Adaptive reconstruction of radar reflectivity in clutter-contaminated areas by accounting for the space-time variability

Identification and elimination of clutter is necessary for ensuring data quality in radar Quantitative Precipitation Estimates (QPE). For uncorrected scanning reflectivity after signal processing, the removed areas have been often reconstructed by horizontal interpolation, extrapolation of non-contaminated PPIs aloft, or combining both based on a classification of the precipitation type. We present a general reconstruction method based on the interpolation of clutter-free observations. The method adapts to the type of precipitation by considering the spatial and temporal variability of the field provided by the multi-dimensional semivariogram. Six different formulations have been tested to analyze the gain introduced by each source of information: (1) horizontal interpolation, (2) vertical extrapolation, (3) extrapolation of past observations, (4) volumetric reconstruction, (5) horizontal and temporal reconstruction, and (6) volumetric and temporal reconstruction. The evaluation of the reconstructed fields obtained with the 6 formulations has been done (i) over clutter-free areas by comparison with the originally observed values, and (ii) over the real clutter-contaminated areas by comparison with the rainfall accumulations from a raingauge network. The results for 24 analyzed events (with a variety of convective and widespread cases) suggest that the contribution of extrapolation of past observations is not fundamental, and that the volumetric reconstruction is the one that overall adapted the best to the different situations.Peer ReviewedPostprint (author’s final draft

Reconstruction of radar reflectivity in clutter areas

The production of Radar Quantitative Precipitation Estimates (QPE) requires processing the observations to ensure their quality and its conversion into the variable of interest (e.g., precipitation rates). Some of the steps involve the reconstruction of the meteorological signal in areas where the signal is lost (e.g. due to total beam blockage or severe path attenuation by heavy rain) or strongly contaminated, for instance, in areas affected by ground or sea clutter. In the latter case, the meteorological signal is often reconstructed through the analysis of the Doppler spectrum. Alternatively, for uncorrected moment data, the reconstruction is done first by identifying clutter-affected areas based on the analysis of statistical properties of radar measurements, and then the reconstruction of the meteorological signal is performed either by horizontal interpolation, by extrapolation of non-contaminated PPIs aloft or a combination of the two, as proposed by Sánchez-Diezma et al. (2001) by adapting the reconstruction to the type of precipitation affecting clutter-contaminated areas. Here, an alternative reconstruction method is proposed here using the space and time structure of the field. The developed method has been implemented to reflectivity fields under different rainfall situations (scattered convection, organized convection, and widespread precipitation –see Section 2). For the evaluation of the method, several formulations of the reconstruction method (presented in Section 3) have been implemented and compared between radar estimates and raingauge observations (Section 4).Peer ReviewedPostprint (author’s final draft

ReAFFIRM: Real-time Assessment of Flash Flood Impacts: a Regional high-resolution Method

Flash floods evolve rapidly in time, which poses particular challenges to emergency managers. One way to support decision-making is to complement models that estimate the flash flood hazard (e.g. discharge or return period) with tools that directly translate the hazard into the expected socio-economic impacts. This paper presents a method named ReAFFIRM that uses gridded rainfall estimates to assess in real time the flash flood hazard and translate it into the corresponding impacts. In contrast to other studies that mainly focus on in- dividual river catchments, the approach allows for monitoring entire regions at high resolution. The method consists of the following three components: (i) an already existing hazard module that processes the rainfall into values of exceeded return period in the drainage network, (ii) a flood map module that employs the flood maps created within the EU Floods Directive to convert the return periods into the expected flooded areas and flood depths, and (iii) an impact assessment module that combines the flood depths with several layers of socio- economic exposure and vulnerability. Impacts are estimated in three quantitative categories: population in the flooded area, economic losses, and affected critical infrastructures. The performance of ReAFFIRM is shown by applying it in the region of Catalonia (NE Spain) for three significant flash flood events. The results show that the method is capable of identifying areas where the flash floods caused the highest impacts, while some locations affected by less significant impacts were missed. In the locations where the flood extent corresponded to flood observations, the assessments of the population in the flooded area and affected critical infrastructures seemed to perform reasonably well, whereas the economic losses were systematically overestimated. The effects of different sources of uncertainty have been discussed: from the estimation of the hazard to its translation into impacts, which highly depends on the quality of the employed datasets, and in particular on the quality of the rainfall inputs and the comprehensiveness of the flood maps.Peer ReviewedPostprint (published version

