Global Evaluation of Radiosonde Water Vapor Systematic Biases using GPS Radio Occultation from COSMIC and ECMWF Analysis

Ho, Shu-peng; Zhou, Xinjia; Kuo, Ying‐Hwa; Hunt, Douglas; Wang, Junhong

doi:10.3390/rs2051320

Cited by 91 publications

(99 citation statements)

References 17 publications

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“…Kursinski et al (1997), Rocken et al (1997), Kursinski and Hajj (2001), and Colard and Healey (2003) described the retrieval process of humidity profiles from GPSRO observations. Steiner et al (1999), Gorbunov and Kornblueh (2001), Divakarla et al (2006), Ho et al (2007), Chou et al (2009), Ho et al (2010, Sun et al (2010), Gorbunov et al (2011), Kishore et al (2011), Wang et al (2013, and Vergados et al (2014) validated the GPSRO-based humidity retrievals against reanalyses, radiosondes, and satellite observations, while recently Kursinski and Gebhardt (2014) reported an innovative technique to further reduce and eliminate retrieval biases in the middle-troposphere humidity products.…”

Section: Introductionmentioning

confidence: 99%

On the comparisons of tropical relative humidity in the lower and middle troposphere among COSMIC radio occultations and MERRA and ECMWF data sets

Vergados

Mannucci

et al. 2015

Atmos. Meas. Tech.

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Abstract. The spatial variability of the tropical tropospheric relative humidity (RH) throughout the vertical extent of the troposphere is examined using Global Positioning System Radio Occultation (GPSRO) observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission. These high vertical resolution observations capture the detailed structure and moisture budget of the Hadley Cell circulation. We compare the COSMIC observations with the European Center for Medium-range Weather Forecast (ECMWF) Reanalysis Interim (ERA-Interim) and the Modern-Era Retrospective analysis for Research and Applications (MERRA) climatologies. Qualitatively, the spatial pattern of RH in all data sets matches up remarkably well, capturing distinct features of the general circulation. However, RH discrepancies exist between ERA-Interim and COSMIC data sets that are noticeable across the tropical boundary layer. Specifically, ERA-Interim shows a drier Intertropical Convergence Zone (ITCZ) by 15-20 % compared to both COSMIC and MERRA data sets, but this difference decreases with altitude. Unlike ECMWF, MERRA shows an excellent agreement with the COSMIC observations except above 400 hPa, where GPSRO observations capture drier air by 5-10 %. RH climatologies were also used to evaluate intraseasonal variability. The results indicate that the tropical middle troposphere at ±5-25 • is most sensitive to seasonal variations. COSMIC and MERRA data sets capture the same magnitude of the seasonal variability, but ERA-Interim shows a weaker seasonal fluctuation up to 10 % in the middle troposphere inside the dry air subsidence regions of the Hadley Cell. Over the ITCZ, RH varies by maximum 9 % between winter and summer.

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Section: Introductionmentioning

confidence: 99%

On the comparisons of tropical relative humidity in the lower and middle troposphere among COSMIC radio occultations and MERRA and ECMWF data sets

Vergados

Mannucci

et al. 2015

Atmos. Meas. Tech.

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“…Ho et al (2007) combined AIRS and RO data retrieving SH profiles in the lower troposphere with root-mean-square error (RMSE) between 0.40 g kg −1 (at 700 hPa) and 0.05 g kg −1 (at 400 hPa). Ho et al (2010) collocated RO and ECMWF profiles near radiosonde locations and estimated that the standard deviation of the differences between the two data sets is < 0.50 g kg −1 above 3.0 km altitude. Kishore et al (2011) estimated that the differences between the ERA-Interim and COSMIC are −0.15±0.22 g kg −1 at 3.0 km and −0.07±0.06 g kg −1 at 7.0 km in the deep tropics (±20 • ).…”

Section: Atmospheric Infrared Soundermentioning

confidence: 99%

Comparisons of the tropospheric specific humidity from GPS radio occultations with ERA-Interim, NASA MERRA, and AIRS data

Vergados

Mannucci

et al. 2018

Atmos. Meas. Tech.

