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Attenuation correction of measured reflectivity (Z H) and differential reflectivity (Z DR) is critical in short-wavelength radar applications, especially during extreme precipitation events such as heavy rain. This paper develops an improved self-consistent approach (improved ZPHI method) for attenuation correction in practical environment. In particular, a non-negative constraint is imposed on the specific attenuation, which is inferred from the monotonic increasing characteristic of differential propagation phase (Φ DP). The copolar correlation coefficient (ρ HV) is used to partition the Φ DP profiles into independent range segments, which are featured by different hydrometeor phases such as liquid rain or mixed-phase precipitation. Additional minimization constraint is imposed on the cost function of the difference between preprocessed Φ DP and reconstructed Φ DP to ensure its appropriate convergence. In addition, an exponential Z DR − Z H relation derived from local raindrop size distribution (DSD) data is applied in the procedure of Z DR correction. Polarimetric measurements from a C-band radar (CPOL) in Hangzhou of China during two extreme precipitation events are used to demonstrate the improved attenuation correction method. DSD observations from disdrometers and radar data from a nearby system operating at non-attenuated frequency (i.e., S-band) at Huangshan Mountain are used to evaluate the attenuation correction performance. This paper also studies the impact of attenuation correction on the radar-derived quantitative precipitation estimation (QPE) product, through cross-comparison with rain gauge observations. Results show that the polarimetric observations from CPOL radar are effectively enhanced and more consistent with collocated S-band measurements and the simulated radar moments based on DSD data. Hourly rainfall products derived from R (Z H) and R (Z H , Z DR) are significantly improved, which are comparable to R (K DP) after attenuation correction. • An improved attenuation correction method is proposed for C-band polarimetric radar applications. • The routinely operating C-band radar in Hangzhou, China is used to demonstrate the proposed attenuation correction scheme. • Cross-comparison with disdrometer data and collocated S-band observations shows great performance of this correction method. • The QPE products derived from R (Z H) and R (Z H , Z DR) are significantly improved after attenuation correction. [ABSTRACT FROM AUTHOR]

In this paper, 34 western track typhoons making landfall over China during the period of 2006–2016 from Jun. to Oct. are taken as research objects. They are divided into strong and weak monsoon influenced categories according to the East Asian summer monsoon index. Using precipitation product that was derived from hourly precipitation observed at automatic weather stations (AWS) in China and retrievals from CMORPH [CPC (Climate Prediction Center) morphing technique] satellite data, Japanese 55-year Reanalysis products (JRA-55) and Blackbody brightness temperature (TBB) data from FengYun 2E (FY-2E) satellite, precipitation intensity and distribution characteristics of strong monsoon influenced typhoons. In addition, the weak monsoon influenced typhoons was compared through composite analysis. Furthermore, to explore the water vapor conditions, dynamic factors, thermal structure, development conditions of mesoscale convective systems, and long-term typhoon maintenance mechanisms of typhoons under both conditions. This research is attempted to obtain the regularity of influences exerted on typhoon precipitation intensity and distribution by summer monsoon, and the possible influencing mechanisms of summer monsoon on typhoon precipitation. Results show that the stronger (weaker) the monsoon flow is, the stronger (weaker) the precipitation intensity of post-landfall western typhoon. Monsoon provides a sufficient amount of water vapor through direct moisture channel. The stronger monsoon flow is, the more obvious the coupling of low-level convergence and upper-level divergence, and thus provides a stronger lifting for typhoon precipitation. A strong monsoon condition is more conducive to the genesis and development of mesoscale convection system. A strong monsoon condition is also favorable for the maintenance of post-landfall typhoon's warm core structure, thereby prolonging the precipitation through delaying the decay of typhoons. All these factors lead up to the influences on precipitation of typhoons by summer monsoon directly or indirectly. • Strong monsoon flow does lead to a significant increase and asymmetry in precipitation. • Stronger monsoon flow provides stronger lifting for typhoon precipitation through more obvious coupling of low-level convergence and upper-level divergence. • A strong monsoon condition is more conducive to the genesis and development of mesoscale convection system. • A strong monsoon condition can prolong the precipitation through delaying the decay of landfall typhoons by the maintenance of warm core structure. [ABSTRACT FROM AUTHOR]

This paper investigates the annual and seasonal precipitation variability at 20 stations over upper Indus River basin (UIRB) by using an innovative trend analysis (ITA) method, Mann-Kendall (MK) and Sen's slope estimator tests. Moreover, the annual precipitation time series is investigated for five precipitation intensities i.e. light, low, moderate, high and heavy; and on a seasonal scale for extreme values i.e. light and heavy precipitation intensities. Annual precipitation showed an increasing trend at four stations in the northeast region and a decreasing trend at two stations in the southeast region of UIRB. On a seasonal scale, the results showed that Garidopatta, Peshawar, Astore and Gupis stations are more sensitive to seasonal precipitation variabilities. Changes in heavy precipitation (> 90th percentile) are found more severe in winter and summer seasons in the northern regions suggesting a probable flooding aggravation. However, a reduction in flooding event observed during the spring season in the southern regions of the UIRB indicating a shift in the precipitation regime from south to north. The results of light precipitation (< 10th percentile) intensity showed that winter and spring seasons are more vulnerable to the occurrence of prolonged drought events. Moreover, ITA method has the advantage of detecting the sub-trends in the precipitation series because of its ability to present the results in graphical format. The results of this study could help to understand seasonal and annual precipitation variability over UIRB and will have imperative insinuations for further studies. [ABSTRACT FROM AUTHOR]

