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

Predictability assessment of nowcasts in high-impact heavy precipitation events

Postprint (published version

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

A study of the error covariance matrix of radar rainfall estimates in stratiform rain

The contribution of various physical sources of uncertainty affecting radar rainfall estimates at the ground is quantified toward deriving and understanding the error covariance matrix of these estimates. The focus here is on stratiform precipitation at a resolution of 15 km, which is most relevant for data assimilation onto mesoscale numerical models. In the characterization of the error structure, the following contributions are considered: (i) the individual effect of the range-dependent error (associated with beam broadening and increasing height of radar measurements with range), (ii) the error associated with the transformation from reflectivity to rain rate due to the variability of drop size distributions, and (iii) the interaction of the first two, that is, the term resulting from the cross correlation between the effects of the range-dependent error and the uncertainty related to the variability of drop size distributions (DSDs). For this purpose a large database of S-band radar observations at short range (where reflectivity near the ground is measured and the beam is narrow) is used to characterize the range-dependent error within a simulation framework, and disdrometric measurements collocated with the radar data are used to assess the impact of the variability of DSDs. It is noted that these two sources of error are well correlated in the vicinity of the melting layer as result of the physical processes that determine the density of snow (e.g., riming), which affect both the DSD variability and the vertical profile of reflectivity.Postprint (published version

Radar-based rainfall nowcasting at European scale: long-term evaluation and performance assessment

This work studies the performance of CRAHI's algorithm for rainfall nowcasting at European scale using the mosaics produced within the EUMETNET project OPERA (with a resolution of 4 km and 15 minutes) in the framework of the European Civil Protection research project HAREN (www.haren-project.eu). Systematic evaluation has been carried out since June 2012, focusing on the space-time variability of the nowcasting skills, and its dependence on the scale of the forecasted precipitation systems and on rainfall intensities. Also, the probabilistic nowcasting technique SBMcast (Berenguer et al. 2011) has been adapted to the use of OPERA mosaics at European scale to assess the uncertainty in the produced nowcasts. The performance of this probabilistic technique has been evaluated over a number of cases. Finally, the work analyzes the usefulness of these nowcasts for hazard assessment at European scale, based on exceeding the regional rainfall thresholds used by the EUMETNET project METEOALARM.Peer ReviewedPostprint (published version

Adding value to the measurements of an X-band radar on Catalonian coast

Rainscanner@Barcelona is an experiment assessing the hydrological value of a small X-band radar in urban areas.Postprint (published version

Scale analysis of the diurnal cycle of precipitation over Continental United States

Rainfall initiation is related to diurnal and semidiurnal radiation forcing (e.g. Wallace 1975, Carbone et al. 2002, Surcel et al. 2010). Much of the observed warm season rainfall results from a thermodynamic response to strong diurnal cycle of land surface temperature. Therefore, over some continental regions deep convection tends to peak around local afternoon and early evening hours. However, there is regional uniqueness in the precipitation pattern that implies a connection between regional characteristics and the behaviour of the precipitation field (Wallace 1975, Carbone et al. 2002, Lee et al. 2007). Over western US the diurnal precipitation pattern becomes well organized with a late afternoon maximum along the eastern slopes of the Rocky Mountains (Carbone et al. 2002, Ahijevych et al. 2004, among others). This mountain-initiated convection tends to propagate away, leading to the local evening maximum over the adjacent plains (Lee et al. 2007). The daily occurrence of propagating systems has a high impact on the continental diurnal cycle of precipitation. Parker and Ahijevych (2007) found that approximately 90% of the episodes identified in the east-central US were due to propagating systems from the west. A consequence of these systems result on the transport of the diurnal cycle from west to east (Surcel et al. 2010). The objective of this work is to study the scale dependence of the diurnal cycle and the variability of the rainfall field with the location and time of the day, with special focus on the role of the different spatial scales in such variability.Postprint (published version

