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Zhipin Ai and Yonghui Yang


Compared with more comprehensive physical algorithms such as the Penman–Monteith model, the Priestley–Taylor model is widely used in estimating evapotranspiration for its robust ability to capture evapotranspiration and simplicity of use. The key point in successfully using the Priestley–Taylor model is to find a proper Priestley–Taylor coefficient, which is variable under different environmental conditions. Based on evapotranspiration partition and plant physiological limitation, this study developed a new model for estimating the Priestley–Taylor coefficient incorporating the effects of three easily obtainable parameters such as leaf area index (LAI), air temperature, and mulch fraction. Meanwhile, the effects of plastic film on the estimation of net radiation and soil heat flux were fully considered. The reliability of the modified Priestley–Taylor model was testified using observed cotton evapotranspiration from eddy covariance in two growing seasons, with high coefficients of determination of 0.86 and 0.81 in 2013 and 2014, respectively. Then, the modified model was further validated by estimating cotton evapotranspiration under three fractions of mulch cover: 0%, 60%, and 100%. The estimated values agreed well with the measured values via water balance analysis. It can be found that seasonal variation of the modified Priestley–Taylor coefficient showed a more reasonable pattern compared with the original coefficient of 1.26. Sensitivity analysis showed that the modified Priestley–Taylor coefficient was more sensitive to LAI than to air temperature. Overall, the modified model has much higher accuracy and could be used for evapotranspiration estimation under plastic mulch condition.

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Weihong Qian, Jun Du, and Yang Ai


Comparisons between anomaly and full-field methods have been carried out in weather analysis and forecasting over the last decade. Evidence from these studies has demonstrated the superiority of anomaly to full-field in the following four aspects: depiction of weather systems, anomaly forecasts, diagnostic parameters and model prediction. To promote the use and further discussion of the anomaly approach, this article summarizes those findings.

After examining many types of weather events, anomaly weather maps show at least five advantages in weather system depiction: (1) less vagueness in visually connecting the location of an event with its associated meteorological conditions; (2) clearer and more complete depictions of vertical structures of a disturbance; (3) easier observation of time and spatial evolution of an event and its interaction or connection with other weather systems; (4) simplification of conceptual models by unifying different weather systems into one pattern; and (5) extension of model forecast length due to earlier detection of predictors. Anomaly verification is also mentioned. The anomaly forecast is useful for raising one’s awareness of potential societal impact. Combining the anomaly forecast with an ensemble is emphasized, where a societal impact index is discussed. For diagnostic parameters, two examples are given: an anomalous convective instability index for convection, and seven vorticity and divergence related parameters for heavy rain. Both showed positive contributions from the anomalous fields. For model prediction, the anomaly version of the beta-advection model consistently outperformed its full-field version in predicting typhoon tracks with clearer physical explanation. Application of anomaly global models to seasonal forecasts is also reviewed.

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