Expected Error Range Research Articles

Research and Design of A Multifunctional Heart Rate Detection System

Addressing the limitations of conventional heart rate detectors, which solely monitor heart rate without providing alerts during critical incidents, a multifunctional heart rate detection system has been developed. This system incorporates a GD32 as the main controller and utilizes a pulse sensor for heart rate monitoring, an MLX90614 infrared temperature sensor for body temperature measurements, an MPU6050 acceleration sensor for motion posture detection, and a SIM868 module for GPS positioning and server communication. This comprehensive setup allows for real-time monitoring of the wearer's heart rate and body temperature, assessment of fall incidents based on motion posture data, and regular uploading of health data to a server. Additionally, the system is equipped with a one-touch emergency call feature. In the event of abnormal heart rate readings or a fall, it triggers an alarm, acquires GPS coordinates, and automatically dials an emergency contact number. After thorough testing and verification, the system has demonstrated full functionality with heart rate data accuracy within the expected error range, offering a reliable safety mechanism for patients while on the move.

Evaluation and comparison of MODIS and MISR aerosol products with ground-based monitoring stations in the Amazon Basin

This study evaluates the long-term accuracy of six daily aerosol optical depth (AOD) datasets derived from the Moderate Resolution Imaging Spectroradiometers (MODIS) Dark Target (DT) at 3 and 10 km resolution, Deep Blue (DB), combined DT and DB datasets (DTB) and MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms and the Multi-angle Imaging SpectroRadiometer (MISR) Version 23, by comparing them with ground-based AOD observations in 18 Aerosol Robotic Network (AERONET) sites over the Amazon basin between 2000 and 2022. The AOD retrieval accuracy was also assessed under varying seasons, aerosol loading, particle size, elevation and land cover type. Overall results showed that Terra and Aqua-MODIS DB and MISR algorithms perform better, with about 88% of AOD retrievals within the expected error range. However, MISR presented a systematic high negative bias. MODIS DT 3 km overestimated AOD values and indicated unsatisfactory retrieval accuracy in almost all scenarios. MODIS and MISR retrievals showed improved performance under low aerosol loading, whereas Aqua-MODIS MAIAC under intermediate conditions (AOD>0.2). MISR indicated a large dependence on a coarse mode-dominated environment with considerable negative bias. Terra-MODIS MAIAC retrievals exhibited a smaller dependence for aerosol particle size distribution, while Terra-MODIS DB performed better for fine mode and Aqua-MODIS DB for coarse dominance. MAIAC also performed well under polluted scenarios in the dry season in contrast to the majority of satellite-based AOD retrievals, which showed good agreement under clean background conditions in the wet season. Almost all satellite-based retrievals showed higher uncertainty under extremely high altitudes (>3000 m) followed by non-satisfactory correlation and significant biases. Additionally, almost all satellite-based AOD retrievals indicated poor performance as vegetation coverage increases. On the contrary, MISR showed higher accuracy under forest type. In general, MODIS and MISR retrieval accuracy demonstrated distinct levels of dependencies that require further improvement in the Amazon Basin.

A novel model predictive controller for the drifting vehicle to track a circular trajectory

ABSTRACT In rally racing, a high sideslip cornering technique known as drifting is employed to minimise the lap time. However, for autonomous vehicles, tracking the trajectory while drifting is a challenging control problem due to the conflict between the two objectives. This paper proposes a novel model predictive controller (MPC) with two operation modes to address this conflict. In Mode 1, fuzzy expert strategies combined with the lateral error are designed to calculate the drift equilibrium point. Subsequently, the linear MPC controller is utilised to achieve quick adjustments of the lateral error. In Mode 2, terminal state constraints and error constraints are added to the MPC optimisation problem. The stability of the MPC controller is guaranteed to keep the vehicle within the expected error range. Finally, a comparison between the existing expert controller and the proposed controller is carried out on both the CarSim platform and a 1:10 scale race car. The results demonstrate that the proposed controller outperforms the existing expert controller in terms of tracking performance and drift stability.

Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager

Abstract. Originally developed for the moderate resolution imaging spectroradiometer (MODIS) in polar, sun-synchronous low earth orbit (LEO), the Dark Target (DT) aerosol retrieval algorithm relies on the assumption of a surface reflectance parameterization (SRP) over land surfaces. Specifically for vegetated and dark-soiled surfaces, values of surface reflectance in blue and red visible-wavelength bands are assumed to be nearly linearly related to each other and to the value in a shortwave infrared (SWIR) wavelength band. This SRP also includes dependencies on scattering angle and a normalized difference vegetation index computed from two SWIR bands (NDVISWIR). As the DT retrieval algorithm is being ported to new sensors to continue and expand the aerosol data record, we assess whether the MODIS-assumed SRP can be used for these sensors. Here, we specifically assess SRP for the Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite (GOES)-16/East (ABIE). First, we find that using MODIS-based SRP leads to higher biases and artificial diurnal signatures in aerosol optical depth (AOD) retrievals from ABIE. The primary reason appears to be that the geostationary orbit (GEO) encounters an entirely different set of observation geometry than does LEO, primarily with regard to solar angles coupled with fixed-view angles. Therefore, we have developed a new SRP for GEO that draws the angular shape of the surface bidirectional reflectance. We also introduce modifications to the parameterization of both red–SWIR and blue–red spectral relationships to include additional information. The revised red–SWIR SRP includes the solar zenith angle, NDVISWIR, and land-type percentage from an ancillary database. The blue–red SRP adds dependencies on the scattering angle and NDVISWIR. The new SRPs improve the AOD retrieval of ABIE in terms of overall less bias and mitigation of the overestimation around local noon. The average bias of the DT AOD compared to the Aerosol Robotic Network (AERONET) AOD shows a reduction from 0.08 to 0.03, while the bias of local solar noon decreases from 0.12 to 0.03. The agreement between the DT and AERONET AOD is established through a regression slope of 1.06 and a y intercept of 0.01 with a correlation coefficient of 0.74. By using the new SRP, the percentage of data falling within the expected error range (±0.05 % + 15 %) is notably increased from 54 % to 78 %.

Robust Control for UAV Close Formation Using LADRC via Sine-Powered Pigeon-Inspired Optimization

This paper designs a robust close-formation control system with dynamic estimation and compensation to advance unmanned aerial vehicle (UAV) close-formation flights to an engineer-implementation level. To characterize the wake vortex effect and analyze the sweet spot, a continuous horseshoe vortex method with high estimation accuracy is employed to model the wake vortex. The close-formation control system will be implemented in the trailing UAV to steer it to the sweet spot and hold its position. Considering the dynamic characteristics of the trailing UAV, the designed control system is divided into three control subsystems for the longitudinal, altitude, and lateral channels. Using linear active-disturbance rejection control (LADRC), the control subsystem of each channel is composed of two cascaded first-order LADRC controllers. One is responsible for the outer-loop position control and the other is used to stabilize the inner-loop attitude. This control system scheme can significantly reduce the coupling effects between channels and effectively suppress the transmission of disturbances caused by the wake vortex effect. Due to the cascade structure of the control subsystem, the correlation among the control parameters is very high. Therefore, sine-powered pigeon-inspired optimization is proposed to optimize the control parameters for the control subsystem of each channel. The simulation results for two UAV close formations show that the designed control system can achieve stable and robust dynamic performance within the expected error range to maximize the aerodynamic benefits for a trailing UAV.

Modeling Control Delays for Edge-Enabled UAVs in Cellular Networks

Real-time control solutions for unmanned aerial vehicles (UAVs) have attracted great interest in recent years. Most existing control methods use Wi-Fi technology. While Wi-Fi is inexpensive and easy to use, it has only a limited transmission range. Thus, 4G/5G cellular networks have been proposed as an alternative enabling technology. This study focuses on the problem of improving the appropriateness of the control commands sent by the ground control station (GCS) to the UAV over the control and nonpayload communication (CNPC) link of the UAV through the cellular network. To satisfy the low-latency requirement of the CNPC link, multiaccess edge computing (MEC) technology is leveraged to collocate the GCS and base station. The effectiveness of the proposed edge-based approach is demonstrated by conducting experiments on two LTE platforms with different MEC deployment methods. An edge-enabled UAV control solution is proposed in which each end-to-end control delay in the UAV-GCS system is estimated based on the preceding delay such that the location of the UAV at the moment it receives the control command from the GCS can be predicted in advance and taken into consideration by the GCS when formulating an appropriate control decision. To this end, an analytical modeling method is proposed for estimating the expected error range of each control delay based on a bimodal distribution approximation of the empirical control delays observed at the UAV. Finally, an event-driven simulator is developed to confirm the accuracy of the analytical predictions of the control delay based on the expected error between consecutive delays.

