Height Assignment Improvement in Kalpana-1 Atmospheric Motion Vectors

Deb, Sanjib; Wanzong, Steve; Velden, Chris; Kaur, Inderpreet; Kishtawal, C. M.; Pal, P. K.; Menzel, W. Paul

doi:10.1007/s12524-013-0278-z

Cited by 14 publications

(8 citation statements)

References 10 publications

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“…The statistical validation scores [22], such as the mean vector difference (MVD), root-mean-square vector difference (RMSVD), bias, and root-mean-square error (RMSE), were calculated to estimate the performance of the AMVs. For the vector quantity, the vector difference (VD) between the individual wind vector (u i , v i ) and the NWP or rawinsonde (u r , v r ) is given by:…”

Section: Verification Methodsmentioning

confidence: 99%

Development and Intercomparison Study of an Atmospheric Motion Vector Retrieval Algorithm for GEO-KOMPSAT-2A

Borde

Carranza

et al. 2019

Remote Sensing

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We derived an atmospheric motion vector (AMV) algorithm for the Geostationary Korea Multipurpose Satellite (GEO-KOMPSAT-2A; GK-2A) launched on 4 December 2018, using the Advanced Himawari Imager (AHI) onboard Himawari-8, which is very similar to the Advanced Meteorological Imager onboard GK-2A. This study clearly describes the main steps in our algorithm and optimizes it for the target box size and height assignment methods by comparing AMVs with numerical weather prediction (NWP) and rawinsonde profiles for July 2016 and January 2017. Target box size sensitivity tests were performed from 8 × 8 to 48 × 48 pixels for three infrared channels and from 16 × 16 to 96 × 96 pixels for one visible channel. The results show that the smaller box increases the speed, whereas the larger one slows the speed without quality control. The best target box sizes were found to be 16 × 16 for CH07, 08, and 13, and 48 × 48 pixels for CH03. Height assignment sensitivity tests were performed for several methods, such as the cross-correlation coefficient (CCC), equivalent blackbody temperature (EBBT), infrared/water vapor (IR/WV) intercept, and CO 2 slicing methods for a cloudy target as well as normalized total contribution (NTC) and normalized total cumulative contribution (NTCC) for a clear-air target. For a cloudy target, the CCC method is influenced by the quality of the cloud's top pressure. Better results were found when using EBBT and IR/WV intercept methods together rather than individually. Furthermore, CO 2 slicing had the best statistics. For a clear-air target, the combined use of NTC and NTCC had the best statistics. Additionally, the mean vector difference, root-mean-square (RMS) vector difference, bias, and RMS error (RMSE) between GK-2A AMVs and NWP or rawinsonde were smaller by approximately 18.2% on average than in the case of the Communication, Ocean and Meteorology Satellite (COMS) AMVs. In addition, we verified the similarity between GK-2A and Meteosat Third Generation (MTG) AMVs using the AHI of Himawari-8 from 21 July 2016. This similarity can provide evidence that the GK-2A algorithm works properly because the GK-2A AMV algorithm borrows many methods of the MTG AMV algorithm for geostationary data and inversion layer corrections. The Pearson correlation coefficients in the speed, direction, and height of the prescribed GK-2A and MTG AMVs were larger than 0.97, and the corresponding bias/RMSE were0.07/2.19 m/s, 0.21/14.8 • , and 2.61/62.9 hPa, respectively, considering common quality indicator with forecast (CQIF) > 80.

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Section: Verification Methodsmentioning

confidence: 99%

Development and Intercomparison Study of an Atmospheric Motion Vector Retrieval Algorithm for GEO-KOMPSAT-2A

Borde

Carranza

et al. 2019

Remote Sensing

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“…Kalpana-1 AMVs are derived operationally at SAC, ISRO, Ahmedabad, while Meteosat-7 AMVs are derived at EUMETSAT. The operational algorithm used to derive AMVs from Kalpana-1 is described in Kishtawal et al (2009), with recent changes in the quality control technique (Deb et al 2013) and height assignment (Deb et al 2014a) methods. However, in this section, a very brief summary of Kalpana-1 AMV retrieval algorithm is given.…”

Section: Kalpana-1 and Meteosat-7 Atmospheric Motion Vectors (Amvs)mentioning

confidence: 99%

“…It is also well established that the large source of uncertainty in restricting the optimum use of AMVs is the limitation of height assignment (Velden and Bedka 2009) techniques, which basically depends on the radiative transfer calculation of cloud radiometric signature in infrared, CO 2 , or water vapour channels and forecast temperature profiles. The AMVs from the infrared and water vapour channels of Indian Geostationary satellite Kalpana-1 are being retrieved by tracking clouds operationally at every 30-min interval at the Space Applications Centre (SAC), Indian Space Research Organisation (ISRO), Ahmedabad (Deb et al 2008;Kishtawal et al 2009), and recently, some changes have been made to the operational algorithm; especially the use of multiple images for quality control procedure (Deb et al 2013) and changes in the height assignment (Deb et al 2014a) technique (i.e. implementation of infrared window technique, H 2 O intercept method, and cloud base method, etc.).…”

