Radar Precipitation Estimates Research Articles

Can stormwater runoff measurements be used for weather radar rainfall adjustment?

ABSTRACT Predicting the response to rainfall in urban hydrological applications requires accurate precipitation estimates with a high spatiotemporal resolution to reflect the natural variability of rainfall. However, installing rain gauges under nearly ideal measurement conditions is often difficult in urban areas, if not impossible. This paper demonstrates the potential of deriving rainfall measurements in urban areas and bias-adjusting weather radar rainfall measurements using stormwater runoff measurements. As a supplement to point rainfall measurements from rain gauges, the developed bias adjustment approach uses catchment runoff-rainfall estimates derived from water level measurements of a stormwater detention pond. The study shows that the bias-adjusted radar product correlates highly with rain gauge measurements in the catchment. Moreover, the presented approach enables rainfall measurements within a catchment independent of rain gauges located in the catchment, making the technique highly applicable for increasing the density of ground observations and thus improving weather radar precipitation estimates over urban areas. The method also derives the catchment-specific runoff coefficient independently of expensive flow measurements in the catchment, making the method very scalable. This paper highlights the potential of using easily achievable catchment runoff-rainfall measurements to increase the density of available ground observations and thereby improve weather radar precipitation estimates.

Merging with crowdsourced rain gauge data improves pan-European radar precipitation estimates

Abstract. Ground-based radar precipitation products typically need adjustment with rain gauge accumulations to achieve a reasonable accuracy. This is certainly the case for the pan-European radar precipitation products. The density of (near) real-time rain gauge accumulations from official networks is often relatively low. Crowdsourced rain gauge networks have a much higher density than conventional ones and are a potentially interesting (complementary) source to merge with radar precipitation accumulations. Here, a 1-year personal weather station (PWS) rain gauge dataset of ∼ 5 min accumulations is obtained from the private company Netatmo over the period 1 September 2019–31 August 2020, which is subjected to quality control using neighbouring PWSs and, after aggregating to 1 h accumulations, using unadjusted radar data. The PWS 1 h gauge accumulations are employed to spatially adjust OPERA radar accumulations, covering 78 % of geographical Europe. The performance of the merged dataset is evaluated against daily and disaggregated 1 h gauge accumulations from weather stations in the European Climate Assessment & Dataset (ECA&D). Results are contrasted to those from an unadjusted OPERA-based radar dataset and from EURADCLIM. The severe average underestimation for daily precipitation of ∼ 28 % from the unadjusted radar dataset diminishes to ∼ 3 % for the merged radar–PWS dataset. A station-based spatial verification shows that the relative bias in 1 h precipitation is still quite variable and suggests stronger underestimations for colder climates. A dedicated evaluation with scatter density plots reveals that the performance is indeed less good for lower temperatures, which points to limitations in observing solid precipitation by PWS gauges. The outcome of this study confirms the potential of crowdsourcing to improve radar precipitation products in (near) real time.

Assessing sampling and retrieval errors of GPROF precipitation estimates over the Netherlands

