scholarly journals The distribution and variability of chlorophyll-a bloom in the southeastern tropical Indian Ocean using Empirical Orthogonal Function analysis

2017 ◽  
Vol 18 (4) ◽  
pp. 1546-1555
Author(s):  
ISKHAQ ISKANDAR ◽  
QURNIA WULAN SARI ◽  
DEDI SETIABUDIDAYA ◽  
INDRA YUSTIAN ◽  
BRUCE MONGER
Keyword(s):  
Time Series ◽  
Indian Ocean ◽  
Chlorophyll A ◽  
Empirical Orthogonal Function ◽  
Function Analysis ◽  
Principal Component ◽  
Tropical Indian Ocean ◽  
Ekman Pumping ◽  
Orthogonal Function ◽  
Southeastern Tropical Indian Ocean

Iskandar I, Sari QW, Setiabudidaya D, Yustian I, Monger B. 2017. The distribution and variability of chlorophyll-a bloom in the southeastern tropical Indian Ocean using Empirical Orthogonal Function analysis. Biodiversitas 18: 1546-1555. The Indian Ocean Dipole (IOD) events cause anomalously strong upwelling along the sourthen coast of Sumatra-Java leading to the bloom of chlorophylla. An empirical orthogonal function (EOF) analysis was applied to the time series of the satellite-observed chlorophyll-a, sea surface temperature (SST) and surface winds. Spatial eigen functions of the first EOF mode revealed the broad areas of coherent temporal variation in chlorophyll-a, SST and Ekman pumping, which was observed in the southeastern tropical Indian Ocean (SETIO) region. The corresponding time series of principal component of the first EOF mode revealed a robust seasonal variation and relativley weak inter-annual variation. The second EOF mode exhibited a distinct inter-annual variation with the high surface chlorophyll-a concentration was observed along the southern coast of Sumatra-Java. This high chlorophyll-a concentration is co-located with the low SST, the positive Ekman pumping, and the positive wind-induced mixing. An EOF analysis applied on the seasonal time series showed interesting patterns. The leading EOF mode during the peak IOD season from September to November (SON) showed the high concentration of chlorophyll-a was restricted to the southern coast of Java and was co-located with low SST region. The corresponding time series of principal component of the leading EOF mode showed a significant correlation with the Dipole Mode Index (DMI), however it had no correlation with the Ekman pumping. It could be concluded that the chlorophyll-a bloom during the peak phase of the IOD event was generated by the alongshore upwelling-favorable winds in the preceding season.

scholarly journals Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function

2012 ◽  
Vol 5 (2) ◽  
pp. 267-273 ◽  
Author(s):  
A. Devasthale ◽  
K.-G. Karlsson ◽  
J. Quaas ◽  
H. Grassl
Keyword(s):  
Time Series ◽  
Empirical Orthogonal Function ◽  
Diurnal Cycle ◽  
Function Analysis ◽  
Cloud Amount ◽  
Convective Cloud ◽  
Orthogonal Function ◽  
Orbital Drift ◽  
The Impact ◽  
Rotated Empirical Orthogonal Function

Abstract. The Advanced Very High Resolution Radiometer (AVHRR) instruments onboard the series of National Oceanic and Atmospheric Administration (NOAA) satellites offer the longest available meteorological data records from space. These satellites have drifted in orbit resulting in shifts in the local time sampling during the life span of the sensors onboard. Depending upon the amplitude of the diurnal cycle of the geophysical parameters derived, orbital drift may cause spurious trends in their time series. We investigate tropical deep convective clouds, which show pronounced diurnal cycle amplitude, to estimate an upper bound of the impact of orbital drift on their time series. We carry out a rotated empirical orthogonal function analysis (REOF) and show that the REOFs are useful in delineating orbital drift signal and, more importantly, in subtracting this signal in the time series of convective cloud amount. These results will help facilitate the derivation of homogenized data series of cloud amount from NOAA satellite sensors and ultimately analyzing trends from them. However, we suggest detailed comparison of various methods and rigorous testing thereof applying final orbital drift corrections.

scholarly journals Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function

2011 ◽  
Vol 4 (3) ◽  
pp. 3877-3890
Author(s):  
A. Devasthale ◽  
K. Karlsson ◽  
J. Quaas ◽  
H. Grassl
Keyword(s):  
Time Series ◽  
Empirical Orthogonal Function ◽  
Diurnal Cycle ◽  
Function Analysis ◽  
Cloud Amount ◽  
Convective Cloud ◽  
Orthogonal Function ◽  
Orbital Drift ◽  
The Impact ◽  
Rotated Empirical Orthogonal Function

Abstract. The AVHRRs instruments onboard the series of NOAA satellites offer the longest available meteorological data records from space. These satellites have drifted in orbit resulting in shifts in the local time sampling during the life span of sensors onboard. Depending on the amplitude of a diurnal cycle of the geophysical parameters derived, orbital drift may cause spurious trends in their time series. We investigate tropical deep convective clouds, which show pronounced diurnal cycle amplitude, to bracket an upper bound of the impact of orbital drift on their time series. We carry out a rotated empirical orthogonal function analysis and show that the REOFs are useful in delineating orbital drift signal and, more importantly, in correcting this signal in the time series of convective cloud amount. These results will help facilitate the derivation of homogenized data series of cloud amount from NOAA satellite sensors and ultimately analyzing trends from them. However, we suggest detailed comparison of various methods and their rigorous testing before applying final orbital drift corrections.

scholarly journals Evaluation of Technology for the Analysis and Forecasting of Precipitation Using Cyclostationary EOF and Regression Methods

2021 ◽  
Author(s):  
Mingdong Sun ◽  
Gwangseob Kim ◽  
Yan Wang ◽  
Kun Lei
Keyword(s):  
Time Series ◽  
South Korea ◽  
Empirical Orthogonal Function ◽  
Arma Model ◽  
Principal Component ◽  
Composite Model ◽  
Precipitation Data ◽  
Regression Methods ◽  
Orthogonal Function ◽  
Comparison Results

Abstract Precipitation time series exhibit complex fluctuations and statistical changes. We investigate and forecast precipitation variations in South Korea from 1973 to 2019 using cyclostationary empirical orthogonal function (CSEOF) and regression methods. First, empirical orthogonal function (EOF) and CSEOF analyses are used to examine the periodic changes in the precipitation data. Then, the autoregressive moving average (ARMA) method is applied to the principal component (PC) time series derived from the EOF and CSEOF precipitation analyses. The fifteen leading EOF and CSEOF modes and their corresponding PC time series clearly reflect the spatial distribution and temporal evolution characteristics of the precipitation data. Based on the PC forecasts of the EOF and CSEOF models, the EOF-ARMA composite model and CSEOF-ARMA composite model are used to obtain quantitative precipitation forecasts. The comparison results show that both composite models have good performances and similar accuracies. However, the performance of the CSEOF-ARMA model is better than that of the EOF-ARMA model under various measurements. Therefore, the CSEOF-ARMA composite forecast model can be considered an efficient and feasible technology representing an analytical approach for precipitation forecasting in South Korea.

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