Search

Carbon monoxide (CO) is primarily the result of incomplete combustion, which has important impacts on the atmospheric chemical cycle and climate. Improved quantitative characterization of long-term CO trends is important for both atmospheric modeling and the design and implementation of policies to efficiently control multiple pollutants. Due to the limitations of high time-resolution and high-quality long-term observational data, studies on long-term trends in the CO concentration in China are quite limited. In this study, the observational data of the concentration of CO in a rural site of Beijing during 2006–2018 was used to analyze the long-term trend in CO concentration in Beijing. The Theil-Sen method and the generalized additive model (GAM)-based method, were used to conduct the trend estimation analysis. We found that the concentration of CO at the Shangdianzi site showed a significant downward trend during 2006–2018, with a decline rate of 22.8 ± 5.1 ppbV per year. The declining trend in CO also showed phasic characteristics, with a fast decreasing rate during the period of 2006–2008, stable variations during the period of 2009–2013 and a continuous downward trend after 2013. The declining trend in the CO concentration in the south to west (S-W) sectors where the polluted air masses come from is more rapid than that in the sectors where the clean air masses come from. The declining trend in the CO concentration implies the improved combustion efficiency and the successful air pollution control policies in Beijing and the surrounding area.

In support of cropland monitoring, operational Copernicus Sentinel-2 (S2) data became available globally and can be explored for the retrieval of important crop traits. Based on a hybrid workflow, retrieval models for six essential biochemical and biophysical crop traits were developed for both S2 bottom-of-atmosphere (BOA) L2A and S2 top-of-atmosphere (TOA) L1C data. A variational heteroscedastic Gaussian process regression (VHGPR) algorithm was trained with simulations generated by the combined leaf-canopy reflectance model PROSAILat the BOA scale and further combined with the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) atmosphere model at the TOA scale. Established VHGPR models were then applied to S2 L1C and L2A reflectance data for mapping: leaf chlorophyll content (Cab), leaf water content (Cw), fractional vegetation coverage (FVC), leaf area index (LAI), and upscaled leaf biochemical compounds, i.e., LAI ∗ Cab (laiCab) and LAI ∗ Cw (laiCw). Estimated variables were validated using in situ reference data collected during the Munich-North-Isar field campaigns within growing seasons of maize and winter wheat in the years 2017 and 2018. For leaf biochemicals, retrieval from BOA reflectance slightly outperformed results from TOA reflectance, e.g., obtaining a root mean squared error (RMSE) of 6.5 μμg/cm2 (BOA) vs. 8 μμg/cm2 (TOA) in the case of Cab. For the majority of canopy-level variables, instead, estimation accuracy was higher when using TOA reflectance data, e.g., with an RMSE of 139 g/m2 (BOA) vs. 113 g/m2 (TOA) for laiCw. Derived maps were further compared against reference products obtained from the ESA Sentinel Application Platform (SNAP) Biophysical Processor. Altogether, the consistency between L1C and L2A retrievals confirmed that crop traits can potentially be estimated directly from TOA reflectance data. Successful mapping of canopy-level crop traits including information about prediction confidence suggests that the models can be transferred over spatial and temporal scales and, therefore, can contribute to decision-making processes for cropland management.

Ethane ( C2H6 ) is an important greenhouse gas and plays a significant role in tropospheric chemistry and climate change. This study first presents and then quantifies the variability, sources, and transport of C2H6 over densely populated and highly industrialized eastern China using ground-based high-resolution Fourier transform infrared (FTIR) remote sensing along with atmospheric modeling techniques. We obtained a retrieval error of 6.21 ± 1.2 (1 σ )% and degrees of freedom (DOFS) of 1.47 ± 0.2 (1 σ ) in the retrieval of C2H6 tropospheric column-averaged dry-air mole fraction (troDMF) over Hefei, eastern China (32 ∘ N, 117 ∘ E; 30 m a . s . l . ). The observed C2H6 troDMF reached a minimum monthly mean value of 0.36 ± 0.26 ppbv in July and a maximum monthly mean value of 1.76 ± 0.35 ppbv in December, and showed a negative change rate of − 2.60 ± 1.34 % yr−1 from 2015 to 2020. The dependencies of C2H6 troDMF on meteorological and emission factors were analyzed using generalized additive models (GAMs). Generally, both meteorological and emission factors have positive influences on C2H6 troDMF in the cold season (December–January–February/March–April–May, DJF/MAM) and negative influences on C2H6 troDMF in the warm season (June–July–August/September–October–November, JJA/SON). GEOS-Chem chemical model simulation captured the observed C2H6 troDMF variability and was, thus, used for source attribution. GEOS-Chem model sensitivity simulations concluded that the anthropogenic emissions (fossil fuel plus biofuel emissions) and the natural emissions (biomass burning plus biogenic emissions) accounted for 48.1 % and 39.7 % of C2H6 troDMF variability over Hefei, respectively. The observed C2H6 troDMF variability mainly results from the emissions within China (74.1 %), where central, eastern, and northern China dominated the contribution (57.6 %). Seasonal variability in C2H6 transport inflow and outflow over the observation site is largely related to the midlatitude westerlies and the Asian monsoon system. Reduction in C2H6 abundance from 2015 to 2020 mainly results from the decrease in local and transported C2H6 emissions, which points to air quality improvement in China in recent years.

