Modulation of Madden-Julian Oscillation Activity by the Tropical Pacific-Indian Ocean Associated Mode

In this study,the impacts of the tropical Pacific–Indian Ocean associated mode(PIOAM)on Madden–Julian Oscillation(MJO)activity were investigated using reanalysis data.In the positive(negative)phase of the PIOAM,the amplitudes of MJO zonal wind and outgoing longwave radiation are significantly weakened(enhanced)over the Indian Ocean,while they are enhanced(weakened)over the central and eastern Pacific.The eastward propagation of the MJO can extend to the central Pacific in the positive phase of the PIOAM,whereas it is mainly confined to west of 160°E in the negative phase.The PIOAM impacts MJO activity by modifying the atmospheric circulation and moisture budget.Anomalous ascending(descending)motion and positive(negative)moisture anomalies occur over the western Indian Ocean and central-eastern Pacific(Maritime Continent and western Pacific)during the positive phase of the PIOAM.The anomalous circulation is almost the opposite in the negative phases of the PIOAM.This anomalous circulation and moisture can modulate the activity of the MJO.The stronger moistening over the Indian Ocean induced by zonal and vertical moisture advection leads to the stronger MJO activity over the Indian Ocean in the negative phase of the PIOAM.During the positive phase of the PIOAM,the MJO propagates farther east over the central Pacific owing to the stronger moistening there,which is mainly attributable to the meridional and vertical moisture advection,especially low-frequency background state moisture advection by the MJO’s meridional and vertical velocities.

Non-destructive method of small sample sets for the maize moisture content measurement during filling based on NIRS

In maize breeding,limitations on sampling quantity and associated costs for measuring maize grain moisture during filling are imposed by factors like the planting area of new varieties,maize plant density,effective experimental spikes,and other conditions.However,the conventional method of detecting moisture content in maize grains is slow,damages seeds,and necessitates many sample sets,particularly for high moisture content determination.Thus,a strong demand exists for a non-destructive quantitative analysis model of maize moisture content using a small sample set during grain filling.The Bayes-Merged-Bootstrap(BMB)sample optimization method,which built upon the Bayes-Bootstrap sampling method and the concept of merging,was proposed.A critical concern in dealing with small samples is the relationship between data distribution,minimum sample value,and sample size,which has been thoroughly analyzed.Compared to the Bayes-Bootstrap sample selection method,the BMB method offers distinct advantages in the optimized selection of small samples for non-destructive detection.The quantitative analysis model for maize grain moisture content was established based on the support vector machine regression.Results demonstrate that when the optimal resampling size is 1000 times or more than the original sample size using the BMB method,the model exhibits strong predictive capabilities,with a determination coefficient(R2)>0.989 and a relative prediction determination(RPD)>2.47.The results of the 3 varieties experiment demonstrate the generality of the model.Therefore,it can be applied effectively in practical maize breeding and determining grain moisture content during maize machine harvesting.