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This study simulates an unusual extreme rainfall event that occurred in Salvador city, Bahia, Brazil, on December 9, 2017, which was named subtropical storm Guará and had precipitation of approximately 24 mm within less than 1 h. Numerical simulations were conducted using the Weather Research and Forecasting (WRF) model over three domains with horizontal resolutions of 9, 3, and 1 km. Different combinations of seven microphysics, three cumulus, and three planetary boundary layer schemes were evaluated based on their ability to simulate the hourly precipitation during this rainfall event. Statistical indices (MB = –0.69; RMSE = 4.11; MAGE = 1.74; r = 0.55; IOA = 0.66; FAC2 = 0.58) and time series plots showed that the most suitable configurations for this weather event were the Mellor-Yamada-Janjić, Grell-Freitas, and Lin formulations for the planetary boundary layer, cumulus, and microphysics schemes, respectively. The results were compared with the data measured at meteorological stations located in Salvador city. The WRF model simulated well the arrival and occurrence of this extreme weather event in a tropical and coastal region, considering that the region already has intense convective characteristics and is constantly influenced by sea breezes, which could interfere in the model results and compromise the performance of the simulations.

Seasonal forecasts of summer continental United States (CONUS) rainfall have relatively low skill, partly due to a lack of consensus about its sources of predictability. The East Asian monsoon (EAM) can excite a cross-Pacific Rossby wave train, also known as the Asia-North America (ANA) teleconnection. In this study, we analyze the ANA teleconnection in observations and model simulations from the Community Atmospheric Model, version 5 (CAM5), comparing experiments with prescribed climatological SSTs and prescribed observed SSTs. Observations indicate a statistically significant relationship between a strong EAM and increased probability of positive precipitation anomalies over the U.S. west coast and the Plains-Midwest. The ANA teleconnection and CONUS rainfall patterns are improved in the CAM5 experiment with prescribed observed SSTs, suggesting that SST variability is necessary to simulate this teleconnection over CONUS. We find distinct ANA patterns between ENSO phases, with the La Niña-related patterns in CAM5_obsSST disagreeing with observations. Using linear steady-state quasi-geostrophic theory, we conclude that incorrect EAM forcing location greatly contributed to CAM5 biases, and jet stream disparities explained the ENSO-related biases. Finally, we compared EAM forcing experiments with different mean states using a simple dry nonlinear atmospheric general circulation model. Overall, the ANA pattern over CONUS and its modulation by ENSO forcing are well described by dry dynamics on seasonal-to-interannual timescales, including the constructive (destructive) interference between El Niño (La Niña) modulation and the ANA patterns over CONUS.

To improve the ensemble prediction system of the tropical regional atmosphere model for the South China Sea (TREPS) in predicting landfalling tropical cyclones (TCs), the impacts of three new implementing strategies for surface and model physics perturbations in TREPS were evaluated for 19 TCs making landfall in China during 2014–16. For sea surface temperature (SST) perturbations, spatially uncorrelated random perturbations were replaced with spatially correlated ones. The multiplier f, which is used to form perturbed tendency in the Stochastically Perturbed Parameterization Tendency (SPPT) scheme, was inflated in regions with evident convective activity (f-inflated SPPT). Lastly, the Stochastically Perturbed Parameterization (SPP) scheme with 14 perturbed parameters selected from the planetary boundary layer, surface layer, microphysics, and cumulus convection parameterizations was added. Overall, all these methods improved forecasts more significantly for non-intensifying than intensifying TCs. Compared with f-inflated SPPT, the spatially correlated SST perturbations generally showed comparable performance but were more (less) skillful for intensifying (non-intensifying) TCs. The advantages of the spatially correlated SST perturbations and f-inflated SPPT were mainly present in the deterministic guidance for both TC track and wind and in the probabilistic guidance for reliability of wind. For intensifying TCs, adding SPP led to mixed impacts with significant improvements in probability-matched mean of modest winds and in probabilistic forecasts of rainfall; while for non-intensifying TCs, adding SPP frequently led to positive impacts on the deterministic guidance for track, intensity, strong winds, and moderate rainfall and on the probabilistic guidance for wind and discrimination of rainfall.

