Hello there,
When downloading seasonal forecast anomalies from NCEP and JMA following the API request code, and selecting multiple initial months across multiple years, the variable “month” is missing in the retrieved data (name expected in the netcdf: “Forecast_reference_time”).
There is no such issue when downloading similar data from Météo_France, ECMWF, UKMO, DWD or CMCC.
Less important but still annoying, if I select year=2025 and month=[01’, ‘02’, ‘03’, ‘04’, ‘05’, ‘06’, ‘07’, ‘08’, ‘09’, ‘10’, ‘11’, ‘12’], I run into an error because there is data available only for months 1 to 6.
No such error when doing the same for older years for which months are missing: the request retrieves the available months without complaining.
Thanks in advance for your help/guidance
Hi Constantin,
On the first issue you mention, could you provide an example API request NCEP or JMA which results in the behaviour you are seeing?
The issue for 2025 is simply that the real-time forecasts are initialised and published on the CDS each month, for example the next forecast start date, July 2025, for leadtime months 1-6 (July to December), will be available next week.
Thanks for the questions.
Thanks for your reply.
On the second point, I was aware of this. I just meant to point out that the request does not fail when it asks 12 months in years < 2025 , even if the the system has data for, say, only two months (November and December for example) of that particular year. It would be convenient if the behaviour was similar for 2025. But again, this is a minor issue.
Now, for the main point here is an example of a request that provides a file without any “month” dimension :
import cdsapi
dataset = “seasonal-postprocessed-single-levels”
request = {
“originating_centre”: “jma”,
“system”: “3”,
“variable”: [
“2m_temperature_anomaly”,
“total_precipitation_anomalous_rate_of_accumulation”
],
“year”: [“2023”],
“month”: [
“01”, “02”, “03”,
“04”, “05”, “06”,
“07”, “08”, “09”,
“10”, “11”, “12”
],
“leadtime_month”: [
“1”,
“2”,
“3”,
“4”
],
“data_format”: “netcdf”,
“product_type”: [“ensemble_mean”]
}
client = cdsapi.Client()
client.retrieve(dataset, request).download()