Global estimates of water‐vapor‐weighted mean temperature of the atmosphere for GPS applications

[1] Water-vapor-weighted atmospheric mean temperature, Tm, is a key parameter in the retrieval of atmospheric precipitable water (PW) from ground-based Global Positioning System (GPS) measurements of zenith path delay (ZPD), as the accuracy of the GPS-derived PW is proportional to the accuracy of Tm. We compare and analyze global estimates of Tm from three different data sets from 1997 to 2002: the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-year reanalysis (ERA-40), the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis, and the newly released Integrated Global Radiosonde Archive (IGRA) data set. Temperature and humidity profiles from both the ERA-40 and NCEP/NCAR reanalyses produce reasonable Tm estimates compared with those from the IGRA soundings. The ERA-40, however, is a better option for global Tm estimation because of its better performance and its higher spatial resolution. Tm is found to increase from below 255 K in polar regions to 295–300 K in the tropics, with small longitudinal variations. Tm has an annual range of ∼2–4 K in the tropics and 20–35 K over much of Eurasia and northern North America. The day-to-day Tm variations are 1–3 K over most low latitudes and 4–7 K (2–4 K) in winter (summer) Northern Hemispheric land areas. Diurnal variations of Tm are generally small, with mean-to-peak amplitudes less than 0.5 K over most oceans and 0.5–1.5 K over most land areas and a local time of maximum around 16–20 LST. The commonly used Tm-Ts relationship from Bevis et al. (1992) is evaluated using the ERA-40 data. Tm derived from this relationship (referred to as Tmb) has a cold bias in the tropics and subtropics (−1 ∼ −6 K, largest in marine stratiform cloud regions) and a warm bias in the middle and high latitudes (2–5 K, largest over mountain regions). The random error in Tmb is much smaller than the bias. A serious problem in Tmb is its erroneous large diurnal cycle owing to diurnally invariant Tm-Ts relationship and large Ts diurnal variations, which could result in a spurious diurnal cycle in GPS-derived PW and cause 1–2% day-night biases in GPS-based PW.

[1]  Anup K. Prasad,et al.  Climate monitoring using GPS and MODIS water vapor , 2006 .

[2]  Russell S. Vose,et al.  Overview of the Integrated Global Radiosonde Archive , 2006 .

[3]  A. Sterl,et al.  The ERA‐40 re‐analysis , 2005 .

[4]  D. Seidel,et al.  Diurnal cycle of upper-air temperature estimated from radiosondes , 2005 .

[5]  Jean-Pierre Aubagnac,et al.  Comparison of Near–Real Time Estimates of Integrated Water Vapor Derived with GPS, Radiosondes, and Microwave Radiometer , 2005 .

[6]  G. Deblonde,et al.  Evaluation of GPS precipitable water over canada and the IGS network , 2005 .

[7]  Nils Wedi,et al.  Comparison of trends and low-frequency variability in CRU, ERA-40, and NCEP//NCAR analyses of surface air temperature , 2004 .

[8]  Galina Dick,et al.  Near Real Time GPS Water Vapor Monitoring for Numerical Weather Prediction in Germany , 2004 .

[9]  Reinhard Dietrich,et al.  Comparison of Tropospheric Water Vapour over Antarctica Derived from AMSU-B Data, Ground-Based GPS Data and the NCEP/NCAR Reanalysis , 2004 .

[10]  K. Trenberth,et al.  The Diurnal Cycle and Its Depiction in the Community Climate System Model , 2004 .

[11]  L. Isaksen,et al.  The ERA-40 Reanalysis , 2004 .

[12]  Gerd Gendt,et al.  On the determination of atmospheric water vapor from GPS measurements , 2003 .

[13]  Jan-Peter Muller,et al.  Comparison of precipitable water vapor derived from radiosonde, GPS, and Moderate‐Resolution Imaging Spectroradiometer measurements , 2003 .

