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The air traffic management automation imposes stringent requirements on the weather models, in such a way that they should be able to provide reliable short-time forecasts in digital formats in almost real time. The atmospheric boundary layer is one of the regions where aircraft operation and coordination are critical and therefore atmospheric model performance is also vital. This paper presents conventional and innovative techniques to improve the accuracy in the forecasting of winds in the lower atmospheric layer, proposing mechanisms to develop better models including deterministic and stochastic simulations. Accuracy is improved by optimizing the grid, assimilating observations in cycling simulations and managing a number of ensemble members. An operation-driven post-processing stage helps to incorporate detailed terrain definitions and real-time observations without re-running the model. The improvements are checked against mesoscale weather simulations at different scales and a dedicated flight campaign. The results show good performance of the model without sensitively increasing the required throughput.

With the development of the data-driven modeling techniques, using the neural network to simulate the transport process of atmospheric pollutants and constructing ${\textrm {PM}}_{2.5}$ time-series prediction model have become a hot topic. The existing data-driven approaches often ignore the dynamical relationships among multiple sites in urban areas, which results in nonideal prediction accuracy. In response to this problem, this article proposes a long short-term memory (LSTM) autoencoder multitask learning model to predict ${\textrm {PM}}_{2.5}$ time series in multiple locations city wide. The model could implicitly and automatically excavate the intrinsic relevance among the pollutants in different stations. And the meteorological information from the monitoring stations is fully utilized, which is beneficial for the performance of the proposed model. Specifically, multilayer LSTM networks can simulate the spatiotemporal characteristics of urban air pollution particles. And using the stacked autoencoder to encode the key evolution pattern of urban meteorological systems could provide important auxiliary information for ${\textrm {PM}}_{2.5}$ time-series prediction. In addition, multitask learning could automatically discover the dynamical relationship between multiple key pollution time series and solve the problem of insufficient use of multisite information in the modeling process of the traditional data-driven methods. The simulation results of ${\textrm {PM}}_{2.5}$ prediction in Beijing indicate the effectiveness of the proposed method.

This brief is devoted to the experimental study of possibilities for control of the phase shift between two vibration actuators. The control laws for frequency stabilization simultaneously with the prescribed phase shift between the rotors angular positions are proposed and experimentally studied by means of the mechatronic vibration test bed. It is obtained that for the low and medium frequencies, the self-synchronization of unbalanced rotors does not prevent achieving the desired phase shift between the rotors. For a high-frequency band, the Huygens self-synchronization of rotors manifests itself, narrowing the range of the achievable phase shift. The obtained experimental results are in agreement with the simulation results obtained from the 7-DOF mathematical model. Especially important is an agreement in the vibrational field observed from simulation and experiment. The qualitative coincidence of such complex motions means both the validation of the model and the verification of the measurement and experiment methodology.

Many atmospheric correction schemes of radiance-based optical satellite data require the selection of normalized solar spectral irradiance models at the top of atmosphere (TOA). However, there is no scientific consensus in literature as to which available model is most suitable. This article examines five commonly used models applied to Landsat 8 Operational Land Imager (OLI) TOA radiance and reflectance products to assess the accuracy and stability between models used to derive surface reflectance products. It is assumed that the calibration of the United States Geological Survey (USGS) Landsat 8 OLI TOA reflectance and radiance products are accurate to currently claimed levels. The results show that the retrieved surface reflectance can exhibit significant variations when different solar irradiance models are used, especially in the OLI coastal blue band at 443 nm. From the five solar irradiance models, the Kurucz 2005 model showed the least bias compared with OLI TOA reflectance product and least variance in surface reflectance. Furthermore, improvement was obtained by adjusting the total solar irradiance (TSI) normalization, and additional validation was provided using observed in situ water leaving reflectance data. The results from this article are particularly relevant to aquatic applications and to satellite sensors that provide TOA radiance such as previous Landsat and other current and historical missions.

A graphical method is extended to and applied for visual inspection of solution patterns (region of complex natural frequencies $s_{n}$ ) in the complex $s$ -plane for dispersive materials characterized by the Debye and Lorentz models. These patterns are in the form of exponential decay and C-shaped profiles for the Debye and Lorentz models, respectively. Their forms are observed to be affected (e.g., moving left/right) in different manners with a variation of dispersion parameters. This method, thanks to its visual inspection capability, can help in analyzing/synthesizing filters and frequency-selective surfaces.

Rainfall event forecasting is prominently done using climate models (CMs) to produce multiple forecasts for the same rainfall event. The best forecast is complicated to find and hence has not yet been explored in the CMs. Recent advances in deep learning methods have provided an exceptional ability to investigate intricate weather patterns from big climate data. In this article, a hybrid climate learning model (HCLM) is proposed that utilizes both the CM and the deep learning models for improving the rainfall forecast. More specifically, a probabilistic multilayer perceptron (PMLP) network evaluates multiple forecasts from the CM-generated forecasts and selects the best one. The selected forecast is next passed onto a hybrid deep long short-term memory (HD-LSTM) network, which looks back and learns the relationship of the selected forecast with corresponding rainfall and temperature observations to produce the next-day rainfall forecast. The experimental results from various climate zones in Australia show that the HCLM outperforms existing state-of-the-art climate and deep learning models.

With the common use of lidar sensors on intelligent vehicles, the simulation of lidar in autonomous driving is necessary. This study proposes an innovative lidar modeling method, and introduces solutions for the simulation of the lidar detection function and its physical mechanism. The model consists of geometric and physical models, and can simulate both point clouds and targets. The geometric model addresses the spatial relationship between lidar and the environment. The physical model describes how the lidar detection mechanism may influence detection results. Signal attenuation and unwanted raw data caused by raindrops are the main consideration in the physical model. Characteristics of lidar signal attenuation in different weather conditions are modeled, and a simplified lidar equation is derived for use by lidar users, as opposed to designers. Unwanted raw data are simulated in a stochastic model employing the Monte Carlo method, where raindrop size and distance are sampled. The model is calibrated and validated with real lidar data. The application of the proposed lidar model for an autonomous emergency braking system is introduced.

Future climate projections made with high-resolution climate models carry the promise of bridging the gap between weather and climate scales, but the scientific and technical hurdles remain daunting. New simulations carried out on Frontera by a team of climate scientists and software engineers are yielding novel insights into how anthropogenic climate change will impact weather extremes at regional scales over the course of the twenty-first century.

Why are the east sides of formerly industrial cities more deprived? To answer this question, we use individual-level census data and create historical pollution patterns derived from the locations of 5,000 industrial chimneys and an atmospheric model. We show that this observation results from path dependent neighborhood sorting that began during the Industrial Revolution as prevailing winds blew pollution eastwards. Past pollution explains up to 20% of the observed neighborhood segregation in 2011, even though coal pollution stopped in the 1970s. We develop a quantitative model to identify the role of neighborhood effects and relocation rigidities in underlying this persistence.

