Articles | Volume 31, issue 2
https://doi.org/10.5194/npg-31-287-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-31-287-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Jul 2024
Research article |
| 01 Jul 2024
| 01 Jul 2024
Improving ensemble data assimilation through Probit-space Ensemble Size Expansion for Gaussian Copulas (PESE-GC)
Department of Geography, The Ohio State University, Columbus, Ohio, 43210, USA
Advanced Study Program, National Center for Atmospheric Research, Boulder, Colorado, USA
Related subject area
Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
Evolution of small-scale turbulence at large Richardson numbers
Prognostic Assumed-PDF (DDF) Approach: Further Generalization and Demonstrations
Bridging classical data assimilation and optimal transport
How far can the statistical error estimation problem be closed by collocated data?
Using orthogonal vectors to improve the ensemble space of the ensemble Kalman filter and its effect on data assimilation and forecasting
Review article: Towards strongly coupled ensemble data assimilation with additional improvements from machine learning
Toward a multivariate formulation of the parametric Kalman filter assimilation: application to a simplified chemical transport model
Data-driven reconstruction of partially observed dynamical systems
Extending ensemble Kalman filter algorithms to assimilate observations with an unknown time offset
Applying prior correlations for ensemble-based spatial localization
A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter
Ensemble Riemannian data assimilation: towards large-scale dynamical systems
Inferring the instability of a dynamical system from the skill of data assimilation exercises
Multivariate localization functions for strongly coupled data assimilation in the bivariate Lorenz 96 system
Improving the potential accuracy and usability of EURO-CORDEX estimates of future rainfall climate using frequentist model averaging
Ensemble Riemannian data assimilation over the Wasserstein space
An early warning sign of critical transition in the Antarctic ice sheet – a data-driven tool for a spatiotemporal tipping point
Behavior of the iterative ensemble-based variational method in nonlinear problems
A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective
A method for predicting the uncompleted climate transition process
Statistical postprocessing of ensemble forecasts for severe weather at Deutscher Wetterdienst
Correcting for model changes in statistical postprocessing – an approach based on response theory
Brief communication: Residence time of energy in the atmosphere
Seasonal statistical–dynamical prediction of the North Atlantic Oscillation by probabilistic post-processing and its evaluation
Application of a local attractor dimension to reduced space strongly coupled data assimilation for chaotic multiscale systems
Order of operation for multi-stage post-processing of ensemble wind forecast trajectories
Lev Ostrovsky, Irina Soustova, Yuliya Troitskaya, and Daria Gladskikh
Nonlin. Processes Geophys., 31, 219–227, https://doi.org/10.5194/npg-31-219-2024, https://doi.org/10.5194/npg-31-219-2024, 2024
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The nonstationary kinetic model of turbulence is used to describe the evolution and structure of the upper turbulent layer with the parameters taken from in situ observations. As an example, we use a set of data for three cruises made in different areas of the world ocean. With the given profiles of current shear and buoyancy frequency, the theory yields results that satisfactorily agree with the measurements of the turbulent dissipation rate.
Jun-Ichi Yano
EGUsphere, https://doi.org/10.5194/egusphere-2024-287, https://doi.org/10.5194/egusphere-2024-287, 2024
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A methodology for directly predicting the time evolution of the assumed parameters for the distribution densities based on the Liouville equation, as proposed earlier, is extended to multi–dimensional cases as well as when the systems are constrained by integrals over a part of the variable range. The extended methodology is tested against a convective energy cycle system as well as the Lorenz's stranger attractor.
Marc Bocquet, Pierre J. Vanderbecken, Alban Farchi, Joffrey Dumont Le Brazidec, and Yelva Roustan
EGUsphere, https://doi.org/10.5194/egusphere-2023-2755, https://doi.org/10.5194/egusphere-2023-2755, 2023
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A novel approach, the Optimal Transport Data Assimilation, is introduced to merge data assimilation and optimal transport concepts. By leveraging optimal transport's displacement interpolation in space, it minimises mislocation errors within data assimilation applied to physical fields, such as water vapour, hydrometeors, chemical species, etc. Its richness and flexibility are showcased through one- and two-dimensional illustrations.
