摘要
A method combined with the nonlinear least-square regression to quantitatively estimate rainfall rate over water surface from the different spectrum shapes generated by rain- fall in some frequency bands was presented. About 2000 min spectrum data generated by rainfall have been collected in an open-lake in Xiamen city. As a result, the spectrum shape in 15-30 kHz is fitted to log-linear law which allows classification of rain into three categories from the spectrum slope： no rain, drizzle （rainfall rate 〉 10 mm/h） and heavy rain （rainfall rate 〉 10 ram/h）. Then, rainfall detection is made and rainfall rate is quantified in drizzle time with Logistic model in the frequency bands of 2-15 kHz, and the rainfall rate in heavy rainfall also quantified with log-linear regression. Finally, all measured spectrum data are used to calculate rainfall rate with the algorithm. The results show that the estimated rainfall rates are comparably coincident with the synchronously measured ones. The average error of accumulative rainfall per min is only 1%-4%.
A method combined with the nonlinear least-square regression to quantitatively estimate rainfall rate over water surface from the different spectrum shapes generated by rain- fall in some frequency bands was presented. About 2000 min spectrum data generated by rainfall have been collected in an open-lake in Xiamen city. As a result, the spectrum shape in 15-30 kHz is fitted to log-linear law which allows classification of rain into three categories from the spectrum slope： no rain, drizzle （rainfall rate 〉 10 mm/h） and heavy rain （rainfall rate 〉 10 ram/h）. Then, rainfall detection is made and rainfall rate is quantified in drizzle time with Logistic model in the frequency bands of 2-15 kHz, and the rainfall rate in heavy rainfall also quantified with log-linear regression. Finally, all measured spectrum data are used to calculate rainfall rate with the algorithm. The results show that the estimated rainfall rates are comparably coincident with the synchronously measured ones. The average error of accumulative rainfall per min is only 1%-4%.
基金
supported by the Scientific Research Foundation of Third Institute of Oceanography,SOA (No2007020)
