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Creating Skew-T Log-P diagrams from the Rapid Refresh (RAP) model
Published at
Dec 31, 2024
Main Article
python-client
Python client library to interface with GribStream
Leverage: - The National Blend of Models (NBM) - The Global Forecast System (GFS) - The Rapid Refresh (RAP)
GFS and RAP are suitable for SkewT LogP charts. Check the example.
from client import GribStreamClient
import datetime
with GribStreamClient(apikey=None) as client: # DEMO API token
print("Query all NBM weather forecasts for three parameters, over a three hour range, ten hours out, for three coordinates")
start = datetime.datetime.now(datetime.UTC)
df = client.forecasts(
dataset='nbm',
forecasted_from=datetime.datetime(year=2024, month=8, day=10, hour=0),
forecasted_until=datetime.datetime(year=2024, month=8, day=10, hour=3),
coordinates=[
{"lat": 40.75, "lon": -73.98},
{"lat": 29.75, "lon": -95.36},
{"lat": 47.60, "lon": -122.33},
],
variables=[
{"name": "TMP", "level": "2 m above ground", "info": ""},
{"name": "WIND", "level": "10 m above ground", "info": ""},
{"name": "DPT", "level": "2 m above ground", "info": ""},
],
min_horizon=1,
max_horizon=10,
)
print(df.sort_values(['forecasted_time', 'lat', 'lon']).head(20).to_string(index=False))
print('response in:', datetime.datetime.now(datetime.UTC) - start)
print()
print("Query the best GFS historical data for two parameters, for a three day range, for three coordinates, as of the end of the second day")
start = datetime.datetime.now(datetime.UTC)
df = client.history(
dataset='gfs',
from_time=datetime.datetime(year=2022, month=8, day=10, hour=0),
until_time=datetime.datetime(year=2022, month=8, day=13, hour=0),
coordinates=[
{"lat": 40.75, "lon": -73.98},
{"lat": 29.75, "lon": -95.36},
{"lat": 47.60, "lon": -122.33},
],
variables=[
{"name": "TMP", "level": "2 m above ground", "info": ""},
{"name": "TMP", "level": "surface", "info": ""},
],
# Time travel. Before as_of, forecasted_time is history, after it is the forecast at as_of
as_of=datetime.datetime(year=2024, month=8, day=12, hour=0),
min_horizon=0,
max_horizon=264,
)
print(df.sort_values(['forecasted_time', 'lat', 'lon']).head(20).to_string(index=False))
print('response in:', datetime.datetime.now(datetime.UTC) - start)
print("done")
Output:
Warning, missing API token. Running in limited DEMO mode.
Query all NBM weather forecasts for three parameters, over a three hour range, ten hours out, for three coordinates
forecasted_at forecasted_time lat lon DPT|2 m above ground| TMP|2 m above ground| WIND|10 m above ground|
2024-08-10 00:00:00+00:00 2024-08-10 01:00:00+00:00 29.75 -95.36 297.27 305.87 2.0
2024-08-10 00:00:00+00:00 2024-08-10 01:00:00+00:00 40.75 -73.98 295.27 296.27 11.6
2024-08-10 00:00:00+00:00 2024-08-10 01:00:00+00:00 47.60 -122.33 289.27 298.67 2.0
2024-08-10 01:00:00+00:00 2024-08-10 02:00:00+00:00 29.75 -95.36 297.72 304.90 1.6
2024-08-10 00:00:00+00:00 2024-08-10 02:00:00+00:00 29.75 -95.36 297.75 304.90 1.6
2024-08-10 01:00:00+00:00 2024-08-10 02:00:00+00:00 40.75 -73.98 295.32 296.10 11.2
