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Uncertainty assessment and an improved CYGNSS cyclonic wind speed retrieval model for cyclones over North Indian Ocean

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Abstract

An investigation is conducted to study the uncertainty in the cyclonic ocean wind speed (CWS) of Cyclone Global Navigation Satellite System (CYGNSS) satellites over the North Indian Ocean (NIO). For the completion of the assessment, the comparison of CYGNSS wind speed during normal ocean conditions is also carried out with respect to buoy and advanced scatterometer (ASCAT ) data. The de-collocation effect is also studied using the India Meteorological Department’s (IMD) data. The sensitivity of the uncertainty in CWS is evaluated through the soil moisture active passive (SMAP) data. It has been found that the CYGNSS CWS underestimates for CWS > 35.0 m/s. The study shows that the uncertainty for the CWS > 35.0 m/s is dependent on the incidence angle. Therefore, a new incidence angle-based Geophysical Model Function (IGMF) is proposed for CWS. The IMD and SMAP data for the cyclones of the year 2018–2019 have been used for validation of the CWS derived from IGMF. An extremely severe cyclone Fani is analysed in detail. The IGMF reduces the mean error from 1.77 to –0.001 m/s with respect to SMAP for Fani. Overall, the improvement in root mean square difference (RMSD) and the mean error with IGMF are 16.11% and 44.39%, respectively, over CYGNSS for CWS > 35.0 m/s. The maximum CWS from CYGNSS, IGMF, and IMD is 38.8, 52.07, and 54.97 m/s, respectively, for the Bay of Bengal. It can be concluded that IGMF provides better CWS than the CYGNSS CWS.

Research highlights

  • The uncertainty of the CYGNSS Cyclonic Ocean Wind Speed (CWS) is investigated over the North Indian Ocean for the years 2018-2019.

  • The Geo-shifting process with the collocation is included to reduce the collocation error due to the cyclone translational speed.

  • The CYGNSS CWS shows significant under-estimation for the wind speed >35 m/s with the SMAP and the IMD data.

  • A new Incidence angle-based Geo-physical Model Function (IGMF) is developed to improve the bias in the CWS.

  • The IGMF model has better agreement with both SMAP and IMD data than CYGNSS CWS over the NIO region.

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Acknowledgements

The authors would like to thank Director, U R Rao Satellite Centre, ISRO, Bangalore, Deputy Director, Mission Development Area, U R Rao Satellite Centre, ISRO, Bangalore, and Group Head, Space Navigation Group, U R Rao Satellite Centre, ISRO, Bangalore for their support, help and encouragement. Authors would like to acknowledge the help and support from the Head of Department and Staff from CORAL IIT Kharagpur. The guidance for the statistical analysis by Dr Rajkumar Kamaljit Singh, Assistant Professor, Manipur Technical University, is also acknowledged. CYGNSS, ASCAT and SMAP data from https://podaac.jpl.nasa.gov/, RAMA buoy data from https://www.pmel.noaa.gov/ and the best track data from India Meteorological Department are also acknowledged. Remote sensing systems (https://www.remss.com/) is also acknowledged here to provide the images and information about SMAP data which contains cyclonic wind structure.

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Authors and Affiliations

  1. Indian Institute of Technology, Kharagapur, India

    Megha Maheshwari & Arun Chakraborty

  2. U R Rao Satellite Centre, ISRO, Bangalore, India

    Megha Maheshwari, Akhilesh Kumar & S Nirmala

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  1. Megha Maheshwari
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  2. Arun Chakraborty
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  3. Akhilesh Kumar
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  4. S Nirmala
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Contributions

Megha Maheshwari contributed to the conceptualisation, methodology, analysis, and writing of the paper. Arun Chakraborty and Akhilesh Kumar supervised the work and contributed to review and editing of the paper. Nirmala S reviewed and edited the paper.

Corresponding author

Correspondence to Megha Maheshwari.

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Communicated by C Gnanaseelan

Appendix

Appendix

1.1 A1. Validation of SMAP high wind speed with SFMR data for year 2018–2019

The CWS data derived from SMAP is used as a reference wind speed for the work presented in the paper. For CWS, SFMR, which is mounted on the National Oceanic and Atmospheric Administration aircraft, is the most reliable ground truth measurement available (Uhlhorn and Black 2003; Meissener et al. 2017). Therefore, it is widely used to validate satellite-derived wind speed data during cyclones (Meissner et al. 2017; Fore et al. 2018; Ruf et al. 2019; Maheshwari et al. 2021). The data is available every second with ~3 km of spatial resolution along the track of the aircraft (Meissner et al. 2017). Therefore, re-sampling of SFMR data is required to compare with SMAP data. We applied the same collocation scheme to the SFMR and SMAP wind speed as described in section 5.2.2 for the years 2018–2019. In this way, the SMAP data is validated before using it for this work. For comparison, the collocated data points with SFMR wind speed greater than 15 m/s are used.

