The extraction of physical information about the subsurface ocean from surface information obtained from satellite measurements is both important and challenging. We introduce a back-propagation neural network (BPNN) method to determine the subsurface temperature of the North Pacific Ocean by selecting the optimum input combination of sea surface parameters obtained from satellite measurements. In addition to sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS) and sea surface wind (SSW), we also included the sea surface velocity (SSV) as a new component in our study. This allowed us to partially resolve the non-linear subsurface dynamics associated with advection, which improved the estimated results, especially in regions with strong currents. The accuracy of the estimated results was verified with reprocessed observational datasets. Our results show that the BPNN model can accurately estimate the subsurface (upper 1000 m) temperature of the North Pacific Ocean. The corresponding mean square errors were 0.868 and 0.802 using four (SSH, SST, SSS and SSW) and five (SSH, SST, SSS, SSW and SSV) input parameters and the average coefficients of determination were 0.952 and 0.967, respectively. The input of the SSV in addition to the SSH, SST, SSS and SSW therefore has a positive impact on the BPNN model and helps to improve the accuracy of the estimation. This study provides important technical support for retrieving thermal information about the ocean interior from surface satellite remote sensing observations, which will help to expand the scope of satellite measurements of the ocean.

The ocean surface and subsurface biophysical responses and their feedbacks to super typhoon Nida were comprehensively investigated based on a substantial dataset of multiple-satellite observations, Argo profiles, and reanalysis data. Nida experienced two Category 5 stages: a rapid intensification stage that was fast moving along a straight-line track, and a rapid weakening stage that was slowly moving along a sharp-left sudden-turning track. During the straight-line stage, Nida caused an average sea surface temperature (SST) cooling of 1.44 °C and a chlorophyll-a (chl-a) concentration increase of 0.03 mg m⁻³. During the sudden-turning stage, cyclonic sudden-turning induced a strong cold cyclonic eddy (SSHA < −60 cm) by strong upwelling, which caused the maximum SST cooling of 6.68 °C and a long-lasting chl-a bloom of 0.6 mg m⁻³ on the left-hand side of the track, resulting in substantial impacts on the ocean ecological environment. Furthermore, the enhanced ocean cold wake and the longer air–sea interaction in turn decreased the average inner-core SST of 4 °C and the corresponding enthalpy flux of 780 W m⁻², which induced a notable negative feedback to the typhoon intensity by weakening it from Category 5 to Category 2. The left bias response and notable negative feedback are special due to sharp-left sudden-turning of typhoon. Comparing with the previously found slow translation speed (~4 m s⁻¹) of significant ocean response, the negative feedback requires even more restriction of translation speed (<2 m s⁻¹) and sharp sudden-turning could effectively relax restrictions by making equivalent translation speed lower and air-sea interaction time longer. Our findings point out that there are some unique features in ocean–typhoon interactions under sudden-turning and/or lingering tracks comparing with ordinary tracks.

We introduce a novel method to accurately evaluate the satellite-observed sea surface temperature (SST) cooling induced by typhoons with complex tracks, which is widely used but only roughly calculated in previous studies. This method first records the typhoon forcing period and the SST response grid by grid, then evaluates the SST cooling in each grid by choosing the maximum decrease in SST within this time period. This grid-based flexible forcing date method can accurately evaluate typhoon-induced SST cooling and its corresponding date in each grid, as indicated by applying the method to the irregular track of Typhoon Lupit (2009) and three sequential typhoons in 2016 (Malakas, Megi, and Chaba). The method was used to accurately calculate the impact of Typhoon Megi by removing the influence of the other two typhoons. The SST cooling events induced by all typhoons in the northwest Pacific from 2004 to 2018 were extracted well using this method. Our findings provide new insights for accurately calculating the response of the ocean using multi-satellite remote sensing and simulation data, including the sea surface salinity, sea surface height, mixed layer depth, and the heat content of the upper levels of the ocean.