Quantitative precipitation estimation (QPE) is one of the important applications of weather radars. However, in complex terrain such as Tibetan Plateau, it is a challenging task to obtain an optimal Z – R relation due to the complex spatial and temporal variability in precipitation microphysics. This paper develops two radar QPE schemes respectively based on Reflectivity Threshold (RT) and Storm Cell Identification and Tracking (SCIT) algorithms using observations from 11 Doppler weather radars and 3264 rain gauges over the Eastern Tibetan Plateau (ETP). These two QPE methodologies are evaluated extensively using four precipitation events that are characterized by different meteorological features. Precipitation characteristics of independent storm cells associated with these four events, as well as the storm-scale differences, are investigated using short-term vertical profile of reflectivity (VPR) clusters. Evaluation results show that the SCIT-based rainfall approach performs better than the simple RT-based method for all precipitation events in terms of score comparison using validation gauge measurements as references. It is also found that the SCIT-based approach can effectively mitigate the local error of radar QPE and represent the precipitation spatiotemporal variability better than the RT-based scheme. [ABSTRACT FROM AUTHOR]

Regional precipitation patterns may change in a warmer climate, thereby increasing flood and drought risks. In this paper, annual, annual maximum, intense, heavy, moderate, light, and trace precipitation are employed as indicators to assess changes in precipitation patterns under two scenarios in which the global mean temperature increases by 1.5 °C and 2.0 °C relative to pre-industrial levels using the regional climate model COSMO-CLM (CCLM). The results show that annual precipitation in China will be approximately 2.5% higher under 1.5 °C warming relative to the present-day baseline (1980–2009), although it will decrease by approximately 4.0% under an additional 0.5 °C increase in global mean temperature. This trend is spatially consistent for regions with annual precipitation of 400–800 mm, which has experienced a drying trend during the past half century; thus, limiting global warming to 1.5 °C may mitigate these drying conditions. The annual maximum precipitation continues to increase from present day levels to the 2.0 °C warming scenario. Relative to the baseline period, the frequency of trace and light precipitation days exhibits a negative trend, while that of moderate, heavy, and intense precipitation days has a positive trend under the 1.5 °C warming scenario. For the 2.0 °C warming world, the frequency of days is projected to decrease for all precipitation categories, although the intensity of intense precipitation increases. Spatially, a decrease in the number of precipitation days is expected to continue in central and northern China, where a drying trend has persisted over the past half century. Southeastern China, which already suffers greatly from flooding, is expected to face more heavy and intense precipitation with an additional 0.5 °C increase in global mean temperature. Meanwhile, the intensity of intense precipitation is expected to increase in northern China, and the contribution of light and moderate precipitation to the annual precipitation is expected to decrease in southeastern China. Therefore, flood risk in northern China and drought risk in southern China should draw more attention for a global air temperature increase from 1.5 °C to 2.0 °C. [ABSTRACT FROM AUTHOR]

The statistical methods based on extreme value theory have been traditionally used in meteorology and hydrology for a long time. Due to climate change and variability, the hypothesis of stationarity in meteorological or hydrological time series was usually not satisfied. In this paper, a nonstationary extreme value analysis was conducted for annual maximum daily precipitation (AMP) at 631 meteorological stations over China for the period 1951–2013. Stationarity of all 631 AMP time series was firstly tested using KPSS test method, and only 48 AMP time series showed non-stationarity at 5% significance level. The trends of these 48 nonstationary AMP time series were further tested using M-K test method. There were 25 nonstationary AMP time series mainly distributed in southern and western China showing significant positive trend at 5% level. Another 5 nonstationary AMP time series with significant negative trends were near northern urban agglomeration, Sichuan Basin, and central China. For these nonstationary AMP time series with significant positive or negative trends, the location parameter in generalized extreme value (GEV) distribution was assumed to be time-varying, and the trends were successfully characterized by the nonstationary GEV models. For the remaining 18 nonstationary AMP time series mainly in the eastern portion of China, no significant trend was detected. The correlation analysis showed that only 5 nonstationary AMP time series were significantly correlated with one or two of the four climate indices EASMI, WPI, SOI, and PDO. Then, the location and scale parameters in the GEV distribution were modeled as functions of the significantly correlated climate indices. The modeling results in this study showed that the nonstationary GEV distributions performed better than their stationary equivalents. Finally, 20-year and 50-year return levels of precipitation extremes at all 631 stations were estimated using the best fitting distribution for the year 1961 and 2013, respectively. [ABSTRACT FROM AUTHOR]

