Meteorological and Ancillary Data Resources for Climate Research in Urban Areas
Abstract
:1. Introduction
2. Methodology
3. Data Resources
3.1. Meteorological Data Resources
- (a)
- Temporal and spatial stability and homogeneity. Ideally, the observations should be performed in constant locations, quasi-continuously over time, with limited and isolated gaps. The shortcomings related to missing data or changes in station locations can be successfully secured by homogenization procedures [10,11]. The period covered by satellite images can be too short and contain too many gaps for developing climatic studies, but the remote sensing products are valuable for meteorological applications as much as they are consistent temporally and spatially.While any meteorological data retrieved from urban sensors may bring valuable information, the data stability and homogeneity are sometimes difficult to address due to inherent spatial heterogeneity and to the intense changes of the urban morphology and land cover–land use categories.
- (b)
- Reliability. The observations should comply with the WMO standards for the stations monitoring the regional climate or other known standards for monitoring the local climate [12,13]. Many synoptic stations worldwide are placed within the administrative limits of a city, but it is very likely that more sensors will capture more relevant information about of the multifaceted urban climate even if they are not placed in standard conditions [14,15,16].
- (c)
- Metadata. Geographical coordinates, instrument specifications, information about the working procedures, spatial and temporal resolution and any changes which have eventually occurred along time must be associated to any meteorological observations and remote sensing products. Besides, information about the proximities are particularly important for sensors placed in an urban environment.
3.1.1. In Situ Meteorological Data
3.1.2. Gridded Datasets
3.1.3. Remote Sensing Data
3.2. Ancillary Data Resources
3.2.1. Societal Information
3.2.2. Land Cover
- (a)
- The Global Man-Made Impervious Surface (GMIS) is one of the first 30m global dataset estimates of fractional impervious cover derived from the Global Land Survey (GLS) for 2010. The GMIS dataset includes: the global percent of impervious cover and the uncertainty for the global impervious cover. The urban studies are related to urban extend [102];
- (b)
- The Global Human Built-Up and Settlement Extent (HBASE) is one of the first 30 m global datasets and estimates the urban extend cover derived from the Global Land Survey (GLS) for 2010. The urban studies are related to urban extend [102].
- (c)
- The Urban Landsat: Cities from Space (1999–2003) is one of the first 30m global datasets providing composite Landsat images and raw data for urban areas that can be used in interdisciplinary studies of remote sensing and the environment.
- (a)
- The global LC product is available only for 2015 based on Proba-V at 100 m spatial resolution. It uses the FAO Land Cover Classification System (LCCS), totaling 23 classes [107].
- (b)
- The Pan-European LC products include:
- i.
- Corine Land Cover (CLC) is a land cover inventory (in 44 classes) project initiated in 1980s and updated in 1990, 2000, 2006, 2012 and 2018 with 100 m spatial resolution.;
- ii.
- Pan-European High-Resolution Layers (HRL) provide information on specific land cover characteristics (imperviousness density, forest, grassland, wetland and water bodies and the new small woody features [108], and are complementary to the CLC dataset. The imperviousness data have a spatial resolution of 20 m and 100 m for 2006, 2009, 2012 and 2015.
- iii.
- The European Settlement Map (EMS) is a very high-resolution dataset with spatial resolutions of 2.5 m, 10 m and 100 m. The first dataset, released in 2014, mapped the settlements in Europe based on 2010–2013 images. During the last year, different improvements have been developed: benchmarking process with population data; increases the number of classes to 13 (buildings, green, streets, water, railways, airports, open space inside the built-up area and same categories outside the built-up area) [109].
- (c)
- The Local LC Products include the Urban Atlas (UA) which provide reliable, inter-comparable, high-resolution land use maps with 17 classes for large urban zones and their surroundings (more than 100,000 inhabitants), at 10 m spatial resolution for 2006 and 2012. The UA is also including the Building Height at 10 m spatial resolution for 2012.
