Multivariate background error covariances in the assimilation of SAPHIR radiances in the simulation of three tropical cyclones over the Bay of Bengal using the WRF model

Madhu, Dhanya; Chandrasekar, A.

doi:10.1080/01431161.2017.1381350

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Based on the work to use multiple linear regressions to estimate multivariate analysis of humidity in a limited-area model, Berre [33] pointed that the relationships between forecast errors of humidity and those of mass and wind fields cannot be negligible. Dhanya and Chandrasekar [34,35] found that the forecasts of rainfall caused by monsoon depressions and tropical cyclones can be improved by applying data assimilation with CV6, which is better than CV5. However, among existing research work on sea fog over the Yellow Sea involving numerical modeling [24,[36][37][38][39][40], quite a few studies, especially those works with focus on the mechanism of sea fog evolution, have little or no mention of data assimilation.…”

Section: Introductionmentioning

confidence: 99%

Impact of Multivariate Background Error Covariance on the WRF-3DVAR Assimilation for the Yellow Sea Fog Modeling

Gao

2020

Advances in Meteorology

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Numerical modeling of sea fog is highly sensitive to initial conditions, especially to moisture in the marine atmospheric boundary layer (MABL). Data assimilation plays a vital role in the improvement of initial MABL moisture for sea fog modeling over the Yellow Sea. In this study, the weather research and forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation module are employed for sea fog simulations. Two kinds of background error (BE) covariances with different control variables (CV) used in WRF-3DVAR, that is, CV5 and multivariate BE (CV6), are compared in detail by explorative case studies and a series of application experiments. Statistical verification metrics including probability of detection (POD) and equitable threat scores (ETS) of forecasted sea fog area are computed and compared for simulations with the implementations of CV5 and CV6 in the WRF-3DVAR system. The following is found: (1) there exists a dominant negative correlation between temperature and moisture in CV6 near the sea surface, which makes it possible to improve the initial moisture condition in the MABL by assimilation of observed temperature; (2) in general, the performance of the WRF-3DVAR assimilation with CV6 is distinctly better, and the results of 10 additional sea fog cases clearly suggest that CV6 is more suitable than CV5 for sea fog modeling. Compared to those with CV5, the average POD and ETS of forecasted sea fog area using 3DVAR with CV6 can be improved by 27.6% and 21.0%, respectively.

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Section: Introductionmentioning

confidence: 99%

Impact of Multivariate Background Error Covariance on the WRF-3DVAR Assimilation for the Yellow Sea Fog Modeling

Gao

2020

Advances in Meteorology

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“…9 Even on days when the satellite measurements contain missing values, other available products, including aerial and drone photography, buoy measurements, conductivity, temperature, and depth (CTD) profiles, and water sample analyses, can be used to fill the gaps. 10 Indeed, high-resolution satellite-based observations of oceanographic fields, 11 along with their assimilation into numerical ocean prediction models, 12 enable advances in research and operational forecasting in marine sciences. 13 Both active and passive satellite scanning strategies can be utilized to acquire a variety of oceanic parameters, including suspended particulate matter (SPM), 14 sea surface roughness, 15 and wave height.…”

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confidence: 99%

Surface temperature and salinity in the northern Bay of Bengal: in situ measurements compared with satellite observations and model output

Masud-Ul-Alam

Khan²,

Barrett

et al. 2022

J. Appl. Rem. Sens.

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The northern Bay of Bengal (BoB) has been traditionally understudied and undersampled. Satellite and modeling products could compensate for the scarcity of in situ measurements, but this requires evaluating the accuracy of satellite and modeling products first. We present a comparison of sea surface temperature (SST) and sea surface salinity (SSS) products (satellite and model output) with 46 in situ observations in the northern BoB. We used satellite and modeled SST (daily) and SSS (weekly and daily) in this comparison. The results are as follows.(1) Both model and satellite-derived SSTs agreed well with in situ observations and with each other, with small biases (<1°C) and large correlation coefficients (r > 0.77).(2) Neither model nor satellite SSSs agreed well with in situ observations (biases > 0.5 PSU, r < 0.54).(3) Calculations of the d-index support the argument that model and satellite SSTs agreed well with in situ observations (d-index values of 0.68 and 0.65, respectively), while the model and satellite SSSs did not agree well with observations (d-index values of 0.31 and 0.40, respectively). The results suggest that additional work is needed to improve both model prediction and satellite retrieval algorithms for SSS in the northern BoB.

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The Megha-Tropiques Mission After Seven Years in Space

Roca

Dejus

Chambon

et al. 2020

Advances in Global Change Research

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Multivariate background error covariances in the assimilation of SAPHIR radiances in the simulation of three tropical cyclones over the Bay of Bengal using the WRF model

Cited by 5 publications

References 28 publications

Impact of Multivariate Background Error Covariance on the WRF-3DVAR Assimilation for the Yellow Sea Fog Modeling

Impact of Multivariate Background Error Covariance on the WRF-3DVAR Assimilation for the Yellow Sea Fog Modeling

Surface temperature and salinity in the northern Bay of Bengal: in situ measurements compared with satellite observations and model output

The Megha-Tropiques Mission After Seven Years in Space

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