4/14/2022 – An EPIC Dive into Social Science: What is the Community of Community Modeling?
Michael Michaud; University of Delaware, NOAA Lapenta Intern 2021
Over the last several decades, community modeling has become more prevalent within earth system science and is seen as a way to solve our wicked forecasting problems. Most community modeling focuses on the technical infrastructure of designing models overlooking the needed social infrastructure to nurture and connect the people developing the models. This presentation examines key stakeholder perceptions on the meaning of a sense of community within a community model. The key stakeholders interviewed included individuals involved with the Unified Forecast System (UFS) and the Earth Prediction Innovation Center (EPIC) across various sectors of the weather, climate, and water enterprise. Through data from interviews and utilizing theoretical and practical frameworks, this presentation makes recommendations on how EPIC can cultivate necessary social infrastructure through structures like a Community of Practice to continuously engage members to develop a sense of community. In order to utilize the full power of a technical system, members need a sense of community and constant engagement.
4/14/2022 – Climbing Down Charney’s Ladder
V. Balaji; Princeton University and NOAA/GFDL
The advent of digital computing in the 1950s sparked a revolution in the science of weather and climate. Meteorology, long based on extrapolating patterns in space and time, gave way to computational methods in a decade of advances in numerical weather forecasting. Those same methods also gave rise to computational climate science, studying the behaviour of those same numerical equations over intervals much longer than weather events, and changes in external boundary conditions. Several subsequent decades of exponential growth in the power of computing have brought us to the present day, where models ever grow in resolution and complexity, capable of mastery of many small-scale phenomena with global repercussions, and ever more intricate feedbacks in the Earth system.
The current juncture in computing, seven decades later, heralds an end to what is called Dennard scaling, the physics behind ever smaller computational units and ever faster arithmetic. This is prompting a fundamental change in our approach to the simulation of weather and climate, potentially as revolutionary as that wrought by John von Neumann in the 1950s. One approach could return us to an earlier era of pattern recognition and extrapolation, this time aided by computational power. Another approach could lead us to insights that continue to be expressed in mathematical equations. In either approach, or any synthesis of those, it is clearly no longer the steady march of the last few decades, continuing to add detail to ever more elaborate models. In this prospectus, we attempt to show the outlines of how this may unfold in the coming decades, a new harnessing of physical knowledge, computation, and data.
3/17/2022 – The NOAA Precipitation Prediction Grand Challenge – An Historic R2O Opportunity
Dr. David Novak, Director, NOAA/NWS Weather Prediction Center
The impacts from extreme precipitation are deadly, damaging, and increasing in a warming climate. Knowing when it will rain and how much will fall is crucial to every person and business in the U.S. Further, precipitation processes are an integration of many atmospheric processes and have direct impacts to the ocean, ecosystems, hydrology and the cryosphere. Thus, precipitation is a unifying theme across the Weather, Water, and Climate communities. However, partner expectations of accuracy, specificity, and lead time often exceed current capabilities. Further, NOAA models (global models in particular) have seen marginal improvement in precipitation skill (~15%) over the past 2 decades. In response, NOAA has developed a strategy for a decadal effort to improve forecast precipitation (from mesoscale weather to seasonal timescales) – called the NOAA Precipitation Prediction Grand Challenge.
The goal of this effort is to dramatically improve precipitation forecasts in terms of accuracy, extent in time, and reliability. The challenge demands investment across the value chain from basic understanding of precipitation processes and predictability limits, to enhanced observations, data assimilation, improved models, post-processing and tools for the human forecaster, culminating in understandable, actionable, and equitable services — these services being informed by stakeholder engagement. Thus, it is a grand Research-to-Operations challenge. This initiative and early studies will be described, with attention focused on opportunities for community involvement in R2O.
2/11/2022 – Stretched Grids for GEOS Chem High Performance
Liam Bindle; Washington University at St. Louis
We recently added a grid refinement capability to the GEOS-Chem High-Performance model that uses grid-stretching to refine the model grid in a user-defined region. Grid-stretching is a nimble technique because it is controlled by simple runtime parameters and does not require lateral boundary conditions. In this talk, I will discuss grid-stretching along with considerations for using stretched grids for simulations of atmospheric chemistry.
1/13/2022 – Coordinating the Giant: The Earth Prediction Innovation Center
Maoyi Huang; Earth Prediction Innovation Center (EPIC)/NOAA
The Weather Research and Forecasting Innovation Act of 2017 (WRFIA) instructs NOAA to prioritize improving weather data, modeling, computing, forecasting and warnings for the protection of life and property and for the enhancement of the national economy. The National Integrated Drought Information System Reauthorization Act of 2018 (NIDISRA) instructs NOAA to establish the Earth Prediction Innovation Center (EPIC) to accelerate community-developed scientific and technological enhancements into the operational applications for numerical weather prediction (NWP) with the following responsibilities:
- Leveraging the weather enterprise to provide expertise on removing barriers to improving numerical weather prediction;
- Enabling scientists and engineers to effectively collaborate in areas important for improving operational global NWP skill, including model development, data assimilation techniques, systems architecture integration, and computational efficiencies;
- Strengthening NOAA’s ability to undertake research projects in pursuit of substantial advancements in weather forecast skill;
- Utilizing and leverage existing resources across NOAA’s enterprise;
- Creating a community global weather research modeling system that is; 1) Accessible by the public; 2) Meets basic end-user requirements for running on public computers and networks located outside of secure NOAA information and technology systems; and; 3) Utilizes, whenever appropriate and cost-effective, innovative strategies and methods, including cloud-based computing capabilities, for hosting and management of part or all of the system described in this subsection
A fundamental question is how EPIC will accelerate the rate of transitioning innovative research and development into NOAA NWP operational applications. This presentation will highlight several important cultural, organizational and technological developments in the past several years within and external to NOAA that position EPIC to be successful in the near term and in the long term.
