An elegant solution to characterise the residual errors in radar precipitation estimates is to generate anensembleof precipitation fields. The paper proposes a radar ensemble generator designed for usage in the Alps using LU decomposition (REAL), and presents first results from a real‐time implementation coupling the radar ensemble with a semi‐distributed rainfall–runoff model for flash flood modelling in a steep Alpine catchment. Each member of the radar ensemble is a possible realisation of the unknown true precipitation field given the observed radar field and knowledge of the space–time error structure of radar precipitation estimates. Feeding the alternative realisations into a hydrological model yields a distribution of response values, the spread of which represents the sensitivity of runoff to uncertainties in the input radar precipitation field.The presented ensemble generator is based on singular value decomposition of the error covariance matrix, stochastic simulation using the LU decomposition algorithm, and autoregressive filtering. It allows full representation of spatial dependence of the mean and covariances of radar errors. This is of particular importance in a mountainous region with large uncertainty in radar precipitation estimates and strong dependence of error structure on location. The real‐time implementation of the radar ensemble generator coupled with a semi‐distributed hydrological model in the framework of the forecast demonstration project MAP D‐PHASE is one of the first experiments of this type worldwide, and is a fully novel contribution to this evolving area of applied research. Copyright © 2009 Royal Meteorological Society

During the second week of September 2013, a seasonally uncharacteristic weather pattern stalled over the Rocky Mountain Front Range region of northern Colorado bringing with it copious amounts of moisture from the Gulf of Mexico, Caribbean Sea, and the tropical eastern Pacific Ocean. This feed of moisture was funneled toward the east-facing mountain slopes through a series of mesoscale circulation features, resulting in several days of unusually widespread heavy rainfall over steep mountainous terrain. Catastrophic flooding ensued within several Front Range river systems that washed away highways, destroyed towns, isolated communities, necessitated days of airborne evacuations, and resulted in eight fatalities. The impacts from heavy rainfall and flooding were felt over a broad region of northern Colorado leading to 18 counties being designated as federal disaster areas and resulting in damages exceeding $2 billion (U.S. dollars). This study explores the meteorological and hydrological ingredients that led to this extreme event. After providing a basic timeline of events, synoptic and mesoscale circulation features of the event are discussed. Particular focus is placed on documenting how circulation features, embedded within the larger synoptic flow, served to funnel moist inflow into the mountain front driving several days of sustained orographic precipitation. Operational and research networks of polarimetric radar and surface instrumentation were used to evaluate the cloud structures and dominant hydrometeor characteristics. The performance of several quantitative precipitation estimates, quantitative precipitation forecasts, and hydrological forecast products are also analyzed with the intention of identifying what monitoring and prediction tools worked and where further improvements are needed.

A general phenomenological formulation for drop size distribution (DSD), written down as a scaling law, is proposed. It accounts for all previous fitted DSDs. As a main implication of the expression proposed, the integral rainfall variables are related by power functions and agree with experimental evidence. Additional consequences are also analyzed. From this formulation there follows a general methodology for scaling all data in a unique plot, leading to more robust fits of the DSD. An illustrative example on real data is provided. Abstract A general phenomenological formulation for drop size distribution (DSD), written down as a scaling law, is proposed. It accounts for all previous fitted DSDs. As a main implication of the expression proposed, the integral rainfall variables are related by power functions and agree with experimental evidence. Additional consequences are also analyzed. From this formulation there follows a general methodology for scaling all data in a unique plot, leading to more robust fits of the DSD. An illustrative example on real data is provided.