Weather, Climate and Global Warming

  • Joseph L. AwangeEmail author
  • John B. Kyalo Kiema
Part of the Environmental Science and Engineering book series (ESE)


In order to fully appreciate the contribution of geoinformatics in monitoring climate change caused by increase in temperature, a distinction between weather and climate, on one hand, and climate variability and climate change, on the other hand, is essential. Burroughs (2007) points out that weather is what is happening to the atmosphere at any given time (i.e., what one gets), whereas climate is what would be expected to occur at any given time of the year based on statistics built up over many years (i.e., what one expects).


Carbon Stock Numerical Weather Prediction Radio Occultation Numerical Weather Prediction Model Zenith Total Delay 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Abdalati W, Zwally HJ, Bindschadler B, Csatho B, Farrell SL, Fricker HA, Harding D, Kwok R, Lefsky M, Markus T, Marshak A, Neumann T, Palm S, Schutz B, Smith B, Spinhirne J, Webb C (2010) The ICESat-2 laser altimetry mission. Proc IEEE 98(5):735–751. doi: 10.1109/JPROC.2009.2034765 CrossRefGoogle Scholar
  2. Agola NO and Awange JL (2013) Globalized poverty and environment.Google Scholar
  3. Agudelo PA, Curry JA (2004) Analysis of spatial distribution in tropospheric temperature trends. Geophys Res Lett 31(L22207): doi: 10.1029/2004GL02818
  4. Anel JA, Gimeno L, Torre LDI, Nieto R (2006) Changes in tropopause. Naturwissenschaften. doi: 10.1007/S00114-006-0147-5
  5. Anthes RA, Rocken C, Kuo YH (2000) Applications of COSMIC to meteorology and climate. Terr Atmos Ocean Sci 11:115–156Google Scholar
  6. Atheru ZKK, Ogallo LA, Ambenje PG (2000) Regional climate forecasts for enhanced food production to alleviate rural poverty around the Lake Victoria region. KMFRI, Nairobi, pp 28–30Google Scholar
  7. Awange JL, Fukuda Y (2003) On possible use of GPS-LEO satellite for flood forecasting. The international civil engineering conference on sustainable development in the 21st century—“the civil engineer in development”, 12–16 August 2003, Nairobi, KenyaGoogle Scholar
  8. Awange JL, Grafarend EW (2005) Solving algebraic computational problems in geodesy and geoinformatics. Springer, BerlinGoogle Scholar
  9. Awange JL, Ogallo L, Kwang-Ho B, Were P, Omondi P, Omute P, Omulo M (2008) Falling Lake Victoria water levels: Is climate a contribution factor? J Clim Change 89:287–297. doi: 10.1007/s10584-008-9409-x Google Scholar
  10. Awange JL, Grafarend EW, Palánczz B, Zaletnyik P (2010) Algebraic geodesy and geoinformatics, 2nd edn. Springer, BerlinCrossRefGoogle Scholar
  11. Awange JL (2012) Environmental monitoring using GNSS, Global Navigation Satellite Systems. Springer, Berlin, New YorkGoogle Scholar
  12. Baker HC, Dodson AH, Penna NT, Higgins M, Offiler D (2001) Ground-based GPS water vapour estimation: potential for meteorological forecasting. J Atmos Solar-Terr Phys 63(12):1305–1314CrossRefGoogle Scholar
  13. Baur O, Kuhn M, Featherstone W (2009) GRACE-derived ice-mass variations over Greenland by accounting for leakage effects. J Geophys Res 114(B06407). doi: 10.1029/2008JB006239
  14. Beaudoin AB (2002) On the identification and characterization of drought and aridity in postglacial paleoenvironmental records from the northern great plains. Géogr Phys Quat 56(2–3):229–246. E-SCAPE contribution 3. Note: Volume dated 2002, but published in 2004Google Scholar
