4d7c-a1b3-86a55fc4283cint.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 3d3e48b0-dc3f-41c3-927e-5a5fb2fb5b72int.esseric1 Compute target variable 'tmpdcmx' based on variable 'tmpdc', 'region_id', 'date' and 'pop'. Calculate maximum temperature by date for each grid cell to be included in the data. Temperature measured in Kelvin is converted to degrees Celsius (°C) by subtracting 273.15. Calculate average maximums for each region, weighted by variable 'pop' that is based on global human settlements statistics. 9de7f212-44d3-4f68-a938-55fbb50d5b7c int.esseric 1 true true Create variable tmpdcmx https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L42 Python3 e12e6b3e-8cb0-4baf-b68d-6e50a51c43f0int.esseric1 b811f20c-498d-4d7c-a1b3-86a55fc4283cint.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 3d3e48b0-dc3f-41c3-927e-5a5fb2fb5b72int.esseric1 Compute target variable 'tmpdcmn' based on variable 'tmpdc', 'region_id', 'date' and 'pop'. Calculate minimum temperature by date for each grid cell to be included in the data. Temperature measured in Kelvin converted to degrees Celsius (°C) by subtracting 273.15. Calculate average minimums for each region, weighted by variable 'pop' that is based on global human settlements statistics. 056a33e2-0c73-4e1e-9e38-6ac1554f0062 int.esseric 1 true true Create variable tmpdcmn https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L43 Python3 22fb2e6e-23b4-4941-9777-5c86d0f4b5f3int.esseric1 b811f20c-498d-4d7c-a1b3-86a55fc4283cint.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 3d3e48b0-dc3f-41c3-927e-5a5fb2fb5b72int.esseric1 Calculate average temperature week (seven days) before the date, date included for each region, based on variables 'tmpdca', 'date' and 'region_id'. e06e5202-2b1d-4f9b-9d3e-3e864f717ee4 int.esseric 1 true true Create variable tmpdcaw https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L60 Python3 94f5de4f-bb5e-4f9c-b795-c08c8fb9b1e5int.esseric1 d570cff6-39ae-4383-a0d6-8e2e502cd55fint.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Calculate average temperature month (thirty days) before the date, date included for each region, date included, based on variables 'tmpdca', 'date' and 'region_id'. 5e9724c6-6205-4846-920c-881fe01ae6d0 int.esseric 1 true true Create variable tmpdcam https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L61 Python3 5c18635b-1cb7-4604-81c1-7dd7c6b2b80aint.esseric1 d570cff6-39ae-4383-a0d6-8e2e502cd55fint.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Calculate average temperature three months (ninty days) before the date, date included for each region, date included, based on variables 'tmpdca', 'date' and 'region_id'. 770f6e63-8f45-4e8f-a086-c2d2dd774b26 int.esseric 1 true true Create variable tmpdca3m https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L62 Python3 8b6c1f47-8191-4329-82a2-7b3db8be09f4int.esseric1 d570cff6-39ae-4383-a0d6-8e2e502cd55fint.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Calculate average temperature year (three hundred and sixty five days) before the date, date included for each region, based on variables 'tmpdca', 'date' and 'region_id'. 