scholarly journals Analysis of precipitation microstructure characteristic during Madden Julian Oscillation (MJO) using micro rain radar (MRR) and disdrometer in South Tangerang

2021 ◽  
Vol 893 (1) ◽  
pp. 012001
Author(s):  
D Nurheliza ◽  
N J Trilaksono ◽  
F Renggono
Keyword(s):  
Total Concentration ◽  
Liquid Water Content ◽  
Radar Data ◽  
Madden Julian Oscillation ◽  
Fall Velocity ◽  
Index Method ◽  
Distribution Method ◽  
Microphysical Process ◽  
Raindrop Size Distribution ◽  
Rain Radar

Abstract Rain microstructure is a critical aspect to understand the dynamics and microphysics character of the clouds. It is characterized by the distribution of size, fall velocity and shape of raindrop. Raindrop size distribution (DSD) explains the detail of the microphysical process because it represents a process of rain to the surface. One of the phenomena that influence the rain patterns in Indonesia is Madden Julian Oscillation (MJO). Therefore, observing rain microstructure with its relation to MJO can determine the differences in rainfall characteristic and microphysical processes during active and inactive MJO period. The data used in this study are Micro Rain Radar (MRR), disdrometer, and real-time multivariate (RMM) index data. The period/date selection of active MJO event performed using RMM index method is more than 1 in phases 4 and 5 and otherwise for inactive MJO. Types of rain are divided into stratiform and convective rain based on disdrometer data. From that, there are 46 active and 52 inactive MJO events. Rain microstructure in this study focuses on DSD from disdrometer and micro rain radar data analyzed with liquid water content profile, fall velocity, reflectivity, and rain rate from MMR. Besides, there are parameters of DSD, which are the mass-weighted diameter (Dm) and total concentration (Nw), calculated using the moment and gamma distribution method. The result shows that DSD and other parameters are greater during inactive MJO period. It means that process of collision-coalescence, evaporation, and updraft is dominant during inactive MJO period.

scholarly journals An Inverse Model for Raindrop Size Distribution Retrieval with Polarimetric Variables

2018 ◽  
Vol 10 (8) ◽  
pp. 1179 ◽  
Author(s):  
Guang Wen ◽  
Haonan Chen ◽  
Guifu Zhang ◽  
Jiming Sun
Keyword(s):  
Size Distribution ◽  
Liquid Water Content ◽  
Radar Data ◽  
Inverse Model ◽  
Training Data ◽  
Training Dataset ◽  
Drop Diameter ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Nearest Neighborhood

This paper proposes an inverse model for raindrop size distribution (DSD) retrieval with polarimetric radar variables. In this method, a forward operator is first developed based on the simulations of monodisperse raindrops using a T-matrix method, and then approximated with a polynomial function to generate a pseudo training dataset by considering the maximum drop diameter in a truncated Gamma model for DSD. With the pseudo training data, a nearest-neighborhood method is optimized in terms of mass-weighted diameter and liquid water content. Finally, the inverse model is evaluated with simulated and real radar data, both of which yield better agreement with disdrometer observations compared to the existing Bayesian approach. In addition, the rainfall rate derived from the DSD by the inverse model is also improved when compared to the methods using the power-law relations.

scholarly journals Analysis of Raindrop Size Distribution Characteristics in Permafrost Regions of the Qinghai–Tibet Plateau Based on New Quality Control Scheme

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2265 ◽  
Author(s):  
Ma ◽  
Zhao ◽  
Yang ◽  
Xiao ◽  
Zhang ◽  
...  
Keyword(s):  
Particle Size ◽  
Quality Control ◽  
Rain Rate ◽  
Tibet Plateau ◽  
Frozen Ground ◽  
Fall Velocity ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Qinghai Tibet Plateau ◽  
Permafrost Regions

Raindrop size distribution (DSD) can reflect the fundamental microphysics of precipitation and provide an accurate estimation of its amount and characteristics; however, there are few observations and investigations of DSD in cold, mountainous regions. We used the second-generation particle size and velocity disdrometer Parsivel2 to establish a quality control scheme for raindrop spectral data obtained for the Qinghai–Tibet Plateau in 2015. This scheme included the elimination of particles in the lowest two size classes, particles >10 mm in diameter and rain rates <0.01 mm∙h−1. We analyzed the DSD characteristics for different types of precipitation and rain rates in both permafrost regions and regions with seasonally frozen ground. The precipitation in the permafrost regions during the summer were mainly solid with a large particle size and slow fall velocity, whereas the precipitation in the regions with seasonally frozen ground were mainly liquid. The DSD of snow had a broader drop spectrum, the largest particle size, the slowest fall velocity, and the largest number of particles, followed by hail. Rain and sleet shared similar DSD characteristics, with a smaller particle size, slower velocity, and smaller number of particles. The particle concentration for different classes of rain rate decreased with an increase in particle size and decreased gradually with an increase in rain rate. Precipitation with a rain rate >2 mm∙h−1 was the main contributor to the annual precipitation. The dewpoint thresholds for snow and rain in permafrost regions were 0 and 1.5 °C, respectively. The dewpoint range 0–1.5 °C was characterized by mixed precipitation with a large proportion of hail. This study provides valuable DSD information on the Qinghai–Tibet Plateau and can be used as an important reference for the quality control of raindrop spectral data in regions dominated by solid precipitation.