Figure A1 shows the scatter plot between the cyclonic wind speed derived from SFMR and SMAP. The total root mean square error in SMAP CWS is 3.12 m/s with mean bias of 0.14 m/s. The root mean square difference and the mean bias are similar, as quoted in Meissner et al. (2017) and Fore et al. (2018).

Figure A1
figure 16

Scatterplot between cyclonic wind speeds derived from SFMR and SMAP for the years 2018–2019. The collocated points with SFMR wind speed greater than 15 m/s, are only used for the comparison. The red colour line represents the 1:1 agreement line. The statistics are shown in the left corner of the figure.

Full size image

1.2 A2. Best track wind intensity data comparison over North Indian Ocean

Over NIO, IMD and JTWC are the major agencies which provide the best track data of the cyclones.

1.2.1 A2.1 Comparison of IMD and JTWC data

The comparison of the best track wind data from IMD and JTWC for the years 2018–2019 for cyclones over the NIO region is shown in figure A2. Since IMD 3 minutes sustained wind is equivalent to 1-minute sustained wind, no scale factor is applied before comparing IMD and JTWC data (Knapp and Kruk 2010). As shown in figure A2, JTWC and IMD best track wind are correlated very well with each other, with a correlation coefficient of 0.95. However, the mean error between JTWC and IMD data is 3.28 m/s. Similar results are published in Knapp and Kruk (2010).

Figure A2
figure 17

Scatterplot between the cyclone best track wind data from IMD and JTWC for the years 2018–2019 over NIO. The black colour line is the 1:1 agreement line. The statistics of the scatter plot in terms of root mean square difference, mean error, and correlation coefficient are also shown in the left corner of the figure.

Full size image

IMD uses three minutes average period to derive maximum sustained wind speed at the given cyclone location. However, JTWC applies one minute averaging on the maximum sustained wind at the track location (Knapp and Kruk 2010). Along with the major difference in averaging, both the agencies use different methods to determine maximum sustained wind for the estimation of wind intensity during cyclones in NIO. Therefore, it is difficult to find out which one is better than the others (Knapp and Kruk 2010).

1.2.2 A2.2 Comparison of JTWC with CYGNSS maximum wind data

To further analyse, the JTWC maximum sustained wind data is compared with the CYGNSS maximum wind speed data within the collocated track. The JTWC data is scaled before comparison, as mentioned in Meissner et al. (2017). The process of collocation and search radius for IMD data are discussed in detail in section 5.2.1. The same process is followed here. As displayed in figure A3(a–d), the mean error varies from 1.83 to 6.44 m/s. However, the number of matched data points is less to derive statistically significant results. It is noteworthy that the CYGNSS maximum wind speed shows underestimation at high wind regime with respect to JTWC data irrespective of the collocation window. Similar pattern is observed when CYGNSS wind data is compared with IMD and SMAP data in the present paper.

Figure A3
figure 18

JTWC best track maximum sustained wind speed vs. maximum wind speed within CYGNSS track. The CYGNSS track is selected when it is within the different space and time search radius of the IMD cyclone track location and time. (a) 25 km radius and 1 hr window, (b) 25 km radius and 2 hr window, (c) 50 km radius and 1 hr window, and (d) 50 km radius and 2 hr window. The black colour line represents the 1:1 agreement line. The statistics in terms of mean error and standard error are also displayed in each panel.

Full size image

In the present paper, IMD best track data is chosen. There are two main reasons to use IMD data. The first one is the literature survey on the IMD best track wind intensity over NIO. Knapp and Kruk (2010) published a detailed analysis of the comparison of wind intensity from the best track data from IMD and JTWC. The literature suggested using IMD data for NIO than JTWC. Shyam et al. (2022) also used IMD than JTWC best track data to study cyclones over NIO for year 2017. Johny et al. (2019) found that the IMD best track intensity is comparable with the assimilated scatterometer wind intensity for cyclones Vardah, Nada, and Kyant over NIO. The second reason is the time interval. Qualitatively, the performance of CYGNSS maximum wind with JTWC and IMD is similar. In both cases, CYGNSS data underestimate at high wind regimes. However, IMD best track data is available for every 3 hours of interval, while JTWC provides wind intensity at 6 hours of interval. Therefore, IMD data can provide more collocation points with CYGNSS observables to get statistically significant results than best track data from JTWC.

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Maheshwari, M., Chakraborty, A., Kumar, A. et al. Uncertainty assessment and an improved CYGNSS cyclonic wind speed retrieval model for cyclones over North Indian Ocean. J Earth Syst Sci 132, 18 (2023). https://doi.org/10.1007/s12040-022-02026-5

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