Annika Vogel and Richard Ménard
Nonlin. Processes Geophys., 30, 375–398, https://doi.org/10.5194/npg-30-375-2023, https://doi.org/10.5194/npg-30-375-2023, 2023
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Accurate estimation of the error statistics required for data assimilation remains an ongoing challenge, as statistical assumptions are required to solve the estimation problem. This work provides a conceptual view of the statistical error estimation problem in light of the increasing number of available datasets. We found that the total number of required assumptions increases with the number of overlapping datasets, but the relative number of error statistics that can be estimated increases.
Yung-Yun Cheng, Shu-Chih Yang, Zhe-Hui Lin, and Yung-An Lee
Nonlin. Processes Geophys., 30, 289–297, https://doi.org/10.5194/npg-30-289-2023, https://doi.org/10.5194/npg-30-289-2023, 2023
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In the ensemble Kalman filter, the ensemble space may not fully capture the forecast errors due to the limited ensemble size and systematic model errors, which affect the accuracy of analysis and prediction. This study proposes a new algorithm to use cost-free pseudomembers to expand the ensemble space effectively and improve analysis accuracy during the analysis step, without increasing the ensemble size during forecasting.
Eugenia Kalnay, Travis Sluka, Takuma Yoshida, Cheng Da, and Safa Mote
Nonlin. Processes Geophys., 30, 217–236, https://doi.org/10.5194/npg-30-217-2023, https://doi.org/10.5194/npg-30-217-2023, 2023
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Strongly coupled data assimilation (SCDA) generates coherent integrated Earth system analyses by assimilating the full Earth observation set into all Earth components. We describe SCDA based on the ensemble Kalman filter with a hierarchy of coupled models, from a coupled Lorenz to the Climate Forecast System v2. SCDA with a sufficiently large ensemble can provide more accurate coupled analyses compared to weakly coupled DA. The correlation-cutoff method can compensate for a small ensemble size.
Antoine Perrot, Olivier Pannekoucke, and Vincent Guidard
Nonlin. Processes Geophys., 30, 139–166, https://doi.org/10.5194/npg-30-139-2023, https://doi.org/10.5194/npg-30-139-2023, 2023
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This work is a theoretical contribution that provides equations for understanding uncertainty prediction applied in air quality where multiple chemical species can interact. A simplified minimal test bed is introduced that shows the ability of our equations to reproduce the statistics estimated from an ensemble of forecasts. While the latter estimation is the state of the art, solving equations is numerically less costly, depending on the number of chemical species, and motivates this research.
Pierre Tandeo, Pierre Ailliot, and Florian Sévellec
Nonlin. Processes Geophys., 30, 129–137, https://doi.org/10.5194/npg-30-129-2023, https://doi.org/10.5194/npg-30-129-2023, 2023
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The goal of this paper is to obtain probabilistic predictions of a partially observed dynamical system without knowing the model equations. It is illustrated using the three-dimensional Lorenz system, where only two components are observed. The proposed data-driven procedure is low-cost, is easy to implement, uses linear and Gaussian assumptions and requires only a small amount of data. It is based on an iterative linear Kalman smoother with a state augmentation.
Elia Gorokhovsky and Jeffrey L. Anderson
Nonlin. Processes Geophys., 30, 37–47, https://doi.org/10.5194/npg-30-37-2023, https://doi.org/10.5194/npg-30-37-2023, 2023
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Older observations of the Earth system sometimes lack information about the time they were taken, posing problems for analyses of past climate. To begin to ameliorate this problem, we propose new methods of varying complexity, including methods to estimate the distribution of the offsets between true and reported observation times. The most successful method accounts for the nonlinearity in the system, but even the less expensive ones can improve data assimilation in the presence of time error.
Chu-Chun Chang and Eugenia Kalnay
Nonlin. Processes Geophys., 29, 317–327, https://doi.org/10.5194/npg-29-317-2022, https://doi.org/10.5194/npg-29-317-2022, 2022
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This study introduces a new approach for enhancing the ensemble data assimilation (DA), a technique that combines observations and forecasts to improve numerical weather predictions. Our method uses the prescribed correlations to suppress spurious errors, improving the accuracy of DA. The experiments on the simplified atmosphere model show that our method has comparable performance to the traditional method but is superior in the early stage and is more computationally efficient.
Andrey A. Popov, Amit N. Subrahmanya, and Adrian Sandu
Nonlin. Processes Geophys., 29, 241–253, https://doi.org/10.5194/npg-29-241-2022, https://doi.org/10.5194/npg-29-241-2022, 2022
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Numerical weather prediction requires the melding of both computational model and data obtained from sensors such as satellites. We focus on one algorithm to accomplish this. We aim to aid its use by additionally supplying it with data obtained from separate models that describe the average behavior of the computational model at any given time. We show that our approach outperforms the standard approaches to this problem.
