W-band radar observations for fog forecast improvement: an analysis of model and forward operator errors

Bell, Alistair; Martinet, Pauline; Caumont, Olivier; Vié, Benoît; Delanoë, Julien; Dupont, Jean-Charles; Borderies, Mary

doi:10.5194/amt-14-4929-2021

Cited by 8 publications

(15 citation statements)

References 56 publications

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“…To evaluate the significance of the amount of rain and ice in the AROME background profile, radar reflectivity was simulated with only LWC and with all hydrometeors. Profiles significantly altered in the radar reflectivity simulations by other hydrometeors were not considered -as explained in Bell et al (2021). To that end, the radar reflectivity was simulated from the background profiles with all hydrometeors and then with only LWC.…”

Section: The 1d-var Algorithmmentioning

confidence: 99%

“…An evaluation of the radar 5420 A. Bell et al: Optimal estimation of fog thermodynamic properties simulator capability for ground-based 95 GHz cloud radar and sources of uncertainty can be found in Bell et al (2021).…”

Section: The 1d-var Algorithmmentioning

confidence: 99%

“…The AROME model has 90 vertical levels from the surface to a height of approximately 30 km and a horizontal resolution of 1.3 km over the domain centred over mainland France and covering most of western Europe (Brousseau et al, 2011). Bell et al (2021) have shown that large errors due to the spatial and temporal displacement of fog events could be expected from short-term model forecasts during fog events, leading to suboptimal background profiles for future 1D-Var retrievals. An approach to select a more adapted background profile with reference to the observed radar reflectivity within a 27 km sub-domain and within a 6 h time window, named the most resembling profile (MRP) method, was therefore proposed and was shown to significantly reduce innovation (observation minus background) error statistics.…”

Section: Background Profile: the Arome Modelmentioning

confidence: 99%

“…An approach to select a more adapted background profile with reference to the observed radar reflectivity within a 27 km sub-domain and within a 6 h time window, named the most resembling profile (MRP) method, was therefore proposed and was shown to significantly reduce innovation (observation minus background) error statistics. This study follows the work of Bell et al (2021) by using the MRP method to provide an adequate background profile to the 1D-Var retrieval algorithm. The impact of using the MRP method will be discussed in Sect.…”

Section: Background Profile: the Arome Modelmentioning

confidence: 99%

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An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

et al. 2022

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Abstract. 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 are 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- and 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. This dataset was constructed by perturbing a high-resolution forecast dataset of fog and low-cloud cases by its expected errors. The algorithm 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 those found from the retrievals on the dataset of real observations. It was found that LWC can be retrieved in idealised conditions with an uncertainty of less than 0.04 g m−3. With real data, as expected, retrievals with a good correlation (0.7) to in situ measurements were found but with a higher uncertainty than the synthetic dataset of around 0.06 g m−3 (41 %). This was reduced to 0.05 g m−3 (35 %) 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 forward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational background profiles of the AROME (Application of Research to Operations at MEsoscale) model, showing encouraging results for future improvement of the AROME model initial state during fog conditions.

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Section: The 1d-var Algorithmmentioning

confidence: 99%

Section: The 1d-var Algorithmmentioning

confidence: 99%

Section: Background Profile: the Arome Modelmentioning

confidence: 99%

Section: Background Profile: the Arome Modelmentioning

confidence: 99%

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An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

et al. 2022

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“…The selection requirement for this instance is that it contains a sufficient number of LWC profiles to evaluate the behavior of the algorithm.AROME is a French convective scale NWP model, operational since 2008 covering France and western Europe providing high-resolution simulations of fog forecasts at 1.3 km of horizontal resolution and 90 vertical levels of 144 profiles. Detailed setup of the AROME model and fog forecast is explained inBell et al (2021). LWC of a fog structure from AROME shortterm forecasts at the nearest grid location of SIRTA is considered as the true atmospheric state.…”

mentioning

confidence: 99%

Climatology of estimated LWC and scaling factor for warm clouds using radar – microwave radiometer synergy

