## 1 Introduction

[2] Understanding and modeling of the transition from shallow to organized deep convection is a particularly vexing problem in tropical meteorology [*Betts and Jakob*, 2002; *Wu et al.*, 2009]. Deep precipitating convection, whose origins are as incipient cumuli, a small-scale phenomenon, temporally, of tens of minutes and, spatially, of kilometers, can become organized and experience upscale growth into mesoscale convective systems with scales of hours to a day and of 100 km to 1000 km. Furthermore, given the weak rotational constraint near the equator, heating perturbations resulting from deep precipitating convection are quickly redistributed to scales much larger than 1000 km by gravity wave dynamics [*Mapes*, 1997]. For what may be essentially considered a single evolving phenomenon, deep convection covers a range of spatial and temporal scales which are challenging to capture observationally and to represent in numerical models [*Mapes et al.*, 2009; *Moncrieff et al.*, 2012; *Zhang et al.*, 2013]. Apart from scale issues, very complex interactions/feedbacks exist between tropical convection and water vapor (wv) fields (see *Sherwood et al.*[2009] for a review). Tropical deep convection depends on the spatial and temporal distribution of wv, yet deep convection is also critical in determining this distribution [*Grabowski and Moncrieff*, 2004]. For example, it has been pointed out that moist regions experience more deep convection, a positive feedback, though precipitating convection ultimately dries the atmosphere [*Grabowski and Moncrieff*, 2004]. Furthermore, the vertical distribution of wv can influence cumulus dynamics and the shallow-to-deep transition through entrainment of dry air with its deleterious effects on buoyancy [*Khairoutdinov and Randall*, 2006; *Wu et al.*2009].

[3] Unraveling the complex nature of the shallow-to-deep transition and deriving useful metrics that characterize its temporal evolution, long-term (i.e., years), high frequency measurements (≤ 30 min) at spatial resolutions of kilometers are necessary. However, it is precisely at this scale that long-term observations are lacking in the tropics. Satellite observations of clouds and column wv are crucial for characterizing the evolution of tropical convection; nevertheless, these platforms have neither the spatial nor temporal resolution for capturing wv/convection interactions that cover the cumulus stage, the shallow-to-deep transition, and upscale growth to mesoscale convective systems. Moreover, satellite wv measurements from IR radiometers are limited to clear-sky conditions [*Divakarla et al.*, 2006], and satellite microwave radiometers are less reliable over land [*Deeter*, 2007]. Within the last two decades, ground-based Global Navigational Satellite System (GNSS) meteorology has offered high frequency (as frequent as 5 min), all-weather, precipitable water vapor (PWV) values with 1 to 2 *mm* accuracy relative to radiosondes and radiometers [*Mattioli et al.*, 2007; *LeBlanc et al.*2011].

[4] Although PWV is only an integral measure providing no vertical humidity structure, it has proven valuable for deriving empirical precipitation relationships [*Zeng*, 1999; *Bretherton et al.*, 2004] and for theoretical work on deep convective organization in the tropics [*Neelin et al.*, 2009; *Peters et al.*, 2009]. The time evolution of GNSS-derived PWV has been used in studies of deep convection [*Mazany et al.*, 2002; *Kursinski et al.*, 2008], but never within the equatorial tropics. In this paper, we present a new application of GNSS ground-based PWV, namely, examining the wv convergence time scales for a tropical continental region, the central Amazon. The motivations are twofold: (1) to introduce the GNSS-derived wv convergence time scale and, (2) to characterize deep convective events for a tropical continental regime using this metric. Many different time scales exist for deep convection, and time scale analysis lies at the root of understanding which physical processes are dominant [*Mapes et al.*, 2009; *Hohenegger and Stevens*, 2013]. For example, recent work by *Hohenegger and Stevens* [2013] utilizes time scale analysis to determine which process dominates convective outbreaks, cumulus congestus moisture preconditioning or dynamically forced wv convergence, the latter mechanism being dominant.

[5] Long-term, continuous observations/statistics are, however, absolutely necessary to gain further insight into wv/convection interactions. Here, we employ 3.5 years of PWV data from the world's first equatorial GNSS meteorology station in Manaus, Brazil to examine wv convergence time scales. In what follows, we present the derivation and underlying assumptions for relating the time evolution of PWV to wv convergence. The methodology, study area, and data employed in developing the deep convective climatology are then presented. Application of this time scale to Amazon convection is discussed and future directions of GNSS-based studies in the tropics close the paper.