12/9/2021 – Model Diagnostics Task Force – A Walkthrough of the Technical Vision and the Diagnostics Package
Aparna Radhakrishnan; Princeton University/NOAA/GFDL & Wenhao Dong; UCAR/GFDL
Climate and weather model development requires ongoing improvements in the representation of a growing list of physical processes. Process-oriented diagnostics (PODs) seek to give insight into the physical mechanisms needed to guide model development. The Model Diagnostics Task Force package (MDTF Diagnostics) is an open-source Python-based unified framework that runs process-oriented diagnostics (PODs) on weather and climate model data. The software package promotes the development and integration of diagnostics by subject matter experts across government, academia, and the private sector to improve the understanding of underlying processes in models under development by NOAA-GFDL and NCAR.
In this talk, we will provide an overview of the MDTF framework, encompassing both the technical and the scientific aspects. The technical overview will lay out the design goals and vision, encompassing key aspects such as Continuous Integration (CI), cloud computing and containerization to further strengthen collaborative development and foster community engagement. A science blurb centered around the Madden–Julian oscillation (MJO) process diagnostics and mesoscale convective systems based on GFDL simulations will be highlighted.
10/14/2021 – The Common Community Physics Page and its Role as an Enabler of Hierarchical System Development
Ligia Bernardet (NOAA/GSL) & Mike Ek (NCAR)
The Common Community Physics Package (CCPP) is a collection of physical parameterizations and a framework that enables their use by host Earth System Models. The CCPP is now an integral part of the Unified Forecast System (UFS) and is used in developmental versions of its Subseasonal-to-Seasonal, Medium-Range Weather (MRW), Short-Range Weather (SRW), Hurricane, and Atmospheric Composition applications. The CCPP is designed to lower the bar for community involvement in physics testing and development through increased interoperability, improved documentation, and continuous support to the developers and the users.
The CCPP is used with a variety of host models, which increases the breadth of innovations that can benefit the UFS. The CCPP has been adopted in developmental versions of NCAR and Navy models, and is distributed with the CCPP Single Column Model (SCM), which offers a simpler and computationally inexpensive avenue for testing and developing atmospheric physics compared to a full three-dimensional model. Given its capabilities to be initialized and forced both by observational field campaign data and previous UFS simulations, the CCPP SCM is a key element in Hierarchical System Development (HSD), which includes testing small elements (e.g. physics schemes) of an Earth System Model (ESM) first in isolation and then with progressive coupling, all the way up to fully-coupled global system. Scientists can utilize the CCPP package to rapidly develop and test prototype code, as well as to tune and explore the parameter space of their schemes.
The CCPP has been developed as open source code, with public releases and support provided by the Developmental Testbed Center (DTC). It is also embedded in public releases of the CCPP SCM and of the UFS MRW and SRW applications. Other resources for CCPP users and developers are an online tutorial, archived materials from UFS training sessions, scientific and technical documentations, a catalog of case studies that highlights UFS MRW biases, and a community forum. In this seminar, we will provide an update of the CCPP and SCM latest developments, and review the plans for CCPP future development and transition to operations.
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9/9/2021 – Investigation of Land-atmosphere Interaction in UFS and its Influence on Model Mean Bias
Paul Dirmeyer, George Mason University
Abstract: The Unified Forecast System (UFS) is a fully coupled Earth modeling system. It will be the system for NOAA‘s operational numerical weather prediction applications. UFS has been progressing through several prototype simulations (lately P5, P6, and P7) by improving model configurations on the way toward an operational version. The land surface model (LSM) beginning with P7 is the multi-parameterization version of Noah (Noah-MP), replacing the older Noah LSM. The main differences between these LSMs include the number of land tiles and snowpack layers, and the simulation of an aquifer below the bottom layer.
This study investigates land-atmosphere interactions in the UFS prototypes and their influence on model mean state bias, with particular attention to the impact of Noah-MP on the coupled model simulation. For the model evaluations, the modeled states related to land-atmosphere interactions for two July seasons (2012-2013) simulated in sub-prototype P7a are assessed against in situ observations as well as satellite-based products with attention to consistency in vegetated land cover and coupling regimes (energy vs. moisture limit controls on surface fluxes). There is an improvement in the simulation of net radiation and the terrestrial coupling index at the land surface without a large degradation on the other state variables (e.g., surface soil moisture). A remarked improvement in surface air temperature over the central US is related to the increased soil moisture sourced by precipitation. This suppresses sensible heat flux, corresponding to weakened land-atmosphere interaction, which corrects the warm bias by improving the coupling regime.
8/12/2021 – Clouds in the Cloud: Developing NOAA’s Next Generation Convection-Allowing Prediction System with Cloud HPC
Jacob Carley, NOAA/EMC
Abstract: The Rapid Refresh Forecast System (RRFS) is NOAA’s next generation convection-allowing ensemble and is currently planned for implementation in the last quarter of 2023. The planned configuration for RRFS is ambitious and its development requires substantial computational resources. The system will cover North America at 3 km grid-spacing with 65 vertical layers having 36 members deployed for its data assimilation system and a subset of 9 members for the free forecast. The RRFS will be hourly-updating and feature forecasts out to 18 hours every hour with extensions to 60 hours 4 times per day. To develop such a system requires computational resources beyond those available on currently deployed on-prem systems.
Cloud-based high performance computing (HPC) capabilities have grown over the past several years and it is apparent that cloud HPC has a role in research and development of our numerical weather prediction systems, and perhaps in operations. In this talk we will describe our recent and ongoing work in developing and testing, in real-time at NOAA testbeds, a prototype RRFS ensemble forecast system using cloud infrastructure.
