Quantitative Precipitation Estimation Algorithm Research Articles

Comparison of four radar profiling algorithms in the S‐band

AbstractRadar profiling algorithms are widely used for precipitation‐induced path‐attenuation correction. In this study, we investigate the impact of the four well‐known profiling algorithms on the efficacy of quantitative precipitation estimation retrievals. The first method, named the “simple PhiDP‐based method”, distributes precipitation‐induced path attenuation, or PhiDP (differential phase shift), of a rain segment to each radar gate according to radar reflectivity (Zh). The second one is a linear version of the ZPHI method derived from an approximate linear relationship between specific attenuation (Ah) and specific differential phase (KDP). The third one is a nonlinear version of the ZPHI that respects the nonlinearity of the Ah–KDP relationship. The fourth method is inspired from the self‐consistent method of Bringi. The two‐moment normalized raindrop size distribution (DSD) with two variables, scaled drop concentration and mean drop diameter , is used to represent natural variability of DSD. Two configurations are considered: a constant over a segment and a variable over a segment. The Ah, KDP, and rain intensity are estimated by the four profiling algorithms. The sensitivity of each algorithm to radar measurements uncertainty is also investigated. The evaluation of the estimates shows the following. First, for the range‐constant situation, the nonlinear ZPHI and the self‐consistent method yield the best estimates if the measurements errors are not considered. However, the nonlinear ZPHI and the self‐consistent method are significantly impacted by the Zh bias and the measurements noises respectively. Second, for the range‐variable situation, the self‐consistent method does not have a good performance even when radar measurements are accurate. The performance of the nonlinear ZPHI method is similar to the simple PhiDP‐based method, whereas the latter is immune to the Zh calibration error. These results suggest that the benefit of introducing in quantitative precipitation estimation algorithms can be significantly reduced if is not constant in reality and/or if the radar measurements are biased and noisy.

An Improved S-Band Polarimetric Radar-Based QPE Algorithm for Typhoons over South China Using 2DVD Observations

Polarimetric radar data are an important tool for quantitative precipitation estimation (QPE), which is essential for monitoring and forecasting precipitation. Previous studies have shown that the drop size distribution (DSD) and polarimetric radar parameters of typhoon-induced precipitation differ significantly from those of other types of rainfall. South China is a region that frequently experiences typhoons and heavy rainfall, which can cause serious disasters. Therefore, it is critical to develop a QPE algorithm that is suitable for typhoon precipitation over South China. In this study, we constructed four simple QPE estimators, R(ZH), R(ZH, ZDR), R(KDP) and R(KDP, ZDR) based on two-dimensional video disdrometer (2DVD) DSD observations of typhoon-induced precipitation over South China in 2017–2018. We analyzed the DSD characteristics and the estimation accuracy of these four QPE estimators in the reflectivity–differential reflectivity (ZH–ZDR) space, as well as the S-band polarimetric radar (S-POL) data of seven typhoon-induced precipitation events that affected South China in 2017–2019. We used these data to quantitatively determine the optimal ranges of the estimators and establish a typhoon precipitation QPE algorithm for typhoon-induced precipitation over South China (2DVD-Typhoon). The evaluation results showed that: (1) compared to R(ZH) and R(KDP), R(ZH, ZDR) and R(KDP, ZDR) had lower performance in estimating typhoon-induced rainfall after incorporating the polarimetric parameter ZDR, as strong crosswind of the typhoon caused some bias in the raindrop-induced ZDR; (2) the 2DVD-Typhoon algorithm utilizes the respective advantages of the individual estimators to generate the best QPE results; (3) the QPE performance of 2DVD-Typhoon and the Colorado State University–Hydrometeor Identification Rainfall Optimization (CSU-HIDRO) is used as a comparison for hourly rainfall, cumulative rainfall and different rainfall intensity. The comparison shows that 2DVD-Typhoon gives a better normalized error (NE), root mean square error (RMSE) and correlation coefficient (CC), indicating its strength in rainfall estimation for typhoons over South China. The above results provide theoretical support for improving typhoon-induced rainfall monitoring and numerical weather forecasting models in South China.

