scholarly journals Downscaling of GCM-Simulated Precipitation Using Model Output Statistics

2014 ◽  
Vol 27 (1) ◽  
pp. 312-324 ◽  
Author(s):  
Jonathan M. Eden ◽  
Martin Widmann
Keyword(s):  
Statistical Downscaling ◽  
Temporal Variability ◽  
General Circulation ◽  
Large Scale ◽  
Principal Component ◽  
General Circulation Models ◽  
Model Output ◽  
Local Scaling ◽  
Simulated Precipitation ◽  
Model Output Statistics

Abstract Producing reliable estimates of changes in precipitation at local and regional scales remains an important challenge in climate science. Statistical downscaling methods are often utilized to bridge the gap between the coarse resolution of general circulation models (GCMs) and the higher resolutions at which information is required by end users. As the skill of GCM precipitation, particularly in simulating temporal variability, is not fully understood, statistical downscaling typically adopts a perfect prognosis (PP) approach in which high-resolution precipitation projections are based on real-world statistical relationships between large-scale atmospheric predictors and local-scale precipitation. Using a nudged simulation of the ECHAM5 GCM, in which the large-scale weather states are forced toward observations of large-scale circulation and temperature for the period 1958–2001, previous work has shown ECHAM5 skill in simulating temporal variability of precipitation to be high in many parts of the world. Here, the same nudged simulation is used in an alternative downscaling approach, based on model output statistics (MOS), in which statistical corrections are derived for simulated precipitation. Cross-validated MOS corrections based on maximum covariance analysis (MCA) and principal component regression (PCR), in addition to a simple local scaling, are shown to perform strongly throughout much of the extratropics. Correlation between downscaled and observed monthly-mean precipitation is as high as 0.8–0.9 in many parts of Europe, North America, and Australia. For these regions, MOS clearly outperforms PP methods that use temperature and circulation as predictors. The strong performance of MOS makes such an approach to downscaling attractive and potentially applicable to climate change simulations.

scholarly journals Conjunction of wavelet-entropy and SOM clustering for multi-GCM statistical downscaling

2018 ◽  
Vol 50 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Aida Hosseini Baghanam ◽  
Vahid Nourani ◽  
Mohammad-Ali Keynejad ◽  
Hassan Taghipour ◽  
Mohammad-Taghi Alami
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
General Circulation Models ◽  
Reanalysis Data ◽  
Self Organizing Map ◽  
Climate Data ◽  
Wavelet Entropy ◽  
Som Clustering ◽  
Artificial Neural Network Ann

Abstract Important issues in statistical downscaling of general circulation models (GCMs) is to select dominant large-scale climate data (predictors). This study developed a predictor screening framework, which integrates wavelet-entropy (WE) and self-organizing map (SOM) to downscale station rainfall. WEs were computed as the representatives of predictors and fed into the SOM to cluster the predictors. SOM-based clustering of predictors according to WEs could lead to physically meaningful selection of the dominant predictors. Then, artificial neural network (ANN) as the statistical downscaling method was developed. To assess the advantages of different GCMs, multi-GCM ensemble approach was used by Can-ESM2, BNU-ESM, and INM-CM4 GCMs. Moreover, NCEP reanalysis data were used to calibrate downscaling model as well for comparison purposes. The calibration, validation, and projection of the proposed model were performed during January 1951 to December 1991, January 1992 to December 2005 and January 2017 to December 2100, respectively. The proposed data screening model could reduce the dimensionality of data and select appropriate predictors for generalizing future rainfall. Results showed better performance of ANN than multiple linear regression (MLR) model. The projection results yielded 29% and 21% decrease of rainfall at the study area for 2017–2050 under RCPs 4.5 and 8.5, respectively.

