The potential of 1 h refractivity changes from an operational C‐band magnetron‐based radar for numerical weather prediction validation and data assimilation†

Abstract

Refractivity changes ( N) derived from radar ground clutter returns serve as a proxy for near-surface humidity changes (1 N unit ≡ 1% relative humidity at 20• C). Previous studies have indicated that better humidity observations should improve forecasts of convection initiation. A preliminary assessment of the potential of refractivity retrievals from an operational magnetron-based C-band radar is presented. The increased phase noise at shorter wavelengths, exacerbated by the unknown position of the target within the 300 m gate, make it difficult to obtain absolute refractivity values, so we consider the information in 1 h changes. These have been derived to a range of 30 km with a spatial resolution of ∼4 km; the consistency of the individual estimates (within each 4 km × 4 km area) indicates that N errors are about 1 N unit, in agreement with in situ observations. Measurements from an instrumented tower on summer days show that the 1 h refractivity changes up to a height of 100 m remain well correlated with near-surface values. The analysis of refractivity as represented in the operational Met Office Unified Model at 1.5, 4 and 12 km grid lengths demonstrates that, as model resolution increases, the spatial scales of the refractivity structures improve. It is shown that the magnitude of refractivity changes is progressively underestimated at larger grid lengths during summer. However, the daily time series of 1 h refractivity changes reveal that, whereas the radar-derived values are very well correlated with the in situ observations, the high-resolution model runs have little skill in getting the right values of N in the right place at the right time. This suggests that the assimilation of these radar refractivity observations could benefit forecasts of the initiation of convection.

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

“…A noisy phase difference field makes dealiasing and the estimation of small-area radial gradients of Df more difficult, lowering the quality of the refractivity retrieval, particularly for shortwavelength radars and for targets at far ranges. To limit this problem, in postprocessing, the noisy phase differences are generally smoothed by either a pyramidal weighting function over a 4 km by 4 km area or a least squares fit (Fabry 2004;Hao et al 2006;Nicol et al 2013). The smoothing process washes out the unrealistic sudden local refractivity change due to the noisy Df problem.…”

“…Therefore, quantifying the noise in Df introduced by different sources of uncertainties will enable improvements to the current retrieval algorithm. Possible sources leading to poorer refractivity estimates are discussed in many works and fall into the following three categories: 1) target uncertainty in its stability, height, and location (Fabry et al 1997;Fabry 2004;Besson et al 2012;Nicol and Illingworth 2013;Nicol et al 2013); 2) propagation conditions associated with dN/dh and height difference between the radar and ground targets (Fabry 2004;Park and Fabry 2010;Bodine et al 2011); and 3) drifts in the transmitter frequency Nicol et al 2013). Here, the focus is on the most basic unsolved part: the effects of atmospheric propagation conditions and the height differences between the radar and the targets.…”

“…Comparisons between refractivity measured by the radar and other instruments in the boundary layer show high correlations in time and space (Fabry et al 1997;Weckwerth et al 2005;Bodine et al 2011). Since hightemporal-(about 5-10 min) and high-spatial-(4 km by 4 km in the horizontal after smoothing) resolution refractivity retrievals have been obtained, many studies have demonstrated the potential utility of refractivity maps for studying near-surface moisture variation associated with a variety of weather phenomena, such as convection initiation, convection evolution, and characteristics of the boundary layer (Weckwerth et al 2005;Fabry 2006;Buban et al 2007;Chen et al 2007;Roberts et al 2008;Koch et al 2008;Besson et al 2012;Nicol et al 2014). Refractivity maps not only provide smallscale moisture variability particularly in those areas without a dense mesonet but also show boundaries prior to the fine line of traditional reflectivity (Weckwerth et al 2005;Heinselman et al 2009;Wakimoto and Murphey 2010;Bodine et al 2011).…”

“…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).…”