53 citations as recorded by crossref.

1. Building a Better Forecast: Reformulating the Ensemble Kalman Filter for Improved Applications to Volcano Deformation J. Albright & P. Gregg https://doi.org/10.1029/2022EA002522
2. A comparison of different versions of the SEEK Filter for assimilation of biogeochemical data in numerical models of marine ecosystem dynamics M. Butenschön & M. Zavatarelli https://doi.org/10.1016/j.ocemod.2012.06.003
3. Sequential data assimilation in an upwelling influenced estuary R. Torres et al. https://doi.org/10.1016/j.jmarsys.2006.02.001
4. A multi-data stream assimilation framework for the assessment of volcanic unrest P. Gregg & J. Pettijohn https://doi.org/10.1016/j.jvolgeores.2015.11.008
5. Efficient Characterization of Uncertain Model Parameters with a Reduced-Order Ensemble Kalman Filter B. Lin & D. McLaughlin https://doi.org/10.1137/130910415
6. Ecoregions in the Mediterranean Sea Through the Reanalysis of Phytoplankton Functional Types and Carbon Fluxes S. Ciavatta et al. https://doi.org/10.1029/2019JC015128
7. Challenges and opportunities for integrating lake ecosystem modelling approaches W. Mooij et al. https://doi.org/10.1007/s10452-010-9339-3
8. A model-independent data assimilation (MIDA) module and its applications in ecology X. Huang et al. https://doi.org/10.5194/gmd-14-5217-2021
9. EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters J. Bruggeman et al. https://doi.org/10.5194/gmd-17-5619-2024
10. Data assimilation strategies for volcano geodesy Y. Zhan & P. Gregg https://doi.org/10.1016/j.jvolgeores.2017.02.015
11. Assimilation of ocean colour data into a Biogeochemical Flux Model of the Eastern Mediterranean Sea G. Triantafyllou et al. https://doi.org/10.5194/os-3-397-2007
12. Emerging ocean observations for interdisciplinary data assimilation systems T. Dickey https://doi.org/10.1016/S0924-7963(03)00011-3
13. Towards a Multi‐Platform Assimilative System for North Sea Biogeochemistry J. Skákala et al. https://doi.org/10.1029/2020JC016649
14. Can ocean color assimilation improve biogeochemical hindcasts in shelf seas? S. Ciavatta et al. https://doi.org/10.1029/2011JC007219
15. Assimilation of Ocean‐Color Plankton Functional Types to Improve Marine Ecosystem Simulations S. Ciavatta et al. https://doi.org/10.1002/2017JC013490
16. Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling M. Schartau et al. https://doi.org/10.5194/bg-14-1647-2017
17. Decadal reanalysis of biogeochemical indicators and fluxes in the North West European shelf‐sea ecosystem S. Ciavatta et al. https://doi.org/10.1002/2015JC011496
18. On the stability and the uniform propagation of chaos properties of Ensemble Kalman–Bucy filters P. Del Moral & J. Tugaut https://doi.org/10.1214/17-AAP1317
19. Variable update strategy to improve water quality forecast accuracy in multivariate data assimilation using the ensemble Kalman filter S. Park et al. https://doi.org/10.1016/j.watres.2020.115711
20. Calibration of a coupled biological–physical model for prediction in a coastal inlet M. Guarracino et al. https://doi.org/10.1016/j.csr.2011.07.012
21. A hybrid ensemble-OI Kalman filter for efficient data assimilation into a 3-D biogeochemical model of the Mediterranean K. Tsiaras et al. https://doi.org/10.1007/s10236-017-1050-7
22. Data assimilation with a local Ensemble Kalman Filter applied to a three-dimensional biological model of the Middle Atlantic Bight J. Hu et al. https://doi.org/10.1016/j.jmarsys.2011.11.016
23. A theoretical analysis of one-dimensional discrete generation ensemble Kalman particle filters P. Del Moral & E. Horton https://doi.org/10.1214/22-AAP1843
24. Application of the Gaussian anamorphosis to assimilation in a 3-D coupled physical-ecosystem model of the North Atlantic with the EnKF: a twin experiment E. Simon & L. Bertino https://doi.org/10.5194/os-5-495-2009
25. A data assimilation tool for the Pagasitikos Gulf ecosystem dynamics: Methods and benefits G. Korres et al. https://doi.org/10.1016/j.jmarsys.2011.11.004
26. Improving Weather Forecasts for Sailing Events Using a Combination of a Numerical Forecast Model and Machine Learning Postprocessing S. Beimel et al. https://doi.org/10.3390/app14072950
27. Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model D. Ford et al. https://doi.org/10.5194/os-8-751-2012
28. A Novel Data Sampling Driven Kalman Filter Is Designed by Combining the Characteristic Sampling of UKF and the Random Sampling of EnKF T. Cui et al. https://doi.org/10.3390/s22041343
29. Nutrient transports in the Baltic Sea – results from a 30-year physical–biogeochemical reanalysis Y. Liu et al. https://doi.org/10.5194/bg-14-2113-2017
30. Dynamics and enzymatic degradation of exopolymer particles under increasing concentrations of silver ions and nanoparticles during a marine mesocosm experiment L. Scheidemann et al. https://doi.org/10.3389/fmars.2022.955488
31. A sequential Monte Carlo approach for marine ecological prediction M. Dowd https://doi.org/10.1002/env.780
32. State and parameter update of a hydrodynamic-phytoplankton model using ensemble Kalman filter J. Huang et al. https://doi.org/10.1016/j.ecolmodel.2013.04.022
33. On the mathematical theory of ensemble (linear-Gaussian) Kalman–Bucy filtering A. Bishop & P. Del Moral https://doi.org/10.1007/s00498-023-00357-2
34. Assimilation of remotely-sensed optical properties to improve marine biogeochemistry modelling S. Ciavatta et al. https://doi.org/10.1016/j.pocean.2014.06.002
35. Adaptive forecasting of phytoplankton communities T. Page et al. https://doi.org/10.1016/j.watres.2018.01.046
36. Estimating time-dependent parameters for a biological ocean model using an emulator approach J. Mattern et al. https://doi.org/10.1016/j.jmarsys.2012.01.015
37. Assessing a robust ensemble-based Kalman filter for efficient ecosystem data assimilation of the Cretan Sea G. Triantafyllou et al. https://doi.org/10.1016/j.jmarsys.2012.12.006
38. Operational monitoring and forecasting for marine environmental applications in the Aegean Sea K. Nittis et al. https://doi.org/10.1016/j.envsoft.2004.04.023
39. Forecasting dryland vegetation condition months in advance through satellite data assimilation S. Tian et al. https://doi.org/10.1038/s41467-019-08403-x
40. Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis R. Baatz et al. https://doi.org/10.1029/2020RG000715
41. Modelling the hydrodynamics and ecosystem of the North-West European continental shelf for operational oceanography J. Siddorn et al. https://doi.org/10.1016/j.jmarsys.2006.01.018
42. Skill assessment in ocean biological data assimilation W. Gregg et al. https://doi.org/10.1016/j.jmarsys.2008.05.006
43. Sequential Assimilation of Volcanic Monitoring Data to Quantify Eruption Potential: Application to Kerinci Volcano, Sumatra Y. Zhan et al. https://doi.org/10.3389/feart.2017.00108
44. Marine data assimilation in the UK: the past, the present, and the vision for the future J. Skákala et al. https://doi.org/10.5194/os-21-1709-2025
45. Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo M. Dowd https://doi.org/10.1016/j.jmarsys.2007.01.007
46. Marine ecosystem models for earth systems applications: The MarQUEST experience J. Allen et al. https://doi.org/10.1016/j.jmarsys.2009.12.017
47. Development of an Aerosol Retrieval Method: Description and Preliminary Tests G. Carrió et al. https://doi.org/10.1175/2008JAMC1729.1
48. Eastern Mediterranean biogeochemical flux model – Simulations of the pelagic ecosystem G. Petihakis et al. https://doi.org/10.5194/os-5-29-2009
49. Sequential data assimilation applied to a physical–biological model for the Bermuda Atlantic time series station J. Mattern et al. https://doi.org/10.1016/j.jmarsys.2009.08.004
50. Assessment of a regional physical–biogeochemical stochastic ocean model. Part 1: Ensemble generation V. Vervatis et al. https://doi.org/10.1016/j.ocemod.2021.101781
51. Improving assimilation of SeaWiFS data by the application of bias correction with a local SEIK filter L. Nerger & W. Gregg https://doi.org/10.1016/j.jmarsys.2007.09.007
52. An Ensemble Kalman Filter Data Assimilation Method for the Sea Surface Temperature in the China Seas: Implementation and Simulation Experiments Z. Li et al. https://doi.org/10.1088/1742-6596/2486/1/012028
53. Experimental and modeling study on Cr(VI) transfer from soil into surface runoff C. Tan et al. https://doi.org/10.1007/s00477-015-1161-y