Overview of Deterministic Computer Models Spring 2016 Kyle Imhoff

What do we mean by deterministic? The computer model solves the equations of motion (discussed on the first day of class) for each grid point. At each grid point, model output provides information on exactly what the temperature will be, how much rain/snow falls, what wind speeds will be, etc. NO probabilities involved, no statistical techniques Strictly physically-driven

Basic Idea (review from last week) Weather is governed by laws of physics that we understand and can approximate through the use of equations The key word there is approximate System of equations are very complex and not easily solved by humans – use of calculus/different equations Computer power used to solve the equations and to provide useful guidance tools to a human forecaster We will get into physics and atmospheric processes later in the class – this lecture will focus on how a model works

Step 1: Observational Data The atmosphere works in 3-dimensions (i.e. in the horizontal and vertical) – we must measure atmospheric processes both at the surface and aloft In order for the model to even have a chance at forecasting accurately, significant amounts of data (and GOOD data) must be provided to the model The use of all of the surface observational data and weather balloon/satellite/aircraft data for the upper atmosphere discussed last week is provided as initial input to the model

Step 2: Data Assimilation This essentially means the model brings in the data and sifts out the bad from the good. Once the model has completed this process, the model can use that data to start solving the equations There are many different ways to “sift” through the data – we will discuss this in some more detail later in the class

Step 3: Solving the equations The model solves the equations in time steps Recall from one of our first lectures a sample equation from the governing set of equations: w, in this case, represents vertical motion (wind speed) The model will take initial observations and solve the equation in time steps So, the model solves the equation in increments of time (e.g. every 3 hours, every 6 hours, etc.) This technique is called “finite differencing” Thus, by solving the equation in steps, in this example you would get values of vertical wind speed for future hours

Step 4: Bring numbers to life The computer model must now interpret numerical output and place it on a grid system The sketch to the right shows the basics of how a model estimates the atmosphere through a series of grids

Step 4: Bring numbers to life (cont’d) The model solves the equations at each grid point (as shown to the right) Each grid point represents the average value for the surrounding air (we call this a “parcel” of air in meteorology So, the model approximates the atmosphere as a series of points and cubes of air

Step 4: Bring numbers to life (cont’d) An example of a grid cell (or “parcel” of air) is shown to the right The greater the number of grid points, the smaller the grid cell Smaller grid cells mean the model has a “higher” resolution

Step 5: Model Output Displays Graphical Output Tabular Output

Computer Model Resolution The resolution of a model refers to how far apart the grid points are from one another The higher the resolution, the smaller the difference between grid points When grid points are closer together, finer details can be seen in the model Model resolution plays a key role in how accurately the model can forecast certain types of phenomena

Computer Model Resolution Differences Lower resolution (12 km between grid points) Higher resolution (4km between grid points)

Computer Model Types – Global vs. Regional Global-scale models: United States: Global Forecast System (GFS) Europe: European Centre for Medium-Range Weather Forecasts (ECMWF) Canada: Canadian Meteorological Centre (CMC) United Kingdom: United Kingdom Meteorological Office (UKMET) Regional-scale models: Many different models United States: North American Model (NAM) United States: High Resolution Rapid Update (HRRR) United States: Rapid Update Cycle (RUC)

Computer Models – Operational Structure Computer models are typically run every 6 or 12 hours i.e. 00z, 06z, 12z, 18z Weather balloons are launched at 00z and 12z everyday worldwide – this makes 00z and 12z runs of models the most accurate because they contain better upper-level data Models produce forecast output for as little as 12-16 hours in their entirety or as much as 384 hours out from the model initialization time Resolution affects how long a model can run The higher the resolution, the more grid points must have the equations solved, thus more time it takes the model to run and complete each time step So, higher resolution models tend to be short-range forecast models (within 72-84 hours) Lower resolution models forecast in the long-range (beyond 84 hours) The same holds true for special extent of the model global-scale models have more grid points, thus take longer for each time step – they are lower resolution compared to regional-scale models

Why create so many model “runs” each day? Running consistent model runs allows the more recent run of the model to include the most recent observations The idea is that including newer observational data will create more accurate forecasts So, the 18-hour forecast in the 00z run of the model should not be as accurate as the 12-hour forecast of the 06z run of that same model

One Main Problem The use of multiple models and multiple runs of each model creates an information overload to the forecaster Which model should you focus on? Should you average all of the models together? If all of the models are radically different, where should they even begin? If the GFS did better for the last snowstorm, will it do well for the one that is forecast for next week? This problem has driven the industry to the point where weather forecasters must now be thought of as professional interpreters of computer model data

Summary Computer models have vastly improved our ability to predict the weather – and more accurately farther out in time They simulate what the real atmosphere looks like and what it will look like in the future – it is not the real thing by any stretch! They provide guidance tools for forecasters With so many tools available, weather forecasters can have a hard time trying to find the signal in all of the noise