vdis: Video Disdrometer

A disdrometer measures the drop size distribution and velocity of falling hydrometeors.

The 2-dimensional video-disdrometer (VDIS) comprises video cameras capable of observing individual hydrometeors from views perpendicular to each other. Two CCD line scan cameras are directed towards the measurement area. Objects passing through the measurement area—which is determined by the cross-section of the two optical paths as seen from above—obstruct the light and are detected as shadows by the cameras.

Each camera contains a small embedded computer responsible for handling the data-capture process, analysis of the data, and its conversion and compression into a format suitable for further processing. Subsequently the data are transferred to the computer used for instrument control and final analysis.

In order to identify individual precipitation particles by matching their views as seen by each of the cameras, it is necessary to synchronize the shutter and control both cameras with a synchronous line trigger signal.

To reconstruct observables like falling velocity, oblateness, etc. from the datastreams of the two cameras, the two optical paths are displaced vertically by about 6mm, typically. Measuring this distance and adjusting the background illumination are the two major calibration and maintenance tasks necessary for successful operation of the device.



  • Fixed
  • AMF1
  • AMF2
  • AMF3

Related Publications


Tokay A, L D’Adderio, F Porcù, D Wolff, and W Petersen. 2017. "A Field Study of Footprint-Scale Variability of Raindrop Size Distribution." Journal of Hydrometeorology, 18(12), 10.1175/JHM-D-17-0003.1.

Bartholomew MJ. 2017. Two-Dimensional Video Disdrometer (VDIS) Instrument Handbook. Ed. by Robert Stafford, ARM Research Facility. DOE/SC-ARM-TR-111.

von Lerber A, D Moisseev, L Bliven, W Petersen, A Harri, and V Chandrasekar. 2017. "Microphysical properties of snow and their link to Ze–S relation during BAECC 2014." Journal of Applied Meteorology and Climatology, 56(6), 10.1175/JAMC-D-16-0379.1.


Giangrande SE, T Toto, MP Jensen, M Bartholomew, Z Feng, A Protat, C Williams, C Schumacher, and L Machado. 2016. "Convective cloud vertical velocity and mass-flux characteristics from radar wind profiler observations during GoAmazon2014/5." Journal of Geophysical Research: Atmospheres, 121(21), 10.1002/2016jd025303. ONLINE.
Research Highlight


Thompson EJ, SA Rutledge, B Dolan, and M Thurai. 2015. "Drop Size Distributions and Radar Observations of Convective and Stratiform Rain over the Equatorial Indian and West Pacific Oceans." Journal of the Atmospheric Sciences, 72(11), 10.1175/jas-d-14-0206.1.

Chandra A, C Zhang, P Kollias, S Matrosov, and W Szyrmer. 2015. "Automated rain rate estimates using the Ka-band ARM zenith radar (KAZR)." Atmospheric Measurement Techniques, 8(9), 10.5194/amt-8-3685-2015.
Research Highlight

Tridon F and A Battaglia. 2015. "Dual-frequency radar Doppler spectral retrieval of rain drop size distributions and entangled dynamics variables." Journal of Geophysical Research: Atmospheres, 120(11), 10.1002/2014jd023023.


Giangrande SE, S Collis, A Theisen, and A Tokay. 2014. "Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign." Journal of Applied Meteorology and Climatology, 53(9), 10.1175/jamc-d-13-0321.1.
Research Highlight

Yano J and TP Lane. 2014. "Convectively generated gravity waves simulated by NAM-SCA." Journal of Geophysical Research: Atmospheres, 119(15), 10.1002/2013jd021419.


Yoneyama K, C Zhang, and CN Long. 2013. "Tracking Pulses of the Madden–Julian Oscillation." Bulletin of the American Meteorological Society, 94(12), 10.1175/bams-d-12-00157.1.

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