The present paper deals with the retrieval of the atmospheric layer averaged relative humidity profiles using data from the Microwave Humidity Sounder (MHS) onboard the MetOp satellite. The retrieval has been innovatively performed by firstly retrieving humidity for pairs of thick overlapping layers (TOLs) used subsequently to derive humidity for associated thin isolated layer (TIL). A water vapour dependent (WVD) algorithm has been developed and applied to infer the humidity of TOLs. Thus, the retrieved profiles have been finally compared with standard algorithm (NORM). These algorithms have been developed based on radiative transfer simulations and study of sensitivities of MHS channels on humidity of various types of layers (TOL, TIL). The algorithm has been tested with MHS data and validated using concurrent radiosonde as well as NCEP reanalysis data indicating profile errors of ~15% and ~19%, respectively. 1. Introduction Being the strongest greenhouse gas, water vapor is the most important constituents in the Earth’s atmosphere, as its spatial and temporal variations affect various meteorological phenomena like formation of clouds, development of severe storms, and global warming [1]. The latent heat released during the condensation of water vapour is crucial in the triggering and development of the convective systems [2]. Nearly all-weather capability of microwave sounders provides an added advantage for remote sensing of water vapor from such sensors. In microwave region, water vapour has two absorption line peaks at 22.235 and 183.31?GHz. The line strength at 22.235?GHz is weak, and in clear conditions the atmosphere typically absorbs less than 20% of the radiation propagating through it at this frequency. Therefore, most of the attempts to retrieve water vapor by remote sensing near this frequency are necessarily limited to total integrated precipitable water [3, 4]. To retrieve water vapor in a few layers over oceans or land requires a strong absorption line such that around 183.31?GHz. Profiling of the atmospheric water vapour has been made with radiometric measurements from the airborne microwave moisture sounder (AMMS) and millimeter wave Imaging radiometer (MIR) by [5, 6] to study the effects of clouds on these frequencies. A similar sounder operating around 183.31?GHz, namely, SAPHIR, onboard MEGHA-TROPIQUES a joint ISRO-CNES mission is in orbit since October 2011. SAPHIR provides measurements in six water vapour channels around 183.31?GHz for sounding the atmospheric humidity. Brogniez et al. [7], Gohil et al. [8] have shown the
References
[1]
R. W. Spencer and W. D. Braswell, “How dry is the tropical free troposphere? Implications for global warming theory,” Bulletin of the American Meteorological Society, vol. 78, no. 6, pp. 1097–1106, 1997.
[2]
K. J. Haydu and T. N. Krishnamurti, “Moisture analysis from radiosonde and microwave spectrometer data.,” Journal of Applied Meteorology, vol. 20, no. 10, pp. 1177–1191, 1981.
[3]
D. H. Staelin, K. F. Kunzi, R. L. Pettyjohn, R. K. L. Poon, R. W. Wilcox, and J. W. Waters, “Remote sensing of atmospheric water vapor and liquid water with NIHPAUS-5 microwave spectrometer,” Journal of Applied Meteorology, vol. 15, no. 11, pp. 1204–1214, 1976.
[4]
A. T. C. Chang and T. T. Wilheit, “Remote sensing of atmospheric water vapor, liquid water, and wind speed at the ocean surface by passive microwave techniques from the nimbus 5 satellite,” Radio Science, vol. 14, no. 5, pp. 793–802, 1979.
[5]
J. R. Wang, J. L. King, T. T. Wilheit et al., “Profiling atmospheric water vapour by microwave radiometry,” Journal of Climate and Applied Meteorology, vol. 22, no. 5, pp. 779–788, 1987.
[6]
J. R. Wang, P. Racette, and L. A. Chang, “Mir measurements of atmospheric water vapor profiles,” IEEE Transactions on Geoscience and Remote Sensing, vol. 35, no. 2, pp. 212–223, 1997.
[7]
H. Brogniez, P. E. Kirstetter, and L. Eymard, “Expected improvements in the atmospheric humidity profile retrieval using the Megha-Tropiques microwave payload,” Quarterly Journal of the Royal Meteorological Society, vol. 139, no. 673, pp. 842–851, 2013.
[8]
B. S. Gohil, R. M. Gairola, A. K. Mathur et al., “Algorithms for retrieving geophysical parameters from the MADRAS and SAPHIR sensors of the Megha-Tropiques satellite: Indian scenario,” Quarterly Journal of the Royal Meteorological Society, vol. 139, no. 673, pp. 954–963, 2013.
[9]
A. K. Mathur, R. K. Gangwar, B. S. Gohil et al., “Humidity profile retrieval from SAPHIR on-board the Megha-Tropiques,” Current Science, vol. 104, no. 12, pp. 1650–1655, 2013.
[10]
B. S. Gohil, A. K. Mathur, A. Sarkar, and V. K. Agarwal, “Atmospheric humidity profile retrieval algorithms for Megha-Tropiques SAPHIR: a simulation study and analysis of AMSU-B data,” in Remote Sensing of the Atmosphere and Clouds, vol. 6408 of Proceedings of SPIE, November 2006.
[11]
G. Liu, “A fast and accurate model for microwave radiance calculations,” Journal of the Meteorological Society of Japan, vol. 76, no. 2, pp. 335–343, 1998.
[12]
H. J. Liebe, G. A. Hufford, and M. G. Cotton, “Propagation modeling ofmoist air and suspended water/ice particles at frequencies below1000?GHz,” in Proceedings of AGARD Conference on Electromagnetic Wave Propagation, p. 10, Palma De Mallorca, Spain, 1993.
[13]
T. T. Wilheit Jr., “Amodel for microwave emissivity of the oceans surfaceas function of wind speed,” IEEE Transactions on Geoscience Electronics, vol. GE-17, no. 4, pp. 244–249, 1979.