Improvement of vertical profiles of raindrop size distribution from micro rain radar using 2D video disdrometer measurements

Adirosi, Elisa; Baldini, Luca; Roberto, Nicoletta; Gatlin, Patrick; Tokay, Ali

doi:10.1016/j.atmosres.2015.07.002

Cited by 46 publications

(29 citation statements)

References 33 publications

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“…The first file is the "Rawdata" which is the lowest level of data available and provided every 10 s. However, our instrument does not provide the "Rawdata" file. The "Rawdata" will be important when the user wants to reprocess the data [31]. The second file type is Processed data which is obtained every 10 s after processing and provides estimated DSD and other rainfall parameters.…”

Section: Micro Rain Radar System Descriptionmentioning

confidence: 99%

“…However, the accuracy of MRR has been questioned due to several problems such as strong rain attenuation at the frequency of 24.1 GHz [30] and the vertical wind that affects the fall speed of the drops [26], particularly in heavy rain. Thus, some correction techniques have been proposed to improve the MRR standard processing method [28,31]. The MRR installed at Kototabang has been overhauled by Meteorologische Messtechnik GmbH (Metek) and the firmware and software were replaced to the newest ones at that time.…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of Micro Rain Radar Over Sumatra Through Comparison With Disdrometer and Wind Profiler

Marzuki¹,

Hashiguchi²,

Shimomai³

et al. 2016

PIER M

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Abstract-Micro Rain Radar (MRR) is a vertical pointing microwave profiler to measure hydrometeors and related parameters in high resolution. However, it is known that the MRR suffers from certain limitations due to several factors. This paper evaluates the performance of the MRR installed at Kototabang, west Sumatra, Indonesia (0.20 • S, 100.32 • E, 864 m above sea level). The DSD and rainfall rate from the MRR standard processing method had been evaluated by using collocated measurements of MRR, Parsivel disdrometer and Optical Rain Gauge (ORG) during 2014. Furthermore, 1.3 GHz wind profiler (BLR) observation was used to examine the vertical profiles of radar reflectivity and Doppler velocity. It was found that there were noticeable differences between the MRR and Parsivel in the small and large size ends of the DSD. At small sized drop (< 1 mm), the DSD spectra of MRR was higher than that obtained by the Parsivel otherwise it was smaller for large sized drop (> 2 mm). Underestimation of large sized drops in the MRR could be responsible for the underestimation of surface rainfall rate and daily rainfall. The source of differences in the DSD seems the measurement shortcomings such as attenuation correction and vertical wind effects, particularly during heavy rain. The shortcomings were observed from the comparison of mean Doppler velocity profiles between the MRR and the BLR. While the melting layer height of the two instruments was the same, the mean Doppler velocities of MRR shown downward increasing (DI) pattern through all rainfall intensities. On the other hand, for the BLR, the DI was only observed for heavy rain (> 10 mm/h), while downward decreasing was observed for light rain (< 5 mm/h). Similar pattern was also observed for the vertical profile of radar reflectivity. Thus, some corrections are needed for heavy rain, nevertheless, the MRR installed at Kototabang can be utilized for light rain. Comparisons indicated that the mean Doppler velocity and the DSD for the light rain as well as Z-R relation were in reasonable agreement with the reference of BLR, Parsivel and previous studies using the MRR.

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Section: Micro Rain Radar System Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Micro Rain Radar Over Sumatra Through Comparison With Disdrometer and Wind Profiler

Marzuki¹,

Hashiguchi²,

Shimomai³

et al. 2016

PIER M

View full text Add to dashboard Cite

show abstract

“…Disdrometer observations of PSD are also used to derive relationships between radar reflectivity and rainfall rates (known usually as Z-R relationships), despite the difficulties due to differences in altitude of the measurement (surface vs. cloud base) and the sensing area (a few cm 2 vs. km 2 ; Krajewski et al, 1998;Löffler-Mang and Blahak, 2001;Miriousky et al, 2004;Thurai and Bringi, 2008;Marzano et al, 2010;Jaffrain and Berne, 2012;Jameson et al, 2015;Raupach and Berne, 2016;Gires et al, 2016). Many of these studies took place within the Precipitation Measurement Missions helping the development of better sensors and algorithms for precipitation detection and quantification; some examples are Ioannidou et al (2016) for the Tropical Rainfall Measurement Mission (TRMM), Liao et al (2014) and Tan et al (2016) for the Global Precipitation Measurement mission (GPM), Adirosi et al (2016) for the Hydrological cycle in the Mediterranean Experiment (HyMex) or Calheiros and Machado (2014) for the Cloud Processes of the Main Precipitation Systems in Brazil (CHUVA) campaign.…”

Section: Introductionmentioning

confidence: 99%

“…Information on the PSD/PSVD is required for a proper understanding of hydrometeorological regimes (Iguchi et al, 2000;Krajewski et al, 2006;Adirosi et al, 2016), soil erosion (Sempere-Torres et al, 1998;Loik et al, 2004;Cruse et al, 2006;Petan et al, 2010;Fernández-Raga et al, 2010;Shuttlewort, 2012;Iserloh et al, 2013;Angulo-Martínez and Barros, 2015;Angulo-Martínez et al, 2016) and other applications such as pollution wash off in urban environments (Kathiravelu et al, 2016;Castro et al, 2010) or interactions of rainfall with crop and forest canopies Nanko et al, 2004;Nanko et al, 2013). Rainfall estimation by remote sensing, radar and satellite also rely on PSD information (Olsen et al, 1978;Atlas et al, 1999;Uijlenhoet and Sempere-Torres, 2006;Tapiador et al, 2017b).…”