7/1/2021 – The Unified Gravity-Wave Physics in the UFS
Michael Toy, NOAA/GSL & CIRES/CU Boulder
Abstract:The GFS model physics has long included the parameterization of subgrid-scale drag forces and dissipative heating due to the vertical propagation and breaking of gravity waves forced by topography and non-stationary sources such as convection. Two additional parameterizations of orographic sources of subgrid drag have recently been implemented in the RAP/HRRR regional forecasting models developed by the Global Systems Laboratory (GSL). These schemes, which represent gravity wave breaking in highly stable boundary layers and turbulent orographic form drag, account for drag forces generated by horizontal topographic variations down to the ~1km scale. All of these schemes are now available in one “unified” physics module within the FV3GFS/CCPP framework. This presentation will provide a physical overview of the parameterizations. Preliminary test results with the global FV3GFS will be presented, and some possible future directions will be described.
6/17/2021 – Advancing Probabilistic Prediction of High-Impact Weather using Ensemble Reforecasts and Machine Learning
Russ Schumacher, Colorado State University
Abstract: Convective weather hazards—excessive rainfall, tornadoes, large hail, and damaging winds—occur on spatial and temporal scales that are not well represented in numerical weather prediction model output. The predictability limit for these hazards is short, so reliable probabilistic forecasts are needed rather than deterministic predictions that will inevitably have large errors. To address these challenges, over the past several years we have developed a suite of probabilistic forecast systems, referred to as Colorado State University-Machine Learning Probabilities (CSU-MLP), that use the Global Ensemble Forecast System (GEFS) Reforecast datasets, historical observations of hazardous weather, and machine learning algorithms to generate skillful, reliable guidance that operational forecasters can use as a “first guess” when generating outlooks. Through close collaboration, testbed evaluations, and iterative improvements, CSU-MLP excessive rainfall guidance has been transitioned into operational use at the Weather Prediction Center. We have recently established a similar collaboration with the Storm Prediction Center with the goal of a similar operational transition for probabilistic severe weather guidance. This presentation will summarize some of the challenges and successes in operations-to-research-to-operations for incorporating machine learning into the operational forecast process, and plans for future work using the Unified Forecast System.
6/3/2021 – Reanalysis Efforts at NASA GMAO: From MERRA-2 to GEOS-R21C and MERRA-3
Amal EL Akkraoui; NASA Global Modeling and Assimilation Office (GMAO)
Abstract: Building on the success of the Modern-Era Retrospective analysis for Research and Applications, MERRA, and its successor MERRA-2 (released in 2009 and 2015 respectively), the NASA Global Modeling and Assimilation Office (GMAO) continues its incremental effort towards a decadal goal of an Integrated Earth System retrospective analysis, MERRA-3 (~2025), coupling components of the atmosphere, ocean, chemistry, land, and ice. While aspects of the coupled Goddard Earth Observing System (GEOS) model and data assimilation are currently under active development, an intermediate reanalysis featuring recent advances in the GEOS atmospheric component is planned as a stepping-stone towards MERRA-3. The GEOS-5 Retrospective analysis for the 21st Century, GEOS-R21C, is a hybrid 4D-EnVar atmospheric reanalysis that will cover the period 2000-onwards and feature the NASA’s Earth Observing System EOS and post-EOS satellite observations. Analysis fields from GEOS-R21C are planned to drive two other retrospective products, an off-line chemistry reanalysis (GEOS-R21C-Chem) and a high-resolution downscaled product for the polar regions (PolarMERRA). Both are also expected to aid in the ongoing coupling effort in advance of MERRA-3.
This talk will present an overview of GMAO retrospective analysis products and focus on the strategy for the two upcoming reanalyses GEOS-R21C and MERRA-3. Configuration details and preliminary results from prototype-R21C will be presented along with an overall review of new and revised features from MERRA-2.
5/20/2021 – Boundary Layer Scheme Development for the United Forecast System
Joseph Olson, NOAA – GSL
Abstract: The Mellor–Yamada–Nakanishi–Niino (MYNN) Eddy Diffusivity-Mass Flux (EDMF) planetary boundary layer (PBL) scheme has been used in the operational High-Resolution Rapid Refresh (HRRR) since 2014. It has been developed primarily to help provide operational short-range convection-allowing forecasts for the contiguous United States (CONUS). This scheme has been selected for inclusion within the set of advanced experimental physical parameterizations in support of the Unified Forecast System (UFS). This expanded scope requires testing the MYNN-EDMF at much longer forecast lead times, at much longer time steps, over more regions of the Earth, and within a new dynamical core. The characterization of model errors specific to the MYNN-EDMF has been accomplished since its implementation into the FV3/CCPP framework, and subsequent development has progressed for improving clouds, solar radiation, and many other sensible weather variables for this expanded application scope. This presentation will overview key features of the MYNN-EDMF and highlight some recent development activities and associated performance examples. Successes and remaining challenges are identified for further research.
5/6/2021 – Design Strategies for Skillful and Reliable Regional UFS Ensemble Forecasts
Dr. Glen Romine, NCAR
Abstract: NOAA’s Rapid Refresh Forecast System development includes plans to utilize high-resolution ensemble analysis methods to initialize forecasts. Traditionally, the design process for convection-permitting ensemble forecast systems has been largely ad hoc. More recently, teams that include collaborations between NCAR and NOAA have identified a simplified forecast system design approach that can use a single-physics modeling system based on the UFS short-range weather application. A key benefit of this approach includes a simplified code repository and workflow, but challenges remain to achieve both skillful and reliable predictions. Here, we present highlights from several activities contributing toward convection-permitting ensemble forecast system design, including: i) tools to trace sources of systematic model error to individual model components, such as specific physics packages; ii) approaches to reduce systematic errors in continuous cycling by increasing analysis system resolution along with large-scale analysis blending approaches; iii) diagnosing scale dependent perturbation growth rates within ensemble forecasts; and iv) post-processing methods to enhance the use and usability of ensemble forecasts. Collectively, these activities help guide forecasts system development within NOAA. During the presentation we will also touch on plans to extend systematic model error detection toward situation dependent errors within the global UFS medium range weather application.
