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Physical statistical algorithm for precipitable water vapor inversion on land surface based on multi-source remotely sensed data

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Abstract

Water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales, from global climate to micrometeorology. The determination of water vapor distribution in the atmosphere and its changing pattern is very important. Although atmospheric scientists have developed a variety of means to measure precipitable water vapor (PWV) using remote sensing data that have been widely used, there are some limitations in using one kind satellite measurements for PWV retrieval over land. In this paper, a new algorithm is proposed for retrieving PWV over land by combining different kinds of remote sensing data and it would work well under the cloud weather conditions. The PWV retrieval algorithm based on near infrared data is more suitable to clear sky conditions with high precision. The 23.5 GHz microwave remote sensing data is sensitive to water vapor and powerful in cloud-covered areas because of its longer wavelengths that permit viewing into and through the atmosphere. Therefore, the PWV retrieval results from near infrared data and the indices combined by microwave bands remote sensing data which are sensitive to water vapor will be regressed to generate the equation for PWV retrieval under cloud covered areas. The algorithm developed in this paper has the potential to detect PWV under all weather conditions and makes an excellent complement to PWV retrieved by near infrared data. Different types of surface exert different depolarization effects on surface emissions, which would increase the complexity of the algorithm. In this paper, MODIS surface classification data was used to consider this influence. Compared with the GPS results, the root mean square error of our algorithm is 8 mm for cloud covered area. Regional consistency was found between the results from MODIS and our algorithm. Our algorithm can yield reasonable results on the surfaces covered by cloud where MODIS cannot be used to retrieve PWV.

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References

  • Aires F, Prigent C, Rossow W B. 2001. A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations. J Geophys Res, 106: 14887–14907

    Article  Google Scholar 

  • Bennartz R, Fischer J. 2001. Retrieval of columnar water vapor over land from backscattered solar radiation using the medium resolution imaging spectrometer. Remote Sens Environ, 78: 274–283

    Article  Google Scholar 

  • Bennouna Y S, Torres B, Cachorro V E. 2013. The annual cycle of total precipitable water vapor derived from different remote sensing techniques: An application to several sites of the Iberian Peninsula. In: AIP Conference Proceedings. Berlin. 1531

    Google Scholar 

  • Brodzik M J, Knowles K W. 2002. EASE-Grid: A versatile set of equal-area projections and grids. Discrete Global Grids, 5: 110–125

    Google Scholar 

  • Chang L, Jin S G. 2013. MODIS infrared water vapor calibration model and assessment. In: 21st International Conference on Geoinformatics (Geoinformatics). Kaifeng. 1–5

    Google Scholar 

  • Charles I, Yoram K. 2004. Modis water vapor description, Product Description. User’s Gui

    Google Scholar 

  • de Chen K, Wu T D, Tsang T, Li Q, Shi J C, Fung A K. 2003. Emission of rough surfaces calculated by the integral equation method with comparison to three-dimensional moment method simulations. IEEE Trans Geosci Remote Sensing, 41: 90–101

    Article  Google Scholar 

  • Chen S H, Zhao Z, Haase J, Chen A, Vandenberghe F. 2008. A study of the characteristics and assimilation of retrieved MODIS total precipitable water data in severe weather simulations. Mon Weather Rev, 136: 3608–3628

    Article  Google Scholar 

  • Cooney J A. 1970. Remote measurements of atmospheric water vapor profiles using the Raman component of laser back-scatter. J Appl Meteorol, 9: 182–184

    Article  Google Scholar 

  • Dalu G. 1986. Satellite remote sensing of atmospheric water vapor. Int J Remote Sens, 7: 1089–1097

    Article  Google Scholar 

  • Desportes C, Obligis E, Eymard L. 2007. On the wet tropospheric correction for altimetry in coastal regions. IEEE Trans Geosci Remote Sensing, 45: 2139–2149

