Precipitation retrieval over high latitudes, particularly snowfall retrieval over ice and snow, using
satellite-based passive microwave spectrometers, is currently an unsolved problem. The challenge
results from the large variability of microwave emissivity spectra for snow and ice surfaces, which
can mimic, to some degree, the spectral characteristics of snowfall.
This work focuses on the investigation of a new snowfall detection algorithm specific for high
latitude regions, based on a combination of active and passive sensors able to discriminate between
snowing and non snowing areas.
The space-borne Cloud Profiling Radar (on CloudSat), the Advanced Microwave Sensor units A
and B (on NOAA-16) and the infrared spectrometer MODIS (on AQUA) have been co-located for
365 days, from October 1st 2006 to September 30th, 2007.
CloudSat products have been used as truth to calibrate and validate all the proposed algorithms.
The methodological approach followed can be summarised into two different steps.
In a first step, an empirical search for a threshold, aimed at discriminating the case of no snow, was
performed, following Kongoli et al. [2003]. This single-channel approach has not produced
appropriate results, a more statistically sound approach was attempted.
Two different techniques, which allow to compute the probability above and below a Brightness
Temperature (BT) threshold, have been used on the available data. The first technique is based upon
a Logistic Distribution to represent the probability of Snow given the predictors. The second
technique, defined Bayesian Multivariate Binary Predictor (BMBP), is a fully Bayesian technique
not requiring any hypothesis on the shape of the probabilistic model (such as for instance the
Logistic), which only requires the estimation of the BT thresholds.
The results obtained show that both methods proposed are able to discriminate snowing and non
snowing condition over the Polar regions with a probability of correct detection larger than 0.5,
highlighting the importance of a multispectral approach.
| Identifer | oai:union.ndltd.org:unibo.it/oai:amsdottorato.cib.unibo.it:982 |
| Date | 27 June 2008 |
| Creators | Todini, Giulio <1979> |
| Contributors | Rizzi, Rolando |
| 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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