Detecting the melting layer with a micro rain radar using a neural network approach

Brast, Maren; Markmann, Piet

doi:10.5194/amt-13-6645-2020

Cited by 7 publications

(7 citation statements)

References 15 publications

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“…The MRR was originally deployed and evaluated for measuring rainfall in remote regions (Maahn & Kollias, 2012; Peters et al., 2002). In addition, MRR observations have been used in identifying melting in stratiform rain (Brast & Markmann, 2020; Cha et al., 2009; Garcia‐Benadi et al., 2020). The MRR observations were processed using the Maahn and Kollias (2012) method providing 1‐min resolution of equivalent radar reflectivity (Ze; dBZ), Doppler velocity ( V d ; ms −1 ) and spectral width (ms −1 ).…”

Section: Instrumentation and Datamentioning

confidence: 99%

“…Studies have explored implications for local hydrology (Henn et al., 2020; Lundquist et al., 2008; White et al., 2002), glaciology (Perry et al., 2017; Schauwecker et al., 2017), and microphysical differences between rain with and without a bright band (Lin et al., 2020; Martner et al., 2008). Methods for identifying melting through vertically profiling radar observations use variables including reflectivity (Cha et al., 2009), Doppler velocity (Perry et al., 2017), Doppler spectra skewness (Garcia‐Benadi et al., 2020), and neural networks (Brast & Markmann, 2020). Weather radars employ other methods using dual polarization capabilities to identify melting (Giangrande et al., 2008; Matrosov et al., 2017; Ryzhkov & Zrnic, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Multi‐Year Analysis of Rain‐Snow Levels at Marquette, Michigan

et al. 2023

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Precipitation is changing as the climate warms. In northern mid and high latitudes, warming is resulting in a shift from snow to rain (Bintanja & Andry, 2017;Tamang et al., 2020). In the Upper Great Lakes region, the mean annual wet-bulb temperature is increasing, and snowfall and snowfall-precipitation ratio is decreasing (Tamang et al., 2020). Rainfall in the place of snowfall impacts water resources (Knowles et al., 2006), and glacier mass balance (Perry et al., 2017;Schauwecker et al., 2017), and potentially the global energy balance (Screen & Simmonds, 2012). Observed changes in precipitation including a reduction in heavy snow cover and the shift from snow to rain impacts soil moisture, watershed hydrology, and streamflow in the Midwest and Great Lakes region (Byun et al., 2019). From a societal standpoint, precipitation processes also impact transportation safety. Hazardous wintertime events such as freezing rain result in especially dangerous conditions that affect vehicle crash risks (Tobin et al., 2021). The height of the melting level in the atmosphere influences the precipitation phase at the surface (

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Section: Instrumentation and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi‐Year Analysis of Rain‐Snow Levels at Marquette, Michigan

et al. 2023

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“…The maximums mostly relate to BB. There are many methods for automatic detection of the ML, but they can be found easily on the radar reflectivity diagrams by the BB position (Brast & Markmann, 2020). The current case occurred from 08 UTC December 5 to 03 UTC December 6, when wet snow and rain were observed.…”

Section: Model Conformity Check and Observation Analysismentioning

confidence: 99%

“…When the temperature of the lower layers of air is positive, the falling snow melts and transforms into rain. The layer where this transformation takes place is called the melting layer (ML) (Drummond et al, 1996), commonly investigated by radars (Brast & Markmann, 2020). Due to the high reflectivity of wet snow, the ML is seen in a radar scan as the well-known "bright band" (BB) (Aus tin & Bemis, 1950).…”

Section: Introductionmentioning

confidence: 99%

Measured and modeled vertical structure of precipitation during mixed-phase event near the West Coast of the Antarctic Peninsula

Pishniak¹,

Razumnyi²

2022

UAJ

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Precipitation structures are easy to detect, however, the mesoscale atmospheric processes which they reflect are challenging to understand in Polar Regions and hard to model numerically. Currently, the spatial distribution of precipitation can be tracked at the resolution of minutes and seconds. For this purpose, the researchers at the Ukrainian Antarctic Akademik Vernadsky station employ several near-ground measurement systems and the Micro Rain Radar for remote vertical measurements. Measurements show stochastic precipitation variability caused by turbulence, precipitation bands related to the atmospheric processes of its formation, phase transition (melting) zones, and wind shears. The time scale of bands in the stratiform precipitation typically varied in the range of 5—15 minutes and corresponded to the 2—15 km spatial scale of atmospheric circulations according to the modeled parameters of the atmosphere. The Polar Weather Research and Forecast (Polar WRF) model was used to reveal the general atmospheric conditions. We also tested and evaluated its ability to reproduce small structures. A simple method based on typical model variables is proposed to identify the precipitation melting layer in the simulation data, similar to that determined by radars. The results were satisfyingly consistent with the position of the 0 °C isotherm in the model and with the radar measurements. In addition, the method highlighted supercooled mixed-phase precipitation. Modeling showed good results for large-scale processes like atmospheric fronts and general air mass features in the case study. However, even at the 1 km resolution the simulation reproduced thin mesoscale precipitation features smoothly, which sometimes looks unrealistic. As for other precipitation peculiarities, like band inclination, melting layer position, and mixed-phase zones, the Polar WRF model demonstrates high consistency with observations. The model can describe the atmospheric conditions except for the investigation of precipitation-initiating mechanisms, which still is a challenge for modeling at a small scale.

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“…The ML product is calculated using an Artificial Intelligence approach [41], providing probability values; a probability greater than 0.75 is considered a reliable ML height determination, being the maximum probability of the selected height for the melting level. The BB peak has been described in Section 3.…”

Section: Manufacturer ML Height Vs Raprom-pro Bb Peak Heightmentioning

confidence: 99%

A New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations

et al. 2021

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The detection and characterisation of the radar Bright Band (BB) are essential for many applications of weather radar quantitative precipitation estimates, such as heavy rainfall surveillance, hydrological modelling or numerical weather prediction data assimilation. This study presents a new technique to detect the radar BB levels (top, peak and bottom) for Doppler radar spectral moments from the vertically pointing radars applied here to a K-band radar, the MRR-Pro (Micro Rain Radar). The methodology includes signal and noise detection and dealiasing schemes to provide realistic vertical Doppler velocities of precipitating hydrometeors, subsequent calculation of Doppler moments and associated parameters and BB detection and characterisation. Retrieved BB properties are compared with the melting level provided by the MRR-Pro manufacturer software and also with the 0 °C levels for both dry-bulb temperature (freezing level) and wet-bulb temperature from co-located radio soundings in 39 days. In addition, a co-located Parsivel disdrometer is used to analyse the equivalent reflectivity of the lowest radar height bins confirming consistent results of the new signal and noise detection scheme. The processing methodology is coded in a Python program called RaProM-Pro which is freely available in the GitHub repository.

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Detecting the melting layer with a micro rain radar using a neural network approach

Cited by 7 publications

References 15 publications

Multi‐Year Analysis of Rain‐Snow Levels at Marquette, Michigan

Multi‐Year Analysis of Rain‐Snow Levels at Marquette, Michigan

Measured and modeled vertical structure of precipitation during mixed-phase event near the West Coast of the Antarctic Peninsula

A New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations

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