Aberson S. D.,2003: Targeted observations to improve operational tropical cyclone track forecast guidance. Mon. Wea. Rev., 131, 1613-1628, https://doi.org/10.1175//2550.1
Aberson S. D.,S. J. Majumdar, C. A. Reynolds, and B. J. Etherton, 2011: An observing system experiment for tropical cyclone targeting techniques using the global forecast system. Mon. Wea. Rev., 139, 895-907, https://doi.org/10.1175/2010MWR3397.1
Ancell B.,G. J. Hakim, 2007: Comparing adjoint- and ensemble-sensitivity analysis with applications to observation targeting. Mon. Wea. Rev., 135, 4117-4134, https://doi.org/10.1175/2007MWR1904.1
Bishop C. H.,Z. Toth, 1999: Ensemble transformation and adaptive observations. J. Atmos. Sci., 56, 1748-1765, https://doi.org/10.1175/1520-0469(1999)056<1748:ETAAO>2.0.CO;2
Bishop C. H.,B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420-436, https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2
Chou K. H.,C. C. Wu, P. H. Lin, S. D. Aberson, M. Weissmann, F. Harnisch, and T. Nakazawa, 2011: The impact of dropwindsonde observations on typhoon track forecasts in DOTSTAR and T-PARC. Mon. Wea. Rev., 139, 1728-1743, https://doi.org/10.1175/2010MWR3582.1
Daescu D. N.,I. M. Navon, 2004: Adaptive observations in the context of 4D-Var data assimilation. Meteor. Atmos. Phys., 85, 205-226, https://doi.org/10.1007/s00703-003-0011-5
Dennis Jr., J. E., and R. B. Schnabel, 1996: Numerical Methods for Unconstrained Optimization and Nonlinear Equations. SIAM, 378 pp.4eb9a5e542e1992910dd762b0e299fb8http%3A%2F%2Fwww.ams.org%2Fmathscinet-getitem%3Fmr%3D702023
Evensen G.,1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. Oceans, 99, 10 143-10 162, https://doi.org/10.1029/94JC00572
Hossen M. J.,I. M. Navon, and D. N. Daescu, 2012: Effect of random perturbations on adaptive observation techniques. International Journal for Numerical Methods in Fluids, 69(1), 110-123, https://doi.org/10.1002/fld.2545
Houtekamer P. L.,H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123-137, https://doi.org/10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2
Joly, A., Coauthors, 1997: The fronts and Atlantic storm-track experiment (FASTEX): Scientific objectives and experimental design. Bull. Amer. Meteor. Soc., 78, 1917-1940, https://doi.org/10.1175/1520-0477(1997)078<1917:TFAAST>2.0.CO;2
Joly, A., Coauthors, 1999: Overview of the field phase of the fronts and Atlantic Storm-Track EXperiment (FASTEX) project. Quart. J. Roy. Meteor. Soc., 125, 3131-3163, https://doi.org/10.1002/qj.49712556103
Langland, R. H.,G. D. Rohaly, 1996: Adjoint-based targeting of observations for FASTEX cyclones. Proc. Seventh Conf. on Mesoscale Processes, United Kingdom, Amer. Meteor. Soc., 359- 371.
Langland, R. H., R. Gelaro, G. D. Rohaly, M. A. Shapiro, 1999a: Targeted observations in FASTEX: Adjoint-based targeting procedures and data impact experiments in IOP17 and IOP18. Quart. J. Roy. Meteor. Soc., 125, 3241-3270, https://doi.org/10.1002/qj.49712556107
Langland, R. H.,Coauthors, 1999b: The North Pacific experiment (NORPEX-98): Targeted observations for improved North American weather forecasts. Bull. Amer. Meteor. Soc., 80, 1363-1384, https://doi.org/10.1175/1520-0477(1999)080<1363:TNPENT>2.0.CO;2
Majumdar S. J.,2016: A review of targeted observations. Bull. Amer. Meteor. Soc., 97, 2287-2303, https://doi.org/10.1175/BAMS-D-14-00259.1
Matsuno T.,1966: Numerical integrations of the primitive equations by a simulated backward difference method. J. Meteor. Soc. Japan, 44, 76- 84.10.2151/jmsj1965.44.1_76https://www.jstage.jst.go.jp/article/jmsj1965/44/1/44_1_76/_article
Mu M.,2013: Methods, current status, and prospect of targeted observation. Science China: Earth Sciences, 56, 1997-2005, https://doi.org/10.1007/s11430-013-4727-x
Mu M.,W. S. Duan, and B. Wang, 2003: Conditional nonlinear optimal perturbation and its applications. Nonlinear Processes in Geophysics, 10, 493-501, https://doi.org/10.5194/npg-10-493-2003
Mu M.,F. F. Zhou, and H. L. Wang, 2009: A method for identifying the sensitive areas in targeted observations for tropical cyclone prediction: Conditional nonlinear optimal perturbation. Mon. Wea. Rev., 137, 1623-1639, https://doi.org/10.1175/2008MWR2640.1
