Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Atmospheric Measurement Techniques

Volumn 10, Issue 5, 2017, Pages 1859-1864

  Lee, Sanggyun ^a   Han, Hyangsun ^b   Im, Jungho ^a   Jang, Eunna ^a   Lee, Myong In ^a  

^a Ulsan National Institute of Science and Technology   (South Korea)

^b Korea Polar Research Institute (KOPRI)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

BRIGHTNESS TEMPERATURE; CLIMATE MODELING; CONVECTION; CONVECTIVE CLOUD; MODEL VALIDATION; PRECIPITATION INTENSITY; SOCIOECONOMIC IMPACT; THUNDERSTORM;

KOREA;

EID: 85019910702     PISSN: 18671381     EISSN: 18678548     Source Type: Journal    
DOI: 10.5194/amt-10-1859-2017     Document Type: Article

References (61)

1. Amorati, R., Alberoni, P. P., Levizzani, V., and Nanni, S.: IR-based satellite and radar rainfall estimates of convective storms over northern Italy, Meteorol. Appl., 7, 1-18, doi:10.1017/S1350482700001328, 2000.
2. Banacos, P. C. and Schultz, D. M.: The Use of Moisture Flux Convergence in Forecasting Convective Initiation: Historical and Operational Perspectives, Weather Forecast., 20, 351-366, doi:10.1175/WAF858.1, 2005.
3. Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y., Miyakawa, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y., Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama, H., Uesawa, D., Yokota, H., and Yoshida, R.: An Introduction to Himawari-8/9; Japan's New-Generation Geostationary Meteorological Satellites, J. Meteorol. Soc. Jpn., 94, 151-183, doi:10.2151/jmsj.2016-009, 2016.
4. Breiman, L.: Random Forests, Machine Learning, 45, 5-32, doi:10.1023/a:1010933404324, 2001.
5. Craven, J. P., Jewell, R. E., and Brooks, H. E.: Comparison between Observed Convective Cloud-Base Heights and Lifting Condensation Level for Two Different Lifted Parcels, Weather Forecast., 17, 885-890, doi:10.1175/1520-0434 (2002) 017<0885:CBOCCB>2.0. CO;2, 2002.
6. Guo, Z. and Du, S.: Mining parameter information for building extraction and change detection with very high resolution imagery and GIS data, GIS. Remote Sens., 54, 38-63, 2017.
7. Haile, A. T., Rientjes, T., Gieske, A., and Gebremichael, M.: Multispectral remote sensing for rainfall detection and estimation at the source of the Blue Nile River, Int. J. Appl. Earth Obs. Geoinf., S76-S82, 2010.
8. Han, H., Lee, S., Im, J., Kim, M., Lee, M. I., Ahn, M. H., and Chung, S. R.: Detection of convective initiation using Meteorological Imager onboard Communication, Ocean, and Meteorological satellite based on machine learning approaches, Remote Sens., 7, 9184-9204, 2015.
9. Hane, C. E., Rabin, R. M., Crawford, T. M., Bluestein, H. B., and Baldwin, M. E.: A Case Study of Severe Storm Development along a Dryline within a Synoptically Active Environment, Part II: Multiple Boundaries and Convective Initiation, Mon. Weather Rev., 130, 900-920, doi:10.1175/1520-0493 (2002) 130<0900:ACSOSS>2.0. CO;2, 2002.
10. Hosmer, D. W. and Lemeshow, S.: Applied Logistic Regressio, John Wiley and Sons, Inc., New York, 528 pp., 2000.
11. Houze, R. A.: Mesoscale convective systems, Rev. Geophys., 42, RG4003, 10.1029/2004RG000150, 2004.
12. Im, J., Jensen, J. R., and Tullis, J. A.: Object-based change detection using correlation image analysis and image segmentation, Int. J. Remote Sens., 29, 399-423, doi:10.1080/01431160601075582, 2008.
13. Im, J., Jensen, J., Jensen, R., Gladden, J., Waugh, J., and Serrato, M.: Vegetation cover analysis of hazardous waste sites in utah and arizona using hyperspectral remote sensing, Remote Sens., 4, 327-353, 2012.