Long-term analysis of gauge-adjusted radar rainfall accumulations at European scale

Monitoring continental precipitation over Europe with high resolution (2 km, 15 min) has been possible since the operational production of the OPERA composites from the European weather radar networks. The OPERA data are the essential input to a hazard assessment tool for identifying localized rainfall-induced flash floods at European scale, and their quality determines the performance of the tool. This paper analyses the OPERA data quality during the warm seasons of 2015–2017 by comparing the estimated rainfall accumulations with the SYNOP rain gauge records over Europe. To compensate the OPERA underestimation, a simple spatially-variable bias adjustment method has been applied. The long-term comparison between the OPERA and gauge point daily rainfall accumulations at the gauge locations shows the benefit of the bias adjustment. Additionally, the daily monitoring shows gradual improvement of the OPERA data year by year. The impact of the quality of the OPERA data for effective flash flood identification is demonstrated for the case of the flash floods that occurred from 29 May to 3 June 2016 in central Europe.Peer ReviewedPostprint (author's final draft

Real-time assessment of flash flood impacts at pan-European scale: the ReAFFINE method

The development of early warning systems (EWSs) is a key element for the effective mitigation of flash flood impacts. Emergency managers and other end-users increasingly recognise the benefit of tools that automatically translate the forecasted flash flood hazard (e.g. expressed in terms of peak discharge or return period) into the expected socio-economic impacts (e.g. the affected population). While previous studies aimed at forecasting flash flood impacts at local or regional scales, this paper presents a simple approach for estimating in real time the flash flood impacts at pan-European scale. The proposed method – named ReAFFINE – is designed to be integrated into an EWS for end-users operating over large spatial domains (e.g. across regions or countries). ReAFFINE uses the pan-European flash flood hazard estimates from the ERICHA system to retrieve the potentially flooded areas from the national flood maps (generated in the framework of the EU Floods Directive). By combining the potentially flooded areas with socio-economic exposure information, ReAFFINE estimates in real time the exposed population and critical infrastructures. For two catastrophic flash flood events affecting Europe in recent years, ReAFFINE has demonstrated the capability to identify impacts over large spatial scales. Also at sub-regional level, the method has mostly been able to locate the areas and municipalities where the most important impacts occurred. The results also show that the performance is sensitive to the quality of the rainfall estimates that drive the hazard estimation, and to the comprehensiveness of the employed flood maps.The EU Horizon 2020 project ANYWHERE (H2020-DRS-1-2015-700099) financed the initial period of this work. The study was finalised in the framework of the TAMIR project (UCPM-874435-TAMIR). We would like to express our gratitude to OPERA, WMO and the Spanish State Meteorological Agency (AEMET) for the provision of meteorological data, and the Spanish National Geographic Institute (IGN) and the German Federal Institute of Hydrology (BfG) for access to the national flood maps. Furthermore, we would like to thank OpenStreetMaps, Milan Kalas, and the Joint Research Centre for providing the pan-European exposure datasets, and the European Severe Weather Database (ESWD), the Bavarian Environment Agency (LfU), the Spanish Insurance Compensation Consortium (CCS), the Spanish Directorate-General for Civil Protection and Emergencies (DGPCE), and Jens de Bruijn (Vrije Universiteit Amsterdam) from the Global Flood Monitor for meticulously reporting the impacts of the analysed flood events.Peer ReviewedPostprint (published version