3.2.3. Urban Morphology
3.2.4. Climate Change, Adaptation and Urban Resilience
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Sensor | Satellite | Spatial Resolution | Temporal Resolution | Time Span |
---|---|---|---|---|
SEVIRI | MSG | 3 to 5 km | 15 min | 2004 to date |
AVHRR | NOAA | 1.1 km | 2 images/24 h | 1981 to date |
MODIS | Terra/Aqua | 1 km | 4 images/24 h | 2000/2002 to date |
SLSTR | Copernicus Sentinel-3 | 1 km | 1 image/24 h | 2017 to date |
TM, ETM+, OLI, TIRS | Landsat 4, 5, 7, 8 | 60–120 m (30 m resampled) | 1 image/8 or16 days | 1982 to date |
Category | Dataset | Data Format | Spatial Resolution | Temporal Resolution | Temporal Coverage | Source |
---|---|---|---|---|---|---|
In situ | Integrated Surface Database (ISD) | ASCII | Data from 35,000 weather stations worldwide | Hourly, daily | 1901 to present | https://www.ncdc.noaa.gov/isd |
Global Summary of the Day (GSOD) | ASCII | Data from over 9000 weather stations | 1929 to present (since 1973 data are the most complete) | http://www.climate.gov/global-summary-day-gsod | ||
European Climate Assessment and Dataset (ECA&D) | ASCII | Data from 18,909 weather stations across Europe and the Mediterranean | Daily | 1900 to present (blended series) and 1900 to a certain year (depending on the ECA&D participant) | https://www.ecad.eu/ | |
Urban Meteorological Networks (UMNs) | ASCII | Various | Hourly or sub-hourly | Various | http://en.urban-path.hu/monitoring-system.html https://www.birmingham.ac.uk/schools/gees/centres/bucl/maps-data/index.aspx Data could be accessed by contacting the owners | |
Crowdsourcing | ASCII | Various | Sub-hourly | Various | https://dev.netatmo.com/ https://www.wunderground.com/pws/overview | |
Gridded Datasets | E-OBS | Grid (NetCDF- 4) | 0.1 to 0.25° regular grids | Daily | 1950-01-01 to 2019-07-31 (v20.0e the latest version —released on October 2019) | https://www.ecad.eu/download/ensembles/download.php |
Climatologies at High Resolution for the Earth’s Land Surface Areas (CHELSA) | Grid (tif) | 30 arcsec, ~1 km | Monthly | 1979–2013 | http://chelsa-climate.org/ | |
Berkeley Earth | ASCII, graphs | - | Monthly and annual summaries | 1750-present (land only) 1850-present (land and ocean) | http://berkeleyearth.org/ | |
Service for Water Indicators in Climate Change Adaptation (SWICCA) | http://swicca.eu/ | |||||
Remote Sensing Data | Moderate-resolution Imaging Spectro-radiometer (MODIS) | HDF4 | 1 km | Daily eight-day mean | 2000-present | https://lpdaac.usgs.gov/tools/data-pool/ https://lpdaac.usgs.gov/tools/earthdata-search/ |
Spinning Enhanced Visible Infra-Red Imager (SEVIRI) | HDF5 | 3 km | 15 min Hourly Daily Weekly Monthly Seasonal Yearly | 1991-present | https://landsaf.ipma.pt/ChangeSystemProdLong.do?system=LandSAF+MSG&algo=LST https://wui.cmsaf.eu/safira/action/viewProduktSearch?menuName=PRODUKT_SUCHE, https://land.copernicus.eu/global/products/lst | |
NOAA’s Advanced Very High Resolution Radiometer (AVHRR) | NetCDF | 4 km | Daily | 1978-present | https://www.bou.class.noaa.gov/saa/products/search?sub_id=0&datatype_family=AVHRR&submit.x=15&submit.y=6 | |
the Landsat’s Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) | 30 m | 1982-present | http://rslab.gr/downloads_LandsatLST.html | |||
Sea and Land Surface Temperature Radiometer (SLSTR) | NC | 1 km | Daily | 2016-present | https://scihub.copernicus.eu/dhus/#/home https://search.earthdata.nasa.gov/ | |
Copernicus Atmosphere Monitoring Service (CAMS) | Grid (NetCDF, Grib Edition2) | - | Daily and hourly | Near real-time data and hourly forecast | http://copernicus-atmosphere.eu |
Dataset | Details (e.g., Data Format, Resolution, Year of Compliance) | Source | |
---|---|---|---|
Societal information | Urban Audit data collection of EUROSTAT | Data format: ASCII Temporal coverage: 2010–2019 (depending on the indicator) | https://ec.europa.eu/eurostat/web/cities/data/database |
LandScan High Resolution global Population Dataset | Data format: ESRI Grid Spatial resolution: 30 arc seconds or ~1km at equator | https://www.eastview.com/resources/e-collections/landscan/ | |
Land cover | Global Land Survey (GLS) | Data format: GeoTIFF Spatial resolution: 30 m Temporal coverage: 2010 | http://earthexplorer.usgs.gov/ |
The Global Man-made Impervious Surface (GMIS) | https://sedac.ciesin.columbia.edu/data/set/ulandsat-gmis-v1/data-download | ||
Global Human Built-up And Settlement Extent (HBASE) | https://sedac.ciesin.columbia.edu/data/set/ulandsat-hbase-v1/data-download | ||
Urban Landsat: Cities from Space | https://sedac.ciesin.columbia.edu/data/set/ulandsat-cities-from-space/data-download | ||
