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Accurate, timely, and reliable precipitation observations are mandatory for hydrological forecast and early warning systems. In the case of convective precipitation, traditional rain gauge networks often miss precipitation maxima, due to density limitations and the high spatial variability of the rainfall field. Despite several limitations like attenuation or partial beam blocking, the use of C-band weather radar has become operational in most European weather services. Traditionally, weather-radar-based quantitative precipitation estimation (QPE) is derived from horizontal reflectivity data. Nevertheless, dual-polarization weather radar can overcome several shortcomings of the conventional horizontal-reflectivity-based estimation. As weather radar archives are growing, they are becoming increasingly important for climatological purposes in addition to operational use. For the first time, the present study analyses one of the longest datasets from fully operational polarimetric C-band weather radars; these are located in Estonia and Italy, in very different climate conditions and environments. The length of the datasets used in the study is 5 years for both Estonia and Italy. The study focuses on long-term observations of summertime precipitation and their quantitative estimations by polarimetric observations. From such derived QPEs, accumulations for 1 h, 24 h, and 1-month durations are calculated and compared with reference rain gauges to quantify uncertainties and evaluate performances. Overall, the radar products showed similar results in Estonia and Italy when compared to each other. The product where radar reflectivity and specific differential phase were combined based on a threshold exhibited the best agreement with gauge values in all accumulation periods. In both countries reflectivity-based rainfall QPE underestimated and specific differential-phase-based product overestimated gauge measurements.

For the estimation of rainfall in northern Algeria, a new method is proposed in this study. It based on the k nearest weighted neighbours (WkNN) classification algorithm using the dataset from the radiometer-imager SEVIRI (Spinning Enhanced Visible and Infra-Red Imager) boarded on the satellite MSG (Meteosat Second Generation) to determine the pixel rainfall rate among the 16 predefined intensity levels observed in the Setif meteorological radar. The implementation of the classification consists in using the spectral characteristics of a new sample (pixel) as input variables of the WkNN classifier to predict his class of membership based on the weighted distances separating him from the samples of the set of learning. The results thus obtained are validated with respect to the rainfall intensity classes observed and co-located by ground radar. The results showed a significant improvement for instantaneous estimate of precipitation during the day, with a correlation coefficient r = 0.87 and statistical parameters R M S E = 2.29 m m / h , B i a s = 0.45 m m / h , M A E = 0.35 m m / h , and we have obtained the results similar overnight.

During the last years we made great progress with the country-wide rainfall estimation from commerical microwave link (CML) data in Germany (Graf et al. 2020, Polz et al. 2020). Using the derived results in different applications has, however, revealed that undetected erratic behaviour of CML raw data is still limiting data quality and that data gaps during heavy rain can lead to underestimation of peak rain rates. Hence, we have extended our processing methods and, for the first time, have carried out a large-scale intercomparison with other available methods. Albeit we are constantly improving our CML rainfall estimation, we already apply these data to operationally generate rainfall maps for Germany, also in combination with radar data from the German Meteorological Service (DWD).In this contribution we will present our current research on the following interconnected topics:1. Detecting erratic signal fluctuations: In contrast to the existing methods that focus on detecting rainy-periods in the noisy raw data we have developed a dedicated classification method for periods with erratic signal fluctuations, which can easily lead to rainfall overestimation from CMLs. Our method, which is based on an artificial neural network, is designed to reduce the number of falsely classified rainy periods during dry periods with strong signal fluctuation.2. Large scale method intercomparison: For the first time, we compare the widely used RAINLINK algorithm, which is based on analysing data from nearby CMLs, with purely time-series based processing methods. First results show that both methods have advantages that, when combined, could improve the overall processing.3. The effect and mitigation of data gaps during heavy rainfall: CML networks are designed so that very heavy rain events lead to a complete loss of signal, and hence to gaps in the data we use for rainfall estimation. We analyse the occurrence of these gaps and show the impact on CML-derived rainfall estimation as well as mitigation methods.4. Real-time application: We use the CML data that we acquire in real-time to generate rainfall maps for Germany and merge the CML rainfall estimates with DWD radar data. Our approach is an extension of the existing RADOLAN-method. Results show that merging with the path-averaged CML rainfall information provides similar results than merging with gauges. In regions where the addition of CMLs significantly increases the density of observations, the joint Radar-gauge-CML product is expected to show improved quality.References:Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data, Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020, 2020Polz, J., Chwala, C., Graf, M., and Kunstmann, H.: Rain event detection in commercial microwave link attenuation data using convolutional neural networks, Atmos. Meas. Tech., 13, 3835–3853, https://doi.org/10.5194/amt-13-3835-2020, 2020

