Systematic Comparison of Convection-Allowing Models during the 2017 NOAA HWT Spring Forecasting Experiment

2019 ◽  
Vol 34 (5) ◽  
pp. 1395-1416 ◽  
Author(s):  
Corey K. Potvin ◽  
Jacob R. Carley ◽  
Adam J. Clark ◽  
Louis J. Wicker ◽  
Patrick S. Skinner ◽  
...  
Keyword(s):  
Model Design ◽  
Model Intercomparison ◽  
Grid Spacing ◽  
Single Model ◽  
Operational Model ◽  
Object Based ◽  
The One ◽  
Horizontal Grid ◽  
Cam Systems ◽  
Better Than

Abstract The 2016–18 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments (SFE) featured the Community Leveraged Unified Ensemble (CLUE), a coordinated convection-allowing model (CAM) ensemble framework designed to provide empirical guidance for development of operational CAM systems. The 2017 CLUE included 81 members that all used 3-km horizontal grid spacing over the CONUS, enabling direct comparison of forecasts generated using different dynamical cores, physics schemes, and initialization procedures. This study uses forecasts from several of the 2017 CLUE members and one operational model to evaluate and compare CAM representation and next-day prediction of thunderstorms. The analysis utilizes existing techniques and novel, object-based techniques that distill important information about modeled and observed storms from many cases. The National Severe Storms Laboratory Multi-Radar Multi-Sensor product suite is used to verify model forecasts and climatologies of observed variables. Unobserved model fields are also examined to further illuminate important intermodel differences in storms and near-storm environments. No single model performed better than the others in all respects. However, there were many systematic intermodel and intercore differences in specific forecast metrics and model fields. Some of these differences can be confidently attributed to particular differences in model design. Model intercomparison studies similar to the one presented here are important to better understand the impacts of model and ensemble configurations on storm forecasts and to help optimize future operational CAM systems.

scholarly journals Toward 1-km Ensemble Forecasts over Large Domains

2017 ◽  
Vol 145 (8) ◽  
pp. 2943-2969 ◽  
Author(s):  
Craig S. Schwartz ◽  
Glen S. Romine ◽  
Kathryn R. Fossell ◽  
Ryan A. Sobash ◽  
Morris L. Weisman
Keyword(s):  
The United States ◽  
Grid Spacing ◽  
Ensemble Forecasts ◽  
Convective Systems ◽  
Object Based ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective ◽  
Computational Resources ◽  
Horizontal Grid ◽  
Better Than

Precipitation forecasts from convection-allowing ensembles with 3- and 1-km horizontal grid spacing were evaluated between 15 May and 15 June 2013 over central and eastern portions of the United States. Probabilistic forecasts produced from 10- and 30-member, 3-km ensembles were consistently better than forecasts from individual 1-km ensemble members. However, 10-member, 1-km probabilistic forecasts usually were best, especially over the first 12 h and at rainfall rates ≥ 5.0 mm h−1 at later times. Further object-based investigation revealed that better 1-km forecasts at heavier rainfall rates were associated with more accurate placement of mesoscale convective systems compared to 3-km forecasts. The collective results indicate promise for 1-km ensembles once computational resources can support their operational implementation.

Initial Conditions for Convection-Allowing Ensembles over the Conterminous United States

2020 ◽  
Vol 148 (7) ◽  
pp. 2645-2669
Author(s):  
Craig S. Schwartz ◽  
May Wong ◽  
Glen S. Romine ◽  
Ryan A. Sobash ◽  
Kathryn R. Fossell
Keyword(s):  
United States ◽  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Limited Area ◽  
Grid Spacing ◽  
Forecast System ◽  
Skill Scores ◽  
Ensemble Mean ◽  
Horizontal Grid ◽  
Better Than

Abstract Five sets of 48-h, 10-member, convection-allowing ensemble (CAE) forecasts with 3-km horizontal grid spacing were systematically evaluated over the conterminous United States with a focus on precipitation across 31 cases. The various CAEs solely differed by their initial condition perturbations (ICPs) and central initial states. CAEs initially centered about deterministic Global Forecast System (GFS) analyses were unequivocally better than those initially centered about ensemble mean analyses produced by a limited-area single-physics, single-dynamics 15-km continuously cycling ensemble Kalman filter (EnKF), strongly suggesting relative superiority of the GFS analyses. Additionally, CAEs with flow-dependent ICPs derived from either the EnKF or multimodel 3-h forecasts from the Short-Range Ensemble Forecast (SREF) system had higher fractions skill scores than CAEs with randomly generated mesoscale ICPs. Conversely, due to insufficient spread, CAEs with EnKF ICPs had worse reliability, discrimination, and dispersion than those with random and SREF ICPs. However, members in the CAE with SREF ICPs undesirably clustered by dynamic core represented in the ICPs, and CAEs with random ICPs had poor spinup characteristics. Collectively, these results indicate that continuously cycled EnKF mean analyses were suboptimal for CAE initialization purposes and suggest that further work to improve limited-area continuously cycling EnKFs over large regional domains is warranted. Additionally, the deleterious aspects of using both multimodel and random ICPs suggest efforts toward improving spread in CAEs with single-physics, single-dynamics, flow-dependent ICPs should continue.