    Article  Google Scholar 

  • Dessler A E, Zhang Z, Yang P. 2008. Water-vapor climate feedback inferred from climate fluctuations, 2003–2008. Geophys Res Lett, 35: L20704

    Article  Google Scholar 

  • Du J Y, Kimball J S, Lucas A J. 2015. Satellite microwave retrieval of total precipitable water vapor and surface air temperature over land from AMSR2. IEEE Trans Geosci Remote Sensing, 53: 2520–2531

    Article  Google Scholar 

  • Eck T F, Holben B N. 1994. AVHRR split window temperature differences and total precipitable water over land surfaces. Int J Remote Sens, 15: 567–582

    Article  Google Scholar 

  • Eric F V, Nazmi Z E, Christopher O J. 2002. Atmospheric correction of MODIS data in the visible to middle infrared: First results. Remote Sens Environ, 83: 97–111

    Article  Google Scholar 

  • Firsov K M, Chesnokova T Y, Bobrov E V, Klitochenko I I. 2013. Total water vapor content retrieval from sun photometer data. Atmos Ocean Optics, 26: 281–284

    Article  Google Scholar 

  • Gao B C, Kaufman Y J. 2003. Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared channels. J Geophys Res, 108: 4389–4401

    Article  Google Scholar 

  • Gao F L, Hua Z Z, Cui G M, Tao L R. 2013. Impacts of water vapor concentration variation on greenhouse effect quantitative analysis (in Chinese). Environ Sci Tech, 36: 182–186

    Google Scholar 

  • Huang Y B, Dong C H, Fan T C. 2006. Using the information of spacecraft SHEN ZHOU-3 moderate resolution imaging spectrometer to retrieve atmospheric water vapor (in Chinese). J Remote Sens, 10: 742–748

    Google Scholar 

  • Hu X Q, Huang Y B, Lu Q F. 2011. Retrieving precipitable water vapor based on the near infrared data of FY-3A satellite (in Chinese). J Appl Meteorol Sci, 22: 46–56

    Google Scholar 

  • Jedlovec G J. 1990. Precipitable water estimation from high-resolution split window radiance measurement. J Appl Meteorol, 29: 863–877

    Article  Google Scholar 

  • Kevin P, Samuel N, David S, Anita W. 2002. Thermal remote sensing of near-surface water vapor. Remote Sens Environ, 79: 253–265

    Article  Google Scholar 

  • Knabb R D, Fuelberg H E. 1997. A comparison of the first-guess dependence of precipitable water estimates from three techniques using GOES data. J Appl Meteorol, 36: 417–427

    Article  Google Scholar 

  • Li X F, Li Y, Zeng Q M, Zhao Y H. 2009. Correction of atmospheric effects on repeat-pass interferometric SAR using MERIS and ASAR synchronous data (in Chinese). Acta Sci Nat Univ Pekinensis, 45: 1012–1018

    Google Scholar 

  • Liu Q, Weng F. 2005. One-dimensional variational retrieval algorithm of temperature, water vapor, and cloud water profiles from advanced microwave sounding unit (AMSU). IEEE Trans Geosci Remote Sensing, 43: 1087–1095

    Article  Google Scholar 

  • Liu Y J, Yang Z D. 2001. The Principle and Algorithm for MODIS (in Chinese). Beijing: Science Press

    Google Scholar 

  • Liu Y, Guan L. 2011. Study on the inversion of clear sky atmospheric humidity profiles with artificial neural network (in Chinese). Meteorol Mon, 37: 318–324

    Google Scholar 

  • Liu Z Z, Wong M S, Nicol J, Chan P W. 2013. A multi-sensor study of water vapor from radiosonde, MODIS and AERONET: A case study of Hongkong. Int J Climatol, 33: 109–120

    Article  Google Scholar 

  • Ma K L. 2006. Study on Atmospheric Water Vapor Retrieved From Satellite Remote Sensing Data (in Chinese). Changchun: Northeast Normal University