Sagar K. Tamang, Ardeshir Ebtehaj, Peter Jan van Leeuwen, Gilad Lerman, and Efi Foufoula-Georgiou
Nonlin. Processes Geophys., 29, 77–92, https://doi.org/10.5194/npg-29-77-2022, https://doi.org/10.5194/npg-29-77-2022, 2022
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The outputs from Earth system models are optimally combined with satellite observations to produce accurate forecasts through a process called data assimilation. Many existing data assimilation methodologies have some assumptions regarding the shape of the probability distributions of model output and observations, which results in forecast inaccuracies. In this paper, we test the effectiveness of a newly proposed methodology that relaxes such assumptions about high-dimensional models.
Yumeng Chen, Alberto Carrassi, and Valerio Lucarini
Nonlin. Processes Geophys., 28, 633–649, https://doi.org/10.5194/npg-28-633-2021, https://doi.org/10.5194/npg-28-633-2021, 2021
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Chaotic dynamical systems are sensitive to the initial conditions, which are crucial for climate forecast. These properties are often used to inform the design of data assimilation (DA), a method used to estimate the exact initial conditions. However, obtaining the instability properties is burdensome for complex problems, both numerically and analytically. Here, we suggest a different viewpoint. We show that the skill of DA can be used to infer the instability properties of a dynamical system.
Zofia Stanley, Ian Grooms, and William Kleiber
Nonlin. Processes Geophys., 28, 565–583, https://doi.org/10.5194/npg-28-565-2021, https://doi.org/10.5194/npg-28-565-2021, 2021
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In weather forecasting, observations are incorporated into a model of the atmosphere through a process called data assimilation. Sometimes observations in one location may impact the weather forecast in another faraway location in undesirable ways. The impact of distant observations on the forecast is mitigated through a process called localization. We propose a new method for localization when a model has multiple length scales, as in a model spanning both the ocean and the atmosphere.
Stephen Jewson, Giuliana Barbato, Paola Mercogliano, Jaroslav Mysiak, and Maximiliano Sassi
Nonlin. Processes Geophys., 28, 329–346, https://doi.org/10.5194/npg-28-329-2021, https://doi.org/10.5194/npg-28-329-2021, 2021
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Climate model simulations are uncertain. In some cases this makes it difficult to know how to use them. Significance testing is often used to deal with this issue but has various shortcomings. We describe two alternative ways to manage uncertainty in climate model simulations that avoid these shortcomings. We test them on simulations of future rainfall over Europe and show they produce more accurate projections than either using unadjusted climate model output or statistical testing.
Sagar K. Tamang, Ardeshir Ebtehaj, Peter J. van Leeuwen, Dongmian Zou, and Gilad Lerman
Nonlin. Processes Geophys., 28, 295–309, https://doi.org/10.5194/npg-28-295-2021, https://doi.org/10.5194/npg-28-295-2021, 2021
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Data assimilation aims to improve hydrologic and weather forecasts by combining available information from Earth system models and observations. The classical approaches to data assimilation usually proceed with some preconceived assumptions about the shape of their probability distributions. As a result, when such assumptions are invalid, the forecast accuracy suffers. In the proposed methodology, we relax such assumptions and demonstrate improved performance.
Abd AlRahman AlMomani and Erik Bollt
Nonlin. Processes Geophys., 28, 153–166, https://doi.org/10.5194/npg-28-153-2021, https://doi.org/10.5194/npg-28-153-2021, 2021
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This paper introduces a tool for data-driven discovery of early warning signs of critical transitions in ice shelves from remote sensing data. Our directed spectral clustering method considers an asymmetric affinity matrix along with the associated directed graph Laplacian. We applied our approach to reprocessing the ice velocity data and remote sensing satellite images of the Larsen C ice shelf.
Shin'ya Nakano
Nonlin. Processes Geophys., 28, 93–109, https://doi.org/10.5194/npg-28-93-2021, https://doi.org/10.5194/npg-28-93-2021, 2021
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The ensemble-based variational method is a method for solving nonlinear data assimilation problems by using an ensemble of multiple simulation results. Although this method is derived based on a linear approximation, highly uncertain problems, in which system nonlinearity is significant, can also be solved by applying this method iteratively. This paper reformulated this iterative algorithm to analyze its behavior in high-dimensional nonlinear problems and discuss the convergence.
