Fast Ensemble Smoothing

Smoothing is essential to many oceanographic, meteorological and hydrological applications. The interval smoothing problem updates all desired states within a time interval using all available observations. The fixed-lag smoothing problem updates only a fixed number of states prior to the observation at current time. The fixed-lag smoothing problem is, in general, thought to be computationally faster than a fixed-interval smoother, and can be an appropriate approximation for long interval-smoothing problems. In this paper, we use an ensemble-based approach to fixed-interval and fixed-lag smoothing, and synthesize two algorithms. The first algorithm produces a linear time solution to the interval smoothing problem with a fixed factor, and the second one produces a fixed-lag solution that is independent of the lag length. Identical-twin experiments conducted with the Lorenz-95 model show that for lag lengths approximately equal to the error doubling time, or for long intervals the proposed methods can provide significant computational savings. These results suggest that ensemble methods yield both fixed-interval and fixed-lag smoothing solutions that cost little additional effort over filtering and model propagation, in the sense that in practical ensemble application the additional increment is a small fraction of either filtering or model propagation costs. We also show that fixed-interval smoothing can perform as fast as fixed-lag smoothing and may be advantageous when memory is not an issue

The 3D Grazing Collision of Two Black Holes

We present results for two colliding black holes (BHs), with angular momentum, spin, and unequal mass. For the first time gravitational waveforms are computed for a grazing collision from a full 3D numerical evolution. The collision can be followed through the merger to form a single BH, and through part of the ringdown period of the final BH. The apparent horizon is tracked and studied, and physical parameters, such as the mass of the final BH, are computed. The total energy radiated in gravitational waves is shown to be consistent with the total mass of the spacetime and the final BH mass. The implication of these simulations for gravitational wave astronomy is discussed.Comment: 4 pages, 7 figures, revte

WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework

Data assimilation is a common technique employed to estimate the state and its associated uncertainties in numerical models. Ensemble-based methods are a prevalent choice, although they can be computationally expensive due to the required ensemble integrations. In this study, we enhance the capabilities of the Weather Research and Forecasting–Advanced Research WRF (WRF-ARW) model by coupling it with the Parallel Data Assimilation Framework (PDAF) in a fully online mode. Through minimal modifications to the WRF-ARW model code, we have developed an efficient data assimilation system. This system leverages parallelization and in-memory data transfers between the model and data assimilation processes, greatly reducing the need for file I/O and model restarts during assimilation. We detail the necessary program modifications in this study. One advantage of the resulting assimilation system is a clear separation of concerns between data assimilation method development and model application resulting from PDAF's model-agnostic structure. To evaluate the assimilation system, we conduct a twin experiment simulating an idealized tropical cyclone. Cycled data assimilation experiments focus on the impact of temperature profiles. The assimilation not only significantly enhances temperature field accuracy but also improves the initial U and V fields. The assimilation process introduces only minimal overhead in runtime when compared to the model without data assimilation and exhibits excellent parallel performance. Consequently, the online WRF-PDAF system emerges as an efficient framework for implementing high-resolution mesoscale forecasting and reanalysis.</p

The Importance of Ile716 toward the Mutagenicity of 8-Oxo-2’-deoxyguanosine with Bacillus Fragment DNA Polymerase

8-oxo-2’-deoxyguanosine (OdG) is a prominent DNA lesion that can direct the incorporation of dCTP or dATP during replication. As the latter reaction can lead to mutation, the ratio of dCTP/dATP incorporation can significantly affect the mutagenic potential of OdG. Previous work with the A-family polymerase BF and seven analogues of OdG identified a major groove amino acid, Ile716, which likely influences the dCTP/dATP incorporation ratio opposite OdG. To further probe the importance of this amino acid, dCTP and dATP incorporations opposite the same seven analogues were tested with two BF mutants, I716M and I716A. Results from these studies support the presence of clashing interactions between Ile716 and the C8-oxygen and C2-amine during dCTP and dATP incorporations, respectively. Crystallographic analysis suggests that residue 716 alters the conformation of the template base prior to insertion into the active site, thereby affecting enzymatic efficiency. These results are also consistent with previous work with A-family polymerases, which indicate they have tight, rigid active sites that are sensitive to template perturbations

NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components

Identifying PM2.5 chemical components is crucial for formulating emission strategies, estimating radiative forcing, and assessing human health effects. However, accurately describing spatiotemporal variations in PM2.5 chemical components remains a challenge. In our earlier work, we developed an aerosol extinction coefficient data assimilation (DA) system (Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v1.0) that was suboptimal for chemical components. This paper introduces a novel hybrid nonlinear chemical DA system (NAQPMS-PDAF v2.0) to accurately interpret key chemical components (SO42-, NO3-, NH4+, OC, and EC). NAQPMS-PDAF v2.0 improves upon v1.0 by effectively handling and balancing stability and nonlinearity in chemical DA, which is achieved by incorporating the non-Gaussian distribution ensemble perturbation and hybrid localized Kalman-nonlinear ensemble transform filter with an adaptive forgetting factor for the first time. The dependence tests demonstrate that NAQPMS-PDAF v2.0 provides excellent DA results with a minimal ensemble size of 10, surpassing previous reports and v1.0. A 1-month DA experiment shows that the analysis field generated by NAQPMS-PDAF v2.0 is in good agreement with observations, especially in reducing the underestimation of NH4+ and NO3- and the overestimation of SO42-, OC, and EC. In particular, the Pearson correlation coefficient (CORR) values for NO3-, OC, and EC are above 0.96, and the R2 values are above 0.93. NAQPMS-PDAF v2.0 also demonstrates superior spatiotemporal interpretation, with most DA sites showing improvements of over 50 %-200 % in CORR and over 50 %-90 % in RMSE for the five chemical components. Compared to the poor performance in the global reanalysis dataset (CORR: 0.42-0.55, RMSE: 4.51-12.27 μg m-3) and NAQPMS-PDAF v1.0 (CORR: 0.35-0.98, RMSE: 2.46-15.50 μg m-3), NAQPMS-PDAF v2.0 has the highest CORR of 0.86-0.99 and the lowest RMSE of 0.14-3.18 μg m-3. The uncertainties in ensemble DA are also examined, further highlighting the potential of NAQPMS-PDAF v2.0 for advancing aerosol chemical component studies

EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters

Data assimilation (DA) in marine and freshwater systems combines numerical models and observations to deliver the best possible characterization of a waterbody's physical and biogeochemical state. DA underpins the widely used 3D ocean state reanalyses and forecasts produced operationally by, e.g., the Copernicus Marine Service. The use of DA in natural waters is an active field of research, but testing new developments in realistic setting can be challenging as operational DA systems are demanding in terms of computational resources and technical skill. There is a need for test beds that are sufficiently realistic but also efficient to run and easy to operate. Here, we present the Ensemble and Assimilation Tool (EAT), a flexible and extensible software package that enables data assimilation of physical and biogeochemical variables in a one-dimensional water column. EAT builds on established open-source components for hydrodynamics (GOTM), biogeochemistry (FABM), and data assimilation (PDAF). It is easy to install and operate and is flexible through support for user-written plugins. EAT is well suited to explore and advance the state of the art in DA in natural waters thanks to its support for (1) strongly and weakly coupled data assimilation, (2) observations describing any prognostic and diagnostic element of the physical–biogeochemical model, and (3) the estimation of biogeochemical parameters. Its range of capabilities is demonstrated with three applications: ensemble-based coupled physical–biogeochemical assimilation, the use of variational methods (3D-Var) to assimilate sea surface chlorophyll, and the estimation of biogeochemical parameters.</p