4/22/2021 -Developing Regional Ocean Modeling Capabilities With MOM6 for Use in UFS
Dr. Enrique Curchitser, Rutgers University
Abstract: Nearly 40% of the US population lives in what is considered coastal regions. The economic
services of the coastal regions such as shipping, tourism, fisheries, an industry constitute a
significant portion of the economy. US waters and exclusive economic zones are characterized by their diversity: from sub-tropical islands (e.g., Hawaii), eastern and western boundary currents (west and east coasts of the continental US), sub-Arctic (e.g., Bering Sea) to the Arctic shelf (e.g., Chukchi Sea). At the same time many of the coastal regions are susceptible to extreme weather events. Providing accurate forecasts to these regions is paramount for the safety of the population and for the economic vitality of coastal areas.
In coastal regions, improving the ocean component of the forecast system can lead both to
improved weather forecasts and estimates of impacts such as the ones resulting from storm surge and precipitation events. In this talk we describe the development and implementation of regional ocean modeling capabilities using the NOAA GFDL MOM6 ocean circulation model. We describe a strategy for a robust and holistic coastal and regional modeling capacity that leverages sustained NOAA investments in ocean model development at the Geophysical Fluid Dynamics Laboratory. The strategy is designed to provide a lasting and improved capability for fundamental process studies and weather and subseasonal-to-seasonal predictions in coastal systems.
4/8/2021 – A Preliminary Report Out on the 2021 DTC UFS Evaluation Metrics Workshop
Tara Jensen, NCAR/RAL – DTC
Abstract: The Developmental Testbed Center (DTC), in collaboration with the National Oceanic and Atmospheric Administration (NOAA) and the Unified Forecast System’s Verification and Validation Cross-Cutting Team (UFS-V&V), a three-day workshop to identify key verification and validation metrics for UFS applications. The workshop was held remotely 22-24 February, 2021. Approximately 300 participants registered for this event from across the research and operational community.
The goal of this workshop was to identify and prioritize key metrics to apply during the evaluation of UFS research products and guiding their transition from research-to-operations (R2O). Because all UFS evaluation decisions affect a diverse set of users, workshop organizers encouraged members from government, academic, and private-sector organizations to participate in the workshop. The organizing committee used the outcome of the 2018 DTC Community Unified Forecast System Test Plan and Metrics Workshop to form the foundation of the workshop and to prepare and disseminate a series of three pre-workshop surveys to interested parties. The results of the surveys were used to prepare the discussion points of the breakout groups to streamline the metrics prioritization process.
During the workshop, the opening plenary was focused on providing background information to the participants. The participants then joined breakout groups to discuss how to apply the prioritized metrics to the full R2O development stages and gates. The breakout groups were stratified by time scales (Short Range, Medium Range, Sub-Seasonal and Seasonal). There was also a break-out group focused on how to define the research-to-operation gates and assign metrics to them.
This presentation will provide a preliminary report out of the findings from the workshop and discuss what the committee perceives are the next steps.
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3/25/2021 – GPU Acceleration of FV3 and the UFS: History, Progress, and Prospects
Lucas Harris, NOAA/GFDL
Abstract: One of the major strengths of the GFDL Finite-Volume Cubed-Sphere Dynamical Core (FV3) is its efficiency and scalability on traditional CPUs. FV3 was designed to fit the MPI-OpenMP paradigm which has served HPC well for three decades. But this may be changing as emerging exascale systems are using manycore processors, especially GPUs. Even newer architectures like ARMs and FPGAs are on the horizon. Previous results to re-write FV3 for GPUs have shown 10x or more speedups in the entire core. More recent results have shown 50x GPU speedups to some modules within FV3. However, each new architecture requires re-writing of scientific codes for best performance. We are collaborating with Vulcan Inc, NASA Goddard, and university partners to port FV3 and the UFS into the GT4py Domain-Specific Language. By doing so the model can be re-compiled for target processors in a way so the code and data can be laid out for optimum performance. Progress and prospects for both traditional and DSL ports will be discussed.
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3/25/2021 – FV3 Applications on GPU
Mark Govet, NOAA/OAR Global Systems Division
Abstract: Graphics Processing Units (GPUs) have become the dominant type of processor on the TOP500 list of most powerful HPC systems. NOAA’s Global Systems Laboratory has been exploring GPUs since 2010. As part of the work, GSL developed the Non-hydrostatic Icosahedral Model (NIM) and demonstrated performance-portability of the model on CPUs, GPUs and MIC processors with a single source code. Performance results reported in 2015 showed GPUs to be about 3 times faster than CPUs with minimal changes to the code. The design and parallelization strategy was successfully adopted by the MPAS model, achieving similar performance characteristics. The successful work with the NIM led the GSL team to apply the same approach to the FV3 dynamical core used in the UFS today. The two year effort (2016 – 2018) to port the FV3 to GPUs was not ultimately successful however. This presentation will describe and compare the NIM and FV3 parallelization efforts, and share lessons learned in developing performance-portable codes.
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3/11/2021 – UFS Land: The Development of a Community Effort to Expand NOAA Land Model Capabilities
Michael Barlage NOAA/NWS/NCEP/EMC, Land Team Lead
Abstract: A collaborative effort is currently underway to develop NOAA’s next generation Unified Forecast System (UFS) framework. Within the UFS, there are multiple major earth system components, including atmosphere, oceans, and land. UFS applications span local to global domains and predictive time scales from sub-hourly to seasonal predictions. These pose challenges and provide opportunities for the development and evaluation of its land components. This presentation will discuss on-going efforts in addressing and coordinating a land evaluation framework and land model physics advances for UFS. Models lacking proper coupled land-atmosphere behavior will underperform at all temporal and spatial scales, and will be prone to systematic biases in temperature, humidity and precipitation. The overarching initial UFS Land goal is to ensure equal, and achieve superior, model performance compared to the baseline UFS Land model. Progress will be presented on the community activities such as the UFS Land Working Group and the UFS Land Workshop. To facilitate UFS community engagement and accelerate R2O transition, a hierarchical testing approach is being developed that involves a spectrum of LSM-only simulations, a single-column coupled land-atmosphere modeling system, and coupled simulations both without and with a prognostic ocean. This approach is used to isolate and quantify the impacts of individual components before systematically increasing complexity and inherently introducing non-linear, difficult to track interactions. This provides a direct pathway for candidate models to diagnose problem areas in the model process chain, which enables identification of specific parameterizations, that are the source of poor model performance.
