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GNSS Sensing of Climate Variability

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Environmental Geoinformatics

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Abstract

Poor reliability of radiosonde records across most developing countries in the southern hemisphere imposes serious challenges in understanding the structure of upper-tropospheric and lower-stratospheric (UTLS) region, i.e., the tropopause. The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission launched in April 2006 has overcome many observational limitations inherent in conventional atmospheric sounding instruments. This chapter presents the study undertaken by Khandu et al. [2] that examined the interannual variability of UTLS temperature over the Ganges-Brahmaputra-Meghna (GBM) River Basin in South Asia using monthly averaged COSMIC radio occultation (RO) data, together with two global reanalyses.

Real-time GNSS measurements have the potential to contribute to climate modeling and weather forecasting through integrative measurement of atmospheric water vapor in GNSS signal delays and measurements of soil moisture flux.

— W. C. Hammond et al. [1]

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Notes

  1. 1.

    http://cdaac-www.cosmic.ucar.edu/cdaac/status.html.

  2. 2.

    http://cdaac-www.cosmic.ucar.edu/cdaac/status.html.

  3. 3.

    http://disc.sci.gsfc.nasa.gov/daac-bin/FTPSubset.pl.

  4. 4.

    http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/.

  5. 5.

    http://www.esrl.noaa.gov/psd/data/climateindices/list/.

  6. 6.

    http://www.jamstec.go.jp/frsgc/research/d1/iod/.

References

  1. Hammond WC, Brooks BA, Bürgmann R, Heaton T, Jackson M, Lowry AR, Anandakrishnan S (2011) Scientific value of real-time global positioning system data. Eos 92(15):125–126. https://doi.org/10.1029/2011EO150001

    Article  Google Scholar 

  2. Khandu Awange J, Forootan E (2016) Interannual variability of upper tropospheric and lower stratospheric (UTLS) temperature over Ganges-Brahmaputra-Meghna basin based on COSMIC GNSS RO data. Atmos Measur Tech. https://doi.org/10.5194/amt-9-1-2016

  3. Reid GC, Gage KS (1985) Interannual variations in the height of the tropical tropopause. J Geophys Res 90:5629–5635. https://doi.org/10.1029/JD090iD03p05629

    Article  Google Scholar 

  4. Randel WJ, Wu F, Gaffen DJ (2000) Interannual variability of the tropical tropopause derived from radiosonde data and NCEP reanalyses. J Geophys Res 105(D12):15509–15523. https://doi.org/10.1029/2000JD900155

    Article  Google Scholar 

  5. Karl TR, Hassol SJ, Miller CD, Murray WL (eds) (2006) Temperature trends in the lower atmosphere: steps for understanding and reconciling differences. Tech. rep., U.S. Climate Change Science Program, Washington, DC

    Google Scholar 

  6. Lott FC, Stott PA, Mitchell DM, Christidis N, Gillett NP, Haimberger L, Perlwitz J, Thorne PW (2013) Models versus radiosondes in the free atmosphere: A new detection and attribution analysis of temperature. J Geophys Res 118. https://doi.org/10.1002/jgrd.50255

  7. Thorne PW, Brohan P, Titchner HA, McCarthy MP, Sherwood SC, Peterson TC, Haimberger L, Parker DE, Tett SFB, Santer BD, Fereday DR, Kennedy JJ (2013) A quantification of uncertainties in historical tropical tropospheric temperature trends from radiosondes. J Geophys Res 116. https://doi.org/10.1029/2010JD015487

  8. Santer BD, Thorne PW, Haimberger L, Taylor KE, Wigley TML, Lanzante JR, Solomon S, Free M, Gleckler PJ, Jones PD, Karl TR, Klein SA, Mears C, Nychka D, Schmidt GA, Sherwood SC, Wentz FJ (2008) Consistency of modelled and observed temperature trends in the tropical troposphere. Int J Climatol 13:1703–1722. https://doi.org/10.1002/joc.1756