During daylight hours, the mean radiant temperature Tmrt$T_{\text{mrt}}$ is one of the most important meteorological parameters to analyse heat stress for humans. This study conducts a spatio-temporal analysis of Tmrt$T_{\text{mrt}}$ for a summer period in 2018 for the city of Berlin, Germany. To this end, the mesoscale climate model COSMO-CLM (CCLM) is coupled with the urban Double Canyon Effect Parameterization scheme with a building energy model (DCEP–BEM) to derive Tmrt$T_{\text{mrt}}$. This coupled model system CCLM/DCEP–BEM enables a dynamic calculation of Tmrt$T_{\text{mrt}}$ for the microscale urban street canyons using a mesoscale model. To bring a more accurate comparison, a two-step approach is applied to assess the radiative fluxes and Tmrt$T_{\text{mrt}}$ from CCLM/DCEP–BEM. The radiation model SOLWEIG is first validated against measurement and then used to evaluate the DCEP–BEM model. Overall good agreement in Tmrt$T_{\text{mrt}}$ is found between CCLM/DCEP–BEM and SOLWEIG (R2=0.96$\text{R}^2 =\nobreak 0.96$). Nighttime Tmrt$T_{\text{mrt}}$ simulated with CCLM/DCEP–BEM is higher than that with SOLWEIG (MBE=2.9K$\text{MBE}=\nobreak 2.9\,\text{K}$), yet closer to measurements. Tmrt$T_{\text{mrt}}$ during the afternoon hours modeled with CCLM/DCEP–BEM is underestimated compared to SOLWEIG (MBE=-3.1K$\text{MBE}=\nobreak -3.1\,\text{K}$). Further, excluding vegetation, higher values for nighttime Tmrt$T_{\text{mrt}}$ are found in the densely built-up city center than in the suburbs with more open structures, while the city center has lower values for Tmrt$T_{\text{mrt}}$ during midday. This study provides a reliable representation of Tmrt$T_{\text{mrt}}$ in a mesoscale model and would be beneficial for future implementation of human-biometeorological variables such as the Universal Thermal Climate Index or Physiological Equivalent Temperature. These quantities are calculated using Tmrt$T_{\text{mrt}}$.

Ammonia (NH3) plays a central role in the chemistry of inorganic secondary aerosols in the atmosphere. The largest emission sector for NH3 is agriculture, where NH3 is volatilized from livestock wastes and fertilized soils. Although the NH3 volatilization from soils is driven by the soil temperature and moisture, many atmospheric chemistry models prescribe the emission using yearly emission inventories and climatological seasonal variations. Here we evaluate an alternative approach where the NH3 emissions from agriculture are simulated interactively using the process model FANv2 (Flow of Agricultural Nitrogen, version 2) coupled to the Community Atmospheric Model with Chemistry (CAM-chem). We run a set of 6-year global simulations using the NH3 emission from FANv2 and three global emission inventories (EDGAR, CEDS and HTAP) and evaluate the model performance using a global set of multi-component (atmospheric NH3 and NH4+, and NH4+ wet deposition) in situ observations. Over East Asia, Europe and North America, the simulations with different emissions perform similarly when compared with the observed geographical patterns. The seasonal distributions of NH3 emissions differ between the inventories, and the comparison to observations suggests that both FANv2 and the inventories would benefit from more realistic timing of fertilizer applications. The largest differences between the simulations occur over data-scarce regions. In Africa, the emissions simulated by FANv2 are 200 %–300 % higher than in the inventories, and the available in situ observations from western and central Africa, as well as NH3 retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) instrument, are consistent with the higher NH3 emissions as simulated by FANv2. Overall, in simulating ammonia and ammonium concentrations over regions with detailed regional emission inventories, the inventories based on these details (HTAP, CEDS) capture the atmospheric concentrations and their seasonal variability the best. However these inventories cannot capture the impact of meteorological variability on the emissions, nor can these inventories couple the emissions to the biogeochemical cycles and their changes with climate drivers. Finally, we show with sensitivity experiments that the simulated time-averaged nitrate concentration in air is sensitive to the temporal resolution of the NH3 emissions. Over the CASTNET monitoring network covering the US, resolving the NH3 emissions hourly instead monthly reduced the positive model bias from approximately 80 % to 60 % of the observed yearly mean nitrate concentration. This suggests that some of the commonly reported overestimation of aerosol nitrate over the US may be related to unresolved temporal variability in the NH3 emissions.

