Relationships between Large Precipitating Systems and Atmospheric Factors at a Grid Scale

Abstract

In this study, TRMM-observed precipitation in the tropics is decomposed according to the horizontal area of radar precipitation features, with special emphasis on large systems (rain area > 104 km2) that contribute roughly half of tropical rainfall. Statistical associations of rain-weighted radar precipitation feature (RPF) size distributions with atmospheric variables on the 1.5° grid of ERA-Interim data are explored. In one-predictor distributions, the association with total precipitable water vapor (TPWV) is the strongest, while relative humidity at low and midlevels and low-level wind shear are also positively related to large-RPF rain fraction. Standard CAPE and CIN variables computed from grid-mean thermodynamic profiles are only weakly related to the size of rain systems. Joint distributions over two variables are also reported. The relative importance of predictors varies over different regions. The eastern Pacific is distinctive for having large rain systems in environments with a moist boundary layer but a dry midtroposphere, with strong shallow wind shear and small CAPE. In contrast, the large-storm environment over the western Pacific is found to be moister in the whole troposphere, with relatively weaker wind shear and larger CAPE. Over tropical land, the Sahel and central Africa stand out as having a great fractional rainfall contributed by large RPFs. Their associated environment is characterized by lower TPWV but stronger shallow wind shear and larger CIN and CAPE, in comparison to the equatorial Amazon basin and the Maritime Continent. Based on these associations, statistical reconstructions of the geographical distribution of large-RPF rain fraction from grid-mean atmospheric predictors are attempted.

On the Relative Sensitivity of a Tropical Deep Convective Storm to Changes in Environment and Cloud Microphysical Parameters

“…The convective thermodynamic and dynamic (e.g., shear) environment is known to strongly influence the development of moist convection (Stevens 2005;Sherwood 2010; Sherwood et al 2010;de Rooy et al 2013;Kim et al 2014;Virman et al 2018). These sensitivities have been examined from an observational perspective (Johnson and Lin 1997;Holloway andNeelin 2009, 2010;Tobin et al 2012;Masunaga 2012Masunaga , 2013Kumar et al 2014;Pereira de Oliveira and Oyama 2015;Bergemann and Jakob 2016;Hannah et al 2016;Schiro et al 2016;Yuan 2016;Chen et al 2017;Stevens et al 2017;Virman et al 2018), as well as via the use of high-resolution numerical models (Crook 1996;Derbyshire et al 2004;Mapes 2004;Raymond and Sessions 2007;Eitzen and Xu 2008;Wandishin et al 2008;Kuang 2010;Tulich and Mapes 2010;Cui et al 2011;Kirshbaum 2011;Hagos and Leung 2012;Böing et al 2012a,b;Hohenegger and Stevens 2013;Sessions et al 2015;Takemi 2015;Hernandez-Deckers and Sherwood 2016;Hannah 2017;Tsai and Wu 2017;Wang and Sobel 2017). Cloud microphysical processes have also bee...…”

A Bayesian‐Like Approach to Describe the Regional Variation of High‐Flash Rate Thunderstorms From Thermodynamic and Kinematic Environment Variables

“…The relatively high spatial correlations for LCL and RHM suggest that they are also useful in the prediction of intense thunderstorms. TCWV, a useful predictor for large precipitating systems (Chen et al, ), with the combination of CAPE, CIN, and SHEAR 1–3 km , does not show good performance in the estimation of intense thunderstorms with high‐flash rate. The spatial correlation related to LI 500 is relatively low, compared to other variables.…”

A climatology of tropical wind shear produced by clustering wind profiles from a climate model

“…Although some studies have shown that shear at higher levels can be important for organization (e.g., Chen et al, 2015), focusing on the lower troposphere can be justified from the results of theoretical studies such as those of Rotunno et al (1988); Thorpe et al (1982), and observational studies such as LeMone et al (1998). Likewise, Chen et al (2017) showed that deep shear was a poor predictor for MCS activity, whereas low-level shear was a better predictor. Second, we do not expect the relative rotation of the wind profiles to have a large effect on the organization that they induce.…”

“…The motivation for their work was to choose optimal vertical bins for the Atmospheric Dynamics Mission Aeolus satellite, and so opportunities for comparison against the work presented here are limited as they are focused on answering different questions which means their analysis cannot easily be compared with ours. Chen et al (2017) investigated the link between large-scale predictor variables and large (rain area > 10000 km 2 ) precipitation features. They used data from the Tropical Rainfall Measuring Mission (TRMM) satellite to provide information about the distribution of MCSs, and ERA-Interim to obtain information about the large-scale environment.…”