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.

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 Identifying the Ideal Number Components of the Bayesian Principal Component Analysis Model for Missing Daily Precipitation Data Treatment

2018 ◽  
Vol 7 (4.30) ◽  
pp. 5 ◽  
Author(s):  
Zun Liang Chuan ◽  
Azlyna Senawi ◽  
Wan Nur Syahidah Wan Yusoff ◽  
Noriszura Ismail ◽  
Tan Lit Ken ◽  
...  
Keyword(s):  
Time Series ◽  
Daily Precipitation ◽  
Principal Component ◽  
Precipitation Data ◽  
Analysis Model ◽  
Principal Component Analysis Model ◽  
Data Treatment ◽  
Daily Precipitation Data ◽  
Ideal Number ◽  
The Ideal

The grassroots of the presence of missing precipitation data are due to the malfunction of instruments, error of recording and meteorological extremes. Consequently, an effective imputation algorithm is indeed much needed to provide a high quality complete time series in assessing the risk of occurrence of extreme precipitation tragedy. In order to overcome this issue, this study desired to investigate the effectiveness of various Q-components of the Bayesian Principal Component Analysis model associates with Variational Bayes algorithm (BPCAQ-VB) in missing daily precipitation data treatment, which the ideal number of Q-components is identified by using The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The effectiveness of BPCAQ-VB algorithm in missing daily precipitation data treatment is evaluated by using four distinct precipitation time series, including two monitoring stations located in inland and coastal regions of Kuantan district, respectively. The analysis results rendered the BPCA5-VB is superior in missing daily precipitation data treatment for the coastal region time series compared to the single imputation algorithms proposed in previous studies. Contrarily, the single imputation algorithm is superior in missing daily precipitation data treatment for an inland region time series rather than the BPCAQ-VB algorithm.   

scholarly journals Analysis of the Potential Contributors to Common Mode Error in Chuandian Region of China

2020 ◽  
Vol 12 (5) ◽  
pp. 751
Author(s):  
Weijie Tan ◽  
Junping Chen ◽  
Danan Dong ◽  
Weijing Qu ◽  
Xueqing Xu
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Principal Component ◽  
Mass Loading ◽  
Common Mode ◽  
Surface Mass ◽  
Common Mode Error ◽  
Comparison Results ◽  
Wide Range ◽  
Gps Time Series

Common mode error (CME) in Chuandian region of China is derived from 6-year continuous GPS time series and is identified by principal component analysis (PCA) method. It is revealed that the temporal behavior of the CME is not purely random, and contains unmodeled signals such as nonseasonal mass loadings. Its spatial distribution is quite uniform for all GPS sites in the region, and the first principal component, uniformly distributed in the region, has a spatial response of more than 70%. To further explore the potential contributors of CME, daily atmospheric mass loading and soil moisture mass loading effects are evaluated. Our results show that ~15% of CME can be explained by these daily surface mass loadings. The power spectral analysis is used to assess the CME. After removing atmospheric and soil moisture loadings from the CME, the power of the CME reduces in a wide range of frequencies. We also investigate the contribution of CME in GPS filtered residuals time series and it shows the Root Mean Squares (RMSs) of GPS time series are reduced by applying of the mass loading corrections in CME. These comparison results demonstrate that daily atmosphere pressure and the soil moisture mass loadings are a part of contributors to the CME in Chuandian region of China.

scholarly journals Merging Multiple-Partial-Depth Data Time Series Using Objective Empirical Orthogonal Function Fitting

2010 ◽  
Vol 35 (4) ◽  
pp. 710-721 ◽  
Author(s):  
Ying-Tsong Lin ◽  
Arthur E. Newhall ◽  
Timothy F. Duda ◽  
Pierre F. J. Lermusiaux ◽  
Patrick J. Haley
Keyword(s):  
Time Series ◽  
Empirical Orthogonal Function ◽  
Depth Data ◽  
Orthogonal Function ◽  
Function Fitting

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 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 Vertical Structure of Annular Modes

2018 ◽  
Vol 75 (10) ◽  
pp. 3507-3519 ◽  
Author(s):  
Aditi Sheshadri ◽  
R. Alan Plumb ◽  
Erik A. Lindgren ◽  
Daniela I. V. Domeisen
Keyword(s):  
Time Series ◽  
Empirical Orthogonal Function ◽  
Zonal Wind ◽  
Vertical Structure ◽  
Zonal Flow ◽  
Annular Modes ◽  
Orthogonal Function ◽  
Linearized System ◽  
Set Up ◽  
Stratospheric Variability

Stratosphere–troposphere interactions are conventionally characterized using the first empirical orthogonal function (EOF) of fields such as zonal-mean zonal wind. Perpetual-winter integrations of an idealized model are used to contrast the vertical structures of EOFs with those of principal oscillation patterns (POPs; the modes of a linearized system governing the evolution of zonal flow anomalies). POP structures are shown to be insensitive to pressure weighting of the time series of interest, a factor that is particularly important for a deep system such as the stratosphere and troposphere. In contrast, EOFs change from being dominated by tropospheric variability with pressure weighting to being dominated by stratospheric variability without it. The analysis reveals separate tropospheric and stratospheric modes in model integrations that are set up to resemble midwinter variability of the troposphere and stratosphere in both hemispheres. Movies illustrating the time evolution of POP structures show the existence of a fast, propagating tropospheric mode in both integrations, and a pulsing stratospheric mode with a tropospheric extension in the Northern Hemisphere–like integration.

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.

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