The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Physical modeling and analysis of rain and clouds by anisotropic scaling multiplicative processes
Abstract
We argue that the basic properties of rain and cloud fields (particularly their scaling and intermittency) are best understood in terms of coupled (anisotropic and scaling) cascade processes. We show how such cascades provide a framework not only for theoretically and empirically investigating these fields, but also for constructing physically based stochastic models. This physical basis is provided by cascade scaling and intermittency, which is of broadly the same sort as that specified by the dynamical (nonlinear, partial differential) equations. Theoretically, we clarify the links between the divergence of high‐order statistical moments, the multiple scaling and dimensions of the fields, and the multiplicative and anisotropic nature of the cascade processes themselves. We show how such fields can be modeled by fractional integration of the product of appropriate powers of conserved but highly intermittent fluxes. We also empirically test these ideas by exploiting high‐resolution radar rain reflectivities. The divergence of moments is established by direct use of probability distributions, whereas the multiple scaling and dimensions required the development of new empirical techniques. The first of these estimates the “trace moments” of rain reflectivities, which are used to determine a moment‐dependent exponent governing the variation of the various statistical moments with scale. This exponent function in turn is used to estimate the dimension function of the moments. A second technique called “functional box counting,” is a generalization of a method first developed for investigating strange sets and permits the direct evaluation of another dimension function, this time associated with the increasingly intense regions. We further show how the different intensities are related to singularities of different orders in the field. This technique provides the basis for another new technique, called “elliptical dimensional sampling,” which permits the elliptical dimension rain (describing its stratification) to be directly estimated: it yields d el = 2.22 ± 0.07, which is less than that of an isotropic rain field (d el = 3), but significantly greater than that of a completely flat (stratified) two‐dimensional field (d el = 2).
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Fletcher, T. D., Andrieu, H., & Hamel, P. (2013) · Advances in Water Resources · doi:10.1016/j.advwatres.2012.09.001 · Cited by 1,015
Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)
Pulkkinen, S., Nerini, D., Pérez Hortal, A. A., Velasco-Forero, C., Seed, A., Germann, U., & Foresti, L. (2019) · Geoscientific Model Development · doi:10.5194/gmd-12-4185-2019 · Cited by 304
Davis, A. B., & Marshak, A. (2010) · Reports on Progress in Physics · doi:10.1088/0034-4885/73/2/026801 · Cited by 89
Metadata
- Authors
- Schertzer, D., & Lovejoy, S.
- Venue
- Journal of Geophysical Research: Atmospheres
- Publisher
- American Geophysical Union (AGU)
- Published
- 1987-08-20
- Volume
- 92
- Issue
- D8
- Pages
- 9693-9714
- ISSN
- 0148-0227 (ISSN-L)
- OpenAlex
- W2002596522
- Cited by
- 1,121
- References
- 20
- FWCI
- 6.91
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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