  15. Belvis M, Businger S, Herring TA, Rocken C, Anthes RA, Ware RH (1992) GPS Meteorology: remote sensing of water vapour using global positioning system. J Geophys Res 97:15787–15801CrossRefGoogle Scholar
  16. Belvis M, Businger S, Chiswell S, Herring TA, Anthes RA, Rocken C, Ware RH (1994) GPS Meteorology: mapping zenith wet delays onto precipitable water. J Appl Meteorol 33:379–386CrossRefGoogle Scholar
  17. Bergthorsson P, Döös B (1955) Numerical weather map analysis. Tellus 7:329–340CrossRefGoogle Scholar
  18. Bjerknes V (1904) Das Problem der Wettervorhersage, betrachtet vom Standpunkt der Mechanik und der Physik. Meteor Zeits 21:1–7Google Scholar
  19. Blair JB, Rabine D, Hofton M (1999) The Laser Vegetation Imaging Sensor (LVIS): a medium-altitude, digitization only, airborne laser altimeter for mapping. ISPRS 54:115–122CrossRefGoogle Scholar
  20. Bonino EE (2006) Changes in carbon pools associated with a land-use gradient in the Dry Chaco, Argentina. For Ecol Manag 223:181–189CrossRefGoogle Scholar
  21. Brutsaert W (2005) Hydrology. An introduction, 4th edn. Cambridge University Press, New YorkGoogle Scholar
  22. Bureau of Meteorology, Australia (2009) Australia’s climate change and variability. Accessed 25 Oct 2009
  23. Burroughs WJ (2007) Climate change: a multidisciplinary approach, 2nd edn. Cambridge University Press, CambridgeGoogle Scholar
  24. Charney JG (1955) The use of primitive equations of motion in numerical prediction. Tellus 7:22–26CrossRefGoogle Scholar
  25. Charney JG, Fjørtoft R, von Neuman J (1950) Numerical integration of the barotropic vorticity equation. Tellus 2:237–254CrossRefGoogle Scholar
  26. Christy JR, Spencer RW, Lobl ES (1998) Analysis of the merging procedure for the MSU daily temperature series. J Clim 11:2016–2041CrossRefGoogle Scholar
  27. Christy JR, Spencer RW, Braswell WD (2000) MSU tropospheric temperatures: dataset construction and radiosonde comparisons. J Atmos Ocean Technol 17:1153–1170CrossRefGoogle Scholar
  28. Christy JR, Spencer RW, Norris WB, Braswell WD, Parker DE (2003) Error estimates of version 5.0 of MSU-AMSU bulk atmospheric temperatures. J Atmos Ocean Technol 20(5):613–629CrossRefGoogle Scholar
  29. Daley R (1991) Atmospheric data analysis. Cambridge University Press, CambridgeGoogle Scholar
  30. Dole RM, Hoerling M, Schubert S (eds) (2008) Reanalysis of historical climate data for key atmospheric features: implications for attribution of causes of observed change. A report by the U.S. Climate Change Science Program (CCSP) and the Subcommittee on Global Change Research. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, 156 ppGoogle Scholar
  31. Elliot WP, Gaffen DJ (1991) On the utility of radiosonde humidity archives for climate studies. Bull Am Meteorol Soc 72:1507–1520CrossRefGoogle Scholar
  32. Emanuel K (2005) Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436:686–688CrossRefGoogle Scholar
  33. Fearnside PM, Laurance WF (2003) Determination of deforestration rates of the world’s humid tropical forests. Science 299:10–15CrossRefGoogle Scholar
  34. Flores A, Ruffini G, Rius A (2000) 4D tropospheric tomography using GPS slant wet delay. Ann Geophys 18:223–234CrossRefGoogle Scholar
  35. Foelsche U, Kirchengast G, Steiner AK (2006a) Atmosphere and climate. Studies by occultation methods. Springer, BerlinCrossRefGoogle Scholar
  36. Foelsche U, Gobiet A, Steiner AK, Borsche M, Wickert J, Schmidt T, Kirchengast G (2006b) Global climatologies based on radio occultation data: the CHAMPCLIM project. In: Foelsche U, Kirchengast G, Steiner A (eds) Atmosphere and climate studies by occultation methods. Springer, Berlin, pp 303–314Google Scholar