2/25/2021 – A JEDI Based Ocean Sea Ice Data Assimilation System for the UFS
Guillaume Vernieres JCSDA
Abstract: The Joint Effort for Data Assimilation Integration (JEDI) is a generic data assimilation (DA) software infrastructure developed at the Joint Center for Satellite Data Assimilation (JCSDA) to facilitate and eventually expedite transfer between research and operation. The JCSDA is also spearheading a JEDI based marine DA effort for NOAA-EMC and NASA-GMAO, for the initialization of the ocean and sea ice component of the Unified Forecast System (UFS) and the Goddard Earth Observing System (GEOS). This presentation will briefly describe the scope of the marine DA effort at the JCSDA and focus on the specific application of the JEDI to the initialization of the marine components of the UFS for NOAA’s Next Generation Global Ocean Data Assimilation System (NG-GODAS). The NG-GODAS is a joint effort between NOAA-EMC, NOAA-CPC and the JCSDA, the first objective is to replace the aging GODAS based ocean sea ice reanalysis.
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2/11/2021 – Improving weather forecast skill and rainfall climatology of FV3GFS using machine learning
Chris Bretherton, Senior Director, Vulcan Climate Modeling and Professor of Atmospheric Sciences, University of Washington
Abstract: Vulcan Climate Modeling (a small philanthropically-supported group in Seattle) and NOAA/GFDL are collaborating on a pilot project to use machine learning to develop a skillful corrective parameterization for a full-complexity global atmospheric model that helps it evolve more like a reference data set, which could be a reanalysis or a finer-grid global model. We have applied this approach to climate-oriented versions of FV3GFS with 100-200 km grids. Encouragingly, it significantly improves their 0-7 day weather forecasts and time-mean precipitation distribution, both in present and SST-perturbed climates.
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1/28/2021 – Development of Global Aerosol Forecast Model (GEFS-Aerosols) into NOAA’s Unified Forecast System (UFS)
Li (Kate) Zhang; NOAA GSL & CIRES CU Boulder
Abstract: NOAA’s National Weather Service (NWS) is on its way to deploy various operational prediction applications using the Unified Forecast System, a community-based coupled, comprehensive Earth modeling system. A chemical component developed in collaboration among the Global Systems Laboratory (GSL), Chemical Science Laboratory (CSL), and Air Resource Laboratory (ARL) was coupled online with the FV3 Global Forecast System (FV3GFS) using the National Unified Operational Prediction Capability (NUOPC)-based NOAA Environmental Modeling System (NEMS) software. It replaced the previous operational global aerosol prediction NEMS GFS Aerosol Component (NGAC) system on September 24th, 2020 as an ensemble member in the operational Global Ensemble Forecast System version 12 (GEFSv12), named GEFS-Aerosols. This chemical component of atmospheric composition in GEFS-Aerosols is based on WRF-Chem with aerosol modules based on the Goddard Chemistry Aerosol Radiation and Transport model (GOCART). Compared to WRF-Chem and the GOCART aerosol modules, the major updates in GEFS-Aerosols include the FENGSHA dust scheme implemented and developed by ARL, the Blended Global Biomass Burning Emissions Product (GBBEPx V3) from NESDIS, which provides biomass burning emission and Fire Radiative Power (FRP) data with the biomass burning plume rise modules from WRF-Chem, the global anthropogenic emission inventories derived from the Community Emissions Data System (CEDS). Sub-grid scale tracer transport and deposition are handled inside the physics routines, including consistent implementation of positive definite tracer transport and wet scavenging in the Simplified Arakawa-Schubert (SAS) scheme. This study describes the details of GEFS-Aerosols model development and corresponding evaluation of the real-time and retrospective experiments using various observations from in situ measurement, satellite and aircraft data. GEFS-Aerosols have the capability to forecast the aerosols and the hazardous air quality caused by fire and dust events. The predictions demonstrate a substantial improvement for both composition and variability of aerosol distributions over those from the currently operational NGAC global aerosol prediction system.
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12/17/2020 – Using AI to Create Situational Intelligence for Storm Responders
Vijay Jayachandran, CEO, ACW Analytics and Peter Watson is the CTO of ACW Analytics
Abstract: Extreme weather events cause billions of dollars of damage every year. Response during storms and restoration of damaged infrastructure afterwards is a perennial problem in many sectors. In the immediate aftermath of an extreme weather event, obtaining accurate ground truth information about “what just happened” is often a time consuming and manual process. This leads to operational delays and inefficiencies, which in turn drive higher costs and poor customer satisfaction.
ACW Analytics is attempting to drastically shorten the post-storm damage assessment window by using AI to confidently estimate the impacts of events in real-time. We have developed an analytical system that applies machine learning algorithms to a blend of NOAA nowcasting, analysis, and observations (HRRR, RTMA, NEXRAD) together with infrastructural and environmental data to produce detailed estimates of the severity and location of storm damages. This information can help infrastructure managers gain the situational intelligence they need to quickly adapt to severe weather as it happens, and ensure a fast and efficient return to normal.