A Radar-Based Quantitative Precipitation Estimation Algorithm to Overcome the Impact of Vertical Gradients of Warm-Rain Precipitation: The Flood in Western Germany on 14 July 2021

Abstract The demand of accurate, near-real-time radar-based quantitative precipitation estimation (QPE), which is key to feed hydrological models and enable reliable flash flood predictions, was highlighted again by the disastrous floods following after an intense stratiform precipitation field passing western Germany on 14 July 2021. Three state-of-the-art rainfall algorithms based on reflectivity Z, specific differential phase KDP, and specific attenuation A were applied to observations of four polarimetric C-band radars operated by the German Meteorological Service [DWD (Deutscher Wetterdienst)]. Due to the large vertical gradients of precipitation below the melting layer suggesting warm-rain processes, all QPE products significantly underestimate surface precipitation. We propose two mitigation approaches: (i) vertical profile (VP) corrections for Z and KDP and (ii) gap filling using observations of a local X-band radar, JuXPol. We also derive rainfall retrievals from vertically pointing Micro Rain Radar (MRR) profiles, which indirectly take precipitation gradients in the lower few hundreds of meters into account. When evaluated with DWD rain gauge measurements, those retrievals result in pronounced improvements, especially for the A-based retrieval partly due to its lower sensitivity to the variability of raindrop size distributions. The VP correction further improves QPE by reducing the normalized root-mean-square error by 23% and the normalized mean bias by 20%. With the use of gap-filling JuXPol data, the A-based retrieval gives the lowest errors followed by the Z-based retrievals in combination with VP corrections. The presented algorithms demonstrate the increased value of radar-based QPE application for warm-rain events and related potential flash flooding warnings.

Modeling of Rain Drop Size Distribution in Association With Convective and Cloud Parameter Over a Tropical Location

The difference in raindrop size distribution (DSD) characteristics between convective and stratiform rain and in monsoon and pre-monsoon season have been investigated over a tropical location, Kolkata, using seven years data collected from a Joss-Waldvogel disdrometer. During the Indian Summer Monsoon (ISM) and the pre-monsoon season, Kolkata gets substantial rain. Interannual variability of DSD has been evident. The normalized drop size distributions of DSD show double peak at 20-50 mm/h rain rate due to drop break up and coalescence process. For convective rain, concentration of higher drops (>3 mm) are more significant compared to stratiform rain. In the pre-monsoon period, significant contribution of normalized DSD in the afternoon to late evening has been evident due to nor’westers. Contribution of normalized DSD of higher drops (>3 mm) are more in the pre-monsoon convective rain compared to monsoon convective rain. The larger slope of μ-Λ relationship has been observed in pre-monsoon convective period which corresponds to higher <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> values. The relationship between radar reflectivity ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Z</i> ) and rain rate ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> ) distinguishes the monsoon and pre-monsoon seasons. Radiometric measurements have been used to examine the effects of the atmospheric instability parameter CAPE and cloud base height (CBH) on the formation of larger drop sizes. This study will be useful for understanding precipitation microphysics and improving the quantitative precipitation estimation algorithm.

Radar Quantitative Precipitation Estimation Algorithm Based on Precipitation Classification and Dynamical Z-R Relationship

The choice of Z-R relationship is an essential source of error in radar Quantitative Precipitation Estimation (QPE). A QPE algorithm combining the Optimization process of precipitation Classification and Dynamical adjustments (OCD) is proposed to improve the accuracy of QPE in Yinchuan city, China. A detailed evaluation and study of Z = 300R1.4 (fixed Z-R), Optimization Processing (OP), Optimization processing of Dynamical Adjustments (ODA) and OCD were performed using various evaluation metrics. The results show that ODA and OCD can significantly reduce the error of QPE, with OCD being the best estimator, reaching a correlation coefficient (CC) of 0.7 and reducing mean absolute error (MAE) and root mean square error (RMSE) by 31% and 34%, respectively. OCD outperforms other algorithms in terms of MAE and RMSE for different rain rates (RR), and the various assessment metrics at hourly scales are also more concentrated in reasonable intervals. OP gives fair results at weaker rain rates (0.2 ≤ RR &lt; 8 mm/h) but underestimates rainfall more incorrectly at stronger rain rates (8 mm/h ≤ RR). Both the OCD and ODA provide a more significant improvement in the estimation of the area and magnitude of strong rainfall, with the OCD providing a better description of the local characteristics of the rainfall distribution, further demonstrating the advantages of the ODA.

