Web system for comparative analysis of regional climatic changes Gordov E.P. (1,2), Okladnikov I.G. (1) and Titov A.G(1,2) (1)Siberian Center for Environmental Research and Training, Tomsk, Russia, (2)(2) Institute of Monitoring of Climatic and Ecological Systems SB RAS, Tomsk, Russia
Plan • The Earth and Space Sciences Informatics • Problem statement • Approach adopted • Some results
EGU - ESSI The Earth and Space Sciences Informatics programme group of the European Geosciences Union was mainly conceived as a European forum for an Earth System Science multidisciplinary community, in the framework of the Spatial Information Community Strong liaison with AGU – ESSI. EGU-ESSI Position Paper Stefano Nativi, Mohan Ramamurthy, Bernd Ritschel EGU Scientific Committee As the technology of web services accessible by computer programs evolves, the challenge for those studying the Earth from an interdisciplinary perspective is to develop interoperable data models that can span the specific models employed in individual disciplines. Moreover, these interoperable models have to be integrated with the semi-structured and service oriented framework of the Web itself.
An Advanced Multidisciplinary Infrastructures for Earth System Science Community Earth scientists are engaged in integrating knowledge stemming from different disciplines about the constituents parts of the complex Earth system with the objective of understanding its properties as a whole. Such system analysis is a challenge for scientists as well as for information technology. In fact, the scope and complexity of Earth system investigations demand the formation of distributed, multidisciplinary collaborative teams. This requires the integration of different discipline information systems, characterized by: heterogeneous and distributed data and metadata models, different semantics and expertise, diverse protocols and interfaces.
Challenges for Earth & Space Sciences Informatics • Advanced informatics infrastructures must be conceived in order to support the formation and operation of an Earth System Science Community, based on multidisciplinary knowledge integration. These web systems will provide scientists, researchers and decision makers with a persistent set of independent services and information that scientists can integrate into a range of more complex analyses. Hence, there exists the need to shift from a “traditional” data centric approach to a more effective and advanced service-based solutions for Earth System Science information. • An holistic approach to mediate and harmonize the different models and interfaces characterizing the two communities (i.e. Information Society and Earth & Space Sciences) is becoming essential for a real interoperability.
Disparate Data Models: Different Ways of Thinking about Data • To the GIS (solid earth and societal impacts) community, the world is: features (e.g., roads, lakes, plots of land) – A collection of static features with geographic footprints on the Earth (surface). features are discrete geometric objects – The features discrete geometric objects with attributes which can be stored and manipulated conveniently in a database. database. • To the Fluid Earth Sciences (FES -- atmosphere and oceans) communities, the world is: parameters (e.g., pressure, temperature, wind speed) – A set of parameters which vary as continuous functions continuous functions in 3-dimensional space and time. parameters in space and time is governed – The behavior of the parameters equations. by a set of equations. – Data are simply discrete points in the mathematical function space.
Traditional GIS view Features Attributes as points, in DBMS lines, tables polygons
Слайд 7 SN1 In the realm of geolocated datasets, data stored in Geographic Information Systems (GIS) are highly structured and most often stored in an underlying relational database. While this may be a gross simplification, GIS datasets typically consist of “features” on the surface of the Earth that can be represented by points, lines and polygons. An example is a county plat which can show natural features such as streams and rivers, infrastructure like roads and bridges and buildings, and plots of land such as towns, lots, and so forth. The attributes of these features lend themselves to storage in the tables of a relational database. There can be a table for the roads, another for the towns, yet another for the rivers, etc. Each specific feature is a record in a table which provides a very useful way of keeping track of the characteristics of each instance of each feature. Visualization is conceptualized in terms of a set of “layers.” In the physical world, transparent mylar sheets are often used to overlay various sets of features on a given base map. The same idea is used for manipulating the visualization of the classes of features in GIS visualization systems stefano; 09.01.2004
Typical NetCDF Visualization
Problem statement • Tasks of climate state monitoring, research and forecast at regional and global levels • Archives of meteorological data containing results of field observations and modeling • SIRS - Northern Eurasia Earth Science Partnership Initiative (NEESPI) mega project – Investigation of regional environment dynamics – Information-computational infrastructure to support multidisciplinary environmental investigations is required • Earth and Space Sciences Informatics approach
Specific problems • Task 1. How to handle huge arrays of heterogeneous environmental data? • Task 2. How to provide access to the data for a scientific community? • Task 3. The problem of unification and comparison of the results obtained.
Approach adopted • To collect, structure, systemize meteorological datasets and put them onto powerful computational server • To unify data processing procedures implementing mathematical and statistical methods • To develop Information-computational web-system
GI OVANNI - GES-DI SC DAAC I nteractive Online Visualization and ANalysis I nfrastructure • Goddard Earth Sciences Data and Information Services Center • Remote sensing data • Spatial visualization http://daac.gsfc.nasa.gov/techlab/giovanni/
SDI – Spatial Data I nfrastructure • SIB-ESS-C - A spatial data infrastructure to facilitate Earth system science in Siberia • Objective: remote sensing product generation, data management, scientific data analysis based on Web Services technology • http://www.sibessc.uni-jena.de/ • Standards developed by: OGC, W3C, ISO • Friedrich-Schiller-University, Jena, Institute of Methodologies for Environmental Analysis (IMAA)
Web-system for processing and visualization of meteorological data • Web interface is based on the web-portal ATMOS engine (http://atmos.iao.ru/) • Technologies: DHTML, PHP, Java; GrADS, IDL • Mathematical and statistical processing of data arrays, visualization of results • NCEP/NCAR Reanalysis and NCEP/DOE Reanalysis AMIP II data • http://climate.risks.scert.ru/
Meteorological parameters
Mathematical and statistical operations • Maximum, minimum, average, variance and standard deviation values for parameter • Number of days with the parameter value within given range • Time smoothing of parameter values using moving averaging window • Correlation coefficient for an arbitrary pair of parameters • First/Last warm/cold period of the year • Climate Change Indices approved by WMO
Graphic user interface example • Characteristic of interest • Geographic domain • Altitude level • Time interval • Visualization parameters
Atmosphere temperature trend calculation Summer, NCEP/DOE AMIP II Summer, NCEP/NCAR dataset, dataset, 1979-1986/1994-2001 1950-1957/1994-2001
Result examples
Correlation coefficients, Spring, 2000
Determination of the first warm day of year Calculation of the first warm (mean daily t > 0°) day of the year, 1979-1989, 2 datasets
Dataset comparison system functionality • Comparison of key meteorological and climatic characteristics based on different datasets • Absolute difference: AD = |b -a| • Normalized difference: ND=|(b-a)/a|*100%
Dataset comparison graphic user interface
Atmosphere pressure normalized difference for April, 1999 - 2001
Climate Change I ndices • Developed by CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI) • http://cccma.seos.uvic.ca/ETCCDMI/indices.sht ml • 12 of 27 core indices currently implemented – Number of frost/summer/icing days – Number of tropical nights – Monthly maximum/minimum value of daily maximum/minimum temperature – Daily temperature range – Monthly maximum 1-day precipitation – Annual count of days when PRCP ≥ 20mm – Etc...
Monthly maximum value of daily minimum temperature
Future plans • Extending list of datasets used – ECMWF Reanalysis ERA-40, http://www.ecmwf.int/ – JRA-25 Reanalysis, http://jra.kishou.go.jp/ – Meteorological station data for the territory of Russia – Satellite data usage (MODIS) • Extending functional capabilities of the system – Implementation of 27 core Climate Change Indices – Trend calculation for complex characteristics
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