Satellite drag-coefficient is a highly dynamic parameter that captures the physics of the interactions between the atmosphere and the satellite surface. Physical models, known as Gas-Surface Interaction Models (GSIMs), can be used to compute the time variations in the drag-coefficient in orbit. But many input parameters to such models are not known accurately, that consequently introduce uncertainties in the drag-coefficient. This work outlines a framework to invert GSIM parameters from estimates of the drag-coefficient that are obtained during orbit determination, under the assumption of a known atmospheric density. The Fourier drag-coefficient models, developed previously by the authors, are used to model the drag-coefficient in the filtering methodology. These models allow estimation of higher-order frequencies in the drag-coefficient that are functions of the orbital and satellite attitudinal variations. In this work, it is demonstrated that information about the GSIM parameters can be obtained from estimated Fourier drag-coefficients. To that end, an extensive observability analysis of the Fourier drag-coefficient models is carried to determine the coefficients that can be estimated from tracking data.

This work analyzes and discusses the influence of human activities on the meteorological conditions related to winter haze events in Beijing, Tianjin, and Hebei (i.e., the Jing–Jin–Ji region) during 1961–2016, using the results of two numerical simulation experiments based on the Community Atmosphere Model version 5.1.1 (http://www.cesm.ucar.edu/models/cesm1.0/cam/docs/ug5_1_1/book1.html) used in the international Climate Variability and Predictability Programme (CLIVAR) Climate of the 20th Century. Detection and Attribution Project (C20C+ D&A). The results show that, under the influence of human activities, the changes in dynamical and thermal meteorological conditions related to winter haze events in the Jing–Jin–Ji region are conducive to the formation and accumulation of haze, and prevent the diffusion of pollutants. The dynamical conditions mainly include the obvious weakening of the East Asian winter monsoon (EAWM) and the enhancement of the near-surface anomalous southerly wind. The thermal conditions include the obvious increase in surface temperature, and the enhancement of water vapor transport and near-surface inversion. The relative contribution of dynamical and thermal conditions to the variation of haze days in the Jing–Jin–Ji region is analyzed using statistical methods. The results show that the contribution of human activities to the increase of haze days in the Jing–Jin–Ji region is greater than that of natural forcing for the study period. To be specific, the dynamical meteorological factors contribute more to the haze days than the thermal meteorological factors. The contribution of thermal meteorological factors is basically the same in both scenarios.

Recent increases in boreal forest burned area, which have been linked with climate warming, highlight the need to better understand the composition of wildfire emissions and their atmospheric impacts. Here we quantified emission factors for CO and CH 4 from a massive regional fire complex in interior Alaska during the summer of 2015 using continuous high-resolution trace gas observations from the Carbon in Arctic Reservoirs Vulnerability Experiment (CRV) tower in Fox, Alaska. Averaged over the 2015 fire season, the mean CO / CO 2 emission ratio was 0.142 ± 0.051, and the mean CO emission factor was 127 ± 40 g kg −1 dry biomass burned. The CO / CO 2 emission ratio was about 39 % higher than the mean of previous estimates derived from aircraft sampling of wildfires from boreal North America. The mean CH 4 / CO 2 emission ratio was 0.010 ± 0.004, and the CH 4 emission factor was 5.3 ± 1.8 g kg −1 dry biomass burned, which are consistent with the mean of previous reports. CO and CH 4 emission ratios varied in synchrony, with higher CH 4 emission factors observed during periods with lower modified combustion efficiency (MCE). By coupling a fire emissions inventory with an atmospheric model, we identified at least 34 individual fires that contributed to trace gas variations measured at the CRV tower, representing a sample size that is nearly the same as the total number of boreal fires measured in all previous field campaigns. The model also indicated that typical mean transit times between trace gas emission within a fire perimeter and tower measurement were 1–3 d, indicating that the time series sampled combustion across day and night burning phases. The high CO emission ratio estimates reported here provide evidence for a prominent role of smoldering combustion and illustrate the importance of continuously sampling fires across time-varying environmental conditions that are representative of a fire season.