scholarly journals European daily precipitation according to EURO-CORDEX regional climate models (RCMs) and high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP)

2020 ◽  
Vol 13 (11) ◽  
pp. 5485-5506
Author(s):  
Marie-Estelle Demory ◽  
Ségolène Berthou ◽  
Jesús Fernández ◽  
Silje L. Sørland ◽  
Roman Brogli ◽  
...  
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Daily Precipitation ◽  
Global Climate ◽  
Regional Climate ◽  
Global Climate Models ◽  
Model Intercomparison ◽  
Grid Spacing ◽  
Intercomparison Project ◽  
Horizontal Grid

Abstract. In this study, we evaluate a set of high-resolution (25–50 km horizontal grid spacing) global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP), developed as part of the EU-funded PRIMAVERA (Process-based climate simulation: Advances in high resolution modelling and European climate risk assessment) project, and from the EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment) regional climate models (RCMs) (12–50 km horizontal grid spacing) over a European domain. It is the first time that an assessment of regional climate information using ensembles of both GCMs and RCMs at similar horizontal resolutions has been possible. The focus of the evaluation is on the distribution of daily precipitation at a 50 km scale under current climate conditions. Both the GCM and RCM ensembles are evaluated against high-quality gridded observations in terms of spatial resolution and station density. We show that both ensembles outperform GCMs from the 5th Coupled Model Intercomparison Project (CMIP5), which cannot capture the regional-scale precipitation distribution properly because of their coarse resolutions. PRIMAVERA GCMs generally simulate precipitation distributions within the range of EURO-CORDEX RCMs. Both ensembles perform better in summer and autumn in most European regions but tend to overestimate precipitation in winter and spring. PRIMAVERA shows improvements in the latter by reducing moderate-precipitation rate biases over central and western Europe. The spatial distribution of mean precipitation is also improved in PRIMAVERA. Finally, heavy precipitation simulated by PRIMAVERA agrees better with observations in most regions and seasons, while CORDEX overestimates precipitation extremes. However, uncertainty exists in the observations due to a potential undercatch error, especially during heavy-precipitation events. The analyses also confirm previous findings that, although the spatial representation of precipitation is improved, the effect of increasing resolution from 50 to 12 km horizontal grid spacing in EURO-CORDEX daily precipitation distributions is, in comparison, small in most regions and seasons outside mountainous regions and coastal regions. Our results show that both high-resolution GCMs and CORDEX RCMs provide adequate information to end users at a 50 km scale.

scholarly journals Further Evaluation of Probabilistic Convective Precipitation Forecasts Using the QPF–PoP Neighborhood Relationship

2017 ◽  
Vol 32 (4) ◽  
pp. 1423-1440 ◽  
Author(s):  
Michael C. Kochasic ◽  
William A. Gallus ◽  
Christopher J. Schaffer
Keyword(s):  
Convective Precipitation ◽  
Grid Spacing ◽  
Single Model ◽  
Ensemble Forecasts ◽  
Fine Grid ◽  
Neighborhood Relationship ◽  
Probability Of Precipitation ◽  
Horizontal Grid ◽  
Day By Day ◽  
Good Agreement

Abstract A neighborhood postprocessing approach that relates quantitative precipitation forecasts (QPF) to probability of precipitation (PoP) forecasts applied to a single model run was found by Schaffer et al. to be as good as traditional ensemble-based approaches using 10 members in 30-h forecasts of convective precipitation. The present study evaluates if PoP forecasts derived from additional variations of the approach can improve PoP forecasts further compared with previous methods. Ensemble forecasts from the Center for Analysis and Prediction of Storms (CAPS) are used for neighborhood tests comparing a single model run and a traditional ensemble. In the first test, PoP forecasts for different combinations of training and testing datasets using a single model member with 4-km grid spacing are compared against those obtained with a 10-member traditional ensemble. Overall, forecasts for the neighborhood approach with just one member are only slightly less accurate to those using a more traditional neighborhood approach with the ensemble. PoP forecasts improve when using older data for training and newer data for testing. Assessments of the sensitivity of the neighborhood PoPs suggest that thinning of the horizontal grid at fine grid spacing is an effective way of maintaining the accuracy of PoP forecasts while reducing computational expenses. In an additional test, the diurnal variation of the forecast is examined on a day-by-day basis, showing good agreement between the two approaches for all but a few cases during 2008.

