Parameter estimation for ocean biogeochemical component in a global model using Ensemble Kalman Filter: a twin experiment

2021 ◽  
Author(s):  
Tarkeshwar Singh ◽  
Francois Counillon ◽  
Jerry F. Tjiputra ◽  
Mohamad El Gharamti
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Parameter Values

<p>Ocean biogeochemical (BGC) models utilize a large number of poorly-constrained global parameters to mimic unresolved processes and reproduce the observed complex spatio-temporal patterns. Large model errors stem primarily from inaccuracies in these parameters whose optimal values can vary both in space and time. This study aims to demonstrate the ability of ensemble data assimilation (DA) methods to provide high-quality and improved BGC parameters within an Earth system model in idealized twin experiment framework.  We use the Norwegian Climate Prediction Model (NorCPM), which combines the Norwegian Earth System Model with the Dual-One-Step ahead smoothing-based Ensemble Kalman Filter (DOSA-EnKF). The work follows on Gharamti et al. (2017) that successfully demonstrates the approach for one-dimensional idealized ocean BGC models. We aim to estimate five spatially varying BGC parameters by assimilating Salinity and Temperature hydrographic profiles and surface BGC (Phytoplankton, Nitrate, Phosphorous, Silicate, and Oxygen) observations in a strongly coupled DA framework – i.e., jointly updating ocean and BGC state-parameters during the assimilation. The method converges quickly (less than a year), largely reducing the errors in the BGC parameters and eventually it is shown to perform nearly as well as that of the system with true parameter values. Optimal parameter values can also be recovered by assimilating climatological BGC observations and challenging sparse observational networks. The findings of this study demonstrate the applicability of the approach for tuning the system in a real framework.</p><p> </p><p><strong>References</strong>:</p><p>Gharamti, M. E., Tjiputra, J., Bethke, I., Samuelsen, A., Skjelvan, I., Bentsen, M., & Bertino, L. (2017). Ensemble data assimilation for ocean biogeochemical state and parameter estimation at different sites. Ocean Modelling, 112, 65-89.</p>

scholarly journals Seasonal-to-decadal predictions with the ensemble Kalman filter and the Norwegian Earth System Model: a twin experiment

2014 ◽  
Vol 66 (1) ◽  
pp. 21074 ◽  
Author(s):  
Francois Counillon ◽  
Ingo Bethke ◽  
Noel Keenlyside ◽  
Mats Bentsen ◽  
Laurent Bertino ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Decadal Predictions

An Efficient Bi-Gaussian Ensemble Kalman Filter for Satellite Infrared Radiance Data Assimilation

2020 ◽  
Vol 148 (12) ◽  
pp. 5087-5104
Author(s):  
Man-Yau Chan ◽  
Jeffrey L. Anderson ◽  
Xingchao Chen
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Relative Performance ◽  
Proof Of Concept ◽  
Numerical Weather ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Ensemble Statistics ◽  
Gaussian Ensemble

AbstractThe introduction of infrared water vapor channel radiance ensemble data assimilation (DA) has improved numerical weather forecasting at operational centers. Further improvements might be possible through extending ensemble data assimilation methods to better assimilate infrared satellite radiances. Here, we will illustrate that ensemble statistics under clear-sky conditions are different from cloudy conditions. This difference suggests that extending the ensemble Kalman filter (EnKF) to handle bi-Gaussian prior distributions may yield better results than the standard EnKF. In this study, we propose a computationally efficient bi-Gaussian ensemble Kalman filter (BGEnKF) to handle bi-Gaussian prior distributions. As a proof-of-concept, we used the 40-variable Lorenz 1996 model as a proxy to examine the impacts of assimilating infrared radiances with the BGEnKF and EnKF. A nonlinear observation operator that constructs radiance-like bimodal ensemble statistics was used to generate and assimilate pseudoradiances. Inflation was required for both methods to effectively assimilate pseudoradiances. In both 800- and 20-member experiments, the BGEnKF generally outperformed the EnKF. The relative performance of the BGEnKF with respect to the EnKF improved when the observation spacing and time between DA cycles (cycling interval) are increased from small values. The relative performance then degraded when observation spacing and cycling interval become sufficiently large. The BGEnKF generated less noise than the EnKF, suggesting that the BGEnKF produces more balanced analysis states than the EnKF. This proof-of-concept study motivates future investigation into using the BGEnKF to assimilate infrared observations into high-order numerical weather models.