The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the two Sentinel-3 satellites provides daily global coverage observation at daytime and nighttime. The split-window (SW) algorithm is currently used to retrieve the land surface temperature (LST) from SLSTR images; however, this algorithm has to utilize visible and near-infrared (VNIR) images and land cover to determine pixel emissivity. For nighttime observation, VNIR cannot be observed, and this limitation complicates the LST retrieval from nighttime images using the SW algorithm. This article proposed a three-channel temperature–emissivity separation (TES) algorithm that estimates the nighttime LST and emissivity from one middle-infrared (MIR) and two thermal-infrared (TIR) nighttime Sentinel-3 SLSTR images. The sensitive analysis showed that the algorithm could theoretically retrieve the LST and emissivity with errors less than 0.8 K and 0.015, respectively. Ground validation showed that the nighttime LST retrieval error was approximately 1.84 K and the bias was approximately −0.33 K. Finally, the TES algorithm was applied to obtain the LST and emissivity images over northern China as an example. The emissivity retrieved from the nighttime observation can be used in the daytime SW algorithm to improve its feasibility in the LST retrieval process.

Methane is one of the main greenhouse trace gases and seriously affects the radiation balance of Earth systems due to its strong heat absorption capacity and long atmospheric retention time. Based on the methane stratification data simulated by the community atmospheric model with chemistry (CAM-chem), near-surface methane concentrations were estimated by utilizing the Gaussian function, and the spatiotemporal variation in the near-surface methane concentration in China from 2001 to 2019 was discussed in this research. The results show that (1) based on the methane stratification concentration data simulated by the atmospheric chemical model, the near-surface CH4 concentration estimated by Gaussian function model is reliable, which provides a new method to estimate the near-surface CH4 concentration over China; (2) from 2001 to 2019, the near-surface methane concentration in China showed an increasing trend with an annual growth rate of 7.20±0.23 ppb·a-1. The annual maximum near-surface methane concentration was measured in winter, and the minimum was measured in summer; (3) the spatial distribution differences are obvious: the methane concentration in the east was higher than that in the west, and the methane concentration in the north was higher than that in the south. Moreover, the distributions of methane in the east and west are consistent with the division of Hu Huanyong population line.

This study assesses the performance of the Weather Research and Forecasting (WRF) model in simulating the Arabian Peninsula summer climate for the period 2001–2016. The European Centre for Medium range Weather Forecast (ECMWF) reanalysis is downscaled using WRF without (CTRL) and with the Spectral Nudging (SPN) method. Our results suggest that the noticeable cold biases in surface temperatures (mean, minimum, and maximum) over the Arabian Peninsula in CTRL are significantly reduced in SPN. The seasonal patterns of surface pressure, cloud cover, lower and upper tropospheric circulation, and mid-tropospheric anticyclone are also simulated more realistically with SPN. The evaluation of mean vertical profiles of dynamical and thermo-dynamical features over the Arabian Peninsula further confirms the enhanced simulations with SPN with respect to CTRL. Though SPN captures better the observed evolution of rainfall compared to that of CTRL, it produces a positive rainfall bias over the Southwestern Arabian Peninsula. Stronger vertical motions associated with the local topography enhance the higher water vapor loading, condenses in the upper layers, and results in excess amount of rainfall in SPN. Furthermore, with SPN, WRF is further able to better simulate the synoptic features of heat waves. Overall, SPN enhances WRF simulation skill of the horizontal structures and vertical profiles of the Arabian Peninsula summer climate by enforcing a better balance between the small and large scale features and associated feedbacks.

The influence of early spring sea ice at Barents Sea on midsummer rainfall in Northeast China (NEC) is identified based on observational analyses and atmospheric modeling experiments in this study. Increased sea ice area (SIA) in the Barents Sea is ensued by positive rainfall anomalies at north of NEC and by negative anomalies at south, and vice verse. Specifically, due to a good seasonal persistence from spring to summer, the preceding sea ice anomalies exert an impact on midsummer surface air temperature anomalies and vertical stability over Barents Sea via the modulation on turbulent heat flux. The anomalous circulation is further triggered over Europe and the Mediterranean Sea through meridional vertical cells, with a barotropic structure. Accordingly, an effective Rossby wave source is excited over the eastern Mediterranean by the advection of vorticity by divergence wind, and causes an eastward propagation of Silk Road Pattern to East Asia. In addition, another SIA-related wave-like train can diffuse directly southeastward from Arctic to NEC in a polar path. Observations and numerical simulations indicate that, in response to increased sea ice at Barents Sea, an anomalous cyclone emerges over NEC, along with easterly or southeasterly over north of NEC and with northwesterly over south, leading to moisture convergence anomalies at north and divergence anomalies at south. Jointly, ascending (descending) motion anomalies favors a wet (dry) summer over north (south) of NEC.

The effects of sea surface temperature (SST) on ocean radar backscattering are investigated under both the neutral and nonneutral atmospheric conditions for the applications of wind retrieval. The impact factors are parameterized as functions of SST. The SST effects on the variations in ocean scattering and wind retrieval are evaluated using an analytic model which combines the KHCC03 spectrum and the second-order small slope approximation (SSA-II) model. Under the neutral condition, we present the following new insights at three commonly used bands: 1) the seawater permittivity accounts for a dominant effect of SST at the L-band. The SST effects induce a wind underestimation of 0.3 m/s over cold seawater and a wind overestimation of 0.24 m/s over warm seawater at the L-band and a wind speed of 8 m/s. The seawater viscosity plays a significant role in the SST effects on ocean scattering at the C- and Ku-bands, while its variation induced by SST has insignificant effects on L-band scattering; 2) for the C-band, the SST-induced wind retrieval error can be neglected at a medium wind speed due to the neutralization of the effects of various factors on surface roughness. Yet, the SST effects are not negligible at low and high wind speeds; and 3) both the dielectric and dynamic factors play significant roles in the SST effects on ocean scattering at the Ku-band. Under the nonneutral condition, the simulation results show that the air–sea interaction governs the SST effects on ocean scattering and wind velocity variations. Other than the air–sea interaction, the wind retrieval errors induced by other SST-related factors are negligible at the L- and C-bands.

In this study, we designed a sensitivity test using the half number concentration of sulfate in the nucleation calculation process to study the aerosol-cloud interaction (ACI) of sulfate on clouds, precipitation, and monsoon intensity in the summer over the eastern China monsoon region (ECMR) with the National Center for Atmospheric Research Community Atmosphere Model version 5. Numerical experiments show that the ACI of sulfate led to an approximately 30% and 34% increase in the cloud condensation nuclei and cloud droplet number concentrations, respectively. Cloud droplet effective radius below 850 hPa decreased by approximately 4% in the southern ECMR, while the total liquid water path increased by 11%. The change in the indirect radiative forcing due to sulfate at the top of the atmosphere in the ECMR during summer was − 3.74 W·m−2. The decreased radiative forcing caused a surface cooling of 0.32 K and atmospheric cooling of approximately 0.3 K, as well as a 0.17 hPa increase in sea level pressure. These changes decreased the thermal difference between the land and sea and the gradient of the sea-land pressure, leading to a weakening in the East Asian summer monsoon (EASM) and a decrease in the total precipitation rate in the southern ECMR. The cloud lifetime effect has a relatively weaker contribution to summer precipitation, which is dominated by convection. The results show that the ACI of sulfate was one possible reason for the weakening of the EASM in the late 1970s.