Abstract. The Goddard Profiling algorithm (GPROF) converts radiometer observations from Global Precipitation Measurement (GPM) constellation satellites into precipitation estimates. Typically, high-quality ground-based estimates serve as reference to evaluate GPROF's performance. To provide a fair comparison, the ground-based estimates are often spatially aligned to GPROF. However, GPROF combines observations from various sensors and channels, each associated with a distinct footprint. Consequently, uncertainties related to the representativeness of the sampled areas are introduced in addition to the uncertainty when converting brightness temperatures into precipitation intensities. The exact contribution of resampling precipitation estimates, required to spatially and temporally align different resolutions when combining or comparing precipitation observations, to the overall uncertainty remains unknown. Here, we analyze the current performance of GPROF over the Netherlands during a 4-year period (2017–2020) while investigating the uncertainty related to sampling. The latter is done by simulating the reference precipitation as satellite footprints that vary in size, geometry, and applied weighting technique. Only GPROF estimates based on observations from the conical-scanning radiometers of the GPM constellation are used. The reference estimates are gauge-adjusted radar precipitation estimates from two ground-based weather radars from the Royal Netherlands Meteorological Institute (KNMI). Echo top heights (ETHs) retrieved from the same radars are used to classify the precipitation as shallow, medium, or deep. Spatial averaging methods (Gaussian weighting vs. arithmetic mean) minimally affect the magnitude of the precipitation estimates. Footprint size has a higher impact but cannot explain all discrepancies between the ground- and satellite-based estimates. Additionally, the discrepancies between GPROF and the reference are largest for low ETHs, while the relative bias between the different footprint sizes and implemented weighting methods increase with increasing ETHs. Lastly, our results do not show a clear difference between coastal and land simulations. We conclude that the uncertainty introduced by merging different channels and sensors cannot fully explain the discrepancies between satellite- and ground-based precipitation estimates. Hence, uncertainties related to the retrieval algorithm and environmental conditions are found to be more prominent than resampling uncertainties, in particular for shallow and light precipitation.

A Comprehensive Assessment of Multiple High-Resolution Precipitation Grid Products for Monitoring Heavy Rainfall during the “7.20” Extreme Rainstorm Event in China

Precipitation products play an important role in monitoring rainstorm processes. This study takes a rare historical event of extreme, heavy precipitation that occurred in Henan Province, China, in July 2021 as a research case. By analyzing the distribution of the spatial and temporal characteristics of precipitation errors, using a probability density function of the occurrence of precipitation and the daily variation pattern, we assess the capability of a radar precipitation estimation product (RADAR), satellite precipitation products (IMERG and GSMAP), a reanalysis product (ERA5) and a precipitation fusion product (the CMPAS) to monitor an extreme rainstorm in the Henan region. The CMPAS has the best fit with the gauge observations in terms of the precipitation area, precipitation maximum and the evolution of the whole process, with a low spatial variability of errors. However, the CMPAS slightly underestimated the precipitation extremum at the peak moment (06:00–08:00). The RADAR product was prone to a spurious overestimation of the originally small rainfall, especially during peak precipitation times, with deviations concentrated in the core precipitation area. The IMERG, GSMAP and ERA5 products have similar performances, all of which failed to effectively capture heavy precipitation in excess of 60 mm/h, with negative deviations in precipitation at mountainfront locations west of northern Henan Province. There is still a need for terrain-specific error revisions for areas with large topographic relief. By merging and processing precipitation data from multiple sources, the accuracy of the CMPAS is better than any single-source precipitation product. The CMPAS has the characteristic advantage of high spatial and temporal resolutions (0.01° × 0.01°/1 h), which play a positive role in precipitation dynamic monitoring, providing early warnings of heavy rainfall processes and hydrological application research.

Application of Machine Learning Techniques to Improve Multi-Radar Mosaic Precipitation Estimates in Shanghai

In this study, we applied an explainable machine learning technique based on the LightGBM method, a category of gradient boosting decision tree algorithm, to conduct a quantitative radar precipitation estimation and move to understand the underlying reasons for excellent estimations. By introducing 3D grid radar reflectivity data into the LightGBM algorithm, we constructed three LightGBM models, including 2D and 3D LightGBM models. Ten groups of experiments were carried out to compare the performances of the LightGBM models with traditional Z–R relationship methods. To further assess the performances of the LightGBM models, rainfall events with 11,483 total samples during August-September of 2022 were used for statistical analysis, and two heavy rainfall events were specifically chosen for the spatial distribution evaluation. The results from both the statistical analysis and spatial distribution demonstrate that the performance of the LightGBM 3D model with nine points is the best method for quantitative precipitation estimation in this study. Through analyzing the explainability of the LightGBM models from Shapley additive explanations (SHAP) regression values, it can be inferred that the superior performance of the LightGBM 3D model is mainly attributed to its consideration of the rain gauge station attributes, diurnal variation characteristics, and the influence of spatial offset.

