Search

The 1D-Var retrieval framework described hereused the NWPSAF 1D package. This can be downloaded fromthe NWPSAF website (https://nwp-saf.eumetsat.int/site/software/1d-var/download/, last access: 22 August 2022, Pavelin and Collard, 2009). The radiative transfer model RTTOV-gb v1.0 is available to licensed users free of charge. RTTOV-gb may be obtainedby registering (https://www.nwpsaf.eu/site/register/, last access: 22August 2022) and then selecting RTTOV-gb in your software preferences. Developments and updates of the RTTOV-gb code willbe published on the MWRnet website (http://cetemps.aquila.infn.it/rttovgb/rttovgb_updates.html, last access: 8 September 2022). Foraccess to the radar simulator and modified 1D-Var code, please contact pauline.martinet@meteo.fr; International audience; A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution Numerical Weather Prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D/4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. It is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than that found from the retrievals on the dataset of real observations. It was found that retrieval of LWC can be obtained on idealised conditions with an uncertainty of less than 0.04 gm −3. With real data, as expected, retrievals with a good correlation (0.7) to in-situ measurements, but with a higher uncertainty than the synthetic dataset, of around 0.06 gm −3 , was found. This was reduced to 0.05 gm −3 when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the for- ward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational back- ground profiles of the AROME (Application of Research to Operations at MEsoscale) model, showing encouraging re- sults for future improvement of the AROME model initial state during fog conditions.

The fog lifecyle and more especially formation and dissipation times are still poorly represented in even the highest resolution operational numerical weather prediction (NWP) models, causing large economic costs in the aviation industry. A new, continuous transmission, 95 GHz cloud radar (CR), sensitive to cloud and fog droplets, opens up the possibility of retrieving vertical profiles of fog microphysical properties with unprecedented capabilities. Additionally, well-know ground-based microwave radiometers (MWR) can provide detailed information on the fog thermodynamics and total liquid water path. This work aims at combining ground-based MWR and CR measurements constrained with short-term-forecasts (called background profiles) from the high-resolution operational model AROME into a one dimensional variational approach (1DVAR) in order to retrieve physically consistent temperature, humidity and liquid water content profiles within fog conditions. Firstly, background and forward model errors during fog conditions are investigated and a methodology of improving the background profile is proposed. Then, a first evaluation of the algorithm using a synthetic dataset will be presented. This evaluation aims at quantifying the capabilities of the algorithm on idealized conditions, providing an upper limit on the performance that could be reached by the algorithm. The algorithm is then applied to real observations from the SOFOG3D (SOuth FOGs 3D experiment for fog processes study) field experiment led by Météo-France during the winter 2019/2020 where the 95 GHz BASTA cloud radar has been collocated with the HATPRO MWR together with in-situ observations. The capability of the algorithm to retrieve liquid water contents with an approximate error of 0.05 g.m-3 but also temperature and humidity profiles with an accuracy better than 1 K and 1 g.kg-1 respectively thanks to comparison with in-situ measurements (radiosondes and cloud droplet probe measurements) has been demonstrated. A sensitivity study will also be presented to identify the major sources of uncertainties in the algorithm (microphysics assumption and 1D-Var background-error-covariance matrix). These developments open new capabilities for future assimilation of the retrieved profiles in the new AROME ensemble data assimilation scheme and to better understand physical processes taking part in the fog lifecycle.

• Understand biomolecular systems through their motions •Numerical integration of Newton's laws of motion — Model atoms as point masses — Compute forces on every atom based on current positions — Update atom velocities and positions in discrete time steps of a few femtoseconds • Force computation described by a model: the force field

