scholarly journals Improving the thermal structure predictions in the Yellow Sea by conducting targeted observations in the CNOP-identified sensitive areas

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kun Liu ◽  
Wuhong Guo ◽  
Lianglong Da ◽  
Jingyi Liu ◽  
Huiqin Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Yellow Sea ◽  
Thermal Structure ◽  
The Yellow Sea ◽  
Perturbation Approach ◽  
Optimal Perturbation ◽  
Sensitive Areas ◽  
Targeted Observations ◽  
Targeted Observation ◽  
The Impact

AbstractTargeted observation is an appealing procedure for improving model predictions. However, studies on oceanic targeted observations have been largely based on modeling efforts, and there is a need for field validating operations. Here, we report the results of a field targeted observation that is designed based on the sensitive areas identified by the Conditional Nonlinear Optimal Perturbation approach to improve the 7th day thermal structure prediction in the Yellow Sea. By introducing the technique of cycle data assimilation and the new concept of time-varying sensitive areas, an observing strategy is designed and validated by a set of Observing System Simulation Experiments. Then, the impact of targeted observations was investigated by a choreographed field campaign in the summer of 2019. The results of the in-field Observing System Experiments show that, compared to conventional local data assimilation, conducting targeted observations in the sensitive areas can yield more benefit at the verification time. Furthermore, dynamic analysis demonstrates that the refinement of vertical thermal structures is mainly caused by the changes in the upstream horizontal temperature advection driven by the Yellow Sea Cold Water Mass circulation. This study highlights the effectiveness of targeted observations on reducing the forecast uncertainty in the ocean.

Targeted observations based on identified sensitive areas by CNOP to improve the thermal structures prediction in the summer Yellow Sea: preparatory work for the campaign in the field

2020 ◽  
Author(s):  
Jingyi Liu ◽  
Wuhong Guo ◽  
Baolong Cui ◽  
Kun Liu ◽  
Huiqin Hu
Keyword(s):  
Yellow Sea ◽  
The Yellow Sea ◽  
Sensitive Area ◽  
Optimal Perturbation ◽  
Field Campaign ◽  
Sensitive Areas ◽  
Initial Errors ◽  
Thermal Structures ◽  
Targeted Observation ◽  
Preparatory Work

<p>Targeted observation is an appealing procedure to improve oceanic model predictions by taking additional assimilation of collected measurements. However, studies on targeted observation in the oceanic field have been largely based on modeling efforts, and there is a need for field validating observations. Here, we report the preparatory work of a field campaign, which is designed based on the identified sensitive area by the Conditional Nonlinear Optimal Perturbation (CNOP) approach, to improve the short-range summer thermal structures prediction in the Yellow Sea (YS). We firstly simulated the hindcasting (2016-2018) temperature structures in the summertime, and found that the locations of the sensitive areas are generally consistent in space for each hindcast year. Then, we introduced the technique of multiple-assimilation and the definition of time-varying sensitive area, and designed observing strategies for the YS summer campaign. Observing System Simulation Experiments (OSSEs) were conducted prior to address the plan on field campaign in the Yellow Sea in August 2019. Results show that, reducing the initial errors in the sensitive area can lead to more improvement on thermal structures prediction than that in other area.</p>

scholarly journals The Use of Remote Sensing Analysis for Evaluating the Impact of Development Projects in the Yellow Sea Large Marine Ecosystem

2020 ◽  
Vol 12 (9) ◽  
pp. 3628
Author(s):  
Gabriel Sidman ◽  
Sydney Fuhrig ◽  
Geeta Batra
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Yellow Sea ◽  
Marine Pollution ◽  
Marine Ecosystem ◽  
Environmental Indicators ◽  
The Yellow Sea ◽  
Series Data ◽  
Large Marine Ecosystem ◽  
The Impact

Remote sensing has long been valued as a data source for monitoring environmental indicators and detecting trends in ecosystem stress from anthropogenic causes such as deforestation, river dams and air and water pollution. More recently, remote sensing analyses have been applied to evaluate the impacts of environmental projects and programs on reducing environmental stresses. Such evaluation has focused primarily on the change in above-surface vegetation such as forests. This study uses remote sensing ocean color products to evaluate the impact on reducing marine pollution of the Global Environment Facility’s (GEF) portfolio of projects in the Yellow Sea Large Marine Ecosystem. Chlorophyll concentration was derived from satellite images over a time series from the 1990s, when GEF projects began, until the present. Results show a 50% increase in chlorophyll until 2011 followed by a 34% decrease until 2019, showing a potential delayed effect of pollution control efforts. The rich time series data is a major advantage to using geospatial analysis for evaluating the impacts of environmental interventions on marine pollution. However, one drawback to the method is that it provides insights into correlations but cannot attribute the results to any particular cause, such as GEF interventions.

scholarly journals Impact of Multivariate Background Error Covariance on the WRF-3DVAR Assimilation for the Yellow Sea Fog Modeling