Estimating Precipitation Using LSTM-Based Raindrop Spectrum in Guizhou

The change in raindrop spectrum characteristics is an important factor affecting the accuracy of estimations of precipitation. The in-depth study of raindrop spectrum characteristics is of great interest for understanding precipitation process and improving quantitative radar precipitation estimation. In this paper, the raindrop size distributions at Longli (57913), Puding (57808) and Luodian (57916) stations in Guizhou were analyzed from the perspective of precipitation microphysical characteristics. The results showed that the raindrop size distribution was different among different regions. The correlation coefficients of the mass-weighted average diameter for the rain intensities at these three stations were 46.89%, 49.51%, and 47.03%, respectively, which were slightly lower than the normal correlation coefficients of the average volume diameter for the rain intensities: 67.80%, 71.28%, and 71.46%, respectively. Based on the data from the Guiyang weather radar, raindrop spectrometer, and automatic rain gauge, the dynamic Z-I relationship method and the LSTM neural network method were used to estimate precipitation. The correlation coefficients of the dynamic Z-I relationship method and the LSTM neural network method at the three stations studied were 0.8432, 0.7763, and 0.8658 and 0.8745, 0.9125, and 0.8676, respectively. Regarding the process of stratiform cloud precipitation, the correlation coefficients of the dynamic Z-I relationship method and LSTM neural network method at the three stations were 0.6933, 0.0902, and 0.1409 and 0.7114, 0.4984, and 0.4902, respectively. In the estimation of cumulative precipitation for 45 days from 1 July to 16 August 2020, the relative errors of the neural network estimation at the three stations were −4.25%, −11.35%, and −8.68% and the relative errors of the dynamic Z-I relationship estimation were −2.68%, −7.41%, and −21.23%, respectively. The final relative error of the neural network was slightly worse than that of the dynamic Z-I relationship in the cumulative precipitation estimations of Longli station and Puding station, but the overall correlation coefficients of the LSTM neural network method were better than those of the dynamic Z-I relationship method.

Верификация детерминистского и вероятностного радиолокационного наукастинга осадков в теплый и холодный периоды года на Европейской территории России

Comparative quality analysis of ensemble radar precipitation nowcasting based on test results for the warm (May–September 2020) and cold (November 2021–March 2022) seasons are presented. Composite precipitation intensity fields obtained from radar observations were used as control data for verification. In both periods, a slight but systematic advantage of forecasts of the mean ensemble field was revealed, which indicates the expediency of using ensembles of even a small volume. For all the skill scores used (except for the frequency bias), forecasts in the cold season turn out to be better than forecasts in the warm season, however, the sample sizes for verification in the cold season may be significantly lower than the corresponding sample sizes in the warm season. The problems of comparative quality analysis are discussed, which are caused, in particular, by the loss of spatial connectivity of the composite field during the cold season. Keywords: ensemble nowcasting of meteorological fields, radar precipitation estimates, composite precipitation field, point and spatial field forecast verification

Characteristics and Variations of Raindrop Size Distribution in Chengdu of the Western Sichuan Basin, China

Knowledge of the microphysical characteristics of precipitation plays a significant role in meteorology, hydrology, and natural hazards management, especially in the western Sichuan Basin (WSB), which is located east of the Tibetan Plateau (TP) in southwestern China and thus has unique terrain conditions and weather systems. Nonetheless, the literature regarding raindrop size distribution (RSD) in the WSB is still very limited. This work investigates RSD characteristics and temporal variations in a site (Chengdu, CD) of the WSB by employing three years of quality-controlled RSD observation collected from a second-generation PARSIVEL disdrometer. The results show that RSD has noticeable seasonal and diurnal variations in CD. Specifically, the broadest mean raindrop spectra can be found in summer and the narrowest in winter, and the raindrop spectra of a day can be the narrowest during 1400–1500 BJT (Beijing Standard Time, UTC+8). In addition, the mass-weighted mean diameter (Dm) is lower in the daytime than in the nighttime, while the logarithm of the generalized intercept parameter (log10Nw, the unit of the Nw is m−3 mm−1) has a larger value in the daytime than in the nighttime. In addition, intercomparisons indicate that the mean Dm of convective rains in CD is smaller than in South China and it is higher than in the eastern slope of TP, East China, and North China; on the other hand, the corresponding mean log10Nw is close to the value at the middle TP. Local empirical relations of shape–slope parameters (μ–Λ) and reflectivity–rain rate (Z–R) are also presented to provide references for optimizing the RSD parameterization scheme and radar precipitation estimation in the local area.

Enhancing the usability of weather radar data for the statistical analysis of extreme precipitation events

Abstract. Spatially explicit quantification on design storms is essential for flood risk assessment and planning. Due to the limited temporal data availability from weather radar data, design storms are usually estimated on the basis of rainfall records of a few precipitation stations only that have a substantially long time coverage. To achieve a regional picture, these station-based estimates are spatially interpolated, incorporating a large source of uncertainty due to the typical low station density, in particular for short event durations. In this study we present a method to estimate spatially explicit design storms with a return period of up to 100 years on the basis of statistically extended weather radar precipitation estimates, based on the ideas of regional frequency analyses and subsequent bias correction. Associated uncertainties are quantified using an ensemble-sampling approach and event-based bootstrapping. With the resulting dataset, we compile spatially explicit design storms for various return periods and event durations for the federal state of Baden Württemberg, Germany. We compare our findings with two reference datasets based on interpolated station estimates. We find that the transition in the spatial patterns of the design storms from a rather random (short-duration events, 15 min) to a more structured, orographically influenced pattern (long-duration events, 24 h) seems to be much more realistic in the weather-radar-based product. However, the absolute magnitude of the design storms, although bias-corrected, is still generally lower in the weather radar product, which should be addressed in future studies in more detail.

Верификация радиолокационного наукастинга областей осадков значительной площади с помощью обобщенного распределения Парето. Часть 2: приложение к прогнозам в теплый и холодный периоды 2017–2018 гг.

The generalized Pareto distribution was used to model the distribution of precipitation area sizes that were observed and predicted for the coverage zones of individual radars by the Hydrometeorological Research Center of the Russian Federation's nowcasting scheme in 2017-2018. Various methods for estimating the distribution parameters and confidence intervals were tested. The main attention was paid to the estimates of the shape parameter that determines the behavior of the distribution tail. The generalized assessment of the nowcasting quality is built on the intersection ratio of the corresponding confidence intervals. It is shown that for most cases the Pareto threshold of 625 points, which is equivalent to a square of 5050 km, separates objects of larger sizes that are satisfactorily modeled by the heavy-tailed distribution and which are quite acceptably (on "climatological" average) predicted by the precipitation nowcasting system for specific periods of the year. Keywords: precipitation nowcasting, spatial verification, radar precipitation estimates, statistical analysis of threshold exceedances, mathematical packages for extreme value analysis

RETRACTED ARTICLE: Mountain rainfall estimation and online English teaching evaluation based on RBF neural network

In order to solve the problem of the main parameters setting of traditional RBF neural network learning algorithm and the lack of local information based on space, this paper proposes a particle swarm hybrid optimization RBF neural network prediction algorithm based on genetic operation, which solves the early problems of PSO on the basis of the cross and mutation fusion of PSO and GA advantages. At the same time, we analyze the limitations of using the mountain rainfall simulation process and taking the mountain rainfall estimation as the model input, and put forward the idea of using the radar estimation technology from the perspective of spatial rainfall, and carrying out the mountain rainfall evaluation and simulation according to the spatial rainfall. The application of flood forecasting focuses on the research of mountain rainfall estimation simulation method based on radar precipitation estimation technology, and it is also the combination of mountain rainfall simulation process. Finally, this paper evaluates and analyzes online English teaching, and the new network environment has become the carrier of College English learning. In the new network environment, students will form the awareness of reading, listening, and speaking English, and conduct conscious online English learning guidance and enhance their language ability to find their own understanding and perception of English. In this paper, we reveal the problems and shortcomings of the current college English education evaluation in China, put forward various new intelligence evaluation theories, and try to explore the practical functions of several education evaluation systems in education, so as to better use the means of education evaluation to improve the efficiency of College English teaching. According to the research of the RBF neural network, it is applied to rainfall estimation and teaching evaluation, which can effectively improve rainfall estimation and teaching evaluation.

