11
First off very few people are using Basemap from Matplotlib these days. From this link matplot basemap
Basemap is deprecated in favor of the Cartopy project. See notes in Cartopy, New Management, and EoL Announcement for more details.
So we are going to use cartopy in addition with matplotlib to plot the grib file that you have provided.
Before I ...
8
In general, the relation between the three datasets is as follows:
The simulation model is initiated with a set of boundary and initial conditions, and will on the long-term provide good mean conditions. However, depending on the frequency of data assimilation, the results on short-term time spans can deviate from the observations, due to internal ...
6
First of all, these netCDF files follow the CF Metadata Conventions, which describe the use of scale_factor and add_offset in section 8.1 Packed data of the conventions description. In short, you're applying them correctly:
If both attributes are present, the data are scaled before the offset is added.
However, I think that you've selected the wrong ...
6
The "step" is the number of hours the ERA re-forecast has been run into the future from the "time" of the re-analysis.
For example, asking for ERA data with time = 12:00 and step = 0 means asking for data valid at 12:00 based on a re-analysis for 12:00. But asking for data with time = 12:00 and step = 6 means asking for data valid at 18:00, i.e. for data ...
5
After my original answer, and some back and forth in the comments, turns out I got the answer right by a bit of fools luck, and we sorted it out...
The thing is, despite flux being commonly thought of in physics as BetterExplained.com suggests:
Timing: We measure flux at a single point in time. Freeze time and ask
“Right now, at this moment, how much ...
5
The ERA archive description document, Berrisford et al (2011) "The ERA-Interim archive Version 2.0", provides additional information about what is available. It's less explicit on the why, but we can read between the lines a bit.
The archive consists of re-analyses of the system state at 00, 06, 12, and 18 UTC and re-forecasts out to 10 days initialised ...
5
There is no traceable uncertainty associated with each datum in the ERA-Interim reanalysis.
However, in ERA-5 there is an ensemble. This multiplies the data volume, but it means you can use the spread between the ensemble members to get an estimate. See ERA-5 page. ECMWF want us to use ERA-5 now anyway.
5
To me I am not sure why the subset option should not work with a python script. According to me it does work. Here is a sample script.
import cdsapi
c = cdsapi.Client()
c.retrieve(
'reanalysis-era5-single-levels',
{
'variable':'total_precipitation',
'product_type':'reanalysis',
'year':'2010',
'month':'04',
'day':'07',
'area':'60/0/...
4
You need to use the following syntax
yes? fill 't'[k=1,l=1]
This is because ferret treats t variable name as time and to distinguish your variable name (if it is the same in the file), you have to provide it in double quotes.
4
Just leaving this for future readers. I finally found in some ECMWF documentation (referring to a different variable) that: "The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation". This applies also to other variables.
Thanks to Daniel ...
4
I haven't looked into ERA-Interim wave data a huge amount but I know around 1991 it started to assimilate ocean wave data from satellite altimeters (ERS-1).
See the first figure here
https://climatedataguide.ucar.edu/climate-data/era-interim
I remember Jean-Raymond Bidlot who runs WAM at ECWMF showing me so experiments with and without the assimilation into ...
3
It is and is not an an error depending on how it is used. They are Eons but in older literature they were also, sort of, eras as the Eons were not subdivided as they are now. Especially in literature dealing with the transitions they might refer to them indirectly as eras, such as "In the previous Proterozoic era", proterozoic being possessive. At the time ...
2
The simplest way is to download another variable in which data is only available over the ocean, such as Sea Surface Temperature (SST). You should be able to download it in the very same way you do with precipitation (let us call it PREC hereafter).
Then, you can use any software that handles this type of data (Matlab, Python, NCL, GrADS...) to mask out the ...
2
There are two ways to get this done:
1- you can select the specific domain of interest when downloading the files. This is highly preferred when you have to download a large number of variables and years. ERA5 netCDF are offered in two different formats: either at hourly time steps, or at monthly averages (see this). If you need a sub-monthly time steps the ...
1
I have two solutions.
Use MetPy to calculate the height from the geopotential height.
Use the hypsometric equation, averaging the temperature below the level.
1
If I understood the question right:
CDO can interpolate from model level to height level with the operator
ml2hl infile.nc outfile.nc
additionally, for the lower atmospheric levels, you can add an extrapolation option.
BUT I don't recommend it since its extrapolation performance is questionable.
1
I had the same problem but have just found the solution after an hour or so of trawling through forums! The problem was that the code 'api.py' could not find the '.cdsapirc' file. The file type has to actually be 'CDSAPIRC' and not a 'txt' file. The icon for the file will then be displayed in your folder as blank and the name will be '.cdsapirc'. I found the ...
1
I would recommend changing from ERA-Interim to ERA-5, since ERA-Interim will be outdated soon. This is the information currently given by the data provider:
ERA Interim is being phased out. Users are strongly advised to migrate to ERA5. The last date to be made available in ERA Interim will be 31 August 2019, which will be released at the end of October ...
1
The land-sea mask for ERA Interim is available here.
1
Any Python/R packages that can extract and plot these data in an efficient way?
I can highly recommend using the Python package xarray or iris for data analysis and Cartopy for plotting.
1
Did you use the right Vtable? That error message is saying that the soil temperature variable is missing/couldn't be read.
1
This doesn't answer your question but I can't comment; It is worth noting that there are several changes in SST packages which drive the models. The most significant is the difference from before December 2001 and after January 2002 - it could be that there is a difference from before 1991 and after for similar reasons.
It might also be worth checking if ...
1
Some confusion comes from the mixture of analysis data (such as 2m-air temperature) and reanalysis products (such as downward radiation) in the final reanalysis product. The following text directly from ECMWF may help
https://confluence.ecmwf.int/pages/viewpage.action?pageId=56658233
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