Model Initialization Spring 2016 Kyle Imhoff

Recall from last class..

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

Where does the computer start? First, the observational data (sporadic and unorganized) must be placed on to an easier-to-use grid This is called an analysis analysis

Model Analysis The computer must do two things to complete the analysis phase Filter out bad observations or “outliers” Interpolate from station information to nearest grid points The model uses the prior run’s forecast as the starting point -> tweaks it based on new observations

Example If the GFS was initialized at 00z with the most recent observations, the 06z run will start with the 6-hour forecast from the 00z run New observations at 6z will allow the model to tweak its old 6-hour forecast This becomes the new analysis

It’s chaos out there! Getting the initial state right is crucial to the remainder of the forecasts produced by the model

Nonlinear system The atmosphere is a chaotic system by nature Starting at point A does not always land you at the same point B – there are multiple possibilities for point B!

Chaos Theory Ed Lorenz, in 1961, started a numerical forecast model late in the evening to simulate atmospheric processes He wanted to double-check the simulation so he ran the model starting a little later in its calculations The results were dramatically different Why? It was a rounding error in the computer that caused dramatic changes in the forecast! Example: Temperature was actually 56.01123 and was rounded to 56.0112 This sparked the idea that the atmosphere is chaotic and nonlinear (it does not behave as one would expect) and is highly sensitive to its initial state

How do we account for chaos? Computer model initialization is one step in the process – the most important step It is still imperfect even though the computer tries its best We try to account for these imperfections by using ensembles (discussed later)