scholarly journals Multimodel Combination Techniques for Analysis of Hydrological Simulations: Application to Distributed Model Intercomparison Project Results

2006 ◽  
Vol 7 (4) ◽  
pp. 755-768 ◽  
Author(s):  
Newsha K. Ajami ◽  
Qingyun Duan ◽  
Xiaogang Gao ◽  
Soroosh Sorooshian
Keyword(s):  
Bias Correction ◽  
Weighted Average ◽  
Distributed Model ◽  
Streamflow Forecasting ◽  
Model Intercomparison ◽  
Single Model ◽  
Model Simulations ◽  
Model Average ◽  
Intercomparison Project ◽  
Better Than

Abstract This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the best-calibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction.

scholarly journals European daily precipitation according to EURO-CORDEX regional climate models (RCMs) and high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP)

2021 ◽  
Author(s):  
Marie-Estelle Demory ◽  
Ségolène Berthou ◽  
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Daily Precipitation ◽  
Global Climate ◽  
Regional Climate ◽  
Global Climate Models ◽  
Model Intercomparison ◽  
Grid Spacing ◽  
Intercomparison Project ◽  
Horizontal Grid

<p>In this study, we evaluate a set of high-resolution (25–50 km horizontal grid spacing) global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP), developed as part of the EU-funded PRIMAVERA (Process-based climate simulation: Advances in high resolution modelling and European climate risk assessment) project, and from the EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment) regional climate models (RCMs) (12–50 km horizontal grid spacing) over a European domain. It is the first time that an assessment of regional climate information using ensembles of both GCMs and RCMs at similar horizontal resolutions has been possible. The focus of the evaluation is on the distribution of daily precipitation at a 50 km scale under current climate conditions. Both the GCM and RCM ensembles are evaluated against high-quality gridded observations in terms of spatial resolution and station density. We show that both ensembles outperform GCMs from the 5th Coupled Model Intercomparison Project (CMIP5), which cannot capture the regional-scale precipitation distribution properly because of their coarse resolutions. PRIMAVERA GCMs generally simulate precipitation distributions within the range of EURO-CORDEX RCMs. Both ensembles perform better in summer and autumn in most European regions but tend to overestimate precipitation in winter and spring. PRIMAVERA shows improvements in the latter by reducing moderate-precipitation rate biases over central and western Europe. The spatial distribution of mean precipitation is also improved in PRIMAVERA. Finally, heavy precipitation simulated by PRIMAVERA agrees better with observations in most regions and seasons, while CORDEX overestimates precipitation extremes. However, uncertainty exists in the observations due to a potential undercatch error, especially during heavy-precipitation events.</p><p>The analyses also confirm previous findings that, although the spatial representation of precipitation is improved, the effect of increasing resolution from 50 to 12 km horizontal grid spacing in EURO-CORDEX daily precipitation distributions is, in comparison, small in most regions and seasons outside mountainous regions and coastal regions. Our results show that both high-resolution GCMs and CORDEX RCMs provide adequate information to end users at a 50 km scale.</p>

scholarly journals The Impacts of Using Mixed Physics in the Community Leveraged Unified Ensemble

2019 ◽  
Vol 34 (4) ◽  
pp. 849-867 ◽  
Author(s):  
William A. Gallus ◽  
Jamie Wolff ◽  
John Halley Gotway ◽  
Michelle Harrold ◽  
Lindsay Blank ◽  
...  
Keyword(s):  
High Resolution ◽  
Software Package ◽  
Initial Conditions ◽  
Detailed Examination ◽  
Lateral Boundary ◽  
Grid Spacing ◽  
Object Based ◽  
Lateral Boundary Conditions ◽  
The Impact ◽  
Horizontal Grid

Abstract A well-known problem in high-resolution ensembles has been a lack of sufficient spread among members. Modelers often have used mixed physics to increase spread, but this can introduce problems including computational expense, clustering of members, and members that are not all equally skillful. Thus, a detailed examination of the impacts of using mixed physics is important. The present study uses two years of Community Leveraged Unified Ensemble (CLUE) output to isolate the impact of mixed physics in 36-h forecasts made using a convection-permitting ensemble with 3-km horizontal grid spacing. One 10-member subset of the CLUE used only perturbed initial conditions (ICs) and lateral boundary conditions (LBCs) while another 10-member ensemble used the same mixed ICs and LBCs but also introduced mixed physics. The cases examined occurred during NOAA’s Hazardous Weather Testbed Spring Forecast Experiments in 2016 and 2017. Traditional gridpoint metrics applied to each member and the ensemble as a whole, along with object-based verification statistics for all members, were computed for composite reflectivity and 1- and 3-h accumulated precipitation using the Model Evaluation Tools (MET) software package. It is found that the mixed physics increases variability substantially among the ensemble members, more so for reflectivity than precipitation, such that the envelope of members is more likely to encompass the observations. However, the increased variability is mostly due to the introduction of both substantial high biases in members using one microphysical scheme, and low biases in other schemes. Overall ensemble skill is not substantially different from the ensemble using a single physics package.

