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Stephanie M. Wingo and Kevin R. Knupp

Abstract

Previous work has shown that vorticity mixing in the tropical cyclone (TC) inner core can promote mesovortex (MV) formation and impact storm intensity. Observations of MVs have largely been serendipitous but are necessary to improve understanding of these features and their role in TC dynamics. This study presents nearly 10 h of ground-based dual-Doppler analysis of MVs in the eyewall of Hurricane Ike (2008) near and during landfall. Derived 3D winds, vertical vorticity, horizontal divergence, and perturbation pressures are analyzed. Results indicate persistent kinematic field arrangements and evolving vertical structures. Perturbation pressure retrievals suggest local pressure minima associated with the MVs. Preferential updraft locations appear to transition cyclonically about the local vorticity maximum as the MVs progress around the eye. Based on published observational datasets, the dual-Doppler updraft magnitudes in Ike’s MVs are within the top 5%–10% of TC vertical velocities. The MVs are marked by peak vorticity in the lowest 2 km and contain vertically coherent vorticity structures extending to 8 km AGL. After prolonged land interaction, the MV structures deteriorate. First, the vertical extent of localized vorticity diminishes, followed by a deterioration in the prelandfall characteristic kinematic arrangements. This supports the notion that the replenishment of a high vorticity annulus contributes to MV production and maintenance, and when the elevated vorticity aloft is not maintained, MV kinematic patterns become less consistent. It is unclear whether the decay of the vertically coherent vorticity structures occurs in response to land interaction, TC inner core processes, or some combination of both.

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Charanjit S. Pabla, David B. Wolff, David A. Marks, Stephanie M. Wingo, and Jason L. Pippitt

Abstract

The Wallops Precipitation Research Facility (WPRF) at NASA Goddard Space Flight Center, Wallops Island, Virginia, has been established as a semipermanent supersite for the Global Precipitation Measurement (GPM) Ground Validation (GV) program. WPRF is home to research-quality precipitation instruments, including NASA’s S-band dual-polarimetric radar (NPOL), and a network of profiling radars, disdrometers, and rain gauges. This study investigates the statistical agreement of the GPM Core Observatory Dual-Frequency Precipitation Radar (DPR), combined DPR–GPM Microwave Imager (GMI) and GMI level II precipitation retrievals compared to WPRF ground observations from a 6-yr collection of satellite overpasses. Multisensor observations are integrated using the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) software package. SIMBA ensures measurements recorded in a variety of formats are synthesized into a common reference frame for ease in comparison and analysis. Given that instantaneous satellite measurements are observed above ground level, this study investigates the possibility of a time lag between satellite and surface mass-weighted mean diameter (Dm), reflectivity (Z), and precipitation rate (R) observations. Results indicate that time lags vary up to 30 min after overpass time but are not consistent between cases. In addition, GPM Core Observatory Dm retrievals are within level I mission science requirements as compared to WPRF ground observations. Results also indicate GPM algorithms overestimate light rain (<1.0 mm h−1). Two very different stratiform rain vertical profiles show differing results when compared to ground reference data. A key finding of this study indicates multisensor DPR/GMI combined algorithms outperform single-sensor DPR algorithm.

Significance Statement

Satellites are beneficial for global precipitation surveillance because extensive ground instruments are lacking, especially over oceans. Ground validation studies are required to calibrate and improve precipitation algorithms from satellite sensors. The primary goal of this study is to quantify the differences between satellite raindrop size and rain-rate retrieval with ground-based observations. Rainfall-rate algorithms require assumptions about the mean raindrop size. Results indicate Global Precipitation Measurement (GPM)/satellite-based mean raindrop size is within acceptable error (±0.5 mm) with respect to ground measurements. In addition, GPM satellite measurements overestimate light rain (<1.0 mm h−1), which is important during the winter months and at high latitudes. Illuminating the challenges of GPM satellite-based precipitation estimation can guide algorithm developers to improve retrievals.

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Stephanie M. Wingo, Walter A. Petersen, Patrick N. Gatlin, Charanjit S. Pabla, David A. Marks, and David B. Wolff

Abstract

Researchers now have the benefit of an unprecedented suite of space- and ground-based sensors that provide multidimensional and multiparameter precipitation information. Motivated by NASA’s Global Precipitation Measurement (GPM) mission and ground validation objectives, the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) has been developed as a unique multisensor precipitation data fusion tool to unify field observations recorded in a variety of formats and coordinate systems into a common reference frame. Through platform-specific modules, SIMBA processes data from native coordinates and resolutions only to the extent required to set them into a user-defined three-dimensional grid. At present, the system supports several ground-based scanning research radars, NWS NEXRAD radars, profiling Micro Rain Radars (MRRs), multiple disdrometers and rain gauges, soundings, the GPM Microwave Imager and Dual-Frequency Precipitation Radar on board the Core Observatory satellite, and Multi-Radar Multi-Sensor system quantitative precipitation estimates. SIMBA generates a new atmospheric column data product that contains a concomitant set of all available data from the supported platforms within the user-specified grid defining the column area in the versatile netCDF format. Key parameters for each data source are preserved as attributes. SIMBA provides a streamlined framework for initial research tasks, facilitating more efficient precipitation science. We demonstrate the utility of SIMBA for investigations, such as assessing spatial precipitation variability at subpixel scales and appraising satellite sensor algorithm representation of vertical precipitation structure for GPM Core Observatory overpass cases collected in the NASA Wallops Precipitation Science Research Facility and the GPM Olympic Mountain Experiment (OLYMPEX) ground validation field campaign in Washington State.

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