Cloud Reflectivity Research Articles

Low cloud-SST variability over the summertime subtropical Northeast Pacific: Role of extratropical atmospheric modes

Abstract Over the subtropical Northeast Pacific (NEP), highly reflective low clouds interact with underlying sea-surface temperature (SST) to constitute a local positive feedback. Recent modeling studies showed that, together with wind-evaporation-SST (WES) feedback, the summertime low cloud-SST feedback promotes nonlocal trade wind variations, modulating subsequent evolution of El Niño-Southern Oscillation (ENSO). This study aims to identify drivers of summertime low-cloud variations, using satellite observations and global atmosphere model simulations forced with observed SST. A trans-basin teleconnection is identified, where the North Tropical Atlantic (NTA) warming induced by the North Atlantic Oscillation (NAO) increases precipitation, exciting warm Rossby waves that extend into the NEP. The resultant enhancement of static stability promotes summertime low cloud-SST variability. By regressing out the effects of the preceding ENSO and NTA SST, atmospheric internal variability over the extratropical North Pacific, including the North Pacific Oscillation (NPO), is found to drive the NEP cooling by latent heat loss and subsequent summer low cloud-SST variability. With the help of the background trade winds and WES feedback, the SST anomalies extend southwestward from the low-cloud region, accompanied by ENSO in the following winter. This suggests the nonlocal effects of low clouds identified by recent studies. Analysis of a 500-year climate model simulation corroborates the NTA and NPO forcing of NEP low cloud-SST variability and subsequent ENSO.

Microphysical Evolution in Mixed-Phase Midlatitude Marine Cold-Air Outbreaks

Abstract Five cold-air outbreaks are investigated with aircraft offshore of continental northeast America. Flight paths aligned with the cloud-layer flow from January through March span cloud-top temperatures from −5° to −12°C, in situ liquid water paths of up to 500 g m−2, while in situ cloud droplet number concentrations exceeding 500 cm−3 maintain effective radii below 10 μm. Rimed ice is detected in the four colder cases within the first cloud pass. After further fetch, ice particle number concentrations reaching 2.5 L−1 support an interpretation that secondary ice production is occurring. Rime splintering is clearly evident, with dendritic growth increasing ice water contents within deeper clouds with colder cloud-top temperatures. Buoyancy fluxes reach 400–600 W m−2 near the Gulf Stream’s western edge, with 1-s updrafts reaching 5 m s−1 supporting closely spaced convective cells. Near-surface rainfall rates of the three more intense cold-air outbreaks are a maximum near the Gulf Stream’s eastern edge, just before the clouds transition to more open-celled structures. The milder two cold-air outbreaks transition to lower-albedo cumulus with little or no precipitation. The clouds thin through cloud-top entrainment. Significance Statement Cold-air outbreaks off of the eastern U.S. seaboard are visually spectacular in satellite imagery, with overcast, high-albedo clouds transitioning to more broken cloud fields. We use data from the recent NASA Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) aircraft campaign to examine the microphysics and environmental context of five such outbreaks. We find the clouds are not ice-deprived, but updrafts still supply significant liquid water. Cloud transitions are encouraged through near-surface rain for the deeper clouds, and otherwise, clouds thin and break through mixing in drier air from above. These observations support understanding and further modeling examining how mixed-phase cloud microphysics affect cloud reflectivity and surface rainfall rates, important for both weather and climate forecasting.

Increasing aerosol direct effect despite declining global emissions in MPI-ESM1.2

Abstract. Anthropogenic aerosol particles partially mask global warming driven by greenhouse gases, both directly by reflecting sunlight back to space and indirectly by increasing cloud reflectivity. In recent decades, emissions of anthropogenic aerosols have declined globally and at the same time shifted from the North American and European regions, foremost to Southeast Asia. Using simulations with the Max Planck Institute Earth System Model version 1.2 (MPI-ESM1.2), we find that the direct effect of aerosols has continued to increase despite declining emissions. Concurrently, the indirect effect has diminished in approximate proportion to the emissions. In this model, which employs parameterized aerosol effects with constant regional direct effect efficiency, the enhanced efficiency of aerosol radiative forcing in emissions is associated with less cloud masking, longer atmospheric residence times, and differences in aerosol optical properties.

