Reading MERRA data¶

This Example show how to read the MERRA atmospheric data and to show the structure of the MERRA data

Workflow:

1. Initialize MERRA data file
2. Take a look at the variables
3. Preload data in the region
4. Get timeseries for a specific location
5. Introduction of standard loaders

In [1]:

import reskit as rk

import reskit as rk

Initialize a MERRA data file¶

• In this case, rk._TEST_DATA_["weather_data"] points to a netCDF4 file on your machine with climate model weather datasets
• If rk._TEST_DATA_["weather_data"] points to a folder, all netCDF4 files in that folder will be scanned

In [2]:

print(rk.TEST_DATA["merra-like"])

print(rk.TEST_DATA["merra-like"])

/home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/latest/reskit/_test/data/merra-like

In [3]:

# The 'bounds' input limits the spatial domain of the weath data which is loaded into memory
#  - given as: [low-lat, low-lon, high-lat, high-lon]

# The files scanned will be listed, unless verbose=False is specified

src = rk.weather.MerraSource(rk.TEST_DATA["merra-like"], bounds=[5, 49, 7, 52])

# The 'bounds' input limits the spatial domain of the weath data which is loaded into memory # - given as: [low-lat, low-lon, high-lat, high-lon] # The files scanned will be listed, unless verbose=False is specified src = rk.weather.MerraSource(rk.TEST_DATA["merra-like"], bounds=[5, 49, 7, 52])

/home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/latest/reskit/_test/data/merra-like/MERRA_tavg1_2d.2015.Europe.PS.nc4
/home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/latest/reskit/_test/data/merra-like/MERRA_tavg1_2d.2015.Europe.SWGDN.nc4
/home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/latest/reskit/_test/data/merra-like/MERRA_tavg1_2d.2015.Europe.T10M.nc4
/home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/latest/reskit/_test/data/merra-like/MERRA_tavg1_2d.2015.Europe.T2M.nc4
/home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/latest/reskit/_test/data/merra-like/MERRA_tavg1_2d.2015.Europe.T2MDEW.nc4
/home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/latest/reskit/_test/data/merra-like/MERRA_tavg1_2d.2015.Europe.U10M.nc4
/home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/latest/reskit/_test/data/merra-like/MERRA_tavg1_2d.2015.Europe.U2M.nc4
/home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/latest/reskit/_test/data/merra-like/MERRA_tavg1_2d.2015.Europe.U50M.nc4
/home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/latest/reskit/_test/data/merra-like/MERRA_tavg1_2d.2015.Europe.V10M.nc4
/home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/latest/reskit/_test/data/merra-like/MERRA_tavg1_2d.2015.Europe.V2M.nc4
/home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/latest/reskit/_test/data/merra-like/MERRA_tavg1_2d.2015.Europe.V50M.nc4

In [4]:

# See all of the variables in this file...
src.variables

# See all of the variables in this file... src.variables

Out[4]:

	name	units	path	shape
time	time	minutes since 2015-01-01 00:30:00	/home/docs/checkouts/readthedocs.org/user_buil...	(71,)
lon	longitude	degrees_east	/home/docs/checkouts/readthedocs.org/user_buil...	(5,)
lat	latitude	degrees_north	/home/docs/checkouts/readthedocs.org/user_buil...	(7,)
PS	surface_pressure	Pa	/home/docs/checkouts/readthedocs.org/user_buil...	(71, 7, 5)
SWGDN	surface_incoming_shortwave_flux	W m-2	/home/docs/checkouts/readthedocs.org/user_buil...	(71, 7, 5)
T10M	10-meter_air_temperature	K	/home/docs/checkouts/readthedocs.org/user_buil...	(71, 7, 5)
T2M	2-meter_air_temperature	K	/home/docs/checkouts/readthedocs.org/user_buil...	(71, 7, 5)
T2MDEW	dew_point_temperature_at_2_m	K	/home/docs/checkouts/readthedocs.org/user_buil...	(71, 7, 5)
U10M	10-meter_eastward_wind	m s-1	/home/docs/checkouts/readthedocs.org/user_buil...	(71, 7, 5)
U2M	2-meter_eastward_wind	m s-1	/home/docs/checkouts/readthedocs.org/user_buil...	(71, 7, 5)
U50M	eastward_wind_at_50_meters	m s-1	/home/docs/checkouts/readthedocs.org/user_buil...	(71, 7, 5)
V10M	10-meter_northward_wind	m s-1	/home/docs/checkouts/readthedocs.org/user_buil...	(71, 7, 5)
V2M	2-meter_northward_wind	m s-1	/home/docs/checkouts/readthedocs.org/user_buil...	(71, 7, 5)
V50M	northward_wind_at_50_meters	m s-1	/home/docs/checkouts/readthedocs.org/user_buil...	(71, 7, 5)

