ارزیابی و مقایسه کارایی الگوریتم بخار آب قابل بارش جو MODIS و AMSR2 در سطح خشکی در نیمه غربی ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار /گروه مدیریت منابع آب، دانشکده مهندسی عمران، دانشگاه صنعتی خواجه نصیرالدین طوسی،

2 دانشجو دکتری /مدیریت منابع آب، دانشکده مهندسی عمران، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران.

چکیده

بخار آب قابل بارش (TPW) جو یکی از پارامترهای مهم در هواشناسی و هیدرولوژی می‌باشد. هدف مطالعه حاضر، ارزیابی کارایی الگوریتم‌های سنجش از دور برآورد TPW در سطح خشکی در محدوده امواج مایکروویو و اپتیکی در نیمه غربی ایران است. در این راستا، الگوریتم برآورد TPW در محدوده امواج مایکروویو با به کارگیری داده‌های رادیومتر اسکن کننده پیشرفته مایکروویو 2 (AMSR2) در روزهای ابری و فاقد ابر و محصول TPW مادون قرمز نزدیک اسپکترورادیومتر تصویربردار با قدرت تفکیک متوسط (MODIS) با نام MOD05 تنها در روزهای فاقد ابر مورد ارزیابی قرار گرفتند زیرا این محصول در روزهای ابری کارایی ندارد. نتایج ارزیابی با استفاده از داده‌های رادیوساند در روزهای فاقد ابر نشان دهنده دقت بالاتر محصول MOD05 نسبت به الگوریتم AMSR2 بود، طوری که ضریب تعیین (R2) در TPW حاصل از سنجنده AMSR2 و محصول MOD05 به ترتیب 0.516 و 0.650 و مقدار جذر میانگین مربعات خطا (RMSE) به ترتیب 5.129 و 4.542 میلی‌متر بود. در روزهای ابری مقدار R2 و RMSE مقادیر حاصل از سنجنده AMSR2 به ترتیب 0.284 و 7.367 میلی‌متر به دست آمدند. مجموعاً در روزهای ابری و فاقد ابر مقدار R2 و RMSE در مقادیر TPW سنجنده AMSR2 به ترتیب 0.420 و 5.976 میلی‌متر محاسبه گردیدند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation and Comparison of the Efficiency of the Total Precipitable Water Vapor Algorithm of MODIS and AMSR2 over Land in the Western Part of IRAN

نویسندگان [English]

  • M. Rahimzadegan, 1
  • M.H. Merrikhpour 2
1 Assistant Professor in Water Resources Management, Department of Civil Engineering, K. N. Toosi University of Technology
2 Ph. D. Candidate of Water Resources Management in Department of Civil Engineering, K. N. Toosi University of Technology
چکیده [English]

Total Precipitable Water Vapor is one of the important parameters in meteorology and hydrology. The aim of this study is the evaluation of remote sensing algorithms to estimate TPW over land using microwave and optical wavelengths in the western part of Iran. In this regard, the algorithm of TPW estimation in the microwave wavelength range by using Advanced Microwave Scanning Radiometer 2 (AMSR2) data on cloudy and clear sky days as well as the product of near infrared TPW of Moderate Resolution Imaging Spectroradiometer (MODIS) MOD05 in clear sky days were evaluated. MOD05 product does not work on cloudy days. The evaluation results based on radiosonde data on clear sky days showed that MOD05 product had higher precision than AMSR2 algorithm, so that the coefficient of determination (R2) derived from AMSR2 and MOD05 were 0.516 and 0.650, respectively. Moreover, the Root Mean Square Error (RMSE) of AMSR2 and MOD05 were acquired as 5.129 and 4.542 mm, respectively. On cloudy days R2 and RMSE of the obtained TPW from AMSR2 algorithm were 0.284 and 7.367 mm, respectively. Overall, on cloudy and clear sky days, the value of R2 and RMSE of estimated TPW from AMSR2 was 0.420 and 5.976 mm, respectively.

