scholarly journals PEMODELAN STATISTICAL DOWNSCALING DENGAN PEUBAH DUMMY BERDASARKAN TEKNIK CLUSTER HIERARKI DAN NON- HIERARKI UNTUK PENDUGAAN CURAH HUJAN

2019 ◽  
Vol 3 (3) ◽  
pp. 295-309
Author(s):  
Sitti Sahriman ◽  
Anisa Kalondeng ◽  
Vieri Koerniawan
Keyword(s):  
Statistical Downscaling ◽  
Principal Component Regression ◽  
Circulation Model ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Statistical Technique ◽  
Rainfall Data ◽  
Global Circulation Model ◽  
Cluster Technique ◽  
Dummy Variables

Statistical downscaling (SD) is a statistical technique used to predict local scale rainfall based on global atmospheric circulation. The global scale climate variable used is precipitation from GCM (Global Circulation Model). However, the precipitation data of GCM outputs have a large dimension, giving rise to multicollinearity in the data. This problem is handled by the Principal Component Regression (PCR) method. In addition, the SD models have heterogeneous error variances. The dummy variable is added to the PCR models to solve the problem. Hierarchical (k-means) and non-hierarchical cluster techniques (average linkage, median linkage, and ward linkage) are used in modeling to determine rainfall data groups. Furthermore, the group formed is the basis of the formation of dummy variables. This study aims to estimate local rainfall data in Pangkep district as a salt-producing area in South Sulawesi. There are 4 dummy variables based on the 5 groups formed. Dummy variables are able to improve predictions from the PCR models. R2 values of the PCR-dummy models (ranging from 89.89% to 95.58%) are relatively higher than the PCR models (ranging from 55.87% to 57.61%). This result is also consistent with the model validation stage. The PCR-dummy models based on non-hierarchical cluster techniques (k-means) are better than the PCR-dummy models based on cluster hierarchy techniques. In general, the best model is the PCR-dummy model of the non-hierarchical cluster technique (k-means ) and involves 4 main components.

Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Khairunnisa Khairunnisa ◽  
Rizka Pitri ◽  
Victor P Butar-Butar ◽  
Agus M Soleh
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Principal Component Regression ◽  
Circulation Model ◽  
Principal Component ◽  
Meteorological Station ◽  
Grid Size ◽  
Partial Least Square ◽  
Least Square ◽  
Output Data

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

scholarly journals Performance Study of Global Circulation Model HADCM3 Using SDSM for Temperature and Rainfall in North-Eastern Bangladesh

2013 ◽  
Vol 6 (1) ◽  
pp. 87-96 ◽  
Author(s):  
A. H. Nury ◽  
M. J. B. Alam
Keyword(s):  
Statistical Downscaling ◽  
Circulation Model ◽  
Rainfall Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Global Circulation Model ◽  
Statistical Downscaling Model ◽  
Global Circulation ◽  
North Eastern ◽  
Index Of Agreement

This paper describes application of a statistical downscaling model to study the performance of the global circulation model HADCM3 (Hadley centre coupled model, version 3) for the Sylhet and Moulvibazar districts (North-eastern region) of Bangladesh. Predictors of HADCM3 have been downscaled by statistical downscaling model (SDSM). Daily observed temperature and rainfall data from 1981 to 2006 was used to conduct the calibration and 2007 to 2011 was used for validation using SDSM. Percent of bias (PBIAS), Nash-Sutcliffe efficiency (NSE) and modified index of agreement are also used for the assessment of downscaled temperature and rainfall data. PBIAS of downscaled temperature is the least (-0.30%), NSE (0.80) and modified index of agreement (0.83) is the highest for daily maximum temperature at Sylhet station. Among five rainfall stations, PBIAS of downscaled rainfall is the least (1.31%), NSE (0.76) and modified index of agreement (0.79) is the highest at Kanairghat station. The downscaled temperature and rainfall data approximately agree with the observed data.  Keywords: Temperature; Rainfall; SDSM; Downscaling; Validation; PBIAS; NSE.  © 2014 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.  doi: http://dx.doi.org/10.3329/jsr.v6i1.16511 J. Sci. Res. 6 (1), 87-96 (2014)  

scholarly journals Statistical downscaling and scenario construction of precipitation in Scania, southern Sweden