Detailed meteorological analyses based on observations extending through the middle atmosphere (∼ 15 to 100 km altitude) can provide key information to whole atmosphere modeling systems regarding the physical mechanisms linking day-to-day changes in ionospheric electron density to meteorological variability near the Earth's surface. However, the extent to which independent middle atmosphere analyses differ in their representation of wave-induced coupling to the ionosphere is unclear. To begin to address this issue, we present the first intercomparison among four such analyses, JAGUAR-DAS, MERRA-2, NAVGEM-HA, and WACCMX+DART, focusing on the Northern Hemisphere (NH) 2009–2010 winter, which includes a major sudden stratospheric warming (SSW). This intercomparison examines the altitude, latitude, and time dependences of zonal mean zonal winds and temperatures among these four analyses over the 1 December 2009 to 31 March 2010 period, as well as latitude and altitude dependences of monthly mean amplitudes of the diurnal and semidiurnal migrating solar tides, the eastward-propagating diurnal zonal wave number 3 nonmigrating tide, and traveling planetary waves associated with the quasi-5 d and quasi-2 d Rossby modes. Our results show generally good agreement among the four analyses up to the stratopause (∼ 50 km altitude). Large discrepancies begin to emerge in the mesosphere and lower thermosphere owing to (1) differences in the types of satellite data assimilated by each system and (2) differences in the details of the global atmospheric models used by each analysis system. The results of this intercomparison provide initial estimates of uncertainty in analyses commonly used to constrain middle atmospheric meteorological variability in whole atmosphere model simulations.

The Bay of Bengal (BoB) sea surface temperatures (SSTs) are found to be negatively correlated with the Indian summer monsoon (ISM) rainfall at seasonal, monthly and daily time scales. It is intriguing that the ISM seasonal mean rainfall is reported to be decreasing in the recent decades while no prominent warming has been observed over the BoB. We posit that the reason why BoB warming is not apparent is because BoB surface waters stay largely above the convective threshold of ~ 28 °C and further warming may be perceivable only in terms of an atmospheric response. This is consistent with prominent increase in the latent heat flux (LHF) over BoB during the pre-monsoon season. However such an increasing trend in LHF is not seen for the monsoon season. The increased surface evaporation leading to an increase in LHF over BoB enhances convection aloft which drives a stronger low level pre-monsoon circulation. We hypothesize that the dynamic response of the atmosphere associated with the pre-monsoon circulation changes driven by the BoB warming can explain, at least in part, the observed ISM rainfall trend. This hypothesis is tested by diagnosing SST-forced simulations with the Community Atmosphere Model version 6. The results suggest that the BoB warming does generate dynamic and thermodynamic responses which enhance moisture convergence (divergence) and convection over BoB (central India) during the pre-monsoon season. As a consequence, evapotranspiration is enhanced over the Indian subcontinent leaving the ground drier and colder. The net result is a weaker land–ocean thermal contrast and a weaker monsoon circulation and hence reduced seasonal rainfall totals.

Evidence clearly shows that the mid-tropospheric North Atlantic subtropical high (NASH) is much stronger than the North Pacific subtropical high (NPSH) in summer, but the mechanism is unknown. To understand the mechanism for such zonal difference between the two systems, we perform a series of sensitivity simulations using a community atmosphere model (CAM) and a linear baroclinic model (LBM). The CAM results indicate that the zonal variation of tropical sea-surface temperatures (SSTs), followed by Tibetan Plateau (TP), play leading roles in the formation of these mid-level subtropical high differences through modulating precipitation-related heating sources. The impacts of such heat sources are then investigated using the LBM. The tropical SST variation-related convective heating over the northeastern equatorial Pacific acts to enhance the NASH via exciting an eastward-propagating wave pattern resembling the Pacific-North American teleconnection; the TP-induced large-scale latent heating can also cause the NASH-NPSH difference by exciting a circumglobal teleconnection (CGT)-like wave pattern with westward-propagating part and eastward-propagating part along the westerly jet stream. Thus, their combined effect makes the NASH much stronger than the NPSH in the mid troposphere during summertime.

