PREDICTORS AND THEIR DOMAIN FOR STATISTICAL DOWNSCALING OF CLIMATE IN BANGLADESH

2016 ◽  
Vol 78 (6-12) ◽  
Author(s):  
Mahiuddin Alamgir ◽  
Sahar Hadi Pour ◽  
Morteza Mohsenipour ◽  
M. Mehedi Hasan ◽  
Tarmizi Ismail
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Regional Scale ◽  
Circulation Model ◽  
Local Scale ◽  
Mitigation Measures ◽  
Grid Points ◽  
Hydrological Services ◽  
Atmospheric Variables

Reliable projection of future rainfall in Bangladesh is very important for the assessment of possible impacts of climate change and implementation of necessary adaptation and mitigation measures. Statistical downscaling methods are widely used for downscaling coarse resolution general circulation model (GCM) output at local scale. Selection of predictors and their spatial domain is very important to facilitate downscaling future climate projected by GCMs. The present paper reports the finding of the study conducted to identify the GCM predictors and demarcate their climatic domain for statistical downscaling in Bangladesh at local or regional scale. Twenty-six large scale atmospheric variables which are widely simulated GCM predictors from 45 grid points around the country were analysed using various statistical methods for this purpose. The study reveals that large-scale atmospheric variables at the grid points located in the central-west part of Bangladesh have the highest influence on rainfall.  It is expected that the finding of the study will help different meteorological and agricultural organizations of Bangladesh to project rainfall and temperature at local scale in order to provide various agricultural or hydrological services.

scholarly journals Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods

2007 ◽  
Vol 4 (5) ◽  
pp. 3413-3440 ◽  
Author(s):  
E. P. Maurer ◽  
H. G. Hidalgo
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Impact Analysis ◽  
Climate Model ◽  
Daily Precipitation ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Temperature Extremes ◽  
Daily Data

Abstract. Downscaling of climate model data is essential to most impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km² per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscaling (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA downscales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to generate daily values. The methods produce comparable skill in producing downscaled, gridded fields of precipitation and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit some skill in reproducing both observed wet and dry extremes and the difference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the reanalysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demonstrates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day correspondence of weather from the GCM needs to be reproduced for some applications. The ability to produce skillful downscaled daily data depends primarily on the ability of the climate model to show daily skill.

scholarly journals Present and LGM permafrost from climate simulations: contribution of statistical downscaling

2011 ◽  
Vol 7 (3) ◽  
pp. 1647-1692 ◽  
Author(s):  
G. Levavasseur ◽  
M. Vrac ◽  
D. M. Roche ◽  
D. Paillard ◽  
A. Martin ◽  
...  
Keyword(s):  
Air Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Regional Scale ◽  
Kappa Statistic ◽  
Local Scale ◽  
Additive Models ◽  
Simple Relationship ◽  
Global Agreement

Abstract. We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the inter-variation between them. Studying an heterogeneous variable such as permafrost implies to conduct analysis at a smaller spatial scale compared with climate models resolution. Our approach consists in applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local-scale permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of air temperature at the surface. Then, we define permafrost distribution over Eurasia by air temperature conditions. In a first validation step on present climate (CTRL period), this method shows some limitations with non-systemic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a Multinomial Logistic GAM (ML-GAM), which directly predicts the occurrence probabilities of local-scale permafrost. The obtained permafrost distributions appear in a better agreement with data. In average for the nine PMIP2 models, we measure a global agreement by kappa statistic of 0.80 with CTRL permafrost data, against 0.68 for the GAM method. In both cases, the provided local information reduces the inter-variation between climate models. This also confirms that a simple relationship between permafrost and the air temperature only is not always sufficient to represent local-scale permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. The prediction of the SDMs is not significantly better than large-scale fields with 0.46 (GAM) and 0.49 (ML-GAM) of global agreement with LGM permafrost data. At the LGM, both methods do not reduce the inter-variation between climate models. We show that LGM permafrost distribution from climate models strongly depends on large-scale air temperature at the surface. LGM simulations from climate models lead to larger differences with permafrost data, than in the CTRL period. These differences reduce the contribution of downscaling and depend on several other factors deserving further studies.

scholarly journals Global atmospheric response to specific linear combinations of the main SST modes. Part I: numerical experiments and preliminary results

1996 ◽  
Vol 14 (10) ◽  
pp. 1066-1077 ◽  
Author(s):  
S. Trzaska ◽  
V. Moron ◽  
B. Fontaine
Keyword(s):  
Numerical Experiments ◽  
General Circulation ◽  
Large Scale ◽  
Southern Oscillation ◽  
Regional Scale ◽  
Circulation Model ◽  
Atmospheric Model ◽  
Equatorial Atlantic ◽  
Preliminary Results ◽  
The Impact