An Evaluation of Cloud‐Precipitation Structures in Mixed‐Phase Stratocumuli Over the Southern Ocean in Kilometer‐Scale ICON Simulations During CAPRICORN

AbstractA persistent shortwave radiative bias of Southern Ocean (SO) clouds in climate models is strongly associated with incorrect cloud phase representation, which impacts precipitation. Measurements characterizing precipitation in low‐level mixed‐phase clouds, which frequently form over the SO, are rare, and our understanding of precipitation efficacy within these clouds remains limited. The simulated surface precipitation bias has an indirect effect on determining global climate sensitivity and a direct impact on the hydrological cycle. This study investigates the representation of low clouds, cloud variability, and precipitation statistics over the SO in real‐case Icosahedral Nonhydrostatic (ICON) simulations at the kilometer scale. The simulations are contrasted with 48 hr of continuous shipborne observations of open and closed‐cell stratocumuli, south of Tasmania. Our simulations show the significance of heavily rimed particle formation, their in‐cloud growth, and subcloud melting to capture the observed cloud‐precipitation vertical structure. In addition, supercooled drizzle formation impacts the vertical structure and precipitation statistics. ICON captures the observed intermittency of precipitation even at a standard vertical resolution of 200 m in the boundary layer but only captures the observed sparse distribution of intense precipitation (>1 mm hr−1) when the maximum vertical resolution is reduced to 100 m. However, the simulations of the 2‐day accumulated precipitation and the radiative effect are largely insensitive to the vertical resolution. The cloud reflectivity of the broken cloud deck is underestimated due to negative biases in cloud optical depth.

A framework for natural resource management with geospatial machine learning: a case study of the 2021 Almora forest fires

BackgroundWildfires have a substantial impact on air quality and ecosystems by releasing greenhouse gases (GHGs), trace gases, and aerosols into the atmosphere. These wildfires produce both light-absorbing and merely scattering aerosols that can act as cloud condensation nuclei, altering cloud reflectivity, cloud lifetime, and precipitation frequency. Uttarakhand province in India experiences frequent wildfires that affect its protected ecosystems. Thus, a natural resource management system is needed in this region to assess the impact of wildfire hazards on land and atmosphere. We conducted an analysis of a severe fire event that occurred between January and April 2021 in the Kumaun region of Uttarakhand, by utilizing open-source geospatial data. Near-real-time satellite observations of pre- and post-fire conditions within the study area were used to detect changes in land and atmosphere. Supervised machine learning algorithm was also implemented to estimate burned above ground biomass (AGB) to monitor biomass stock.ResultsThe study found that 21.75% of the total burned area burned with moderate to high severity, resulting in a decreased Soil Adjusted Vegetation Index value (> 0.3), a reduced Normalized Differential Moisture Index value (> 0.4), and a lowered Normalized Differential Vegetation Index (> 0.5). The AGB estimate demonstrated a significant simple determination (r2 = 0.001702) and probability (P < 2.2 10−16), along with a positive correlation (r ≤ 0.24) with vegetation and soil indices. The algorithm predicted that 17.56 tonnes of biomass per hectare burned in the Kumaun forests. This fire incident resulted in increased emissions of carbon dioxide (CO2; ~ 0.8 10−4 kg carbon h−1), methane (CH4; ~ 200 10−9 mol fraction in dry air), carbon monoxide (CO; 2000 1015 molecules cm−2 total column), and formaldehyde (HCHO; 3500 1013 molecules cm−2 total column), along with increased aerosol optical thickness (varying from 0.2 to 0.5).ConclusionsWe believe that our proposed operational framework for managing natural resources and assessing the impact of natural hazards can be used to efficiently monitor near-real-time forest-fire-caused changes in land and atmosphere. This method makes use of openly accessible geospatial data that can be employed for several objectives, including monitoring carbon stocks, greenhouse gas emissions, criterion air pollution, and radiative forcing of the climate, among many others. Our proposed framework will assist policymakers and the scientific community in mitigating climate change problems and in developing adaptation policies.

