scholarly journals An adaptive echo attenuation correction method for airborne Ka-band precipitation cloud radar based on melting layer

2021 ◽  
Author(s):  
Dongfei Zuo ◽  
Deping Ding ◽  
Yichen Chen ◽  
Ling Yang ◽  
Delong Zhao ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Attenuation Correction ◽  
Relative Error ◽  
Water Film ◽  
Liquid Water Content ◽  
Correction Method ◽  
Ka Band ◽  
Melting Layer ◽  
Mean Relative Error ◽  
Cloud Radar

Abstract. In this study, an airborne Ka-band Precipitation Cloud Radar (KPR) is used to carry out a cloud observation experiment.By analyzing the attenuation of the snow echo, it is found that during the snowfall, due to the low liquid water content, the KPR attenuation is small on the detection path, and after preliminary comparative analysis, the maximum attenuation correction value is 0.5 dBZ. According to the echo attenuation analysis of mixed precipitation, the melting layer is found to be the key factor affecting the attenuation correction. This study hereby proposes an adaptive echo attenuation correction method based on the melting layer (AEC), and uses the ground-based S-band radar to extract the echo on the aircraft trajectory to verify the correction results. The results show that the echo attenuation correction value above the melting layer is related to the flight position. The aircraft above the melting layer is dominated by ice particles, with small attenuation correction value, the maximum correction amount of 0.13 dBZ; when the aircraft is at and just below the melting layer, a water film is prone to be on the antenna, which leads to serious attenuation of the KPR detection path, with the attenuation correction value 1~2 dBZ. For the precipitation echo below the melting layer, due to the abundant rain and water vapor content, the KPR attenuation is significant with maximum correction value of about 5 dBZ. Compared with the S-band radar, before attenuation correction, the total mean relative error is 15 %, and the correlation coefficient is 0.82; after correction, the total mean relative error is 6 %, and the correlation coefficient is 0.90, indicating the significant improvement of the KPR data quality.

The Attenuation Correction for Ka Band Cloud Radar

2019 ◽  
Author(s):  
Shuangyu Yao ◽  
Jianxin He ◽  
Hao Wang ◽  
Shunxian Tang ◽  
Xiaofeng Liang
Keyword(s):  
Attenuation Correction ◽  
Ka Band ◽  
Cloud Radar

scholarly journals Estimation of the liquid water content and Z -LWC relationship using Ka-band cloud radar and a microwave radiometer

2018 ◽  
Vol 25 (3) ◽  
pp. 423-434 ◽  
Author(s):  
Su-Bin Oh ◽  
Yong Hee Lee ◽  
Jong-Hoon Jeong ◽  
Yeon-Hee Kim ◽  
Sangwon Joo
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Microwave Radiometer ◽  
Liquid Water Content ◽  
Ka Band ◽  
Cloud Radar

scholarly journals Attenuation Correction for Ka-band Cloud Radar Using X-band Weather Radar Data

2016 ◽  
Author(s):  
Peng Zhang ◽  
Yunjie Chen
Keyword(s):  
Attenuation Correction ◽  
Weather Radar ◽  
Radar Data ◽  
Contributory Factor ◽  
Ka Band ◽  
Millimeter Wavelength ◽  
Cloud Radar ◽  
X Band ◽  
Bin Method ◽  
Variation Trends

In order to correct attenuated millimeter-wavelength (Ka-band) radar data and address the problem of instability, an attenuation correction methodology (attenuation correction with variation trend constraint; VTC) was developed. Using synchronous observation conditions and multi-band radars, the VTC method adopts the variation trends of reflectivity in X-band radar data captured with wavelet transform as a constraint to adjust reflectivity factors of millimeter-wavelength radar. The correction was evaluated by comparing reflectivities obtained by millimeter-wavelength cloud radar and X-band weather radar. Experiments showed that attenuation was a major contributory factor in the different reflectivities of the two radars when relatively intense echoes exist, and the attenuation correction developed in this study significantly improved data quality for millimeter-wavelength radar. Reflectivity differences between the two radars were reduced and reflectivity correlations were enhanced. Errors caused by attenuation were eliminated, while variation details in the reflectivity factors were retained. The VTC method is superior to the bin-by-bin method in terms of correction amplitude and can be used for attenuation correction of shorter wavelength radar assisted by longer wavelength radar data.

scholarly journals Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

2021 ◽  
Vol 13 (2) ◽  
pp. 284
Author(s):  
Dan Lu ◽  
Yahui Wang ◽  
Qingyuan Yang ◽  
Kangchuan Su ◽  
Haozhe Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Relative Error ◽  
Urban Areas ◽  
Large Scale ◽  
Population Distribution ◽  
Spatial Optimization ◽  
Distribution Data ◽  
Mountainous Areas ◽  
Mean Relative Error ◽  
Nighttime Light

