ارزیابی عملکرد داده‌های بازتحلیل ‌شده Era-Interim در تخمین بارش روزانه و ماهانه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه مهندسی آب/ دانشگاه بین المللی امام خمینی قزوین

2 استادیار گروه مهندسی آب دانشگاه بین المللی امام خمینی قزوین

چکیده

تخمین صحیح بارش  در شبیه­سازی سیلاب، پایش خشکسالی و مدیریت منابع آب امری ضروری و مهم بشمار می­آید. در حال حاضر بخش­های عمده­ای از جهان، فاقد ایستگاه­های اندازه­گیری بارش زمینی هستند و حتی در صورت وجود از نظر زمانی و مکانی دارای پوشش مناسبی نیستند و همین مساله مطالعات منابع آب را با چالشی اساسی روبرو می­نماید. یکی از مهم­ترین منابع بارشی موجود، پایگاه­های بارشی مدل مبنا می­باشد که با تلفیق فن­آوری­های ماهواره­ای، مدلهای سطح زمین (LSMs) و مدل­های عمومی گردش جو (GCMs) داده­های شبکه­بندی شده با توان تفکیک مکانی و زمانی بالا را برای تمامی نقاط دنیا ارائه می­نماید. این گزینه می‌تواند کمبود اطلاعات ایستگاه‌های زمینی را به ویژه در مناطقی که از این حیث با کمبود مواجه هستند تا حدود زیادی برطرف سازد. در پژوهش حاضر به ارزیابی عملکرد یکی از مهم­ترین پایگاه­های بارشی  مدل مبنا به نام پایگاه ECMWF در گام­های زمانی روزانه و ماهانه در سطح حوضه آبریز سفیدرود (در بازه زمانی 2000 تا 2008) پرداخته شده است. همچنین برای ارزیابی هرچه بهتر پایگاه مذکور از داده­های بارش مبتنی بر سنجش از دور TRMM نیز استفاده گردید. نتایج حاصل از ارزیابی عملکرد پایگاه بارش ECMWF در سطح این حوضه در دو مقیاس زمانی روزانه و ماهانه حاکی از آن است که این منبع دارای همبستگی بالایی با ایستگاه­های زمینی به ویژه در بخش­های جنوبی، مرکزی و غربی حوضه است. به عنوان مثال در هر دو گام زمانی روزانه و ماهانه، همبستگی بین متوسط داده­های بارشی این منبع با داده­های بارش زمینی به ترتیب در حدود 83/0 و 94/0 برآورد گردید در حالیکه در صورت استفاده از پایگاه TRMM مقادیر مذکور به ترتیب معادل 32/0 و 57/0 بدست آمد. برخلاف پایگاه­های بارشی بازتحلیل شده، یکی از نقاط ضعف پایگاه­های بارشی همچون TRMM، تخمین ضخامت ابر و میزان آب قابل بارش توسط آن، تنها بر اساس تکنیک­های مبتنی بر سنجش از دور می­باشد. همچنین از نظر آماره­های طبقه­بندی، پایگاه بارش ECMWF در هر دو گام زمانی روزانه و ماهانه با دارا بودن مقادیر کم شاخص FAR (گزارش­های اشتباه)، مقادیر بالای شاخص Accuracy (صحت پیش­بینی­های درست) و نیز مقدار بالا در تشخیص روزهای بارانی (POD) دارای عملکرد بسیار مناسبی می­باشد. از آنجائی که حوضه آبریز سفیدرود با توجه به وسعت زیاد دارای تنوع اقلیمی، توپوگرافیکی و پوشش گیاهی متفاوتی است، نتایج بدست آمده در آن می­تواند راهنمای مناسبی برای استفاده در حوضه­های مشابه مدنظر قرار گیرد. لذا در حوضه­های فاقد آمار که امکان دسترسی به داده­های زمینی برای ارزیابی عملکرد پایگاه­های بارش مختلف میسر نمی­باشد، استفاده از این منبع بارشی ارزشمند می­تواند سودمند باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Assessing the Accuracy of European Center for Medium Range Weather Forecasts (ECMWF) Reanalysis Datasets for Estimation of Daily and Monthly Precipitation