کلیدواژه‌ها [English]

  • Total Precipitable Water Vapor
  • Iran
  • AMSR2
  • MODIS
  • Radiosonde
Bordi I, Raziei T, Pereira LS and Sutera A (2015) Ground-based GPS measurements of precipitable water vapor and their usefulness for hydrological applications. Water resources management, Springer 29(2):471-486
Bordi I, Zhu X and Fraedrich K (2016) Precipitable water vapor and its relationship with the Standardized Precipitation Index: ground-based GPS measurements and reanalysis data. Theoretical and applied climatology, Springer 123(1–2):263-275
Bowker D, Davis R, Myrick D, Stacy K and Jones W (1985) Spectral reflectances of natural targets for use in remote sensing studies. NASA, 188p
Casas MC, Rodríguez R, Prohom M, Gázquez A and Redaño A (2011) Estimation of the probable maximum precipitation in Barcelona (Spain). International Journal of Climatology. Wiley Online Library 31(9):1322-1327
Chaboureau J, Chédin A and Scott NA (1998) Remote sensing of the vertical distribution of atmospheric water vapor from the TOVS observations: Method and validation. Journal of Geophysical Research: Atmospheres. Wiley Online Library 103(D8):8743-8752
Deeter MN (2007) A new satellite retrieval method for precipitable water vapor over land and ocean. Geophysical research letters, Wiley Online Library 34(2)
Du J, Kimball JS and Jones LA (2015) Satellite microwave retrieval of total precipitable water vapor and surface air temperature over land from AMSR2. IEEE Transactions on Geoscience and Remote Sensing. IEEE 53(5):2520-2531
Froidevaux M, Higgins CW, Simeonov V, Ristori P, Pardyjak E, Serikov I, Calhoun R, Van Den Bergh H and Parlange MB (2013) A Raman lidar to measure water vapor in the atmospheric boundary layer. Advances in Water Resources 51:345-356
Frouin R, Deschamps P-Y and Lecomte P (1990) Determination from space of atmospheric total water vapor amounts by differential absorption near 940 nm: Theory and airborne verification. Journal of Applied Meteorology 29(6):448-460
Gao B and Kaufman Y (1998) The MODIS Near-IR water vapor algorithm (Algorithm theoretical basis document, ATBD-MOD05). NASA, 25p
Gao B and Kaufman Y (2003) Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near‐infrared channels. Journal of Geophysical Research: Atmospheres, Wiley Online Library 108(D13)
Grant WB (1991) Differential absorption and Raman lidar for water vapor profile measurements: a review. Optical Engineering. International Society for Optics and Photonics 30(1):40-49
Halthore RN, Eck TF, Holben BN and Markham BL (1997) Sun photometric measurements of atmospheric water vapor column abundance in the 940nm band. Journal of Geophysical Research: Atmospheres. Wiley Online Library 102(D4):4343-4352
Holben BN and Eck TF (1990) Precipitable water in the Sahel measured using sun photometry. Agricultural and Forest Meteorology 52(1-2):95-107
Japan Aerospace Exploration Agency Website (2016) Available at: https://gcom-w1.jaxa.jp/auth.html
Ji D and Shi J (2014) Water vapor retrieval over cloud cover area on land using AMSR-E and MODIS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE 7(7):3105-3116
Jones LA, Ferguson CR, Kimball JS, Zhang K, Chan STK, McDonald KC, Njoku EG and Wood EF (2010) Satellite microwave remote sensing of daily land surface air temperature minima and maxima from AMSR-E. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE 3(1):111-123
Justice CO, Townshend JRG, Vermote EF, Masuoka E, Wolfe RE, Saleous N, Roy DP and Morisette JT (2002) An overview of MODIS land data processing and product status. Remote sensing of Environment, Elsevier 83(1-2):3-15
Kachi M, Hori M, Maeda T and Imaoka K (2014) Status of validation of AMSR2 on board the GCOM-W1 satellite. Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, IEEE, 110–113
Kaufman YJ and Gao B-C (1992) Remote sensing of water vapor in the near IR from EOS/MODIS. IEEE Transactions on Geoscience and Remote Sensing 30(5):871-884
Kneizys F, Shettle E, Abreu L, Chetwynd J and Anderson G (1988) Users guide to LOWTRAN 7. DTIC Document
Lee O, Park Y, Kim ES and Kim S (2016) Projection of Korean probable maximum precipitation under future climate change scenarios. Advances in Meteorology. Hindawi
Lu N, Qin J, Yang K, Gao Y, Xu X and Koike T (2011) On the use of GPS measurements for moderate resolution imaging spectrometer precipitable water vapor evaluation over southern Tibet. Journal of Geophysical Research: Atmospheres, Wiley Online Library 116(D23)
