CAJ | 학술논문

提海量数据的利用是建立自适应预报模型的基础，但随着数据的不断增加，新引入数据的作用会逐渐降低，有可能导致预报模型失效。为克服因数据量增加引起的所谓“数据饱和”现象对天气预报效果的影响，本文给出了考虑遗忘因子的线性自适应最小二乘建模算法的原理和方法，并利用该算法进行了最高气温和最低气温预报试验。结果表明，考虑遗忘因子的线性自适应建模算法优于传统的线性自适应建模算法，加入遗忘因子可以避免产生“数据饱和”现象，适当地选择遗忘因子有助于提高模型的预报准确率。
제해량수거적이용시건립자괄응예보모형적기출，단수착수거적불단증가，신인입수거적작용회축점강저，유가능도치예보모형실효。위극복인수거량증가인기적소위“수거포화”현상대천기예보효과적영향，본문급출료고필유망인자적선성자괄응최소이승건모산법적원리화방법，병이용해산법진행료최고기온화최저기온예보시험。결과표명，고필유망인자적선성자괄응건모산법우우전통적선성자괄응건모산법，가입유망인자가이피면산생“수거포화”현상，괄당지선택유망인자유조우제고모형적예보준학솔。
A mass of data is the foundation of adaptive forecasting model.However,the role of new incom-ing data will be gradually reduced and the performance of the model will become poor with the data increas-ing.In order to overcome the influence of “data saturation”on the weather forecast,the method of adap-tive linear least square modeling algorithm considering forgetting factors is developed and applied in max-min temperature forecast.The results show that this adaptive linear least square modeling algorithm con-sidering forgetting factors is superior to the traditional adaptive linear modeling algorithm,it can reduce the effect of “data saturation”by using the forgetting factor,and it is possible to improve the model’s forecast accuracy by choosing the appropriate forgetting factors.