scholarly journals Surface energy partitioning over four dominant vegetation types across the United States in a coupled regional climate model (Weather Research and Forecasting Model 3-Community Land Model 3.5)

2012 ◽  
Vol 117 (D6) ◽  
pp. n/a-n/a ◽  
Author(s):  
Yaqiong Lu ◽  
Lara M. Kueppers
Keyword(s):  
United States ◽  
Surface Energy ◽  
Climate Model ◽  
Regional Climate ◽  
The United States ◽  
Energy Partitioning ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Vegetation Types ◽  
Community Land Model

scholarly journals On the Potential Change in Surface Water Vapor Deposition over the Continental United States due to Increases in Atmospheric Greenhouse Gases

2006 ◽  
Vol 19 (8) ◽  
pp. 1576-1585
Author(s):  
Zaitao Pan ◽  
Moti Segal ◽  
Charles Graves
Keyword(s):  
United States ◽  
Water Vapor ◽  
Surface Water ◽  
Vapor Deposition ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Scenario ◽  
The United States ◽  
Eastern United States ◽  
Present Climate

Abstract Characteristics of surface water vapor deposition (WVD) over the continental United States under the present climate and a future climate scenario reflecting the mid-twenty-first-century increased greenhouse gas concentrations were evaluated by using a regional climate model forced by initial and lateral boundary conditions generated by a GCM. Simulated seasonal WVD frequency and daily amounts are presented and elaboration on their relation to potential surface dew/frost is also provided. The climate scenario showed in winter a noticeable decline in WVD frequency over snow-covered areas in the Midwest and over most of the elevated terrain in the western United States, contrasted by an overall increase in the eastern United States. In summer, a decline in frequency was simulated for most of the United States, particularly over the mountains in the west. A spatially mixed trend of change in the frequency was indicated in spring and fall. The trend of change in WVD amount resembled that of the frequency in summer, whereas a largely reversed relation was shown in winter. Quantitatively, changes in frequency and amount of WVD in the range of −30% to +30% generally were indicated for all locations and seasons, except for the western half of the United States, where the change was larger in summer. While areas passing a local statistical test on WVD changes ranged from 11% to 36% of land domain, the WVD differences as a whole field between present climate and future scenarios are significant.

Estimations of Hazardous Convective Weather in the United States Using Dynamical Downscaling

2014 ◽  
Vol 27 (17) ◽  
pp. 6581-6589 ◽  
Author(s):  
Vittorio A. Gensini ◽  
Thomas L. Mote
Keyword(s):  
United States ◽  
High Resolution ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate Model ◽  
Regional Climate ◽  
The United States ◽  
Regional Model ◽  
Severe Weather ◽  
Convective Weather

Abstract High-resolution (4 km; hourly) regional climate modeling is utilized to resolve March–May hazardous convective weather east of the U.S. Continental Divide for a historical climate period (1980–90). A hazardous convective weather model proxy is used to depict occurrences of tornadoes, damaging thunderstorm wind gusts, and large hail at hourly intervals during the period of record. Through dynamical downscaling, the regional climate model does an admirable job of replicating the seasonal spatial shifts of hazardous convective weather occurrence during the months examined. Additionally, the interannual variability and diurnal progression of observed severe weather reports closely mimic cycles produced by the regional model. While this methodology has been tested in previous research, this is the first study to use coarse-resolution global climate model data to force a high-resolution regional model with continuous seasonal integration in the United States for purposes of resolving severe convection. Overall, it is recommended that dynamical downscaling play an integral role in measuring climatological distributions of severe weather, both in historical and future climates.