The past four decades have seen an increase of terrestrial hot extremes during summer in the northern extratropics, accompanied by the Northern Hemisphere (NH) sea surface temperature (SST) warming (mainly over 10°–70°N, 0°–360°) and CO2 concentration rising. This study aims to understand possible causes for the increasing hot extremes, which are defined on a daily basis. We conduct a series of numerical experiments using the Community Atmosphere Model version 5 model for two periods, 1979–1995 and 2002–2018. The experiment by changing the CO2 concentration only with the climatological SST shows less increase of hot extremes days than that observed, whereas that by changing the NH SST (over 10°–70°N, 0°–360°) with constant CO2 concentration strengthens the hot extremes change over mid-latitudes. The experiment with both SST and CO2 concentration changes shows hot extremes change closer to the observation compared to the single-change experiments, as well as more similar simulations of atmospheric circulations and feedbacks from cloud and radiative processes. Also discussed are roles of natural variability (e.g., Pacific Decadal Oscillation and Atlantic Multidecadal Oscillation) and other factors (e.g., Arctic sea ice and tropical SST).

A low-level-jet (LLJ) event that occurred over a coastal area in complex terrain is analyzed to investigate its effect on the dispersion of potential air pollutants released in the area. The atmospheric model RAMS (Regional Atmospheric Modeling System) is employed with a high vertical resolution close to the surface, adopting a rarely used nesting approach, in order to allow a detailed analysis of the flow and to characterize the specific features of the LLJ. After a comparison with meteorological variables measured by radiosondes, numerical experiments are performed adding a scalar tracer in the simulation. As a first test, the tracer is distributed uniformly throughout the domain to follow the dynamics of the LLJ and its effect on the tracer dispersion. Then, continuous releases from virtual point sources are simulated to address their possible impact in the area under LLJ conditions. This allows the identification of “hotspots” of pollutant accumulation due to very local circulations and convective cells that develop from the combined effect of terrain-induced flow and the interaction of the LLJ flow with complex topography. An original mass analysis is applied on the dispersion results for an advanced exploration of the LLJ impact on the tracer. The RAMS model provides reliable results demonstrating that with which atmospheric numerical models are useful tools with which to study LLJ dynamics and their effect on local circulation and pollutant dispersion.

Atmospheric transient eddy dynamical forcing (TEDF)-driven midlatitude unstable air–sea interaction has recently been recognized as a crucial positive feedback for the maintenance of the extratropical decadal variabilities. Our recent theoretical work (Chen et al., Clim Dyn 10.1007/s00382-020-05405-0, 2020) has characterized such an interaction through building an analytical midlatitude barotropic atmospheric model coupled to a simplified upper oceanic model. This study extends the analytical model to including a two-layer quasi-geostrophic baroclinic atmospheric model and then identifies the roles of vertical distributions of atmospheric TEDF and diabatic heating in midlatitude unstable air–sea interaction. It is found that midlatitude air–sea coupling with more realistic vertical profiles of atmospheric TEDF and diabatic heating destabilizes oceanic Rossby wave modes over the entire range of zonal wavelengths, in which the most unstable coupled mode features an equivalent barotropic atmospheric low (high) pressure over a cold (warm) oceanic surface. Spatial structure and period of the most unstable mode are more consistent with the observation than those from in previous model. Although either TEDF or diabatic heating alone can lead to a destabilized coupled mode, the former makes a dominant contribution to the instability. The increase of low-layer TEDF stimulates the instability more effectively if the TEDF in upper layer is larger than in lower layer, while the TEDF in either high or low layers can individually cause the instability. The surface heating always destabilizes the air–sea interaction, while the mid-level heating always decays the coupled mode. The results of this study further confirm the TEDF-driven positive feedback mechanism in midlatitude air–sea interaction proposed by recent observational and numerical experiment studies.

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.

High-resolution modeling is implemented to investigate an evolution of two quasi-tropical cyclones (QTC) over the Black Sea: in September of 2005 and 2018. For comparison, the quasi-tropical hurricane Zorba, that developed over the Mediterranean Sea at the end of September 2018, is considered. An assessment of factors leading to the origination and intensification of QTCs is carried out, and the predictability of the phenomenon is studied. The authors tried to reduce the analysis to the parameters obtained directly as a result of mesoscale nonhydrostatic modeling. In all the considered cases, the period of active development of cyclones was preceded by the formation of a core of warm air above the sea surface; the moisture inflow from the lateral boundaries was more important in the moisture balance. It is found that coarser spatial resolution predictions are more successful for large QTCs.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

