LEAD-TO-LEARN
UNDERSTANDING THE PROCESS OF USING OBSERVATIONS TO INITIALIZE
WEATHER PREDICTION MODELS
WORK SHEET
Introduction
In this activity you will learn about the different data sources used to
initialize numerical weather prediction (NWP) models as well as complexity
of the data assimilation process used in most models.
Objectives
By the end of this module you should be able to:
• Identify and explain the data sources used to initialize
numerical weather prediction models.
• Explain the data assimilation process used by most
numerical weather prediction models.
• Evaluate the effect of different data assimilation
techniques on the accuracy of the model output.
Background
Numerical models require a set of initial conditions for the dependent
variables (pressure, temperature). These initial conditions are derived
not only by using synoptic or other observations, but with a “first guess”
field, which is most often a short-term forecast from a previous model run.
This ensures that there are reasonable data over regions of the earth where
observations are scarce.1 The technique by which observations are combined
with a NWP forecast (the first guess field) and their respective error statistics
to provide an improved estimate of the atmospheric state is known as data
assimilation.2 The initialization of the model is very important to
the quality of the model forecast. Poor initialization will likely
result in a poor model forecast. Therefore, data assimilation is essential
to weather forecasts1. This module will discuss the process of using
observations to initialize weather prediction models in the following sections:
1) Initial Conditions/Observations
2) Data Assimilation Techniques
3) Numerical Weather Prediction Model Output
4) Questions
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