Vol.21, NO.4, 2010
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Article Navigation >  Journal of Applied Meteorological Science  > 2010  >  21(4): 405-415
Zhao Xiaolin, Zhu Guofu, Li Zechun. Applied research on adaptive observation for identifying sensitive regions based upon TIGGE data. J Appl Meteor Sci, 2010, 21(4): 405-415.
Citation: Zhao Xiaolin, Zhu Guofu, Li Zechun. Applied research on adaptive observation for identifying sensitive regions based upon TIGGE data. J Appl Meteor Sci, 2010, 21(4): 405-415.
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Applied Research on Adaptive Observation for Identifying Sensitive Regions Based upon TIGGE Data

  • 1.

    Nanjing University of Information Science & Technology, Nanjing 210044

  • 2.

    National Meteorological Center, Beijing 100081

  • Received Date: 2009-09-09
  • Rev Recd Date: 2010-04-21
  • Publish Date: 2010-08-31
    Abstract
  • Accurate prediction of high impact weather is very important. Adaptive observation has an immediate significance to improve the quality of high impact weather forecast. THORPEX (THe Observing system Research and Predictability EXperiment) is a ten year international atmospheric research program with the primary objective to improve the accuracy of 1 day to 2 week high impact weather forecasts for the benefit of society and economy. THORPEX is developed under four sub programs, and two of them are related to adaptive observation, which are observing systems, data assimilation and observing strategies, so adaptive observation is the foundation and prerequisite to achieve the ultimate goal of THORPEX. Adaptive observation is a type of observation interactive between weather forecast (or weather service) and observation. The key to adaptive observation lies in identifying sensitive regions. Sensitive regions are defined as localized regions from where the analysis errors grow significantly and thereby the forecast skills are degraded. As a new method for identification of sensitive regions which is based on ensemble predictions, ETKF (Ensemble Transform Kalman Filter) has many advantages, such as the program simplicity, independency and so on. On the basis of an ensemble prediction solely, ETKF solves Kalman equation in the space of ensemble, and it can estimate the reductions of prediction error covariance caused by adding observation immediately. The research of ETKF has a proactive effect on both adaptive observation and ensemble predictions. On the basis of TIGGE (THORPEX Interactive Grand Global Ensemble) data, through identification and comparison of the sensitive regions of two different types of heavy precipitation events, specific aspects in the actual application of ETKF method to the adaptive observation are analyzed in detail. The results indicate that the appropriate horizontal resolution and coverage of ensemble data can be used for a reasonable result and shorter computational time, and the reasonable result shows that the geographical distribution of the signal variance maxima calculated from different resolutions and coverages are basically coincident and a relatively small coverage can even be used for the heavy precipitation event under regional weather circulation comparatively to that under macroscale weather circulation; identified sensitive regions are more credible by using ensemble predictions available which are initialized more recently; identified sensitive regions calculated from different meteorological centers' ensemble data are more consistent and reliable for the heavy precipitation event under clear macroscale weather circulation than under regional weather circulation; a calculation of signal variance using nine adjacent grids at one time makes the wide distribution of signal variance maxima, and makes it not conducive to locate sensitive regions more precisely. To the result of sensitive regions, the effects could negligible if the analysis errors of routine observation are changed moderately. Sensitive regions under different circulation are dependent on the selected metric to some extent. In conclusion, the sensitive regions identified by ETKF are understandable and reasonable.
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    References(20)

  • Fig. 1  500 hPa geopotential height from 16 Jul 2007 to 19 Ju l2007 (unit:dagpm)

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    Fig. 2  Standardized signal variance of ETKF (shadow region) and 850 hPa geopotential height of ensemble average (isoline, unit:gpm)(verification region is denoted by the rectangle)

    (a) Shandong Rainstorm, resolution is 2.0°×2.0°, (b) Shandong Rainstorm, resolution is1.0°×1.0°, (c) Rainstorm of Sichuan Basins, resolution is 2.0°×2.0°, (d) Rainstorm of Sichuan Basins, resolution is 1.0°×1.0°

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    Fig. 3  Standardized signal variance of ETKF (shadow region) using ensemble data of different regions, and 850 hPa geopotential height of ensemble average (isoline, unit:gpm) (a) Shandong Rainstorm, the scope of ensemble data is Northern Hemisphere, (b) Shandong Rainstorm, thescope of ensembledatais10°-90°N, 70°-150°E, (c) Rainstormof Sichuan Basins, the scope of ensemble datais Northern Hemisphere, (d) Rainstorm of Sichuan Basins, the scope of ensemble data is 10°-90°N, 70°-150°E

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    Fig. 4  Standardized signal variance of ETKF (shadow region) using ensemble data of different initial time, and 850 hPa geopotential height of ensemble average (isoline, unit:gpm) (a) Shandong Rainstorm, ta is 00:00 18 Jul 2007, ti is 00:00 16 Jul 2007 (time interval is48h), (b) Shandong Rainstorm, butta is verification time, (c) Rainstorm of Sichuan Basins, ta is 00:00 17 Jul 2007, ti is 00:00 15 Jul 2007 (time interval is48h), (d) Rainstorm of Sichuan Basins, butta is verification time

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    Fig. 5  Standardized signal variance (shadow region) of Shandong Rainstorm using ECMWF, CMA and NCEP ensemble prediction with 850 hPa geopotential height of ensemble average (isoline, unit:gpm) (a) ECMWF, ta is 00:00 18 Jul 2007, (b) CMA, ta is 00:00 18 Jul 2007, (c) NCEP, ta is 00:00 18 Jul 2007, (d) ECMWF, ta is verification time, (e) CMA, ta is verification time, (f) NCEP, ta is verification time

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    Fig. 6  Standardized signal variance (shadow region) of Rainstorm of Sichuan Basins using ECMWF, CMA and NCEP ensemble pridiction with 850 hPa geopotential height of ensemble average (isoline, unit:gpm) (a) ECMWF, ta is 00:00 17 Jul 2007, (b) CMA, ta is 00:00 17 Jul 2007, (c) NCEP, ta is 00:00 17 Jul 2007, (d) ECMWF, ta is verification time, (e) CMA, ta is verification time, (f) NCEP, ta is verification time

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    Fig. 7  Standardized signal variance (shadow region) of Shandong Rainstorm using ECMWF (a), CMA (b), NCEP (c) ensemble prediction and 850 hPa geopotential height of ensemble average (isoline, unit:gpm) (the metric of kinetic energy)

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    Fig. 8  Standardized signal variance (shadow region) of Rainstorm of Sichuan Basins using ECMWF (a), CMA (b), NCEP (c) ensemble prediction and 850 hPa geopotential height of ensemble average (isoline, unit:gpm)(the metric of kinetic energy)

    DownLoad: Full-Size Img PowerPoint
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    • Received : 2009-09-09
    • Accepted : 2010-04-21
    • Published : 2010-08-31
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