Four sequential tropical cyclones generated and developed in the Northwest Pacific Ocean (NWP) in 2014, which had significant impacts on the oceanic environment and coastal regions. Based on a substantial dataset of multiple-satellite observations, Argo profiles, and reanalysis data, we comprehensively investigated the interactions between the oceanic environment and sequential tropical cyclones. Super typhoon Neoguri (2014) was the first typhoon-passing studied area, with the maximum sustained wind speed of 140 kts, causing strong cold wake along the track. The location of the strongest cold wake was consistent with the pre-existing cyclonic eddy (CE), in which the average sea surface temperature (SST) cooling exceeded −5 °C. Subsequently, three tropical cyclones passed the ocean environment left by Neoguri, namely, the category 2 typhoon Matmo (2014), the tropical cyclone Nakri (2014) and the category 5 typhoon Halong (2014), which caused completely different subsequent responses. In the CE, due to the fact that the ocean stratification was strongly destroyed by Neoguri and difficult to recover, even the weak Nakri could cause a secondary response, but the secondary SST cooling would be overridden by the first response and thus could cause no more serious ocean disasters. If the subsequent typhoon was super typhoon Halong, it could cause an extreme secondary SST cooling, exceeding −8 °C, due to the deep upwelling, exceeding 700 m, surpassing the record of the maximum cooling caused by the first typhoon. In the anti-cyclonic eddy (AE), since the first typhoon Neoguri caused strong seawater mixing, it was difficult for the subsequent weak typhoons to mix the deeper, colder and saltier water into the surface, thus inhibiting secondary SST cooling, and even the super typhoon Halong would only cause as much SST cooling as the first typhoon. Therefore, the ocean responses to sequential typhoons depended on not only TCs intensity, but also TCs track order and ocean mesoscale eddies. In turn, the cold wake caused by the first typhoon, Neoguri, induced different feedback effects on different subsequent typhoons.

We report on the observation of six red sprites produced by negative cloud‐to‐ground (CG) lightning strokes in a tropical thunderstorm that later evolved into Hurricane Harvey (2017). Most of these sprites conformed to the morphological characteristics of typical negative sprites previously observed over continental thunderstorms. The sprite‐producing CG strokes were located in or near the deep convection region and their impulse charge moment changes all exceeded −500 C‐km. During the sprite generation stage, the CG lightning stroke rate declined and the tropical thunderstorm structure appeared stable. Our analysis suggests that tropical marine meteorological systems, such as tropical disturbances, depressions, and thunderstorms are more likely to be main production systems of negative sprites. The frequent occurrence of 18 gigantic jets produced by the same thunderstorm further indicates that the thundercloud charge structures of sprite‐producing oceanic thunderstorms are significantly different from that of continental thunderstorms.

Multi-satellite and buoy observation data were used to systematically analyze the ocean response offshore of Taiwan to Super Typhoon Nepartak in 2016. The satellite data showed that a high sea surface temperature combined with a thick warm water layer and deep mixed layer provided a good thermal environment for continuous intensification of the typhoon. Two high-resolution buoys (NTU1 and NTU2) moored 375 and 175 km offshore of southeastern Taiwan were used to clarify the typhoon–ocean interaction as the typhoon approached Taiwan. The ocean conditions were similar at the two buoys before the typhoon, and both buoys were on the left side of the typhoon track and suffered similar typhoon factors (e.g., typhoon intensity and translation speed) during its passage. However, the ocean response differed significantly at the two buoys. During the forced period, the entire upper ocean was cooled at NTU1. In contrast, there was a clear three-layer vertical structure at NTU2 consisting of cool surface and deep layers with a warmer layer between the two cool layers. These responses can be attributed to strong upwelling of a cold eddy at NTU1 and vertical mixing at NTU2. These results indicate that, under similar preexisting conditions and typhoon factors, the movement of ocean eddies under typhoon forcing is an unexpected mechanism that results in upwelling and thus needs to be considered when predicting changes in the ocean environment and typhoon intensity.