Real Challenge of Data Assimilation for Tornadogenesis
Successful recent numerical simulation of tornadogenesis with horizontal resolution of the order of 10 m, O(10 m), and associated temporal resolution of O (0.1 s and 0.01 s for a time-split scheme) requires a vast amount of computer time, impractical to use the simulation model for the model constraint of variational data assimilation and ensemble Kalman filter.
Also, recent advanced observations such as phased array radar have revealed spatial and temporal details of the similar high resolutions important for tornadogenesis, which should be properly reflected in the data assimilation.
To deal with them, data assimilation for operational uses requires special strategy. The author discusses, in this article, one promising strategy, especially use of the entropic balance model, which sounds computationally practical in variational data assimilation.
The entropic balance theory with entropic source and sink simplification is favorably compared to other historically proposed tornadogenesis theories and models. The theory explains the overshooting of hydrometeors against head-wind upper level westerlies and middle-level south-westerlies, mesocyclone, rear frank downdraft and tornado development. Furthermore, the theory suggests transition from dipole structure of early stage to monopole type mature stage, similar to an attractor of nonlinear system, of tornadogenesis, which explains of tilting of tornado vortex axis.
It is a real challenge to develop an operational data assimilation technology for accurate diagnosis and prediction of tornadogenesis.
KeywordsData Assimilation Vortex Tube Convective Storm Variational Data Assimilation Environmental Wind
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- Asai T (1970a) Three dimensional features of thermal convection in a plane Couette flow. J Meteor Soc Japan 48: 18–29Google Scholar
- Asai T (1970b) Stability of a plane parallel flow with variable vertical shear and unstable stratification. J Meteor Soc Japan 48:129–139Google Scholar
- Atkins P, de Paula J (2002) Atkins’ physical chemistry (7th ed.). Oxford University Press, New YorkGoogle Scholar
- Atkinson BW (1981) Meso-scale atmospheric circulations. Academic Press, London (marked by A with figure number of this book when used for reference in the present article)Google Scholar
- Bateman H (1932) Partial differential equations of mathematical physics. Cambridge Univ Press, Cambridge (reprinted by Dover Publ., 1944)Google Scholar
- Bluestein HB (1993) Synoptic-dynamic meteorology in midlatitudes, Vol II, Observations and theory of weather systems. Oxford Univ Press, New York (marked by B with figure number of this book when used for reference in the present article)Google Scholar
- Browning KA (1968) The organization of severe local storms. Weather 23:429–434Google Scholar
- Browning KA (1977) The structure and mechanism of hailstorms. Hail: A review of hail, science and hail suppression. Meteor Monogr 38 Amer Meteor Soc: 116–122Google Scholar
- Burgess D et al (1982) Mesocyclone evolution statistics. Preprints, 12th conf. on Severe Local Storms, San Antonio, Texas, Amer Meteor Soc: 422–424Google Scholar
- Byers HR, Braham RR (1949) The Thunderstorm. Dept. Commerce, USAGoogle Scholar
- Chandrasekar C (2007) CASA vision of dense low-level network. National Symposium on Multifunction Phased Array Radar: Laveraging technology for a next-generation national radar system. October 10–12, 2007, National Weather Center, Norman OklahomaGoogle Scholar
- Chisholm A J, Renick JH (1972) The kinematics of multicell and supercell Alberta hail stdies. Research Council of Alberta Hail Studies, Report 72–2: 24–31Google Scholar
- Clebsch A (1859) Über ein algemeine transformation der Hydrodynamischen Gleichungen. Crelle J für Math 54:293–312Google Scholar
- Davies-Jones R, Brooks HE (1993) Mesocyclogenesis from a theoretical perspective. The Tornado: Its Structure, Dynamics, Prediction, and Hazards. Geophys Monogr 79, Amer Geophys Union: 105–114Google Scholar
- Dutton JA (1976) The ceaseless wind. McGraw-Hill, New YorkGoogle Scholar