    Google Scholar 

  • Mario M, Nazzareno P, Frank S. 2012. Spectral downscaling of integrated water vapor fields from satellite infrared observations. IEEE Trans Geosci Remote Sensing, 50: 415–428

    Article  Google Scholar 

  • Menzel W P, Seemann S W, Li J. 2002. MODIS Atmospheric Profile Retrieval Algorithm Theoretical Basis Document. MODIS ATBD, version 6

    Google Scholar 

  • Merritt N D. 2007. A new satellite retrieval method for precipitable water vapor over land and ocean. Geophys Res Lett, 34: L02815

    Google Scholar 

  • Michael K, Yoram K, Paul M, Tanre D. 1992. Remote Sensing of cloud, aerosol, and water vapor properties from the Moderate Resolution Imaging Spectrometer (MODIS). IEEE Trans Geosci Remote Sensing, 30: 2–27

    Article  Google Scholar 

  • Nakamura H, Koizumi K, Mannoji N. 2004. Data assimilation of GPS precipitable water vapor into the JMA meso-scale numerical weather prediction model and its impact on rainfall forecast. J Meteorol Soc Jpn, 82: 441–452

    Article  Google Scholar 

  • Onn F, Zebker H. 2006. Correction for interferometric synthetic aperture radar atmospheric phase artifacts using time series of zenith wet delay observations from a GPS network. J Geophys Res, 111: B09102

    Google Scholar 

  • Ottle C, Francois C. 1999. Further insights into the use of the Split-Window covariance technique for precipitable water retrieval. Remote Sens Environ, 69: 84–86

    Article  Google Scholar 

  • Randolph H W, David W F, Seth A S, David N A. 2000. SuomiNet: A real-time national GPS network for atmospheric research and education. Bull Amer Meteorol Soc, 81: 677–694

    Article  Google Scholar 

  • Raval A, Ramanathan V. 1989. Observational determination of the greenhouse effect. Nature, 342: 758–761

    Article  Google Scholar 

  • Rodgers C D. 1976. Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation. Rev Geophy Space Phys, 14: 609–624

    Article  Google Scholar 

  • Schroedter H M, Drews A, Heise S. 2008. Total water vapor column retrieval from MSG-SEVIRI split window measurements exploiting the daily cycle of land surface temperatures. Remote Sens Environ, 112: 249–258

    Article  Google Scholar 

  • Seemann S W, Li J, Menzel W P, Gumley L E. 2003. Operational retrieval of atmospheric temperature, moisture, and ozone from MODIS infrared radiances. J Appl Meteorol, 42: 1072–1091

    Article  Google Scholar 

  • Shi J C, Jackson T, Tao J, Du J Y, Bindlish R, Lu L, Chen K S. 2008. Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E. Remote Sens Environ, 112: 4285–4300

    Article  Google Scholar 

  • Shi J C, Jiang L M, Zhang L X, Chen K S, Wigneron J P, Chanzy A. 2005. A parameterized multifrequency polarization surface emission model. IEEE Trans Geosci Remote Sensing, 43: 2831–2841

    Article  Google Scholar 

  • Smith T L, Benjamin S G, Schwartz B E, Gutman S I. 2000. Using GPSIPW in a 4-D data assimilation system. Earth Planets Space, 52: 921–926

    Article  Google Scholar 

  • Solheim F S, Vivekanandan J, Ware R H, Rocken C. 1999. Propagation delays induced in GPS signals by dry air, water vapor, hydrometeors, and other particulates. J Geophys Res, 104: 9663–9670

    Article  Google Scholar 

  • Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K B, Tignor M, Miller H L. 2007. Intergovernmental Panel on Climate Change, Climate Change 2007. The Physical Science Basis. New York: Cambridge University Press. 591–662

    Google Scholar 

  • Song Z F, Wei H L, Wu X Q. 1996. Infrared remote sensing of atmospheric water vapor (in Chinese). Remote Sens Environ, 11: 130–137