Traditional radar-based rainfall estimation is typically done by known functional relationships between the rainfall intensity (R) and radar measurables, such as R–Zh, R–(Zh, ZDR), etc. One of the biggest advantages of machine learning algorithms is the applicability to a non-linear relationship between a dependent variable and independent variables without any predefined relationships. We explored the potential use of two supervised machine learning methods (regression tree and random forest) in rainfall estimation using dual-polarization radar variables. The regression tree does not require normalization and scaling of data; however, this method is quite unstable since each split depends on the parent split. Since the random forest is an ensemble method of regression trees, it has less variability in prediction compared with regression trees, but consumes more computer resources. We considered several different configurations for machine learning algorithms with different sets of dependent and independent variables. The random forest model was appropriately tuned. In the test of variable importance, the specific differential phase (differential reflectivity) was the most important variable to predict the rainfall rate (residual that is the difference between the true rainfall rate and the one estimated from the R–Z relationship). The models were evaluated by 10-fold cross-validation. The best model was the random forest model using a residual with the non-classified training set. The results indicated that the machine learning algorithms outperformed the traditional R–Z relationship. Then, we applied the best machine learning model to an S-band dual-polarization radar (Mt. Myeonbong) and validated the result with ground rain gauges. The results of the application to radar data showed that the estimates of the residuals had spatial variability. The stratiform and weak rain areas had positive residuals while convective areas had negative residuals, indicating that the spatial error structure driven by the R–Z relationship was well captured by the model. The rainfall rates of all pixels over the study area were adjusted with the estimated residuals. The rainfall rates adjusted by residual showed excellent agreement with the rain gauge, especially at high rainfall rates.

Modern ground-based weather radars are mostly dual-polarized, and they are rich in information content in multiple dimensions, and are ideal candidates for effective artificial intelligence (AI) applications. There are a large number of dual-polarization radars around the world. In addition, space borne weather radars such as Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and Global Precipitation Measurement (GPM) mission Dual-frequency Precipitation Radar (DPR) have also produced rich observations that are great to be analyzed using AI. AI has already been used with weather radar information long before it became popular in mainstream, such as the use of neural networks for rainfall estimation through ingesting vertical reflectivity profiles and the application of neuro-fuzzy systems for hydrometeor classification. The primary goal of this paper is to introduce some of the major application areas for weather radar AI, namely, quantitative precipitation estimation (QPE), precipitation classification, and nowcasting. These three application domains have engaged very different types of AI technologies.

The project aimed at quantifying the impact of Precipitation Measurement Biases (PMBs) on the modelling of hydrological processes at different catchment scales. Both local and areal methods for estimating precipitation rates were addressed, as well as instrumental and environmental sources of inaccuracy in measurements.

Dual-polarization radar rainfall estimation relationships have been extensively tested in continental and subtropical coastal rain regimes, with little testing over tropical oceans where the majority of rain on Earth occurs. A 1.5-yr Indo-Pacific warm pool disdrometer dataset was used to quantify the impacts of tropical oceanic drop-size distribution (DSD) variability on dual-polarization radar variables and their resulting utility for rainfall estimation. Variables that were analyzed include differential reflectivity Zdr; specific differential phase Kdp; reflectivity Zh; and specific attenuation Ah. When compared with continental or coastal convection, tropical oceanic Zdr and Kdp values were more often of low magnitude (−1) and Zdr was lower for a given Kdp or Zh, consistent with observations of tropical oceanic DSDs being dominated by numerous, small, less-oblate drops. New X-, C-, and S-band R estimators were derived: R(Kdp), R(Ah), R(Kdp, ζdr), R(z, ζdr), and R(Ah, ζdr), which use linear versions of Zdr and Zh, namely ζdr and z. Except for R(Kdp), convective/stratiform partitioning was unnecessary for these estimators. All dual-polarization estimators outperformed updated R(z) estimators derived from the same dataset. The best-performing estimator was R(Kdp, ζdr), followed by R(Ah, ζdr) and R(z, ζdr). The R error was further reduced in an updated blended algorithm choosing between R(z), R(z, ζdr), R(Kdp), and R(Kdp, ζdr) depending on Zdr > 0.25 dB and Kdp > 0.3° km−1 thresholds. Because of these thresholds and the lack of hail, R(Kdp) was never used. At all wavelengths, R(z) was still needed 43% of the time during light rain (R < 5 mm h−1, Zdr < 0.25 dB), composing 7% of the total rain volume. As wavelength decreased, R(Kdp, ζdr) was used more often, R(z, ζdr) was used less often, and the blended algorithm became increasingly more accurate than R(z).

