Articles | Volume 12, issue 10
https://doi.org/10.5194/amt-12-5669-2019
https://doi.org/10.5194/amt-12-5669-2019
Research article
 | 
25 Oct 2019
Research article |  | 25 Oct 2019

Combined use of volume radar observations and high-resolution numerical weather predictions to estimate precipitation at the ground: methodology and proof of concept

Tony Le Bastard, Olivier Caumont, Nicolas Gaussiat, and Fatima Karbou

Related authors

Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery
Marin Kneib, Amaury Dehecq, Fanny Brun, Fatima Karbou, Laurane Charrier, Silvan Leinss, Patrick Wagnon, and Fabien Maussion
The Cryosphere, 18, 2809–2830, https://doi.org/10.5194/tc-18-2809-2024,https://doi.org/10.5194/tc-18-2809-2024, 2024
Short summary
Severe hail detection with C-band dual-polarisation radars using convolutional neural networks
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloe David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
EGUsphere, https://doi.org/10.5194/egusphere-2024-1336,https://doi.org/10.5194/egusphere-2024-1336, 2024
Short summary
Assimilation of surface pressure observations from personal weather stations in AROME-France
Alan Demortier, Marc Mandement, Vivien Pourret, and Olivier Caumont
Nat. Hazards Earth Syst. Sci., 24, 907–927, https://doi.org/10.5194/nhess-24-907-2024,https://doi.org/10.5194/nhess-24-907-2024, 2024
Short summary
Assimilation of Meteosat Third Generation (MTG) Lightning Imager (LI) pseudo-observations in AROME-France – proof of concept
Felix Erdmann, Olivier Caumont, and Eric Defer
Nat. Hazards Earth Syst. Sci., 23, 2821–2840, https://doi.org/10.5194/nhess-23-2821-2023,https://doi.org/10.5194/nhess-23-2821-2023, 2023
Short summary
An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties
Alistair Bell, Pauline Martinet, Olivier Caumont, Frédéric Burnet, Julien Delanoë, Susana Jorquera, Yann Seity, and Vinciane Unger
Atmos. Meas. Tech., 15, 5415–5438, https://doi.org/10.5194/amt-15-5415-2022,https://doi.org/10.5194/amt-15-5415-2022, 2022
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Scale separation for gravity wave analysis from 3D temperature observations in the mesosphere and lower thermosphere (MLT) region
Björn Linder, Peter Preusse, Qiuyu Chen, Ole Martin Christensen, Lukas Krasauskas, Linda Megner, Manfred Ern, and Jörg Gumbel
Atmos. Meas. Tech., 17, 3829–3841, https://doi.org/10.5194/amt-17-3829-2024,https://doi.org/10.5194/amt-17-3829-2024, 2024
Short summary
Estimating the refractivity bias of FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) radio occultation in the deep troposphere
Gia Huan Pham, Shu-Chih Yang, Chih-Chien Chang, Shu-Ya Chen, and Cheng Yung Huang
Atmos. Meas. Tech., 17, 3605–3623, https://doi.org/10.5194/amt-17-3605-2024,https://doi.org/10.5194/amt-17-3605-2024, 2024
Short summary
High Spectral Resolution Lidar – generation 2 (HSRL-2) retrievals of ocean surface wind speed: methodology and evaluation
Sanja Dmitrovic, Johnathan W. Hair, Brian L. Collister, Ewan Crosbie, Marta A. Fenn, Richard A. Ferrare, David B. Harper, Chris A. Hostetler, Yongxiang Hu, John A. Reagan, Claire E. Robinson, Shane T. Seaman, Taylor J. Shingler, Kenneth L. Thornhill, Holger Vömel, Xubin Zeng, and Armin Sorooshian
Atmos. Meas. Tech., 17, 3515–3532, https://doi.org/10.5194/amt-17-3515-2024,https://doi.org/10.5194/amt-17-3515-2024, 2024
Short summary
Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms
Laura M. Tomkins, Sandra E. Yuter, and Matthew A. Miller
Atmos. Meas. Tech., 17, 3377–3399, https://doi.org/10.5194/amt-17-3377-2024,https://doi.org/10.5194/amt-17-3377-2024, 2024
Short summary
Noise filtering options for conically scanning Doppler lidar measurements with low pulse accumulation
Eileen Päschke and Carola Detring
Atmos. Meas. Tech., 17, 3187–3217, https://doi.org/10.5194/amt-17-3187-2024,https://doi.org/10.5194/amt-17-3187-2024, 2024
Short summary

Cited articles

Andrieu, H. and Creutin, J. D.: Identification of vertical profiles of radar reflectivity for hydrological applications using an inverse method, Part I: Formulation, J. Appl. Meteorol., 34, 225–239, 1995. a
Auger, L., Dupont, O., Hagelin, S., Brousseau, P., and Brovelli, P.: AROME–NWC: a new nowcasting tool based on an operational mesoscale forecasting system, Q. J. Roy. Meteor. Soc., 141, 1603–1611, 2015. a, b
Augros, C., Caumont, O., Ducrocq, V., Gaussiat, N., and Tabary, P.: Comparisons between S-, C-and X-band polarimetric radar observations and convective-scale simulations of the HyMeX first special observing period, Q. J. Roy. Meteor. Soc., 142, 347–362, 2016. a, b
Augros, C., Caumont, O., Ducrocq, V., and Gaussiat, N.: Assimilation of radar dual-polarization observations in the AROME model, Q. J. Roy. Meteor. Soc., 144, 1352–1368, 2018. a, b
Bauer, H.-S., Schwitalla, T., Wulfmeyer, V., Bakhshaii, A., Ehret, U., Neuper, M., and Caumont, O.: Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF–a performance test, Tellus A, 67, 25047, https://doi.org/10.3402/tellusa.v67.25047, 2015. a
Download
Short summary
The estimation of surface rainfall from radars becomes less effective at long ranges or in mountainous regions where the radar beam is far from the ground. The method proposed in this paper investigates how vertical profiles simulated from high-resolution model can be used to predict the evolution of the precipitation below the radar beam. Our results show that this novel method leads to better results than the current operational methods that either use climatological or idealised profiles.