13fb673d-08a1-4ad3-b7c5-282fdd6030df int.esseric 1 true true Create variable tmpdcay https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L63 Python3 48ae1e59-3ea2-4d23-9c73-683b9a53c255int.esseric1 d570cff6-39ae-4383-a0d6-8e2e502cd55fint.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'tmpdcacm' based on variables 'tmpdca', 'region_id' and 'date'. Calculate average temperature for each calendar month for each region. 51b90bb6-30b0-4e1c-8c0b-19b2a56e49c4 int.esseric 1 true true Create variable tmpdcacm https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L101 Python3 770dbb42-3ea1-4ff6-9e29-06dd2826a102int.esseric1 d570cff6-39ae-4383-a0d6-8e2e502cd55fint.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'tmpdcamb' based on variables 'tmpdca', 'region_id' and 'date'. Calculate average temperature for each calendar month for each region, across the baseline period 1991 - 2020. 63d9f86f-b32b-4687-b753-662e9f56747f int.esseric 1 true true Create variable tmpdcamb https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L89 Python3 a87745ad-3f82-454b-bb54-1e7537687c8aint.esseric1 d570cff6-39ae-4383-a0d6-8e2e502cd55fint.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'tmp95pacmb' based on variable 'tmpdc', 'date', 'region_id'.and 'pop' Calculate 95 percentile values for each calendar month for each grid cell as an average of the 1991 - 2020 baseline period values. Temperature measured in Kelvin converted to degrees Celsius (°C) by subtracting 273.15. Calculate calendar month averages for each region as an average of 95 the percentil values for each calendar month for each grid for the baseline period 1991 - 2020, weighted by variable 'pop' that is based on global human settlements statistics. d25d0d69-51d7-4e18-8eef-d7dc124a2a63 int.esseric 1 true true Create variable tmp95pacmb https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L93 Python3 0845a5f2-5a15-4ae9-b4db-4e4b01237b0eint.esseric1 b811f20c-498d-4d7c-a1b3-86a55fc4283cint.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 3d3e48b0-dc3f-41c3-927e-5a5fb2fb5b72int.esseric1 Compute target variable 'tmpanod' based on variables 'tmpdca', 'tmpdcamb', 'region_id' and 'date'. Calculate 'tmpanod' as day average for each region, minus calendar month average base value 1991 - 2020 (tmpdca - tmpdcamb). a56e71e4-f9e9-47d7-8c49-6625de7c6804 int.esseric 1 true true Create variable tmpanod https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L97 Python3 2f43806d-3b16-4577-96c9-2f48a65a3a9eint.esseric1 d570cff6-39ae-4383-a0d6-8e2e502cd55fint.esseric1 a87745ad-3f82-454b-bb54-1e7537687c8aint.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'tmpanocm' based on variables 'tmpdcacm', 'tmpdcamb', 'region_id' and 'date'. Calculate 'tmpanocm' as calendar month average for each region, minus calendar month average base value 1991 - 2020 (tmpdcacm - tmpdcamb). 2c3f42b4-1ed3-4b19-9799-f2e228a66c07 int.esseric 1 true true Create variable tmpanocm https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L102 Python3 22d6e6c6-53c6-48b1-bd84-3e9c5dd8765bint.esseric1 770dbb42-3ea1-4ff6-9e29-06dd2826a102int.esseric1 a87745ad-3f82-454b-bb54-1e7537687c8aint.