    Article  Google Scholar 

  9. Santer BD, Wehner MF, Wigley TML, Sausen R, Meehl GA, Taylor KE, Ammann C, Arblaster J, Washington WM, Boyle JS, Bruggemann W (2003) Contributions of anthropogenic and natural forcing to recent tropopause height changes. Science 301:479–483. https://doi.org/10.1126/science.1084123

    Article  Google Scholar 

  10. Santer BD, Sausen R, Wigley TML, Boyle JS, Doutriaux C, AchutaRao K, Hansen JE, Meehl GA, Roeckner E, Ruedy R, Schmidt G, Taylor KE (2003b) Behavior of tropopause height and atmospheric temperature in models, reanalyses, and observations: Decadal changes. J Geophys Res 108, 4ACL 11ACL:122. https://doi.org/10.1029/2002JD00225

  11. Gettelman A, Randel WJ, Massie S, Wu F, Read WG, Russell JM (2001) El Ni no as a natural experiment for studying the tropical tropopause region. J Clim 14:3375–3392. https://doi.org/10.1175/1520-0442(2001)0143375:ENOAAN2.0.CO;2

  12. Wilcox LJ, Hoskins BJ, Shine KP (2011) A global blended tropopause based on ERA data. Part II: trends and tropical broadening. J Geophys Res 138:576–584. https://doi.org/10.1002/qj.910

    Article  Google Scholar 

  13. Trenberth KE (1990) Recent observed interdecadal climate changes in the Northern hemisphere. Bull Amer Meteor Soc 71:988–993. https://doi.org/10.1175/1520-0477(1990)0710988:ROICCI2.0.CO;2

  14. Baldwin MP, Gray LJ, Dunkerton TJ, Hamilton K, Haynes PH, Randel WJ, Holton JR, Alexander MJ, Hirota I, Horinouchi T, Jones DBA, Kinnersley JS, Marquardt C, Sato K, Takahashi M (2001) The quasi-biennial oscillation. Rev Geophys 39:229. https://doi.org/10.1029/1999RG000073

    Article  Google Scholar 

  15. Ansari MI, Madan R, Bhaita S (2015) Verification of quality of GPS based radiosonde data. Mausam 66:367–374. Available at: metnet.imd.gov.in/mausamdocs/16632\(\_\)F.pdf

    Google Scholar 

  16. Das Gupta M, Das S, Prasanthi K, Pradhan PK (2005) Validation of upper-air observations taken during the ARMEX-I and its impact on the global analysis-forecast system. MAUSAM 56:139–146

    Google Scholar 

  17. Sun B, Reale A, Seidel DJ, Hunt, DC (2010) Comparing radiosonde and COSMIC atmospheric profile data to quantify differences among radiosonde types and the effects of imperfect collocation on comparison statistics. J Geophys Res 115. https://doi.org/10.1029/2010JD014457

  18. Kumar G, Madan R, Saikrishnan K, Kundu SK, Jain PK (2011) Technical and operational characteristics of GPS sounding system in the upper air network of IMD. Mausam 62:403–416. Available at: metnet.imd.gov.in/mausamdocs/16632\(\_\)F.pdf

    Google Scholar 

  19. Anthes RA, Ector D (2008) The COSMIC/FORMOSAT-3 mission: early results. Bull Amer Meteor Soc 89:313–333. https://doi.org/10.1175/BAMS-89-3-313

    Article  Google Scholar 

  20. Rocken C, Anthes R, Exner M, Hunt D, Sokolovski S, Ware R, Gorbunov M, Schreiner S, Feng D, Hermann B, Kuo Y-H, Zou X (1997) Analysis and validation of GPS/MET data in the neutral atmosphere. J Geophys Res 102(D25):29849–29866. https://doi.org/10.1029/97JD02400

