This paper reviews the development of the ensemble Kalman filter (EnKF) for atmospheric data assimilation. Particular attention is devoted to recent advances and current challenges. The ...distinguishing properties of three well-established variations of the EnKF algorithm are first discussed. Given the limited size of the ensemble and the unavoidable existence of errors whose origin is unknown (i.e., system error), various approaches to localizing the impact of observations and to accounting for these errors have been proposed. However, challenges remain; for example, with regard to localization of multiscale phenomena (both in time and space). For the EnKF in general, but higher-resolution applications in particular, it is desirable to use a short assimilation window. This motivates a focus on approaches for maintaining balance during the EnKF update. Also discussed are limited-area EnKF systems, in particular with regard to the assimilation of radar data and applications to tracking severe storms and tropical cyclones. It seems that relatively less attention has been paid to optimizing EnKF assimilation of satellite radiance observations, the growing volume of which has been instrumental in improving global weather predictions. There is also a tendency at various centers to investigate and implement hybrid systems that take advantage of both the ensemble and the variational data assimilation approaches; this poses additional challenges and it is not clear how it will evolve. It is concluded that, despite more than 10 years of operational experience, there are still many unresolved issues that could benefit from further research. Contents 1. 1. Introduction...4490 2. 2. Popular flavors of the EnKF algorithm...4491 1. a. General description...4491 2. b. Stochastic and deterministic filters...4492 1. The stochastic filter...4492 2. The deterministic filter...4492 3. c. Sequential or local filters...4493 1. Sequential ensemble Kalman filters...4493 2. The local ensemble transform Kalman filter...4494 4. d. Extended state vector...4494 5. e. Issues for the development of algorithms...4495 3. 3. Use of small ensembles...4495 1. a. Monte Carlo methods...4495 2. b. Validation of reliability...4497 3. c. Use of group filters with no inbreeding...4498 4. d. Sampling error due to limited ensemble size: The rank problem...4498 5. e. Covariance localization...4499 1. Localization in the sequential filter...4499 2. Localization in the LETKF...4499 3. Issues with localization...4500 6. f. Summary...4501 4. 4. Methods to increase ensemble spread...4501 1. a. Covariance inflation...4501 1. Additive inflation...4501 2. Multiplicative inflation...4502 3. Relaxation to prior ensemble information...4502 4. Issues with inflation...4503 2. b. Diffusion and truncation...4503 3. c. Error in physical parameterizations...4504 1. Physical tendency perturbations...4504 2. Multimodel, multiphysics, and multiparameter approaches...4505 3. Future directions...4505 4. d. Realism of error sources...4506 5. 5. Balance and length of the assimilation window...4506 1. a. The need for balancing methods...4506 2. b. Time-filtering methods...4506 3. c. Toward shorter assimilation windows...4507 4. d. Reduction of sources of imbalance...4507 6. 6. Regional data assimilation...4508 1. a. Boundary conditions and consistency across multiple domains...4509 2. b. Initialization of the starting ensemble...4510 3. c. Preprocessing steps for radar observations...4510 4. d. Use of radar observations for convective-scale analyses...4511 5. e. Use of radar observations for tropical cyclone analyses...4511 6. f. Other issues with respect to LAM data assimilation...4511 7. 7. The assimilation of satellite observations...4512 1. a. Covariance localization...4512 2. b. Data density...4513 3. c. Bias-correction procedures...4513 4. d. Impact of covariance cycling...4514 5. e. Assumptions regarding observational error...4514 6. f. Recommendations regarding satellite observations...4515 8. 8. Computational aspects...4515 1. a. Parameters with an impact on quality...4515 2. b. Overview of current parallel algorithms...4516 3. c. Evolution of computer architecture...4516 4. d. Practical issues...4517 5. e. Approaching the gray zone...4518 6. f. Summary...4518 9. 9. Hybrids with variational and EnKF components...4519 1. a. Hybrid background error covariances...4519 2. b. E4DVar with the alpha control variable...4519 3. c. Not using linearized models with 4DEnVar...4520 4. d. The hybrid gain algorithm...4521 5. e. Open issues and recommendations...4521 10. 10. Summary and discussion...4521 1. a. Stochastic or deterministic filters...4522 2. b. The nature of system error...4522 3. c. Going beyond the synoptic scales...4522 4. d. Satellite observations...4523 5. e. Hybrid systems...4523 6. f. Future of the EnKF...4523 APPENDIX A...4524 Types of Filter Divergence...4524 1. a. Classical filter divergence...4524 2. b. Catastrophic filter divergence...4524 APPENDIX B...4524 Systems Available for Download...4524 References...4525
Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised ...clustering followed by manual annotation or via 'mapping' to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma.
