scholarly journals Automated rain rate estimates using the Ka-band ARM Zenith Radar (KAZR)

2014 ◽  
Vol 7 (2) ◽  
pp. 1807-1833
Author(s):  
A. Chandra ◽  
C. Zhang ◽  
P. Kollias ◽  
S. Matrosov ◽  
W. Szyrmer
Keyword(s):  
Rain Rate ◽  
Robust Statistics ◽  
Tropical Indian Ocean ◽  
Ka Band ◽  
Weather Radars ◽  
Automated Algorithm ◽  
Microphysical Processes ◽  
The Tropics ◽  
Rain Rates ◽  
The Impact

Abstract. The use of millimeter wavelength radars for probing precipitation has recently gained interest. However, estimation of precipitation variables is not straightforward due to strong attenuation, radar receiver saturation, antenna wet radome effects and natural microphysical variability. Here, an automated algorithm is developed for routinely retrieving rain rates from profiling Ka-band (35-GHz) ARM zenith radars (KAZR). A 1-D simple, steady state microphysical model is used to estimate the impact of microphysical processes and attenuation on the profiles of the radar observables at 35-GHz and thus provide criteria for identifying when attenuation or microphysical processes dominate KAZR observations. KAZR observations are also screened for saturation and wet radome effects. The proposed algorithm is implemented in two steps: high rain rates are retrieved by using the amount of attenuation in rain layers, while lower rain rates by the Ze–R (reflectivity-rain rate) relation is implemented. Observations collected by the KAZR, disdrometer and scanning weather radars during the DYNAMO/AMIE field campaign at Gan Island of the tropical Indian Ocean are used to validate the proposed approach. The results indicate that the proposed algorithm can be used to derive robust statistics of rain rates in the tropics from KAZR observations.

scholarly journals Automated rain rate estimates using the Ka-band ARM zenith radar (KAZR)

2015 ◽  
Vol 8 (9) ◽  
pp. 3685-3699 ◽  
Author(s):  
A. Chandra ◽  
C. Zhang ◽  
P. Kollias ◽  
S. Matrosov ◽  
W. Szyrmer
Keyword(s):  
Rain Rate ◽  
Tropical Indian Ocean ◽  
Rain Gauge ◽  
Signal Attenuation ◽  
Ka Band ◽  
Automated Algorithm ◽  
Atmospheric Radiation Measurement ◽  
Microphysical Processes ◽  
The Tropics ◽  
Rain Rates

Abstract. The use of millimeter wavelength radars for probing precipitation has recently gained interest. However, estimation of precipitation variables is not straightforward due to strong signal attenuation, radar receiver saturation, antenna wet radome effects and natural microphysical variability. Here, an automated algorithm is developed for routinely retrieving rain rates from the profiling Ka-band (35-GHz) ARM (Atmospheric Radiation Measurement) zenith radars (KAZR). A 1-dimensional, simple, steady state microphysical model is used to estimate impacts of microphysical processes and attenuation on the profiles of radar observables at 35-GHz and thus provide criteria for identifying situations when attenuation or microphysical processes dominate KAZR observations. KAZR observations are also screened for signal saturation and wet radome effects. The algorithm is implemented in two steps: high rain rates are retrieved by using the amount of attenuation in rain layers, while low rain rates are retrieved from the reflectivity–rain rate (Ze–R) relation. Observations collected by the KAZR, rain gauge, disdrometer and scanning precipitating radars during the DYNAMO/AMIE field campaign at the Gan Island of the tropical Indian Ocean are used to validate the proposed approach. The differences in the rain accumulation from the proposed algorithm are quantified. The results indicate that the proposed algorithm has a potential for deriving continuous rain rate statistics in the tropics.

scholarly journals Microphysics Regime Impacts on the Relationship between Orographic Rain and Orographic Forcing in the Coastal Mountains of Northern California

2016 ◽  
Vol 17 (11) ◽  
pp. 2905-2922 ◽  
Author(s):  
David E. Kingsmill ◽  
Paul J. Neiman ◽  
Allen B. White
Keyword(s):  
Rain Rate ◽  
Correlation Coefficients ◽  
Northern California ◽  
Orographic Enhancement ◽  
Orographic Forcing ◽  
Warm Rain Process ◽  
Bodega Bay ◽  
Rain Rates ◽  
The Impact ◽  
The Relationship

