1D-Var temperature retrievals from microwave radiometer and convective scale model

Abstract

This paper studies the potential of ground-based microwave radiometers (MWR) for providing accurate temperature retrievals by combining convective scale numerical models and brightness temperatures (BTs). A one-dimensional variational (1D-Var) retrieval technique has been tested to optimally combine MWR and 3-h forecasts from the French convective scale model AROME. A microwave profiler HATPRO (Humidity and Temperature PROfiler) was operated during 6 months at the meteorological station of Bordeaux (Me´te´o France). MWR BTs were monitored against simulations from the Atmospheric Radiative Transfer Simulator 2 radiative transfer model. An overall good agreement was found between observations and simulations for opaque V-band channels but large errors were observed for channels the most affected by liquid water and water vapour emissions (51.26 and 52.28 GHz). 1D-Var temperature retrievals are performed in clear-sky and cloudy conditions using a screening procedure based on cloud base height retrieval from ceilometer observations, infrared radiometer temperature and liquid water path derived from the MWR observations. The 1D-Var retrievals were found to improve the AROME forecasts up to 2 km with a maximum gain of approximately 50 % in root-mean-square-errors (RMSE) below 500 m. They were also found to outperform neural network retrievals. A static bias correction was proposed to account for systematic instrumental errors. This correction was found to have a negligible impact on the 1D-Var retrievals. The use of low elevation angles improves the retrievals up to 12 % in RMSE in cloudy-sky in the first layers. The present implementation achieved a RMSE with respect to radiosondes within 1 K in clear-sky and 1.3 K in cloudy-sky conditions for temperature.

RTTOV-gb – adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations

“…This result is very important as it suggests that forward model errors due to the fast model approximation are not dominant. Note that bias values of the same order of magnitude for the 51-54 GHz range were previously reported Löhnert and Maier, 2012;Martinet et al, 2015;Blumberg et al, 2015), employing MWRs of different types and manufacturers. This may be attributed to a combination of uncertainties from instrument calibration and gas absorption models.…”

“…Of course the priority of LBL models is more accuracy than speed, though settings may be tuned to improve the computation performances. Although a detailed analysis on computation speed goes beyond the scope of this paper, we found that RTTOV-gb is faster than our implementation of ARTS (Martinet et al, 2015) for both the direct and Jacobian calculations. Moreover, our tests demonstrate that the computation time for Jacobians is shorter by a factor of 8 for the RTTOV-gb K-module than for the direct module with the brute force method.…”

Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network

“…In addition, the infrared radiometer (not available in Payerne, SIRTA, and CESAR) is sensitive to cloud base temperature and indicates the presence of thick clouds when the infrared temperature is high (meaning no contribution from the cold background above the cloud; Martinet et al, 2015). Thresholds for this screening procedure were determined in order to have a good compromise between a sufficient data sample and a high confidence of cloudy-sky rejections (Martinet et al, 2015). Periods with σ C > 0.5 K (Turner at al., 2007) or T IR > −30 • C (Martinet et al, 2015) were rejected.…”

“…Thresholds for this screening procedure were determined in order to have a good compromise between a sufficient data sample and a high confidence of cloudy-sky rejections (Martinet et al, 2015). Periods with σ C > 0.5 K (Turner at al., 2007) or T IR > −30 • C (Martinet et al, 2015) were rejected. In addition, O-B TB differences larger than 3 standard deviations with respect to the mean difference were rejected to remove outliers (e.g.…”

“…This has been recently investigated in a few sporadic cases, assimilating retrieved temperature and humidity profiles into NWP models (Cimini et al, 2012(Cimini et al, , 2014Caumont et al, 2016). Martinet et al (2015) illustrate the attempt to assimilating the primary observable, i.e. brightness temperature (TB) instead of retrieved profiles, within a simplified 1-D framework, showing positive impact on the NWP forecasts in the PBL.…”