Supplementary material to "A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0"

In order to explore the influence of different assimilation indicators on the sequential data assimilation,in this paper, based on Lorenz-1963 model, we attempt to test the sensitivities of three typical assimilation methods, including En KF, DEn KF and En SRF. We have investigated the effects of different indicators on the data assimilation results. These indicators include the total assimilation time, the assimilation step, the ensemble number, the inflation factor, the observation number and the localization radius. The experimental results show that the total assimilation time, the assimilation step, the ensemble number, the inflation factor, the observation number and the localization radius directly affect the data assimilation result. The improved algorithm based on En KF is superior to the En KF under a given condition. When some preconditions are satisfied, the data assimilation results of all above methods tend to be the same. In practice, this work is of great significance in choosing the optimal sequential assimilation method.

Assimilation (phonology)

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Short Course SC1.1: Data Assimilation in the Geosciences - Practical Data Assimilation with the Parallel Data Assimilation Framework

Lars Nerger Maria Broadbridge Gernot Geppert Peter Jan van Leeuwen

Data assimilation combines observational data with a numerical model. It is commonly used in numerical weather prediction, but is't also applied also in oceanography and hydrology. The integrating observations with models in a quantitative way, data assimilation allows to estimate and improve the model states, e.g. to initialize model forecasts. Also it can estimate parameters that control processes in the model or fluxes, which can be difficult and even impossible to measure. As such data assimilation can use observations to provide information about unobservable quantities if the model represents those. The combination of model and observation requires to have error estimates of both information sources. In ensemble data assimilation the error in the model state is estimated by an ensemble of model state realizations. This ensemble not only provides estimates of uncertainties, but also of cross-correlations between different model variables or parameters. The ensemble information is then used by the assimilation method, whose most widely known is the ensemble Kalman filter. To simplify the implementation and use of ensemble data assimilation, the Parallel Data Assimilation Framework - PDAF - has been developed. PDAF is a freely-available open-source software (http://pdaf.awi.de) that provides ensemble-based data assimilation methods like the ensemble Kalman filter, but also support to perform ensemble simulations. PDAF is designed so that it can be used from small toy problems running on notebook computers up to high-dimensional Earth Systems models running on supercomputers. This short course aims at geoscientists who have a modeling application or observations and are interested in applying data assimilation, but haven't found a starting point yet. The course will first provide an introduction to the ensemble data assimilation methodology. Then, it will explain the implementation concept of PDAF and finally provide a hands-on example of building a data assimilation system based on a numerical model. This practical introduction will prepare the participants to build a data assimiliton system by combining their numerical model with PDAF, hence providing a quick starting point for apply the ensemble data assimilation.

Ensemble forecasting

Ensemble Learning

Errors-in-Variables Models

Assimilation (phonology)

Unobservable

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A regulated localization method for ensemble-based Kalman filters

Lars Nerger Tijana Janjic Pfander Jens Schröter Wolfgang Hiller

Data assimilation applications with large-scale numerical models exhibit extreme requirements on computational resources. Good scalability of the assimilation system is necessary to make these applications feasible. Sequential data assimilation methods based on ensemble forecasts, like ensemble-based Kalman filters, provide such good scalability, because the forecast of each ensemble member can be performed independently. However, this parallelism has to be combined with the parallelization of both the numerical model and the data assimilation algorithm. In order to simplify the implementation of scalable data assimilation systems based on existing numerical models, the Parallel Data Assimilation Framework PDAF (http://pdaf.awi.de) has been developed. PDAF provides support for implementing a data assimilation system with parallel ensemble forecasts and parallel numerical models. Further, it includes several optimized parallel filter algorithms, like the Ensemble Transform Kalman Filter. We will discuss the philosophy behind PDAF as well as features and scalability of data assimilation systems based on PDAF on the example of data assimilation with the finite element ocean model FEOM.