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 Statistical Downscaling of Precipitation in Korea Using Multimodel Output Variables as Predictors

2009 ◽  
Vol 137 (6) ◽  
pp. 1928-1938 ◽  
Author(s):  
Hongwen Kang ◽  
Chung-Kyu Park ◽  
Saji N. Hameed ◽  
Karumuri Ashok
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Summer Precipitation ◽  
General Circulation Models ◽  
Prediction Skill ◽  
Dynamical Models ◽  
Pattern Projection ◽  
Multiple Variables ◽  
Leave One Out

Abstract A pattern projection downscaling method is applied to predict summer precipitation at 60 stations over Korea. The predictors are multiple variables from the output of six operational dynamical models. The hindcast datasets span a period of 21 yr from 1983 to 2003. A downscaled prediction was made for each model separately within a leave-one-out cross-validation framework. The pattern projection method uses a moving window, which scans globally, in order to seek the most optimal predictor for each station. The final forecast is the average of six model downscaled precipitation forecasts using the best predictors and will be referred to as “DMME.” It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse-resolution predictions of general circulation models. Although Korea’s precipitation is strongly influenced by local mountainous terrain, DMME performs well at 59 stations with correlation skill significant at the 95% confidence level. The improvement of the prediction skill is attributed to three steps: coupled pattern selection, optimal predictor selection, and the multimodel downscaled precipitation ensemble. This study indicates that the large-scale circulation variables, which are predicted by the current operational dynamical models, if selected, can be used to make skillful predictions of the local precipitation by using appropriate statistical downscaling methods.

scholarly journals Skill, Correction, and Downscaling of GCM-Simulated Precipitation

2012 ◽  
Vol 25 (11) ◽  
pp. 3970-3984 ◽  
Author(s):  
Jonathan M. Eden ◽  
Martin Widmann ◽  
David Grawe ◽  
Sebastian Rast
Keyword(s):  
Real World ◽  
General Circulation ◽  
Large Scale ◽  
General Circulation Models ◽  
Atmospheric Variability ◽  
Promising Alternative ◽  
Global Correlation ◽  
Simulated Precipitation ◽  
Simple Scaling ◽  
Set Up

The ability of general circulation models (GCMs) to correctly simulate precipitation is usually assessed by comparing simulated mean precipitation with observed climatologies. However, to what extent the skill in simulating average precipitation indicates how well the models represent temporal changes is unclear. A direct assessment of the latter is hampered by the fact that freely evolving climate simulations for past periods are not set up to reproduce the specific evolution of internal atmospheric variability. Therefore, model-to-real-world comparisons of time series of daily, monthly, or annual precipitation are not meaningful. Here, for the first time, the authors quantify GCM skill in simulating precipitation variability using simulations in which the temporal evolution of the large-scale atmospheric state closely matches that of the real world. This is achieved by nudging the atmospheric states in the ECHAM5 GCM, but crucially not the precipitation field itself, toward the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40). Global correlation maps between observed and simulated seasonal precipitation allow areas in which simulated future precipitation changes are likely to be meaningful to be identified. In many areas, correlations higher than 0.8 are found. This means also that in these regions the simulated precipitation is a very good predictor for the true precipitation, and thus a statistical correction of the simulated precipitation, which can include a downscaling component, can provide useful estimates for local-scale precipitation. The authors show that a simple scaling of the simulated precipitation performs well in a cross validation and thus appears to be a promising alternative to standard statistical downscaling approaches.

scholarly journals A non-local model output statistics approach for the downscaling of CMIP5 GCMs for the projection of rainfall in Peninsular Malaysia

2019 ◽  
Vol 11 (4) ◽  
pp. 944-955 ◽  
Author(s):  
Muhammad Noor ◽  
Tarmizi bin Ismail ◽  
Shahid Ullah ◽  
Zafar Iqbal ◽  
Nadeem Nawaz ◽  
...  
Keyword(s):  
General Circulation ◽  
General Circulation Models ◽  
Peninsular Malaysia ◽  
Local Model ◽  
Annual Rainfall ◽  
Model Output ◽  
Local Regression ◽  
Maximum Increase ◽  
Non Local ◽  
Model Output Statistics

