Spatial and Temporal Variability of Rainfall in Eastern Amazon during the Rainy Season

Empirical orthogonal functions (EOF) and composites analysis were employed on pentad data in order to investigate the tropical atmospheric-ocean patterns over the Atlantic Ocean and the spatial-temporal characteristics of the rainfall in eastern Amazon during the peak of the rainy season (February to April). The EOF results evidenced that the Intertropical Convergence Zone (ITCZ) is the main rainfall-producing system in eastern Amazon during the rainy season. Conditions associated with the southward SST gradient in the intertropical Atlantic formed the dynamic patterns that favored the position of the ITCZ to south of the equator, thus explaining the predominance of positive precipitation anomalies in eastern Amazon, especially in the state of Maranhão and northeastern Pará during the February and April months.

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Analysis of the Monthly and Spring-Neap Tidal Variability of Satellite Chlorophyll-a and Total Suspended Matter in a Turbid Coastal Ocean Using the DINEOF Method

Remote Sensing ◽

10.3390/rs13040632 ◽

2021 ◽

Vol 13 (4) ◽

pp. 632

Author(s):

Mengmeng Yang ◽

Faisal Ahmed Khan ◽

Hongzhen Tian ◽

Qinping Liu

Keyword(s):

Sea Level ◽

Satellite Data ◽

Temporal Variability ◽

Total Suspended Matter ◽

Empirical Orthogonal Functions ◽

Spatial And Temporal Variability ◽

Ariake Sea ◽

Orthogonal Functions ◽

Chl A ◽

Tidal Variability

Missing spatial data is one of the major concerns associated with the application of satellite data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has been proven to be an effective tool for filling spatial gaps in various satellite data products. The Ariake Sea, which is a turbid coastal sea, shows the large spatial and temporal variability of chlorophyll-a (Chl-a) and total suspended matter (TSM). However, ocean color satellite data for this region usually have large gaps, which affects the accurate analysis of Chl-a and TSM variability. In this study, we applied the DINEOF method to fill the missing pixels from the regionally tuned Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua (hereafter, MODIS) Chl-a and MODIS-derived TSM datasets for the period 2002–2017. The validation results showed that the DINEOF reconstructed data were accurate and reliable. Furthermore, the Empirical Orthogonal Functions (EOF) analysis based on the reconstructed data was used to quantitatively analyze the spatial and temporal variability of Chl-a and TSM at both monthly and individual events of spring-neap tidal scales. The first three EOF modes of Chl-a showed seasonal variability mainly caused by precipitation, the sea surface temperature (SST), and river discharge for the first EOF mode and the sea level amplitude for the second. The first three EOF modes of TSM exhibited both seasonal and spring-neap tidal variability. The first and second EOF modes of TSM displayed spring-neap tidal variability caused by the sea level amplitude. The second EOF mode of TSM also showed seasonal variability caused by the sea level amplitude. In this study, we first applied the DINEOF method to reconstruct the satellite data and to capture the major spatial and temporal variability of Chl-a and TSM for the Ariake Sea. Our results demonstrate that the DINEOF method can reconstruct patchy oceanic color datasets and improve spatio-temporal variability analysis.

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Dynamics of the Intertropical Convergence Zone of the East Pacific

Journal of the Atmospheric Sciences ◽

10.1175/jas3642.1 ◽

2006 ◽

Vol 63 (2) ◽

pp. 582-597 ◽

Cited By ~ 40

Author(s):

David J. Raymond ◽

Christopher S. Bretherton ◽

John Molinari

Keyword(s):

Boundary Layer ◽

Boundary Layer Flow ◽

Deep Convection ◽

Intertropical Convergence Zone ◽

Surface Fluxes ◽

Balance Model ◽

Convergence Zone ◽

East Pacific ◽

Layer Flow ◽

Sst Gradient

Abstract The dynamical factors controlling the mean state and variability of the east Pacific intertropical convergence zone (ITCZ) and the associated cross-equatorial boundary layer flow are investigated using observations from the East Pacific Investigation of Climate (EPIC2001) project. The tropical east Pacific exhibits a southerly boundary layer flow that terminates in the ITCZ. This flow is induced by the strong meridional sea surface temperature (SST) gradient in the region. Away from the equator and from deep convection, it is reasonably well described on a day-to-day basis by an extended Ekman balance model. Variability in the strength and northward extent of this flow is caused by variations in free-tropospheric pressure gradients that either reinforce or oppose the pressure gradient associated with the SST gradient. These free-tropospheric gradients are caused by easterly waves, tropical cyclones, and the Madden–Julian oscillation. Convergence in the boundary layer flow is often assumed to be responsible for destabilizing the atmosphere to deep convection. An alternative hypothesis is that enhanced total surface heat fluxes associated with high SSTs and strong winds act to produce the necessary destabilization. Analysis of the moist entropy budget of the planetary boundary layer shows that, on average, surface fluxes generate over twice the destabilization produced by boundary layer convergence in the east Pacific ITCZ.

