NCL: convert-GRIB-to-netCDF
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/temp.yaml")
ds=cat.netcdf.read()Metadata
| title | NCL: convert-GRIB-to-netCDF |
| location | /project/atlantic_var/OBS/Ishii/Ishii/DATA |
| tags | PLACEHOLDER |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/temp.yaml |
| last updated | 2017-04-21 |
Dataset Contents
<xarray.Dataset>
Dimensions: (g0_lat_2: 180, g0_lon_3: 360, initial_time0_hours: 816, lv_DBSL1: 24, time: 68)
Coordinates:
* initial_time0_hours (initial_time0_hours) float64 1.271e+06 ... 1.867e+06
* g0_lat_2 (g0_lat_2) float32 -89.5 -88.5 -87.5 ... 88.5 89.5
* g0_lon_3 (g0_lon_3) float32 0.5 1.5 2.5 ... 357.5 358.5 359.5
* lv_DBSL1 (lv_DBSL1) int32 0 10 20 30 ... 1200 1300 1400 1500
Dimensions without coordinates: time
Data variables:
WTMP_GDS0_DBSL (time, initial_time0_hours, lv_DBSL1, g0_lat_2, g0_lon_3) float32 dask.array<chunksize=(1, 816, 24, 180, 360), meta=np.ndarray>
MSLSA_GDS0_DBSL (time, initial_time0_hours, lv_DBSL1, g0_lat_2, g0_lon_3) float32 dask.array<chunksize=(1, 816, 24, 180, 360), meta=np.ndarray>
initial_time0_encoded (time, initial_time0_hours) float64 dask.array<chunksize=(1, 816), meta=np.ndarray>
initial_time0 (time, initial_time0_hours) object dask.array<chunksize=(1, 816), meta=np.ndarray>
Attributes:
creation_date: Thu Mar 9 19:01:50 MST 2017
NCL_Version: 6.4.1-09Mar2017_0113
system: Linux geyser06 2.6.32-358.el6.x86_64 #1 SMP Wed Nov 2 11:...
Conventions: None
grib_source: temp.1945.grb.grb
title: NCL: convert-GRIB-to-netCDFxarray.Dataset
- g0_lat_2: 180
- g0_lon_3: 360
- initial_time0_hours: 816
- lv_DBSL1: 24
- time: 68
- initial_time0_hours(initial_time0_hours)float641.271e+06 1.272e+06 ... 1.867e+06
- units :
- hours since 1800-01-01 00:00
- long_name :
- initial time
array([1271376., 1272120., 1272792., ..., 1865256., 1866000., 1866720.])
- g0_lat_2(g0_lat_2)float32-89.5 -88.5 -87.5 ... 88.5 89.5
- La1 :
- -89.5
- Lo1 :
- 0.5
- La2 :
- 89.5
- Lo2 :
- 359.5
- Di :
- 1.0
- Dj :
- 1.0
- units :
- degrees_north
- GridType :
- Cylindrical Equidistant Projection Grid
- long_name :
- latitude
array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5, -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5, -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5, -9.5, -8.5, -7.5, -6.5, -5.5, -4.5, -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5], dtype=float32) - g0_lon_3(g0_lon_3)float320.5 1.5 2.5 ... 357.5 358.5 359.5
- La1 :
- -89.5
- Lo1 :
- 0.5
- La2 :
- 89.5
- Lo2 :
- 359.5
- Di :
- 1.0
- Dj :
- 1.0
- units :
- degrees_east
- GridType :
- Cylindrical Equidistant Projection Grid
- long_name :
- longitude
array([ 0.5, 1.5, 2.5, ..., 357.5, 358.5, 359.5], dtype=float32)
- lv_DBSL1(lv_DBSL1)int320 10 20 30 ... 1200 1300 1400 1500
- units :
- m
- long_name :
- depth below sea level
array([ 0, 10, 20, 30, 50, 75, 100, 125, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500], dtype=int32)
- WTMP_GDS0_DBSL(time, initial_time0_hours, lv_DBSL1, g0_lat_2, g0_lon_3)float32dask.array<chunksize=(1, 816, 24, 180, 360), meta=np.ndarray>
- forecast_time_units :
- days
- forecast_time :
- 0
- parameter_number :
- 80
- parameter_table_version :
- 2
- gds_grid_type :
- 0
- level_indicator :
- 160
- units :
- K
- long_name :
- Water temperature
- center :
- Japanese Meteorological Agency - Tokyo (RSMC)
Array Chunk Bytes 345.18 GB 5.08 GB Shape (68, 816, 24, 180, 360) (1, 816, 24, 180, 360) Count 822 Tasks 68 Chunks Type float32 numpy.ndarray - MSLSA_GDS0_DBSL(time, initial_time0_hours, lv_DBSL1, g0_lat_2, g0_lon_3)float32dask.array<chunksize=(1, 816, 24, 180, 360), meta=np.ndarray>
- forecast_time_units :
- days
- forecast_time :
- 0
- parameter_number :
- 128
- parameter_table_version :
- 2
- gds_grid_type :
- 0
- level_indicator :
- 160
- units :
- Pa
- long_name :
- Mean sea level pressure (Std Atm)
- center :
- Japanese Meteorological Agency - Tokyo (RSMC)
Array Chunk Bytes 345.18 GB 5.08 GB Shape (68, 816, 24, 180, 360) (1, 816, 24, 180, 360) Count 822 Tasks 68 Chunks Type float32 numpy.ndarray - initial_time0_encoded(time, initial_time0_hours)float64dask.array<chunksize=(1, 816), meta=np.ndarray>
- units :
- yyyymmddhh.hh_frac
- long_name :
- initial time encoded as double
Array Chunk Bytes 443.90 kB 6.53 kB Shape (68, 816) (1, 816) Count 612 Tasks 68 Chunks Type float64 numpy.ndarray - initial_time0(time, initial_time0_hours)objectdask.array<chunksize=(1, 816), meta=np.ndarray>
- NCL_converted_from_type :
- string
- units :
- mm/dd/yyyy (hh:mm)
- long_name :
- Initial time of first record
Array Chunk Bytes 443.90 kB 6.53 kB Shape (68, 816) (1, 816) Count 680 Tasks 68 Chunks Type object numpy.ndarray
- creation_date :
- Thu Mar 9 19:01:50 MST 2017
- NCL_Version :
- 6.4.1-09Mar2017_0113
- system :
- Linux geyser06 2.6.32-358.el6.x86_64 #1 SMP Wed Nov 2 11:00:18 MDT 2016 x86_64 x86_64 x86_64 GNU/Linux
- Conventions :
- None
- grib_source :
- temp.1945.grb.grb
- title :
- NCL: convert-GRIB-to-netCDF