  37. Free M, Seidel DJ (2005) Causes of differing temperature trends in radiosonde upper air data sets. J Geophys Res 110. doi: 10.1029/2004JD005481
  38. Gibbs HK, Brown S, Niles JO, Foley JA (2007) Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ Res Lett 2:23–45Google Scholar
  39. Hammond WC, Brooks BA, Bürgmann R, Heaton T, Jackson M, Lowry AR, and Anandakrishnan S (2010) The scientific value of high-rate, low-latency GPS data. A white paperGoogle Scholar
  40. 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. doi: 10.1029/2011EO150001 CrossRefGoogle Scholar
  41. Hanssen RF, Weckwerth TM, Zebker HA, Klees R (1999) High-resolution water vapour mapping from interferometric radar measurements. Science 283:1297–1299CrossRefGoogle Scholar
  42. Healey SB, Thépaut JN (2006) Assimilation experiment with CHAMP GPS radio occultation measurements. Quart J R Meterol Soc 132:605–623. doi: 10.1256/qj.04.182 CrossRefGoogle Scholar
  43. Healey SB, Jupp AM, Marquardt C (2005) Forecast impact experiment with GPS radio occultation measurements. Geophys Res Lett 32: L03804.1–L03804.4Google Scholar
  44. Highwood EJ, Hoskins BJ, Berrisforde P (2000) Properties of the Arctic tropopause. Meteorol Soc 126:1515–1532CrossRefGoogle Scholar
  45. Houghton RA (2005) Above ground forest biomass and the global carbon balance. Global Change Biol 11:945–958CrossRefGoogle Scholar
  46. IPCC (Intergovernmental Panel on Climate Change) (2001) Climate change 2001: the scientific basis. Cambridge University Press, Cambridge, 881 ppGoogle Scholar
  47. IPCC (Intergovernmental Panel on Climate Change) (2007) Contribution of Working Group I to the fourth assessment reportGoogle Scholar
  48. Jallow BP, Barrow MKA, Leatherman SP (1996) Vulnerability of the coastal zone of the Gambia to sea level rise and development of response options. Clim Res 6:165–177CrossRefGoogle Scholar
  49. Jiang H, Apps MJ, Peng CH, Zhang YL, Liu JX (2002) Modelling the influence of harvesting on Chinese boreal forest carbon dynamics. For Ecol Manag 169:65–82CrossRefGoogle Scholar
  50. Kalnay E (2003) Atmospheric modeling, data assimilation and predictability. Cambridge University Press, CambridgeGoogle Scholar
  51. Khandu, Awange JL, Wickert J, Schmidt T, Sharifi MA, Heck B, Fleming K (2010) GNSS remote sensing of the Australian tropopause. Climatic Change 105(3-4): 597-618, doi: 10.1007/s10584-010-9894-6
  52. Krabill WE, Hanna P, Huybrechts W, Abdalati J, Cappelen B, Csatho B, Frefick E, Manizade S, Martin C, Sonntag J, Swift R, Thomas R, Yungel J (2004) Greenland ice sheet: increased coastal thinning. Geophys Res Lett 31:L24402. doi: 10.1029/2004GL021533
  53. Kumi-Boateng B (2012) A spatio-temporal based estimation of vegetation changes in the Tarkwa mining area of Ghana. Doctor of Philosophy. Dissertation, University of Mines and Technology, Ghana, 165 ppGoogle Scholar
  54. Kuo Y-H, Sokolovski SV, Anthens RA, Vandenberghe F (2000) Assimilation of the GPS radio occultation data for numerical weather prediction. Terr Atmos Ocean Sci 11:157–186Google Scholar
  55. Kursinski ER, Hajj GA, Schofield JT, Linfield RP, Hardy KR (1997) Observing Earth’s atmosphere with radio occultation measurements using global positioning system. J Geophys Res 102(D19): 23429–23465Google Scholar
  56. Le Toan T, Ribbes F, Floury N, Wang LF, Ding KH, Kong JA, Fujita M, Kurosu T (1997) Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results. IEEE Trans Geosci Remote Sens 35:41–56CrossRefGoogle Scholar