250-m Aerosol Retrieval from FY-3 Satellite in Guangzhou

Aerosol distribution with fine spatial resolution is crucial for atmospheric environmental management. This paper proposes an improved algorithm of aerosol retrieval from 250-m Medium Resolution Spectral Image (MERSI) data of Chinese FY-3 satellites. A mixing model of soil and vegetation was used to calculate the parameters of the algorithm from moderate-resolution imaging spectroradiometer (MODIS) reflectance products in 500-m resolution. The mixing model was used to determine surface reflectance in blue band, and the 250-m aerosol optical depth (AOD) was retrieved through removing surface contributions from MERSI data over Guangzhou. The algorithm was used to monitor two pollution episodes in Guangzhou in 2015, and the results displayed an AOD spatial distribution with 250-m resolution. Compared with the yearly average of MODIS aerosol products in 2015, the 250-m resolution AOD derived from the MERSI data exhibited great potential for identifying air pollution sources. Daily AODs derived from MERSI data were compared with ground results from CE318 measurements. The results revealed a correlation coefficient between the AODs from MERSI and those from the ground measurements of approximately 0.85, and approximately 68% results were within expected error range of ±(0.05 + 15%τ).

SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe

This paper presents the validation results of Aerosol Optical Depth (AOD) retrieved from the Spinning Enhanced Visible Infrared Radiometer (SEVIRI) data using the near-real-time algorithm further developed in the frame of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project. The SEVIRI AOD was compared against multiple data sources: six stations of the Aerosol Robotic Network (AERONET) in Romania and Poland, three stations of the Aerosol Research Network in Poland (Poland–AOD) and Moderate Resolution Imaging Spectroradiometer (MODIS) data overlapping Romania, Czech Republic and Poland. The correlation values between a four-month dataset (June–September 2014) from SEVIRI and the closest temporally available data for both ground-based and satellite products were identified. The comparison of the SEVIRI AOD with the AERONET AOD observations generally shows a good correlation (r = 0.48–0.83). The mean bias is 0.10–0.14 and the root mean square error RMSE is between 0.11 and 0.15 for all six stations cases. For the comparison with Poland–AOD correlation values are 0.55 to 0.71. The mean bias is 0.04–0.13 and RMSE is between 0.10 and 0.14. As for the intercomparison to MODIS AOD, correlations values were generally lower (r = 0.33–0.39). Biases of −0.06 to 0.24 and RMSE of 0.04 to 0.28 were in good agreement with the ground–stations retrievals. The validation of SEVIRI AOD with AERONET results in the best correlations followed by the Poland–AOD network and MODIS retrievals. The average uncertainty estimates are evaluated resulting in most of the AOD values falling above the expected error range. A revised uncertainty estimate is proposed by including the observed bias form the AERONET validation efforts.

Retrieving the Optical Properties of Aerosols Over Land With Directional Polarimetric Camera Observations and an Adaptive Algorithm

The Directional Polarimetric Camera (DPC) is a polarization sensor mounted on Chinese Gaofen-5 satellite. It has the capability of observing multispectral, multiangular, and polarized light, and can be used to monitor global aerosols and clouds. This work developed an adaptive algorithm to retrieve aerosol properties based on DPC measurements. It performs initial surface property estimations, aerosol parameter retrievals, surface parameter adjustments, and result assessments. In the algorithm, it allows global aerosol optical depths (AODs) and Angstrom exponents (AEs) to be retrieved. The AOD values show good consistency with that from AErosol RObotic NETwork (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS). The regression line between AODs from DPC and AERONET is y = 0.895x + 0.056, with a correlation coefficient of 0.894, and 49% of the DPC-retrieved AODs are within the expected error range of ±(15%AOD + 0.05). For the AEs, the regression line is y = 0.728x + 0.395, the correlation coefficient is 0.763, and 41.4% of the AEs are within the error range of (AE ± 0.4). We also investigated the possible influence of AOD errors retrieved from the DPC measurements over three typical areas. It was found that though the DPC-retrieved dust aerosols did not show so good consistency with the MODIS measurements as the smoke aerosols, the retrievals still matched well on the whole. It demonstrates the potential of DPC and the adaptive algorithm for aerosol remote sensing.

