Web system for comparative analysis of regional climatic changes - - PowerPoint PPT Presentation

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

• f 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

• bjective 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:

– A collection of static features

features (e.g., roads, lakes, plots of land)

with geographic footprints on the Earth (surface). – The features

features are discrete geometric objects

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:

– A set of parameters

parameters (e.g., pressure, temperature, wind speed)

which vary as continuous functions continuous functions in 3-dimensional space and time. – The behavior of the parameters

parameters in space and time is governed

by a set of equations.

equations.

– Data are simply discrete points in the mathematical function space.

Traditional GIS view

Attributes in DBMS tables Features as points, lines, polygons

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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

• f 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
• bservations 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

• 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.

Specific problems

• To collect, structure, systemize

meteorological datasets and put them

• nto powerful computational server
• To unify data processing procedures

implementing mathematical and statistical methods

• To develop

Information-computational web-system

Approach adopted

GI OVANNI - GES-DI SC DAAC I nteractive Online Visualization and ANalysis I nfrastructure http://daac.gsfc.nasa.gov/techlab/giovanni/

• Goddard Earth

Sciences Data and Information Services Center

• Remote sensing

data

• Spatial

visualization

• 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)

SDI – Spatial Data I nfrastructure

• 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/

Web-system for processing and visualization of meteorological data

Meteorological parameters

• 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

Mathematical and statistical operations

Graphic user interface example

• Characteristic of

interest

• Geographic

domain

• Altitude level
• Time interval
• Visualization

parameters

Atmosphere temperature trend calculation

Summer, NCEP/NCAR dataset, 1950-1957/1994-2001 Summer, NCEP/DOE AMIP II dataset, 1979-1986/1994-2001

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

• 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 system functionality

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

• 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

Future plans

• Application in meteorological and climatology

investigations

• Providing access to data archives
• Simplifying handling of huge arrays of spatially

distributed meteorological data

• Reliability of the results obtained
• One of the key elements of the SIRS

information-computational infrastructure under development

Conclusion

• This work is partially supported by SB

RAS Integration Project 34, SB RAS Basic Program Project 4.5.2.2, and FP6 Enviro- RISKS project (INCO-CT-2004-013427)

Thank you for attention!

Acknowledgements