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 (T_bs) 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 T_b 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.

The best water vapor information layer (BWIL) of the 6.7 μm water vapor absorption infrared (IR) band for the FengYun‐2E is investigated over the Tibetan Plateau with standard atmospheric profile and European Centre for Medium‐Range Weather Forecasts (ECMWF) operational model analysis data. The sensitivity tests show that surface characteristics over the Tibetan Plateau have a significant influence on the BWIL. To be specific, topographic elevation, colder skin temperature, and lower emissivity tend to lift the altitude of the BWIL, decrease its magnitude, and narrow the half‐width range. The results from statistical analysis indicate that the altitude of the BWIL reaches the highest in summer and the lowest in winter. Meanwhile, the altitude of the BWIL is highly correlated with the water vapor amount above 500 hPa over the Tibetan Plateau and above 300 hPa over the East China Plain, respectively. The diurnal variation in the BWIL is synchronous with the diurnal variation in the surface skin temperature. It can be concluded from the study that surface characteristics over high terrain in dry and cold atmospheres have more significant impacts on the BWIL. With multiple water vapor absorption IR bands, the imagers on board the new generation of geostationary satellites will provide crucial improvement in water vapor remote sensing over the current single water vapor band on board the FY‐2 series according to the analysis in this study.

(1) Raindrop size distribution (DSD) is a vital microphysical characteristic of clouds and precipitations. The vertical evolution of DSD also provides a reference for the microphysical mechanisms and dynamic processes involved in clouds and precipitations. (2) Here we analyzed the characteristics and vertical evolution of DSDs, which were obtained from Micro Rain Radar (MRR) data of two typical stratiform rain cases. (3) First, we compared MRR-observed reflectivity (Z) and DSD at 400 m with data from a distrometer on the ground. This ensured the reliability of the MRR data of the two cases. Then it was found that the DSD was wider just below the 0 °C level than at lower levels. The larger DSDs width formed a bulge shape in the vertical direction, and large particles in the ‘bulge’ then constantly collided as they were falling down. The DSD was broadened and the echo of the warm layer was strengthened. We referred to this as the bulge phenomenon (BP), which appeared occasionally, and broader DSD propagated from high to low intermittently during the stratiform rain. Next, by combining the detailed cloud structures detected by cloud radar with BP, we found that a BP was always accompanied by higher developing cloud tops, stronger Z and larger falling velocity. It was inferred that ice particles formed near cloud top intermittently and fell through the underlying cloud, causing the gustiness and instability of particle aggregation, which was reflected by the BP below the 0 °C layer. BP triggered quick collision and falling down along the warm layer, enhancing the Z and falling velocity transiently. Thus, BP was considered as one of the mechanisms of rain variation in stratocumulus and stratiform rain in North China. Finally, we defined the cycle time of a BP (BPT), which was composed of broadening stage (BS) and stable stage (SS). We found that changes of DSD parameters for both MRR and distrometer responded to each BP occurring, showing the same intermittency. From each BP occurring time to the corresponding BS ending time, D_m basically grew from small to large. After this, D_m decreased immediately or maintained for a while and then decreased. N_w had the opposite trend to D_m. Also, it was found that larger R accelerated the fluency of BP occurring (BPT).

Error evaluation is essential for the improvement and application of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) Version 06 daily precipitation estimates, including early-, late-, and final-run products (IMERG-DE, IMERG-DL, and IMERG-DF, respectively), especially for summer precipitation in complex topographical areas. However, many existing works mainly focus on comparing the error statistical metrics of precipitation estimates, but few further analyze the internal relationships between these error statistics and geographical features. Therefore, taking Sichuan Province of China as a case study of the complex topographic and mountainous area, we adopt statistical metrics, error decomposition schemes, systematic and random error separation models, and regression methods to analyze the relationships between the spatial distribution of IMERG summer precipitation error metrics and geographical features. These features include longitude, latitude, distance from Sichuan Basin edge (DFBE), digital elevation model (DEM), normalized difference vegetation index (NDVI), slope, aspect, and topographic position index (TPI). The results show that: (1) DEM and DFBE are the two most important geographical features affecting the spatial distribution of error metrics, while both aspect and TPI have negligible effects on these metrics; (2) the variations in DEM, DFBE, and latitude have the negative relationships with error metrics; (3) longitude and DFBE do not have a direct impact on the errors, but indirectly affect the precipitation errors through the changing DEM; (4) slope shows a strong negative correlation with hit bias, and its increase significantly amplifies the sensitivity of systematic errors of hit bias from IMERG-DE and DL; and (5) the high detection probability and small missed precipitation error of the three IMERG estimates are virtually unaffected by changes in geographical features.