ilustraciones, gráficas, tablas Se diseñó e implementó una metodología para la estimación de campos de precipitación y su pronóstico para las siguientes tres horas, en intervalos de 15 minutos, empleando imágenes provenientes de la plataforma satelital GOES 16 (canal 13) y haciendo uso de información de radar, estaciones meteorológicas automáticas y convencionales en campo. Esta metodología se desarrolló en tres etapas consecutivas. Entendiendo que el radar es el sensor remoto que mejor representa los fenómenos de precipitación (en términos espaciales, temporales y cuantitativos) y que, a partir de los registros de precipitación en campo, es posible establecer la relación reflectividad / precipitación, en la primera etapa se realizó un modelamiento estadístico para convertir sus valores registrados (reflectividad) en precipitación, obteniendo errores en acumulados de 24 horas cercanos a los 3.3 mm. Para la segunda etapa, un segundo modelamiento estadístico fue realizado, encaminado a la conversión de los valores de los niveles digitales de las imágenes GOES 16 a valores de precipitación; para esto se estableció la relación precipitación radar / niveles digitales, obteniendo errores cercanos a los 6 mm en acumulados de 24 horas, adicionalmente se comparó su desempeño contra dos de las estimaciones satelitales más empleadas. Una tercera etapa consistió en la aplicación de métodos estadísticos de series de tiempo, ARIMA y Holt-Winters, para modelar el comportamiento de los valores de precipitación estimados por satélite para las siguientes tres horas, en donde el modelo de Holt-Winters presenta consistentemente mejores resultados que el modelo ARIMA a lo largo de los intervalos pronosticados. (Texto tomado de la fuente). A methodology was designed and implemented for the rainfall estimation and their forecast for the following three hours, by 15-minute intervals, using images from the GOES 16 satellite platform (channel 13) and using radar information, automatic stations and conventional meteorological stations in ground. This methodology was developed in three consecutive stages. The radar is the remote sensing that best represents rainfall, usually the reflectivity values are using in combination with the ground rainfall records to establish the reflectivity / rainfall relationship. statistic modeling was performed to convert their radar information values (reflectivity) in precipitation, obtaining errors in accumulated 24 hours close to 3.3 mm, in this first stage. A second statistical modeling was developed, in the second stage, aimed at converting the values of the digital levels from GOES 16 images to precipitation values; the ratio radar precipitation / digital levels was established, obtaining errors close to 6 mm in accumulated 24 hours, additionally its performance was compared against two of the most used satellite estimates in the region. A third stage consisted in the application of statistical time series methods, ARIMA and Holt-Winters, to model the behavior of satellite precipitation values for the next three hours, where the Holt-Winters model presents consistently better results that the ARIMA model along the predicted intervals. Incluye anexos Maestría Magíster en Geomática Tecnologías geoespaciales Ciencias Agronómicas

Traditionally, radar rainfall algorithms are derived through nonlinear regression of rain rates and simulated radar observables from raindrop size distribution (DSD). The performance of such empirical relations is highly dependent on the physical model of DSD and the parametric relation between the physical model and radar parameters. Such algorithms also have large uncertainties that need to be adaptively adjusted based on local DSD properties. In this research, we propose an alternative approach to dual-polarization radar rainfall estimation. In particular, a non-parametric machine learning model is designed and trained using simulated radar data based on DSD measurements in different climatological regimes. The trained model is applied to real radar measurements to produce rain rate estimates. Preliminary results show the promising performance of this novel method compared to traditional parametric rainfall relations.

Radar-based rainfall information has been widely used in hydrological and meteorological applications, as it provides data with a high spatial and temporal resolution that improve rainfall representation. However, the broad diversity of studies makes it difficult to gather a condensed overview of the usefulness and limitations of radar technology and its application in particular situations. In this paper, a comprehensive review through a categorization of radar-related topics aims to provide a general picture of the current state of radar research. First, the importance and impact of the high temporal resolution of weather radar is discussed, followed by the description of quantitative precipitation estimation strategies. Afterwards, the use of radar data in rainfall nowcasting as well as its role in preparation of initial conditions for numerical weather predictions by assimilation is reviewed. Furthermore, the value of radar data in rainfall-runoff models with a focus on flash flood forecasting is documented. Finally, based on this review, conclusions of the most relevant challenges that need to be addressed and recommendations for further research are presented. This review paper supports the exploitation of radar data in its full capacity by providing key insights regarding the possibilities of including radar data in hydrological and meteorological applications.

