Publication Date:
2018-02-24
Description:
Despite significant progress made in snowfall estimation from space, methods utilizing passive microwave measurements continue to be plagued by low detectability compared to those that estimate rainfall. This paper presents a hybrid snowfall detection algorithm that combines the output from a statistical algorithm utilizing satellite passive microwave measurements with the output from a statistical algorithm trained with in-situ data that uses meteorological variables derived from a global forecast model as predictors. The satellite algorithm computes the probability of snowfall over land using logistic regression and the principal components of the high frequency brightness temperature measurements at AMSU/MHS and ATMS channel frequencies 89 GHz and above. In a separate investigation, analysis of modelled data derived from NOAA’s Global Forecast System (GFS) showed that cloud thickness and relative humidity at 1- to 3-km height were the best predictors of snowfall occurrence. A statistical logistical regression model that combined cloud thickness, relative humidity and vertical velocity was selected among statistically significant variants as the one with the highest overall classification accuracy. Next, the weather-based and satellite model outputs were combined in a weighting scheme to produce a final probability of snowfall output, which was then used to classify a weather event as “snowing” or “no snowing” based on an a-priori threshold probability. Statistical analysis indicated that a scheme with equal weights applied to the weather-based and satellite model significantly improved satellite snowfall detection. Example applications of the hybrid algorithm over continental US demonstrated the improvement for a major snowfall event and for an event dominated by lighter snowfall.
Print ISSN:
0035-9009
Electronic ISSN:
1477-870X
Topics:
Geography
,
Physics