Offline Single-Polarization Radar Quantitative Precipitation Estimation Based on a Spatiotemporal Deep Fusion Model

Quantitative precipitation estimation (QPE) based on Doppler radar plays an important role in severe weather monitoring, industrial and agricultural production, and natural disaster prediction and prevention. However, the temporal and spatial variability of precipitation leads to large errors in radar estimates of mixed precipitation. To improve the accuracy of radar QPE, we propose an offline spatiotemporal deep fusion model that uses the reflectivity data of the Shijiazhuang Doppler radar Z9311 and the precipitation data from 17 national weather stations (NWSs) and 260 automatic weather stations (AWSs). Considering the abrupt spatial changes in precipitation, a three-dimensional radar data structure is proposed, and the spatial features of multielevation and multiscale radar data are extracted and merged using the feature fusion network (FFNet). Finally, the time dependence of the precipitation is captured using the long short-term memory (LSTM) network, and the precipitation estimation is obtained. Based on a comparison of the results of the proposed model (FFNet-LSTM) with those of the ordinary kriging (OK) interpolation, two Z-R relationship, the multilayer perceptron (MLP), the LSTM, and the FFNet, the proposed method is superior to these models, has a promising performance, and is a general-purpose rainfall algorithm.

Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda

Tropical Storm Imelda struck the southeast coastal regions of Texas from 17–19 September, 2019, and delivered precipitation above 500 mm over about 6000 km2. The performance of the three IMERG (Early-, Late-, and Final-run) GPM satellite-based precipitation products was evaluated against Stage-IV radar precipitation estimates. Basic and probabilistic statistical metrics, such as CC, RSME, RBIAS, POD, FAR, CSI, and PSS were employed to assess the performance of the IMERG products. The products captured the event adequately, with a fairly high POD value of 0.9. The best product (Early-run) showed an average correlation coefficient of 0.60. The algorithm used to produce the Final-run improved the quality of the data by removing systematic errors that occurred in the near-real-time products. Less than 5 mm RMSE error was experienced in over three-quarters (ranging from 73% to 76%) of the area by all three IMERG products in estimating the Tropical Storm Imelda. The Early-run product showed a much better RBIAS relatively to the Final-run product. The overall performance was poor, as areas with an acceptable range of RBIAS (i.e., between −10% and 10%) in all the three IMERG products were only 16% to 17% of the total area. Overall, the Early-run product was found to be better than Late- and Final-run.

Use of Radar Quantitative Precipitation Estimates (QPEs) for Improved Hydrological Model Calibration and Flood Forecasting

AbstractFlood forecasting is essential to minimize the impacts and costs of floods, especially in urbanized watersheds. Radar rainfall estimates are becoming increasingly popular in flood forecasting because they provide the much-needed real-time spatially distributed precipitation information. The current study evaluates the use of radar Quantitative Precipitation Estimates (QPEs) in hydrological model calibration for streamflow simulation and flood mapping in an urban setting. Firstly, S-band and C-band radar QPEs were integrated into event-based hydrological models to improve the calibration of model parameters. Then, rain gauge and radar precipitation estimates’ performances were compared for hydrological modeling in an urban watershed to assess radar QPE's effects on streamflow simulation accuracy. Finally, flood extent maps were produced using coupled hydrological-hydraulic models integrated within the Hydrologic Engineering Center- Real-Time Simulation (HEC-RTS) framework. It is shown that the bias correction of radar QPEs can enhance the hydrological model calibration. The radar-gauge merging obtained a KGE, MPFC, NSE, and VE improvement of about + 0.42, + 0.12, + 0.78, and − 0.23, respectively for S-band and + 0.64, + 0.36, + 1.12, and − 0.34, respectively for C-band radar QPEs. Merged radar QPEs are also helpful in running hydrological models calibrated using gauge data. The HEC-RTS framework can be used to produce flood forecast maps using the bias-corrected radar QPEs. Therefore, radar rainfall estimates could be efficiently used to forecast floods in urbanized areas for effective flood management and mitigation. Canadian flood forecasting systems could be efficiently updated by integrating bias-corrected radar QPEs to simulate streamflow and produce flood inundation maps.