Merging rain gauge and radar data improves the accuracy of precipitation estimation for urban areas. Since the rain gauge network around the ungauged urban catchment is fixed, the relevant question relates to the optimal merging area that produces the best rainfall estimation inside the catchment. Thus, an incremental radar-gauge merging was performed by gradually increasing the distance from the centre of the study area, the number of merging gauges around it and the radar domain. The proposed adaptive merging scheme is applied to a small urban catchment in west Yorkshire, Northern England, for 118 extreme events from 2007 to 2009. The performance of the scheme is assessed using four experimental rain gauges installed inside the study area. The result shows that there is indeed an optimum radar-gauge merging area and consequently there is an optimum number of rain gauges that produce the best merged rainfall data inside the study area. Different merging methods produce different results for both classified and unclassified rainfall types. Although the scheme was applied on daily data, it is applicable to other temporal resolutions. This study has importance for other studies such as urban flooding analysis, since it provides improved rainfall estimation for ungauged urban catchments.

Radar-based rainfall estimation is one of the most important inputs for various meteorological applications. Although exciting progresses have been made in this area, accurate real-time rainfall estimation is still a significant opening topic that requires practical modeling. The research study presented in this letter improves rainfall estimation accuracy by proposing a random forest and linear chain conditional random-field-based spatiotemporal model (RANLIST). To apply this model for rainfall estimation, the implementing approach is presented. The advantages are listed as follows: 1) RANLIST improves rainfall estimation accuracy by exploiting both underlying local spatial structure of multiple radar reflectivity factors and time-series information of rain processes. 2) The time-series information of rain processes can be utilized in virtue of the presented implementation method. Experiments have been carried out over the radar-covered area of Quanzhou, China, in June and July 2014. Results show that RANLIST is superior to previous works.

This research aimed to study the rainfall estimation with weather radar in southern Thailand. Both precipitation data from Sathing-Phra radar station, Songkhla, and hourly rainfall data from automatic tipping bucket rain gauges are of the Thai Meteorological Department (TMD) in 2012-2013. These data were used to study the relationship between radar reflectivity (Z) and intensity of the rainfall (R) in both monsoon season. The rain-rate obtained from radar with Z-R equation was a precise rain estimate method. The calculation provided the rain-rate more accurate than original equation often used. The equation of this study was best used during the northeast monsoon which occurred in rainy season in eastern coast of the South (Thailand's Gulf). However, the result of this study should be improved to estimate rain-rate by radar for southern Thailand and, as input data, applied for applications of early warning systems.

Dual polarization radar provides comprehensive information about rainfall by measuring multiple parameters. In Korea, for the rainfall estimation, JPOLE and CSU-HIDRO algorithms are generally used. This study evaluated the local applicability of JPOLE and CSU-HIDRO algorithms in Korea by using the observed rainfall data collected on the August, 2014 by the Biseulsan dual polarization radar data and KMA AWS. A total of 11,372 pairs of radar-ground rain rate data were classified according to thresholds of synthetic algorithms into suitable and unsuitable data. Then, evaluation criteria were derived by comparing radar rain rate and ground rain rate, respectively, for entire, suitable, and unsuitable data. The results are as follows: (1) The radar rain rate equation including specific differential phase was found better in the rainfall estimation than the other equations for both JPOLE and CSU-HIDRO algorithms. The thresholds were found to be adequately applied for both algorithms including specific differential phase. (2) The radar rain rate equation including horizontal reflectivity and differential reflectivity were found poor compared to the others. The result was not improved even when only the suitable data were applied.

