Spatial interpolation for climate data : the use of GIS in climatology and meterology /

Detalles Bibliográficos
Otros Autores: Dobesch, Hartwig (ed.), Dumolard, Pierre (coed.), Dyras, Izabela (coed.)
Formato: Libro
Lenguaje:English
Publicado: London ; Newport Beach, CA : ISTE, 2007.
Colección:Geographical information systems series
Materias:
Climatología > Procesamiento de datos.
Meteorología > Procesamiento de datos.
Datos geoespaciales > Modelos matemáticos.
Sistemas de información geográfica.
Infraestructura de datos espaciales.
Acceso en línea:Tabla de contenido
Información biográfica
Tabla de Contenidos:
  • Table of contents
  • Preface
  • Part 1. GIS to manage and distribute climate data
  • Chapter 1. GIS, climatology and meteorology
  • 1.1. GIS technology and spatial data (working group 1)
  • 1.2. Data and metadata
  • 1.3. Interoperability
  • 1.4. Conclusions
  • 1.5. Bibliography
  • Chapter 2. SIGMA: A Web-based GIS for environmental applications
  • 2.1. Introduction
  • 2.2. CPTEC-INPE
  • 2.3. SIGMA
  • 2.4. Impacts of weather conditions on the economy
  • 2.5. Severe Weather Observation System (SOS)
  • 2.6. SOS interface
  • 2.7. Conclusions
  • 2.8. Acknowledgements
  • 2.9. Bibliography
  • Chapter 3. Web mapping: different solutions using GIS
  • 3.1. Introduction
  • 3.2. Examples of Web mapping based on the usage of GIS technology in offline mode
  • 3.3. Examples of Web mapping using GIS tools in online mode
  • 3.4. Conclusion
  • 3.5. Bibliography
  • Chapter 4. Comparison of geostatistical and meteorological interpolation methods (what is what?)
  • 4.1. Introduction
  • 4.2. Mathematical statistical model of spatial interpolation
  • 4.3. Geostatistical interpolation methods
  • 4.4. Meteorological interpolation
  • 4.5. Software and connection of topics
  • 4.6. Example of the MISH application
  • 4.7. Bibliography
  • Chapter 5. Uncertainty from spatial sampling: a case study in the French alps
  • 5.1. Introduction
  • 5.2. The sample as a whole
  • 5.3. Looking in detail where the sample is not representative
  • 5.4. Summarizing the sampling uncertainty
  • 5.5. Conclusion
  • 5.6. Bibliography
  • Part 2. Spatial interpolation of climate data
  • Chapter 6. The developments in spatialization of meteorological and climatological elements
  • 6.1. Introduction
  • 6.2. Spatialization
  • 6.3. Why spatialization?
  • 6.4. The role of GIS in developing spatialization within climatology
  • 6.5. Methodology
  • 6.6. Data representativity, quality and reliability
  • 6.7. Applications
  • 6.8. Climate indices
  • 6.9. Gridded datasets
  • 6.10. Recommendations and future outlook
  • 6.11. Bibliography
  • Chapter 7. The spatial analysis of the selected meteorological fields in the example of Poland
  • 7.1. Introduction
  • 7.2. Spatialization problems using standard observation data
  • 7.3. Spatialization using remote sensing data
  • 7.4. Conclusions
  • 7.5. Acknowledgements
  • 7.6. Bibliography
  • Chapter 8. Optimizing the interpolation of temperatures by GIS: a space analysis approach
  • 8.1. Limits of the interpolation in a heterogenous space
  • 8.2. Optimizing the spatial distribution of the stations
  • 8.3. Underlying space assumptions
  • 8.4. Theoretical structure of our model
  • 8.5. The process of linear modeling for the selected factors
  • 8.6. Determination of the optimal positioning of P
  • 8.7. An example of implementation
  • 8.8. Consequences and spatial/structural understanding
  • 8.9. Determination of authorized spaces
  • 8.10. Taking uncertainty into account: a choice/given couple
  • 8.11. The standardization process
  • 8.12. Results for the addition of stations...

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