    Article  Google Scholar 

  21. Anthes RA (2011) Exploring earths atmosphere with radio occultation: contributions to weather, climate and space weather. Atmos Meas Tech 4:1077–1103. https://doi.org/10.5194/amt-4-1077-2011

    Article  Google Scholar 

  22. Schmidt T, Wickert J, Haser A (2010) Variability of the upper troposphere and lower stratosphere observed with GPS radio occultation bending angles and temperatures. Adv Space Res 46:150–161. https://doi.org/10.1016/j.asr.2010.01.021

    Article  Google Scholar 

  23. Healy SB, Thépaut JN (2006) Assimilation experiment with CHAMP GPS radio occultation measurements. Q J R Meteorol Soc 132:605–623. https://doi.org/10.1256/qj.04.182

    Article  Google Scholar 

  24. Cucurull L, Derber JC, Treadon R, Purser R (2007) Assimilation of global positioning system radio occultation observations into NCEPs global data assimilation system. Mon Weather Rev 35:3174–3193. https://doi.org/10.1175/MWR3461.1

    Article  Google Scholar 

  25. Poli P, Healy SB, Rabier F, Pailleux J (2008) Preliminary assessment of the scalability of GPS radio occultations impact in numerical weather prediction. Geophys Res Lett 35. https://doi.org/10.1029/2008GL035873

  26. Poli P, Healy SB, Dee DP (2010) Assimilation of global positioning system radio occultation data in the ECMWF ERA-Interim reanalysis. Q J R Meteorol Soc 136. https://doi.org/10.1002/qj.722

  27. Foelsche U, Borsche M, Steiner AK, Gobiet M, Pirscher B, Kirchengast G, Wickert J, Schmidt T (2008) Observing upper troposphere-lower stratosphere climate with radio occultation from the CHAMP satellite. Clim Dyn 31:49–65. https://doi.org/10.1007/s00382-007-0337-7

    Article  Google Scholar 

  28. Steiner AK, Hunt D, Ho S-P, Kirchengast G, Mannucci AJ, Scherllin-Pirscher B, Gleisner H, von Engeln A, Schmidt T, Ao C, Leroy SS, Kursinski ER, Foelsche U, Gorbunov M, Heise S, Kuo Y-H, Lauritsen KB, Marquardt C, Rocken C, Schreiner W, Sokolovskiy S, Syndergaard S, Wickert J (2013) Quantification of structural uncertainty in climate data records from GPS radio occultation. Atmos Chem Phys 13:1469–1484. https://doi.org/10.5194/acp-13-1469-2013

    Article  Google Scholar 

  29. Lee IT, Matsuo T, Richmon AD, Liu JY, Wang W, Lin CH, Anderson JL, Chen MQ (2012) Assimilation of FORMOSAT-3/COSMIC electron density profiles into a coupled thermosphere/ionosphere model using ensemble Kalman filtering. J Geophys Res 117. https://doi.org/10.1029/2012JA017700

  30. Zhang ML, Liu L, Wan W, Ning B (2014) An update global model of hmF2 from values estimated from ionosonde and COSMIC/FORMOSAT-3 radio occultation. Adv Space Res 53:395–402. https://doi.org/10.1016/j.asr.2013.11.053

    Article  Google Scholar 

  31. Wickert J, Reigber C, Beyerle G, Koenig R, Marquardt C, Schmidt T, Grunwaldt L, Galas R, Meehan TK, Melbourne WG, Hocke K (2001) Atmosphere sounding by GPS radio occultation: first results from CHAMP. Geophys Res Lett 28(29):849–866. https://doi.org/10.1029/2001GL013117

    Article  Google Scholar 

  32. Wickert J, Michalak G, Schmidt T, Beyerle G, Cheng CZ, Healy SB, Heise S, Huang CY, Jakowski N, Kohler W, Mayer C, Offiler D, Ozawa E, Pavelyev AG, Rothacher M, Tapley B, Arras C (2009) GPS radio occultation: results from CHAMP, GRACE and FORMOSAT-3/COSMIC. Terr Atmos Ocean Sci 20:35–50. https://doi.org/10.3319/TAO.2007.12.26.01(F3C)