Recently in the field of disaster risk management, the applications of x display--inline-block" style="background: var(--highlight-yellow); color: inherit;">rainfall data from X-band Polarimetric radar are rapidly increasing due to the advancement of radar technology and analytical tools. In most cases, these x display--inline-block" style="background: var(--highlight-yellow); color: inherit;">rainfall data are used in the development and application of the meteorological and hydrological models. One of the typical purposes of such models is to report and disseminate disaster information such as x display--inline-block" style="background: var(--highlight-yellow); color: inherit;">rainfall to the government, related agencies, and the public for disaster prevention or mitigation. To accomplish this, the reliability of x display--inline-block" style="background: var(--highlight-yellow); color: inherit;">rainfall data from X-band Polarimetric radar is one of the most important factors as the data are the fundamental input. Equally important is the appropriate analytical method of the data to ensure the predicted x display--inline-block" style="background: var(--highlight-yellow); color: inherit;">rainfall information based on the collected data is reliable and safe to be used for further development and applications. Under such circumstances, this paper aim to investigate and evaluate the types of analytical methods for x display--inline-block" style="background: var(--highlight-yellow); color: inherit;">rainfall estimation by using X-band Polarimetric radar data. Content analysis was carried out and as a result, the statistical analyses were found as an appropriate analytical method to determine the reliability of x display--inline-block" style="background: var(--highlight-yellow); color: inherit;">rainfall data from the X-band Polarimetric radar. Further, the result from this study will be used to apply to a set of actual x display--inline-block" style="background: var(--highlight-yellow); color: inherit;">rainfall data from X-band Polarimetric radar and Automated Meteorological Data Acquisition System implemented in a region in Japan.

Three methods for determining the reflectivity bias of single polarization radar using dual polarization radar reflectivity and disdrometer data (i.e., the equidistance line, overlapping area, and disdrometer methods) are proposed and evaluated for two low-pressure rainfall events that occurred over the Korean Peninsula on 25 August 2014 and 8 September 2012. Single polarization radar reflectivity was underestimated by more than 12 dB and 7 dB in the two rain events, respectively. All methods improved the accuracy of rainfall estimation, except for one case where DSDs were not observed, as the precipitation system did not pass through the disdrometer location. The use of these bias correction methods reduced the RMSE by as much as 50%. Overall, the most accurate rainfall estimates were obtained using the overlapping area method to correct radar reflectivity. A combination of all three methods would produce more accurate rainfall estimates, provided optimal values are determined for the domain size for the overlapping area method, the sample number threshold for the equidistance line method, and the reflectivity threshold for the disdrometer method.

Measured differential phase shift ФDP is known to be a noisy unstable polarimetric radar variable, such that the quality of ФDP data has direct impact on specific differential phase shift KDP estimation, and subsequently, the KDP-based rainfall estimation. Over the past decades, many ФDP de-noising methods have been developed; however, the de-noising effects in these methods and their impact on KDP-based rainfall estimation lack comprehensive comparative analysis. In this study, simulated noisy ФDP data were generated and de-noised by using several methods such as finite-impulse response (FIR), Kalman, wavelet, traditional mean, and median filters. The biases were compared between KDP from simulated and observed ФDP radial profiles after de-noising by these methods. The results suggest that the complicated FIR, Kalman, and wavelet methods have a better de-noising effect than the traditional methods. After ФDP was de-noised, the accuracy of the KDP-based rainfall estimation increased significantly based on the analysis of three actual rainfall events. The improvement in estimation was more obvious when KDP was estimated with ФDP de-noised by Kalman, FIR, and wavelet methods when the average rainfall was heavier than 5 mm h ≥1. However, the improved estimation was not significant when the precipitation intensity further increased to a rainfall rate beyond 10 mm h ≥1. The performance of wavelet analysis was found to be the most stable of these filters.

One of main benefits of a dual polarization radar is improvement of quantitative rainfall estimation. In this paper, performance of two representative rainfall estimation methods for a dual polarization radar, JPOLE and CSU algorithms, have been compared by using data from a MOLIT S-band dual polarization radar. In addition, this paper presents evaluation of specific differential phase () retrieval algorithm proposed by Lim et al. (2013). Current retrieval methods are based on range filtering technique or regression analysis. However, these methods can result in underestimating peak or negative values in convective regions, and fluctuated in low rain rate regions. To resolve these problems, this study applied the distribution method suggested by Lim et al. (2013) and evaluated by adopting new to JPOLE and CSU algorithms. Data were obtained from the Mt. Biseul radar of MOLIT for two rainfall events in 2012. Results of evaluation showed improvement of the peak and did not show fluctuation and negative values. Also, in heavy rain (daily rainfall > 80 mm), accumulated daily rainfall using new was closer to AWS observation data than that using legacy , but in light rain(daily rainfall is used mostly in case of heavy rain rate of quantitative rainfall estimation algorithm.

