Melentiev Energy Systems Institute, SB RAS GeoGIPSAR – A SYSTEM INTENDED FOR LONG-TERM FORECASTING AND ANALYSIS OF NATURAL-CLIMATIC FACTORS IN ENERGETICS N.V. Abasov Enviromis-2012
Hydro-energetic resources of Russia. The level of the hydroenergetic potential exploited 23,4% North 6% 60% Russian Far East North-West 68% 33% 49% Volgo-Vyatsky 2% East Siberia Urals West Siberia 74% Volga Region 34% North Caucasus 23% of the total economic hydroenergetical potential of Russia is exploited IEA-2004 . Melentiev Energy Systems Institute, SB RAS 2
Hydro-energetic potential of the Anagara cascade HPPs (annual and monthly variability) Melentiev Energy Systems Institute, SB RAS 3
Stages in development of the technology of long-term forecasting in Melentiev Energy Systems Institute, SB of RAS (generations) � Generation one (1960–1994) Technique of Forecastiong ( I.P.Druzhynin , A .P. Reznikov, T.V.Berejnykh ) space-time regularities + approximation (neural network) techniques. ( Programming technology → Fortran) . � Generation two (1995–2004) – an information-forecasting system GIPSAR → complex automation of the process of forecasting: data preparation ; choosing the forecasting technique; verifications; finding a generalized forecast; analysis ( A.P. Reznikov , T.V.Berejnykh ). Programming technology → a universal programming environment ZIRUS( N.Abasov ) . Generation three (2005-2012) → a sharp increase of the volume of the � information component employed in forecasting, which is gained at the expense of: satellite monitoring ; global climate change ; application of geoinformation technologies (GeoGIPSAR) ( T.V. Berejnykh , E. Osipchuk, O. Marchenko ) Melentiev Energy Systems Institute, SB RAS 4
Space-time decomposition of the hydropower potential model (Angara cascade of HPPs) Melentiev Energy Systems Institute, SB RAS 5
Output data of the information-forecasting system GIPSAR TECHNIQUES OF LONG-TERM FORECASTING Basis one (approximation, Experimantal (wavelet, global analogs, probabilistic, qualitative) astronimic indioces paramter 1 paramter n paramter n paramte r 1 finding a generalized forecast Melentiev Energy Systems Institute, SB RAS 6
Local extremes and Investigation of the attractors Attractors of (a) inflows (annual and 3 rd quarter, resp.) for Lake Baikal (1,2) and Bratsk reservoir (3,4). Indicated are many- year series (b), envelopes of the maxima (d) and ( с ) minima of inflows and the centers of these attractors (e). Melentiev Energy Systems Institute, SB RAS 7
Basic functions of the system GeoGIPSAR Melentiev Energy Systems Institute, SB RAS 8
The technology for processing geoclimatic data Melentiev Energy Systems Institute, SB RAS 9
Wavelet analysis Wavelet analysis ∞ 1 − τ ∞ ∞ t − τ 1 τ t dsd ∫ ψ ∫ ∫ ( , ) ( ) * W s τ = f t ⋅ ψ dt ( ) = − 1 ( , τ ) ψ f t C W s s − масштаб s s s s 2 s − ∞ 2 − ∞ − ∞ d τ − временной сдвиг 1 2 ( ) − / 2 ( 1 2 ) − / 2 ψ t = e t = − t e t s , τ ∈ R , τ > 0 , s > 0 2 dt 1 : 0 , 0 ; 2 : 0 , 2 ; s = τ = s = τ = 3 : = 2 , τ = 0 s MHAT ψ ( t − ) базисный вейвлет ∞ ∫ 2 1 . ( ) 2 ( ) : ( ) ψ t ∈ L R ψ t dt < ∞ − ∞ ∞ ∫ 2 . ψ ( ) = 0 t dt − ∞ ∞ ∫ 3 . . : ψ ( t ) t m dt = 0 Доп условие 2 2 2 2 / 4 − t / α ik t − k α − ∞ ψ ( ) = [ − ] t e e e 0 0 ( ) ∫ 2 ( , ) E w s = W s τ d τ 1 , 0 1 / 2 ≤ t < для ψ ( ) = − 1 , 1 / 2 ≤ < 1 t для t 0 , в остальных случаях Melentiev Energy Systems Institute, SB RAS 10
Wavelet analysis of inflows for the Lake Baikal Wavelet analysis of inflows for the Lake Baikal and Bratsk Bratsk reservoir + (spectral characteristics) reservoir + (spectral characteristics) and Scale-1 Baikal Bratsk Scale-2 high Baikal Bratsk low Melentiev Energy Systems Institute, SB RAS 11
Classification and Clastering of spatial cells of intergral- difference curves representing summer precipitation, temperature and pressure data Melentiev Energy Systems Institute, SB RAS 12
Melentiev Energy Systems Institute, SB RAS 13
hgt500 precipitations Summer (6-8) uwnd temperature Melentiev Energy Systems Institute, SB RAS 14
Analogs with respect to geoclimatic indices Melentiev Energy Systems Institute, SB RAS 15
Correlations between the inflow into Lake Baikal (3 rd quarter (4-5 months)) and the surface temperatures (advance time being 1 year) Melentiev Energy Systems Institute, SB RAS 16
Correlations between the inflow into Lake Baikal (3 rd quarter [4-5 months]) and the surface temperatures (advance time being 1 year) Melentiev Energy Systems Institute, SB RAS 17
11-13.05.2012 HGT-500 (anomaly) UV-850 Melentiev Energy Systems Institute, SB RAS 18
11-13.05.2012 HGT- 850-500 (trajectories) 850 500 Melentiev Energy Systems Institute, SB RAS 19
The process of constructing HTML-files in course of analysis of geoclimatic data, a special language of retrievals being employed Melentiev Energy Systems Institute, SB RAS 20
Modeling water regimes of the Irkutsk HPP Melentiev Energy Systems Institute, SB RAS 21
Modeling of the volume and the surface of the Boguchany water reservoir Усть - Илимская ГЭС V=63 км 3 V полезн =2.3 км 3 H УМО =207 м H НПУ =208 м H ФПУ =209.5 м Melentiev Energy Systems Institute, SB RAS 22
Various terms of filling in the Boguchany HPP Melentiev Energy Systems Institute, SB RAS 23
Modeling of potential HPPs on the river Irkut. (Mondy Reservoir) Melentiev Energy Systems Institute, SB RAS 24
Conclusion 1. An easy-to-learn portable technology intended for processing geoclimatic data has been developed. 2 Gb – monthly data (NCEP reanalysis, GPCC-precipitations) 5Mb – basic system software (extended version of Lua with ZIRUS, Gnuplot, SciTE -editor) 50 Mb – gis data 2. 50 Gb daily data (NCEP reanalysis) 3. This technology is extensively employed in energetics for the purpose of emproving reliability of forecasting of hydroenergetic potential under the conditions of global and regional climate changes. This tecnology is simple and portable for different kind of users. Melentiev Energy Systems Institute, SB RAS 25
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