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V. Chandrasekar and Nitin Bharadwaj

Abstract

Dual-polarization weather radars typically measure the radar reflectivity at more than one polarization state for transmission and reception. Historically, dual-polarization radars have been operated at copolar and cross-polar states defined with respect to the transmit polarization states. Recently, based on the improved understanding of the propagation properties of electromagnetic waves in precipitation media, the simultaneous transmit and receive (STAR) mode has become common to simplify the hardware. In the STAR mode of operation, horizontal and vertical polarization states are transmitted simultaneously and samples of both horizontal and vertical copolar returns are obtained. A drawback of the current implementation of STAR mode is its inability to measure parameters obtained from cross-polar signals such as linear depolarization ratio (LDR). In this paper, a technique to obtain cross-polar signals with STAR mode waveform is presented. In this technique, the horizontally and vertically polarized transmit waveforms are coded with orthogonal phase sequences. The performance of the phase-coded waveform is determined by the properties of the phase codes. This orthogonal phase coding technique is implemented in the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar. This paper outlines the methodology and presents the performance of the cross-polar and copolar parameter estimation based on the simulation as well as data collected from the CSU–CHILL radar.

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Nitin Bharadwaj and V. Chandrasekar

Abstract

The use of solid-state transmitters is becoming increasingly viable for atmospheric radars and is a key part of the strategy to realize any dense network of low-powered radars. However, solid-state transmitters have low peak powers and this necessitates the use of pulse compression waveforms. In this paper frequency diversity in a wideband waveform design is proposed to mitigate the low sensitivity of solid-state transmitters. In addition, the waveforms mitigate the range-eclipsing problem associated with long pulse compression. An analysis of the performance of pulse compression using mismatched compression filters designed to minimize sidelobe levels is presented. The impact of the range sidelobe level on the retrieval of Doppler moments is discussed. Realistic simulations are performed based on both the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) and the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) Integrated Project I (IP1) radar data.

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Nitin Bharadwaj, V. Chandrasekar, and Francesc Junyent

Abstract

This paper describes the waveform design space and signal processing system for dual-polarization Doppler weather radar operating at X band. The performance of the waveforms is presented with ground clutter suppression capability and mitigation of range–velocity ambiguity. The operational waveform is designed based on operational requirements and system/hardware requirements. A dual–Pulse Repetition Frequency (PRF) waveform was developed and implemented for the first generation X-band radars deployed by the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). This paper presents an evaluation of the performance of the waveforms based on simulations and data collected by the first-generation CASA radars during operations.

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Pavlos Kollias, Nitin Bharadwaj, Kevin Widener, Ieng Jo, and Karen Johnson

Abstract

The acquisition of scanning cloud radars by the Atmospheric Radiation Measurement (ARM) program and research institutions around the world generates the need for developing operational scan strategies for cloud radars. Here, the first generation of sampling strategies for the scanning ARM cloud radars (SACRs) is presented. These scan strategies are designed to address the scientific objectives of ARM; however, they introduce an initial framework for operational scanning cloud radars. While the weather community uses scan strategies that are based on a sequence of scans at constant elevations, the SACR scan strategies are based on a sequence of scans at constant azimuth. This is attributed to the cloud geometrical properties, which are vastly different from the rain and snow shafts that are the primary targets of precipitation radars; the need to cover the cone of silence; and the scanning limitations of the SACRs. A “cloud surveillance” scan strategy is introduced that is based on a sequence of horizon-to-horizon range–height indicator (RHI) scans that sample the hemispherical sky (HS) every 30° azimuth (HSRHI). The HSRHI scan strategy is complimented with a low-elevation plan position indicator (PPI) scan. The HSRHI and PPI are repeated every 30 min to provide a static view of the cloud conditions around the SACR location. Between the HSRHI and PPI scan strategies, other scan strategies are introduced depending on the cloud conditions. In the future, information about the atmospheric cloud state will be used in a closed-loop process to optimize the selection of the SACR scan strategy.

