15 citations as recorded by crossref.

1. Indirect assimilation of radar reflectivity data with an adaptive hydrometer retrieval scheme for severe short-term weather forecasts L. Song et al.
2. Definition of the Moist-Air Exergy Norm: A Comparison with Existing “Moist Energy Norms” P. Marquet et al.
3. Assimilation of Meteosat Third Generation (MTG) Lightning Imager (LI) pseudo-observations in AROME-France – proof of concept F. Erdmann et al.
4. Improvement of mesoscale numerical weather prediction WRF-ARW in the Republic of Belarus by assimilation of radar wind and reflectivity data P. Zaiko et al.
5. Data assimilation of weather radar reflectivity with a blending hydrometer retrieval scheme for two convective storms in East China F. Shen et al.
6. Mobility-Enhanced Nitrogen-Polar GaN/AlGaN Heterostructure by Stress Modulation H. Ma et al.
7. Overview towards improved understanding of the mechanisms leading to heavy precipitation in the western Mediterranean: lessons learned from HyMeX S. Khodayar et al.
8. One‐dimensional maximum‐likelihood estimation for spaceborne precipitation radar data assimilation Y. Ikuta et al.
9. Impact of WIVERN Wind Observations on ARPEGE Numerical Weather Prediction Model Forecasts Using an Ensemble of Data Assimilation Method N. Sasso et al.
10. Assimilation of Moderate-Resolution Imaging Spectroradiometer Level Two Cloud Products for Typhoon Analysis and Prediction H. Zhang et al.
11. Increasing model vertical resolution may not necessarily lead to improved atmospheric predictability S. Moon et al.
12. Data assimilation impact studies with the AROME-WMED reanalysis of the first special observation period of the Hydrological cycle in the Mediterranean Experiment N. Fourrié et al.
13. The impact of GPS and high-resolution radiosonde nudging on the simulation of heavy precipitation during HyMeX IOP6 A. Caldas-Alvarez et al.
14. Comprehensive thematic T-matrix reference database: a 2017–2019 update M. Mishchenko
15. Combined use of volume radar observations and high-resolution numerical weather predictions to estimate precipitation at the ground: methodology and proof of concept T. Le Bastard et al.