    Article  Google Scholar 

  33. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimbergere L, Healy SB, Hersbach H, Holm EV, Isaksen L, Kllberg P, Khler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette J-J, Park B-K, Peubey C, de Rosnay P, Tavolato C, Thpaut J-N, Vitarta F (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. https://doi.org/10.1002/qj.828

    Article  Google Scholar 

  34. Rienecker MM, Suarez MJ, Gelaro R, Todling R, Bacmeister J, Liu E, Bosilovich MG, Schubert SD, Takacs L, Kim GK, Bloom S, Chen J, Collins D, Conaty A, da Silva A, Gu W, Joiner J, Koster RD, Lucchesi R, Molod A, Owens T, Pawson S, Pegion P, Redder CR, Reichle R, Robertson FR, Ruddick AG, Sienkiewicz M, Woollen J (2011) MERRA: NASAs modern-era retrospective analysis for research and applications. J Clim 24:3624–3648. https://doi.org/10.1175/JCLI-D-11-00015.1

    Article  Google Scholar 

  35. Chowdhury MDR, Ward N (2004) Hydro-meteorological variability in the greater Ganges-Brahmaputra-Meghna basins. Int J Climatol 24:1495–1508. https://doi.org/10.1002/joc.1076

    Article  Google Scholar 

  36. Chowdhury MR (2003) The El Nino-Southern Oscillation (ENSO) and seasonal flooding -Bangladesh. Theoret Appl Climatol 76:105–124. https://doi.org/10.1007/s00704-003-0001-z

    Article  Google Scholar 

  37. Mirza MMQ, Warrick R, Ericksen N, Kenny G (1998) Trends and persistence in precipitation in the Ganges, Brahmaputra and Meghna river basins. Hydrol Sci 43. https://doi.org/10.1080/02626669809492182

  38. Ashok K, Saji NH (2007) On the impacts of ENSO and Indian Ocean dipole events on sub-regional Indian summer monsoon rainfall. Nat Hazards 42:273–285. https://doi.org/10.1007/s11069-006-9091-0

    Article  Google Scholar 

  39. Gautam R, Hsu NC, Lau KM, Tsay SC, Kafatos M (2009) Enhanced pre-monsoon warming over the himalayan-gangetic region from 1979 to 2007. Geophys Res Lett 36. https://doi.org/10.1029/2009GL037641

  40. Lau WKM, Kim KM, Hsu CN, Holben BN (2009) Possible influences of air pollution, dust- and sandstorms on the Indian monsoon. Bull World Meteorol Organ 58:22–30

    Google Scholar 

  41. Ho SP, Zhou X, Kuo YK, Hunt D, Wang JH (2010) Global evaluation of radiosonde water vapor systematic biases using GPS radio occultation from COSMIC and ECMWF analysis. Remote Sens 2(5):13201330. https://doi.org/10.3390/rs2051320

    Article  Google Scholar 

  42. Melbourne WG, Davis ES, Duncan CB, Hajj GA, Hardy K, Kursinski R, Mechan TK, Young LE, Yunck TP (1994) The application of spaceborne GPS to atmospheric limb sounding and global change monitoring. JPL Publication 94-18

    Google Scholar 

  43. Rienecker MM, Suarez MJ, Todling R, Bacmeister J, Takacs L, Liu HC, Gu W, Sienkiewicz M, Koster RD, Gelaro R, Stajner I, Nielsen JE (2008) The GEOS-5 data assimilation system documentation of versions 5.0.1, 5.1.0, and 5.2.0. NASA technical report series on global modeling and data assimilation vol 27, NASA/TM2008104606. NASA Center for AeroSpace Information, Maryland, US