Olivier Pannekoucke, Richard Ménard, Mohammad El Aabaribaoune, and Matthieu Plu
Nonlin. Processes Geophys., 28, 1–22, https://doi.org/10.5194/npg-28-1-2021, https://doi.org/10.5194/npg-28-1-2021, 2021
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Numerical weather prediction involves numerically solving the mathematical equations, which describe the geophysical flow, by transforming them so that they can be computed. Through this transformation, it appears that the equations actually solved by the machine are then a modified version of the original equations, introducing an error that contributes to the model error. This work helps to characterize the covariance of the model error that is due to this modification of the equations.
Pengcheng Yan, Guolin Feng, Wei Hou, and Ping Yang
Nonlin. Processes Geophys., 27, 489–500, https://doi.org/10.5194/npg-27-489-2020, https://doi.org/10.5194/npg-27-489-2020, 2020
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A system transiting from one stable state to another has to experience a period. Can we predict the end moment (state) if the process has not been completed? This paper presents a method to solve this problem. This method is based on the quantitative relationship among the parameters, which is used to describe the transition process of the abrupt change. By using the historical data, we extract some parameters for predicting the uncompleted climate transition process.
Reinhold Hess
Nonlin. Processes Geophys., 27, 473–487, https://doi.org/10.5194/npg-27-473-2020, https://doi.org/10.5194/npg-27-473-2020, 2020
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Forecasts of ensemble systems are statistically aligned to synoptic observations at DWD in order to provide support for warning decision management. Motivation and design consequences for extreme and rare meteorological events are presented. Especially for probabilities of severe wind gusts global logistic parameterisations are developed that generate robust statistical forecasts for extreme events, while local characteristics are preserved.
Jonathan Demaeyer and Stéphane Vannitsem
Nonlin. Processes Geophys., 27, 307–327, https://doi.org/10.5194/npg-27-307-2020, https://doi.org/10.5194/npg-27-307-2020, 2020
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Postprocessing schemes used to correct weather forecasts are no longer efficient when the model generating the forecasts changes. An approach based on response theory to take the change into account without having to recompute the parameters based on past forecasts is presented. It is tested on an analytical model and a simple model of atmospheric variability. We show that this approach is effective and discuss its potential application for an operational environment.
Carlos Osácar, Manuel Membrado, and Amalio Fernández-Pacheco
Nonlin. Processes Geophys., 27, 235–237, https://doi.org/10.5194/npg-27-235-2020, https://doi.org/10.5194/npg-27-235-2020, 2020
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We deduce that after a global thermal perturbation, the Earth's
atmosphere would need about a couple of months to come back to equilibrium.
André Düsterhus
Nonlin. Processes Geophys., 27, 121–131, https://doi.org/10.5194/npg-27-121-2020, https://doi.org/10.5194/npg-27-121-2020, 2020
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Seasonal prediction of the of the North Atlantic Oscillation (NAO) has been improved in recent years by improving dynamical models and ensemble predictions. One step therein was the so-called sub-sampling, which combines statistical and dynamical predictions. This study generalises this approach and makes it much more accessible. Furthermore, it presents a new verification approach for such predictions.
Courtney Quinn, Terence J. O'Kane, and Vassili Kitsios
Nonlin. Processes Geophys., 27, 51–74, https://doi.org/10.5194/npg-27-51-2020, https://doi.org/10.5194/npg-27-51-2020, 2020
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This study presents a novel method for reduced-rank data assimilation of multiscale highly nonlinear systems. Time-varying dynamical properties are used to determine the rank and projection of the system onto a reduced subspace. The variable reduced-rank method is shown to succeed over other fixed-rank methods. This work provides implications for performing strongly coupled data assimilation with a limited number of ensemble members on high-dimensional coupled climate models.
Nina Schuhen
Nonlin. Processes Geophys., 27, 35–49, https://doi.org/10.5194/npg-27-35-2020, https://doi.org/10.5194/npg-27-35-2020, 2020
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We present a new way to adaptively improve weather forecasts by incorporating last-minute observation information. The recently measured error, together with a statistical model, gives us an indication of the expected future error of wind speed forecasts, which are then adjusted accordingly. This new technique can be especially beneficial for customers in the wind energy industry, where it is important to have reliable short-term forecasts, as well as providers of extreme weather warnings.
