scholarly journals Statistical Downscaling Multimodel Forecasts for Seasonal Precipitation and Surface Temperature over the Southeastern United States

2014 ◽  
Vol 27 (22) ◽  
pp. 8384-8411 ◽  
Author(s):  
Di Tian ◽  
Christopher J. Martinez ◽  
Wendy D. Graham ◽  
Syewoon Hwang
Keyword(s):  
United States ◽  
Bias Correction ◽  
Polynomial Regression ◽  
Southeastern United States ◽  
Mapping Technique ◽  
Lead Times ◽  
Equal Weight ◽  
Seasonal Precipitation ◽  
Multimodel Ensemble ◽  
Spatial Disaggregation

Abstract This study compared two types of approaches to downscale seasonal precipitation (P) and 2-m air temperature (T2M) forecasts from the North American Multimodel Ensemble (NMME) over the states of Alabama, Georgia, and Florida in the southeastern United States (SEUS). Each NMME model forecast was evaluated. Two multimodel ensemble (MME) schemes were tested by assigning equal weight to all forecast members (SuperEns) or by assigning equal weights to each model’s ensemble mean (MeanEns). One type of downscaling approach used was a model output statistics (MOS) method, which was based on direct spatial disaggregation and bias correction of the NMME P and T2M forecasts using the quantile mapping technique [spatial disaggregation with bias correction (SDBC)]. The other type of approach used was a perfect prognosis (PP) approach using nonparametric locally weighted polynomial regression (LWPR) models, which used the NMME forecasts of Niño-3.4 sea surface temperatures (SSTs) to predict local-scale P and T2M. Both SDBC and LWPR downscaled P showed skill in winter but no skill or limited skill in summer at all lead times for all NMME models. The SDBC downscaled T2M were skillful only for the Climate Forecast System, version 2 (CFSv2), model even at far lead times, whereas the LWPR downscaled T2M showed limited skill or no skill for all NMME models. In many cases, the LWPR method showed significantly higher skill than the SDBC. After bias correction, the SuperEns mostly showed higher skill than the MeanEns and most of the single models, but its skill did not outperform the best single model.

scholarly journals Precipitation Contribution of Tropical Cyclones in the Southeastern United States from 1998 to 2009 Using TRMM Satellite Data

2013 ◽  
Vol 26 (3) ◽  
pp. 1047-1062 ◽  
Author(s):  
Olivier P. Prat ◽  
Brian R. Nelson
Keyword(s):  
United States ◽  
Tropical Cyclones ◽  
Southeastern United States ◽  
Monthly Precipitation ◽  
Seasonal Precipitation ◽  
Monthly Basis ◽  
Precipitation Total ◽  
Precipitation Analysis ◽  
Southern Florida ◽  
Precipitation Events

Abstract The objective of this paper is to characterize the precipitation amounts originating from tropical cyclones (TCs) in the southeastern United States during the tropical storm season from June to November. Using 12 years of precipitation data from the Tropical Rainfall Measurement Mission (TRMM), the authors estimate the TC contribution on the seasonal, interannual, and monthly precipitation budget using TC information derived from the International Best Track Archive for Climate Stewardship (IBTrACS). Results derived from the TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 showed that TCs accounted for about 7% of the seasonal precipitation total from 1998 to 2009. Rainfall attributable to TCs was found to contribute as much as 8%–12% for inland areas located between 150 and 300 km from the coast and up to 15%–20% for coastal areas from Louisiana to the Florida Panhandle, southern Florida, and coastal Carolinas. The interannual contribution varied from 1.3% to 13.8% for the period 1998–2009 and depended on the TC seasonal activity, TC intensity, and TC paths as they traveled inland. For TCs making landfall, the rainfall contribution could be locally above 40% and, on a monthly basis, TCs contributed as much as 20% of September rainfall. The probability density functions of rainfall attributable to tropical cyclones showed that the percentage of rainfall associated with TC over land increased with increasing rain intensity and represent about 20% of heavy rainfall (>20 mm h−1), while TCs account for less than 5% of all seasonal precipitation events.

scholarly journals Evaluation of Statistical Downscaling of North American Multimodel Ensemble Forecasts over the Western United States

2017 ◽  
Vol 32 (1) ◽  
pp. 327-341 ◽  
Author(s):  
Renaud Barbero ◽  
John T. Abatzoglou ◽  
Katherine C. Hegewisch
Keyword(s):  
United States ◽  
Statistical Downscaling ◽  
North American ◽  
Southern Oscillation ◽  
Western United States ◽  
Seasonal Temperature ◽  
Multimodel Ensemble ◽  
Temperature And Precipitation ◽  
Spatial Disaggregation ◽  
North American Multimodel Ensemble