First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia

Abstract. Aerosol optical properties have been provided by the Geostationary Environment Monitoring Spectrometer (GEMS), the world's first geostationary-Earth-orbit (GEO) satellite instrument designed for air quality monitoring. This study describes improvements made to the GEMS aerosol retrieval (AERAOD) algorithm, including spectral binning, surface reflectance estimation, cloud masking, and post-processing, along with validation results. These enhancements aim to provide more accurate and reliable aerosol-monitoring results for Asia. The adoption of spectral binning in the lookup table (LUT) approach reduces random errors and enhances the stability of satellite measurements. In addition, we introduced a new high-resolution database for surface reflectance estimation based on the minimum-reflectance method, which was adapted to the GEMS pixel resolution. Monthly background aerosol optical depth (BAOD) values were used to estimate hourly GEMS surface reflectance consistently. Advanced cloud-removal techniques have been implemented to significantly improve the effectiveness of cloud detection and enhance aerosol retrieval quality. An innovative post-processing correction method based on machine learning has been introduced to address artificial diurnal biases in aerosol optical depth (AOD) observations. In this study, we investigated selected aerosol events, highlighting the capability of GEMS in monitoring and providing insights into hourly aerosol optical properties during various atmospheric events. The performance of the GEMS AERAOD products was validated against the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data for the period from November 2021 to October 2022. GEMS AOD at 443 nm demonstrated a strong correlation with AERONET AOD at 443 nm (R = 0.792). However, it exhibited biased patterns, including the underestimation of high AOD values and overestimation of low-AOD conditions. Different aerosol types (highly absorbing fine aerosols, dust aerosols, and non-absorbing aerosols) exhibited distinct validation results. The retrievals of GEMS single-scattering albedo (SSA) at 443 nm agreed well with the AERONET SSA at 440 nm within reasonable error ranges, with variations observed among aerosol types. For GEMS AOD at 443 nm exceeding 0.4 (1.0), 42.76 % (56.61 %) and 67.25 % (85.70 %) of GEMS SSA data points fell within the ±0.03 and ±0.05 error bounds, respectively. Model-enforced post-processing correction improved GEMS AOD and SSA performance, thereby reducing the diurnal variation in the biases. The validation of the retrievals of GEMS aerosol layer height (ALH) against the CALIOP data demonstrates good agreement, with a mean bias of −0.225 km and 55.29 % (71.70 %) of data points falling within ±1 km (1.5 km).

Incorporating Climate Engineering into Secondary Education: A New Direction for Indiana’s Science Classrooms

Climate change represents a significant existential challenge in modern times, with widespread anxiety over its impacts. There's a growing desire among students to explore climate solutions and identify actions they can personally undertake to address climate change. Despite mitigation efforts, current greenhouse gas emission reduction measures are insufficient, and the development of negative emission technologies is both slow and costly. Consequently, the past two decades have witnessed an escalating interest in alternative strategies to temporarily and intentionally cool the planet. These strategies include injecting reflective particles into the stratosphere or increasing the reflectivity of low-lying ocean clouds. Collectively known as climate engineering, also called geoengineering, these approaches could serve as a temporary shield against the most severe outcomes of climate change, buying time while efforts to mitigate emissions and enhance carbon sequestration reach the required scale.In line with the Indiana state science standards (HS-ESS3-4), this article presents the Climate Engineering Teaching Module (CETM) and recounts firsthand experiences from its application in high school settings. Launched over three years ago, the CETM has been effectively integrated into fifteen Indiana classrooms. As the future citizens and leaders of Indiana, it is crucial that students are well-informed on climate engineering. Educating them about the scientific, ethical, political, and economic facets of climate engineering is imperative for fostering responsible decision-making. By examining the trade-offs associated with climate engineering and encouraging students to conceptualize ways to implement these technologies beneficially while minimizing risks, the CETM offers an innovative and practical approach to teaching climate change and engineering design. This method not only prepares students for active engagement in future discussions on climate engineering but also equips them with a comprehensive understanding of its complexities.