Scale characterization and correction of diurnal cycle errors in MAPLE

The most widely used technique for nowcasting of quantitative precipitation in operational and research centers is the Lagrangian extrapolation of the latest radar observations. However, this technique has a limited forecast skill because of the assumptionmade on its formulation, such as the fact that the motion vectors do not change and, evenmore important for convective events, neglect any growth or decay in the precipitation field. In this work, the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) errors have been computed for 10 yr of radar composite data over the continental United States. The study of these errors shows systematic bias depending on the time of day. This effect is related to the solar cycle, whose heating energy results in an increase in the average rainfall in the afternoon. This external forcing interacts with the atmospheric system, creating local initiation and dissipation of convection depending on orography, land use, cloud coverage, etc. The signal of the diurnal cycle inMAPLEprecipitation forecast has been studied in different locations and spatial scales as a function of lead time in order to recognize where, when, and for which spatial scales the signal is significant. This information has been used in the development of a scaling correction scheme where the mean errors due to the diurnal cycle are adjusted. The results show that the developed methodology improves the forecast for the spatial scales and locations where the diurnal cycle signal is significant.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

Critical rainfall conditions for the initiation of torrential flows: results from the Rebaixader catchment (Central Pyrenees)

Torrential flows like debris flows or debris floods are fast movements formed by a mix of water and different amounts of unsorted solid material. They generally occur in steep torrents and pose high risk in mountainous areas. Rainfall is their most common triggering factor and the analysis of the critical rainfall conditions is a fundamental research task. Due to their wide use in warning systems, rainfall thresholds for the triggering of torrential flows are an important outcome of such analysis and are empirically derived using data from past events. In 2009, a monitoring system was installed in the Rebaixader catchment, Central Pyrenees (Spain). Since then, rainfall data of 25 torrential flows (“TRIG rainfalls”) were recorded, with a 5-min sampling frequency. Other 142 rainfalls that did not trigger torrential flows (“NonTRIG rainfalls”) were also collected and analyzed. The goal of this work was threefold: (i) characterize rainfall episodes in the Rebaixader catchment and compare rainfall data that triggered torrential flows and others that did not; (ii) define and test Intensity–Duration (ID) thresholds using rainfall data measured inside the catchment by with different techniques; (iii) analyze how the criterion used for defining the rainfall duration and the spatial variability of rainfall influences the value obtained for the thresholds. The statistical analysis of the rainfall characteristics showed that the parameters that discriminate better the TRIG and NonTRIG rainfalls are the rainfall intensities, the mean rainfall and the total rainfall amount. The antecedent rainfall was not significantly different between TRIG and NonTRIG rainfalls, as it can be expected when the source material is very pervious (a sandy glacial soil in the study site). Thresholds were derived from data collected at one rain gauge located inside the catchment. Two different methods were applied to calculate the duration and intensity of rainfall: (i) using total duration, Dtot, and mean intensity, Imean, of the rainfall event, and (ii) using floating durations, D, and intensities, Ifl, based on the maximum values over floating periods of different duration. The resulting thresholds are considerably different (Imean = 6.20 Dtot-0.36 and Ifl_90% = 5.49 D-0.75, respectively) showing a strong dependence on the applied methodology. On the other hand, the definition of the thresholds is affected by several types of uncertainties. Data from both rain gauges and weather radar were used to analyze the uncertainty associated with the spatial variability of the triggering rainfalls. The analysis indicates that the precipitation recorded by the nearby rain gauges can introduce major uncertainties, especially for convective summer storms. Thus, incorporating radar rainfall can significantly improve the accuracy of the measured triggering rainfall. Finally, thresholds were also derived according to three different criteria for the definition of the duration of the triggering rainfall: (i) the duration until the peak intensity, (ii) the duration until the end of the rainfall; and, (iii) the duration until the trigger of the torrential flow. An important contribution of this work is the assessment of the threshold relationships obtained using the third definition of duration. Moreover, important differences are observed in the obtained thresholds, showing that ID relationships are significantly dependent on the applied methodology.Peer ReviewedPostprint (author's final draft