Microphysical Characteristics of Raindrop Size Distribution and Implications for Radar Rainfall Estimation Over the Northeastern Tibetan Plateau

AbstractRaindrop size distribution (DSD) observations with OTT PARSIVEL2 disdrometers at Dari and Delingha in the northeast of the Tibetan Plateau (TP) during summer 2019 and 2020 were utilized to investigate the DSD characteristics and DSD‐based polarimetric radar quantitative precipitation estimation (QPE) relations on the X, Ku, and Ka bands. The DSD characteristics were examined for different rain rates (R) and for stratiform and convective rainfall types. The raindrop spectra became wider and flatter as the R increased. Gamma parameters N0, μ, and λ (representing intercept, shape, and slope parameters, respectively) were smaller for convective compared to stratiform rainfall, corresponding to the broader and flatter convective raindrop spectra. The μ–λ and Dm–log10Nw relationships were also examined in our study. There was a significant μ–λ correlation, with a quadratic function relationship. The summer convective rainfall at both Dari and Delingha showed continental‐like clusters based on the Dm–log10Nw relationship. The DSD‐based two‐parameter polarimetric radar QPE relations (R(Zh, ZDR), R(KDP, ZDR)), and one‐parameter relations (R(Zh), R(KDP)) at the X, Ku, and Ka bands were proposed, and the R computed from DSD was implemented to evaluate these relations. The performance of two‐parameter models was stronger than that of one‐parameter models. Moreover, R(Zh, ZDR) and R(KDP, ZDR) performed better for the X and Ku bands, while the R(KDP, ZDR) model performed best for the Ka band. The ground‐based Ka/Ku band measurement Z at Dari was used to evaluate the Z–R and R−Dm QPE relations. The R−Dm relations had a better performance compared to the Z–R relations. The Ku radar‐derived R series are more consistent with the DSD measured counterparts. The research of the dual‐polarization radar parameter models is vital to develop radar QPE algorithms on the TP.

Raindrop Size Distribution Characteristics of the Western Pacific Tropical Cyclones Measured in the Palau Islands

Due to the severe threat of tropical cyclones to human life, recent years have witnessed an increase in the investigations on raindrop size distributions of tropical cyclones to improve their quantitative precipitation estimation algorithms and modeling simulations. So far, the raindrop size distributions of tropical cyclones using disdrometer measurements have been conducted at coastal and inland stations, but such studies are still missing for oceanic locations. To the authors’ knowledge, the current study examines—for the first time—the raindrop size distributions of fourteen tropical cyclones observed (during 2003–2007) at an oceanic station, Aimeliik, located in the Palau islands in the Western Pacific. The raindrop size distributions of Western Pacific tropical cyclones measured in the Palau islands showed unlike characteristics between stratiform and convective clusters, with a larger mass-weighted mean diameter and smaller normalized intercept parameter in the convective type. The contribution of the drop diameters to the total number concentration showed a gradual decrease with the increase in drop diameter size. Raindrop size distributions of Western Pacific tropical cyclones measured in the Palau islands differed slightly from Taiwan and Japan. The helpfulness of empirical relations in raindrop size distribution in rainfall estimation algorithms of ground-based (Z–R, μ–Λ, Dm–R, and Nw–R) and remote-sensing (σm–Dm, μo–Dm, Dm–Zku, and Dm–Zka) radars are evaluated. Furthermore, the present study also related the rainfall kinetic energy of fourteen tropical cyclones with rainfall rate and mass-weighted mean diameter (KEtime–R, KEmm–R, and KEmm–Dm). The raindrop size distribution empirical relations appraised in this study offer a chance to: (1) enhance the rain retrieval algorithms of ground-based, remote sensing radars; and (2) improve rainfall kinetic energy estimations using disdrometers and GPM DPR in rainfall erosivity studies.

Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements

Abstract. Deriving large-scale and high-quality precipitation products from satellite remote-sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited studies have been conducted even using China's latest Fengyun-4A (FY-4A) geostationary satellite. Taking three rainstorm events over South China as examples, a machine-learning-based regression model was established using the random forest (RF) method to derive QPE from FY-4A observations, in conjunction with cloud parameters and physical quantities. The cross-validation results indicate that both daytime (DQPE) and nighttime (NQPE) RF algorithms performed well in estimating QPE, with the bias score, correlation coefficient and root-mean-square error of DQPE (NQPE) of 2.17 (2.42), 0.79 (0.83) and 1.77 mm h−1 (2.31 mm h−1), respectively. Overall, the algorithm has a high accuracy in estimating precipitation under the heavy-rain level or below. Nevertheless, the positive bias still implies an overestimation of precipitation by the QPE algorithm, in addition to certain misjudgements from non-precipitation pixels to precipitation events. Also, the QPE algorithm tends to underestimate the precipitation at the rainstorm or even above levels. Compared to single-sensor algorithms, the developed QPE algorithm can better capture the spatial distribution of land-surface precipitation, especially the centre of strong precipitation. Marginal difference between the data accuracy over sites in urban and rural areas indicate that the model performs well over space and has no evident dependence on landscape. In general, our proposed FY-4A QPE algorithm has advantages for quantitative estimation of summer precipitation over East Asia.

Assessing the benefits of specific attenuation for quantitative precipitation estimation with a C-band radar network

AbstractRecent advances demonstrate the benefits of radar-derived specific attenuation at horizontal polarization (AH) for quantitative precipitation estimation (QPE) at S and X band. To date the methodology has, however, not been adapted for the widespread European C-band radars such as installed in the network of the German Meteorological Service (DWD, Deutscher Wetterdienst). Simulations based on a large dataset of drop size distributions (DSDs) measured over Germany are performed to investigate the DSD dependencies of the attenuation parameter αH for the AH estimates. The normalized raindrop concentration (Nw) and the change of differential reflectivity (ZDR) with reflectivity at horizontal polarization (ZH) are used to categorize radar observations into regimes for which scan-wise optimized αH values are derived. For heavier continental rain with ZH &gt; 40 dBZ, the AH-based rainfall retrieval R(AH) is combined with a rainfall estimator using a substitute of specific differential phase (). We also assess the performance of retrievals based on specific attenuation at vertical polarization (AV). Finally, the regime-adapted hybrid QPE algorithms are applied to four convective cases and one stratiform case from 2017 to 2019, and compared to DWD’s operational RAdar-OnLine-ANeichung (RADOLAN) RW rainfall product, which is based on Zh only but adjusted to rain gauge measurements. For the convective cases, our hybrid retrievals outperform the traditional R(Zh) and pure R(AH/V) retrievals with fixed αH/V values when evaluated with gauge measurements and outperform RW when evaluated by disdrometer measurements. Potential improvements using ray-wise αH/V and segment-wise applications of the ZPHI method along the radials are discussed.

Deep Learning for Polarimetric Radar Quantitative Precipitation Estimation during Landfalling Typhoons in South China

Heavy rain associated with landfalling typhoons often leads to disasters in South China, which can be reduced by improving the accuracy of radar quantitative precipitation estimation (QPE). At present, raindrop size distribution (DSD)-based nonlinear fitting (QPEDSD) and traditional neural networks are the main radar QPE algorithms. The former is not sufficient to represent the spatiotemporal variability of DSDs through the generalized Z–R or polarimetric radar rainfall relations that are established using statistical methods since such parametric methods do not consider the spatial distribution of radar observables, and the latter is limited by the number of network layers and availability of data for training the model. In this paper, we propose an alternative approach to dual-polarization radar QPE based on deep learning (QPENet). Three datasets of “dual-polarization radar observations—surface rainfall (DPO—SR)” were constructed using radar observations and corresponding measurements from automatic weather stations (AWS) and used for QPENetV1, QPENetV2, and QPENetV3. In particular, 13 × 13, 25 × 25, and 41 × 41 radar range bins surrounding each AWS location were used in constructing the datasets for QPENetV1, QPENetV2, and QPENetV3, respectively. For training the QPENet models, the radar data and AWS measurements from eleven landfalling typhoons in South China during 2017–2019 were used. For demonstration, an independent typhoon event was randomly selected (i.e., Merbok) to implement the three trained models to produce rainfall estimates. The evaluation results and comparison with traditional QPEDSD algorithms show that the QPENet model has a better performance than the traditional parametric relations. Only when the hourly rainfall intensity is less than 5 mm (R &lt; 5 mm·h−1), the QPEDSD model shows a comparable performance to QPENet. Comparing the three versions of the QPENet model, QPENetV2 has the best overall performance. Only when the hourly rainfall intensity is less than 5 mm (R &lt; 5 mm·h−1), QPENetV3 performs the best.