Northern hemisphere land-atmosphere feedback from prescribed plant phenology in CESM

Abstract Plant phenology influences both the terrestrial carbon cycle and land-atmosphere interactions, and therefore can potentially modify large-scale circulations in the atmosphere. However, considerable discrepancies are present among models and between model simulations and observations of plant phenology, adding large uncertainties to future climate projections. Here we modified plant phenology in the Northern Hemisphere in the Community Earth System Model and conducted simulations to characterize how differences in plant phenology influence land-atmosphere coupling. Plant phenology changes the land surface and land-atmosphere interactions by directly modulating absorbed solar radiation and evapotranspiration and indirectly modifying cloud feedback and snow-albedo feedback. Over the Northern Hemisphere, the largest effects occur from March to June when seasonal deciduous phenology is modified from satellite-derived values to model simulations, which results in a &gt;3K increase in surface temperature that propagates to 500hPa (~5km height). Phenology-induced changes in canopy evapotranspiration and surface temperature depend on soil moisture availability during the growing season. Surface temperature decreases significantly due to increasing latent heat flux and cloud reflection where soil moisture is abundant, while soil moisture control over evapotranspiration increases and surface temperature remains little-changed or even increases in more arid regions. Characterizing the influence of phenology on biogeophysical processes is critical, as significant impacts are present both at the land surface and in the atmospheric layers above.

The Impact of the Explicit Representation of Convection on the Climate of a Tidally Locked Planet in Global Stretched-mesh Simulations

Convective processes are crucial in shaping exoplanetary atmospheres but are computationally expensive to simulate directly. A novel technique of simulating moist convection on tidally locked exoplanets is to use a global 3D model with a stretched mesh. This allows us to locally refine the model resolution to 4.7 km and resolve fine-scale convective processes without relying on parameterizations. We explore the impact of mesh stretching on the climate of a slowly rotating TRAPPIST-1e-like planet, assuming it is 1:1 tidally locked. In the stretched-mesh simulation with explicit convection, the climate is 5 K colder and 25% drier than that in the simulations with parameterized convection(with both stretched and quasi-uniform meshes). This is due to the increased cloud reflectivity—because of an increase in low-level cloudiness—and exacerbated by the diminished greenhouse effect due to less water vapor. At the same time, our stretched-mesh simulations reproduce the key characteristics of the global climate of tidally locked rocky exoplanets, without any noticeable numerical artifacts. Our methodology opens an exciting and computationally feasible avenue for improving our understanding of 3D mixing in exoplanetary atmospheres. Our study also demonstrates the feasibility of a global stretched-mesh configuration for LFRic-Atmosphere, the next-generation Met Office climate and weather model.

Road Weather Condition Recognition via Fusing Images and Upsampled Point Cloud Reflection Intensities

Road Weather Condition Recognition via Fusing Images and Upsampled Point Cloud Reflection Intensities

Evaluation of Lightning Flash Rate Parameterizations in a Cloud‐Resolved WRF‐Chem Simulation of the 29–30 May 2012 Oklahoma Severe Supercell System Observed During DC3

AbstractEighteen lightning flash rate parameterization schemes (FRPSs) were investigated in a Weather Research and Forecasting model coupled with chemistry cloud‐resolved simulation of the 29–30 May 2012 supercell storm system observed during the Deep Convective Clouds and Chemistry (DC3) field campaign. Most of the observed storm's meteorological conditions were well represented when the model simulation included both convective damping and lightning data assimilation techniques. Newly‐developed FRPSs based on DC3 radar observations and Lightning Mapping Array data are implemented in the model, along with previously developed schemes from the literature. The schemes are based on relationships between lightning and various kinematic, structural, and microphysical thunderstorm characteristics (e.g., cloud top height, hydrometeors, reflectivity, and vertical velocity) available in the model. The results suggest the model‐simulated graupel and snow/ice hydrometeors require scaling factors to more closely represent proxy observations. The model‐simulated lightning flash trends and total flashes generated by each scheme over the simulation period are compared with observations from the central Oklahoma Lightning Mapping Array. For this supercell system, 13 of the 18 schemes overpredicted flashes by &gt;100% with the group of FRPSs based on storm kinematics and structure (particularly updraft volume) performing slightly better than the hydrometeor‐based schemes. During the storm's first 4 hr, the upward cloud ice flux FRPS, which is based on the combination of vertical velocity and hydrometeors, well represents the observed total flashes and flash rate trend; while, the updraft volume scheme well represents the observed flash rate peak and subsequent sharp decline in flash rate.