Black carbon (BC) particles over the Himalayas and Tibetan Plateau (HTP), both airborne and those deposited on snow, have been shown to affect snowmelt and glacier retreat. Since BC over the HTP may ...originate from a variety of geographical regions and emission sectors, it is essential to quantify the source-receptor relationships of BC in order to understand the contributions of natural and anthropogenic emissions and provide guidance for potential mitigation actions. In this study, we use the Community Atmosphere Model version 5 (CAM5) with a newly developed source-tagging technique, nudged towards the MERRA meteorological reanalysis, to characterize the fate of BC particles emitted from various geographical regions and sectors. Evaluated against observations over the HTP and surrounding regions, the model simulation shows a good agreement in the seasonal variation in the near-surface airborne BC concentrations, providing confidence to use this modeling framework for characterizing BC source-receptor relationships. Our analysis shows that the relative contributions from different geographical regions and source sectors depend on season and location in the HTP. The largest contribution to annual mean BC burden and surface deposition in the entire HTP region is from biofuel and biomass (BB) emissions in South Asia, followed by fossil fuel (FF) emissions from South Asia, then FF from East Asia. The same roles hold for all the seasonal means except for the summer, when East Asia FF becomes more important. For finer receptor regions of interest, South Asia BB and FF have the largest impact on BC in the Himalayas and central Tibetan Plateau, while East Asia FF and BB contribute the most to the northeast plateau in all seasons and southeast plateau in the summer. Central Asia and Middle East FF emissions have relatively more important contributions to BC reaching the northwest plateau, especially in the summer. Although local emissions only contribute about 10% of BC in the HTP, this contribution is extremely sensitive to local emission changes. Lastly, we show that the annual mean radiative forcing (0.42 W m-2) due to BC in snow outweighs the BC dimming effect (-0.3 W m-2) at the surface over the HTP. We also find strong seasonal and spatial variation with a peak value of 5 W m-2 in the spring over the northwest plateau. Such a large forcing of BC in snow is sufficient to cause earlier snow melting and potentially contribute to the acceleration of glacier retreat.
The Energy Exascale Earth System Model Atmosphere Model version 1, the atmospheric component of the Department of Energy's Energy Exascale Earth System Model is described. The model began as a fork ...of the well‐known Community Atmosphere Model, but it has evolved in new ways, and coding, performance, resolution, physical processes (primarily cloud and aerosols formulations), testing and development procedures now differ significantly. Vertical resolution was increased (from 30 to 72 layers), and the model top extended to 60 km (~0.1 hPa). A simple ozone photochemistry predicts stratospheric ozone, and the model now supports increased and more realistic variability in the upper troposphere and stratosphere. An optional improved treatment of light‐absorbing particle deposition to snowpack and ice is available, and stronger connections with Earth system biogeochemistry can be used for some science problems. Satellite and ground‐based cloud and aerosol simulators were implemented to facilitate evaluation of clouds, aerosols, and aerosol‐cloud interactions. Higher horizontal and vertical resolution, increased complexity, and more predicted and transported variables have increased the model computational cost and changed the simulations considerably. These changes required development of alternate strategies for tuning and evaluation as it was not feasible to “brute force” tune the high‐resolution configurations, so short‐term hindcasts, perturbed parameter ensemble simulations, and regionally refined simulations provided guidance on tuning and parameterization sensitivity to higher resolution. A brief overview of the model and model climate is provided. Model fidelity has generally improved compared to its predecessors and the CMIP5 generation of climate models.
