Tài liệu kỹ thuật


08/05/2021 GeoLink Thu Giang 0 Nhận xét

(Tiếng Việt ở đây)

TerraClimate is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2019. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. The data cover the period from 1958-2020. We plan to update these data periodically (annually).

New: We have also provided future TerraClimate layers commensurate with global mean temperatures +2C and +4C above preindustrial levels. These data are available for pseudo years 1985-2015 and described in more detail below.

Primary Climate Variables: Maximum temperature, minimum temperature, vapor pressure, precipitation accumulation, downward surface shortwave radiation, wind-speed
​Derived variables: Reference evapotranspiration (ASCE Penman-Montieth), Runoff, Actual Evapotranspiration, Climate Water Deficit, Soil Moisture, Snow Water Equivalent, Palmer Drought Severity Index, Vapor pressure deficit
TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser spatial resolution, but time-varying data from CRU Ts4.0 and the Japanese 55-year Reanalysis (JRA55). Conceptually, the procedure applies interpolated time-varying anomalies from CRU Ts4.0/JRA55 to the high-spatial resolution climatology of WorldClim to create a high-spatial resolution dataset that covers a broader temporal record.

Temporal information is inherited from CRU Ts4.0 for most global land surfaces for temperature, precipitation, and vapor pressure. However, JRA55 data is used for regions where CRU data had zero climate stations contributing (including all of Antarctica, and parts of Africa, South America, and scattered islands).For primary climate variables of temperature, vapor pressure and precipitation, we provide additional data on the number of stations (between 0 and 8) that contributed to the CRU Ts4.0 data used by TerraClimate. JRA55 was used exclusively for solar radiation and wind speeds.

TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. We used a modified Thornthwaite-Mather climatic water-balance model and extractable soil water storage capacity data at a 0.5° grid from Wang-Erlandsson et al. (2016).


Netcdf files from THREDDS web server (guide for dataset abbreviations)
Individual years (1958-present)
Aggregated years (1958-present)
Individual years for +2C climate futures
Individual years for +4C climate futures
Climatologies (1961-1990 and 1981-2010; and +2C and +4C future scenarios)​

Read me first: Best practices for accessing our datasets
Download individual netCDF files for individual variables and years
directly from data catalogs
using wget script tool to batch download files
Download subsets and point data using THREDDS web services 
using OPeNDAP and these example scripts
 rectangular subsets  (MATLAB, Python, and R [alternative R version]) 
 point data (MATLAB, Python, and R) 
using NCSS and these example batch scripts for subsets and points
Google Earth Engine
'Get an account' -> https://earthengine.google.com/new_signup/
'code in the playground'  -> https://developers.google.com/earth-engine/
'data' -> https://code.earthengine.google.com/dataset/IDAHO_EPSCOR/TERRACLIMATE

Netcdf files from THREDDS web server (guide for dataset abbreviations)

  1. Individual years (1958-present)
  2. Aggregated years (1958-present)
  3. Individual years for +2C climate futures
  4. Individual years for +4C climate futures
  5. Climatologies (1961-1990 and 1981-2010; and +2C and +4C future scenarios)​

Read me first: Best practices for accessing our datasets

  1. Download individual netCDF files for individual variables and years
    1. directly from data catalogs
    2. using wget script tool to batch download files
  2. Download subsets and point data using THREDDS web services 
    1. using OPeNDAP and these example scripts
      1.  rectangular subsets  (MATLABPython, and R [alternative R version]) 
      2.  point data (MATLABPython, and R
    2. using NCSS and these example batch scripts for subsets and points
  3. Google Earth Engine
    1. 'Get an account' -> https://earthengine.google.com/new_signup/
    2. 'code in the playground'  -> https://developers.google.com/earth-engine/
    3. 'data' -> https://code.earthengine.google.com/dataset/IDAHO_EPSCOR/TERRACLIMATE

Sources: Climatologylab 

Bình luận



Số lượng:

Tổng tiền: