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This study addresses the longstanding question of the reliability of gridded visible/infrared satellite cloud properties in partially cloudy scenes. By using in‐situ cloud probes and airborne Research Scanning Polarimeter (RSP) observations, we analyze bias changes in satellite retrievals from the Spinning Enhanced Visible Infra‐Red Imager (SEVIRI) geostationary sensor during the ORACLES campaign. Biases in cloud optical depth (τ) and droplet effective radius (re) modestly change for cloud area fraction greater than 35%. The agreement between SEVIRI and RSP re substantially improves when the retrievals are averaged after removing pixels with τ < 3.0, yielding biases indistinguishable from overcast scenes. In addition, satellite and RSP show an excellent agreement for closed‐ and open‐cell stratocumulus clouds, showing that the satellite retrievals capture spatial changes of re, and confirming that satellites can faithfully reproduce real physical features for optically thick and partially cloudy scenes. We demonstrate that a simple methodology can minimize uncertainties in satellite‐based climate studies. Plain Language Summary A long‐standing question originated from using satellite gridded cloud properties derived from satellite sensors is whether physical inferences can be made for partially cloudy grids, given the potential artifacts in the satellite data. This is a critical issue as the general way of reducing uncertainties in satellite products is by limiting the analysis to overcast scenes. However, this choice inadvertently introduces a sampling bias by neglecting a wide range of cloud morphological structures and associated atmospheric conditions. Here we investigate ways to address this issue by directly analyzing the dependency of satellite biases on cloud fraction. Here we show with the use of airborne data that satellite observations can yield physically meaningful cloud microphysical properties for scenes with cloud area coverage as small as 35%. Moreover, the bias reduction and the retrievals lack of sensitivity to changes in cloud fraction is a particularly robust result for optically thick clouds. We demonstrate that by removing thin clouds prior to gridding the data, as well as by filtering the data based on a minimum cloud fraction threshold (35%), satellite biases can effectively be reduced. Consequently, satellite data can be reliable applied to multiple climate applications. Key Points We used ORACLES airborne observations to evaluate satellite cloud retrievals from Spinning Enhanced Visible Infra‐Red Imager and their dependency on cloud area fraction Satellite retrievals feature a negligible sensitivity to cloud fraction when optically thin clouds are removed prior to gridding the data The proposed filtering criteria reduce systematic biases, making the satellite data suitable for climate and aerosol‐cloud interactions research

Long-term observations of deep convective cloud (DCC) vertical velocity and mass flux were collected during the Observations and Modelling of the Green Ocean Amazon (GoAmazon2014/5) experiment. Precipitation echoes from a surveillance weather radar near Manaus, Brazil, are tracked to identify and evaluate the isolated DCC lifecycle evolution during the dry and wet seasons. A radar wind profiler (RWP) provides precipitation and air motion profiles to estimate the vertical velocity, mass flux, and mass transport rates within overpassing DCC cores as a function of the tracked cell lifecycle stage. The average radar reflectivity factor (Z), DCC area (A), and surface rainfall rate (R) increased with DCC lifetime as convective cells were developing, reached a peak as the cells matured, and decreased thereafter as cells dissipated. As the convective cells mature, cumulative DCC properties exhibit stronger updraft behaviors with higher upward mass flux and transport rates above the melting layer (compared with initial and later lifecycle stages). In comparison, developing DCCs have the lowest Z associated with weak updrafts, as well as negative mass flux and transport rates above the melting layer. Over the DCC lifetime, the height of the maximum downward mass flux decreased, whereas the height of the maximum net mass flux increased. During the dry season, the tracked DCCs had higher Z, propagation speed, and DCC area, and were more isolated spatially compared with the wet season. Dry season DCCs exhibit higher Z, mass flux, and mass transport rate while developing, whereas wet season DCCs exhibit higher Z, mass flux, and mass transport rates at later stages.

This study focuses on quantifying the conditional relationship between aerosol and convective precipitation properties of isolated deep convective clouds (DCCs) in the Houston–Galveston region, after adjusting for confounding effects. We leverage comprehensive ground-based observations from the TRacking Aerosol Convection interactions ExpeRiment (TRACER) to estimate aerosol effects on convective echo top height (ETH), intensity, and area separately. Our results show that greater aerosol number concentrations generally have a limited impact on these convective properties, showing relationships consistent with the possibility of both invigoration and suppression effects. Under certain conditions, where ultrafine particles are abundant, aerosols exhibit a positive effect on ETH, increasing it by about 1 km. However, it is inevitable to consider measurement uncertainties and the limitations of temporal and spatial resolution in the data, as these factors can further contribute to uncertainties in our estimates. In DCCs associated with sea breezes, the estimated aerosol effects on DCCs are found to be more pronounced. However, this heightened effect could be attributed to the exclusion of key confounders such as boundary layer updrafts in the analysis.

Malnutrition remains a significant cause of death and disability for children in low-income countries. To address this, multi-sectoral interventions have shown potential. However, there is a lack of economic evaluations for such programs. This study aimed to assess the cost-effectiveness and benefit-cost analysis of a multi-sectoral nutrition intervention in Nepal. We used costing estimates of the Suaahara II program from SEEMS-Nutrition group at University of Washington. We measured program outcomes in terms of prevented premature deaths, reduced stunting cases, and reduced diarrhea incidence using the Lived Saved Tool (LiST). We evaluated the benefits by estimating averted DALYs (disability-adjusted life years) and the value resulting from lowered premature mortality, increased lifetime productivity, and reduced non-fatal health risks. We estimated cost-effectiveness ratio at $24,352 per DALY averted assuming standard life expectancy. We estimated the benefit-cost ratio at 1.93-1.95. In conclusion, although the program is not cost-effective, it could still be considered a good investment as the benefit-cost analysis confirmed that the program yielded a positive return on investment.

Southern Africa produces almost a third of the Earth’s biomass burning (BB) aerosol particles, yet the fate of these particles and their influence on regional and global climate is poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) is a 5-year NASA EVS-2 (Earth Venture Suborbital-2) investigation with three intensive observation periods designed to study key atmospheric processes that determine the climate impacts of these aerosols. During the Southern Hemisphere winter and spring (June–October), aerosol particles reaching 3–5 km in altitude are transported westward over the southeast Atlantic, where they interact with one of the largest subtropical stratocumulus (Sc) cloud decks in the world. The representation of these interactions in climate models remains highly uncertain in part due to a scarcity of observational constraints on aerosol and cloud properties, as well as due to the parameterized treatment of physical processes. Three ORACLES deployments by the NASA P-3 aircraft in September 2016, August 2017, and October 2018 (totaling ~ 350 science flight hours), augmented by the deployment of the NASA ER-2 aircraft for remote sensing in September 2016 (totaling ~ 100 science flight hours), were intended to help fill this observational gap. ORACLES focuses on three fundamental science themes centered on the climate effects of African BB aerosols: (a) direct aerosol radiative effects, (b) effects of aerosol absorption on atmospheric circulation and clouds, and (c) aerosol–cloud microphysical interactions. This paper summarizes the ORACLES science objectives, describes the project implementation, provides an overview of the flights and measurements in each deployment, and highlights the integrative modeling efforts from cloud to global scales to address science objectives. Significant new findings on the vertical structure of BB aerosol physical and chemical properties, chemical aging, cloud condensation nuclei, rain and precipitation statistics, and aerosol indirect effects are emphasized, but their detailed descriptions are the subject of separate publications. The main purpose of this paper is to familiarize the broader scientific community with the ORACLES project and the dataset it produced.

The representations of clouds, aerosols, and cloud–aerosol–radiation impacts remain some of the largest uncertainties in climate change, limiting our ability to accurately reconstruct past climate and predict future climate. The south-east Atlantic is a region where high atmospheric aerosol loadings and semi-permanent stratocumulus clouds are co-located, providing an optimum region for studying the full range of aerosol–radiation and aerosol–cloud interactions and their perturbations of the Earth's radiation budget. While satellite measurements have provided some useful insights into aerosol–radiation and aerosol–cloud interactions over the region, these observations do not have the spatial and temporal resolution, nor the required level of precision to allow for a process-level assessment. Detailed measurements from high spatial and temporal resolution airborne atmospheric measurements in the region are very sparse, limiting their use in assessing the performance of aerosol modelling in numerical weather prediction and climate models. CLARIFY-2017 was a major consortium programme consisting of five principal UK universities with project partners from the UK Met Office and European- and USA-based universities and research centres involved in the complementary ORACLES, LASIC, and AEROCLO-sA projects. The aims of CLARIFY-2017 were fourfold: (1) to improve the representation and reduce uncertainty in model estimates of the direct, semi-direct, and indirect radiative effect of absorbing biomass burning aerosols; (2) to improve our knowledge and representation of the processes determining stratocumulus cloud microphysical and radiative properties and their transition to cumulus regimes; (3) to challenge, validate, and improve satellite retrievals of cloud and aerosol properties and their radiative impacts; (4) to improve the impacts of aerosols in weather and climate numerical models. This paper describes the modelling and measurement strategies central to the CLARIFY-2017 deployment of the FAAM BAe146 instrumented aircraft campaign, summarizes the flight objectives and flight patterns, and highlights some key results from our initial analyses.

