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 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 Simulation of Daily Extreme Precipitation over the United States in the CMIP5 30-Yr Decadal Prediction Experiment

2019 ◽  
Vol 58 (4) ◽  
pp. 875-886 ◽  
Author(s):  
Steve T. Stegall ◽  
Kenneth E. Kunkel
Keyword(s):  
United States ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
The United States ◽  
Natural Variability ◽  
Return Level ◽  
Historical Period ◽  
Regional Variability ◽  
Anthropogenic Forcing ◽  
Near Term

AbstractThe CMIP5 decadal hindcast (“Hindcast”) and prediction (“Predict”) experiment simulations from 11 models were analyzed for the United States with respect to two metrics of extreme precipitation: the 10-yr return level of daily precipitation, derived from the annual maximum series of daily precipitation, and the total precipitation exceeding the 99.5th percentile of daily precipitation. Both Hindcast simulations and observations generally show increases for the 1981–2010 historical period. The multimodel-mean Hindcast trends are statistically significant for all regions while the observed trends are statistically significant for the Northeast, Southeast, and Midwest regions. An analysis of CMIP5 simulations driven by historical natural (“HistoricalNat”) forcings shows that the Hindcast trends are generally within the 5th–95th-percentile range of HistoricalNat trends, but those outside that range are heavily skewed toward exceedances of the 95th-percentile threshold. Future projections for 2006–35 indicate increases in all regions with respect to 1981–2010. While there is good qualitative agreement between the observations and Hindcast simulations regarding the direction of recent trends, the multimodel-mean trends are similar for all regions, while there is considerable regional variability in observed trends. Furthermore, the HistoricalNat simulations suggest that observed historical trends are a combination of natural variability and anthropogenic forcing. Thus, the influence of anthropogenic forcing on the magnitude of near-term future changes could be temporarily masked by natural variability. However, continued observed increases in extreme precipitation in the first decade (2006–15) of the “future” period partially confirm the Predict results, suggesting that incorporation of increases in planning would appear prudent.

scholarly journals The Potential Predictability of Precipitation Occurrence, Intensity, and Seasonal Totals over the Continental United States*

2014 ◽  
Vol 27 (18) ◽  
pp. 6904-6918 ◽  
Author(s):  
Daniel J. Short Gianotti ◽  
Bruce T. Anderson ◽  
Guido D. Salvucci
Keyword(s):  
United States ◽  
High Frequency ◽  
Variance Component ◽  
Daily Precipitation ◽  
Seasonal Precipitation ◽  
Signal To Noise ◽  
Potential Predictability ◽  
Precipitation Occurrence ◽  
Weather Model ◽  
Precipitation Characteristics

Abstract Using weather station data, the parameters of a stationary stochastic weather model (SSWM) for daily precipitation over the contiguous United States are estimated. By construct, the model exactly captures the variance component of seasonal precipitation characteristics (intensity, occurrence, and total amount) arising from high-frequency variance. By comparing the variance of the lower-frequency accumulations (on the order of months) between the SSWM and the original measurements, potential predictability (PP) is estimated. Decomposing the variability into contributions from occurrence and intensity allows one to establish two contributing sources of total PP. Aggregated occurrence is found to have higher PP than either intensity or the seasonal total precipitation, and occurrence and intensity are found to interfere destructively when convolved into seasonal totals. It is recommended that efforts aimed at forecasting seasonal precipitation or attributing climate variability to particular processes should analyze occurrence and intensity separately to maximize signal-to-noise ratios. Significant geographical and seasonal variations exist in all PP components.

scholarly journals Multiscale Variability of the Flow during the North American Monsoon Experiment

2007 ◽  
Vol 20 (9) ◽  
pp. 1628-1648 ◽  
Author(s):  
Richard H. Johnson ◽  
Paul E. Ciesielski ◽  
Brian D. McNoldy ◽  
Peter J. Rogers ◽  
Richard K. Taft
Keyword(s):  
United States ◽  
North American ◽  
Gulf Of California ◽  
Large Scale ◽  
Regional Scale ◽  
North American Monsoon ◽  
Return Flow ◽  
Southwestern United States ◽  
The North ◽  
Upper Level

Abstract The 2004 North American Monsoon Experiment (NAME) provided an unprecedented observing network for studying the structure and evolution of the North American monsoon. This paper focuses on multiscale characteristics of the flow during NAME from the large scale to the mesoscale using atmospheric sounding data from the enhanced observing network. The onset of the 2004 summer monsoon over the NAME region accompanied the typical northward shift of the upper-level anticyclone or monsoon high over northern Mexico into the southwestern United States, but in 2004 this shift occurred slightly later than normal and the monsoon high did not extend as far north as usual. Consequently, precipitation over the southwestern United States was slightly below normal, although increased troughiness over the Great Plains contributed to increased rainfall over eastern New Mexico and western Texas. The first major pulse of moisture into the Southwest occurred around 13 July in association with a strong Gulf of California surge. This surge was linked to the westward passages of Tropical Storm Blas to the south and an upper-level inverted trough over northern Texas. The development of Blas appeared to be favored as an easterly wave moved into the eastern Pacific during the active phase of a Madden–Julian oscillation. On the regional scale, sounding data reveal a prominent sea breeze along the east shore of the Gulf of California, with a deep return flow as a consequence of the elevated Sierra Madre Occidental (SMO) immediately to the east. Subsidence produced a dry layer over the gulf, whereas a deep moist layer existed over the west slopes of the SMO. A prominent nocturnal low-level jet was present on most days over the northern gulf. The diurnal cycle of heating and moistening (Q1 and Q2) over the SMO was characterized by deep convective profiles in the mid- to upper troposphere at 1800 LT, followed by stratiform-like profiles at midnight, consistent with the observed diurnal evolution of precipitation over this coastal mountainous region. The analyses in the core NAME domain are based on a gridded dataset derived from atmospheric soundings only and, therefore, should prove useful in validating reanalyses and regional models.

