Detection of the bright band with a vertically pointing k-band radar

Pfaff, Thomas; Engelbrecht, Alexander; Seidel, Jochen

doi:10.1127/metz/2014/0605

Cited by 11 publications

(12 citation statements)

References 6 publications

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“…Thus, BB detection and correction schemes are essential in order to make accurate surface rain rate estimations. The widely adopted methods for BB detection and correction analyze the vertical profiles of parameters measured by weather radar, such as radar reflectivity, Doppler vertical velocity (DVV), signal-to-noise ratio (SNR), and spectral width (e.g., Mittermaier and Illingworth 2003;Villarini and Krajewski 2010;Pfaff et al 2014).…”

Section: A Brightband Detection Algorithmsmentioning

confidence: 99%

“…The BB is located where a significant increase in fall velocity occurs. Pfaff et al (2014) illustrated that a DVV algorithm is likely to derive a more accurate BBH than the algorithms analyzing VPR or the vertical gradients of VPR. Some weather radars also measure spectral width and SNR.…”

Section: A Brightband Detection Algorithmsmentioning

confidence: 99%

“…Some weather radars also measure spectral width and SNR. These two parameters can be used to detect the BB, but the behavior of spectral width resembles that of the DVV (Emory et al 2014), and the changes in SNR are similar to those seen with radar reflectivity (Pfaff et al 2014). White et al (2002) established an algorithm to identify BB using both the SNR and DVV.…”

Section: A Brightband Detection Algorithmsmentioning

confidence: 99%

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Relating the Radar Bright Band and Its Strength to Surface Rainfall Rate Using an Automated Approach

Lin

Pickering

Neely

2020

Journal of Hydrometeorology

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In radar observations of hydrometeors, the 0°C isotherm in the atmosphere (i.e., the freezing level) usually appears as a region of enhanced reflectivity. This region is known as the bright band (BB). In this study, observations over 12 months from a vertically pointing 35-GHz radar and a collocated disdrometer at the Natural Environment Research Council (NERC) Facility for Atmospheric and Radio Research (NFARR) are used to identify and compare microphysical differences between BB and non-brightband (NBB) periods. From these observations, the relationship between radar reflectivity Z and rainfall intensity R is found to be Z = 772R0.57 for BB periods and Z = 108R0.99 for NBB periods. Additionally, the brightband strength (BBS) was calculated using a novel method derived from the Michelson contrast equation in an attempt to explain the observed variability in BB precipitation. A series of Z–R relationships are computed with respect to BBS. The coefficients increase with increasing BBS from 227 to 926, while the exponents decrease with increasing BBS from 0.85 to 0.38. The results also indicate that NBB periods identified in the presence of a 0°C isotherm in other studies may be misclassified due to their inability to identify weak brightband periods. As such, it is hypothesized that NBB periods are solely due to warm rain processes.

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Section: A Brightband Detection Algorithmsmentioning

confidence: 99%

Section: A Brightband Detection Algorithmsmentioning

confidence: 99%

Section: A Brightband Detection Algorithmsmentioning

confidence: 99%

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Relating the Radar Bright Band and Its Strength to Surface Rainfall Rate Using an Automated Approach

Lin

Pickering

Neely

2020

Journal of Hydrometeorology

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“…As input data, profiles of both ZEA and VEL proved to be the best choice. The combination of these two variables have also produced the best results in Pfaff et al (2014). Before using these profiles within the NN, they need to be prepared.…”

Section: Neural Networkmentioning

confidence: 99%

“…Cha et al (2009) detect an ML where the largest positive and negative vertical gradient of the rain rate embrace the maximum rain rate. A large dataset was used, excluding the winter months with surface temperatures 20 below 0 • C. Pfaff et al (2014) have compared this algorithm to two other algorithms: fitting an analytical function to the reflectivity profile, and combining reflectivity and falling velocity to derive the ML height. They conclude that the combination of reflectivity and falling velocity gives the best results.…”

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confidence: 99%

Detecting the Melting Layer with a Micro Rain Radar Using a Neural Network Approach

Brast¹,

Markmann²

2019

Preprint

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A new method using the Micro Rain Radar (MRR) to determine the melting layer height is presented. The MRR is a small vertically pointing frequency modulated continuous wave radar which measures Doppler spectra of precipitation. From these Doppler spectra, various variables such as Doppler velocity or spectral width can be derived. The melting layer is visible through a higher reflectivity and an acceleration of the falling particles, among others. These characteristics are fed to a neural network to determine the melting layer height. For the training of the neural network, the melting layer height is determined 5 manually. The neural network is trained and tested using data from two sites covering all seasons. For most cases, it is well able to detect the correct melting layer height. Sensitivity studies show that the neural network is able to handle different settings of the MRR. Comparisons to radiosonde data and cloud radar data show a good agreement in melting layer heights.

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Rainfall Microphysical Properties of Landfalling Typhoon Yagi (201814) Based on the Observations of Micro Rain Radar and Cloud Radar in Shandong, China

Hong

Wang

et al. 2021

Adv. Atmos. Sci.

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Detection of the bright band with a vertically pointing k-band radar

Cited by 11 publications

References 6 publications

Relating the Radar Bright Band and Its Strength to Surface Rainfall Rate Using an Automated Approach

Relating the Radar Bright Band and Its Strength to Surface Rainfall Rate Using an Automated Approach

Detecting the Melting Layer with a Micro Rain Radar Using a Neural Network Approach

Rainfall Microphysical Properties of Landfalling Typhoon Yagi (201814) Based on the Observations of Micro Rain Radar and Cloud Radar in Shandong, China

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