Title: Climate short-term self-learning climate model for Northern Europe
1Climate short-term self-learning climate model
for Northern Europe
-
- O.M.Pokrovsky
- Main Geophysical Observatory
- St.Petersburg
2Outlines
- Theoretical background
- Observing data filtering by low oscillation
approach - Nonlinear forward and feedback link description
by neural network tools - Encoding and decoding of the atmospheric
circulation patterns by fuzzy analysis - Implementation
- Climate short-term self-learning model
model for Northern Europe - Result verification
3Air Temperature at Northern Europe
4Global Irradiance at Northern Europe
5Atlantic Ocean Surface Temperature
6INSTABILITY IN WEATHER ANALYSIS AND FORECAST
- Ill-condition feature of objective analysis of
meteorological fields - Lorenz forecasting equations
- instability generated by
- linear
- nonlinear terms
7Why slow oscillationswere selected?
- Slow oscillations exhibit the highest amplitudes
among other frequencies - Slow oscillations explain the most part of total
variability - Slow oscillations are most stable to
perturbation in initial conditions for numerical
weather models based on differential equation
solution
8Spatial Differentiation
9Time Differentiation
10Multi-Layer Neuron Model
11Neural network methodology advantages
- Flexibility in description of the arbitrary
non-linear dependencies - Easy incorporation of the additional and renewal
feedbacks - Fast self-learning feature
- High approximation fit to measurement data
achieved by NN model
12(No Transcript)
13 March monthly SAT for St. Petersburg observed
and explained by model (NAO, AO)
14 March monthly SAT for St. Petersburg observed
and explained by model (AO, NAO , EA, EAJ, S,
EAWR )
15March, 1996
16May, 1996
17May, 1998
18Conclusions
- First attempt to develop an intellectual climate
model has been carried out - Its principal distinctive features are as
following - Self-learning ability
- Accumulation of all past observing information
with changing inter-parameter links - Adaptation of model feedbacks to changes in
observing samples and its trends