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Abstract. We construct a 9-year data record (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) of the tropospheric specific humidity using Global Positioning System radio occultation (GPS RO) observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission. This record covers the ±40 • latitude belt and includes estimates of the zonally averaged monthly mean specific humidity from 700 up to 400 hPa. It includes three major climate zones: (a) the deep tropics (±15 • ), (b) the trade winds belts (±15-30 • ), and (c) the subtropics (±30-40 • ). We find that the RO observations agree very well with the European Centre for Medium-Range Weather Forecasts Re-Analysis Interim (ERA-Interim), the Modern-Era Retrospective Analysis for Research and Applications (MERRA), and the Atmospheric Infrared Sounder (AIRS) by capturing similar magnitudes and patterns of variability in the monthly zonal mean specific humidity and interannual anomaly over annual and interannual timescales. The JPL and UCAR specific humidity climatologies differ by less than 15 % (depending on location and pressure level), primarily due to differences in the retrieved refractivity. In the middle-to-upper troposphere, in all climate zones, JPL is the wettest of all data sets, AIRS is the driest of all data sets, and UCAR, ERA-Interim, and MERRA are in very good agreement, lying between the JPL and AIRS climatologies. In the lower-to-middle troposphere, we present a complex behavior of discrepancies, and we speculate that this might be due to convection and entrainment. Conclusively, the RO observations could potentially be used as a climate variable, but more thorough analysis is required to assess the structural uncertainty between centers and its origin.

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“…The RS at the four stations are generally launched twice daily during the hour before . The VIZ/Sippican B2 humidity sensor has a nighttime wet bias (Wang and Zhang, 2008;Ho et al, 2010), and performs poorly in dry conditions (H. Vömel, personal communication, 2017). Ho et al (2010) found no obvious bias for the Meisei sensor.…”

Section: Radiosonde Airs and Gfsmentioning

confidence: 99%

“…The VIZ/Sippican B2 humidity sensor has a nighttime wet bias (Wang and Zhang, 2008;Ho et al, 2010), and performs poorly in dry conditions (H. Vömel, personal communication, 2017). Ho et al (2010) found no obvious bias for the Meisei sensor. The Vaisala RS92 sensor is known for its dry bias (Vömel et al, 2007) of ∼9 % at surface, and up to 50 % at 15 km altitude, and several correction schemes have been developed to address this 5 (Miloshevich et al, 2006;Vömel et al, 2007).…”

Section: Radiosonde Airs and Gfsmentioning

confidence: 99%

Evaluating tropospheric humidity from GPS radio occultation, radiosonde, and AIRS from high-resolution time series

Rieckh¹,

Anthes²,

Randel³

et al. 2018

Preprint

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Abstract. While water vapor is the most important tropospheric greenhouse gas, it is also highly variable in both space and time, and water vapor concentrations range over three orders of magnitude in the troposphere. These properties challenge all observing systems to accurately measure and resolve the vertical structure and variability of tropospheric humidity. In this study we characterize the humidity measurements of various observing techniques, including four separate Global Positioning System (GPS) Radio Occultation (RO) humidity retrievals (UCAR direct, UCAR 1D-Var, WEGC 1D-Var, Jet Propulsion 5 Laboratory (JPL) direct), radiosonde, and Atmospheric Infrared Sounder (AIRS) data. Furthermore, we evaluate how well the ERA-Interim reanalysis and National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model perform in analyzing water vapor at different levels. To investigate detailed vertical structure, we used time-height cross sections over specific locations (radiosonde stations in the tropical and subtropical western Pacific) for the year 2007. We found that RO humidity has comparable or better accuracy than both radiosonde and AIRS humidity over 800 hPa to 400 hPa, 10 as well as below 800 hPa if super-refraction is absent. The various RO retrievals of specific humidity agree within 20 % in the 1000 hPa to 400 hPa layer, and differences are most pronounced above 600 hPa.

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Global Evaluation of Radiosonde Water Vapor Systematic Biases using GPS Radio Occultation from COSMIC and ECMWF Analysis

Cited by 91 publications

References 17 publications

On the comparisons of tropical relative humidity in the lower and middle troposphere among COSMIC radio occultations and MERRA and ECMWF data sets

On the comparisons of tropical relative humidity in the lower and middle troposphere among COSMIC radio occultations and MERRA and ECMWF data sets

Comparisons of the tropospheric specific humidity from GPS radio occultations with ERA-Interim, NASA MERRA, and AIRS data

Evaluating tropospheric humidity from GPS radio occultation, radiosonde, and AIRS from high-resolution time series

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