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Analyzing Global Meteorological Networks Using Satellite Data

Explore interconnections of evaporation and precipitation networks through advanced statistical methods. Investigate climate patterns and physical properties for insights in weather systems. Analyze datasets from HOAPS and GPCC for 1992-2005. Utilize complex network models for network construction. Identify teleconnection patterns like NAO+ and NAO-. Evaluate climate model (CMIP5 ESM-MPI) using HOAPS dataset for validation. Conclusions include network structure similarities and statistical analyses.

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Analyzing Global Meteorological Networks Using Satellite Data

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  1. EGU: 25Apr2012, Wed Structural Interrelationships of the Evaporation-Precipitation Satellite-based Fields: Application of the Complex NetworksBy Irina Petrova 1,2*Alexander Löw 1, Christian Klepp 1,2 1 Max-Planck Institute for Meteorology, MPI, www.mpimet.mpg.de 2 Meteorological Institute, KlimaCampus, University of Hamburg, Hamburg, Germany * Irina.Petrova@zmaw.de

  2. Goals and Objectives: • Analyze topology of the single/ coupled precipitation • and evaporation networks • 2.Investigate the prominent global teleconnection patterns in the constructed networks • 3. Identify physical properties within the Climate Networks fields 2 d(2,3) 3 1 d(1,3)

  3. Datasets and Experimental Setup: Datasets used:: HOAPS: Hamburg Ocean Atmosphere Parameters and fluxes from Satellite data [http//:www.hoaps.org] GPCC: The Global Precipitation Climatology Center [http//:www.gpcc.dwd.de] HOAPS (satellite, ocean) 1987-2005 HOAPS-3 (ocean) GPCC (gauge, land) Evaporation • T63 (1.8) • Monthly • 1992-2005 • 7968 vertices Precipitation • T63 (1.8) • Monthly • 1992-2005 • 13834 vertices 1

  4. Datasets and Experimental Setup: Datasets used:: HOAPS: Hamburg Ocean Atmosphere Parameters and fluxes from Satellite data [http//:www.hoaps.org] GPCC: The Global Precipitation Climatology Center [http//:www.gpcc.dwd.de] HOAPS (satellite, ocean) 1987-2005 HOAPS-3 (ocean) GPCC (gauge, land) Evaporation • T63 (1.8) • Monthly • 1992-2005 • 7968 vertices Precipitation • T63 (1.8) • Monthly • 1992-2005 • 13834 vertices 1

  5. CCN Construction: Model used:: PYCLIMATENETWORK:[J.F Donges et al. 2009] Single/ Coupled Evap-Precip Networks: • Similarity Criteria: • - Pearson correlation (zero lag) • - Spearman rank (zero lag) • Threshold: 99,9% significance level (t-test)

  6. Teleconnection Patterns: ENSO Degree Centrality field of the Precipitation Network Source - point Correlation values of the ICTZ location neighbours within the Precipitation Network

  7. Teleconnection Patterns: NAO+ and NAO- Degree centrality of the precipitation network [Dec-Mar]Thrsh=0.43 Correlation of monthly mean Dec-Mar precipitation with the station-based NAO index [Andersson et al. 2010]

  8. Teleconnection Patterns: NAO+ and NAO- Positive Correlations within the precipitation network [Dec-Mar] of the neighbours at two locations: Bergen (Norway) and Gibraltar Correlation of monthly mean Dec-Mar precipitation with the station-based NAO index [Andersson et al. 2010]

  9. Coupled EVAP-PRECIP Fields: Evaporation Precipitation 1 Cross Degree Centralities of coupled Precipitation-Evaporation Networks (thresh. = 0,25)

  10. Downstream Source Feature: Precipitation field: Receiver - point 1 Correlation Values of the Precipitation Neighbours Within the Evaporation Field and Cross-Degree Centrality (background filed) (Thrsh=0,25)

  11. Outlook: Conclusions and Expectations • 1. Evap-Precip Network structure resembles major teleconnection patterns • 2. High DC coupled evap-precip fields reveal spatial source-receptor feature • 3. Statistical analysis operation V.S. CCN physical features • (Surrogate data, Transfer Entropy) • 4. Comparison/ validation of the CMIP5 ESM-MPI using HOAPS dataset 2 d(2,3) 3 1 d(1,3)

  12. Acknowledgements: The author is grateful for the fruitful collaboration, information and PYCLIMATENETWORK software support to the Potsdam Institute for Climate Research(PIK), and especially to Jonathan Donges and Norbert Marwan The author is also thankful to the HOAPS group at MPI-M, UniHH and DWD for the provided HOAPS - GPCC datasets.

  13. THANK YOU Irina.Petrova@zmaw.de

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