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Remote Sensing of Cloud Ice Water Path from SAPHIR Microwave Sounder Onboard Megha- Tropiques

  • Durgesh Nandan PiyushEmail author
  • J Satapathy
  • J. Srinivasan
Original Article

Abstract

This study derives the ice water path of the atmospheric column from the microwave sounder SAPHIR onboard Megha-Tropiques. SAPHIR (Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie) is a cross-track, multichannel microwave humidity sounder with six channels ranging from 183.3 ± 0.2 to 183.3 ± 11 GHz near the 183.31 GHz water vapor absorption line. It measures the earth emitted radiation at these six frequencies. In this paper, Concurrent and collocated observations of Channel 183.31 ± 6.6 GHz, and 183.3 ± 11 GHz from SAPHIR and IWP (Ice water Path) from CloudSat have been used in the development the algorithm. A total of five sets of neural network model, each for 10° of incidence angle of SAPHIR have been developed. The model shows a correlation of 0.83 and RMSE of 195 g/m2 with an independent test dataset. The validation of the algorithm has been done by comparing the retrieval with various satellite derived IWP products such as CloudSat, GMI (Global precipitation measuring mission Microwave Imager) and MSPPS (Microwave Surface and Precipitation Products System). The instantaneous comparisons of IWP over a cyclonic storm ROANU demonstrate a good agreement between NN (Neural Network) derived IWP and CloudSat. A probability distribution of IWP indicates consistency between SAPHIR and CloudSat. A comparison of zonal mean between all the IWP products shows that SAPHIR performs better than GMI, and MSPPS.

Keywords

Megha-Tropiques SAPHIR Ice Water Path Neural Network 

Notes

Acknowledgements

SAPHIR data was downloaded from CNES ICARE (www.icare.univ-lille1.fr). MSPPS data was acquired from NOAA CLASS and TRMM-TMI from DAAC. We acknowledge the NASA CloudSat project for the CloudSat data. The radiative transfer model (mwrt) provided by Prof Guosheng Liu, Florida State University is gratefully acknowledged. INSAT3D data is acquired from MOSDAC (www.mosdac.gov.in). The authors thank the Department of Science and Technology (DST), Government of India for financial support for the Centre for Excellence at Divecha Centre for Climate Change.