Considering the wide acceptability and practicability of single-scatter path loss (PL) model of non-line-of-sight (NLOS) ultraviolet communications (UVC) in noncoplanar geometry and the corresponding complexity of the triple integrals in the exact single-scatter PL model, a novel simplified PL model is proposed in this work with better analytical tractability and high accuracy. By approximately regarding the differential volume as a circular and thin plate, the triple integrals in the exact single-scatter PL model can be reduced to a single integral. With different baseline ranges between transmitter and receiver, beam divergence angles, receiver field-of-view (FOV) angles, inclination angles and azimuth angles taken into account, the results of the proposed PL model are compared with those of an exact single-scatter PL model, a Monte Carlo numerical model, and another recently reported approximate single-scatter PL model, which is called spherical crown (SC) model. Compared with the SC model, the proposed single-scatter PL model is obtained in closed form. Besides, taking the exact single-scatter PL model as the benchmark, the root-mean-square error (RMSE) of the proposed model and the SC model are calculated for comparison. It is demonstrated that the proposed model could achieve better accuracy than SC model under most conditions of our interest. Besides, it is shown that the RMSE of the proposed model is less than 0.4 dB for all considered transceiver geometry and the execution time of the proposed PL model is significantly reduced to less than 1% of the exact single-scatter PL model’s, which manifests the effectiveness of the proposed model. Finally, the experimental results are also provided to verify the correctness of the proposed model.

The Micro-Sized Microwave Atmospheric Satellite (MicroMAS-2A) is a 3U CubeSat that launched in January 2018 as a technology demonstration for future microwave sounding constellation missions, such as the NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission now in development. MicroMAS-2A has a miniaturized 1U 10-channel passive microwave radiometer with channels near 90, 118, 183, and 206 GHz for moisture and temperature profiling and precipitation imaging [4]. MicroMAS-2A provided the first CubeSat atmospheric vertical sounding data from orbit and to date is the only CubeSat to provide temperature and moisture sounding and surface imaging. In this paper, we analyze six segments of data collected from MicroMAS-2A in April 2018 and compare them to ERA5 reanalysis fields coupled with the Community Radiative Transfer Model (CRTM). This initial assessment of CubeSat radiometric accuracy shows biases relative to ERA5 with magnitudes ranging from 0.4 to 2.2 K (with standard deviations ranging from 0.7 to 1.2 K) for the four mid-tropospheric temperature channels and biases of 2.2 and 2.8 K (standard deviations 1.8 and 2.6 K) for the two lower tropospheric water vapor channels., NASA (Award NNX16AM73H)

Single-image dehazing techniques are extensively used in outdoor optical image acquisition equipment. Most existing methods pay attention to use various priors to estimate scene transmission. In this paper, a fast single-image dehazing algorithm is proposed based on a piecewise transformation model between the minimum channels of the hazy image and the haze-free image in optical model. The minimum channel of the haze-free image is obtained by the piecewise transformation, which is a quadratic function model that we establish for the dark region, and a linear transformation model is established for the bright region. Using the minimum channels of the hazy image and the haze-free image, a transmission estimation model is established based on the haze optical model with adjustment variables. To obtain an accurate estimation of atmospheric light, we estimate the atmospheric light twice. Finally, the haze-free image is restored. Experimental results show that the proposed algorithm has minimal halo artifacts and color distortion in various depths of field, flat areas and sky areas. From the subjective evaluation, objective evaluation and running time analysis, it can be seen that the algorithm in this paper is superior to most existing technologies.

Estimates of the contribution of direct radiative forcing (DRF) to the surface air temperature trends over the Northern Hemisphere (NH) continents for the period 1979–2012 have been obtained based on an ensemble of numerical experiments with an atmospheric general circulation model using the prescribed historical sea surface temperature and sea ice concentration data with and without time-varying concentrations of greenhouse gases and aerosols in the atmosphere. The contribution of DRF to the simulated ensemble mean temperature variations over land in the NH in winter and summer is approximately 30% or 0.3°С and 0.2°С, respectively. The maximum warming associated with DRF was found, in particular, in winter and summer over the eastern part of the European territory of Russia. The results indicate that the response of atmosphere circulation to DRF may lead to a nonlinear DRF contribution to the temperature changes in the NH, in particular mitigating the temperature increase in the winter season.

The visible-shortwave infrared Advanced Hyperspectral Imager (AHSI) is a payload onboard the Gaofen-5 satellite, which is China’s first hyperspectral satellite and is part of the Chinese High-Resolution Earth Observation System. As a supplement to the onboard radiometric calibration of the AHSI instrument, vicarious calibration is also required, which is independent of the instrument-based calibration. In this article, a reflectance-based vicarious calibration approach is presented, which takes surface reflectance data, aerosol data, and atmospheric water vapor data into account. The Dunhuang test site, which is one of the China Radiometric Calibration Sites (CRCS) for the vicarious calibration of spaceborne sensors, possesses stable, uniform, and measurable surface objects, so it was chosen as the radiation source to replace the laboratory and onboard calibrators. A Spectra Vista Corporation (SVC) spectral radiometer and a CE318 sun photometer were utilized for the measurement of the surface reflectance and the condition of the aerosol, respectively. The radiance at the entrance pupil at the top of atmosphere was then obtained through the MODerate resolution atmospheric TRANsmission (MODTRAN) atmospheric transmission model. The surface reflectance was obtained using the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) atmospheric model for validation. The results show that, with regard to the calibration coefficients, the calibrated AHSI instrument presents a stable radiometric performance among different land-cover types. The ratios on all the bands are between 0.8 and 1.2 and are consistent with the reflectance data from the Dunhuang test site. The ${R} ^{{2}}$ values are all greater than 0.95 and the spectral angle is all less than 2°. The standard deviations of the ratios are less than 3% for each chosen band, which proves that the calibrated data have a high consistency with the in situ measurements. When compared with Landsat 8 and Sentinel-2, the mean errors of the surface reflectance are all under 0.06, which further demonstrates that the calibrated reflectance has a high accuracy.

Incorporation of mid-wave infrared (MWIR) channel/s into the prevalent regression-based split-window technique (SWT) for operational daytime sea surface temperature (SST) retrieval is challenging. However, the MWIR channels are highly desirable to obtain unambiguous information from the surface since these channels offer high transparency with respect to the earth’s atmosphere and are very sensitive to the thermal emission from the surface. On the other hand, the MWIR channel/s can be easily incorporated into any physical-based SST retrieval scheme. Daytime SST retrieval using various physical-based methods is studied and it is found that the physical deterministic sea surface temperature (PDSST) retrieval scheme is the best choice. This article discusses various scientific aspects of the daytime PDSST retrieval including MWIR channels from a theoretical point of view and its application on real data from Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua. Daytime SST retrievals from PDSST, including MWIR channels, are also compared with the currently operational SWT-based SSTs from MODIS-Aqua and MODIS-Terra by NASA, without MWIR channels. The root-mean-square differences in PDSST from the in situ buoys using the global matchup data for daytime MODIS-Aqua SSTs is ~0.28 K for complete cloud-free set and is ~0.38 K for MODIS-Aqua and MODIS-Terra when quasi-deterministic cloud and error masking algorithm is applied for cloud detection. The information gain is defined by combining the two metrics, quality improvement and the increase in cloud-free data. The PDSST suite rendered two to three times as much information as the NASA-produced daytime regression-based SST.

