An inter‐comparison of <scp>K</scp>alpana‐1 and <scp>M</scp>eteosat‐7 atmospheric motion vectors against radiosonde winds and <scp>NWP</scp> forecasts during monsoon 2011

Rani, S. Indra; Gupta, M. Das

doi:10.1002/met.1411

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…RMSD is more below 5 km and less above 5 km than the RMSD in the Mie‐cloudy winds in the respective layers. Validation of AMVs against the short‐term forecasts also showed similar values of RMSD (Rani and Das Gupta, 2013; Sharma et al ., 2021). Le Marshall et al .…”

Section: Resultsmentioning

confidence: 65%

“…RMSD is more below 5 km and less above 5 km than the RMSD in the Mie-cloudy winds in the respective layers. Validation of AMVs against the short-term forecasts also showed similar values of RMSD (Rani and Das Gupta, 2013;Sharma et al, 2021). Le Marshall et al (2002) reported that the difference wind threshold, the mean wind difference between AMV and the model equivalent, is situation dependent, near a jet stream or low-level jet, etc., and is typically between 5 and 7 m⋅s −1 , and in general, the difference wind threshold should be less than 10 m⋅s −1 to be considered for assimilation.…”

Section: Nwp Model Backgroundmentioning

confidence: 64%

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Assessing the quality of novel Aeolus winds for NWP applications at NCMRWF

Rani

Jangid

Kumar

et al. 2022

Quart J Royal Meteoro Soc

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This study illustrates the validation of Aeolus Horizontal Line-Of-Sight (HLOS) winds, both Rayleigh-clear and Mie-cloudy, using in situ satellite wind (Atmospheric Motion Vectors, AMVs) observations, and NWP equivalents for three months, June-August 2020, covering the Indian summer monsoon season. Estimated errors in the Mie-cloudy (Rayleigh-clear) winds are clustered around 0.5-4 m⋅s −1 (3-8 m⋅s −1 ), and the differences between Aeolus Mie-cloudy (Rayleigh-clear) and sonde winds are within ±5 m⋅s −1 (± 8 m⋅s −1 ), but the systematic error is close to zero over the Northern Hemisphere where there are more sonde reports. Validation shows the quality of Mie-cloudy winds is better than Rayleigh-clear winds. Though the comparison against the observations like sonde (radiosonde and pilot balloons together) and aircraft indicate the quality of the Aeolus winds, their sparse spatial and temporal coverage limits the validation. Validation of Aeolus winds against AMVs provides similar results but with a better and nearly complete picture of the quality and quantity, with more information over the data-sparse and remote regions. Statistical scores suggest the characteristics of the Aeolus winds at different vertical levels and geographical regions remain the same irrespective of the validation reference datasets. The Indian summer monsoon features like Low-Level Jet (LLJ) and Tropical Easterly Jet (TEJ) are well represented in the Aeolus winds. This study also investigated the impact of the Aeolus HLOS winds over the Indian region through the collocated radiosonde and ALADIN wind profile assimilation experiments. Observing System Experiments (OSEs) suggest assimilation of Aeolus winds produced marginal improvement in the simulation of north Indian Ocean cyclones.

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Section: Resultsmentioning

confidence: 65%

Section: Nwp Model Backgroundmentioning

confidence: 64%

Assessing the quality of novel Aeolus winds for NWP applications at NCMRWF

Rani

Jangid

Kumar

et al. 2022

Quart J Royal Meteoro Soc

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“…We hypothesized that the satellite wind measurements assimilated in this region could be biased. Previous work has documented upper-level wind speed biases associated with Meteosat-7 over the Southern Hemisphere extending into the higher latitudes (Rani and Das Gupta 2014;Lean and Bormann 2018). Since satellite observations are the only observations at the upper levels in outer portions of the domain, this could degrade the analyses and lead to poor forecasts produced by A-DART.…”

Section: Discussionmentioning

confidence: 95%

Mesoscale Prediction in the Antarctic Using Cycled Ensemble Data Assimilation

Riedel

Cavallo

Parsons

2021

Monthly Weather Review

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Due in part to sparse conventional observation coverage in the Antarctic region, atmospheric studies in this part of the globe often rely more heavily on numerical models. Model representation of atmospheric processes in the Antarctic remains inferior to representation in the Northern Hemisphere midlatitudes. Poor representation may be related to inaccurate model analyses that do not optimally utilize the limited observation network. Here, the ensemble Kalman filter (EnKF) data assimilation (DA) technique is employed in lieu of variational DA techniques to investigate impacts on model analysis accuracy. This DA technique [provided by the Data Assimilation Research Testbed (DART)] is coupled with a polar-modified, mesoscale numerical model that together compose Antarctic-DART (A-DART). A-DART is cycled with DA and run over a 1-month period, assimilating only conventional observations. Results show relatively good agreement between A-DART and observations. Comparison with radiosonde temperature and geostationary satellite wind observations shows large differences between RMSE and ensemble spread in the upper troposphere. The analysis increment shows large values in the eastern Atlantic–western Indian Oceans associated with geostationary satellite wind observations. Further evaluation determines that geostationary satellite wind observations may be biased in this region. Overall, this baseline demonstration of ensemble-based modeling applied in the Antarctic produced short-term forecasts that were competitive with two operational modeling systems while assimilating on the O(106) fewer observations. A-DART is capable of assimilating additional observations for a variety of applications. This study highlights the capability of applying this ensemble-based DA technique for process and forecast studies in an observation-sparse region.

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“…Several studies have shown the benefit of assimilating AMVs on improving the weather forecasts over the tropics (eg., Velden et al 1992;Leslie et al 1998;Soden et al 2001;Rani and Gupta 2014;Mounika et al 2018). A study by Kaur et al (2015) during the Indian summer monsoon (ISM) reported a positive impact of Kalpana-1 AMV assimilation over the tropical region in the 3DVAR DA system.…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of the impact of INSAT-3D atmospheric motion vectors in 3DVAR and hybrid ensemble-3DVAR data assimilation systems during Indian summer monsoon

Gogoi

Kutty

Boroghain

2021

Theor Appl Climatol

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and INSAT-3D satellite derived atmospheric motion vectors from https://www.mosdac.gov.in. Code availability: The atmospheric model used in this study is Weather Research and Forecast (ARW-WRF) of version 3.8.1, which is openly available for download in https://www2.mmm.ucar.edu/wrf/users/download/get_sources.html. The data assimilation package comes from WRFDA system of 3.8.1 version archived in https://www2.mmm.ucar.edu/wrf/users/download/get_sources.html#WRFDA. Authors' contributions:Rekha Bharali performed the experiments and wrote the manuscript. G Kutty analyzed the results.

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An inter‐comparison of Kalpana‐1 and Meteosat‐7 atmospheric motion vectors against radiosonde winds and NWP forecasts during monsoon 2011

Cited by 5 publications

References 23 publications

Assessing the quality of novel Aeolus winds for NWP applications at NCMRWF

Assessing the quality of novel Aeolus winds for NWP applications at NCMRWF

Mesoscale Prediction in the Antarctic Using Cycled Ensemble Data Assimilation

Intercomparison of the impact of INSAT-3D atmospheric motion vectors in 3DVAR and hybrid ensemble-3DVAR data assimilation systems during Indian summer monsoon

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