Ensemble forecasting

Ensemble Learning

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Interactive comment on “Mitigation of Model Bias Influences on Wave Data Assimilation with Multiple Assimilation Systems Using WaveWatch III v5.16 and SWAN v41.20” from anonymous referee #2

Jiangyu Li

Assimilation (phonology)

10.5194/gmd-2019-243-ac2

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Scalable sequential data assimilation with the Parallel Data Assimilation Framework PDAF

Lars Nerger Wolfgang Hiller Jens Schröter

Data assimilation applications with large-scale numerical models exhibit extreme requirements on computational resources. Good scalability of the assimilation system is necessary to make these applications feasible. Sequential data assimilation methods based on ensemble forecasts, like ensemble-based Kalman filters, provide such good scalability, because the forecast of each ensemble member can be performed independently. However, this parallelism has to be combined with the parallelization of both the numerical model and the data assimilation algorithm. In order to simplify the implementation of scalable data assimilation systems based on existing numerical models, the Parallel Data Assimilation Framework PDAF (http://pdaf.awi.de) has been developed. PDAF provides support for implementing a data assimilation system with parallel ensemble forecasts and parallel numerical models. Further, it includes several optimized parallel filter algorithms, like the Ensemble Transform Kalman Filter. We will discuss the philosophy behind PDAF as well as features and scalability of data assimilation systems based on PDAF on the example of data assimilation with the finite element ocean model FEOM.

Assimilation (phonology)

Ensemble forecasting

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Scalable sequential data assimilation with the Parallel Data Assimilation Framework PDAF

Lars Nerger Wolfgang Hiller Jens Schröter

Data assimilation applications with high-dimensional numerical modelsshow extreme requirements on computational resources. Thus, goodscalability of the assimilation system is necessary to make theseapplications feasible. Sequential data assimilation methods based onensemble forecasts, like ensemble-based Kalman filters, provide suchgood scalability, because the forecast of each ensemble member can beperformed independently. However, this parallelism has to be combinedwith the parallelization of both the numerical model and the data assimilation algorithm. In order to simplify the implementation ofscalable data assimilation systems based on existing numerical models,the Parallel Data Assimilation Framework PDAF has been developed. Itprovides support for parallel ensemble forecasts and parallelnumerical models. Further, it includes several optimized parallel filteralgorithms, like the ensemble transform Kalman filter. We will discussthe features and scalability of data assimilation systems based onPDAF on the example of data assimilation with the finite element oceanmodel FEOM.

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Adjoint Data Assimilation Methods

Springer eBooks (2011)

Andrew M. Moore

Assimilation (phonology)

10.1007/978-94-007-0332-2_14

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Local Volume Solvers for Earth System Data Assimilation: Implementation in the Framework for Joint Effort for Data Assimilation Integration

Journal of Advances in Modeling Earth Systems (2024)

Sergey Frolov Anna Shlyaeva Wei Huang Travis Sluka Clara Draper

Abstract The Joint Effort for Data assimilation Integration (JEDI) is an international collaboration aimed at developing an open software ecosystem for model agnostic data assimilation. This paper considers implementation of the model‐agnostic family of the local volume solvers in the JEDI framework. The implemented solvers include the Local Ensemble Transform Kalman Filter (LETKF), the Gain form of the Ensemble Transform Kalman Filter (GETKF), and the optimal interpolation variant of the LETKF (LETKF‐OI). This paper documents the implementation strategy for the family of the local volume solvers within the JEDI framework. We also document an expansive set of localization approaches that includes generic distance‐based localization, localization based on modulated ensemble products, and localizations specific to ocean (based on the Rossby radius of deformation), and land (based on the terrain difference between observation and model grid point). Finally, we apply the developed solvers in a limited set of experiments, including single‐observation experiments in atmosphere and ocean, and cycling experiments for the atmosphere, ocean, land, and aerosol assimilation. We also illustrate how JEDI Ensemble Kalman Filter solvers can be used in a strongly coupled framework using the interface solver approximation, which provides increments to the ocean based on observations from the ocean and atmosphere.

10.1029/2023ms003692

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Susquehanna Chorale Spring Concert "Roots and Wings"

Deborah Hawkins

The nationally-recognized Susquehanna Chorale will delight audiences of all ages with a diverse mix of classic and contemporary pieces. The ChoraleAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂ¢AÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂs performances have been described as AÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂ¢AÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂemotionally unfiltered, honest music making, successful in their aim to make the audience feel, to be moved, to be part of the performance - and all this while working at an extremely high musical level.AÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂ¢AÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂ Experience choral singing that will take you to new heights!

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Advances in the Meteorological Data Assimilation

Di He

The paper gives a comprehensive and systemical description on advances in the meteorological data assimilation,and presents an introduction to the theories and application of the main methods(especially the Adjoint method and Genetic Algorithm) in Data Assimilation.The GA (Genetic Algorithm)has opened a new way in solving the data assimilation problems.

Assimilation (phonology)

Genetic data

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