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Zhe Feng, Sally A. McFarlane, Courtney Schumacher, Scott Ellis, Jennifer Comstock, and Nitin Bharadwaj

Abstract

To improve understanding of the convective processes key to the Madden–Julian oscillation (MJO) initiation, the Dynamics of the MJO (DYNAMO) and the Atmospheric Radiation Measurement Program (ARM) MJO Investigation Experiment (AMIE) collected 4 months of observations from three radars—the S-band dual-polarization Doppler radar (S-Pol), the C-band Shared Mobile Atmospheric Research and Teaching Radar (SMART-R), and Ka-band ARM zenith radar (KAZR)—along with radiosonde and comprehensive surface meteorological instruments on Addu Atoll, Maldives, in the tropical Indian Ocean. One DYNAMO/AMIE hypothesis suggests that the evolution of shallow and congestus cloud populations is essential to the initiation of the MJO. This study focuses on evaluating the ability of these three radars to document the full spectrum of cloud populations and to construct a merged cloud–precipitation radar dataset that can be used to test this hypothesis. Comparisons between collocated observations from the three radars show that KAZR provides the only reliable estimate of shallow clouds, while S-Pol/SMART-R can reasonably detect congestus within the 30–50-km range in addition to precipitating deep clouds. On the other hand, KAZR underestimates cloud-top heights due to rainfall attenuation in ~34% of the precipitating clouds, and an empirical method to correct KAZR cloud-top height bias is proposed. Finally, a merged KAZR–S-Pol dataset is produced to provide improved cloud-top height estimates, total hydrometeor microphysics, and radiative heating rate retrievals. With this dataset the full spectrum of tropical convective clouds during DYNAMO/AMIE can be reliably constructed and, together with complimentary radiosonde data, it can be used to study the role of shallow and congestus clouds in the initiation of the MJO.

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Pavlos Kollias, Ieng Jo, Paloma Borque, Aleksandra Tatarevic, Katia Lamer, Nitin Bharadwaj, Kevin Widener, Karen Johnson, and Eugene E. Clothiaux

Abstract

The scanning Atmospheric Radiation Measurement (ARM) Program cloud radars (SACRs) are the primary instruments for documenting the four-dimensional structure and evolution of clouds within a 20–30-km radius of the ARM fixed and mobile sites. Here, the postprocessing of the calibrated SACR measurements is discussed. First, a feature mask algorithm that objectively determines the presence of significant radar returns is described. The feature mask algorithm is based on the statistical properties of radar receiver noise. It accounts for atmospheric emission and is applicable even for SACR profiles with few or no signal-free range gates. Using the nearest-in-time atmospheric sounding, the SACR radar reflectivities are corrected for gaseous attenuation (water vapor and oxygen) using a line-by-line absorption model. Despite having a high pulse repetition frequency, the SACR has a narrow Nyquist velocity limit and thus Doppler velocity folding is commonly observed. An unfolding algorithm that makes use of a first guess for the true Doppler velocity using horizontal wind measurements from the nearest sounding is described. The retrieval of the horizontal wind profile from the hemispherical sky range–height indicator SACR scan observations and/or nearest sounding is described. The retrieved horizontal wind profile can be used to adaptively configure SACR scan strategies that depend on wind direction. Several remaining challenges are discussed, including the removal of insect and second-trip echoes. The described algorithms significantly enhance SACR data quality and constitute an important step toward the utilization of SACR measurements for cloud research.