References

  1. Atkinson, N.C.: Calibration, monitoring and validation of AMSU-B. Adv. Space Res. 28(1), 117–126 (2001)CrossRefGoogle Scholar
  2. Austin, R.T., Heymsfield, A.J., Stephens, G.L.: Retrieval of ice cloud microphysical temperature. J. Geophys. Res.-Atmos. (1984–2012). 114(D8), (2009)Google Scholar
  3. Bennartz, R., Bauer, P.: Sensitivity of microwave radiances at 85–183 GHz to precipitating ice particles. Radio Sci. 38(4), (2003)Google Scholar
  4. Burns, B., Wu, X., Diak, G.R.: Effects of precipitation and cloud ice on brightness temperatures in AMSU moisture channels. IEEE Trans. Geosci. Remote Sens. 35(6), 1429–1437 (1997)CrossRefGoogle Scholar
  5. Draper, D.W., Newell, D.A., Wentz, F.J., Krimchansky, S., Skofronick-Jackson, G.M.: The global precipitation measurement (GPM) microwave imager (GMI): instrument overview and early on-orbit performance. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 8(7), 3452–3462 (2015)CrossRefGoogle Scholar
  6. Eliasson, S., Buehler, S.A., Milz, M., Eriksson, P., John, V.O.: Assessing observed and modelled spatial distributions of ice water path using satellite data. Atmos. Chem. Phys. 11(1), 375–391 (2011)CrossRefGoogle Scholar
  7. Evans, K.F., Stephens, G.L.: Microwave radiative transfer through clouds composed of realistically shaped ice crystals. Part I. single scattering properties. J. Atmos. Sci. 52(11), 2041–2057 (1995)CrossRefGoogle Scholar
  8. Evans, K.F., Wang, J.R., Racette, P.E., Heymsfield, G., Li, L.: Ice cloud retrievals and analysis with the compact scanning submillimeter imaging radiometer and the cloud radar system during CRYSTAL FACE. J. Appl. Meteorol. 44(6), 839–859 (2005)CrossRefGoogle Scholar
  9. Ferraro, R.R., Weng, F., Grody, N.C., Zhao, L., Meng, H., Kongoli, C., et al.: NOAA operational hydrological products derived from the advanced microwave sounding unit. IEEE Trans. Geosci. Remote Sens. 43(5), 1036–1049 (2005)CrossRefGoogle Scholar
  10. Fusina, F., Spichtinger, P., Lohmann, U.: Impact of ice supersaturated regions and thin cirrus on radiation in the midlatitudes. J. Geophys. Res.-Atmos. 112(D24), (2007)Google Scholar
  11. Ghosh, A., Varma, A.K., Shah, S., Gohil, B.S., Pal, P.K.: Rain identification and measurement using Oceansat-II scatterometer observations. Remote Sens. Environ. 142, 20–32 (2014)CrossRefGoogle Scholar
  12. Gohil, B.S., Gairola, R.M., Mathur, A.K., Varma, A.K., Mahesh, C., Gangwar, R.K., Pal, P.K.: Algorithms for retrieving geophysical parameters from the MADRAS and SAPHIR sensors of the Megha-Tropiques satellite: Indian scenario. Q. J. R. Meteorol. Soc. 139(673), 954–963 (2013)CrossRefGoogle Scholar
  13. Gong, J., Wu, D.L.: CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances. Meas. Tech. 7(6), 1873–1890 (2014)CrossRefGoogle Scholar
  14. Goyal, J. M., Srinivasan, J., Satheesh, S. K.: Can SAPHIR Instrument Onboard MEGHATROPIQUES Retrieve Hydrometeors and Rainfall Characteristics?. In: AGU Fall Meeting Abstracts, vol. 1, p. 3153. (2014)Google Scholar
  15. Heymsfield, A.J., Protat, A., Bouniol, D., Austin, R.T., Hogan, R.J., Delanoë, J., Okamoto, H., Sato, K., van Zadelhoff, G.J., Donovan, D.P., Wang, Z.: Testing IWC retrieval methods using radar and ancillary measurements with in situ data. J. Appl. Meteorol. Climatol. 47(1), 135–163 (2008)CrossRefGoogle Scholar
  16. Holl, G., Buehler, S.A., Rydberg, B., Jimenez, C.: Collocating satellite-based radar and radiometer measurements–methodology. Atmos. Meas. Tech. 3(3), 693–708 (2010)CrossRefGoogle Scholar
  17. Holl, G., Eliasson, S., Mendrok, J., Buehler, S.A.: SPARE- ICE: synergistic ICE water path from passive operational sensors. J. Geophys. Res.-Atmos. 119(3), 1504–1523 (2014)CrossRefGoogle Scholar
  18. Hong, G., Heygster, G., Miao, J., Kunzi, K.: Detection of tropical deep convective clouds from AMSU-B water vapor channels measurements. J. Geophys. Res.-Atmos. 110(D5), (2005)Google Scholar
  19. Hornik, K., Stinchcombe, M., White, H.: Multilayer feed forward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)CrossRefGoogle Scholar
  20. Hou, A.Y., Kakar, R.K., Neeck, S., Azarbarzin, A.A., Kummerow, C.D., Kojima, M., Oki, R., Nakamura, K., Iguchi, T.: The global precipitation measurement mission. Bull. Am. Meteorol. Soc. 95(5), 701–722 (2014)CrossRefGoogle Scholar
  21. Islam, T., Srivastava, P.K.: Synergistic multi-sensor and multi-frequency retrieval of cloud ice water path constrained by CloudSat collocations. J. Quant. Spectrosc. Radiat. Transf. 161, 21–34 (2015)CrossRefGoogle Scholar
  22. Islam, T., Rico-Ramirez, M.A., Han, D.W., Srivastava, P.K.: Artificial intelligence techniques for clutter identification with polarimetry radar signatures. Atmos. Res. 109, 95–113 (2012).  https://doi.org/10.1016/j.atmosres.2012.02.007 CrossRefGoogle Scholar
  23. John, V.O., Holl, G., Atkinson, N., Buehler, S.A.: Monitoring scan asymmetry of microwave humidity sounding channels using simultaneous all angle collocations (SAACs). J. Geophys. Res.-Atmos. 118(3), 1536–1545 (2013)CrossRefGoogle Scholar
