Detection and characterization of heavy drizzle cells within subtropical marine stratocumulus using AMSR-E 89-GHz passive microwave measurements

Abstract

Abstract. This empirical study demonstrates the feasibility of using 89-GHz Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) passive microwave brightness temperature data to detect heavily drizzling cells within subtropical marine stratocumulus. For the purpose of this paper, we define heavily drizzling cells as areas ≥ 6 km × 4 km with C-band Z > 0 dBZ; equivalent to > 0.084 mm h −1 . A binary heavy drizzle product is described that can be used to determine areal and feature statistics of drizzle cells within the major marine stratocumulus regions. Current satellite liquid water path (LWP) and cloud radar products capable of detecting drizzle are either lacking in resolution (AMSR-E LWP), diurnal coverage (MODIS LWP), or spatial coverage (CloudSat). The AMSR-E 89-GHz data set at 6 km × 4 km spatial resolution is sufficient for resolving individual heavily drizzling cells. Radiant emission at 89 GHz by liquid-water cloud and precipitation particles from drizzling cells in marine stratocumulus regions yields local maxima in brightness temperature against an otherwise cloud-free background brightness temperature. The background brightness temperature is primarily constrained by column-integrated water vapor for moderate sea surface temperatures. Clouds containing ice are screened out. Once heavily drizzling pixels are identified, connected pixels are grouped into discrete drizzle cell features. The identified drizzle cells are used in turn to determine several spatial statistics for each satellite scene, including drizzle cell number and size distribution. The identification of heavily drizzling cells within marine stratocumulus regions with satellite data facilitates analysis of seasonal and regional drizzle cell occurrence and the interrelation between drizzle and changes in cloud fraction.

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

“…Similarly, the Low cloud DSDs indicate a substantial contribution from very light rain that is close to the lower sampling limits of these disdrometers. Satellite cloud radars (i.e., CloudSat) and passive microwave applications should be sensitive to these conditions (e.g., Miller & Yuter, ; Stephens et al, ) for future comparison. However, this high frequency of smaller drop DSDs as in Figures and may explain some of the discrepancy in total rainfall between ground and longer wavelength radar‐based satellite estimates for these regions.…”

Characterization of shallow oceanic precipitation using profiling and scanning radar observations at the Eastern North Atlantic ARM observatory

“…Although satellite-based microwave sensors can infer the spatial distribution of liquid water path (Wood and Hartmann, 2006;Miller and Yuter, 2013) and precipitation rate (Ellis et al, 2009;Adler et al, 2009;Rapp et al, 2013), they have poor horizontal resolution and suffer from surface inference, causing them to under-sample the cloud field variability and to underreport boundary layer cloud and precipitation occurrence (Schumacher and Houze, 2000;Rapp et al, 2013). In contrast, airborne (Stevens et al, 2005;Wood et al, 2011;Moyer and Young, 1994;Vali et al, 1998;Paluch and Lenschow, 1991;Sharon et al, 2006) and ship-based (Yuter et al, 2000;Comstock et al, 2005;Feingold et al, 2010) sensors can resolve the spatial and temporal variability of the cloud and precipitation field, but field campaigns deploying such sensors are often expensive to conduct and limited in temporal duration (Stevens et al, 2003;Bretherton et al, 2004;Rauber et al, 2007).…”

“…However, it only identifies a fraction of the atmospheric returns in the X band at 0.5 • elevation observations. There, additional filtering, beyond the scope of this study, would be required to suppress the remaining sea clutter and recover the missing atmospheric returns (see Chandrasekar, 2009 andUnal, 2009, who propose advanced techniques). Given this, XSAPR2 cross validation and precipitation rate maps will be estimated using observations collected at 1.0 • elevation since it offers the best compromise between proximity to the surface and minimum sea-clutter contamination.…”

Numerical simulation of tropical cumulus congestus during TOGA COARE

“…In order to better compare with the results of Johnson et al . [], we conducted a feature identification analysis using the North Carolina State University “blob” detection toolbox [ Miller and Yuter , ]. The blob detection method employs accepted image processing algorithms for the identification of contiguous features.…”