Quantitative Precipitation Estimation Research Articles

Evaluation of multi-satellite precipitation products in estimating precipitation extremes over mainland China at annual, seasonal and monthly scales

The wide and consistent global coverage of satellite-based quantitative precipitation estimates (QPEs) has shown great potential for monitoring precipitation (PR) at large spatial scales. Evaluation of QPEs in estimating PR extremes is vital to forecasting hydrologic extremes. Here, we present a systematic evaluation of four commonly used QPEs: (1) the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), (2) Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42 V7), (3) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and (4) Multi-Source Weighted-Ensemble Precipitation (MSWEP), in their abilities to detect PR extremes in mainland China at annual-seasonal-monthly scales. Four Standard Extreme Precipitation Indices (SEPIs) are chosen as the assessment metrics, including the maximum 1-day PR (Rx1day), the simple PR intensity index (SDII), the count of days with PR ≥ 20 mm (R20mm), and the consecutive dry days (CDD). Results indicate that PERSIANN-CDR performs best for all SEPIs, followed by CHIRPS and TRMM 3B42 V7. All QPEs (except MSWEP) perform better (worse) in capturing CDD (SDII) than other SEPIs at all three timescales. However, large differences among the performance of QPEs in estimated seasonal SEPIs are found - CHIRPS (PERSIANN-CDR) outperforms the other QPEs in spring (summer and autumn), while PERSIANN-CDR and CHIRPS outperform the other QPEs in winter. All QPEs perform better in detecting extreme PR occurrence in summer than in other seasons, and spatially most of them perform better in humid southeastern China than in arid northwestern China. CHIRPS and TRMM 3B42 V7 overestimate Rx1day, R20mm and SDII in most of China, while MSWEP notably underestimates CDD in most of China and overestimates Rx1day, R20mm and SDII in western China.

Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather Events.

During extreme events such as tropical cyclones, the precision of sensors used to sample the meteorological data is vital to feed weather and climate models for storm path forecasting, quantitative precipitation estimation, and other atmospheric parameters. For this reason, periodic data comparison between several sensors used to monitor these phenomena such as ground-based and satellite instruments, must maintain a high degree of correlation in order to issue alerts with an accuracy that allows for timely decision making. This study presents a cross-evaluation of the radar reflectivity from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement Mission (GPM) and the U.S. National Weather Service (NWS) Next-Generation Radar (NEXRAD) ground-based instrument located in the Caribbean island of Puerto Rico, USA, to determine the correlation degree between these two sensors’ measurements during extreme weather events and normal precipitation events during 2015–2019. GPM at Ku-band and Ka-band and NEXRAD at S-band overlapping scanning regions data of normal precipitation events during 2015–2019, and the spiral rain bands of four extreme weather events, Irma (Category 5 Hurricane), Beryl (Tropical Storm), Dorian (Category 1 hurricane), and Karen (Tropical Storm), were processed using the GPM Ground Validation System (GVS). In both cases, data were classified and analyzed statistically, paying particular attention to variables such as elevation angle mode and precipitation type (stratiform and convective). Given that ground-based radar (GR) has better spatial and temporal resolution, the NEXRAD was used as ground-truth. The results revealed that the correlation coefficient between the data of both instruments during the analyzed extreme weather events was moderate to low; for normal precipitation events, the correlation is lower than that of studies that compared GPM and NEXRAD reflectivity located in other regions of the USA. Only Tropical Storm Karen obtained similar results to other comparative studies in terms of the correlation coefficient. Furthermore, the GR elevation angle and precipitation type have a substantial impact on how well the rain reflectivity correlates between the two sensors. It was found that the Ku-band channel possesses the least bias and variability when compared to the NEXRAD instrument’s reflectivity and should therefore be considered more reliable for future tropical storm tracking and tropical region precipitation estimates in regions with no NEXRAD coverage.