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'paccta' based on variable 'pac', 'region_id','date' and 'pop'. Calculate total precipitation by date in mm for each grid cell to be included in the data. The total precipitation over 24 hours is the sum of the individual total precipitation values for each hour. Precipitation measured in m converted to mm. Calculate total precipitation by date for each region as an average of the grid cell value, weighted by variable 'pop' that is based on global human settlements statistics. 5cfa2f2b-9b94-4e34-b64e-3b808f39e4c7 int.esseric 1 true true Create variable paccta https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L44 Python3 d8db2e66-7d6f-4899-98b2-43d2454be2b2int.esseric1 e68f489e-0d26-4c35-8eac-2e6e7d5d6f66int.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 3d3e48b0-dc3f-41c3-927e-5a5fb2fb5b72int.esseric1 Compute target variable 'pacctaw' based on variables 'paccta', 'date' and 'region_id'. Calculate average total precipitation for the week (seven days) before the date, date included, for each region. 6a68be34-64d3-4a72-8847-4e0645f474c0 int.esseric 1 true true Create variable pacctaw https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L64 Python3 7b6452de-0b77-4b29-8606-14aeb87df963int.esseric1 d8db2e66-7d6f-4899-98b2-43d2454be2b2int.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'pacctam' based on variables 'paccta', 'date' and 'region_id'. Calculate average total precipitation for the month (thirty days) before the date, date included, for each region. b779a530-0e55-4b61-9c2d-7c9dc2017fb2 int.esseric 1 true true Create variable pacctam https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L65 Python3 6e4e6d68-2093-4ab1-b6f5-b4c6a23f7e2eint.esseric1 d8db2e66-7d6f-4899-98b2-43d2454be2b2int.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'paccta3m' based on variables 'paccta', 'date' and 'region_id'. Calculate average total precipitation for the three months (ninty days) before the date, date included, for each region. 7f344be0-8be6-4c1b-91d2-c689bde5f31f int.esseric 1 true true Create variable paccta3m https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L66 Python3 c39e44a0-4cd9-4d49-85d1-b7c2b7158495int.esseric1 d8db2e66-7d6f-4899-98b2-43d2454be2b2int.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'pacctay' based on variables 'paccta', 'date' and 'region_id'. Calculate average total precipitation for the year (three hundred and sixty five days) before the date, date included, for each region. 2d3eb36e-15b7-45e6-8f42-824956c550c2 int.esseric 1 true true Create variable pacctay https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L67 Python3 43dd6df3-485d-48a4-97e5-d77e15c8d6f3int.esseric1 d8db2e66-7d6f-4899-98b2-43d2454be2b2int.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'pacctcm' based on variables 'paccta', 'date' and 'region_id'. Calculate 'pacctcm' as an average of total precipitation date values for the calendar month. f047699d-0c01-4289-89fd-bf8d01efb9b0 int.esseric 1 true true Create variable pacctcm https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L106 Python3 3a864b8d-84b5-446a-94ce-833f234ab1c9int.esseric1 d8db2e66-7d6f-4899-98b2-43d2454be2b2int.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'pacctmb' based on variables 'pacctm', 'region_id' and 'date'.Calculate accumulated precipitation averages for each calendar month for each region over the baseline period 1991 to 2020. 