Analysis of updraft velocity in mesoscale convective systems using satellite and WRF model simulations

نوع مقاله: مقاله تحقیقی‌ (پژوهشی‌)

نویسندگان

1 Faculty of Geography, University of Tehran, Tehran, Iran

2 Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, USA

3 Institute of Geophysics, University of Tehran, Tehran, Iran

چکیده

Updraft vertical velocity is an important dynamical quantity which is strongly related to storm intensity and heavy precipitation. It can be calculated by direct observations, NWP model, and geostationary satellites which can provide the possibility of measuring this quantity with high temporal resolution. This research analyzed updraft velocity based on six derived parameters from INSAT3-D and high temporal and spatial resolution simulations of WRF model in the west and southwest of Iran. The interrelationship among the derived variables was investigated from the immature to mature stages of convective cells in Mesoscale Convective Systems (MCS). Updraft velocity was calculated based on a theoretical framework and real observations. The was a large results discrepancy among the results. This finding was in company with previous studies which concluded that updraft velocity is the resultant of other bulk buoyancy forces and environmental variables. Also, the estimated updraft velocities showed a positive correlation with height. The authors proposed linear regression, as a parametric, and Random Forest (RF), as a non-parametric, machine learning methods for estimation of updraft velocity based on satellite variables. A forward–backward method was applied to reach the best modeling in both methods. In linear regression modeling, the cloud-top cooling rate was the most significant factor, and in the RF, band difference of water vapor, thermal infrared 1, and elevation data had the maximum importance. Results showed that the RF could better estimate updraft velocity.

کلیدواژه‌ها


عنوان مقاله [English]

Analysis of updraft velocity in mesoscale convective systems using satellite and WRF model simulations

نویسندگان [English]

  • Reza Khandan 1
  • Seyed Kazem Alavipanah 1
  • Arastoo Pour Biazar 2
  • Maryam Gharaylou 3
1 Faculty of Geography, University of Tehran, Tehran, Iran
2 Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, USA
3 Institute of Geophysics, University of Tehran, Tehran, Iran
چکیده [English]

Updraft vertical velocity is an important dynamical quantity which is strongly related to storm intensity and heavy precipitation. It can be calculated by direct observations, NWP model, and geostationary satellites which can provide the possibility of measuring this quantity with high temporal resolution. This research analyzed updraft velocity based on six derived parameters from INSAT3-D and high temporal and spatial resolution simulations of WRF model in the west and southwest of Iran. The interrelationship among the derived variables was investigated from the immature to mature stages of convective cells in Mesoscale Convective Systems (MCS). Updraft velocity was calculated based on a theoretical framework and real observations. The was a large results discrepancy among the results. This finding was in company with previous studies which concluded that updraft velocity is the resultant of other bulk buoyancy forces and environmental variables. Also, the estimated updraft velocities showed a positive correlation with height. The authors proposed linear regression, as a parametric, and Random Forest (RF), as a non-parametric, machine learning methods for estimation of updraft velocity based on satellite variables. A forward–backward method was applied to reach the best modeling in both methods. In linear regression modeling, the cloud-top cooling rate was the most significant factor, and in the RF, band difference of water vapor, thermal infrared 1, and elevation data had the maximum importance. Results showed that the RF could better estimate updraft velocity.

کلیدواژه‌ها [English]

  • MCS
  • updraft velocity
  • NWP
  • geostationary satellite
  • CAPE

Ackerman, SA., 1996, Global Satellite Observations of Negative Brightness Temperature Differences between 11 and 6.7 µm: Journal of the Atmospheric Sciences, 53(19), 2803-2812.

Adlerman, EJ., & Droegemeier, KK., 2005, The Dependence of Numerically Simulated Cyclic Mesocyclogenesis Upon Environmental Vertical Wind Shear: Monthly Weather Review, 133(12), 3595-3623.