Determination of the Z-R Relationship through Spatial Analysis of X-Band Weather Radar and Rain Gauge Data

In weather radar applications, the Z-R relationship is considered one of the most crucial factors for providing quality quantitative precipitation estimates. However, the relationship’s parameters vary in time and space, making the derivation of an optimal relationship for a specific weather radar system challenging. This research focused on the analysis of the spatiotemporal variability of the parameters for a newly installed X-Band weather radar in Athens, Greece, by performing correlation and optimization analyses between high temporal resolution weather radar and rain gauge datasets. The correlation analysis was performed to assess the available datasets and provide the base of quality control. Multiple Z-R relationships were then derived for the following three optimization procedures; event-based relationships, station-based relationships, and a single area-based relationship. The results highlighted the region’s spatial variability regarding the Z-R relationship and the correlation between the station location and its parameter values. Moreover, it was found that stations near the coast and the front end of precipitation systems featured parameter values typical of convective type events. Finally, a single Z-R relationship was determined under a calibration and validation scheme, Z = 321R1.53,, which was validated with good agreement.

Multiscale and multi event evaluation of short-range real-time flood forecasting in large metropolitan areas

Many large metropolitan areas are especially susceptible to floods generated by heavy, short-duration rainfall. The high density of people, buildings and infrastructure in these areas underline the importance of developing flood resilient cities and communities. An accurate real-time flood forecasting system can support decision making for launching preparedness and response actions in short-range (hours to days), and assist mitigating the disturbances caused by floods. This study explores the short-range predictive capability of a real-time flood forecast system by coupling the High-Resolution Rapid Refresh (HRRR) meteorological forecasted variables with a fully distributed hydrological model (WRF-Hydro). We provide a comprehensive analysis of short-range (36 h) forecasts for 19flood events generated by heavy rainfall in small urban and suburban watersheds (<200 km2) in the National Capital Region of the United States. Flood forecast performance are then assessed using different metrics based on observed data from U.S. Geological Survey stream gauges and reanalysis simulations using the StageIV quantitative precipitation estimates. Results show that despite the high variability of the HRRR cycle-to-cycle precipitation forecasts, the real-time flood forecast system can produce skillful forecasts with similar overall performance as the reanalysis simulations, achieving 65 % of flood detection rate. This variability had more impact on forecast skill in smaller subbasins, and flood forecasts were more consistent for longer duration and larger spatial extent rainfall. Hourly flood forecasts performed well even in smaller watersheds, correctly detecting flood occurrence with up to 34 h lead time and resulting in low peak flow magnitude and timing errors.

Assessment of three gridded satellite-based precipitation products and their performance variabilities during typhoons over Zhejiang, southeastern China

• IMERG-Early, GSMaP-NRT and FY4A QPE were evaluated for overall performance and five typhoons during 2018–2019. • IMERG-Early and GSMaP-NRT performed much better than FY4A QPE. • Satellite-based products tended to underestimate heavy precipitation but overestimate light precipitation. • Satellite-based products showed unstable performance and were more likely to present low estimations during typhoons. • Satellite-based products were difficult to detect heavy precipitation centers during typhoons in some parts of southwestern Zhejiang with complex topography. Effective and accurate estimation of heavy precipitation from typhoons is critical for the early warning of hydrological extremes and disaster mitigation. Satellite-based precipitation products are widely used for various purposes, including supporting climate, hydrology, and severe weather event monitoring and research activities. In this study, the performance of Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation (GSMaP), and Chinese Fengyun-4A quantitative precipitation estimates (FY4A QPE) were compared to daily observations from 2018 to 2019. Further comparison of these products at an hourly scale was implemented for five typhoons that made landfall in the coastal areas of southeastern China. The results demonstrate that IMERG-Early and GSMaP-NRT performed much better than FY4A QPE, in both overall assessment and specific typhoons. All the satellite-based products showed the best correlations with the gauge observations for heavy precipitation events, and their correlations became worse with lower precipitation intensity. They tended to underestimate heavy precipitation but overestimate light precipitation. Furthermore, their random errors increased with increasing precipitation, according to an analysis of unbiased root mean squared error (ubRMSE). Based upon analyses of the probability of detection (POD) and critical success index (CSI), satellite-based products performed better during light precipitation events than moderate and heavy precipitation events. During typhoons, IMERG-Early and GSMaP-NRT had better accuracy in detecting typhoon precipitation with higher POD and CSI values. FY4A QPE showed instability in different typhoons and a low correlation with gauge observations. In addition, satellite-based products produced precipitation estimates more reliably in northern and eastern Zhejiang, but were difficult to detect heavy precipitation centers in some parts of southwestern Zhejiang with complex topography. These findings not only provide valuable references for the development of satellite-based retrieval algorithms to enhance the ability to monitor typhoon heavy precipitation but also support diverse hydrological models and applications.

