A tropical cyclone is a catastrophic natural disaster and this complex synoptic storm environment always brings together destructive high wind speed (greater than 15 ms −1 ) combined with heavy rain. In order to assess the intensity and radial extent of catastrophic effects, wind speed information inside tropical cyclone structure is crucial (Elsner et al., 2008;Emanuel, 2000;Emanuel et al., 2004). Buoy measurement error due to notable tilt and sheltered by large waves has caused difficulties in measuring wind speed at 10 m from the sea surface (hereafter known U 10 ) in tropical cyclone (Bender et al., 2010). By the advancement of satellite remote sensing, various sensors help the responsible agencies to monitor the tropical cyclone in almost real-time. The aerial view of cloud-top pattern from optical geostationary satellite always contributes significantly to forecasting the trajectory (Sawada et al., 2020) yet the U 10 information in tropical cyclone is still hard to achieve.

The reflectivity (Z)—rain rate (R) model has not been tested on single polarization radar for estimating monsoon rainfall in Southeast Asia, despite its widespread use for estimating heterogeneous rainfall. The artificial neural network (ANN) regression has been applied to the radar reflectivity data to estimate monsoon rainfall using parametric Z-R models. The 10-min reflectivity data recorded in Kota Bahru radar station (in Malaysia) and hourly rain record in nearby 58 gauge stations during 2013–2015 were used. The three-dimensional nearest neighbor interpolation with altitude correction was applied for pixel matching. The non-linear Levenberg Marquardt (LM) regression, integrated with ANN regression minimized the spatiotemporal variability of the proposed Z-R model. Results showed an improvement in the statistical indicator, when LM and ANN overestimated (6.6%) and underestimated (4.4%), respectively, the mean total rainfall. For all rainfall categories, the ANN model has a positive efficiency ratio of >0.2.

The ability of satellite altimeter to estimate wind speed in tropical cyclone condition has been investigated. In the extreme condition with higher spatio-temporal variation, the ocean-atmosphere interaction is very complex and makes the existing algorithm become an ill-posed solution. In such condition, the developed algorithm from single frequency backscatter and significant waves height were insufficient. Besides, wind speed estimates become saturated at high regimes and the reflected backscatter was contaminated by rain. Therefore, other simultaneously observed parameters are needed to comprehensively account for this condition and is expected to improve the accuracy of wind speed retrieval. Aside from altimeter instrument, the microwave radiometer onboard Jason-2 concurrently records the brightness temperature and the rain information. To accommodate related multiple parameters for wind speed derivation, the neural network approach is proposed. Its unique advantage is relationship among multi-parameters can be easily established without prior knowledge on their physical attributes. Therefore, this study intended to determine the multi-parameter neural network (MPNN) model in estimating altimeter wind speed during the tropical cyclone condition. The results proved that the MPNN technique has potential in reducing the root mean square error by 30% in comparison between tropical cyclone wind speed estimate by the existing algorithm.