Cited articles
Amezcua, J. and Van Leeuwen, P. J.: Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach, Tellus A, 66, 23493, https://doi.org/10.3402/tellusa.v66.23493, 2014. a
Anderson, J. L.: A Local Least Squares Framework for Ensemble Filtering, Mon. Weather Rev., 131, 634–642, https://doi.org/10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2, 2003. a, b, c
Anderson, J. L.: Spatially and temporally varying adaptive covariance inflation for ensemble filters, Tellus A, 61A, 72–83, https://doi.org/10.1111/j.1600-0870.2008.00361.x, 2009. a
Anderson, J. L.: A Non-Gaussian Ensemble Filter Update for Data Assimilation, Mon. Weather Rev., 138, 4186–4198, https://doi.org/10.1175/2010MWR3253.1, 2010. a, b, c, d
Anderson, J. L.: A marginal adjustment rank histogram filter for non-Gaussian ensemble data assimilation, Mon. Weather Rev., 148, 3361–3378, https://doi.org/10.1175/MWR-D-19-0307.1, 2020. a
Anderson, J. L.: A Quantile-Conserving Ensemble Filter Framework. Part II: Regression of Observation Increments in a Probit and Probability Integral Transformed Space, Mon. Weather Rev., 151, 2759–2777, https://doi.org/10.1175/MWR-D-23-0065.1, 2023. a, b
Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758, https://doi.org/10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2, 1999. a
Anderson, J. L., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The data assimilation research testbed a community facility, B. Am. Meteorol. Soc., 90, 1283–1296, https://doi.org/10.1175/2009BAMS2618.1, 2009. a
Bishop, C. H. and Hodyss, D.: Ensemble covariances adaptively localized with ECO-RAP. Part 2: A strategy for the atmosphere, Tellus A, 61A, 97–111, https://doi.org/10.1111/j.1600-0870.2008.00372.x, 2009. a, b
Bishop, C. H. and Hodyss, D.: Adaptive ensemble covariance localization in ensemble 4D-VAR state estimation, Mon. Weather Rev., 139, 1241–1255, https://doi.org/10.1175/2010MWR3403.1, 2011. a
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects, Mon. Weather Rev., 129, 420–436, https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2, 2001. a, b
Bishop, C. H., Whitaker, J. S., and Lei, L.: Gain form of the ensemble transform Kalman Filter and its relevance to satellite data assimilation with model space ensemble covariance localization, Mon. Weather Rev., 145, 4575–4592, https://doi.org/10.1175/MWR-D-17-0102.1, 2017. a
Burgers, G., Jan van Leeuwen, P., Evensen, G., Van Leeuwen, P. J., and Evensen, G.: Analysis scheme in the ensemble Kalman filter, Mon. Weather Rev., 126, 1719–1724, https://doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2, 1998. a, b
Castruccio, F. S., Karspeck, A. R., Danabasoglu, G., Hendricks, J., Hoar, T., Collins, N., and Anderson, J. L.: An EnOI-Based Data Assimilation System With DART for a High-Resolution Version of the CESM2 Ocean Component, J. Adv. Model. Earth Sy., 12, e2020MS002176, https://doi.org/10.1029/2020MS002176, 2020. a
Chan, M.-Y.: Improving the analysis and forecast of tropical mesoscale convective systems through advancing the ensemble data assimilation of geostationary satellite infrared radiance observations, PhD thesis, The Pennsylvania State University, State College, https://www.proquest.com/dissertations-theses/improving-analysis-forecast-tropical-mesoscale/docview/2812055538/se-2?accountid=9783 (last access: 17 June 2024), 2022. a
Chan, M.-Y.: Code for PESE-GC Lorenz 96 study, Zenodo [software and data set], https://doi.org/10.5281/zenodo.10126956, 2023. a, b, c
Chan, M.-Y., Anderson, J. L., and Chen, X.: An efficient bi-Gaussian ensemble Kalman filter for satellite infrared radiance data assimilation, Mon. Weather Rev., 148, 5087–5104, https://doi.org/10.1175/mwr-d-20-0142.1, 2020. a, b, c, d
