Long-range precipitation forecasts using paleoclimate reconstructions in the western United States  

作　　者：Christopher Allen CARRIER Ajay KALRA Sajjad AHMAD 

机构地区：[1]Department of Civil and Environmental Engineering,University of Nevada,Las Vegas,4505 S.Maryland Parkway,Las Vegas,IVV89154-4015,USA [2]Development Review Division,Clark County Public Works,500 S.Grand Central Parkway,Las Vegas,NV 89155,USA [3]Department of Civil and Environmental Engineering,Southern Illinois University,1230 Lincoln Drive,Carbondale,IL 62901-6603,USA

出　　处：《Journal of Mountain Science》2016年第4期614-632,共19页山地科学学报（英文）

摘　　要：Long-range precipitation forecasts are useful when managing water supplies.Oceanicatmospheric oscillations have been shown to influence precipitation.Due to a longer cycle of some of the oscillations,a short instrumental record is a limitation in using them for long-range precipitation forecasts.The influence of oscillations over precipitation is observable within paleoclimate reconstructions;however,there have been no attempts to utilize these reconstructions in precipitation forecasting.A data-driven model,KStar,is used for obtaining long-range precipitation forecasts by extending the period of record through the use of reconstructions of oscillations.KStar is a nearest neighbor algorithm with an entropy-based distance function.Oceanic-atmospheric oscillation reconstructions include the El Nino-Southern Oscillation(ENSO),the Pacific Decadal Oscillation(PDO),the North Atlantic Oscillation(NAO),and the Atlantic Multi-decadal Oscillation(AMO).Precipitation is forecasted for 20 climate divisions in the western United States.A 10-year moving average is applied to aid in the identification of oscillation phases.A lead time approach is used to simulate a one-year forecast,with a 10-fold cross-validation technique to test the models.Reconstructions are used from 1658-1899,while the observed record is used from 1900-2007.The model is evaluated using mean absolute error(MAE),root mean squared error(RMSE),RMSE-observations standard deviation ratio(RSR),Pearson's correlation coefficient(R),NashSutcliffe coefficient of efficiency(NSE),and linear error in probability space(LEPS) skill score(SK).The role of individual and coupled oscillations is evaluated by dropping oscillations in the model.The results indicate 'good' precipitation estimates using the KStar model.This modeling technique is expected to be useful for long-term water resources planning and management.

关 键 词：Precipitation Oscillations Paleoclimate reconstruction Forecast KStar 

分 类 号：P457.6[天文地球—大气科学及气象学] P532