Validation and inter-comparison of MODIS and VIIRS aerosol optical depth products against data from multiple observation networks over East China

Based on ground-based aerosol optical property observations from the Central China Comprehensive Experimental Site and Aerosol Robotic Network, Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol optical depth (AOD) products between January 2016 and February 2020 over East China were evaluated and compared on both regional and site scales. In addition, the relationship between the retrieval error and season, the Ångström exponent, and real AOD values was investigated. The results revealed that on the regional scale, the Deep Blue (DB) algorithm outperformed Dark Target (DT) for all three platforms of Terra/Aqua MODIS and NPP VIIRS (with 57%, 57%, and 70%, respectively, of collocations falling within the expected error range), yet the MODIS DB AOD failed to reach a satisfactory result of at least 67% matchups falling within the expected error range. On the site scale, the VIIRS DB aerosol products were the best over most cropland sites. The AOD retrieval errors at some cropland sites exhibited seasonal patterns, which might be related to field burning events and long-distance transport. The results indicate that the DT algorithm of both MODIS 10-km AOD and VIIRS products over croplands in East China needs further improvement.

Quantisation of angular momenta in atoms

The aim of the work was to see if the direction of angular momenta in atoms is quantised. In order to do it, a beam of atoms is split, and deflected because of interactions between atomic dipole moments and an externally applied magnetic field. In which case, the angular momenta in atoms is definitively quantised. Firstly, the detector was checked and then the operation of the detector was characterised; secondly, the beam was detected and characterised with residual magnetisation minimised; thirdly, the beam was detected and characterised with magnetic field present. The sample used to determine the magnetic moment was Potassium. The Bohr’s magneton constant value was found to be (9.22±0.7)*10−24 J/T. The answer is lower than the nominal value but lies within the expected error range. This proved the accuracy of the work done and proved the quantisation of the angular momentum, since it proved the presence of magnetic moment.

Application of Back Propagation Neural Network Model in Prediction and Diagnosis of Osteoporosis

The classification model of osteoporosis was established, the diagnostic index system of osteoporosis was analyzed, and the BP neural network system was designed. The data of 1261 people from 402 nuclear families were collected, including age, gender and medical history. The actual data were sampled and tested by using the computational function of ANN, so that the model error could be controlled within a set range. The results of neural network analysis suggested that among the 12 factors influencing bone mineral density, gender had the most obvious effect, followed by height, weight and age. The polymorphism of estrogen receptor PvuII had the greatest impact on bone mineral density (ER PvuII) of all candidate genes, and the factor that had the most serious effect was BGP gene. A large number of BP network training test results were within the expected error range, and the same conclusion was drawn using the same sample and traditional statistical methods.Therisk factors application has certain feasibility and accuracy.

Comparison of pulse oximetry and earlobe blood gas with CO-oximetry in children with sickle cell disease: a retrospective review

ObjectivesTo investigate the agreement between pulse oximetry (SpO2) and oxygen saturation (SaO2) measured by CO-oximetry on arterialised earlobe blood gas (EBG) in children and adolescents with sickle cell disease (SCD).Design...

A comparison study between AOD data from MODIS deep blue collections 51 and 06 and from AERONET over Saudi Arabia

This study compares the precision of the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) Collection–51 (C–51) and Collection–06 (C–06) Aerosol Optical Depth (AOD, at 550 nm) products with surface-based aerosol robotic network (AERONET) observations for the period 2002–2013 at the Solar Village, Saudi Arabia. In general, MODIS captures the patterns of AERONET AOD although C−51 tends to underestimate them while C−06 overestimates them. We found a slightly higher correlation for C–06 (0.79) than for C–51 (0.74) over the Solar Village. The C–06 retrievals are typically of better quality than those of C−51 with a smaller root mean square error (RMSE) and mean absolute error (MAE) and more AODs fall within the expected error range and relative mean bias. Overall, both the C–51 and C–06 MODIS DB algorithms show significant uncertainties and errors. The errors in AOD measurements arise due to imperfect aerosol model schemes and underestimation of surface reflectance over the Solar Village. This study suggests that further quantitative research is required to provide better estimates of satellite-based AOD over Saudi Arabia.

