@workshop{physics-workshop, title = {The First Annual UFS Physics Workshop White Paper: Summary and Key Conclusions}, author = {Jian-Wen Bao; Fanglin Yang; Ligia Bernardet; Lisa Bengtsson; and Gary Wick}, url = {https://ufscommunity.org/wp-content/uploads/2023/11/white-paper-1st-Annual-UFS-Physics-Workshop-for-all-to-comment.pdf}, year = {2023}, date = {2023-11-02}, urldate = {2023-11-02}, keywords = {}, pubstate = {published}, tppubtype = {workshop} } @article{Krishnamurthy2022, title = {Prediction of extreme events in precipitation and temperature over CONUS during boreal summer in the UFS coupled model}, author = {Krishnamurthy, V. and C. Stan, 2022}, doi = {10.1007/s00382-021-06120-0}, year = {2022}, date = {2022-01-10}, urldate = {2022-01-10}, journal = {Climate Dynamics}, volume = {59}, issue = {1-2}, pages = {109-125}, abstract = {The predictions of extreme events by the Unified Forecast System (UFS) Coupled Model Prototype 5 of the National Centers for Environmental Prediction over the contiguous United States during boreal summer are assessed. The extreme events in precipitation and daily maximum and minimum surface air temperature in weeks 1–4 predictions are analyzed in the deterministic retrospective forecasts of UFS during 2011–2017. The spatial structures of the extreme events in precipitation are reasonably well predicted but with higher values. Although the predictions of the temperature are closer to observation over central and eastern parts of the US, the model fails to generate the extreme events over large western regions. There is no appreciable growth of forecast errors of extreme events during weeks 1–4. While the spatial correlation of the number of extreme events between the forecasts and observation is very low for precipitation and temperature, the correlation of the temperature per event is very high. The model is able to better predict the observed location and magnitude of temperature events whenever it can generate such events. The number of precipitation events in the forecasts is higher than in the observation but with less accuracy in location and magnitude. The influence of slowly varying modes related to El Niño-Southern Oscillation (ENSO), intraseasonal oscillation (ISO) and warming trend of the ocean on the extreme events are also studied. All three modes have enhancing influence on precipitation while only the ENSO mode enhances the maximum temperature events. The minimum temperature events are enhanced by ENSO and ISO but diminished by the warming trend.}, keywords = {}, pubstate = {published}, tppubtype = {article} } @article{Krishnamurthy2021, title = {Sources of subseasonal predictability over CONUS during Boreal Summer}, author = {Krishnamurthy, V., J. Meixner, L. Stephanova, J. Wang, D. Worthen, S. Moorthi, B. Li, T. Sluka, and C. Stan, 2021}, doi = {10.1175/JCLI-D-20-0586.1}, year = {2021}, date = {2021-05-01}, urldate = {2022-05-01}, journal = {Journal of Climate}, volume = {34}, issue = {9}, pages = {3273-3294}, abstract = {The predictability of the Unified Forecast System (UFS) Coupled Model Prototype 2 developed by the National Centers for Environmental Prediction is assessed for the boreal summer over the continental United States (CONUS). The retrospective forecasts of low-level horizontal wind, precipitation and 2-m temperature for 2011–17 are examined to determine the predictability at subseasonal time scale. Using a data-adaptive method, the leading modes of variability are obtained and identified to be related to El Niño–Southern Oscillation (ENSO), intraseasonal oscillation (ISO), and warming trend. In a new approach, the sources of enhanced predictability are identified by examining the forecast errors and correlations in the weekly averages of the leading modes of variability. During the boreal summer, the ISO followed by the trend in UFS are found to provide better predictability in weeks 1–4 compared to the ENSO mode and the total anomaly. The western CONUS seems to have better predictability on weekly time scale in all three modes.