Research Applications LaboratoryNational Center for Atmospheric ResearchBoulder

Sun City Center, United States

Research Applications LaboratoryNational Center for Atmospheric ResearchBoulder

Sun City Center, United States
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Clark M.P.,Research Applications LaboratoryNational Center for Atmospheric ResearchBoulder | Hanson R.B.,Publications
Water Resources Research | Year: 2017

We examine a suite of journal-level productivity and citation statistics for six leading hydrology journals in order to help authors understand the robustness and meaning of journal impact factors. The main results are (1) the probability distribution of citations is remarkably homogenous across hydrology journals; (2) hydrology papers tend to have a long-lasting impact, with a large fraction of papers cited after the 2 year window used to calculate the journal impact factor; and (3) journal impact factors are characterized by substantial year-to-year variability (especially for smaller journals), primarily because a small number of highly cited papers have a large influence on the journal impact factor. Consequently, the ranking of hydrology journals with respect to the journal impact factor in a given year does not have much information content. These results highlight problems in using citation data to evaluate hydrologic science. We hope that this analysis helps authors better understand journal-level citation statistics, and also helps improve research assessments in institutions and funding agencies. © 2017. American Geophysical Union. All Rights Reserved.

Brown B.R.,University of Hawaii at Manoa | Thompson G.,Research Applications LaboratoryNational Center for Atmospheric ResearchBoulder
Journal of Advances in Modeling Earth Systems | Year: 2017

Polarimetric upgrades to the U.S. radar network have allowed new insight into the precipitation processes of tropical cyclones. Previous work by the authors compared the reflectivity at horizontal polarization and differential reflectivity observations from two hurricanes to simulated radar observations from the WRF model, and found that the aerosol-aware Thompson-Eidhammer microphysical scheme performed the best of several commonly used bulk microphysical parameterizations. Here we expand our investigation of the Thompson-Eidhammer scheme, and find that though it provided the most accurate forecast in terms of wind speed and simulated radar signatures, the scheme produces areas in which the differential reflectivity was much higher than observed. We conclude that the Thompson-Eidhammer scheme produces drop size distributions that have a larger median drop size than observed in regions of light stratiform precipitation. Examination of the vertical structure of simulated differential reflectivity indicates that the source of the discrepancy between the model and radar observations likely originates within the melting layer. The treatment of number production of rain drops from melting snow in the microphysical scheme is shown to be the ultimate source of the enhancement of differential reflectivity. A modification to the scheme is shown to result in better fidelity of the radar variables with the observations without degrading the short-term intensity forecast. Additional tests with an idealized squall line simulation are consistent with the hurricane results, suggesting the modification is generally applicable. The modifications to the Thompson-Eidhammer scheme shown here have been incorporated into updates of the WRF model starting with version 3.8.1. © 2017. The Authors.

Shukla S.,University of California at Santa Barbara | Wood A.W.,Research Applications LaboratoryNational Center for Atmospheric ResearchBoulder | Cheng L.,University of Colorado BoulderBoulder | Svoboda M.,National United University
Water Resources Research | Year: 2016

Improving water management in water stressed-regions requires reliable seasonal precipitation predication, which remains a grand challenge. Numerous statistical and dynamical model simulations have been developed for predicting precipitation. However, both types of models offer limited seasonal predictability. This study outlines a hybrid statistical-dynamical modeling framework for predicting seasonal precipitation. The dynamical component relies on the physically based North American Multi-Model Ensemble (NMME) model simulations (99 ensemble members). The statistical component relies on a multivariate Bayesian-based model that relates precipitation to atmosphere-ocean teleconnections (also known as an analog-year statistical model). Here the Pacific Decadal Oscillation (PDO), Multivariate ENSO Index (MEI), and Atlantic Multidecadal Oscillation (AMO) are used in the statistical component. The dynamical and statistical predictions are linked using the so-called Expert Advice algorithm, which offers an ensemble response (as an alternative to the ensemble mean). The latter part leads to the best precipitation prediction based on contributing statistical and dynamical ensembles. It combines the strength of physically based dynamical simulations and the capability of an analog-year model. An application of the framework in the southwestern United States, which has suffered from major droughts over the past decade, improves seasonal precipitation predictions (3-5 month lead time) by 5-60% relative to the NMME simulations. Overall, the hybrid framework performs better in predicting negative precipitation anomalies (10-60% improvement over NMME) than positive precipitation anomalies (5-25% improvement over NMME). The results indicate that the framework would likely improve our ability to predict droughts such as the 2012-2014 event in the western United States that resulted in significant socioeconomic impacts. © 2016. American Geophysical Union. All Rights Reserved.

Clark M.P.,Research Applications LaboratoryNational Center for Atmospheric ResearchBoulder | Kavetski D.,University of South Australia | Rupp D.E.,Oregon State University | Gutmann E.D.,Research Applications LaboratoryNational Center for Atmospheric ResearchBoulder | And 6 more authors.
Water Resources Research | Year: 2015

This work advances a unified approach to process-based hydrologic modeling, which we term the "Structure for Unifying Multiple Modeling Alternatives (SUMMA)." The modeling framework, introduced in the companion paper, uses a general set of conservation equations with flexibility in the choice of process parameterizations (closure relationships) and spatial architecture. This second paper specifies the model equations and their spatial approximations, describes the hydrologic and biophysical process parameterizations currently supported within the framework, and illustrates how the framework can be used in conjunction with multivariate observations to identify model improvements and future research and data needs. The case studies illustrate the use of SUMMA to select among competing modeling approaches based on both observed data and theoretical considerations. Specific examples of preferable modeling approaches include the use of physiological methods to estimate stomatal resistance, careful specification of the shape of the within-canopy and below-canopy wind profile, explicitly accounting for dust concentrations within the snowpack, and explicitly representing distributed lateral flow processes. Results also demonstrate that changes in parameter values can make as much or more difference to the model predictions than changes in the process representation. This emphasizes that improvements in model fidelity require a sagacious choice of both process parameterizations and model parameters. In conclusion, we envisage that SUMMA can facilitate ongoing model development efforts, the diagnosis and correction of model structural errors, and improved characterization of model uncertainty. © 2015. American Geophysical Union. All Rights Reserved..

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