Urban cloud patterns and environmental factors dataset supporting for paper: "How do Cities Modify Local Cloud Patterns?" Vo T.T., Hu L., Xue L., Li Q., Chen S. (2023)

The dataset supporting for the publication: "How do Cities Modify Local Cloud Patterns?" Vo T.T., Hu L., Xue L., Li Q., Chen S. (2023), PNAS. 

• The data format is in tabular form (.csv, comma delimited). Each row is the monthly aggregated \(\Delta_{CloudCover}\) for each city for daytime and nighttime, associated with the environmental factors used to study the relationship in the research. More specifically, the description of each factor associated with its unit formatted in the table are listed as follows:
  • the spatial differences in cloud coverage (in percent) between urban and adjacent background domain (\(\Delta_{CloudCover}\)): cldmeand (in percent) 
  • the city size (\(log_{10}A\), A in km²) as an indicator of surface roughness and anthropogenic emission: CitySize (in \(log_{10}(km^{2})\))
  • the differential urban-background surface heating measured by land surface temperature (\(\Delta LST\)): Tempdiff (in K)
  • the moisture availability measured by annual precipitation (P): precipannualbackground (in mm)
  • the energy availability measured by mean annual temperature (\(\overline{T}\)): tempannualbackground (in \(^{o}C\))
  • the longitudes and latitudes for the defined city: xcoords (longitudes, in meters) and ycoords (latitudes, in meters)
• Other columns:
  • NAME10: the name of urban domain or city defined in the study (CENSUS 2010)
  • time: daytime (around 13 LT) or nighttime (around 1 LT). 
  • monthtext: specifies the month (January to December)
  • urban_type: the geographical location of the defined urban area (either inland, coastal or mountainous city)