Accurate cloud detection using infrared (IR) data is very challenging due to the limitations and uncertainties from many aspects in the satellite IR remote sensing. This article proposes an end-to-end cloud detection method for the Cross-track IR Sounder (CrIS) using machine learning (ML) techniques. The brightness temperatures from paired CrIS channels in the longwave and midwave water vapor bands and the longwave and shortwave CO2 bands are used. After obtaining the linear regression coefficients for each of the selected channel pairs, a complete set of CrIS full spectral resolution (FSR) cloud detection index (FCDI) is derived from the temperature difference between the regression and observation for each channel pair. It is shown that FCDI captures cloud location and structure well by comparing with the cloud products (CPs) from the Visible IR Imaging Radiometer Suite (VIIRS). After collocating FCDI with VIIRS CP, ML techniques such as the extreme learning machine, support vector machine, and multilayer perceptron are used to train the collocated FCDIs for cloud detection. Simulation results show that the accuracy of FCDI cloud detection is slightly above 80%. Moreover, the results encourage the use of water vapor bands in FCDI, in addition to CO2 bands.

Since a new generation of geostationary satellite data has incredibly high temporal, spatial, and spectral resolutions, new methodologies are now needed to take advantage of both the temporal and spectral signatures of them for accurate estimation of Earth’s environmental variables. This article describes a novel optimization method to estimate a suite of 11 physically consistent land surface and atmospheric variables under all-sky conditions from the geostationary advanced Himawari imager (AHI) top-of-atmosphere (TOA) observations. This method is based on a coupled soil, snow, vegetation, and atmospheric radiative transfer (RT) model from 0.28 to $14~\mu \text{m}$ . The inversion algorithm consists of three major steps. First, the “clearest” observations at each moment during a temporal window were determined and then the essential variables that characterize surface RT models, such as leaf area index (LAI), leaf chlorophyll concentration, and soil parameters were estimated. Second, the atmospheric variables, including aerosol optical depth (AOD) under clear-sky conditions, and cloud optical thickness (COT) and cloud effective particle radius (CER) under cloudy-sky conditions, were inverted given surface reflectance calculated by the surface RT models. Finally, the inverted atmospheric and land surface variables were fed into the coupled RT model to calculate the remaining set of variables, including spectral directional reflectance, surface broadband albedo, thermal emissivity, incident shortwave radiation (ISR), photosynthetically active radiation (PAR), fraction of absorbed PAR by green vegetation (FAPAR), and TOA shortwave albedo. The retrieved variables were validated using in-situ measurements from Ozflux network sites and compared with the other existing satellite products. Intercomparisons demonstrate that the AHI-retrieved atmospheric variables (AOD, CER, and COT) and surface variables (surface reflectance, LAI, FAPAR, PAR, and surface emissivity) are well correlated with the corresponding JAXA released AHI, NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and Clouds and the Earth’s Radiant Energy System (CERES), and the Global LAnd Surface Satellite (GLASS) products. Direct validation using in-situ measurements indicates that the retrieved ISR achieves higher accuracy than the CERES ISR product (with $R^{2}$ values of 0.95 and 0.89, and root-mean-square error (RMSE) of 20.3 and 29.6 W/ $\text{m}^{2}$ for the AHI-retrieved and CERES daily ISR, respectively). Validation also shows that the estimated daily surface albedo has an accuracy comparable to the MODIS daily albedo product (RMSE = 0.03). Both the direct validation and product comparisons have demonstrated that this proposed inversion framework works very well for the AHI data. Unlike other algorithms that are usually used for estimating an individual parameter and rely heavily on a separate atmospheric correction, this inversion framework can effectively estimate a group of atmospheric and land surface variables and be easily applied to other similar multispectral geostationary satellite data. A comprehensive sensitivity and validation study is still needed to quantify the uncertainties of the retrieval variables.

The autumn precipitation over the Sichuan Basin (APSB) experienced a significant wet-to-dry shift around the mid-1980s during the 1961–2010 period. Thus, the dataset is divided into two subperiods, 1961–1985 (P1) and 1986–2010 (P2), which are denoted as the wet period and dry period, respectively. The possible mechanisms accounting for the wet-to-dry shift in the APSB are investigated using both observational data and a baroclinic atmospheric model. Analysis shows that the sea surface temperature (SST) in the tropical Indian-western Pacific (TIWP) regions displays continuous warming during the period under examination, which may intensify the ascent motion over the warm pool and favor the descent motion over the Sichuan Basin. Meanwhile, the Gill-type atmospheric response to anomalous TIWP warm SST is accompanied by anomalous northerly winds prevailing over central and Southwest China. Both the intensified descent motion and anomalous northerly winds over central and Southwest China favor the switch to dry conditions in the APSB. Further analysis shows that the decadal changes in the North Atlantic SST lead to an atmospheric wave pattern prevailing over the extratropical Eurasian continent. An anomalous cyclone over central-eastern Eurasia, which is one component of the wave pattern, is accompanied by significant northerly winds penetrating southward to central China. These northerly winds can cause more-than-normal snow cover around Lake Baikal, which may in turn modulate the atmospheric circulation through positive feedback and therefore result in intensified northerly winds, favorable for the switch to the anomalous dry state of the APSB.

As a unique natural satellite of the Earth, the Moon has always attracted much attention, and one subject of interest is the establishment of an Earth observation platform on the Moon. Compared with the existing spaceborne and airborne observation platforms, a Moon-based Earth observation platform would have the unique advantage of the ability to obtain global and large-scale observation data. At present, the study on Moon-based Earth observations is in theory, so it is necessary to use simulation methods to understand the observation performance of a Moon-based Earth observation platform. In this study, a method for Moon-based Earth observation imaging simulation in the thermal infrared band is developed to study the Moon-based thermal infrared imaging process. The method comprises three parts, including the estimation of Moon-based imaging coverage, the acquisition of the radiation intensity at the entrance pupil, and the simulated image output from the Moon-based thermal infrared sensor. Then, the simulated results are validated with the existing spaceborne observation data. Results show that the absolute error of Moon-based thermal infrared simulations is between 1.3–5.7 K, the RMSE is between 1.38 – 3.12 K, and the relative error is between 0.44% – 2.12%, indicating that the thermal infrared imaging model can more realistically simulate the true conditions of ground surface and that a Moon-based Earth observation platform could observe the Earth in the thermal infrared band.