A Probabilistic Forecast Approach for Daily Precipitation Totals

2008 ◽  
Vol 23 (4) ◽  
pp. 659-673 ◽  
Author(s):  
Petra Friederichs ◽  
Andreas Hense
Keyword(s):  
Large Scale ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Reanalysis Data ◽  
Forecast System ◽  
Large Scale Circulation ◽  
Censored Quantile Regression ◽  
Circulation Patterns ◽  
Probabilistic Forecasts ◽  
Continuous Character

Abstract Commonly, postprocessing techniques are employed to calibrate a model forecast. Here, a probabilistic postprocessor is presented that provides calibrated probability and quantile forecasts of precipitation on the local scale. The forecasts are based on large-scale circulation patterns of the 12-h forecast from the NCEP high-resolution Global Forecast System (GFS). The censored quantile regression is used to estimate selected quantiles of the precipitation amount and the probability of the occurrence of precipitation. The approach accounts for the mixed discrete-continuous character of daily precipitation totals. The forecasts are verified using a new verification score for quantile forecasts, namely the censored quantile verification (CQV) score. The forecast approach is as follows: first, a canonical correlation is employed to correct systematic deviations in the GFS large-scale patterns compared with the NCEP–NCAR reanalysis or the 40-yr ECMWF Re-Analysis (ERA-40). Second, the statistical quantile model between the large-scale circulation and the local precipitation quantile is derived using NCEP and ERA-40 reanalysis data. Then, the statistical quantile model is applied to 12-h forecasts provided by the GFS forecast system. The probabilistic forecasts are reliable and the relative gain in performance of the quantile as well as the probability forecasts compared to the climatological forecasts range between 20% and 50%. The importance of the various parts of the postprocessing is assessed, and the performance is compared to forecasts based on the direct precipitation output from the ECMWF forecast system.

scholarly journals Large-Scale Pinyon Ips (Ips confusus) Outbreak in Southwestern United States Tied with Elevation and Land Cover

2012 ◽  
Vol 110 (4) ◽  
pp. 194-200 ◽  
Author(s):  
Sabrina J. Kleinman ◽  
Thomas E. DeGomez ◽  
Gary B. Snider ◽  
Kelly E. Williams
Keyword(s):  
United States ◽  
Land Cover ◽  
Large Scale ◽  
Southwestern United States ◽  
Ips Confusus

scholarly journals Probabilistic Projections of Climate Change for the Mid-Atlantic Region of the United States: Validation of Precipitation Downscaling during the Historical Era*

2012 ◽  
Vol 25 (2) ◽  
pp. 509-526 ◽  
Author(s):  
Liang Ning ◽  
Michael E. Mann ◽  
Robert Crane ◽  
Thorsten Wagener
Keyword(s):  
United States ◽  
General Circulation ◽  
Large Scale ◽  
Daily Precipitation ◽  
Circulation Model ◽  
The United States ◽  
Monthly Precipitation ◽  
Atlantic Region ◽  
Daily Precipitation Data ◽  
Training Interval

Abstract This study uses a statistical downscaling method based on self-organizing maps (SOMs) to produce high-resolution, downscaled precipitation estimates over the state of Pennsylvania in the mid-Atlantic region of the United States. The SOMs approach derives a transfer function between large-scale mean atmospheric states and local meteorological variables (daily point precipitation values) of interest. First, the SOM was trained using seven coarsely resolved atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis dataset to model observed daily precipitation data from 17 stations across Pennsylvania for the period 1979–2005. Employing the same coarsely resolved variables from nine general circulation model (GCM) simulations taken from the historical analysis of the Coupled Model Intercomparison Project, phase 3 (CMIP3), the trained SOM was subsequently applied to simulate daily precipitation at the same 17 sites for the period 1961–2000. The SOM analysis indicates that the nine model simulations exhibit similar synoptic-scale features to the (NCEP) observations over the 1979–2007 training interval. An analysis of the sea level pressure signatures and the precipitation distribution corresponding to the trained SOM shows that it is effective in differentiating characteristic synoptic circulation patterns and associated precipitation. The downscaling approach provides a faithful reproduction of the observed probability distributions and temporal characteristics of precipitation on both daily and monthly time scales. The downscaled precipitation field shows significant improvement over the raw GCM precipitation fields with regard to observed average monthly precipitation amounts, average monthly number of rainy days, and standard deviations of monthly precipitation amounts, although certain caveats are noted.

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