نویسندگان [English]

  • Asghar Azizian 1
  • hadi ramezani etedali 2
1 Assistant Professor in Water Engineering Department/ Imam Khomeini International University
2 Assistant Professor in Water Engineering Dept/ IKIU University, Qazvin, Iran
چکیده [English]

An accurate estimation of precipitation is important and necessary for flood simulation, drought monitoring and water resources management. Currently, most parts of the world are suffering from the lack of the rain gauge observations and the spatial coverage of ground observations aren’t enough and continues. One of the most important precipitation datasets is the model-based precipitation datasets, by which the satellite techniques, the general circulation models (GCMs) and the land surface models (LSMs) are integrated to provide high temporal and high resolution datasets for all parts of the world. This datasets can compensate the lack of adequate ground observation gauges or can be considered as an alternative for ground observations, especially in ungauged regions. In this research the accuracy of the most important reanalysis datasets, called ECMWF, for estimation of daily and monthly precipitation over the SefidRood watershed for the time period of 2000-2008 was investigated. In addition, for better assessment of the proposed precipitation datasets, TRMM dataset was used. Findings on daily and monthly time scales, show that the correlation coefficient (CC) between observed and ECMWF dataset is so remarkable, especially in south, central and west parts of the study area. For instance, the CC values of the average precipitation of ECMWF data versus gauge datasets in both daily and monthly time steps were estimated to be about 0.83, 0.94, respectively, while the CC values for TRMM dataset versus gauge datasets were estimated to be 0.32 and 0.57, respectively. In contrast to reanalysed datasets, one of the most important weakness of the precipitation datasets such as TRMM is that they estimate the rainfall only based on the cloud thickness and its available water. Moreover, according to the categorical verification statistics in both time spans, ECMWF due to having low value of false alarm ratio (FAR) and high values for accuracy and probability of detection (POD) yields acceptable results over the SefidRood watershed. SefidRood watershed is a large scale region and contains different climate and topographical conditions and hence the results of this research can be used as an appropriate guidance for other similar areas. Based on the findings in this study it’s highly recommended for using this rainfall dataset as one of the best alternatives for ground observations, especially in data sparse regions that accessing to ground datasets is so hard or almost impossible.  

کلیدواژه‌ها [English]