51b3843d-4f7b-4f2d-b2be-9e9a50c3e7b2 int.esseric 1 true true Create variable pacctmb https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L112 Python3 36e523f4-53b9-4cde-96c2-947a9e4c0e2aint.esseric1 3a864b8d-84b5-446a-94ce-833f234ab1c9int.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'paccdcm' based on variables 'pacctcm', 'pacctmb', 'region_id' and 'date'.Compute variable for calendar month precipitation anomaly for each region as (current month/norml)*100. Using variables: (pacctcm/pacctmb)* 100 e2decc61-8a29-49eb-96fc-c255b48598b0 int.esseric 1 true true Create variable paccdcm https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L116 Python3 0e8e8e24-7171-4d6b-9df8-5e39c61a23feint.esseric1 3a864b8d-84b5-446a-94ce-833f234ab1c9int.esseric1 36e523f4-53b9-4cde-96c2-947a9e4c0e2aint.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'iwg10mx' based on variables 'iwg10', 'region_id' and 'date'. Calculate maximum wind gust value pr. date for each grid cell. The maximum wind gust value pr. date for a grid cell within the region becomes the value of the target variable. 032365cc-6b7f-49df-a8c6-2207f9475032 int.esseric 1 true true Create variable iwg10mx https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L45 Python3 46b4db67-47e9-4db4-81f7-51a7740b9d97int.esseric1 70e66b17-19d5-4a85-9b60-4c195fe5aaf2int.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'iwg10mxaw' based on variables 'iwg10mx', 'region_id' and 'date'. Calculate the average maximum wind gust value for the week (seven days) before the date, date included, for each region. c06776b1-81ea-4e7a-bc3b-b168a98b4984 int.esseric 1 true true Create variable iwg10mxaw https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L68 Python3 eacdb92d-13d7-4b18-9d06-2607c4de0ff0int.esseric1 46b4db67-47e9-4db4-81f7-51a7740b9d97int.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'iwg10mxam' based on variables 'iwg10mx', 'region_id' and 'date'. Calculate the average maximum wind gust value for the month (thirty days) before the date, date included, for each region. 0c31e2ac-b408-4b4f-80e5-8e2e813ce25c int.esseric 1 true true Create variable iwg10mxam https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L69 Python3 8b8a2d8c-1653-4193-8b44-93761e140c0dint.esseric1 46b4db67-47e9-4db4-81f7-51a7740b9d97int.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'iwg10mxa3m' based on variables 'iwg10mx', 'region_id' and 'date'. Calculate the average maximum wind gust value for the three months (ninty days) before the date, date included, for each region. 52dcbefa-0633-44da-8cc2-ee42b535f3fd int.esseric 1 true true Create variable iwg10mxa3m https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L70 Python3 81d75a45-25e0-4a67-9870-32080d0b1e84int.esseric1 46b4db67-47e9-4db4-81f7-51a7740b9d97int.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'iwg10mxay' based on variables 'iwg10mx', 'region_id' and 'date'. Calculate the average maximum wind gust value for the year (three hundred and sixty five days) before the date, date included, for each region. 