2004 ◽  
Vol 35 (3) ◽  
pp. 261-278 ◽  
Author(s):  
Maj-Lena Linderson ◽  
Christine Achberger ◽  
Deliang Chen
Keyword(s):  
Atmospheric Circulation ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Circulation Model ◽  
Summer Precipitation ◽  
Atmospheric Composition ◽  
Global Circulation Model ◽  
Southern Sweden ◽  
Monthly Basis ◽  
Projected Changes

Statistical downscaling models for precipitation in Scania, southern Sweden, have been developed and applied to calculate the changes in the future Scanian precipitation climate due to projected changes in the atmospheric composition. The models are based on multiple linear regression, linking large-scale predictors at monthly time resolution to regional statistics of daily precipitation on a monthly basis. To account for spatial precipitation variability within the area, the precipitation statistics were derived for different regions in Scania. The final downscaling models, developed for different regions and seasons, use atmospheric circulation, large-scale humidity and precipitation as predictors. Among the precipitation statistics examined, only the models for estimating the mean precipitation and the frequency of wet days were skilful. Based on the Canadian Global Circulation Model 1 (CGCM1), a future scenario of these two statistics was created. The downscaled scenario shows a significant increase of the annual mean precipitation by about 10% and a slight decrease in the frequency of wet days, indicating an increase in the precipitation amounts as well as in the precipitation intensity. The main increase of precipitation amounts and intensity occur during winter, while the summer precipitation amounts decrease slightly. The seasonal changes found in precipitation are likely attributed to changes in the westerly flow of the atmospheric circulation.

scholarly journals Application of gas chromatography analysis to quality control of residual organic solvents in clopidogrel bisulphate

2014 ◽  
Vol 79 (10) ◽  
pp. 1279-1293 ◽  
Author(s):  
Aleksandar Pavlovic ◽  
Ljubisa Ignjatovic ◽  
Sasa Popov ◽  
Aleksandar Mladenovic ◽  
Igor Stankovic
Keyword(s):  
Degradation Products ◽  
Direct Injection ◽  
Principal Component ◽  
Process Variations ◽  
Hierarchical Cluster ◽  
Statistical Technique ◽  
Dimethyl Formamide ◽  
Chromatography Analysis ◽  
Flame Ionization ◽  
Gas Chromatographic Method

A direct-injection, split-mode capillary gas chromatographic procedure with a flame ionization detection is developed for the analysis of eight solvents used in the synthesis and purification of an anti-thrombotic drug clopidogrel bisulphate. The solvents analyzed were methanol, acetone, dichloromethane (DCM), 2-butanol, cyclohexane, toluene, acetic acid and N, N-dimethyl formamide (DMF). In addition, as a result of dehydration of 2-butanol during drying process, in clopidogrel bisulphate samples, significant amounts of 2-butanol dehydration products (1-butene, cis and trans isomers of 2-butene, 2,2'-oxydibutane and 1-(1-methylpropoxy)butane) may be detected. The content of each of these volatile products can be evaluated using the same gas-chromatographic method, with quantification based on the response factor established for the chromatographic peak of 2-butanol. For each solvent used in the process of clopidogrel bisulphate preparation, the procedure is validated for selectivity, linearity, recovery, precision, robustness, quantitation limit, and detection limit. All eight solvents plus five 2-butanol degradation products are fully separated. System suitability test is validated, and requirements are set. Based on a large number of result sets, retrospectively, from many different batches analyzed, conclusions were made about process variations and reliability and a lack of consistency was identified in the quality of the active substance from a particular producer source. Multivariate analysis was used as statistical technique to classify samples. From the analyzed set of 11 solvents, 6 of them were preselected based upon their occurrence in the samples and both Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were performed.

scholarly journals Principal Component Regression in Statistical Downscaling with Missing Value for Daily Rainfall Forecasting

2021 ◽  
Vol 2 (3) ◽  
pp. 139-146
Author(s):  
M Dika Saputra ◽  
Alfian Futuhul Hadi ◽  
Abduh Riski ◽  
Dian Anggraeni
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
Statistical Downscaling ◽  
State Space Model ◽  
Principal Component Regression ◽  
Daily Rainfall ◽  
Principal Component ◽  
Rainfall Forecasting ◽  
Space Model ◽  
Circular Model