Abstract. This article investigates through numerical experiments the controversial question of the impact of El Niño-Southern Oscillation (ENSO) phenomena on climate according to large-scale and regional-scale interhemispheric thermal contrast. Eight experiments (two considering only inversed Atlantic thermal anomalies and six combining ENSO warm phase with large-scale interhemispheric contrast and Atlantic anomaly patterns) were performed with the Météo-France atmospheric general circulation model. The definition of boundary conditions from observed composites and principal components is presented and preliminary results concerning the month of August, especially over West Africa and the equatorial Atlantic are discussed. Results are coherent with observations and show that interhemispheric and regional scale sea-surface-temperature anomaly (SST) patterns could significantly modulate the impact of ENSO phenomena: the impact of warm-phase ENSO, relative to the atmospheric model intercomparison project (AMIP) climatology, seems stronger when embedded in global and regional SSTA patterns representative of the post-1970 conditions [i.e. with temperatures warmer (colder) than the long-term mean in the southern hemisphere (northern hemisphere)]. Atlantic SSTAs may also play a significant role.

scholarly journals Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods

2008 ◽  
Vol 12 (2) ◽  
pp. 551-563 ◽  
Author(s):  
E. P. Maurer ◽  
H. G. Hidalgo
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Impact Analysis ◽  
Climate Model ◽  
Daily Precipitation ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Temperature Extremes ◽  
Daily Data

Abstract. Downscaling of climate model data is essential to local and regional impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km2 per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscaling (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA downscales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to generate daily values. The methods produce generally comparable skill in producing downscaled, gridded fields of precipitation and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit limited skill in reproducing both observed wet and dry extremes and the difference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the reanalysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demonstrates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day correspondence of weather from the GCM needs to be reproduced for some applications. The ability to produce skillful downscaled daily data depends primarily on the ability of the climate model to show daily skill.

scholarly journals Statistical downscaling of climate data to estimate streamflow in a semi-arid catchment

2012 ◽  
Vol 9 (4) ◽  
pp. 4869-4918 ◽  
Author(s):  
S. Samadi ◽  
G. J. Carbone ◽  
M. Mahdavi ◽  
F. Sharifi ◽  
M. R. Bihamta
Keyword(s):  
General Circulation Model ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Atmospheric Precipitation ◽  
Circulation Model ◽  
Climate Data ◽  
Catchment Scale ◽  
Artificial Neural Network Ann ◽  
Semi Arid

Abstract. Linear and non-linear statistical 'downscaling' study is done to relate large-scale climate information from a general circulation model (GCM) to local-scale river flows in west Iran. This study aims to investigate and evaluate the more promising downscaling techniques, and provides a through inter comparison study using the Karkheh catchment as an experimental site in a semi arid region for the years of 2040 to 2069. A hybrid conceptual hydrological model was used in conjunction with modeled outcomes from a General Circulation Model (GCM), HadCM3, along with two downscaling techniques, Statistical Downscaling Model (SDSM) and Artificial Neural Network (ANN), to determine how future streamflow may change in a semi arid catchment. The results show that the choice of a downscaling algorithm having a significant impact on the streamflow estimations for a semi-arid catchment, which are mainly, influenced, respectively, by atmospheric precipitation and temperature projections. According to the SDSM and ANN projections, daily temperature will increase up to +0.58° (+3.90%) and +0.48° (+3.48%) and daily precipitation will decrease up to −0.1mm (−2.56%) and −0.4 mm (−2.82%) respectively. Moreover streamflow changes corresponding to downscaled future projections presented a reduction in mean annual flow of −3.7 m3 s−1 and −9.47 m3 s−1 using SDSM and ANN outputs respectively. The results suggest a significant decrease of streamflow in both downscaling projections, particularly in winter. The discussion considers the performance of each statistical method for downscaling future flow at catchment scale as well as the relationship between atmospheric processes and flow variability and changes.

scholarly journals A Comparison of Statistical Approaches for Seasonal Precipitation Prediction in Pakistan

2013 ◽  
Vol 28 (5) ◽  
pp. 1116-1132 ◽  
Author(s):  
Ting Ding ◽  
Zongjian Ke
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Circulation Model ◽  
Consistent Pattern ◽  
Seasonal Precipitation ◽  
Model Output ◽  
Statistical Regression ◽  
Precipitation Prediction ◽  
Statistical Approaches

Abstract The present study focuses on two statistical approaches for improving seasonal precipitation prediction skills for Pakistan. Precipitation over Pakistan is concentrated in July–August (JA), when droughts and floods occur recurrently and cause disasters. Empirical orthogonal function (EOF) analysis is used to assess spatial patterns of precipitation, and two precipitation patterns are identified: a consistent pattern and a north–south dipole pattern. Two statistical approaches, the statistical regression method using prewinter predictors and statistical downscaling, are employed to perform rainfall predictions for JA in Pakistan. Linear regression (LR) and optimal subset regression (OSR) are used for each approach, and the regression forecast methods are compared with the raw model outputs. Historical data for large-scale variables from the NCEP–NCAR reanalysis and version 1.0 of the coupled atmosphere–ocean general circulation model from the Beijing Climate Center (CGCM1.0/BCC) outputs in 1986–2011 are used as predictors for the statistical prewinter method and statistical downscaling, respectively. In the majority of the years, the statistical prewinter method and statistical downscaling are able to correct the erroneous signs of the raw dynamical model output for the consistent pattern. The statistical prewinter method is found to provide more skillful predictions than the statistical downscaling on the prediction of the dipolelike pattern. The best prediction skills for the consistent pattern and dipolelike pattern are provided by NCEP-OSR and NCEP-LR, which have significant correlations of 0.39 and 0.40, respectively. For all the forecast methods in this study, prewinter prediction and downscaled prediction show considerable improvements when compared with model output. These statistical methods provide valuable approaches for studying local climates.

