New evidence based on recent satellite data is presented to provide a rare opportunity in quantifying the long‐speculated contribution of tropical cyclones to enhance ocean primary production. In July 2000, moderate cyclone Kai‐Tak passed over the South China Sea (SCS). During its short 3‐day stay, Kai‐Tak triggered an average 30‐fold increase in surface chlorophyll‐a concentration. The estimated carbon fixation resulting from this event alone is 0.8 Mt, or 2–4% of SCS's annual new production. Given an average of 14 cyclones passing over the SCS annually, we suggest the long‐neglected contribution of tropical cyclones to SCS's annual new production may be as much as 20–30%.

On 2 May 2008, category‐4 tropical cyclone Nargis devastated Myanmar. It was observed that just prior to its landfall, Nargis rapidly intensified from a weak category‐1 storm to an intense category‐4 storm within only 24 h. Using in situ ocean depth‐temperature measurements and satellite altimetry, it is found that Nargis' rapid intensification took place on a pre‐existing warm ocean anomaly in the Bay of Bengal. In the anomaly, the subsurface ocean is evidently warmer than climatology, as characterized by the depth of the 26°C isotherm of 73–101 m and the tropical cyclone heat potential of 77–105 kj cm−2. This pre‐existing deep, warm subsurface layer leads to reduction in the cyclone‐induced ocean cooling, as shown from the ocean mixed layer numerical experiments. As a result, there was a near 300% increase in the air‐sea enthalpy flux to support Nargis' rapid intensification.

Dropwindsonde, Geostationary Operational Environmental Satellite-11 (GOES-11) rapid-scan atmospheric motion vectors, and NASA Quick Scatterometer (QuikSCAT) near-surface wind data collected during NASA's Tropical Cloud Systems and Processes (TCSP) field experiment in July 2005 were assimilated into an advanced research version of the Weather Research and Forecasting (WRF) model using its three-dimensional variational data assimilation (3DVAR) system. The impacts of the mesoscale data assimilation on WRF numerical simulation of Tropical Storms Cindy and Gert (2005) near landfall are examined. Sensitivity of the forecasts to the assimilation of each single data type is investigated. Specifically, different 3DVAR strategies with different analysis update cycles and resolutions are compared in order to identify the better methodology for assimilating the data from research aircraft and satellite for tropical cyclone study.The results presented herein indicate the following. 1) Assimilation of dropwindsonde and satellite wind data into the WRF model improves the forecasts of the two tropical storms up to the landfall time. The QuikSCAT wind information is very important for improving the storm track forecast, whereas the dropwindsonde and GOES-11 wind data are also necessary for improved forecasts of intensity and precipitation. 2) Data assimilation also improves the quantitative precipitation forecasts (QPFs) near landfall of the tropical storms. 3) A 1-h rapid-update analysis cycle at high resolution (9 km) provides more accurate tropical cyclone forecasts than a regular 6-h analysis cycle at coarse (27 km) resolution. The high-resolution rapidly updated 3DVAR analysis cycle might be a practical way to assimilate the data collected from tropical cyclone field experiments.

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