Takemasa Miyoshi

Atmospheric Science · 2007–2018

A researcher in the Science corpus: 12 papers; trusts 67 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Atmospheric Science — 190 citations into it; #161 of 4,804 by trust here, #805 by citations
• Global and Planetary Change — 185 citations into it; #319 of 10,225 by trust here, #2,122 by citations
• Environmental Engineering — 31 citations into it
• Oceanography — 26 citations into it; #564 of 4,047 by trust here, #2,150 by citations
• Computational Mechanics — 8 citations into it
• Artificial Intelligence — 6 citations into it
• Statistics and Probability — 5 citations into it

Whom Takemasa Miyoshi trusts overall

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

1. Jeffrey L. Anderson
2. J. Whitaker
3. P. Houtekamer
4. Brian R. Hunt
5. Thomas M. Hamill
6. H. Mitchell
7. Fuqing Zhang
8. Eric J. Kostelich
9. G. Desroziers
10. S. Yamane

Who trusts Takemasa Miyoshi overall

1. Marc Bocquet
2. Mohamad E. Gharamti
3. Juan J. Ruiz
4. Shunji Kotsuki
5. Masaru Kunii
6. Jeffrey L. Anderson
7. Julia Tödter
8. Martin Leutbecher
9. Manuel Pulido
10. Yoichiro Ota

Papers from Takemasa Miyoshi's perspective

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

1. An Ensemble Adjustment Kalman Filter for Data Assimilation
2. Ensemble Data Assimilation without Perturbed Observations
3. The Ensemble Kalman Filter: theoretical formulation and practical implementation
4. A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation
5. The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var
6. Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter
7. A Hybrid Ensemble Kalman Filter-3D Variational Analysis Scheme
8. Ensemble Kalman filtering
9. Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches
10. An Adaptive Ensemble Kalman Filter

Their papers

• A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation (2018) — 88 citations
• Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere (2017) — 62 citations
• A local particle filter for high-dimensional geophysical systems (2015) — 88 citations
• The Non-hydrostatic Icosahedral Atmospheric Model: description and development (2014) — 369 citations
• The 10,240‐member ensemble Kalman filtering with an intermediate AGCM (2014) — 94 citations
• Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review (2013) — 131 citations
• “Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation (2011) — 146 citations
• The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter (2011) — 289 citations
• Accounting for Model Errors in Ensemble Data Assimilation (2009) — 90 citations
• Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter (2009) — 317 citations
• Local Ensemble Transform Kalman Filtering with an AGCM at a T159/L48 Resolution (2007) — 300 citations
• Localizing the Error Covariance by Physical Distances within a Local Ensemble Transform Kalman Filter (LETKF) (2007) — 111 citations