| Year |
Citation |
Score |
| 2019 |
Abbaszadeh P, Moradkhani H, Daescu DN. The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework. Water Resources Research. 55: 2407-2431. PMID 31217643 DOI: 10.1029/2018Wr023629 |
0.514 |
|
| 2017 |
Shaw JA, Daescu DN. Sensitivity of the model error parameter specification in weak-constraint four-dimensional variational data assimilation Journal of Computational Physics. 343: 115-129. DOI: 10.1016/J.Jcp.2017.04.050 |
0.515 |
|
| 2016 |
Daescu DN, Langland RH. Innovation-Weight Parametrization in Data Assimilation: Formulation & Analysis with NAVDAS-AR/NAVGEM Ifac-Papersonline. 49: 176-181. DOI: 10.1016/J.Ifacol.2016.10.159 |
0.49 |
|
| 2013 |
Daescu DN, Langland RH. The adjoint sensitivity guidance to diagnosis and tuning of error covariance parameters Data Assimilation For Atmospheric, Oceanic and Hydrologic Applications (Vol. Ii). 205-232. DOI: 10.1007/978-3-642-35088-7_9 |
0.415 |
|
| 2013 |
Daescu DN, Langland RH. Error covariance sensitivity and impact estimation with adjoint 4D-Var: Theoretical aspects and first applications to NAVDAS-AR Quarterly Journal of the Royal Meteorological Society. 139: 226-241. DOI: 10.1002/Qj.1943 |
0.478 |
|
| 2012 |
Hossen MJ, Navon IM, Daescu DN. Effect of random perturbations on adaptive observation techniques International Journal For Numerical Methods in Fluids. 69: 110-123. DOI: 10.1002/Fld.2545 |
0.495 |
|
| 2011 |
Sandu A, Constantinescu E, Carmichael G, Chai T, Daescu D, Seinfeld J. Ensemble methods for dynamic data assimilation of chemical observations in atmospheric models Journal of Algorithms and Computational Technology. 5: 667-692. DOI: 10.1260/1748-3018.5.4.667 |
0.473 |
|
| 2011 |
Godinez HC, Daescu DN. Observation targeting with a second-order adjoint method for increased predictability Computational Geosciences. 15: 477-488. DOI: 10.1007/S10596-010-9217-Z |
0.468 |
|
| 2010 |
Daescu DN. Forecast sensitivity to the observation error covariance in variational data assimilation Procedia Computer Science. 1: 1277-1285. DOI: 10.1016/j.procs.2010.04.142 |
0.448 |
|
| 2010 |
Daescu DN, Todling R. Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters Quarterly Journal of the Royal Meteorological Society. 136: 2000-2012. DOI: 10.1002/Qj.693 |
0.517 |
|
| 2009 |
Daescu DN. On the deterministic observation impact guidance: A geometrical perspective Monthly Weather Review. 137: 3567-3574. DOI: 10.1175/2009Mwr2954.1 |
0.474 |
|
| 2009 |
Daescu DN, Todling R. Adjoint estimation of the variation in model functional output due to the assimilation of data Monthly Weather Review. 137: 1705-1716. DOI: 10.1175/2008Mwr2659.1 |
0.492 |
|
| 2009 |
Veerman JJP, Daescu D, Romero-Vallés MJ, Torres PJ. A single particle impact model for motion in avalanches Physica D: Nonlinear Phenomena. 238: 1897-1908. DOI: 10.1016/J.Physd.2009.06.017 |
0.312 |
|
| 2008 |
Daescu DN. On the sensitivity equations of four-dimensional variational (4D-Var) data assimilation Monthly Weather Review. 136: 3050-3065. DOI: 10.1175/2007Mwr2382.1 |
0.516 |
|
| 2008 |
Daescu DN, Navon IM. A dual-weighted approach to order reduction in 4DVAR data assimilation Monthly Weather Review. 136: 1026-1041. DOI: 10.1175/2007Mwr2102.1 |
0.501 |
|
| 2008 |
Carmichael GR, Sandu A, Chai T, Daescu DN, Constantinescu EM, Tang Y. Predicting air quality: Improvements through advanced methods to integrate models and measurements Journal of Computational Physics. 227: 3540-3571. DOI: 10.1016/J.Jcp.2007.02.024 |
