Dfs2 - Meteo data

Conversion of NetCDF from Global Forecasting System to Dfs2

import xarray
import pandas as pd
import mikeio

The file gfs_wind.nc contains a small sample of the GFS forecast data downloaded via their OpenDAP service

ds = xarray.open_dataset('../../data/gfs_wind.nc')
ds

<xarray.Dataset> Size: 32kB
Dimensions:   (time: 3, lat: 41, lon: 21)
Coordinates: (3)
Data variables:
    msletmsl  (time, lat, lon) float32 10kB ...
    ugrd10m   (time, lat, lon) float32 10kB ...
    vgrd10m   (time, lat, lon) float32 10kB ...
Attributes: (4)

xarray.Dataset

• Dimensions:
  • time: 3
  • lat: 41
  • lon: 21
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2021-09-02T12:00:00 ... 2021-09-...

    grads_dim :

    t

    grads_mapping :

    linear

    grads_size :

    129

    grads_min :

    12z02sep2021

    grads_step :

    3hr

    long_name :

    time

    minimum :

    12z02sep2021

    maximum :

    12z18sep2021

    resolution :

    0.125

    array(['2021-09-02T12:00:00.000000000', '2021-09-02T15:00:00.000000000',
           '2021-09-02T18:00:00.000000000'], dtype='datetime64[ns]')

  • lat
    (lat)
    float64
    30.0 30.25 30.5 ... 39.5 39.75 40.0

    grads_dim :

    y

    grads_mapping :

    linear

    grads_size :

    721

    units :

    degrees_north

    long_name :

    latitude

    minimum :

    -90.0

    maximum :

    90.0

    resolution :

    0.25

    array([30.  , 30.25, 30.5 , 30.75, 31.  , 31.25, 31.5 , 31.75, 32.  , 32.25,
           32.5 , 32.75, 33.  , 33.25, 33.5 , 33.75, 34.  , 34.25, 34.5 , 34.75,
           35.  , 35.25, 35.5 , 35.75, 36.  , 36.25, 36.5 , 36.75, 37.  , 37.25,
           37.5 , 37.75, 38.  , 38.25, 38.5 , 38.75, 39.  , 39.25, 39.5 , 39.75,
           40.  ])

  • lon
    (lon)
    float64
    10.0 10.25 10.5 ... 14.5 14.75 15.0

    grads_dim :

    x

    grads_mapping :

    linear

    grads_size :

    1440

    units :

    degrees_east

    long_name :

    longitude

    minimum :

    0.0

    maximum :

    359.75

    resolution :

    0.25

    array([10.  , 10.25, 10.5 , 10.75, 11.  , 11.25, 11.5 , 11.75, 12.  , 12.25,
           12.5 , 12.75, 13.  , 13.25, 13.5 , 13.75, 14.  , 14.25, 14.5 , 14.75,
           15.  ])

• Data variables: (3)
  • msletmsl
    (time, lat, lon)
    float32
    ...

    long_name :

    ** mean sea level mslp (eta model reduction) [pa]

    [2583 values with dtype=float32]

  • ugrd10m
    (time, lat, lon)
    float32
    ...

    long_name :

    ** 10 m above ground u-component of wind [m/s]

    [2583 values with dtype=float32]

  • vgrd10m
    (time, lat, lon)
    float32
    ...

    long_name :

    ** 10 m above ground v-component of wind [m/s]

    [2583 values with dtype=float32]

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2021-09-02 12:00:00', '2021-09-02 15:00:00',
                   '2021-09-02 18:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • lat
    PandasIndex

    PandasIndex(Index([ 30.0, 30.25,  30.5, 30.75,  31.0, 31.25,  31.5, 31.75,  32.0, 32.25,
            32.5, 32.75,  33.0, 33.25,  33.5, 33.75,  34.0, 34.25,  34.5, 34.75,
            35.0, 35.25,  35.5, 35.75,  36.0, 36.25,  36.5, 36.75,  37.0, 37.25,
            37.5, 37.75,  38.0, 38.25,  38.5, 38.75,  39.0, 39.25,  39.5, 39.75,
            40.0],
          dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([ 10.0, 10.25,  10.5, 10.75,  11.0, 11.25,  11.5, 11.75,  12.0, 12.25,
            12.5, 12.75,  13.0, 13.25,  13.5, 13.75,  14.0, 14.25,  14.5, 14.75,
            15.0],
          dtype='float64', name='lon'))

• Attributes: (4)

  title :

  GFS 0.25 deg starting from 12Z02sep2021, downloaded Sep 02 17:14 UTC

  Conventions :

  COARDS GrADS

  dataType :

  Grid

  history :

  Thu Sep 02 17:27:02 GMT 2021 : imported by GrADS Data Server 2.0

Running a Mike 21 HD model, needs at least three variables of meteorological forcing * Mean Sea Level Pressure * U 10m * V 10m

Let’s take a look the U 10m

ds.ugrd10m.isel(time=0).plot();

Convert to dfs2

Time

time = pd.DatetimeIndex(ds.time)
time

DatetimeIndex(['2021-09-02 12:00:00', '2021-09-02 15:00:00',
               '2021-09-02 18:00:00'],
              dtype='datetime64[ns]', freq=None)

Variable types

mikeio.EUMType.Air_Pressure

Air Pressure

mikeio.EUMType.Air_Pressure.units

[hectopascal, millibar]

mikeio.EUMType.Wind_Velocity

Wind Velocity

mikeio.EUMType.Wind_Velocity.units

[meter per sec, feet per sec, miles per hour, km per hour, knot]

mslp = ds.msletmsl.values / 100 # conversion from Pa to hPa
u = ds.ugrd10m.values
v = ds.vgrd10m.values

geometry = mikeio.Grid2D(x=ds.lon.values, y=ds.lat.values, projection="LONG/LAT")
geometry

<mikeio.Grid2D>
x: [10, 10.25, ..., 15] (nx=21, dx=0.25)
y: [30, 30.25, ..., 40] (ny=41, dy=0.25)
projection: LONG/LAT

from mikeio import ItemInfo, EUMType, EUMUnit

mslp_da = mikeio.DataArray(data=mslp,time=time, geometry=geometry, item=ItemInfo("Mean Sea Level Pressure", EUMType.Air_Pressure, EUMUnit.hectopascal))
u_da = mikeio.DataArray(data=u,time=time, geometry=geometry, item=ItemInfo("Wind U", EUMType.Wind_Velocity, EUMUnit.meter_per_sec))
v_da = mikeio.DataArray(data=v,time=time, geometry=geometry, item=ItemInfo("Wind V", EUMType.Wind_Velocity, EUMUnit.meter_per_sec))

mds = mikeio.Dataset([mslp_da, u_da, v_da])
mds

<mikeio.Dataset>
dims: (time:3, y:41, x:21)
time: 2021-09-02 12:00:00 - 2021-09-02 18:00:00 (3 records)
geometry: Grid2D (ny=41, nx=21)
items:
  0:  Mean Sea Level Pressure <Air Pressure> (hectopascal)
  1:  Wind U <Wind Velocity> (meter per sec)
  2:  Wind V <Wind Velocity> (meter per sec)

mds.to_dfs("gfs.dfs2")

Clean up

import os

os.remove("gfs.dfs2")