Simulation of historical temperatures using a multi-site, multivariate block resampling algorithm with perturbation

Abstract

Stochastic weather generators have evolved as tools for creating long time series of synthetic meteorological data at a site for risk assessments in hydrologic and agricultural applications. Recently, their use has been extended as downscaling tools for climate change impact assessments. Non‐parametric weather generators, which typically use a K‐nearest neighbour (K‐NN) resampling approach, require no statistical assumptions about probability distributions of variables and can be easily applied for multi‐site use. Two characteristics of traditional K‐NN models result from resampling daily values: (1) temporal correlation structure of daily temperatures may be lost, and (2) no values less than or exceeding historical observations can be simulated. Temporal correlation in simulated temperature data is important for hydrologic applications. Temperature is a major driver of many processes within the hydrologic cycle (for example, evaporation, snow melt, etc.) that may affect flood levels. As such, a new methodology for simulation of climate data using the K‐NN approach is presented (named KnnCAD Version 4). A block resampling scheme is introduced along with perturbation of the reshuffled daily temperature data to create 675 years of synthetic historical daily temperatures for the Upper Thames River basin in Ontario, Canada. The updated KnnCAD model is shown to adequately reproduce observed monthly temperature characteristics as well as temporal and spatial correlations while simulating reasonable values which can exceed the range of observations. Copyright © 2012 John Wiley & Sons, Ltd.

Improving Stochastic Modelling of Daily Rainfall Using the ENSO Index: Model Development and Application in Chile

“…This model was implemented as WGEN (Weather GENerator) by Richardson and Wright (1984) [1], which used a simple Markov Chain for precipitation occurrence, a gamma distribution for simulation of rainfall amounts, and an autoregressive model for the remaining variables. A number of subsequent WGs, such as WXGEN [8], CLIGEN [9,10], LARS-WG [11][12][13], ClimGen [14], WeaGETS [15,16], Met and Roll [17], MOFRBC [18,19], WeatherMan [20], MarkSim [21], AAFC-WG [22,23], WM2 [24], KnnCAD [25][26][27], and the WG used by the UK Met Office (UKCP09) [28,29], all share the basic principles of stochastic simulation presented in WGEN. These WGs are station-scale generators, with time scales that range from daily (or even hourly in the case of rainfall) to annual, daily resolution being the most common.…”

Uncertainty in Precipitation Projection under Changing Climate Conditions: A Regional Case Study

“…RCP 2.6 represents lower carbon emission scenario, RCP 4.5 and RCP 6.0 represent intermediate carbon emission scenarios and RCP 8.5 assumes high and unabated carbon emission by the end of 2100. Six Downscaling methods applied in this study are as follows: 1) bias corrected spatial disaggregation (BCSD) [11] [15], 2) bias correction constructed analogues with quantile mapping reordering (BCCAQ) [16], 3) delta change method coupled with a non-parametric K-nearest neighbor weather generator [17], 4) delta change method coupled with maximum entropy based weather generator [18], 5) non-parametric statistical downscaling model based on the kernel regression [19], and 6) beta regression based statistical downscaling model [20]. BCSD and BCAAQ were successfully applied across Canada in the past, however these methods cannot explicitly capture changes in daily extremes [16] where other four downscaling methods can capture changes in daily extremes and can produce extreme values outside of the historical boundaries [18]- [21].…”

Global Climate Model Selection for Analysis of Uncertainty in Climate Change Impact Assessments of Hydro-Climatic Extremes

“…The scaled climate series using CFM are then used with KNN-CADv4, a multi-site, multivariate weather generator developed by King et al [31]. The main purpose of this step is to explore plausible temporal variability within the historical record through the process of bootstrap resampling, while generating a long climate series using the future scaled baseline period that will then be used for streamflow-frequency analysis.…”

“…For this analysis, a total of 150 years of simulated weather is generated. Using this method, the spatial correlation among meteorological stations, as well as the temporal correlation of the generated variables will be preserved, as 10-day block periods are used for resampling among meteorological stations [31].…”