  • Reanalysis datasets
  • ECMWF
  • Rainfall estimation
  • remote sensing
  • SefidRood Catchment
AghaKouchak, A, Nasrollahi, N. and Habibi, E. (2009). Accounting for uncertainties of the TRMM satellite estimates. Remote sensing. 1(3): 606-619.
Ashouri, H., P. Nguyen, A. Thorstensen, K.-l. Hsu, S. Sorooshian and D. Braithwaite. (2016). Assessing the Efficacy of High-Resolution Satellite-Based PERSIANN-CDR Precipitation Product in Simulating Streamflow. Journal of Hydrometeorology. 17(7): 2061-2076.
Bajracharya, S.R., Shrestha, M.S. and Shrestha, A.B. (2014). Assessment of high-resolution satellite rainfall estimation products in a streamflow model for flood prediction in the Baghmati basin, Nepal. Journal of Flood Risk Management. 10: 5-16.
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T. and Vitart, F.  (2015). ERA-Interim/Land: a global land surface reanalysis data set, Hydrology and Earth System Science. 19: 389-407.
De Leeuw, J., J. Methven., and M. Blackburn. (2015). Evaluation of ERA-Interim reanalysis precipitation products using England and Wales observations. Quarterly Journal of the Royal Meteorological Society. 141(688): 798-806.
Dee, D., S. Uppala, A. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. Balmaseda, G. Balsamo., and P. Bauer. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society. 137: 553–597.
Dembele, M., and S. J. Zwart. (2016). Evaluation and comparison of satellite-based rainfall products in Burkina Faso, West Africa. International Journal of Remote Sensing. 37(17): 3995-4014.
Dezfuli, D., Hosseini-Moghari, S.M., and Ebrahimi, K. (2016). Comparison of TRMM-3B42 V7 and PERSIANN Satellites Precipitation Data with Ground-Based Data (Case study: Gorganrood Basin, Iran). J. Sci. & Technol. Agric. & Natur. Resour. Water and Soil Sci., 20(6): 10-22 (In Farsi).
Duan, Z., Liu, J., Tuo, Y., Chiogna, C., and Disse, M. (2016). Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales. Science of the Total Environment. 573: 1536-1553.
Ebert, E.E., Janowiak, J.E. and Kidd, C. (2007). Comparison of near-real-time precipitation estimates from satellite observations and numerical models. American Meteorological Society. 88: 47-64.
Ghajarnia, N., Liaghat, A., and Arasteh, P. D. (2015). Comparison and evaluation of high resolution precipitation estimation products in Urmia BasinIran. Atmospheric Research.158: 50-65.
Greene, J. and Morrissey, M. (2000). Validation and Uncertainty Analysis of Satellite Rainfall Algorithms. The Professional Geographer. 52: 247–258.
Hsieh, H.H., Cheng S. J., Liou, J.Y., Chou, S.C., and Siao, B. R. (2006). Characterization of spatially distributed summer daily rainfall. Journal of Chinese Agricultural Engineering. 52:  47–55.
Hargreaves, G.H. and Z.A. Samani. (1985). Reference crop evapotranspiration from temperature. Transaction of ASCE. 1(2): 96-99.
Harris, I., Jones, P.D., Osborn, T.J., Lister, D.H. (2013). Updated high-resolution grids of monthly climatic observations the CRU TS3.10 Dataset. International Journal of Climatology. 34: 623–642.
Harris, I.C., and Jones, P.D. (2015). CRU TS3.23: Climatic Research Unit (CRU) Time-Series (TS) Version 3.23 of High Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901 - Dec. 2014). Centre for Environmental Data Analysis.
Hong, Y., K. Hsu, H. Moradkhani. and S. Sorooshian. (2006). Uncertainty Quantification of Satellite Precipitation Estimation and Monte Carlo Assessment of the Error Propagation into Hydrologic Response. Water Resources Research. 42: W08421.
Hughes, D.A. (2006). Comparison of satellite rainfall data with observations from gauging station networks. J. Hydrol. 327: 399–410.
Javanmard, S., A. Yatagai, M. Nodzu, J. BodaghJamali., and H. Kawamoto. (2010). Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM_3B42 over Iran. Advances in Geosciences. 25: 119-125.
Dirks, K. N., Hay, J. E., Stow C. D., and Harris, D., (1998). High resolution studies of rainfall on Norfolk island part II: interpolation of rainfall data. Journal of Hydrology. 208(3-4): 187–193.
Katiraie-Boroujerdy, P.-S., N. Nasrollahi, K.-l. Hsu., and S. Sorooshian (2013). Evaluation of satellite-based precipitation estimation over Iran. Journal of arid environments. 97: 205-219.
Khodadost, S., Saghafian, B., and Moazami, S. (2017). Comprehensive evaluation of 3-hourly TRMM and half-hourly GPM-IMERG satellite precipitation products. International Journal of Remote Sensing. 38(2): 558-571.
Kidd, C., Dawkins, E. and Huffman, G. (2013). Comparison of precipitation derived from the ECMWF operational forecast model and satellite precipitation datasets. American Meteorological Society. 14: 1463-1482.
Krogh, S. A., J. W. Pomeroy., and J. McPhee. (2015). Physically Based Mountain Hydrological Modeling Using Reanalysis Data in Patagonia. Journal of Hydrometeorology 16(1): 172-193.
Kumar, D., Pandey, A., Sharma, N. and Flugel, W.A. (2015). Evaluation of TRMM-Precipitation with Rain-Gauge Observation Using Hydrological Model J2000. Journal of Hydrologic Engineering. E5015007.
Li, Z., Yang, D. and Hong, Y. (2013). Multi-scale evaluation of high-resolution multi-sensor blended global precipitation products over the Yangtze River. Journal of Hydrology. 500: 157-169.
Lo Conti, F., Hsu, k. L., Noto, V. L. and Sorooshian, S. (2014). Evaluation and comparison of satellite precipitation estimations with reference to a local area in the Mediterranean Sea. Atmospheric Research. 138: 189-204.
Miri, M., Raziei, T., and Rahimi, M. (2016). Evaluation and statistically comparison of TRMM and GPCC datasets with observed precipitation in Iran. Journal of the Earth and Space Physics. 42 (3): 657-672 (In Farsi).
Moazami, S., S. Golian, Y. Hong, C. Sheng., and M. R. Kavianpour. (2016). Comprehensive evaluation of four high-resolution satellite precipitation products under diverse climate conditions in Iran. Hydrological Sciences Journal. 61(2): 420-440.
Moreau, E., P. Bauer. and F. Chevallier. (2003). Variational retrieval of rain profiles from space borne passive microwave radiance observations. J. Geophysics. Res. 108: 4521.
Morice, C.P., Kennedy, J.J., Rayner, N.A., and Jones, P. (2012). quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. Journal of Geophysical Research. 117: 1-22.
Raziei, T., Sotodeh, F. (2017). Investigation of the accuracy of the European Center for Medium Range Weather Forecast (ECMWF) in forecasting observed precipitation in different climates of Iran. Journal of the Earth and Space Physics. 43(1): 133-147.
Ly, S., Charles, C., and Degré, A. (2011). Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Outré and Ambleve catchments, Belgium. Hydrology and Earth System Sciences. 15(7): 2259–2274.
Sahlu, D., Nikolopoulos, E.I., Moges, S.A., Anagnostou, E.N. and Hailu, D. (2016). First evaluation of the day-1 IMERG over the upper Blue Nile Basin. J. Hydrometeorol.
17:  2875–2882.
Sharifi, E., Steinacker, R., and Saghafian, B. (2016). Performance Evaluation of the Newest Generation    Satellite-Based     Precipitation Product with high Spatio-Temporal Resolution. 6th Iranian national water resources management conference, Kurdistan University.
Steiner, M., T. Bell, Y. Zhang. And E. Wood. (2003). Comparison of Two Methods for Estimating the Sampling-Related Uncertainty of Satellite Rainfall Averages Based on a Large Radar Dataset. Journal of Climate. 16: 3759–3778.
Su F., Hong Y. and Lettenmaier D.P. (2008). Evaluation of TRMM Multisatellite precipitation analysis (TMPA) and its utility in hydrologic prediction in the La Plata Basin. J Hydrometeorol. 9: 622–640.
 
