@article{li_peng_yan_xie_2013, title={On improving storm surge forecasting using an adjoint optimal technique}, volume={72}, ISSN={["1463-5011"]}, DOI={10.1016/j.ocemod.2013.08.009}, abstractNote={A three-dimensional ocean model and its adjoint model are used to simultaneously optimize the initial conditions (IC) and the wind stress drag coefficient (Cd) for improving storm surge forecasting. To demonstrate the effect of this proposed method, a number of identical twin experiments (ITEs) with a prescription of different error sources and two real data assimilation experiments are performed. Results from both the idealized and real data assimilation experiments show that adjusting IC and Cd simultaneously can achieve much more improvements in storm surge forecasting than adjusting IC or Cd only. A diagnosis on the dynamical balance indicates that adjusting IC only may introduce unrealistic oscillations out of the assimilation window, which can be suppressed by the adjustment of the wind stress when simultaneously adjusting IC and Cd. Therefore, it is recommended to simultaneously adjust IC and Cd to improve storm surge forecasting using an adjoint technique.}, journal={OCEAN MODELLING}, author={Li, Yineng and Peng, Shiqiu and Yan, Jing and Xie, Lian}, year={2013}, month={Dec}, pages={185–197} }
 @article{peng_xie_liu_semazzi_2010, title={Application of Scale-Selective Data Assimilation to Regional Climate Modeling and Prediction}, volume={138}, ISSN={["1520-0493"]}, DOI={10.1175/2009mwr2974.1}, abstractNote={Abstract}, number={4}, journal={MONTHLY WEATHER REVIEW}, author={Peng, Shiqiu and Xie, Lan and Liu, Bin and Semazzi, Fredrick}, year={2010}, month={Apr}, pages={1307–1318} }
 @article{shiqiu_xiangde_xiaohui_dongxiao_yuxiang_jingjiao_2009, title={The early-warning effects of assimilation of the observations over the large-scale slope of the "World Roof" on its downstream weather forecasting}, volume={54}, ISSN={["1861-9541"]}, DOI={10.1007/s11434-008-0560-6}, number={4}, journal={CHINESE SCIENCE BULLETIN}, author={ShiQiu, Peng and XiangDe, Xu and XiaoHui, Shi and DongXiao, Wang and YuXiang, Zhu and JingJiao, Pu}, year={2009}, month={Feb}, pages={706–710} }
 @article{xu_shi_wang_peng_shi_2008, title={Data analysis and numerical simulation of moisture source and transport associated with summer precipitation in the Yangtze River Valley over China}, volume={100}, DOI={10.1007/s00703-008-0305-8}, number={1-4}, journal={Meteorology and Atmospheric Physics}, author={Xu, X. D. and Shi, X. Y. and Wang, Y. Q. and Peng, S. Q. and Shi, X. H.}, year={2008}, pages={217–231} }
 @article{peng_xie_pietrafesa_2007, title={Correcting the errors in the initial conditions and wind stress in storm surge simulation using an adjoint optimal technique}, volume={18}, DOI={10.1016/j.ocemod.2007.04.002}, abstractNote={An adjoint data assimilation methodology is applied to the Princeton Ocean Model and is evaluated by obtaining "optimal" initial conditions, sea surface forcing conditions, or both for coastal storm surge modelling. By prescribing different error sources and setting the corresponding control variables, we performed several sets of identical twin experiments by assimilating model-generated water levels. The experiment results show that, when the forecasting errors are caused by the initial (or surface boundary) conditions, adjusting initial (or surface boundary) conditions accordingly can significantly improve the storm surge simulation. However, when the forecasting errors are caused by surface boundary (or initial) conditions, data assimilation targeting improving the initial (or surface boundary) conditions is ineffective. When the forecasting errors are caused by both the initial and surface boundary conditions, adjusting both the initial and surface boundary conditions leads to the best results. In practice, we do not know whether the errors are caused by initial conditions or surface boundary conditions, therefore it is better to adjust both initial and surface boundary conditions in adjoint data assimilation.}, number={3-4}, journal={Ocean Modelling (Oxford, England)}, author={Peng, S. Q. and Xie, L. and Pietrafesa, L. J.}, year={2007}, pages={175–193} }
 @article{pietrafesa_buckley_peng_bao_liu_peng_xie_dickey_2007, title={On coastal ocean systems, coupled model architectures, products and services: Morphing from observations to operations and applications}, volume={41}, ISSN={["1948-1209"]}, DOI={10.4031/002533207787442268}, abstractNote={The national build-up of “coastal ocean observing systems” (COOSs) to establish the coastal observing component of the national component of the Integrated Ocean Observing System (IOOS) network must be well organized and must acknowledge, understand and address the needs
 of the principal clients, the federal, and in some cases state as well, agencies that provide financial support if it is to have substantive value. The funds being spent in support of COOS should be invested in pursuit of the establishment of the National Backbone (NB) that is needed: to greatly
 improve atmospheric, oceanic and coastal “weather” forecasting, broadly defined; for ecosystem management; and to document climate variability and change in coastal zones. However, this process has not occurred in a well conceived, orderly, well integrated manner due to historical
 and cultural bases and because of local priorities. A sub-regional effort that is designed to meet federal agency needs and mission responsibilities with an emphasis on meeting societal needs is presented by way of example to show that university and industry partners with federal agencies
 have an important role to play in the future of building out ocean and coastal observing and prediction systems and networks.}, number={1}, journal={MARINE TECHNOLOGY SOCIETY JOURNAL}, author={Pietrafesa, L. J. and Buckley, E. B. and Peng, M. and Bao, S. and Liu, H. and Peng, S. and Xie, L. and Dickey, D. A.}, year={2007}, pages={44–52} }
 @misc{peng_xie_2006, title={Effect of determining initial conditions by four-dimensional variational data assimilation on storm surge forecasting}, volume={14}, ISSN={["1463-5011"]}, DOI={10.1016/j.ocemod.2006.03.005}, abstractNote={A tangent linear model and an adjoint model of the three-dimensional, time-dependent, nonlinear Princeton Ocean Model (POM) are developed to construct a four-dimensional variational data assimilation (4D-Var) algorithm for coastal ocean prediction. To verify and evaluate the performance of this 4D-Var method, a suite of numerical experiments are conducted for a storm surge case using model-generated "pseudo-observations". The pseudo-observations are generated by a nested-grid high-resolution numerical model which is coupled to an inundation/drying scheme that is not included in the original POM. The 4D-Var algorithm based on POM is tested thoroughly for both code accuracy and the potential application in storm surge forecasting. The assimilation cycles lead to effective convergence between the forecasts and the "observations". Assimilating water level alone or together with surface currents both lead to significant improvements in storm surge forecasts within and several hours beyond the data assimilation window. It is worth noting that, assimilating water level alone produces improvements in storm surge forecasts that are comparable to those by assimilating both water level and surface currents, suggesting that optimizations of water level and surface currents are linked through the 4D-Var assimilation cycles. However, it is also worth noting that, the benefit resulting from the reduction of initial error in water level and/or surface currents through data assimilation decreases rapidly in time outside the assimilation window. This suggests that determining initial conditions of water level and/or surface currents via data assimilation is only effective within and a few hours beyond the assimilation window for storm surge forecasting. Thus, alternative data assimilation approaches are needed to improve the accuracy and lead time in operational storm surge forecasting.}, number={1-2}, journal={OCEAN MODELLING}, author={Peng, S. -Q. and Xie, L.}, year={2006}, pages={1–18} }