Drought is a serious problem that often arises during the dry season. Hydrometeorologically, drought is caused by reduced rainfall in a certain period. Therefore, it is necessary to take the latest actions that can overcome this problem. This research aims to predict the potential for a drought to occur again in the Kupang City, Indonesia by developing a rainfall forecasting model. Incomplete daily local climate data for Kupang City is an obstacle in this analysis of rainfall forecasting. Data correction was then carried out through imputed missing values using the Kalman Filter method with Arima State-Space model. The Kalman Filter and Arima State-Space model (2,1,1) produces the best missing data imputation with a Root Mean Square Error (RMSE) of 0.930. The rainfall forecasting process is carried out using Statistical Downscaling with the Principal Component Regression (PCR) model that considers global atmospheric circulation from the Global Circular Model (GCM). The results showed that the PCR model obtained was quite good with a Mean Absolute Percent Error (MAPE) value of 2.81%. This model is used to predict the daily rainfall of Kupang City by utilizing GCM data.

scholarly journals Statistical Downscaling to Predict Monthly Rainfall Using Generalized Linear Model with Gamma Distribution

2016 ◽  
Vol 24 (2) ◽  
pp. 215 ◽  
Author(s):  
Agus M Soleh
Keyword(s):  
Linear Model ◽  
Gamma Distribution ◽  
Statistical Downscaling ◽  
Generalized Linear Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Linear Modeling ◽  
Rainfall Prediction ◽  
Generalized Linear Modeling ◽  
Significant Difference

Statistical Downscaling (SDS) models might involve ill-conditioned covariates (large dimension and high correlation/multicollinear). This problem could be solved by a variable selection technique using L1 regularization/LASSO or a dimension reduction approach using principal component analysis (PCA). In this paper, both methods were applied to generalized linear modeling with gamma distribution and compared to predict rainfall models at 11 rain posts in Indramayu. More over, generalized linear model with gamma distribution was used to obtain non-negative rainfall prediction and compared with principal component regression (PCR). Two types of ill-conditioned data with different characteristics (CMIP5 and GPCP version 2.2) were used as covariates in SDS modeling. The results show that three methods (PCR, Gamma-PC, and Gamma-L1) did not demonstrate significant difference in term of Root Mean Square Error (RMSE) after addition of dummy variables (month) in the models. However, a generalized linear modeling with gamma distribution could be considered as the best methods since it provided non-negative rainfall predictions.

scholarly journals Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe

2007 ◽  
Vol 3 (4) ◽  
pp. 899-933 ◽  
Author(s):  
M. Vrac ◽  
D. Paillard ◽  
P. Naveau
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
Western Europe ◽  
Generalized Additive Models ◽  
Circulation Model ◽  
Additive Models ◽  
Future Climate Change ◽  
Small Scale ◽  
Global Circulation Model ◽  
Non Linear

Abstract. The needs of small-scale climate information have become prevalent to study the impacts of future climate change as well as for paleoclimate researches where the reconstructions from proxies are obviously local. In this study we develop a non-linear statistical downscaling method to generate local temperatures and precipitation values from large-scale variables (e.g. Global Circulation Model – GCM – outputs), through Generalized Additive Models (GAMs) calibrated on the present Western Europe climate. First, various monthly GAMs (i.e. one model for each month) are tested for preliminary analysis. Then, annual GAMs (i.e. one model for the 12 months altogether) are developed and tailored for two sets of predictors (geographical and physical) to downscale local temperatures and precipitation. As an evaluation of our approach under large-scale conditions different from present Western Europe, projections are realized (1) for present North America and Northern Europe and compared to local observations (spatial test); and (2) for the Last Glacial Maximum (LGM) period, and compared to local reconstructions and GCMs outputs (temporal test). In general, both spatial and temporal evaluations indicate that the GAMs are flexible and efficient tools to capture and downscale non-linearities between large- and local-scale variables. More precisely, the results emphasize that, while physical predictors alone are not capable of downscaling realistic values when applied to climate strongly different from the one used for calibration, the inclusion of geographical-type variables – such as altitude, advective continentality and W-slope – into GAM predictors brings robustness and improvement to the method and its local projections.

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