Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Metsat: a MATLAB code to calculate, and visualize METOP B satellite data for global climatic monitoring

  • Software Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Global warming and Climatic changes in common are the most famous topics in the present days, to study the substantial size of this problem an advancement in the data collecting sensors and platform is taking place to increase the measurement’s density and frequency, this led to the generation of a huge amount of data. Data include Temperature, Pressure, and water vapor consider one of the foremost important parameters within the atmosphere. Accurate measurements of water vapor pressure within the troposphere are significant for understanding and precursor weather changes also it is important to follow the global warming progression. In this research, we tried generating a modest computer program that can handle this kind of data and represent a tool to help climatologist to deal with this data, the program read and collect separated data file, plot climatic daily variables, and calculates the average total precipitable water from Global Positioning satellite radio occultation. The program results are formed in 2D and 3D global figures that can visualize the measured atmospheric parameters, in addition to the calculation of the total precipitable water.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Boukabara S, Garrett K, Chen W (2010) Global coverage of Total Precipitable water using a microwave Variational algorithm. IEEE Trans Geosci Remote Sens 48(10):3608–3621. https://doi.org/10.1109/TGRS.2010.2048035

    Article  Google Scholar 

  • Chen B, Liu Z (2016) Global water vapor variability and trend from the latest 36 year (1979 to 2014) data of ECMWF and NCEP reanalyses, radiosonde, GPS, and microwave satellite. J Geophys Res Atmos 121:11,442–11,462. https://doi.org/10.1002/2016JD024917

    Article  Google Scholar 

  • Eyre M (2008) An introduction to GPS radio occultation and its use in numerical weather prediction. Proceedings of the ECMWF GRAS SAF workshop on applications of GPS radio occultation measurements, 16–18 June: 1–10. ECMWF. http://www.ecmwf.int/publications/. Accessed 24 Apr 2009

  • Guan J-P, Yin Y-T, Zhang L-F, Wang J-N, Zhang M-Y (2019) Comparison analysis of Total Precipitable water of satellite-borne microwave radiometer retrievals and island Radiosondes. Atmosphere 10:390

    Article  Google Scholar 

  • Ho S, Kuo Y, Sokolovskiy S (2007) Improvement of the temperature and moisture retrievals in the lower troposphere using AIRS and GPS radio occultation measurements. J Atmos Ocean Technol 2007(24):1726–1739

    Article  Google Scholar 

  • Ho S-p, Peng L (2018) Global water vapor estimates from measurements from active GPS RO sensors and passive infrared and microwave sounders. Green Chemistry Applications, Murat Eyvaz and Ebubekir Yüksel, IntechOpen. https://doi.org/10.5772/intechopen.79541

  • Ho S-P, Peng L, Mears C, Anthes RA (2018) Comparison of global observations and trends of total precipitable water derived from microwave radiometers and COSMIC radio occultation from 2006 to 2013. Atmos Chem Phys 18:259–274. https://doi.org/10.5194/acp-18-259-2018

    Article  Google Scholar 

  • Hu F, Yang C, Schnase JL, Duffy DQ, Xu M, Bowen MK, Lee T, Song W (2018) Climatespark: An in-memory distributed computing framework for big climate data analytics. Comput Geosci 115:154–166

    Article  Google Scholar 

  • Jaffrés JBD (2019) GHCN-daily: A treasure trove of climate data awaiting discovery. Comput Geosci 122:35–44

    Article  Google Scholar 

  • Kursinski ER, Hajj GA, Bertiger WI, Leroy SS (1996) Initial results of radio occultation observations of Earth’s atmosphere using the global positioning system. United States: N. p. Web. https://doi.org/10.1126/science.271.5252.1107.