Section: Introductionmentioning

confidence: 99%

Comparison of precipitation measurements by OTT Parsivel<sup>2</sup> and Thies LPM optical disdrometers

Angulo‐Martínez

Beguería

Latorre

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Optical disdrometers are present weather sensors with the ability of providing detailed information on precipitation such as rain intensity, radar reflectivity or kinetic energy, together with discrete information on the particle size and fall velocity distribution (PSVD) of the hydrometeors. Disdrometers constitute a step forward towards a more complete characterization of precipitation, being useful in several research fields and applications. In this article the performance of two extensively used optical disdrometers, the most recent version of OTT Parsivel 2 disdrometer and Thies Clima Laser Precipitation Monitor (LPM), is evaluated. During 2 years, four collocated optical disdrometers, two Thies Clima LPM and two OTT Parsivel 2 , collected up to 100 000 min of data and up to 30 000 min with rain in more than 200 rainfall events, with intensities peaking at 277 mm h −1 in 1 minute. The analysis of these records shows significant differences between both disdrometer types for all integrated precipitation parameters, which can be explained by differences in the raw PSVD estimated by the two sensors. Thies LPM recorded a larger number of particles than Parsivel 2 and a higher proportion of small particles than OTT Parsivel 2 , resulting in higher rain rates and totals and differences in radar reflectivity and kinetic energy. These differences increased greatly with rainfall intensity. Possible causes of these differences, and their practical consequences, are discussed in order to help researchers and users in the choice of sensor, and at the same time pointing out limitations to be addressed in future studies.

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“…Information on the PSD / PSVD is required for a proper understanding of hydrometeorological regimes (Iguchi et al, 2000;Krajewski et al, 2006;Adirosi et al, 2016;Angulo-Martínez and Barros, 2015), soil erosion (Sempere-Torres et al, 1998;Loik et al, 2004;Cruse et al, 2006;Petan et al, 2010;Fernández-Raga et al, 2010;Shuttlewort, 2012;Iserloh et al, 2013;Angulo-Martínez et al, 2016) and other applications such as pollution wash off in urban environments (Kathiravelu et al, 2016;Castro et al, 2010).…”

mentioning

confidence: 99%

Comparison of precipitation measurements by Ott Parsivel<sup>2</sup> and Thies LPM optical disdrometers

Angulo‐Martínez¹,

Beguería²,

Latorre³

et al. 2017

Preprint

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Abstract.Optical disdrometers are present weather sensors with the ability of providing detailed information of precipitation such as rain intensity, kinetic energy or radar reflectivity, together with discrete information on the distribution of particle sizes and fall velocities (PSVD) of the hydrometeors. Disdrometers constitute a step forward towards a more complete characterisation of precipitation, being highly useful in several research fields and applications. In this article the performance of the two optical . The results show significant differences between both disdrometer types for all integrated precipitation parameters, which can be explained by differences in the raw particle size and 10 velocity distribution (PSVD). Thies LPM recorded in average double number of particles than PARSIVEL 2 . PSVD percentile comparison showed Thies LPM measuring more small particles than Ott Parsivel 2 , resulting in higher rain rates and totals.These differences increased greatly with rainfall intensity. At rain rates above 10 mm h

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Improvement of vertical profiles of raindrop size distribution from micro rain radar using 2D video disdrometer measurements

Cited by 46 publications

References 33 publications

Performance Evaluation of Micro Rain Radar Over Sumatra Through Comparison With Disdrometer and Wind Profiler

Performance Evaluation of Micro Rain Radar Over Sumatra Through Comparison With Disdrometer and Wind Profiler

Comparison of precipitation measurements by OTT Parsivel<sup>2</sup> and Thies LPM optical disdrometers

Comparison of precipitation measurements by Ott Parsivel<sup>2</sup> and Thies LPM optical disdrometers

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Improvement of vertical profiles of raindrop size distribution from micro rain radar using 2D video disdrometer measurements

Cited by 46 publications

References 33 publications

Performance Evaluation of Micro Rain Radar Over Sumatra Through Comparison With Disdrometer and Wind Profiler

Performance Evaluation of Micro Rain Radar Over Sumatra Through Comparison With Disdrometer and Wind Profiler

Comparison of precipitation measurements by OTT Parsivel&lt;sup&gt;2&lt;/sup&gt; and Thies LPM optical disdrometers

Comparison of precipitation measurements by Ott Parsivel&lt;sup&gt;2&lt;/sup&gt; and Thies LPM optical disdrometers

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Comparison of precipitation measurements by OTT Parsivel<sup>2</sup> and Thies LPM optical disdrometers

Comparison of precipitation measurements by Ott Parsivel<sup>2</sup> and Thies LPM optical disdrometers