Time series interferometric synthetic aperture radar (TS-InSAR) has been a powerful tool for monitoring land surface deformation over the last two decades. Atmospheric effects cause large-scale delays in InSAR observations, which is one of the difficulties facing deformation calculations from differential InSAR and time-series InSAR. Currently, the atmospheric delay is derived mainly from auxiliary data from sources such as the global navigation satellite system (GNSS) and moderate-resolution imaging spectroradiometry (MODIS), but GNSS data are limited by the sparse distribution of observation stations. MODIS data may also not temporally match SAR image acquisition, which leads to low accuracy in atmospheric phase correction. This article presents a decomposition method to remove the atmospheric delay. We consider the atmospheric phase to be caused by the combined changes in spatial position and elevation. Therefore, quadtree segmentation is applied to divide the topographic units, and we improve the drift function of universal kriging by adding an elevation component. We then interpolate the whole atmospheric phase space from reliable sampling points estimated by the coherence coefficient. Using Sentinel-1 data, we test the proposed method in discriminating and monitoring a mining subsidence area in Shanxi Province and compare the results with the results from interferometric point target analysis and the network-based variance-covariance estimation method. The results demonstrate that the proposed method is superior to existing methods for the detection of deformation inverted from TS-InSAR.

Errors in soil moisture adversely impact the modeling of land–atmosphere water and energy fluxes and, consequently, near-surface atmospheric conditions in atmospheric data assimilation systems (ADAS). To mitigate such errors, a land surface analysis is included in many such systems, although not yet in the currently operational NASA Goddard Earth Observing System (GEOS) ADAS. This article investigates the assimilation of L-band brightness temperature (Tb) observations from the Soil Moisture Active Passive (SMAP) mission in the GEOS weakly coupled land–atmosphere data assimilation system (LADAS) during boreal summer 2017. The SMAP Tb analysis improves the correlation of LADAS surface and root-zone soil moisture versus in situ measurements by ∼0.1–0.26 over that of ADAS estimates; the unbiased root-mean-square error of LADAS soil moisture is reduced by 0.002–0.008 m3/m3 from that of ADAS. Furthermore, the global land average RMSE versus in situ measurements of screen-level air specific humidity (q2m) and daily maximum temperature (T2mmax) is reduced by 0.05 g/kg and 0.04 K, respectively, for LADAS compared to ADAS estimates. Regionally, the RMSE of LADAS q2m and T2mmax is improved by up to 0.4 g/kg and 0.3 K, respectively. Improvement in LADAS specific humidity extends into the lower troposphere (below ∼700 mb), with relative improvements in bias of 15–25%, although LADAS air temperature bias slightly increases relative to that of ADAS. Finally, the root mean square of the LADAS Tb observation-minus-forecast residuals is smaller by up to ∼0.1 K than in a land-only assimilation system, corroborating the positive impact of the Tb analysis on the modeled land–atmosphere coupling.

Medium resolution spectral imager II (MERSI-2) is a payload for the Chinese meteorological satellite Feng Yun 3 (FY-3). China's satellite remote sensing observation capabilities such as climate change research can be improved during MERSI-2's operation in orbit, and the sensor is the world's first imaging instrument that can obtain a global infrared split-window data with a spatial resolution of 250 m. We developed an operational spilt-window (SW) algorithm to retrieve land surface temperature (LST) accurately from the MERSI-2 data. The SW algorithm coefficients were derived from a simulation dataset that was established with the Moderate spectral resolution atmospheric Transmittance model version 5.2 and the thermodynamic initial guess retrieval dataset. In the practical retrieval, the precise algorithm coefficients were determined by view zenith angle and atmospheric water vapor content (WVC), the atmospheric WVC were obtained from the ERA5 dataset, and the land surface emissivity was dynamically estimated using the advanced spaceborne thermal emission and reflection radiometer global emissivity dataset, considering the fractional of vegetation cover and snow cover. The retrieved LST compared with in situ LST, which was highly consistent with the in situ LST and that the root-mean-square error of the two is within 3 K. The retrieved LST was compared with the MYD11_L2 and MYD21_L2 LST products, and the results indicated that MERSI-2 LST was more consistent with the MYD21 LST. The operational SW algorithm for FY-3D MERSI-2 developed in this study could retrieve LST accurately and has a wide range of popularization and application values.

