1D-VAR Retrieval of Temperature and Humidity Profiles from Ground-based Microwave Radiometers

A variational retrieval method is described to combine observations from microwave and infrared radiometers and surface sensors with background from short-range numerical weather prediction (NWP) forecasts in an optimal way, accounting for their error characteristics. The required forward models are described. Observation errors are found to be dominated by representativeness, due to their sensitivity to atmospheric variability on smaller scales than the NWP model gird. Their effect can be reduced by evaluating this dynamically. Profiles of temperature and total water content are retrieved from synthetic data using Newtonian iteration. An error analysis shows these are expected to improve mesoscale NWP, retrieving temperature profiles with an uncertainty of <1 K up to 5 km and humidity with <40% up to 3 km, albeit both with poor vertical resolution. A cloud classification scheme is introduced to address convergence problems and constrain the retrievals. This Bayesian method can be extended to form a basis for future integrated observing systems