scholarly journals A Novel Approach to Obtain Diurnal Variation of Bio-Optical Properties in Moving Water Parcel Using Integrated Drifting Buoy and GOCI Data: A Case Study in Yellow and East China Seas

2021 ◽  
Vol 13 (11) ◽  
pp. 2115
Author(s):  
Yuying Xu ◽  
Weibing Guan ◽  
Jianyu Chen ◽  
Zhenyi Cao ◽  
Feng Qiao
Keyword(s):  
Time Series ◽  
Optical Properties ◽  
Diurnal Variation ◽  
Mass Movement ◽  
East China ◽  
China Seas ◽  
Diurnal Variability ◽  
Drifting Buoy ◽  
Novel Approach ◽  
Water Parcel

Ocean processes that can influence rapidly changing ocean color include water-mass movement and bio-optical property changes in the water parcel. Traditionally, diurnal variability of bio-optical properties relies on daily time series at fixed locations by satellite sensors or in situ observations. There is a lack of an effective way to observe diurnal variation of bio-optical properties in a moving water parcel on a large scale. In this paper, we propose a new method to acquire diurnal variation of bio-optical properties in a moving water parcel. The novel approach integrates drifting buoy data and GOCI data. The movement of surface current was tracked by a drifting buoy, and its spatiotemporally matching bio-optical properties were obtained via the GOCI data. The results in the Yellow and East China seas during the summers of 2012 and 2013 show that the variation of time series following the movement of water parcel was obviously different from that obtained at fixed locations. The hourly differences of the former are 15.7% and 16.3% smaller than those of the latter for Chl a and total suspended sediment (TSS), respectively. The value of ag440 was more stable within the moving water parcel than in the fixed location. Our approach provides a simple and feasible way for observing diurnal variability of bio-optical properties in a moving surface water parcel.

scholarly journals Long term measurements of optical properties and their hygroscopic enhancement

2014 ◽  
Vol 14 (20) ◽  
pp. 27731-27767 ◽  
Author(s):  
M. Hervo ◽  
K. Sellegri ◽  
J. M. Pichon ◽  
J. C. Roger ◽  
P. Laj
Keyword(s):  
Optical Properties ◽  
Diurnal Variation ◽  
Asymmetry Factor ◽  
Optical Parameters ◽  
Strong Impact ◽  
Aerosol Optical Properties ◽  
Diurnal Variability ◽  
Air Mass ◽  
The Impact

Abstract. Optical properties of aerosols were measured from the GAW Puy de Dôme station (1465 m) over a seven year period (2006–2012). The impact of hygroscopicity on aerosol optical properties was calculated over a two year period (2010–2011). The analysis of the spatial and temporal variability of the optical properties showed that while no long term trend was found, a clear seasonal and diurnal variation was observed on the extensive parameters (scattering, absorption). Scattering and absorption coefficients were highest during the warm season and daytime, in concordance with the seasonality and diurnal variation of the PBL height reaching the site. Intensive parameters (single scattering albedo, asymmetry factor, refractive index) did not show such a strong diurnal variability, but still indicated different values depending on the season. Both extensive and intensive optical parameters were sensitive to the air mass origin. A strong impact of hygroscopicity on aerosol optical properties was calculated, mainly on aerosol scattering, with a dependence on the aerosol type. At 90% humidity, the scattering factor enhancement (fσsca) was more than 4.4 for oceanic aerosol that have mixed with a pollution plume. Consequently, the aerosol radiative forcing was estimated to be 2.8 times higher at RH = 90% and 1.75 times higher at ambient RH when hygroscopic growth of the aerosol was considered. The hygroscopicity enhancement factor of the scattering coefficient was parameterized as a function of humidity and air mass type.

scholarly journals Retrieval of Daily Mean VIIRS SST Products in China Seas

2021 ◽  
Vol 13 (24) ◽  
pp. 5158
Author(s):  
Qianmei Li ◽  
Qingyou He ◽  
Chuqun Chen
Keyword(s):  
Diurnal Variation ◽  
Infrared Imaging ◽  
Accurate Estimation ◽  
High Temporal Resolution ◽  
China Seas ◽  
Retrieval Method ◽  
Diurnal Variability ◽  
Satellite Sst ◽  
Sinusoidal Pattern ◽  
Transient Measurements

Sea surface temperature (SST) is one of the most important factors in regulating air-sea heat flux and, thus, climate change. Most of current global daily SST products are derived from one or two transient measurements of polar-orbiting satellites, which are not the same to daily mean SST values. In this study, high-temporal-resolution SST measurements (32–40 snapshots per day) from a geostationary satellite, FengYun-4A (FY–4A), are used to analyze the diurnal variation of SST in China seas. The results present a sinusoidal pattern of the diurnal variability in SST, with the maximum value at 13:00–15:00 CST and the minimum at 06:00–08:00 CST. Based on the diurnal variation of SST, a retrieval method for daily mean SST products from polar-orbiting satellites is established and applied to 7716 visible infrared imaging radiometer (VIIRS) data in China seas. The results suggest that it is feasible and practical for the retrieval of daily mean SST with an average RMSE of 0.133 °C. This retrieval method can also be utilized to other polar-orbiting satellites and obtain more daily mean satellite SST products, which will contribute to more accurate estimation and prediction between atmosphere and ocean in the future.

