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Impact of GOES Atmospheric Motion Vector Data Assimilation on Forecasts over South America: Akará Cyclone Case Study.

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Title: Impact of GOES Atmospheric Motion Vector Data Assimilation on Forecasts over South America: Akará Cyclone Case Study.
Authors: Barros, Luana O.1 (AUTHOR), Sapucci, Luiz F.2 (AUTHOR) luiz.sapucci@inpe.br, Viezel, Caroline2,3 (AUTHOR), Ranieri, Victor A.3,4 (AUTHOR), Baños, Ivette H.1,4 (AUTHOR), Bastarz, Carlos F.2 (AUTHOR), Vendrasco, Eder P.2,3 (AUTHOR), Lopes, Thaisa G.1,4 (AUTHOR), Almeida, Sindy S. S.1 (AUTHOR), de Mattos, João G. Z.2 (AUTHOR), Aravequia, José A.2 (AUTHOR)
Source: Remote Sensing. Jun2026, Vol. 18 Issue 11, p1799. 21p.
Subjects: Data assimilation, GOES (Meteorological satellite), Cyclone tracking, Tropical cyclones, Satellite meteorology, Numerical weather forecasting, Climate change, Weather forecasting
Geographic Terms: South America, South Atlantic Ocean, Brazil
Abstract: Highlights: This study evaluates the impact of assimilating GOES-derived Atmospheric Motion Vectors (AMVs) on the predictability of Tropical Cyclone Akará using the Numerical Modeling and Assimilation System (SMNA) at CPTEC/INPE, the only South American center producing global Numerical Weather Prediction (NWP). The results show a positive impact of GOES AMV assimilation on the cyclone representation, with clear improvements in track, intensity, and circulation structure. These findings are particularly relevant given the observed increase in the frequency and intensity of South Atlantic cyclones under climate change, highlighting the growing need for high-quality weather forecasts in the region. Atmospheric Motion Vectors (AMVs) from geostationary satellites are a critical observational source for data assimilation, particularly in regions with sparse observations, such as the Southern Hemisphere. This study evaluates the impact of assimilating AMVs from the Geostationary Operational Environmental Satellite (GOES) series into the Numerical Modeling and Assimilation System (SMNA) used at the Center for Weather Forecasting and Climate Studies of the National Institute for Space Research (CPTEC/INPE). The SMNA consists of the Brazilian Global Atmospheric Model (BAM) coupled with the Gridpoint Statistical Interpolation (GSI) data assimilation system. Two experiments were conducted in February 2024: a control experiment that assimilated all conventional observations along with AMVs from GOES-16 and GOES-18 satellites, and a second experiment (data denial), in which the AMVs were excluded. This time period coincided with the formation of the tropical cyclone Akará offshore the southeast coast of Brazil. The diagnostic analysis of the assimilation process indicates a substantial increase in the relative contribution of wind observations to the cost function and a reduction in the differences between the background and the analysis, particularly in the mid and upper troposphere. Forecast verification showed that assimilating AMV data led to a reduction in RMSE and an increase in anomaly correlations for several variables, including wind and temperature at various vertical levels. The positive impact of GOES AMV data on the representation of the tropical cyclone Akará is evident in the improved positioning, intensity, and circulation structure of the cyclone, particularly during its intensification phase. With tropical cyclone events over South America becoming more frequent in recent years, results from this study indicate the critical need to assimilate AMV data to improve forecast skill. Furthermore, the assimilation of GOES AMVs significantly enhances the representation of atmospheric circulation over South America, particularly improving the predictability of large-scale events such as cyclones in the South Atlantic. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Impact of GOES Atmospheric Motion Vector Data Assimilation on Forecasts over South America: Akará Cyclone Case Study.
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  Data: <searchLink fieldCode="AR" term="%22Barros%2C+Luana+O%2E%22">Barros, Luana O.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sapucci%2C+Luiz+F%2E%22">Sapucci, Luiz F.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> luiz.sapucci@inpe.br</i><br /><searchLink fieldCode="AR" term="%22Viezel%2C+Caroline%22">Viezel, Caroline</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ranieri%2C+Victor+A%2E%22">Ranieri, Victor A.</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Baños%2C+Ivette+H%2E%22">Baños, Ivette H.</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bastarz%2C+Carlos+F%2E%22">Bastarz, Carlos F.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vendrasco%2C+Eder+P%2E%22">Vendrasco, Eder P.</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lopes%2C+Thaisa+G%2E%22">Lopes, Thaisa G.</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Almeida%2C+Sindy+S%2E+S%2E%22">Almeida, Sindy S. S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22de+Mattos%2C+João+G%2E+Z%2E%22">de Mattos, João G. Z.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Aravequia%2C+José+A%2E%22">Aravequia, José A.</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 11, p1799. 21p.
