Vertical profile of reflectivity (VPR) correction of bright band (BB) effects has been a challenge for single‐polarization radar quantitative precipitation estimations (QPEs) for mesoscale convective systems and for cool season stratiform precipitation when the freezing level is low. BB is often found in the radar observations of stratiform precipitation, and the inflated reflectivity intensities in the BB often cause positive biases in radar QPEs. A VPR correction is desirable to mitigate the BB contamination and reduce the bias. However, a well‐defined BB bottom, while critical for an effective correction of the bias, is often not found in the VPRs. Fortunately, polarimetric radar variables, especially the copolar correlation coefficient (ρ_HV), can provide a much better depiction of vertical BB structure than does reflectivity. In the current study, an apparent vertical profile of ρ_HV (AVPρ_HV) correction scheme is developed. For each tilt of the radar volume scan data, the precipitation echoes are segregated into convective and stratiform regions. An apparent VPR (AVPR) and AVPρ_HV are computed for the stratiform region in the given tilt. Then the bright band top, peak, and bottom are identified from the AVPR and AVPρ_HV, and a linear VPR correction model is fit to the AVPR in the BB layer. VPR corrections are applied to the reflectivity field in the given tilt based on the linear correction model, and radar QPEs are derived from the corrected reflectivity field. The new AVPR and AVPρ_HV combined scheme was tested on two mesoscale convective system events and one cold season event in the United States and was shown to be more effective in reducing the radar QPE bias associated with the BB than did the AVPR correction alone.

The spatio-temporal distribution of geological hazards, including collapses, landslides, and debris flows, in Shaanxi province, China was studied based on data from 1951 to 2018. The potential impact factors, including the geomorphologic types, rivers, roads, rainfall, and earthquakes, were analyzed using Random Forests. The results indicated that most hazards occurred in summer (i.e., July–September) and were triggered by rainstorms. The freeze–thaw effect had a considerable contribution to hazards in the north. Spatially, most hazards in the north occurred in valley terraces of the Loess Plateau, while medium-relief terrane (relief ranged from 500 to 1000 m) in the southern Qinling Mountains were hazard-prone areas. The collapses and landslides were mainly affected by human factors in Northern Shaanxi, whereas in Southern Shaanxi geomorphology was the primary factor. Permeability was a dominant factor for debris flows. In addition, the 2008 Wenchuan earthquake had a remarkable influence on the spatial distribution of hazards. In contrast, for the situation in the Sichuan province, which was close to the earthquake epicenter, the Wenchuan earthquake triggered many collapse and landslide events in the southwest regions of Shaanxi province only on 12 May 2008. The thresholds for the three hazard types in the north and south regions were almost the same despite their distinctly different geologic characteristics. Through a sensitivity analysis, we found an appropriate dry period of 12 h for the area.

Understanding the characteristics of the raindrop size distribution (DSD) is crucial to improve our knowledge of the microphysical processes of precipitation and to improve the accuracy of radar quantitative precipitation estimation (QPE). In this study, the spatial variability of DSD in different regions of Beijing and its influence on radar QPE are analyzed using 11 disdrometers. The DSD data are categorized into three regions: Urban, suburban, and mountainous according to their locations. The DSD exhibits evidently different characteristics in the urban, suburban, and mountain regions of Beijing. The average raindrop diameter is smaller in the urban region compared to the suburban region. The average rain rate and raindrop number concentration are lower in the mountainous region compared to both urban and suburban regions. The difference in DSD between urban and suburban regions is due to the difference in DSD for the same precipitation types, while the difference in DSD between mountain and plains (i.e., urban and suburban regions) is the combined effect of the convection/stratiform ratio and the difference of DSD for the same precipitation types. Three DSD-based polarimetric radar QPE estimators were retrieved and estimated. Among these three QPE estimators, R(Z_H), R(K_dp), and R(K_dp, Z_DR), R(K_dp, Z_DR) performs best, followed by R(K_dp), and R(Z_H) performs worst. R(K_dp) is more sensitive to the representative parameters, while R(Z_H) and R(K_dp, Z_DR) are more sensitive to observational error and systematic bias (i.e., calibration).

This study conducted evaluation and analysis on various precipitation products over the eastern Tibetan Plateau (ETP), including four sets of satellite precipitation data (i.e., IMERG_Uncal, IMERG_Cal, GSMaP_MVK, GSMaP_Gauge) and one set of model reanalysis data (i.e., ERA5‐land, hereafter ERA5‐L). We evaluated the spatial‐temporal distribution of their quality at an hourly temporal scale and 0.1° spatial scale, with a special focus on capturing different types of precipitation. The results show that: (a) GSMaP_Gauge exhibits the highest correlation with ground‐based gauges, while IMERG_Uncal and IMERG_Cal perform best in the estimation of the amount of precipitation. Satellite products generally perform better during summer while ERA5‐L sometimes outperforms satellite products in spring and autumn. (b) The evaluation results for different precipitation types reveal that all the QPE products face significant challenges in accurately describing convective precipitation. They tend to underestimate convective precipitation and fail to properly capture the intensity and location of heavy precipitation. (c) In heavy convective precipitation cases, the evaluated QPE products show various issues in accurately capturing the intensity and spatiotemporal variation of precipitation. Almost all QPE products underestimate maximum precipitation (both hourly precipitation and accumulated precipitation) and small‐scale (about 50 km or less) spatial variability of precipitation. IMERG_Uncal, IMERG_Cal, and GSMaP_MVK perform better than other products in heavy convective precipitation cases. This study provides new insights into the quality of QPE products in different types of precipitation. The analysis of the quality of these QPE products serves as a valuable indicator of their potential applications, particularly in flash flood simulations, while also underscoring the critical need for improving precipitation product quality.

Microtopography on a building roof will direct rainfall from roofs to the ground through downspouts and transform the rainfall spatial distribution from plane to points. However, the issues on whether and how the building-induced rainfall redistribution (BIRR) influences hydrologic responses are still not well understood despite the numerous downspouts in the urban area. Hence, this study brings the roof layer into a grid-based urban hydrologic model (gUHM) to quantitatively evaluate the impacts of BIRR, aiming to enhance the understanding of building effects in urban hydrology and subsequently to identify the necessity of incorporating BIRR into flood forecasting. Nine land development strategies and 27 rainfall conditions are considered herein to characterize the changing circumstance. Results indicate that the impacts of BIRR depend on multiple circumstance factors and are nonnegligible in urban hydrology. The BIRR causes not only bidirectional impacts on the hydrologic characteristic values (e.g., peak flow and runoff volume) but also an obvious alteration of the hydrograph. Overall, the BIRR tends to increase the peak flow, and more importantly, the impact will be aggravated by the increase of rainfall intensity with the maximum relative error of peak flow approaching 10%. This study contributes to a better understanding of building effects on urban hydrology and a step forward to reduce the uncertainty in urban flood warnings.