Coastal regions are generally densely populated and have become highly vulnerable to the occurrence of extreme events. In recent years, Brazil’s southern coastal region has been affected by several different extreme weather events that have caused coastal flooding, with economic losses as well as fatalities. Understanding and improving the predictability of these events has become a major issue for the local population. In this study, state-of-the-art numerical modeling was applied to this region to assess the ability of the Ocean-Land Atmosphere Model to represent major extreme events. The model was applied to the region with a high-resolution grid refinement technique capable of simultaneously representing global and local atmospheric phenomena. The main events that affected Brazil’s southern coastal region between 2000 and 2018 were identified and then simulated. All selected events were associated with cyclonic and/or anticyclonic systems near the coastal region of the study area. Those systems were responsible for bringing heavy rain, strong winds and sea level rise, causing impacts for the coastal region. The results of the numerical simulations were compared with observational data to evaluate model performance. The model simulated well the air temperature and wind fields. Correlation values for sea level pressure were high despite a maximum positive bias of approximately 2 hPa. Precipitation presented a negative bias for most events. Finally, the results show that the methodology allowed for a detailed representation of sensible and latent heat fluxes for the region, allowing a better representation of local mesoscale features.

With rapid industrial development, air pollution problems, especially in urban and metropolitan centers, have become a serious societal problem and require our immediate attention and comprehensive solutions to protect human and animal health and the environment. Because bad air quality brings prominent effects on our daily life, how to forecast future air quality accurately and tenuously has emerged as a priority for guaranteeing the quality of human life in many urban areas worldwide. Existing models usually neglect the influence of wind and do not consider both distance and similarity to select the most related stations, which can provide significant information in prediction. Therefore, we propose a Geographic Self-Organizing Map (GeoSOM) spatiotemporal gated recurrent unit (GRU) model, which clusters all the monitor stations into several clusters by geographical coordinates and time-series features. For each cluster, we build a GRU model and weighted different models with the Gaussian vector weights to predict the target sequence. The experimental results on real air quality data in Beijing validate the superiority of the proposed method over a number of state-of-the-art ones in metrics, such as ${R} ^{2}$ , mean relative error (MRE), and mean absolute error (MAE). The MAE, MRE, and ${R} ^{2}$ are 16.1, 0.79, and 0.35 at the Gucheng station and 19.53, 0.82, and 0.36 at the Dongsi station.

In cold region, conceptual models assigned precipitation phase, liquid (rain) or solid (snow), cause vastly different atmospheric, hydrological, and ecological responses, along with significant differences in evaporation, runoff, and infiltration fates for measured precipitation mass. A set air temperature threshold (ATT) applied to the over 30% annual precipitation events occurring with surface air temperatures between −3 and 5 °C resulted in 11.0 and 9.8% misclassified precipitation in Norway and Sweden, respectively. Surface air temperatures do not account for atmospheric properties causing precipitation phase changes as snow falls toward the ground. However, cloud base height and relative humidity (RH) measured from the surface can adjust ATT for expected hydrometeor-atmosphere interactions. Applying calibrated cloud base height ATTs or a linear RH function for Norway (Sweden) reduced misclassified precipitation by 4.3% (2.8%) and 14.6% (8.9%) misclassified precipitation, respectively. Cloud base height ATTs had lower miss-rates with low cloud bases, 100 m in Norway and 300 m in Sweden. Combining the RH method with cloud base ATT in low cloud conditions resulted in 16.1 and 10.8% reduction in misclassified precipitation in Norway and Sweden, respectively. Therefore, the conceptual model output should improve through the addition of available surface data without coupling to an atmospheric model. HIGHLIGHTS This paper lays out two new methods to decrease misclassified precipitation in conceptual surface-based models using air temperature thresholds (ATTs) to distinguish between rain and snow, common in hydrological modeling.; The cloud base height method has not been written about in scientific publications and gives another simple solution to help reduce misclassified precipitation phase in surface-based models.; The linear relative humidity ATT formula suggested in this paper simplifies earlier attempts to include relative humidity in surface-based precipitation phase determination.; The information needed for both methods is widely available in surface meteorological reports and improves surface-based models without coupling to an atmospheric model for precipitation phase.; In this paper, misclassified precipitation between −3 and 5 °C was reduced by 4.3% (2.8%) using cloud base height ATTs, 14.6% (8.9%) using a linear relative humidity formula, and 16.1% (10.8%) using a combination of both methods in Norway (Sweden), showing great potential for reducing model uncertainties when applying this work.

