A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations

A spatial quality control method, ARF, is proposed. The ARF method incorporates the optimization ability of the artificial fish swarm algorithm and the random forest regression function to provide quality control for multiple surface air temperature stations. Surface air temperature observations were recorded at stations in mountainous and plain regions and at neighboring stations to test the performance of the method. Observations from 2005 to 2013 were used as a training set, and observations from 2014 were used as a testing set. The results indicate that the ARF method is able to identify inaccurate observations; and it has a higher rate of detection, lower rate of change for the quality control parameters, and fewer type I errors than traditional methods. Notably, the ARF method yielded low performance indexes in areas with complex terrain, where traditional methods were considerably less effective. In addition, for stations near the ocean without sufficient neighboring stations, different neighboring stations were used to test the different methods. Whereas the traditional methods were affected by station distribution, the ARF method exhibited fewer errors and higher stability. Thus, the method is able to effectively reduce the effects of geographical factors on spatial quality control

Spatial Quality Control Method for Surface Temperature Observations Based on Multiple Elements

Quality control can effectively improve the quality of surface meteorological observations. To ensure the stability and effectiveness of a quality control model under different terrain and climate conditions, it is necessary to structure a quality control model with strong generalization ability. Algorithms such as the Random Forest provide such generalization ability. However, machine learning algorithms are slower than traditional mathematical models. Therefore, a Random Forest quality control algorithm based on the Principal Component Analysis (PCA-RF) is proposed in this paper. Fifteen target stations under different climatic and geomorphological conditions were selected and tested using observations collected four times daily at neighboring stations from 2005-2014. The results show that using PCA to analyze the elemental composition and select elements with high correlation factors, as well as applying the Random Forest algorithm, can effectively reduce the run time and keep the accuracy of the model. The training sample dependence, model prediction accuracy and error detection rate of the PCA-RF model are superior to those of the Spatial Regression method. Therefore, the PCA-RF method is a better-quality control model for the spatial quality control of multiple elements of surface air temperature observations.El control de calidad puede mejorar efectivamente la calidad de las observaciones meteorológicas. Para asegurar la estabilidad y efectividad de un modelo de control de calidad bajo condiciones diferentes de terreno y climáticas es necesario estructurar un esquema con una fuerte habilidad de generalización. Algoritmos como el método de bosques aleatorios (del inglés Random Forest) cumplen con estas condiciones. Sin embargo, los algoritmos de maquinas de aprendizaje son más lentos que los modelos matemáticos tradicionales. En este artículo se propone un algoritmo de control de calidad tipo bosques aleatorios basado en el Análisis de Componentes Principales (PCA-RF). Se seleccionaron 15 estaciones objetivo bajo diferentes condiciones climáticas y geomorfológicas y se evaluaron con observaciones realizadas cuatro veces por día en estaciones vecinas desde 2005 hasta 2014. Los resultados muestran que usando PCA para analizar la composición elemental y seleccionar elementos con factores de correlación alta, al igual que la aplicación del algoritmo Random Forest, se puede reducir efectivamente el tiempo de ejecución y mantener la exactitud del modelo. La dependencia de la muestra de prueba, la exactitud del modelo de predicción y la tasa de detección de error del modelo PCA-RF son superiores a aquellos del método de Regresión Espacial. Por lo tanto, el método PCA-RF es un mejor modelo para el control de calidad de elementos múltiples en las observaciones superficiales de aire y temperatura

General Physical Properties of Gamma-Ray-emitting Radio Galaxies

We study the radio galaxies with known redshift detected by the Fermi satellite after 10 years of data (4FGL-DR2). We use a one-zone leptonic model to fit the quasi-simultaneous multiwavelength data of these radio galaxies and study the distributions of the derived physical parameter as a function of black hole mass and accretion disk luminosity. The main results are as follows. (1) We find that the jet kinetic power of most radio galaxies can be explained by the hybrid jet model based on ADAFs surrounding Kerr black holes. (2) After excluding the redshift, there is a significant correlation between the radiation jet power and the accretion disk luminosity, while the jet kinetic power is weakly correlated with the accretion disk luminosity. (3) We also find a significant correlation between inverse Compton luminosity and synchrotron luminosity. The slope of the correlation for radio galaxies is consistent with the synchrotron self-Compton (SSC) process. The result may suggest that the high-energy component of radio galaxies is dominated by the SSC process.Comment: 9 pages,7 figures, accept for publication in ApJ