The presence of haze could seriously contaminate the observations of optical satellite imagery. Haze not only significantly affects the visual interpretation but also reduces the accuracy of map products. In this article, a semiphysical approach is proposed to reduce the haze effects for Landsat image. The proposed approach is based on the physical model of radiative transfer theory and the presence of dark objects. As the depth map of satellite remotely sensed image is almost a constant value, the coarse transmission map of atmosphere is estimated by the haze thickness, other than the scene depth map. The derived coarse transmission is utilized to correct the color shift induced by airlight. For haze veiled textural information, the guided-filter based approach is adopted to refine the coarse transmission map to restore the textural information. Experiments are conducted upon the images acquired by Landsat at different dates and spatial locations. The visual interpretation upon the dehazed results suggests that the proposed approach could generate visually promising results and preserve spectral properties of land surfaces. Moreover, it also performs favorably against several state-of-the-art deep learning based methods and a classic algorithm. The results of quantitative assessments demonstrate that the developed approach could enhance the information of Landsat scene with minimum spectrum changes. With the visual and quantitative assessments, we can conclude that the proposed approach is a promising solution for Landsat image dehazing. It is expected to improve the data quality of hazy scene and expand the usability of Landsat data.

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.

Near-surface relative humidity (RHns) is an essential meteorological parameter for water, carbon, and climate studies. However, spatially continuous RHns estimation is difficult due to the spatial discontinuity of in situ observations and the cloud contamination of satellite-based data. This article proposed machine learning-based models to estimate spatially continuous daily RHns at 1 km resolution over Japan and South Korea under all sky conditions and examined the spatiotemporal patterns of RHns. All sky estimation of RHns using machine learning has been rarely conducted, and it can be an alternative to the currently available RHns data mostly from numerical models, which have relatively low spatial resolution. We combined two schemes for clear sky conditions (scheme A, which uses satellite and reanalysis data) and cloudy sky conditions (scheme B, which uses reanalysis data solely). The relatively small numbers of data in extremely low and high RHns conditions (i.e., 70%, respectively) were augmented by applying an oversampling method to avoid biased training. The machine learning models based on random forest (RF) and XGBoost were trained and validated using 94 in situ observation sites from meteorological administrations of both countries from 2012 to 2017. The results showed that XGBoost produced slightly better performance than RF, and the spatially continuous RHns model combined based on XGBoost yielded the coefficient of determination of 0.72 and a root-mean-square error of 10.61%. Spatiotemporal patterns of the estimated RHns agreed with in situ observations, reflecting the effect of topography on RHns. We expect that the proposed RHns model could be used in various environmental studies that require RHns under all sky conditions as input data.

The atmosphere-land exchange inverse disaggregation (DisALEXI) algorithm is a multi-scale energy balance model that estimates evapotranspiration (ET) using land-surface temperature (LST) as a driving remote sensing input. Using LST products from ECOSTRESS, a thermal radiometer mounted on the International Space Station, DisALEXI ET products have been produced over the contiguous United States (CONUS) at 70 m resolution. The goal of this study is to demonstrate the accuracy of the CONUS-wide ET produced by the Jet Propulsion Laboratory (JPL) and to compare the results with the original DisALEXI ET produced by researchers at the United States Department of Agriculture (USDA). DisALEXI-USDA has been produced ad-hoc using Landsat LST, and is routinely produced over six target sites using ECOSTRESS LST. DisALEXI-JPL was implemented in order to expand the spatial coverage. DisALEXI-JPL was evaluated at 26 CONUS eddy covariance sites, showing good correlation, with R2 = 0.80 and RMSE = 0.81 mm/day, which is comparable to previous DisALEXI validation studies (RMSE ∼1 mm/day). The two DisALEXI implementations compared well, with R2 = 0.92. This article evaluates DisALEXI-JPL and shows that the algorithm is valid over a larger segment of CONUS. We also show the impact of quality flags, as pixels with high view zenith angles or high aerosol optical depth showed greater deviation from field measurements. As a product demonstration, we show a regional map of fine-scale ET, where the fine-scale variation over wider areas can detect small areas of stress much sooner than products with coarse resolution representing average conditions.