On the estimation of near-surface atmospheric refraction uing scanning radar

Near-surface atmospheric refraction, often characterized by the quantity refractivity, affects the propagation of the radar beam, yet it is poorly measured due to its complex pattern. The aim of this thesis is to characterize the structure of near-surface refractivity and its errors. The refractivity retrieval in the horizontal is obtained from the radar phase measurements that can be affected systematically by the variability of ground target heights over complex terrain coupled with propagation conditions. This study characterizes such factors statistically and reproduces the expected uncertainty (noisiness) by simulating phase for the assessment of the radar refractivity retrieval. However, the noisiness of simulated phase is much smaller compared with that of observations suggesting that such factors are incapable of characterizing moving ground targets and thus insufficient to fully explain the phase noisiness. The vertical structure of refractivity is, on the other hand, characterized in order to inform about low-level propagation conditions. The coverage of radar ground echo observed at low elevation angles is affected by the path of the radar beam determined with the vertical gradient of refractivity. Hence, this study simulates the coverage of ground targets with given vertical gradient of refractivity and compares it with the observed one. The best match between the simulation and observation is used to determine the radar estimate of refractivity changes in the vertical. The results are validated with the estimates from several sounding instruments. Although the identification of ground targets is required for better performance, this novel technique shows certain skill in extracting additional low-level atmospheric information out of radar measurements from ground targets. In both studies, the characterization of ground targets observed by radar plays a critical role: on one hand, it allows us to extract the structure of the near-ground refractivityPrès de la surface terrestre, la réfraction atmosphérique affecte la propagation des ondes radars. Cette caractéristique de l'atmosphère, qu'on nomme la réfractivité, est spatialement complexe et mal connue. Dans cette thèse, la structure de la réfractivité ainsi que ses erreurs sont caractérisés. La réfractivité est mesurée à partir du déphasage des échos radars provenant de cibles terrestres. Ce déphasage est une fonction de la complexité du terrain ainsi que des conditions de propagations. Ces deux facteurs sont analysés statistiquement afin de simuler l'incertitude attendue (le bruit) de la phase des échos radars affectant les mesures de réfractivité. Les simulations ainsi conduites possèdent un niveau de bruit sur la phase beaucoup plus petit que celui des mesures instrumentales. Cette observation suggère que le bruit sur la phase causé par le terrain et les conditions de propagation a un impact limité sur les mesures de réfractivité comparé au bruit provenant d'autres sources tel que les cibles mobiles. Dans un deuxième temps, la structure verticale de la réfractivité est étudiée afin de déduire les conditions de propagation en basse altitude. L'étendue des échos de sol observés à faibles élévations est affectée par la trajectoire des ondes radars qui à son tour est affectée par le gradient vertical de la réfractivité. Cette étude simule l'étendue des échos de sols en supposant différents gradients de réfractivité. Les gradients qui mènent à la meilleure ressemblance entre l'étendue des échos simulée et observée sont utilisés pour estimer les gradients réels de l'atmosphère. La validation des résultats est ensuite faite par comparaison avec d'autres instruments. Malgré le fait que les échos de sols doivent être identifiés pour une performance optimale, cette nouvelle technique démontre la possibilité d'inférer de nouvelles informations sur l'atmosphère en basse altitude à partir d'éc

Water vapor estimation using near-surface radar refractivity during IHOP_2002

A ground-based radar refractivity mapping technique is used to measure water vapor near the surface during the International H2O Project in May and June, 2002 (IHOP_2002). Radar-measured refractivity is compared with refractivity estimated from surface station observations during this field experiment. Bias in radar and station refractivity is found to occur often when humidity is high. Possible reasons for this difference between radar and station observations are discussed. Most of the biases were associated either with inaccurate humidity observations by stations or with the small height difference of the two measurements. With confirming this last observation further during these wet ground conditions, radar refractivity shows much better agreement with radiosonde sounding refractivity just above the surface than with station refractivity.In addition, columnar water vapor is computed using the mixing ratios retrieved from radar and station refractivity and using the observed height of the convective boundary layer from a FM-CW radar. Surface moisture fluxes are computed as a residual of the columnar water vapor and compared with observations from flux-towers, which compute this using the eddy-covariance technique. Although the results show that the radar-based measurements may have some skill over longer time periods, the technique completely fails to reproduce observations over scales smaller than 1 hour

Adaptive reconstruction of radar reflectivity in clutter-contaminated areas by accounting for the space-time variability