 
Tan, M. L., Ibrahim, A. L., Duan, ZH., Cracknell, A. P. and Chaplot, V. (2015). Evaluation of six high-resolution satellite and ground-based precipitation products over Malaysia. Remote Sensing. 7: 1504-1528.
Tianobao, Z.H., and Congbin, F. (2006). Comparison of products from ERA-40, NCEP-2 and CRU with station data for summer precipitation over China. Advance in Atmospheric Science. 23(4):593-604.
Tong, K., Su, F., Yang, D., Hao, Z. (2014). Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan Plateau. Journal of Hydrology. 519: 432-437.
Worqlul, A.W., Collick, A. S., Tilahun, S. A., Langan, S., Rientjes, T. H. and Steenhuis, T. S. (2015). Comparing TRMM 3B42, CFSR and ground-based rainfall estimates as input for hydrological models, in data scarce regions: the Upper Blue Nile Basin, Ethiopia.  Hydrology and Earth System Sciences. 12(2): 2081–2112.
Zhao, T., and A. Yatagai. (2014). Evaluation of TRMM 3B42 product using a new gauge‐based analysis of daily precipitation over China. International Journal of Climatology. 34(8): 2749-2762.
Zhao, T., and Fu, C. (2006). Comparison of products from ERA-40, NCEP-2, and CRU with station data for summer precipitation over China. Advances in Atmospheric Sciences. 23: 593-604.