Observational Assessment of Changes in Earth’s Energy Imbalance Since 2000

AbstractSatellite observations from the Clouds and the Earth’s Radiant Energy System show that Earth’s energy imbalance has doubled from 0.5 ± 0.2 Wm−2 during the first 10 years of this century to 1.0 ± 0.2 Wm−2 during the past decade. The increase is the result of a 0.9 ± 0.3 Wm−2 increase absorbed solar radiation (ASR) that is partially offset by a 0.4 ± 0.25 Wm−2 increase in outgoing longwave radiation (OLR). Despite marked differences in ASR and OLR trends during the hiatus (2000–2010), transition-to-El Niño (2010–2016) and post-El Niño (2016–2022) periods, trends in net top-of-atmosphere flux (NET) remain within 0.1 Wm−2 per decade of one another, implying a steady acceleration of climate warming. Northern and southern hemisphere trends in NET are consistent to 0.06 ± 0.31 Wm−2 per decade due to a compensation between weak ASR and OLR hemispheric trend differences of opposite sign. We find that large decreases in stratocumulus and middle clouds over the sub-tropics and decreases in low and middle clouds at mid-latitudes are the primary reasons for increasing ASR trends in the northern hemisphere (NH). These changes are especially large over the eastern and northern Pacific Ocean, and coincide with large increases in sea-surface temperature (SST). The decrease in cloud fraction and higher SSTs over the NH sub-tropics lead to a significant increase in OLR from cloud-free regions, which partially compensate for the NH ASR increase. Decreases in middle cloud reflection and a weaker reduction in low-cloud reflection account for the increase in ASR in the southern hemisphere, while OLR changes are weak. Changes in cloud cover in response to SST increases imply a feedback to climate change yet a contribution from radiative forcing or internal variability cannot be ruled out.

The tidal deformation and atmosphere of WASP-12 b from its phase curve

Context. Ultra-hot Jupiters present a unique opportunity to understand the physics and chemistry of planets, their atmospheres, and interiors at extreme conditions. WASP-12 b stands out as an archetype of this class of exoplanets, with a close-in orbit around its star that results in intense stellar irradiation and tidal effects. Aims. The goals are to measure the planet’s tidal deformation, atmospheric properties, and also to refine its orbital decay rate. Methods. We performed comprehensive analyses of the transits, occultations, and phase curves of WASP-12b by combining new CHEOPS observations with previous TESS and Spitzer data. The planet was modeled as a triaxial ellipsoid parameterized by the second-order fluid Love number of the planet, h2, which quantifies its radial deformation and provides insight into the interior structure. Results. We measured the tidal deformation of WASP-12b and estimated a Love number of h2 = 1.55−0.49+0.45 (at 3.2σ) from its phase curve. We measured occultation depths of 333 ± 24 ppm and 493 ± 29 ppm in the CHEOPS and TESS bands, respectively, while the nightside fluxes are consistent with zero, and also marginal eastward phase offsets. Our modeling of the dayside emission spectrum indicates that CHEOPS and TESS probe similar pressure levels in the atmosphere at a temperature of ~2900 K. We also estimated low geometric albedos of Ag = 0.086 ± 0.017 and Ag = 0.01 ± 0.023 in the CHEOPS and TESS passbands, respectively, suggesting the absence of reflective clouds in the high-temperature dayside of the planet. The CHEOPS occultations do not show strong evidence for variability in the dayside atmosphere of the planet at the median occultation depth precision of 120 ppm attained. Finally, combining the new CHEOPS timings with previous measurements refines the precision of the orbital decay rate by 12% to a value of −30.23 ± 0.82 ms yr−1, resulting in a modified stellar tidal quality factor of Q′★ = 1.70 ± 0.14 × 105. Conclusions. WASP-12 b becomes the second exoplanet, after WASP-103b, for which the Love number has been measured from the effect of tidal deformation in the light curve. However, constraining the core mass fraction of the planet requires measuring h2 with a higher precision. This can be achieved with high signal-to-noise observations with JWST since the phase curve amplitude, and consequently the induced tidal deformation effect, is higher in the infrared.

