Abstract
Abstract. Ground-based microwave radiometers (MWRs) offer a new capability to provide continuous observations of the atmospheric thermodynamic state in the planetary boundary layer. Thus, they are potential candidates to supplement radiosonde network and satellite data to improve numerical weather prediction (NWP) models through a variational assimilation of their data. However in order to assimilate MWR observations, a fast radiative transfer model is required and such a model is not currently available. This is necessary for going from the model state vector space to the observation space at every observation point. The fast radiative transfer model RTTOV is well accepted in the NWP community, though it was developed to simulate satellite observations only. In this work, the RTTOV code has been modified to allow for simulations of ground-based upward-looking microwave sensors. In addition, the tangent linear, adjoint, and K-modules of RTTOV have been adapted to provide Jacobians (i.e., the sensitivity of observations to the atmospheric thermodynamical state) for ground-based geometry. These modules are necessary for the fast minimization of the cost function in a variational assimilation scheme. The proposed ground-based version of RTTOV, called RTTOV-gb, has been validated against accurate and less time-efficient line-by-line radiative transfer models. In the frequency range commonly used for temperature and humidity profiling (22–60 GHz), root-mean-square brightness temperature differences are smaller than typical MWR uncertainties (∼ 0.5 K) at all channels used in this analysis. Brightness temperatures (TBs) computed with RTTOV-gb from radiosonde profiles have been compared with nearly simultaneous and co-located ground-based MWR observations. Differences between simulated and measured TBs are below 0.5 K for all channels except for the water vapor band, where most of the uncertainty comes from instrumental errors. The Jacobians calculated with the K-module of RTTOV-gb have been compared with those calculated with the brute force technique and those from the line-by-line model ARTS. Jacobians are found to be almost identical, except for liquid water content Jacobians for which a 10 % difference between ARTS and RTTOV-gb at transparent channels around 450 hPa is attributed to differences in liquid water absorption models. Finally, RTTOV-gb has been applied as the forward model operator within a one-dimensional variational (1D-Var) software tool in an Observing System Simulation Experiment (OSSE). For both temperature and humidity profiles, the 1D-Var with RTTOV-gb improves the retrievals with respect to the NWP model in the first few kilometers from the ground.
Highlights
The planetary boundary layer (PBL) is the single most important undersampled part of the atmosphere (National Research Council, 2008)
The performance of RTTOV-gb has been tested in four different ways, reported in the following subsections: validation against the LBL radiative transfer (RT) model used as reference for the training and against another independent reference LBL RT model (3.1); a comparison of TB simulated with RTTOV-gb from a radiosonde profile dataset with nearly co-located microwave radiometers (MWRs) measurements (3.2); a comparison of Jacobians calculated with the RTTOV-gb K-module and the brute force method, and with Jacobians computed with an analytical model (3.3); the exploitation of RTTOV-gb as a forward model operator within a one-dimensional variational scheme (3.4)
In addition to the direct module, which allows ground-based MWR observations to be simulated, the tangent linear (TL), AD, and K-modules of RTTOV have been modified in order to provide temperature, humidity, and cloud liquid water Jacobians for the ground-based perspective
Summary
The planetary boundary layer (PBL) is the single most important undersampled part of the atmosphere (National Research Council, 2008). These experiments used retrieved variables (temperature and humidity profiles), whereas the assimilation of raw measurement (TBs) is found to have more impact on the NWP forecasts in the case of satellite data (Geer et al, 2008). The Jacobians (i.e., partial derivatives with respect to the state vector) of the radiative transfer model are required to minimize the distances of the atmospheric state from both the first guess and the observations in a variational data assimilation process.
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