ارزیابی و کاربرد انواع مجموعه داده‌های دیدبانی (زمینی و ماهواره‌ای) بارش بر روی ایران

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه علم و صنعت ایران

2 دانشگاه آزاد اسلامی واحد تهران شمال

چکیده

هدف این مقاله معرفی، ارزیابی و کاربرد انواع مجموعه داده‌های بارش دیدبانی زمینی و ماهواره‌ای معتبر است که بر روی ایران داده مستمر و به روز دارند. تولید و کاربرد مجموعه‌های بارش بر اساس داده‌های ماهواره‌ای به دلیل تفکیک مکانی و زمانی بالا و همچنین پوشش مکانی تقریبا کامل جهانی در سال‌های اخیر به سرعت رو به گسترش است. در این مقاله  توزیع مکانی هفت مجموعه بارش جهانی دیدبانی بر روی ایران با داده‌های باران‌سنجی در 228 پیکسل 25/0 درجه طول و عرض جغرافیایی که حداقل شامل سه باران‌سنج هستند مقایسه و بررسی شده است. مقایسه این نتایج نشان می‌دهند که مجموعه‌ها اختلاف زیادی در مقدار بارش سالانه بر روی پهنه ایران نشان می‌دهد (mm 180-260). این اختلاف در کرانه دریای خزر بالغ بر80 درصد میانگین بارش سالانه (حدود 300 میلی‌متر در سال) می‌رسد. داده‌های ماهواره‌ای روی منطقه سواحل دریای خزر و مناطق پر ارتفاع کوه‌های زاگرس واقع در جنوب غرب ایران بارش را با دقت کمتری نسبت به سایر نقاط برآورد می‌کنند. مجموعه‌های بارش زمینی بیشترین سهم از بارش سالانه را برای فصل بهار و سایر مجموعه‌ها بیشترین سهم را برای بارش زمستانی نشان می‌دهند. مقایسه بارش ماهانه، فصلی و سالانه مجموعه‌ها با داده های باران‌سنچی نشان می‌دهد مجموعه‌های ماهواره‌ای که با داده‌های باران‌سنجی تصحیح شده‌اند نتایج بهتری حتی نسبت به مجموعه‌های بارش زمینی دارند. مجموعه‌های ماهواره‌ای حال حاضر نیز بیش از سایرین بارش را کم برآورد می‌کنند.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation and Application of Different Observational (Land and Satellite) Datasets Over Iran

نویسندگان [English]

  • Ali Chavoshian 1
  • P.S. Katiraie-Boroujerdy 2
1 Civil Engineering, Faculty of Engineering, Iran University of Science and technology, Tehran, Iran
2 Tehran North Branch, Islamic Azad University
چکیده [English]

