A World of Weather
Fundamentals of Meteorology

2a. Playing Weather on the Computer

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Playing Weather on the Computer: Making a "Mesh" out of Things

For U.S. forecasters, the “board” used for “playing the game” of NWP is typically North America and adjacent oceans. Like a chess board, this geographical arena can be neatly divided into a mesh of regularly spaced points called a grid. For the record, these grid points are the locations at which the computer calculates the numerical forecast (meteorologists refer to this type of model as a grid-point model). The spacing between grid points varies from grid-point model to grid-point model (and sometimes even within a single model). Some grid-point models have a "coarse" mesh, with large spacing between relatively few grid points.  Other grid-point models are "fine" mesh, with a small spacing between relatively many grid points.  Whether the mesh of grid points is coarse or fine ideally governs the quality of the forecast (we'll explore this idea in just a moment).

Mimicking a game of three-dimensional chess, there are also meshes of regularly spaced points at specified altitudes, stacking from the ground to the upper reaches of the atmosphere (see the figure below). Thus, a three-dimensional array emerges that covers a great volume of the atmosphere, ready to be filled with weather data at each grid point. The typical horizontal spacing of grid points for operational computer models is typically on the order of tens of kilometers. The vertical spacing of grid points varies from tens to hundreds of meters.

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Like three-dimensional chess, numerical weather prediction requires meteorological variables to be calculated at grid points on a number of different levels in the atmosphere.

Once a simulation (sometimes called a "model run" or just plain “run”) has begun, the computer predicts (calculates) the values of moisture, temperature, wind, and so on, at each grid point at a future time, typically a few virtual minutes into the future. To perform this feat, powerful, high-speed supercomputers make billions of calculations each second (see photograph farther down on the page). Thereafter, the computer calculates the same parameters for the next forecast time (a few more minutes into the future), and so on. For a short-range prediction, this "leapfrog" time scheme typically ends 60 to 84 hours into the virtual future, taking on the order of an hour of real time to complete.

Even leap-frogging just a few virtual minutes into the future is fraught with error because the computer makes calculations for one time and then “leaps” to make calculations a few more virtual minutes later, skipping calculations for intermediate times between the starting and ending points of the leap. Like taking short cuts while solving a complicated algebra problem, skipping steps inevitably leads to errors.

This sacrifice in accuracy is a gambit with which forecasters must live. They could, theoretically, make a more accurate computer forecast by reducing the size of the time interval in the leapfrog scheme. However, a smaller time interval would require faster and more powerful computers to support the increased computational load. Also, meteorologists could increase the number of grid points to improve forecasts with the hope that smaller grid spacings could better capture smaller-scale weather phenomena. But such a scheme also demands faster and faster supercomputers. Though technology continues to advance, there is a practical limit to what computers can do, so there will never be a perfect computer forecast. Never.

An IBM supercomputer at the National Weather Service runs numerical weather models that create guidance for weather forecasters. This supercomputer can make more than 450 billion calculations per second.
Courtesy of NOAA

Another shortcoming of computer guidance results from the way each simulation is initialized. By initialization, we mean the mathematical scheme used to represent the state of the atmosphere at the time the computer simulation begins. In other words, to initialize a model means to assign appropriate values of pressure, temperature, moisture, wind, and so on, to each grid point before the leapfrog scheme begins. These values might come from observations or previous forecasts, for example.

Another complication arises in grid-point models because the grid points don’t necessarily fall directly on the locations where weather observations are routinely taken. In addition, the observations themselves have deficiencies: Instrument error is unavoidable, and the observational network, particularly over the oceans, has many gaps. As a result, the best “first guess” for the initialization is often a forecast from the previous model run. This first guess is then adjusted by incorporating real weather observations, taken both at the surface and aloft. Weather-balloon-toted radiosondes take upper-air measurements at 00Z and 12Z each day, so these are typically the times at which computer runs are initialized (short-range models are also initialized at 06Z and 18Z). By way of example, the “first-guess” initialization for a model initialized at 12Z might be the 12-hour forecast from the previous 00Z run. Though the complicated process of initialization is imperfect, it’s the best meteorologists can do to produce the starting values for a computer simulation.

Worldwide, there are many different computer models run on a day-to-day basis that provide guidance for weather forecasters. The various models differ in their geographical area, initialization technique, representation of topography, mathematical formulation, length of forecast (in other words, how far into the future they are run), and other factors.

On October 18, 2011, the NEMS NMM-B became the flagship grid-point model that meteorologists use for short-term weather forecasts. NOAA is big on acronyms, and NEMS stands for NOAA Environmental Modeling System, which is the super-framework within which the National Centers for Environmental Prediction (NCEP) in Washington, DC, initializes, runs, and post-processes its suite of computer models. NMM-B is the Non-Hydrostatic Multiscale Model based on a "B staggering in the horizontal." Don't get nervous. Without getting too complicated here, "B-staggering in the horizontal" means that the predicted north-south and east-west components of the wind lie on the four corners of each grid cell. All other forecast variables (temperature, pressure, vertical motion, etc.) lie in the center of the cell. Yes, it's inside baseball, and you really don't need to know such details, but we at least wanted you to have a sense for what the "B" in "NMM-B" means.

Let's just drop all the formality here and call the NEMS NMM-B the NAM, which is short for the North American Model (now that's an acronym we can live with). For the record, the NAM has a horizontal grid spacing of 12 kilometers. NCEP runs the NAM four times a day at 00Z, 06Z, 12Z and 18Z. The NAM produces numerical weather forecasts out to 84 hours into the future.

A few years prior to October 18, 2011, the Weather Research and Forecasting Model (WRF, for short) served as the North American Model. The only reason we point this out to you is that you'll see "WRF" labeled on some of the progs we present as case studies in this chapter. Don't get nervous. All the concepts you'll learn about interpreting NAM progs are universal (they apply to both the old WRF and the newer NEMS NMM-B).

Other grid-point models used by weather forecasters include the Rapid Update Cycle (RUC), which only produces 12- to 24-hour forecasts; and the Naval Operational Regional Atmospheric Prediction Systems (NORAPS).

We'll talk more about the NAM later in the chapter, but, for now, we point out that the list of models above doesn't include the Global Forecast System (GFS), which NCEP runs daily at 0Z, 6Z, 12Z and 18Z.  As it turns out, the GFS is not a grid-point model.  Let's investigate.