Assimilation of satellite chlorophyll measurements into a coupled biophysical model of the Indian Ocean with a guided particle filter

Ratheesh, Smitha; Chakraborty, Abhisek; Sharma, Rashmi; Basu, Samik

doi:10.1080/2150704x.2016.1143985

Cited by 8 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The weights, in principle, should be proportional to likelihoods or conditional probabilities. This, of course, is unknown and has to be approximated (Ratheesh et al, ). In this study, the likelihood is approximated using multiple criteria.…”

Section: Methodsmentioning

confidence: 99%

Assimilating INSAT‐3D Thermal Infrared Window Imager Observation With the Particle Filter: A Case Study for Vardah Cyclone

Kumar

Shukla

2019

JGR Atmospheres

View full text Add to dashboard Cite

A big challenge in the satellite data assimilation is the effective use of InfraRed (IR) window channel radiances in the high‐resolution weather model. A hybrid data‐assimilation method is used in this study for very severe cyclonic storm “Vardah,” in which three‐dimensional variational method is used to assimilate control observations, and particle filter method is used to assimilate Indian geostationary satellite INSAT‐3D data. In the context of imperfect weather model, various particles (or ensembles) are prepared with different combinations of model physics. To implement particle filter, INSAT‐3D thermal IR window channel 1 (TIR‐1; center wavelength 11 μm) measured brightness temperature (BT) and cloud mask product are used to assign appropriate weights to different particles to reduce model uncertainties. This step is followed by resampling step in which new particles are generated from high weight particles using stochastic kinetic‐energy backscatter scheme method and dynamical variables are perturbed into the model physics. Results suggest that simulated TIR‐1 BT analysis and forecasts from WPF (with INSAT‐3D data using particle filter experiments) are closer to INSAT‐3D measured TIR‐1 BT in comparison to WCNT (without INSAT‐3D data using particle filter) experiments. Furthermore, approximately 10% to 50% improvements are found in mean track error forecasts in WPF experiments. An improvement of ~10% is noticed in cyclone center position in analysis. Prediction of storm intensity is also improved after assimilation. Results also suggest that vertical structure of WPF simulated humidity, temperature, wind speed, and surface pressure is improved over WCNT runs.

show abstract

Section: Methodsmentioning

confidence: 99%

Assimilating INSAT‐3D Thermal Infrared Window Imager Observation With the Particle Filter: A Case Study for Vardah Cyclone

Kumar

Shukla

2019

JGR Atmospheres

View full text Add to dashboard Cite

show abstract

“…In ocean biogeochemical modelling, the assimilation of satellite ocean-colour observations has been successfully applied in research and operational applications at both global and regional scales (Fennel et al, 2019;Groom et al, 2019). Chlorophyll concentration is the most commonly assimilated variable since the first applications of ocean biogeochemical DA (Ciavatta et al, 2016;Dorofeyev and Sukhikh, 2018;Ford and Barciela, 2017;Ford, 2020;Gehlen et al, 2015;Mattern et al, 2017;Pradhan et al, 2019;Ratheesh et al, 2016;Santana-Falcón et al, 2020;Song et al, 2016;Teruzzi et al, 2018;Tsiaras et al, 2017). However, assimilation of the ocean-colour diffuse attenuation coefficient, phytoplankton functional types, particulate organic carbon, and inherent optical properties has been suggested as promising alternative to chlorophyll assimilation (Ciavatta et al, 2019(Ciavatta et al, , 2018(Ciavatta et al, , 2014Dutkiewicz et al, 2019;Jones et al, 2016;Pradhan et al, 2020;Shulman et al, 2013;Skákala et al, 2018;Xiao and Friedrichs, 2014).…”

Section: Introductionmentioning

confidence: 99%

Deep chlorophyll maximum and nutricline in the Mediterranean Sea: emerging properties from a multi-platform assimilated biogeochemical model experiment

et al. 2021

View full text Add to dashboard Cite

Abstract. Data assimilation has led to advancements in biogeochemical modelling and scientific understanding of the ocean. The recent operational availability of data from BGC-Argo (biogeochemical Argo) floats, which provide valuable insights into key vertical biogeochemical processes, stands to further improve biogeochemical modelling through assimilation schemes that include float observations in addition to traditionally assimilated satellite data. In the present work, we demonstrate the feasibility of joint multi-platform assimilation in realistic biogeochemical applications by presenting the results of 1-year simulations of Mediterranean Sea biogeochemistry. Different combinations of satellite chlorophyll data and BGC-Argo nitrate and chlorophyll data have been tested, and validation with respect to available independent non-assimilated and assimilated (before the assimilation) observations showed that assimilation of both satellite and float observations outperformed the assimilation of platforms considered individually. Moreover, the assimilation of BGC-Argo data impacted the vertical structure of nutrients and phytoplankton in terms of deep chlorophyll maximum depth, intensity, and nutricline depth. The outcomes of the model simulation assimilating both satellite data and BGC-Argo data provide a consistent picture of the basin-wide differences in vertical features associated with summer stratified conditions, describing a relatively high variability between the western and eastern Mediterranean, with thinner and shallower but intense deep chlorophyll maxima associated with steeper and narrower nutriclines in the western Mediterranean.

show abstract

“…Most of the previous studies based on variational method and ensemble Kalman filter (EnKF) assume/convert non-Gaussian distribution of rainfall to Gaussian error statistics which lead to suboptimal analysis (e.g., Posselt et al, 2014;Posselt & Bishop, 2012;Van Leeuwen, 2009, 2010. One well-known advantage of the particle filter over EnKF is that the particle filter can works for non-Gaussian distribution (Kumar & Shukla, 2019;Mattern et al, 2013;Ratheesh et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Assimilation of the Rain Gauge Measurements Using Particle Filter

Kumar

2020

Earth and Space Science

View full text Add to dashboard Cite

The well-recognized constraint of nonlinear and non-Gaussian distribution of rainfall observation limits its assimilation in the high-dimensional numerical weather prediction (NWP) model. In this study, rainfall observed from Indian Meteorological Department (IMD) rain gauges over Indian landmass is assimilated in the Weather Research and Forecasting (WRF) model using particle filter. In the framework of imperfect weather models, particles (or ensembles) for rainfall predictions are created with various combinations of model physics (viz., cumulus parameterization, microphysics and planetary boundary layer schemes). The multiple hypotheses are used to determine the weights for different particles, and this is the step where IMD rainfall data are used for assimilation. Further, a resampling step is performed to generate new particles from high weight particles using stochastic kinetic-energy backscatter scheme (SKEBS) method in which dynamical variables are perturbed into the model physics. Results, based on rainfall verification scores, suggest that the assimilation of the rainfall using particle filter could improve the prediction of rainfall over CNT runs (unweighted particles; without assimilation). Moreover, surface and vertical profile of temperature, water vapor mixing ratio (WVMR), and wind speed are also improved in 24-hr forecasts.

show abstract

Assimilation of satellite chlorophyll measurements into a coupled biophysical model of the Indian Ocean with a guided particle filter

Cited by 8 publications

References 19 publications

Assimilating INSAT‐3D Thermal Infrared Window Imager Observation With the Particle Filter: A Case Study for Vardah Cyclone

Assimilating INSAT‐3D Thermal Infrared Window Imager Observation With the Particle Filter: A Case Study for Vardah Cyclone

Deep chlorophyll maximum and nutricline in the Mediterranean Sea: emerging properties from a multi-platform assimilated biogeochemical model experiment

Assimilation of the Rain Gauge Measurements Using Particle Filter

Contact Info

Product

Resources

About