Identification and elimination of clutter is necessary for ensuring data quality in radar Quantitative Precipitation Estimates (QPE). For uncorrected scanning reflectivity after signal processing, the removed areas have been often reconstructed by horizontal interpolation, extrapolation of non-contaminated PPIs aloft, or combining both based on a classification of the precipitation type. We present a general reconstruction method based on the interpolation of clutter-free observations. The method adapts to the type of precipitation by considering the spatial and temporal variability of the field provided by the multi-dimensional semivariogram. Six different formulations have been tested to analyze the gain introduced by each source of information: (1) horizontal interpolation, (2) vertical extrapolation, (3) extrapolation of past observations, (4) volumetric reconstruction, (5) horizontal and temporal reconstruction, and (6) volumetric and temporal reconstruction. The evaluation of the reconstructed fields obtained with the 6 formulations has been done (i) over clutter-free areas by comparison with the originally observed values, and (ii) over the real clutter-contaminated areas by comparison with the rainfall accumulations from a raingauge network. The results for 24 analyzed events (with a variety of convective and widespread cases) suggest that the contribution of extrapolation of past observations is not fundamental, and that the volumetric reconstruction is the one that overall adapted the best to the different situations.Peer Reviewe

Reconstruction of radar reflectivity in clutter areas

The production of Radar Quantitative Precipitation Estimates (QPE) requires processing the observations to ensure their quality and its conversion into the variable of interest (e.g., precipitation rates). Some of the steps involve the reconstruction of the meteorological signal in areas where the signal is lost (e.g. due to total beam blockage or severe path attenuation by heavy rain) or strongly contaminated, for instance, in areas affected by ground or sea clutter. In the latter case, the meteorological signal is often reconstructed through the analysis of the Doppler spectrum. Alternatively, for uncorrected moment data, the reconstruction is done first by identifying clutter-affected areas based on the analysis of statistical properties of radar measurements, and then the reconstruction of the meteorological signal is performed either by horizontal interpolation, by extrapolation of non-contaminated PPIs aloft or a combination of the two, as proposed by Sánchez-Diezma et al. (2001) by adapting the reconstruction to the type of precipitation affecting clutter-contaminated areas. Here, an alternative reconstruction method is proposed here using the space and time structure of the field. The developed method has been implemented to reflectivity fields under different rainfall situations (scattered convection, organized convection, and widespread precipitation –see Section 2). For the evaluation of the method, several formulations of the reconstruction method (presented in Section 3) have been implemented and compared between radar estimates and raingauge observations (Section 4).Peer Reviewe

Anticipating cascading effects of extreme precipitation with pathway schemes: three case studies from Europe

Extreme precipitation events with high local precipitation intensities, heavy snowfall or extensive freezing rain can have devastating impacts on society and economy. Not only is the quantitative forecast of such events sometimes difficult and associated with large uncertainties, also are the potential consequences highly complex and challenging to predict. It is thus a demanding task to anticipate or nowcast the impacts of extreme precipitation, even more so in situations where human lives or critical infrastructure might be at risk. In recent years, the term “cascading effects” has been increasingly used to describe events in which an initial trigger leads to a sequence of consequences with significant magnitude. We here analyze three examples for different precipitation types where the initial triggering event generated a cascade of events and impacts, namely a convective precipitation event in the Swiss Prealps, a freezing rain in Slovenia, and a heavy snowfall episode in Catalonia. With the aim to improve process understanding of complex precipitation-triggered events, we assess the prediction of the selected events and analyze the cascading effects that caused diverse impacts. To this end, we use a framework of cascading effects which should ultimately allow the development of a better design risk assessment and management strategies. Our findings confirm that damage of extreme precipitation events is clearly related to the knowledge of potential cascading effects. Major challenges of predicting cascading effects are the high complexity, the interdependencies and the increasing uncertainty along the cascade. We propose a framework for cascading effects including two approaches: (i) one to analyze cascading effects during past extreme precipitation events, which then serves as a basis for a (ii) more generalized approach to increase the preparedness level of operational services before and during future extreme precipitation events and to anticipate potential cascading effects of extreme precipitation. Both approaches are based on pathway schemes that can be used in addition to numerical models or hazard maps to analyze and predict potential cascading effects, but also as training tools.Peer Reviewe