Upper-ocean thermal structure and heat content off the US West Coast during the 1997–1998 El Niño event based on AXBT and satellite altimetry data

2007 ◽  
Vol 74 (1) ◽  
pp. 48-70 ◽  
Author(s):  
James M. Wilczak ◽  
Robert R. Leben ◽  
David S. McCollum
Keyword(s):  
West Coast ◽  
Satellite Altimetry ◽  
Thermal Structure ◽  
Heat Content ◽  
Upper Ocean ◽  
Altimetry Data ◽  
The Us ◽  
Event Based ◽  
Us West ◽  
Satellite Altimetry Data

scholarly journals A Method to Estimate Three-Dimensional Thermal Structure from Satellite Altimetry Data

2009 ◽  
Vol 26 (12) ◽  
pp. 2655-2664 ◽  
Author(s):  
Akiko Takano ◽  
Hidekatsu Yamazaki ◽  
Takeyoshi Nagai ◽  
Osamu Honda
Keyword(s):  
Satellite Altimetry ◽  
Thermal Structure ◽  
Three Dimensional ◽  
Empirical Method ◽  
Altimetry Data ◽  
Spatiotemporal Mapping ◽  
Strong Linear Correlation ◽  
Globally Distributed ◽  
Satellite Altimetry Data

Abstract A new empirical method to estimate mesoscale three-dimensional oceanic thermal structures from near-real-time satellite altimetry data is presented. The method uses a two-layer model with a novel set of empirical parameters for stratification. Empirical parameters, which are calculated from altimetry data and in situ temperature and salinity profiles obtained from globally distributed Argo floats, allowed for significant increases in the spatiotemporal mapping of mesoscale temperature distributions. The accuracy of the method was validated by comparing the estimated isothermal depths with in situ temperature data. Three case studies in different regions and seasons showed excellent agreement, with a strong linear correlation between the estimated and observed isothermal depths. The authors present the details of the method, discuss its limitations, and demonstrate the ecological relevance of the method for fisheries management by successfully predicting pelagic fish distributions.

Eddy properties in the Pacific sector of the Southern Ocean from satellite altimetry data

2016 ◽  
Vol 35 (11) ◽  
pp. 28-34 ◽  
Author(s):  
Yongliang Duan ◽  
Hongwei Liu ◽  
Weidong Yu ◽  
Yijun Hou
Keyword(s):  
Southern Ocean ◽  
Satellite Altimetry ◽  
Altimetry Data ◽  
The Pacific ◽  
Satellite Altimetry Data

Kuroshio variations in the upstream region as seen by HF radar and satellite altimetry data

2008 ◽  
Vol 29 (21) ◽  
pp. 6417-6426 ◽  
Author(s):  
K. Ichikawa ◽  
R. Tokeshi ◽  
M. Kashima ◽  
K. Sato ◽  
T. Matsuoka ◽  
...  
Keyword(s):  
Satellite Altimetry ◽  
Hf Radar ◽  
Upstream Region ◽  
Altimetry Data ◽  
Satellite Altimetry Data

scholarly journals Evaluation of Marine Gravity Anomaly Calculation Accuracy by Multi-Source Satellite Altimetry Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Shanwei Liu ◽  
Yinlong Li ◽  
Qinting Sun ◽  
Jianhua Wan ◽  
Yue Jiao ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Gravity Anomaly ◽  
Mean Square Error ◽  
Spatial Resolution ◽  
Satellite Altimetry ◽  
Gravity Anomalies ◽  
Mean Square ◽  
Altimetry Data ◽  
Marine Gravity ◽  
Satellite Altimetry Data

The purpose of this paper is to analyze the influence of satellite altimetry data accuracy on the marine gravity anomaly accuracy. The data of 12 altimetry satellites in the research area (5°N–23°N, 105°E–118°E) were selected. These data were classified into three groups: A, B, and C, according to the track density, the accuracy of the altimetry satellites, and the differences of self-crossover. Group A contains CryoSat-2, group B includes Geosat, ERS-1, ERS-2, and Envisat, and group C comprises T/P, Jason-1/2/3, HY-2A, SARAL, and Sentinel-3A. In Experiment I, the 5′×5′ marine gravity anomalies were obtained based on the data of groups A, B, and C, respectively. Compared with the shipborne gravity data, the root mean square error (RMSE) of groups A, B, and C was 4.59 mGal, 4.61 mGal, and 4.51 mGal, respectively. The results show that high-precision satellite altimetry data can improve the calculation accuracy of gravity anomaly, and the single satellite CryoSat-2 enables achieving the same effect of multi-satellite joint processing. In Experiment II, the 2′×2′ marine gravity anomalies were acquired based on the data of groups A, A + B, and A + C, respectively. The root mean square error of the above three groups was, respectively, 4.29 mGal, 4.30 mGal, and 4.21 mGal, and the outcomes show that when the spatial resolution is satisfied, adding redundant low-precision altimetry data will add pressure to the calculation of marine gravity anomalies and will not improve the accuracy. An effective combination of multi-satellite data can improve the accuracy and spatial resolution of the marine gravity anomaly inversion.

scholarly journals Estimating bathymetry from multi-mission satellite altimetry data using Gravity-Geologic Method over Malaysian Seas

2021 ◽  
Vol 32 (5.2) ◽  
Author(s):  
Astina Tugi ◽  
Ami Hassan Md Din ◽  
Nornajihah Mohammad Yazid ◽  
Abdullah Hisam Omar ◽  
Amalina Izzati Abdul Hamid ◽  
...  
Keyword(s):  
Satellite Altimetry ◽  
Altimetry Data ◽  
Mission Satellite ◽  
Satellite Altimetry Data

scholarly journals Analysis of the effectiveness of face-coverings on the death ratio of COVID-19 using machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ali Lafzi ◽  
Miad Boodaghi ◽  
Siavash Zamani ◽  
Niyousha Mohammadshafie ◽  
Veeraraghava Raju Hasti
Keyword(s):  
Machine Learning ◽  
West Coast ◽  
Economic Condition ◽  
Preventive Measure ◽  
Accuracy Score ◽  
Machine Learning Classification ◽  
Monthly Average ◽  
The Us ◽  
Recent Outbreak ◽  
Us West

AbstractThe recent outbreak of the COVID-19 led to death of millions of people worldwide. To stave off the spread of the virus, the authorities in the US employed different strategies, including the mask mandate order issued by the states’ governors. In the current work, we defined a parameter called average death ratio as the monthly average of the number of daily deaths to the monthly average number of daily cases. We utilized survey data to quantify people’s abidance by the mask mandate order. Additionally, we implicitly addressed the extent to which people abide by the mask mandate order, which may depend on some parameters such as population, income, and education level. Using different machine learning classification algorithms, we investigated how the decrease or increase in death ratio for the counties in the US West Coast correlates with the input parameters. The results showed that for the majority of counties, the mask mandate order decreased the death ratio, reflecting the effectiveness of such a preventive measure on the West Coast. Additionally, the changes in the death ratio demonstrated a noticeable correlation with the socio-economic condition of each county. Moreover, the results showed a promising classification accuracy score as high as 90%.

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