Rainfall measured by Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) is important for studying precipitation distribution in the tropical regions. The ground validation of TRMM PR is difficult because the ground sensing systems have different characteristics from TRMM PR in terms of resolution, scale, view aspect and sensing environments. In this paper, we introduce a machine learning system to train ground radars for rainfall estimation using rain gauge data and subsequently using the trained ground radar rainfall estimation to train TRMM PR. This system can build a connection between ground gauge measurements and ground radar observations, and transfer this connection to TRMM PR observations for rainfall estimation. The rain gauge, ground radar and satellite data collected from Melbourne, Florida are used for demonstration purposes. The rainfall estimation product derived from this new system is compared against the TRMM standard products, which shows improvement brought by the new machine learning system.

Propagation effects play in important role in the application of weather radar. Attenuation and depolarization have negative effects on the quality of radar data and hinder rainfall estimation, whereas the differential propagation phase can be used for the quantification of precipitation and even correction of attenuation effects.

Between 25 and 27 August 2010 a long-duration mesoscale convective system was observed above the Netherlands, locally giving rise to rainfall accumulations exceeding 150 mm. Correctly measuring the amount of precipitation during such an extreme event is important, both from a hydrological and meteorological perspective. Unfortunately, the operational weather radar measurements were affected by multiple sources of error and only 30% of the precipitation observed by rain gauges was estimated. Such an underestimation of heavy rainfall, albeit generally less strong than in this extreme case, is typical for operational weather radar in The Netherlands. In general weather radar measurement errors can be subdivided into two groups: (1) errors affecting the volumetric reflectivity measurements (e.g. ground clutter, radar calibration, vertical profile of reflectivity) and (2) errors resulting from variations in the raindrop size distribution that in turn result in incorrect rainfall intensity and attenuation estimates from observed reflectivity measurements. A stepwise procedure to correct for the first group of errors leads to large improvements in the quality of the estimated precipitation, increasing the radar rainfall accumulations to about 65% of those observed by gauges. To correct for the second group of errors, a coherent method is presented linking the parameters of the radar reflectivity-rain rate (ZZ–RR) and radar reflectivity-specific attenuation (ZZ–kk) relationships to the normalized drop size distribution (DSD). Two different procedures were applied. First, normalized DSD parameters for the whole event and for each precipitation type separately (convective, stratiform and undefined) were obtained using local disdrometer observations. Second, 10,000 randomly generated plausible normalized drop size distributions were used for rainfall estimation, to evaluate whether this Monte Carlo method would improve the quality of weather radar rainfall products. Using the disdrometer information, the best results were obtained in case no differentiation between precipitation type (convective, stratiform and undefined) was made, increasing the event accumulations to more than 80% of those observed by gauges. For the randomly optimized procedure, radar precipitation estimates further improve and closely resemble observations in case one differentiates between precipitation type. However, the optimal parameter sets are very different from those derived from disdrometer observations. It is therefore questionable if single disdrometer observations are suitable for large-scale quantitative precipitation estimation, especially if the disdrometer is located relatively far away from the main rain event, which was the case in this study. In conclusion, this study shows the benefit of applying detailed error correction methods to improve the quality of the weather radar product, but also confirms the need to be cautious using locally obtained disdrometer measurements.

The empirical relationships used to estimate rainfall amounts from polarimetric radar observations were developed and tested by using long-period drop size distribution (DSD) data and monitoring of rainfall events prior to operational use in Korea. Rainfall associated with Typhoon Meari in 2011 was selected to assess the performance of these relationships for point and areal rainfall amounts. Data quality was checked in regions of light rain to enable the quantitative use of polarimetric variables. The distributions of the cross-correlation co-efficient and standard deviation of the differential phase shift agreed with those established in a previous study, but the absolute average deviation of differential reflectivity (ZDR) was a little distorted. Biases in reflectivity (Z) and differential reflectivity were calculated following established methods and found to be –0.18 and 0.47 dB, respectively. The accuracy of rainfall amounts calculated from R(KDP) and R(KDP, ZDR) was poor. The best estimates of rainfall were obtained using R(Z, ZDR) based on DSDs from Oklahoma (OKC) in the USA and Busan (BSC) for both the point and areal mean cases. Correlation co-efficients of R(Z, ZDR) using the BSC DSDs were better than those using the OKC DSDs for areal mean rainfall amounts. Rainfall amounts in this particular case in Korea were estimated more accurately using the Brandes drop shape for R(Z, ZDR) than the equilibrium drop shape.

