scholarly journals An Evaluation of Statistical Downscaling Techniques for Simulating Daily Rainfall Occurrences in the Upper Ping River Basin

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 63
Author(s):  
Sirikanya Cheevaprasert ◽  
Rajeshwar Mehrotra ◽  
Sansarith Thianpopirug ◽  
Nutchanart Sriwongsitanon
Keyword(s):  
River Basin ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Low Frequency ◽  
Daily Rainfall ◽  
General Circulation Models ◽  
Research Area ◽  
Single Site ◽  
Future Research ◽  
Rainfall Occurrences

This study presents an exhaustive evaluation of the performance of three statistical downscaling techniques for generating daily rainfall occurrences at 22 rainfall stations in the upper Ping river basin (UPRB), Thailand. The three downscaling techniques considered are the modified Markov model (MMM), a stochastic model, and two variants of regression models, statistical models, one with single relationship for all days of the year (RegressionYrly) and the other with individual relationships for each of the 366 days (Regression366). A stepwise regression is applied to identify the significant atmospheric (ATM) variables to be used as predictors in the downscaling models. Aggregated wetness state indicators (WIs), representing the recent past wetness state for the previous 30, 90 or 365 days, are also considered as additional potential predictors since they have been effectively used to represent the low-frequency variability in the downscaled sequences. Grouping of ATM and all possible combinations of WI is used to form eight predictor sets comprising ATM, ATM-WI30, ATM-WI90, ATM-WI365, ATM-WI30&90, ATM-WI30&365, ATM-WI90&365 and ATM-WI30&90&365. These eight predictor sets were used to run the three downscaling techniques to create 24 combination cases. These cases were first applied at each station individually (single site simulation) and thereafter collectively at all sites (multisite simulations) following multisite downscaling models leading to 48 combination cases in total that were run and evaluated. The downscaling models were calibrated using atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis database and validated using representative General Circulation Models (GCM) data. Identification of meaningful predictors to be used in downscaling, calibration and setting up of downscaling models, running all 48 possible predictor combinations and a thorough evaluation of results required considerable efforts and knowledge of the research area. The validation results show that the use of WIs remarkably improves the accuracy of downscaling models in terms of simulation of standard deviations of annual, monthly and seasonal wet days. By comparing the overall performance of the three downscaling techniques keeping common sets of predictors, MMM provides the best results of the simulated wet and dry spells as well as the standard deviation of monthly, seasonal and annual wet days. These findings are consistent across both single site and multisite simulations. Overall, the MMM multisite model with ATM and wetness indicators provides the best results. Upon evaluating the combinations of ATM and sets of wetness indicators, ATM-WI30&90 and ATM-WI30&365 were found to perform well during calibration in reproducing the overall rainfall occurrence statistics while ATM-WI30&365 was found to significantly improve the accuracy of monthly wet spells over the region. However, these models perform poorly during validation at annual time scale. The use of multi-dimension bias correction approaches is recommended for future research.

scholarly journals Comparison of Statistical Downscaling Methods for Monthly Total Precipitation: Case Study for the Paute River Basin in Southern Ecuador

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
L. Campozano ◽  
D. Tenelanda ◽  
E. Sanchez ◽  
E. Samaniego ◽  
J. Feyen
Keyword(s):  
River Basin ◽  
Statistical Downscaling ◽  
General Circulation ◽  
General Circulation Models ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Local Conditions ◽  
Southern Ecuador ◽  
Monthly Total Precipitation ◽  
Precipitation Estimates