  57. Leroy SS (1997) Measurements of geopotential heights by GPS radio occultation. J Geophys Res 102:6971–6986CrossRefGoogle Scholar
  58. Leroy SS, Dykema JA, Anderson JG (2006) Climate benchmarking using GNSS occultation. In: Foelsche U, Kirchengast G, Steiner A (eds) Atmosphere and climate studies by occultation methods. Springer, Berlin, pp 287–301Google Scholar
  59. Li XY, Xu HY, Sun YL, Zhang DS, Yang ZP (2007) Lake-level change and water balance analysis at Lake Qinghai, West China during recent decades. Water Resour Manag 21:1505–1516. doi: 10.1007/s11269-006-9096-1 CrossRefGoogle Scholar
  60. Magadza CHD (1996) Climate change: some likely multiple impacts in southern Africa. In: Downing TE (ed) Climate change and world food security. Springer, Heidelberg, pp 449–483CrossRefGoogle Scholar
  61. Malhi Y, Grace J (2000) Tropical forests and atmospheric carbon dioxide. Trends Ecol Evol 15:332–337CrossRefGoogle Scholar
  62. Manneh A (1997) Vulnerability of the water resources sector of The Gambia to climate change. In: Republic of the Gambia: final report of The Gambia/U.S. Country study program project on assessment of the vulnerability of the major economic sectors of the Gambia to the projected climate change. Banjul, The Gambia (unpublished)Google Scholar
  63. Martens WJM (1998) Health impacts of climate change and ozone depletion: an ecoepidemiologic modeling approach. Environ Health Perspect 106:241–251Google Scholar
  64. Martens WJM, Niessen LW, Rotmans J, Jetten TH, McMichael AJ (1995) Potential impact of global climate change on malaria risk. Environ Health Perspect 103:458–464CrossRefGoogle Scholar
  65. Mears C, Schabel M, Wents F (2003) A reanalysis of MSU channel 2 tropospheric temperature trend. J Clim 16(22):3560–3664CrossRefGoogle Scholar
  66. 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, Pasadena, pp 94–18Google Scholar
  67. Mistry VV, Conway D (2003) Remote forcing of East African rainfall and relationships with fluctuations in levels of Lake Victoria. Int J Clim 23:67–89CrossRefGoogle Scholar
  68. Mitrovica JX, Gomez N, Clark PU (2009) The sea-level fingerprint of West Antarctic collapse. Science 323(5915):753. doi: 10.1126/science.1166510 CrossRefGoogle Scholar
  69. Myneni RB, Dong JR, Tucker CJ, Kaufmann RK, Kauppi PE, Liski J, Zhou L, Alexeyev V, Hudges MK (2001) A large carbon sink in the woody biomass of Northern forest. Proc Natl Acad Sci 98:14784–14789CrossRefGoogle Scholar
  70. Nagurny AP (1998) Climatic characteristics of the tropopause over the Arctic Basin. Ann Geophys 16:110–115CrossRefGoogle Scholar
  71. Nyakwada W (2000) The use of weather and climate forecasts by rural people to enhance food production. In: Akunda E, Mango C, Oteng’l SBB et al (eds) Sustainable environmental management for poverty alleviation in the Lake Victoria Basin. KMFRI, pp 38–42Google Scholar
  72. Okoola RE (2000) Climate change as related to food production for the alleviation of rural poverty in the Lake basin region. In: Akunda E, Mango C, Oteng’i SBB et al (eds) Sustainable environmental management for poverty alleviation in the Lake Victoria Basin. KMFRI, pp 43–45Google Scholar
  73. Otengi SBB (2000) Weather and climate hazards that affect food production in the Lake Victoria Basin. In: Akunda E, Mango C, Oteng’i SBB et al (eds) Sustainable environmental management for poverty alleviation in the Lake Victoria Basin, Kisii, 3–5 Oct 1995. KMFRI, pp 24–27Google Scholar
