Improving Convective Precipitation Forecasts Using Ensemble‐Based Background Error Covariance in 3DVAR Radar Assimilation System
Abstract Skillful quantitative precipitation forecast using the numerical weather prediction model relies on an accurate estimate of the atmospheric state as an initial condition. Variational assimilation methods (VAR) have the potential to provide improved initial state estimation to the numerical...
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2020-04-01
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| Series: | Earth and Space Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2019EA000667 |
| _version_ | 1828413890951643136 |
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| author | P. Thiruvengadam J. Indu Subimal Ghosh |
| author_facet | P. Thiruvengadam J. Indu Subimal Ghosh |
| author_sort | P. Thiruvengadam |
| collection | DOAJ |
| description | Abstract Skillful quantitative precipitation forecast using the numerical weather prediction model relies on an accurate estimate of the atmospheric state as an initial condition. Variational assimilation methods (VAR) have the potential to provide improved initial state estimation to the numerical weather prediction model using observations, prior data (background), and their respective error covariance. The quality of variational assimilation hinges on the background error statistics (BES) as it weights the error in prior state and determines the spread of assimilated observations in model space. Traditional approaches used to model stationary BES in a three‐dimensional variational assimilation system often fail to represent the model error in BES. In this study, we have proposed an ensemble method using Stochastically Perturbed Parameterization Tendency to represent the model error in BES. The characteristics of the proposed BES are compared with the traditional approaches using the National Meteorological Centre method for different control variables choices. We have further tested the performance of the proposed method in improving the skill of precipitation forecast for an extreme rainfall event, which caused devastating flood over Chennai city, India, on December 2015. Results demonstrate that the use of the proposed method results in better forecast skill of convective precipitation in terms of both position and intensity than traditional National Meteorological Centre‐based BES. Best results are obtained when zonal and meridional momentum control variables are used for modeling ensemble‐based BES. |
| first_indexed | 2024-12-10T13:17:34Z |
| format | Article |
| id | doaj.art-8dd20e2026084b398f4a5eab2172fa90 |
| institution | Directory Open Access Journal |
| issn | 2333-5084 |
| language | English |
| last_indexed | 2024-12-10T13:17:34Z |
| publishDate | 2020-04-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Earth and Space Science |
| spelling | doaj.art-8dd20e2026084b398f4a5eab2172fa902022-12-22T01:47:29ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842020-04-0174n/an/a10.1029/2019EA000667Improving Convective Precipitation Forecasts Using Ensemble‐Based Background Error Covariance in 3DVAR Radar Assimilation SystemP. Thiruvengadam0J. Indu1Subimal Ghosh2Department of Civil Engineering Indian Institute of Technology Bombay Mumbai IndiaDepartment of Civil Engineering Indian Institute of Technology Bombay Mumbai IndiaDepartment of Civil Engineering Indian Institute of Technology Bombay Mumbai IndiaAbstract Skillful quantitative precipitation forecast using the numerical weather prediction model relies on an accurate estimate of the atmospheric state as an initial condition. Variational assimilation methods (VAR) have the potential to provide improved initial state estimation to the numerical weather prediction model using observations, prior data (background), and their respective error covariance. The quality of variational assimilation hinges on the background error statistics (BES) as it weights the error in prior state and determines the spread of assimilated observations in model space. Traditional approaches used to model stationary BES in a three‐dimensional variational assimilation system often fail to represent the model error in BES. In this study, we have proposed an ensemble method using Stochastically Perturbed Parameterization Tendency to represent the model error in BES. The characteristics of the proposed BES are compared with the traditional approaches using the National Meteorological Centre method for different control variables choices. We have further tested the performance of the proposed method in improving the skill of precipitation forecast for an extreme rainfall event, which caused devastating flood over Chennai city, India, on December 2015. Results demonstrate that the use of the proposed method results in better forecast skill of convective precipitation in terms of both position and intensity than traditional National Meteorological Centre‐based BES. Best results are obtained when zonal and meridional momentum control variables are used for modeling ensemble‐based BES.https://doi.org/10.1029/2019EA000667radarvariational assimilationbackground error statistics3DVARNWPensemble forecast |
| spellingShingle | P. Thiruvengadam J. Indu Subimal Ghosh Improving Convective Precipitation Forecasts Using Ensemble‐Based Background Error Covariance in 3DVAR Radar Assimilation System Earth and Space Science radar variational assimilation background error statistics 3DVAR NWP ensemble forecast |
| title | Improving Convective Precipitation Forecasts Using Ensemble‐Based Background Error Covariance in 3DVAR Radar Assimilation System |
| title_full | Improving Convective Precipitation Forecasts Using Ensemble‐Based Background Error Covariance in 3DVAR Radar Assimilation System |
| title_fullStr | Improving Convective Precipitation Forecasts Using Ensemble‐Based Background Error Covariance in 3DVAR Radar Assimilation System |
| title_full_unstemmed | Improving Convective Precipitation Forecasts Using Ensemble‐Based Background Error Covariance in 3DVAR Radar Assimilation System |
| title_short | Improving Convective Precipitation Forecasts Using Ensemble‐Based Background Error Covariance in 3DVAR Radar Assimilation System |
| title_sort | improving convective precipitation forecasts using ensemble based background error covariance in 3dvar radar assimilation system |
| topic | radar variational assimilation background error statistics 3DVAR NWP ensemble forecast |
| url | https://doi.org/10.1029/2019EA000667 |
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