  • Meko DM, Touchan R, Anchukaitis Seascorr KJ (2011) A MATLAB program for identifying the seasonal climate signal in an annual tree-ring time series. Comput Geosci 37:1234–1241. https://doi.org/10.1016/j.cageo.2011.01.013

    Article  Google Scholar 

  • Roman J, Knuteson R, Ackerman S (2014) Time-to-detect trends in Precipitable water vapor with varying measurement error. J Clim 27:8259–8275. https://doi.org/10.1175/JCLI-D-13-00736.1

    Article  Google Scholar 

  • Soden BJ, Held IM (2006) An assessment of climate feedbacks in coupled ocean–atmosphere models. J Clim 19:3354–3360. https://doi.org/10.1175/JCLI3799.1

    Article  Google Scholar 

  • Steiner AK, Lackner BC, Ladstädter F, Scherllin-Pirscher B, Foelsche U, Kirchengast G (2011) GPS radio occultation for climate monitoring and change detection. Radio Sci 46:RS0D24. https://doi.org/10.1029/2010RS004614

    Article  Google Scholar 

  • Sun X, Shen S, Leptoukh G, Wang P, Di L, Lu M (2012) Development of a web-based visualization platform for climate research using Google earth. Comput Geosci 47:160–168

    Article  Google Scholar 

  • Teng W, Huang C, Ho S, Kuo Y, Zhou X (2013) Characteristics of global precipitable water in ENSO events revealed by COSMIC measurements. J Geophys Res 2013(118):8411–8425

    Article  Google Scholar 

  • Unidata (2019) Netcdf 4.6.1. Boulder, CO: UCAR/Unidata Program Center. https://doi.org/10.5065/D6H70CW6

  • Voigt A et al (2016) The tropical rain belts with an annual cycle and a continent model intercomparison project: TRACMIP. J Adv Model Earth Syst 8:1868–1891. https://doi.org/10.1002/2016MS000748

    Article  Google Scholar 

  • Zhang Y, Xu J, Yang N, Lan P (2018) Variability and trends in global Precipitable water vapor retrieved from COSMIC radio occultation and Radiosonde observations. Atmosphere 2018(9):174. https://doi.org/10.3390/atmos9050174

    Article  Google Scholar 

  • Zhang W, Lou Y, Cao Y, Liang H, Shi C, Huang J et al (2019) Corrections of radiosonde-based precipitable water using ground-based GPS and applications on historical radiosonde data over China. J Geophys Res Atmos 124:3208–3222. https://doi.org/10.1029/2018JD029662

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohamed Al Deep.

Additional information

Communicated by: H. Babaie

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(P 1 kb)

ESM 2

(P 827 bytes)

ESM 3

(P 712 bytes)

ESM 4

(P 490 bytes)

ESM 5

(P 425 bytes)

ESM 6

(P 435 bytes)

ESM 7

(TXT 1.07 KB)

ESM 8

(metsat.M 2 kb)

ESM 9

(CPG 5 bytes)

ESM 10

(DBF 585 kb)

ESM 11

(SHP 8594 kb)

ESM 12

(SHX 2 kb)

ESM 13

(P 1 kb)

ESM 14

(P 850 bytes)

ESM 15

(P 877 bytes)

ESM 16

(MD 1 kb)

ESM 17

(P 262 bytes)

ESM 18

(P 264 bytes)

ESM 19

(P 989 bytes)

ESM 20

(P 490 bytes)

Annex 1

Annex 1

The description of all generated function and what they do in the program.

Read_Variables: in this step, we will be prompted to select the folder that contains all day’s data files to sort and collect single day climatic Parameters, the code will save the sorted data from all NC files into text file starting with the word “Data”,

Read_Calc_TPW: in this step, we will be prompted to select the folder that contains all day’s data files (the same folder as in the previous step), the total precipitable water (TPW) will be calculated and saved in a separate text file starting with the word “TPW”.

Profiles: This code will plot all the vertical profiles measured in the selected day.

gene_Parameters: in order to generate the parameters to plot the 2D and 3D figures for each day separately, we will be prompted to select the file starting by the world “data” generated in the first step.

TPW_Parameters: in this part will be prompted to select the file starting by the world “TPW” generated in the second step, to generate mesh and grids needed for 2D plots in the next step.

TPW_2D_Plot_: This Code will generate a 2D plot of the daily TPW generated in the previous step

ANU_AVG_TPW_Plot: this code will plot the annual average TPW global map, in addition to some statistics about the daily variation, also this code can run for all the subfolder in the main directory, so that mean if we put a month it will plot the average monthly TPW, and so on

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al Deep, M. Metsat: a MATLAB code to calculate, and visualize METOP B satellite data for global climatic monitoring. Earth Sci Inform 14, 2423–2431 (2021). https://doi.org/10.1007/s12145-021-00686-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-021-00686-3

Keywords