scholarly journals Stochastic Modeling of Daily Summertime Rainfall over the Southwestern United States. Part I: Interannual Variability

2006 ◽  
Vol 7 (4) ◽  
pp. 739-754 ◽  
Author(s):  
Jingyun Wang ◽  
Bruce T. Anderson ◽  
Guido D. Salvucci
Keyword(s):  
United States ◽  
Interannual Variability ◽  
Daily Precipitation ◽  
Temporal Structure ◽  
Daily Rainfall ◽  
Second Order ◽  
Precipitation Process ◽  
Total Precipitation ◽  
Southwestern United States ◽  
Dependent Model

Abstract The interannual variability of summertime daily precipitation at 78 stations in the southwestern United States is studied using chain-dependent models and nonparametric empirical distributions of daily rainfall amounts. Modeling results suggest that a second-order chain-dependent model can optimally portray the temporal structure of the summertime daily precipitation process over the southwestern United States. The unconditioned second-order chain-dependent model, in turn, can explain approximately 75% of the interannual variance in the seasonal total wet days over the region and 83% of the interannual variance in the seasonal total precipitation. In addition, only a small fraction (generally smaller than 20%) of the observed years at any given station show statistically significant changes in the occurrence and intensity characteristics, related to either the number of seasonal total wet days or the distributions of daily rainfall amounts. Investigations of the year-to-year variations in the occurrence and intensity characteristics indicate that both variations are random (on interannual time scales), and they display similar significance in explaining the remaining 17% of interannual variance of seasonal total precipitation over the region. However, numerical tests suggest that the interannual variations of the two are not independent for the summertime monsoon precipitation, and that complex covariability that cannot be described with simple stochastic statistical models may exist between them.

scholarly journals Influence of Daily Rainfall Characteristics on Regional Summertime Precipitation over the Southwestern United States

2009 ◽  
Vol 10 (5) ◽  
pp. 1218-1230 ◽  
Author(s):  
Bruce T. Anderson ◽  
Jingyun Wang ◽  
Suchi Gopal ◽  
Guido Salvucci
Keyword(s):  
United States ◽  
Large Scale ◽  
Daily Precipitation ◽  
Daily Rainfall ◽  
Southwestern United States ◽  
Regional Variability ◽  
Rainfall Frequency ◽  
Circulation Patterns ◽  
Precipitation Characteristics ◽  
Precipitation Anomalies

Abstract The regional variability in the summertime precipitation over the southwestern United States is studied using stochastic chain-dependent models generated from 70 yr of station-based daily precipitation observations. To begin, the spatiotemporal structure of the summertime seasonal mean precipitation over the southwestern United States is analyzed using two independent spatial cluster techniques. Four optimal clusters are identified, and their structures are robust across the techniques used. Next, regional chain-dependent models—comprising a previously dependent occurrence chain, an empirical rainfall coverage distribution, and an empirical rainfall amount distribution—are constructed over each subregime and are integrated to simulate the regional daily precipitation evolution across the summer season. Results indicate that generally less than 50% of the observed interannual variance of seasonal precipitation in a given region lies outside the regional chain-dependent models’ stochastic envelope of variability; this observed variance, which is not captured by the stochastic model, is sometimes referred to as the “potentially predictable” variance. In addition, only a small fraction of observed years (between 10% and 20% over a given subregime) contain seasonal mean precipitation anomalies that contribute to this potentially predictable variance. Further results indicate that year-to-year variations in daily rainfall coverage are the largest contributors to potentially predictable seasonal mean rainfall anomalies in most regions, whereas variations in daily rainfall frequency contribute the least. A brief analysis for one region highlights how the identification of years with potentially predictable precipitation characteristics can be used to better understand large-scale circulation patterns that modulate the underlying daily rainfall processes responsible for year-to-year variations in regional rainfall.

scholarly journals Observed Trends in Summertime Precipitation over the Southwestern United States

2010 ◽  
Vol 23 (7) ◽  
pp. 1937-1944 ◽  
Author(s):  
Bruce T. Anderson ◽  
Jingyun Wang ◽  
Guido Salvucci ◽  
Suchi Gopal ◽  
Shafiqul Islam
Keyword(s):  
United States ◽  
Daily Rainfall ◽  
Southwestern United States ◽  
Storm Activity ◽  
Rainfall Events ◽  
Precipitation Characteristics ◽  
Monsoon Area ◽  
Intense Storm ◽  
Dry Spell ◽  
Current Century