Abstract In this study, a non-local model output statistics (MOS) approach is proposed for the downscaling of daily rainfall of coupled model intercomparison project phase 5 (CMIP5) general circulation models (GCMs) for the projections of rainfall in Peninsular Malaysia for two representative concentration pathway (RCP) scenarios, RCP4.5 and RCP8.5. Projections of eight GCMs for both the mentioned RCPs were used for this purpose. The GCM simulations were downscaled at 19 observed stations distributed over Peninsular Malaysia. Random forest (RF) was used for the development of non-local regression-based MOS models. The results revealed a high accuracy of the models in downscaling rainfall at all the observed stations. The mean absolute error (MAE) of the models were found in the range of 0.8–0.39; normalized root mean square error (NRMSE) between 7.4 and 41.7, Percent Bias (PBIAS) between –0.3 and 10.1, Nash–Sutcliffe coefficient (NSE) between 0.81 and 0.99 and R2 between 0.89 and 0.99. The increase in annual rainfall was in the range of 7.3–29.5%. The increase was higher for RCP8.5 compared to RCP4.5. The maximum increase was observed in the northern part of Peninsular Malaysia in the range of 20.7–29.5%, while the minimum in the south-west region was in the range of 7.6–15.2%.

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 Analog Downscaling of Seasonal Rainfall Forecasts in the Murray Darling Basin

2013 ◽  
Vol 141 (3) ◽  
pp. 1099-1117 ◽  
Author(s):  
Andrew Charles ◽  
Bertrand Timbal ◽  
Elodie Fernandez ◽  
Harry Hendon
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Seasonal Prediction ◽  
General Circulation Models ◽  
Meridional Wind ◽  
Seasonal Forecasts ◽  
Local Climate ◽  
Continental Scale ◽  
Predictive Skill ◽  
Murray Darling Basin

Abstract Seasonal predictions based on coupled atmosphere–ocean general circulation models (GCMs) provide useful predictions of large-scale circulation but lack the conditioning on topography required for locally relevant prediction. In this study a statistical downscaling model based on meteorological analogs was applied to continental-scale GCM-based seasonal forecasts and high quality historical site observations to generate a set of downscaled precipitation hindcasts at 160 sites in the South Murray Darling Basin region of Australia. Large-scale fields from the Predictive Ocean–Atmosphere Model for Australia (POAMA) 1.5b GCM-based seasonal prediction system are used for analog selection. Correlation analysis indicates modest levels of predictability in the target region for the selected predictor fields. A single best-match analog was found using model sea level pressure, meridional wind, and rainfall fields, with the procedure applied to 3-month-long reforecasts, initialized on the first day of each month from 1980 to 2006, for each model day of 10 ensemble members. Assessment of the total accumulated rainfall and number of rainy days in the 3-month reforecasts shows that the downscaling procedure corrects the local climate variability with no mean effect on predictive skill, resulting in a smaller magnitude error. The amount of total rainfall and number of rain days in the downscaled output is significantly improved over the direct GCM output as measured by the difference in median and tercile thresholds between station observations and downscaled rainfall. Confidence in the downscaled output is enhanced by strong consistency between the large-scale mean of the downscaled and direct GCM precipitation.

scholarly journals Using Different Formulations of the Transformed Eulerian Mean Equations and Eliassen–Palm Diagnostics in General Circulation Models

2010 ◽  
Vol 67 (6) ◽  
pp. 1983-1995 ◽  
Author(s):  
Steven C. Hardiman ◽  
David G. Andrews ◽  
Andy A. White ◽  
Neal Butchart ◽  
Ian Edmond
Keyword(s):  
General Circulation Model ◽  
General Circulation ◽  
Circulation Model ◽  
General Circulation Models ◽  
Primitive Equations ◽  
Model Output ◽  
Vertical Coordinate ◽  
Order Of Magnitude ◽  
Transformed Eulerian Mean

Abstract Transformed Eulerian mean (TEM) equations and Eliassen–Palm (EP) flux diagnostics are presented for the general nonhydrostatic, fully compressible, deep atmosphere formulation of the primitive equations in spherical geometric coordinates. The TEM equations are applied to a general circulation model (GCM) based on these general primitive equations. It is demonstrated that a naive application in this model of the widely used approximations to the EP diagnostics, valid for the hydrostatic primitive equations using log-pressure as a vertical coordinate and presented, for example, by Andrews et al. in 1987 can lead to misleading features in these diagnostics. These features can be of the same order of magnitude as the diagnostics themselves throughout the winter stratosphere. Similar conclusions are found to hold for “downward control” calculations. The reasons are traced to the change of vertical coordinate from geometric height to log-pressure. Implications for the modeling community, including comparison of model output with that from reanalysis products available only on pressure surfaces, are discussed.