91e18b23-0e5e-42f5-9231-238c22f40274 int.esseric 1 true true Create variable iwg10mxay https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L71 Python3 a34a2324-8c44-4be3-8f19-02db87644f0cint.esseric1 46b4db67-47e9-4db4-81f7-51a7740b9d97int.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 Compute target variable 'iwg10mxamb' based on variables 'iwg10mx', 'region_id' and 'date'. Calculate the average maximum wind gust for each calendar month for each region, across the baseline period 1991 - 2020. 7b9e29e4-4a25-496d-9f28-8dca7c3e94d2 int.esseric 1 true true Create variable iwg10mxamb https://github.com/sikt-no/ess-labs-data-sp9/blob/master/era5-prepare.py#L119 Python3 f0d9925b-4f72-422e-bcfb-8f6f30b5f2e2int.esseric1 46b4db67-47e9-4db4-81f7-51a7740b9d97int.esseric1 a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6int.esseric1 30f06f3d-2e0b-4066-8e89-618be51244ceint.esseric1 https://ess-search.nsd.no/en/variable/1ccbcbf9-588f-46bd-b233-914a59db32aa fa27c73e-7e8c-4b9a-b2cd-2de28f8352b2 int.esseric 1 true true Variable time https://ess-search.nsd.no/en/variable/b0c15dec-6161-4f99-8b22-a95f5bb1fd69 6226e8c5-20ef-4f8a-8549-3f78abefc7d7 int.esseric 1 true true Variable longitude https://ess-search.nsd.no/en/variable/a8a3f3b0-ad4d-42da-a19b-2c26bb9a12a2 893db376-5b23-4156-88e1-d1c8c44c519d int.esseric 1 true true Variable latitude https://ess-search.nsd.no/en/variable/30dc45a1-3597-4b42-8bdb-35b61f7089c2 b811f20c-498d-4d7c-a1b3-86a55fc4283c int.esseric 1 true true Variable tmpdc https://ess-search.nsd.no/en/variable/9cd32e48-b5bb-4617-ad01-a53195d0fdd9 e68f489e-0d26-4c35-8eac-2e6e7d5d6f66 int.esseric 1 true true Variable pac https://ess-search.nsd.no/en/variable/530463d3-ac6e-439d-876f-dff966dac65c 70e66b17-19d5-4a85-9b60-4c195fe5aaf2 int.esseric 1 true true Variable iwg10 https://ess-search.nsd.no/en/variable/812c4035-6886-43aa-a18c-8d797390a8b4 30f06f3d-2e0b-4066-8e89-618be51244ce int.esseric 1 true true Variable region_id https://ess-search.nsd.no/en/variable/d96727bb-96b8-4fad-a6b4-7e65cb63326f 734811f7-7854-48ed-9eb3-99b4187b2992 int.esseric 1 true true Variable grid_id https://ess-search.nsd.no/en/variable/982b1f8a-f1d8-4651-9d87-ef708d0c88b2 3d3e48b0-dc3f-41c3-927e-5a5fb2fb5b72 int.esseric 1 true true Variable pop https://ess-search.nsd.no/en/variable/291fc2b7-2914-4398-9d8b-cef4f1ec4bad 73c0ac4f-9d3a-4e67-bf2f-9a9d3094e16d int.esseric 1 true true Variable __index_level_0__ https://ess-search.nsd.no/en/variable/0cae5306-79bf-4976-b25a-3f790aa312be a4c5a88e-5f12-4bb1-9be7-3f54c54ff4f6 int.esseric 1 true true Variable date https://ess-search.nsd.no/en/variable/b00ef56c-0653-4646-9226-5a1679613da0 238c6473-4806-40f1-9f25-809b0d49f63c int.esseric 1 true true Variable region https://ess-search.nsd.no/en/variable/d977ac41-6e88-4413-8999-b7960a988c9f d570cff6-39ae-4383-a0d6-8e2e502cd55f int.esseric 1 true true Variable tmpdca https://ess-search.nsd.no/en/variable/cff883f7-3577-4ae5-b7c1-adf676f09120 e12e6b3e-8cb0-4baf-b68d-6e50a51c43f0 int.esseric 1 true true Variable tmpdcmx https://ess-search.nsd.no/en/variable/35aa21af-4b85-4a79-a02f-336a09af600c 22fb2e6e-23b4-4941-9777-5c86d0f4b5f3 int.esseric 1 true true Variable tmpdcmn https://ess-search.nsd.no/en/variable/8dfdc09d-f0cd-4849-bf61-3f980c1db775 94f5de4f-bb5e-4f9c-b795-c08c8fb9b1e5 int.esseric 1 true true Variable tmpdcaw