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Methods for Yield Forecast Based on Crop Model with Incomplete Weather Observations
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摘要: 针对基于作物模型开展产量实时预报后期气象资料的获取问题，提出通过相似类比，从历史气象资料库中获取替代资料的方案，基于CERES-Rice模型系统评估了平均值处理方案和历史相似类比方案的可预报性和误差分布特征。结果表明：水稻产量对成熟前2个月内的气象条件较为敏感，基于气象资料和作物模型开展产量预测，在5%误差范围内可获得60%以上的预测概率；以多年气候平均值替代起报日后期气象资料，在成熟前2个月起报预测概率约为60%，成熟前1个月约为70%，但预报误差系统性偏高；采用气候相似类比方法，从历史气象资料中获取起报日后期替代资料，可有效降低预报误差的系统偏差，若引入后期气候趋势信息，成熟前2个月起报预测概率可达80%以上，较采用历史平均值有显著提高。研究结果为基于作物模型和气象观测及气候预测信息开展产量预报提供了技术方案。
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关键词:
- CERES-Rice模型;
- 相似类比;
- 可预报性;
- 误差分布
Abstract: Crop simulation models are important tools for identifying climate-crop relationships as well as for yield prediction, while complete daily weather data for whole growing season is required for running crop model, which cannot be satisfied only by weather observations in the real-time operation. Formal studies have generally used averages of daily weather calculated from the historical weather database as replacement, which may destroy its temporal distribution, and thus introduce another source of bias. Aiming at preparing meteorological data after the forecasting day that required by the crop model in the real-time yield forecasting operation, the climate analogues methodology is proposed, which can generate new climate series for the desired period from history observations that with similar climates across space and time, based on a distance metric such as Euclidean, and the new proposed methodology is tested with the CERES-Rice model for its predictability and error distribution, comparing with a general arithmetic mean method. Results show that rice yield is sensitivity to meteorological conditions during two months before maturity, yield forecasting with CERES-Rice model driven by weather data at two-month lead-time leads to a more than 60% prediction probability with an error no more than 5%, and such predictability increases steadily with weather observations updated, showing considerable potential for operational application. Considering there is no priori knowledge on the climate trend for the remainder growing season, using a multi-year mean weather data instead, there is a 60% prediction probability when forecasted at two months before maturity and a 70% prediction probability one month before, however, obvious systematic overestimate is observed, and there exist systematic errors among different decades using 30-year means due to the climate trend under global warning, by using the latest 10-year or 5-year means, the decadal systematic errors decrease while the predictability increase for the poor ability in representing climate variability among years. Finally, using the historical analogue approach that generating downscaled daily weather data from historical observations, the prediction probabilities increase slightly, while the systematic errors reduce considerably compared with that of using the general arithmetic average approach, in addition, the historical analogues approach allows to include climate trend for the upcoming growing season, and by doing so, the predictability increases to more than 80% at two month in advance, much higher than that with multi-year mean. It is concluded that the analogue approach has great potential in bridging the gap between crop model and climate forecasting.-
Key words:
- CERES-Rice model;
- climate analogue;
- predictability;
- error distribution
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图 1 不完整气象观测资料下基于作物模型的产量预报技术流程
Fig. 1 Flow diagram of yield forecast based on crop model with incomplete weather observations
图 2 1981—2010年不同起报时间模拟的最终产量
(不同颜色曲线代表1981—2010年不同年份的模拟结果，下同)
Fig. 2 Simulated final yield at different forecast day for 1981-2010
(different color represents result of different year)
图 3 1981—2010年不同起报时间模拟的产量与利用实际气象资料模拟结果的相对偏差
Fig. 3 Relative yield bias between simulated yield at different forecast day and that with weather observations for 1981-2010
(different color represents different year)
图 4 95%置信水平下不同起报时间产量预报能力
Fig. 4 Predictability of yield at different forecast day at 95% confidence level
图 7 气候要素采用近10年平均 (a) 和近5年平均 (b) 产量偏差年代际分布特征
Fig. 7 Yield bias among different decades using synthetic weather data from latest 10-year mean (a) and 5-year mean (b)
图 8 95%置信水平下不同起报时间产量预报能力后期气象资料采用近10年平均 (a) 及近5年平均 (b)
Fig. 8 Predictability of yield at different forecast day at 95% confidence level using synthetic weather data from latest 10-year mean (a) and 5-year mean (b)
图 9 不同起报时间相似类比获取的气候要素与实际气候要素的偏差
Fig. 9 Bias between observed climate variables and those generated by the historical analogue approach at different forecast day
表 1 起报日至成熟期气象资料处理方案
Table 1 Methodology for generating weather data from the initial forecast day to maturity
处理方案 基准值 近30年平均 近10年平均 近5年平均 气候预测 平均值处理 Mean30 Meanb10 Meanb5 历史相似类比 Ana30 Anab10 Anab5 Anab0 
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表 2 不同处理方案的可预报性及误差分布
Table 2 Predictability and error distribution of different methodology
气象资料
处理方案不同起报时间 (移栽后日数/d) 可预报性 误差分布特征 0 10 20 30 40 50 60 70 80 90 100 110 平均相对
偏差/%偏度
系数年际趋
势/%Mean30 0.55 0.55 0.55 0.56 0.59 0.60 0.63 0.67 0.74 0.79 0.88 0.99 3.53 0.86 0.26 Meanb10 0.46 0.46 0.46 0.48 0.51 0.53 0.56 0.60 0.69 0.74 0.87 0.99 4.01 0.63 0.08 Meanb5 0.51 0.51 0.52 0.53 0.57 0.57 0.60 0.64 0.72 0.75 0.86 0.98 3.70 0.53 0.05 Anam30 0.54 0.54 0.54 0.55 0.59 0.58 0.63 0.67 0.71 0.74 0.82 0.94 3.63 0.54 0.27 Anab10 0.56 0.58 0.56 0.57 0.60 0.60 0.61 0.67 0.71 0.73 0.82 0.94 3.56 0.45 0.07 Anab5 0.59 0.59 0.59 0.61 0.62 0.63 0.64 0.67 0.72 0.74 0.83 0.94 3.44 0.32 0.04 Anab0 0.77 0.79 0.81 0.81 0.83 0.83 0.86 0.87 0.87 0.89 0.92 0.97 2.28 0.20 0.03
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