GLOBAL CLIMATE MONITOR
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1 FP7 INCO INCO- LAB project GLOBAL CLIMATE MONITOR Natalia Limones ([email protected]) José I. Alvarez ([email protected]) Juan M. Camarillo ([email protected]) María Fernanda Pita ([email protected]) CLIMATE RESEARCH GROUP, UNIVERSITY OF SEVILLE
2 SCHEME OF THE PRESENTATION 1. Background 2. Objectives 3. Climate data used 4. Data model and data flow. Technology and Information System 5. Climatic indicators designed 6. GLOBAL CLIMATE MONITOR 7. Further research. 8. Some final ideas
3 1. Background (The climate data sets) TRADITIONAL CLIMATE DATA SETS - Irregular coverage - Heterogeneus time span (very short) - Poorly quality controlled - No global coverage RECENT CLIMATE DATA SETS - Regular coverage - Long and homogeneous time span - Highly quality controlled - GLOBAL COVERAGE NOAA IPCC MET OFFICE GLOBAL PRECIPITATION CENTRE
4 1. Background (Data dissemination and the viewers) GEO- VISUALIZATION INFORMATION GETTING DATA Climate datasets are available in very complex data formats GETTING INDICATORS (NEW INFORMATION OR KNOWLEDGE)
5 1. Background (The viewers) 1st EXAMPLE: GREAT DATA, LITTLE VISUALIZATION CRU WEB:
6 1. Background (Data dissemination and the viewers) GEO- VISUALIZATION INFORMATION GETTING DATA Climate datasets are available in very complex data formats Some are Specialized in data extraction tools Climatie web mapping is normally on 30 years climatology No spatial reference integration No interactive access to data GETTING INDICATORS (NEW INFORMATION OR KNOWLEDGE)
7 1. Background (The viewers) 2nd EXAMPLE: GOOD EXTRACTION TOOLS BUT 30 Y. AVERAGE VALUES NCDC NOAA #app=cdo&cfg=isdsummaries&theme=isdsummaries
8 1. Background (The viewers) 3rd EXAMPLE: AVERAGE VALUES, GOOD EXTRACTION AND LOTS OF DATA BUT NO GOOD DISPLAY, NO PIXEL INFO, NO MAP BACKGROUND IPCC data.org/maps/
9 1. Background (The viewers) 4th EXAMPLE: SOME NICE INDICATORS (FUTURE TRENDS) BUT NO GOOD DISPLAY, NO PIXEL INFO, NO MAP BACKGROUND US GEOLOGICAL SURVEY global- climate- change- viewer/
10 1. Background (Data dissemination and the viewers) GEO- VISUALIZATION INFORMATION GETTING DATA Climate datasets are available in very complex data formats Some are Specialized in data extraction tools Climatic data web mapping is normally on 30 years climatology No spatial reference integration Poor interactive access to data on viewer GETTING INDICATORS (NEW INFORMATION OR KNOWLEDGE) Many indicators with no visualization or Good visualization with very few indicators
11 1. Background (The viewers) 5th EXAMPLE: LOTS OF USEFUL INDICATORS, BUT ONLY STATIC MAPS EURO4M FP7 European_Temperature
12 1. Background (The viewers) 6th EXAMPLE: THE «PERFECT» MONITOR BUT JUST FOR DROUGHT SPEI GLOBAL DROUGHT MONITOR
13 SCHEME OF THE PRESENTATION 1. Background 2. Objectives 3. Climate data used 4. Data model and data flow. Technology and Information System 5. Climatic indicators designed 6. GLOBAL CLIMATE MONITOR 7. Further research. 8. Some final ideas
14 2. Objectives To make complex scientific data accesible To build climate indicators To design an operational web mapping tool for climate data visualization and monitoring: GLOBAL CLIMATE MONITOR To GET KNOWLEDGE FROM DATA and to share it
15 SCHEME OF THE PRESENTATION 1. Background 2. Objectives 3. Climate data used 4. Data model and data flow. Technology and Information System 5. Climatic indicators designed 6. GLOBAL CLIMATE MONITOR 7. Further research. 8. Some final ideas
16 3. Climate data used CLIMATIC RESEARCH UNIT, UNIVERSITY OF EAST ANGLIA ( High- resolution gridded datasets (CRU TS3.21 )à Tested and recommended by IPCC Variables: pre, tmp, tmx, tmn, pet, etc. Data format: netcdf Space: 0.5, global coverage Time: , monthly time- series DATA à 5*10 8
17 SCHEME OF THE PRESENTATION 1. Background 2. Objectives 3. Climate data used 4. Data model and data flow. Technology and Information System 5. Climatic indicators designed 6. GLOBAL CLIMATE MONITOR 7. Further research. 8. Some final ideas
18 4. Data model and dataflow II 1 *.txt files CRU TS CQL FILTERING (TO GET THE INDICATORS) 1 Netcdf EXTRACTION 6 3 TABLES INDEXING WMS GENERATION 2 DATABASE INSERTION 2 GCM SPATIAL DATABASE 4 SQL SPATIAL VIEWS GCM VIEWER 5 MAP LAYERS DEFINITION
19 4. Data model and dataflow I
20 4. Technology and information system design PostGis MAP SERVER WEB APPLICATIONS SERVER GEO- VIEWER
21 SCHEME OF THE PRESENTATION 1. Background 2. Objectives 3. Climate data used 4. Data model and data flow. Technology and Information System 5. Climatic indicators designed 6. GLOBAL CLIMATE MONITOR 7. Further research. 8. Some final ideas
22 6. Climatic indicators design TIME SCALE TEMPERATURE PRECIPITATION EVAPOTRANSPIRATION MONTHLY ANNUAL Monthly mean temperatures Monthly maximum temperatures Monthly minimum temperatures Anomalies Mean temperature Minimun temperature Maximum temperature Trends Monthly total precipitation Anomalies (%) Total precipitation Seasonality index Monthly mean evapotranspiration Anomalies Mean evapotranspiration 30 YEARS CLIMAT. Mean temperature Minimun temperature Maximum temperature Total precipitation Evapotranspiration
23 SCHEME OF THE PRESENTATION 1. Background 2. Objectives 3. Climate data used 4. Data model and data flow. Technology and Information System 5. Climatic indicators designed 6. GLOBAL CLIMATE MONITOR 7. Further research. 8. Some final ideas
24 6. GLOBAL CLIMATE MONITOR
25 SCHEME OF THE PRESENTATION 1. Background 2. Objectives 3. Climate data used 4. Data model and data flow. Technology and Information System 5. Climatic indicators designed 6. GLOBAL CLIMATE MONITOR 7. Further research. 8. Some final ideas
26 7. Further research VARIABLES - New variables acquirement: - Future climate scenarios data - Near real time climate data from GHCN_CAMS / GPCC INDICATORS - New indicators calculation: - Trends - IESP drought index - Climate extremes by percentiles WEB MAPPING TOOLS Improvements on the viewer: - Point extraction tool - Point trend graphic tool - Double screen for data comparison - Animations
27 8. Some final ideas FEEDBACK, DISSEMINATION & COLLABORATION FEASIBILITY TO GENERATE OTHER SIMILAR VIEWERS WITH THIS TECHNOLOGY GEO- VISUALIZATION IS THE MOST IMPORTANT WAY TO MAKE SUCH LARGE DATABASES ACCESSIBLE TO EVERYBODY, NOT ONLY A SCIENTIFIC COMMUNITY HERITAGE
28 THANK YOU
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