In the present paper, an artificial neural network (ANN) based technique has been developed to estimate instantaneous rainfall by using brightness temperature from the IR sensors of SEVIRI radiometer, onboard Meteosat Second Generation (MSG) satellite. The study is carried out over north of Algeria. For estimation of rainfall, weight matrices of two ANNs namely MLP1 and MLP2 are developed. MLP1 is to identify raining or non-raining pixels. When rainy pixels are identified, then for those pixels, instantaneous rainfall is estimated by using MLP2. For identification of raining and non raining pixels, 7 input parameters from the IR sensors are utilized. Corresponding data of raining/non-raining pixels are taken from radar. For instantaneous rainfall estimation, 14 input parameters are utilized, where 7 parameters are information about raining pixels and 7 parameters are related with cloud features. The results obtained show the neural network performs reasonably well.

The ultimate objective of this paper is the estimation of rainfall over an area in Algeria using data from the SEVIRI radiometer (Spinning Enhanced Visible and Infrared Imager). To achieve this aim, we use a new Convective/Stratiform Rain Area Delineation Technique (CS-RADT). The satellite rainfall retrieval technique is based on various spectral parameters of SEVIRI that express microphysical and optical cloud properties. It uses a multispectral thresholding technique to distinguish between stratiform and convective clouds. This technique (CS-RADT) is applied to the complex situation of the Mediterranean climate of this region. The tests have been conducted during the rainy seasons of 2006/2007 and 2010/2011 where stratiform and convective precipitation is recorded. The developed scheme (CS-RADT) is calibrated by instantaneous meteorological radar data to determine thresholds, and then rain rates are assigned to each cloud type by using radar and rain gauge data. These calibration data are collocated with SEVIRI data in time and space.

More than 70% of South Korea has mountainous terrain, which leads to significant spatiotemporal variability of rainfall. The country is exposed to the risk of flash floods owing to orographic rainfall. Rainfall observations are important in mountainous regions because flood control measures depend strongly on rainfall data. In particular, radar rainfall data are useful in these regions because of the limitations of rain gauges. However, radar rainfall data include errors despite the development of improved estimation techniques for their calculation. Further, the radar does not provide accurate data during heavy rainfall in mountainous areas. This study presents a radar rainfall adjustment method that considers the elevation in mountainous regions. Gauge rainfall and radar rainfall field data are modified by using standardized ordinary cokriging considering the elevation, and the conditional merging technique is used for combining the two types of data. For evaluating the proposed technique, the Han River basin was selected; a high correlation between rainfall and elevation can be seen in this basin. Further, the proposed technique was compared with the mean field bias and original conditional merging techniques. Comparison with kriged rainfall showed that the proposed method has a lesser tendency to oversmooth the rainfall distribution when compared with the other methods, and the optimal mean areal rainfall is very similar to the value obtained using gauges. It reveals that the proposed method can be applied to an area with significantly varying elevation, such as the Han River basin, to obtain radar rainfall data of high accuracy.

Operational weather radars are usually equipped with a radome to reduce the wind load on the antenna and to allow continuous operation under bad weather conditions. The study is focused on quantifying the impact on radar polarimetric measurements due to the presence of a radome. Analysis refers to the transportable X-band polarimetric radar of the Agenzia Regionale per la Protezione Ambientale (ARPA) Piemonte (ARX), which uses a “bullet shaped” radome to provide environmental protection to the antenna and receiver apparatus system. Radar calibration is performed using wet and dry radomes. Differential reflectivity calibration results are compared with those obtained using the sun as a radiation reference source. Estimates have comparable mean values, with their differences being within the standard deviation. The paper describes a method based on the self-consistency principle that takes into account the loss induced by radome wetting due to rain to adjust the absolute calibration of the radar in real time. To verify how well this method overcomes the problem of excess attenuation caused by the presence of precipitation on the radome, two validation methods are presented. The first uses an empirical model to estimate the two-way wet radome losses for a qualitative comparison, with the corresponding losses obtained by the self-consistency principle. The second employs comparisons between radar and gauge rainfall accumulations with and without radome correction. This approach offers a clear understanding of the response of the correction, providing quantitative performance in a real case.