The mesosphere and lower thermosphere (MLT) is a critical region that must be accurately reproduced in general circulation models (GCMs) that aim to include the coupling between the lower and middle atmosphere and the thermosphere. An accurate representation of the MLT is thus important for improved climate modelling and the development of a whole atmosphere model. This is because the atmospheric waves at these heights are particularly large, and so the energy and momentum they carry is an important driver of climatological phenomena through the whole atmosphere, affecting terrestrial and space weather. The Extended Unified Model (ExUM) is the recently developed version of the Met Office's Unified Model which has been extended to model the MLT. The capability of the ExUM to model atmospheric winds and tides in the MLT is currently unknown. Here, we present the first study of winds and tides from the ExUM. We make a comparison against meteor radar observations of winds and tides from 2006 between 80 and 100 km over two radar stations – Rothera (68∘ S, 68∘ W) and Ascension Island (8∘ S, 14∘ W). These locations are chosen to study tides in two very different tidal regimes – the equatorial regime, where the diurnal (24 h) tide dominates, and the polar regime, where the semi-diurnal (12 h) tide dominates. The results of this study illustrate that the ExUM is capable of reproducing atmospheric winds and tides that capture many of the key characteristics seen in meteor radar observations, such as zonal and meridional wind maxima and minima, the increase in tidal amplitude with increasing height, and the decrease in tidal phase with increasing height. In particular, in the equatorial regime some essential characteristics of the background winds, tidal amplitudes and tidal phases are well captured but with significant differences in detail. In the polar regime, the difference is more pronounced. The ExUM zonal background winds in austral winter are primarily westward rather than eastward, and in austral summer they are larger than observed above 90 km. The ExUM tidal amplitudes here are in general consistent with observed values, but they are also larger than observed values above 90 km in austral summer. The tidal phases are generally well replicated in this regime. We propose that the bias in background winds in the polar regime is a consequence of the lack of in situ gravity wave generation to generate eastward fluxes in the MLT. The results of this study indicate that the ExUM has a good natural capability for modelling atmospheric winds and tides in the MLT but that there is room for improvement in the model physics in this region. This highlights the need for modifications to the physical parameterization schemes used in the model in this region – such as the non-orographic spectral gravity wave scheme – to improve aspects such as polar circulation. To this end, we make specific recommendations of changes that can be implemented to improve the accuracy of the ExUM in the MLT.

The retrieval algorithm for CO2 column mixing ratio from measurements of a pulsed multi-wavelength integrated path differential absorption (IPDA) lidar is described. The lidar samples the shape of the 1572.33 nm CO2 absorption line at multiple wavelengths. The algorithm uses a least-squares fit between the CO2 line shape computed from a layered atmosphere model and that sampled by the lidar. In addition to the column-average CO2 dry-air mole fraction (XCO2), several other parameters are also solved simultaneously from the fit. These include the Doppler shift at the received laser signal wavelength, the product of the surface reflectivity and atmospheric transmission, and a linear trend in the lidar receiver's spectral response. The algorithm can also be used to solve for the average water vapor mixing ratio, which produces a secondary absorption in the wings of the CO2 absorption line under humid conditions. The least-squares fit is linearized about the expected XCO2 value, which allows the use of a standard linear least-squares fitting method and software tools. The standard deviation of the retrieved XCO2 is obtained from the covariance matrix of the fit. The averaging kernel is also provided similarly to that used for passive trace-gas column measurements. Examples are presented of using the algorithm to retrieve XCO2 from measurements of the NASA Goddard airborne CO2 Sounder lidar that were made at constant altitude and during spiral-down profile maneuvers.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

