By combining high temporal and spatial resolution Multifunctional Transport Satellite-1R (MTSAT-1R) infrared (IR) images and precipitation data from the Climate Prediction Center morphing technique (CMORPH), this study tracked mesoscale convective systems (MCSs) from May to August in 2008 and 2009 in the middle of east China with an automatic tracking algorithm based on an areal overlapping methodology. This methodology is adjusted to include those MCSs with a relative weak intensity before formation. The unique advantage of combining high temporal and spatial resolution geostationary satellite brightness temperature images and the precipitation measurements for tracking MCSs is that the cloud-top height along with the coverage and the precipitation intensity can be well identified. Results showed that the MCSs formed most frequently in the southwest Henan Province and at the border of four provinces-Shandong, Henan, Anhui, and Jiangsu-which is east of the convergence zone near the terrain's edge. Locations of the highest cloud tops and of the heaviest precipitation rates did not always match. In addition, the MCSs in the study region tended to first reach the maximum precipitation rate, followed soon by the minimum brightness temperature, then the maximum associated precipitation area, and finally the maximum in system area.

This study combined measurements from the Chinese operational geostationary satellite Fengyun‐2E (FY‐2E) and ground‐based weather radars to conduct a statistical survey of isolated convection initiation (CI) over central eastern China (CEC). The convective environment in CEC is modulated by the complex topography and monsoon climate. From May to August 2010, a total of 1,630 isolated CI signals were derived from FY‐2E using a semiautomated method. The formation of these satellite‐derived CI signals peaks in the early afternoon and occurs with high frequency in areas with remarkable terrain inhomogeneity (e.g., mountain, water, and mountain‐water areas). The high signal frequency areas shift from northwest CEC (dry, high altitude) in early summer to southeast CEC (humid, low altitude) in midsummer along with an increasing monthly mean frequency. The satellite‐derived CI signals tend to have longer lead times (the time difference between satellite‐derived signal formation and radar‐based CI) in the late morning and afternoon than in the early morning and night. During the early morning and night, the distinction between cloud top signatures and background terrestrial radiation becomes less apparent, resulting in delayed identification of the signals and thus short and even negative lead times. A decline in the lead time is observed from May to August, likely due to the increasing cloud growth rate and warm‐rain processes. Results show increasing lead times with increasing landscape elevation, likely due to more warm‐rain processes over the coastal sea and plain, along with a decreasing cloud growth rate from hill and mountain to the plateau.

Deep convective storms have contributed to airplane accidents, making them a threat to aviation safety. The most common method to identify deep convective clouds (DCCs) is using the brightness temperature difference (BTD) between the atmospheric infrared (IR) window band and the water vapor (WV) absorption band. The effectiveness of the BTD method for DCC detection is highly related to the spectral resolution and signal‐to‐noise ratio (SNR) of the WV band. In order to understand the sensitivity of BTD to spectral resolution and SNR for DCC detection, a BTD to noise ratio method using the difference between the WV and IR window radiances is developed to assess the uncertainty of DCC identification for different instruments. We examined the case of AirAsia Flight QZ8501. The brightness temperatures (Tbs) over DCCs from this case are simulated for BTD sensitivity studies by a fast forward radiative transfer model with an opaque cloud assumption for both broadband imager (e.g., Multifunction Transport Satellite imager, MTSAT‐2 imager) and hyperspectral IR sounder (e.g., Atmospheric Infrared Sounder) instruments; we also examined the relationship between the simulated Tb and the cloud top height. Results show that despite the coarser spatial resolution, BTDs measured by a hyperspectral IR sounder are much more sensitive to high cloud tops than broadband BTDs. As demonstrated in this study, a hyperspectral IR sounder can identify DCCs with better accuracy.

Boundary layer convergence lines (boundaries), an important trigger of convective storms, can be produced by land surface contrasts. This study explored a five‐year summertime radar climatology of boundaries and their associated convection in response to vegetation contrast around the bend of the Yellow River in North China. A total of 323 boundaries were identified with 44% being convection‐associated. The boundaries especially the convective boundaries were more frequent over the arid area than those over the vegetated area and tended to have an orientation parallel to the vegetation contrast line. The boundary activities collocated well with the diurnal variation in surface temperature difference across the vegetation contrast. Compared with the nonconvective boundaries, the convective boundaries formed earlier and moved faster into the inner arid area, obtained maximum length around midday, and then initiated convection. Vegetation contrast might also affect the high‐frequency location and magnitude of boundary‐associated convective precipitation.

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