Downscaling improves considerably the results of General Circulation Models (GCMs). However, little information is available on the performance of downscaling methods in the Andean mountain region. The paper presents the downscaling of monthly precipitation estimates of the NCEP/NCAR reanalysis 1 applying the statistical downscaling model (SDSM), artificial neural networks (ANNs), and the least squares support vector machines (LS-SVM) approach. Downscaled monthly precipitation estimates after bias and variance correction were compared to the median and variance of the 30-year observations of 5 climate stations in the Paute River basin in southern Ecuador, one of Ecuador’s main river basins. A preliminary comparison revealed that both artificial intelligence methods, ANN and LS-SVM, performed equally. Results disclosed that ANN and LS-SVM methods depict, in general, better skills in comparison to SDSM. However, in some months, SDSM estimates matched the median and variance of the observed monthly precipitation depths better. Since synoptic variables do not always present local conditions, particularly in the period going from September to December, it is recommended for future studies to refine estimates of downscaling, for example, by combining dynamic and statistical methods, or to select sets of synoptic predictors for specific months or seasons.

scholarly journals Statistical Downscaling for Rainfall Forecasts Using Modified Constructed Analog Method in Thailand

2017 ◽  
Vol 2017 ◽  
pp. 1-24 ◽  
Author(s):  
Patchalai Anuchaivong ◽  
Dusadee Sukawat ◽  
Anirut Luadsong
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Historical Data ◽  
Weather Forecasting ◽  
Daily Rainfall ◽  
General Circulation Models ◽  
Rainfall Simulation ◽  
Percentage Error ◽  
Analog Method ◽  
Simulation Accuracy

The simulations of rainfall from historical data were created in this study by using statistical downscaling. Statistical downscaling techniques are based on a relationship between the variables that are solved by the General Circulation Models (GCMs) and the observed predictions. The Modified Constructed Analog Method (MCAM) is a technique in downscaling estimation, suitable for rainfall simulation accuracy using weather forecasting. In this research, the MCAM was used to calculate the Euclidean distance to obtain the number of analog days. Afterwards, a linear combination of 30 analog days is created with simulated rainfall data which are determined by the corresponding 5 days from the adjusted weights of the appropriate forecast day. This method is used to forecast the daily rainfall and was received from the Thai Meteorological Department (TMD) from the period during 1979 to 2010 at thirty stations. The experiment involved the use of rainfall forecast data that was combined with the historical data during the rainy season in 2010. The result showed that the MCAM gave the correlation value of 0.8 resulting in a reduced percentage error of 13.66%. The MCAM gave the value of 1094.10 mm which was the closest value to the observed precipitation of 1119.53 mm.

scholarly journals Evaluation of General Circulation Models over the Upper Ouémé River Basin in the Republic of Benin

Hydrology ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 11 ◽  
Author(s):  
André Attogouinon ◽  
Agnidé E. Lawin ◽  
Jean-François Deliège
Keyword(s):  
Climate Change ◽  
River Basin ◽  
General Circulation ◽  
Climate Model ◽  
Coupled Model ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
General Circulation Models ◽  
Future Climate Change ◽  
Data Set

This study assessed the performance of eight general circulation models (GCMs) implemented in the upper Ouémé River basin in Benin Republic (West Africa) during the Fifth Assessment Report on Climate Change. Historical rainfall simulations of the climate model of Rossby Regional Centre (RCA4) driven by eight Coupled Model Intercomparison Project (CMIP5) GCMs over a 55-year period (1951 to 2005) are evaluated using the observational data set. Apart from daily rainfall, other rainfall parameters calculated from observed and simulated rainfall were compared. U-test and other statistical criteria (R2, MBE, MAE, RMSE and standard of standard deviations) were used. According to the results, the simulations correctly reproduce the interannual variability of precipitation in the upper Ouémé River basin. However, the models tend to produce drizzle. Especially, the overestimation of April, May and November rains not only explains the overestimation of seasonal and annual cumulative rainfall but also the early onset of the rainy season and its late withdrawal. However, we noted that this overestimation magnitude varies from one model to another. As for extreme rainfall indices, the models reproduced them poorly. The CanESM2, CNRM-CM5 and EC-EARTH models perform well for daily rainfall. A trade-off is formulated to select the common MPI-ESM-LR, GFDL-ESM2M, NorESM1-M and CanESM2 models for different rainfall parameters for the reliable projection of rainfall in the area. However, the MPI-ESM-LR model is a valuable tool for studying future climate change.