14. Jensen, J. R. and Im, J.: Remote Sensing Change Detection in Urban Environments, in: Geo-Spatial Technologies in Urban Environments: Policy, Practice, and Pixels, edited by: Jensen, R. R., Gatrell, J. D., and McLean, D., Springer Berlin Heidelberg, Berlin, Heidelberg, 7-31, 2007.
15. Jewett, C. P. and Mecikalski, J. R.: Adjusting thresholds of satellitebased convective initiation interest fields based on the cloud environment, J. Geophys. Res-Atmos., 118, 12649-612660, doi:10.1002/2013JD019700, 2013.
16. Jorgensen, D. P. and LeMone, M. A.: Vertical Velocity Characteristics of Oceanic Convection, J. Atmos. Sci., 46, 621-640, doi:10.1175/1520-0469 (1989) 046<0621:VVCOOC>2.0. CO;2, 1989.
17. Kar, S. K. and Ha, K.-J.: Characteristic Differences of Rainfall and Cloud-to-Ground Lightning Activity over South Korea during the Summer Monsoon Season, Mon. Weather Rev., 131, 2312-2323, doi:10.1175/1520-0493 (2003) 131<2312:CDORAC>2.0. CO;2, 2003.
18. Kim, D. H. and Ahn, M. H.: Introduction of the in-orbit test and its performance for the first meteorological imager of the Communication, Ocean, and Meteorological Satellite, Atmos. Meas. Tech., 7, 2471-2485, 10.5194/amt-7-2471-2014, 2014.
19. Kim, H. W. and Lee, D. K.: An Observational Study of Mesoscale Convective Systems with Heavy Rainfall over the Korean Peninsula, Weather Forecast., 21, 125-148, doi:10.1175/WAF912.1, 2006.
20. Kim, M., Im, J., Han, H., Kim, J., Lee, S., Shin, M., and Kim, H.-C.: Landfast sea ice monitoring using multisensor fusion in the Antarctic, GIS. Remote Sens., 52, 239-256, doi:10.1080/15481603.2015.1026050, 2015.
21. Kim, Y. H., Im, J., Ha, H. K., Choi, J.-K., and Ha, S.: Machine learning approaches to coastal water quality monitoring using GOCI satellite data, GIS. Remote Sens., 51, 158-174, doi:10.1080/15481603.2014.900983, 2014.
22. Lawrence, R. L. and Wright, A.: Rule-based classification systems using classification and regression tree (CART) analysis, Photogramm. Eng. Rem. S., 67, 1137-1142, 2001.
23. Li, M., Im, J., and Beier, C.: Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest, GIS. Remote Sens., 50, 361-384, doi:10.1080/15481603.2013.819161, 2013.
24. Li, M., Im, J., Quackenbush, L. J., and Liu, T.: Forest Biomass and Carbon Stock Quantification Using Airborne LiDAR Data: A Case Study Over Huntington Wildlife Forest in the Adirondack Park, IEEE J. Sel. Top. Appl. Earth Obser. Remote Sens., 7, 3143-3156, doi:10.1109/JSTARS.2014.2304642, 2014.
25. Liu, T., Im, J., and Quackenbush, L. J.: A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification, ISPRS J. Photogramme., 110, 34-47, doi:10.1016/j.isprsjprs.2015.10.002, 2015.
26. Lu, Z., Im, J., Quackenbush, L. J., and Yoo, S.: Remote Sensingbased House Value Estimation Using an Optimized Regional Regression Model, Photogramm. Eng. Remote Sens., 79, 809-820, doi:10.14358/PERS.79.9.809, 2013.
27. Lu, Z., Im, J., Rhee, J., and Hodgson, M.: Building type classification using spatial and landscape attributes derived from Li-DAR remote sensing data, Landscape, Urban Plan., 130, 134-148, doi:10.1016/j.landurbplan. 2014.07.005, 2014.
28. Mecikalski, J. R. and Bedka, K. M.: Forecasting Convective Initiation by Monitoring the Evolution of Moving Cumulus in Daytime GOES Imagery, Mon. Weather Rev., 134, 49-78, doi:10.1175/MWR3062.1, 2006.
29. Mecikalski, J. R., Bedka, K. M., Paech, S. J., and Litten, L. A.: A Statistical Evaluation of GOES Cloud-Top Properties for Nowcasting Convective Initiation, Mon. Weather Rev., 136, 4899-4914, doi:10.1175/2008MWR2352.1, 2008.