Introduction: Precipitation has an important role not only in the variety of scientific applications including climate change, climate simulations, weather modeling, and forecasting but also in decision making such as water management, hydrology, agriculture, drought, and crisis management. Different temporal resolutions and coverages of data are required for this and other applications. For example, long term meteorological data are needed for monitoring the climate variability and trends and for climate simulation assessments in local and global scales. Also, present data are used to assimilate into forecast models to improve the predictions. Historical and present precipitation data are the main requirements to monitor and predict droughts which help to early warning system and water management decisions in a country. The recent rainfall data are also the primary input of hydrological models to flood forecast in a basin. The accurate estimation of precipitation amount is vital for these applications.
Materials and Methods: However, rainfall is discontinuous and varies greatly both in time and space which makes it parallel with difficulties in the actual measurements. The two main sources of observational precipitation datasets are ground-based rain gauge measurements and space-based remote sensing satellite estimations each one with its own limitations and strengths. Historically, rain-gauge measurements have been considered as the “ground truth”, but they have mostly limited to land surface, the measurements are sparse or nonexistent in some regions like deserts or high topographic areas. Although rain gauges measure rainfall directly, their data are only representative for a limited spatial extent and may be subjected to some errors caused by local effects such as topography or wind-induced undercatch. An alternative approach which can provide relatively homogenous estimates with complete coverage over most of the globe is based on using satellite observations. Therefore, satellite data are capable to estimate precipitation over the oceans and over remote areas where few or no ground measurements are available. The satellite-based precipitation estimates are derived mainly from visible, infrared (IR) and passive microwave (PMW) radiances which are measured by satellites. Although the visible channels cannot be used at night, the IR data are available in fine spatial resolution (about 3-4 km) with high temporal sampling (15 min) which are provided by geosynchronous satellites. Another source of data is PMW that can be used to estimate rainfall more directly. Low-altitude polar-orbiting satellites serve to measure the PMW data. Although, the microwave sensors can penetrate into the clouds and provide more information about the cloud characteristics such as water vapor, cloud particles, and structure of hydrometeors, but at the expense of temporal sampling. In recent years, different algorithms have been developed using the combination of the IR, Visible (VIS) and PWM observations to provide more accurate rainfall estimations in high spatial and temporal resolutions. To demonstrate the similarities and differences between the spatial distribution of different satellite-based and gauge-based precipitation datasets over Iran we compared seven different datasets. For comparisons all datasets are regridded to 0.25-degree latitude longitude spatial resolutions. Then the spatial distribution of the mean and relative standard deviations of annual precipitation of these datasets have been calculated. We also used more than 2000 rain gauges to evaluate the selected datasets. To reduce error only 228 pixels, include at least 3 rain gauges are used for comparisons of spatial average of monthly, seasonal and annual precipitation of gauge and seven datasets.
Results and Discussion: The results showed a large amount of differences in annual precipitation between seven selected datasets. The most differences pronounce in wet areas in the north of Alborz Mountain, in the semi-arid and arid regions of the central desert and in the high mountainous areas of the southern Zagros. The reason for these differences is that not only satellite-based but gauge-based datasets have large uncertainties estimating areal precipitation in such high topographic areas. The satellite products are prone to some errors arising from not fully understood physical process, sampling error and parameter estimation. Therefore, verification of precipitation datasets is one of the most important parts of the data development and refinements. In this paper, the spatial distribution of seven different global-observational precipitation datasets over Iran are compared for the period 2003-2007. At first all datasets were regridded to 0.25° spatial resolutions using linear interpolation method. Then, the mean and relative standard deviation of annual precipitation of the datasets were calculated to analyze the spatial discrepancies between datasets. The areal average of annual precipitation and the contribution of seasonal precipitation were calculated for comparison purposes. The results showed that areal average of annual and seasonal precipitation for 228 selected pixels for PERSIANN-CDR, TRMM, and GPCP which are satellite-based and gauge adjusted datasets are more similar to the rain gauge data than other datasets. The results for the above datasets are even better than CRU and APHRODITE which are gauge-based datasets.
Conclusion: The results showed that the satellite estimates are not capable to show the precipitation (detection and amount) over the coast of Caspian Sea and the high areas of the Zagros Mountain as well as other parts of the country. There are some useful recommendations for data users at the end of this paper. In fact, in this paper our spatial focus is on Iran and we introduced a web address which data users can access freely from one of the most popular and widely used satellite-based products in easy-to-use format only for Iran. The results show considerable differences between the datasets. The difference is about 0.8 times of mean annual precipitation (about 300 mm in a year) for the coast of Caspian Sea. The satellite-based estimations were less accurate over the coast of Caspian Sea and high mountainous area of the southwest of Zagros comparing to other parts of the country. While spring precipitation shows maximum contributions in annul precipitation for in-situ datasets, winter precipitation shows maximum contribution in annual precipitation for other datasets. The results showed that areal average of monthly, seasonal and annual precipitation over 228 selected pixels for PERIANN-CDR, TRMM and GPCP were consistent with rain gauge data. CMORPH and PERSIANN underestimate areal average of monthly and seasonal precipitation over the pixels.

کلیدواژه‌ها [English]