Evaluation of land-atmosphere processes of the Polar WRF in the summertime Arctic tundra

2020 ◽  
Author(s):  
Jeongwon Kim ◽  
Junhong Lee ◽  
Je-Woo Hong ◽  
Jinkyu Hong ◽  
Ja-Ho Koo ◽  
...  
Keyword(s):  
Surface Energy ◽  
Polar Region ◽  
Climate Model ◽  
Regional Climate ◽  
Arctic Tundra ◽  
The Arctic ◽  
Weather Research And Forecasting ◽  
Physical Parameters ◽  
Observation Data ◽  
Surface Energy Fluxes

<p>Arctic tundra is changing rapidly under the influence of global warming and it is important to know its impact on local and regional climate. Polar Weather Research and Forecasting (PWRF) model is a regional climate model optimized for the polar region and it is a useful tool for studying the Arctic tundra in high resolution. In this study, we evaluate the performance of the PWRF model over the Arctic tundra on clear summer days, when the transition is taking place the most, based on the surface energy fluxes and PBL observations in Cambridge Bay, Nunavut, Canada.</p><p>The PWRF simulates a drier and warmer environment in PBL than the observations. Our analysis shows that it is due to the surface energy imbalance in the model caused by the uncertainties in prescribed initial input data and physical parameters rather than structural flaws in the model physics. The performance of the PWRF model over the Arctic tundra is improved when those values are modified based on the observation data.</p>

scholarly journals Summer Land–Atmosphere Coupling Strength over the United States: Results from the Regional Climate Model RegCM4–CLM3.5

2013 ◽  
Vol 14 (3) ◽  
pp. 946-962 ◽  
Author(s):  
Rui Mei ◽  
Guiling Wang ◽  
Huanghe Gu
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Regional Climate Model ◽  
Coupling Strength ◽  
Time Scale ◽  
Climate Model ◽  
Regional Climate ◽  
The United States ◽  
Regional Model ◽  
Forecast Skill

Abstract This study investigates the land–atmosphere coupling strength during summer over the United States using the Regional Climate Model version 4 (RegCM4)–Community Land Model version 3.5 (CLM3.5). First, a 10-yr simulation driven with reanalysis lateral boundary conditions (LBCs) is conducted to evaluate the model performance. The model is then used to quantify the land–atmosphere coupling strength, predictability, and added forecast skill (for precipitation and 2-m air temperature) attributed to realistic land surface initialization following the Global Land–Atmosphere Coupling Experiment (GLACE) approaches. Similar to previous GLACE results using global climate models (GCMs), GLACE-type experiments with RegCM4 identify the central United States as a region of strong land–atmosphere coupling, with soil moisture–temperature coupling being stronger than soil moisture–precipitation coupling, and confirm that realistic soil moisture initialization is more promising in improving temperature forecasts than precipitation forecasts. At a 1–15-day lead, the added forecast skill reflects predictability (or land–atmosphere coupling strength) indicating that that model can capture the realistic land–atmosphere coupling at a short time scale. However, at a 16–30-day lead, predictability cannot translate to added forecast skill, implying that the coupling at the longer time scale may not be represented well in the model. In addition, comparison of results from GLACE2-type experiments with RegCM4 driven by reanalysis LBCs and those driven by GCM LBCs suggest that the intrinsic land–atmosphere coupling strength within the regional model is the dominant factor for the added forecast skill at a 1–15-day lead, while the impact of LBCs from the GCM may play a dominant role in determining the signal of added forecast skill in the regional model at a 16–30-day lead. It demonstrates the complexities of using regional climate model for GLACE-type studies.

Does School Climate Mean the Same Thing in the United States as in Mexico? A Focus on Measurement Invariance

2017 ◽  
Vol 37 (1) ◽  
pp. 55-68 ◽  
Author(s):  
Kathan D. Shukla ◽  
Tracy E. Waasdorp ◽  
Sarah Lindstrom Johnson ◽  
Mercedes Gabriela Orozco Solis ◽  
Amanda J. Nguyen ◽  
...  
Keyword(s):  
United States ◽  
School Climate ◽  
Measurement Invariance ◽  
School Environment ◽  
Climate Model ◽  
The United States ◽  
School Students ◽  
Scale Scores ◽  
Partial Invariance ◽  
The U.S

