Abstract
Abstract. This paper investigates the potential benefit of ground-based microwave radiometers (MWRs) to improve the initial state (analysis) of current numerical weather prediction (NWP) systems during fog conditions. To this end, temperature, humidity and liquid water path (LWP) retrievals have been performed by directly assimilating brightness temperatures using a one-dimensional variational technique (1D-Var). This study focuses on a fog-dedicated field-experiment performed over winter 2016–2017 in France. In situ measurements from a 120 m tower and radiosoundings are used to assess the improvement brought by the 1D-Var analysis to the background. A sensitivity study demonstrates the importance of the cross-correlations between temperature and specific humidity in the background-error-covariance matrix as well as the bias correction applied on MWR raw measurements. With the optimal 1D-Var configuration, root-mean-square errors smaller than 1.5 K (respectively 0.8 K) for temperature and 1 g kg−1 (respectively 0.5 g kg−1) for humidity are obtained up to 6 km altitude (respectively within the fog layer up to 250 m). A thin radiative fog case study has shown that the assimilation of MWR observations was able to correct large temperature errors of the AROME (Application of Research to Operations at MEsoscale) model as well as vertical and temporal errors observed in the fog life cycle. A statistical evaluation through the whole period has demonstrated that the largest impact when assimilating MWR observations is obtained on the temperature and LWP fields, while it is neutral to slightly positive for the specific humidity. Most of the temperature improvement is observed during false alarms when the AROME forecasts tend to significantly overestimate the temperature cooling. During missed fog profiles, 1D-Var analyses were found to increase the atmospheric stability within the first 100 m above the surface compared to the initial background profile. Concerning the LWP, the RMSE with respect to MWR statistical regressions is decreased from 101 g m−2 in the background to 27 g m−2 in the 1D-Var analysis. These encouraging results led to the deployment of eight MWRs during the international SOFOG3D (SOuth FOGs 3D experiment for fog processes study) experiment conducted by Météo-France.
Highlights
Each year large human and economical losses are due to fog episodes, which, by the large reduction of visibility, affect air, marine, and land transportation (Gultepe et al, 2007)
The impact of microwave radiometers (MWRs) brightness temperatures on temperature, humidity and liquid water content profiles forecast by AROME has been evaluated during the 6-month period against in situ data collected during intensive observation periods (IOPs) and continuous measurements deployed on a 120 m instrumented tower
The statistical study performed here is useful to evaluate the expected impact on the AROME analyses if MWR observations were assimilated and the liquid water path (LWP) included in the control variables
Summary
Each year large human and economical losses are due to fog episodes, which, by the large reduction of visibility, affect air, marine, and land transportation (Gultepe et al, 2007). A real network of 13 MWRs was assimilated by Caumont et al (2016) into the 2.5 km horizontal resolution convective-scale model AROME in the context of heavy-precipitation events in the western Mediterranean The impact of this network was found to be neutral on temperature and humidity fields but positive on quantitative precipitation forecasts up to 18 h. The purpose of this article is to evaluate the expected benefit of MWRs on kilometre-scale NWP analyses during fog events on an extended dataset over a 6-month fog experiment This expands the studies by Martinet et al (2015, 2017) to humidity and liquid water path retrievals and evaluates the impact of new tools developed to optimize the assimilation of MWRs during COST Actions TOPROF (Illingworth et al, 2019) and PROBE (Cimini et al, 2020).
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