GeoGIPSAR A SYSTEM INTENDED FOR LONG-TERM FORECASTING AND ANALYSIS - - PowerPoint PPT Presentation

Melentiev Energy Systems Institute, SB RAS

GeoGIPSAR – A SYSTEM INTENDED FOR LONG-TERM FORECASTING AND ANALYSIS OF NATURAL-CLIMATIC FACTORS IN ENERGETICS

Enviromis-2012

N.V. Abasov

Melentiev Energy Systems Institute, SB RAS 2

Hydro-energetic resources of Russia. The level of the hydroenergetic potential exploited

IEA-2004.

23% of the total economic hydroenergetical potential of Russia is exploited

6% 33% 2% 49% 34% 74% Russian Far East East Siberia West Siberia Urals North Caucasus Volga Region 68% Volgo-Vyatsky North-West 60% 23,4% North

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Hydro-energetic potential of the Anagara cascade HPPs (annual and monthly variability)

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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)

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Space-time decomposition of the hydropower potential model (Angara cascade of HPPs)

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Output data of the information-forecasting system GIPSAR

TECHNIQUES OF LONG-TERM FORECASTING

paramtern

paramter1

paramtern paramter1

finding a generalized forecast

Experimantal (wavelet, global analogs,

astronimic indioces

Basis one (approximation,

probabilistic, qualitative)

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Local extremes and Investigation of the attractors

Attractors

• f

(a) inflows (annual and 3rd quarter, resp.) for Lake Baikal (1,2) and Bratsk reservoir (3,4). Indicated are many- year series (b), envelopes of the maxima (d) and minima (с)

• f

inflows and the centers

• f

these attractors (e).

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Basic functions of the system GeoGIPSAR

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The technology for processing geoclimatic data

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∫

∞ ∞ −

      − ⋅ = dt s t t f s s W τ ψ τ

*

) ( 1 ) , (

2 1

1 ) , ( ) ( s dsd s s t s W C t f τ τ ψ τ

ψ ∫ ∫ ∞ ∞ − ∞ ∞ − −

      − =

вейвлет базисный t − ) ( ψ

) ( : . . 3 ) ( . 2 ) ( : ) ( ) ( . 1

2 2

= = ∞ < ∈

∫ ∫ ∫

∞ ∞ − ∞ ∞ − ∞ ∞ −

dt t t условие Доп dt t dt t R L t

m

ψ ψ ψ ψ

MHAT , 2 : 3 ; 2 , : 2 ; , : 1 , , , = = = = = = > > ∈ − − τ τ τ τ τ τ s s s s R s сдвиг временной масштаб s

2 / 2 2 / 2 2

2 1

) 1 ( ) (

t t

e t e dt d t

− −

− = = ψ ] [ ) (

4 / /

2 2 2 2

α α

ψ

k t ik t

e e e t

− −

− =      < ≤ − < ≤ = случаях остальных в t для t для t , 1 2 / 1 , 1 2 / 1 , 1 ) ( ψ ( ) ∫

= τ τ d s W s Ew ) , (

2

Wavelet analysis Wavelet analysis

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Wavelet analysis of inflows for the Lake Baikal Wavelet analysis of inflows for the Lake Baikal and and Bratsk Bratsk reservoir + (spectral characteristics) reservoir + (spectral characteristics)

Baikal Baikal Bratsk Bratsk

high low

Scale-1 Scale-2

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Classification and Clastering of spatial cells of intergral- difference curves representing summer precipitation, temperature and pressure data

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precipitations temperature hgt500 uwnd

Summer (6-8)

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Analogs with respect to geoclimatic indices

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Correlations between the inflow into Lake Baikal (3rd quarter (4-5 months)) and the surface temperatures (advance time being 1 year)

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Correlations between the inflow into Lake Baikal (3rd quarter [4-5 months]) and the surface temperatures (advance time being 1 year)

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11-13.05.2012 HGT-500 (anomaly) UV-850

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11-13.05.2012 HGT- 850-500 (trajectories)

500 850

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The process of constructing HTML-files in course of analysis of geoclimatic data, a special language of retrievals being employed

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Modeling water regimes of the Irkutsk HPP

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Modeling of the volume and the surface of the Boguchany water reservoir

Усть-Илимская ГЭС

V=63км3 Vполезн=2.3км3 HУМО=207м HНПУ=208м HФПУ=209.5м

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Various terms of filling in the Boguchany HPP

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Modeling of potential HPPs on the river Irkut.

(Mondy Reservoir)

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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.