تعیین مقیاس زمانی مناسب پیش‎بینی‎ های کوتاه و میان‎ مدت مدلهای عددی هواشناسی جهانی در بخشهای مختلف ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته رشته مهندسی منابع آب، گروه مهندسی آب دانشگاه بین المللی امام خمینی، قزوین

2 عضو هیئت علمی گروه مهندسی آب دانشگاه بین المللی امام خمینی قزوین

3 دانشیار گروه مهندسی آب، دانشگاه بین المللی امام خمینی، قزوین

چکیده

مدل‎های پیش‎بینی بارش نقش اساسی در عملکرد هر چه بهتر سامانه‌های پیش‎بینی هواشناسی و سیلاب ایفا می‌کنند. در مطالعه حاضر، عملکرد پیش‎بینی‎های پنج مدل عددی هواشناسی موجود در پایگاه TIGGE به منظور بررسی دقت پیش‎بینی‎ها طی گام زمانی‎های 1 تا 10 روزه در اقلیم‌های مختلف کشور ایران (در محل 38 ایستگاه سینوپتیک) طی بازه زمانی 2014 تا 2018 مورد ارزیابی و اصلاح اریبی قرار گرفتند. بررسی شاخص‎های آماری و جدولی حاکی از کاهش دقت پیش‎بینی‎ها با افزایش گام زمانی می‌باشد. طبق نتایج بدست آمده عمده مدلهای هواشناسی به ویژه دو مدل ECMWF و UKMO حداکثر تا افق زمانی 3 روزه از همبستگی مناسبی با داده‌های زمینی برخوردار بوده و در عین حال نیز دارای خطای کمتری (در تخمین مقدار بارش و پیش‌بینی روزهای بارانی) می‌باشند. با اصلاح اریبی داده‌های خام پیش‌بینی عملکرد مدلهای عددی آب و هوا به طور قابل توجهی افزایش یافت، به طوری که در گام زمانی 10 روزه در مدلهای ECMWF، JMA و KMA به ترتیب بیش از 70، 65 و 73 درصد از مقدار شاخص RMSE کاهش یافت. پس از اصلاح داده‌های بارش، عملکرد عمده مدلهای عددی به غیر از JMA حتی تا گام زمانی 7 روزه نیز در اکثر اقلیمهای کشور منجربه نتایج قابل قبولی گردید. مدل JMA در اقلیم‌های مرطوب که شامل مناطق غربی و شمالی کشور است، به دلیل ساختار مدل آشفتگی موجود در این مدل دارای اریبی زیادی بوده و نتایج غیرقابل اعتمادی ارائه نموده است.

کلیدواژه‌ها


عنوان مقاله [English]

Determining the Appropriate Temporal Resolution of Short and mid-terms of Global Precipitation Forecasting Systems over Iran

نویسندگان [English]

  • Setareh Amini 1
  • Asghar Azizian 2
  • Peyman Arasteh 3
1 MSc of water resources engineering, Water engineering Department, IKIU University, Qazvin,
2 Assistant Professor in Water Engineering Dept./ Imam Khomeini International University
3 Assistance Professor, Water Engineering Department, IKIU University, Qazvin
چکیده [English]

Precipitation forecasting models play important role in the performance of flood and meteorological warning systems. In this research, the efficiency of five numerical weather prediction (NWP) models, which exist in the TIGGE database, are assessed to determine the best temporal resolution of forecasted datasets at distinct climate regions of Iran, during 2014-2018. Findings show that by increasing the lead time the accuracy of all forecasts decreases significantly. Moreover, most of the NWP models, especially the ECMWF and UKMO perform well, based on correlation coefficient (CC) and RMSE metrics, up to lead time of 3 days. Also, results indicate that by removing biases from the raw forecast datasets, the performance of all NWP models in different lead times increases considerably. After bias correction, the RMSE values of ECMWF, JMA, and KMA models in the lead time of 10 days reduces about 70, 65, and 73%, respectively, and, except for JMA, all NWP models perform well in most climate regions. The JMA model in humid climate zones (north and west parts of Iran) has a high level of bias and leads to unreliable forecasts.

کلیدواژه‌ها [English]

  • Prediction
  • Flood warning
  • Rainfall
  • Remote-Sensing
Aminyavari S, Saghafian B, and Delavar M (2018) Evaluation of TIGGE ensemble forecasts of precipitation in distinct climate regions in Iran. Advances in Atmospheric Sciences 35(4):457–468
 
