DATA ASSIMILATION OF THE GLOBAL OCEAN USING THE 4D LOCAL ENSEMBLE TRANSFORM KALMAN FILTER (4D-LETKF) AND THE MODULAR OCEAN MODEL (MOM2)

The 4D Local Ensemble Transform Kalman Filter (4D-LETKF), originally designed for atmospheric applications, has been adapted and applied to the Geophysical Fluid Dynamics Laboratory's (GFDL) Modular Ocean Model (MOM2). This new ocean assimilation system provides an estimation of the evolving errors in the global oceanic domain for all state variables. Multiple configurations of LETKF have been designed to manage observation coverage that is sparse relative to the model resolution. An Optimal Interpolation (OI) method, implemented through the Simple Ocean Data Assimilation (SODA) system, has also been applied to MOM2 for use as a benchmark. Retrospective 7-year analyses using the two systems are compared for validation. The oceanic 4D-LETKF assimilation system is demonstrated to be an effective method for data assimilation of the global ocean as determined by comparisons of global and regional `observation minus forecast' RMS, as well as comparisons with temperature/salinity relationships and independent observations of altimetry and velocity

Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence

The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit from long records of training data, it is common to use datasets that are temporally subsampled relative to the time steps required for the numerical integration of differential equations. Here, we investigate how this often overlooked processing step affects the quality of an emulator's predictions. We implement two ML architectures from a class of methods called reservoir computing: (1) a form of Nonlinear Vector Autoregression (NVAR), and (2) an Echo State Network (ESN). Despite their simplicity, it is well documented that these architectures excel at predicting low dimensional chaotic dynamics. We are therefore motivated to test these architectures in an idealized setting of predicting high dimensional geophysical turbulence as represented by Surface Quasi-Geostrophic dynamics. In all cases, subsampling the training data consistently leads to an increased bias at small spatial scales that resembles numerical diffusion. Interestingly, the NVAR architecture becomes unstable when the temporal resolution is increased, indicating that the polynomial based interactions are insufficient at capturing the detailed nonlinearities of the turbulent flow. The ESN architecture is found to be more robust, suggesting a benefit to the more expensive but more general structure. Spectral errors are reduced by including a penalty on the kinetic energy density spectrum during training, although the subsampling related errors persist. Future work is warranted to understand how the temporal resolution of training data affects other ML architectures

Best practice strategies for process studies designed to improve climate modeling

Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 101(10), (2020): E1842-E1850, doi:10.1175/BAMS-D-19-0263.1.Process studies are designed to improve our understanding of poorly described physical processes that are central to the behavior of the climate system. They typically include coordinated efforts of intensive field campaigns in the atmosphere and/or ocean to collect a carefully planned set of in situ observations. Ideally the observational portion of a process study is paired with numerical modeling efforts that lead to better representation of a poorly simulated or previously neglected physical process in operational and research models. This article provides a framework of best practices to help guide scientists in carrying out more productive, collaborative, and successful process studies. Topics include the planning and implementation of a process study and the associated web of logistical challenges; the development of focused science goals and testable hypotheses; and the importance of assembling an integrated and compatible team with a diversity of social identity, gender, career stage, and scientific background. Guidelines are also provided for scientific data management, dissemination, and stewardship. Above all, developing trust and continual communication within the science team during the field campaign and analysis phase are key for process studies. We consider a successful process study as one that ultimately will improve our quantitative understanding of the mechanisms responsible for climate variability and enhance our ability to represent them in climate models.We gratefully acknowledge U.S. CLIVAR for supporting the PSMI panel, as well as all the principal investigators that contributed to our PSMI panel webinars. JS was inspired by participation in the process studies funded by NASA NNH18ZDA001N-OSFC and NOAA NA17OAR4310257; GF was supported by base funds to NOAA/AOML’s Physical Oceanography Division; and HS was supported by NOAA NA19OAR4310376 and NA17OAR4310255.2021-04-0