    Google Scholar 

  • Stefania B, Vinia M, Patrizia B, Ciotti P, Pierdicca N. 2011. Satellite based retrieval of precipitable water vapor over land by using a neural network approach. IEEE Trans Geosci Remote Sensing, 49: 3236–3248

    Article  Google Scholar 

  • Treuhaft R N, Lanyi G E. 1987. The effect of dynamic wet troposphere on radio interferometric measurements. Radio Sci, 22: 251–265

    Article  Google Scholar 

  • Turner D D, Clough S A, Liljegren J C, Clothiaux E E, Cady-Pereira K E, Gaustad K L. 2007. Retrieving liquid water path and precipitable water vapor from the Atmospheric Radiation Measurement (ARM) microwave radiometers. IEEE Trans Geosci Remote Sensing, 45: 3680–3690

    Article  Google Scholar 

  • Wang Y Q, Shi J C, Liu Z H. 2012. Estimating of atmospheric parameters on land using AMSR-E, part I: Inferring precipitable water vapor. In: Second International Workshop on Earth Observation and Remote Sensing Applications (EORSA). Shanghai. 125–129

    Google Scholar 

  • Wang Y Q, Shi J C, Liu Z H, Peng Y J, Liu W J. 2013. Retrieval algorithm microwave surface emissivities based on multi-source, remote sensing data: An assessment on the Qinghai-Tibet Plateau. Sci China Earth Sci, 56: 93–101

    Article  Google Scholar 

  • Wang Y Q, Shi J C, Jiang L M, Du J Y, Tian B S. 2011. The development of an algorithm to enhance and match the resolution of satellite measurements from AMSR-E. Sci China Earth Sci, 54: 410–419

    Article  Google Scholar 

  • Wang Y Q, Shi J C, Liu Z H, Feng W L. 2015. Passive microwave remote sensing of precipitable water vapor over Beijing-Tianjin-Hebei region based on AMSR-E (in Chinese). Geom and Inform Sci Wuhan Univ, 40: 17–24

    Google Scholar 

  • Wang L M. 2009. The Inversion of the Content of PWV Based on RS Images (in Chinese). Fuxin: Liaoning Technical University

    Google Scholar 

  • Wolfe D E, Gutman S I. 2000. Developing an operational, surface based, GPS, water vapor observing system for NOAA: Network design and results. J Atmos Ocean Tech, 17: 426–440

    Article  Google Scholar 

  • Xu N, Hu X Q, Chen L, Zhang Y X. 2012. Cross-calibration of FY-2E/VISSR infrared window and water vapor channels with TERRA/MODIS (in Chinese). J Infrared Millim Waves, 32: 319–324

    Article  Google Scholar 

  • Xu X R. 2005. Remote Sensing Physics (in Chinese). Beijing: Peking University Press

    Google Scholar 

  • Zhang H, Xu J M, Huang Y F. 2003. Remote sensing of total column precipitable water vapor with two sun reflectance channels of FY-1C satellite (in Chinese). J Appl Meteorol Sci, 14: 385–394

    Google Scholar 

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Authors and Affiliations

  1. College of Environmental and Resource Science, Chengdu University of Information Technology, Chengdu, 610225, China

    YongQian Wang, WenLan Feng & YanJun Wang

  2. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China

    YongQian Wang

  3. Chongqing Institute of Meteorological Sciences, Chongqing, 401147, China

    YongQian Wang & JianCheng Shi

  4. College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, 610225, China

    Hao Wang

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  1. YongQian Wang
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  2. JianCheng Shi
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  3. Hao Wang
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  4. WenLan Feng
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  5. YanJun Wang
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Correspondence to YongQian Wang.

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Wang, Y., Shi, J., Wang, H. et al. Physical statistical algorithm for precipitable water vapor inversion on land surface based on multi-source remotely sensed data. Sci. China Earth Sci. 58, 2340–2352 (2015). https://doi.org/10.1007/s11430-015-5211-6

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  • DOI: https://doi.org/10.1007/s11430-015-5211-6

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