Fog forecasts still remain quite inaccurate due to the complexity, non linearities and fine scale of the main physical processes driving the fog lifecycle. Additionally to the complex modelling of fog processes, current numerical weather prediction models are known to suffer from a lack of operational observations in the atmospheric boundary layer and more generally during cloudy-sky conditions. Continuous observations of both thermodynamics and microphysics during the fog lifecycle are thus essential to develop future operational networks with the aim of validating current physical parameterizations and improving the model initial state through data assimilation techniques. In this context, an international network of 8 ground-based microwave radiometers (MWRs) has been deployed at a regional-scale on a 300 x 300 km domain during the SOFOG3D (SOuth FOGs 3D experiment for fog processes study) that has been conducted from October 2019 to April 2020. The MWR network has been extended with ceilometers at all MWR sites and additional microphysical observations from the 95 GHz cloud radar BASTA at two major sites as well as wind measurements from a Doppler lidar deployed at the super-site. After an overview of the SOFOG3D objectives and experimental set-up, preliminary results exploiting mainly the MWR network and cloud radar observations will be presented. Firstly, the capability of MWRs to provide temperature and humidity retrievals within fog and stratus clouds will be evaluated and discussed against radiosoundings launched during intensive observation periods (IOPs). Secondly, first retrievals of liquid water content profiles within fog and stratus clouds derived from the synergy between MWRs and the BASTA cloud radar will be presented. To that end, a one dimensional variational approach (1D-Var) directly assimilating MWR brightness temperatures and cloud-radar reflectivities has been developed. 1D-Var retrievals will be validated through a dataset of simulated observations and real fog cases of the SOFOG3D experiment. The capability of MWR and cloud radar observations to improve the initial state of the AROME model during fog conditions will be discussed with a focus on selected case studies. Finally, the usefulness of ground-based remote sensing networks to improve our understanding of fog processes and to validate physical parameterizations will be illustrated using the operational AROME model and the AROME Ensemble Prediction System

Transportation especially aviation sector all around the world is severely hindered due to Fog and hence observations and specific research for fog is necessary. The SOFOG3D (SOuth west FOGs 3D) experiment took place in South-West of France which is particularly prone to fog occurrence, during the period between November 2019 to March 2020 with primary objective to advance our understanding of fog processes and to improve fog forecast. Simultaneous measurements from various remote sensing instruments like BASTA: a 95 GHz cloud radar with scanning capability, HATPRO Microwave radiometer (MWR), doppler lidar, and balloon-borne in-situ measurements were collected to characterize the spatio-temporal evolution of Fog. On the supersite, detailed measurements of meteorological conditions, aerosol properties, fog microphysics, water deposition, radiation budget, heat, and momentum fluxes are collected to provide 3D structure of the boundary layer during fog events. The improvement in the retrieval of fog parameters and understanding of fog dynamics based on cloud radar and microwave (MWR) synergy will be addressed. We will present our work on the retrieval of key fog parameters like dynamics and microphysics using a combination of cloud radar and MWR observations. The retrievals will be validated with the tethered-balloon and radio-sounding observations. In-situ measurements and remote-sensing retrievals of fog microphysical properties will be compared. We will show a detailed analysis of retrieved LWP derived from BASTA radar only with LWP derived from HATPRO microwave radiometer, considering instrumental uncertainty and sensitivity. A closer analysis of the in-situ data (measured by granulometer) will be presented in order to assess and improve the retrieval derived with cloud radar in vertically pointing mode. Radar attenuation will be quantified by measuring the backscattered radar signal on well-known calibrated reflectivity metallic targets installed at the top of 20 m mast. The integrated attenuation along the radar beam path will be measured by the cloud radar and used as a new constraint to improve the microphysical properties.

The development of ground-based cloud radars offers a new capability to continuously monitor fog structure. Retrievals of fog microphysics are key for future process studies, data assimilation, or model evaluation and can be performed using a variational method. Both the one-dimensional variational retrieval method (1D-Var) or direct 3D/4D-Var data assimilation techniques rely on the combination of cloud radar measurements and a background profile weighted by their corresponding uncertainties to obtain the optimal solution for the atmospheric state. In order to prepare for the use of ground-based cloud radar measurements for future applications based on variational approaches, the different sources of uncertainty due to instrumental, background, and forward operator errors need to be properly treated and accounted for. This paper aims at preparing 1D-Var retrievals by analysing the errors associated with a background profile and a forward operator during fog conditions. For this, the background was provided by a high-resolution numerical weather prediction model and the forward operator by a radar simulator. Firstly, an instrumental dataset was taken from the SIRTA observatory near Paris, France, for winter 2018–2019 during which 31 fog events were observed. Statistics were calculated comparing cloud radar observations to those simulated. It was found that the accuracy of simulations could be drastically improved by correcting for significant spatio-temporal background errors. This was achieved by implementing a most resembling profile method in which an optimal model background profile is selected from a domain and time window around the observation location and time. After selecting the background profiles with the best agreement with the observations, the standard deviation of innovations (observations–simulations) was found to decrease significantly. Moreover, innovation statistics were found to satisfy the conditions needed for future 1D-Var retrievals (un-biased and normally distributed).