  74. 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. doi: 10.1029/2004JD004982
  75. Parker DE, Gorden M, Cullum DPN, Sexton DMH, Folland CK, Rayner N (1997) A new global gridded radiosonde temperature database and recent temperature trends. Geophys Res Lett 24:1499–1502CrossRefGoogle Scholar
  76. Patenaude G, Milne R, Dawson TP (2005) Synthesis of remote sensing approaches for forest carbon estimation: reporting to the Kyoto Protocol. Environ Sci Policy 8(2):161–178CrossRefGoogle Scholar
  77. Phillips S (2006) Water crisis. COSMOS, issue 9, June 2006.
  78. Pittcock B (2003) Climate change: an Australian guide to the science and potential impacts. Climate change, Australian Greenhouse Office, CanberraGoogle Scholar
  79. Poli P (2006) Assimilation of GNSS radio occultation data into numerical weather prediction. In: Foelsche U, Kirchengast G, Steiner A (eds) Atmosphere and climate studies by occultation methods. Springer, Berlin, pp 195–204CrossRefGoogle Scholar
  80. Poli P, Pailleux J, Ducrocq V, Moll P, Rabier F, Mauprivez M, Dufour S, Grondin M, Lechat-Carvalho F, De Latour A, Issler J, Ries L (2008) Weather report. Meteorological applications of GNSS from space and on the ground. InsideGNSS 3(8):30–39Google Scholar
  81. Prince SD, Goward S (1995) Global primary production: a remote sensing approach. J Biogeogr 22:815–835CrossRefGoogle Scholar
  82. Randall DA, Tjemkes S (1991) Clouds, the Earth’s radiation budget and the hydrological cycle. Palaeogeogr Palaeoclimatol Palaeoecol 90:3–9Google Scholar
  83. Randel WJ, Wu F, Gaffen DJ (2000) Interannual variability of the tropical tropopause derived from radiosonde data and NCEP reanalyses. J Geophys Res 105:15509–15524CrossRefGoogle Scholar
  84. Ranson KJ, Sun G, Weishample JF, Knox RG (1997) Forest biomass from combined ecosystem and radar backscatter. Remote Sens Environ 59:118–133CrossRefGoogle Scholar
  85. Richards TS, Gallego J, Achard F (2000) Sampling for forest cover change assessment at the pantropical scale. Int J Remote Sens 21:1473–1490CrossRefGoogle Scholar
  86. Richardson LF (2007) Weather prediction by numerical process, 2nd edn. Cambridge Mathematical Library (the first edition appeared in 1922), CambridgeGoogle Scholar
  87. Rocken C, Ware R, Hove TV, Solheim F, Alber C, Johnson J, Belvis M, Businger S (1993) Sensing atmospheric water vapour with the global positioning system. Geophys Res Lett 20(23):2631–2634CrossRefGoogle Scholar
  88. 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 102:29849–29860CrossRefGoogle Scholar
  89. Rosenqvist Å, Imhoff M, Milne A, Dobson C (eds) (1999) Remote sensing and the Kyoto Protocol: a review of available and future technology for monitoring treaty compliance. Report of a workshop, Ann Arbor, Michigan, USA, 20–22 Oct 1999Google Scholar
  90. Rosenqvist A, Milne T, Lucas R, Imhoff M, Dobson C (2003) A review of remote sensing technology in support of the Kyoto Protocol. Environ Sci Policy 6(5):441–455CrossRefGoogle Scholar
  91. 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 301:479–483CrossRefGoogle Scholar
  92. Santer BD, Wigley TML, Simmons AJ, Kallberg PW, Kelly GA, Uppala SM, Ammann C, Boyle JS, Bruggemann W, Doutriaux C, Fiorino M, Mears C, Meehl GA, Sausen R, Taylor KE, Washington WM, Wehner MF, Wentz FJ (2004) Identification of anthropogenic climate change using a second-generation reanalysis. J Geophys Res 109. doi: 10.1029/2004JD005075
  93. Sausen R, Santer BD (2003) Use of changes in tropopause height to detect influences on climate. Meteorol Zeits 12(3):131–136CrossRefGoogle Scholar
