Keywords
environmental datasets, regional scale, data-transformation modelling, semantic array programming, local-average invariant downscaling
Start Date
1-7-2012 12:00 AM
Abstract
Environmental datasets grow in size and specialization while models designed for local scale are often unsuitable at regional/continental scale. At regional scale, data are usually available as georeferenced collections of spatially distributed despite semantically atomic information. Complex data intrinsically impose modellers to manipulate nontrivial information structures. For example, multi-dimensional arrays of time series may be composed by slices of raster spatial matrices for each time step, whilst heterogeneous collections of uneven arrays are common when dealing with data analogous to precipitation events, and these structures may ask for integration at several spatial scales, projections and temporal extents. Interestingly, it might be far more difficult to practically implement such a complexity rather than conceptually describe it: a subset of modelling generalizations may deal more with abstraction rather than with the explosion of lines of code. Many environmental modelling algorithms are composed by chains of data-transformations or trees of domain specific sub-algorithms. Concisely expressing them without the need for paying attention on the enormous set of spatio-temporal details, is a highly recommendable practice in both mathematical formulation and implementation. The use of semantic array programming paradigm is here exemplified as a powerful conceptual and practical (with the free software library Mastrave) tool for easing scalability and semantic integration in environmental modelling. Array programming, AP, is widely used for its computational effectiveness but often underexploited in reducing the gap between mathematical notation and algorithm implementations, i.e. by promoting arrays (vectors, matrices, tensors) as atomic quantities with extremely compact manipulating operators. Coherent array-based mathematical description of models can simplify complex algorithm prototyping while moving mathematical reasoning directly into the source code – because of its substantial size reduction – where the mathematical description is actually expressed in a completely formalized and reproducible way. The proposed paradigm suggests to complement the characteristic AP weak typing with semantics, both by composing generalized modular submodels and via array oriented – thus concise – constraints. The Mastrave library use is exemplified with a regional scale benchmark application to local-average invariant (LAI) downscaling of climate raster data. Unnecessary errors frequently introduced by non-LAI upsampling are shown to be easily detected and removed when the scientific modelling practice is terse enough to let mathematical reasoning and model coding merge together.
Semantic Array Programming for Environmental Modelling: Application of the Mastrave Library
Environmental datasets grow in size and specialization while models designed for local scale are often unsuitable at regional/continental scale. At regional scale, data are usually available as georeferenced collections of spatially distributed despite semantically atomic information. Complex data intrinsically impose modellers to manipulate nontrivial information structures. For example, multi-dimensional arrays of time series may be composed by slices of raster spatial matrices for each time step, whilst heterogeneous collections of uneven arrays are common when dealing with data analogous to precipitation events, and these structures may ask for integration at several spatial scales, projections and temporal extents. Interestingly, it might be far more difficult to practically implement such a complexity rather than conceptually describe it: a subset of modelling generalizations may deal more with abstraction rather than with the explosion of lines of code. Many environmental modelling algorithms are composed by chains of data-transformations or trees of domain specific sub-algorithms. Concisely expressing them without the need for paying attention on the enormous set of spatio-temporal details, is a highly recommendable practice in both mathematical formulation and implementation. The use of semantic array programming paradigm is here exemplified as a powerful conceptual and practical (with the free software library Mastrave) tool for easing scalability and semantic integration in environmental modelling. Array programming, AP, is widely used for its computational effectiveness but often underexploited in reducing the gap between mathematical notation and algorithm implementations, i.e. by promoting arrays (vectors, matrices, tensors) as atomic quantities with extremely compact manipulating operators. Coherent array-based mathematical description of models can simplify complex algorithm prototyping while moving mathematical reasoning directly into the source code – because of its substantial size reduction – where the mathematical description is actually expressed in a completely formalized and reproducible way. The proposed paradigm suggests to complement the characteristic AP weak typing with semantics, both by composing generalized modular submodels and via array oriented – thus concise – constraints. The Mastrave library use is exemplified with a regional scale benchmark application to local-average invariant (LAI) downscaling of climate raster data. Unnecessary errors frequently introduced by non-LAI upsampling are shown to be easily detected and removed when the scientific modelling practice is terse enough to let mathematical reasoning and model coding merge together.