An applied mathematics perspective on stochastic modelling for climate

2008 ◽  
Vol 366 (1875) ◽  
pp. 2427-2453 ◽  
Author(s):  
Andrew J Majda ◽  
Christian Franzke ◽  
Boualem Khouider
Keyword(s):  
Stochastic Model ◽  
General Circulation ◽  
Applied Mathematics ◽  
Low Frequency ◽  
Lattice Models ◽  
Stochastic Modelling ◽  
General Circulation Models ◽  
Teleconnection Patterns ◽  
Slowing Down ◽  
Low Dimensional

Systematic strategies from applied mathematics for stochastic modelling in climate are reviewed here. One of the topics discussed is the stochastic modelling of mid-latitude low-frequency variability through a few teleconnection patterns, including the central role and physical mechanisms responsible for multiplicative noise. A new low-dimensional stochastic model is developed here, which mimics key features of atmospheric general circulation models, to test the fidelity of stochastic mode reduction procedures. The second topic discussed here is the systematic design of stochastic lattice models to capture irregular and highly intermittent features that are not resolved by a deterministic parametrization. A recent applied mathematics design principle for stochastic column modelling with intermittency is illustrated in an idealized setting for deep tropical convection; the practical effect of this stochastic model in both slowing down convectively coupled waves and increasing their fluctuations is presented here.

scholarly journals Weights for general circulation models from CMIP3/CMIP5 in a statistical downscaling framework and the impact on future Mediterranean precipitation

2019 ◽  
Vol 39 (8) ◽  
pp. 3639-3654 ◽  
Author(s):  
Irena Kaspar‐Ott ◽  
Elke Hertig ◽  
Severin Kaspar ◽  
Felix Pollinger ◽  
Christoph Ring ◽  
...  
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
General Circulation Models ◽  
The Impact

Efficient estimation and ensemble generation in climate modelling

2007 ◽  
Vol 365 (1857) ◽  
pp. 2077-2088 ◽  
Author(s):  
J.D Annan ◽  
J.C Hargreaves
Keyword(s):  
Monte Carlo ◽  
Particle Filtering ◽  
General Circulation ◽  
Climate Models ◽  
General Circulation Models ◽  
Monte Carlo Sampling ◽  
Efficient Estimation ◽  
Future Research ◽  
Great Promise ◽  
Climate Modelling

In this paper, we review progress towards efficiently estimating parameters in climate models. Since the general problem is inherently intractable, a range of approximations and heuristic methods have been proposed. Simple Monte Carlo sampling methods, although easy to implement and very flexible, are rather inefficient, making implementation possible only in the very simplest models. More sophisticated methods based on random walks and gradient-descent methods can provide more efficient solutions, but it is often unclear how to extract probabilistic information from such methods and the computational costs are still generally too high for their application to state-of-the-art general circulation models (GCMs). The ensemble Kalman filter is an efficient Monte Carlo approximation which is optimal for linear problems, but we show here how its accuracy can degrade in nonlinear applications. Methods based on particle filtering may provide a solution to this problem but have yet to be studied in any detail in the realm of climate models. Statistical emulators show great promise for future research and their computational speed would eliminate much of the need for efficient sampling techniques. However, emulation of a full GCM has yet to be achieved and the construction of such represents a substantial computational task in itself.

scholarly journals Mathematics applied to the climate system: outstanding challenges and recent progress

2013 ◽  
Vol 371 (1991) ◽  
pp. 20120518 ◽  
Author(s):  
Paul D. Williams ◽  
Michael J. P. Cullen ◽  
Michael K. Davey ◽  
John M. Huthnance
Keyword(s):  
General Circulation ◽  
Recent Progress ◽  
Initial Conditions ◽  
Global Climate ◽  
Climate Science ◽  
General Circulation Models ◽  
Climate System ◽  
Future Research ◽  
Theme Issue ◽  
Physically Based

