Snowfall is a prominent component of the Earth’s hydrologic cycle. Global observations of snowfall are essential for the monitoring of the status of the Earth system, and because of their wide coverage, nowadays, remote sensing instruments are fundamental tools in the measurement of precipitation.
The principal uncertainty in the interpretation of radar data are the scattering properties of the hydrometeors which are strictly connected to their microphysical characteristics. The presented study propose a comprehensive approach that analyze the all snow physical characteristics: single particle modeling, snowfall automatic microphysical retrieval, scattering simulations and remote sensing.
A state of the art snow aggregation algorithm (SAM) has been implemented to model the snowflake accurate morphology, simulating the basic physical governing phenomena of snow formation and growth. The algorithm has been further extended to model the initial stage of snowflake melting. The snowflake models are used as input of computer scattering simulations.
The analysis of the radiative properties obtained with the spherical models and the complex aggregated particles produced by SAM shows that the former are inadequate to represent the scattering characteristics of large aggregated particles.
An innovative methodology has been developed to automatically estimate the mean snow mass-size relation using particle size distribution, velocity fits, snow accumulation and Rayleigh radar reflectivity. The radar reflectivities at Ka and W band simulated with T-matrix spheroidal models and using the retrieved mass-dimensional relation cannot match the observation. When the same simulation is performed with the usage of DDA scattering calculations the results reproduce better the observed radar reflectivities. This outcome gives validity to both the microphysical and the scattering model.
A multi-perspective approach, that simultaneously include the microphysical and scattering simulation of snowflake properties, is the way forward to solve the uncertainties related to snowfall remote sensing.
| Identifer | oai:union.ndltd.org:unibo.it/oai:amsdottorato.cib.unibo.it:7521 |
| Date | 15 April 2016 |
| Creators | Ori, Davide <1987> |
| Contributors | Maestri, Tiziano |
| Publisher | Alma Mater Studiorum - Università di Bologna |
| Source Sets | Università di Bologna |
| Language | English |
| Detected Language | English |
| Type | Doctoral Thesis, PeerReviewed |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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