Abstract In this paper, the authors evaluate the significance of multidecadal trends in seasonal-mean summertime precipitation and precipitation characteristics over the southwestern United States using stochastic, chain-dependent daily rainfall models. Unlike annual-mean precipitation, trends during the summertime monsoon, covering the period 1931–2000, indicate an overall increase in seasonal precipitation, the number of rainfall events, and the coverage of rainfall events in peripheral regions north of the “core” monsoon area of Arizona and western New Mexico. In addition, there is an increasing trend in intense storm activity and a decreasing trend in extreme dry-spell lengths. Over other regions of the domain, there are no discernible trends found in any of the observed characteristics. These trends are robust to the choice of start dates and, in the case of seasonal-mean precipitation, appear to persist into the current century.

scholarly journals Stochastic Modeling of Daily Summertime Rainfall over the Southwestern United States. Part II: Intraseasonal Variability

2007 ◽  
Vol 8 (4) ◽  
pp. 938-951 ◽  
Author(s):  
Jingyun Wang ◽  
Bruce T. Anderson ◽  
Guido D. Salvucci
Keyword(s):  
United States ◽  
Stochastic Models ◽  
Intraseasonal Variability ◽  
Daily Rainfall ◽  
Summer Season ◽  
Southwestern United States ◽  
Rainfall Events ◽  
Seasonal Evolution ◽  
Dry Spells ◽  
Dry Spell

Abstract The intraseasonal variability of summertime precipitation over the southwestern United States is examined using stochastic daily occurrence models combined with empirical daily rainfall distributions to document 1) the seasonal evolution of the frequency and intensity of rainfall events across the summertime monsoon season and 2) the climatological evolution of wet spells, dry spells, and storm events. Study results indicate that the evolution of the North American monsoon system (NAMS) is most apparent in the occurrence of daily rainfall events, which exhibit clear time dependence across the summer season over the southwestern United States and can be principally portrayed by stochastic models. In contrast, the seasonal evolution of NAMS is largely absent in the averaged daily rainfall amount time series. There is also a significant seasonal evolution in the length of dry spells. In the central area of the domain (approximately 39 out of 78 stations) dry-spell lengths tend to increase over the course of the summer season, while on the western fringe (8 out of 78 stations) dry-spell lengths tend to decrease. In contrast, wet spells tend to exhibit constant lengths over the course of the season (44 out of 78 stations). The seasonal trend for storms indicates that the number and duration of storms tend to decrease in September; however, the storm depths tend to be more intense, particularly over the western portion of the domain. Overall, 90% of the area-averaged variance for dry-spell lengths can be explained by the random daily evolution of the stochastic model alone. For wet-spell lengths, the area-averaged variance explained by the stochastic models is 98% and for storm amounts it is 92%. These results suggest that the characteristics of most intraseasonal events over this region (i.e., spell lengths and storm amounts) can be captured by the random evolution of daily rainfall models, even with constant year-to-year statistical parameters, indicating that systematic variations in the background climatic conditions from one year to the next may contribute little to the characteristics of these events.

scholarly journals Statistical tool for modeling of a daily precipitation process in the context of climate change

2019 ◽  
Author(s):  
Myeong-Ho Yeo ◽  
Hoang-Lam Nguyen ◽  
Van-Thanh-Van Nguyen
Keyword(s):  
Climate Change ◽  
Logistic Regression ◽  
Regression Model ◽  
Daily Precipitation ◽  
Daily Rainfall ◽  
Cold Climate ◽  
Precipitation Process ◽  
Correction Coefficient ◽  
Subtropical Climate ◽  
Daily Precipitation Data

Abstract The present study proposes a climate change assessment tool based on a statistical downscaling (SD) approach for describing the linkage between large-scale climate predictors and observed daily rainfall characteristics at a local site. The proposed SD of the daily rainfall process (SDRain) model is based on a combination of a logistic regression model for representing the daily rainfall occurrences and a nonlinear regression model for describing the daily precipitation amounts. A scaling factor (SR) and correction coefficient (CR) are suggested to improve the accuracy of the SDRain model in representing the variance of the observed daily precipitation amounts in each month without affecting the monthly mean precipitation. SDRain facilitates the construction of daily precipitation models for the current and future climate conditions. The tool is tested using the National Center for Environmental Prediction re-analysis data and the observed daily precipitation data available for the 1961–2001 period at two study sites located in two completely different climatic regions: the Seoul station in subtropical-climate Korea and the Dorval Airport station in cold-climate Canada. Results of this illustrative application have indicated that the proposed functions (e.g. logistic regression, SR, and CR) contribute marked improvement in describing daily precipitation amounts and occurrences. Furthermore, the comparison analyses show that the proposed SD method could provide more accurate results than those given by the currently popular SDSM method.

Southern oscillation effects on daily precipitation in the southwestern United States

1993 ◽  
Vol 29 (4) ◽  
pp. 1287-1295 ◽  
Author(s):  
D. A. Woolhiser ◽  
T. O. Keefer ◽  
K. T. Redmond
Keyword(s):  
United States ◽  
Daily Precipitation ◽  
Southern Oscillation ◽  
Southwestern United States
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