Supporting‐electrolyte‐free electrochemical methoxymethylation of alcohols using a 3D‐printed electrosynthesis continuous flow cell system

We describe the development of a novel low‐cost small‐footprint 3D‐printed electrosynthesis continuous flow cell system that was designed and adapted to fit a commercially available Electrasyn 2.0. The utility and effectiveness of the combined flow/electrochemistry system over the batch process was demonstrated in the development of an improved and supporting‐electrolyte‐free version of our anodic methoxymethylation of alcohols

Activation induced changes in GABA: functional MRS at 7 T with MEGA-sLASER

Functional magnetic resonance spectroscopy (fMRS) has been used to assess the dynamic metabolic responses of the brain to a physiological stimulus non-invasively. However, only limited information on the dynamic functional response of γ-aminobutyric acid (GABA), the primary inhibitory neurotransmitter in the brain, is available. We aimed to measure the activation-induced changes in GABA unambiguously using a spectral editing method, instead of the conventional direct detection techniques used in previous fMRS studies. The Mescher-Garwood-semi-localised by adiabatic selective refocusing (MEGA-sLASER) sequence was developed at 7 T to obtain the time course of GABA concentration without macromolecular contamination. A significant decrease (−12±5%) in the GABA to total creatine ratio (GABA/tCr) was observed in the motor cortex during a period of 10 minutes of hand-clenching, compared to an initial baseline level (GABA/tCr = 0.11±0.02) at rest. An increase in the Glx (glutamate and glutamine) to tCr ratio was also found, which is in agreement with previous findings. In contrast, no significant changes in NAA/tCr and tCr were detected. With consistent and highly efficient editing performance for GABA detection and the advantage of visually identifying GABA resonances in the spectra, MEGA-sLASER is demonstrated to be an effective method for studying of dynamic changes in GABA at 7 T

Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges, and Recommendations

The purpose of this report is to identify fundamental issues for coupled data assimilation (CDA), such as gaps in science and limitations in forecasting systems, in order to provide guidance to the World Meteorological Organization (WMO) on how to facilitate more rapid progress internationally. Coupled Earth system modeling provides the opportunity to extend skillful atmospheric forecasts beyond the traditional two-week barrier by extracting skill from low-frequency state components such as the land, ocean, and sea ice. More generally, coupled models are needed to support seamless prediction systems that span timescales from weather, subseasonal to seasonal (S2S), multiyear, and decadal. Therefore, initialization methods are needed for coupled Earth system models, either applied to each individual component (called Weakly Coupled Data Assimilation - WCDA) or applied the coupled Earth system model as a whole (called Strongly Coupled Data Assimilation - SCDA). Using CDA, in which model forecasts and potentially the state estimation are performed jointly, each model domain benefits from observations in other domains either directly using error covariance information known at the time of the analysis (SCDA), or indirectly through flux interactions at the model boundaries (WCDA). Because the non-atmospheric domains are generally under-observed compared to the atmosphere, CDA provides a significant advantage over single-domain analyses. Next, we provide a synopsis of goals, challenges, and recommendations to advance CDA: Goals: (a) Extend predictive skill beyond the current capability of NWP (e.g. as demonstrated by improving forecast skill scores), (b) produce physically consistent initial conditions for coupled numerical prediction systems and reanalyses (including consistent fluxes at the domain interfaces), (c) make best use of existing observations by allowing observations from each domain to influence and improve the full earth system analysis, (d) develop a robust observation-based identification and understanding of mechanisms that determine the variability of weather and climate, (e) identify critical weaknesses in coupled models and the earth observing system, (f) generate full-field estimates of unobserved or sparsely observed variables, (g) improve the estimation of the external forcings causing changes to climate, (h) transition successes from idealized CDA experiments to real-world applications. Challenges: (a) Modeling at the interfaces between interacting components of coupled Earth system models may be inadequate for estimating uncertainty or error covariances between domains, (b) current data assimilation methods may be insufficient to simultaneously analyze domains containing multiple spatiotemporal scales of interest, (c) there is no standardization of observation data or their delivery systems across domains, (d) the size and complexity of many large-scale coupled Earth system models makes it is difficult to accurately represent uncertainty due to model parameters and coupling parameters, (e) model errors lead to local biases that can transfer between the different Earth system components and lead to coupled model biases and long-term model drift, (e) information propagation across model components with different spatiotemporal scales is extremely complicated, and must be improved in current coupled modeling frameworks, (h) there is insufficient knowledge on how to represent evolving errors in non-atmospheric model components (e.g. as sea ice, land and ocean) on the timescales of NWP