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

In this research work, an analysis is conducted concerning the impact on rainfall-runoff simulations of utilizing rain gauge precipitation measurements against weather radar quantitative precipitation estimates. The study area is the Sarantapotamos river basin, a peri-urban basin located in the greater area of Athens, and measurements from a newly installed X-Band weather radar system, referred to as rainscanner, along with ground rain gauge stations were used. Rainscanner, in contrast to rain gauges, is able to provide with higher resolution surface precipitation datasets, but due to signal errors, uncertainty is involved, and thus proper calibration and evaluation of these estimates must be first performed. In this context, this research work evaluates the impact of adopting different precipitation datasets and interpolation methods for generating runoff, through the use of a lumped based rainfall-runoff model. Initially, the analysis focuses on the correlation between the rain gauge and the rainscanner estimations for each station, as well as for the calculated mean areal precipitation. The results of the rainfall-runoff simulations show that even though a different spatial and temporal variability of the rainfall field is calculated through the two datasets, in a lumped-based scheme, the most important factor that dictates the runoff generation is the amount of total precipitation.

Novel Application of Uncertainty Analysis Methods for Quantitative Precipitation Estimation Based on Weather Radars in the Korean Peninsula

Several sources of bias are involved at each stage of a quantitative precipitation estimation process because weather radars measure precipitation amounts indirectly. Conventional methods compare the relative uncertainties between different stages of the process but seldom present the total uncertainty. Therefore, the objectives of this study were as follows: (1) to quantify the uncertainty at each stage of the process and in total; (2) to elucidate the ratio of the uncertainty at each stage in terms of the total uncertainty; and (3) to explain the uncertainty propagation process at each stage. This study proposed novel application of three methods (maximum entropy method, uncertainty Delta method, and modified-fractional uncertainty method) to determine the total uncertainty, level of uncertainty increase, and percentage of uncertainty at each stage. Based on data from 18 precipitation events that occurred over the Korean Peninsula, the applicability of the three methods was tested using a radar precipitation estimation process that comprised two quality control algorithms, two precipitation estimation methods, and two post-processing precipitation bias correction methods. Results indicated that the final uncertainty of each method was reduced in comparison with the initial uncertainty, and that the uncertainty was different at each stage depending on the method applied.

Improving Radar Rainfall Estimations with Scaled Raindrop Size Spectra in Mei-Yu Frontal Rainstorms.

Hydrological calibration of raw weather radar rainfall estimation relies on in situ rainfall measurements. Raindrop size distribution (DSD) was collected during three typical Mei-Yu rainstorms in July 2014 using three particle size velocity (Parsivel) DSD sensors along the Mei-Yu front in Nanjing, Chuzhou, and the western Pacific, respectively. To improve the radar precipitation estimation in different parts of the Mei-Yu front, a scaling method was adopted to formulate the DSD model and further derive the Z–R relations. The results suggest a distinct variation of DSDs in different parts of the Mei-Yu front. Compared with statistical radar Z–ARb relations obtained by mathematical fitting techniques, the use of a DSD model fitting based on a scaling law formulation theoretically shows a significant improvement in both stratiform (33.9%) and convective (2.8%) rainfall estimations of the Mei-Yu frontal system, which indicates that using a scaling law can better reflect the DSD variations in different parts of the Mei-Yu front. Polarimetric radar has indisputable advantages with multiparameter detection ability. Several dual-polarization radar estimators are also established by DSD sensor data, and the R(ZH, ZDR) estimator is proven to be more accurate than traditional Z–R relations in Mei-Yu frontal rainfall, with potential applications for operational C-band polarimetric radar.

