Massimo Bonavita

Atmospheric Science · 2016–2020

A researcher in the Science corpus: 2 papers; trusts 11 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

Whom Massimo Bonavita trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Xuguang Wang
  2. A. Lorenc
  3. Thomas M. Hamill
  4. M. Buehner
  5. Chengsi Liu
  6. C. Snyder
  7. Margarida Pereira
  8. L. Berre
  9. Q. Xiao
  10. Adam Clayton

Who trusts Massimo Bonavita overall

  1. Alan J. Geer
  2. R. Bannister
  3. Hans Hersbach

Papers from Massimo Bonavita's perspective

Ordered by how strongly the researchers Massimo Bonavita trusts endorse each paper. Same corpus, a different order for every researcher.

  1. The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var
  2. Ensemble‐derived stationary and flow‐dependent background‐error covariances: Evaluation in a quasi‐operational NWP setting
  3. A Hybrid Ensemble Kalman Filter-3D Variational Analysis Scheme
  4. An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test
  5. An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW)
  6. Ensemble Data Assimilation without Perturbed Observations
  7. Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office
  8. An Ensemble Adjustment Kalman Filter for Data Assimilation
  9. A Hybrid ETKF-3DVAR Data Assimilation Scheme for the WRF Model. Part I: Observing System Simulation Experiment
  10. Ensemble Kalman filtering

Their papers