https://ess-search.nsd.no/en/variable/e4f97b28-f945-4eef-860c-60a85158e8da 8b6c1f47-8191-4329-82a2-7b3db8be09f4 int.esseric 1 true true Variable tmpdca3m https://ess-search.nsd.no/en/variable/3e9d6336-4c36-4a18-a86b-cf826c7d91e7 5c18635b-1cb7-4604-81c1-7dd7c6b2b80a int.esseric 1 true true Variable tmpdcam https://ess-search.nsd.no/en/variable/ce707a90-c97f-46dd-bfab-f921a53dfa7f 48ae1e59-3ea2-4d23-9c73-683b9a53c255 int.esseric 1 true true Variable tmpdcay https://ess-search.nsd.no/en/variable/ae4f0001-f59d-47b2-8ded-e163391587de a87745ad-3f82-454b-bb54-1e7537687c8a int.esseric 1 true true Variable tmpdcamb https://ess-search.nsd.no/en/variable/74308e4a-3f55-4f0e-b7bf-53dc07128ee5 0845a5f2-5a15-4ae9-b4db-4e4b01237b0e int.esseric 1 true true Variable tmp95pacmb https://ess-search.nsd.no/en/variable/9927e365-be1d-4fca-8f8b-e23720345667 2f43806d-3b16-4577-96c9-2f48a65a3a9e int.esseric 1 true true Variable tmpanod https://ess-search.nsd.no/en/variable/2f8614ac-8662-46a3-b60f-813dcb7db4c2 22d6e6c6-53c6-48b1-bd84-3e9c5dd8765b int.esseric 1 true true Variable tmpanocm https://ess-search.nsd.no/en/variable/7bc1b4ca-b1e1-42b1-b594-7e8b30458eeb 770dbb42-3ea1-4ff6-9e29-06dd2826a102 int.esseric 1 true true Variable tmpdcacm https://ess-search.nsd.no/en/variable/19fc2d5a-6475-4c71-84fd-b8175f7729f3 d8db2e66-7d6f-4899-98b2-43d2454be2b2 int.esseric 1 true true Variable paccta https://ess-search.nsd.no/en/variable/721daf7b-787e-4892-a676-0d80ec4945da 7b6452de-0b77-4b29-8606-14aeb87df963 int.esseric 1 true true Variable pacctaw https://ess-search.nsd.no/en/variable/14cca74d-d801-4432-a268-38cb5860ed52 6e4e6d68-2093-4ab1-b6f5-b4c6a23f7e2e int.esseric 1 true true Variable pacctam https://ess-search.nsd.no/en/variable/4aa2df31-83fc-43cc-8a2e-731f649bd39a c39e44a0-4cd9-4d49-85d1-b7c2b7158495 int.esseric 1 true true Variable paccta3m https://ess-search.nsd.no/en/variable/5727b93d-b4e6-4b0d-8f34-3a9f240751b6 43dd6df3-485d-48a4-97e5-d77e15c8d6f3 int.esseric 1 true true Variable pacctay https://ess-search.nsd.no/en/variable/e61d2a62-9756-4a4e-b346-d09c38c6d58e 36e523f4-53b9-4cde-96c2-947a9e4c0e2a int.esseric 1 true true Variable pacctmb https://ess-search.nsd.no/en/variable/f508c569-611b-4fef-be64-d9cb3fa41004 3a864b8d-84b5-446a-94ce-833f234ab1c9 int.esseric 1 true true Variable pacctcm https://ess-search.nsd.no/en/variable/47a5086e-eaf8-4224-ace8-f1486b59d557 0e8e8e24-7171-4d6b-9df8-5e39c61a23fe int.esseric 1 true true Variable paccdcm https://ess-search.nsd.no/en/variable/11ec4397-ffed-4a96-a951-8e176a066d80 46b4db67-47e9-4db4-81f7-51a7740b9d97 int.esseric 1 true true Variable iwg10mx https://ess-search.nsd.no/en/variable/f87075c0-bf22-4103-9aa5-c1f9a791bf26 8b8a2d8c-1653-4193-8b44-93761e140c0d int.esseric 1 true true Variable iwg10mxam https://ess-search.nsd.no/en/variable/2c983e17-8135-48bc-a5d7-e472b22ef1b9 eacdb92d-13d7-4b18-9d06-2607c4de0ff0 int.esseric 1 true true Variable iwg10mxaw https://ess-search.nsd.no/en/variable/f611c43a-9ab2-45c9-aca7-e0a7b3c8757a 81d75a45-25e0-4a67-9870-32080d0b1e84 int.esseric 1 true true Variable iwg10mxa3m https://ess-search.nsd.no/en/variable/bb81fa13-5c1c-49ce-ba97-6260c88fbd56 a34a2324-8c44-4be3-8f19-02db87644f0c int.esseric 1 true true Variable iwg10mxay