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Xiaoyu Gao ◽  
Shanhong Gao
Keyword(s):  
Data Assimilation ◽  
Yellow Sea ◽  
Initial Conditions ◽  
Wrf Model ◽  
Probability Of Detection ◽  
Dominant Negative ◽  
The Yellow Sea ◽  
Background Error ◽  
Moisture Condition ◽  
Sea Fog

Numerical modeling of sea fog is highly sensitive to initial conditions, especially to moisture in the marine atmospheric boundary layer (MABL). Data assimilation plays a vital role in the improvement of initial MABL moisture for sea fog modeling over the Yellow Sea. In this study, the weather research and forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation module are employed for sea fog simulations. Two kinds of background error (BE) covariances with different control variables (CV) used in WRF-3DVAR, that is, CV5 and multivariate BE (CV6), are compared in detail by explorative case studies and a series of application experiments. Statistical verification metrics including probability of detection (POD) and equitable threat scores (ETS) of forecasted sea fog area are computed and compared for simulations with the implementations of CV5 and CV6 in the WRF-3DVAR system. The following is found: (1) there exists a dominant negative correlation between temperature and moisture in CV6 near the sea surface, which makes it possible to improve the initial moisture condition in the MABL by assimilation of observed temperature; (2) in general, the performance of the WRF-3DVAR assimilation with CV6 is distinctly better, and the results of 10 additional sea fog cases clearly suggest that CV6 is more suitable than CV5 for sea fog modeling. Compared to those with CV5, the average POD and ETS of forecasted sea fog area using 3DVAR with CV6 can be improved by 27.6% and 21.0%, respectively.

scholarly journals A Comparison between 3DVAR and EnKF for Data Assimilation Effects on the Yellow Sea Fog Forecast

2018 ◽  
Author(s):  
Xiaoyu Gao ◽  
Shanhong Gao ◽  
Yue Yang
Keyword(s):  
Data Assimilation ◽  
Yellow Sea ◽  
Initial Conditions ◽  
Surface Wind ◽  
Probability Of Detection ◽  
The Other ◽  
Moisture Distribution ◽  
The Yellow Sea ◽  
Background Error ◽  
Sea Fog

The data assimilation method to improve sea fog forecast over the Yellow Sea is usually three-dimensional variational assimilation (3DVAR), whereas ensemble Kalman filter (EnKF) has not yet been applied on this weather phenomenon. In this paper, two sea fog cases over the Yellow sea, one spread widely and the other spread narrowly along the coastal area, are studied in detail by a series of numerical experiments with 3DVAR and EnKF based on the Grid-point Statistical Interpolation (GSI) system and the Weather Research and Forecasting (WRF) model. The results show that the assimilation effect of EnKF outperforms that of 3DVAR: for the widespread-fog case, the probability of detection and equitable threat scores of the forecasted sea fog area get improved respectively by ~57.9% and ~55.5%; the sea fog of the other case completely mis-forecasted by 3DVAR is produced successfully by EnKF. These improvements of EnKF relative to 3DVAR are benefited from its flow-dependent background error, resulting in more realistic depiction of sea surface wind for the widespread-fog case and better moisture distribution for the other case in the initial conditions. More importantly, the correlation between temperature and humidity in the background error of EnKF plays a vital role in the response of moisture to the assimilation of temperature, which leads to a great improvement on the initial moisture conditions for sea fog forecast.

Assessment of fish vulnerability to climate change in the Yellow Sea and Bohai Sea

2020 ◽  
Vol 71 (7) ◽  
pp. 729
Author(s):  
Yunlong Chen ◽  
Xiujuan Shan ◽  
Ning Wang ◽  
Xianshi Jin ◽  
Lisha Guan ◽  
...  
Keyword(s):  
Climate Change ◽  
Fish Species ◽  
Yellow Sea ◽  
Bohai Sea ◽  
Fish Assemblages ◽  
The Yellow Sea ◽  
Risk Indices ◽  
Vulnerability To Climate Change ◽  
Impact Risk ◽  
The Impact

Vulnerability assessments provide a feasible yet infrequently used approach to expanding our understanding and evaluating the effects of climate change on fish assemblages. Here, we first used a fuzzy-logic expert system to quantitatively estimate the vulnerability and potential impact risks of climate change for fish species in the Bohai Sea and Yellow Sea (BSYS). The mean (±s.d.) vulnerability and the impact-risk indices for 25 dominant fish species were 51±22 and 62±12 respectively (with the highest possible value being 100 under the Representative Concentration Pathway 8.5 scenario). Miiuy croaker (Miichthys miiuy) was found to have the highest impact risk, whereas the glowbelly (Acropoma japonicum) had the lowest. Demersal fishes tended to be more vulnerable than pelagic fishes, whereas the opposite was found for impact risks. No significant correlation was found between species biomass and vulnerability (P>0.05). The assessment provided a comprehensive framework for evaluating climate effects in the BSYS and suggested that interspecific and habitat group differences should be considered when developing future climate-adaptive fishery policies and management measures in this region, as well as similar systems elsewhere in the world.

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