Section: Introductionmentioning

confidence: 99%

A comparative assessment of Kalpana-1 and MISR cloud tracked winds over the Indian Ocean region

Deb

Kaur

Kishtawal

et al. 2014

Theor Appl Climatol

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In this study, an attempt has been made to advance the error characteristic of atmospheric motion vectors (AMVs) derived from the infrared and water vapour channels of Kalpana-1 very high resolution radiometer by comparing against stereo motion vectors (SMVs) retrieved by tracking clouds from the multi-angle imaging spectro-radiometer (MISR) for a period of 9 months. Two different versions of the MISR SMVs with horizontal resolutions 70.4 and 17.6 km, respectively, are used for the inter-comparison. It is found that the Kalpana-1 AMV has stronger westerlies and southerlies than the MISR SMV at all latitudes and levels in majority of times. The performances of Kalpana-1 AMVs against MISR SMVs are assessed by doing a similar analysis where Meteosat-7 AMVs (infrared and water vapour AMVs) are also evaluated against the MISR SMVs for the same region. It is found that results of both AMVs (Kalpana-1 and Meteosat-7) with both sets of MISR SMVs are comparable with few exceptions. The zonal wind components of the MISR SMVs showed smaller mean wind difference and root mean square difference (RMSD) compared to the meridional wind components. The SMVs are typically assigned to higher altitudes than AMVs. Analysis related to the height discrepancies between MISR SMVs and AMVs shows that in the multi-layer cloud AMVs are tracked in upper level cloud targets, while SMVs are skewed more towards lower level. The accuracy is better for the low level where collocations are highly dense and gradually decreases towards the higher levels. Because of improvement in the MISR SMV retrieval algorithm, the errors in the meridional component of SMVs have improved in the recently released version with horizontal resolution of 17.6 km.

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“…The operational derivation of AMVs started at the Space Applications Centre (SAC), Indian Space Research Organisation (ISRO), a few years back (Kishtawal et al , ) using three successive images (called as a triplet algorithm) from the Indian geostationary meteorological satellites (Kalpana‐1, INSAT‐3A). The height assignment component of the operational Kalpana‐1 AMV retrieval algorithm is upgraded recently by incorporating widely used methods: the infrared window (WIN) technique or the water vapour histogram method (HIST), the H 2 O intercept method and the cloud base method (Deb et al , ). One of the criteria to accept a wind vector during automatic quality control (Holmlund, ) in the standard triplet method is to test its consistency with wind in the neighbourhood.…”

Section: Introductionmentioning

confidence: 99%

“…The present study is focussed only on demonstrating a new concept of quality control based on wind buffer. To assess the impact of the new quality control technique, the other components such as tracer selection, tracking and height assignment methods (Deb et al , ; Kishtawal et al , ; Deb et al , ) are kept identical for the old algorithm (OA) and the new algorithm (NA). The only difference between the OA and NA is the quality control procedure.…”

Section: Introductionmentioning

confidence: 99%

Assessment of a new quality control technique in the retrieval of atmospheric motion vectors

et al. 2013

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Atmospheric motion vectors (AMVs) are generally derived using three successive images of a geosynchronous satellite in most of the operational centres in world. This study presents a new quality control technique used during the retrieval of AMVs from the images of infrared and water vapour channels of the Indian geostationary satellite Kalpana-1. The new technique proposed here is based on wind retrieval using the previous eight images. The AMVs retrieved using a new algorithm and the old algorithm that uses the automatic quality control procedure of European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), are compared with collocated radiosonde data and numerical weather prediction model analysis. The new algorithm shows an improvement over the old one at all levels both in terms of root mean square vector difference and spatial pattern. It has also produced significantly larger numbers of valid retrievals.

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Height Assignment Improvement in Kalpana-1 Atmospheric Motion Vectors

Cited by 14 publications

References 10 publications

Development and Intercomparison Study of an Atmospheric Motion Vector Retrieval Algorithm for GEO-KOMPSAT-2A

Development and Intercomparison Study of an Atmospheric Motion Vector Retrieval Algorithm for GEO-KOMPSAT-2A

A comparative assessment of Kalpana-1 and MISR cloud tracked winds over the Indian Ocean region

Assessment of a new quality control technique in the retrieval of atmospheric motion vectors

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