- Forsyth DE et al (2007) Update on the National Weather Radar Testbed (phased array). Preprint 23rd Conf. on Interactive Information Processing Systems for Meteorology, Oceanography, and Hydrology, San Antonio, TX, Amer Meteor Soc CD-ROM,7.4Google Scholar
- Fujita T (1958) Mesoanalysis of the Illinois tornadoes of April 9, 1953. J Meteor 15: 288–296Google Scholar
- Henselman PD (2007) Rapid sampling of severe storms by the National Weather Radar Testbed Phased Array Radar. Presented at National Weather Center Seminar on 02 October 2007Google Scholar
- Henselman PD et al (2006) Comparison of storm evolution characteristics: The NWRT and WSR-88D. Preprint, 23rd Conf. on Severe Local Storms, St Louis, MO, Amer Meteo SocGoogle Scholar
- Ishikawa Y, Koizumi K (2002) One-month cycle experiments of the JMA mesoscale 4-dimensional variational data assimilation (4D-Var) system. Research Activities in Atmospheric and Oceanic Modelling WNO/TD-No.1105 1: 1.26–1.27Google Scholar
- Kalney E (2003) Atmospheric modeling, data assimiltion and predictability. Cambridge University Press, CambridgeGoogle Scholar
- Lamb H (1932) Hydrodynamics, 2nd ed. Cambridge Univ. Press, Dover Publ, New YorkGoogle Scholar
- Lemon LR (1977) New severe thunderstorm radar identification techniques and warning criteria: A preliminary report. NOAA Tech. Memo. NWS NSSFC-1, TDU, NSSFC, Kansas CityGoogle Scholar
- Lewis JM et al (2006) Dynamic data assimilation: A least squares approache. Cambridge University Press, CambridgeGoogle Scholar
- Lilly DK (1982) The development and maintenance of rotation in convective storms. In: Bengtsson L and Lighthill J (eds) Intense atmospheric vortices. Springer-Verlag, New York, pp 149–160Google Scholar
- McQuarrie DA, Simon JD (1997) Physical chemistry: A molecular approach. University Science Books, Sansalito, CaliforniaGoogle Scholar
- Newton CW (1966) Circulations in large sheared cumulonimbus. Tellas 18: 669–712Google Scholar
- Noda A (2002) Numerical simulation of supercell tornadogenesis and its structure. (Doc. of Science dissertation in Japanese) Graduate College of Science, Tokyo University, TokyoGoogle Scholar
- Rasmussen EN et al (1982) Evolutionary characteristics and photogrammetric determination of wind speeds within the Tulia outbreak tornadoes 28 May 1980. Preprints, 12th Conf. on Severe Local Storms. San Antonio, Texas, Amer Meteor Soc: 301–304Google Scholar
- Romine G (2007) Assesment and assimilation of polarimetric radar observations: Improving convective storm analyses. Presented at National Weather Center, University of Oklahoma, senimar on 6 September, 2007 (personal communication)Google Scholar
- Sasaki Y (1955) A fundamental study of the numerical prediction based on the variational principle. J Meteor Soc Japan 33: 262–275Google Scholar
- Sasaki Y (1958) An objective analysis based on the variational method. J Meteor Soc Japan 36: 77–88Google Scholar
- Sasaki Y (1999) Tornado and hurricane—Needs of accurate prediction and effective dissemination of information. (in Japanese). J Visualization Soc Japan 19, 74: 187–192Google Scholar
- Sasaki Y, Baxter TL (1982) The gust front. Thunderstorms: A social, scientific, & technological documentary, Vol. 2, Thunderstorm morphology and dynamics, U. S. Department of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories: 281–296Google Scholar
- Seo EK, Biggerstaff MI (2006) Impact of cloud model microphysics on passive microwave retrievals of cloud properties. Part II: Uncertainty in rain, hydrometeor structure, and latent heating retrievals. (personal communication, through the seminar of National Weather Center, University of Oklahoma)Google Scholar
- Tsuboki K (2007) Recent progress of simulation on convective storm and tornadogenesis (in Japanese). Tornado Symposium held on January 13, 2007, organized by Japan Meteorological Agency (personal communication)Google Scholar
- Tsuyuki T et al (2002) The JMA mesoscale 4D-Var system and assimilation of precipitation and moisture data. Proceedings of the ECMWF/GEMEX Workshop on Humidity Analysis (8–11 July 2002, Reading, UK), ECMWF: 59–67Google Scholar
- Xue M et al (1995) ARPS Version 4.0 User’s Guide. The center for analysis and prediction of storms, University of OklahomaGoogle Scholar
- Zrnic D S et al (2007) Agile-beam phased array radar for weather observations. BAMS Nov 2007: 1753–1766Google Scholar