scholarly journals Raindrop Size Distribution and Rain Characteristics during the 2013 Great Colorado Flood

2015 ◽  
Vol 17 (1) ◽  
pp. 53-72 ◽  
Author(s):  
Katja Friedrich ◽  
Evan A. Kalina ◽  
Joshua Aikins ◽  
Matthias Steiner ◽  
David Gochis ◽  
...  
Keyword(s):  
Drop Size ◽  
Doppler Radar ◽  
Liquid Water Content ◽  
Size Distributions ◽  
Intense Rainfall ◽  
Wind Fields ◽  
Raindrop Size Distribution ◽  
Median Volume ◽  
Drop Size Distributions ◽  
Analysis System

Abstract Drop size distributions observed by four Particle Size Velocity (PARSIVEL) disdrometers during the 2013 Great Colorado Flood are used to diagnose rain characteristics during intensive rainfall episodes. The analysis focuses on 30 h of intense rainfall in the vicinity of Boulder, Colorado, from 2200 UTC 11 September to 0400 UTC 13 September 2013. Rainfall rates R, median volume diameters D0, reflectivity Z, drop size distributions (DSDs), and gamma DSD parameters were derived and compared between the foothills and adjacent plains locations. Rainfall throughout the entire event was characterized by a large number of small- to medium-sized raindrops (diameters smaller than 1.5 mm) resulting in small values of Z (&lt;40 dBZ), differential reflectivity Zdr (&lt;1.3 dB), specific differential phase Kdp (&lt;1° km−1), and D0 (&lt;1 mm). In addition, high liquid water content was present throughout the entire event. Raindrops observed in the plains were generally larger than those in the foothills. DSDs observed in the foothills were characterized by a large concentration of small-sized drops (d &lt; 1 mm). Heavy rainfall rates with slightly larger drops were observed during the first intense rainfall episode (0000–0800 UTC 12 September) and were associated with areas of enhanced low-level convergence and vertical velocity according to the wind fields derived from the Variational Doppler Radar Analysis System. The disdrometer-derived Z–R relationships reflect how unusual the DSDs were during the 2013 Great Colorado Flood. As a result, Z–R relations commonly used by the operational NEXRAD strongly underestimated rainfall rates by up to 43%.

Fusion of rain radar images and wind forecasts in adeep learning model applied to rain nowcasting

2021 ◽  
Author(s):  
Anastase Charantonis ◽  
Vincent Bouget ◽  
Dominique Béréziat ◽  
Julien Brajard ◽  
Arthur Filoche
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Weather Forecast ◽  
Forecast Model ◽  
Radar Data ◽  
Learning Model ◽  
Rainfall Forecasting ◽  
Radar Images ◽  
Deep Learning Model ◽  
Rain Radar

&lt;p&gt;Short or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risks monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only rainfall radar images as inputs. In order to determine whether using other meteorological parameters such as wind would improve forecasts, we trained a deep learning model on a fusion of rainfall radar images and wind velocity produced by a weather forecast model. The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow. Our network outperforms by 8% the F1-score calculated for the optical flow on moderate and higher rain events for forecasts at a horizon time of 30 minutes. Furthermore, it outperforms by 7% the same architecture trained using only rainfall radar images. Merging rain and wind data has also proven to stabilize the training process and enabled significant improvement especially on the difficult-to-predict high precipitation rainfalls. These results can also be found in Bouget, V., B&amp;#233;r&amp;#233;ziat, D., Brajard, J., Charantonis, A., &amp; Filoche, A. (2020). Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting. arXiv preprint arXiv:2012.05015&lt;/p&gt;

scholarly journals Distribusi Arah Vertikal Butiran Hujan dari Hujan Stratiform di Kototabang dari Pengamatan Micro Rain Radar (MRR)

2019 ◽  
Vol 8 (3) ◽  
pp. 252-259 ◽  
Author(s):  
Ravidho Ramadhan ◽  
Marzuki Marzuki
Keyword(s):  
Size Distribution ◽  
Melting Layer ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Rain Radar

Distribusi ukuran butiran hujan atau raindrop size distribution (RSD) arah vertikal hujan stratiform dari ketinggian 0,45 km hingga 4,65 km di atas permukaan tanah di Kototabang, Sumatera Barat (0,20o LS; 100,32o BT; 865 m di atas permukaan laut ), telah diteliti melalui pengamatan Micro Rain Radar (MRR) selama Januari 2012 sampai Agustus 2016. RSD dari MRR dimodelkan dengan distribusi gamma dan parameternya didapatkan menggunakan metode momen. Pertumbuhan RSD dari hujan stratiform pada ketinggian 3,9 – 3,4 km sangat kuat untuk semua ukuran butiran, yang menandakan  daerah melting layer di Kototabang. Di bawah daerah melting layer terjadi penurunan konsentrasi butiran berukuran kecil dan peningkatan konsentrasi butiran besar. Hal ini diperkirakan disebabkan oleh proses evaporasi dan updraft pada butiran kecil dan coalescence yang teramati pada hujan stratiform dengan intensitas tinggi. Hal ini juga ditandai dengan perubahan parameter gamma dan koefisien persamaan Z-R (Z=ARb) terhadap penurunan ketinggian. Dengan demikian, asumsi persamaan Z-R yang konstan untuk setiap ketinggian bagi hujan stratiform pada radar meteorologi khususnya di Kototabang kurang akurat.Kata kunci: Hujan stratiform, Kototabang, Micro Rain Radar (MRR), raindrop size distribution (RSD)

scholarly journals A Synergy Approach to Estimate Properties of Raindrop Size Distributions Using a Doppler Lidar and Cloud Radar

