Automated aircraft temperatures exhibit considerable variance with aircraft models and on average they are warmerthan radiosonde temperatures; therefore, field studies and bias corrections for NWP models are recommended.

Radiation‐induced biases in global operational radiosonde temperature data from May 2008 to August 2011 are examined by using spatially and temporally collocated Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) data as estimates of the truth. The data on average from most radiosonde types show a nighttime cold bias and a daytime warm bias relative to COSMIC. Most daytime biases increase with altitude and solar elevation angle (SEA). The global average biases in the 15–70 hPa layer are −0.05 ± 1.89 K standard deviation (~52,000 profiles) at night and 0.39 ± 1.80 K standard deviation (~64,500 profiles) in daytime (SEA > 7.5°). Daytime warm biases associated with clouds are smaller than those under clear conditions. Newer sondes (post‐2000) have smaller biases and appear to be less sensitive to effects of clouds. Biases at night show greater seasonal and zonal variations than those for daytime. In general, warm night biases are associated with warm climate regimes and less warm or cold night biases with cold climate regimes. Bias characteristics for 13 major radiosonde types are provided, as a basis for updating radiosonde corrections used in numerical weather predictions, for validating satellite retrievals, and for adjusting archived radiosonde data to create consistent climate records.

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Various studies have noted that aircraft temperature data have a generally warm bias relative to radiosonde data around 200 hPa. In this study, variational aircraft temperature bias correction is incorporated in the Gridpoint Statistical Interpolation analysis system at the National Centers for Environmental Prediction. Several bias models, some of which include information about aircraft ascent/descent rate, are investigated. The results show that the aircraft temperature bias correction cools down the atmosphere analysis around 200 hPa, and improves the analysis and forecast fits to the radiosonde data. Overall, the quadratic aircraft ascent/descent rate bias model performs better than other bias models tested here, followed closely by the aircraft ascent/descent rate bias model. Two other issues, undocumented in previous studies, are also discussed in this paper. One is the bias correction of aircraft report (AIREP) data. Unlike Aircraft Meteorological Data Relay (AMDAR) data, where unique corrections are applied for each aircraft, bias correction is applied indiscriminately (without regard to tail numbers) to all AIREP data. The second issue is the problem of too many aircraft not reporting time in seconds, or too infrequently, to be able to determine accurate vertical displacement rates. In addition to the finite-difference method employed to estimate aircraft ascent/descent rate, a tensioned-splines method is tested to obtain more continuously smooth aircraft ascent/descent rates and mitigate the missing time information.

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