GlobeLand30 (GLOB) | Data format: GeoTIFF Spatial resolution: 30 m Temporal coverage: 2010 (2009–2011) | http://www.globallandcover.com/GLC30Download/index.aspx | |
Copernicus Land Monitoring Service (CLMS) | Data format: GeoTIFF Spatial resolution: 100 m Temporal coverage: 2015 | https://lcviewer.vito.be/download | |
Corine Land Cover (CLC) | Data format: GeoTIFF, ESRI Geodatabase, SQLite Database Spatial resolution: 100 m Temporal coverage: 1990 (1986–1998), 2000 (+/- 1 year), 2006 (+/- 1 year), 2012 (2011–2012), 2018 (2017–2018) | https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download | |
Pan-European High-Resolution Layers (HRL) | Data format: GeoTIFF Spatial resolution: 20 m and 100 m Temporal coverage: 2006 (2005–2007), 2009 (2008–2010), 2012 (2011–2013), 2015 (2014–2016) | https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/2015?tab=download | |
European Settlement Map (EMS) | Data format: GeoTIFF Spatial resolution: 2.5 m, 10 m and 100 m Temporal coverage: 2012 (2010–2013) Release: 2014, 2016, 2017 | https://land.copernicus.eu/pan-european/GHSL/european-settlement-map/esm-2012-release-2017-urban-green?tab=download | |
Urban Atlas (UA) | Data format: vector file ESRI format Spatial resolution: 10 m Temporal coverage: 2006 (2005–2007), 2012 (2011–2013) | https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012?tab=download | |
Building Height | Data format: GeoTIFF Spatial resolution: 10 m Temporal coverage: 2012 (2011–2014) | https://land.copernicus.eu/local/urban-atlas/building-height-2012?tab=download | |
MODIS Land Cover | Data format: HDF4 Spatial resolution: 500 m Temporal coverage: 2001-2018 | https://lpdaac.usgs.gov/tools/earthdata-search/, https://lpdaac.usgs.gov/tools/usgs-earthexplorer/, https://lpdaac.usgs.gov/tools/data-pool/ | |
GlobCover Portal | Data format: GeoTIFF Spatial resolution: 300 m Temporal coverage: 2005, 2009 | http://due.esrin.esa.int/page_globcover.php | |
Open-ECOCLIMAP | Data format: ASCII Spatial resolution: 1 km Temporal coverage: 2005, 2009 | https://opensource.umr-cnrm.fr/projects/ecoclimap/files | |
Urban morphology | World Urban Database and Access Portal Tool (WUDAPT) | http://www.wudapt.org/ | |
Climate change, adaptation and urban resilience | Urban Audit dataset | Data format: ESRI shapefile and geodatabase Scale 1:100000 (UA2018, 2011–2014), scale 1:3 Million (UA2004, 2001) Temporal availability: 2015–2018 (UA2018), 2011–2014 (UA2011–2014), 2004 (UA2004), 2001 (UA2001) | https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit |
Urban Adaptation Map Viewer (UAMV) | Available only for visualization (e.g., in ArcGIS JavaScript, ArcGIS Online Map Viewer, ArcGis Earth) Dataset comprises 15 indicators of climate and climate-related hazards (e.g., heat waves, heavy precipitation, meteorological drought, fire danger); 7 exposure indicators of cities to climate-related hazards (e.g., wildfires, river flooding, sea level rise); 4 indicators of physical characteristics of urban areas (e.g., urban morphological zone; percentage of impervious area in core city); 10 socio-economic indicators for cities (e.g., population, total use of water); adaptation activities of cities. | https://climate-adapt.eea.europa.eu/knowledge/tools/urban-adaptation/Urban-Adaptation-datasets | |
LOBELIA EARTH | Available only for visualization as distribution maps and graphs using Climate Explorer (past climate) | https://www.lobelia.earth/ |
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Cheval, S.; Micu, D.; Dumitrescu, A.; Irimescu, A.; Frighenciu, M.; Iojă, C.; Tudose, N.C.; Davidescu, Ș.; Antonescu, B. Meteorological and Ancillary Data Resources for Climate Research in Urban Areas. Climate 2020, 8, 37. https://doi.org/10.3390/cli8030037
Cheval S, Micu D, Dumitrescu A, Irimescu A, Frighenciu M, Iojă C, Tudose NC, Davidescu Ș, Antonescu B. Meteorological and Ancillary Data Resources for Climate Research in Urban Areas. Climate. 2020; 8(3):37. https://doi.org/10.3390/cli8030037
Chicago/Turabian StyleCheval, Sorin, Dana Micu, Alexandru Dumitrescu, Anișoara Irimescu, Maria Frighenciu, Cristian Iojă, Nicu Constantin Tudose, Șerban Davidescu, and Bogdan Antonescu. 2020. "Meteorological and Ancillary Data Resources for Climate Research in Urban Areas" Climate 8, no. 3: 37. https://doi.org/10.3390/cli8030037
APA StyleCheval, S., Micu, D., Dumitrescu, A., Irimescu, A., Frighenciu, M., Iojă, C., Tudose, N. C., Davidescu, Ș., & Antonescu, B. (2020). Meteorological and Ancillary Data Resources for Climate Research in Urban Areas. Climate, 8(3), 37. https://doi.org/10.3390/cli8030037