Abrupt temperature drops pose serious concerns for rice production in northern Japan. Previous early warning systems have been based on projected temperature tendencies, and alerts have announced for the occurrence of low temperatures. The rice crop has low-temperature-sensitive stages; however, previous systems have not considered them because of the difficulty of simulating rice growth at the local scale. The forecast system would be more valuable by considering both the rice growth stage and current meteorological forecast techniques. In this study, we synthesized ensemble numerical weather prediction and a cultivar-based rice growth model to forecast 14-day cold damage risk. The ensemble mean forecast with nine members predicted surface air temperatures more skillfully for seven days with lower root-mean-square errors (RMSEs) (1.3-1.9°C) than those of the climatological forecast (2.1-2.4°C) that is derived from historical observations over 30 years. The single deterministic forecast predicted the temperatures better for five days with 1.3-2.0°C of RMSEs, showing the extension of the predictable period by two days with ensemble forecasting. For the cooling degree-days, both the single and ensemble mean forecasts showed lower RMSEs than the climatological forecast throughout the forecast period of 14 days (4.1, 3.8, and 5.2°C at the forecast time = 14 day for single deterministic, ensemble mean, and climatological forecasts, respectively). Although the climatological forecast estimated the rice growth stages reasonably, the performance for cooling degree-days was inferior to the ensemble mean and single deterministic forecasts. The meteorological mean state is sufficient to estimate the rice growth stage, but an accurate temporal pattern of the surface air temperature provided by numerical weather forecast is essential for reliable cold damage forecasting. Moreover, ensemble forecasting is more effective than the single deterministic forecast to reduce prediction errors for both the surface air temperature and cold damage.

中文翻译：

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气象合奏预报在预测水稻生长中夏季受冷害中的适用性

突然的温度下降给日本北部的水稻生产带来了严重的问题。先前的预警系统已基于预测的温度趋势，并且已宣布发生低温警报。水稻作物具有低温敏感性阶段。但是，以前的系统没有考虑它们，因为很难在本地范围内模拟水稻的生长。通过同时考虑水稻的生长期和当前的气象预报技术，预报系统将具有更大的价值。在这项研究中，我们综合了整体数值天气预报和基于品种的水稻生长模型来预测14天的冷害风险。由9位成员组成的集合平均预报可以更熟练地预测7天的地面气温，并且具有较低的均方根误差（RMSE）（1.3-1。比30年历史观测得出的气候预报（2.1-2.4°C）高9°C）。单一确定性预报在1.3-2.0°C的RMSE的情况下预测了5天的温度更好，这表明集合预报将可预测的时间延长了2天。就凉爽度日而言，在整个14天的预测期内（预测时间的4.1、3.8和5.2°C = 14天，单一确定的集合平均数），单一和集合平均预测均显示出低于气象预报的RMSE。 ，以及气候预测）。尽管气候预报能合理地估算出水稻的生长期，但降温天数的表现却不如整体平均预报和单确定性预报。气象平均状态足以估算水稻的生长期，但是由数值天气预报提供的地表气温的准确时间模式对于可靠的冷害预报至关重要。此外，集合预报比单一确定性预报更有效，可减少地面气温和冷害的预测误差。