Vishwakarma

Delanoë

Jorquera

et al. 2022

Preprint

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Abstract. Cloud radars are capable of providing continuous high-resolution observations of the cloud. These observations are related to the microphysical properties of clouds. Power law relations in the form of Z = a · LWCb are generally used to estimate liquid water content (LWC) profiles. The constants a and b from the power-law relation vary with the cloud type and cloud characteristics. Due to the variety of such parameterizations, selecting the most appropriate Z-LWC relation for a continuous cloud system is complicated. Additional information such as Liquid water path (LWP) from a co-located microwave radiometer is used to scale the LWC of the cloud profile. An algorithm for estimating the LWC of warm clouds using radar-microwave radiometer synergy in a variational framework is presented. This method also accounts for attenuation due to cloud droplets and retrieves a suitable scaling factor (lna) of the profile in addition to the LWC. The optimal estimation techniques incorporate a priori information of desired variables, and the forward model converts these variables into observation parameters. In this algorithm formulation, the measure of uncertainty in observations, forward model and, a priori acts as weights in the retrieved quantities. These uncertainties in the retrieval are analyzed in the sensitivity analysis of the algorithm. The retrieval algorithm is first tested on a synthetic profile for different perturbations in sensitivity parameters. The sensitivity study has shown that this method is susceptible to LWP information. The algorithm is then implemented to various cloud and fog cases at SIRTA observatory to estimate LWC and the scaling factor. The scaling factor changes for each cloud profile, and the range of lna are consistent with suggested values in literature. The validation of such an algorithm is challenging, as we need reference measurements of LWC co-located with the retrieved values. During the SOFOG-3D campaign (South-West of France, October 2019 to March 2020), in-situ measurements of LWC were collected in the vicinity of a cloud radar and a microwave radiometer, allowing comparison of retrieved and measured LWC. The comparison demonstrated that the cloud-fog heterogeneity was playing a key role in the assessment. The proposed synergistic retrieval algorithm is applied to 39 cloud and fog cases at SIRTA, and the behavior of the scaling factor is studied. This statistical analysis of scaling is carried out to develop a radar-only retrieval method. The climatology revealed that the scaling factor can be linked to the maximum reflectivity of the profile. From climatology, the statistical relations for scaling factor are proposed for fog and cloud. Thanks to the variational framework, a stand-alone radar version of the algorithm is adapted from the synergistic retrieval algorithm, which incorporates the climatology of scaling factor as a priori information to estimate the LWC of warm cloud. This method allows the LWC estimation using only radar reflectivity and climatology of scaling factor.

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Formation of fog due to stratus lowering: An observational and modelling case study

Fathalli

Lac

Burneta

et al. 2022

Quart J Royal Meteoro Soc

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We numerically investigate the processes responsible for a fog event formed by stratus cloud lowering, observed on December 1-2, 2016, during the experimental campaign in the northeast of France. The observations revealed a complex temporal evolution with stratus followed by a relatively drier period and then its reformation leading to fog formation by stratus lowering. Microphysical observations below a tethered balloon exhibit different vertical profiles of liquid water content and droplet concentration between the stratus and the fog formed below. A simulation at 100-m grid spacing reproduced the main observed characteristics of the cloud life cycle despite a time lag in stratus formation due to large-scale conditions. The advection of cloud water in the stratus and at its top appears crucial to feed the stratus lowering, resulting in radiative cooling, vertical transport, droplet sedimentation, evaporation, and cooling of the sub-cloud layer. The advection of cold or warm air in the lowest 250 m, mainly driven by fine-scale orographic circulations, impacts the fog formation due to stratus cloud lowering. When non-local conditions are favourable, the most important microphysical process to favour fog formation is the droplet sedimentation, leading to the cooling and moistening in the sub-cloud layer by evaporation. Droplet sedimentation appears more efficient when the droplet concentration is low, and a two-moment microphysical scheme more appropriate than a one-moment scheme to reproduce the observed variability of the droplet concentration. Given the predominance of non-local processes on this case study, a three-dimensional high-resolution model appears crucial to perform accurate forecasts of fog by stratus lowering.

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W-band radar observations for fog forecast improvement: an analysis of model and forward operator errors

Cited by 8 publications

References 56 publications

An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

Climatology of estimated LWC and scaling factor for warm clouds using radar – microwave radiometer synergy

Formation of fog due to stratus lowering: An observational and modelling case study

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