ارزیابی اثر نمایه چند متغیره انسو بر بارش زمستانه خراسان شمالی

از آنجا که کشاورزی در خراسان شمالی از اهمیت و جایگاه ویژه ای برخوردار است. پیش بینی فصلی بارش می تواند تاثیر بسیار مهمی در تابستان و پاییز در پیش بینی بارش زمستانه استان خراسان شمالی با به کار بردن نرم افزارcpt(climate predictability tool) می‌باشد. بدین منظور از مدل تحلیل هم بستگی متعارف cca (canonical correlation analysis) و رگرسیون خطی چند گانه در نرم افزار cpt استفاده شده است. سری های زمانی فصلی نمایه mei به عنوان پیشگوکننده و بارش زمستانه دوره 19862008 هفده ایستگاه خراسان شمالی به عنوان پیشگو شونده در نظر گرفته شده است. در روش تحلیل هم بستگی متعارف به منظور کاهش تعداد متغیرهای پیشگوکننده از روش متعامد تجربی eof (empirical orthogonal function) استفاده شد و 5 مولفه اصلی که 89% از کل واریانس مجموعه داده ها را شرح می دهند، انتخاب گردید. نتایج به دست آمده از دو مدل مذکور نشان می دهند که بین بارش زمستان و نمایه mei در فصل بهار همبستگی ضعیفی وجود دارد و نمایه پاییز mei همبستگی قوی تری با بارش زمستان خراسان شمالی دارد و بیشترین همبستگی از ایستگاه تازه قلعه و کمترین همبستگی از ایستگاه منگلی به دست آمد. منفی بودن همبستگی نشان دهنده این است که با افزایش نمایه mei بارش زمستان کاهش می یابد و برعکس. بارش ها در تمامی ایستگاه ها نسبت به سال 2008 که خشکسالی به وقوع پیوسته بود، افزایش داشتند که مشاهدات نیز این افزایش بارش را تایید می کنند. اختلاف بین داده های خروجی مدل ها و بارش مشاهده شده نشان دهنده این است که فقط با تعیین فاز mei نمی توان بی هنجاری بارش زمستان را از نظر علامت و  شدت پیش بینی نمود.

Evaluate the MEI Effect on Winter Precipitation In Northern Khorasan

IntroductionNorth Khorasan province is in north east of IRAN and has different climates. West side has cold semiarid, east side has cold arid and north part has cold semiwet. Agriculture has special situation so seasonal prediction is very important. If precipitation predictions show above normal or below normal, agriculture should take a right decision for type of farming, dry farming or water farming. They are many studies about climate prediction in IRAN some of them are about effects of sea surface temperature of Atlantic, Pacific and Indian Ocean on IRAN precipitation and the others are about the effects of teleconnections on climate prediction. These studies concluded that there is a relationship between changes of SST and precipitation fluctuations.Present study use CCA and MLR model of CPT to predict winter precipitation and evaluate the effect of seasonal MEI on North Khorasan winter precipitation.Materials and MethodsPrecipitation DataAt this study the monthly precipitation data (Jan, Feb, and March) of 17 synoptic and rain gauge stations of North Khorasan during 19862008 is used and then the mean of winter precipitation is calculated.MEI dataThe MEI data (time series 12 month) is used of NOAA data bank from 1986 to 2008 and then the seasonal MEI data is calculated in excel and SPSS. MEI is computed on the six main observed variables over the tropical Pacific. These six variables are: sealevel pressure (P), zonal (U) and meridional (V) components of the surface wind, sea surface temperature (S), surface air temperature (A), and total cloudiness fraction of the sky (C).Reconstruction and Normal test dataThe Ratio Method is used to construct and complete the monthly precipitation data and then normal test data by using JMP4 software is done. The results show that yearly, winter and autumn data are normal.CCA ModelVarious dynamical and statistical models are used to predict seasonal and climate prediction. The most popular method and model that is used for seasonal prediction is CCA. IRI[1] released the Climate Predictability Tool (CPT) that provides a Windows package for constructing a seasonal climate forecast model, performing model validation, and producing forecasts given updated data at 2002. Although the software is specifically tailored for these applications, it can be used in more general settings to perform canonical correlation analysis (CCA), principal components regression (PCR), or multiple linear regressions (MLR) on any data, and for any application. For this study we use the last version of CPT (11.10) CCA and MLR model.Two data sets are required by CPT. the first data set contains the X variables here are seasonal MEI data from 1986 to 2008 and these variables are sometimes called predictors, independent variables. The Y variables are sometimes called predictands, dependent variables here are winter precipitation (19862008). At first we choose spring MEI data as predictor and winter precipitation as predictant and by using CCA model we study the effects of spring MEI on winter precipitation and then choose summer and autumn MEI data and again implement the model. Model assumes a linear relationship between the predictor, x, and the predictand, y:                                     y=β0+β1x                                                 (1)Where β0 and β1 are regression constant and regression coefficient or the “slope”.Correlation coefficient is a widely used measure of the strength of linear association between the predictor and the predictand. Where sx and sy are the standard deviations of x and y, respectively. The numerator in Eq. (2) is related to the covariance by a factor of n, and will be positive if positive anomalies in both the predictor and the predictand tend to occur in corresponding cases, and will be negative if opposite anomalies tend to occur. At this study negative correlations refer to winter precipitation will decrease by increasing MEI.The model expressesThe value of a predictand variable as a linear function of one or more predictor variables and an error term.           (3)Here  is regression constant,  is coefficient on the kth predictor, k is total number of predictors,  is predictand in year i and  is error term.Model ValidationAfter implementing the model by choosing cross validation method, Forecast performance scores and graphics can be obtained for the crossvalidated forecasts. The performance window for an individual series provides a variety of forecast performance scores divided into those based on continuous measures, and those based on measures in which the observations, and in some cases the forecasts as well, are divided into three categories. The continuous forecast measures calculated are: Pearson correlation, spearman correlation, Mean squared error, Root mean squared error (RMSE) … and the categorical forecast measures are: Hit score, ROC area. Jajarm and Mokhaberat stations have the minimum RMSE that means their forecasts accuracy is high. Hit rate versus false alarm rate plots are also provided (ROC curve), which indicate how well the models forecast winter precipitation. The perfect prediction system would have a hit rate of 1.0 and a false alarm rate of 0.0. Winter precipitation forecasts with summer and autumn MEI have better results.ResultsThe first five EOF modes can explain 89% of total precipitation variance. The model forecast winter precipitation for every station from beginning of duration (1986) so every station has time series of observations and hindcast. Most stations have positive correlation between winter precipitation and spring MEI and negative correlation between winter precipitation and summer and autumn MEI. The maximum correlation belongs to Langar winter precipitation and autumn MEI in CCA Model and Tazehgale in MLR Model and the minimum correlation is for Bolgan.ConclusionWith regard to effect of teleconnection on precipitation, the relationship between MEI and precipitation is evaluated. The MEI time series are predictors and winter precipitation are predictants at CCA and MLR model. The results of this study are very important for North Khorasan agriculture. Comparison of model forecasts and precipitation observations of 2009 show us that we haven’t skillful forecast of seasonal precipitation by only use MEI and other teleconnections effect on seasonal precipitation, too. 1. IRI, INTERNATIONAL RESEARCH INSTITUE FOR CLIMATE PREDICTION