Zhu lijuan, Gong Jiandong, Huang Liping, et al. Three-dimensional cloud initial field created and applied to GRAPES numerical weather prediction nowcasting. J Appl Meteor Sci, 2017, 28(1): 38-51. DOI:  10.11898/1001-7313.20170104.
Citation: Zhu lijuan, Gong Jiandong, Huang Liping, et al. Three-dimensional cloud initial field created and applied to GRAPES numerical weather prediction nowcasting. J Appl Meteor Sci, 2017, 28(1): 38-51. DOI:  10.11898/1001-7313.20170104.

Three-dimensional Cloud Initial Field Created and Applied to GRAPES Numerical Weather Prediction Nowcasting

DOI: 10.11898/1001-7313.20170104
  • Received Date: 2016-03-22
  • Rev Recd Date: 2016-10-12
  • Publish Date: 2017-01-31
  • In order to get more accurate cloud initial fields in GRAPES_Meso model, the ARPS cloud analysis scheme is introduced. With some modifications or improvements based on the rational law in cloud macro-characteristic and micro-characteristic, the cloud analysis scheme is used to set up a local cloud analysis scheme which suitable for domestic numerical weather prediction model and local synoptic observations. Based on the background field, it integrates data sources from Doppler weather radar three-dimensional mosaic reflectivity data, geostationary meteorological satellite data, and surface observation. The cloud initial information is analyzed based on cloud physical laws of thermodynamics and dynamics and the observed empirical relationship. After the cloud analysis, analyzed three dimensional fields which include information of cloud hydrometeors are introduced by nudging technique for initialization of GRAPES_Meso model. One-month (15 Jul 2014-14 Aug 2014) time serial of experiments in different horizontal resolution (0.03°×0.03°, 0.1°×0.1°) are designed to verify the performance of the cloud analysis scheme. Case study shows that cloud macro-characteristic and cloud initial hydrometeors of synoptic system, such as typhoon, squall line etc., could be represented better by using cloud analysis scheme. The satellite cloud simulation technology of university of Wisconsin is adopted to produce the satellite cloud simulation product, which is convenient to compare the cloud product of model output with FY-2 meteorological satellite cloud products. The comparing result shows that 1 h nowcasting cloud of GRAPES_Meso model with cloud information initialized is close to satellite measurment in cloud macro-characteristic and cloud spatial distribution, while the one without cloud information initialized is missing and the brightness temperature is higher than satellite measurment. Until 6 hours, the nowcasting cloud of cloud analysis scheme is more similar to satellite measurment than the one without cloud analysis scheme, and the brightness temperature simulation is reasonable. As for performance of precipitation forecast, it is found that forecast with the cloud analysis has a significant positive impact on short range precipitation forecast. The 1-hour precipitation forecast with cloud analysis is closer to observation, and the positive effect can last for over twelve hours, which meets the demand for the short time nowcasting operational system. Furthermore, the spin-up time is also shortened. In long time experiments, the statistical variable of equitable threat score (ETS) of the precipitation forecast is calculated. At the first 6 h forecast in horizontal resolution of both 0.03°×0.03° and 0.1°×0.1°, the ETS of the precipitation forecast with cloud analysis is obviously increased compared with the one without cloud analysis. In the following three 6 h forecast, the positive effects decrease as forecast time increasing.
  • Fig. 1  Flowchart of cloud initial field construction

    Fig. 2  The relationship between relative humidity and grid cloud fraction

    Fig. 3  Radar composite reflectiveity at 1200 UTC 18 Jul 2014(a) and 6 h accumulate precipitation observation from 1200 UTC to 1800 UTC on 18 Jul 2014(b)

    Fig. 4  Distribution of experiment data source (a) FY-2E total amount of cloud, (b) hourly TBB of FY-2E, (c) radar reflectivity factor

    Fig. 5  Vertically integrated cloud hydrometer at 1200 UTC 18 Jul 2014(unit:g·kg-1) (a) cloud water, (b) cloud ice, (c) rain water, (d) snow, (e) graupel, (f) vapor

    Fig. 6  TBB of FY-2E 6.8 μm vaper channel on 18 Jul 2014 (a) measurement at 1300 UTC, (b) control test forecast at 1300 UTC, (c) sensitivity test forecast at 1300 UTC, (d) measurement at 1500 UTC, (e) control test forecast at 1500 UTC, (f) sensitivity test forecast at 1500 UTC, (g) measurement at 1800 UTC, (h) control test forecast at 1800 UTC, (i) sensitivity test forecast at 1800 UTC

    Fig. 7  TBB of FY-2E 11 μm infrared channel 18 Jul 2014 (a) measurement at 1300 UTC, (b) control test forecast at 1300 UTC, (c) sensitivity test forecast at 1300 UTC, (d) measurement at 1500 UTC, (e) control test forecast at 1500 UTC, (f) sensitivity test forecast at 1500 UTC, (g) measurement at 1800 UTC, (h) control test forecast at 1800 UTC, (i) sensitivity test forecast at 1800 UTC

    Fig. 8  Radar composite reflectivity factor on 18 Jul 2014 (a) observation at 1500 UTC, (b) control test forecast at 1500 UTC, (c) sensitivity test forecast at 1500 UTC, (d) observation at 1700 UTC, (e) control test forecast at 1700 UTC, (f) sensitivity test forecast at 1700 UTC

    Fig. 9  6 h accumulate precipitation from 1200 UTC to 1800 UTC on 18 Jul 2014 (a) control test forecast, (b) sensitivity test forecast

    Fig. 10  ETS of GRAPES_Meso with 0.03°×0.03° batch experiments from 15 Jul to 14 Aug in 2014 (a) forecast of 0-6 h, (b) forecast of 6-12 h, (c) forecast of 12-18 h, (d) forecast of 18-24 h

    Fig. 11  ETS of GRAPES_Meso with 0.1°×0.1° batch experiments from 15 Jul to 14 Aug in 2014 (a) forecast of 0-6 h, (b) forecast of 6-12 h, (c) forecast of 12-18 h, (d) forecast of 18-24 h

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    • Received : 2016-03-22
    • Accepted : 2016-10-12
    • Published : 2017-01-31

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