Improving S-Band Polarimetric Radar Monsoon Rainfall Estimation with Two-Dimensional Video Disdrometer Observations in South China

The capability to estimate monsoon rainfall is investigated by using S-band polarimetric radar (S-POL) and two-dimensional Video Disdrometer (2DVD) during 2017–2018 in South China. Based on 2 years of 2DVD raindrop size distribution (DSD) observations of monsoon precipitation systems, four different quantitative precipitation estimation (QPE) algorithms were obtained, including R(ZH), R(ZH, ZDR), R(KDP), and R(KDP, ZDR). In order to clearly demarcate the optimal ranges of the four QPE algorithms by considering the impact of the monsoon precipitation system of South China, the optimal ranges of the four QPE algorithms were integrated together according to the characteristics of different QPE algorithms in the reflectivity-differential reflectivity (ZH-ZDR) space distribution by reference to 8 monsoon rainfall events from 2016 to 2020 observed in Guangzhou and Yangjiang S-POL. Then, an optimal algorithm was proposed for the quantitative estimation of monsoon precipitation in South China (2DVD-SCM) using S-POL. The 2DVD-SCM was tested by comparing it with a traditional radar QPE algorithm PPS (WSR-88D Precipitation Processing System); a classical QPE algorithm CSU-HIDRO (Colorado State University-Hydrometeor Identification Rainfall Optimization) for the polarimetric radar; a piecewise fitting algorithm LPA-PFM (Piecewise Fitting Method) based on laser raindrop spectrum. The rainfall event one-by-one test results show that the 2DVD-SCM algorithm performs obviously better than the other three algorithms in most of the rainfall events. The hourly accumulated rainfalls estimated by the 2DVD-SCM algorithm are agreed well with rain gauge observations. The normalized errors (NE) and the root mean square errors (RMSE) values of 2DVD-SCM are remarkably less than the other three algorithms, and the correlation coefficient (CC) values are higher. The results of the classified rain rate test show that the NE and RMSE values of the 2DVD-SCM algorithm are the lowest in all classified rain rates. The overall evaluation results show that the 2DVD-SCM algorithm performs obviously better than the existing three algorithms and have the potential to apply in S-band polarimetric radar monsoon rainfall estimation operational system in South China.

Challenges of the Polarimetric Update on Operational Radars in China—Ground Clutter Contamination of Weather Radar Observations

China New Generation Doppler Weather Radar (CINRAD) plans to upgrade its hardware and software to achieve polarimetric function. However, the small-magnitude polarimetric measurements were negatively affected by the scattering characteristics of ground clutter and the filter’s response to the ground clutter. This polarimetric contamination was characterized by decreased differential reflectivity (ZDR) and cross-correlation coefficient (ρhv), as well as an increased standard deviation of the differential phase (ΦDP), generating a large-area and long-term observational anomaly for eight polarimetric radars in South China. Considering that outliers simultaneously appeared in the radar mainlobe and sidelobe, the variations in the reflectivity before and after clutter mitigation (ΔZH) and ρhv were used for quantitatively describing the random dispersion caused by mainlobe and sidelobe clutters. The performance of polarimetric algorithms was also reduced by clutter contamination. The deteriorated membership functions in the hydrometeor classification algorithm changed the proportion of classified echoes. The empirical relations of R(ZH, ZDR) and R(KDP) were broken in the quantitative precipitation estimation algorithm and the extra error considerably exceeded the uncertainty caused by the drop-size distribution (DSD) variability of R(ZH). The above results highlighted the negative impact of clutter contamination on polarimetric applications that need to be further investigated.

