Abstract : This minisymposium focuses on the coupled weather/climate prediction systems, whose components are atmosphere, land surface, chemistry, etc.; topics include but not limited to parameter estimation, data assimilation, targeted observation, sensitivity analysis, uncertainty quantification, and predictability in the coupled systems.
Organizer(s) : Sujeong Lim, Ji Won Yoon, Xiaohao Qin, Ting-Chi Wu, Shigenori Otsuka
02570 (1/3) : 5B @D407 [Chair: Shigenori Otsuka, Ji Won Yoon]
[04638] Improving Numerical Forecast Skill: Combinational Parameter Optimization and Coupled Data Assimilation
Format : Talk at Waseda University
Author(s) :
Seon Ki Park (Ewha Womans University)
Abstract : Numerical weather prediction (NWP) requires coupled modeling and data assimilation, and its forecast skill depends on uncertainties in physical parameterizations and initial conditions. This study illustrates that NWP skill can be improved through optimization of physical parameterizations and data assimilation; the former includes combinational optimization which seeks for the optimal set of parameterizations followed by optimal parameter estimation, whereas the latter develops the coupled data assimilation systems such as the WRF-Noah LSM and the WRF-Chem.
[05388] Application of the CNOP-PEP method in hydrological ensemble prediction in China to reduce model parameter uncertainties
Author(s) :
Guodong Sun (Institute of Atmospheric Physics, Chinese Academy of Sciences)
Mu Mu (Fudan University)
Abstract : In this talk, a conditional nonlinear optimal parameter perturbation ensemble prediction (CNOP-PEP) method is proposed. The CNOP-PEP method is employed to carry out ensemble prediction of evapotranspiration (ET) over Tibetan Plateau (TP). The numerical results show that ensemble prediction experiments conducted with the CNOP-PEP method exhibit better prediction skills compared to the reference ET over the TP. The prediction skill by employing the CNOP-PEP method is more excellent than those of the traditional methods.
[05401] The effect of Westerly Wind Burst on ENSO
Author(s) :
Youmin Tang (University of Northern British Columbia)
Abstract : Westerly wind bursts (WWBs), as a semi-stochastic process, play a vital role in El Niño–Southern Oscillation (ENSO). However, current dynamical models have large challenges in the representation of WWBs. In this study, we introduced and developed several WWB parameterization schemes, including a novel scheme developed using the deep learning technique. The effect of these parameterization schemes on ENSO simulation and prediction was comprehensively evaluated and systematically compared using coupled models with varied complexity.
[04422] Improving Model Uncertainty in Physical Parameterizations: Combinational Optimizations Using Genetic Algorithm in the Coupled Atmosphere-Chemistry Model
Format : Talk at Waseda University
Author(s) :
Ji Won Yoon (Ewha Womans University)
Abstract : The Asian dust storm is one of the important air pollution problems in South Korea; thus, it is significant to improve the air quality forecasting skill using a numerical prediction system. In this study, we developed an optimization system by applying the micro-genetic algorithm (μGA) interfaced with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to enhance air quality forecasting skills in East Asia. We introduce the results of the combinational optimizations.
[03616] A novel approach of data assimilation: application to ENSO diversity predictions
Author(s) :
Wansuo Duan (Institute of Atmospheric Physics, Chinese Academy of Sciences)
Abstract : The talk introduces an approach of data assimilation (DA) entitled nonlinear forcing singular vector (NFSV) to neutralize combined effect of initial and model errors. The approach is applied to an intermediate-complexity ENSO model and reproduces the conditions of the emergence of both EP- and CP-El Niño events, eventually distinguishing El Niño types at two-season lead time in predictions. The NFSV-DA is a useful DA approach for offsetting initial and model error effects for ENSO predictions.
Abstract : This study explores the data-driven sparse sensor placement (SSP) to determine rain gauge locations for efficiently estimating the spatiotemporal interpolation of precipitation. The SSP determines the rain gauge locations using dominant modes extracted from spatiotemporal precipitation data over a training period. Through evaluations using radar-analyzed precipitation, we found that the SSP-based rain gauges enable to provide more accurate precipitation fields compared to the current operational rain gauge network in Japan.
[04176] The Conditional Nonlinear Optimal Perturbation method and it's application to the targeting observation for tropical cyclones
Author(s) :
Xiaohao Qin (LASG, Institute of Atmospheric Physics, Chinese Academy of Science)
Mu Mu (Fudan University)
Feifan Zhou (LACS, Institute of Atmospheric Physics, Chinese Academy of Science)
Boyu Chen (Chinese Meteorology Administration)
Jie Feng (Fudan University)
Abstract : To augment the routine observational network for better forecasts of tropical cyclones (TCs), targeting observations (TOs) have developed rapidly during the past several decades over China. In consequence, TC forecasts have benefitted a lot from these field campaigns. In this talk, research work and field campaigns of TOs are briefly overviewed. After that, we introduce a method named the conditional nonlinear optimal perturbation (CNOP), which is utilized to identify those areas should be additionally observed with priority in TOs. Using some examples, we explain how to use the CNOP method in mathematics, its impacts on improving TC forecasts, and its latest application in real time operational forecasts.
[04998] Towards targeted observations of meteorological state for improving PM2.5 forecasts
Author(s) :
Lichao Yang (Institute of Atmospheric Physics, Chinese Academy of Sciences)
Wansuo Duan (Institute of Atmospheric Physics, Chinese Academy of Sciences)
Abstract : An advanced approach of conditional non-linear optimal perturbation (CNOP) was introduced to identify the sensitive area for targeted observations of meteorological fields associated with PM2.5 concentration forecasts of a heavy haze event that occurred in the Beijing–Tianjin–Hebei (BTH) region, China. We show numerically and physically that preferentially deploying additional observations in the sensitive areas identified by the CNOP approach can greatly improve the forecasting skill of PM2.5 forecasts.
Milija Zupanski (Colorado State University/Cooperative Institute for Research in the Atmosphere)
Abstract : This study demonstrates the capability of an atmosphere-aerosol coupled data assimilation RAMS-MLEF (Regional Atmospheric Modeling System - Maximum Likelihood Ensemble Filter) using a dust event over the Arabian Peninsula, which is an area known to be severely under-sampled. One important lesson learned is that the location and timing of observations largely determines the improvements achieved by data assimilation. The lack of observations also makes it very challenging to perform a quantitative verification of results.
[04249] Impact of Soil Moisture Observation in the Coupled Atmosphere-Land Data Assimilation System
Format : Talk at Waseda University
Author(s) :
Sujeong Lim (Ewha Womans University)
Seon Ki Park (Ewha Womans University)
Milija Zupanski (Colorado State University/Cooperative Institute for Research in the Atmosphere)
Abstract : Soil moisture is important in a coupled atmosphere-land surface model because it propagates to atmospheric variables in the planetary boundary layer through the latent and sensible heat fluxes. In this study, we introduce the results of the assimilation of both atmospheric and soil moisture observations within a strongly coupled data assimilation system, taking into account the cross-covariance between the atmosphere and land.
[04599] Application and improvement of Land Data Assimilation System at CWB
Author(s) :
PO-HSUN LIN (CWB / International Integrated Systems, Inc. (IISI))
Abstract : Interactions between land and atmospheric components are critical for coupled model forecasting. Improved accuracy of soil initial conditions has been shown to enhance land-atmosphere interactions in coupled processes. The Central Weather Bureau of Taiwan has collaborated with the National Center for Atmospheric Research to optimize the use of the High-Resolution Land Data Assimilation System in order to improve the deterministic forecast over Taiwan. The results of this collaboration will be presented in this summary.
