1. MPO 674 Lecture 15 3/3/15

2. Bayesian update Jeff Anderson’s Tutorial A | C = Prior based on previous information C A | BC = Posterior based on previous information C and new information B B = New observational information A | BC B | AC A | C Posterior Observation Prior

3. B A | CA | BC Courtesy Tom Hamill (NOAA/ESRL)

4. Forecast Model Cycle MODEL “FIRST GUESS” OBSERVATION PREPARATION DATA ASSIMILATION INITIAL CONDITIONS 00 UTC MODEL “FIRST GUESS” OBSERVATION PREPARATION DATA ASSIMILATION INITIAL CONDITIONS 06 UTC MODEL “FIRST GUESS” OBSERVATION PREPARATION DATA ASSIMILATION INITIAL CONDITIONS 12 UTC MODEL “FIRST GUESS” OBSERVATION PREPARATION DATA ASSIMILATION INITIAL CONDITIONS 18 UTC Integrate 6 hours FORECAST

5. Locations of meteorological stations from which Richardson obtained upper-air observations for his first numerical weather prediction. Squares marked 'P' provided atmospheric pressure values; those marked 'M' gave atmospheric momentum. “Perhaps some day in the dim future it will be possible to advance the computations faster than the weather advances and at a cost less than the saving to mankind due to the information gained. But that is a dream.” L. F. Richardson

6. Subjective Analysis Objective Analysis 700 hPa Z

7. Gilchrist and Cressman (1954)

8. Successive Corrections: Cressman Analysis

9. Cressman Analysis Three kinds of observation handled by the Cressman successive corrections scheme: height only, wind only, and height and wind together. R is the scan radius and d is the distance from the gridpoint at the center of the circle to the observation: height alone (i index), wind only (j index), height and wind (k index). The radius of influence is R; its value decreases on successive scans.

10. Cressman Analysis Advantages – Simple and computationally fast – Uses forecast information in background field Disadvantages – Does not include observation errors (bad obs?) – Does not account for distribution of observations – Level of detail depends on observation density – How to analyze wind versus height? – Does not account for background error

11. Barnes Analysis w n = exp(-d n 2 / 4c).

12. Barnes Analysis Advantages – No need for a model – No need to set influence radius – Control of fine-scale analysis Disadvantages – Same as for Cressman analysis

13. Nudging Adds a “nudging” term to the prognostic equations for the field variables. Initialize with first guess (background field) and integrate forward. Nudging term forces integration towards observations. Balanced initial conditions. Good for small-scale obs (e.g. radar). No covariance matrices.