Abstract This study examines the impact of microphysics regime on the relationship between orographic forcing and orographic rain in the coastal mountains of Northern California using >4000 h of data from profiling Doppler radars, rain gauges, and a GPS receiver collected over 10 cool seasons. Orographic forcing is documented by hourly upslope flow, integrated water vapor (IWV), and IWV flux observed along the coast at Bodega Bay (BBY; 15 m MSL). Microphysics regime is inferred in the coastal mountains at Cazadero (CZC; 478 m MSL), where hourly periods of brightband (BB) and nonbrightband (NBB) rain are designated. BB rain is associated with a microphysics regime dominated by the seeder–feeder process while NBB rain is associated with a microphysics regime dominated by the warm-rain process. Mean BBY upslope flow, IWV, and IWV flux are ~16%, ~5%, and ~19% larger, respectively, for NBB rain compared to BB rain, while mean CZC rain rate is ~33% larger for BB rain compared to NBB rain. The orographic enhancement ratio of CZC to BBY rain rate is 3.7 during NBB rain and 2.7 during BB rain. Rain rate at CZC increases as orographic forcing at BBY increases. For a given amount of BBY orographic forcing, mean CZC rain rates are larger for BB rain compared to NBB rain. Correlation coefficients associated with the relationship between CZC rain rate and BBY orographic forcing are smaller for NBB rain relative to BB rain, but these differences are not statistically significant.

scholarly journals A Study of Vertical Structures and Microphysical Characteristics of Different Convective Cloud–Precipitation Types Using Ka-Band Millimeter Wave Radar Measurements

2019 ◽  
Vol 11 (15) ◽  
pp. 1810 ◽  
Author(s):  
Zheng ◽  
Zhang ◽  
Liu ◽  
Liu ◽  
Che
Keyword(s):  
Millimeter Wave ◽  
Convective Cloud ◽  
Radar Data ◽  
Radar Signal ◽  
Dynamic Features ◽  
Ka Band ◽  
Microphysical Parameters ◽  
Wave Radar ◽  
Microphysical Processes ◽  
Rain Rates

Millimeter wave cloud radar (MMCR) is one of the primary instruments employed to observe cloud–precipitation. With appropriate data processing, measurements of the Doppler spectra, spectral moments, and retrievals can be used to study the physical processes of cloud–precipitation. This study mainly analyzed the vertical structures and microphysical characteristics of different kinds of convective cloud–precipitation in South China during the pre-flood season using a vertical pointing Ka-band MMCR. Four kinds of convection, namely, multi-cell, isolated-cell, convective–stratiform mixed, and warm-cell convection, are discussed herein. The results show that the multi-cell and convective–stratiform mixed convections had similar vertical structures, and experienced nearly the same microphysical processes in terms of particle phase change, particle size distribution, hydrometeor growth, and breaking. A forward pattern was proposed to specifically characterize the vertical structure and provide radar spectra models reflecting the different microphysical and dynamic features and variations in different parts of the cloud body. Vertical air motion played key roles in the microphysical processes of the isolated- and warm-cell convections, and deeply affected the ground rainfall properties. Stronger, thicker, and slanted updrafts caused heavier showers with stronger rain rates and groups of larger raindrops. The microphysical parameters for the warm-cell cloud–precipitation were retrieved from the radar data and further compared with the ground-measured results from a disdrometer. The comparisons indicated that the radar retrievals were basically reliable; however, the radar signal weakening caused biases to some extent, especially for the particle number concentration. Note that the differences in sensitivity and detectable height of the two instruments also contributed to the compared deviation.

