Category Archives: Basic Weather Facts

It’s not just snowstorm accumulations that are difficult to predict….

Today’s models are a perfect example of where model prediction of QPF (total accumulated precipitation) is so difficult.

Had this been snowfall, we would have been predicting 1-2 feet of snow for today.  (QPF values were 1-2 inches of water).  Instead, last night’s late models revised that down to 0. 50 inches of water.  So a forecast of 2 foot  snowstorm would have had to be revised down to 5 inches!

(A spectacular change- the evening NAM was predicting 1.29 inches for PHL during the afternoon alone.  The 1 AM model run reduced that to about 0.30!)

Significant errors in forecast QPF  is very a frequent occurrence.   When it rains, no one really notices or cares when the QPF is not forecast properly.  But if it’s going snow, the models let you down in a way that is much more obvious.

It’s often not about “changes in the track of the storm” as the TV people like to talk about.  It’s more about the three dimensional distribution of energy and moisture in a storm that can’t be accurately measured and then modeled.

Today’s forecast is a case in point.

10 AM Update – Yet another turnaround….this morning’s NAM shows a QPF of 2.32 inches for PHL with a band of the heaviest rain right though the Delaware Valley.  A totally different precipitation coverage map compared to last night.   Even last night’s experimental “National Blend of Models” had decreased the precipitation forecast for today in PHL.

Good thing this isn’t falling as snow.

In addition to these large swings in forecast QPF for certain areas, snowfall amounts can be anywhere from QPFx10 or QPF x20!  So errors in QPF are multiplied by a factor of ten or more when we have snow.

While we’re at it, I get a real kick out of the TV “Future Tracker” where they depict the locations of the heavy rain in advance.  I want you all to know those forecasts are not to be ever taken literally.



Basic Weather Model Facts

Here is some basic weather information for understanding my blog:

GFS model is a global weather model and is the “Global Forecast System” model.  (It is what used to be called the AVN/MRF model (AVN=aviation, MRF= medium range forecast model

The GFS is a global spectral model (its physics is based on waveform physics)  and it models the entire globe.  It’s a relatively coarse resolution model, not as fine a resolution as the NAM described below, however it is very advanced. The GFS is run four times a day. While not as ‘fine’ a resolution as the NAM model, the GFS model has wonderful features and is the one I usually bet on.  The GFS is constantly improved and its most recent update was in January 2015.

Another subset of the model, the GFSX (X for extended) also predicts over longer ranges, as much as 144 hours to 384 hours into the future.

NAM stands for the North American Mesoscale model. Previously known as the Eta model, it was renamed on 1/25/05.

On 6/20/06, the NAM was converted from using older Eta physics to WRF (Weather Research Forecast) model physics.  There are also variations of the NAM model which use different physics for their initial conditions.

As mentioned NAM has different physics than the GFS and is a higher resolution model. It attempts to predict in areas as small as 4 km in area. In meteorology,  that scale is referred to as a “mesoscale” area. (Thunderstorms are mesoscale weather events.)

The NAM is not a global model and only predicts for North America. It’s run  four times daily and the usual form predicts 84 hours into the future. Experimental extended versions are being developed.

NAVGEM is the model developed and used by the US NAVY. A global model, somewhat similar in coverage as the GFS, but different physics. Run four times a day. I find it very useful for hurricane predictions

GFS MOS and the NAM (Eta) MOS: MOS stands for Model Output Statistics. MOS data are statistically based forecasts for up to 84 hours (NAM and GFS) or 144 hours (GFSX or GFS eXtended) and

The MOS predicts temperature, humidity and precipitation probabilities every 3, 6 and 12 hours, depending upon the specific MOS output . MOS forecasts use historical data and reinterpret the raw data for specific locations. However, MOS forecasts do not correct for model biases.

RAP: Rapid Update Cycle model.  Based on the NAM model, this model is a short term model that is updated hourly.  There is also a High Resolution Rapid Refresh (HRRR) model.

LAMP Forecast:  An hourly forecast, rerun every hour and based on the GFS

Other models include the DIGEX, Canadian GEMS, MM5, UKMET, ECMWF.

All models have their specific biases, inaccuracies, etc. New forms of each model are constantly being developed and tested.

The Acronyms and other meteorological concepts

QPF is “quantity of precipitation falling”. Most of the models predict the amount of precip that will fall in a given period of time, based on the total amount of available moisture that can precipitate (PWAT = precipitable water) and the physical conditions (lift, convection, etc.) that will cause it to precipitate. The amount of snow (snow:water ratio) is usually calculated by multiplying the QPF in inches by a factor of 12-20, depending upon the temperature.

Atmospheric Thickness Levels: Heights in the atmosphere are often measured based on where the pressure is a constant value. The ‘thickness’ of the atmosphere is the three dimensional depth of the atmosphere between two pressure values. A useful thickness value is the thickness (or depth) of the atmosphere between the pressure of 500 millibars (mb) (about 18,000 feet) and 1000 mb (millibars) (about the earth surface near sea level). The thickness values become higher when the upper atmosphere is warmer and become lower when the upper atmosphere is colder. Thickness values are useful in predicting rain/snow or sleet. They correlate with temperatures at specific heights that are correlated with snow or sleet or rain.

Useful Temperature Levels: 800 millibars and 900 millibars- Both of these levels must be at or below 0 degrees C (freezing) for snow to form.