Rain identification and measurement using Oceansat-II scatterometer observations

We exhibit a technique to detect and estimate precipitation over the global oceans using the radar back scattering coefficient and brightness temperature measurements from Oceansat-II scatterometer along with numerical weather prediction model derived rain sensitive parameters via a neural network (NN) based setup. Rain/no-rain labels are generated by analyzing rainfall observations from Tropical Rainfall Measuring Mission (TRMM) and Advanced Microwave Scanning Radiometer for Earth Observation Satellite (EOS) (AMSR-E) which are concurrent (within a spatiotemporal bin 0.25° × 0.25° latitude–longitude and 900 s) to Oceansat-II overpasses. The rain sensitivity of all the parameters is examined. NN is applied in two stages: (1) rain identification and (2) rain quantification with training samples from five different overlapping geographical regions [I(25°N–25°S), II(15°N–45°N), III(35°N–70°N), IV(15°S–45°S) and V(35°S–70°S)]. Rain identification accuracy is about 93%, 87%, 90%, 79%, and 85%, and no-rain detection accuracy of about 97%, 87%, 86%, 84% and 86% for these regions. The missing rain cases are few compared to the size of no-rain samples and are largely from the low rain regime. The RMS error of instantaneous rain estimation for regions I to V (rain rates varying from > 0 to approximately 45, 25, 25, 45, and 20 mm h− 1) is found to be 1.86, 0.69, 0.47, 0.56, and 0.46 mm h− 1, respectively. The qualitative comparisons of instantaneous, 3-days, monthly and seasonal rain rates from scatterometer and AMSR-E demonstrate a good agreement between them. Probability distribution of monthly rain rates from scatterometer and AMSR-E is also compared, indicating the consistency of scatterometer derived rain with AMSR-E rain in a climatic scale.