High spatiotemporal resolution land surface temperature (LST) plays an important role in various environment applications. However, the limitation of thermal infrared sensors and the effect of clouds and other atmospheric conditions result in discontinuous daily thermal observations of the Moderate Resolution Imaging Spectroradiometer (MODIS). Annual temperature cycle (ATC) models can help to supply daily continuous LSTs via limited observations, but these ATC models seldom consider the disturbance of weather conditions or the land cover change. On the other hand, spatial interpolation techniques also limit in implementation when available data in one day or several days are not able to obtain enough spatiotemporal information for LST reconstruction. The objective of this study is to propose a phenology-based ATC model (termed PATC), which takes the phenology change and local weather change into account, to reconstruct daily unscanned LSTs at an annual scale. Daily MODIS LSTs collected in 2015 were utilized to analyze the performance of PATC compared with other ATC models. Results show that PATC improved the accuracy by 1.6 and 0.5 K compared to the classic ATC model in the daytime and nighttime, respectively. Compared to the enhanced ATC model, PATC also shows better performance with higher accuracies, especially during the growing season of vegetation in the daytime. Future research may focus on an incorporation with Landsat observations and diurnal temperature cycle models to implement LST reconstruction at a diurnal scale.

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.

As ground-level O3 has become one of the major pollutants affecting air quality in recent years, monitoring and revealing the spatial distribution pattern of O3 are of great significance to study air-pollution characteristics. Based on the multilayer backpropagation neural network, one O3 estimation model is proposed to obtain the continuous spatial distribution of O3 concentrations, where the Landsat 8 images, meteorological parameters, and air quality data have been integrated together as the input for the model training and validation. In order to enhance the estimation accuracy, the proposed model has been optimized with respect to the influencing factors and spatial extent. In the test areas of Beijing, Tianjin, and Shijiazhuang of China, the proposed O3 estimation model has demonstrated quite satisfactory performance—with the average coefficient of determination (R2) larger than 0.90 and root-mean-square error smaller than 19.0 μg/m3. It is worth mentioning that all the data employed in this research are freely available and can be applied nationwide in the mainland of China. Taking advantage of the generic nature and the positive O3 estimation results with high accuracy and spatial resolution, the proposed model can be expected to be a new way for studying air-pollution characteristics and, thus, support the decision making for environmental governance.

Monitoring pollution of PM2.5 on walkable roads is important for resident health in high-density cities. Due to the spatiotemporal resolution limitations of aerosol optical depth observation, fixed-point monitoring, or traditional mobile measurement instruments, the microscale PM2.5 distribution in the walking environment cannot be fully estimated at the fine scale. In this article, by the integration of mobile measurement data, OpenStreetMap (OSM) data, Landsat images, and other multisource data in land-use regression (LUR) models, a novel framework is proposed to estimate and map PM2.5 distribution in a typical microscale walkable environment of the high-density city Hong Kong. First, the PM2.5 data on the typical walking paths were collected by the handheld mobile measuring instruments, to be selected as the dependent variables. Second, geographic prediction factors calculated by Google Street View, OSM data, Landsat images, and other multisource data were further selected as independent variables. Then, these dependent and independent variables were put into the LUR models to estimate the PM2.5 concentration on sidewalks, footbridges, and footpaths in the microscale walkable environment. The proposed models showed high performance relative to those in similar studies (adj R2, 0.593 to 0.615 [sidewalks]; 0.641 to 0.682 [footpaths]; 0.783 to 0.797 [footbridges]). This article is beneficial for mapping PM2.5 concentration in the microscale walking environment and the identification of hot spots of air pollution, thereby helping people avoid the PM2.5 hotspots and indicating a healthier walking path.

Kernel-driven models provide an effective way for correcting the thermal radiation directionality effect. Under a general kernel-driven modeling framework proposed by Cao et al., by using three fixed-width hotspot kernels, and considering whether to combine two existing base shape kernels, this article proposed nine kernel-driven models with different coefficient requirements. Specifically, the three hotspot kernels are four-component kernel (KGO4), three-component kernel (KGO3), and sunlit soil kernel (KGOg), and the two base shape kernels are temperature difference kernel (KLSF) and emissivity kernel (KEmi). Based on discrete anisotropic radiative transfer model, a comprehensive simulation data set, including three fraction of vegetation covers (FVC) and 36 component temperature profiles, was generated for evaluating the performances of novel models. First, the order of model performances was determined, and the result is basically independent of FVC and component temperature difference. In the case of five-parameter, four-parameter, three-parameter, and two-parameter, the best models are GO4_LSF model or GO4_Emi model, GO3_LSF model or GO3_Emi model, GOg_LSF model or GOg_Emi model, and GOg model, respectively. In addition, for three-parameter models, the comparison with two current linear kernel-driven models, i.e., LSF_Li model and Vinnikov model, was discussed in detail, and it is revealed that newly proposed GOg_LSF and GOg_Emi model in this article have a better performance.