Performance of a PDE-Based Hydrologic Model in a Flash Flood Modeling Framework in Sparsely-Gauged Catchments

Modeling and nowcasting of flash floods remains challenging, mainly due to uncertainty of high-resolution spatial and temporal precipitation estimates, missing discharge observations of affected catchments and limitations of commonly used hydrologic models. In this study, we present a framework for flash flood hind- and nowcasting using the partial differential equation (PDE)-based ParFlow hydrologic model forced with quantitative radar precipitation estimates and nowcasts for a small 18.5 km2 headwater catchment in Germany. In the framework, an uncalibrated probabilistic modeling approach is applied. It accounts for model input uncertainty by forcing the model with precipitation inputs from different sources, and accounts for model parameter uncertainty by perturbing two spatially uniform soil hydraulic parameters. Thus, sources of uncertainty are propagated through the model and represented in the results. To demonstrate the advantages of the proposed framework, a commonly used conceptual model was applied over the same catchment for comparison. Results show the framework to be robust, with the uncalibrated PDE-based model matching streamflow observations reasonably. The model lead time was further improved when forced with precipitation nowcasts. This study successfully demonstrates a parsimonious application of the PDE-based ParFlow model in a flash flood hindcasting and nowcasting framework, which is of interest in applications to poorly or ungauged watersheds.

Improved Attenuation-Based Radar Precipitation Estimation Considering the Azimuthal Variabilities of Microphysical Properties

AbstractThe attenuation-based rainfall estimator is less sensitive to the variability of raindrop size distributions (DSDs) than conventional radar rainfall estimators. For the attenuation-based quantitative precipitation estimation (QPE), the key is to accurately estimate the horizontal specific attenuation AH, which requires a good estimate of the ray-averaged ratio between AH and specific differential phase KDP, also known as the coefficient α. In this study, a variational approach is proposed to optimize the coefficient α for better estimates of AH and rainfall. The performance of the variational approach is illustrated using observations from an S-band operational weather radar with rigorous quality control in south China, by comparing against the α optimization approach using a slope of differential reflectivity ZDR dependence on horizontal reflectivity factor ZH. Similar to the ZDR-slope approach, the variational approach can obtain the optimized α consistent with the DSD properties of precipitation on a sweep-to-sweep basis. The attenuation-based hourly rainfall estimates using the sweep-averaged α values from these two approaches show comparable accuracy when verified against the gauge measurements. One advantage of the variational approach is its feasibility to optimize α for each radar ray, which mitigates the impact of the azimuthal DSD variabilities on rainfall estimation. It is found that, based on the optimized α for radar rays, the hourly rainfall amounts derived from the variational approach are consistent with gauge measurements, showing lower bias (1.0%), higher correlation coefficient (0.92), and lower root-mean-square error (2.35 mm) than the results based on the sweep-averaged α.

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an exponential relation between radar reflectivity factors and precipitation called Z-R relationship which has a low accuracy in precipitation estimation. With the rapid development of computing power in cloud computing, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of precipitation estimation, given an ample account of radar data. In this study, we introduce these approaches to the precipitation estimation, proposing two models based on the back propagation neural networks (BPNN) and convolutional neural networks (CNN) respectively, to compare with the traditional method in meteorological service systems. The results of the three approaches show that deep learning algorithms outperform the traditional method with 75.84% and 82.30% lower mean square errors respectively. Meanwhile, the proposed method with CNN achieves a better performance than that with BPNN for its ability to preserve the spatial information by maintaining the interconnection between pixels, which improves 26.75% compared to that with BPNN.