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 ...


6

It looks like a Gibbs oscillation, a basic feature of spectral analysis. In a spectral atmospheric model this means that near high orography artificial surface waves exist which may reflect in different variables. They are usually not considered to affect meteorology, however. onlinelibrary.wiley.com/doi/10.1029/2010JC006927/full http://journals.ametsoc.org/...


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 ...


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

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

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

Unfortunately, your question has no answer. Both have errors. Both can be unreliable. Your choice really depends on how you plan on using them. Satellites instrument contain sources of error, such as bad observations (wildfires in the NIR), mapping problems (especially near the poles), and representation error. Don't underestimate those sources of error- ...


4

The ERA Interim reanalysis is made by ECMWF and the other reanalysis is made by NOAA. Reanalyzes of the past are made by running global climate models and these differ because NOAA and ECMWF use different models and perhaps even slightly different data. The models on the other hand can be very different for example on implementation of data assimilation, ...


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

Some variables in reanalyses have to be forecast and cannot be assimilated to allow the model to converse mass, momentum and energy. Precipitation is a good example. The model will assimilate observations which give information about moisture (someone who knows more about assimilation may want to chirp in here). This will be used in the analysis (e.g. 00z) ...


3

The height coordinate varies and is described for the various variables on the site you link. For example: Levels: Surface or near the surface (.995 sigma level), or entire atmosphere (eatm) and Levels: 17 Pressure levels (mb): 1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10 Some variables have less: omega (to 100mb) and ...


2

Beam radiation is direct radiation, e.g. photons that have not been scattered. Diffuse radiation is indirect and has been scattered. Examples of beam radiation from the sky would be from the sun directly to your eye. Diffuse solar radiation would be the blue sky (scattered out of the direct beam by the atmosphere), clouds and anything you can see that is ...


2

Fractional Snow Cover (FSC) is a property of snow cover. The fractional snow cover property is used in a variety of contexts, most common of which is meteorological models. Snow cover algorithms applied to visible imagery remote sensing data are used to derive fractional snow cover for each pixel. This information is typically re-gridded and time-averaged ...


2

Sounds like you want a dataset that is unadjusted for rainfall. I'm not sure what you have to gain from that, but there are ways to do it. One way to do this would be to find the inverse correlation between rain and black carbon concentrations, and extrapolate to zero rainfall. Or, if you have any months that have zero rainfall, you could use that for ...


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

When I visit that link, the first option at the top ("Data Product") defaults to inst3_3d_asm_Np, which is atmospheric variables in pressure levels. I suspect you need to change that dropdown option to inst1_2d_asm_Nx, which is surface and near-surface variables. The list is shows me for that option is, DISPH = zero plane displacement height PS = surface ...


2

tl;dr I would suggest using the Toolbox of the Copernicus CDS. Via the Toolbox, you can choose datasets, process them and download the processed results. You have to be registered and logged-in to use the Toolbox. How to find out how to select the dataset of interest Use the search of the CDS and go to the bottom of the page. There, you can click/tab Show ...


2

TL;DR: It is not a problem, it is a feature. As it was pointed in the comments, 1000 hPa over land (and sometimes over ocean) is below the surface. MERRA is just fair enough not to put any values where they do not exist. Other reanalyses use some procedure to fill the gaps and make fields without holes. The procedure is reanalysis specific, and, can ususlly ...


2

Welcome to StackExchange SE! I don't think I fully understand your question, but I'll try to answer it. Reanalysis data is the use of weather models and data assimilation to piece back the weather. It is a 4-dimensional dataset (Latitude, longitude, pressure levels, and time). You can do whatever you wish with that data (provided you follow the legal ...


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

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

If you check the 6-hourly products in the links you shared, you can see that CFSR includes 50 variables in the "complete" dataset. The hourly dataset has a limited number of variables (25 in total). That's what they mean by "selected". Hope this help.


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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