Comparing partial and continuously cycling ensemble Kalman filter data assimilation systems for convection-allowing ensemble forecast initialization

2021 ◽  
Author(s):  
Craig S. Schwartz ◽  
Jonathan Poterjoy ◽  
Jacob R. Carley ◽  
David C. Dowell ◽  
Glen S. Romine ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Limited Area ◽  
Computational Domain ◽  
Grid Spacing ◽  
Pass Filter ◽  
Continuous Cycling ◽  
Horizontal Grid ◽  
Better Than

AbstractSeveral limited-area 80-member ensemble Kalman filter (EnKF) data assimilation systems with 15-km horizontal grid spacing were run over a computational domain spanning the conterminous United States (CONUS) for a 4-week period. One EnKF employed continuous cycling, where the prior ensemble was always the 1-h forecast initialized from the previous cycle’s analysis. In contrast, the other EnKFs used a partial cycling procedure, where limited-area states were discarded after 12 or 18 h of self-contained hourly cycles and re-initialized the next day from global model fields. “Blended” states were also constructed by combining large scales from global ensemble initial conditions (ICs) with small scales from limited-area continuously cycling EnKF analyses using a low-pass filter. Both the blended states and EnKF analysis ensembles initialized 36-h, 10-member ensemble forecasts with 3-km horizontal grid spacing. Continuously cycling EnKF analyses initialized ~1–18-h precipitation forecasts that were comparable to or somewhat better than those with partial cycling EnKF ICs. Conversely, ~18–36-h forecasts with partial cycling EnKF ICs were comparable to or better than those with unblended continuously cycling EnKF ICs. However, blended ICs yielded ~18–36-h forecasts that were statistically indistinguishable from those with partial cycling ICs. ICs that more closely resembled global analysis spectral characteristics at wavelengths > 200 km, like partial cycling and blended ICs, were associated with relatively good ~18–36-h forecasts. Ultimately, findings suggest that EnKFs employing a combination of continuous cycling and blending can potentially replace the partial cycling assimilation systems that currently initialize operational limited-area models over the CONUS without sacrificing forecast quality.

Subjective Time Preference and Willingness to Pay for an Energy-Saving Durable Good

2001 ◽  
Vol 32 (3) ◽  
pp. 133-141 ◽  
Author(s):  
Gerrit Antonides ◽  
Sophia R. Wunderink
Keyword(s):  
Willingness To Pay ◽  
Energy Saving ◽  
Subjective Time ◽  
Durable Good ◽  
Discount Function ◽  
Hyperbolic Discount ◽  
The One ◽  
Two Parameter ◽  
Difference Effect ◽  
Better Than

Summary: Different shapes of individual subjective discount functions were compared using real measures of willingness to accept future monetary outcomes in an experiment. The two-parameter hyperbolic discount function described the data better than three alternative one-parameter discount functions. However, the hyperbolic discount functions did not explain the common difference effect better than the classical discount function. Discount functions were also estimated from survey data of Dutch households who reported their willingness to postpone positive and negative amounts. Future positive amounts were discounted more than future negative amounts and smaller amounts were discounted more than larger amounts. Furthermore, younger people discounted more than older people. Finally, discount functions were used in explaining consumers' willingness to pay for an energy-saving durable good. In this case, the two-parameter discount model could not be estimated and the one-parameter models did not differ significantly in explaining the data.

Exploring the Multidimensional Facets of Authoritarianism: Authoritarian Aggression and Social Dominance Orientation

2008 ◽  
Vol 67 (1) ◽  
pp. 51-60 ◽  
Author(s):  
Stefano Passini
Keyword(s):  
Social Dominance ◽  
Factor Model ◽  
Three Dimensional ◽  
Social Dominance Orientation ◽  
Model Fit ◽  
Regression Analyses ◽  
One Dimensional ◽  
Confirmatory Factor ◽  
The One ◽  
Better Than

The relation between authoritarianism and social dominance orientation was analyzed, with authoritarianism measured using a three-dimensional scale. The implicit multidimensional structure (authoritarian submission, conventionalism, authoritarian aggression) of Altemeyer’s (1981, 1988) conceptualization of authoritarianism is inconsistent with its one-dimensional methodological operationalization. The dimensionality of authoritarianism was investigated using confirmatory factor analysis in a sample of 713 university students. As hypothesized, the three-factor model fit the data significantly better than the one-factor model. Regression analyses revealed that only authoritarian aggression was related to social dominance orientation. That is, only intolerance of deviance was related to high social dominance, whereas submissiveness was not.

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