ERA5 versus ERA-Interim Total Precipitation

Dear Jessica,

ERA5 does suffer from "rain bombs", mainly over Africa in regions of high orography, which usually occur a few times per year. Rain bombs are concentrated areas of high rainfall. This is probably why the range of values is larger for ERA5 than ERA-Interim. You might be interested in reading about comparisons of ERA5 and ERA-Interim precipitation with various observational products, see Sections 9.2 and 9.3 in Hersbach et al. 2020:

https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803

The rain bombs are also mentioned in this reference, in Section 10.2.

For the ERA5 data, I presume you downloaded the Product type "Monthly averaged reanalysis".

Best wishes,

Paul

Hi Jessica,

I am Ervin, the colleague who looked into these rain bombs.

I have tried to create a simple filtering, that would cap these extreme single grid values, but unfortunately these rain bombs are not easy to remove 'correctly' at all.

These are single grid point spikes in the data. They (as mentioned earlier above) tend to appear over high/extensive orographical areas, maybe up to few times a year (in the most extreme cases/points even 10% of the year).

But there is no exact rule on what is a 'too high' single point outlier and what is still a physically reasonable one.

I have analysed the extended range ENS precipitation fields (24-hour totals), which has a similar resolution and does not suffer from rainbombs, in order to see how big outlier single grid point values are still physically possible. And in fact there are rather surprisingly high single value cases. 

As a good assumption, one can use the 50 mm as a generic threshold though. So, taking all the neighbouring grid points (in the original ERA5 grid) and if the value is higher by at least 50 mm than all the neighbours, then simply cut this outlier to the maximum value amongst the neighbours. This would be done on the 24-hour fields, for all the days and for all the grid points, as I have been working on those.

This 50 mm, I have found to be working quite well for all value ranges. For the small values, line outlier points when all neighbours are below 0.1 mm (so basically a single point precip), the highest 'real' value of this kind I saw in the extended range ENS was ~30 mm. For the <1 mm case, the highest I have found was ~60 mm, for <10mm it was ~100mm, for <50mm it was 120-150mm, for <200mm it was 250-300mm and so on. So, as you can see, the 50mm is a good assumption for the 24-hour totals.

Then the next question is how to correct the original hourly values based on the 24-hour, in case you wanted to use 1-3-6-12-hourly values, not just 24-hourly. I have not actually done this, but as a simple approach one could replace all the hourly values by the values at the neighbour which had the maximum in the 24-hour data (in case of an outlier point). Alternatively, one should find the equivalent cut-off threshold for the other accumulation periods (1-, 3-, 6-, 12-hour). I do not know at all, how they translate from the 50mm / 24-hour.

So, as a summary, I suggest you do a correction on the 24-hour values by cutting off outliers above 50 mm and then applying the correction to the hourly data (if needed) as above. This will remove most of the rain bombs (although not all as some smaller than 50mm outliers will of course still be rain bombs) and probably remove only very few real outlier cases which are not rain bombs.

Let me know if this makes sense to you and how you are getting on with it and also what the corrected ERA5 result looks like in your original comparison.

Cheers, Ervin

Hi Jessica,

So we don't have a method to correct the monthly data. Ervin developed his method on 24 hour data, so that would be the best method to use. There is some documentation on calculating 24 hourly data:

ERA5: How to calculate daily total precipitation

Let us know if you have any questions/problems and let us know how you get on.

Best wishes,

Paul

Hi Ervin and Paul

Thank you for this detailed methodology. I will give it a try and let you know how I get on.

Kind regards

Jessica

Hi Jessica,

There's a CDS application that calculates daily values of ERA5 and ERA5-Land data, which you might find useful:

https://cds.climate.copernicus.eu/cdsapp#!/software/app-c3s-daily-era5-statistics?tab=app

You can use this web page to calculate the daily data or you can use the CDS API, which is particularly useful to produce the daily data for longer time periods. There is a forum discussion of this:

Retrieve daily ERA5/ERA5-Land data using the CDS API

The advantage of using this CDS application, over the scripts I mentioned before, is that you only need to download the results, the daily data, rather than the hourly data.

Hope this is useful.

Best wishes,

Paul