Chan, M.-Y., Chen, X., and Anderson, J. L.: The potential benefits of handling mixture statistics via a bi-Gaussian EnKF: tests with all-sky satellite infrared radiances, J. Adv. Model. Earth Sy., 15, e2022MS003357, https://doi.org/10.1029/2022MS003357, 2023. a
Cheng, R. C. H. and Amin, N. A. K.: Estimating Parameters in Continuous Univariate Distributions with a Shifted Origin, J. Roy. Stat. Soc. B, 45, 394–403, https://doi.org/10.1111/j.2517-6161.1983.tb01268.x, 1983. a
El Gharamti, M.: Hybrid ensemble-variational filter: A spatially and temporally varying adaptive algorithm to estimate relative weighting, Mon. Weather Rev., 149, 65–76, https://doi.org/10.1175/MWR-D-20-0101.1, 2020. a
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, https://doi.org/10.1256/smsqj.55416, 1999. a
Gasperoni, N. A., Wang, X., and Wang, Y.: Using a Cost-Effective Approach to Increase Background Ensemble Member Size within the GSI-Based EnVar System for Improved Radar Analyses and Forecasts of Convective Systems, Mon. Weather Rev., 150, 667–689, https://doi.org/10.1175/mwr-d-21-0148.1, 2022. a, b
Gordon, N. J., Salmond, D. J., and Smith, A. F.: Novel approach to nonlinear/non-gaussian Bayesian state estimation, IEE Proc. F, 140, 107–113, https://doi.org/10.1049/ip-f-2.1993.0015, 1993. a
Grooms, I.: Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders, Q. J. Roy. Meteor. Soc., 147, 139–149, https://doi.org/10.1002/qj.3910, 2021. a
Grooms, I.: A comparison of nonlinear extensions to the ensemble Kalman filter: Gaussian anamorphosis and two-step ensemble filters, Comput. Geosci., 26, 633–650, https://doi.org/10.1007/s10596-022-10141-x, 2022. a
Huang, B. and Wang, X.: On the use of cost-effective Valid-Time-Shifting (VTS) method to increase ensemble size in the GFS hybrid 4DEnVar System, Mon. Weather Rev., 146, 2973–2998, https://doi.org/10.1175/MWR-D-18-0009.1, 2018. a, b
Ide, K., Courtier, P., Ghil, M., and Lorenc, A. C.: Unified notation for data assimilation: Operational, sequential and variational, J. Meteorol. Soc. Jpn., 75, 181–189, https://doi.org/10.2151/jmsj1965.75.1B_181, 1997. a
Kotsuki, S. and Bishop, C. H.: Implementing Hybrid Background Error Covariance into the LETKF with Attenuation-Based Localization: Experiments with a Simplified AGCM, Mon. Weather Rev., 150, 283–302, https://doi.org/10.1175/MWR-D-21-0174.1, 2022. a
Kurosawa, K. and Poterjoy, J.: A Statistical Hypothesis Testing Strategy for Adaptively Blending Particle Filters and Ensemble Kalman Filters for Data Assimilation, Mon. Weather Rev., 151, 105–125, https://doi.org/10.1175/MWR-D-22-0108.1, 2023. a
Lei, L., Wang, Z., and Tan, Z. M.: Integrated Hybrid Data Assimilation for an Ensemble Kalman Filter, Mon. Weather Rev., 149, 4091–4105, https://doi.org/10.1175/MWR-D-21-0002.1, 2021. a
Lorenz, E. N.: Predictability-a problem partly solved, in: Predictability of Weather and Climate, ISBN 9780511617652, https://doi.org/10.1017/CBO9780511617652.004, 2006. a
Monache, L. D., Anthony Eckel, F., Rife, D. L., Nagarajan, B., and Searight, K.: Probabilistic weather prediction with an analog ensemble, Mon. Weather Rev., 141, 3498–3516, https://doi.org/10.1175/MWR-D-12-00281.1, 2013. a
National Center for Atmospheric Research: The Data Assimilation Research Testbed, UCAR/NSF NCAR/CISL/DAReS [software], https://doi.org/10.5065/D6WQ0202, 2024. a
Poterjoy, J.: A localized particle filter for high-dimensional nonlinear systems, Mon. Weather Rev., 144, 59–76, https://doi.org/10.1175/MWR-D-15-0163.1, 2016. a
Sakov, P. and Oke, P. R.: A deterministic formulation of the ensemble Kalman filter: an alternative to ensemble square root filters, Tellus A, 60, 361–371, https://doi.org/10.1111/j.1600-0870.2007.00299.x, 2008. a, b