Deorbit kit demonstration mission

In Low Earth Orbit, it is possible to use the ambient plasma and the geomagnetic field to exchange momentum with the Earth's magnetosphere without using propellant. A device that allows an efficient momentum exchange is the electrodynamic tether (EDT), a long conductor attached to the satellite. EDT technology has been demonstrated in several past missions, being the Plasma Motor Generator mission (NASA 1993) one of the most successful. Nevertheless, it is not until today that reality has imposed a strong need and a concrete use case for developing this technology. In March 2019, the European Commission project Electrodynamic Tether technology for PAssive Consumable-less deorbit Kit (E.T.PACK) started the design of a new generation EDT. After completing the design phase, the consortium manufactured and is currently testing a Deorbit Kit Demonstrator (DKD) breadboard based on EDT technology. The objective of E.T.PACK is to reach Technology Readiness Level equal to 4 by 2022. The DKD is a standalone 24-kg satellite with the objective to demonstrate the performances of the improved EDT solution and validate its ultra-compact deployment system. The DKD is composed of two modules that will separate in orbit extending a 500-m long tape-like tether. The deployed bare-Aluminium tether will capture electrons from the ambient plasma passively and the circuit will be closed with the ionospheric plasma by using an active electron emitter. E.T.PACK tether will take advantage of several novelties with respect to the mission flown in the past that will allow to optimize the system volume and mass. Once successful demonstrated in orbit, the team plans to develop a suite of EDT systems capable of deorbiting satellites between 200 and 1000 kg from an altitude up to 1200 km in a few months. The work presents the current design status of the de-orbit kit demonstrator breadboard, the simulations of the system deorbit performances and the development approach.This work was supported by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No.828902 (3M€ E.T.PACK project) and No.101034874 (100K€ BMOM project). SG is supported by an Industrial Ph.D funded by Comunidad de Madrid (135K€ IND2019/TIC17198). The team has recently got 2.5M€ additional financial support from European Union (ETPACK-F project No. 101058166) for the manufacturing and qualification of the In Orbit Demonstration (IOD) by the end of 2025

HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model

This article describes a modular ensemble-based data assimilation (DA) system which is developed for an integrated surface–subsurface hydrological model. The software environment for DA is the Parallel Data Assimilation Framework (PDAF), which provides various assimilation algorithms like the ensemble Kalman filters, non-linear filters, 3D-Var and combinations among them. The integrated surface–subsurface hydrological model is HydroGeoSphere (HGS), a physically based modelling software for the simulation of surface and variably saturated subsurface flow, as well as heat and mass transport. The coupling and capabilities of the modular DA system are described and demonstrated using an idealised model of a geologically heterogeneous alluvial river–aquifer system with drinking water production via riverbank filtration. To demonstrate its modularity and adaptability, both single and multivariate assimilations of hydraulic head and soil moisture observations are demonstrated in combination with individual and joint updating of multiple simulated states (i.e. hydraulic heads and water saturation) and model parameters (i.e. hydraulic conductivity). With the integrated model and this modular DA framework, we have essentially developed the hydrologically and DA-wise robust toolbox for developing the basic model for operational management of coupled surface water–groundwater resources.</p

The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system

In the past decade groundbreaking new satellite observations of the Arctic sea ice cover have been made, allowing researchers to understand the state of the Arctic sea ice system in greater detail than before. The derived estimates of sea ice thickness are useful but limited in time and space. In this study the first results of a new sea ice data assimilation system are presented. Observations assimilated (in various combinations) are monthly mean sea ice thickness and monthly mean sea ice thickness distribution from CryoSat-2 and NASA daily Bootstrap sea ice concentration. This system couples the Centre for Polar Observation and Modelling's (CPOM) version of the Los Alamos Sea Ice Model (CICE) to the localised ensemble transform Kalman filter (LETKF) from the Parallel Data Assimilation Framework (PDAF) library. The impact of assimilating a sub-grid-scale sea ice thickness distribution is of particular novelty. The sub-grid-scale sea ice thickness distribution is a fundamental component of sea ice models, playing a vital role in the dynamical and thermodynamical processes, yet very little is known of its true state in the Arctic. This study finds that assimilating CryoSat-2 products for the mean thickness and the sub-grid-scale thickness distribution can have significant consequences for the modelled distribution of the ice thickness across the Arctic and particularly in regions of thick multi-year ice. The assimilation of sea ice concentration, mean sea ice thickness and sub-grid-scale sea ice thickness distribution together performed best when compared to a subset of CryoSat-2 observations held back for validation. Regional model biases are reduced: the thickness of the thickest ice in the Canadian Arctic Archipelago (CAA) is decreased, but the thickness of the ice in the central Arctic is increased. When comparing the assimilation of mean thickness with the assimilation of sub-grid-scale thickness distribution, it is found that the latter leads to a significant change in the volume of ice in each category. Estimates of the thickest ice improve significantly with the assimilation of sub-grid-scale thickness distribution alongside mean thickness.</p