681 Predicting of lightning activity in Japan using mesoscale model output statistics

Wednesday, 26 January 2011
Washington State Convention Center
Syugo Hayashi, MRI, Tsukuba, Japan

Handout (1.2 MB)

LIghtning DEtection Network system (LIDEN) are operated over Japan by Japan Meteorological Agency (JMA) since 2001. Using LIDEN, the horizontal and temporal distribution of lightning is revealed in Japan. Moreover, we are developing lightning prediction system with numerical weather prediction for short-term forecast based on LIDEN observation.

Nowadays, a mesoscale model (MSM) with 5 km horizontal resolution is conducted for short-term (15 or 33 hour) forecast to prevent weather disaster in every 3 hour every day by JMA in operational. For the lightning prediction, MSM-PoT (Potential of Thunderstorm), for which logistic regression analysis using MSM outputs is applied, are provided in every 3-hours from forecasts in 20 km grid using MSM outputs. In the MSM-PoT, the various traditional lightning indexes from MSM outputs (e.g. CAPE, SSI, temperature, wind speed, etc.) are adequately combined in statistical. Therefore, the accuracy of MSM-PoT is better than that of a traditional index alone.

On the other hand, 1 km horizontal resolution Non-Hydrostatic Model (NHM-1km) are conducted every day for research purpose in Meteorological Research Institute of JMA. It is expected that more realistic physical and cloud micro-physical outputs are obtained from NHM-1km results. Using the outputs, we investigate whether the better lightning prediction based on atmospheric electricity is possible or not. We make indexes for lightning prediction from NHM-1km, for example the maximum vertical velocity, vertical accumulated graupel amount, collision frequency between graupel and ice-crystal, and so on.

Statistical verification in 20 km grid for the above mentioned indexes (MSM-PoT, NHM-1km indexes) in 1-month 2-seasons are conducted. As a result, MSM-PoT and vertical accumulated graupel amount in NHM-1km show the best score in other traditional indexes. In the future, we'll make a appropriate index for high resolution cloud resolving model on the basics of these results.

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