School climate is an important construct for guiding violence prevention efforts in U.S. schools, but there has been less consideration of this concept in its neighboring country Mexico, which has a higher prevalence of violence. The U.S. Department of Education outlined a three-domain conceptualization of school climate (i.e., safe and supportive schools model) that includes engagement, safety, and the school environment. To examine the applicability of this school climate model in Mexico, the present study tested its measurement invariance across middle school students in the United States ( n = 15,099) and Mexico ( n = 2,211). Findings supported full invariance for engagement and modified-safety scales indicating that factor loadings and intercepts contributed almost equally to factor means, and scale scores were comparable across groups. Partial invariance was found for the environment scales. Results of a multigroup confirmatory factor analysis (MGCFA) consisting of all 13 school climate scales indicated significantly positive associations among all scales in the U.S. sample and among most scales in the Mexico sample. Implications of these findings are discussed.

Investigation of the Summer Climate of the Contiguous United States and Mexico Using the Regional Atmospheric Modeling System (RAMS). Part II: Model Climate Variability

2007 ◽  
Vol 20 (15) ◽  
pp. 3866-3887 ◽  
Author(s):  
Christopher L. Castro ◽  
Roger A. Pielke ◽  
Jimmy O. Adegoke ◽  
Siegfried D. Schubert ◽  
Phillip J. Pegion
Keyword(s):  
United States ◽  
Climate Model ◽  
Weather Prediction ◽  
Regional Climate ◽  
Summer Rainfall ◽  
Atmospheric Modeling ◽  
Regional Atmospheric Modeling System ◽  
Prediction Mode ◽  
Regional Atmospheric Modeling ◽  
Seasonal Weather

Abstract Summer simulations over the contiguous United States and Mexico with the Regional Atmospheric Modeling System (RAMS) dynamically downscaling the NCEP–NCAR Reanalysis I for the period 1950–2002 (described in Part I of the study) are evaluated with respect to the three dominant modes of global SST. Two of these modes are associated with the statistically significant, naturally occurring interannual and interdecadal variability in the Pacific. The remaining mode corresponds to the recent warming of tropical sea surface temperatures. Time-evolving teleconnections associated with Pacific SSTs delay or accelerate the evolution of the North American monsoon. At the period of maximum teleconnectivity in late June and early July, there is an opposite relationship between precipitation in the core monsoon region and the central United States. Use of a regional climate model (RCM) is essential to capture this variability because of its representation of the diurnal cycle of convective rainfall. The RCM also captures the observed long-term changes in Mexican summer rainfall and suggests that these changes are due in part to the recent increase in eastern Pacific SST off the Mexican coast. To establish the physical linkage to remote SST forcing, additional RAMS seasonal weather prediction mode simulations were performed and these results are briefly discussed. In order for RCMs to be successful in a seasonal weather prediction mode for the summer season, it is required that the GCM provide a reasonable representation of the teleconnections and have a climatology that is comparable to a global atmospheric reanalysis.

Bayesian Forcasting Model – 2020 Presidential Elections in the United States of America

2021 ◽  
Vol 704 (1) ◽  
pp. 91-104
Author(s):  
Maria Raczyńska
Keyword(s):  
United States ◽  
United States Of America ◽  
Presidential Elections ◽  
Bayesian Models ◽  
The United States ◽  
Point Of View ◽  
Forecasting Model ◽  
Bayesian Forecasting ◽  
Technical Point ◽  
Made In

The article describes and explains a prior centric Bayesian forecasting model for the 2020 US elections.The model is based on the The Economist forecasting project, but strongly differs from it. From the technical point of view, it uses R and Stan programming and Stan software. The article’s focus is on theoretical decisions made in the process of constructing the model and outcomes. It describes why Bayesian models are used and how they are used to predict US presidential elections.

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