}, keywords = {}, pubstate = {published}, tppubtype = {article} } @misc{nokey, title = {UFS Strategic Plan 2021-2025}, author = {Unified Forecast System - Steering Committee (UFS-SC) and Writing Team}, url = {https://vlab.noaa.gov/documents/12370130/12437941/20210406_UFS_Strategic_Plan_2021-2025_v1.0.pdf/6c42f8c7-9a08-7255-86d1-cb6113e636e8?t=1618491726122}, year = {2021}, date = {2021-04-06}, urldate = {2021-04-06}, keywords = {}, pubstate = {published}, tppubtype = {misc} } @misc{nokey, title = {UFS Organization and Governance}, author = {Unified Forecast System -Steering Committee (UFS-SC)}, year = {2021}, date = {2021-04-06}, urldate = {2021-04-06}, keywords = {}, pubstate = {published}, tppubtype = {misc} } @techreport{Tolman2020b, title = {2017-2018 Roadmap for the Production Suite at NCEP (signed in 2020)}, editor = {Hendrik Tolman and John Cortinas}, url = {https://ufscommunity.org/wp-content/uploads/2020/06/20200423_2017-2018_Roadmap_for_PSN.pdf}, year = {2020}, date = {2020-04-23}, keywords = {R2O}, pubstate = {published}, tppubtype = {techreport} } @techreport{Tolman2020, title = {A Strategic Vision for the NOAA’s Physical Environmental Modeling Enterprise (signed in 2020)}, editor = {Hendrik Tolman and John Cortinas}, url = {https://ufscommunity.org/wp-content/uploads/2020/06/20200416_Strategic_Vision_for_Modeling.pdf}, year = {2020}, date = {2020-04-16}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } @article{Gallo2020, title = {Initial Development and Testing of a Convection-Allowing Model Scorecard}, author = {Burkely T. Gallo and Christina P. Kalb and John Halley Gotway and Henry H. Fisher and Brett Roberts and Israel L. Jirak and Adam J. Clark and Curtis Alexander and Tara L. Jensen}, doi = {https://doi.org/10.1175/BAMS-D-18-0218.1}, year = {2020}, date = {2020-01-07}, journal = {Bull. Amer. Meteor. Soc}, volume = {100}, pages = {ES367–ES384}, abstract = {Evaluation of numerical weather prediction (NWP) is critical for both forecasters and researchers. Through such evaluation, forecasters can understand the strengths and weaknesses of NWP guidance, and researchers can work to improve NWP models. However, evaluating high-resolution convection-allowing models (CAMs) requires unique verification metrics tailored to high-resolution output, particularly when considering extreme events. Metrics used and fields evaluated often differ between verification studies, hindering the effort to broadly compare CAMs. The purpose of this article is to summarize the development and initial testing of a CAM-based scorecard, which is intended for broad use across research and operational communities and is similar to scorecards currently available within the enhanced Model Evaluation Tools package (METplus) for evaluating coarser models. Scorecards visualize many verification metrics and attributes simultaneously, providing a broad overview of model performance. A preliminary CAM scorecard was developed and tested during the 2018 Spring Forecasting Experiment using METplus, focused on metrics and attributes relevant to severe convective forecasting. The scorecard compared attributes specific to convection-allowing scales such as reflectivity and surrogate severe fields, using metrics like the critical success index (CSI) and fractions skill score (FSS). While this preliminary scorecard focuses on attributes relevant to severe convective storms, the scorecard framework allows for the inclusion of further metrics relevant to other applications. Development of a CAM scorecard allows for evidence-based decision-making regarding future operational CAM systems as the National Weather Service transitions to a Unified Forecast system as part of the Next-Generation Global Prediction System initiative.}, keywords = {Short-Range Weather}, pubstate = {published}, tppubtype = {article} } @article{Clark2019, title = { Comparisons of QPFs Derived from Single- and Multicore Convection-Allowing Ensembles}, author = {Adam J. Clark}, doi = {https://doi.org/10.1175/WAF-D-19-0128.1}, year = {2019}, date = {2019-12-06}, journal = {Wea. Forecasting,}, volume = {34}, pages = {1955-1964}, abstract = {This study compares ensemble precipitation forecasts from 10-member, 3-km grid-spacing, CONUS domain single- and multicore ensembles that were a part of the 2016 Community Leveraged Unified Ensemble (CLUE) that was run for the 2016 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. The main results are that a 10-member ARW ensemble was significantly more skillful than a 10-member NMMB ensemble, and a 10-member MIX ensemble (5 ARW and 5 NMMB members) performed about the same as the 10-member ARW ensemble. Skill was measured by area under the relative operating characteristic curve (AUC) and fractions skill score (FSS). Rank histograms in the ARW ensemble were flatter than the NMMB ensemble indicating that the envelope of ensemble members better encompassed observations (i.e., better reliability) in the ARW. Rank histograms in the MIX ensemble were similar to the ARW ensemble. In the context of NOAA’s plans for a Unified Forecast System featuring a CAM ensemble with a single core, the results are positive and indicate that it should be possible to develop a single-core system that performs as well as or better than the current operational CAM ensemble, which is known as the High-Resolution Ensemble Forecast System (HREF). However, as new modeling applications are developed and incremental changes that move HREF toward a single-core system are made possible, more thorough testing and evaluation should be conducted.