Extreme Precipitation Analysis with Serial Dependency for a Changing Climate

2020 ◽  
pp. 83-96
Author(s):  
Huiling Hu ◽  
Bilal M. Ayyub
Keyword(s):  
Extreme Precipitation ◽  
Changing Climate ◽  
Serial Dependency ◽  
Precipitation Analysis

scholarly journals Representing Extremes in a Daily Gridded Precipitation Analysis over the United States: Impacts of Station Density, Resolution, and Gridding Methods

2014 ◽  
Vol 27 (14) ◽  
pp. 5201-5218 ◽  
Author(s):  
Melissa Gervais ◽  
L. Bruno Tremblay ◽  
John R. Gyakum ◽  
Eyad Atallah
Keyword(s):  
United States ◽  
Extreme Precipitation ◽  
The United States ◽  
Low Resolution ◽  
Spatial Homogeneity ◽  
Precipitation Analysis ◽  
Precipitation Station ◽  
Density Resolution ◽  
Analysis Point ◽  
Station Density

Abstract This study focuses on errors in extreme precipitation in gridded station products incurred during the upscaling of station measurements to a grid, referred to as representativeness errors. Gridded precipitation station analyses are valuable observational data sources with a wide variety of applications, including model validation. The representativeness errors associated with two gridding methods are presented, consistent with either a point or areal average interpretation of model output, and it is shown that they differ significantly (up to 30%). An experiment is conducted to determine the errors associated with station density, through repeated gridding of station data within the United States using subsequently fewer stations. Two distinct error responses to reduced station density are found, which are attributed to differences in the spatial homogeneity of precipitation distributions. The error responses characterize the eastern and western United States, which are respectively more and less homogeneous. As the station density decreases, the influence of stations farther from the analysis point increases, and therefore, if the distributions are inhomogeneous in space, the analysis point is influenced by stations with very different precipitation distributions. Finally, ranges of potential percent representativeness errors of the median and extreme precipitation across the United States are created for high-resolution (0.25°) and low-resolution areal averaged (0.9° lat × 1.25° lon) precipitation fields. For example, the range of the representativeness errors is estimated, for annual extreme precipitation, to be from +16% to −12% in the low-resolution data, when station density is 5 stations per 0.9° lat × 1.25° lon grid box.

scholarly journals Bias correction of gauge-based gridded product to improve extreme precipitation analysis in the Yarlung Tsangpo-Brahmaputra River Basin

2019 ◽  
Author(s):  
Xian Luo ◽  
Xuemei Fan ◽  
Yungang Li ◽  
Xuan Ji
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Extreme Precipitation ◽  
Precipitation Data ◽  
Brahmaputra River ◽  
Spatial Coverage ◽  
Extreme Precipitation Indices ◽  
Precipitation Analysis ◽  
Precipitation Indices ◽  
Yarlung Tsangpo

Abstract. Critical gaps in the amount, quality, consistency, availability, and spatial distribution of rainfall data limit extreme precipitation analysis, and the application of gridded precipitation data are challenging because of their considerable biases. This study corrected Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) in the Yarlung Tsangpo-Brahmaputra River Basin (YBRB) using two linear and two nonlinear methods, and assessed their influence on extreme precipitation indices. The results showed that the original APHRODITE data tended to underestimate precipitation during the summer monsoon season, especially in the topographically complex Himalayan belt. Bias correction using complementary rainfall observations to add spatial coverage in data-sparse regions greatly improved the performance of extreme precipitation analysis. Although all methods could correct mean precipitation, their ability to correct the wet-day frequency and coefficient of variation were substantially different, leading to considerable differences in extreme precipitation indices. Generally, higher-skill bias-corrected APHRODITE data are expected to perform better than those corrected by lower-skill approaches. This study would provide reference for using gridded precipitation data in extreme precipitation analysis and selecting bias-corrected method for rainfall products in data-sparse regions.

Evaluation of the TRMM 3B42 and GPM IMERG products for extreme precipitation analysis over China

2019 ◽  
Vol 223 ◽  
pp. 24-38 ◽  
Author(s):  
Jian Fang ◽  
Wentao Yang ◽  
Yibo Luan ◽  
Juan Du ◽  
Aiwen Lin ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Precipitation Analysis ◽  
Trmm 3B42

scholarly journals Extreme Precipitation and Runoff under Changing Climate in Southern Maine

2016 ◽  
Author(s):  
Eugene Yan ◽  
Alissa Jared ◽  
Vinod Mahat ◽  
Mark Picel ◽  
Duane Verner ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Changing Climate

scholarly journals Bias correction of a gauge-based gridded product to improve extreme precipitation analysis in the Yarlung Tsangpo–Brahmaputra River basin

2020 ◽  
Vol 20 (8) ◽  
pp. 2243-2254
Author(s):  
Xian Luo ◽  
Xuemei Fan ◽  
Yungang Li ◽  
Xuan Ji
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Extreme Precipitation ◽  
Precipitation Data ◽  
Brahmaputra River ◽  
Extreme Precipitation Indices ◽  
Precipitation Analysis ◽  
Precipitation Indices ◽  
Yarlung Tsangpo ◽  
Local Intensity

Abstract. Critical gaps in the amount, quality, consistency, availability, and spatial distribution of rainfall data limit extreme precipitation analysis, and the application of gridded precipitation data is challenging because of their considerable biases. This study corrected Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) estimates in the Yarlung Tsangpo–Brahmaputra River basin (YBRB) using two linear and two nonlinear methods, and their influence on extreme precipitation indices was assessed by cross-validation. Bias correction greatly improved the performance of extreme precipitation analysis. The ability of four methods to correct wet-day frequency and coefficient of variation were substantially different, leading to considerable differences in extreme precipitation indices. Local intensity scaling (LOCI) and quantile–quantile mapping (QM) performed better than linear scaling (LS) and power transformation (PT). This study would provide a reference for using gridded precipitation data in extreme precipitation analysis and selecting a bias-corrected method for rainfall products in data-sparse regions.

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