1. Donat Perler, MeteoSwiss Joint work with Oliver Marchand, MeteoSwiss
Automatic weather interpretation using modern classification algorithms
Donat Perler, MeteoSwiss
Joint work with Oliver Marchand, MeteoSwiss

2. Overview: The aLMo Model
Model features:
Non-hydrostatic
Prognostic variables (u, v, w, T, p´, qv, qc, qi)
aLMo7 (today) aLMo2 (planned)
2 times per day, +72h 8 times per day, +18h
358 x 325 x 45 grid points 480 x 350 x 45 grid points
7km horizontal grid scale 2km horizontal grid scale
COSMO Meeting, Date, Zürich

3. Result: Output of aLMo and its interpretation
2000 plots per simulation
COSMO Meeting, Date, Zürich

4. Operational Weather Forecasting
COSMO Meeting, Date, Zürich
?Thunderstorm risk?  ?Thunderstorm alert?

5. Automatic Weather Interpretation at DWD
Pseudo Code for DWD Expert System:
[…]
if ((STABILITY_INDEX < -6) AND
(P_CLOUD_BAS-P_CLOUD_TOP > 400hPa) AND
(T_CLOUD_TOP < -45C) AND
(PERCIP_CONV > 2.0mm/h))
then
report(`Thunderstorm´)
end
COSMO Meeting, Date, Zürich

6. Output of the System used at DWD
Thunder-
storm
COSMO Meeting, Date, Zürich

7. Main Drawbacks of DWD`s Method
Rules are manually chosen
Parameters/threshold values set by hand
No measure for the certainty of classification given
Questions
How can the parameters be tuned more exact?
Do all rules have the same importance?
Are there other important rules? How does one find these rules?
COSMO Meeting, Date, Zürich

8. Machine Learning use! Training set Actual data Neural Networks
Boosting
SVM
train!
COSMO Meeting, Date, Zürich
Output
Classification Problem

9. Select appropriate model features
Steps in this work
Select appropriate model features
Principal Component Analysis
Use DWD experience from Expert System
Compare and evaluate different supervised machine learning algorithms
Boosting
Support Vector Machines
Bayesian- and Neural Networks
Introduce the best algorithm to the aLMo for operational use
COSMO Meeting, Date, Zürich

10. Thank you for your attention! We very much welcome discussions.
COSMO Meeting, Date, Zürich