Combined Radar Quality Index for Quantitative Precipitation Estimation of Heavy Rainfall Events

For quantitative precipitation estimation (QPE) based on polarimetric radar (PR) and rain gauges (RGs), the quality of the radar data is crucial for estimation accuracy. This paper proposes a combined radar quality index (CRQI) to represent the quality of the radar data used for QPE and an algorithm that uses CRQI to improve the QPE performance. Nine heavy rainfall events that occurred in Guangdong Province, China, were used to evaluate the QPE performance in five contrast tests. The QPE performance was evaluated in terms of the overall statistics, spatial distribution, near real-time statistics, and microphysics. CRQI was used to identify good-quality data pairs (i.e., PR-based QPE and RG observation) for correcting estimators (i.e., relationships between the rainfall rate and the PR parameters) in real-time. The PR-based QPE performance was improved because estimators were corrected according to variations in the drop size distribution, especially for data corresponding to 1.1 mm &lt; average Dm &lt; 1.4 mm, and 4 &lt; average log10Nw &lt; 4.5. Some underestimations caused by the beam broadening effect, excessive beam height, and partial beam blockages, which could not be mitigated by traditional algorithms, were significantly mitigated by the proposed algorithm using CRQI. The proposed algorithm reduced the root mean square error by 17.5% for all heavy rainfall events, which included three precipitation types: convective precipitation (very heavy rainfall), squall line (huge raindrops), and stratocumulus precipitation (small but dense raindrops). Although the best QPE performance was observed for stratocumulus precipitation, the biggest improvement in performance with the proposed algorithm was observed for the squall line.

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.

Toward optimal rainfall – Hydrologic QPE correction in headwater basins

Large errors in Quantitative Precipitation Estimates (QPE) tied to remote-sensing retrieval algorithms remain a challenge especially in complex terrain with fast hydrologic response. We propose a new framework to derive dynamic hydrologic corrections of rainfall in headwater basins that enforces water budget closure, and distributes transient rainfall corrections by Lagrangian backtracking along runoff trajectories constrained by realistic time-of-travel distributions. Downscaled QPE products (250 m resolution) are applied first as input to a distributed hydrologic model to predict runoff trajectories and the event hydrograph at the basin's outlet. Second, time-varying rainfall corrections are derived from the residuals between predicted and observed discharge at the outlet. Finally, the corrections are spatially distributed following the runoff trajectories backward (i.e. trajectories are used as streaklines originating at the basin's outlet). Because nonlinear interactions between rainfall, runoff and storage are transient, the corrections are applied recursively until the shape and volume of the predicted hydrograph are stable. The framework is applied to ground-based (e.g. Stage IV) and satellite-based remote-sensing QPEs (e.g. IMERG) associated with the 49 largest floods 2008–2018 in the Southern Appalachian Mountains, USA. The results show improvements in hydrograph prediction efficiency skill at 15 min timescale from −0.5 to 0.6 on average and up to 0.9 for warm season events, bounding event runoff volume errors with a mean of 3%, and reducing time to peak errors by half an hour on average. Corrected QPEs exhibit nearly perfect correlation and no bias at high elevation gauge locations. Uncertainty in the water budget closure at the event scale is less than the uncertainty in streamflow measurements. Error attribution shows strong organization of QPE corrections according to seasonal weather and rainfall regime, thus providing a path to generalization to ungauged mountain basins.

Quantitative Precipitation Estimation Model Integrating Meteorological and Geographical Factors at Multiple Spatial Scales

Heavy precipitation tends to cause mountain torrents, urban waterlogging and other disasters. It poses a serious threat to people’s life and property safety. Therefore, real-time quantitative precipitation estimation is especially important to keep track of precipitation changes and reduce negative impacts. However, high-resolution and high-accuracy quantitative precipitation estimation is a challenging task due to the complex spatial and temporal variability of microphysics in precipitation processes. Previous studies have focused only on small-scale radar reflectivity factors above rain gauges and did not pay enough attention to the contribution of covariates to model performance. Meteorological and geographical factors play an important role in rain process, so these factors are taken into account during our research. In this study, a quantitative precipitation estimation model that can employ multi-scale radar reflectivity factors and fuse meteorological and geographical factors is proposed to further improve precipitation accuracy. In addition, we propose the muti-scale self-attention (MS-SA) module that can further utilize information at multiple spatial scales to improve the accurate precipitation estimation. The proposed model reduced the root mean square error of precipitation estimation by 83.8% compared to the conventional Z-R relationship that correlates the rainfall and radar reflectivity factors, i.e., Z=aRb, and by 43.7, 24.6, and 22.7% compared to the back propagation neural network (BPNN), convolutional neural network (CNN), and convolutional neural network with the addition of meteorological factors and geographical factors as covariates in the proposed model, respectively. Therefore, we can conclude that multi-scale radar reflectivity factors fused with meteorological and geographical factors can produce more accurate precipitation estimation.