X-band-radar-based quantitative precipitation estimation (QPE) system is increasingly gaining interest thanks to its strength in providing high spatial resolution rainfall information for urban hydrological applications. However, prior to such applications, a variety of errors associated with X-band radars are mandatory to be corrected. In general, X-band radar QPE systems are affected by two types of errors: 1) common errors (e.g. mis-calibration, beam blockage, attenuation, non-precipitation clutter, variations in the raindrop size distribution) and 2) “wind drift” errors resulting from non-vertical falling of raindrops. In this study, we first assess the impacts of different corrections of common error using a dataset consisting of one-year reflectivity observations collected at an X-band radar site and a distrometer along with rainfall observations in Beijing urban area. The common error corrections demonstrate promising improvements in the rainfall estimates, even though an underestimate of 24.6% by the radar QPE system in the total accumulated rainfall still exists as compared with gauge observations. The most significant improvement is realized by beam integration correction. The DSD-related corrections (i.e., convective–stratiform classification and local Z-R relationship) also lead to remarkable improvement and highlight the necessity of deriving the localized Z-R relationships for specific rainfall systems. The effectiveness of wind drift correction is then evaluated for a fast-moving case, whose results indicate both the total accumulation and the temporal characteristics of the rainfall estimates can be improved. In conclusion, considerable potential of X-band radar in high-resolution rainfall estimation can be realized by necessary error corrections.

This study presents an easy and convenient empirical method to optimize polarimetric variables and produce more accurate dual polarization radar rainfall estimation. Weather Radar Center (WRC) in Korea Meteorological Administration (KMA) suggested relations between polarimetric variables (Z–ZDR and Z–KDP) based on a 2-D Video Distrometer (2DVD) measurements in 2014. Observed polarimetric variables from CAPPI (Constant Altitude Plan Position Indicator) images composed at 1 km of height were adjusted using the WRC's relations. Then dual polarization radar rainfalls were estimated by six different radar rainfall estimation algorithms, which are using either Z, Z and ZDR, or Z, ZDR and KDP. Accuracy of radar rainfall estimations derived by the six algorithms using the adjusted variables was assessed through comparison with raingauge observations. As a result, the accuracy of the radar rainfall estimation using adjusted polarimetric variables has improved from 50 % to 70 % approximately. Three high rainfall events with more than 40 mm of maximum hourly rainfall were shown the best accuracy on the rainfall estimation derived by using Z, ZDR and KDP. Meanwhile stratiform event was gained better radar rainfalls estimated by algorithms using Z and ZDR.

Summary Debris flow occurrence is generally forecasted by means of empirical rainfall depth–duration thresholds based on raingauge observations. Rainfall estimation errors related to the sparse nature of raingauge data are enhanced in case of convective rainfall events characterized by limited spatial extent. Such errors have been shown to cause underestimation of the rainfall thresholds and, thus, less efficient forecasts of debris flows occurrence. This work examines the spatial organization of debris flows-triggering rainfall around the debris flow initiation points using high-resolution, carefully corrected radar data for a set of short duration ( On average, triggering rainfall presents a local peak corresponding to the debris flow initiation point, with rain depth at 5 km (10 km) distance being on average around 70% (40%) of rain depth observed at the debris flow initiation points. The peak is consistently enhanced for events characterized by short durations and causes a systematic underestimation of the rainfall depth–duration thresholds when rainfall is measured away from the debris flow initiation points. We develop an analytical framework that exploits the general characteristics of the spatial rainfall organization to predict the systematic underestimation of the depth–duration thresholds when rainfall is sampled away from the initiation points. Predictions obtained based on this analytical framework are assessed using a Monte Carlo sampling technique.

The different quantities measured by dual-polarization radar systems are closely linked to each other. An extended Kalman filter framework is proposed in order to make use of constraints on individual radar observables that are induced by these relations. This new approach simultaneously estimates the specific differential phase on propagation Kdp, the attenuation-corrected reflectivity at horizontal polarization Zh, and the attenuation-corrected differential reflectivity Zdr, as well as the differential phase shift on backscatter δhυ. In a simulation experiment it is found that Kdp and δhυ can be retrieved with higher accuracy and spatial resolution than existing estimators that solely rely on a smoothed measurement of the differential phase shift Ψdp. Attenuation-corrected Zh was retrieved with an accuracy similar to standard algorithms, but improvements were found for attenuation-corrected Zdr. In addition, the algorithm can be used for radar calibration by comparing the directly retrieved differential phase shift on propagation Φdp with the accumulated Kdp estimates. The extended Kalman filter estimation scheme was applied to data collected with an X-band polarimetric radar in the Swiss Alps in 2010. Radome attenuation appears to be significant (up to 5 dB) in moderate to intense rain events and hence needs to be corrected in order to have reliable quantitative precipitation estimates. Measurements corrected for radome and propagation attenuation were converted into rain-rate R with a newly developed relation between R, Kdp, and Zdr. The good agreement between rain-rate values inferred from ground observations and from the radar measurements confirms the reliability of the proposed radar processing technique.