30. Mecikalski, J. R., MacKenzie, W. M., Koenig, M., and Muller, S.: Cloud-Top Properties of Growing Cumulus prior to Convective Initiation as Measured by Meteosat Second Generation. Part I: Infrared Fields, J. Appl. Meteorol. Climatol., 49, 521-534, doi:10.1175/2009JAMC2344.1, 2009.
31. Mecikalski, J. R., MacKenzie, W. M., König, M., and Muller, S.: Cloud-Top Properties of Growing Cumulus prior to Convective Initiation as Measured by Meteosat Second Generation. Part II: Use of Visible Reflectance, J. Appl. Meteorol. Climatol., 49, 2544-2558, doi:10.1175/2010JAMC2480.1, 2010.
32. Mecikalski, J. R., Williams, J. K., Jewett, C. P., Ahijevych, D., LeRoy, A., and Walker, J. R.: Probabilistic 0-1-h Convective Initiation Nowcasts that Combine Geostationary Satellite Observations and Numerical Weather Prediction Model Data, J. Appl. Meteorol. Climatol., 54, 1039-1059, doi:10.1175/JAMC-D-14-0129.1, 2015.
33. Merk, D. and Zinner, T.: Detection of convective initiation using Meteosat SEVIRI: implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM, Atmos. Meas. Tech., 6, 1903-1918, 10.5194/amt-6-1903-2013, 2013.
34. Miyamoto, Y., Kajikawa, Y., Yoshida, R., Yamaura, T., Yashiro, H., and Tomita, H.: Deep moist atmospheric convection in a subkilometer global simulation, Geophys. Res. Lett., 40, 4922-4926, doi:10.1002/grl.50944, 2013.
35. Morel, C. and Senesi, S.: A climatology of mesoscale convective systems over Europe using satellite infrared imagery. I: Methodology, Q. J. Roy. Meteorol. Soc., 128, 1953-1971, doi:10.1256/003590002320603485, 2002.
36. Mueller, C., Saxen, T., Roberts, R., Wilson, J., Betancourt, T., Dettling, S., Oien, N., and Yee, J.: NCAR Auto-Nowcast System, Weather Forecast., 18, 545-561, doi:10.1175/1520-0434 (2003) 018<0545:NAS>2.0. CO;2, 2003.
37. Nyarko, B., Diekkruger, B., Van De Giesen, N., and Vlek, P.: Floodplain wetland mapping in the White Volta River Basin of Ghana, GIS. Remote Sens. 52, 374-395, 2015.
38. Park, S., Im, J., Jang, E., and Rhee, J.: Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions, Agr. Forest Meteorol., 216, 157-169, doi:10.1016/j.agrformet.2015.10.011, 2016.
39. Quinlan, J. R.: Data mining tools See5 and C4.5, version 2.10, available at: https://www.rulequest.com/see5-info.html (last access: 10 February 2016), 2015.
40. Rhee, J., Im, J., Carbone, G. J., and Jensen, J. R.: Delineation of climate regions using in-situ and remotely-sensed data for the Carolinas, Remote Sens. Environ., 112, 3099-3111, doi:10.1016/j.rse.2008.03.001, 2008.
41. Rhee, J., Park, S., and Lu, Z.: Relationship between land cover patterns and surface temperature in urban areas, GIS. Remote Sens., 51, 521-536, doi:10.1080/15481603.2014.964455, 2014.
42. Roberts, N. M. and Lean, H. W.: Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events, Mon. Weather Rev., 136, 78-97, doi:10.1175/2007MWR2123.1, 2008.
43. Roberts, R. D. and Rutledge, S.: Nowcasting storm initiation and growth using GOES-8 and WSR-88D data, Weather Forecast., 18, 562-584, 2003.
44. Rosenfeld, D., Woodley, W. L., Lerner, A., Kelman, G., and Lindsey, D. T.: Satellite detection of severe convective storms by their retrieved vertical profiles of cloud particle effective radius and thermodynamic phase, J. Geophys. Res-Atmos., 113, D04208, doi:10.1029/2007JD008600, 2008.
45. Schmit, T. J., Gunshor, M. M., Menzel, W. P., and Gurka, J. J.: Introducing the next-generation Advanced Baseline Imager on GOES-R, B. Am. Meteorol. Soc. 86, 1079-1096, 2005.
