1. Model Initialization
Spring 2016
Kyle Imhoff

2. Recall from last class..

3. 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

4. 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

5. 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

6. 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

7. 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

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

9. 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!

10. 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 and was rounded to
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

11. 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)