0.362 |
|
| 2007 |
Daescu DN, Navon IM. Efficiency of a POD-based reduced second-order adjoint model in 4D-Var data assimilation International Journal For Numerical Methods in Fluids. 53: 985-1004. DOI: 10.1002/Fld.1316 |
0.452 |
|
| 2006 |
Zupanski M, Fletcher SJ, Navon IM, Uzunoglu B, Heikes RP, Randall DA, Ringler TD, Daescu D. Initiation of ensemble data assimilation Tellus A. 58: 159-170. DOI: 10.3402/Tellusa.V58I2.14766 |
0.48 |
|
| 2006 |
Chai T, Carmichael GR, Sandu A, Tang Y, Daescu DN. Chemical data assimilation of Transport and Chemical Evolution over the Pacific (TRACE-P) aircraft measurements Journal of Geophysical Research Atmospheres. 111. DOI: 10.1029/2005Jd005883 |
0.414 |
|
| 2005 |
Sandu A, Daescu DN, Carmichael GR, Chai T. Adjoint sensitivity analysis of regional air quality models Journal of Computational Physics. 204: 222-252. DOI: 10.1016/J.Jcp.2004.10.011 |
0.524 |
|
| 2005 |
Navon IM, Daescu DN, Liu Z. The impact of background error on incomplete observations for 4D-Var data assimilation with the FSU GSM Lecture Notes in Computer Science. 3515: 837-844. |
0.394 |
|
| 2004 |
Daescu DN, Navon IM. Adaptive observations in the context of 4D-Var data assimilation Meteorology and Atmospheric Physics. 85: 205-226. DOI: 10.1007/S00703-003-0011-5 |
0.5 |
|
| 2004 |
Chai T, Carmichael GR, Daescu DN, Sandu A. Analysis of TRACE-P observations using a four-dimensional variational data assimilation technique Bulletin of the American Meteorological Society. 6213-6218. |
0.36 |
|
| 2003 |
Daescu DN, Carmichael GR. An adjoint sensitivity method for the adaptive location of the observations in air quality modeling Journal of the Atmospheric Sciences. 60: 434-450. DOI: 10.1175/1520-0469(2003)060<0434:Aasmft>2.0.Co;2 |
0.527 |
|
| 2003 |
Daescu DN, Navon IM. An analysis of a hybrid optimization method for variational data assimilation International Journal of Computational Fluid Dynamics. 17: 299-306. DOI: 10.1080/1061856031000120510 |
0.468 |
|
| 2003 |
Daescu DN, Sandu A, Carmichael GR. Direct and adjoint sensitivity analysis of chemical kinetic systems with KPP: II - Numerical validation and applications Atmospheric Environment. 37: 5097-5114. DOI: 10.1016/J.Atmosenv.2003.08.020 |
0.483 |
|
| 2003 |
Sandu A, Daescu DN, Carmichael GR. Direct and adjoint sensitivity analysis of chemical kinetic systems with KPP: Part I - Theory and software tools Atmospheric Environment. 37: 5083-5096. DOI: 10.1016/J.Atmosenv.2003.08.019 |
0.476 |
|
| 2003 |
Carmichael GR, Daescu DN, Sandu A, Chai T. Computational aspects of chemical data assimilation into atmospheric models Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2660: 269-278. |
0.4 |
|
| 2002 |
Le Dimet FX, Navon IM, Daescu DN. Second-order information in data assimilation Monthly Weather Review. 130: 629-648. DOI: 10.1175/1520-0493(2002)130<0629:Soiida>2.0.Co;2 |
0.411 |
|
| 2000 |
Daescu D, Carmichael GR, Sandu A. Adjoint implementation of Rosenbrock methods applied to variational data assimilation problems Journal of Computational Physics. 165: 496-510. DOI: 10.1006/Jcph.2000.6622 |
0.503 |
|
| 1999 |
Carmichael GR, Sandu A, Song CH, He S, Phadnis MJ, Daescu D, Damian‐Iordache V, Potra FA. Computational challenges of modelling interactions between aerosol and gas phase processes in large‐scale air pollution models Environmental Management and Health. 10: 99-136. DOI: 10.1108/09566169910276157 |
0.373 |
|
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