Sensitivity analysis of attenuation in convective rainfall at X-band frequency using the mountain reference technique

Abstract. The RadAlp experiment aims at improving quantitative precipitation estimation (QPE) in the Alps thanks to X-band polarimetric radars and in situ measurements deployed in the region of Grenoble, France. In this article, we revisit the physics of propagation and attenuation of microwaves in rain. We first derive four attenuation–reflectivity (AZ) algorithms constrained, or not, by path-integrated attenuations (PIAs) estimated from the decrease in the return of selected mountain targets when it rains compared to their dry weather levels (the so-called mountain reference technique – MRT). We also consider one simple polarimetric algorithm based on the profile of the total differential phase shift between the radar and the mountain targets. The central idea of the work is to implement these five algorithms all together in the framework of a generalized sensitivity analysis in order to establish useful parameterizations for attenuation correction. The parameter structure and the inherent mathematical ambiguity of the system of equations makes it necessary to organize the optimization procedure in a nested way. The core of the procedure consists of (i) exploring with classical sampling techniques the space of the parameters allowed to be variable from one target to the other and from one time step to the next, (ii) computing a cost function (CF) quantifying the proximity of the simulated profiles and (iii) selecting parameters sets for which a given CF threshold is exceeded. This core is activated for a series of values of parameters supposed to be fixed, e.g., the radar calibration error for a given event. The sensitivity analysis is performed for a set of three convective events using the 0∘ elevation plan position indicator (PPI) measurements of the Météo-France weather radar located on top of the Moucherotte mountain (altitude of 1901 m a.s.l. – above sea level). It allows the estimation of critical parameters for radar QPE using radar data alone. In addition to the radar calibration error, this includes the time series of radome attenuation and estimations of the coefficients of the power law models relating the specific attenuation and the reflectivity (A–Z relationship) on the one hand and the specific attenuation and the specific differential phase shift (A–Kdp relationship) on the other hand. It is noteworthy that the A–Z and A–Kdp relationships obtained are consistent with those derived from concomitant drop size distribution measurements at ground level, in particular with a slightly non-linear A–Kdp relationship (A=0.28 Kdp1.1). X-Band radome attenuations as high as 15 dB were estimated, leading to the recommendation of avoiding the use of radomes for remote sensing of precipitation at such a frequency.

A High-Resolution Quantitative Precipitation Estimate over Alaska through Kriging-Based Merging of Rain Gauges and Short-Range Regional Precipitation Forecasts

Abstract A method is presented to generate quantitative precipitation estimates over Alaska using kriging to merge sparse, unevenly distributed rain gauge observations with quantitative precipitation forecasts from a three-member ensemble of high-resolution numerical weather prediction models. The estimated error variance of the analysis is computed by starting with the estimated error variance from kriging and then refining the variance in k-fold cross validation by an empirically derived inflation factor. The method combines dynamical model forecast information with observational data to deliver a best linear unbiased estimate of precipitation, along with an analysis uncertainty estimate, that provides a much-needed precipitation analysis in a region where sparse in situ observations, poor coverage by remote sensing platforms, and complex terrain introduce large uncertainties that need to be quantified. For 6-hourly accumulation estimates produced four times daily from 1 August 2019 to 31 July 2020, three analysis configurations are tested to measure the value added by including model forecast data and how those data are best utilized in the analysis. Several directions for further improvement and validation of the analysis product are provided.

