Review on bio-inspired flight systems and bionic aerodynamics

Humans’initial desire for flight stems from the imitation of flying creatures in nature.The excellent flight performance of flying animals will inevitably become a source of inspiration for researchers.Bio-inspired flight systems have become one of the most exciting disruptive aviation technologies.This review is focused on the recent progresses in bio-inspired flight systems and bionic aerodynamics.First,the development path of Biomimetic Air Vehicles(BAVs)for bio-inspired flight systems and the latest mimetic progress are summarized.The advances of the flight principles of several natural creatures are then introduced,from the perspective of bionic aerodynamics.Finally,several new challenges of bionic aerodynamics are proposed for the autonomy and intelligent development trend of the bio-inspired smart aircraft.This review will provide an important insight in designing new biomimetic air vehicles.

Evaluation of nine machine learning methods for estimating daily land surface radiation budget from MODIS satellite data

The all-wave net radiation(Rn)at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water cycles.Many studies have been conducted to estimate from satellite top-of-atmosphere(TOA)data using various methods,particularly the application of machine learning(ML)and deep learning(DL).However,few studies have been conducted to provide a comprehensive evaluation about various ML and DL methods in retrieving.Based on extensive in situ measurements distributed at mid-low latitudes,the corresponding Moderate Resolution Imaging Spectroradiometer(MODIS)TOA observations,and the daily from the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)used as a priori knowledge,this study assessed nine models for daily estimation,including six classic ML methods(random forest-RF,adaptive boosting-Adaboost,extreme gradient boosting-XGBoost,multilayer perceptron-MLP,radial basis function neural network-RBF,and support vector machine-SVM)and three DL methods(multilayer perceptron neural network with stacked autoencoders-SAE,deep belief network-DBN and residual neural network-ResNet).The validation results showed that the three DL methods were generally better than the six ML methods except XGBoost,although they all performed poorly in certain conditions such as winter days,rugged terrain,and high elevation.ResNet had the most robust performance across different land cover types,elevations,seasons,and latitude zones,but it has disadvantages in practice because of its highly configurable implementation environment and low computational efficiency.The estimated daily values from all nine models were more accurate than the corresponding Global LAnd Surface Satellite(GLASS)product.

Significant discrepancies of land surface daily net radiation among ten remotely sensed and reanalysis products

Land surface all-wave net radiation(R_(n))is crucial in determining Earth’s climate by contributing to the surface radiation budget.This study evaluated seven satellite and three reanalysis long-term land surface R_(n)products under different spatial scales,spatial and temporal variations,and different conditions.The results showed that during 2000-2018,Global Land Surface Satellite Product(GLASS)-Moderate Resolution Imaging Spectroradiometer(MODIS)performed the best(RMSE=25.54 Wm^(-2),bias=-1.26 Wm^(-2)),followed by ERA5(the fifth-generation of European Centre for Medium-Range Weather Forecast Reanalysis)(RMSE=32.17 Wm^(-2),bias=-4.88 Wm^(-2))and GLASS-AVHRR(Advanced Very-High-Resolution Radiometer)(RMSE=33.10 Wm^(-2),bias=4.03 Wm^(-2)).During 1983-2018,GLASS-AVHRR and ERA5 ranked top and performed similarly,with RMSE values of 31.70 and 33.08 Wm^(-2)and biases of-4.56 and 3.48 Wm^(-2),respectively.The averaged multi-annual mean R_(n)over the global land surface of satellite products was higher than that of reanalysis products by about 10~30 Wm^(-2).These products differed remarkably in long-term trends variations,particularly pre-2000,but no significant trends were observed.Discrepancies were more frequent in satellite data,while reanalysis products showed smoother variations.Large discrepancies were found in regions with high latitudes,reflectance,and elevation which could be attributed to input radiative components,meteorological variables(e.g.,cloud properties,aerosol optical thickness),and applicability of the algorithms used.While further research is needed for detailed insights.