| Sensitivity and uncertainty analysis, volume II: applications to large-scale systems DG Cacuci, M Ionescu-Bujor, IM Navon CRC press, 2005 | 1214 | 2005 |
| Practical and theoretical aspects of adjoint parameter estimation and identifiability in meteorology and oceanography IM Navon Dynamics of atmospheres and oceans 27 (1-4), 55-79, 1998 | 345 | 1998 |
| Conjugate-gradient methods for large-scale minimization in meteorology IM Navon, D LEGLER Monthly Weather Review 115 (8), 1479-1502, 1987 | 279 | 1987 |
| Data assimilation for numerical weather prediction: a review IM Navon Data assimilation for atmospheric, oceanic and hydrologic applications, 21-65, 2009 | 251 | 2009 |
| Second-order information in data assimilation FX Le Dimet, IM Navon, DN Daescu Monthly Weather Review 130 (3), 629-648, 2002 | 242 | 2002 |
| A reduced‐order approach to four‐dimensional variational data assimilation using proper orthogonal decomposition Y Cao, J Zhu, IM Navon, Z Luo International Journal for Numerical Methods in Fluids 53 (10), 1571-1583, 2007 | 238 | 2007 |
| Numerical experience with limited-memory quasi-Newton and truncated Newton methods X Zou, IM Navon, M Berger, KH Phua, T Schlick, FX Le Dimet SIAM Journal on Optimization 3 (3), 582-608, 1993 | 190 | 1993 |
| The second order adjoint analysis: theory and applications Z Wang, IM Navon, FX Le Dimet, X Zou Meteorology and atmospheric physics 50, 3-20, 1992 | 188 | 1992 |
| An optimal nudging data assimilation scheme using parameter estimation X Zou, IM Navon, FX LeDimet Quarterly Journal of the Royal Meteorological Society 118 (508), 1163-1186, 1992 | 180 | 1992 |
| Non-linear model reduction for the Navier–Stokes equations using residual DEIM method D Xiao, F Fang, AG Buchan, CC Pain, IM Navon, J Du, G Hu Journal of Computational Physics 263, 1-18, 2014 | 178 | 2014 |
| Long lead-time daily and monthly streamflow forecasting using machine learning methods M Cheng, F Fang, T Kinouchi, IM Navon, CC Pain Journal of Hydrology 590, 125376, 2020 | 167 | 2020 |
| POD/DEIM nonlinear model order reduction of an ADI implicit shallow water equations model R Ştefănescu, IM Navon Journal of Computational Physics 237, 95-114, 2013 | 165 | 2013 |
| Optimality of variational data assimilation and its relationship with the Kalman filter and smoother Z Li, IM Navon Quarterly Journal of the Royal Meteorological Society 127 (572), 661-683, 2001 | 160 | 2001 |
| Non-intrusive reduced order modelling of the Navier–Stokes equations D Xiao, F Fang, AG Buchan, CC Pain, IM Navon, A Muggeridge Computer Methods in Applied Mechanics and Engineering 293, 522-541, 2015 | 156 | 2015 |
| Reduced-order modeling of the upper tropical pacific ocean model using proper orthogonal decomposition Y Cao, J Zhu, Z Luo, IM Navon Computers & mathematics with Applications 52 (8-9), 1373-1386, 2006 | 154 | 2006 |
| Objective analysis of pseudostress over the Indian Ocean using a direct-minimization approach DM Legler, IM Navon, JJ O'Brien Monthly Weather Review 117 (4), 709-720, 1989 | 143 | 1989 |
| Optimal control of cylinder wakes via suction and blowing Z Li, IM Navon, MY Hussaini, FX Le Dimet Computers & Fluids 32 (2), 149-171, 2003 | 134 | 2003 |
| Variational data assimilation with moist threshold processes using the NMC spectral model X Zou, IM Navon, JG Sela Tellus A: Dynamic Meteorology and Oceanography 45 (5), 370-387, 1993 | 126 | 1993 |
| Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method R Hu, F Fang, CC Pain, IM Navon Journal of Hydrology 575, 911-920, 2019 | 124 | 2019 |
| Comparison of POD reduced order strategies for the nonlinear 2D shallow water equations R Ştefănescu, A Sandu, IM Navon International Journal for Numerical Methods in Fluids 76 (8), 497-521, 2014 | 123 | 2014 |
Fangxin FangSenior Research Fellow, Earth Science and Engineering, Imperial College LondonVerified email at imperial.ac.uk
