Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E

Abstract

Abstract. The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site includes a heterogeneous distributed scanning Doppler radar network suitable for collecting coordinated Doppler velocity measurements in deep convective clouds. The surrounding National Weather Service (NWS) Next Generation Weather Surveillance Radar 1988 Doppler (NEXRAD WSR-88D) further supplements this network. Radar velocity measurements are assimilated in a three-dimensional variational (3DVAR) algorithm that retrieves horizontal and vertical air motions over a large analysis domain (100 km × 100 km) at storm-scale resolutions (250 m). For the first time, direct evaluation of retrieved vertical air velocities with those from collocated 915 MHz radar wind profilers is performed. Mean absolute and root-meansquare differences between the two sources are of the order of 1 and 2 m s −1 , respectively, and time-height correlations are of the order of 0.5. An empirical sensitivity analysis is done to determine a range of 3DVAR constraint weights that adequately satisfy the velocity observations and anelastic mass continuity. It is shown that the vertical velocity spread over this range is of the order of 1 m s −1 . The 3DVAR retrievals are also compared to those obtained from an iterative upwards integration technique. The results suggest that the 3DVAR technique provides a robust, stable solution for cases in which integration techniques have difficulty satisfying velocity observations and mass continuity simultaneously.

Cloud‐resolving model intercomparison of an MC3E squall line case: Part I—Convective updrafts

“…The multi‐Doppler retrievals may underestimate vertical velocity by 2–4 m s −1 (2 m s −1 for the 90th percentile and 4 m s −1 for the 99th percentile in Figure ) based on North et al . []. Thus, for some schemes such as FSBM and NSSL, the overestimation aloft reduces to 30% or less after considering the potential observational bias.…”

“…During the 20 May 2011 squall line event, nearby well‐calibrated operational Vance Air Force Base, OK (KVNX) and Wichita, KS (KICT) NEXRAD WSR‐88D 2.8‐GHz (S‐band) [e.g., Whiton et al ., ] radars routinely collected reflectivity and Doppler velocity measurements. This triplet of CSAPR and WSR‐88D radar volumes matched fairly well in time, and therefore, wind field retrievals could be performed as long as the time offset between their respective volume scans was less than 60 s. Radial velocity observations from CSAPR, KVNX, and KICT were assimilated in a three‐dimensional variational algorithm capable of retrieving wind fields that satisfy the input velocity observations and anelastic mass continuity simultaneously, among other physical constraints [ North et al ., ]. The wind retrievals were performed on a regular 0.5 km spaced Cartesian grid surrounding the SGP CF covering 125 km × 100 km × 10 km in meridional, zonal, and vertical distances, respectively.…”

Cloud‐Resolving Model Intercomparison of an MC3E Squall Line Case: Part II. Stratiform Precipitation Properties

“…In addition to vertical air motion retrieved by the VPRs, we utilize a multi‐Doppler radar 3‐D wind field retrieval as described in Part I to evaluate the zonal wind in the stratiform region. Radial velocity observations from a 6.3‐GHz C‐band Scanning ARM Precipitation Radar (e.g., Giangrande et al, ) located ~20 km north of the SGP CF and Next Generation Radar (NEXRAD) WSR‐88D 2.8‐GHz (S‐band) radars (e.g., Whiton et al, ) located at Vance Air Force Base, OK (KVNX), and Wichita, KS (KICT), were assimilated in a three‐dimensional variational algorithm to retrieve the 3‐D wind field (North et al, ). Additionally, vertical profiles of temperature and relative humidity data at 10‐min frequency retrieved from Raman lidar measurements are used to characterize the thermodynamic characteristics of the observed stratiform region below the melting level.…”

Dependence of Vertical Alignment of Cloud and Precipitation Properties on Their Effective Fall Speeds

“…The data set is described by Kumar et al (2015), who used it to analyze mass flux characteristics of tropical cumulus clouds, and by Schumacher et al (2015), who documented vertical motion statistics segregated by cloud type. This study relies on vertical air motions retrieved from Doppler spectra from the two profilers using an algorithm by Williams (2012), which avoids making assumptions on hydrometeor fall speeds needed for a single profiler retrieval (e.g., Giangrande et al, 2016) or mass continuity assumptions used in multi-Doppler retrievals (e.g., North et al, 2017).…”