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  • Author or Editor: Scott M. Ellis x
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Jothiram Vivekanandan
,
Virendra P. Ghate
,
Jorgen B. Jensen
,
Scott M. Ellis
, and
M. Christian Schwartz

Abstract

This paper describes a technique for estimating the liquid water content (LWC) and a characteristic particle diameter in stratocumulus clouds using radar and lidar observations. The uncertainty in LWC estimate from radar and lidar measurements is significantly reduced once the characteristic particle diameter is known. The technique is independent of the drop size distribution. It is applicable for a broad range of W-band reflectivity Z between −30 and 0 dBZ and all values of lidar backscatter β observations. No partitioning of cloud or drizzle is required on the basis of an arbitrary threshold of Z as in prior studies. A method for estimating droplet diameter and LWC was derived from the electromagnetic simulations of radar and lidar observations. In situ stratocumulus cloud and drizzle probe spectra were input to the electromagnetic simulation. The retrieved droplet diameter and LWC were validated using in situ measurements from the southeastern Pacific Ocean. The retrieval method was applied to radar and lidar measurements from the northeastern Pacific. Uncertainty in the retrieved droplet diameter and LWC that are due to the measurement errors in radar and lidar backscatter measurements are 7% and 14%, respectively. The retrieved LWC was validated using the concurrent G-band radiometer estimates of the liquid water path.

Open access
Scott M. Ellis
,
Peisang Tsai
,
Christopher Burghart
,
Ulrike Romatschke
,
Michael Dixon
,
Jothiram Vivekanandan
,
Jonathan Emmett
, and
Eric Loew

Abstract

A technique for correcting radar radial velocity Vr in airborne, nadir-pointing radar data using the surface of Earth as a reference is proposed and tested. Operating airborne Doppler radars requires correcting the radial velocity for platform motion. This can be accomplished with accurate beam-pointing and platform motion measurements. However, there are often residual pointing errors due to drift in inertial navigation systems (INS) and/or errors in platform-relative pointing. The technique proposed here takes advantage of the fact that the surface is stationary and the mean of the measured Vr at the surface Vr surf meas should be 0 m s−1. Therefore, if a good estimate of the mean Vr surf meas is made, it can be subtracted from the measured Vr to correct for errors due to residual pointing errors. The Vr surf meas data contain many independent deviations from 0 m s−1 due to various causes, including measurement variance and large deviations due to surface features. These deviations must be filtered out of Vr surf meas before the surface reference can be applied to correct the Vr data. A two-step filtering process was developed and tested. The first step removes large deviations in Vr surf meas and the second step removes the measurement noise. The technique was examined using data from three field campaigns and was found to improve the quality of Vr in all cases. The Vr bias was removed and the variance was substantially reduced. The approach is generally applicable to nadir-pointing airborne radar data.

Full access
David J. Serke
,
Scott M. Ellis
,
Sarah A. Tessendorf
,
David E. Albo
,
John C. Hubbert
, and
Julie A. Haggerty

Abstract

Detection of in-flight icing hazard is a priority of the aviation safety community. The “Radar Icing Algorithm” (RadIA) has been developed to indicate the presence, phase, and relative size of supercooled drops. This paper provides an evaluation of RadIA via comparison to in situ microphysical measurements collected with a research aircraft during the 2017 “Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment” (SNOWIE) field campaign. RadIA uses level-2 dual-polarization radar moments from operational National Weather Service WSR-88D and a numerical weather prediction model temperature profile as inputs. Moment membership functions are defined based on the results of previous studies, and fuzzy logic is used to combine the output of these functions to create a 0 to 1 interest for detecting small-drop, large-drop, and mixed-phase icing. Data from the two-dimensional stereo (2D-S) particle probe on board the University of Wyoming King Air aircraft were categorized as either liquid or solid phase water with a shape classification algorithm and binned by size. RadIA interest values from 17 cases were matched to statistical measures of the solid/liquid particle size distributions (such as maximum particle diameter) and values of LWC from research aircraft flights. Receiver operating characteristic area under the curve (AUC) values for RadIA algorithms were 0.75 for large-drop, 0.73 for small-drop, and 0.83 for mixed-phase cases. RadIA is proven to be a valuable new capability for detecting the presence of in-flight icing hazards from ground-based precipitation radar.

Restricted access
M. Christian Schwartz
,
Virendra P. Ghate
,
Bruce. A. Albrecht
,
Paquita Zuidema
,
Maria P. Cadeddu
,
Jothiram Vivekanandan
,
Scott M. Ellis
,
Pei Tsai
,
Edwin W. Eloranta
,
Johannes Mohrmann
,
Robert Wood
, and
Christopher S. Bretherton

Abstract

The Cloud System Evolution in the Trades (CSET) aircraft campaign was conducted in the summer of 2015 in the northeast Pacific to observe the transition from stratocumulus to cumulus cloud regime. Fourteen transects were made between Sacramento, California, and Kona, Hawaii, using the NCAR’s High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Gulfstream V (GV) aircraft. The HIAPER W-band Doppler cloud radar (HCR) and the high-spectral-resolution lidar (HSRL), in their first deployment together on board the GV, provided crucial cloud and precipitation observations. The HCR recorded the raw in-phase (I) and quadrature (Q) components of the digitized signal, from which the Doppler spectra and its first three moments were calculated. HCR/HSRL data were merged to develop a hydrometeor mask on a uniform georeferenced grid of 2-Hz temporal and 20-m vertical resolutions. The hydrometeors are classified as cloud or precipitation using a simple fuzzy logic technique based on the HCR mean Doppler velocity, HSRL backscatter, and the ratio of HCR reflectivity to HSRL backscatter. This is primarily applied during zenith-pointing conditions under which the lidar can detect the cloud base and the radar is more sensitive to clouds. The microphysical properties of below-cloud drizzle and optically thin clouds were retrieved using the HCR reflectivity, HSRL backscatter, and the HCR Doppler spectrum width after it is corrected for the aircraft speed. These indicate that as the boundary layers deepen and cloud-top heights increase toward the equator, both the cloud and rain fractions decrease.

Open access