Virtual Research Environment for Northern Eurasia Future Initiative - - PowerPoint PPT Presentation

Virtual Research Environment for Northern Eurasia Future Initiative

• E. Gordov, A. Titov, I. Okladnikov and A. Fazliev

Institute of Monitoring of Climatic and Ecological Systems V.E. Zuev Institute of Atmospheric Optics Russian Academy of Science, Siberian Branch, Tomsk, Russia

The work is supported by the Russian Science Foundation grant No 16-19-10257

n Global climate changes during last 50 years set conditions for rapid development of environmental monitoring and modeling technologies

n Frequencies of climate extremes such as droughts, excessive moistening periods,heat waves, show positive trends n It is necessary to analyze impacts of such events on the environment, to predict them and minimize their consequences

n Methods of processing of environmental geospatial datasets

n Modern statistical methods of extracting extreme events from meteorological time series n Assessment of spatio-temporal dynamics of extreme climate events

n Regional consequences of global warming are strongly manifested in Northern Eurasia and Siberia

n Responses of boreal forests and Siberian wetlands to climate change effects and the feedback influencing climate dynamics are poorly understood n Consequences: shift of permafrost borders, what else??? Introduction

n Environmental geospatial datasets as results of global and regional climate change research projects

n Meteorological observations n Modeling and reanalysis results n Remote sensing data

n Inherent heterogeneity of environmental datasets

n Different sets of parameters, file formats n Syntactic and semantic differences n Complexity of data models used

n Increasing volume (terabytes and petabytes)

n Geographically distributed archives

n Task of development of environmental data access, processing and visualization services and applications to provide researchers with relevant climatic characteristics

and tools for their in-depth statistical analysis

n Based on SDI approach n Combined usage of Web and GIS technologies n Open source software Introduction

Development of Distributed Research Center (including Web GIS as a core element) for analysis of regional climatic and environmental changes

INSPIRE requirements to data visualization

n Data overview n Image navigation n Scrolling n Scaling n Graphical overlay n Displaying map legends and corresponding metadata n Etc.

Example: Udig (User-friendly Desktop Internet GIS)

Geospatial data visualization system n Various computational functions n Meteorological and hydrological datasets n Web GIS: MapServer based n Disregarding SDI and OGC standards q Non-standard WMS implementation

RIMS (Regional Integrated Mapping and Analysis System), http://RIMS.unh.edu/

2 4 3 5 8 7 1 6 Interactive Map

Architecture of Distributed Research Center

n Tier of data and calculations providing processing, cartographical and visualization services

n Modular computational backend as a standalone software implemented in GNU Data Language (GDL) and Python, providing data access, statistical processing and visualization of datasets (netCDF, Shapefile, PostGIS) n OGC WMS, WFS, WCS, WPS based on Geoserver

n Tier of middleware representing geoportal including metadata database and server-side web applications

n Metadata database describing available geospatial datasets n Metadata catalog based on GeoNetwork to describe using ISO 19115 geoinformation resources (used in climate researches n Web portal implementing: web applications as reusable PHP modules; interconnections with OGC web services, target computing systems and metadata database n Central Geoserver repository containing basic cartographical layers

n Tier of client-side web-applications providing GIS functionality (Web-GIS client, etc.) n As a result standalone node is provided with:

n High-performance computing system with a set of data storage systems n Geoportal & computational backend

Datasets available

Dataset Source organization Time coverage Spatial resolution APHRODITE Reanalysis RIHN-MRI/JMA 1951 - 2007 0.25°×0.25°, precipitations only ERA-40 Reanalysis ECMWF 1957 – 2004 2.5°×2.5°, 23 vertical levels ERA Interim Reanalysis ECMWF 1979 – 2016 0.75°×0.75°, surface GPCC Reanalysis GPCC 1901 - 2009 0.5°×0.5°, precipitations only JRA-25 Reanalysis JMA/CRIEPI 1979 – 2009 2.5°×2.5°, 23 vertical levels JRA-55 Reanalysis JMA/CRIEPI 1958 – 2013 1.25°×1.25°, 27 vertical levels MERRA Reanalysis ECMWF 1979 - 2014 0.67°×0.5°, 42 vertical levels NCEP/DOE AMIP II Reanalysis NCEP/DOE 1979 – 2003 2.5°×2.5°, 17 vertical levels 20th Century Global Reanalysis Version II NOAA/OAR/ESRL PSD 1869 – 2011 2.0°×2.0°; 24 vertical levels NCEP Climate Forecast System Reanalysis NCEP 1979-2010 0.5°×0.5°, 37 vertical levels PlaSim dataset IMCES SB RAS 2000 - 2100 2.5°×2.5°, 10 vertical levels Meteostations RIHMI-WDC 1910 – 2016 600 stations for Russia

Data storage scheme

Datasets are stored

• n

dedicated storage systems as collections of netCDF files. Data files are distributed in a hierarchy of specially named directories:

/<data root directory>/ <data collection name>/ <spatial domain resolution>/ <time domain resolution>/ <files and directories with data>

Computing backend modules

# Computational procedures 1 Mean value 2 Standard deviation 3 Maximum/minimum value 4 Daily temperature range 5 Number of frost days 6 Number of summer days 7 Number of icing days 8 Number of tropical nights 9 Growing season length 10 Growing degree days 11 Simple precipitation intensity index 12 Monthly maximum 1-day precipitation 13 Monthly maximum consecutive 5-day precipitation 14 Maximum length of dry spell 15 Maximum length of wet spell 16 Hydrothermal Coefficient 17 Linear trend 18 Coefficient of correlation … …

Demonstration example

18 – 22 July, 2017 FOSS4G Europe 2017, Paris | Marne-la-Vallée, France

n The software presented is currently actively used in the education process at Tomsk State University n Several tutorials, lectures and labs are available via integrated Moodle environment for students of Meteorology and Climatology department n Basic statistical approaches to meteorological data analysis n Global climate change and their consequences

Education

18 – 22 July, 2017 FOSS4G Europe 2017, Paris | Marne-la-Vallée, France

Illustrations: extemes

Baseline period 1979 – 2008, norm for considered characteristics 10 and 90 percentile of near surface temperature for Cold days/nights and Warm days/nights duration and frequency Trends for 2009-2012

Trend of duration of cold days Trend of duration of warm days

Trend of cold night frequency Trend of warm night frequency

n Usage of the metadata database improved system functional capabilities in terms of adding new datasets and statistical procedures as well as providing computational resources as services. n Frontend developed comply with general GIS requirements while backend technical realization allows unified geospatial dataset processing regardless of client application type.

n Web GIS presented has shown its effectiveness in the process of solving real climate change research problems and disseminating investigation results in cartographical form.

n It is developed for decision makers and specialists working in affiliated sciences and provides accurate environmental characteristics required for studies of economic and social consequences of global climate change at the regional level.

Conclusion

n Development of the following Web-GIS applications:

n Verification of statistical hypotheses on the spatio-temporal characteristics of climatic variables n Verification of simulation data using real meteorological

• bservations data

n Publishing computational backend functional capabilities as OGC WPS n Queue manager development for the computational backend n Making sources available on GitHub n Implementation of algorithms as backend software modules for: n Identification and analysis of climate extremes n Analysis of essential climate variables using multivariate statistics of extremes and the theory of spatio-temporal dynamical systems.

Future plans

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This work is supported by the Russian Science Foundation grant #16-19-10257

Thank you for your attention!