An Observation Operator for Radar Refractivity Change: Comparison of Observations and Convective-Scale Simulations

Abstract

International audienceWeather radar refractivity depends on low-level moisture, temperature, and pressure and is available at high space-time resolutions over large areas. It is of definite meteorological interest for assimilation, verification, and process-study purposes. In this study, the path-averaged refractivity change is simulated from the Arome cloud-resolving atmospheric system analyses and compared with corresponding radar observations over a 35-day period with various meteorological conditions. For that, a novel post-processing procedure is applied to radar data to improve its quality. Also, an observation operator is developed that ingests Arome analyses and simulates a 3-h path-averaged refractivity change. A sensitivity study shows that simulated path-averaged refractivity change is immune to the modelling of the beam height as long as it remains below approximately 60 m above the ground. Comparisons show overall consistency between observed and simulated path-averaged refractivity change, with discrepancies at times that suggest an improvement in analyses once radar refractivity change observations are assimilated. Finally, errors introduced when retrieving local refractivity from path-averaged refractivity are estimated and it is found for our dataset that such retrievals halve the range of usable observations

Improving Radar Refractivity Retrieval by Considering the Change in the Refractivity Profile and the Varying Altitudes of Ground Targets

“…The smoothing process washes out the unrealistic sudden local refractivity change due to the noisy Df problem. Caumont et al (2013) also suggested a new weighting parameter for extracting meaningful signal and smoothing the noisiness of retrieved refractivity change. Nonetheless, the smoothing process reduces the spatial resolution of the data and does not fully resolve the incorrect physical biases introduced by dN/dh and target height variability.…”

“…The newly added information not only modified the low-level humidity field but also changed the spatial variability of moisture, which enhanced the intensity of the storm, leading to better quantitative precipitation forecasting. As a result, the research community has been preparing to assimilate the composite refractivity data from operational radar networks to numerical models in order to improve short-term forecasting skill Caumont et al 2013;Gasperoni et al 2013;Nicol et al 2013;Nicol and Illingworth 2013;Nicol et al 2014). …”

“…Although the quality of the retrieval has been discussed from different aspects and improved in the last decade (Fabry 2004;Park and Fabry 2010;Besson et al 2012;Parent du Chatelet et al 2012;Caumont et al 2013;Nicol et al 2013;Nicol and Illingworth 2013), the unsolved problem associated with the vertical gradient of refractivity (dN/dh) and uneven target heights (H T ) remains challenging. A consequence of these issues is the difficulties of assigning a height to the retrieved refractivity fields and the prominent diurnal periodicity of the refractivity difference between in situ observations and the radar estimations (Fabry 2004;Weckwerth et al 2005;Bodine et al 2011).…”

Comparison of real‐time refractivity measurements by radar with automatic weather stations, AROME‐WMED and WRF forecast simulations during SOP1 of the HyMeX campaign

“…From mesoscale non-hydrostatic (Méso-NH) high-resolution simulations, Besson et al (2012) showed the usefulness of the refractivity in order to characterize fine-scale deep convection. Moreover, studies highlighted that weather radar refractivity could be interesting for data assimilation in numerical weather prediction (NWP) systems (Montmerle et al, 2002;Sun, 2005;Caumont et al, 2013;Nicol et al, 2014).…”

Solutions for Improving the Radar Refractivity Measurement by Taking Operational Constraints into Account

“…Besson et al (2012) showed that it can be used to characterize finescale deep convection structures. Moreover, recent studies (Montmerle et al 2002;Sun 2005;Caumont et al 2013) highlighted that data assimilation of weather radar refractivity into numerical weather prediction (NWP) systems can be interesting. Finally, Heinselman et al (2009) showed that the use of refractivity fields by forecasters provided complementary information that somewhat enhanced the forecasters' capability to analyze the near-surface environment and boosted their confidence in moisture trends.…”