AbstractThe skill of two statistical downscaled seasonal temperature and precipitation forecasts from the North American Multimodel Ensemble (NMME) was evaluated across the western United States at spatial scales relevant to local decision-making. Both statistical downscaling approaches, spatial disaggregation (SD) and bias correction spatial disaggregation (BCSD), exhibited similar correlative skill measures; however, the BCSD method showed superior tercile-based skill measures since it corrects for variance deflation in NMME ensemble averages. Geographic and seasonal variations in downscaled forecast skill revealed patterns across the complex topography of the western United States not evident using coarse-scale skill assessments, particularly in regions subject to inversions and variability in orographic precipitation ratios. Similarly, differences in the skill of cool-season temperature and precipitation forecasts issued when the fall El Niño–Southern Oscillation (ENSO) signal was strong versus ENSO-neutral years were evident across topographic gradients in the northwestern United States.

Downscaling large-scale NCEP CFS to resolve fine-scale seasonal precipitation and extremes for the crop growing seasons over the southeastern United States

2009 ◽  
Vol 35 (2-3) ◽  
pp. 449-471 ◽  
Author(s):  
Young-Kwon Lim ◽  
Steven Cocke ◽  
D. W. Shin ◽  
Justin T. Schoof ◽  
Timothy E. LaRow ◽  
...  
Keyword(s):  
United States ◽  
Large Scale ◽  
Southeastern United States ◽  
Seasonal Precipitation ◽  
Fine Scale ◽  
Growing Seasons ◽  
Ncep Cfs

scholarly journals The Advantage of Using International Multimodel Ensemble for Seasonal Precipitation Forecast over Israel

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Amir Givati ◽  
Mashor Housh ◽  
Yoav Levi ◽  
Dror Paz ◽  
Itzhak Carmona ◽  
...  
Keyword(s):  
Global Climate ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Seasonal Precipitation ◽  
Multimodel Ensemble ◽  
Mean Values ◽  
Precipitation Forecasting ◽  
Seasonal Model ◽  
Small Domain ◽  
Forecast Models

This study analyzes the results of monthly and seasonal precipitation forecasting from seven different global climate forecast models for major basins in Israel within October–April 1982–2010. The six National Multimodel Ensemble (NMME) models and the ECMWF seasonal model were used to calculate an International Multimodel Ensemble (IMME). The study presents the performance of both monthly and seasonal predictions of precipitation accumulated over three months, with respect to different lead times for the ensemble mean values, one per individual model. Additionally, we analyzed the performance of different combinations of models. We present verification of seasonal forecasting using real forecasts, focusing on a small domain characterized by complex terrain, high annual precipitation variability, and a sharp precipitation gradient from west to east as well as from south to north. The results in this study show that, in general, the monthly analysis does not provide very accurate results, even when using the IMME for one-month lead time. We found that the IMME outperformed any single model prediction. Our analysis indicates that the optimal combinations with the high correlation values contain at least three models. Moreover, prediction with larger number of models in the ensemble produces more robust predictions. The results obtained in this study highlight the advantages of using an ensemble of global models over single models for small domain.

Does Enhancing Mind Perception Affect Conspiracy Belief?

2019 ◽  
Author(s):  
Jorge Noguera
Keyword(s):  
United States ◽  
Southeastern United States ◽  
Social Discourse ◽  
Psychological Processes ◽  
Social Psychological ◽  
Conspiracy Theories ◽  
Mind Perception ◽  
Mixed Anova ◽  
Interaction Term ◽  
The Mind

This study was conducted to determine the effectiveness of a novel mind perception manipulation. Mind perception is currently theorized to be an essential aspect of a number of human social psychological processes. Thus, a successful manipulation would allow for the causal study of those processes. This manipulation was created in an attempt to explore the downstream impact of mind perception on the endorsement of conspiracy theories. Conspiracy theories are steadily becoming more and more prominent in social discourse. Endorsement of conspiracy theories are beginning to show real world ramifications such as a danger to human health (e.g., in the anti-vaccination movement). A sample of college students (valid N = 53) from a large rural institution in the southeastern United States participated for course credit. These participants completed a mind perception pretest, were randomly assigned to either the manipulation in question (in which participants are asked to consider the ‘mind’ of several targets and write their thoughts about them) or the control condition, and then they completed a posttest. The mixed ANOVA revealed that the interaction term between Time and Condition was not significant. Because the manipulation did not work, other analyses were aborted, in accord with the pre-registration. My Discussion focuses on the procedures and potential shortcomings of this manipulation, in an effort to lay the groundwork for a successful one.

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