1. GEO Cold Regions Initiative Side Meeting
Russian State Hydrometeorological University (RSHU)
GEO Cold Regions Initiative Side Meeting
Statistical Approach in the Seasonal Weather Forecasts for the Northern Eurasia and Adjacent Areas
Done by: Master’s student Mikhail Latonin
and Dr. in Math&Phys. Sci.,
professor Oleg Pokrovsky
Saint-Petersburg,
2016

2. The Current State of Problem
We all have to underline that the accuracy of these forecasts is still not at the very high level. For example , in the recent paper entitled “Advancing Polar Prediction Capabilities on Daily to Seasonal Time Scales” a team of scientists notes that existing polar prediction systems do not yet meet users’ needs.
The article is written by Thomas Jung, Neil D. Gordon, Peter Bauer, David H. Bromwich, Matthieu Chevallier, Jonathan J. Day, Jackie Dawson, Francisco Doblas-Reyes, Christopher Fairall, Helge F. Goessling, Marika Holland, Jun Inoue, Trond Iversen, Stefanie Klebe, Peter Lemke, Martin Losch, Alexander Makshtas, Brian Mills, Pertti Nurmi, Donald Perovich, Philip Reid, Ian A. Renfrew, Gregory Smith, Gunilla Svensson, Mikhail Tolstykh, and Qinghua Yang
Septemper 2016 American Meteorological Society

3. Our Approach Original Idea New Knowledge Applications
Improvement of the Existing Methods
A New Forecasting Technique

4. Statement of Problem
The main goal of our research is to assess the benefits from climate indices in seasonal weather forecasts. And as a case study we consider polar air outbreaks.

5. Climate Variables that we have been Studying
Sea Level Pressure (SLP)
Surface Air Temperature (SAT)
Sea Ice Parameters
Atmospheric Circulation Patterns

6. The Region of Investigation

9. Stations for the Calculation of Atlantic Arctic Oscillation Index

13. Conclusions
This research provides a very good starting point for the development of an alternative approach in the seasonal weather forecasts. Now, actually these forecasts are made only by means of ensemble hydrodynamic models, which can’t essentially resolve the problem with the butterfly effect.  
This work has shown that statistical analysis in combination with physical reasoning can reveal many important linkages. So, if we apply very advanced statistical methods of analysis and physical modeling based on real data, it is possible to achieve the desired accuracy of seasonal weather forecasts.

14. Fuzzy-neural model for prediction of monthly SAT fields in 3-6 months
INPUT
FUZZY
LAYER
HIDDEN LAYER
OUTPUT
FUZZY
LAYER
INPUT PREDICTOR PARAMETERS
PREDICTED PARAMETER
ATMOSPHERIC
REGIME AND/OR SEASONAL CYCLE CLASSIFICATION
ATMOSPHERIC
REGIME AND /OR SEASONAL CYCLE DECLASSIFICATION
NONLINEAR TRANSFORMATION OF HIDDEN LAYER VARIABLES

15. Circulation regime (U850-V850) and mean rain rate for late winter and early spring

16. Circulation regime (U850-V850) and mean rain rate for late spring and summer

17. March, 1996

18. May, 1996

19. May, 1998

20. Conclusions
First successful 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