The monsoon-driven river basins are more vulnerable to the flash-floods triggered by intense rainfall activities. Due to the lack of adequate high resolution forecast of real-time hydro-meteorological variables, a reliable forecast of flash floods remains a challenge. One plausible way to generate such a high-resolution forecast of hydro-meteorological variables is to use a coupled atmospheric–hydrologic modelling system. Thus, in this study, a physically-based, fully distributed, multi-scale hydrologic modelling framework, WRF-Hydro with optimized configurations (stand-alone and coupled-mode) is used to simulate the important hydro-meteorological variables like precipitation, runoff, soil moisture, and land surface heat fluxes over Cauvery river basin, India. In stand-alone mode, the model is driven by the high-resolution gridded data from the Global Land Data Assimilation System; while the coupled model is run with the WRF atmospheric model. In this study, the ability of a fully coupled WRF–WRF-Hydro modelling framework , with 3 km grid spacing is used to simulate the hydro-meteorological conditions during an extreme rainfall event (08–09 August 2019). The innermost domain of WRF-Hydro in conjunction with a high resolution hydrological routing grid (300 m) is also utilized, to include the subgrid scale disaggregation–aggregation weighting procedures to generate the land–atmospheric feedbacks on the hydrometeorological variables. The resulting variables have been validated through relevant observations; the overall performance of the coupled WRF-Hydro is shown to be relatively good when compared to the WRF-only simulations.

Detection of moving objects in outdoor environments is an extremely researched topic. However, studies on moving object detection in complex atmospheric/weather conditions are limited, primarily because of the absence of any relevant benchmark dataset. To address this disparity, we introduce a novel benchmark video dataset entitled “Extended Tripura University Video Dataset (E-TUVD)” which is a diverse dataset of complex atmospheric/weather conditions. Currently, E-TUVD is the largest video dataset for moving object detection under degraded atmospheric/weather conditions. The dataset comprises 147 video clips spanning 1-5 minutes in duration of each video clips. Because of the requirement of evaluating any object detection model, this study emphasizes on generation of ground-truth images of salient moving objects on E-TUVD. Using this dataset, we assessed the performance of several state-of-the-art algorithms, considering both the ability to detect moving objects and visibility enhancement under such complex conditions. The method with the best performance was used to investigate the effectiveness of visibility enhancement of atmospheric/weather degraded image sequences for accurate moving object detection. Results and analysis reveal that effective enhancement can significantly improve the ability of detection algorithms under degraded atmospheric/weather conditions to resemble the true properties of moving objects in terms of pixel oriented binary masks.

Distributions of angles of departure (AODs) and angles of arrival (AOAs) are essential for mobile communications since they are the basis of channel modeling, channel estimation, antenna beamforming, precoding, and positioning. However, distributions of AODs and AOAs for air-to-air (A2A) communications have not been presented precisely, not to mention the time-variant probability density functions (PDFs). This letter presents analytical derivations of the time-variant PDFs of AODs and AOAs of ground scattering paths over open area based on uniformly distributed scatterers in maximum delay determined scattering area for low altitude A2A communication, both hundreds-meter scenario and under 60m scenario are simulated to observe the time-variant features of the PDFs due to movements of the aircraft.

To assess the impact of four-dimensional variational (4D-Var) data assimilation on the performance of a land–atmosphere coupled model, the satellite precipitation of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) was assimilated into the Weather Research Forecast (WRF) Model, and the WRF was coupled to the hydrological model TOPX. Precipitation and evaporation were both investigated as connecting elements in the coupled model WRF–TOPX. Differing in whether the 4D-Var data assimilation and evaporation were applied, one control experiment and four experiments were performed to simulate a historical flood event that happened in the Wangjiaba watershed in eastern China. The key hydrological variables of precipitation, potential evaporation, soil moisture, and discharge in the studied flood process were evaluated. The results showed that 1) the 4D-Var data assimilation with the IMERG could reduce both the overestimations of the WRF-predicted precipitation and potential evaporation; 2) the applied 4D-Var data assimilation could improve considerably the accuracy of the soil moisture and discharges from the coupled model WRF–TOPX; and 3) evaporation was also an important factor to influence the net precipitation to affect the performance of the coupled land–atmosphere model. With the two connecting elements of precipitation and evaporation, the 4D-Var assimilation based on IMERG could improve the Nash–Sutcliffe coefficient of the coupled model WRF–TOPX from 0.483 to 0.521 at the hourly scale. These investigations can provide important implications for the land–atmosphere coupling with both the precipitation and evaporation and using the 4D-Var data assimilation with IMERG for flood simulation at a large scale.

Sea salt aerosol (SSA) significantly affects the atmospheric albedo, the radiation balance of the Earth, and has consequences for global and regional climate. There is currently no reliable radiative large-scale aerosol model of the marine and coastal atmosphere. All these lead to uncertainties in radiation boxes, short-term weather forecasts, and climate models and to uncertainties in interpretation of the lidar return data obtained and at the development of new lidar systems. The MaexPro model is shown to calculate adequately the aerosol extinction coefficients $\varepsilon (\lambda)$ and the concentration of SSA particles for different wind regimes, fetches, and humidity. The calculated $\varepsilon (\lambda)$ profiles well agree with the experimental data and with calculations using Advanced Navy Aerosol Model (ANAM) and MODTRAN codes. A new approach in studying the influence of the meteorological parameters on spectral $\varepsilon (\lambda)$ gives a new explanation for previously obtained conflicting experimental data, namely, to the effect of decreasing transparency, manifested in the coastal zone, and makes it possible indirectly to account for the type of air mass (marine or coastal). It is shown that the particle size distribution functions and spectral $\varepsilon (\lambda)$ , calculated using the model with the input parameters corresponding most closely to the conditions of experiment, agree well with observations, using OLAF transmissometer, and calculations using MODTRAN code and the experimental results receiving the same conditions of observations (numerical or field). The results obtained are of interest both in the calculations of radiative characteristics of the atmosphere and in interpretation of the lidar return data and calibration signal when developing new lidar systems.

A stochastic deep convection parameterization is implemented into the US Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1.0 (EAMv1). This study evaluates its performance in simulating precipitation. Compared to the default model, the probability distribution function (PDF) of rainfall intensity in the new simulation is greatly improved. The well-known problem of “too much light rain and too little heavy rain” is alleviated, especially over the tropics. As a result, the contribution from different rain rates to the total precipitation amount is shifted toward heavier rain. The less frequent occurrence of convection contributes to suppressed light rain, while more intense large-scale and convective precipitation contributes to enhanced heavy total rain. The synoptic and intraseasonal variabilities of precipitation are enhanced as well to be closer to observations. The sensitivity of the rainfall intensity PDF to the model vertical resolution is examined. The relationship between precipitation and dilute convective available potential energy in the stochastic simulation agrees better with that in the Atmospheric Radiation Measurement (ARM) observations compared with the standard model simulation. The annual mean precipitation is largely unchanged with the use of the stochastic scheme except over the tropical western Pacific, where a moderate increase in precipitation represents a slight improvement. The responses of precipitation and its extremes to climate warming are similar with or without the stochastic deep convection scheme.