A Preliminary Examination of Warm Season Precipitation Displacement Errors in the Upper Midwest in the HRRRE and HREF Ensembles

Abstract Increased operational use of convection-allowing models and ensembles offers substantial improvements for some aspects of convective weather forecasting; however, errors in quantitative precipitation forecasts (QPFs) from these models, especially those related to incorrect placement of heavy rainfall systems, limit their usefulness as an input into hydrological models. To improve understanding of QPF location errors, this study quantifies the displacement errors for the centroids of both 0–18-h accumulated rainfall and rainfall in the first hour after initiation of precipitation systems in both the High-Resolution Rapid Refresh Ensemble (HRRRE) and the High-Resolution Ensemble Forecast (HREF) for 30 events in the 2018 warm season. Ensemble member QPFs are compared to quantitative precipitation estimates (QPEs) obtained from the North Central River Forecast Center (NCRFC). HRRRE is found to have less spread in centroid locations than HREF, and both HRRRE and HREF 0–18-h QPF accumulations have less spread than the 1-h QPF accumulation when the precipitation event initiates. Furthermore, QPF centroids are most often displaced to the west in HRRRE for both 0–18-h QPF accumulation and the 1-h QPF accumulation when the precipitation event initiates. The 0–18-h QPF accumulation displacement errors can be reduced when adjustments are made to the forecasted position based upon displacement errors present in the first hour of precipitation, but only when the adjustments are a function of the intercardinal quadrant in which the initial hour QPF centroid was displaced.

SUBANG RADAR CAPPI DATA PROCESSING AND Z-R OPTIMIZATION FOR QUANTITATIVE PRECIPITATION ESTIMATES (QPE) OVER LANGAT RIVER BASIN

The main advantage of radar data over point gauge rainfall is its ability to provide continuous spatial and temporal resolution of rainfall details over a large area. Weather radar transmits electromagnetic wave, interacts with raindrops and reflects some of the intercepted power (backscattering) that is subsequently converted into rainfall intensity. Despite its advantages, indirect rainfall estimation using radar reflectivity factor suffers from various sources of error such as ground clutter, partial beam occultation, beam blockage and attenuation effects. Literatures on the use of radar quantitative precipitation estimates (QPE) in Malaysia have been increasing since the past 15 years ago. However, none of the previous work have detailed out the sources of radar data used and its processing for rainfall rate conversion. This paper will discuss the fundamentals in radar data acquisition and processing for rainfall input to a case study of Langat river basin, Malaysia. The methodology in raw radar data processing is decribed in details and the use of CAPPI data for rainfall estimation over Langat river basin is discussed. The findings indicate a good performance of the radar CAPPI data as an alternative source to the rainfall measurement for Langat river basin with correlation coefficient between radar rainfall and gauged rainfall ranging from 0.69 to 0.75. Improvement on radar rainfall estimates is also recommended by newly derived optimized Z-R equations based on monsoon season. The results presented in this study are encouraging, especially for the application of water resources management for the river basin.

Investigation of raindrop size distribution and its potential influential factors during warm season over China

The nation-wide feature of raindrop size distribution (hereafter referred to as DSD) remains largely unknown across China. We here analyzed the DSD using high-resolution disdrometer measurements collected throughout China during the warm season (May through August) of 2020. Overall, pronounced unimodal structures were found for the monthly mean DSD, while little differences were revealed in the monthly variation of DSD. Spatially, the regions with a high value of mass-weighted mean diameter Dm generally witnessed a low value of intercept parameter Nw. The DSD parameters were further investigated by six regions of interest: Northeast China Plain (ECP), North China Plain (NCP), Yangtze River Delta (YRD), Pearl River Delta (PRD), Sichuan Basin (SCB), and Qinghai-Tibet Plateau (QTP). Specially, Dm (log10Nw) over ECP, NCP, YRD, PRD and SCB was found to have a larger (smaller) value than the surrounding areas, whereas the Dm (log10Nw) in the QTP had a smaller (larger) value. Furthermore, explicit analyses were performed in attempt to better understand the influential factors accounting for the regional disparity of DSD by two categories: stratiform and convective precipitation. Dm, log10Nw, spectral width and peak concentration all showed larger values in the convective precipitation than those in the stratiform precipitation. Interestingly, the Dm-log10Nw pairs for convective precipitation resembled roughly the “maritime” cluster, indicating a maritime nature of convective precipitation throughout the warm season in China. The PRD had the largest Dm and the smallest log10Nw, while the QTP had the smallest Dm and the largest log10Nw in the stratiform precipitation. In addition, several widely used empirical equations of Z-R relations tended to underestimate R compared with our actual fitting. The impacts of environmental variables were further studied, including convection available potential energy (CAPE), surface temperature, wind shear and vertically integrated moisture flux divergence (MFD). The dependence of DSD on environmental variable was revealed to varying degrees. It is noteworthy that larger CAPE but lower MFD was found to be accompanied with heavy rain produced by convective precipitation than by stratiform precipitation. The invaluable DSD dataset hereby will lay a solid foundation for potentially improved quantitative precipitation estimation from weather radar in the future in China.