Palmer T. N.,R. Gelaro, J. Barkmeijer, and R. Buizza, 1998: Singular vectors, metrics, and adaptive observations. J. Atmos. Sci., 55, 633-653, https://doi.org/10.1175/1520-0469(1998)055<0633:SVMAAO>2.0.CO;2
Pu Z. X.,E. Kalnay, J. Sela, and I. Szunyogh, 1997: Sensitivity of forecast errors to initial conditions with a quasi-inverse linear method. Mon. Wea. Rev., 125, 2479-2503, https://doi.org/10.1175/1520-0493(1997)125<2479:SOFETI>2.0.CO;2
Qin X. H.,M. Mu, 2011: A study on the reduction of forecast error variance by three adaptive observation approaches for tropical cyclone prediction. Mon. Wea. Rev., 139, 2218-2232, https://doi.org/10.1175/2010MWR3327.1
Qiu C. J.,A. M. Shao, Q. Xu, and L. Wei, 2007: Fitting model fields to observations by using singular value decomposition: An ensemble-based 4DVar approach. J. Geophys. Res. Atmos., 112, D11105, https://doi.org/10.1029/2006JD007994
Rabier F.,H. Järvinen E. Klinker, J. F. Mahfouf, and A. Simmons, 2000: The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics. Quart. J. Roy. Meteor. Soc., 126, 1143-1170, https://doi.org/10.1002/qj.49712656415
Tian X. J.,Z. H. Xie, 2012: Implementations of a square-root ensemble analysis and a hybrid localisation into the POD-based ensemble 4DVar. Tellus A, 64, 18375, https://doi.org/10.3402/tellusa.v64i0.18375
Tian X. J.,X. B. Feng, 2015: A non-linear least squares enhanced POD-4DVar algorithm for data assimilation. Tellus A, 67, 25340, https://doi.org/10.3402/tellusa.v67.25340
Tian X. J.,Z. H. Xie, and A. G. Dai, 2008: An ensemble-based explicit four-dimensional variational assimilation method. J. Geophys. Res. Atmos., 113, D21124, https://doi.org/10.1029/2008JD010358
Tian X. J.,Z. H. Xie, and A. G. Dai, 2010: An ensemble conditional nonlinear optimal perturbation approach: Formulation and applications to parameter calibration. Water Resour. Res., 46, W09540, https://doi.org/10.1029/2009WR008508
Tian X. J.,Z. H. Xie, and Q. Sun, 2011: A POD-based ensemble four-dimensional variational assimilation method. Tellus A, 63, 805-816, https://doi.org/10.1111/j.1600-0870.2011.00529.x
Tian X. J.,X. B. Feng, H. Q. Zhang, B. Zhang, and R. Han, 2016: An enhanced ensemble-based method for computing CNOPs using an efficient localization implementation scheme and a two-step optimization strategy: Formulation and preliminary tests. Quart. J. Roy. Meteor. Soc., 142, 1007-1016, https://doi.org/10.1002/qj.2703
Tian X. J.,X. B. Feng, 2017: A nonlinear least-squares-based ensemble method with a penalty strategy for computing the conditional nonlinear optimal perturbations. Quart. J. Roy. Meteor. Soc., 143, 641-649, https://doi.org/10.1002/qj.2946
Tian X. J.,H. Q. Zhang, X. B. Feng, and Y. F. Xie, 2018: Nonlinear least squares En4DVar to 4DEnVar methods for data assimilation: Formulation, analysis, and preliminary evaluation. Mon. Wea. Rev., 146, 77-93, https://doi.org/10.1175/MWR-D-17-0050.1
Wu C. C.,K. H. Chou, P. H. Lin, S. D. Aberson, M. S. Peng, and T. Nakazawa, 2007a: The impact of dropwindsonde data on typhoon track forecasts in DOTSTAR. Wea. Forecasting, 22, 1157-1176, https://doi.org/10.1175/2007WAF2006062.1
Xu Q.,1996: Generalized adjoint for physical processes with parameterized discontinuities. Part I: Basic issues and heuristic examples. J. Atmos. Sci., 53, 1123-1142, https://doi.org/10.1175/1520-0469(1996)053<1123:GAFPPW>2.0.CO;2
Zhang H. Q.,X. J. Tian, 2018a: An efficient local correlation matrix decomposition approach for the localization implementation of ensemble-based assimilation methods. J. Geophys. Res. Atmos., 123, 3556-3573, https://doi.org/10.1002/2017JD027999
Zhang H. Q.,X. J. Tian, 2018b: A multigrid nonlinear least squares four-dimensional variational data assimilation scheme with the advanced research weather research and forecasting model. J. Geophys. Res. Atmos., 123, 5116-5129, https://doi.org/10.1029/2017JD027529
Zhang Y.,Y. F. Xie, H. L. Wang, D. H. Chen, and Z. Toth, 2016: Ensemble transform sensitivity method for adaptive observations. Adv. Atmos. Sci., 33(1), 10-20, https://doi.org/10.1007/s00376-015-5031-9
Zhou F. F.,M. Mu, 2011: The impact of verification area design on tropical cyclone targeted observations based on the CNOP method. Adv. Atmos. Sci., 28(5), 997-1010, https://doi.org/10.1007/s00376-011-0120-x