Cloud Reflectivity Research Articles

Online analysis of Amazon's soils through reflectance spectroscopy and cloud computing can support policies and the sustainable development.

Analyzing soil in large and remote areas such as the Amazon River Basin (ARB) is unviable when it is entirely performed by wet labs using traditional methods due to the scarcity of labs and the significant workforce requirements, increasing costs, time, and waste. Remote sensing, combined with cloud computing, enhances soil analysis by modeling soil from spectral data and overcoming the limitations of traditional methods. We verified the potential of soil spectroscopy in conjunction with cloud-based computing to predict soil organic carbon (SOC) and particle size (sand, silt, and clay) content from the Amazon region. To this end, we request physicochemical attribute values determined by wet laboratory analyses of 211 soil samples from the ARB. These samples were submitted to spectroscopy Vis-NIR-SWIR in the laboratory. Two approaches modeled the soil attributes: M-I) cloud-computing-based using the Brazilian Soil Spectral Service (BraSpecS) platform, and M-II) computing-based in an offline environment using R programming language. Both methods used the Cubist machine learning algorithm for modeling. The coefficient of determination (R2), mean absolute error (MAE) and root mean squared error (RMSE) served as criteria for performance assessment. The soil attributes prediction was highly consistent, considering the measured and predicted by both approaches M-I and M-II. The M-II outperformed the M-I in predicting both particle size and SOC. For clay content, the offline model achieved an R2 of 0.85, with an MAE of 86.16gkg⁻1 and RMSE of 111.73gkg⁻1, while the online model had an R2 of 0.70, MAE of 111.73gkg⁻1, and RMSE of 144.19gkg⁻1. For SOC, the offline model also showed better performance, with an R2 of 0.81, MAE of 3.42gkg⁻1, and RMSE of 4.57gkg⁻1, compared to an R2 of 0.72, MAE of 3.66gkg⁻1, and RMSE of 5.53gkg⁻1 for the M-I. Both modeling methods demonstrated the power of reflectance spectroscopy and cloud computing to survey soils in remote and large areas such as ARB. The synergetic use of these techniques can support policies and sustainable development.

Insights into Cloud Albedo Biases from a Cloud-Controlling Factor Framework

Abstract A long-standing and pervasive problem in climate modeling is the proper representation of cloud albedo over the Southern Ocean (SO). In this study, we investigate the causes of SO cloud albedo biases using phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations and a cloud-controlling factor approach on daily time scales. Cloud albedo, computed from upwelling and downwelling shortwave radiation at the surface and top of the atmosphere, is averaged into bins of vertical velocity, surface wind, and sea surface temperature. The performance of 15 models in both atmosphere-only and ocean-coupled configurations is evaluated against Clouds and the Earth’s Radiant Energy System (CERES) satellite retrievals in combination with ERA5 reanalysis for the 2000–14 period. We find that the SO cloud biases maximize in the 50°–65° oceanic band and that models tend to underestimate SO cloud reflectivity for cold conditions and weak surface winds. In turn, descent (ascent) conditions are consistently underestimated (overestimated) across models for both hemispheres in the same latitude band. With a very similar approach, we evaluate how models represent the observed cloud albedo hemispheric asymmetry over oceans, which also maximizes in the 50°–65° band. The sign of the asymmetry is consistently predicted by all models, many of which also predict a similar magnitude to observations. However, this is also a consequence of compensating global biases as individually most models tend to either overpredict or underpredict cloud albedo in both hemispheres. We propose that surface temperatures less than 4°C are important in explaining SO bias in cloud albedo and that they also partly explain the observed hemispheric asymmetry. Further, we hypothesize that the 4°C threshold sets a hemispheric asymmetry in cloud phase.

Cloud reflectivity and hydrometeors profile characterization over the North-West Himalayan region based on decade-long GPM- GMI and DPR observations

Cloud reflectivity and hydrometeors profile characterization over the North-West Himalayan region based on decade-long GPM- GMI and DPR observations

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 transbasin 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-yr climate model simulation corroborates the NTA and NPO forcing of NEP low cloud–SST variability and subsequent ENSO.

Exploring Marine Cloud Brightening with a Reduced Complexity Model

Throughout the industrial period, anthropogenic aerosols have likely offset approximately one-third of the warming caused by greenhouse gases. Marine cloud brightening aims to capitalize on one aspect of this phenomenon to potentially mitigate global warming by enhancing cloud reflectivity through adjustments in cloud droplet concentration. This study employs a simplified yet comprehensive modeling framework, integrating an open-source parcel model for aerosol activation, a radiation transport model based on commercial computational fluid dynamics code, and assimilated meteorological data. The reduced complexity model addresses the challenges of rapid radiation transfer calculations while managing uncertainties in aerosol–cloud-radiation (ACR) parameterizations. Despite using an uncoupled ACR mechanism and omitting feedback between clouds and aerosols, our results closely align with observations, validating the robustness of our assumptions and methodology. This demonstrates that even simplified models, supported by parcel modeling and observational constraints, can achieve accurate radiation transfer calculations comparable to advanced climate models. We analyze how variations in droplets size and concentration affect cloud albedo for geoengineering applications. Optimal droplet sizes, typically within the 20–35-µm range, significantly increase cloud albedo by approximately 28%–57% across our test cases. We find that droplets transmit about 29% more solar radiation than droplets. Effective albedo changes require injection concentrations exceeding background levels by around 30%, diminishing as concentrations approach ambient levels. Considerations must also be given to the spray pattern of droplet injections, as effective deployment can influence cloud thickness and subsequently impact cloud albedo. This research provides insights into the feasibility and effectiveness of using a reduced complexity model for marine cloud brightening with frontal cyclone and stratus cumulus clouds, and emphasizes the need to also consider background droplets size and concentration than just meteorological conditions.

Soil Moisture-Cloud-Precipitation Feedback in the Lower Atmosphere From Functional Decomposition of Satellite Observations.

The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of weather and climate models. Here, we use functional decomposition of satellite-derived SM and cloud vertical profiles (CVP) to quantify the relationship between SM and the vertical distribution of cloud water in the central US. High-dimensional model representation is used to disentangle the contributions of SM and other land-surface and atmospheric variables to the CVP. Results show that the sign and strength of the SM-cloud-precipitation feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in antecedent 7-hr SM and land-surface temperature enhance cloud reflectivity up to 4 dBZ in the lower atmosphere about 1-3km above the surface. Our approach presents new insights into the SM-cloud-precipitation feedback and aids in the diagnosis of land-atmosphere interactions simulated by weather and climate models.

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.

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

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