Investigating the relations between land surface temperature (LST) and biophysical compositions can help the understanding of the surface biophysical process. However, there are still uncertainties in determining the impacts of biophysical compositions on LST due to the atmospheric effects. In this article, four atmospheric correction algorithms were used to correct 12 Landsat 8 images in Xi’an, Beijing, Wuhan, and Guangzhou, China, including the Atmospheric Correction for Flat Terrain (ATCOR2), Quick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and Second Simulation of Satellite Signal in the Solar Spectrum (6S). Then, geodetector was used to investigate the atmospheric correction differences in the spatial heterogeneity relationships between LST and normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and bare soil index (BSI). Results indicate that the selected composition factors were greatly improved after atmospheric correction, and the relations between LST and three factors were characterized by obvious atmospheric correction differences in four study areas. On the whole, the 6S algorithm performed the best in improving the factor values and impacting the spatial heterogeneity relations between LST and biophysical compositions, followed by FLAASH, QUAC, and ATCOR2 algorithms. Except for Wuhan, 6S, FLAASH, and QUAC algorithms significantly enhanced the correlation between LST and NDVI. However, all algorithms weakened the correlations between LST, NDVI, and BSI, except Guangzhou. These findings have been verified using the regression analysis. In addition, with geodetector, combinations of any two composition factors all had strongly enhanced impacts on LST, and a combination between NDVI and NDBI performed the strongest in most cases.

Snow-depth retrieval from passive microwave observations without a priori information is a highly undetermined problem. Achieving accurate snow-depth retrievals requires a priori information on the snowpack properties, such as grain size, density, physical temperature, and stratigraphy. On a practical level, however, retrieval algorithms must consider prior information, while minimizing the dependence on it, as accurate ancillary data are not globally available. In this study, we build on the previously published Bayesian Algorithm for Snow Water Equivalent Estimation (BASE) to retrieve snow depth using an airborne passive microwave data set over the tundra snow in the Eureka region. The method computes the optimal estimates of snow depth, density, grain size, and other variables, given the brightness temperature observations and prior information, using Markov chain Monte Carlo (MCMC). The airborne data set includes passive microwave brightness temperature ( $T_{b}$ ) at 18.7 and 36.5 GHz. The in situ measurements of the snow depth provide validation data for 464 sensor footprints. The microwave radiative transfer (RT) model used is the Dense Media RT-Multilayered (DMRT-ML) model. We use a two-layer wind slab and depth hoar assumption based on the local snow cover knowledge from the previous research on the study area. To improve our understanding of the results using the airborne $T_{b}\text{s}$ , the inversion was also applied using the synthetic observations, where $T_{b}\text{s}$ were generated from the RT model. For the case with synthetic observations, the snow-depth RMSE was 0.07 cm. When the airborne $T_{b}\text{s}$ are used, the snow-depth RMSE was 21.8 cm. This discrepancy is due to the large spatial variability in the MagnaProbe snow-depth measurements and the fact that not all physical processes affecting the airborne $T_{b}\textrm {s}$ are represented in the RT model. Our work verifies the feasibility and applicability of the proposed methodology regionally for the airborne retrievals and reinforces the tractable applicability of a physics-based RT model in the SWE retrievals.

The Sun was dimmer earlier in Earth’s history, but glaciation was rare in the Precambrian: this is the ‘faint young Sun problem’. Most solutions rely on changes to the chemical composition of the atmosphere to compensate via a stronger greenhouse effect, whereas physical feedbacks have received less attention. We perform global climate model experiments, using two versions of the Community Atmosphere Model, in which a reduced solar constant is offset by higher CO2. Model runs corresponding to past climate show a substantial decrease in low clouds and hence planetary albedo compared with present, which contributes 40% of the required forcing to offset the faint Sun. Through time, the climatically important stratocumulus decks have grown in response to a brightening Sun and decreasing greenhouse effect, driven by stronger cloud-top radiative cooling (which drives low cloud formation) and a stronger inversion (which sustains clouds against dry air entrainment from above). We find that systematic changes to low clouds have had a major role in stabilizing climate through Earth’s history, which demonstrates the importance of physical feedbacks on long-term climate stabilization, and a smaller role for geochemical feedbacks. Reduced planetary albedo due to fewer low clouds on early Earth could explain some 40% of the required forcing to offset the faint young Sun, according to global climate model experiments.

Cirrus cloud radiative effects are largely affected by ice microphysical properties, including ice water content (IWC), ice crystal number concentration (Ni) and mean diameter (Di). These characteristics vary significantly due to thermodynamic, dynamical and aerosol conditions. In this work, a global-scale observation dataset is used to examine regional variations of cirrus cloud microphysical properties, as well as several key controlling factors, i.e., temperature, relative humidity with respect to ice (RHi), vertical velocity (w) and aerosol number concentrations (Na). Results are compared with simulations from the National Center for Atmospheric Research (NCAR) Community Atmosphere Model version 6 (CAM6). Observed and simulated ice mass and number concentrations are constrained to ≥62.5 µm to reduce potential uncertainty from shattered ice in data collection. The differences between simulations and observations are found to vary with latitude and temperature. Comparing with averaged observations at ∼100 km horizontal scale, simulations are found to underestimate (overestimate) IWC by a factor of 3–10 in the Northern (Southern) Hemisphere. Simulated Ni is overestimated in most regions except the Northern Hemisphere midlatitudes. Simulated Di is underestimated by a factor of 2, especially for warmer conditions (−50 to −40 ∘C), possibly due to misrepresentation of ice particle growth/sedimentation. For RHi effects, the frequency and magnitude of ice supersaturation are underestimated in simulations for clear-sky conditions. The simulated IWC and Ni show bimodal distributions with maximum values at 100 % and 80 % RHi, differing from the unimodal distributions that peak at 100 % in the observations. For w effects, both observations and simulations show variances of w (σw) decreasing from the tropics to polar regions, but simulations show much higher σw for the in-cloud condition than the clear-sky condition. Compared with observations, simulations show weaker aerosol indirect effects with a smaller increase of IWC and Di at higher Na. These findings provide an observation-based guideline for improving simulated ice microphysical properties and their relationships with key controlling factors at various geographical locations.

This paper discusses the interannual variation of winter cold surges (CSs) over East Asia (EA) from the perspective of the cold air path and its relationship with autumn Barents Sea ice concentration (BSIC). Flexible particle dispersion model for large-scale atmospheric transport process simulation is used to track the cold air path of 301 CSs in EA in winters from 1979 to 2017, and the probability density distribution of cold air path is calculated by kernel density estimation. The cold air mainly originates from the Ural Mountains, and is continuously cooled by the impact of cold land, then accumulates and strengthens near the Lake Baikal, and finally invades EA under the guidance of the northwest airflow. The autumn BSIC has a significant negative correlation with CSs over EA. The decreased autumn BSIC hinders the spread of zonal warm advection over Eurasia and makes the soil temperature near Lake Baikal drop, and the negative anomaly of soil temperature persists from autumn to winter, strengthening cold source therein, which provides suitable thermal conditions for occurring more CSs over EA. Moreover, the decreased autumn BSIC can also persist to winter, which leads to the positive anomaly of the geopotential height over the polar region. Through the propagation of Rossby wave, the negative geopotential height anomaly over EA is excited and provides the appropriate dynamic conditions for forming more CSs in EA. The simulations from the Community Atmosphere Model version 5.3 (CAM5.3) further confirm the proposed possible mechanism.

