Articles | Volume 9, issue 7
Atmos. Meas. Tech., 9, 3145–3163, 2016
https://doi.org/10.5194/amt-9-3145-2016
Atmos. Meas. Tech., 9, 3145–3163, 2016
https://doi.org/10.5194/amt-9-3145-2016

Research article 20 Jul 2016

Research article | 20 Jul 2016

4-D-VAR assimilation of disdrometer data and radar spectral reflectivities for raindrop size distribution and vertical wind retrievals

François Mercier et al.

Related authors

Data-driven clustering of rain events: microphysics information derived from macro-scale observations
Mohamed Djallel Dilmi, Cécile Mallet, Laurent Barthes, and Aymeric Chazottes
Atmos. Meas. Tech., 10, 1557–1574, https://doi.org/10.5194/amt-10-1557-2017,https://doi.org/10.5194/amt-10-1557-2017, 2017
Short summary
A layer-averaged relative humidity profile retrieval for microwave observations: design and results for the Megha-Tropiques payload
R. G. Sivira, H. Brogniez, C. Mallet, and Y. Oussar
Atmos. Meas. Tech., 8, 1055–1071, https://doi.org/10.5194/amt-8-1055-2015,https://doi.org/10.5194/amt-8-1055-2015, 2015
Rainfall measurement from the opportunistic use of an Earth–space link in the Ku band
L. Barthès and C. Mallet
Atmos. Meas. Tech., 6, 2181–2193, https://doi.org/10.5194/amt-6-2181-2013,https://doi.org/10.5194/amt-6-2181-2013, 2013

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Linking rain into ice microphysics across the melting layer in stratiform rain: a closure study
Kamil Mróz, Alessandro Battaglia, Stefan Kneifel, Leonie von Terzi, Markus Karrer, and Davide Ori
Atmos. Meas. Tech., 14, 511–529, https://doi.org/10.5194/amt-14-511-2021,https://doi.org/10.5194/amt-14-511-2021, 2021
Short summary
Classification of lidar measurements using supervised and unsupervised machine learning methods
Ghazal Farhani, Robert J. Sica, and Mark Joseph Daley
Atmos. Meas. Tech., 14, 391–402, https://doi.org/10.5194/amt-14-391-2021,https://doi.org/10.5194/amt-14-391-2021, 2021
Short summary
The development of rainfall retrievals from radar at Darwin
Robert Jackson, Scott Collis, Valentin Louf, Alain Protat, Die Wang, Scott Giangrande, Elizabeth J. Thompson, Brenda Dolan, and Scott W. Powell
Atmos. Meas. Tech., 14, 53–69, https://doi.org/10.5194/amt-14-53-2021,https://doi.org/10.5194/amt-14-53-2021, 2021
Short summary
Retrieved wind speed from the Orbiting Carbon Observatory-2
Robert R. Nelson, Annmarie Eldering, David Crisp, Aronne J. Merrelli, and Christopher W. O'Dell
Atmos. Meas. Tech., 13, 6889–6899, https://doi.org/10.5194/amt-13-6889-2020,https://doi.org/10.5194/amt-13-6889-2020, 2020
Short summary
Probabilistic analysis of ambiguities in radar echo direction of arrival from meteors
Daniel Kastinen and Johan Kero
Atmos. Meas. Tech., 13, 6813–6835, https://doi.org/10.5194/amt-13-6813-2020,https://doi.org/10.5194/amt-13-6813-2020, 2020
Short summary

Cited articles

Arakawa, A. and Lamb, V. R.: Computational design of the basic dynamical processes of the UCLA general circulation model, Methods in Computational Physics, 17, 173–265, 1977.
Atlas, D., Srivastava, R., and Sekhon, R. S.: Doppler radar characteristics of precipitation at vertical incidence, Rev. Geophys., 11, 1–35, 1973.
Barros, A. P., Prat, O. P., Shrestha, P., Testik, F. Y., and Bliven, L. F.: Revisiting low and list (1982): evaluation of raindrop collision parameterizations using laboratory observations and modeling, J. Atmos. Sci., 65, 2983–2993, 2008.
Barthes, L. and Mallet, C.: Vertical evolution of raindrop size distribution: impact on the shape of the DSD, Atmos. Res., 119, 13–22, 2013.
Beheng, K.: The evolution of raindrop spectra: a review of microphysical essentials, in: Rainfall: State of the Science, edited by: Testik, F. Y., and Gebremichael, M., Wiley Online Library, 29–48, 2010.
Download
Short summary
The aim of this study is to retrieve vertical profiles of raindrop size distributions and vertical winds from radar and ground measurements. This is crucial to understand the phenomena acting on the raindrops at small scale during their fall and then to be able to merge measurements of rain at different heights and scales (from radar, rain gauges, satellites etc.). It could also help to improve the treatment of radar data and to better parameterize rain in numerical weather prediction models.