Performance of a Radar Mosaic Quantitative Precipitation Estimation Algorithm Based on a New Data Quality Index for the Chinese Polarimetric Radars

The quality of radar data is crucial for its application. In particular, before radar mosaic and quantitative precipitation estimation (QPE) can be conducted, it is necessary to know the quality of polarimetric parameters. The parameters include the horizontal reflectivity factor, ZH; the differential reflectivity factor, ZDR; the specific differential phase, KDP; and the correlation coefficient, ρHV. A novel radar data quality index (RQI) is specifically developed for the Chinese polarimetric radars. Not only the influences of partial beam blockages and bright band upon radar data quality, but also those of bright band correction performance, signal-to-noise ratio, and non-precipitation echoes are considered in the index. RQI can quantitatively describe the quality of various polarimetric parameters. A new radar mosaic QPE algorithm based on RQI is presented in this study, which can be used in different regions with the default values adjusted according to the characteristics of local radar. RQI in this algorithm is widely used for high-quality polarimetric radar data screening and mosaic data merging. Bright band correction is also performed to errors of polarimetric parameters caused by melting ice particles for warm seasons in this algorithm. This algorithm is validated by using nine rainfall events in Guangdong province, China. Major conclusions are as follows. ZH, ZDR, and KDP in bright band become closer to those under bright band after correction than before. However, the influence of KDP correction upon QPE is not as good as that of ZH and ZDR correction in bright band. Only ZH and ZDR are used to estimate precipitation in the bright band affected area. The new mosaic QPE algorithm can improve QPE performances not only in the beam blocked areas and the bright band affected area, which are far from radars, but also in areas close to the two radars. The sensitivity tests show the new algorithm can perform well and stably for any type of precipitation occurred in warm seasons. This algorithm lays a foundation for regional polarimetric radar mosaic precipitation estimation in China.

Short-Term Dynamic Radar Quantitative Precipitation Estimation Based on Wavelet Transform and Support Vector Machine

Currently, Doppler weather radar in China is generally used for quantitative precipitation estimation (QPE) based on the Z-R relationship. However, the estimation error for mixed precipitation is very large. In order to improve the accuracy of radar QPE, we propose a dynamic radar QPE algorithm with a 6-min interval that uses the reflectivity data of Doppler radar Z9002 in the Shanghai Qingpu District and the precipitation data at automatic weather stations (AWSs) in East China. Considering the time dependence and abrupt changes of precipitation, the data during the previous 30-min period were selected as the training data. To reduce the complexity of radar QPE, we transformed the weather data into the wavelet domain by means of the stationary wavelet transform (SWT) in order to extract high and low-frequency reflectivity and precipitation information. Using the wavelet coefficients, we constructed a support vector machine (SVM) at all scales to estimate the wavelet coefficient of precipitation. Ultimately, via inverse wavelet transformation, we obtained the estimated rainfall. By comparing the results of the proposed method (SWT-SVM) with those of Z = 300 × R1.4, linear regression (LR), and SVM, we determined that the root mean square error (RMSE) of the SWT-SVM method was 0.54 mm per 6 min and the average Threat Score (TS) could exceed 40% with the exception of the downpour category, thus remaining at a high level. Generally speaking, the SWT-SVM method can effectively improve the accuracy of radar QPE and provide an auxiliary reference for actual meteorological operational forecasting.

PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation

AbstractPrecipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time precipitation estimation with high precision is pivotal for the monitoring and managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data as input are attractive owing to their rich spatiotemporal resolution and near-instantaneous availability, their sole reliance on cloud-top brightness temperature (Tb) readings causes underestimates in wet regions and overestimates in dry regions—this is especially evident over the western contiguous United States (CONUS). We introduce an algorithm, the Precipitation Estimations from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain rate model (PDIR), which utilizes climatological data to construct a dynamic (i.e., laterally shifting) Tb–rain rate relationship that has several notable advantages over other quantitative precipitation-estimation algorithms and noteworthy skill over the western CONUS. Validation of PDIR over the western CONUS shows a promising degree of skill, notably at the annual scale, where it performs well in comparison to other satellite-based products. Analysis of two extreme landfalling atmospheric rivers show that solely IR-based PDIR performs reasonably well compared to other IR- and PMW-based satellite rainfall products, marking its potential to be effective in real-time monitoring of extreme storms. This research suggests that IR-based algorithms that contain the spatiotemporal richness and near-instantaneous availability needed for rapid natural hazards response may soon contain the skill needed for hydrologic and water resource applications.