scholarly journals The MJO in a Coarse-Resolution GCM with a Stochastic Multicloud Parameterization

2015 ◽  
Vol 72 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Qiang Deng ◽  
Boualem Khouider ◽  
Andrew J. Majda
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Deterministic Model ◽  
Weather Prediction ◽  
General Circulation Models ◽  
Radar Data ◽  
Equation Model ◽  
Death Process ◽  
Relevant Parameter ◽  
Convective Systems

Abstract The representation of the Madden–Julian oscillation (MJO) is still a challenge for numerical weather prediction and general circulation models (GCMs) because of the inadequate treatment of convection and the associated interactions across scales by the underlying cumulus parameterizations. One new promising direction is the use of the stochastic multicloud model (SMCM) that has been designed specifically to capture the missing variability due to unresolved processes of convection and their impact on the large-scale flow. The SMCM specifically models the area fractions of the three cloud types (congestus, deep, and stratiform) that characterize organized convective systems on all scales. The SMCM captures the stochastic behavior of these three cloud types via a judiciously constructed Markov birth–death process using a particle interacting lattice model. The SMCM has been successfully applied for convectively coupled waves in a simplified primitive equation model and validated against radar data of tropical precipitation. In this work, the authors use for the first time the SMCM in a GCM. The authors build on previous work of coupling the High-Order Methods Modeling Environment (HOMME) NCAR GCM to a simple multicloud model. The authors tested the new SMCM-HOMME model in the parameter regime considered previously and found that the stochastic model drastically improves the results of the deterministic model. Clear MJO-like structures with many realistic features from nature are reproduced by SMCM-HOMME in the physically relevant parameter regime including wave trains of MJOs that organize intermittently in time. Also one of the caveats of the deterministic simulation of requiring a doubling of the moisture background is not required anymore.

Evaluating the Consistency between Statistically Downscaled and Global Dynamical Model Climate Change Projections

2008 ◽  
Vol 21 (22) ◽  
pp. 6052-6059 ◽  
Author(s):  
B. Timbal ◽  
P. Hope ◽  
S. Charles
Keyword(s):  
Global Warming ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Specific Humidity ◽  
Model Output ◽  
Winter Rainfall ◽  
Direct Model ◽  
The Impact

Abstract The consistency between rainfall projections obtained from direct climate model output and statistical downscaling is evaluated. Results are averaged across an area large enough to overcome the difference in spatial scale between these two types of projections and thus make the comparison meaningful. Undertaking the comparison using a suite of state-of-the-art coupled climate models for two forcing scenarios presents a unique opportunity to test whether statistical linkages established between large-scale predictors and local rainfall under current climate remain valid in future climatic conditions. The study focuses on the southwest corner of Western Australia, a region that has experienced recent winter rainfall declines and for which climate models project, with great consistency, further winter rainfall reductions due to global warming. Results show that as a first approximation the magnitude of the modeled rainfall decline in this region is linearly related to the model global warming (a reduction of about 9% per degree), thus linking future rainfall declines to future emission paths. Two statistical downscaling techniques are used to investigate the influence of the choice of technique on projection consistency. In addition, one of the techniques was assessed using different large-scale forcings, to investigate the impact of large-scale predictor selection. Downscaled and direct model projections are consistent across the large number of models and two scenarios considered; that is, there is no tendency for either to be biased; and only a small hint that large rainfall declines are reduced in downscaled projections. Among the two techniques, a nonhomogeneous hidden Markov model provides greater consistency with climate models than an analog approach. Differences were due to the choice of the optimal combination of predictors. Thus statistically downscaled projections require careful choice of large-scale predictors in order to be consistent with physically based rainfall projections. In particular it was noted that a relative humidity moisture predictor, rather than specific humidity, was needed for downscaled projections to be consistent with direct model output projections.

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