I’m seeking the total precipitation for all months of 2023 and 2024 for every location in the world. As an example, I obtained the monthly_averaged_reanalysis of total_precipitation from the ERA5 monthly averaged data on single levels from 1940 to present dataset for May 2024. Here’s the code I used to retrieve the dataset:
import cdsapi
c = cdsapi.Client()
c.retrieve(
'reanalysis-era5-single-levels-monthly-means',
{
'format': 'grib',
'variable': 'total_precipitation',
'product_type': 'monthly_averaged_reanalysis',
'month': '05',
'time': '00:00',
'year': '2024',
},
'download.grib'
)
The minimum, maximum, and mean values in millimeters are 0.0, 93.10, and 2.42 respectively.
As a sample, I looked for a location near or inside Paris. I know the total precipitation in Paris was around 95 mm in May 2024. However, in the dataset, I found 3.4 mm for latitude=48.75 and longitude=2.25.
Initially, I thought this might be the average daily amount and that I should multiply it by the number of days (31) in May. This would result in 105.4 mm, which is closer to the expected value. However, applying this method to another location (latitude=0.5, longitude=29.75) yields 2886.15 mm of precipitation, which seems unrealistic.
Is multiplying by 31 the correct approach? Is there any data noise in the dataset that should be removed? I’m simply trying to collect rainfall data and didn’t anticipate such challenges. Any help would be greatly appreciated.