Search

The Rwenzori Mountains, in southwest Uganda, are prone to precipitation-related hazards such as flash floods and landslides. These natural hazards highly impact the lives and livelihoods of the people living in the region. However, our understanding of the precipitation patterns and their impact on related hazardous events and/or agricultural productivity is hampered by a dearth of in situ precipitation observations. Here, we propose an evaluation of gridded precipitation products as potential candidates filling this hiatus. We evaluate three state-of-the-art gridded products, the ERA5 reanalysis, IMERG satellite observations, and a simulation from the convection-permitting climate model (CPM), COSMO-CLM, for their ability to represent precipitation totals, timing, and precipitation probability density function. The evaluation is performed against observations from 11 gauge stations that provide at least 2.5 years of hourly and half-hourly data, recorded between 2011 and 2016. Results indicate a poor performance of ERA5 with a persistent wet bias, mostly for stations in the rain shadow of the mountains. IMERG gives the best representation of the precipitation totals as indicated by bias score comparisons. The CPM outperforms both ERA5 and IMERG in representing the probability density function, while both IMERG and the CPM have a good skill in capturing precipitation seasonal and diurnal cycles. The better performance of CPM is attributable to its higher resolution. This study highlights the potential of using IMERG and CPM precipitation estimates for hydrological and impact modeling over the Rwenzori Mountains, preferring IMERG for precipitation totals and CPM for precipitation extremes. [ABSTRACT FROM AUTHOR]

Subdaily precipitation extremes are high-impact events that can result in flash floods, sewer system overload, or landslides. Several studies have reported an intensification of projected short-duration extreme rainfall in a warmer future climate. Traditionally, regional climate models (RCMs) are run at a coarse resolution using deep-convection parameterization for these extreme events. As computational resources are continuously ramping up, these models are run at convection-permitting resolution, thereby partly resolving the small-scale precipitation events explicitly. To date, a comprehensive evaluation of convection-permitting models is still missing. We propose an evaluation strategy for simulated subdaily rainfall extremes that summarizes the overall RCM performance. More specifically, the following metrics are addressed: the seasonal/diurnal cycle, temperature and humidity dependency, temporal scaling, and spatiotemporal clustering. The aim of this paper is as follows: (i) to provide a statistical modeling framework for some of the metrics, based on extreme value analysis, (ii) to apply the evaluation metrics to a microensemble of convection-permitting RCM simulations over Belgium against high-frequency observations, and (iii) to investigate the added value of convection-permitting scales with respect to coarser 12-km resolution. We find that convection-permitting models improved precipitation extremes on shorter time scales (i.e., hourly or 2 hourly), but not on 6-24-h time scales. Some metrics such as the diurnal cycle or the Clausius-Clapeyron rate are improved by convection-permitting models, whereas the seasonal cycle appears to be robust across spatial scales. On the other hand, the spatial dependence is poorly represented at both convection-permitting scales and coarser scales. Our framework provides perspectives for improving high-resolution atmospheric numerical modeling and datasets for hydrological applications. [ABSTRACT FROM AUTHOR]

Although mesoscale convective systems (MCSs) are the main source of precipitation in the semi-arid Sahel region, the relationship between MCS characteristics and their generated precipitation remain unclear. However, a thorough understanding of this relation is essential to work towards a classification scheme for MCSs and eventually to improve quantitative precipitation estimates in which cloud parameters are used as proxy variables for the total or maximum intense rainfall from a system. Accordingly, this study aims to analyse the cloud parameters and rain variables distributions and their concurrence before quantifying the relationships between them. This is done using hourly EUMETSAT's Meteosat-8 infrared (10.8 µm) images, 3-hourly precipitation data from National Aeronautics and Space Administration (NASA)'s Tropical Rainfall Measuring Mission and an MCS tracking algorithm. The period of interest extends from 1 June till 22 September 2006 and the area of interest covers the Lake Chad region. Results indicate that MCSs in the Sahel region generally show a maximum cloud coverage around 57 000 km2, a life duration of 9 h, an embedded convective core during 6 h and precipitation peaks around 12.3 mm h−1. A recurrent sequence of cloud and rain variables is also noticed; maximum in cloud coverage is mostly preceded by a minimum in brightness temperature in the cold convective core and is followed by a peak in precipitation. Longer-lived and larger MCSs as well as MCSs embedding very cold and long-lived convective cores exhibit an increased likelihood to induce more intense precipitation. Focussing on the characteristics of the cold convective core rather than on the characteristics of the entire system appears to be more relevant to predict the precipitation as the former are better correlated with the generated precipitation and can be used as proxy parameter for estimations of maximum intense precipitation using two-dimensional nonlinear regression models. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

In this research the impact of modifying the size distribution assumptions of the precipitating hydrometeors in a bulk one-moment microphysics scheme on simulated surface precipitation and storm dynamics has been explored for long-lived low-topped supercells in Belgium. It was shown that weighting the largest precipitating ice species of the microphysics scheme to small graupel results in an increase of surface precipitation because of counteracting effects. On the one hand, the precipitation formation process slowed down, resulting in lower precipitation efficiency. On the other hand, latent heat release associated with freezing favored more intense storms. In contrast to previous studies finding decreased surface precipitation when graupel was present in the microphysics parameterization, storms were rather shallow in the authors'' simulations. This left little time for graupel sublimation. The impact of size distribution assumptions of snow was found to be small, but more realistic size distribution assumptions of rain led to the strongest effect on surface precipitation. Cold pools shrunk because of weaker rain evaporation at the cold pool boundaries, leading to a decreased surface rain area. [ABSTRACT FROM AUTHOR]