کاربرد وایازش خطی چندمتغیره در پس‌پردازش مقادیر بارش حاصل از مدل regcm4

پیش‌بینی‌‌های فصلی بارش در تصمیم گیریهای مدیریتی در بخشهای کشاورزی و منابع آب و نیز ارزیابی و پایش پدیده‌‌های فرین نظیرخشکسالی و سیل اهمیت ویژه‌‌ای دارد. در این تحقیق بهمنظور ارزیابی کارایی مدل regcm4در پیش‌بینی بارش ماهانه، فصلی و سالانه در چند ایستگاه منتخب شمال غرب کشور و همچنین بررسی میزان دقت پیش‌بینیها بعد از پس‌پردازش روی برون‌داد مدل، مدل در دوره 30 ساله 1982 تا 2011 با طرحواره بارش کوkuoو گام زمانی 100 ثانیه اجرا شد. دادههای موردنیاز برای اجرای مدل از مرکز ictpبا قالب netcdfشامل دادههای دوباره تحلیلشده وضعجوّی (nnrp1، دمای سطح دریا sstو دادههای سطح زمین surfaceشامل داده‌هایgtopoglccو glzbدریافت شد. با در نظر داشتن آمار موجود در مقطع زمانی پیش‌گفته، چهار ایستگاه همدیدی ارومیه، تبریز، اردبیل و خوی مورد بررسی قرار گرفت. نتایج روشن ساخت که برون‌داد خام (بدون پس‌پردازش آماری) بارش مدل regcm4، در همه ماهها به‌جز ماه‌های گرم سال، دارای اُریبی تر و بیش‌برآوردی است. اجرای پس‌پردازش آماری با استفاده از همبستگی چندمتغیره خطی (و در بعضی موارد دومتغیره خطی) بر برون‌داد مدل پیش‌گفته منجر به تعدیل مقادیر بارش و همخوانی بیشتر آن با مقادیر دیده‌بانی شده، شد به‌طوری‌که اُریبی و خطای نسبی در پیش‌بینیهای ماهانه، فصلی و سالانه در 75% موارد کاهش یافت و در همه ایستگاه های تحت بررسی، به‌جز ارومیه، پس‌پردازش به روش پیش‌گفته، در همه مقاطع زمانی موجب بهبود برون‌داد مدل دینامیکی regcmشد.

Application of multiple linear regression for postprocessing of the RegCM4 model outputs in forecasting precipitation

The seasonal forecasts of precipitation play a major role in agricultural and water resource management and also in monitoring extreme events such as drought and flood. The Earth Systems Physics (ESP) group of the Abdus Salam International Centre for Theoretical Physics (ICTP) maintains and distributes a stateof thescience regional climate model called the ICTP Regional Climate Model (RegCM), which has been successfully used in different regions of the world for a diverse range of climaterelated studies. This study was performed with two aims: (1) to evaluate the performance of the RegCM4 dynamic model in forecasting monthly, seasonal and annual precipitation in four selected stations in the northwest of Iran, i.e. Tabriz, Ardabil, Khouy and Urumia; and (2) to examine the accuracy of a stepwise regression technique for post processing of the outputs of the model for a 30year period from 1982 to 2011. In order to run the RegCM4, the required observed weather data of the study stations were collected from the Iran Meteorological Organization (IRIMO) archive, while the rest of the data were collected from the ICTP database including three sets of the weather data: NCEP/NCAR Reanalysis Product version 1 (NNRP1) with a 6hour time step and a horizontal resolution of 2.5°×2.5° on the reanalysis data from the National Center of Environmental Prediction (NCEP) of the United States, Sea Surface Temperature (SST) of the Optimum Interpolation Sea Surface Temperature (OISST) type, retrieved from the National Oceanic and Atmospheric Administration (NOAA) database and surface data (SURFACE), which were consisted of three topographic features: Global Topographic (GTOPO), vegetation or land use Global Land Cover Characterization (GLCC), and soil type Global Zobler (GLZB) data, with a horizontal resolution of 30×30 seconds from the United States Geological Survey, for the period 1982–2011. To determine a suitable rainfall scheme, the normal year of 2009 was chosen for running the model using different schemes. Accordingly, the Kuo scheme with a minimum bias compared to the observed precipitation amounts in the entire 36 synoptic stations of the region was selected as the best scheme. The time step was set to 100 seconds, with a spatial resolution of 30×30 km2, and the number of grid points were 152 in longitude (iy) and 168 in latitude (ix) during the study period. The geographical area center was placed at 30.5° N and 50° E. Nine significant variables (excluding, total precipitation; tpr) having the highest correlation with precipitation were determined as q2m, t2m, ps, v1000, v500, u1000, u500, omega1000, and omega500. For postprocessing of the outputs of the model, the multiple linear regressions (MLR) approach was used. Except for the warm months, the output of the RegCM4 showed a wet bias, and overestimation. Applying the multivariate linear regression equation (and sometimes twovariables) to the output of the model led to a better agreement between the observed and simulated values of precipitation, such that in 75% of the cases, the bias and relative error decreased for the monthly, seasonal and annual forecasts. At all stations, except for Urumia, performing the post processing improved the accuracy of the RegCM4 output at all time scales. Further scrutiny is recommended for explaining the variations among the stations.