The Indo-Gangetic Plain (IGP) experienced an intensive air pollution episode during November 2017. Weather Research and Forecasting model coupled to Chemistry (WRF-Chem), a coupled meteorology–chemistry model, was used to simulate this episode. In order to capture PM2.5 peaks, we modified input chemical boundary conditions and biomass burning emissions. The Community Atmosphere Model with Chemistry (CAM-chem) and Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) global models provided gaseous and aerosol chemical boundary conditions, respectively. We also incorporated Visible Infrared Imaging Radiometer Suite (VIIRS) active fire points to fill in missing fire emissions in the Fire INventory from NCAR (FINN) and scaled by a factor of 7 for an 8 d period. Evaluations against various observations indicated the model captured the temporal trend very well although missed the peaks on 7, 8, and 10 November. Modeled aerosol composition in Delhi showed secondary inorganic aerosols (SIAs) and secondary organic aerosols (SOAs) comprised 30 % and 27 % of total PM2.5 concentration, respectively, during November, with a modeled OC/BC ratio of 2.72. Back trajectories showed agricultural fires in Punjab were the major source for extremely polluted days in Delhi. Furthermore, high concentrations above the boundary layers in vertical profiles suggested either the plume rise in the model released the emissions too high or the model did not mix the smoke down fast enough. Results also showed long-range-transported dust did not affect Delhi's air quality during the episode. Spatial plots showed averaged aerosol optical depth (AOD) of 0.58 (±0.4) over November. The model AODs were biased high over central India and low over the eastern IGP, indicating improving emissions in the eastern IGP can significantly improve the air quality predictions. We also found high ozone concentrations over the domain, which indicates ozone should be considered in future air quality management strategies alongside particulate matter.

This article describes the development and ocean forecast evaluation of an atmosphere–ocean coupled prediction system for the Maritime Continent (MC) domain, which includes the eastern Indian and western Pacific oceans. The coupled system comprises regional configurations of the atmospheric model MetUM and ocean model NEMO at a uniform horizontal resolution of 4.5 km × 4.5 km, coupled using the OASIS3-MCT libraries. The coupled model is run as a pre-operational forecast system from 1 to 31 October 2019. Hindcast simulations performed for the period 1 January 2014 to 30 September 2019, using the stand-alone ocean configuration, provided the initial condition to the coupled ocean model. This paper details the evaluations of ocean-only model hindcast and 6 d coupled ocean forecast simulations. Direct comparison of sea surface temperature (SST) and sea surface height (SSH) with analysis, as well as in situ observations, is performed for the ocean-only hindcast evaluation. For the evaluation of coupled ocean model, comparisons of ocean forecast for different forecast lead times with SST analysis and in situ observations of SSH, temperature, and salinity have been performed. Overall, the model forecast deviation of SST, SSH, and subsurface temperature and salinity fields relative to observation is within acceptable error limits of operational forecast models. Typical runtimes of the daily forecast simulations are found to be suitable for the operational forecast applications.

Process-based ecosystem models are increasingly used to simulate the effects of a changing environment on vegetation growth in the past, present, and future. To improve the simulation, the multimodel ensemble mean (MME) and ensemble Bayesian model averaging (EBMA) methods are often used in optimizing the integration of ecosystem model ensemble. These two methods were compared with four other optimization techniques, including genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search (CS), and interior-point method (IPM), to evaluate their efficiency in this article. Here, we focused on eight commonly used ecosystem models to simulate vegetation growth, represented by the growing season leaf area index (LAIgs), collected globally from 2000 to 2014. The performances of the multimodel ensembles and individual models were compared using the satellite-observed LAI products as the reference. Generally, ensemble simulations provide more accurate estimates than individual models. There were significant performance differences among the six tested methods. The IPM ensemble model simulated LAIgs more accurately than the other tested models, as the reduction in the root-mean-square error was 84.99% higher than the MME results and 61.50% higher than the EBMA results. Thus, IPM optimization can reproduce LAIgs trends accurately for 91.62% of the global vegetated area, which is double the area of the results from MME. Furthermore, the contributions and uncertainties of the individual models in the final simulated IPM LAIgs changes indicated that the best individual model (CABLE) showed the greatest area fraction for the maximum IPM weight (32.49%), especially in the low-lalitude to midlatitude areas.

This letter models the high-altitude air-to-ground (A2G) channel for aerial base stations (BSs), which are carried by fixed-wing unmanned aerial vehicles (UAVs). Different from ground BS, fixed-wing UAVs have to periodically hover to provide coverage to ground users (GUs), which may result in the instability of A2G wireless links. On the one hand, pathloss is modeled as a function of distance from UAV to GUs, which includes hover radius and flight altitude, and the elevation angle of GUs. On the other hand, the model of multipath and shadow (MS) fading is dependent on the elevation angle as well. Specifically, we show that generalized Gamma distribution is suitable to model MS fading under large elevation angle, while log-normal fading is suitable otherwise. Moreover, we further investigate the spatial-temporal correlation of the A2G channel, which is shown to depend on the movement distance/direction of GU and the distance from GU to the hover center.

In terms of analytical models used in the initial motor design, the subdomain (SD), complex permeance (CP), and relative permeance (RP) models are among the main ones. This article presents an analysis and comparison of these three models for a wide family of permanent-magnet (PM) machines. The finite-element models are used as a reference for comparison. The fundamental harmonic of the flux-linkage generated by the PMs is selected to evaluate the precision of the analytical model in the initial design step. The number of slots, PM pole-pairs, and ratio of tooth are selected as the key parameters to generate 749 designs for the test. The error distributions and the relationships between the error and the key parameters in the different analytical models are illustrated in this article. The SD and CP models give the best and worst results, respectively. This article also explains how to select the analytical model for the high-torque-density motors.

This paper investigates the second-order statistics of the level crossing rate (LCR) and average fade duration (AFD) for the optical wireless channels between a satellite and a vehicle. Specifically, the novel expressions of LCR and AFD for the low earth orbit (LEO) satellite-to-unmanned aerial vehicles (UAVs) laser links are derived in the presence of the generalized misalignments caused by the satellite vibration together with the UAV hovering, which is modeled by a four-parameter Beckmann distribution, and the atmospheric turbulence conditions. The analysis can also be applied to other kinds of vehicles, which, in fact, require simpler channel models. Several numerical examples are presented to quantitatively demonstrate the impact of both atmospheric turbulence and pointing errors on the LCR and AFD. The Monte Carlo simulations are performed to validate the correctness of the theoretical derivations.