Quantitative Evaluation of Polarimetric Estimates from Scanning Weather Radars using a Vertically Pointing Micro Rain Radar

2020 ◽  
Author(s):  
Ricardo Reinoso-Rondinel ◽  
Marc Schleiss
Keyword(s):  
Drop Size ◽  
Limited Attention ◽  
Size Distributions ◽  
Weather Radars ◽  
X Band ◽  
Microphysical Processes ◽  
Drop Size Distributions ◽  
Differential Reflectivity ◽  
The Impact ◽  
Rain Radar

AbstractConventionally, micro rain radars (MRRs) have been used as a tool to calibrate reflectivity from weather radars, estimate the relation between rainfall rate and reflectivity, and study microphysical processes in precipitation. However, limited attention has been given to the reliability of the retrieved drop size distributions DSDs from MRRs. This study sheds more light on this aspect by examining the sensitivity of retrieved DSDs to the assumptions made to map Doppler spectra into size distributions, and investigates the capability of an MRR to assess polarimetric observations from operational weather radars. For that, an MRR was installed near the Cabauw observatory in the Netherlands, between the IDRA X-band radar and the Herwijnen operational C-band radar. The measurements of the MRR from November 2018 to February 2019 were used to retrieve DSDs and simulate horizontal reflectivity Ze, differential reflectivity ZDR, and specific differential phase KDP in rain. Attention is given to the impact of aliased spectra and right-hand side truncation on the simulation of polarimetric variables. From a quantitative assessment, the correlations of Ze and ZDR between the MRR and Herwijnen radar were 0.93 and 0.70, respectively, while those between the MRR and IDRA were 0.91 and 0.69. However, Ze and ZDR from the Herwijnen radar showed slight biases of 1.07 and 0.25 dB. For IDRA, the corresponding biases were 2.67 and -0.93 dB. Our results show that MRR measurements are advantageous to inspect the calibration of scanning radars and validate polarimetric estimates in rain, provided that the DSDs are correctly retrieved and controlled for quality assurance.

scholarly journals Performance of the DBS Satellite Receiver under the Impact of Rainfall and Terrestrial Interference

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zaid Ahmed Shamsan ◽  
Ahmed Al-Saman
Keyword(s):  
Rain Rate ◽  
Measured Data ◽  
Semiarid Region ◽  
Receiver Performance ◽  
Mitigation Techniques ◽  
Rain Rates ◽  
The Impact ◽  
Satellite Receiver ◽  
K Band

This article presents a new study on the feasibility of operating a direct broadcasting satellite (DBS) system under the effect of both precipitation and interference from a fixed service (FS) at K-band in a semiarid region. The carrier-to-noise plus interference ratio (CNIR) as a protection criterion has been adopted to make sure that the receiver of the DBS system operates with an acceptable performance under rainfall and interference from FS. Various measured data for rainfall in different areas have been utilized to investigate different rain rate exceedance percentages. Results have been shown that areas with high rain rates have a small CNIR at the DBS receiver and require large protection distances compared to low-rain rate areas and vice versa. Some mitigation techniques have been suggested to alleviate the effect of rain and terrestrial interference on the DBS receiver performance.

scholarly journals Measuring Hydrometeors Using a Precipitation Microphysical Characteristics Sensor: Sampling Effect of Different Bin Sizes on Drop Size Distribution Parameters

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xichuan Liu ◽  
Taichang Gao ◽  
Yuntao Hu ◽  
Xiaojian Shu
Keyword(s):  
Drop Size ◽  
Rain Rate ◽  
Monte Carlo Model ◽  
Rain Gauge ◽  
Sampling Schemes ◽  
Distribution Parameters ◽  
Optimum Sampling ◽  
Simulation Results ◽  
The Impact ◽  
Good Agreement

In order to improve the measurement of precipitation microphysical characteristics sensor (PMCS), the sampling process of raindrops by PMCS based on a particle-by-particle Monte-Carlo model was simulated to discuss the effect of different bin sizes on DSD measurement, and the optimum sampling bin sizes for PMCS were proposed based on the simulation results. The simulation results of five sampling schemes of bin sizes in four rain-rate categories show that the raw capture DSD has a significant fluctuation variation influenced by the capture probability, whereas the appropriate sampling bin size and width can reduce the impact of variation of raindrop number on DSD shape. A field measurement of a PMCS, an OTT PARSIVEL disdrometer, and a tipping bucket rain Gauge shows that the rain-rate and rainfall accumulations have good consistencies between PMCS, OTT, and Gauge; the DSD obtained by PMCS and OTT has a good agreement; the probability of N0, μ, and Λ shows that there is a good agreement between the Gamma parameters of PMCS and OTT; the fitted μ-Λ and Z-R relationship measured by PMCS is close to that measured by OTT, which validates the performance of PMCS on rain-rate, rainfall accumulation, and DSD related parameters.