12/03/2020 – Hurricane Analysis and Forecast System (HAFS): A Unified Forecast System Hurricane Application
Dr. Avichal Mehra – Chief, Dynamics and Coupled Modeling Group, Modeling and Data Assimilation Branch, NOAA/NWS/NCEP/EMC
Abstract: NOAA/NCEP/EMC has embarked on advancing the next generation operational Hurricane Analysis and Forecast System (HAFS) at NWS as a Unified Forecast System (UFS) application with active participation from other NOAA Laboratories (AOML, GFDL and ESRL), NCAR and operational centers (NHC and AOC). FV3-based HAFS will be a multi-scale model and data assimilation package capable of providing analyses and forecasts of the inner core structure of the Tropical Cyclones (TC) out to 7 days, which is key to improving size and intensity predictions, as well as the large-scale environment that is known to influence the TC’s motion. It will provide an advanced analysis and forecast system for cutting-edge research on modeling, physics, data assimilation, and coupling to earth system components for high-resolution TC predictions to address the outlined objectives of the UFS and the Hurricane Forecast Improvement Plan (HFIP). HAFS will provide Hurricane forecasters with reliable, robust and skillful guidance on TC track and intensity (including RI), storm size, genesis, storm surge, rainfall and tornadoes associated with TCs.
A number of different experiments based on alternate HAFS configurations were run in real-time for the 2020 Hurricane season which included stand-alone-regional domains; nested domains within global models; alternate grid projections and ensembles. In this presentation, performance of these real-time configurations will be compared and contrasted and details discussed along with plans for Hurricane model improvements in the next two to five years at NWS/NCEP.
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11/19/2020 – Development of Data Assimilation and Ensemble Forecasting Capabilities for Rapid Refresh Forecast System at CAPS
Dr. Ming Xue, Center for Analysis and Prediction of Storms (CAPS) and School of Meteorology, University of Oklahoma
Abstract: Under the support of NOAA JTTI, Warn-on-Forecast and GOES-R program fundings, CAPS has been developing capabilities for directly assimilating radar reflectivity, radial velocity, and GOES-R Geostationary Lightning Mapper (GLM) observations directly into the GSI EnKF and hybrid EnVar systems, for HRRR-like CAM forecasting systems including the future FV3-based Rapid Refresh Forecast System (RRFS). Special techniques and treatments have to be devised and implemented in GSI hybrid EnVar to be able to effectively assimilate radar reflectivity data directly within the variational framework due to the high nonlinearity of reflectivity observation operator. The operator also needs to be consistent with the preferred multi-moment microphysics scheme used. For GLM lightning flesh extent density (FED) data, tuned observation operators based on graupel mass and graupel volume are implemented within GSI and experiments show that the assimilation of FED data can achieve similar level of impacts as assimilating radar data. To effectively assimilate observations sampling synoptic (e.g. rawinsonde) through convective (e.g., radar) scales on continental-scale CAM grids utilizing ensemble error covariances, a multi-scale algorithm is developed and tested with GSI EnKF. Assimilation and forecasting results with above schemes with individual cases in an extended period will be presented.
Most recently, the direct radar reflectivity assimilation capabilities in GSI have been tentatively implemented within the first public release of JEDI, and single-time 3DVar and En3DVar analyses of hydrometeors yield reasonable results, on a local FV3-LAM grid or a stretched global FV3 grid. The presentation will also briefly report on results of FV3-LAM forecasts with multiple physics configurations for the HMT realtime forecast experiments.
11/5/2020 – The MOM6 Community Ocean Model and Its Operational Use Across NOAA
Robert Hallberg, NOAA/GFDL
Abstract: The Modular Ocean Model, version 6 (MOM6) is the latest in a long line of NOAA-supported community ocean models. With the advent of a new Open Community Development paradigm, MOM6 draws upon a broader range of precursor ocean models, and there is now engagement in the development and deployment of MOM6 by several major groups from academia and agencies in the U.S. and abroad. Extensive testing and modern version control make this Open Community Development possible, while giving each scientific group the control it needs to ensure the quality of its MOM6-based model configurations. This talk will describe some of the notable features of MOM6 (such as the use of Lagrangian Vertical Dynamics to facilitate the use of range of different vertical coordinates and cost-efficient simulations in tracer-rich configurations) that make MOM6 well suited for wide range of research and operational applications. MOM6 being used in a number of different operational or pre-operational applications in NOAA, which this talk will also describe. Examples of such applications range from centennial-scale Earth System projections, to seasonal-to-interannual global coupled forecasts, to high-resolution global near-term forecasts and regional simulations with extensive marine ecosystem components for fishery-related studies.
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10/22/2020 – Development and Evaluation of NCEP’s Global Forecast System GFSv16
Dr. Fanglin Yang, NOAA/NWS/NCEP/EMC
Abstract: The National Centers for Environmental Prediction (NCEP/NOAA) will upgrade its Global Forecast System (GFS) to version 16 in February 2021. Development of the GFSv16 started one and a half years ago, building upon the implementation of version 15, which featured a new FV3 atmospheric model dynamic core, in June 2019. This talk will present several upgrades included in GFSv16, their impact on downstream applications, and testing and evaluation results from pre-implementation real-time and retrospective parallel experiments. GFSv16 is an implementation of the Unified Forecast System (UFS) featuring an increase in the number of model vertical layers from 64 to 127, whereas the model top is extended from the upper stratosphere to the mesopause (~80 km height). Major upgrades in model physics include: (1) employing a new scheme to parameterize sub-grid scale stationary and non-stationary gravity waves; (2) adopting a scale-aware TKE-EDMF scheme to better represent the PBL processes; and (3) updating the RRTMG radiation package. Major changes in data assimilation include: (1) spinning up an offline land model with observed precipitation to provide improved land initial conditions, (2) using LETKF with model space localization and linearized observation operator to replace the Ensemble Square Root Filter, (3) employing the 4-Dimensional Incremental Analysis Update technique, (4) adopting SKEB perturbation technique in the ensemble forecast component, updating variational quality control, applying Hilbert curve to aircraft data, and inter-channel correlated observation error for CrIS and IASI observations, and (5) assimilating new satellite observations. In addition, GFSv16 includes a one-way coupled wave component that will replace the current operational stand-alone global deterministic wave model.