scholarly journals Present and LGM permafrost from climate simulations: contribution of statistical downscaling

2011 ◽  
Vol 7 (4) ◽  
pp. 1225-1246 ◽  
Author(s):  
G. Levavasseur ◽  
M. Vrac ◽  
D. M. Roche ◽  
D. Paillard ◽  
A. Martin ◽  
...  
Keyword(s):  
Air Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Generalized Additive Models ◽  
Regional Scale ◽  
Local Scale ◽  
Additive Models ◽  
Simple Relationship ◽  
Temperature Conditions

Abstract. We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the variability between their results. Studying a heterogeneous variable such as permafrost implies conducting analysis at a smaller spatial scale compared with climate models resolution. Our approach consists of applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local-scale permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of air temperature at the surface. Then we define permafrost distribution over Eurasia by air temperature conditions. In a first validation step on present climate (CTRL period), this method shows some limitations with non-systematic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a Multinomial Logistic GAM (ML-GAM), which directly predicts the occurrence probabilities of local-scale permafrost. The obtained permafrost distributions appear in a better agreement with CTRL data. In average for the nine PMIP2 models, we measure a global agreement with CTRL permafrost data that is better when using ML-GAM than when applying the GAM method with air temperature conditions. In both cases, the provided local information reduces the variability between climate models results. This also confirms that a simple relationship between permafrost and the air temperature only is not always sufficient to represent local-scale permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. The prediction of the SDMs (GAM and ML-GAM) is not significantly in better agreement with LGM permafrost data than large-scale fields. At the LGM, both methods do not reduce the variability between climate models results. We show that LGM permafrost distribution from climate models strongly depends on large-scale air temperature at the surface. LGM simulations from climate models lead to larger differences with LGM data than in the CTRL period. These differences reduce the contribution of downscaling.

scholarly journals Projection Pursuit Regression (PPR) on Statistical Downscaling Modeling for Daily Rainfall Forecasting

2021 ◽  
Vol 5 (2) ◽  
pp. 326-332
Author(s):  
Rio Pradani Putra ◽  
Dian Anggraeni ◽  
Alfian Futuhul Hadi
Keyword(s):  
Observational Data ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Regional Scale ◽  
Projection Pursuit ◽  
Predictor Variable ◽  
Circulation Model ◽  
Rainfall Forecasting ◽  
Projection Pursuit Regression ◽  
Response Variable

Rainfall forecasting has an important role in people's lives. Rainfall forecasting in Indonesia has complex problems because it is located in a tropical climate. Rainfall prediction in Indonesia is difficult due to the complex topography and interactions between the oceans, land and atmosphere. With these conditions, an accurate rainfall forecasting model on a local scale is needed, of course taking into account the information about the global atmospheric circulation obtained from the General Circulation Model (GCM) output. GCM may still be used to provide local or regional scale information by adding Statistical Downscaling (SD) techniques. SD is a regression-based model in determining the functional relationship between the response variable and the predictor variable. Rainfall observations obtained from the Meteorology Climatology and Geophysics Council (BMKG) are a response variable in this study. The predictor variable used in this study is the global climate output from GCM. This research was conducted in a place, namely Kupang City, East Nusa Tenggara because it has low rainfall. The Projection Pursuit Regression (PPR) will be used in this SD method for this study. In PPR modeling, optimization needs to be done and model validation is carried out with the smallest Root Mean Square Error (RMSE) criteria. The expected results must have a pattern between the results of forecasts and observations showing or approaching the observational data. The PPR model is a good model for predicting rainfall because The results of the forecast and observation show that the results of the rainfall forecast are observational data.

scholarly journals Pemodelan Statistical Downscaling dengan Regresi Modifikasi Jackknife Ridge Dummy Berbasis K-means untuk Pendugaan Curah Hujan

2021 ◽  
pp. 19-28
Author(s):  
Dewi Santika Upa P. ◽  
Sitti Sahriman
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Circulation Model ◽  
Local Scale ◽  
Dry Season ◽  
Mean Square ◽  
Grid Data

Indonesia is a tropical country, which only has two seasons throughout the year, namely the dry season and the rainy season. Thus, it is likely that rain will continue to fall during the dry season, which has a serious impact on various sectors of life. General Circulation Model (GCM) is used to deal with climate change, but the GCM cannot conduct simulations well for local scale climate variables. Therefore, Statistical Downscaling (SD) is used to predict local scale rainfall in the district of Pangkep based on square GCM (CMIP5) 8 × 8 grid data. Modified jackknife ridge regression is used to overcome multicollinearity problems that occur in GCM-lag data. Three dummy variables were added as predictor variables for the model to overcome the heterogeneity of the various forms. SD model MJR dummy regression gives good results based on the coefficient of determination and high correlation with lower root mean square error and root mean square error prediction.

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.

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