Maghrabi A and Al Dajani HM (2013) Estimation of precipitable water vapour using vapour pressure and air temperature in an arid region in central Saudi Arabia. Journal of the Association of Arab Universities for Basic and Applied Sciences 14(1):1-8
Merrikhpour MH and Rahimzadegan M (2017a) Improving the algorithm of extracting regional total precipitable water vapor over land from MODIS images. IEEE Transactions on Geoscience and Remote Sensing 55(10)
Merrikhpour MH and Rahimzadegan M (2017b) An introduction to an algorithm for extracting precipitable water vapor over land from AMSR2 Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(9)
Mobasheri MR, Purbagher Kordi SM, Farajzadeh M and Sadeghi Naeini A (2008) Improvement of remote sensing techniques in TPW assessment using radiosonde data. Journal of Applied Sciences 8:480-488
NASA Website (2016) Available at: https://ladsweb.nascom.nasa.gov
Niell AE, Coster AJ, Solheim FS, Mendes VB, Toor PC, Langley RB and Upham CA (2001) Comparison of measurements of atmospheric wet delay by radiosonde, water vapor radiometer, GPS, and VLBI. Journal of Atmospheric and Oceanic Technology 18(6):830-850
Pérez‐Ramírez D, Whiteman DN, Smirnov A, Lyamani H, Holben BN, Pinker R, Andrade M and Alados‐Arboledas L (2014) Evaluation of AERONET precipitable water vapor versus microwave radiometry, GPS, and radiosondes at ARM sites. Journal of Geophysical Research: Atmospheres, Wiley Online Library 119(15):9596-9613
Reagan J, Thome K, Herman B and Gall R (1987) Water vapor measurements in the 0. 94 micron absorption band- calibration, measurements and data applications. In: International Geoscience and Remote Sensing Symposium (IGARSS87), The University of Michigan, United States, 63-67
Rousseau AN, Klein IM, Freudiger D, Gagnon P, Frigon A and Ratté-Fortin C (2014) Development of a methodology to evaluate probable maximum precipitation (PMP) under changing climate conditions: Application to southern Quebec, Canada. Journal of hydrology 519:3094-3109
Seemann SW, Li J, Menzel WP and Gumley LE (2003) Operational retrieval of atmospheric temperature, moisture, and ozone from MODIS infrared radiances. Journal of applied meteorology 42(8):1072-1091
Sohn B-J and Smith EA (2003) Explaining sources of discrepancy in SSM/I water vapor algorithms. Journal of climate 16(20):3229-3255
Valeo C, Skone SH, Ho CLI, Poon SKM and Shrestha SM (2005) Estimating snow evaporation with GPS derived precipitable water vapour. Journal of Hydrology 307(1-4):196-203
Wang JR and Manning W (2003) Near concurrent MIR, SSM/T-2, and SSM/I observations over snow-covered surfaces. Remote Sensing of Environment 84(3):457-470
Wentz FJ (1997) A well‐calibrated ocean algorithm for special sensor microwave/imager. Journal of Geophysical Research: Oceans, Wiley Online Library 102(C4):8703-8718
Whiteman DN, Rush K, Rabenhorst S, Welch W, Cadirola M, McIntire G, Russo F, Adam M, Venable D and Connell R (2010) Airborne and ground-based measurements using a high-performance Raman lidar. Journal of Atmospheric and Oceanic Technology 27(11):1781-1801
Woodhouse IH (2017) Introduction to microwave remote sensing. CRC press
Wu Q, Liu H, Wang L and Deng C (2016) Evaluation of AMSR2 soil moisture products over the contiguous United States using in situ data from the International Soil Moisture Network. International Journal of Applied Earth Observation and Geoinformation 45:187-199
Wyoming University Website (2016) Radiosonde data. http://weather.uwyo.edu/upperair/sounding.html
Zhou F-C, Song X, Leng P, Wu H and Tang B-H (2016) An algorithm for retrieving precipitable water vapor over land based on passive microwave satellite data. Advances in Meteorology, Hindawi, http://dx.doi.org/10.1155/2016/4126393
Marbut B, Ashrafzadeh A, Vazifehdoust M and Khaledian MR (2018) Comparing actual evapotranspiration rates derived from MOD16 product and simulated using SWAP model (Case study: Corn fields in Qazvin Province).  Iran-Water Resources Research 14(2):81-93 (In Persian)
Sima S and Tajrishi M (2015) Estimation of Urmia Lake evaporation using remote sensing data. Iran-Water Resources Research 11(1):32-48 (In Persian)
Tasdighian M and Rahimzadegan M (2017) Evaluation and improvement of snow cover detection from MODIS images. Iran-Water Resources Research 13(1):163-177 (In Persian)