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Analysis of spatio-temporal variability of aerosol optical depth with empirical orthogonal functions in the Changjiang River Delta, China

Frontiers of Earth Science ◽

10.1007/s11707-014-0444-9 ◽

2014 ◽

Vol 9 (1) ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

Tianyong Zhai ◽

Qing Zhao ◽

Wei Gao ◽

Runhe Shi ◽

Weining Xiang ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Temporal Variability ◽

Empirical Orthogonal Functions ◽

River Delta ◽

Changjiang River ◽

Orthogonal Functions ◽

The Changjiang River ◽

Changjiang River Delta ◽

Spatio Temporal

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The Influence of European Climate Variability Mechanism on Air Temperatures in Romania

Present Environment and Sustainable Development ◽

10.2478/pesd-2014-0001 ◽

2014 ◽

Vol 8 (1) ◽

pp. 5-16 ◽

Cited By ~ 2

Author(s):

Nicoleta Ionac ◽

Monica Matei

Keyword(s):

Time Series ◽

Climate Variability ◽

Large Scale ◽

Empirical Orthogonal Functions ◽

Spatial And Temporal Variability ◽

Orthogonal Functions ◽

Mean Values ◽

European Climate ◽

Air Temperatures ◽

And Shifts

Abstract The present paper investigates on the spatial and temporal variability of maximum and minimum air-temperatures in Romania and their connection to the European climate variability. The European climate variability is expressed by large scale parameters, which are roughly represented by the geopotential height at 500 hPa (H500) and air temperature at 850 hPa (T850). The Romanian data are represented by the time series at 22 weather stations, evenly distributed over the entire country’s territory. The period that was taken into account was 1961-2010, for the summer and winter seasons. The method of empirical orthogonal functions (EOF) has been used, in order to analyze the connection between the temperature variability in Romania and the same variability at a larger scale, by taking into consideration the atmosphere circulation. The time series associated to the first two EOF patterns of local temperatures and large-scale anomalies were considered with regard to trends and shifts in their mean values. The non- Mann-Kendall and Pettitt parametric tests were used in this respect. The results showed a strong correlation between T850 parameter and minimum and maximum air temperatures in Romania. Also, the ample variance expressed by the first EOF configurations suggests a connection between local and large scale climate variability.

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The Spatial and Temporal Variability of Mn Speciation in the Coastal Northwest Atlantic Ocean

Journal of Geophysical Research Oceans ◽

10.1029/2019jc015167 ◽

2020 ◽

Vol 125 (1) ◽

Cited By ~ 1

Author(s):

V. E. Oldham ◽

C. H. Lamborg ◽

C. M. Hansel

Keyword(s):

Atlantic Ocean ◽

Temporal Variability ◽

Spatial And Temporal Variability ◽

Northwest Atlantic

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Spatial and temporal variability of the remotely sensed chlorophyllasignal associated with Rossby waves in the South Atlantic Ocean

Journal of Geophysical Research Atmospheres ◽

10.1029/2009jc005291 ◽

2010 ◽

Vol 115 (C5) ◽

Cited By ~ 7

Author(s):

E. Gutknecht ◽

I. Dadou ◽

G. Charria ◽

P. Cipollini ◽

V. Garçon

Keyword(s):

Atlantic Ocean ◽

Temporal Variability ◽

Rossby Waves ◽

Remotely Sensed ◽

South Atlantic ◽

South Atlantic Ocean ◽

Spatial And Temporal Variability ◽

The South

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Spatial–Temporal Variability of the Calculated Characteristics of the Ocean in the Arctic Zone of Russia by Using the NEMO Model with Altimetry Data Assimilation

Journal of Marine Science and Engineering ◽

10.3390/jmse8100753 ◽

2020 ◽

Vol 8 (10) ◽

pp. 753

Author(s):

Konstantin Belyaev ◽

Andrey Kuleshov ◽

Ilya Smirnov

Keyword(s):

Data Assimilation ◽

Temporal Variability ◽

Satellite Observation ◽

Sea Surface Height ◽

Empirical Orthogonal Functions ◽

Sea Surface ◽

The Arctic ◽

Observation Data ◽

Arctic Zone ◽

Orthogonal Functions

The spatial–temporal variability of the calculated characteristics of the ocean in the Arctic zone of Russia is studied. In this study, the known hydrodynamic model of the ocean Nucleus for European Modelling of the Ocean (NEMO) is used with assimilation of observation data on the sea surface height taken from the Archiving, Validating and Interpolation Satellite Observation (AVISO) archive. We use the Generalized Kalman filter (GKF) method, developed earlier by the authors of this study, in conjunction with the method of decomposition of symmetric matrices into empirical orthogonal functions (EOF, Karhunen–Loeve decomposition). The investigations are focused mostly on the northern seas of Russia. The main characteristics of the ocean, such as the current velocity, sea surface height, and sea surface temperature are calculated with data assimilation (DA) and without DA (the control calculation). The calculation results are analyzed and their spatial–temporal variability over a time period of 14 days is studied. It is shown that the main spatial variability of characteristics after DA is in good agreement with the localization of currents in the North Atlantic and in the Arctic zone of Russia. The contribution of each of the eigenvectors and eigenvalues of the covariation matrix to the spatial–temporal variability of the calculated characteristics is shown by using the EOF analysis.

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