Model grid interpolations – an aid to precipitation forecasting

By Matt Gallagher

Forecasting precipitation, as you’ve seen, is really challenging when attempting to forecast for a specific point. Forecasts are generally good for an area – e.g. it’s likely to rain in Chicago on a given day with a 70% probability, but that doesn’t really help with coming up with a forecast for a specific amount of precipitation at a specific point in that area (the rain sensor at KORD).  Models such as the GFS or NAM as shown on the Penn State E-Wall help you understand broad areas which are likely to see precip, and even give you a sense of the amount of QPF you should expect, but they aren’t much help in providing the detailed forecast you need to provide in the Challenge.

Today I want to highlight a resource available which can help with your QPF forecast. However, you should always remember to first understand the big picture setup, and then look to the big picture models, before turning to these specialized tools. (I’ll hit another available resource for this in the next couple of days, SREF). 

The first tool are grid interpolations of the models, available from the Texas A&M site.  They do exactly what it sounds like – they interpolate between grid points in the models to provide you a solution for the exact amount of QPF forecast for a given airport.  To find them on the A&M site, click on Model data, and then pick GFS or NAM or RUC grid interpolation (all three models provide grid interpolations, and you should check all 3!).  Put in the station identifier (KORD) and click on ‘get data’. Your results will look like this:

Interpreting this chart is a bit rough.  The  DAY / HOUR line tells you the calendar day and period in Zulu time – so in the example above, the first column with data is for 19/18, or May 19 for 18Z.  Looking down the left, you’ll see the various parameters that the model interpolation provides.  Look for ‘total precip (IN)’ and read across. You’ll have to sum the amounts shown for the periods in question (in this case, 6 hour periods). To get the total for a given calendar day using this example  you’d add the 20/06, 20/12, 20/18 and 21/00 columns.  (In this case, 0.00”).  

Remember this is NOT “the answer”. It is one more source of info, but model grid interpolations do at least give you precise numbers to look at (but maybe not accurate ones).  You need to start with the big picture and then model charts to make sure the interpolations make sense. Also, don’t rely on one model – look at all available ones and see if they’re consistent. Only then should you start thinking about a specific number forecast for QPF for your forecast location. Then look to our old friend MOS and see what guidance it provides. Your job as forecaster is to take all these (differing) sources of guidance and using them to formulate a forecast that makes sense.

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