Plain Language Summary
This study provides an overview of a new computer model of the Earth's atmosphere that is used as one component of the Department of Energy's latest Earth system model. The model can be used to help understand past, present, and future changes in Earth's behavior as the system responds to changes in atmospheric composition (like pollution and greenhouse gases), land, and water use and to explore how the atmosphere interacts with other components of the Earth system (ocean, land, biology, etc.). Physical, chemical, and biogeochemical processes treated within the atmospheric model are described, and pointers to previous and recent work are listed to provide additional information. The model is compared to present‐day observations and evaluated for some important tests that provide information about what could happen to clouds and the environment as changes occur. Strengths and weaknesses of the model are listed, as well as opportunities for future work.
Key Points
A brief description and evaluation is provided for the atmospheric component of the Department of Energy's Energy Exascale Earth System Model
Model fidelity has generally improved compared to predecessors and models participating in past international model evaluations
Strengths and weaknesses of the model, as well as opportunities for future work, are described
We developed incremental reactivity (IR) scales for 116 volatile organic compounds (VOCs) in a Chinese megacity (Guangzhou) and elucidated their
application in calculating the ozone (O3) formation ...potential (OFP) in China. Two sets of model inputs (emission-based and observation-based) were designed to localize the IR scales in Guangzhou using the Master Chemical Mechanism (MCM) box model and were also compared with those of the US. The two inputs differed in how primary pollutant inputs in the model were derived, with one based on emission data and the
other based on observed pollutant levels, but the maximum incremental reactivity (MIR) scales derived from them were fairly similar. The IR scales showed a strong dependence on the chemical mechanism (MCM vs. Statewide Air Pollution Research Center), and a higher consistency was found in IR scales between China and the US using a similar chemical mechanism. With a given chemical mechanism, the MIR scale for most VOCs showed a relatively small dependence on environmental conditions. However, when the NOx availability decreased, the IR scales became more sensitive to environmental conditions and the discrepancy between the IR scales obtained from emission-based and observation-based inputs increased, thereby implying the necessity to localize IR scales over mixed-limited or NOx-limited areas. This study provides recommendations for the application of IR scales, which has great significance for VOC control in China and other countries suffering from serious O3 air pollution.
The epidemiology of candidaemia varies between hospitals and geographic regions. Although there are many studies from Asia, a large-scale cross-sectional study across Asia has not been performed. We ...conducted a 12-month, laboratory-based surveillance of candidaemia at 25 hospitals from China, Hong Kong, India, Singapore, Taiwan and Thailand. The incidence and species distribution of candidaemia were determined. There were 1601 episodes of candidaemia among 1.2 million discharges. The overall incidence was 1.22 episodes per 1000 discharges and varied among the hospitals (range 0.16–4.53 per 1000 discharges) and countries (range 0.25–2.93 per 1000 discharges). The number of Candida blood isolates and the total number of fungal isolates were highly correlated among the six countries (R² = 0.87) and 25 hospitals (R² = 0.77). There was a moderate correlation between incidence of candidaemia and the intensive care unit (ICU)/total bed ratio (R² = 0.47), although ICUs contributed to only 23% of candidaemia cases. Of 1910 blood isolates evaluated, Candida albicans was most frequently isolated (41.3%), followed by Candida tropicalis (25.4%), Candida glabrata (13.9%) and Candida parapsilosis (12.1%). The proportion of C. tropicalis among blood isolates was higher in haemato-oncology wards than others wards (33.7% versus 24.5%, p 0.0058) and was more likely to be isolated from tropical countries than other Asian countries (46.2% versus 18.9%, p 0.04). In conclusion, the ICU settings contribute, at least in part, to the incidence variation among hospitals. The species distribution is different from Western countries. Both geographic and healthcare factors contribute to the variation of species distribution.
Magnetotail reconnection plays a crucial role in explosive energy conversion in geospace. Because of the lack of in-situ spacecraft observations, the onset mechanism of magnetotail reconnection, ...however, has been controversial for decades. The key question is whether magnetotail reconnection is externally driven to occur first on electron scales or spontaneously arising from an unstable configuration on ion scales. Here, we show, using spacecraft observations and particle-in-cell (PIC) simulations, that magnetotail reconnection starts from electron reconnection in the presence of a strong external driver. Our PIC simulations show that this electron reconnection then develops into ion reconnection. These results provide direct evidence for magnetotail reconnection onset caused by electron kinetics with a strong external driver.
