W-band Radar Observations for Fog Forecast Improvement: an Analysis of Model and Forward Operator Errors

Abstract

Abstract. The development of ground based cloud radars offers a new capability to continuously monitor the fog structure. Retrievals of fog microphysics is 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 the 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 2018–19 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 best background profile a good agreement was found between observations and simulations. Moreover, observation minus simulation errors were found to satisfy the conditions needed for future 1D-var retrievals (un-biased and normally distributed).

Reply on RC1

“…A total of 31 fog events were observed over the period, the numbers of each type are detailed in table 3. In line with previous studies performed by (Philip et al, 2016;Dupont et al, 2016) and which looked at fog events in Paris, and Román-Cascón et al ( 2019), which examined fog events over a short period in January 2016 on the Spanish Northern Plateau, the majority of fog events were either cloud base lowering or radiative.…”

“…Scores of FBI and CSI were found to be 1.59 and 0.32 respectively. The scores agree well with the work ofPhilip et al (2016) who calculated a score of 1.24 and 0.37 respectively as well asMartinet et al (2020) who found scores of 1.77 and 0.35. The FBI score indicates that the model over-predicts the occurrence of fog with a large number of false alarms and the CSI score means that only 32 % of fog events (observed and/or predicted) are correctly forecast by the model.…”

“…For monostatic radars, the receiver must be switched off during the transmission of a pulse, meaning that signal backscattered close to the radar cannot be detected, and a minimum detectable range of over 100m is typical for cloud radars sounding in a boundary layer mode (Liu et al (2017)). The fact that BASTA employs a seperate receiver and transmitter (bistatic) thus allows the minimum measurement distance of the radar to be relatively small compared to that of a monostatic radar.…”

“…Fog can occur through several atmospheric processes, not all of which are modelled equally well. Philip et al (2016) has shown that the AROME model seems to succeed in predicting certain types of fog better than others. Notably, CBL events are badly predicted compared to radiative fog.…”

“…The development of high-resolution numerical weather prediction (NWP) models, with horizontal resolutions in the order of 1 km, and vertical resolutions in the order of 10 m near the surface, offers the possibility to represent fog events with fine spatial and temporal resolutions. However, fog events are generally still poorly forecast with current NWP models (Steeneveld et al, 2015;Philip et al, 2016).…”