scholarly journals Seasonal variation in the vertical profile of the raindrop size distribution for stratiform rain as inferred from micro rain radar observations at Kototabang

2020 ◽  
Author(s):  
Ravidho Ramadhan ◽  
Marzuki ◽  
Mutya Vonnisa ◽  
Harmadi ◽  
H. Hashiguhci ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Size Distribution ◽  
Vertical Profile ◽  
Radar Observations ◽  
Stratiform Rain ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Rain Radar

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)

Seasonal variation of raindrop size distribution over a coastal station Thumba: A quantitative analysis

2019 ◽  
Vol 229 ◽  
pp. 86-99 ◽  
Author(s):  
S. Lavanya ◽  
N.V.P. Kirankumar ◽  
S. Aneesh ◽  
K.V. Subrahmanyam ◽  
S. Sijikumar
Keyword(s):  
Seasonal Variation ◽  
Quantitative Analysis ◽  
Size Distribution ◽  
Coastal Station ◽  
Raindrop Size Distribution ◽  
Raindrop Size

scholarly journals Invariance of the Double-Moment Normalized Raindrop Size Distribution through 3D Spatial Displacement in Stratiform Rain

2017 ◽  
Vol 56 (6) ◽  
pp. 1663-1680 ◽  
Author(s):  
Timothy H. Raupach ◽  
Alexis Berne
Keyword(s):  
Size Distribution ◽  
Vertical Displacement ◽  
Statistical Moments ◽  
Stratiform Rain ◽  
Vertical Range ◽  
Raindrop Size Distribution ◽  
Double Moment ◽  
Raindrop Size ◽  
Polarimetric Weather Radar ◽  
Using Data

AbstractDouble-moment normalization of the drop size distribution (DSD) summarizes the DSD in a compact way, using two of its statistical moments and a “generic” double-moment normalized DSD function. Results are presented of an investigation into the invariance of the double-moment normalized DSD through horizontal and vertical displacement in space, using data from disdrometers, vertically pointing K-band Micro Rain Radars, and an X-band polarimetric weather radar. The invariance of the double-moment normalized DSD is tested over a vertical range of up to 1.8 km and a horizontal range of up to approximately 100 km. The results suggest that for practical use, with well-chosen input moments, the double-moment normalized DSD can be assumed invariant in space in stratiform rain. The choice of moments used to characterize the DSD affects the amount of DSD variability captured by the normalization. It is shown that in stratiform rain, it is possible to capture more than 85% of the variability in DSD moments zero to seven using the technique. Most DSD variability in stratiform rain can thus be explained through the variability of two of its statistical moments. The results suggest similar behavior exists in transition and convective rain, but the limited data samples available do not allow for robust conclusions for these rain types. The results have implications for practical uses of double-moment DSD normalization, including the study of DSD variability and microphysics, DSD-retrieval algorithms, and DSD models used in rainfall retrieval.

scholarly journals Measurements of Rainfall Velocity and Raindrop Size Distribution Using Coherent Doppler Lidar

2016 ◽  
Vol 33 (9) ◽  
pp. 1949-1966 ◽  
Author(s):  
Makoto Aoki ◽  
Hironori Iwai ◽  
Katsuhiro Nakagawa ◽  
Shoken Ishii ◽  
Kohei Mizutani
Keyword(s):  
Size Distribution ◽  
Wind Velocity ◽  
Vertical Wind ◽  
Doppler Spectrum ◽  
Doppler Lidar ◽  
Stratiform Rain ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Rain Events ◽  
Vertical Wind Velocity

AbstractRainfall velocity, raindrop size distribution (DSD), and vertical wind velocity were simultaneously observed with 2.05- and 1.54-μm coherent Doppler lidars during convective and stratiform rain events. A retrieval method is based on identifying two separate spectra from the convolution of the aerosol and precipitation Doppler lidar spectra. The vertical wind velocity was retrieved from the aerosol spectrum peak and then the terminal rainfall velocity corrected by the vertical air motion from the precipitation spectrum peak was obtained. The DSD was derived from the precipitation spectrum using the relationship between the raindrop size and the terminal rainfall velocity. A comparison of the 1-min-averaged rainfall velocity from Doppler lidar measurements at a minimum range and that from a collocated ground-based optical disdrometer revealed high correlation coefficients of over 0.89 for both convective and stratiform rain events. The 1-min-averaged DSDs retrieved from the Doppler lidar spectrum using parametric and nonparametric methods are also in good agreement with those measured with the optical disdrometer with a correlation coefficient of over 0.80 for all rain events. To retrieve the DSD, the parametric method assumes a mathematical function for the DSD and the nonparametric method computes the direct deconvolution of the measured Doppler lidar spectrum without assuming a DSD function. It is confirmed that the Doppler lidar can retrieve the rainfall velocity and DSD during relatively heavy rain, whereas the ratio of valid data significantly decreases in light rain events because it is extremely difficult to separate the overlapping rain and aerosol peaks in the Doppler spectrum.

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