Comparison of AVHRR aerosol optical depth production against CARSNET data in China

This study presents a dedicated evaluation of a newly released satellite aerosol product derived from Advanced Very-High-Resolution Radiometer (AVHRR) measurements by using the Deep Blue/SOAR aerosol retrieval algorithm. Aerosol product from the Chinese Aerosol Network (CARSNET) is used as the ground truth in the comparison. Comparison of AVHRR and CARSNET aerosol optical depth (AOD) products during 2006–2011 shows that the accuracy and precision of AVHRR AOD demonstrate complicated spatial and seasonal dependence. First, AVHRR AOD is in good agreement with CARSNET AOD over eastern urban areas in China. 45%–78% of data points fall within the expected error range (±0.05 ± 0.25× AOD) over there. Second, in regions with natural vegetation cover and relatively clean environment conditions, low correlation is observed due to small AOD loadings (<0.2 and < 0.6, respectively) and small AOD variations (<1.0). Third, for dust affected arid and semi-arid dry regions and western urban regions, AVHRR AOD products need further great improvement. A large regional variation of AOD bias with increasing CARSNET AOD may be due to insufficient representation of aerosol properties in the algorithm. The systematic underestimation of AVHRR AOD in most sites likely attributes to improper surface reflectance estimation, which should be furthermore improved in the deep blue algorithm. AVHRR has great potential in the retrieval of the historical AOD record from 1979 to the present, therefore, one would expect that AVHRR aerosol products would contribute to furthering our understanding of aerosol effects on climate and environment.

Aerosol retrieval algorithm based on adaptive land–atmospheric decoupling for polarized remote sensing over land surfaces

Land–atmospheric decoupling is important for the retrieval accuracy in atmospheric remote sensing. In this study, we developed an algorithm that adaptively decouples the land surface and atmosphere reflected solar radiation and retrieves the aerosol properties (adaptive land–atmospheric decoupling algorithm, ALAD). The ALAD algorithm allows precise global aerosol optical depth (AOD) retrievals. The four sub-procedures in ALAD comprise estimating a first guess for surface properties, aerosol property retrieval, atmospheric correction, and assessing the results. The ALAD algorithm was applied to PARASOL (Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) and the retrieved AODs were compared with the AOD products from the Moderate resolution Imaging Spectroradiometer (MODIS) and AErosol RObotic NETwork (AERONET). ALAD and MODIS retrieved AOD values were with good consistency in terms of their spatial distribution, regardless of the region or the global distribution. The correlation coefficient between the global ALAD and AERONET AOD was 0.875, and about 60.7% of the AODs retrieved by ALAD were within the expected error range of ±(15% AOD + 0.05). The results obtained using ALAD agreed well with the AERONET values. We also investigated the aerosol and land surface properties that might introduce errors into the AOD values retrieved by ALAD. It was found that the value of AOD had the greatest effect on the accuracy of the retrieval results. The errors introduced by the Angstrom exponent and Normalized Difference Vegetation Index varied slightly but they made no significant contributions.

A NEW ALGORITHM FOR THE AOD INVERSION FROM NOAA/AVHRR DATA

Abstract. The advanced very high resolution radiometer (AVHRR) data from the National Oceanic and Atmospheric Administration satellite is one of the earliest data applied in aerosol research. The dense dark vegetation (DDV) algorithm is a popular method for the present land aerosol retrieval. One of the most crucial steps in the DDV algorithm with AVHRR data is estimating the land surface reflectance (LSR). However, LSR cannot be easily estimated because of the lack of a 2.13 μm band. In this article, the moderate resolution imaging spectroradiometer (MODIS) vegetation index product (MYD13) is introduced to support the estimation of AVHRR LSR. The relationship between MODIS NDVI and the AVHRR LSR of the visible band is analysed to retrieve aerosol optical depth (AOD) from AVHRR data. Retrieval experiments are carried out in mid-eastern America. The AOD data from AErosol RObotic NETwork (AERONET) measurements are used to evaluate the aerosol retrieval from AVHRR data, the results indicate that about 74 % of the retrieved AOD are within the expected error range of ±(0.05 + 0.2), and a cross comparison of the AOD retrieval results with the MODIS aerosol product (MYD04) shows that the AOD datasets have a similar spatial distribution.

