12 citations as recorded by crossref.

1. Flow dependence of forecast uncertainty in a large convection‐permitting ensemble M. Puh et al. https://doi.org/10.1002/qj.4860
2. Distributions and convergence of forecast variables in a 1,000‐member convection‐permitting ensemble G. Craig et al. https://doi.org/10.1002/qj.4305
3. Revisiting Online and Offline Data Assimilation Comparison for Paleoclimate Reconstruction: An Idealized OSSE Study A. Okazaki et al. https://doi.org/10.1029/2020JD034214
4. Weight structure of the Local Ensemble Transform Kalman Filter: A case with an intermediate atmospheric general circulation model S. Kotsuki et al. https://doi.org/10.1002/qj.3852
5. Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA https://doi.org/10.2151/jmsj.2022-027
6. Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction J. Ruiz et al. https://doi.org/10.5194/npg-28-615-2021
7. Deterministic ensemble Kalman filter based on two localization techniques for mitigating sampling errors with a quasi-geostrophic model M. Chang et al. https://doi.org/10.1007/s00703-024-01015-1
8. Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models? Y. Sawada https://doi.org/10.5194/hess-24-3881-2020
9. Convergence of forecast distributions in a 100,000‐member idealised convective‐scale ensemble K. Tempest et al. https://doi.org/10.1002/qj.4410
10. ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package D. Giles et al. https://doi.org/10.5194/gmd-17-2427-2024
11. On‐line machine‐learning forecast uncertainty estimation for sequential data assimilation M. Sacco et al. https://doi.org/10.1002/qj.4743
12. A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF S. Kotsuki et al. https://doi.org/10.5194/gmd-15-8325-2022