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The inversion data of the microwave radiometer atmospheric temperature profile were systematically analyzed with the temperature observation data of the microwave radiometer in coastal areas of Fujian. By comparing the microwave radiometer temperature inversion data with sounding and ERA5 temperatures, the ability of microwave radiometer to detect atmospheric temperature and the quality of the inversion data were analyzed, and the microwave radiometer inversion data were further applied to analyze the daily variation of atmospheric temperature in the boundary layer. The results show that the microwave radiometer temperature is in good agreement with the sounding temperature, with a correlation coefficient of 0.971, a mean deviation of 2.716 °C, and a root mean square error of 5.131 °C; the greater the intensity of rainfall, the more significant the effect of rainfall on the accuracy of the microwave radiometer in detecting atmospheric temperature. The correlation coefficients, mean deviations and root mean square errors of ERA5 and microwave radiometer are consistent from month to month; the temperature deviations between ERA5 and microwave radiometer in the middle atmosphere are smaller than those in the near-surface and upper atmosphere in each month; above 2 km in height, the temperature profiles of ERA5 and microwave radiometer are consistent. Above 2 km in altitude, the trend of ERA5 and microwave radiometer temperature profiles are basically the same. Below 3 km altitude, the daily variation of summer temperature is smaller than that of winter temperature at the same altitude. With the increase of altitude, the variation of atmospheric temperature in summer is large and that in winter is small [ABSTRACT FROM AUTHOR]

The seasonal temperature forecasting models in greenhouse based on multiple regression (MR), BP artificial neural networks (BPANN), random forest (RF) and support vector machine (SVM) were constructed using meteorological observations inside and outside the Venlo-type glass greenhouses from 27th February 2021 to 4th March 2023 at Nanjing University of Information Science & Technology, and then to validate all the models. The results showed that the fitting accuracy of the seasonal forecasting models for daily average and minimum air temperatures in the greenhouse was significantly higher than that of the seasonal forecasting models for daily maximum air temperature; the fitting accuracy of each model for air temperature inside the greenhouse in spring, summer and autumn was higher than that for winter. For the seasonal forecasting models of daily average air temperature and minimum air temperature, the fitting accuracy of the spring, summer and autumn forecasting models constructed by the four algorithms was higher, but the simulation effect of the RF model was more stable, and the coefficient of determination (R²) of the simulated values and the actual observed values were above 0.94, and the root mean square error (RMSE) and absolute error (MAE) were within 1.5 °C. For the seasonal forecast models of daily maximum temperature, the fitting accuracy of RF model for spring, summer and autumn was higher than other models in general, and its R² value was above 0.75. The MR model had higher accuracy for winter indoor air temperature and was more suitable for predicting winter greenhouse air temperature. The study concluded that it is feasible to choose the RF model to forecast the air temperature inside the glass greenhouse in spring, summer and autumn and the MR model to forecast the air temperature inside the glass greenhouse in winter, and this study can provide an important reference for the seasonal forecast of air temperature in Venlo-type glass greenhouse. [ABSTRACT FROM AUTHOR]

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China has the largest waterfowl industry, accounting for almost 75% of the waterfowl production in the world. The air temperature in the lion-head goose breeding environment is crucial to the survival of lion-head goose. The air temperature is susceptible to many factors such as relative humidity, illumination intensity, total suspended particulates, etc. So, it is significant to understand timely and accurately the change of the air temperature which can prevent environment deterioration, disease outbreaks, and optimize breeding management. Grasping the trend of the air temperature value timely and accurately is the key to the high-density healthy lion-head goose culture. The traditional prediction methods in temperature have problems such as low prediction accuracy, poor robustness, and poor generalization ability, etc. To improve the prediction accuracy of the air temperature of the lion-head goose breeding environment, a predicting model of air temperature fusing Principal Component Analysis (PCA), Support Vector Regression (SVR) machine, and Autoregressive Moving Average (ARMA) model was proposed for lion-head goose breeding environment. First, the key impact factors of air temperature in lion-head goose breeding were extracted by principal component analysis, which eliminated redundant information between variables and reduced the complexity of the prediction model structure. Therefore, the key impact factors were selected air temperature, relative humidity, illumination intensity, total suspended particulates, respectively. Then, SVR-ARMA model was used to build the nonlinear prediction model between the temperature and these key impact factors for lion-head goose breeding, in which, first, the air temperature was predicted by SVR, then the air temperature prediction values were corrected by the residual prediction value of ARMA model. Because the kernel parameter and the regularization parameter in the SVR training procedure significantly influence forecasting accuracy, a leave-one-out cross-validation method was utilized to optimize the SVR model parameters and biases under a parameter space design. With this model, the air temperature change had been predicted for the lion-head goose breeding environment from July 21st, 2018 to July 30th, 2018 in Shanwei city, Guangdong province. The experimental results showed that the proposed combination prediction model of PCA-SVR-ARMA had a better prediction effect than the standard BP neural network, standard support vector regression machine, PCA-BPNN, PCA-SVR, and PCA-BPNN-ARMA method. And the relative Mean Absolute Percent Error (MAPE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) for the temperature between the PCA-SVR-ARMA and PCA-SVR models were 29.59%, 40.37%, and 60.75% respectively under the same experimental conditions. The relative MAPE, RMSE, and MAE for the temperature between the PCA-SVR and standard SVR models were 31.78%, 15.89%, and 29.45% respectively. The relative MAPE, RMSE, and MAE for the temperature between the PCA-SVR-ARMA and PCA-BPNN-ARMA models were 55.64%, 35.66%, and 55.26% respectively. The relative MAPE, RMSE, and MAE for the temperature between the PCA-BPNN-ARMA and PCA-BPNN models were 43.16%, 30.63%, and 44.16% respectively. The relative MAPE, RMSE, and MAE for the temperature between the PCA-BPNN and BPNN models were 10.34%, 4.80%, and 7.98% respectively. To sum up, the SVR model had a better prediction performance under the condition of small samples, while the residual error correction method based on the ARMA model effectively improved prediction performance. The temperature prediction model based on PCA-SVR-ARMA exhibited best prediction accuracy and generalization performance when compared with other traditional forecasting models. Therefore, the presented model based on PCA-SVR-ARMA could meet the actual demand for accurate forecasting of temperature and provide a reference for environment control in healthy breeding and seedling breeding of lion-head goose. As well as it also could help farmers make decisions and reduce farming risks. [ABSTRACT FROM AUTHOR]

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Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)