Preload the data in this region¶

In [5]:

# Load a specific variable
src.load("U50M", name="wind_speed")

# Load a specific variable src.load("U50M", name="wind_speed")

Get the time series for a specific location¶

In [6]:

# Get a time series for a single location
# Locations are given as (Lon, lat)
t = src.get("wind_speed", locations=(6.0, 50.0))

t.head()

# Get a time series for a single location # Locations are given as (Lon, lat) t = src.get("wind_speed", locations=(6.0, 50.0)) t.head()

Out[6]:

2015-01-01 00:30:00+00:00    3.564828
2015-01-01 01:30:00+00:00    3.401189
2015-01-01 02:30:00+00:00    3.127785
2015-01-01 03:30:00+00:00    2.838547
2015-01-01 04:30:00+00:00    2.414742
Name: (6.0, 50.0), dtype: float32

In [7]:

# Get a time series for multiple locations
t = src.get("wind_speed", locations=[(5.5, 50.0), (6.0, 50.0), (6.7, 50.0)])

t.head()

# Get a time series for multiple locations t = src.get("wind_speed", locations=[(5.5, 50.0), (6.0, 50.0), (6.7, 50.0)]) t.head()

Out[7]:

	(5.5, 50.0)	(6.0, 50.0)	(6.7, 50.0)
2015-01-01 00:30:00+00:00	2.886117	3.564828	3.810922
2015-01-01 01:30:00+00:00	2.529118	3.401189	3.825017
2015-01-01 02:30:00+00:00	2.138527	3.127785	3.693214
2015-01-01 03:30:00+00:00	1.761887	2.838547	3.489914
2015-01-01 04:30:00+00:00	1.258858	2.414742	3.195015

In [8]:

# Get a time series for a single location, but interpolate the surrounding MERRA points the specified location(s)
# Options are:
#  'bilinear' -> Uses linear interpolation. Requires the surrounding 4 index points
#  'cubic' -> Uses cubic interpolation. Requires the surrounding 16 index points
t = src.get("wind_speed", (6.0, 50.0), interpolation="bilinear")

t.head()

# Get a time series for a single location, but interpolate the surrounding MERRA points the specified location(s) # Options are: # 'bilinear' -> Uses linear interpolation. Requires the surrounding 4 index points # 'cubic' -> Uses cubic interpolation. Requires the surrounding 16 index points t = src.get("wind_speed", (6.0, 50.0), interpolation="bilinear") t.head()

Out[8]:

2015-01-01 00:30:00+00:00    3.293344
2015-01-01 01:30:00+00:00    3.052360
2015-01-01 02:30:00+00:00    2.732082
2015-01-01 03:30:00+00:00    2.407883
2015-01-01 04:30:00+00:00    1.952389
Name: (6.0, 50.0), dtype: float64

Standard Loaders¶

Many weather sources have similar (but nevertheless slightly different) variables which often need to be transformed into a standard unit. Therefore RESKit configures each weather source with numerous available 'standard loader' functions

In [9]:

src.list_standard_variables()

src.list_standard_variables()

sload_elevated_wind_direction
sload_elevated_wind_speed
sload_global_horizontal_irradiance
sload_surface_air_temperature
sload_surface_dew_temperature
sload_surface_pressure
sload_surface_wind_direction
sload_surface_wind_speed
sload_wind_direction_at_10m
sload_wind_direction_at_2m
sload_wind_direction_at_50m
sload_wind_speed_at_10m
sload_wind_speed_at_2m
sload_wind_speed_at_50m

In [10]:

# Load, for example, "surface air temperature", which will always be output in degrees-Celcius
src.sload_surface_air_temperature()

# Load, for example, "surface air temperature", which will always be output in degrees-Celcius src.sload_surface_air_temperature()

In [11]:

# Access the data using the same name
t = src.get("surface_air_temperature", (6.0, 50.0), interpolation="bilinear")

t.head()

# Access the data using the same name t = src.get("surface_air_temperature", (6.0, 50.0), interpolation="bilinear") t.head()

Out[11]:

2015-01-01 00:30:00+00:00   -2.400848
2015-01-01 01:30:00+00:00   -2.561975
2015-01-01 02:30:00+00:00   -2.745178
2015-01-01 03:30:00+00:00   -2.974139
2015-01-01 04:30:00+00:00   -3.179675
Name: (6.0, 50.0), dtype: float64