[14]  Norman T. O'Neill,et al.  Multisensor analysis of integrated atmospheric water vapor over Canada and Alaska , 2003 .

[15]  Fujio Kimura,et al.  Diurnal Variation of Precipitable Water over a Mountainous Area of Sumatra Island , 2003 .

[16]  Dean Lauritsen,et al.  Performance of operational radiosonde humidity sensors in direct comparison with a chilled mirror dew‐point hygrometer and its climate implication , 2003 .

[17]  Guergana Guerova,et al.  Validation of NWP Mesoscale Models with Swiss GPS Network AGNES , 2003 .

[18]  David Carlson,et al.  Corrections of Humidity Measurement Errors from the Vaisala RS80 Radiosonde—Application to TOGA COARE Data , 2002 .

[19]  Junhong Wang,et al.  Diurnal variation in water vapor over North America and its implications for sampling errors in radiosonde humidity , 2002 .

[20]  Hans van der Marel,et al.  Integrated atmospheric water vapor estimates from a regional GPS network , 2002 .

[21]  Yuei-An Liou,et al.  Comparison of Precipitable Water Observations in the Near Tropics by GPS, Microwave Radiometer, and Radiosondes , 2001 .

[22]  Isao Naito,et al.  Comparisons of GPS‐derived precipitable water vapors with radiosonde observations in Japan , 2000 .

[23]  T. Emardson,et al.  On the relation between the wet delay and the integrated precipitable water vapour in the European atmosphere , 2000 .

[24]  Richard B. Langley,et al.  An Evaluation of the Accuracy of Models for the Determination of the Weighted Mean Temperature of the Atmosphere , 2000 .

[25]  Rebecca J. Ross,et al.  Correction to “Estimating mean weighted temperature of the atmosphere for Global Positioning System applications” by Rebecca J. Ross and Simon Rosenfeld , 1999 .

[26]  Paul Tregoning,et al.  Accuracy of absolute precipitable water vapor estimates from GPS observations , 1998 .

[27]  Niklaus Kämpfer,et al.  Weighted mean tropospheric temperature and transmittance determination at millimeter‐wave frequencies for ground‐based applications , 1998 .

[28]  P. Watson,et al.  Comparison of integrated precipitable water vapour obtained by GPS and radiosondes , 1998 .

[29]  Jan M. Johansson,et al.  Three months of continuous monitoring of atmospheric water vapor with a network of Global Positioning System receivers , 1998 .

[30]  Christian Rocken,et al.  Near real‐time GPS sensing of atmospheric water vapor , 1997 .

[31]  Rebecca J. Ross,et al.  Estimating mean weighted temperature of the atmosphere for Global Positioning System applications , 1997 .

[32]  G. Stephens,et al.  A new global water vapor dataset , 1996 .

[33]  Steven Businger,et al.  GPS Meteorology: Mapping Zenith Wet Delays onto Precipitable Water , 1994 .

[34]  J. Worley,et al.  An introduction to atmospheric and oceanographic datasets , 1994 .

[35]  Steven Businger,et al.  Sensing atmospheric water vapor with the global positioning system , 1993 .

[36]  S. Klein,et al.  The Seasonal Cycle of Low Stratiform Clouds , 1993 .

[37]  Ying-Hwa Kuo,et al.  Assimilation of Precipitable Water Measurements into a Mesoscale Numerical Model , 1993 .

[38]  T. Herring,et al.  GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System , 1992 .

[39]  T. Barnett,et al.  Space and Time Scales of Global Tropospheric Moisture , 1991 .

[40]  Gunnar Elgered,et al.  Geodesy by radio interferometry - Water vapor radiometry for estimation of the wet delay , 1991 .

[41]  I. Shapiro,et al.  Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length , 1985 .

[42]  G. Smith,et al.  Further Study on Atmospheric Lapse Rate Regimes , 1985 .

[43]  J. Giraytys.  symposium on meteorological observations and instrumentation , 1969 .