scholarly journals Rain‐rate estimation algorithm using signal attenuation of Ka‐band cloud radar

2019 ◽  
Vol 27 (1) ◽  
Author(s):  
Su‐Bin Oh ◽  
Pavlos Kollias ◽  
Jeong‐Soon Lee ◽  
Seung‐Woo Lee ◽  
Yong Hee Lee ◽  
...  
Keyword(s):  
Rain Rate ◽  
Estimation Algorithm ◽  
Signal Attenuation ◽  
Ka Band ◽  
Rate Estimation ◽  
Cloud Radar

scholarly journals Stratiform and Convective Precipitation Observed by Multiple Radars during the DYNAMO/AMIE Experiment

2014 ◽  
Vol 53 (11) ◽  
pp. 2503-2523 ◽  
Author(s):  
Min Deng ◽  
Pavlos Kollias ◽  
Zhe Feng ◽  
Chidong Zhang ◽  
Charles N. Long ◽  
...  
Keyword(s):  
Rain Rate ◽  
Convective Precipitation ◽  
Doppler Velocity ◽  
Ka Band ◽  
Stratiform Rain ◽  
Rate Estimation ◽  
Precipitation Occurrence ◽  
Cloud Radar ◽  
Stratiform Precipitation ◽  
Two Parameter

AbstractIn this study, methods of convective/stratiform precipitation classification and surface rain-rate estimation based on the Atmospheric Radiation Measurement Program (ARM) cloud radar measurements were developed and evaluated. Simultaneous and collocated observations of the Ka-band ARM zenith radar (KAZR), two scanning precipitation radars [NCAR S-band/Ka-band Dual Polarization, Dual Wavelength Doppler Radar (S-PolKa) and Texas A&M University Shared Mobile Atmospheric Research and Teaching Radar (SMART-R)], and surface precipitation during the Dynamics of the Madden–Julian Oscillation/ARM MJO Investigation Experiment (DYNAMO/AMIE) field campaign were used. The motivation of this study is to apply the unique long-term ARM cloud radar observations without accompanying precipitation radars to the study of cloud life cycle and precipitation features under different weather and climate regimes. The resulting convective/stratiform classification from KAZR was evaluated against precipitation radars. Precipitation occurrence and classified convective/stratiform rain fractions from KAZR compared favorably to the collocated SMART-R and S-PolKa observations. Both KAZR and S-PolKa radars observed about 5% precipitation occurrence. The convective (stratiform) precipitation fraction is about 18% (82%). Collocated disdrometer observations of two days showed an increased number concentration of small and large raindrops in convective rain relative to dominant small raindrops in stratiform rain. The composite distributions of KAZR reflectivity and Doppler velocity also showed distinct structures for convective and stratiform rain. These evidences indicate that the method produces physically consistent results for the two types of rain. A new KAZR-based, two-parameter [the gradient of accumulative radar reflectivity Ze (GAZ) below 1 km and near-surface Ze] rain-rate estimation procedure was developed for both convective and stratiform rain. This estimate was compared with the exponential Z–R (reflectivity–rain rate) relation. The relative difference between the estimated and surface-measured rainfall rates showed that the two-parameter relation can improve rainfall estimation relative to the Z–R relation.

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 Optimal rain rate estimation algorithm for light and heavy rain using polarimetric measurements

2011 ◽  
Vol 11 (11) ◽  
pp. 3067-3079 ◽  
Author(s):  
A. Elmzoughi ◽  
R. Abdelfattah ◽  
V. Santalla Del Rio ◽  
Z. Belhadj
Keyword(s):  
Rain Rate ◽  
Heavy Rain ◽  
Estimation Algorithm ◽  
Radar Data ◽  
Accurate Estimation ◽  
Rate Estimation ◽  
Rayleigh Approximation ◽  
Physically Based ◽  
Rain Rates

Abstract. In this paper, we propose an ameliorated physically-based rain rate estimation algorithm for semi-arid regions using the Rayleigh approximation. The proposed algorithm simultaneously uses the reflectivity and the specific differential phase to provide an accurate estimation for both small and large rain rates. In order to validate the proposed estimator, simulated polarimetric rain rate data based on a dual approach, referring to both physical and statistical models of the rain target, are used. Moreover, experimental radar data (the same as used in Matrosov et al., 2006) taken in light to moderate stratiform rainfalls with rain rates varying between 2 and 15 mm h−1 were collected as part of the GPM pilot experiment. It is shown that the proposed algorithm for rain rate estimation based on the full set of polarimetric radar measurements agree better with in situ disdrometer ones.