Brocca L, Filippucci P, Hahn S, Ciabatta L, Massari C, Camici S, Schüller L, Bojkov B, Wagner W (2019) SM2RAIN-ASCAT (2007-2018): Global daily satellite rainfall data from ASCAT soil moisture observations. Earth System Science Data 11:1583–1601
Brocca L, Ciabatta L, Massari C, Moramarco T, Hahn S, Hasenauer S, Kidd R, Dorigo W, Wagner W, Levizzani V (2014) Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data.Journal of Geophysical Research: Atmospheres 119:5128–5141
Buizza R, Houtekamer P L, Toth Z, Pellerin G, Wei M, Zhu Y (2005) Acomparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Monthly Weather Review 133(5):1076
Cai C, Wang J, Li Z (2018)  Improving TIGGE precipitation forecasts using an SVR ensemble approach in the Huaihe River Basin. Advance in Meteorology 2018:1-15
Casati B, Wilson L, Stephenson D, Nurmi P, Ghelli A, Pocernich M, Damrath U, Ebert E, Browne B, Masonh S (2008) Forecast verification: Current status and future directions. Meteorolical Application 15(1):3–18
Cloke H.L, Pappenberger F (2009) Ensemble flood forecasting: A review. Journal of Hydrology 37(4):613-626
Domroes M, Kaviani M, Schaefer D (1998) An analysis of regional and intra-annual precipitation variability over Iran using multivariate statistical methods. Theoretical and Applied Climatology (61):151–159
Duan M, Ma J, Wang P (2012) Preliminary comparison of the CMA, ECMWF, NCEP, and JMA ensemble prediction systems. Acta Meteorologica Sinica 26(1):26–40
Ebert  E E, Manton M J, Arkin  P A, Allam R J,  Holpin C E and Gruber A (1996) Results from the GPCP Algorithm Intercomparison Programme. Bulletin of the American Meteorological Society 77(12):2875–2887
Gupta  R, Bhattarai R, and Mishra A (2019) Development of Climate Data Bias Corrector (CDBC) tool and its application over the agro-ecological zones of India. Journal of Water 11(5):1102
Hamill T M (2012) Verification of TIGGE multimodel and ECMWF reforecast-calibrated probabilistic precipitation forecasts over the contiguous United States. Monthly Weather Review 140(7):2232–2252
Jabbari  A, Bae D H (2020) Improving ensemble forecasting using total least squares and lead-time dependent bias correction. Journal of Atmosphere 11(3):300
Javanmard M, Delavar M, and Morid S (2016) Evaluation and uncertainty analysis of the results of the global weather forecast models to apply in flood warning systems (Case study: Karoon River basin, Iran). Iran-Water Resources Research 14(3):1-14 (In Persian)
Katiraie Boroujerdy  P, Naeini R M, Asanjan A A, Chavoshian A, Hsu K, Sorooshian S (2020) Bias correction of satellite-based precipitation estimations using quantile mapping approach in different climate regions of Iran. Remot Sensing 12(13):2102
Kay J K, kim H M (2014) Characteristics of initial perturbations in the ensemble prediction system of the Korea Meteorological Administration. America Meteorological Society 29(3):563-581
Liu L, Gao C, Zhu Q, Xu Y (2019) Evaluation of TIGGE daily accumulated precipitation forecasts over the Qu River Basin, China. Journal of Meteorological Research 33(4):747-764
Louvet S, Sultan B, Kamsu-Tamo  P H, Ndiaye O (2016) Evaluation of TIGGE precipitation forecasts over West Africa at intraseasonal timescale. Climate Dynamics 47:31-47
Rahimi J, Ebrahimpour M, and Khalili A (2013) Spatial changes of extended De Martonne climatic zones affected by climate change in Iran. Theoretical and Applied Climatology 112(3-4):409-418
Raziei T, Mofidi A, Santos JA, Bordi I (2012) Spatial patterns and regimes of daily precipitation in Iran in relation to large-scale atmospheric circulation. International Journal of Climatology 32(8):1226–1237
Saedi A, Saghafian B, Moazami S, Aminyavari S (2019) Performance evaluation of sub-daily ensemble precipitation forecasts. Meteorological Application 27(1):1872
Shapiro M, Thorpe A (2004) THORPEX international science plan. 2:51
Su  X, Yuan H L, Zhu  Y J, Luo Y, and Wang Y ( 2014)  Evaluation of TIGGE ensemble predictions of Northern Hemisphere summer precipitation during 2008–2012. Journal of Geophysical Research 119(12):7292–7310
Shrestha D L, Robertson D E, Wang Q J (2013) Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose. Hydrolgic Earth System Sciences 17:1913–1931
Saedi A, Saghafian B, Moazami S (2020) Uncertainty of flood forecasts via ensemble precipitation forecasts of seven NWP Models for Spring 2019 Golestan Flood. Iran-Water Resources Research 16(1):347-359 (In Persian)
Kolacian R, Saghafian B, Moazami S (2021) Evaluation of post-processing and bias correction of monthly precipitation and temperature forecasts in Karun Basin. Iran-Water Resources Research (In Persian) (In Press)
Toth Z, Kalnay E (1997) Ensemble forecasting at NCEP and the breeding method. Monthly Weather Review 125(12):3297–3319
Wang J, Wang H J, Hong Y (2016) Comparison of satellite-estimated and model-forecasted rainfall data during a deadly debris-flow event in Zhouqu, Northwest China. Atmospheric and Oceanic Science Letters 9(2):139–145
Wilks D S (1995) Statistical methods in the atmospheric sciences: An introduction. International Geophysics Series  59
Shayeghi A, Azizian A, and Brocca  L (2020) Reliability of reanalysis and remotely sensed precipitation products for hydrological simulation over the SefidRood River Basin, Iran. Hydrological Sciences Journal 65(2):296-310