https://ess-search.nsd.no/en/variable/953d6279-518a-4c0b-adda-ca045a0d4dbe f0d9925b-4f72-422e-bcfb-8f6f30b5f2e2 int.esseric 1 true true Variable iwg10mxamb https://ess-search.nsd.no/en/variable/c51d6cf3-20d2-41bf-a1a7-1bf0d6490a61 15f93862-58a3-4f13-aef2-0a5c7f250a8e int.esseric 1 true true Variable DatetimeBegin https://ess-search.nsd.no/en/variable/93da81ca-7beb-4727-b345-220f7c4667ad 64f584c5-d271-4e60-89a2-7ff6c5a0ed1a int.esseric 1 true true Variable DatetimeEnd https://ess-search.nsd.no/en/variable/fd2f98ae-4b0c-4f48-b5da-b7036536102a 1b87f6c7-d1ce-4321-8e76-f94a16e780b6 int.esseric 1 true true Variable AirQualityStation https://ess-search.nsd.no/en/variable/2cd960f7-3d9a-4aa2-bf34-b40ccaa239e1 2eddc4ae-156e-4b85-8449-09487a26c0a0 int.esseric 1 true true Variable AirPollutant https://ess-search.nsd.no/en/variable/9458698a-abc5-4907-b542-b5e9d741ad00 1c576b4d-0eaa-4542-8c17-4b74ff1a854b int.esseric 1 true true Variable Concentration https://ess-search.nsd.no/en/variable/8f21effd-5e25-4b82-af63-69ea47a5ca26 96f0f540-815f-4e17-b2d5-0c929b4c2a12 int.esseric 1 true true Variable pollutant https://ess-search.nsd.no/en/variable/e84f7948-e66d-4b17-bc2f-9de9754883e2 79ea1be6-4f62-49e4-80e7-f27ed73e7f9e int.esseric 1 true true Variable region https://ess-search.nsd.no/en/variable/a4797603-2e56-45ea-af39-2287e4f132b2 94f92cd0-80e2-4e1e-bc72-0e0f9d7b3c50 int.esseric 1 true true Variable station_id https://ess-search.nsd.no/en/variable/bcace1b8-7533-42ed-bb43-77daa56b5c83 5573a1c5-6489-464d-8852-78f9e0a8e0d8 int.esseric 1 true true Variable __index_level_0__ https://ess-search.nsd.no/en/variable/c9ae5e2b-376b-436d-abd8-f86879f8dc45 7d6e0e95-572c-40a5-82e4-bc51a2a68c77 int.esseric 1 true true Variable date https://ess-search.nsd.no/en/variable/99d07034-b859-4c28-801e-6accb5e9f9da 87419b24-5963-48e7-8db9-0c6edf44bce6 int.esseric 1 true true Variable region https://ess-search.nsd.no/en/variable/06107588-d36d-43c2-be89-b4659999c0c4 83ef6dcf-2d56-4fd6-94e1-8cbbdca65f55 int.esseric 1 true true Variable aqiwdpm10 https://ess-search.nsd.no/en/variable/48622b44-b269-4183-9bbc-af8c6885252a 046a6012-3f1e-4945-8e57-7d24b4b9a73f int.esseric 1 true true Variable aqiwdpm2_5 https://ess-search.nsd.no/en/variable/5e5337ea-78db-4368-a73f-a7b7d8b273c1 5d1db345-d4d5-4d64-a3dd-fd2e7d8c8e50 int.esseric 1 true true Variable aqiwdso2 https://ess-search.nsd.no/en/variable/d72f021d-bb04-4b0f-bcca-d8d28daaf6d1 0cb5b141-7c62-4d67-84a5-9c4a6e3cb8d1 int.esseric 1 true true Variable aqiwdno2 https://ess-search.nsd.no/en/variable/56904641-4c6d-48c4-be32-272f166689a3 b5ee4a8f-84ea-4b17-8e3a-0f7146cc6f6a int.esseric 1 true true Variable aqiwdo3 https://ess-search.nsd.no/en/variable/1cf81dad-0ec7-405a-b1ce-cc98da722d0d 977a1c8e-2fe5-4a2f-bba9-7b7f2a663ee8 int.esseric 1 true true Variable aqiwd https://ess-search.nsd.no/en/variable/ca2e01fc-c818-4ad4-9426-2fc9f6922b72 0f79a5f7-d011-441b-a6fe-5c8a34e7bc3e int.esseric 1 true true Variable aqiw2dpm10 https://ess-search.nsd.no/en/variable/e242aed0-7aba-4f61-a7d9-d06ae07f469d 1198b158-2a18-44b6-b6b3-739f7f7f53a4 int.esseric 1 true true Variable aqiw2dpm2_5 https://ess-search.nsd.no/en/variable/08ed7ffc-37ee-447d-8b6f-b4d8a66fe73f 3e5a1e7a-0c99-40f3-a8e2-935fbaa3be42 int.esseric 1 true true Variable aqiw2dso2 