2010 ◽  
Vol 27 (6) ◽  
pp. 1095-1100 ◽  
Author(s):  
Katja Träumner ◽  
Jan Handwerker ◽  
Andreas Wieser ◽  
Jens Grenzhäuser
Keyword(s):  
Size Distribution ◽  
Analytical Description ◽  
Doppler Lidar ◽  
Fall Velocity ◽  
Rayleigh Approximation ◽  
Size Dependent ◽  
Cloud Radar ◽  
Raindrop Size Distribution ◽  
Remote Sensing Systems ◽  
Raindrop Size

Abstract Remote sensing systems like radars and lidars are frequently used in atmospheric measurement campaigns. Because of their different wavelengths, they operate in different scattering regimes. Combined use may result in new measurement options. Here, an approach to estimate raindrop size distribution using vertical velocities measured by a lidar–radar combination is introduced and tested using a 2-μm Doppler lidar and a 35.5-GHz cloud radar. The lidar spectra are evaluated to deduce air motion from the aerosol peak and the fall velocity of the raindrops from the rain peak. The latter is weighted by the area (D2) of the scatters. The fall velocity derived from radar measurements is weighted by D6 (Rayleigh approximation). Assuming a size-dependent fall velocity and an analytical description of the drop size distribution, its parameters are calculated from these data. Comparison of the raindrop size distribution from the lidar–radar combination with in situ measurements on the ground yields satisfying results.

scholarly journals Dataset on raindrop size distribution, raindrop fall velocity and precipitation data measured by disdrometers and rain gauges over Peruvian central Andes (12.0°S)

Data in Brief ◽  
2020 ◽  
Vol 29 ◽  
pp. 105215
Author(s):  
Jairo M. Valdivia ◽  
Kevin Contreras ◽  
Daniel Martinez-Castro ◽  
Elver Villalobos-Puma ◽  
Luis F. Suarez-Salas ◽  
...  
Keyword(s):  
Size Distribution ◽  
Central Andes ◽  
Precipitation Data ◽  
Fall Velocity ◽  
Rain Gauges ◽  
Raindrop Size Distribution ◽  
Raindrop Size

scholarly journals The accuracy of weather radar in heavy rain: a comparative study for Denmark, the Netherlands, Finland and Sweden

2020 ◽  
Vol 24 (6) ◽  
pp. 3157-3188
Author(s):  
Marc Schleiss ◽  
Jonas Olsson ◽  
Peter Berg ◽  
Tero Niemi ◽  
Teemu Kokkonen ◽  
...  
Keyword(s):  
The Netherlands ◽  
Weather Radar ◽  
Heavy Rain ◽  
Radar Data ◽  
Small Scale ◽  
Differential Phase Shift ◽  
Urban Flood ◽  
Raindrop Size Distribution ◽  
Invaluable Tool ◽  
Urban Pluvial Flooding

Abstract. Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by measuring at higher resolutions and making use of dual-polarization radar, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. However, since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 h. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events in a 10-year database of radar data were used to quantify the overall agreement between radar and gauges as well as the bias affecting the peaks. Results show that the overall agreement in heavy rain is fair (correlation coefficient 0.7–0.9), with apparent multiplicative biases on the order of 1.2–1.8 (17  %–44  % underestimation). However, after taking into account the different sampling volumes of radar and gauges, actual biases could be as low as 10 %. Differences in sampling volumes between radar and gauges play an important role in explaining the bias but are hard to quantify precisely due to the many post-processing steps applied to radar. Despite being adjusted for bias by gauges, five out of six radar products still exhibited a clear conditional bias, with intensities of about 1 %–2 % per mmh−1. As a result, peak rainfall intensities were severely underestimated (factor 1.8–3.0 or 44 %–67 %). The most likely reason for this is the use of a fixed Z–R relationship when estimating rainfall rates (R) from reflectivity (Z), which fails to account for natural variations in raindrop size distribution with intensity. Based on our findings, the easiest way to mitigate the bias in times of heavy rain is to perform frequent (e.g., hourly) bias adjustments with the help of rain gauges, as demonstrated by the Dutch C-band product. An even more promising strategy that does not require any gauge adjustments is to estimate rainfall rates using a combination of reflectivity (Z) and differential phase shift (Kdp), as done in the Finnish OSAPOL product. Both approaches lead to approximately similar performances, with an average bias (at 10 min resolution) of about 30 % and a peak intensity bias of about 45 %.

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