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 Filling the gaps of in-situ hourly PM<sub>2.5</sub> concentration data with the aid of empirical orthogonal function constrained by diurnal cycles

2019 ◽  
Author(s):  
Kaixu Bai ◽  
Ke Li ◽  
Jianping Guo ◽  
Yuanjian Yang ◽  
Ni-Bin Chang
Keyword(s):  
Time Series ◽  
Empirical Orthogonal Function ◽  
Diurnal Cycle ◽  
Spline Interpolation ◽  
Gap Filling ◽  
Concentration Data ◽  
Diurnal Variability ◽  
Orthogonal Function ◽  
Data Gaps ◽  
Pm2.5 Concentration

Abstract. Data gaps are frequently observed in the hourly PM2.5 mass concentration records measured from the China national air quality monitoring network. In this study, we proposed a novel gap filling method called the diurnal cycle constrained empirical orthogonal function (DCCEOF) to fill in data gaps present in hourly PM2.5 concentration records. This method mainly calibrates the diurnal cycle of PM2.5 that is reconstructed from discrete PM2.5 neighborhood fields in space and time to the level of valid PM2.5 data values observed at adjacent times. Prior to gap filling, possible impacts of varied number of data gaps in the time series of hourly PM2.5 concentration on PM2.5 daily averages were examined via sensitivity experiments. The results showed that PM2.5 data suffered from the gaps on about 40% of days, indicating a high frequency of missing data in the hourly PM2.5 records. These gaps could introduce significant bias to daily-averaged PM2.5. Particularly, given the same number of gaps, larger biases would be introduced to daily-averaged PM2.5 during clean days than polluted days. The cross-validation results indicate that the predicted missing values from the DCCEOF method with the consideration of the local diurnal phases of PM2.5 are more accurate and reasonable than those from the conventional spline interpolation approach, especially for the reconstruction of daily peaks and/or minima that cannot be restored by the latter method. To fill the gaps in the hourly PM2.5 records across China during 2014 to 2019, as a practical application, the DCCEOF method can be able to reduce the averaged frequency of missingness from 42.6 % to 5.7 %. In general, the present work implies that the DCCEOF method is realistic and robust to be able to handle the missingness issues in time series of geophysical parameters with significant diurnal variability and can be expectably applied in other data sets with similar barriers because of its self-consistent capability.

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.

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