A Novel Symbol Rate Estimation Algorithm for Phase Modulating Signals in Wireless Communications

2013 ◽  
Vol 443 ◽  
pp. 392-396
Author(s):  
Peng Zhou ◽  
Chi Sheng Li
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Frequency Resolution ◽  
Phase Shift Keying ◽  
Phase Jump ◽  
Rate Estimation ◽  
Awgn Channel ◽  
Symbol Rate ◽  
Shift Keying ◽  
Simulation Results

In this paper, we proposed a new symbol rate estimation algorithm for phase shift keying (PSK) and qua drawtube amplitude modulation (QAM) signals in AWGN channel First we constructe a delay-multiplied signal, from which we obtaine the modulated information. Then we calculated the instantaneous autocorrelation of the delay-multiplied signal to pick out the phase jump. To eliminate the restriction of frequency resolution in fast Fourier transform, we performed a Chirp-Z transform to find out the exact spectral line which represente the symbol rate of the signal to be analyzed. Compared with the existing algorithms, it is a simple solution that has a better performance and accuracy in low signal-to-noise-ratio channel conditions. Simulation results show that the probability of relative estimating deviation below 0.1% reaches 100% and the average and standard variance of absolute estimation deviation are at the magnitude of 10-2 when SNR is over 2dB.

scholarly journals Estimating Rain Rates from Tipping-Bucket Rain Gauge Measurements

2008 ◽  
Vol 25 (1) ◽  
pp. 43-56 ◽  
Author(s):  
Jianxin Wang ◽  
Brad L. Fisher ◽  
David B. Wolff
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Time Shift ◽  
Estimation Errors ◽  
Rate Estimation ◽  
Reflectivity Data ◽  
Rain Rates ◽  
Event Definition

Abstract This paper describes the cubic spline–based operational system for the generation of the Tropical Rainfall Measuring Mission (TRMM) 1-min rain-rate product 2A-56 from tipping-bucket (TB) gauge measurements. A simulated TB gauge from a Joss–Waldvogel disdrometer is employed to evaluate the errors of the TB rain-rate estimation. These errors are very sensitive to the time scale of rain rates. One-minute rain rates suffer substantial errors, especially at low rain rates. When 1-min rain rates are averaged over 4–7-min intervals or longer, the errors dramatically reduce. Estimated lower rain rates are sensitive to the event definition whereas the higher rates are not. The median relative absolute errors are about 22% and 32% for 1-min rain rates higher and lower than 3 mm h−1, respectively. These errors decrease to 5% and 14% when rain rates are used at the 7-min scale. The radar reflectivity–rain-rate distributions drawn from the large amount of 7-min rain rates and radar reflectivity data are mostly insensitive to the event definition. The time shift due to inaccurate clocks can also cause rain-rate estimation errors, which increase with the shifted time length. Finally, some recommendations are proposed for possible improvements of rainfall measurements and rain-rate estimations.

Monsoon cloud observations using a low power vertical looking Ka Band Cloud radar

2016 ◽  
Author(s):  
A. Agarwal ◽  
J. S. Pillai ◽  
K. Aurobindo ◽  
J. D. Abhyankar ◽  
G. Isola ◽  
...  
Keyword(s):  
Low Power ◽  
Ka Band ◽  
Cloud Radar

scholarly journals Classification of Hydrometeors Using Measurements of the Ka-Band Cloud Radar Installed at the Milešovka Mountain (Central Europe)

2018 ◽  
Vol 10 (11) ◽  
pp. 1674 ◽  
Author(s):  
Zbyněk Sokol ◽  
Jana Minářová ◽  
Petr Novák
Keyword(s):  
Central Europe ◽  
Terminal Velocity ◽  
Small Particles ◽  
Cloud Droplets ◽  
Vertical Temperature Profile ◽  
Ka Band ◽  
Cloud Radar ◽  
Weather Radars ◽  
Cloud Particles ◽  
Linear Depolarization Ratio

In radar meteorology, greater interest is dedicated to weather radars and precipitation analyses. However, cloud radars provide us with detailed information on cloud particles from which the precipitation consists of. Motivated by research on the cloud particles, a vertical Ka-band cloud radar (35 GHz) was installed at the Milešovka observatory in Central Europe and was operationally measuring since June 2018. This study presents algorithms that we use to retrieve vertical air velocity (Vair) and hydrometeors. The algorithm calculating Vair is based on small-particle tracers, which considers the terminal velocity of small particles negligible and, thereby, Vair corresponds to the velocity of the small particles. The algorithm classifying hydrometeors consists of calculating the terminal velocity of hydrometeors and the vertical temperature profile. It identifies six hydrometeor types (cloud droplets, ice, and four precipitating particles: rain, graupel, snow, and hail) based on the calculated terminal velocity of hydrometeors, temperature, Vair, and Linear Depolarization Ratio. The results of both the Vair and the distribution of hydrometeors were found to be realistic for a thunderstorm associated with significant lightning activity on 1 June 2018.

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