You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
164 lines
6.8 KiB
Markdown
164 lines
6.8 KiB
Markdown
# 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 ##
|
|
```python
|
|
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 ###
|
|
|
|
```python
|
|
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 ###
|
|
|
|
```python
|
|
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 |
|