  • Evaluation
  • Iran
  • Remote sensing
  • Precipitation Datasets
  • Satellite-based Precipitation
1- Adler R., Sapiano M., Huffman G., Bolvin D., Gu G., Wang J., and Ferraro R. 2016. The new version 2.3 of the global precipitation climatology project (GPCP) monthly analysis product. University of Maryland, April.
2- Adler R., Huffman G., Chang A., Ferraro R., Xie P., Janowiak J., and Bolvin D. 2003. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). Journal of Hydrometeorology 4(6): 1147-1167.
3- Aonashi K., Awaka J., Hirose M., Kozu T., Kubota T., Liu G., and Takahashi N. 2009. GSMaP passive microwave precipitation retrieval algorithm: Algorithm description and validation. Journal of the Meteorological Society of Japan. Ser. II 87: 119-136.
4- Ashouri H., Hsu K.L., Sorooshian S., Braithwaite D. K., Knapp K.R., Cecil L.D., and Prat O.P. 2015. PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bulletin of the American Meteorological Society 96(1): 69-83.
5- Beck H.E., Van Dijk A.I.J.M., Levizzani V., Schellekens J., Miralles D.G., Martens B., and Roo A.D. 2017. MSWEP: 3-hourly 0.25 global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data. Hydrology and Earth System Sciences 21(1):589-615.
6- Ebert E.E. 2007. Methods for Verifying Satellite Precipitation Estimates. In: Levizzani V., Bauer P., Turk F.J. (eds) Measuring Precipitation From Space. Advances In Global Change Research, vol 28. Springer, Dordrecht.
7- Funk C., Peterson P., Landsfeld M., Pedreros D., Verdin J., Shukla S., and Hoell A. 2015. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific data, 2, 150066.
8- Galindo, Francisco J, & Palacio, Juan. (1999). Estimating the instabilities of N correlated clocks: REAL OBSERVATORIO DE LA ARMADA (SPAIN).
9- Golian S., Moazami S., Kirstetter P.E., and Hong Y. 2015. Evaluating the performance of merged multi-satellite precipitation products over a complex terrain. Water Resources Management 29(13): 4885-4901.
10- Harris I.P.D.J., Jones P.D., Osborn T.J., and Lister D.H. 2014. Updated high‐resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset. International journal of climatology 34(3): 623-642.
11- Hong Y., Hsu K.L., Sorooshian S., and Gao X. 2004. Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. Journal of Applied Meteorology 43(12): 1834-1853.
12- Huffman G.J., Bolvin D.T., Nelkin E.J., Wolff D.B., Adler R.F., Gu G., and Stocker E.F. 2007. The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of hydrometeorology 8(1): 38-55.
13- Javanmard S., Yatagai A., Nodzu MI., BodaghJamali J., and Kawamoto H. 2010. Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM_3B42 over Iran. Advances in Geosciences 25: 119-125.
14- Joyce R.J., Janowiak J.E., Arkin P.A., and Xie P. 2004. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology 5(3): 487-503.
15- Katiraie-Boroujerdy P.S., Asanjan A.A., Hsu K.L., and Sorooshian S. 2017. Intercomparison of PERSIANN-CDR and TRMM-3b42v7 precipitation estimates at monthly and daily time scales. Atmospheric Research 193: 36-49.
16- Katiraie-Boroujerdy P.S., Ashouri H., Hsu K.L., and Sorooshian S. 2017. Trends of precipitation extreme indices over a subtropical semi-arid area using PERSIANN-CDR. Theoretical and Applied Climatology 130(1-2): 249-260.
17- Katiraie-Boroujerdy P.S., Nasrollahi N., Hsu K.L., and Sorooshian S. 2013. Evaluation of satellite-based precipitation estimation over Iran. Journal of Arid Environments 97: 205-219.
18- Kubota T., Shige S., Hashizume H., Aonashi K., Takahashi N., Seto S., and Nakagawa K. 2007. Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: Production and validation. IEEE Transactions on Geoscience and Remote Sensing 45(7): 2259-2275.
19- Moazami S., Golian S., Hong Y., Sheng C., and Kavianpour M.R. 2016. Comprehensive evaluation of four high-resolution satellite precipitation products under diverse climate conditions in Iran. Hydrological Sciences Journal 61(2): 420-440.
20- Moazami S., Golian S., Kavianpour M. R., and Hong Y. 2013. Comparison of PERSIANN and V7 TRMM Multi-satellite Precipitation Analysis (TMPA) products with rain gauge data over Iran. International Journal of Remote Sensing 34(22): 8156-8171.
21- Rudolf B., and Schneider U. 2005. Calculation of gridded precipitation data for the global land-surface using in-situ gauge observations. P. 231-247, Paper presented at the Proc. Second Workshop of the Int. Precipitation Working Group, October 2004, Monterey,Germany, EUMETSAT, ISBN 92-9110-070-6, ISSN 1727-432X, 231-247.
22- Schamm K., Ziese M., Becker A., Finger P., Meyer-Christoffer A., Schneider U., and Stender P. 2014. Global gridded precipitation over land: A description of the new GPCC First Guess Daily product. Earth System Science Data 6(1): 49-60.
23- Schneider U., Becker A., Finger P., Meyer-Christoffer A., Ziese M., and Rudolf B. 2014. GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theoretical and Applied Climatology 115(1-2): 15-40.
24- Sorooshian S., AghaKouchak A., Arkin P., Eylander J., Foufoula-Georgiou E., Harmon R., Skahill B. 2011. Advanced concepts on remote sensing of precipitation at multiple scales. Bulletin of the American Meteorological Society 92(10): 1353-1357.
25- Sorooshian S., Hsu K.L., Gao X., Gupta H.V., Imam B., and Braithwaite D. 2000. Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bulletin of the American Meteorological Society 81(9): 2035-2046.
26- Sun Q., Miao C., Duan Q., Ashouri H., Sorooshian S., and Hsu K.L. 2018. A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Reviews of Geophysics 56(1): 79-107.
27- Ushio T., Sasashige K., Kubota T., Shige S., Okamoto K., Aonashi K., and Kachi M. 2009. A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. Journal of the Meteorological Society of Japan. Ser. II, 87: 137-151.
28- Wilks D.S. 2006. Statistical Methods in the Atmospheric Sciences. Burlington, MA: Academic Press.
29- Willmott C.J., and Robeson S.M. 1995. Climatologically aided interpolation (CAI) of terrestrial air temperature. International Journal of Climatology 15(2): 221-229.
30- Xie P., and Arkin P.A. 1997. Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bulletin of the American Meteorological Society 78(11): 2539-2558.
31- Yatagai A., Kamiguchi K., Arakawa O., Hamada A., Yasutomi N., and Kitoh A. 2012. APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bulletin of the American Meteorological Society 93(9): 1401-1415.
32- Zhang X., Alexander L., Hegerl G.C., Jones P., Tank A.K., Peterson T.C., and Zwiers F.W. 2011. Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdisciplinary Reviews: Climate Change 2(6): 851-870.