Abstract
One approach to improve advection methods is the short-term ensemble prediction system (STEPS). STEPS decomposes precipitation fields into different spatial scales and filters those having a short lifetime. The latter is achieved by using an auto-regressive (AR) model that considers a sequence of observations. However, such a model tends to smooth nowcasting fields especially in small but convective precipitation areas and at longer lead-times. With focus on the deterministic configuration of STEPS, i.e., the spectral prognosis model (SPROG), this article 1) extends the STEPS approach by estimating spatially localized parameters of the AR process, 2) conducts a sensitivity analysis of the SPROG model to the order of the AR process, the spatial decomposition levels, and post-processing, and 3) analyzes the forecast skill of the extended STEPS. For such purpose, the performance of the localized AR model was demonstrated and evaluated at several precipitation thresholds and window sizes using a varied set of precipitation events collected by the radar network of the German Weather Service. The statistical results exhibited an improved performance of the localized AR model over SPROG when both are evaluated at precipitation thresholds and window sizes larger than 0.1 mm h <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-1}$</tex-math></inline-formula> and 1 km, respectively, and for lead-times up to 2 h. The analysis suggested a first-order AR process, six cascade levels, and a mean adjustment post-processing procedure. Our results show a key role of the localization aspect when generating nationwide forecasts in scenarios that include large precipitation areas which are non-uniformly distributed having isolated convective features.
Highlights
Prolonged heavy precipitation induced by low pressure systems or short intense rainfall due to deep convection are typical severe weather phenomena in Europe [1]
Radar-based precipitation nowcasting methods have offered the capability of prediction at high temporal and spatial resolutions with increasing lead-time, providing support to, amongst others, decision makers in the hydrological and meteorological communities
We explored and presented 1) a model referred to as the SPROG-LOC to improve the skill of the SPROG model by estimating localized 2D parameters of the auto-regressive (AR) process, 2) a sensitivity analysis of the SPROG model to its parameters, such as the order of the AR process and the spatial decomposition levels, and 3) the degree of dependency to post-processing
Summary
Prolonged heavy precipitation induced by low pressure systems or short intense rainfall due to deep convection are typical severe weather phenomena in Europe [1]. Despite the immense progress made in improving the quality of forecasts made by numerical weather prediction (NWP) over the last decades [6], forecast errors of the above described small-scale phenomena are quite high compared to extrapolation-based nowcasting methods [7]. This may be caused by inaccurate initial and outdated boundary conditions or effects that are not well captured by numerical models in particular with respect to parameterized processes (e.g. cloud microphysics; [8]). Observation-based precipitation nowcasting techniques typically outperform the NWP up to lead-times of the order of 4 to 6 h (e.g. [9]), especially when considering the low update frequency compared to extrapolation-based nowcasting
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