Characterization of the Long-term Radiosonde Temperature Biases in the Lower Stratosphere using COSMIC and Metop-A/GRAS Data from 2006 to 2014
Other literature type
(issn: 1680-7324, eissn: 1680-7324)
Radiosonde observations (RAOBs) have provided the only long-term global <i>in situ</i> temperature measurements in the troposphere and lower stratosphere since 1958. In this study, we use consistently reprocessed Global Positioning System (GPS) radio occultation (RO) temperature data derived from COSMIC and Metop-A/GRAS missions from 2006 to 2014 to characterize the inter-seasonal and inter-annual variability of temperature biases in the lower stratosphere for different sensor types. The results show that the RAOB temperature biases for different RAOB sensor types are mainly owing to i) uncorrected solar zenith angle dependent errors, and ii) change of radiation correction. The mean daytime temperature difference (<i>ΔT</i>) for Vaisala RS92 is equal to 0.18 K in Australia, 0.20 K in Germany, 0.10 K in Canada, 0.13 K in England, and 0.33 K in Brazil. The mean daytime <i>ΔT</i> is equal to −0.06 K for Sippican, 0.71 K for VIZ-B2, 0.66 K for AVK-MRZ, and 0.18 K for Shanghai. The daytime trend of anomalies for Vaisala RS92 and RO temperature at 50 hPa is equal to 0.00 K/5 yrs over United States, −0.02 K/5 yrs over Germany, 0.17 K/5 yrs over Australia, 0.23 K/5 yrs over Canada, 0.26 K/5 yrs over England, and 0.12 K/5 yrs over Brazil, respectively. Although there still exist uncertainties for Vaisala RS92 temperature measurements over different geographical locations, the global trend of temperature anomaly between Vaisala RS92 and RO from June 2006 to April 2014 is within +/−0.09 K/5 yrs globally. Comparing with Vaisala RS80, Vaisala RS90 and sondes from other manufacturers, the Vaisala RS92 seems to provide the best RAOB temperature measurements, which can potentially be used to construct long term temperature CDRs. Results from this study also demonstrate the feasibility to use RO data to correct RAOB temperature biases for different sensor types.