The Energy Exascale Earth System Model (E3SM) developed by the Department of Energy has a goal of addressing challenges in understanding the global water cycle. Success depends on correct simulation of cloud and precipitation elements. However, lack of appropriate evaluation metrics has hindered the accurate representation of these elements in general circulation models. We derive metrics from the three-dimensional data of the ground-based Next-Generation Radar (NEXRAD) network over the US to evaluate both horizontal and vertical structures of precipitation elements. We coarsened the resolution of the radar observations to be consistent with the model resolution and improved the coupling of the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) and E3SM Atmospheric Model Version 1 (EAMv1) to obtain the best possible model output for comparison with the observations. Three warm seasons (2014–2016) of EAMv1 simulations of 3-D radar reflectivity features at an hourly scale are evaluated. A general agreement in domain-mean radar reflectivity intensity is found between EAMv1 and NEXRAD below 4 km altitude; however, the model underestimates reflectivity over the central US, which suggests that the model does not capture the mesoscale convective systems that produce much of the precipitation in that region. The shape of the model-estimated histogram of subgrid-scale reflectivity is improved by correcting the microphysical assumptions in COSP. Different from previous studies that evaluated modeled cloud top height, we find the model severely underestimates radar reflectivity at upper levels – the simulated echo top height is about 5 km lower than in observations – and this result is not changed by tuning any single physics parameter. For more accurate model evaluation, a higher-order consistency between the COSP and the host model is warranted in future studies.

Regional seasonal forecasting requires accurate simulation of the variability of local climate drivers. The South Pacific Convergence Zone (SPCZ) is a large region of low-level convergence, clouds and precipitation in the South Pacific, whose effects extend as far as northeast Australia (NEA). The location of the SPCZ is modulated by the El Niño-Southern Oscillation (ENSO) which causes rainfall variability in the region. Correctly simulating the ENSO-SPCZ teleconnection and its interplay with local conditions is essential for improving seasonal rainfall forecasts. Here we analyse the ability of the ACCESS-S1 seasonal forecast system to predict the SPCZ’s relationship with ENSO including its latitudinal shifts, zonal slope and rainfall magnitude between 1990 and 2012 for the December–January–February (DJF) season. We found improvements in ACCESS-S1’s SPCZ prediction capability compared to its predecessor (POAMA), although prediction of the slope is still limited. The inability of ACCESS-S1 to replicate seasons with a strong anti-zonal SPCZ slope is attributed to its atmospheric model. This has implications for accurate seasonal rainfall forecasts for NEA and South Pacific Islands. Future challenges in seasonal prediction facing regional communities and developers of coupled ocean–atmosphere forecast models are discussed.

Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

Various uncertainties exist in a hindcast due to the inabilities of numerical models to resolve all the complicated atmosphere-sea interactions, and the lack of certain ground truth observations. Here, a comprehensive analysis of an atmospheric model performance in hindcast mode (Hurricane Weather and Research Forecasting model—HWRF) and its 40 ensembles during severe events is conducted, evaluating the model accuracy and uncertainty for hurricane track parameters, and wind speed collected along satellite altimeter tracks and at stationary source point observations. Subsequently, the downstream spectral wave model WAVEWATCH III is forced by two sets of wind field data, each includes 40 members. The first ones are randomly extracted from original HWRF simulations and the second ones are based on spread of best track parameters. The atmospheric model spread and wave model error along satellite altimeters tracks and at stationary source point observations are estimated. The study on Hurricane Irma reveals that wind and wave observations during this extreme event are within ensemble spreads. While both Models have wide spreads over areas with landmass, maximum uncertainty in the atmospheric model is at hurricane eye in contrast to the wave model.

Snow depth over sea ice is an essential variable for understanding the Arctic energy budget. In this study, we evaluate snow depth over Arctic sea ice during 1993–2014 simulated by 31 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) against recent satellite retrievals. The CMIP6 models capture some aspects of the observed snow depth climatology and variability. The observed variability lies in the middle of the models’ simulations. All the models show negative trends of snow depth during 1993–2014. However, substantial spatiotemporal discrepancies are identified. Compared to the observation, most models have late seasonal maximum snow depth (by two months), remarkably thinner snow for the seasonal minimum, an incorrect transition from the growth to decay period, and a greatly underestimated interannual variability and thinning trend of snow depth over areas with frequent occurrence of multi-year sea ice. Most models are unable to reproduce the observed snow depth gradient from the Canadian Arctic to the outer areas and the largest thinning rate in the central Arctic. Future projections suggest that snow depth in the Arctic will continue to decrease from 2015 to 2099. Under the SSP5-8.5 scenario, the Arctic will be almost snow-free during the summer and fall and the accumulation of snow starts from January. Further investigation into the possible causes of the issues for the simulated snow depth by some models based on the same family of models suggests that resolution, the inclusion of a high-top atmospheric model, and biogeochemistry processes are important factors for snow depth simulation.