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

Abstract. The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER) version 1 algorithm was developed to retrieve hourly aerosol optical depth at 550 nm (AOD) and other subsidiary aerosol optical properties over East Asia. The GOCI YAER AOD had accuracy comparable to ground-based and other satellite-based observations but still had errors because of uncertainties in surface reflectance and simple cloud masking. In addition, near-real-time (NRT) processing was not possible because a monthly database for each year encompassing the day of retrieval was required for the determination of surface reflectance. This study describes the improved GOCI YAER algorithm version 2 (V2) for NRT processing with improved accuracy based on updates to the cloud-masking and surface-reflectance calculations using a multi-year Rayleigh-corrected reflectance and wind speed database, and inversion channels for surface conditions. The improved GOCI AOD τG is closer to that of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD than was the case for AOD from the YAER V1 algorithm. The V2 τG has a lower median bias and higher ratio within the MODIS expected error range (0.60 for land and 0.71 for ocean) compared with V1 (0.49 for land and 0.62 for ocean) in a validation test against Aerosol Robotic Network (AERONET) AOD τA from 2011 to 2016. A validation using the Sun-Sky Radiometer Observation Network (SONET) over China shows similar results. The bias of error (τG−τA) is within −0.1 and 0.1, and it is a function of AERONET AOD and Ångström exponent (AE), scattering angle, normalized difference vegetation index (NDVI), cloud fraction and homogeneity of retrieved AOD, and observation time, month, and year. In addition, the diagnostic and prognostic expected error (PEE) of τG are estimated. The estimated PEE of GOCI V2 AOD is well correlated with the actual error over East Asia, and the GOCI V2 AOD over South Korea has a higher ratio within PEE than that over China and Japan.

Micro-mechanical failure prediction of radar-absorbing structure dispersed with multi-walled carbon nanotubes considering multi-scale modeling

Conventional radar-absorbing structure is typically manufactured with high weight percentage (wt.%) of carbonaceous nano-conductive particles in the polymer matrix to tailor its microwave absorbing performance. However, these manufacturing methods have some physical limitations with regard to fabrication, due to the high viscosity in the polymer matrix and, inhomogeneous in mechanical and electrical properties. No study has been conducted with micro-mechanical failure prediction of radar-absorbing structure dispersed with multi-walled carbon nanotubes. In order to address these limitations, radar-absorbing structures dispersed with multi-walled carbon nanotubes were designed in the Ku-band (12.4–18 GHz). Additionally, to establish and verify the micro-mechanical failure analysis based on multiscale modeling, finite element analysis was carried out using the Mori–Tanaks mean-field homogenization model within the representative volume element model in the microstructure. In order to verify the Hashin criteria of radar-absorbing structure dispersed with multi-walled carbon nanotube (0.5 wt.%, 1.0 wt.% and 1.5 wt.%), mechanical tests (tensile, compressive and shear test) were conducted according to ASTM standards. In this paper, radar-absorbing structure with irregularly arranged filler and matrix with representative volume element was modeled from the micro-mechanical point of view and the results from Hashin failure criterion were verified both by simulations and experimental results of prediction strengths within the expected error range (lower than 6%). The reliability of application in micro-mechanical prediction of radar-absorbing structure was confirmed considering the multi-scale modeling.

Long Temporal Analysis of 3-km MODIS Aerosol Product Over East China

The 3-km resolution MODerate resolution Imaging Spectroradiometer (MODIS) aerosol product has advantages for local-scale aerosol monitoring over land. This study assessed the accuracy and feasibility of the product over East China and investigated the potential for aerosol climatology studies. The long-temporal aerosol optical depth (AOD) of the product from 2002 to 2015 was collected and analyzed. Validation results show good overall accuracy. The correlation coefficient between MODIS AOD and ground measurements of the Aerosol Robotic Network (AERONET) is 0.79, and 63.1% data points are within the expected error range. However, in some areas, the MODIS AODs are highly overestimated because of bias and noise. Seasonal average AOD maps indicate the spatio-temporal distributions of aerosol. In general, seasonal AOD values follow the sequence summer > spring > fall > winter. Higher AODs (>1.0) usually occur over urban areas and cropland whereas lower values coincide with forest, shrub, and grassland. A simple moving average technique was applied to remove noise. Trend slopes were calculated and the significances were tested. Most areas show remarkable increases in AOD values prior to 2010, followed by significant downward trends. Differences in MODIS AOD were calculated between 2002 and 2009 and 2010 and 2015. Despite significant downward trends after 2010, the AODs are still higher than before 2010. The study demonstrates potential application of the 3 km product in aerosol climatology but confirms that it is crucial to first remove noise.