Primary Modes of Global Drop Size Distributions

Abstract

Understanding drop size distribution (DSD) variability has important implications for remote sensing and numerical modeling applications. Twelve disdrometer datasets across three latitude bands are analyzed in this study, spanning a broad range of precipitation regimes: light rain, orographic, deep convective, organized midlatitude, and tropical oceanic. Principal component analysis (PCA) is used to reveal comprehensive modes of global DSD spatial and temporal variability. Although the locations contain different distributions of individual DSD parameters, all locations are found to have the same modes of variability. Based on PCA, six groups of points with unique DSD characteristics emerge. The physical processes that underpin these groups are revealed through supporting radar observations. Group 1 (group 2) is characterized by high (low) liquid water content (LWC), broad (narrow) distribution widths, and large (small) median drop diameters D0. Radar analysis identifies group 1 (group 2) as convective (stratiform) rainfall. Group 3 is characterized by weak, shallow radar echoes and large concentrations of small drops, indicative of warm rain showers. Group 4 identifies heavy stratiform precipitation. The low latitudes exhibit distinct bimodal distributions of the normalized intercept parameter N w, LWC, and D0 and are found to have a clustering of points (group 5) with high rain rates, large N w, and moderate D0, a signature of robust warm rain processes. A distinct group associated with ice-based convection (group 6) emerges in the midlatitudes. Although all locations exhibit the same covariance of parameters associated with these groups, it is likely that the physical processes responsible for shaping the DSDs vary as a function of location.

Midlatitude Oceanic Cloud and Precipitation Properties as Sampled by the ARM Eastern North Atlantic Observatory

“…Numerous disdrometer data sets have expanded the archive of detailed, quantitative precipitation characteristics into remote global environments, strengthening opportunities for cloud process studies in support of global climate model improvement (e.g., Dolan et al, ; Giangrande, Bartholomew, et al, ; Leinonen et al, ; Thompson et al, ; Wang et al, ). Several efforts focus on surface‐based DSD regime classifications to isolate convective clouds and processes (typically, higher drop concentrations) from stratiform (typically, smaller drop concentrations), as introduced by Bringi et al (, ) predominantly in Darwin, Australia, for maritime continental deeper convective system contexts.…”

“…Several efforts focus on surface‐based DSD regime classifications to isolate convective clouds and processes (typically, higher drop concentrations) from stratiform (typically, smaller drop concentrations), as introduced by Bringi et al (, ) predominantly in Darwin, Australia, for maritime continental deeper convective system contexts. Recent global summaries (Dolan et al, ) attempt to incorporate a wider range of cloud types and physical process implications to multiparameter DSD subsets.…”

“…However, there are discrepancies between the PARS and 2DVD sampling, with the PARS favoring larger drop sizes, number concentrations, and higher LWC for similar DSDs. Interestingly, ENA DSD data sets are unique for global compilation studies (e.g., Dolan et al, ) in terms of their relatively small D 0 values, especially if compared to previous midlatitude deployments. Moreover, ENA data sets reside to the left of these depictions and similar to the light rain, smaller‐drop periphery of the DSD characteristics shown for higher and lower latitude conditions and/or shallower‐topped clouds (Dolan et al, ).…”

“…This site hosts several instruments for continuous cloud sampling, including vertically pointing millimeter wavelength radars collocated with multiple surface precipitation gauge and disdrometer units. This data set adds to the growing global precipitation archive, sampling oceanic precipitation from both deep and shallow clouds not well captured by previous deployments (e.g., Dolan et al, ). Azores observations also complement the oceanic cloud information primarily available from satellite sensors (e.g., Elsaesser et al, ).…”

Response to Anonymous Referees

“…Thus, a traditional observational approach in support of convective modeling has been to document global precipitation variability and improve basic rainfall retrievals. Nevertheless, improving model capabilities introduces new challenges that motivate multi-scale, multi-sensor observations to better constrain cloud microphysics and dynamics closer to the process levels future GCMs attempt to represent (e.g., Mather and Voyles, 2013;Donner et al, 2016). certainty when developing useful precipitation retrievals for model development is the shortage of long-term surface gauge and disdrometer observations within tropical regions.…”