Vertical structures of abrupt heavy rainfall events over southwest China with complex topography detected by dual‐frequency precipitation radar of global precipitation measurement satellite

AbstractSouthwest China is with complex topography including basins, mountains, hills and plains, where abrupt heavy rainfall events (AHREs) occur frequently and are difficult to quantitatively estimate due to limited ground‐based observations. Using the data of ground rain gauges and GPM dual‐frequency precipitation radar during April to September in 2014–2020, this study investigates vertical structures of AHREs over southwest China. Both mass‐weighted mean diameter (Dm) and reflectivity (Ze) of AHREs increase rapidly in ice‐phase process but slowly in liquid‐phase process. For convective rainfall of AHREs, abundant water vapour and strong atmospheric convective motion cause higher Dm and Ze but lower generalized intercept parameter (dBNw) than those for stratiform rainfall. Moreover, ice‐phase process is active while liquid‐phase process is weak in the mountains, but the situation is opposite in the plains, which results in large‐size and low‐concentration raindrops in the mountains while small‐size and high‐concentration raindrops in the plains. Furthermore, statistical models of vertical profile of reflectivity (VPR) indicate that VPR patterns are affected by surface rain intensity and present different trends around the 0°C level between stratiform and convective rainfalls. In addition, higher rain top height in the mountains is conducive to ice‐phase process while lower terrain height in the plains is favourable for liquid‐phase process. Therefore, the VPR pattern depends on rain type, terrain and rainfall intensity, and its fine model is beneficial for understanding microphysical process of AHREs and improving quantitative precipitation estimation by ground‐based radars.

Polarimetric Radar Quantitative Precipitation Estimation

Radar quantitative precipitation estimation (QPE) is one of the primary tasks of weather radars. The QPE quality was substantially improved after polarimetric upgrade of the radars. This study provides an overview of existing polarimetric methodologies for rain and snow estimation and their operational implementation. The variability of drop size distributions (DSDs) is a primary factor affecting the quality of rainfall estimation and its impact on the performance of various radar rainfall relations at S, C, and X microwave frequency bands is one of the focuses of this review. The radar rainfall estimation algorithms based on the use of specific attenuation A and specific differential phase KDP are the most efficient. Their brief description is presented and possible ways for their further optimization are discussed. Polarimetric techniques for the vertical profile of reflectivity (VPR) correction at longer distances from the radar are also summarized. Radar quantification of snow is particularly challenging and it is demonstrated that polarimetric methods for snow measurements show good promise. Finally, the article presents a summary of the latest operational radar QPE products available in the US by integration of the information from the WSR-88D radars via the Multi-Radar Multi-Sensor (MRMS) platform.

Evaluating the accuracy of two satellite-based Quantitative Precipitation Estimation products and their application for meteorological drought monitoring over the Lake Victoria Basin, East Africa

ABSTRACT This study evaluates the high-resolution satellite estimated long-term precipitation data for monitoring the drought condition over the Lake Victoria Basin (LVB) from 1984 to 2020. Standardized Precipitation Indices (SPI) were used to capture the short, medium and long-term meteorological drought conditions at multiple time scales (i.e. 3, 6, and 12). For these, the following two primaries Quantitative Precipitation Estimation (QPEs) products were employed – 1) Climate Hazards group Infra-Red Precipitation with Station (CHIRPS), and 2) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network -Climate Data Record (PERSIANN-CDR). This dataset was compared based on the observation data obtained from the Climate Research Unit (CRU) over the nine selected regions surrounding lake basins. The performance of these two QPEs products was evaluated using seven statistical metrics. The findings of this study indicate that the CHIRPS and PERSIANN-CDR datasets could capture the behavior of drought magnitude based on the time scale of SPI-3, SPI-6, SPI-12. The results indicate that 2012 and 2017 are significant severe drought years in the recent decade over LVB. However, the CHIRPS datasets provide good agreement (Correlation Coefficient (CC) = 0.65) with observation, whereas PERSIANN-CDR present satisfactory results (CC = 0.54). In addition, Hurst (H) exponent was used to predict the future drought trend and found that the CHIRPS performed well to predict the degree of drought trend. Therefore, this study considers the CHIRPS product for near-real-time drought monitoring and PERSIANN-CDR for historical drought assessment. Moreover, the outcome from the H values is greater than 0.5, which indicates the future drought trend would be decreased over LVB. These results are useful for developing the strategies for drought hazards and water resource management in LVB.