https://ess-search.nsd.no/en/variable/99440d59-e468-4eb1-b46a-294afd20461d 54b03ec0-08d3-449f-8a6b-2a51370d7c6f int.esseric 1 true true Variable aqiw2dno2 https://ess-search.nsd.no/en/variable/36d4e8e3-55c5-4e16-852a-3091eaa0e1d4 1726b265-7d4d-4e11-bf14-d6dd4e8a8fa1 int.esseric 1 true true Variable aqiw2do3 https://ess-search.nsd.no/en/variable/c008ece0-d1b1-44fd-b3f0-087fb7af636a 4cddfb70-6f52-4844-8c1a-df5a27e0c8cd int.esseric 1 true true Variable aqiw2d https://ess-search.nsd.no/en/variable/ffc12aab-2f08-4509-aa95-188cdf1fc8bf 8e8c7a55-682d-4eae-bb81-4e120dfb92f5 int.esseric 1 true true Variable ndyprwpm10 https://ess-search.nsd.no/en/variable/128b9565-308a-4f3b-a5da-642425a9b444 618a1449-0c2b-43c4-a1c0-ceddb988fb06 int.esseric 1 true true Variable ndyprwpm2_5 https://ess-search.nsd.no/en/variable/a66c12c8-afc6-4aa2-8731-02d346b2917e 9e9826c5-2e42-4e29-ba01-f4e2f45553c4 int.esseric 1 true true Variable ndyprwso2 https://ess-search.nsd.no/en/variable/cfe6be06-b590-4d28-8cb7-10d6ca4ed2f9 0f2db12e-4c23-48b0-bbb3-fcb38afec775 int.esseric 1 true true Variable ndyprwno2 https://ess-search.nsd.no/en/variable/2a737686-d8f8-4a6c-9cdb-551afb9207f0 3764a9e4-b82b-4087-9384-46037772f3dc int.esseric 1 true true Variable ndyprwo3 https://ess-search.nsd.no/en/variable/451bb92d-fdf2-4abb-91f7-ceb8867929b0 50b171a3-ef1a-4a25-8707-0451d71d69b5 int.esseric 1 true true Variable ndyprw https://ess-search.nsd.no/en/variable/a7b5b1ab-ffa3-4848-8616-0dd8578b72fe 08c32e0a-9d9a-4b1f-8813-4a61a97f5eb5 int.esseric 1 true true Variable ndyprmpm10 https://ess-search.nsd.no/en/variable/b2fb1c70-e44e-429b-b34d-e8246a1fb283 06f845d3-6eef-4c86-8f08-2ff09cb8f336 int.esseric 1 true true Variable ndyprmpm2_5 https://ess-search.nsd.no/en/variable/567a93be-020f-446c-9a02-a0470fd6220b a0f03669-7d69-456a-b0a2-5dc2f5ceee0e int.esseric 1 true true Variable ndyprmso2 https://ess-search.nsd.no/en/variable/e0e3227a-8c78-40e1-a288-0f8007f3ca3e 48ad7d75-3a71-4960-b234-faf6b43eb0d0 int.esseric 1 true true Variable ndyprmno2 https://ess-search.nsd.no/en/variable/45db50e3-f91f-446c-beb2-01be2c04d46b 94f04e4e-9a9c-4c1a-8bf5-ba09d7087c7c int.esseric 1 true true Variable ndyprmo3 https://ess-search.nsd.no/en/variable/03cc70b2-5530-44a2-879d-1b9fcd7d6b27 53a7b2b0-6e47-4ae3-939a-3e4dbb87a5e1 int.esseric 1 true true Variable ndyprm https://ess-search.nsd.no/en/variable/c782a6ac-5358-48c7-b37e-dcd9a6f99317 28e2e3f7-5d32-43d5-9967-dc8c2b52a812 int.esseric 1 true true Variable ndyprypm10 https://ess-search.nsd.no/en/variable/46b4aecc-2397-4b61-8765-d9daac5d1d6b 63c688b1-7eac-4467-ae78-9ffdcf61a7e0 int.esseric 1 true true Variable ndyprypm2_5 https://ess-search.nsd.no/en/variable/41e9f980-ab5d-4327-b4c3-20c3fa3b056e e126afbf-d4d0-4489-a1ff-68d24d6b2739 int.esseric 1 true true Variable ndypryso2 https://ess-search.nsd.no/en/variable/73748d0a-98e7-4fb9-b563-df305e81d0cb 8200aede-3eaa-45b9-ae3f-2252e5fe9a6b int.esseric 1 true true Variable ndypryno2 https://ess-search.nsd.no/en/variable/3adcc1ab-f199-42a0-b8e0-0362880b8c9c b23dadd2-c0e7-450d-a5e2-95ff625cbb46 int.esseric 1 true true Variable ndypryo3 https://ess-search.nsd.no/en/variable/f3c2ae1b-df8f-4ad6-8d67-cc2f1d6c4491 4a68ab66-0c79-42a7-bb1b-d69be74eef1d int.esseric 1 true true Variable ndypry