Near-surface air temperature (NSAT) is a key parameter in climate changes, environmental ecosystem monitoring, and human settlement issues. As it is difficult for in situ observations to capture the spatial distribution characteristics of NSAT in great detail, various methods have been developed to use remotely sensed land surface temperature and other auxiliary variables to estimate the NSAT. Among them, machine learning turns out to be an exhilarating choice due to its superior performance. However, for long-term dataset estimation, when using machine learning methods with abundant related variables, computation loads and data storage cannot be ignored. Fortunately, Google Earth Engine (GEE) provides a wealth of geospatial datasets and powerful parallel computation capability. In this article, GEE has been utilized to demonstrate the feasibility of estimating long-term 1-km NSAT with two machine-learning models (random forest and deep neural network). After testing the effects of the input variables and the model portability, the 20-year monthly mean 1 km × 1 km NSAT over the Yellow River basin was generated and analyzed. Compared with in situ observations, the overall RMSE, MAE, R 2, and R of the NSAT product are 0.429, 0.302, 0.998, and 0.999, respectively. Specifically, for each observation station, R and R 2 are greater than 0.997, and RMSE and MAE are smaller than 0.5 and 0.4, respectively. The comparisons with three existing NSAT products show that our product outperforms the other three products. Once again, it is demonstrated in this study that GEE is a powerful platform to generate valuable products with ready-to-use datasets and computation resources.

The Southern Ocean comprises 25% of the global ocean surface area, accounts for nearly half of the total carbon sink of the global oceans, and is a place that significantly reduces the impacts of anthropogenic CO2 emissions. Due to the sparsity of observational data, the changes in Southern Ocean carbon sinks over time remain uncertain. In this study, we integrated correlation analysis and a feedforward neural network to improve the accuracy of carbon flux estimations in the Southern Ocean. Based on observation data from 1998–2018, we reconstructed the Southern Ocean's pCO2 grid data during this period. The root-mean-square error obtained by fitting the observation data was 8.86 μatm, indicating that the results were better than those of the two primary statistically based models in the Surface Ocean pCO2 mapping intercomparison. The results also showed that the Southern Ocean's capacity to act as a carbon sink has gradually increased since 2000; it reduced during 2010–2013 but increased significantly after that. The Southern Ocean's seasonality is characterized by minimum carbon uptake in winter due to increased upwelling; this is followed by a rapid increase toward maximum uptake in summer, which is mainly biologically driven. There is an apparent double-ring structure in the Southern Ocean, as noted in other studies. This study confirms that the inner ring (50–70°S) is a carbon source area gradually transforming into a carbon sink, while the outer ring (35–50°S) continues to serve as a carbon sink.

A multimodal generative modeling approach combined with permutation-invariant set attention is investigated in this article to support long-wave infrared (LWIR) in-scene atmospheric compensation. The generative model can produce realistic atmospheric state vectors ( $\text{T}, \text{H}_2\text{O}, \text{O}_3$ ) and their corresponding transmittance, upwelling radiance, and downwelling radiance (TUD) vectors by sampling a low-dimensional space. Variational loss, LWIR radiative transfer loss, and atmospheric state loss constrain the low-dimensional space, resulting in lower reconstruction error compared to standard mean-squared error approaches. A permutation-invariant network predicts the generative model low-dimensional components from in-scene data, allowing for simultaneous estimates of the atmospheric state and TUD vector. Forward modeling the predicted atmospheric state vector results in a second atmospheric compensation estimate. Results are reported for collected LWIR data and compared against fast line-of-sight atmospheric analysis of hypercubes-infrared (FLAASH-IR), demonstrating commensurate performance when applied to a target detection scenario. Additionally, an approximate eight times reduction in detection time is realized using this neural network-based algorithm compared to FLAASH-IR. Accelerating the target detection pipeline while providing multiple atmospheric estimates is necessary for many real world, time sensitive tasks.