Verification by Multiple Methods of Precipitation Forecast from HDRFFGS and SisPI Tools during the Impact of the Tropical Storm Isaias over the Dominican Republic

During 2020, the Dominican Republic received the impact of several tropical organisms. Among those that generated the greatest losses in the country, tropical storm Isaias stands out because of the significant precipitation (327.6 mm at Sabana del Mar during 29–31 July 2020) and flooding it caused. The study analyzes the behavior of the products of the Flash Flood Guidance System (FFGS) and the Nowcasting and Very Short Range Prediction System (Spanish acronym SisPI) for the quantitative precipitation forecast (QPF) of the precipitation generated by Isaias on 30 July 2020 over the Dominican Republic. Traditional categorical verification and featured-based spatial verification methods are used in the study, taking as observation the quantitative precipitation estimation of GPM. The results show that both numerical weather prediction systems are powerful tools for QPF and also to contribute to the prevention and mitigation of disasters caused by the extreme hydro-meteorological event analyzed. For the forecast of rain occurrence, the HIRESW-NMMB product of FFGS presented the highest ability with a CSI greater than 0.4. The HIRESW-ARW and SisPI products not only presented high rates of false alarms but also performed better in forecasting heavy rain values. The results of the verification based on objects with the MODE are consistent with those obtained in the verification by categories. The HIRESW-NMMB product underestimated the intense rainfall values by approximately 60 mm, while HIRESW-ARW and SisPI tools presented minor differences, the latter being the one with the greatest skill.

Simulation of Karst Floods with a Hydrological Model Improved by Meteorological Model Coupling

Abstract Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst areas in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting (WRF) Model and quantitative precipitation estimation (QPE) from the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst–Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 h, respectively. Coupling the WRF Model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 h, which is important for flood warning and control. Significance Statement The WRF Model and PERSIANN-CCS can provide precipitation data for mountainous karst areas lacking rainfall gauges, and their rainfall results are forecasted effectively to reduce the uncertainty of input precipitation data. Then, the PERSIANN-CCS QPEs and WRF QPF are coupled with the improved KL model for karst flood simulation and prediction. This coupled model worked well in karst basins.

Calibration of radar differential reflectivity using quasi-vertical profiles

Abstract. Accurate precipitation estimation with weather radars is essential for hydrological and meteorological applications. The differential reflectivity (ZDR) is a crucial weather radar measurement that helps to improve quantitative precipitation estimates using polarimetric weather radars. However, a system bias between the horizontal and vertical channels generated by the radar produces an offset in ZDR. Existing methods to calibrate ZDR measurements rely on the intrinsic values of the ZDR of natural targets (e.g. drizzle or dry snow) collected at high elevation angles (e.g. higher than 40∘ or even at 90∘), in which ZDR values close to 0 dB are expected. However, not all weather radar systems can scan at such high elevation angles or point the antenna vertically to collect precipitation measurements passing overhead. Therefore, there is a need to develop new methods to calibrate ZDR measurements using lower-elevation scans. In this work, we present and analyse a novel method for correcting and monitoring the ZDR offset using quasi-vertical profiles computed from scans collected at 9∘ elevations. The method is applied to radar data collected through 1 year of precipitation events by two operational C-band polarimetric weather radars in the UK. The proposed method shows a relative error of 0.1 dB when evaluated against the traditional approach based on ZDR measurements collected at 90∘ elevations. Additionally, the method is independently assessed using disdrometers located near the radar sites. The results showed a reasonable agreement between disdrometer-derived and radar-calibrated ZDR measurements.