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.

scholarly journals Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1836
Author(s):  
Bo-Hye Choi ◽  
Donghwi Hwang ◽  
Seung-Kwan Kang ◽  
Kyeong-Yun Kim ◽  
Hongyoon Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Attenuation Correction ◽  
Complex Structure ◽  
Scatter Correction ◽  
Amyloid Β ◽  
Correction Method ◽  
Percentage Error ◽  
Similarity Coefficients ◽  
Brain Pet ◽  
Positron Emission

The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission tomography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of facial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET.

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 A Robust Dual-Frequency Radar Profiling Algorithm

2011 ◽  
Vol 50 (7) ◽  
pp. 1543-1557 ◽  
Author(s):  
Mircea Grecu ◽  
Lin Tian ◽  
William S. Olson ◽  
Simone Tanelli
Keyword(s):  
Synthetic Data ◽  
Optimization Procedure ◽  
Correction Method ◽  
Single Frequency ◽  
Retrieval Algorithm ◽  
Dual Frequency ◽  
Ka Band ◽  
Radar Observations ◽  
Distribution Shape ◽  
Ku Band

AbstractIn this study, an algorithm to retrieve precipitation from spaceborne dual-frequency (13.8 and 35.6 GHz, or Ku/Ka band) radar observations is formulated and investigated. Such algorithms will be of paramount importance in deriving radar-based and combined radar–radiometer precipitation estimates from observations provided by the forthcoming NASA Global Precipitation Measurement (GPM) mission. In GPM, dual-frequency Ku-/Ka-band radar observations will be available only within a narrow swath (approximately one-half of the width of the Ku-band radar swath) over the earth’s surface. Therefore, a particular challenge is to develop a flexible radar retrieval algorithm that can be used to derive physically consistent precipitation profile estimates across the radar swath irrespective of the availability of Ka-band radar observations at any specific location inside that swath, in other words, an algorithm capable of exploiting the information provided by dual-frequency measurements but robust in the absence of Ka-band channel. In the present study, a unified, robust precipitation retrieval algorithm able to interpret either Ku-only or dual-frequency Ku-/Ka-band radar observations in a manner consistent with the information content of the observations is formulated. The formulation is based on 1) a generalized Hitschfeld–Bordan attenuation correction method that yields generic Ku-only precipitation profile estimates and 2) an optimization procedure that adjusts the Ku-band estimates to be physically consistent with coincident Ka-band reflectivity observations and surface reference technique–based path-integrated attenuation estimates at both Ku and Ka bands. The algorithm is investigated using synthetic and actual airborne radar observations collected in the NASA Tropical Composition, Cloud, and Climate Coupling (TC4) campaign. In the synthetic data investigation, the dual-frequency algorithm performed significantly better than a single-frequency algorithm; dual-frequency estimates, however, are still sensitive to various assumptions such as the particle size distribution shape, vertical and cloud water distributions, and scattering properties of the ice-phase precipitation.

scholarly journals Attenuation correction for a high-resolution polarimetric X-band weather radar

2010 ◽  
Vol 8 ◽  
pp. 279-284
Author(s):  
T. Otto ◽  
H. W. J. Russchenberg
Keyword(s):  
Attenuation Correction ◽  
Hydrological Cycle ◽  
Correction Method ◽  
Phase Constraint ◽  
Spatial And Temporal Variability ◽  
Range Resolution ◽  
Raindrop Size Distribution ◽  
University Of Technology ◽  
X Band ◽  
Raindrop Size

Abstract. In 2007, IRCTR (Delft University of Technology) installed a new polarimetric X-band LFMCW radar (IDRA) at the meteorological observation site of Cabauw, The Netherlands. It provides plan position indicators (PPI) at a fixed elevation with a high range resolution of either 3 m or 30 m at a maximum observation range of 1.5 km and 15 km, respectively. IDRA aims to monitor precipitation events for the long-term analysis of the hydrological cycle. Due to the specifications of IDRA, the spatial and temporal variability of a large range of rainfall intensities (from drizzle to heavy convective rain) can be studied. Even though the usual observation range of IDRA is limited to 15 km, attenuation due to precipitation can be large enough to seriously affect the measurements. In this contribution we evaluate the application of a combined method to correct for the specific and the differential attenuation, and in the same vein estimate the parameters of the raindrop-size distribution. The estimated attenuations are compared to a phase constraint attenuation correction method.

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