What does it take to provide clinical interventions with temporal consistency? A qualitative study of London hyperacute stroke units.

OBJECTIVES: Seven-day working in hospitals is a current priority of international health research and policy. Previous research has shown variability in delivering evidence-based clinical interventions across different times of day and week. We aimed to identify factors influencing such variations in London hyperacute stroke units (HASUs). DESIGN: Interview and observation study to explain patterns of variation in delivery and outcomes of care described in a quantitative partner paper (Melnychuk et al). SETTING: Eight HASUs in London. PARTICIPANTS: We interviewed HASU staff (n=76), including doctors, nurses, therapists and administrators. We also conducted non-participant observations of delivery of care at different times of the day and week (n=45; ~102 hours). We analysed the data for thematic content relating to the ability of staff to provide evidence-based interventions consistently at different times of the day and week. RESULTS: Staff were able to deliver 'front door' interventions consistently by taking on additional responsibilities out of hours (eg, deciding eligibility for thrombolysis); creating continuities between day and night (through, eg, governance processes and staggering rotas); building trusting relationships with, eg, Radiology and Emergency Departments and staff prioritisation of 'front door' interventions. Variations by time of day resulted from reduced staffing in HASUs and elsewhere in hospitals in the evenings and at the weekend. Variations by day of week (eg, weekend effect) resulted from lack of therapy input and difficulties repatriating patients at weekends, and associated increases in pressure on Fridays and Mondays. CONCLUSIONS: Evidence-based service standards can facilitate 7-day working in acute stroke services. Standards should ensure that the capacity and capabilities required for 'front door' interventions are available 24/7, while other services, for example, therapies are available every day of the week. The impact of standards is influenced by interdependencies between HASUs, other hospital services and social services

3D-printed Franz cells - update on optimization of manufacture and evaluation

OBJECTIVES: Laboratory in vitro permeation processes require the use of modified Franz type diffusion cells which are conventionally fabricated from glass. Fragility and high cost are frequently associated with this type of laboratory apparatus. The purpose of our present research was to develop a simple, economical and versatile approach to manufacture Franz type cells using additive manufacturing (AM). METHODS: Graphical Franz diffusion cell designs were reproduced with a stereolithography (SLA) 3D printer and assessed over a minimum period of 24 h. The surface morphology of AM printouts was analysed before and after compatibility studies using scanning electron microscopy (SEM). Comparative permeation studies in both glass and AM Franz type diffusion cells were conducted using a caffeine solution (1.5 mg mL‑1), applied to a model silicone membrane. RESULTS: Testing of the 3D printed scaffolds confirmed similar recovery of the permeant when compared to glass cells: 1.49 ± 0.01 and 1.50 ± 0.01 mg mL‑1, respectively, after 72 h. No significant differences were visible from the SEM micrographs demonstrating consistent, smooth and non-porous surfaces of the AM Franz cells’ core structure. Permeation studies using transparent 3D printed constructs resulted in 12.85 ± 0.53 μg cm ‑2 caffeine recovery in the receptor solution after 180 min with comparable permeant recovery, 11.49 ± 1.04 μg cm ‑2, for the glass homologues. CONCLUSION: AM constructs can be considered as viable alternatives to the use of conventional glass apparatus offering a simple, reproducible and cost-effective method of replicating specialised laboratory glassware. A wider range of permeants will be investigated in future studies with these novel 3D printed Franz diffusion cells