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Uncertainty estimation and propagation in radar-rain gauge rainfall merging using kriging-based techniques
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The optimal temporal resolution for rainfall applications in urban hydrological models depends on different factors. Accumulations are often used to reduce uncertainty, while a sufficiently fine resolution is needed to capture the variability of the urban hydrological processes. Merging radar and rain gauge rainfall is recognized to improve the estimation accuracy. This work explores the possibility to merge radar and rain gauge rainfall at coarser temporal resolutions to reduce uncertainty, and to downscale the results. A case study in the UK is used to cross-validate the methodology. Rainfall estimates merged and downscaled at different resolutions are compared. As expected, coarser resolutions tend to reduce uncertainty in terms of rainfall estimation. Additionally, an example of urban application in Twenterand, the Netherlands, is presented. The rainfall data from four rain gauge networks are merged with radar composites and used in an InfoWorks model reproducing the urban drainage system of Vroomshoop, a village in Twenterand. Fourteen combinations of accumulation and downscaling resolutions are tested in the InfoWorks model and the optimal is selected comparing the results to water level observations. The uncertainty is propagated in the InfoWorks model with ensembles. The results show that the uncertainty estimated by the ensemble spread is proportional to the rainfall intensity and dependent on the relative position between rainfall cells and measurement points.
The accurate evaluation of the precipitation's time–spatial
structure is a critical step for rainfall–runoff modelling. Particularly for
small catchments, the variability of rainfall can lead to mismatched results.
Large errors in flow evaluation may occur during convective storms,
responsible for most of the flash floods in small catchments in the
Mediterranean area. During such events, we may expect large spatial and
temporal variability. Therefore, using rain-gauge measurements only can be
insufficient in order to adequately depict extreme rainfall events. In this
work, a double-level information approach, based on rain gauges and weather
radar measurements, is used to improve areal rainfall estimations for
hydrological applications. In order to highlight the effect that
precipitation fields with different level of spatial details have on
hydrological modelling, two kinds of spatial rainfall fields were computed
for precipitation data collected during 2015, considering both rain gauges
only and their merging with radar information. The differences produced by
these two precipitation fields in the computation of the areal mean rainfall
accumulation were evaluated considering 999 basins of the region Calabria,
southern Italy. Moreover, both of the two precipitation fields were used to
carry out rainfall–runoff simulations at catchment scale for main
precipitation events that occurred during 2015 and the differences between
the scenarios obtained in the two cases were analysed. A representative case
study is presented in detail.
The accuracy of spatial predictions of rainfall by merging rain-gauge and radar data is partly determined by the sampling design of the rain-gauge network. Optimising the locations of the rain-gauges may increase the accuracy of the predictions. Existing spatial sampling design optimisation methods are based on minimisation of the spatially averaged prediction error variance under the assumption of intrinsic stationarity. Over the past years, substantial progress has been made to deal with non-stationary spatial processes in kriging. Various well-documented geostatistical models relax the assumption of stationarity in the mean, while recent studies show the importance of considering non-stationarity in the variance for environmental processes occurring in complex landscapes. We optimised the sampling locations of rain-gauges using an extension of the Kriging with External Drift (KED) model for prediction of rainfall fields. The model incorporates both non-stationarity in the mean and in the variance, which are modelled as functions of external covariates such as radar imagery, distance to radar station and radar beam blockage. Spatial predictions are made repeatedly over time, each time recalibrating the model. The space-time averaged KED variance was minimised by Spatial Simulated Annealing (SSA). The methodology was tested using a case study predicting daily rainfall in the north of England for a one-year period. Results show that (i) the proposed non-stationary variance model outperforms the stationary variance model, and (ii) a small but significant decrease of the rainfall prediction error variance is obtained with the optimised rain-gauge network. In particular, it pays off to place rain-gauges at locations where the radar imagery is inaccurate, while keeping the distribution over the study area sufficiently uniform.
The aim of this work is to update ground clutter classification methods in weather radar rainfall measurements to more accurately identify clutter pixels from wind farms. Measurements from two dual polarised weather radars, based in the UK, will be used to determine the characteristics of multiple wind farms in the North Sea and the Irish Sea. Currently 21 of the top 25 largest offshore wind farms are located in these regions. The extensive area occupied by the wind farms creates problems for weather radars located in the neighbouring European countries. Data sets of wind farm, precipitation and ground clutter pixels were aggregated from Thurnham Radar measurements to form novel membership functions that can be used in a fuzzy logic classification system to identify wind farm clutter. When only ground clutter data sets were used for classification areas of the radar scans taken up by wind farm clutter were misclassified as rainfall. The inclusion of wind farm measurements lead to an increase in the ability of the algorithm to detect these pixels as clutter as the Heidke Skill Score increased from 67.4% to 97.8%. However there was a slight increase in the number of precipitation pixels incorrectly classified as clutter, with the false alarm rate increasing from 0.05% to 1.24% when all variables are used. The algorithm performed slightly better when applied to another radar in Hameldon Hill showing promise for application to the UK network without recalibration of membership functions.
The HYREX experiment has provided a data set unique in the UK, with a dense network of raingauges available for studying the rainfall at a fine local scale and a network of radar stations allowing detailed examination of the spatial and temporal structure of rainfall at larger scales. In this paper, the properties and characteristics of the rainfall process, as measured by the HYREX recording network of rainguages and radars, are studied from a statistical perspective. The results of these analyses are used to develop various models of the rainfall process, for use in hydrological applications. Some typical results of these various modelling exercises are presented.