•Compensability between indicators needs to be restricted in performance comparison.•A non-compensatory approach with thresholds is proposed.•We assess the low-carbon performance of Chinese cities by ...the proposed approach.•The impact of different thresholds on performance rankings is studied.
Low-carbon development has been widely regarded as a key strategy for tackling the challenges posed by climate change. Measuring low-carbon performance can provide policy makers valuable information for monitoring the progress of low-carbon development in an economy such as a city. Composite indicator, owing to its transparency and ease of communication to the public, has been touted as a useful analytical tool for measuring low-carbon performance. The construction of composite indicators often takes the compensability assumption which allows the full substitutability between underlying indicators. In this paper, we argue that the compensability assumption needs to be restricted in assessing low-carbon performance. A non-compensatory approach based on the outranking relation is used to construct composite low-carbon performance indicator. A more efficient heuristic procedure is proposed to handle the computational complexity in deriving the final comprehensive rankings. The approach has been applied to assess the city-level low-carbon performance in China. A sensitivity analysis is conducted to investigate the impacts of various parameters on the modeling results.
Aerosols have important impacts on air quality and climate, but the processes affecting their removal from the atmosphere are not fully understood and are poorly constrained by observations. This ...makes modelled aerosol lifetimes uncertain. In this study, we make use of an observational constraint on aerosol lifetimes provided by radionuclide measurements and investigate the causes of differences within a set of global models. During the Fukushima Dai-Ichi nuclear power plant accident of March 2011, the radioactive isotopes cesium-137 (137Cs) and xenon-133 (133Xe) were released in large quantities. Cesium attached to particles in the ambient air, approximately according to their available aerosol surface area. 137Cs size distribution measurements taken close to the power plant suggested that accumulation-mode (AM) sulfate aerosols were the main carriers of cesium. Hence, 137Cs can be used as a proxy tracer for the AM sulfate aerosol's fate in the atmosphere. In contrast, the noble gas 133Xe behaves almost like a passive transport tracer. Global surface measurements of the two radioactive isotopes taken over several months after the release allow the derivation of a lifetime of the carrier aerosol. We compare this to the lifetimes simulated by 19 different atmospheric transport models initialized with identical emissions of 137Cs that were assigned to an aerosol tracer with each model's default properties of AM sulfate, and 133Xe emissions that were assigned to a passive tracer. We investigate to what extent the modelled sulfate tracer can reproduce the measurements, especially with respect to the observed loss of aerosol mass with time. Modelled 137Cs and 133Xe concentrations sampled at the same location and times as station measurements allow a direct comparison between measured and modelled aerosol lifetime. The e-folding lifetime τe, calculated from station measurement data taken between 2 and 9 weeks after the start of the emissions, is 14.3 days (95 % confidence interval 13.1–15.7 days). The equivalent modelled τe lifetimes have a large spread, varying between 4.8 and 26.7 days with a model median of 9.4 ± 2.3 days, indicating too fast a removal in most models. Because sufficient measurement data were only available from about 2 weeks after the release, the estimated lifetimes apply to aerosols that have undergone long-range transport, i.e. not for freshly emitted aerosol. However, modelled instantaneous lifetimes show that the initial removal in the first 2 weeks was quicker (lifetimes between 1 and 5 days) due to the emissions occurring at low altitudes and co-location of the fresh plume with strong precipitation. Deviations between measured and modelled aerosol lifetimes are largest for the northernmost stations and at later time periods, suggesting that models do not transport enough of the aerosol towards the Arctic. The models underestimate passive tracer (133Xe) concentrations in the Arctic as well but to a smaller extent than for the aerosol (137Cs) tracer. This indicates that in addition to too fast an aerosol removal in the models, errors in simulated atmospheric transport towards the Arctic in most models also contribute to the underestimation of the Arctic aerosol concentrations.
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