This paper presents an inter-comparison performance assessment of the newly developed Centre for Weather Forecast and Climate Studies (CPTEC) model (the Brazilian Atmospheric Model version 1.2, BAM-1.2) against four sub-seasonal to seasonal (S2S) prediction project models from: Japan Meteorological Agency (JMA), Environmental and Climate Change Canada (ECCC), European Centre for Medium-range Weather Forecasts (ECMWF) and Australian Bureau of Meteorology (BoM). The inter-comparison was performed using hindcasts of weekly precipitation anomalies and the daily evolution of Madden–Julian Oscillation (MJO) for 12 extended austral summers (November–March, 1999/2000–2010/2011), leading to a verification sample of 120 hindcasts. The deterministic assessment of the prediction of precipitation anomalies revealed ECMWF as the model presenting the highest (smallest) correlation (root mean squared error, RMSE) values among all examined models. JMA ranked as the second best performing model, followed by ECCC, CPTEC and BoM. The probabilistic assessment for the event “positive precipitation anomaly” revealed that ECMWF presented better discrimination, reliability and resolution when compared to CPTEC and BoM. However, these three models produced overconfident probabilistic predictions. For MJO predictions, CPTEC crosses the 0.5 bivariate correlation threshold at around 19 days when using the mean of 4 ensemble members, presenting similar performance to BoM, JMA and ECCC. Overall, CPTEC proved to be competitive compared to the S2S models investigated, but with respect to ECMWF there is scope to improve the prediction system, likely by a combination of including coupling to an interactive ocean, improving resolution and model parameterization schemes, and better methods for ensemble generation.

Real-time heatwave risk management with fine-grained spatial resolution is important for analysis of urban heat island (UHI) effects and local heatwaves. This study analyzed the spatio-temporal behavior of ground temperatures and developed methods for modeling them. The developed models consider two higher-order stochastic spatial properties (skewness and kurtosis), which are key to understanding and describing local temperature fluctuations and UHI effects. Application of the developed models to the greater Tokyo metropolitan area demonstrated the feasibility of statistically incorporating a variety of real datasets. Remotely sensed imagery and data from a variety of ground-based monitoring sites were used to build models linking urban covariates to air temperature. Air temperature models were used to capture high-resolution spatial emulator outputs for modeling ground surface temperatures. The main processes studied were the Tukey g-and-h processes for capturing spatial and temporal aspects of heat processes in urban environments. The main finding is that consideration of not only the mean temperature but also the variance, skewness, and kurtosis parameters can reveal hidden heatwave structures.

Carbon fiber composites (CFCs) are widely used in the aeronautics industry because of their excellent properties, including low weight and better corrosion and fatigue resistance compared with metals such as aluminum. However, metals are still required in primary aircraft components when, for example, damage tolerance designs are used. In this study, CFC structures used in aircraft are simulated and experimental tests are carried out. The direct and indirect effects of a high current discharge through the fuselage of an aircraft are compared with experimental data to validate the proposed simulation model.

Long-range transport of biomass burning (BB) aerosol from regions affected by wildfires is known to have a significant impact on the radiative balance and air quality in receptor regions. However, the changes that occur in the optical properties of BB aerosol during long-range transport events are insufficiently understood, limiting the adequacy of representations of the aerosol processes in chemistry transport and climate models. Here we introduce a framework to infer and interpret changes in the optical properties of BB aerosol from satellite observations of multiple BB plumes. Our framework includes (1) a procedure for analysis of available satellite retrievals of the absorption and extinction aerosol optical depths (AAOD and AOD) and single-scattering albedo (SSA) as a function of the BB aerosol photochemical age and (2) a representation of the AAOD and AOD evolution with a chemistry transport model (CTM) involving a simplified volatility basis set (VBS) scheme with a few adjustable parameters. We apply this framework to analyze a large-scale outflow of BB smoke plumes from Siberia toward Europe that occurred in July 2016. We use AAOD and SSA data derived from OMI (Ozone Monitoring Instrument) satellite measurements in the near-UV range along with 550 nm AOD and carbon monoxide (CO) columns retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) and IASI (Infrared Atmospheric Sounding Interferometer) satellite observations, respectively, to infer changes in the optical properties of Siberian BB aerosol due to its atmospheric aging and to get insights into the processes underlying these changes. Using the satellite data in combination with simulated data from the CHIMERE CTM, we evaluate the enhancement ratios (EnRs) that allow isolating AAOD and AOD changes due to oxidation and gas–particle partitioning processes from those due to other processes, including transport, deposition, and wet scavenging. The behavior of EnRs for AAOD and AOD is then characterized using nonlinear trend analysis. It is found that the EnR for AOD strongly increases (by about a factor of 2) during the first 20–30 h of the analyzed evolution period, whereas the EnR for AAOD does not exhibit a statistically significant increase during this period. The increase in AOD is accompanied by a statistically significant enhancement of SSA. Further BB aerosol aging (up to several days) is associated with a strong decrease in EnRs for both AAOD and AOD. Our VBS simulations constrained by the observations are found to be more consistent with satellite observations of strongly aged BB plumes than “tracer” simulations in which atmospheric transformations of BB organic aerosol were disregarded. The simulation results indicate that the upward trends in EnR for AOD and in SSA are mainly due to atmospheric processing of secondary organic aerosol (SOA), leading to an increase in the mass scattering efficiency of BB aerosol. Evaporation and chemical fragmentation of the SOA species, part of which is assumed to be absorptive (to contain brown carbon), are identified as likely reasons for the subsequent decrease in the EnR for both AAOD and AOD. Hence, our analysis reveals that the long-range transport of smoke plumes from Siberian fires is associated with major changes in BB aerosol optical properties and chemical composition. Overall, this study demonstrates the feasibility of using available satellite observations for evaluating and improving representations in atmospheric models of the BB aerosol aging processes in different regions of the world at much larger temporal scales than those typically addressed in aerosol chamber experiments.