10/8/2020 – Climate reanalysis at ECMWF: From research to operational services
Dr. Dick Dee, Joint Center for Satellite Data Assimilation (JCSDA)
Abstract: This presentation will focus on recent developments in climate reanalysis at ECMWF. We will explain the increasing role of reanalysis activities in operational products and services in Europe in the context of the Copernicus Program. Some results from the latest ECMWF atmosphere reanalysis, ERA5, will be presented. We will also discuss the approach to coupled climate reanalysis being explored for ERA6, to be produced in the next phase of Copernicus program.
9/24/2020 – Almost Resolving Convection, but not quite…Challenges for Convective Parameterizations
Georg A. Grell, NOAA, Global Systems Laboratory
Abstract: Convection Parameterizations (CPs) are components of atmospheric models that aim to represent the statistical effects of a sub-grid scale ensemble of convective clouds. This is done in models in which the spatial resolution is not sufficient to resolve the associated convective circulations. Although CPs have been under development for over 50 years, many challenges remain. These parameterizations often differ fundamentally in closure assumptions and parameters used to solve the interaction problem, leading to a large spread and uncertainty in possible solutions. Additionally, more complexity is being added with almost every new development. On the other hand, increasing resolution in Numerical Weather Prediction models introduced additional challenges, since models can now partially resolve convection. We will discuss basic ideas and constraints of parameterizations, challenges with treating gray-scales (when convection is partially resolved) and new ideas for future developments. Simulations with FV3GFS, GEOS-5, and WRF (with and without the Grell-Freitas convection scheme) will be used to illustrate some of the issues.
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9/10/2020 – Model Validation Using GOES-16 Brightness Temperatures
Sarah Griffin, Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison
Abstract: In this presentation, infrared brightness temperatures (BTs) from the GOES-16 Advanced Baseline Imager are used to examine the accuracy of cloud forecasts using two different model approaches. The first approach will be ensemble-based, comparing simulated BTs from a 5-member ensemble where a stochastic perturbed parameter methodology is applied to the widely-used Thompson-Eidhammer cloud microphysics scheme to a 5-member ensemble with white noise perturbations added to the potential temperature fields at initialization time. The second approach compares simulated BTs from several microphysics and planetary boundary layer (PBL) schemes, as well as land surface models and surface layers.
This presentation will utilize both pixel-based and object-based statistics. Some validation metrics include the mean absolute error and mean bias error, as well as the Object-Based Threat Score and Mean-Error Distance calculation. Objects are identified using the Method for Object-Based Diagnostic Evaluation.
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8/27/2020 – Hybridization of Physics-Based Modeling with Machine Learning in Numerical Weather/Climate Prediction Systems
Vladimir Krasnopolsky, NOAA/NWS/NCEP/EMC
Abstract: During the last decade, machine learning (ML) began to play an important role in advancing scientific discovery in domains traditionally dominated by physically based (first principle) models. The use of ML models is particularly promising in scientific problems involving processes that are not completely understood, or where it is computationally infeasible to run physically based models at desired resolutions in space and time. Numerical Weather/Climate Prediction Systems (NWPS) represent one of the most complex systems that deal with such problems. Attempts to completely substitute physically based models with even the state-of-the-art black box ML models have often met with limited success in scientific domains due to inability to provide a meaningful physical understanding of underlying processes, their large data requirements, and their limited generalizability to out-of-sample scenarios. Given that neither an ML-only nor a physically based-only approach can be considered sufficient for complex scientific and operational applications, the research community explores the continuum of hybrids of physically based and ML models, where both scientific knowledge and data are integrated in a synergistic manner. This paradigm is fundamentally different from mainstream practices in the ML community that can only work with simpler forms of heuristics and constraints. This presentation is focused on hybrid NWPSs incorporating a deeper coupling of ML methods with physical knowledge. Advantages and limitations of such an approach are discussed.
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8/13/2020 – The Earth System Modeling Framework (ESMF) in the UFS Architecture
Rocky Dunlap, NCAR/ESMF; Co-Authors: Ben Koziol , Peggy Li , Fei Liu , Raffaele Montuoro , Bob Oehmke , Ryan O’Kuinghttons , Himanshu Pillai , Dan Rosen , Gerhard Theurich , Ufuk Turuncoglu 
1 National Center for Atmospheric Research; 2 NOAA Global Systems Laboratory / U. of Colorado/CIRES; 3 NASA Jet Propulsion Laboratory; 4 Naval Research Laboratory
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Abstract: The Unified Forecast System (UFS) coupled model architecture is based on the Earth System Modeling Framework (ESMF) and the National Unified Operational Prediction Capability (NUOPC) Interoperability Layer. ESMF and the NUOPC Layer provide a unified, standardized approach to implementing coupled models in the UFS, including a single Earth system driver used across applications, and a flexible approach to supporting different configurations of atmosphere, ocean, sea ice, wave, aerosol, and other model components. This talk will provide a technical overview of how ESMF/NUOPC are used in UFS applications and define key concepts important for UFS users, such as “NUOPC cap”, “Mediator,” and “Connector.” This talk will also provide an overview of capabilities provided by the multi-agency ESMF/NUOPC framework and its role in solving technical challenges in building modular, high-performance Earth system models.
7/16/2020 – The UFS Research-to-Operations (R2O) Project
Abstract: The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth modeling system. It can be configured into multiple applications, which span local to global domains and predictive time scales from sub-hourly analyses to seasonal predictions. The UFS is already being used for a wide range of research and operational prediction applications. In early 2020, the National Weather Service and the Office of Oceanic and Atmospheric Research teamed up to sponsor a project specifically targeting research to address critically important operational prediction issues and bring that research to a pre-implementation level of readiness in anticipation of transition to operations. This presentation will describe this UFS R2O Project, which got underway on 1 July 2020.