scholarly journals Convection-Permitting Forecasts Initialized with Continuously Cycling Limited-Area 3DVAR, Ensemble Kalman Filter, and “Hybrid” Variational–Ensemble Data Assimilation Systems

2014 ◽  
Vol 142 (2) ◽  
pp. 716-738 ◽  
Author(s):  
Craig S. Schwartz ◽  
Zhiquan Liu
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Three Dimensional ◽  
Skill Score ◽  
Limited Area ◽  
Computational Domain ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Precipitation Characteristics ◽  
High Precipitation

Abstract Analyses with 20-km horizontal grid spacing were produced from parallel continuously cycling three-dimensional variational (3DVAR), ensemble square root Kalman filter (EnSRF), and “hybrid” variational–ensemble data assimilation (DA) systems between 0000 UTC 6 May and 0000 UTC 21 June 2011 over a domain spanning the contiguous United States. Beginning 9 May, the 0000 UTC analyses initialized 36-h Weather Research and Forecasting Model (WRF) forecasts containing a large convection-permitting 4-km nest. These 4-km 3DVAR-, EnSRF-, and hybrid-initialized forecasts were compared to benchmark WRF forecasts initialized by interpolating 0000 UTC Global Forecast System (GFS) analyses onto the computational domain. While important differences regarding mean state characteristics of the 20-km DA systems were noted, verification efforts focused on the 4-km precipitation forecasts. The 3DVAR-, hybrid-, and EnSRF-initialized 4-km precipitation forecasts performed similarly regarding general precipitation characteristics, such as timing of the diurnal cycle, and all three forecast sets had high precipitation biases at heavier rainfall rates. However, meaningful differences emerged regarding precipitation placement as quantified by the fractions skill score. For most forecast hours, the hybrid-initialized 4-km precipitation forecasts were better than the EnSRF-, 3DVAR-, and GFS-initialized forecasts, and the improvement was often statistically significant at the 95th percentile. These results demonstrate the potential of limited-area continuously cycling hybrid DA configurations and suggest additional hybrid development is warranted.

scholarly journals Tropical climate variability in the Community Earth System Model: Data Assimilation Research Testbed

2019 ◽  
Vol 54 (1-2) ◽  
pp. 793-806 ◽  
Author(s):  
Jonathan Eliashiv ◽  
Aneesh C. Subramanian ◽  
Arthur J. Miller
Keyword(s):  
Data Assimilation ◽  
Climate Variability ◽  
Tropical Climate ◽  
Earth System Model ◽  
The Other ◽  
System Model ◽  
Earth System ◽  
Ocean Atmosphere ◽  
Community Earth System Model ◽  
Free Running

AbstractA new prototype coupled ocean–atmosphere Ensemble Kalman Filter reanalysis product, the Community Earth System Model using the Data Assimilation Research Testbed (CESM-DART), is studied by comparing its tropical climate variability to other reanalysis products, available observations, and a free-running version of the model. The results reveal that CESM-DART produces fields that are comparable in overall performance with those of four other uncoupled and coupled reanalyses. The clearest signature of differences in CESM-DART is in the analysis of the Madden–Julian Oscillation (MJO) and other tropical atmospheric waves. MJO energy is enhanced over the free-running CESM as well as compared to the other products, suggesting the importance of the surface flux coupling at the ocean–atmosphere interface in organizing convective activity. In addition, high-frequency Kelvin waves in CESM-DART are reduced in amplitude compared to the free-running CESM run and the other products, again supportive of the oceanic coupling playing a role in this difference. CESM-DART also exhibits a relatively low bias in the mean tropical precipitation field and mean sensible heat flux field. Conclusive evidence of the importance of coupling on data assimilation performance will require additional detailed direct comparisons with identically formulated, uncoupled data assimilation runs.

scholarly journals Enhanced flood forecasting through ensemble data assimilation and joint state-parameter estimation

2019 ◽  
Vol 577 ◽  
pp. 123924 ◽  
Author(s):  
Matteo G. Ziliani ◽  
Rabih Ghostine ◽  
Boujemaa Ait-El-Fquih ◽  
Matthew F. McCabe ◽  
Ibrahim Hoteit
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
State Parameter ◽  
Flood Forecasting ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Joint State

Ensemble data assimilation for ocean biogeochemical state and parameter estimation at different sites

2017 ◽  
Vol 112 ◽  
pp. 65-89 ◽  
Author(s):  
M.E. Gharamti ◽  
J. Tjiputra ◽  
I. Bethke ◽  
A. Samuelsen ◽  
I. Skjelvan ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Ensemble Data Assimilation ◽  
Ensemble Data
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