scholarly journals Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1250
Author(s):  
Daniel Medina ◽  
Haoqing Li ◽  
Jordi Vilà-Valls ◽  
Pau Closas
Keyword(s):  
Intelligent Transportation Systems ◽  
Robust Statistics ◽  
Real Data ◽  
Transportation Systems ◽  
Global Navigation Satellite Systems ◽  
Mixed Integer ◽  
Robust Filtering ◽  
Precise Positioning ◽  
The Impact ◽  
Filtering Techniques

Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness.

scholarly journals Seasonality and Dynamics of Atmospheric Teleconnection from the Tropical Indian Ocean and the Western Pacific to West Antarctica

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 849
Author(s):  
Hyun-Ju Lee ◽  
Emilia-Kyung Jin
Keyword(s):  
Indian Ocean ◽  
Rossby Wave ◽  
Tropical Indian Ocean ◽  
Western Pacific ◽  
Tropical Region ◽  
Formation Mechanisms ◽  
West Antarctica ◽  
Background Flow ◽  
The Impact ◽  
The Western Pacific

The global impact of the tropical Indian Ocean and the Western Pacific (IOWP) is expected to increase in the future because this area has been continuously warming due to global warming; however, the impact of the IOWP forcing on West Antarctica has not been clearly revealed. Recently, ice loss in West Antarctica has been accelerated due to the basal melting of ice shelves. This study examines the characteristics and formation mechanisms of the teleconnection between the IOWP and West Antarctica for each season using the Rossby wave theory. To explicitly understand the role of the background flow in the teleconnection process, we conduct linear baroclinic model (LBM) simulations in which the background flow is initialized differently depending on the season. During JJA/SON, the barotropic Rossby wave generated by the IOWP forcing propagates into the Southern Hemisphere through the climatological northerly wind and arrives in West Antarctica; meanwhile, during DJF/MAM, the wave can hardly penetrate the tropical region. This indicates that during the Austral winter and spring, the IOWP forcing and IOWP-region variabilities such as the Indian Ocean Dipole (IOD) and Indian Ocean Basin (IOB) modes should paid more attention to in order to investigate the ice change in West Antarctica.

scholarly journals What Polarimetric Weather Radars Offer to Cloud Modelers: Forward Radar Operators and Microphysical/Thermodynamic Retrievals

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 362 ◽  
Author(s):  
Alexander V. Ryzhkov ◽  
Jeffrey Snyder ◽  
Jacob T. Carlin ◽  
Alexander Khain ◽  
Mark Pinsky
Keyword(s):  
Cloud Model ◽  
Weather Prediction ◽  
Primary Source ◽  
Polarimetric Radar ◽  
Microphysical Process ◽  
Weather Radars ◽  
Cloud Models ◽  
Cloud Modeling ◽  
Bin Microphysics ◽  
The Impact

The utilization of polarimetric weather radars for optimizing cloud models is a next frontier of research. It is widely understood that inadequacies in microphysical parameterization schemes in numerical weather prediction (NWP) models is a primary cause of forecast uncertainties. Due to its ability to distinguish between hydrometeors with different microphysical habits and to identify “polarimetric fingerprints” of various microphysical processes, polarimetric radar emerges as a primary source of needed information. There are two approaches to leverage this information for NWP models: (1) radar microphysical and thermodynamic retrievals and (2) forward radar operators for converting the model outputs into the fields of polarimetric radar variables. In this paper, we will provide an overview of both. Polarimetric measurements can be combined with cloud models of varying complexity, including ones with bulk and spectral bin microphysics, as well as simplified Lagrangian models focused on a particular microphysical process. Combining polarimetric measurements with cloud modeling can reveal the impact of important microphysical agents such as aerosols or supercooled cloud water invisible to the radar on cloud and precipitation formation. Some pertinent results obtained from models with spectral bin microphysics, including the Hebrew University cloud model (HUCM) and 1D models of melting hail and snow coupled with the NSSL forward radar operator, are illustrated in the paper.

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