Continuous rainfall measurements from groundbased radars are crucial for monitoring and forecasting heavy rainfall-related events such as floods and landslides. However, complete coverage by ground-based radars is often hampered by terrain blockage and beam-related errors. In this study, we presented a method to fill the radar gap using surrounding radar-estimated precipitation and observations...

Ensemble simulations and forecasts provide probabilistic information about the inherently uncertain climate system. Counting the number of ensemble members in a category is a simple nonparametric method of using an ensemble to assign categorical probabilities. Parametric methods of assigning quantile-based categorical probabilities include distribution fitting and generalized linear regression....

Constructing an accurate, continental, in-situ-based, kilometer-scale, long-term record of the precipitation field and its spatiotemporal changes remains a significant challenge. Here, we determine extreme-value behavior NEXRAD Stage IV radar-based quantitative estimate. We find that climatology 5-year daily return values in contiguous United States East Rocky Mountains shows only slight variab...

The probability distribution of precipitation amount strongly depends on geography, climate zone, and time scale considered. Closed-form parametric distributions are not sufficiently flexible to provide accurate universal models for over different scales. In this paper we derive non-parametric estimates the cumulative function (CDF) wet periods. CDF obtained by integrating kernel density estima...

Infrared observation is an all-weather, real-time, large-scale precipitation method with high spatio-temporal resolution. A high-precision deep learning algorithm of infrared estimation can provide powerful data support for nowcasting and other hydrological studies timeliness requirements. The “classification-estimation” two-stage framework widely used balancing the distribution in algorithms, ...