    Google Scholar 

  44. Pan LL, Randel WJ, Gary BL, Mahony MJ, Hintsa EJ (2004) Definitions and sharpness of the extratropical tropopause: a trace gas perspective. J Geophys Res 109:D23103. https://doi.org/10.1029/2004JD004982

    Article  Google Scholar 

  45. Sausen R, Santer BD (2003) Use of changes in tropopause height to detect influences on climate. Meteorol Z 12(3):131–136. https://doi.org/10.1127/0941-2948/2003/0012-0131

    Article  Google Scholar 

  46. WMO (1957) Definition of tropopause. World Meteorological Organisation, Geneva

    Google Scholar 

  47. PSAS (2004) A quick derivation relating altitude to air pressure. Portland State Aerospace Society, US. http://psas.pdx.edu/RocketScience/PressureAltitude_Derived.pdf

  48. Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228:113–129. https://doi.org/10.1016/S0022-1694(00)00144-X

  49. Preisendorfer RW (1988) Principal component analysis in meteorology and oceanography. Elsevier

    Google Scholar 

  50. Forootan E (2014) Statistical signal decomposition techniques for analyzing time-variable satellite gravimetry data. Ph.D. thesis, University of Bonn, Germany. http://hss.ulb.uni-bonn.de/2014/3766/3766.htm

  51. Resmi E, Mohanakumar K, Appu K (2013) Effect of polar sudden stratospheric warming on the tropical stratosphere and troposphere and its surface signatures over the Indian region. J Atmos Solar-Terr Phys 105–106:15–29. https://doi.org/10.1016/j.jastp.2013.07.003

    Article  Google Scholar 

  52. Liang CK, Eldering A, Gettelman A, Tian B, Wong S, Fetzer EJ, Liou KN (2011) Record of tropical interannual variability of temperature and water vapor from a combined AIRS-MLS data set. J Geophys Res 116. https://doi.org/10.1029/2010JD014841

  53. Sen PK (1968) Estimates of the regression coefficient based on Kendalls Tau. J Am Stat Assoc 63:1379–1389. https://doi.org/10.1080/01621459.1968.10480934

    Article  Google Scholar 

  54. Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259

    Article  Google Scholar 

  55. Kendall MG (1962) Rank correlation methods. J Am Stat Assoc 63:1379–1389. https://doi.org/10.1080/01621459.1968.10480934

    Article  Google Scholar 

  56. Seidel DJ, Gillett NP, Lanzante JR, Shine KP, Thorne PW (2011) Stratospheric temperature trends: our evolving understanding. WIREs Clim Change 2:592–616. https://doi.org/10.1002/wcc.125

    Article  Google Scholar 

  57. Mehta SK, Ratnam MV, Murthy BVK (2010) Variability of the tropical tropopause over Indian monsoon region. J Geophys Res 115. https://doi.org/10.1029/2009JD012655

  58. Schmidt T, Wickert J, Beyerle G, Heise S (2008) Global tropopause height trends estimated from GPS radio occultation data. Geophys Res Lett 35:L11806. https://doi.org/10.1029/2008GL034012

    Article  Google Scholar 

  59. Khandu Awange JL, Wickert J, Schmidt T, Sharifi MA, Heck B, Fleming K (2011) GNSS remote sensing of the Australian tropopause. Clim Change 105(3–4):597–618. https://doi.org/10.1007/s10584-010-9894-6

    Article  Google Scholar 

  60. IPCC (2007) Summary for policymakers. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller H (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, USA

    Google Scholar 

  61. IPCC (2013) Summary for policymakers. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley P (eds) Climate change 2013: the physical science basis. Contribution of working 725 group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, USA

    Google Scholar 

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Awange, J., Kiema, J. (2019). GNSS Sensing of Climate Variability. In: Environmental Geoinformatics. Environmental Science and Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-030-03017-9_26

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