}, keywords = {Short-Range Weather}, pubstate = {published}, tppubtype = {article} } @article{Bengtsson2019, title = {Convectively Coupled Equatorial Wave Simulations Using the ECMWF IFS and the NOAA GFS Cumulus Convection Schemes in the NOAA GFS Model}, author = {Lisa Bengtsson and Juliana Dias and Maria Gehne and Peter Bechtold and Jeffrey Whitaker and Jian-Wen Bao and Linus Magnusson and Sara Michelson and Philip Pegion and Stefan Tulich and George N. Kiladis}, doi = {https://doi.org/10.1175/MWR-D-19-0195.1}, year = {2019}, date = {2019-10-18}, journal = {Mon. Wea. Rev.}, volume = {147}, pages = {4005–4025}, abstract = {There is a longstanding challenge in numerical weather and climate prediction to accurately model tropical wave variability, including convectively coupled equatorial waves (CCEWs) and the Madden–Julian oscillation. For subseasonal prediction, the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) has been shown to be superior to the NOAA Global Forecast System (GFS) in simulating tropical variability, suggesting that the ECMWF model is better at simulating the interaction between cumulus convection and the large-scale tropical circulation. In this study, we experiment with the cumulus convection scheme of the ECMWF IFS in a research version of the GFS to understand which aspects of the IFS cumulus convection scheme outperform those of the GFS convection scheme in the tropics. We show that the IFS cumulus convection scheme produces significantly different tropical moisture and temperature tendency profiles from those simulated by the GFS convection scheme when it is coupled with other physics schemes in the GFS physics package. We show that a consistent treatment of the interaction between parameterized convective plumes in the GFS planetary boundary layer (PBL) and the IFS convection scheme is required for the GFS to replicate the tropical temperature and moisture profiles simulated by the IFS model. The GFS model with the IFS convection scheme, and the consistent treatment between the convection and PBL schemes, produces much more organized convection in the tropics, and generates tropical waves that propagate more coherently than the GFS in its default configuration due to better simulated interaction between low-level convergence and precipitation.}, keywords = {Physics}, pubstate = {published}, tppubtype = {article} } @article{Bengtsson2019b, title = {A Model Framework for Stochastic Representation of Uncertainties Associated with Physical Processes in NOAA’s Next Generation Global Prediction System (NGGPS)}, author = {Lisa Bengtsson and Jian-Wen Bao and Philip Pegion and Cecile Penland and Sara Michelson and Jeffrey Whitaker}, doi = {https://doi.org/10.1175/MWR-D-18-0238.1}, year = {2019}, date = {2019-10-18}, journal = {Mon. Wea. Rev.}, volume = {147}, pages = { 893–911}, abstract = {In this study, we propose a physical-process-based stochastic parameterization scheme using cellular automata for NOAA’s Next Generation Global Prediction System. The cellular automata, used to simulate stochastic processes such as the production and destruction of subgrid convective elements, are conditioned on unresolved vertical motion that follows a prescribed stochastically generated skewed distribution (SGS). The SGS is described by a stochastic differential equation and linked to observations by taking into account the first four moments from an observed dataset. In the proposed parameterization framework, we emphasize the need for a dynamical memory term to be included in physical-process-based stochastic parameterizations, and we illustrate the requirement for the dynamical memory using the Mori–Zwanzig formalism. Although this paper focuses on the methodology, early results indicate that if we apply our stochastic framework to deep cumulus convection, it is found that the frequency distribution of precipitation is improved in a single-member stochastic forecast, and some improved spread–skill relationship in ensemble runs can be found in state variables in the tropics, as well as in the subtropics.