Ahmadi Givi, F., Mohebalhojeh, AR., & Gharaylou, M., 2006, The Dynamics of Cyclonic Systems over Iran Using Potential Vorticity Diagnostics; A Case Study for Nov-Dec 2003: Earth and Space Physics, 32(1), 13.

Cohen, C, & McCaul Jr, EW.2006. The Sensitivity of Simulated Convective Storms to Variations in Prescribed Single-Moment Microphysics Parameters That Describe Particle Distributions, Sizes, and Numbers. Monthly Weather Review, 134(9), 2547-2565.

Ellrod, GP.2004. Impact on Volcanic Ash Detection Caused by the Loss of the 12.0 Μm “Split Window” Band on Goes Imagers. Journal of volcanology and geothermal research, 135(1), 91-103.

Giangrande, SE, Toto, T, Jensen, MP, Bartholomew, MJ, Feng, Z, Protat, A, . . . Machado, L.2016. Convective Cloud Vertical Velocity and Mass‐Flux Characteristics from Radar Wind Profiler Observations During Goamazon2014/5. Journal of Geophysical Research: Atmospheres, 121(21).

Hamada, A, & Takayabu, YN.2016. Convective Cloud Top Vertical Velocity Estimated from Geostationary Satellite Rapid‐Scan Measurements. Geophysical Research Letters, 43(10), 5435-5441.

Inoue, T.1987. An Instantaneous Delineation of Convective Rainfall Areas Using Split Window Data of Noah-7 Avhrr. Journal of the Meteorological Society of Japan. Ser. II, 65(3), 469-481.

James, G, Witten, D, Hastie, T, & Tibshirani, R. 2013. An Introduction to Statistical Learning (Vol. 6): Springer.

Jensen, MP, Petersen, WA, Bansemer, A, Bharadwaj, N, Carey, L, Cecil, D, Gerlach, J.2016. The Midlatitude Continental Convective Clouds Experiment (Mc3e). Bulletin of the American Meteorological Society, 97(9), 1667-1686.

Kain, JS.2004. The Kain–Fritsch Convective Parameterization: An Update. Journal of Applied Meteorology, 43(1), 170-181.

Kirkpatrick, C, McCaul Jr, EW, & Cohen, C.2009. Variability of Updraft and Downdraft Characteristics in a Large Parameter Space Study of Convective Storms. Monthly Weather Review, 137(5), 1550-1561.

Luo, ZJ, Jeyaratnam, J, Iwasaki, S, Takahashi, H, & Anderson, R.2014. Convective Vertical Velocity and Cloud Internal Vertical Structure: An a‐Train Perspective. Geophysical Research Letters, 41(2), 723-729.

McCaul Jr, EW, & Cohen, C.2002. The Impact on Simulated Storm Structure and Intensity of Variations in the Mixed Layer and Moist Layer Depths. Monthly Weather Review, 130(7), 1722-1748.

McCaul Jr, EW, Cohen, C, & Kirkpatrick, C.2005. The Sensitivity of Simulated Storm Structure, Intensity, and Precipitation Efficiency to Environmental Temperature. Monthly Weather Review, 133(10), 3015-3037.

McCaul Jr, EW, & Weisman, ML.1996. Simulations of Shallow Supercell Storms in Landfalling Hurricane Environments. Monthly Weather Review, 124(3), 408-429.

Mecikalski, JR, Jewett, CP, Apke, JM, & Carey, LD.2016. Analysis of Cumulus Cloud Updrafts as Observed with 1-Min Resolution Super Rapid Scan Goes Imagery. Monthly Weather Review, 144(2), 811-830.

Mohammadi, H, Fattahi, E, Shamsi pour, AA, & Akbari, M.2012. Dynamic Analysis of Sudan Low-Pressure Systems and Torrents in Southwest of Iran.