This paper investigates the nonstationarity of precipitation extremes by incorporating time-varying and physical-based explanatory covariates, using daily precipitation data across the Beijing-Tianjin-Hebei (BTH) region, China. We perform the stationary and nonstationary generalized extreme value (GEV) models based on the Bayesian framework to estimate the expected return levels of precipitation extremes with the 90% credible intervals. Results reveal that the nonstationarity of precipitation extremes is not prominently visible for the majority of sites in BTH. However, the nonstationary GEV models exhibit better performance to capture the variations of precipitation extremes by comparison to the stationary models based on four evaluation criteria. Further, this work attempts to determine the best covariate to illustrate the possible effects of environmental changes on the frequency analysis. Results indicate that the El Nino-Southern Oscillation (ENSO) is the top of the best covariates, followed by the East Asian summer monsoon, North Atlantic Oscillation (NAO) and local temperature anomaly. Moreover, the best covariates are dominated by the physical-based covariates, and the best models with nonlinear functions of covariates are found in the majority of sites. Finally, the best-fitted models are used to estimate the design values of return levels in precipitation extremes. Results illustrate that the differences between the stationary modeling and nonstationary modeling in the median condition of covariates are not significant for most of the sites. But the discrepancies will be enhanced if the covariates locate in a high (95-percentile) or low (5-percentile) value. Our findings suggest that the nonstationary modeling of precipitation extremes might prove more useful and reliable, especially in the uncommon conditions of physical-based covariates. Unlabelled Image • Nonstationary frequency analysis can better capture the temporal evolution of precipitation extremes • The best covariates for nonstationary modeling of precipitation extremes are dominated by the physical-based covariates • Higher return period indicates higher uncertainty in expected return levels both for stationary and nonstationary models [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate the projected regional responses of univariate and bivariate behaviors of extreme precipitation to climate change over the upper-middle Huaihe River Basin. Based on twelve GCM outputs under historical, RCP4.5 and the observations at 32 rainfall stations, the equidistant cumulative distribution function matching method (EDCDFm) was utilized to bias correct daily precipitation during the historical (1961–2005) and future (2021–2080) periods. Four precipitation indices combinations were introduced based on eight precipitation indices to characterize the regional-scale changes of precipitation events, which designate the duration, intensity and amount of heavy and weak precipitation in a year. Their dependence structures were captured by Copulas. Kendall return period (KRP) were applied to discuss hazard scenarios and we quantified the spatial variability of KRPs under different marginal values. The results indicated that projected precipitation characteristics including the average intensity, the amount of annual precipitation, the intensity and amount of extreme precipitation together with annual extremes displayed increasing trends, while the changes of consecutive wet and dry days did not present pronounced trends. Decreased KRPs in the vast majority of the territory manifested that the frequency of simultaneous floods and droughts in a year as well as that of extreme heavy precipitation events would augment. Obvious spatial heterogeneity of the changes of KRP was partly attributed to the topography difference, especially the coastal areas along the main stream of the Huaihe River. Consequently, there will be a higher risk of water resources-related issues in this region for upcoming decades. • Significant decreases will occur in the intensity and amount of heavy precipitation for upcoming decades. • Change rates of precipitation indices combinations illustrate obvious spatial heterogeneity. • The frequency of simultaneous floods and droughts in a year and that of extreme heavy precipitation events will augment. [ABSTRACT FROM AUTHOR]

The wide application of satellite-based and reanalysis-based precipitation data has greatly promoted hydrometeorological research in areas where precipitation observations are scarce. However, the suitability of such precipitation products needs to be carefully evaluated before applications in certain basins because their inherited errors vary with different climate zones, seasonal cycles and land surface conditions; in addition, precipitation products have not been evaluated in the Xihe River basin, China. In this paper, two representative satellite-based precipitation products (Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (CDR)) and two reanalysis-based precipitation products (China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and National Centers for Environmental Prediction - Climate Forecast System Reanalysis (CFSR)) were selected for evaluation and corrected against gauge-observed data (OBS). Furthermore, the performances of precipitation products in hydrological simulations were also assessed using the SWAT model calibrated with OBS forcing and not with individual precipitation products. The results show that satellite-based precipitation has a higher quality than reanalysis-based precipitation. The CFSR and CDR overestimate precipitation (the overestimation of CDR precipitation is mainly concentrated in the precipitation intensity range of 1 mm/d to 5 mm/d), while the TRMM and CMADS underestimate precipitation in the Xihe River basin. The TRMM precipitation performs best during the wet season, while the CDR precipitation performed best during the dry season. After bias correction, the quality of TRMM precipitation improves significantly. The Nash-Sutcliffe coefficient (NS) (the percent bias (|PBIAS|)) increases (decreases) by 0.61 (77.27%) and 0.7 (39.15%) under the two different correction scenarios during 2009–2015. Overall, the above original precipitation products cannot be used as supplements to the OBS in the Xihe River basin unless they are corrected by the OBS. • Four precipitation products (TRMM, CDR, CMADS and CFSR) were selected for evaluation and corrected against OBS. • The hydrological simulation results show that satellite-based precipitation performs better than reanalysis precipitation. • TRMM performs best during the wet season, while CDR performes best during the dry season. [ABSTRACT FROM AUTHOR]