A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part I: Algorithm Description

Abstract A prototype quantitative precipitation estimate (QPE) algorithm that utilizes specific attenuation A and specific differential phase KDP was developed for inclusion into the Multi-Radar Multi-Sensor (MRMS) system and the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. Special attention is given to the optimization of the factor α used for computation of a path-integrated attenuation from a total span of differential phase along the propagation path in rain. It is suggested to estimate α from a slope of the ZDR dependence on Z in rain. The use of real-time adjusted α allows us to capture the variations of the drop size distributions, and therefore improve the QPE accuracy. It is demonstrated that the factor α is generally higher for tropical rain type compared to continental rain. Since the R(A) approach is only valid for pure rainfall, the R(KDP) relation is suggested as a complement in areas of hail contamination. The paper contains a description of the basic version of the R(A) and R(KDP) algorithm and recommendations for its further optimization.

Investigation of Weather Radar Quantitative Precipitation Estimation Methodologies in Complex Orography

Near surface quantitative precipitation estimation (QPE) from weather radar measurements is an important task for feeding hydrological models, limiting the impact of severe rain events at the ground as well as aiding validation studies of satellite-based rain products. To date, several works have analyzed the performance of various QPE algorithms using actual and synthetic experiments, possibly trained by measurement of particle size distributions and electromagnetic models. Most of these studies support the use of dual polarization radar variables not only to ensure a good level of data quality but also as a direct input to rain estimation equations. One of the most important limiting factors in radar QPE accuracy is the vertical variability of particle size distribution, which affects all the acquired radar variables as well as estimated rain rates at different levels. This is particularly impactful in mountainous areas, where the sampled altitudes are likely several hundred meters above the surface. In this work, we analyze the impact of the vertical profile variations of rain precipitation on several dual polarization radar QPE algorithms when they are tested in a complex orography scenario. So far, in weather radar studies, more emphasis has been given to the extrapolation strategies that use the signature of the vertical profiles in terms of radar co-polar reflectivity. This may limit the use of the radar vertical profiles when dual polarization QPE algorithms are considered. In that case, all the radar variables used in the rain estimation process should be consistently extrapolated at the surface to try and maintain the correlations among them. To avoid facing such a complexity, especially with a view to operational implementation, we propose looking at the features of the vertical profile of rain (VPR), i.e., after performing the rain estimation. This procedure allows characterization of a single variable (i.e., rain) when dealing with vertical extrapolations. In this work, a definition of complex orography is also given, introducing a radar orography index to objectively quantify the degree of terrain complexity when dealing with radar QPE in heterogeneous environmental scenarios. Three case studies observed by the research C-band polarization agility Doppler radar named Polar 55C, managed by the Institute of Atmospheric Sciences and Climate (ISAC) at the National Research Council of Italy (CNR), were used to prove the concept of VPR. Our results indicate that the combined algorithm, which merges together differential phase shift (Kdp), single polarization reflectivity factor (Zhh), and differential reflectivity (Zdr), once accurately processed, in most cases performs better among those tested and those that make use of Zhh alone, Kdp alone, and Zhh, and Zdr. Improvements greater than 25% are found for the total rain accumulations in terms of normalized bias when the VPR extrapolation is applied.

An Integrated Method of Multiradar Quantitative Precipitation Estimation Based on Cloud Classification and Dynamic Error Analysis

Relationships between radar reflectivity factor and rainfall are different in various precipitation cloud systems. In this study, the cloud systems are firstly classified into five categories with radar and satellite data to improve radar quantitative precipitation estimation (QPE) algorithm. Secondly, the errors of multiradar QPE algorithms are assumed to be different in convective and stratiform clouds. The QPE data are then derived with methods of Z-R, Kalman filter (KF), optimum interpolation (OI), Kalman filter plus optimum interpolation (KFOI), and average calibration (AC) based on error analysis on the Huaihe River Basin. In the case of flood on the early of July 2007, the KFOI is applied to obtain the QPE product. Applications show that the KFOI can improve precision of estimating precipitation for multiple precipitation types.

Automated convective and stratiform precipitation estimation in a small mountainous catchment using X-band radar data in Central Spain

For the purposes of weather nowcasting, flood risk monitoring and water resources assessment, it is often difficult to achieve a reliable spatio-temporal representation of rainfall due to a low rain gauge network density. However, quantitative precipitation estimation (QPE) has acquired new prospects with the introduction of weather radars, thanks to their higher spatio-temporal resolution. Although a wide number of QPE algorithms are available for using C-band radar data, only a few studies have employed X-band radar. In this study the microscale rainfall variability in a small catchment is automatically measured using short-range X-band radar variograms and classifying precipitation into convective and stratiform types with a recently published index. The aim is to apply a straightforward geostatistical algorithm, named ordinary kriging of radar errors (OKRE), to integrate X-band radar and rain gauge measurements in a mountainous catchment (15 km2) in central Spain. As expected, convective events presented higher estimation errors due to their complex spatial and temporal variability. Despite this fact, errors are sufficiently small and results are reliable rainfall estimations. The two main contributions of this work are the adaptation of the OKRE method to small spatial scales and its application automatically differentiating between convective and stratiform events.