}, keywords = {Physics}, pubstate = {published}, tppubtype = {article} } @article{Potvin2019, title = {Systematic Comparison of Convection-Allowing Models during the 2017 NOAA HWT Spring Forecasting Experiment}, author = {Corey K. Potvin and Jacob R. Carley and Adam J. Clark and Louis J. Wicker and Patrick S. Skinner and Anthony E. Reinhart and Burkely T. Gallo and John S. Kain and Glen S. Romine and Eric A. Aligo and Keith A. Brewster and David C. Dowell and Lucas M. Harris and Israel L. Jirak and Fanyou Kong and Timothy A. Supinie and Kevin W. Thomas and Xuguang Wang and Yongming Wang and Ming Xue}, doi = {https://doi.org/10.1175/WAF-D-19-0056.1}, year = {2019}, date = {2019-09-16}, journal = {Wea. Forecasting,}, volume = {34}, pages = {ES367–ES384}, 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.}, keywords = {Short-Range Weather}, pubstate = {published}, tppubtype = {article} } @online{Ek2019, title = {Hierarchical System Development for the UFS}, author = {Michael Ek and Cecelia DeLuca and Ligia Bernardet and Tara Jensen and Mariana Vertenstein and Arun Chawla and James Kinter and Richard Rood}, url = {https://ufscommunity.org/articles/hierarchical-system-development-for-the-ufs/}, year = {2019}, date = {2019-09-10}, keywords = {R2O}, pubstate = {published}, tppubtype = {online} } @misc{Rood2019d, title = {Unified Forecast System: @ EPIC Community Workshop}, author = {Richard Rood and Hendrik Tolman}, url = {https://ufscommunity.org/wp-content/uploads/2019/10/20190806_Richard_Rood_UFS_Introduction_EPIC.pdf}, year = {2019}, date = {2019-08-06}, keywords = {UFS Steering Committee}, pubstate = {published}, tppubtype = {presentation} } @misc{Rood2019b, title = {Unified Forecast System SIP: Research and Operations}, author = {Richard Rood and Hendrik Tolman}, url = {https://ufscommunity.org/wp-content/uploads/2019/10/20190514_UFS_Update_Overview_SIP.pdf}, year = {2019}, date = {2019-05-14}, keywords = {UFS Steering Committee}, pubstate = {published}, tppubtype = {presentation} } @misc{Rood2019, title = {Unified Forecast System Brief: Technical Oversight Board}, author = {Richard Rood and Hendrik Tolman}, url = {https://ufscommunity.org/wp-content/uploads/2019/10/20190509_UFS_Brief_Technical_Oversight_Board_TOB.pdf}, year = {2019}, date = {2019-05-09}, keywords = {UFS Steering Committee}, pubstate = {published}, tppubtype = {presentation} } @misc{Rood2019c, title = {Unified Forecast System Overview}, author = {Richard Rood and Hendrik Tolman}, url = {https://ufscommunity.org/wp-content/uploads/2019/10/201903xx_UFS_Overview.pdf}, year = {2019}, date = {2019-03-01}, keywords = {UFS Steering Committee}, pubstate = {published}, tppubtype = {presentation} } @misc{Davis2019, title = {Memorandum of Agreement among UCAR Acting on Behalf of NCAR and the NWS/NOAA and the OAR/NOAA for Co-Development of Common Modeling Infrastructure}, author = {Chris Davis and Jean-Francois Lamarque and Hendrik Tolman and DaNa Carlis}, url = {https://www.weather.gov/media/sti/nggps/18-064553_SignedMOU.pdf}, year = {2019}, date = {2019-01-30}, keywords = {R2O}, pubstate = {published}, tppubtype = {misc} } @techreport{Carr2018, title = {Community Modeling review Committee Report}, author = {Frederick Carr and James Kinter and Cecilia Bitz and Alicia R. Karspeck and Cliff Mass and Rohit Mathur and Lorenzo Polvani and Richard Rood and Elena Shevliakova and Hendrik Tolman and Ryan Torn and John Wilkin and Eric Wood and Fuqing Zhang}, url = {https://ufscommunity.org/wp-content/uploads/2020/06/20181214_CMrC_Report.pdf}, year = {2018}, date = {2018-12-14}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } @techreport{Rood2018, title = {Organizing Research to Operations Transition}, author = {Richard Rood and Hendrik Tolman}, url = {https://ufscommunity.org/wp-content/uploads/2019/10/20181130_Organizing_Research_to_Operations_Transition.pdf}, year = {2018}, date = {2018-11-30}, keywords = {R2O}, pubstate = {published}, tppubtype = {techreport} } @misc{Benson2018, title = {UFS Infrastructure: Repositories Sub-Group}, author = {Rusty Benson and Arun Chawla and Cristiana Stan and Cecelia DeLuca and Bill Sacks and Gerhard Theurich and Seth Underwood and Mariana Vertenstein and Jun Wang}, url = {https://ufscommunity.org/wp-content/uploads/2019/10/20180608_UFS_Infrastructure_Repositories_Sub-Group.pdf}, year = {2018}, date = {2018-06-08}, keywords = {Infrastructure}, pubstate = {published}, tppubtype = {presentation} } @techreport{Schneider2017, title = {Unified Forecast System Communication and Outreach Plan }, author = {Timothy Schneider and Cecelia DeLuca and Susan Jasko and Bhavana Rakesh and Heather Archambault and Bill Bua and Eric Chassignet and Hui-ya Chuang and Adam Clark and Jimy Dudhia and Rocky Dunlap and Michael Ek and Kate Howard and Isadora Jankov and Daryl Kleist and Sarah Lu and Avichal Mehra and Richard Rood and Hui-Shao and Jennifer Sprague-Hilderbrand and Cristiana Stan and Hendrik Tolman and Steve Warren and Betsy Weatherhead}, url = {https://ufscommunity.org/wp-content/uploads/2019/10/20171221_Unified_Forecast_System_CommunicationOutreach_Plan.pdf}, year = {2017}, date = {2017-12-21}, keywords = {Communication and Outreach}, pubstate = {published}, tppubtype = {techreport} } @techreport{na, title = {UCACN Model Advisory Committee Report}, author = {UMAC members: Frederick Carr, Richard Rood, Alan Blumberg, Chris Bretherton, Eric Chassignet, Brian Colle, James Doyle, Anke Kamrath, Jim Kinter, Cliff Mass, Peter Neilley, Christa Peters-Lidard}, editor = {UMAC members}, url = {https://vlab.noaa.gov/documents/12370130/12994300/20171215_UMAC_Report_201708_Meeting-FC-v5.pdf/ab156261-7c32-f021-e9c3-ea2fa44a6f7f?t=1609789883325}, year = {2017}, date = {2017-12-15}, keywords = {UMAC}, pubstate = {published}, tppubtype = {techreport} } @techreport{Auligne2017, title = {System Architecture for Operational Needs and Research Collaborations}, author = {Tom Auligne and V. Balaji and Rusty Benson and Ligia Bernardet and Arun Chawla and Philip Chu and Anthony Craig and Arlindo da Silva and Cecelia DeLuca and John Derber and James Doyle and Michael Farrar and Mark Iredell and James Kinter and Jean-Francois Lamarque and John Michalakes and Philip Rasch and Suranjana Saha and Vijay Tallapragada and Gerhard Theurich and Samuel Trahan and Mariana Vertenstein and Jun Wang}, url = {https://ufscommunity.org/wp-content/uploads/2019/10/20170331_System_Architecture_for_Operational_Needs_and_Research_Collaborations.pdf}, year = {2017}, date = {2017-03-31}, keywords = {System Architecture}, pubstate = {published}, tppubtype = {techreport} } @techreport{Link2017, title = {High-level NOAA Unified Modeling Overview}, author = {Jason Link and Hendrik Tolman and Eric Bayler and Chris Brown and Pat Burke and Jessie Carman and Scott Cross and John Dunne and Doug Lipton and Annarita Mariotti and Rick Methot and Ed Myers and Tim Schneider and Monica Grasso and Katie Robinson }, url = {https://repository.library.noaa.gov/view/noaa/14156}, doi = {https://doi.org/10.7289/V5GB2248}, year = {2017}, date = {2017-01-01}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } @techreport{nab, title = {UCACN Model Advisory Committee}, author = {UMAC members: Frederick Carr, Richard Rood, Alan Blumberg, Chris Bretherton, Eric Chassignet, Brian Colle, James Doyle, Anke Kamrath, Jim Kinter, Cliff Mass, Peter Neilley, Christa Peters-Lidard}, url = {https://vlab.noaa.gov/documents/12370130/12994300/20160811_UMAC_Outbrief_Doc_20161104_17_37.pdf/4d0b4022-e9ef-2e32-1302-765401479ccc?t=1609789883773}, year = {2016}, date = {2016-10-05}, abstract = {On August 9-11, 2016, the UMAC (UCACN Modeling Advisory Committee) held an in-person meeting in Silver Spring, Maryland. The invitees included the UMAC, NOAA staff, and members of the private, academic, and federal communities. The agenda and the registered attendees are attached at the end. This document includes individual narratives from all of the UMAC members. Members who were not in attendance had access to the presentation materials as well as notes from the meeting. }, keywords = {UMAC}, pubstate = {published}, tppubtype = {techreport} } @article{nac, title = {Report of the UCACN Model Advisory Committee}, author = {UMAC Members: Frederick Carr, Richard Rood, Alan Blumberg, Chris Bretherton, Andy Brown, Eric Chassignet, Brian Colle, James Doyle, Tom Hamill, Anke Kamrath, Jim Kinter, Ben Kirtman, Cliff Mass, Peter Keilley, Christa Peters-Lidard }, url = {https://vlab.noaa.gov/documents/12370130/12994300/UMAC_Final_Report_20151207-v14.pdf/a860cc33-c4f5-999e-253d-533b11e258e8?t=1609789880623}, year = {2015}, date = {2015-12-07}, keywords = {UMAC}, pubstate = {published}, tppubtype = {article} }