The societal need for reliable climate predictions and a proper assessment of their uncertainties is pressing. Uncertainties arise not only from initial conditions and forcing scenarios, but also from model formulation. Here, we identify and document three broad classes of problems, each representing what we regard to be an outstanding challenge in the area of mathematics applied to the climate system. First, there is the problem of the development and evaluation of simple physically based models of the global climate. Second, there is the problem of the development and evaluation of the components of complex models such as general circulation models. Third, there is the problem of the development and evaluation of appropriate statistical frameworks. We discuss these problems in turn, emphasizing the recent progress made by the papers presented in this Theme Issue. Many pressing challenges in climate science require closer collaboration between climate scientists, mathematicians and statisticians. We hope the papers contained in this Theme Issue will act as inspiration for such collaborations and for setting future research directions.

scholarly journals Late-glacial to late-Holocene shifts in global precipitation δ<sup>18</sup>O

2015 ◽  
Vol 11 (10) ◽  
pp. 1375-1393 ◽  
Author(s):  
S. Jasechko ◽  
A. Lechler ◽  
F. S. R. Pausata ◽  
P. J. Fawcett ◽  
T. Gleeson ◽  
...  
Keyword(s):  
General Circulation ◽  
Late Holocene ◽  
Late Glacial ◽  
General Circulation Models ◽  
Future Research ◽  
Quaternary Climate ◽  
Simulated Precipitation ◽  
The Tropics ◽  
Global Precipitation ◽  
Precipitation Δ18o

Abstract. Reconstructions of Quaternary climate are often based on the isotopic content of paleo-precipitation preserved in proxy records. While many paleo-precipitation isotope records are available, few studies have synthesized these dispersed records to explore spatial patterns of late-glacial precipitation δ18O. Here we present a synthesis of 86 globally distributed groundwater (n = 59), cave calcite (n = 15) and ice core (n = 12) isotope records spanning the late-glacial (defined as ~ 50 000 to ~ 20 000 years ago) to the late-Holocene (within the past ~ 5000 years). We show that precipitation δ18O changes from the late-glacial to the late-Holocene range from −7.1 ‰ (δ18Olate-Holocene > δ18Olate-glacial) to +1.7 ‰ (δ18Olate-glacial > δ18Olate-Holocene), with the majority (77 %) of records having lower late-glacial δ18O than late-Holocene δ18O values. High-magnitude, negative precipitation δ18O shifts are common at high latitudes, high altitudes and continental interiors (δ18Olate-Holocene > δ18Olate-glacial by more than 3 ‰). Conversely, low-magnitude, positive precipitation δ18O shifts are concentrated along tropical and subtropical coasts (δ18Olate-glacial > δ18Olate-Holocene by less than 2 ‰). Broad, global patterns of late-glacial to late-Holocene precipitation δ18O shifts suggest that stronger-than-modern isotopic distillation of air masses prevailed during the late-glacial, likely impacted by larger global temperature differences between the tropics and the poles. Further, to test how well general circulation models reproduce global precipitation δ18O shifts, we compiled simulated precipitation δ18O shifts from five isotope-enabled general circulation models simulated under recent and last glacial maximum climate states. Climate simulations generally show better inter-model and model-measurement agreement in temperate regions than in the tropics, highlighting a need for further research to better understand how inter-model spread in convective rainout, seawater δ18O and glacial topography parameterizations impact simulated precipitation δ18O. Future research on paleo-precipitation δ18O records can use the global maps of measured and simulated late-glacial precipitation isotope compositions to target and prioritize field sites.

scholarly journals An Assessment of GCM Skill in Simulating Persistence across Multiple Time Scales