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G. Alexander Sokolowsky, Eugene E. Clothiaux, Cory F. Baggett, Sukyoung Lee, Steven B. Feldstein, Edwin W. Eloranta, Maria P. Cadeddu, Nitin Bharadwaj, and Karen L. Johnson

Abstract

Intrusions of warm, moist air into the Arctic during winter have emerged as important contributors to Arctic surface warming. Previous studies indicate that temperature, moisture, and hydrometeor enhancements during intrusions all make contributions to surface warming via emission of radiation down to the surface. Here, datasets from instrumentation at the Atmospheric Radiation Measurement User Facility in Utqiaġvik (formerly Barrow) for the six months from November through April for the six winter seasons of 2013/14–2018/19 were used to quantify the atmospheric state. These datasets subsequently served as inputs to compute surface downwelling longwave irradiances via radiative transfer computations at 1-min intervals with different combinations of constituents over the six winter seasons. The computed six winter average irradiance with all constituents included was 205.0 W m−2, close to the average measured irradiance of 206.7 W m−2, a difference of −0.8%. During this period, water vapor was the most important contributor to the irradiance. The computed average irradiance with dry gas was 71.9 W m−2. Separately adding water vapor, liquid, or ice to the dry atmosphere led to average increases of 2.4, 1.8, and 1.6 times the dry atmosphere irradiance, respectively. During the analysis period, 15 episodes of warm, moist air intrusions were identified. During the intrusions, individual contributions from elevated temperature, water vapor, liquid water, and ice water were found to be comparable to each other. These findings indicate that all properties of the atmospheric state must be known in order to quantify the radiation coming down to the Arctic surface during winter.

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Pavlos Kollias, Eugene E. Clothiaux, Thomas P. Ackerman, Bruce A. Albrecht, Kevin B. Widener, Ken P. Moran, Edward P. Luke, Karen L. Johnson, Nitin Bharadwaj, James B. Mead, Mark A. Miller, Johannes Verlinde, Roger T. Marchand, and Gerald G. Mace
Introduction

As the ARM Program was getting underway in the early 1990s, studies by Ramanathan et al. (1989) and Cess et al. (1990) highlighted the importance of cloud and radiation interactions to climate. Ramanathan et al. (1989) demonstrated that, on average, clouds cool the climate system but that different cloud types can have different influences upon it. Cess et al. (1990) showed that general circulation models have an array of different responses to the same sea surface temperature change that result from differences in model clouds and their interactions with radiation.

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Yuying Zhang, Shaocheng Xie, Stephen A. Klein, Roger Marchand, Pavlos Kollias, Eugene E. Clothiaux, Wuyin Lin, Karen Johnson, Dustin Swales, Alejandro Bodas-Salcedo, Shuaiqi Tang, John M. Haynes, Scott Collis, Michael Jensen, Nitin Bharadwaj, Joseph Hardin, and Bradley Isom

Clouds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real-world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors that are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the concept of instrument

Open access
David McLaughlin, David Pepyne, V. Chandrasekar, Brenda Philips, James Kurose, Michael Zink, Kelvin Droegemeier, Sandra Cruz-Pol, Francesc Junyent, Jerald Brotzge, David Westbrook, Nitin Bharadwaj, Yanting Wang, Eric Lyons, Kurt Hondl, Yuxiang Liu, Eric Knapp, Ming Xue, Anthony Hopf, Kevin Kloesel, Alfred DeFonzo, Pavlos Kollias, Keith Brewster, Robert Contreras, Brenda Dolan, Theodore Djaferis, Edin Insanic, Stephen Frasier, and Frederick Carr

Dense networks of short-range radars capable of mapping storms and detecting atmospheric hazards are described. Composed of small X-band (9.4 GHz) radars spaced tens of kilometers apart, these networks defeat the Earth curvature blockage that limits today s long-range weather radars and enables observing capabilities fundamentally beyond the operational state-of-the-art radars. These capabilities include multiple Doppler observations for mapping horizontal wind vectors, subkilometer spatial resolution, and rapid-update (tens of seconds) observations extending from the boundary layer up to the tops of storms. The small physical size and low-power design of these radars permits the consideration of commercial electronic manufacturing approaches and radar installation on rooftops, communications towers, and other infrastructure elements, leading to cost-effective network deployments. The networks can be architected in such a way that the sampling strategy dynamically responds to changing weather to simultaneously accommodate the data needs of multiple types of end users. Such networks have the potential to supplement, or replace, the physically large long-range civil infrastructure radars in use today.

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