Methods for Validating HRRR Simulated Cloud Properties for Different Weather Phenomena Using Satellite and Radar Observations

Abstract In this study, we evaluate the ability of the High-Resolution Rapid Refresh (HRRR) model to forecast cloud characteristics through comparison of observed and simulated satellite brightness temperatures (BTs) and radar reflectivity during different weather phenomena in December 2021: the Mayfield, Kentucky, tornado on 11 December, a heavy snow event in Minnesota from 10 to 11 December, and the Midwest derecho on 15 December. This is done to illustrate the importance of examining model accuracy across a range of weather phenomena. Observation and forecast objects were created using the Method for Object-Based Diagnostic Evaluation (MODE). HRRR accurately depicted the spatial displacements between observation cloud (defined using BTs) and radar reflectivity objects, namely, the centers of cloud objects are to the east of the radar objects for the tornado and derecho events, and generally west of the radar objects for the snow event. However, HRRR had higher (less intense) simulated BTs and higher (more intense) radar reflectivity than the observations for the tornado event. Simulated radar reflectivity is higher and BTs are lower than the observations during the middle of the snow event. Also, simulated radar reflectivity is higher and BTs are lower than the observations during the derecho event. Of the three weather events, the HRRR forecasts are most accurate for the snow event, based on the object-based threat score, followed by the derecho and tornado events. The tornado event has lower accuracy because matches between paired simulated and observation objects are worse than for the snow event, with less similarity in size forecast objects and greater distance between paired object centers. Significance Statement The purpose of this study is to assess the accuracy of forecast cloud and radar objects, defined using simulated satellite brightness temperatures and radar reflectivity, from the High-Resolution Rapid Refresh (HRRR) model. This assessment was conducted for a tornado, snow, and derecho event from December 2021. Results from these three events indicate that the HRRR model accurately represents the observed displacement between the center of cloud and radar objects for the tornado and derecho events, and is the most accurate overall for the snow event.

Variability and properties of liquid-dominated clouds over the ice-free and sea-ice-covered Arctic Ocean

Abstract. Due to their potential to either warm or cool the surface, liquid-phase clouds and their interaction with the ice-free and sea-ice-covered ocean largely determine the energy budget and surface temperature in the Arctic. Here, we use airborne measurements of solar spectral cloud reflectivity obtained during the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign in summer 2017 and the Arctic Amplification: FLUXes in the Cloudy Atmospheric Boundary Layer (AFLUX) campaign in spring 2019 in the vicinity of Svalbard to retrieve microphysical properties of liquid-phase clouds. The retrieval was tailored to provide consistent results over sea-ice and open-ocean surfaces. Clouds including ice crystals that significantly bias the retrieval results were filtered from the analysis. A comparison with in situ measurements shows good agreement with the retrieved effective radii and an overestimation of the liquid water path and reduced agreement for boundary-layer clouds with varying fractions of ice water content. Considering these limitations, retrieved microphysical properties of clouds observed over the ice-free ocean and sea ice in spring and early summer in the Arctic are compared. In early summer, the liquid-phase clouds have a larger median effective radius (9.5 µm), optical thickness (11.8) and effective liquid water path (72.3 g m−2) compared to spring conditions (8.7 µm, 8.3 and 51.8 g m−2, respectively). The results show larger cloud droplets over the ice-free Arctic Ocean compared to sea ice in spring and early summer caused mainly by the temperature differences in the surfaces and related convection processes. Due to their larger droplet sizes, the liquid clouds over the ice-free ocean have slightly reduced optical thicknesses and lower liquid water contents compared to the sea-ice surface conditions. The comprehensive dataset on microphysical properties of Arctic liquid-phase clouds is publicly available and could, e.g., help to constrain models or be used to investigate effects of liquid-phase clouds on the radiation budget.