  94. Schmidt T, Heise S, Wickert J, Beyerle G, Reigber C (2005) GPS radio occultation with CHAMP and SAC-C: global monitoring of thermal tropopause parameters. Atmos Chem Phys 5:1473–1488CrossRefGoogle Scholar
  95. Schmidt T, Wickert J, Beyerle G, Heise S (2008) Global tropopause height trends estimated from GPS radio occultation data. Geophys Res Lett 35:L11806. doi: 10.1029/2008GL034012 CrossRefGoogle Scholar
  96. Schröder T, Leroy S, Stendel M, Kaas E (2003) Stratospheric temperatures probed by microwave sounding units or by occultation of the global positioning system. Geophys Res Lett 30. doi: 10.1029/2003GL017588
  97. Seidel DJ, Randel WJ (2006) Variability and trends in the global tropopause estimated from radiosonde data. J Geophys Res 111. doi: 10.1029/2006JD007363
  98. Seidel JD, Ross RJ, Angell JK, Reid GC (2001) Climatological characteristics of the tropical tropopause as revealed by radiosondes. J Geophys Res 106:7857–7878CrossRefGoogle Scholar
  99. Shea DJ, Wifley SJ, Stern IR, Hoar TJ (1994) An introduction to atmospheric and oceanographic data. NCAR Tech. NoteGoogle Scholar
  100. Slaymaker O, Kelly REJ (2007) The cryosphere and global environmental change (Environmental systems and global change series), 1 edn. Wiley-Blackwell, New YorkGoogle Scholar
  101. Spencer RW, Christy JR, Grody NC (1990) Global atmospheric temperature monitoring with satellite microwave measurements: methods and results 1979–84. J Clim 3:1111–1128CrossRefGoogle Scholar
  102. Steffen W, Sanderson A, Tyson PD, Jger J, Matson PA, Moore BIII, Oldfield F, Richardson K, Schellnhuber HJ, Turner BLII, Wasson RJ (2005) Global change and the earth system: a planet under pressure. Springer, BerlinGoogle Scholar
  103. Stendel M (2006) Monitoring climate variability and change by means of GNSS data. In: Foelsche U, Kirchengast G, Steiner A (eds) Atmosphere and climate studies by occultation methods. Springer, Berlin, pp 275–285CrossRefGoogle Scholar
  104. Syndergaard S, Kuo Y-H, Lohmann MS (2006) Observation operators for the assimilation of occultation data into atmospheric models: a review. In: Foelsche U, Kirchengast G, Steiner A (eds) Atmosphere and climate studies by occultation methods. Springer, Berlin, pp 205–224CrossRefGoogle Scholar
  105. Talagrand O (1997) Assimilation of observations, an introduction. J Meteorol Soc Jpn (Spl Issue) 75(1B):191–209Google Scholar
  106. Tao W (2008) Near real-time GPS PPP-inferred water vapour system development and evaluation. MSc thesis, UCGE Reports No. 20275. Accessed 26 Aug 2009
  107. Trenberth K, Guillemot C (1996) Evaluation of the atmospheric moisture and hydrological cycle in the NCEP Reanalyses. NCAR Technical Note TN-430, Dec 1996Google Scholar
  108. Tutu BD (2008) Assessing the effects of land-use/cover change on ecosystem services in Ejisu-Juaben District, Ghana. MSc thesis, International Institute for Geo-Information Science and Earth Observation, Enschede, 88 ppGoogle Scholar
  109. Ummenhofer C, England M, McIntosh P, Meyers G, Pook M, Risbey J, Gupta A, Taschetto A (2009) What causes southeast Australiaś worst droughts? Geophys Res Lett 36:L04706. doi: 10.1029/2008GL036801 CrossRefGoogle Scholar
  110. UN (1998) Kyoto Protocol to the United Nations framework convention on climate change. Accessed 14 July 2010
  111. UNFCCC (2007) Kyoto Protocol reference manual on accounting of emissions and assigned amounts. Framework convention on climate change.