Ground-based measurements of frozen precipitation are heavily influenced by interactions of surface winds with gauge-shield geometry. The Multi-Angle Snowflake Camera (MASC), which photographs hydrometeors in free-fall from three different angles while simultaneously measuring their fall speed, has been used in the field at multiple midlatitude and polar locations both with and without wind shielding. Here, we present an analysis of Arctic field observations – with and without a Belfort double Alter shield – and compare the results to computational fluid dynamics (CFD) simulations of the airflow and corresponding particle trajectories around the unshielded MASC. MASC-measured fall speeds compare well with Ka-band Atmospheric Radiation Measurement (ARM) Zenith Radar (KAZR) mean Doppler velocities only when winds are light (≤5ms-1) and the MASC is shielded. MASC-measured fall speeds that do not match KAZR-measured velocities tend to fall below a threshold value that increases approximately linearly with wind speed but is generally <0.5ms-1. For those events with wind speeds ≤1.5ms-1, hydrometeors fall with an orientation angle mode of 12∘ from the horizontal plane, and large, low-density aggregates are as much as 5 times more likely to be observed. Simulations in the absence of a wind shield show a separation of flow at the upstream side of the instrument, with an upward velocity component just above the aperture, which decreases the mean particle fall speed by 55 % (74 %) for a wind speed of 5 m s−1 (10 m s−1). We conclude that accurate MASC observations of the microphysical, orientation, and fall speed characteristics of snow particles require shielding by a double wind fence and restriction of analysis to events where winds are light (≤5ms-1). Hydrometeors do not generally fall in still air, so adjustments to these properties' distributions within natural turbulence remain to be determined.

The Nonhydrostatic ICosahedral Atmospheric Model (NICAM), a global model with an icosahedral grid system, has been under development for nearly two decades. This paper describes NICAM16-S, the latest stable version of NICAM (NICAM.16), modified for the Coupled Model Intercomparison Project Phase 6, High Resolution Model Intercomparison Project (HighResMIP). Major updates of NICAM.12, a previous version used for climate simulations, included updates of the cloud microphysics scheme and land surface model, introduction of natural and anthropogenic aerosols and a subgrid-scale orographic gravity wave drag scheme, and improvement of the coupling between the cloud microphysics and the radiation schemes. External forcings were updated to follow the protocol of the HighResMIP. A series of short-term sensitivity experiments were performed to determine and understand the impacts of these various model updates on the simulated mean states. The NICAM16-S simulations demonstrated improvements in the ice water content, high cloud amount, surface air temperature over the Arctic region, location and strength of zonal mean subtropical jet, and shortwave radiation over Africa and South Asia. Some long-standing biases, such as the double intertropical convergence zone and smaller low cloud amount, still exist or are even worse in some cases, suggesting further necessity for understanding their mechanisms, upgrading schemes and parameter settings, and enhancing horizontal and vertical resolutions.

Accurate models and fast calculations are required for the design of permanent magnet machines. This article presents a 2-D hybrid model for the surface-mounted permanent-magnet synchronous machine (PMSM) combining the Fourier model and the finite element method (FEM). Both magnetic saturation and parallel teeth are taken into account. The model is validated by FEM. The results show that in structures that are saturated, the hybrid model performs much better than the linear Fourier model and simultaneously reduces a large amount of computation compared with FEM.

To partially meet the communication requirements in Mars, low-frequency ground-to-ground communication is likely to be used in the future and hence, it is desirable to increase electron density in the Martian ionosphere. As a convenient means to this, we have proposed and explored the production of high electron density regions in the Martian atmosphere in daytime by suspension of low work function dust, specifically Cs-coated bronze in the atmosphere. Equations of the kinetics of the dusty air in sunlight have been introduced and a numerical estimate of the daytime electron density at 60–100 km heights has been obtained as a function of the dust parameters. Photoelectric emission from dust and accretion of electrons and ions on dust particles play the key role in getting the desired electron density at high altitudes (60–100 km). A discussion of the numerical results has been given with conclusions.

In this study, two collection 6 (C6) Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 land surface temperature (LST) products (MYD11_L2 and MYD21_L2) from the Aqua satellite were evaluated using temperature-based ( T- based) and radiance-based ( R -based) validation methods over barren surfaces in Northwestern China. The ground measurements collected at four barren surface sites from June 2012 to September 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment were used to perform the T- based evaluation. Ten sand dune sites were selected in six large deserts in Northwestern China to carry out an R -based validation from 2012 to 2018. The T- based validation results indicate that the C6 MYD21 LST product has a better accuracy than the C6 MYD11 product during both daytime and nighttime. The LST is underestimated by the C6 MYD11 products at the four T- based sites during the daytime, with a mean bias of −2.82 K and a mean RMSE of 3.82 K, whereas the MYD21 LST product has a mean bias and RMSE of −0.51 and 2.53 K, respectively. The LST is also underestimated at night by the C6 MYD11 products at the four T- based sites, with a mean bias of −1.40 K and a mean RMSE of 1.72 K, whereas the MYD21 LST product has a mean bias and RMSE of 0.23 and 1.01 K, respectively. For the R -based validation, the MYD11 results are associated with large negative biases during both daytime and nighttime at three sand dune sites and biases within 1 K at the other seven sites, whereas the MYD21 results are more consistent at all ten sand dune sites, with a mean bias of 0.45 and 0.70 K for daytime and nighttime, respectively. The emissivities for these two products in MODIS bands 31 and 32 were compared with each other and then compared with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity and laboratory emissivity. The results indicate that the emissivities in MODIS bands 31 and 32 of MYD11 at the four T- based and three of the R -based validation sites are overestimated and result in LST underestimation, whereas the emissivities of MYD21 are more consistent with the laboratory emissivity. Besides, an experiment was carried out to demonstrate that the physically retrieved dynamic emissivity of the MYD21 product can be utilized to improve the accuracy of the split-window (SW) algorithm for barren surfaces, making it a valuable data source for retrieving LST from different remote sensing data.

The radiance received by satellite sensors viewing the ocean is a mixed signal of the atmosphere and ocean. Accurate decomposition of the radiance components is crucial because any inclusion of atmospheric signal in the water-leaving radiance leads to an incorrect estimation of the oceanic parameters. This is especially true over the turbid coastal waters, where the estimation of the radiance components is difficult. A layer removal scheme for atmospheric correction (LRSAC) has been developed to take the atmospheric and oceanic components as the layer structure according to the sunlight passing in the Sun–Earth-satellite system. Compared with the normal coupled atmospheric column, the uncertainty of the layer structure of Rayleigh and aerosols has a relatively small error with a mean relative error (MRE) of 0.063%. As the aerosol layer was put between Rayleigh and ocean, a new Rayleigh lookup table (LUT) was regenerated using 6SV (Second Simulation of a Satellite Signal in the Solar Spectrum, Vector version 3.2) based on the zero reflectance at the ground to produce the pure Rayleigh reflectance without the Rayleigh–ocean interaction. The accuracy of the LRSAC was validated by in situ water-leaving reflectance, obtaining an MRE of 6.3%, a root-mean-square error (RMSE) of 0.0028, and the mean correlation coefficient of 0.86 based on 430 matchup pairs over the East China Sea. Results show that the LRSAC can be used to decompose the reflectance at the top of each layer for the atmospheric correction over turbid coastal waters.