Calculating 3-Hour Flux Values in the ERA-Interim Reanalysis

Diving into ERA-Interim: Getting Those 3-Hour Flux Values

So, you’re wrestling with ERA-Interim data and need to calculate 3-hour flux values? You’re not alone! ERA-Interim, a product of the European Centre for Medium-Range Weather Forecasts (ECMWF), was the go-to global atmospheric reanalysis dataset for a long time. We’re talking from January 1979 all the way to August 2019. Sure, ERA5 is the new kid on the block, but knowing how to wrangle ERA-Interim is still super useful, especially if you’re digging into older climate studies or comparing results with past research. Let’s get down to how to extract those 3-hour flux values.

Think of ERA-Interim as a souped-up version of its predecessor, ERA-40. It used a better data system and a more advanced forecast model. The clever folks at ECMWF used something called a 4-dimensional variational analysis (4D-Var) – fancy, right? – within a 12-hour window. Spatially, we’re looking at roughly 80 km resolution (that’s T255 spectral, for the nerds among us!) across 60 vertical levels.

Now, here’s where it gets interesting. ERA-Interim didn’t give you everything at the same time intervals. Surface fields came every 3 hours, but upper-air fields were only available every 6. And fluxes? Those were typically presented as accumulated values over a 12-hour chunk of time. Tricky, but not insurmountable!

Even though it’s retired, you can still grab ERA-Interim data from archives like the ECMWF Data Server. The NCAR Research Data Archive is another great place to check. Just a heads up: you might need to register to play nice with the ECMWF licensing. It’s a small price to pay for all that juicy data.

Okay, so how do we actually calculate those 3-hour flux values? The secret sauce is understanding that those archived flux variables are accumulations – totals since the start of the forecast. They’re not snapshots in time.

To get the 3-hour average flux in good old Watts per square meter (W/m2), you need to do a little subtraction and division. You’re essentially finding the difference between two consecutive accumulation values and then spreading that difference out over the 3-hour period.

Here’s the formula, plain and simple:

Flux(t) = (Accumulation(t) – Accumulation(t-3h)) / (3 * 60 * 60)

Let’s break it down:

• Flux(t) is what you’re after: the 3-hour average flux at time t (in W/m2).
• Accumulation(t) is the total accumulated flux at time t (in Joules per square meter, J/m2).
• Accumulation(t-3h) is the accumulated flux value three hours before time t (also in J/m2).
• And that 3 * 60 * 60? That’s just the number of seconds in 3 hours (10800). Easy peasy.

Example Time!

Imagine you’re tracking surface sensible heat flux between 6 AM and 9 AM UTC.

Voila! You’ve got the average surface sensible heat flux in W/m2 for that 3-hour window.

A Few Things to Keep in Mind:

• Units Matter: Double-check that your accumulated flux values are in J/m2.
• Forecast Fun: If you’re playing with forecast data, stick to the shortest forecast hour you can to keep errors at bay.
• Timing is Everything: ERA-Interim gives you analyses four times a day: 00:00, 06:00, 12:00, and 18:00 UTC.
• Know Your Fluxes: Surface latent heat flux? Surface sensible heat flux? Surface solar radiation? They’re all different, so pay attention!

Now, ERA-Interim was a game-changer, but it wasn’t perfect. Like any model, it had its quirks:

• Missing Pieces: We don’t have eyes everywhere, so there are gaps in the observational data.
• Model Quirks: No model is flawless.
• Assimilation Limitations: The way we shove data into the model has its limits.
• Changing Views: The observing systems have changed over time, which can create artificial jumps in the data.
• Too Much Water: ERA-Interim had a bit of a heavy hand with the water cycle, especially over the oceans. It tended to overestimate precipitation and evaporation.

So, there you have it. Calculating 3-hour flux values from ERA-Interim is all about understanding those accumulated flux variables and doing a little bit of math. ERA5 might be the shiny new toy, but these techniques are still gold for certain research scenarios. Just remember to be aware of the dataset’s limitations and always consult the documentation. Happy data crunching!