LEGATES

  1. main
  2. obs
  3. gridded
  4. atm
  5. precip
  6. legates

Load in Python

from intake import open_catalog

cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/legates.yaml")
ds=cat.netcdf.read()

Metadata

title LEGATES
location /shared/SWFluxCorr/LEGATES
tags gridded, obs, atm, precip, climo, global
catalog_dir https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/legates.yaml
last updated 2017-07-01

Dataset Contents

Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • lat: 64
    • lon: 128
    • time: 15
    • lat
      (lat)
      float32
      -87.8638 -85.09653 ... 87.8638
      long_name :
      latitude
      units :
      degrees_north
      array([-87.8638 , -85.09653, -82.31291, -79.52561, -76.7369 , -73.94752,
       -71.15775, -68.36776, -65.57761, -62.78735, -59.99702, -57.20663,
       -54.4162 , -51.62573, -48.83524, -46.04473, -43.25419, -40.46365,
       -37.67309, -34.88252, -32.09194, -29.30136, -26.51077, -23.72017,
       -20.92957, -18.13897, -15.34836, -12.55776,  -9.76715,  -6.97653,
        -4.18592,  -1.39531,   1.39531,   4.18592,   6.97653,   9.76715,
        12.55776,  15.34836,  18.13897,  20.92957,  23.72017,  26.51077,
        29.30136,  32.09194,  34.88252,  37.67309,  40.46365,  43.25419,
        46.04473,  48.83524,  51.62573,  54.4162 ,  57.20663,  59.99702,
        62.78735,  65.57761,  68.36776,  71.15775,  73.94752,  76.7369 ,
        79.52561,  82.31291,  85.09653,  87.8638 ], dtype=float32)
    • lon
      (lon)
      float32
      0.0 2.8125 ... 354.375 357.1875
      long_name :
      longitude
      units :
      degrees_east
      array([  0.    ,   2.8125,   5.625 ,   8.4375,  11.25  ,  14.0625,  16.875 ,
        19.6875,  22.5   ,  25.3125,  28.125 ,  30.9375,  33.75  ,  36.5625,
        39.375 ,  42.1875,  45.    ,  47.8125,  50.625 ,  53.4375,  56.25  ,
        59.0625,  61.875 ,  64.6875,  67.5   ,  70.3125,  73.125 ,  75.9375,
        78.75  ,  81.5625,  84.375 ,  87.1875,  90.    ,  92.8125,  95.625 ,
        98.4375, 101.25  , 104.0625, 106.875 , 109.6875, 112.5   , 115.3125,
       118.125 , 120.9375, 123.75  , 126.5625, 129.375 , 132.1875, 135.    ,
       137.8125, 140.625 , 143.4375, 146.25  , 149.0625, 151.875 , 154.6875,
       157.5   , 160.3125, 163.125 , 165.9375, 168.75  , 171.5625, 174.375 ,
       177.1875, 180.    , 182.8125, 185.625 , 188.4375, 191.25  , 194.0625,
       196.875 , 199.6875, 202.5   , 205.3125, 208.125 , 210.9375, 213.75  ,
       216.5625, 219.375 , 222.1875, 225.    , 227.8125, 230.625 , 233.4375,
       236.25  , 239.0625, 241.875 , 244.6875, 247.5   , 250.3125, 253.125 ,
       255.9375, 258.75  , 261.5625, 264.375 , 267.1875, 270.    , 272.8125,
       275.625 , 278.4375, 281.25  , 284.0625, 286.875 , 289.6875, 292.5   ,
       295.3125, 298.125 , 300.9375, 303.75  , 306.5625, 309.375 , 312.1875,
       315.    , 317.8125, 320.625 , 323.4375, 326.25  , 329.0625, 331.875 ,
       334.6875, 337.5   , 340.3125, 343.125 , 345.9375, 348.75  , 351.5625,
       354.375 , 357.1875], dtype=float32)
    • time
      (time)
      float64
      1.0 2.0 3.0 4.0 ... 6.0 5.0 7.0
      units :
      months
      long_name :
      climatological month
      array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.,  6.,  5.,
        7.])
    • gw
      (time, lat)
      float32
      dask.array<chunksize=(1, 64), meta=np.ndarray>
      long_name :
      gauss weights
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  3.84 kB   256 B 
      
    
       Shape  (15, 64)   (1, 64) 
      
    
       Count  60 Tasks  15 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  

  
  
  

  
  

  
  64
  15
    • ORO
      (time, lat, lon)
      float32
      dask.array<chunksize=(1, 64, 128), meta=np.ndarray>
      units :
      FLAG
      long_name :
      ocean (0), land (1), sea ice (2) flag
      time_op :
      instantaneous
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  491.52 kB   32.77 kB 
      
    
       Shape  (15, 64, 128)   (1, 64, 128) 
      
    
       Count  45 Tasks  15 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  

  
  

  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  128
  64
  15
    • TREFHT
      (time, lat, lon)
      float32
      dask.array<chunksize=(1, 64, 128), meta=np.ndarray>
      source :
      Legates and Wilmott climatology 1920-1980 from NCAR climate analysis section on T42 grid
      units :
      K
      long_name :
      2-meter temperature
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  491.52 kB   32.77 kB 
      
    
       Shape  (15, 64, 128)   (1, 64, 128) 
      
    
       Count  45 Tasks  15 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  

  
  

  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  128
  64
  15
    • PRECT
      (time, lat, lon)
      float32
      dask.array<chunksize=(1, 64, 128), meta=np.ndarray>
      long_name :
      PRECT
      units :
      

      


      
  
    
        Array  Chunk 
      
        
  
    
       Bytes  491.52 kB   32.77 kB 
      
    
       Shape  (15, 64, 128)   (1, 64, 128) 
      
    
       Count  45 Tasks  15 Chunks 
      
    
       Type  float32  numpy.ndarray 
      
        

      
      		



  
  
  

  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  

  
  

  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  

  
  
  

  
  

  
  
  

  
  
  

  
  

  
  128
  64
  15
  • Conventions :
    COARDS
    source :
    Data converted from CCM History Tape Format
    case :
    title :
    hybrid_sigma_pressure :
    Pressure at a grid point (lon(i),lat(j),lev(k)) is computed using the formula: p(i,j,k) = A(k)*PO + B(k)*PS(i,j) where A, B, PO, and PS are contained in the variables whose names are given by the attributes of the vertical coordinate variable A_var, B_var, P0_var, and PS_var respectively.
    history :
    Fri Jun 22 13:58:42 2001: ncrcat -C -A -v PRECT PT4201M2080.nc LEGATES_01_climo.nc Fri Jun 22 13:55:08 2001> ccm2nc -C LCT4201M2080.ccm PT4201M2080.nc