A percentile‐based approach to rainfall scenario construction for surface‐water flood forecasts

A novel technique to produce reasonable worst‐case rainfall scenarios from ensemble forecasts is presented. This type of scenario is relevant for predicting the risk of localized, intense rainfall events with a duration between 15 min and several hours. Such rainfall events can cause surface‐water (pluvial) flooding. Producing useful forecasts of these events at lead times of more than a few hours is challenging due to the precision and accuracy in rainfall intensity, duration and location that is required. The technique described here addresses these challenges by constructing appropriate scenarios using a neighbourhood technique in combination with ensemble forecasting. It is similar to the distance‐dependent depth–duration analysis described in earlier studies, but it introduces an additional post‐processing step based on probability distribution functions of rainfall accumulation near a location of interest. This additional step makes the reasonable worst‐case scenarios less dependent on grid‐scale behaviour, and helps to generate scenarios with a consistent interpretation. The method is used to compare forecasts with a lead time of 6–36 hr to radar data for several case studies that occurred in Yorkshire. These comparisons also introduce new techniques to present maps of the reasonable worst‐case rainfall accumulation at each location

Characterising the shape, size, and orientation of cloud‐feeding coherent boundary‐layer structures

Two techniques are presented for characterisation of cloud-feeding coherent boundary-layer structures through analysis of large-eddy simulations of shallow cumulus clouds, contrasting conditions with and without ambient shear. The first technique is a generalisation of the two-point correlation function, where the correlation length-scale as well as the orientation can be extracted. The second technique identifies individual coherent structures and decomposes their vertical transport by the shape, size, and orientation of these objects. The bulk-correlation technique is shown to capture the elongation and orientation of coherence by ambient wind, but is unable to characterise individual coherent structures. Using the object-based approach, it is found that the individual structures dominating the vertical flux are plume-like in character (extending from the surface into cloud) rather than thermal-like, show small width/thickness asymmetry, and rise near-vertically in the absence of ambient wind. The planar stretching and tilting of boundary-layer structures caused by the introduction of ambient shear is also quantified, demonstrating the general applicability of the techniques for future study of other boundary-layer patterns

Identification of critical paralog groups with indispensable roles in the regulation of signaling flow

Extensive cross-talk between signaling pathways is required to integrate the myriad of extracellular signal combinations at the cellular level. Gene duplication events may lead to the emergence of novel functions, leaving groups of similar genes - termed paralogs - in the genome. To distinguish critical paralog groups (CPGs) from other paralogs in human signaling networks, we developed a signaling network-based method using cross-talk annotation and tissue-specific signaling flow analysis. 75 CPGs were found with higher degree, betweenness centrality, closeness, and ‘bowtieness’ when compared to other paralogs or other proteins in the signaling network. CPGs had higher diversity in all these measures, with more varied biological functions and more specific post-transcriptional regulation than non-critical paralog groups (non-CPG). Using TGF-beta, Notch and MAPK pathways as examples, SMAD2/3, NOTCH1/2/3 and MEK3/6-p38 CPGs were found to regulate the signaling flow of their respective pathways. Additionally, CPGs showed a higher mutation rate in both inherited diseases and cancer, and were enriched in drug targets. In conclusion, the results revealed two distinct types of paralog groups in the signaling network: CPGs and non-CPGs. Thus highlighting the importance of CPGs as compared to non-CPGs in drug discovery and disease pathogenesis