Uncertainty in the representation of biomass burning (BB) aerosol composition and optical properties in climate models contributes to a range in modeled aerosol effects on incoming solar radiation. Depending on the model, the top-of-the-atmosphere BB aerosol effect can range from cooling to warming. By relating aerosol absorption relative to extinction and carbonaceous aerosol composition from 12 observational datasets to nine state-of-the-art Earth system models/chemical transport models, we identify varying degrees of overestimation in BB aerosol absorptivity by these models. Modifications to BB aerosol refractive index, size, and mixing state improve the Community Atmosphere Model version 5 (CAM5) agreement with observations, leading to a global change in BB direct radiative effect of −0.07 W m−2, and regional changes of −2 W m−2 (Africa) and −0.5 W m−2 (South America/Temperate). Our findings suggest that current modeled BB contributes less to warming than previously thought, largely due to treatments of aerosol mixing state., Wildfires produce aerosols known to impact the climate, but the wider-reaching effects of this biomass burning are poorly constrained in models. Here the authors use a suite of observations from 12 campaigns around the globe to determine that the values used by most climate models overestimate the contribution of biomass burning aerosols.

To acquire daytime land surface temperature (LST) in thin cirrus cloudy skies, we have developed a three-channel LST retrieval algorithm based on a widely used two-channel LST retrieval algorithm for the clear-sky conditions. In this algorithm, the LST is expressed as a multiple linear function of MODIS channels 29, 31 and 32 with the coefficients of the linear function dependent on the cirrus optical depth (COD) and cirrus effective radius (R). The influences from land surface emissivities (LSEs) are also considered in this algorithm. The simulated dataset shows that the LST could be estimated using the proposed algorithm with the root mean squire error (RMSE) less than 2.2 K in thin cirrus cloudy skies (COD less than 0.7) when viewing zenith angle (VZA) equivalent to 0°. As VZA is equivalent to 60°, the maximum RMSE are 2.7 K. The widely used generalized split-window (GSW) algorithm proposed for clear-sky conditions are used in cirrus cloudy skies, and the RMSEs of GSW algorithm estimated LST are 16.89 K and 22.32 K for VZA =0° and VZA =60° respectively when COD is 0.7. It indicates that the proposed three-channel algorithm can significantly improve the LST retrieval accuracy using thermal infrared data in cirrus cloudy skies. To estimate the LST errors caused by the uncertainties of COD, R, LSE and instrument noise, a sensitivity analysis was performed. It shows that the accuracy of cirrus COD is more important for the retrieval of LST compared with other parameters. The maximum total LST errors, taking into account all the input parameters’ uncertainty and algorithm error itself, are 3.8 K and 4.3 K when VZA =0° and VZA =60° respectively.

