The quality of humidity measurements from global operational radiosonde sensors in upper, middle, and lower troposphere for the period 2000–2011 were investigated using satellite observations from three microwave water vapor channels operating at 183.31±1, 183.31±3, and 183.31±7 GHz. The radiosonde data were partitioned based on sensor type into 19 classes. The satellite brightness temperatures (Tb) were simulated using radiosonde profiles and a radiative transfer model, then the radiosonde simulated Tb's were compared with the observed Tb's from the satellites. The surface affected Tb's were excluded from the comparison due to the lack of reliable surface emissivity data at the microwave frequencies. Daytime and nighttime data were examined separately to see the possible effect of daytime radiation bias on the sonde data. The error characteristics among different radiosondes vary significantly, which largely reflects the differences in sensor type. These differences are more evident in the mid‐upper troposphere than in the lower troposphere, mainly because some of the sensors stop responding to tropospheric humidity somewhere in the upper or even in the middle troposphere. In the upper troposphere, most sensors have a dry bias but Russian sensors and a few other sensors including GZZ2, VZB2, and RS80H have a wet bias. In middle troposphere, Russian sensors still have a wet bias but all other sensors have a dry bias. All sensors, including Russian sensors, have a dry bias in lower troposphere. The systematic and random errors generally decrease from upper to lower troposphere. Sensors from China, India, Russia, and the U.S. have a large random error in upper troposphere, which indicates that these sensors are not suitable for upper tropospheric studies as they fail to respond to humidity changes in the upper and even middle troposphere. Overall, Vaisala sensors perform better than other sensors throughout the troposphere exhibiting the smallest systematic and random errors. Because of the large differences between different radiosonde humidity sensors, it is important for long‐term trend studies to only use data measured using a single type of sensor at any given station. If multiple sensor types are used then it is necessary to consider the bias between sensor types and its possible dependence on humidity and temperature.

We compared two fast radiative transfer models, Community Radiative Transfer Model (CRTM) and Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV), with the LBL model Atmospheric Radiative Transfer Simulator (ARTS). We used the measurements from Advanced Technology Microwave Sounder (ATMS) and the Global Precipitation Measurement Microwave Imager (GMI) for evaluation of the radiative transfer models. The models in comparison with the observations and each other performed very well with a mean difference less than 0.5 K for the temperature sounding channels operating near the oxygen absorption band at 60 GHz. There was a difference of up to 1 K among the models as well as compared with the observations for humidity sounding channels operating around water vapor absorption line at 183 GHz. The mean difference between the simulations and observations was up to 6 K for surface sensitive channels. Water vapor and surface sensitive channels also showed to be more sensitive than the temperature sounding channels to the spectroscopy models used to calculate the absorption coefficients. There was a small difference, less than 0.1 K, between brightness temperatures calculated using traditional boxcar and actual Sensor or Spectral Response Functions, except for a difference of 0.25 K for ATMS Channel 6. Double difference technique showed about 1 K difference between water vapor channels from ATMS instruments onboard N20 and National Polar‐orbiting Partnership (NPP). However, comparison of a new version of ATMS/NPP observations recently generated using an enhanced calibration algorithm with ATMS/N20 observations showed that the differences between the two instruments are less than 0.5 K after improving the ATMS/NPP calibration.