이중 편파 레이더 자료를 이용한 강수 추정 알고리즘의 오차 분석

강수량의 정확한 추정은 자연 재해의 예측 및 방지를 위해 필수적이다. 최근 강우레이더는 높은 시공간 해상도로 정량적 강수 추정(quantitative precipitation estimation; QPE)을 수행하는 기술로 주목받고 있다. 특히, 레이더의 강수 추정 성능은 다양한 규모에서 여러 수문 기상 변수를 관측하는 이중 편파 기술의 도입에 의해 향상되었다. 이중 편파 레이더는 다양한 매개변수를 측정하기 때문에, 기존의 반사도-강수 관계만을 활용하는 단일 편파 레이더에 비해 강수 추정 정확도 향상이 가능하다. 본 연구의 목적은 이중 편파 레이더에 대한 대표적인 강수 추정 방법인 JPOLE(Ryzhkov et al., 2005a)와 CSU(Cifelli et al., 2011) 알고리즘의 국내 강우레이더 자료에 대한 적용성 및 불확실성을 평가하는 것이다. 이를 위해 S밴드 이중 편파 레이더인 비슬산 강우레이더의 2012년 호우사상을 대상으로 두 가지 알고리즘을 적용하여 강수를 각각 추정하고, 일별 및 시간별 강수의 시공간 분포 정확성에 대해 지상관측 강수와 비교하였다. 또한 관측지점 별 오차를 분석하여 레이더로부터의 거리 및 관측지점 고도와 레이더 정확도와의 상관성을 분석하였다. 분석 결과, JPOLE 및 CSU 알고리즘 모두 표준화된 평균절대오차(nMAE)를 기준으로 일별 강수에 대해 0.23, 시간별 강수 기준으로 0.35 이내의 비교적 정확한 강수량 추정 결과를 산출하였다. 모든 정확도 평가 지표 및 기준에 대해서 JPOLE 알고리즘이 CSU 알고리즘에 비해 우수한 정확도를 보였으나, 그 차이는 평균제곱근오차(RMSE)를 기준으로 50 mm/day 이상 일별 강수에 대해 5.5 mm(약 17%차이), 10 mm/hr 이상 시간별 강수에 대해 0.6 mm(약 8%차이)였다. 관측소 위치 별 오차 분석 결과, 불확실성이 높은 지점의 위치는 일정한 경향이 없었으나, 지상관측지점이 레이더로부터 거리 60 km, 해발고도 고도 100 m 이내의 조건에서 정확도 높은 관측지점이 존재하였다. Accurate estimation of precipitation is essential to predict and prevent natural disasters (e.g. flood, landslide, and heavy snowfall). Recently, weather radars have become a popular tool for quantitative precipitation estimation (QPE) with high spatio-temporal resolution. Especially, in the last decade, QPE performance has been improved by introduction of polarimetric technology to observe multiple hydrometeorological variables at various scales. By being able to measure variables such as the differential reflectivity, specific differential phase, and cross-correlation coefficient, the reliability of estimation has significantly improved as compared to the reflectivity-based method (Ryzhkov et al., 2005a). However, QPEs using dual polarization radar data are still subject to uncertainties resulting from rainfall conversion relationships, combination methods of different parameters, and sampling errors. In this study, we assessed the uncertainty and applicability of conventional QPE algorithms, such as JPOLE (Ryzhkov et al., 2005a) and CSU (Cifelli et al., 2011) algorithms, using the dual polarization radar at Mt. Biseul in the south-eastern part of the Korean Peninsula. Analysis results illustrated that the JPOLE algorithm outperformed the CSU algorithm slightly for daily and hourly rainfall analysis using various metrics. The higher accuracy was found in the stations located within less than 60 km from radar and 100 m in the elevation.