7/2/2020 – Assessing the Influence of UFS Tropical Forecast Errors on Higher Latitude Predictions Using Nudging Experiments
Abstract: The atmospheric response to variations in tropical latent heating extends well beyond its source region, and therefore it is thought that a reduction of tropical forecast errors should also benefit subsequent forecasts over the extratropics. In this presentation, we employ the use of “relaxation experiments” to quantify the remote influence of tropical forecast errors, which is implemented on the National Centers for Environmental Prediction (NCEP) unified forecast system (UFS). This approach involves nudging forecasts towards reanalyses over a tropical region, while allowing the model to run freely elsewhere. By comparing nudged to free running forecasts, this type of experiment generally shows that midlatitude forecasts are improved in association with reducing tropical forecast errors. For example, Week 2-4 forecast errors over the North Pacific and North America in particular are reduced by tropical nudging. The sensitivity of changes in remote forecast errors to nudging parameters is discussed with focus on the location of the nudging region as well as on which state variables are nudged. In addition, potential modulations of the pattern and amplitude of remote error reductions by ENSO as well as by the Madden Julian oscillation are investigated.
6/18/2020 – The NCEP Global Ensemble Forecast System version 12: Reanalysis and Reforecast
Abstract: The upcoming implementation of version 12 of the NCEP Global Ensemble Forecast System (GEFSv12) will be accompanied by a 20-year reanalysis and reforecast. The reanalysis uses a reduced-resolution approximation of the operational global data assimilation system to produce reanalyses with statistical characteristics that approximate those of the real-time system. The reanalyses are used primarily for reforecast initialization. The primary reforecast data spans the period 2000-2020. Every day during this period there were 5-member reforecasts computed to +16 days lead time, and once per week an 11-member ensemble was computed to +35 days lead. These reforecasts will be used for the statistical calibration of precipitation/temperature/freezing level for hydrologic forecasts, for 6-10 and 8-14 day forecasts, and in the future for a broader range of postprocessed products including National Blend of Models precipitation inputs, week 2 fire weather forecasts, and weeks 3-4 S2S forecasts including Atlantic hurricane cyclone energy.
To facilitate research across the enterprise, approximately 200 fields from the reanalysis and reforecast data will be made available on NOAA disk servers and at Amazon Web Services via the NOAA Big Data Program.
The seminar will provide more details on the reanalysis/reforecast, review their statistical characteristics, and will briefly discuss the anticipated future GEFSv13 reanalysis/reforecast, which we envision will utilize a weakly coupled data assimilation system and which may be computed using cloud resources.
6/4/2020 – The Unified Forecast System Short-Range Weather Application for Convection Allowing Model Forecasts
Abstract: Over the past several years, NOAA’s numerical weather prediction (NWP) efforts have organized around a vision of a community-based model system unification, i.e. Unified Forecast System (UFS), across domains from global to regional mesoscale to CONUS-scale convection-allowing (and ultimately cloud-resolving) forecasts. This webinar will focus on describing the development and plans for a UFS Short-Range Weather / Convection Allowing Model (CAM) application including the establishment of a stand-alone regional version of the FV3 dynamic core with extensible grid generation capabilities, an interface with both data assimilation systems and lateral boundary forcings provided from external models and an end-to-end workflow. The webinar will also highlight the plan to consolidate/replace many existing operational CAM systems with a UFS CAM application known as the Rapid Refresh Forecast System (RRFS) including metrics that will be used in the research-to-operations transition process along with physical parameterizations (suite) to be used in the RRFS. Public release(s) of the RRFS components will be described that will help facilitate community engagement in future development efforts. Finally, the webinar will describe some scientific challenges at CAM scales including documented biases in the depiction of convective-scale processes and other CAM initialization challenges.
5/21/2020 – Implementation of Global Ensemble Forecast System (GEFSv12) as the First UFS Sub-Seasonal Weather Application
Abstract: NCEP has implemented the first version of the Finite Volume Cubed Sphere (FV3) dynamical core based Global Forecast System (GFS v15) into operations in June 2019, replacing the spectral model based GFS. This is the first instantiation of NOAA’s Unified Forecast System (UFS) for Medium Range Weather Application in operations. The next major upgrade is for the Global Ensemble Forecast System (GEFSv12) which will use the same FV3 based global model with advanced stochastic physics perturbations, and 2-tiered SSTs, and for the first time, will be extending ensemble based weather predictions for sub-seasonal scales to 35 days. GEFSv12 comes with 20-year reanalysis and 31-year reforecasts to support stakeholder needs for calibration and validation, and 2.5 year retrospective forecasts provide basis for the scientific evaluation of GEFSv12. GEFSv12 will also unify the wave ensembles and aerosol capabilities with this implementation in support of simplifying the NCEP Production Suite. Development of GEFSv12 was a multi-year project involving collaborations with various community partners and stakeholders. This webinar describes the science changes for GEFSv12 and comprehensive evaluation of the ensemble performance for medium and extended range (weeks 3&4) weather predictions along with results from the evaluation of GEFS-Wave ensemble and GEFS-Aerosol components.
5/7/2020 – Ensemble Data Assimilation and Prediction Development for the UFS
Jeffrey S. Whitaker, Philip Pegion, Clara Draper and Anna Shylaeva, NOAA PSL and University of CO/CIRES
Abstract: This talk presented recent results from NGGPS-funded work at the Physical Sciences Laboratory focused on improving the use of ensembles in the UFS for data assimilation and prediction. In order to provide an accurate estimate of the background-error covariance used in data assimilation, and to provide reliable probabilistic predictions, the ensemble prediction system must account for both errors in the initial state and the prediction system itself. PSL has been working on improvements to the stochastic parameterization suite for UFS weather application to better capture uncertainty in the land states, and to improve the consistency of the representation of uncertainty in atmospheric physics tendencies and fluxes across model component interfaces (land/atmosphere and ocean/atmosphere). PSL has also been working to improve the Ensemble Kalman Filter (EnKF) data assimilation system used to initialize the NOAA GFS – including updating the system to work with the a much higher model top (80km instead of 50km), improving assimilation of satellite radiances, and reducing the differences between solutions provided by the variational and EnKF algorithms. Recent progress in both of these areas was be presented.