46. Sieglaff, J. M., Cronce, L. M., Feltz, W. F., Bedka, K. M., Pavolonis, M. J., and Heidinger, A. K.: Nowcasting Convective Storm Initiation Using Satellite-Based Box-Averaged Cloud-Top Cooling and Cloud-Type Trends, J. Appl. Meteorol. Climatol., 50, 110-126, doi:10.1175/2010JAMC2496.1, 2011.
47. Siewert, C. W., Koenig, M., and Mecikalski, J. R.: Application of Meteosat second generation data towards improving the nowcasting of convective initiation, Meteorol. Appl., 17, 442-451, doi:10.1002/met.176, 2010.
48. Sobajima, A.: Rapidly Development Cumulus Areas Derivation Algorithm. Japan Meteorological Agency Algorithm Theoretical Basis Document, Meteorological Satellite Center, Tokyo, Japan, 2012.
49. Sohn, B. J., Ryu, G. H., Song, H. J., and Ou, M. L.: Characteristic features of warm-type rain producing heavy rainfall over the Korean Peninsula inferred from TRMM measurements, Mon. Weather Rev., 141, 3873-3888l, 2013.
50. Song, H. J. and Sohn, B. J.: Two heavy rainfall types over the Korean peninsula in the humid East Asian summer environment: A satellite observation study, Mon. Weather Rev., 143, 363-382, 2015.
51. Torbick, N. and Corbiere, M.: Mapping urban sprawl and impervious surfaces in the northeast United States for the past four decades, GIS. Remote Sens., 52, 746-764, doi:10.1080/15481603.2015.1076561, 2015.
52. Trier, S. B., Chen, F., and Manning, K. W.: A Study of Convection Initiation in a Mesoscale Model Using High-Resolution Land Surface Initial Conditions, Mon. Weather Rev., 132, 2954-2976, doi:10.1175/MWR2839.1, 2004.
53. Tu, J. V.: Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes, J. Clin. Epidemiol., 49, 1225-1231, doi:10.1016/S0895-4356 (96) 00002-9, 1996.
54. Walker, J. R., MacKenzie, W. M., Mecikalski, J. R., and Jewett, C. P.: An Enhanced Geostationary Satellite-Based Convective Initiation Algorithm for 0-2-h Nowcasting with Object Tracking, J. Appl. Meteorol. Climatol., 51, 1931-1949, doi:10.1175/JAMC-D-11-0246.1, 2012.
55. Walker, J. R. and Mecikalski, J. R.: Algorithm theoretical basis document (ATBD) for convective initiation. NOAA NESDIS Center for Satellite Applications and Research, available at: http://www.nsstc.uah.edu/SATCAST/docs/GOES-RAWGATBDAviationConvectiveInitiationv2.0.pdf (last access: 10 February 2016), 2011.
56. Wang, C.-C., Chen, G. T.-J., and Carbone, R. E.: A Climatology of Warm-Season Cloud Patterns over East Asia Based on GMS Infrared Brightness Temperature Observations, Mon. Weather Rev., 132, 1606-1629, doi:10.1175/1520-0493 (2004) 132<1606:ACOWCP>2.0. CO;2, 2004.
57. Weckwerth, T. M. and Parsons, D. B.: A Review of Convection Initiation and Motivation for IHOP-2002, Mon. Weather Rev., 134, 5-22, doi:10.1175/MWR3067.1, 2006.
58. Vondou, D. A., Nzeukou, A., and Kamga, F. M.: Diurnal cycle of convective activity over the West of Central Africa based on meteosat images, Int. J. Appl. Earth Obs. Geoinf., S58-S62, 2010.
59. Yoo, S., Im, J., and Wagner, J. E.: Variable selection for hedonic model using machine learning approaches: A case study in Onondaga County, NY, Landscape. Urban Plan., 107, 293-306, doi:10.1016/j.landurbplan. 2012.06.009, 2012.
60. Zinner, T., Mannstein, H., and Tafferner, A.: Cb-TRAM: Tracking and monitoring severe convection from onset over rapid development to mature phase using multi-channel Meteosat-8 SEVIRI data, Meteorol. Atmos. Phys. 101, 191-210, doi:10.1007/s00703-008-0290-y, 2008.
61. Zuidema, P.: Convective Clouds over the Bay of Bengal, Mon. Weather Rev., 131, 780-798, doi:10.1175/1520-0493 (2003) 131<0780:CCOTBO>2.0. CO;2, 2003.