Morrison, H.2016. Impacts of Updraft Size and Dimensionality on the Perturbation Pressure and Vertical Velocity in Cumulus Convection. Part Ii: Comparison of Theoretical and Numerical Solutions and Fully Dynamical Simulations. Journal of the Atmospheric Sciences, 73(4), 1455-1480.

Nazaripour, H, Dostkamiyan, M, & Alizadeh, S.2015. The Spatial Distribution Patterns of Temperature, Precipitation, and Humidity Using Geostatistical Exploratory Analysis (Case Study: Central Area of Iran).

Parodi, A, & Emanuel, K.2009. A Theory for Buoyancy and Velocity Scales in Deep Moist Convection. Journal of the Atmospheric Sciences, 66(11), 3449-3463.

Prata, A.1989. Observations of Volcanic Ash Clouds in the 10-12 Μm Window Using Avhrr/2 Data. International Journal of Remote Sensing, 10(4-5), 751-761.

Schmetz, J, Tjemkes, S, Gube, M, & Van de Berg, L.1997. Monitoring Deep Convection and Convective Overshooting with Meteosat. Advances in Space Research, 19(3), 433-441.

Schumacher, C, Stevenson, SN, & Williams, CR.2015. Vertical Motions of the Tropical Convective Cloud Spectrum over Darwin, Australia. Quarterly Journal of the Royal Meteorological Society, 141(691), 2277-2288.

Skamarock, WC, Klemp, JB, Dudhia, J, Gill, DO, Barker, DM, Wang, W, & Powers, JG. (2005). A Description of the Advanced Research Wrf Version 2. Retrieved from

Soden, BJ, & Bretherton, FP.1993. Upper Tropospheric Relative Humidity from the Goes 6.7 Μm Channel: Method and Climatology for July 1987. Journal of Geophysical Research: Atmospheres, 98(D9), 16669-16688.

Tang, S, Xie, S, Zhang, Y, Zhang, M, Schumacher, C, Upton, H, . . . Ahlgrimm, M.2016. Large-Scale Vertical Velocity, Diabatic Heating and Drying Profiles Associated with Seasonal and Diurnal Variations of Convective Systems Observed in the Goamazon2014/5 Experiment. Atmospheric Chemistry and Physics, 16(22), 14249.

Thompson, G, Rasmussen, RM, & Manning, K.2004. Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part I: Description and Sensitivity Analysis. Monthly Weather Review, 132(2), 519-542.

Tian, Y, & Kuang, Z.2016. Dependence of Entrainment in Shallow Cumulus Convection on Vertical Velocity and Distance to Cloud Edge. Geophysical Research Letters, 43(8), 4056-4065.

Walker, JR, MacKenzie Jr, WM, Mecikalski, JR, & Jewett, CP.2012. An Enhanced Geostationary Satellite–Based Convective Initiation Algorithm for 0–2-H Nowcasting with Object Tracking. Journal of Applied Meteorology and Climatology, 51(11), 1931-1949.

Wang, X, & Zhang, M.2014. Vertical Velocity in Shallow Convection for Different Plume Types. Journal of Advances in Modeling Earth Systems, 6(2), 478-489.

Weisman, ML, & Klemp, JB.1982. The Dependence of Numerically Simulated Convective Storms on Vertical Wind Shear and Buoyancy. Monthly Weather Review, 110(6), 504-520.

Weisman, ML, & Klemp, JB.1984. The Structure and Classification of Numerically Simulated Convective Stormsin Directionally Varying Wind Shears. Monthly Weather Review, 112(12), 2479-2498.

Xu, K-M, & Randall, DA.2001. Updraft and Downdraft Statistics of Simulated Tropical and Midlatitude Cumulus Convection. Journal of the Atmospheric Sciences, 58(13), 1630-1649.

Yang, J, Wang, Z, Heymsfield, AJ, & French, JR.2016. Characteristics of Vertical Air Motion in Isolated Convective Clouds. Atmospheric Chemistry and Physics, 16(15), 10159-10173.