Skamarock, W., Klemp, J., Dudhi, J., Gill, D., Barker, D., Duda, M., Huang, X.-Y., Wang, W., and Powers, J.: A Description of the Advanced Research WRF Version 3. NCAR Tech. Note NCAR/TN–468+STR, 113 pp., NCAR TECHNICAL NOTE, https://doi.org/10.5065/D68S4MVH, 2008. a
Snyder, C., Bengtsson, T., Bickel, P., and Anderson, J.: Obstacles to high-dimensional particle filtering, Mon. Weather Rev., 136, 4629–4640, https://doi.org/10.1175/2008MWR2529.1, 2008. a
Sun, H., Lei, L., Liu, Z., Ning, L., and Tan, Z. M.: An Analog Offline EnKF for Paleoclimate Data Assimilation, J. Adv. Model. Earth Sy., 14, e2021MS002674, https://doi.org/10.1029/2021MS002674, 2022. a
Sun, H., Lei, L., Liu, Z., Ning, L., and Tan, Z. M.: A Hybrid Gain Analog Offline EnKF for Paleoclimate Data Assimilation, J. Adv. Model. Earth Sy. 16, e2022MS003414, https://doi.org/10.1029/2022MS003414, 2024. a
Tippett, M. K. and Delsole, T.: Constructed analogs and linear regression, Mon. Weather Rev., 141, 2519–2525, https://doi.org/10.1175/MWR-D-12-00223.1, 2013. a
Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M., and Whitaker, J. S.: Ensemble Square Root Filters, Mon. Weather Rev., 131, 1485–1490, https://doi.org/10.1175/1520-0493(2003)131<1485:ESRF>2.0.CO;2, 2003. a, b
Van den Dool, H. M.: Searching for analogues, how long must we wait?, Tellus A, 46, 314–324, https://doi.org/10.1034/j.1600-0870.1994.t01-2-00006.x, 1994. a
van Leeuwen, P. J., Künsch, H. R., Nerger, L., Potthast, R., and Reich, S.: Particle filters for high-dimensional geoscience applications: A review, Q. J. Roy. Meteor. Soc., 145, 2335–2365, https://doi.org/10.1002/qj.3551, 2019. a
Wan, E. and Van Der Merwe, R.: The unscented Kalman filter for nonlinear estimation, in: Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373), 153–158, IEEE, ISBN 0-7803-5800-7, https://doi.org/10.1109/ASSPCC.2000.882463, 2000. a
Wang, X., Chipilski, H. G., Bishop, C. H., Satterfield, E., Baker, N., and Whitaker, J. S.: A multiscale local gain form ensemble transform Kalman filter (MLGETKF), Mon. Weather Rev., 149, 605–622, https://doi.org/10.1175/MWR-D-20-0290.1, 2021. a
Whitaker, J. S. and Hamill, T. M.: Ensemble data assimilation without perturbed observations, Mon. Weather Rev., 130, 1913–1924, https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2, 2002. a, b
Whitaker, J. S. and Hamill, T. M.: Evaluating methods to account for system errors in ensemble data assimilation, Mon. Weather Rev., 140, 3078–3089, https://doi.org/10.1175/MWR-D-11-00276.1, 2012. a
Xu, Q., Wei, L., Lu, H., Qiu, C., and Zhao, Q.: Time-expanded sampling for ensemble-based filters: Assimilation experiments with a shallow-water equation model, J. Geophys. Res.-Atmos., 113, D02114, https://doi.org/10.1029/2007JD008624, 2008. a
Yuan, H., Lu, C., McGinley, J. A., Schultz, P. J., Jamison, B. D., Wharton, L., and Anderson, C. J.: Evaluation of short-range quantitative precipitation forecasts from a time-lagged multimodel ensemble, Weather Forecast., 24, 18–38, https://doi.org/10.1175/2008WAF2007053.1, 2009. a
Short summary
Forecasts have uncertainties. It is thus essential to reduce these uncertainties. Such reduction requires uncertainty quantification, which often means running costly models multiple times. The cost limits the number of model runs and thus the quantification’s accuracy. This study proposes a technique that utilizes users’ knowledge of forecast uncertainties to improve uncertainty quantification. Tests show that this technique improves uncertainty reduction.
Forecasts have uncertainties. It is thus essential to reduce these uncertainties. Such reduction...