MMAF-Net: Multi-view multi-stage adaptive fusion for multi-sensor 3D object detection

In this paper, we propose a 3D object detection method called MMAF-Net that is based on the multi-view and multi-stage adaptive fusion of RGB images and LiDAR point cloud data. This is an end-to-end architecture, which combines the characteristics of RGB images, the front view of point clouds based on reflection intensity, and the bird’s eye view of point clouds. It also adopts a multi-stage fusion approach of “data-level fusion + feature-level fusion” to fully exploit the strength of multimodal information. Our proposed method addresses key challenges found in current 3D object detection methods for autonomous driving, including insufficient feature extraction from multimodal data, rudimentary fusion techniques, and sensitivity to distance and occlusion. To ensure the comprehensive integration of multimodal information, we present a series of targeted fusion methods. Firstly, we propose a novel input form that encodes dense point cloud reflectivity information into the image to enhance its representational power. Secondly, we design the Region Attention Adaptive Fusion module utilizing an attention mechanism to guide the network in adaptively adjusting the importance of different features. Finally, we extend the 2D DIOU (Distance Intersection over Union) loss function to 3D and develop a joint regression loss based on 3D_DIOU and SmoothL1 to optimize the similarity between detected and ground truth boxes. The experimental results on the KITTI dataset demonstrate that MMAF-Net effectively addresses the challenges posed by highly obscured or crowded scenes while maintaining real-time performance and improving the detection accuracy of smaller and more difficult objects that are occluded at far distances.

Evaporative Cooling Does Not Prevent Vertical Dispersion of Effervescent Seawater Aerosol for Brightening Clouds.

Marine cloud brightening (MCB) is a potential intervention to mitigate the effects of climate change by increasing the reflectance of low-level maritime clouds, including those over the Great Barrier Reef. The technique involves dispersing a plume of submicrometer seawater droplets over the ocean, which evaporate, generating nanosized sea-salt aerosols (SSAs) that disperse through the atmosphere with some fraction incorporated into clouds. Droplet evaporation, which occurs in the immediate vicinity (meters to tens of meters) of the source, has been theorized to produce a negatively buoyant plume hindering the mixing of the sea-salt aerosol to cloud height and compromising the effectiveness of MCB. We characterized in situ for the first time the nearfield aerosol dispersion from a point source of atomized seawater produced using the effervescent technique. We observed consistent vertical mixing of the plume up to 150 ± 5 m height at 1 km downwind. The extent of vertical dispersion was influenced by wind velocity and atmospheric stability. We found no evidence that negative buoyancy due to the evaporation of the 0.068 kg/s water fraction significantly suppressed vertical mixing. Our results can be attributed to the small droplet sizes generated by the effervescent spray technology and associated low flow rates required to generate around 1014 droplets s-1. We estimate that, for a hypothetical implementation producing up to 1016 s-1 similarly sized SSAs, evaporative cooling is unlikely to significantly suppress the vertical dispersion of aerosol for MCB.

Robust Susceptibility of Cloud Cover and Radiative Effects to Biases in Retrieved Droplet Concentrations

AbstractThe quantification of cloud droplet number concentration (Nd) is essential in gaining insights into the aerosol‐cloud interactions (ACI), contributing to the most significant source of anthropogenic climate forcing uncertainty. This study compared the retrieved Nd from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) at pixel and scene levels and analyzed the changes in the susceptibility of cloud albedo (CA), cloud fraction (CF), and cloud radiative effect (CRE) to Nd. The results show that the differences between the pixel‐level cloud products from VIIRS and MODIS are caused by a combination of factors including cloud detectability, spatial resolution, and wavelengths of channels. As cloud top inhomogeneity increases, the differences become more significant. This deviation finally results in VIIRS achieving a global average of 40% higher Nd than MODIS. Although the non‐systematic differences cause an inevitable variability in the dependence of CA, CF, and CRE on Nd, the strong susceptibilities of these cloud properties to Nd are similar for both VIIRS and MODIS. This study found minimal impact on the susceptibilities, whether using the average Nd or that of the top 10% reflective clouds. This study further supports the conclusion that cloud cover and radiative effect are highly susceptible to variations in Nd, which can reduce uncertainty when evaluating the cooling effect of ACI in the context of global warming.

