草地贪夜蛾迁飞气象预报方法探索及应用

为了准确预报害虫的迁飞轨迹，将天气预报技术应用于迁飞性害虫的预测预报，基于天气预报平台，利用欧洲中心中短期气象数值预报产品以及大气环流形势及低层风动力对草地贪夜蛾（Spodoptera frugiperda）的迁飞路径、迁入时间（包括首见日及高峰日）、落区等进行预报，并基于2021年草地贪夜蛾迁飞的2次典型预报案，分析2021年草地贪夜蛾春季北迁至湖北（首见日）以及秋季南迁回湖北（高峰日）的典型天气过程以及迁飞层气象要素场，运用HYSPLIT轨迹模型模拟迁飞后向轨迹，再利用草地贪夜蛾田间监测数据、测报灯监测数据以及迁飞轨迹对预报结论进行验证。结果显示，2次典型预报案例的预报结论与草地贪夜蛾田间监测数据及测报灯监测数据以及轨迹模拟的情景吻合度较好，草地贪夜蛾迁入时间、落区及路径预报基本正确。研究表明，将天气预报技术应用于迁飞性害虫的预报具有实际可行性。

Exploration and application of meteorological forecasting model for migration of Spodoptera frugiperda

A meteorological forecasting model for the migration of Spodoptera frugiperda was preliminarily established based on the weather forecast platform and the medium-short term numerical forecast of European Centre for Medium-Range Weather Forecasts （ECMWF） to accurately predict the migration routes of pests and use weather forecasting technology to predict migratory pests. The atmospheric circulation situation and low-level wind dynamic were used to accurately forecast the migration routes， migration time including the first day of emergence and the day with a peak number and falling zone of Spodoptera frugiperda. A detailed review of two typical forecasting cases of the migration of Spodoptera frugiperda in 2021 was conducted. The typical weather processes and meteorological factor fields of the migratory layer of Spodoptera frugiperda， which migrated northward to Hubei Province in spring （first day of sighting） and southward to Hubei Province in autumn （peak day） in 2021， were analyzed. The HYSPLIT route model was used to simulate the backward routes of migration. The prediction conclusion was validated using data from monitoring Spodoptera frugiperda in the field and from monitoring lamp and migration routes. The results showed that the forecasting conclusions of two typical prediction cases were in good agreement with data from monitoring Spodoptera frugiperda in the field and from monitoring lamp， and the scenario simulation of migration routes. The forecasting of the migration time， falling fields and routes was basically correct， which verified the feasibility of using the weather forecast technologies to predict migratory pests.