scholarly journals Aerosol Optical Properties around the East China Seas Based on AERONET Measurements

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 642
Author(s):  
Qianguang Tu ◽  
Zengzhou Hao ◽  
Yunwei Yan ◽  
Bangyi Tao ◽  
Chuyong Chung ◽  
...  
Keyword(s):  
Optical Properties ◽  
Normal Distribution ◽  
Size Distribution ◽  
East China ◽  
China Seas ◽  
Lognormal Size Distribution ◽  
Fine Mode ◽  
Log Normal ◽  
Aerosol Effects ◽  
Autumn And Winter

Understanding aerosols optical properties over the oceans is vital for enhancing our knowledge of aerosol effects on climate and pollutant transport between continents. In this study, the characteristics of aerosol optical thickness (AOT) at 500 nm (τ500nm), Ångström exponent for the wavelength pair 440–870 nm (α) and volume size distribution (VSD), are presented and analyzed over the East China seas based on the observations at four AERONET sites during 1999–2019. The main results are: (1) the mean τ500nm (α) value ranged from 0.31 to 0.36 (1.17–1.31); (2) the distribution of τ500nm (α) is similar to a log-normal distribution with a right-skewed long tail larger than 0.5 (closer to the normal distribution); (3) large AOT (τ500nm>0.6) was frequently observed in summer (June and July) and spring (March to May), followed by autumn and winter; (4) all aerosol types were observed, and urban/industrial aerosols and mixed types were dominant throughout the period. The atmospheric column aerosol was characterized by a bimodal lognormal size distribution with a fine mode at effective radius, Reff = 0.16 ± 0.01 μm, and coarse mode at Reff = 2.05 ± 0.1 μm.

scholarly journals STUDIES ON THE FOOD BASE IN THE YELLOW AND THE EAST CHINA SEAS-IV

1963 ◽  
Vol 29 (2) ◽  
pp. 114-117
Author(s):  
Yoshimasa ENOMOTO
Keyword(s):  
East China ◽  
China Seas

scholarly journals Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 261
Author(s):  
Tianyang Liu ◽  
Zunkai Huang ◽  
Li Tian ◽  
Yongxin Zhu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Rapid Development ◽  
Superior Performance ◽  
Learning Platform ◽  
Novel Approach ◽  
Wind Power Forecast ◽  
Transformation Methods

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

scholarly journals A Process-Oriented Approach to Identify Evolutions of Sea Surface Temperature Anomalies with a Time-Series of a Raster Dataset

2021 ◽  
Vol 10 (8) ◽  
pp. 500
Author(s):  
Lianwei Li ◽  
Yangfeng Xu ◽  
Cunjin Xue ◽  
Yuxuan Fu ◽  
Yuanyu Zhang
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Global Climate ◽  
Sea Surface ◽  
Temperature Anomalies ◽  
Global Climate Changes ◽  
Space And Time ◽  
Novel Approach ◽  
Sea Surface Temperature Anomalies

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge’s parent node and a number of parent nodes from the edge’s child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO’s relevant knowledge, which may provide new references for global change research.

scholarly journals Diurnal Variation of the Diffuse Attenuation Coefficient for Downwelling Irradiance at 490 nm in Coastal East China Sea

2021 ◽  
Vol 13 (9) ◽  
pp. 1676
Author(s):  
Yu Zhang ◽  
Zhantang Xu ◽  
Yuezhong Yang ◽  
Guifen Wang ◽  
Wen Zhou ◽  
...  
Keyword(s):  
Diurnal Variation ◽  
Attenuation Coefficient ◽  
East China Sea ◽  
East China ◽  
Coefficient Of Determination ◽  
Target Area ◽  
Diffuse Attenuation Coefficient ◽  
China Sea ◽  
Mean Square Errors ◽  
Downwelling Irradiance

The diurnal variation of the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd(490)) has complex characteristics in the coastal regions. However, owing to the scarcity of in situ data, our knowledge on the diurnal variation is inadequate. In this study, an optical-buoy dataset was used to investigate the diurnal variation of Kd(490) in the coastal East China Sea, and to evaluate the Kd(490) L2 products of geostationary ocean color imager (GOCI), as well as the performance of six empirical algorithms for Kd(490) estimation in the Case-2 water. The results of validation show that there was high uncertainty in GOCI L2 Kd(490), with mean absolute percentage errors (MAPEs) of 69.57% and 68.86% and root mean square errors (RMSEs) of 0.70 and 0.71 m−1 compared to buoy-measured Kd12(490) and Kd13(490), respectively. Meanwhile, with the coefficient of determination (R2) of 0.71, as well as the lowest MAPE of 27.31% and RMSE of 0.29 m−1, the new dual ratio algorithm (NDRA) performed the best in estimating Kd(490) in the target area, among the six algorithms. Further, four main types of Kd(490) diurnal variation were found from buoy data, showing different variabilities compared to the area closer to the shore. One typical diurnal variation pattern showed that Kd(490) decreased at flood tide and increased at ebb tide, which was confirmed by GOCI images through the use of NDRA. Hydrometeorological factors influencing the diurnal variations of Kd(490) were also studied. In addition to verifying the predominant impact of tide, we found that the dominant effect of tide and wind on the water column is intensifying sediment resuspension, and the change of sediment transport produced by them are secondary to it.

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