Early detection of pine shoot beetle attack using vertical profile of plant traits through UAV-based hyperspectral, thermal, and lidar data fusion

Pine shoot beetle (PSB) is one of the most damaging forest insects of Yunnan pine plantations in southwest China. However, the subtle symptoms of heterogeneous tree crowns make it difficult to accurately detect at early stage of PSB attack. Here, we evaluated the potential of a combination of plant traits (PTs) and vegetation indices (VIs) to distinguish different levels of tree damage by integrating hyperspectral, thermal imagery, and light detection and ranging (lidar) data based on unmanned airborne vehicle (UAV) systems. A voxelization method was used to fuse hyperspectral reflectance, temperature, and lidar point cloud data. Subsequently, PTs such as pigments (i.e., chlorophyll, carotenoid, and anthocyanin contents) were retrieved from a radiative transfer model inversion, and structural, fluorescence, and thermal traits, as well as VIs, were derived from the fusion data. We developed a novel analytical approach using random forest (RF) algorithm with predictors from different spatial distributions (i.e., horizontal directions, vertical layers, and vertical clusters) to compare the performance of tree severity classification. The results showed that the difference of both PTs and VIs in vertical layers between different severity levels are more than that in horizontal directions. The performance of RF model with predictors of the vertical layers (OA = 74 %, kappa = 0.65) was better than that using the predictive variables in horizontal directions (OA = 69 % and kappa = 0.60) of tree crowns. Using the vertical clustering features RF model increased the accuracy (OA = 78 % and kappa = 0.70), especially for slightly and moderately damaged trees, with improvements of 10 % and 12 %, respectively. Among all variables analyzed, chlorophylls were the most important predictor, followed by photochemical reflectance index and structural traits. Our work demonstrates the effectiveness of using fused UAV-based multi-sensor data for early detection of PSB attack, and can be applied other potential forest diseases and insect monitoring.

Experimental statistical analysis of radar signals reflected from weather hazards

The nature of the causes of prerequisites for flight accidents due to meteorological conditions has remained unchanged for many years. For the effective operation of aviation, it is necessary to solve the issues of the timely detection of weather hazards and the identification of their intensity. This article discusses the research into the atmospheric reflectivity and turbulence in cumulonimbus clouds using a near-field meteorological radar system. Experimental studies are carried out with the aim of obtaining estimates of statistical characteristics of the reflectivity distribution and turbulence for weather hazards. The methodology for conducting an experimental study to obtain information about the weather hazards (rain shower, thunderstorm and hail), special features of the radar reflectivity propagation and the specific rate of turbulent energy dissipation in the considered conditions for the Tver region is presented. The analysis of the obtained results has been carried out: comparisons of meteorological data derived, using the meteorological radar complex of the near airfield zone (WR BZ), with the reliable sources of meteorological observation data obtained during experimental studies. Studies of horizontal sections and vertical profiles of the weather hazards parameters under consideration, associated with the cumulonimbus area, have been carried out. A data bank has been formed. Histograms of the information parameter distribution have been constructed. The article considers the results of experimental data approximation, using the χ2 criterion for various distribution laws. It is shown that the distributions of reflectivity and turbulence can be described by the generalized Rice’s law. The results obtained can be used to correct the classification criteria of weather hazards to subsequently increase the justification and reliability of the weather hazard classification. The further development of the method to process meteorological information is also of paramount importance to more correctly interpretate experimental data processing results of remote meteorological phenomena sensing.