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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
John C. Hubbert
,
James W. Wilson
,
Tammy M. Weckwerth
,
Scott M. Ellis
,
Mike Dixon
, and
Eric Loew

Abstract

The National Center for Atmospheric Research (NCAR) operates a state-of-the-art S-band dual-polarization Doppler radar (S-Pol) for the National Science Foundation (NSF). This radar has some similar and some distinguishing characteristics to the National Weather Service (NWS) operational Weather Surveillance Radar-1988 Doppler Polarimetric (WSR-88DP). One key difference is that the WSR-88DP is used for operational purposes where rapid 360° volumetric scanning is required to monitor rapid changes in storm characteristics for nowcasting and issuing severe storm warnings. Since S-Pol is used to support the NSF research community, it usually scans at much slower rates than operational radars. This results in higher resolution and higher data quality suitable for many research studies. An important difference between S-Pol and the WSR-88DP is S-Pol’s ability to use customized scan strategies including scanning on vertical surfaces ([range–height indicators (RHIs)], which are presently not done by WSR-88DPs. RHIs provide high-resolution microphysical structures of convective storms, which are central to many research studies. Another important difference is that the WSR-88DP simultaneously transmits horizontal (H) and vertical (V) polarized pulses. In contrast, S-Pol typically transmits alternating H and V pulses, which results in not only higher data quality for research but also allows for the cross-polar signal to be measured. The cross-polar signal provides estimates of the linear depolarization ratio (LDR) and the co- to cross-correlation coefficient that give additional microphysical information. This paper presents plots and interpretations of high-quality, high-resolution polarimetric data that demonstrate the value of S-Pol’s polarimetric measurements for atmospheric research.

Full access
Evan A. Kalina
,
Katja Friedrich
,
Scott M. Ellis
, and
Donald W. Burgess

Abstract

Microphysical data from thunderstorms are sparse, yet they are essential to validate microphysical schemes in numerical models. Mobile, dual-polarization, X-band radars are capable of providing a wealth of data that include radar reflectivity, drop shape, and hydrometeor type. However, X-band radars suffer from beam attenuation in heavy rainfall and hail, which can be partially corrected with attenuation correction schemes. In this research, the authors compare surface disdrometer observations to results from a differential phase-based attenuation correction scheme. This scheme is applied to data recorded by the National Oceanic and Atmospheric Administration (NOAA) X-band dual-polarized (NOXP) mobile radar, which was deployed during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2). Results are presented from five supercell thunderstorms and one squall line (183 min of data). The median disagreement (radar–disdrometer) in attenuation-corrected reflectivity Z and differential reflectivity Z DR is just 1.0 and 0.19 dB, respectively. However, two data subsets reveal much larger discrepancies in Z (Z DR): 5.8 (1.6) dB in a hailstorm and −13 (−0.61) dB when the radar signal quality index (SQI) is less than 0.8. The discrepancies are much smaller when disdrometer and S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) Z are compared, with differences of −1.5 dB (hailstorm) and −0.66 dB (NOXP SQI < 0.8). A comparison of the hydrometeor type retrieved from disdrometer and NOXP radar data is also presented, in which the same class is assigned 63% of the time.

Full access
Phuong-Nghi Do
,
Kao-Shen Chung
,
Pay-Liam Lin
,
Ching-Yin Ke
, and
Scott M. Ellis

Abstract

This study investigated the effect of the assimilation of the S- and Ka-band dual‐wavelength-retrieved water vapor data with radial wind and reflectivity data. The vertical profile of humidity, which provides environmental information before precipitation occurs, was obtained at low levels and thinned into averaged and four-quadrant profiles. Additionally, the following two strategies were examined: 1) assimilation of water vapor data with radar data for the entire 2 h and 2) assimilation of water vapor data in the first hour, and radial velocity and reflectivity data in the second hour. By using the WRF local ensemble transform Kalman filter data assimilation system, three real cases of the Dynamics of the Madden–Julian Oscillation experiment were examined through a series of experiments. The analysis results revealed that assimilating additional water vapor data more markedly improved the analysis at the convective scale than assimilating radial wind and reflectivity data alone. In addition, the strategy of assimilating only retrieved water vapor data in the first hour and radial wind and reflectivity data in the second hour achieved the optimal analysis and subsequent very short-term forecast. The evaluation of quantitative precipitation forecasting demonstrated that assimilating additional retrieved water vapor data distinctly improved the rain forecast compared with assimilating radar data only. When moisture data were assimilated, improved nowcasting could be extended up to 4 h. Furthermore, assimilating moisture profiles into four quadrants achieved more accurate analysis and forecast. Overall, our study demonstrated that the humidify information in nonprecipitation areas is critical for further improving the analysis and forecast of convective weather systems.

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
Robert M. Rauber
,
Scott M. Ellis
,
J. Vivekanandan
,
Jeffrey Stith
,
Wen-Chau Lee
,
Greg M. McFarquhar
,
Brian F. Jewett
, and
Andrew Janiszeski

Abstract

The newly developed High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Cloud Radar (HCR) is an airborne, W-band, dual-polarization, Doppler research radar that fits within an underwing pod on the National Center for Atmospheric Research Gulfstream-V HIAPER aircraft. On 2 February 2015, the HCR was flown on its maiden research voyage over a cyclone along the Northeast coast of the United States. Six straight flight legs were flown over 6 h between the northern tip of Delaware Bay and Bangor, Maine, crossing the rain–snow line, and passing directly over Boston, Massachusetts, which received over 16 in. of snow during the event. The HCR, which recorded reflectivity, radial velocity, spectral width, and linear depolarization ratio with a 0.7° beam, was pointed at nadir from a flight altitude of 12,800 m (42,000 ft). The along-track resolution ranged between 20 and 200 m, depending on range, at aircraft speeds varying between 200 and 275 m s−1. The range resolution was 19.2 m.

Remarkably detailed finescale structures were found throughout the storm system, including cloud-top generating cells, upright elevated convection, layers of turbulence, vertical velocity perturbations across the melting level, gravity waves, boundary layer circulations, and other complex features. Vertical velocities in these features ranged from 1 to 5 m s−1, and many features were on scales of 5 km or less. The purpose of this paper is to introduce the HCR and highlight the remarkable finescale structures revealed within this Northeast U.S. cyclone by the HCR.

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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
Howard B. Bluestein
,
Robert M. Rauber
,
Donald W. Burgess
,
Bruce Albrecht
,
Scott M. Ellis
,
Yvette P. Richardson
,
David P. Jorgensen
,
Stephen J. Frasier
,
Phillip Chilson
,
Robert D. Palmer
,
Sandra E. Yuter
,
Wen-Chau Lee
,
David C. Dowell
,
Paul L. Smith
,
Paul M. Markowski
,
Katja Friedrich
, and
Tammy M. Weckwerth

To assist the National Science Foundation in meeting the needs of the community of scientists by providing them with the instrumentation and platforms necessary to conduct their research successfully, a meeting was held in late November 2012 with the purpose of defining the problems of the next generation that will require radar technologies and determining the suite of radars best suited to help solve these problems. This paper summarizes the outcome of the meeting: (i) Radars currently in use in the atmospheric sciences and in related research are reviewed. (ii) New and emerging radar technologies are described. (iii) Future needs and opportunities for radar support of high-priority research are discussed. The current radar technologies considered critical to answering the key and emerging scientific questions are examined. The emerging radar technologies that will be most helpful in answering the key scientific questions are identified. Finally, gaps in existing radar observing technologies are listed.

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