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Hanii Takahashi, Matthew Lebsock, Zhengzhao Johnny Luo, Hirohiko Masunaga, and Cindy Wang

Abstract

This paper is the first attempt to document a simple convection tracking method based on the IMERG precipitation product to generate an IMERG-based Convection Tracking (IMERG-CT) dataset. Up to now precipitation datasets have been Eulerian accumulations. Now with IMERG-CT, we can estimate total rainfall based on Lagrangian accumulations, which is a very important step in diagnosing cloud-precipitation process following the evolution of air masses. Convection tracking algorithms have traditionally been developed based on brightness temperature (Tb) from satellite infrared (IR) retrievals. However, vigorous rainfall can be produced by warm-topped systems in moist environment, which cannot be captured by traditional IR-based tracking but is observed in IMERG-CT. Therefore, an advantage of IMERG-CT is its ability to include the previously missing information of shallow clouds that grow into convective storms, which provides us more complete lifecycle records of convective storms than traditional IR-based tracking does. This study also demonstrates the utility of IMERG-CT through investigating various properties of convective systems in terms of the evolution before and after peak precipitation rate and amount. For example, composite analysis reveals a link between evolution of precipitation and convective development: the signature of stratiform anvils remaining after the storm has produced the maximum rainfall, as average Tb stays almost constant for 5 hours after the peak of precipitation. Our study highlights the importance of joint analysis of cloud and precipitation data in time sequence, which helps elucidate the underlying dynamic processes producing tropical rainfall and its resultant effects on the atmospheric thermodynamics.

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Chunpeng Wang, Zhengzhao Johnny Luo, Xiuhong Chen, Xiping Zeng, Wei-Kuo Tao, and Xianglei Huang

Abstract

Cloud-top temperature (CTT) is an important parameter for convective clouds and is usually different from the 11-μm brightness temperature due to non-blackbody effects. This paper presents an algorithm for estimating convective CTT by using simultaneous passive [Moderate Resolution Imaging Spectroradiometer (MODIS)] and active [CloudSat + Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] measurements of clouds to correct for the non-blackbody effect. To do this, a weighting function of the MODIS 11-μm band is explicitly calculated by feeding cloud hydrometer profiles from CloudSat and CALIPSO retrievals and temperature and humidity profiles based on ECMWF analyses into a radiation transfer model. Among 16 837 tropical deep convective clouds observed by CloudSat in 2008, the averaged effective emission level (EEL) of the 11-μm channel is located at optical depth ~0.72, with a standard deviation of 0.3. The distance between the EEL and cloud-top height determined by CloudSat is shown to be related to a parameter called cloud-top fuzziness (CTF), defined as the vertical separation between −30 and 10 dBZ of CloudSat radar reflectivity. On the basis of these findings a relationship is then developed between the CTF and the difference between MODIS 11-μm brightness temperature and physical CTT, the latter being the non-blackbody correction of CTT. Correction of the non-blackbody effect of CTT is applied to analyze convective cloud-top buoyancy. With this correction, about 70% of the convective cores observed by CloudSat in the height range of 6–10 km have positive buoyancy near cloud top, meaning clouds are still growing vertically, although their final fate cannot be determined by snapshot observations.

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Hanii Takahashi, Matthew Lebsock, Zhengzhao Johnny Luo, Hirohiko Masunaga, and Cindy Wang

Abstract

This paper is the first attempt to document a simple convection-tracking method based on the IMERG precipitation product to generate an IMERG-based Convection Tracking (IMERG-CT) dataset. Up to now, precipitation datasets have been Eulerian accumulations. Now with IMERG-CT, we can estimate total rainfall based on Lagrangian accumulations, which is a very important step in diagnosing cloud-precipitation process following the evolution of air masses. Convection-tracking algorithms have traditionally been developed on the basis of brightness temperature (Tb) from satellite infrared (IR) retrievals. However, vigorous rainfall can be produced by warm-topped systems in a moist environment; this situation cannot be captured by traditional IR-based tracking but is observed in IMERG-CT. Therefore, an advantage of IMERG-CT is its ability to include the previously missing information of shallow clouds that grow into convective storms, which provides us more-complete life cycle records of convective storms than traditional IR-based tracking does. This study also demonstrates the utility of IMERG-CT through investigating various properties of convective systems in terms of the evolution before and after peak precipitation rate and amount. For example, composite analysis reveals a link between evolution of precipitation and convective development: the signature of stratiform anvils remaining after the storm has produced the maximum rainfall, as average Tb stays almost constant for 5 h after the peak of precipitation. Our study highlights the importance of joint analysis of cloud and precipitation data in time sequence, which helps to elucidate the underlying dynamic processes producing tropical rainfall and its resultant effects on the atmospheric thermodynamics.

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Zhengzhao Johnny Luo, Dieter Kley, Richard H. Johnson, G. Y. Liu, Susanne Nawrath, and Herman G. J. Smit

Abstract

Multiple years of measurements of tropical upper-tropospheric temperature and humidity by the Measurement of Ozone and Water Vapor by Airbus In-Service Aircraft (MOZAIC) project are analyzed in the vicinity of deep convective outflow to study the variations of temperature and humidity and to investigate the influence of the sea surface temperature (SST) on the outflow air properties. The principal findings are the following. 1) The distribution of relative humidity with respect to ice (RHi) depends on where a convective system is sampled by the MOZAIC aircraft: deep inside the system, RHi is unimodal with the mode at ~114%; near the outskirts of the system, bimodal distribution of RHi starts to emerge with a dry mode at around 40% and a moist mode at 100%. The results are compared with previous studies using in situ measurements and model simulations. It is suggested that the difference in the RHi distribution can be explained by the variation of vertical motions associated with a convective system. 2) Analysis of MOZAIC data shows that a fractional increase of specific humidity with SST, q −1 dq/dSTT, near the convective outflow is about 0.16–0.18 K−1. These values agree well with previous studies using satellite data. Because MOZAIC measurements of temperature and humidity are independent, the authors further analyze the SST dependence of RHi and temperature individually. Temperature increases with SST for both prevalent flight levels (238 and 262 hPa); RHi stays close to constant with respect to SST for 238 hPa but shows an increasing trend for the 262-hPa level. Analysis conducted in this study represents a unique observational basis against which model simulations of upper-tropospheric humidity and its connection to deep convection and SST can be evaluated.

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