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Real-time forecasts
The NCEP real-time forecasts consist of a 16-member ensemble run every day. The S2S archive contains all the NCEP real-time forecasts since 1st January 2015.
1. Real-time forecasts: 1 param, 1 date
Retrieving one field (10 meter U wind here) for all time steps and for the forecast starting on 1st January 2015:
| Code Block | ||
|---|---|---|
| ||
#!/usr/bin/env python
from ecmwfapi import ECMWFDataServer
server = ECMWFDataServer()
server.retrieve({
"class": "s2",
"dataset": "s2s",
"date": "2015-01-01",
"expver": "prod",
"levtype": "sfc",
"origin": "kwbc",
"param": "165",
"step": "24/to/1056/by/24",
"stream": "enfo",
"target": "CHANGEME",
"time": "00",
"type": "cf",
}) |
| Code Block | ||
|---|---|---|
| ||
#!/usr/bin/env python
from ecmwfapi import ECMWFDataServer
server = ECMWFDataServer()
server.retrieve({
"class": "s2",
"dataset": "s2s",
"date": "2015-01-01",
"expver": "prod",
"levtype": "sfc",
"origin": "kwbc",
"param": "165",
"step": "24/to/1056/by/24",
"stream": "enfo",
"target": "CHANGEME",
"time": "00",
"number": "1/to/15",
"type": "pf",
}) |
2. Real-time forecasts: 1 param, series of dates
Retrieving 1 field (10m U wind) for all time steps and for the whole January 2015.
| Code Block | ||
|---|---|---|
| ||
#!/usr/bin/env python
from ecmwfapi import ECMWFDataServer
server = ECMWFDataServer()
server.retrieve({
"class": "s2",
"dataset": "s2s",
"date": "2015-01-01/to/2015-01-31",
"expver": "prod",
"levtype": "sfc",
"origin": "kwbc",
"param": "165",
"step": "24/to/1056/by/24",
"stream": "enfo",
"target": "CHANGEME",
"time": "00",
"type": "cf",
}) |
| Code Block | ||
|---|---|---|
| ||
#!/usr/bin/env python
from ecmwfapi import ECMWFDataServer
server = ECMWFDataServer()
server.retrieve({
"class": "s2",
"dataset": "s2s",
"date": "2015-01-01/to/2015-01-31",
"expver": "prod",
"levtype": "sfc",
"origin": "ecmf",
"param": "165",
"step": "24/to/1056/by/24",
"stream": "enfo",
"target": "CHANGEME",
"time": "00",
"number": "1/to/15",
"type": "pf",
}) |
Re- forecasts
The NCEP re-forecasts dataset is a "fixed" dataset which means that the re-forecasts are produced once from a "frozen" version of the model and are used for a number of years to calibrate real-time forecast.
...