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| Titel |
A Novel Bayesian algorithm for Microwave Retrieval of Precipitation from Space: Applications in Snow and Coastal Hydrology |
| VerfasserIn |
Efi Foufoula, Ardeshir M. Ebtehaj, Rafael L. Bras |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250107867
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| Publikation (Nr.) |
 EGU/EGU2015-7585.pdf |
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| Zusammenfassung |
| Resolving accurately the space-time structure of precipitation over remote areas of the world
where in-situ observations are not available is one of the biggest challenges in hydrology in
view of the pressure to understand and mitigate climate and human-induced hydrologic and
eco-geomorphologic changes. Two especially vulnerable areas are snow covered highlands
(earlier snowmelt and changes in land-atmosphere feedbacks affecting storm dynamics and
hydrologic response) and coastal areas (threats due to extreme storms and flooding in view of
sea level rise and land-use changes affecting hazard potential in these overly populated
low land areas). The GPM constellation of satellites offers the potential to retrieve
precipitation over these complex surfaces but not without significant new ideas in
the retrieval techniques for operational products. Here we present recent results
from a new Bayesian inversion Passive Microwave Rainfall Retrieval algorithm
(called ShARP) which introduces two main innovations: (1) a new distance metric in
the space of retrieval (physically-derived or observational databases of brightness
temperature and rainfall profiles) to create neighborhoods whose closeness is judged
not on the basis of spatial averages but in terms of spatial structure in the space of
spectral brightness temperatures, and (2) computes weights of those elements by
minimizing a log-likelihood function plus a prior density of the spatial precipitation
gradients. Both innovations rely on extending the typical Least squares (/2) distance
metric used in inverse problems to a mixed /2 - /1 metric (via regularization) and
showing that this new metric is consistent with the localized small-scale spatial rainfall
structure of sharp features embedded within more homogeneous domains. Using the
data provided by the Tropical Rainfall Measuring Mission (TRMM) satellite, we
demonstrate marked improvements in the ShARP rainfall retrievals in comparison with the
standard TRMM-2A12 operational products by analysis of case studies in the Tibetan
Highlands and the Ganges-Brahmaputra-Meghna river basin and its coastal delta. |
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