silo

Scientific Information for Land Owners (SILO) is a database of Australian climate data from 1889 (current to yesterday). It provides daily datasets for a range of climate variables in ready-to-use formats suitable for research and climate applications. SILO products provide national coverage with interpolated infills for missing data, which allows you to focus on your research or model development without the burden of data preparation.

https://silo.longpaddock.qld.gov.au

There are two different data types:

Point data:

Get multiple climate variables at a single point (accessible with this python module).

Gridded data:

Get a single climate variable for the entire Australian continent (accessible on the SILO website).

Point data

Installation

git clone http://git.wrl.unsw.edu.au:3000/danh/silo.git
pip install -e silo

Usage

import silo

variables = ['daily_rain', 'max_temp', 'min_temp']
api_key = 'BcfJnotlnVgX9gyLmZh2dURqNH8xAD3m8Am7V9OY'
start = '18990101'
finish = '20180101'
lat = -33
lon = 151

silo.pointdata(variables, api_key, start, finish, lat=lat, lon=lon,
               output='silo-data.csv')

silo-data.csv

date	latitude	longitude	daily_rain_mm	daily_rain_source	max_temp_Celsius	max_temp_source	min_temp_Celsius	min_temp_source
1899-01-01	-33	151	0.9	24	31.4	35	14.8	35
1899-01-02	-33	151	0	24	30.1	35	18.3	35
1899-01-03	-33	151	0	24	33.3	35	14.3	35
...

Gridded data

Gridded datasets are available here:

https://silo.longpaddock.qld.gov.au/gridded-data

Example: generate timeseries at specific location

import xarray as xr
import pandas as pd
import calendar
import matplotlib.pyplot as plt

# Open netcdf dataset
fname = '2000.monthly_rain.nc'
ds = xr.open_dataset(fname)

# Convert to pandas dataframe
df = ds.to_dataframe().drop(columns=['crs'])

# Set coordinates
lat = -33.75
lon = 151.25

# Create temporary dataframe at specific location
df1 = df.loc[lat, lon]

# Plot time series
fig, ax = plt.subplots(1, 1, figsize=(6, 4))
ax.plot(df1.index, df1.values)

# Tidy up figure
ax.set_xticks(df1.index)
ax.set_xticklabels([calendar.month_abbr[i] for i in df1.index.month])
ax.set_ylabel('Rainfall (mm)', labelpad=10)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

plt.show()

Example: generate maps at specific dates

import xarray as xr
import pandas as pd
import calendar
import matplotlib.pyplot as plt

# Open netcdf dataset
fname = '2000.monthly_rain.nc'
ds = xr.open_dataset(fname)

# Convert to pandas dataframe
df = ds.to_dataframe().drop(columns=['crs'])

# Get index values
lats = df.index.get_level_values('lat').unique()
lons = df.index.get_level_values('lon').unique()
dates = df.index.get_level_values('time').unique()

fig, ax = plt.subplots(3, 4)

for i, date in enumerate(dates):
    # Get temporary dataframe with only one date
    df1 = df[df.index.get_level_values('time') == date]

    # Remove date from multi-index
    df1.index = df1.index.droplevel(-1)

    # Split multi-index so that rows=latitude and columns=longitude
    grid = df1.unstack()

    # Plot colour map for current month
    a = ax.ravel()[i]
    im = a.imshow(grid, cmap='Blues', vmin=-100, vmax=800)
    a.set_title(calendar.month_abbr[i + 1])
    a.invert_yaxis()
    a.axis('off')

# Add colour bar
cbax = fig.add_axes([0.95, 0.3, 0.02, 0.4])
cb = fig.colorbar(im, cax=cbax)
cb.set_ticks([0, 200, 400, 600, 800])
cbax.set_ylabel('Monthly rainfall (mm)', labelpad=10)

plt.show()

Available climate variables

code	name	units
daily_rain	Daily rainfall	mm
monthly_rain	Monthly rainfall	mm
max_temp	Maximum temperature	Celsius
min_temp	Minimum temperature	Celsius
vp	Vapour pressure	hPa
vp_deficit	Vapour pressure deficit	hPa
evap_pan	Evaporation - Class A pan	mm
evap_syn	Evaporation - synthetic estimate	mm
evap_comb	Evaporation - combination (synthetic estimate pre-1970, class A pan 1970 onwards)	mm
evap_morton_lake	Evaporation - Morton's shallow lake evaporation	mm
radiation	Solar radiation - total incoming downward shortwave radiation on a horizontal surface	MJm-2
rh_tmax	Relative humidity at the time of maximum temperature	%
rh_tmin	Relative humidity at the time of minimum temperature	%
et_short_crop	Evapotranspiration - FAO56 short crop	mm
et_tall_crop	Evapotranspiration - ASCE tall crop	mm
et_morton_actual	Evapotranspiration - Morton's areal actual evapotranspiration	mm
et_morton_potential	Evapotranspiration - Morton's potential evapotranspiration	mm
et_morton_wet	Evapotranspiration - Morton's wet-environment areal evapotranspiration over land	mm
mslp	Mean sea level pressure	hPa