  112. Uriel K (1998) Landscape ecology and epidemiology of vector-borne diseases: tools for spatial analysis. J Med Entomol 35(4):435–445Google Scholar
  113. Varotsos C, Cartalis C, Vlamakis A, Tzanis C, Keramitsoglou I (2004) The long-term coupling between column ozone and tropopause properties. J Clim 17:3843–3854CrossRefGoogle Scholar
  114. Velicogna I (2009) Increasing rates of ice mass loss from the Greenland and Antarctic ice sheets revealed by GRACE. Geophys Res Lett 36:L19503. doi: 10.1029/2009GL040222 CrossRefGoogle Scholar
  115. Vinnikov KY, Grody NC (2003) Global warming trend of mean tropospheric temperature observed by satellites. Science 302:269–272Google Scholar
  116. Ware H, Fulker D, Stein S, Anderson D, Avery S, Clerk R, Droegmeier K, Kuettner J, Minster B, Sorooshian S (2000) Real-time national GPS networks: opportunities for atmospheric sensing. Earth Planet Space 52:901–905Google Scholar
  117. Ware R, Exner M, Feng D, Gorbunov M, Hardy K, Herman B et al (1996) GPS sounding of atmosphere from low earth orbit: preliminary results. Bull Am Meteorol Soc 77:19–40CrossRefGoogle Scholar
  118. Wickert J, Beyerle G, Konig K, Heise S, Grunwaldt L, Michalak G, Reigber C, Schmidt T (2005) GPS radio occultation with CHAMP and GRACE: a first look at a new and promising satellite configuration for global atmospheric sounding. Ann Geophys 23:653–657Google Scholar
  119. Wickert J, Michalak G, Schmidt T, Beyerle G, Cheng CZ, Healy SB, Heise S, Huang CY, Jakowski N, Köhler W, Mayer C, Offiler D, Ozawa E, Pavelyev AG, Rothacher M, Tapley B, Arras C (2009) GPS ra dio occultation: Re sults from CHAMP, GRACE and FORMOSAT-3/COS MIC. Terr Atmos Ocean Sci., 20:35–50, doi: 10.3319/TAO.2007.12.26.01(F3C)
  120. WMO (1957) Definition of tropopause. World Meteorological Organisation, GenevaGoogle Scholar
  121. Yuan LL, Anthes RA, Ware RH, Rocken C, Bonner WD, Bevis MG, Businger S (1993) Sensing climate-change using the global positioning system. J Geophys Res 98(D8):14925–14937Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Spatial SciencesCurtin University of TechnologyPerthAustralia
  2. 2.Karlsruhe Institute of TechnologyKarlsruheGermany
  3. 3.Kyoto UniversityKyotoJapan
  4. 4.School of EnvironmentMaseno UniversityKisumuKenya
  5. 5.Geospatial and Space TechnologyUniversity of NairobiNairobiKenya

Personalised recommendations