Evaluation of the Radar QPE and Rain Gauge Data Merging Methods in Northern China

Abstract

Radar-rain gauge merging methods have been widely used to produce high-quality precipitation with fine spatial resolution by combing the advantages of the rain gauge observation and the radar quantitative precipitation estimation (QPE). Different merging methods imply a specific choice on the treatment of radar and rain gauge data. In order to improve their applicability, significant studies have focused on evaluating the performances of the merging methods. In this study, a categorization of the radar-rain gauge merging methods was proposed as: (1) Radar bias adjustment category, (2) radar-rain gauge integration category, and (3) rain gauge interpolation category for a total of six commonly used merging methods, i.e., mean field bias (MFB), regression inverse distance weighting (RIDW), collocated co-kriging (CCok), fast Bayesian regression kriging (FBRK), regression kriging (RK), and kriging with external drift (KED). Eight different storm events were chosen from semi-humid and semi-arid areas of Northern China to test the performance of the six methods. Based on the leave-one-out cross validation (LOOCV), conclusions were obtained that the integration category always performs the best, the bias adjustment category performs the worst, and the interpolation category ranks between them. The quality of the merging products can be a function of the merging method that is affected by both the quality of radar QPE and the ability of the rain gauge to capture small-scale rainfall features. In order to further evaluate the applicability of the merging products, they were then used as the input to a rainfall-runoff model, the Hybrid-Hebei model, for flood forecasting. It is revealed that a higher quality of the merging products indicates a better agreement between the observed and the simulated runoff.

Subang Radar Cappi Data Processing and Z-R Optimization for Quantitative Precipitation Estimates (Qpe) Over Langat River Basin

“…Merging radar and rain gauge data is a commonly adopted technique to reduce error between radar rainfall estimates and the gauged rainfall. Qiu et al [10] discussed that Mean Field Bias (MFB) modification is the easiest technique in which the correction factor is calculated as the ratio of the cumulative gauge rainfall, G and the accumulated radar rainfall estimates, R over the specified time duration at the specified locations. Other radar-gauge merging methods include co-kriging [11] kriging with external drift [12] and conditional merging [13].…”

Comparative Analysis of Rain Gauge and Radar Precipitation Estimates towards Rainfall-Runoff Modelling in a Peri-Urban Basin in Attica, Greece

“…Therefore, the Z-R relationship is a static relationship but varies in time and space. The most common approaches are to calibrate the Z-R relationship for use on a specific weather radar based on measurements of a distrometer [7][8][9], an equipment for measuring the drop size distribution, or the correlation of the QPE with rain gauge measurements [3,[10][11][12]. Nevertheless, the parameters of the Z-R relationship should be evaluated for use with only specific storm characteristics, being for instance either convective or stratiform.…”

Modular Neural Networks with Fully Convolutional Networks for Typhoon-Induced Short-Term Rainfall Predictions

“…Many studies have used the statistical relationships between the radar reflectivity and the rain rate or nonlinear regression to establish rainfall estimation models. These studies have achieved favorable outcomes [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. However, the interpretation of these image data is a crucial emerging topic.…”