  24. Kummerow, C., Ringerud, S., Crook, J., Randel, D., Berg, W.: An observationally generated a priori database for microwave rainfall retrievals. J. Atmos. Ocean. Technol. 28, 113–130 (2011)CrossRefGoogle Scholar
  25. Kummerow, C., Randel, D., Kulie, M., Wang, N.-Y., Ferraro, R., Munchak, S.J., Petkovic, V.: The evolution of the Goddard profiling algorithm to a fully parametric scheme. J. Atmos. Ocean. Technol. 32, 2265–2280 (2015)CrossRefGoogle Scholar
  26. Liou, K.N.: Influence of cirrus clouds on weather and climate processes: a global perspective. Mon. Weather Rev. 114(6), 1167–1199 (1986)CrossRefGoogle Scholar
  27. Liu, G.: A fast and accurate model for microwave radiance calculations. J. Meteorol. Soc. Jpn. Ser. II. 76(2), 335–343 (1998)CrossRefGoogle Scholar
  28. Liu, G., Curry, J.A.: An investigation of the relationship between emission and scattering signals in SSM/I data. J. Atmos. Sci. 55(9), 1628–1643 (1998)CrossRefGoogle Scholar
  29. Liu, G., Curry, J.A.: Determination of ice water path and mass median particle size using multichannel microwave measurements. J. Appl. Meteorol. 39(8), 1318–1329 (2000)CrossRefGoogle Scholar
  30. Mahesh, C., Prakash, S., Sathiyamoorthy, V., Gairola, R.M.: Artificial neural network based microwave precipitation estimation using scattering index and polarization corrected temperature. Atmos. Res. 102(3), 358–364 (2011)CrossRefGoogle Scholar
  31. Muller, B.M., Fuelberg, H.E., Xiang, X.: Simulations of the effects of water vapor, cloud liquid water, and ice on AMSU moisture channel brightness temperatures. J. Appl. Meteorol. 33(10), 1133–1154 (1994)CrossRefGoogle Scholar
  32. Piyush, D.N., Goyal, J., Srinivasan, J.: Retrieval of cloud ice water path using SAPHIR on board Megha-Tropiques over the tropical ocean. Adv. Space Res. 59(7), 1895–1906 (2017)CrossRefGoogle Scholar
  33. Rossow, W.B., Schiffer, R.A.: ISCCP cloud data products. Bull. Am. Meteorol. Soc. 72, 2–20 (1991).  https://doi.org/10.1175/1520-0477(1991)072,0002:ICDP.2.0.CO;2
  34. Rossow, W.B., Schiffer, R.A.: Advances in understanding clouds from ISCCP. Bull. Am. Meteorol. Soc. (1999).  https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2
  35. Sanderson, B.M., Piani, C., Ingram, W.J., Stone, D.A., Allen, M.R.: Towards constraining climate sensitivity by linear analysis of feedback patterns in thousands of perturbed-physics GCM simulations. Clim. Dyn. 30(2–3), 175–190 (2008)CrossRefGoogle Scholar
  36. Sheu, R.-S., Curry, J.A., Liu, G.: Vertical stratification of tropical cloud properties as determined from satellite. J. Geophys. Res. 102, 4231–4245 (1997)CrossRefGoogle Scholar
  37. Spencer, R.W., Goodman, H.M., Hood, R.E.: Precipitation retrieval over land and ocean with the SSM/I: identification and characteristics of the scattering signal. J. Atmos. Ocean. Technol. 6(2), 254–273 (1989)CrossRefGoogle Scholar
  38. Srivastava, P.K., Han, D., Ramirez, M.R., Islam, T.: Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application. Water Resour. Manag. 27(8), 3127–3144 (2013)CrossRefGoogle Scholar
  39. Stephens, G.L., Vane, D.G., Tanelli, S., Im, E., Durden, S., Rokey, M., et al.: CloudSat mission: performance and early science after the first year of operation. J. Geophys. Res.-Atmos. 113(D8), (2008)Google Scholar
  40. Sun, N., Weng, F.: Retrieval of cloud ice water path from special sensor microwave imager/sounder (SSMIS). J. Appl. Meteorol. Climatol. 51(2), 366–379 (2012)CrossRefGoogle Scholar
  41. Varma, A.K., Piyush, D.N.: An algorithm for retrieval of precipitation using microwave humidity sounder channels around 183 GHz. In: SPIE Asia-Pacific Remote Sensing, pp. 98760C–98760C. International Society for Optics and Photonics (2016).  https://doi.org/10.1117/12.2222742
  42. Varma, A.K., Piyush, D.N., Gohil, B.S., Pal, P.K., Srinivasan, J.: Rain detection and measurement from Megha-Tropiques microwave sounder—SAPHIR. J. Geophys. Res.-Atmos. 121(15), 9191–9207 (2016)CrossRefGoogle Scholar
  43. Viltard, N., Burlaud, C., Kummerow, C.D.: Rain retrieval from TMI brightness temperature measurements using a TRMM PR-based database. J. Appl. Meteorol. Climatol. 45(3), 455–466 (2006)CrossRefGoogle Scholar
  44. Waliser, D.E., Li, J.L.F., Woods, C.P., Austin, R.T., Bacmeister, J., Chern, J., et al.: Cloud ice: a climate model challenge with signs and expectations of progress. J. Geophys. Res.-Atmos. (1984–2012). 114(D8), (2009)Google Scholar
  45. Weng, F., Grody, N.C.: Retrieval of ice cloud parameters using a microwave imaging radiometer. J. Atmos. Sci. 57(8), 1069–1081 (2000)CrossRefGoogle Scholar

Copyright information

© Korean Meteorological Society and Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Divecha Centre for Climate Change, Centre for Atmospheric and Oceanic SciencesIndian Institute of ScienceBangaloreIndia
  2. 2.Department of PhysicsAmrita Vishwa VidyapeethamAmritapuriIndia

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