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  Data: <searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink><br /><searchLink fieldCode="DE" term="%22GOES+%28Meteorological+satellite%29%22">GOES (Meteorological satellite)</searchLink><br /><searchLink fieldCode="DE" term="%22Cyclone+tracking%22">Cyclone tracking</searchLink><br /><searchLink fieldCode="DE" term="%22Tropical+cyclones%22">Tropical cyclones</searchLink><br /><searchLink fieldCode="DE" term="%22Satellite+meteorology%22">Satellite meteorology</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+weather+forecasting%22">Numerical weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink><br /><searchLink fieldCode="DE" term="%22Weather+forecasting%22">Weather forecasting</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22South+America%22">South America</searchLink><br /><searchLink fieldCode="DE" term="%22South+Atlantic+Ocean%22">South Atlantic Ocean</searchLink><br /><searchLink fieldCode="DE" term="%22Brazil%22">Brazil</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: This study evaluates the impact of assimilating GOES-derived Atmospheric Motion Vectors (AMVs) on the predictability of Tropical Cyclone Akará using the Numerical Modeling and Assimilation System (SMNA) at CPTEC/INPE, the only South American center producing global Numerical Weather Prediction (NWP). The results show a positive impact of GOES AMV assimilation on the cyclone representation, with clear improvements in track, intensity, and circulation structure. These findings are particularly relevant given the observed increase in the frequency and intensity of South Atlantic cyclones under climate change, highlighting the growing need for high-quality weather forecasts in the region. Atmospheric Motion Vectors (AMVs) from geostationary satellites are a critical observational source for data assimilation, particularly in regions with sparse observations, such as the Southern Hemisphere. This study evaluates the impact of assimilating AMVs from the Geostationary Operational Environmental Satellite (GOES) series into the Numerical Modeling and Assimilation System (SMNA) used at the Center for Weather Forecasting and Climate Studies of the National Institute for Space Research (CPTEC/INPE). The SMNA consists of the Brazilian Global Atmospheric Model (BAM) coupled with the Gridpoint Statistical Interpolation (GSI) data assimilation system. Two experiments were conducted in February 2024: a control experiment that assimilated all conventional observations along with AMVs from GOES-16 and GOES-18 satellites, and a second experiment (data denial), in which the AMVs were excluded. This time period coincided with the formation of the tropical cyclone Akará offshore the southeast coast of Brazil. The diagnostic analysis of the assimilation process indicates a substantial increase in the relative contribution of wind observations to the cost function and a reduction in the differences between the background and the analysis, particularly in the mid and upper troposphere. Forecast verification showed that assimilating AMV data led to a reduction in RMSE and an increase in anomaly correlations for several variables, including wind and temperature at various vertical levels. The positive impact of GOES AMV data on the representation of the tropical cyclone Akará is evident in the improved positioning, intensity, and circulation structure of the cyclone, particularly during its intensification phase. With tropical cyclone events over South America becoming more frequent in recent years, results from this study indicate the critical need to assimilate AMV data to improve forecast skill. Furthermore, the assimilation of GOES AMVs significantly enhances the representation of atmospheric circulation over South America, particularly improving the predictability of large-scale events such as cyclones in the South Atlantic. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18111799
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        Text: English
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        PageCount: 21
        StartPage: 1799
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      – SubjectFull: Data assimilation
        Type: general
      – SubjectFull: GOES (Meteorological satellite)
        Type: general
      – SubjectFull: Cyclone tracking
        Type: general
      – SubjectFull: Tropical cyclones
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      – SubjectFull: Satellite meteorology
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      – SubjectFull: Numerical weather forecasting
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      – SubjectFull: Climate change
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      – SubjectFull: Weather forecasting
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      – SubjectFull: South America
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      – SubjectFull: South Atlantic Ocean
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      – SubjectFull: Brazil
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              Text: Jun2026
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