Uncertainty Propagation From Radio Occultation Profiles to Aggregated Atmospheric Gridded Fields

Abstract Radio occultation is a well‐established remote sensing method that provides reliable estimates of atmospheric profiles of diverse variables, including temperature and pressure. However, as with all indirect methods, radio occultation has some inherent systematic and random error effects, which lead to observational uncertainties. While propagation of uncertainties along the processing chain for individual radio occultation profiles was described in recent studies, this uncertainty information has not yet been carried forward to climatological fields. We close this gap and present an uncertainty propagation procedure that provides uncertainty estimates for aggregated means for climate applications. Estimated random uncertainties, basic and apparent systematic uncertainties and sampling uncertainties (due to the discrete sampling by profiles) are propagated through the aggregation process, resulting in uncertainty estimates for gridded fields. We demonstrate the new procedure for two test months and representative variables, inspecting monthly mean profiles for refractivity, dry temperature and physical temperature measurements. Results show that estimated random uncertainties and residual sampling uncertainties (after sampling bias correction) have similar magnitudes, both decreasing with increasing spatial aggregation sizes and corresponding increasing number of aggregated observations. At small aggregation they are the main contributors to uncertainty in refractivity, and important contributors to uncertainty of temperature. Systematic uncertainty, whose magnitude is independent of the number of profiles, is for refractivity the main source of uncertainty for larger aggregation sizes, and for pressure and dry temperature at all commonly used aggregation sizes. All uncertainty components exhibit pronounced spatial variation over the globe, with polar regions showing the greatest uncertainty.