A 10‐year radar‐based analysis of orographic precipitation growth and decay patterns over the Swiss Alpine region

Abstract

The dependence of precipitation growth and decay on the orientation of orographic features, mesoscale flow and freezing‐level height is quantified using a 10‐year archive of composite weather radar images over the Swiss Alpine region. The mesoscale flow is described by the motion of radar precipitation echoes, computed through variational echo tracking, while the freezing‐level height is extracted from the analyses of the numerical weather prediction model COSMO. On the northern side of the Alps, the areas of growth are generally observed on the upwind slopes of the Alpine chain, while on the southern side their location also depends on factors other than the motion of precipitation, such as the convergence of low‐level flows. On the other hand, the decay of precipitation is generally found in the inner Alpine valleys and on the downwind slopes of the Alpine chain. Compared to situations characterized by low freezing level, the areas of precipitation growth penetrate more into the mountain range with high‐freezing‐level conditions. When considering a time lag of 1 h and specific flow conditions, the systematic growth and decay of precipitation can explain up to 30–40% of its total variability over the orography. The relative contribution to the total variability is lower with high freezing level because the non‐systematic variability is larger. The statistical analysis of precipitation growth and decay using large archives of composite radar images highlights the mesoscale flow conditions and geographical locations most prone to orographic precipitation enhancement, but also has potential to improve existing precipitation nowcasting systems in mountainous regions and to provide a basis for the verification of systematic biases of numerical weather prediction models.

Spatial and Temporal Evaluation of Radar Rainfall Nowcasting Techniques on 1,533 Events

“…It is noteworthy that the Netherlands is a lowland country and that the results from this study will likely not hold for mountainous regions. In mountainous regions, growth and decay processes dominate over the advection of rainfall fields (e.g., Foresti & Seed, 2015; Foresti et al., 2018). Hence, larger errors are expected for nowcasts in these regions, which affects the skillfulness of the forecasts.…”

NowPrecip: localized precipitation nowcasting in the complex terrain of Switzerland

“…Growth and decay estimation is especially useful for countries with strong orographic gradients, like Switzerland, where interactions between orography and air masses are important. Research on growth and decay using the most recent observations has been presented before (Tsonis and Austin, ; Li et al ., ; Radhakrishna et al ., ), while research on growth and decay based on multiyear radar archives has been published recently (Foresti et al ., ).…”

“…The same basis can be used for analogue‐based machine‐learning systems, which rely on multiyear radar archives. Such a system has already been published (Foresti et al ., ). Mechanisms that can assist precipitation generation in more targeted ways can potentially improve skill, especially to reduce the number of misses in the rank histogram of Figure . This expectation was the main stimulus for developing a method to this end (Sideris et al ., ).…”

Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)

“…For example, the optical flow modules could be used to study precipitation growth and decay in moving coordinates (e.g. Foresti et al, 2018;Zeder et al, 2018), to correct radar field accumulations accounting for advection (e.g. Wang et al, 2015;Lukach et al, 2017), to synchronize the individual radar elevation scans (e.g.…”