2011 ◽  
Vol 24 (14) ◽  
pp. 3609-3623 ◽  
Author(s):  
Fiona Johnson ◽  
Seth Westra ◽  
Ashish Sharma ◽  
Andrew J. Pitman
Keyword(s):  
Interannual Variability ◽  
General Circulation ◽  
Large Scale ◽  
Low Frequency ◽  
General Circulation Models ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Max Planck ◽  
Reservoir Storage ◽  
Low Frequency Variability

Abstract Climate change impact studies for water resource applications, such as the development of projections of reservoir yields or the assessment of likely frequency and amplitude of drought under a future climate, require that the year-to-year persistence in a range of hydrological variables such as catchment average rainfall be properly represented. This persistence is often attributable to low-frequency variability in the global sea surface temperature (SST) field and other large-scale climate variables through a complex sequence of teleconnections. To evaluate the capacity of general circulation models (GCMs) to accurately represent this low-frequency variability, a set of wavelet-based skill measures has been developed to compare GCM performance in representing interannual variability with the observed global SST data, as well as to assess the extent to which this variability is imparted in precipitation and surface pressure anomaly fields. A validation of the derived skill measures is performed using GCM precipitation as an input in a reservoir storage context, with the accuracy of reservoir storage estimates shown to be improved by using GCM outputs that correctly represent the observed low-frequency variability. Significant differences in the performance of different GCMs is demonstrated, suggesting that judicious selection of models is required if the climate impact assessment is sensitive to low-frequency variability. The two GCMs that were found to exhibit the most appropriate representation of global low-frequency variability for individual variables assessed were the Istituto Nazionale di Geofisica e Vulcanologia (INGV) ECHAM4 and L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4); when considering all three variables, the Max Planck Institute (MPI) ECHAM5 performed well. Importantly, models that represented interannual variability well for SST also performed well for the other two variables, while models that performed poorly for SST also had consistently low skill across the remaining variables.

scholarly journals Multidecadal to Centennial Variability of the AMOC: HadCM3 and a Perturbed Physics Ensemble

2013 ◽  
Vol 26 (7) ◽  
pp. 2390-2407 ◽  
Author(s):  
Laura Jackson ◽  
Michael Vellinga
Keyword(s):  
General Circulation ◽  
Low Frequency ◽  
Coupled Model ◽  
General Circulation Models ◽  
Meridional Overturning Circulation ◽  
The Arctic ◽  
Equatorial Atlantic ◽  
Small Ensemble ◽  
Background Climate ◽  
Perturbed Physics

Abstract Multidecadal to centennial variability of the Atlantic meridional overturning circulation (AMOC) is investigated in a multi-thousand-year simulation of the third version of the Hadley Centre Coupled Model (HadCM3) and in an ensemble of general circulation models (GCMs) based on HadCM3 with perturbed physics. Large changes in the AMOC in the standard HadCM3 are strongly related to salinity anomalies in the deep-water formation regions, with anomalies arriving via two pathways. The first is from a coupled feedback in the equatorial Atlantic Ocean, described previously by Vellinga and Wu, and the second is from variability in the Arctic Ocean, possibly driven by stochastic sea level pressure. The low-frequency variability of the AMOC in HadCM3 is well predicted from salinity anomalies from these two pathways. The sensitivity of these processes to model physics is investigated using a small ensemble based on HadCM3 where parameters relating to physical processes are varied. The AMOC responds consistently to the salinity anomalies in the ensemble members. However, 1) the timing of the response depends on the background climate state and 2) some ensemble members have significantly larger AMOC and salinity variability than in standard HadCM3 simulations. In this small ensemble, the presence and strength of multidecadal to centennial AMOC variability is associated with the variability of salinity exported from the Arctic, with little multidecadal to centennial variability of either in the coldest members. This demonstrates how the background climate state can alter the frequency and strength of AMOC variability and is a first step toward understanding how AMOC variability differs within a multimodel context.

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