2024-08-10 00:00:00+00:00 2024-08-10 02:00:00+00:00 40.75 -73.98 295.35 296.10 11.2
2024-08-10 01:00:00+00:00 2024-08-10 02:00:00+00:00 47.60 -122.33 289.72 296.50 1.6
2024-08-10 00:00:00+00:00 2024-08-10 02:00:00+00:00 47.60 -122.33 289.35 296.50 1.6
2024-08-10 02:00:00+00:00 2024-08-10 03:00:00+00:00 29.75 -95.36 297.47 304.05 1.6
2024-08-10 00:00:00+00:00 2024-08-10 03:00:00+00:00 29.75 -95.36 297.82 304.23 1.2
2024-08-10 01:00:00+00:00 2024-08-10 03:00:00+00:00 29.75 -95.36 298.01 304.23 1.6
2024-08-10 02:00:00+00:00 2024-08-10 03:00:00+00:00 40.75 -73.98 295.07 295.65 10.4
2024-08-10 00:00:00+00:00 2024-08-10 03:00:00+00:00 40.75 -73.98 295.42 295.83 10.4
2024-08-10 01:00:00+00:00 2024-08-10 03:00:00+00:00 40.75 -73.98 295.21 295.83 10.4
2024-08-10 02:00:00+00:00 2024-08-10 03:00:00+00:00 47.60 -122.33 289.87 294.85 1.2
2024-08-10 00:00:00+00:00 2024-08-10 03:00:00+00:00 47.60 -122.33 289.82 295.03 1.6
2024-08-10 01:00:00+00:00 2024-08-10 03:00:00+00:00 47.60 -122.33 289.61 294.63 1.2
2024-08-10 02:00:00+00:00 2024-08-10 04:00:00+00:00 29.75 -95.36 297.89 303.52 1.2
2024-08-10 00:00:00+00:00 2024-08-10 04:00:00+00:00 29.75 -95.36 298.23 303.53 1.2
response in: 0:00:01.427238
Query the best GFS historical data for two parameters, for a three day range, for three coordinates, as of the end of the second day
forecasted_at forecasted_time lat lon TMP|2 m above ground| TMP|surface|
2022-08-10 00:00:00+00:00 2022-08-10 00:00:00+00:00 29.75 -95.36 305.76 306.26
2022-08-10 00:00:00+00:00 2022-08-10 00:00:00+00:00 40.75 -73.98 303.16 303.46
2022-08-10 00:00:00+00:00 2022-08-10 00:00:00+00:00 47.60 -122.33 297.66 298.66
2022-08-10 00:00:00+00:00 2022-08-10 01:00:00+00:00 29.75 -95.36 304.38 304.30
2022-08-10 00:00:00+00:00 2022-08-10 01:00:00+00:00 40.75 -73.98 301.58 301.80
2022-08-10 00:00:00+00:00 2022-08-10 01:00:00+00:00 47.60 -122.33 295.48 296.10
2022-08-10 00:00:00+00:00 2022-08-10 02:00:00+00:00 29.75 -95.36 303.24 303.22
2022-08-10 00:00:00+00:00 2022-08-10 02:00:00+00:00 40.75 -73.98 301.04 301.42
2022-08-10 00:00:00+00:00 2022-08-10 02:00:00+00:00 47.60 -122.33 294.24 294.52
2022-08-10 00:00:00+00:00 2022-08-10 03:00:00+00:00 29.75 -95.36 302.77 302.79
2022-08-10 00:00:00+00:00 2022-08-10 03:00:00+00:00 40.75 -73.98 300.47 300.69
2022-08-10 00:00:00+00:00 2022-08-10 03:00:00+00:00 47.60 -122.33 291.47 290.99
2022-08-10 00:00:00+00:00 2022-08-10 04:00:00+00:00 29.75 -95.36 301.26 300.90
2022-08-10 00:00:00+00:00 2022-08-10 04:00:00+00:00 40.75 -73.98 299.06 299.50
2022-08-10 00:00:00+00:00 2022-08-10 04:00:00+00:00 47.60 -122.33 288.96 288.20
2022-08-10 00:00:00+00:00 2022-08-10 05:00:00+00:00 29.75 -95.36 300.61 300.24
2022-08-10 00:00:00+00:00 2022-08-10 05:00:00+00:00 40.75 -73.98 297.31 297.44
2022-08-10 00:00:00+00:00 2022-08-10 05:00:00+00:00 47.60 -122.33 287.51 286.94
2022-08-10 06:00:00+00:00 2022-08-10 06:00:00+00:00 29.75 -95.36 300.38 299.93
2022-08-10 06:00:00+00:00 2022-08-10 06:00:00+00:00 40.75 -73.98 296.98 296.93
response in: 0:00:00.659955
done
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