Surface soil moisture retrievals were performed by inverting physical scattering models for forests over $\text{30}^\circ$ to $\text{50}^\circ$ incidence angle range and 0.05 to 0.40 m $^3$ /m $^3$ soil moisture range using $L$ -band airborne synthetic aperture radar (SAR) data during a 28-day period. The forward models implemented single-scattering of discrete elements of trees and were validated at $F$ 2 site within 1.5 dB rmse of observation for VV-pol, which was enabled by introducing the gaps between trees. The physical forward models were inverted using the time-series SAR data to retrieve soil moisture and soil surface roughness, which were validated with in situ data at four sites $F$ 1, $F$ 2, $F$ 3, and $F$ 5. Retrievals using VV input over the full dynamic ranges of wetness are accurate to 0.044 m $^3$ /m $^3$ unbiased rmse with correlations of 0.71 to 0.84, which is very encouraging for retrieval under forest canopy. The conditions of these results are the vegetation water content varied from 7.3 to 25.6 kg/m $^2$ and the sensitivity of VV to soil moisture changes ranged from 0.64 to 2.27 dB/(0.1 m $^3$ /m $^3$ ). When both HH and VV were used as inputs to retrieval, the performance did not improve because the benefit of the multichannels was offset by the larger uncertainties in HH modeling. Due to the short duration of in situ data, the efficacy of commonly used relative change index retrieval is diminished. In comparison, the inversion of the scattering model is found to be an effective way to estimate forest soil moisture, it is capable of systematic correction of vegetation effect, and offers accurate retrieval of the dynamic ranges of soil moisture values in terms of unbiased rmse. The retrieval with the physical forward model provides a first step toward global application for the future NISAR satellite.

The F2-layer peak parameters of the International Reference Ionosphere (IRI) model are critical to its subsequent applications because of the vital role of the peak parameters in determining other IRI model outputs. Therefore, we analyzed the statistical deviation and ionospheric climatology of NmF2/hmF2 between the most current version of the IRI model, namely IRI-2016, and ionospheric radio occultation (IRO) data, including Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) and FengYun-3C (FY3C), during the recent solar minimum in 2017–2019. The International Union of Radio Science (URSI) model for F2-layer peak electron density (NmF2) and the Bilitza–Sheikh–Eyfrig (BSE), Altadill-Magdaleno–Torta–Blanch (AMTB), and SHUbin (SHU) models for maximum ionization height (hmF2) in IRI-2016 are compared with the IRO dataset. A statistical analysis shows a systematic offset of less than 10% in NmF2 between IRI-2016 URSI and IRO data, and the SHU option for hmF2 shows a better fit to IRO data than BSE and AMTB. A climatology comparison shows that NmF2 estimated by IRI-2016 URSI is consistent with that of the IRO dataset in terms of the general trend in the typical ionospheric characteristics; except for some discrepancies in the marine area, the IRO data may provide a feasible reference for IRI estimation in marine regions. The hmF2 options of BSE/AMTB/SHU show hemispheric asymmetry like the IRO dataset. Moreover, hmF2 of BSE/AMTB and IRO/SHU show some discrepancies in fine structures, such as the day–night reversal phenomenon.

The simulation and prediction of winter Northern Hemisphere atmospheric blocking in the seasonal prediction systems from the European Centre for Medium‐Range Weather Forecasts (ECMWF) is analysed. Blocking statistics from the operational November‐initialised seasonal hindcasts are evaluated in three generations of models: System3, System4, and System5 (SEAS5). Improvements in the climatological representation of blocking are observed in the most recent model configurations, with reduced bias over North Pacific and Greenland. Minor progress is seen over the European sector, where SEAS5 still underestimates the observed blocking frequency. SEAS5 blocking interannual variability is underestimated too and is proportional to the climatological frequency, highlighting that a negative bias in the blocking frequency implies an underestimation of the interannual variance. SEAS5 predictive skill and signal‐to‐noise ratio remain low, but interesting positive results are found over Western and Central Europe. Improved forecasts with reduced ensemble spread are obtained during El Niño years, especially at low latitudes. Complementary experiments show that the statistics of blocking are improved following atmospheric and oceanic resolution increase. Conversely, they remain largely insensitive to coupled model sea‐surface temperature (SST) errors. On the other hand, the implementation of stochastic parameterisations tends to displace blocking activity equatorward. Finally, by comparing seasonal hindcasts with climate runs using the same model, we highlight that the largest contributors to the chronic underestimation of blocking are persistent errors in the atmospheric model. It is also shown that SST errors have a larger impact on blocking bias in climate runs than in seasonal runs, and that increased ocean model resolution contributes to improved blocking more effectively in climate runs. Seasonal forecasts can thus be considered a suitable test‐bed for model development targeting blocking improvement in climate models.

In this article, a water vapor density inversion model based on the long short-term memory network is proposed for E-band commercial microwave links (CMLs). A full-duplex E-band microwave link located in Prague (two sublinks with frequencies of 73.5 and 83.5 GHz, both vertically polarized) was used to verify the performance of the model. The results show that the model inversion results are in good agreement with the water vapor density calculated by temperature and humidity sensors. Compared with previous water vapor inversion methods based on CMLs, this model has a higher temporal resolution and can realize real-time monitoring.