In a democratic society like India, there could be nothing more authoritative or powerful than having the influence to ability to influence the opinion and action of public. The present era is an epoch of information. Media, in general refers to means of communication; Press comprises of newspaper, magazines and Electronic media i.e. television, radio, mobile, internet are used for the purpose of obtaining information which is fundamental to the functioning of a democratic society.

Aerosol–cloud–precipitation interactions (ACIs) provide the greatest source of uncertainties in predicting changes in Earth's energy budget due to poor representation of marine stratocumulus and the associated ACIs in climate models. Using in situ data from 329 cloud profiles across 24 research flights from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign in September 2016, August 2017, and October 2018, it is shown that contact between above-cloud biomass burning aerosols and marine stratocumulus over the Southeast Atlantic Ocean was associated with precipitation suppression and a decrease in the precipitation susceptibility (So) to aerosols. The 173 “contact” profiles with aerosol concentration (Na) greater than 500 cm−3 within 100 m above cloud tops had a 50 % lower precipitation rate (Rp) and a 20 % lower So, on average, compared to 156 “separated” profiles with Na less than 500 cm−3 up to at least 100 m above cloud tops. Contact and separated profiles had statistically significant differences in droplet concentration (Nc) and effective radius (Re) (95 % confidence intervals from a two-sample t test are reported). Contact profiles had 84 to 90 cm−3 higher Nc and 1.4 to 1.6 µm lower Re compared to separated profiles. In clean boundary layers (below-cloud Na less than 350 cm−3), contact profiles had 25 to 31 cm−3 higher Nc and 0.2 to 0.5 µm lower Re. In polluted boundary layers (below-cloud Na exceeding 350 cm−3), contact profiles had 98 to 108 cm−3 higher Nc and 1.6 to 1.8 µm lower Re. On the other hand, contact and separated profiles had statistically insignificant differences between the average liquid water path, cloud thickness, and meteorological parameters like surface temperature, lower tropospheric stability, and estimated inversion strength. These results suggest the changes in cloud microphysical properties were driven by ACIs rather than meteorological effects, and adjustments to existing relationships between Rp and Nc in model parameterizations should be considered to account for the role of ACIs.

In situ cloud probe data from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign were used to estimate the effective radius (Re), cloud optical thickness (τ), and cloud droplet concentration (Nc) for marine stratocumulus over the southeast Atlantic Ocean. The in situ Re, τ, and Nc were compared with co-located Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals of Re and τ and MODIS-derived Nc. For 145 cloud profiles, a MODIS retrieval was co-located with in situ data with a time gap of less than 1 h. On average, the MODIS Re and τ (11.3 µm and 11.7) were 1.6 µm and 2.3 higher than the in situ Re and τ with Pearson's correlation coefficients (R) of 0.77 and 0.73, respectively. The average MODIS Nc (151.5 cm−3) was within 1 cm−3 of the average in situ Nc with an R of 0.90. The 145 cloud profiles were classified into 67 contact profiles where an aerosol concentration (Na) greater than 500 cm−3 was sampled within 100 m above cloud tops and 78 separated profiles where Na less than 500 cm−3 was sampled up to 100 m above cloud tops. Contact profiles had a higher in situ Nc (by 88 cm−3), higher τ (by 2.5), and lower in situ Re (by 2.2 µm) compared to separated profiles. These differences were associated with aerosol–cloud interactions (ACI), and MODIS estimates of the differences were within 5 cm−3, 0.5, and 0.2 µm of the in situ estimates when profiles with MODIS Re>15 µm or MODIS τ>25 were removed. The agreement between MODIS and in situ estimates of changes in Re, τ, and Nc associated with ACI was driven by small biases in MODIS retrievals of cloud properties relative to in situ measurements across different aerosol regimes. Thus, when combined with estimates of aerosol location and concentration, MODIS retrievals of marine stratocumulus cloud properties over the southeast Atlantic can be used to study ACI over larger domains and longer timescales than possible using in situ data.

Advances in performance and analysis capabilities have accelerated the development of object tracking algorithms for atmospheric research. This has resulted in a growing number of studies using Lagrangian tracking techniques to analyze the evolution of atmospheric phenomena and the underlying processes. However, the increasing complexity and variety of tracking algorithms present a steep learning curve for new users and make it difficult for existing users to compare algorithm performance. We introduce CoCoMET (Community Cloud Model Evaluation Toolkit), an open-source toolkit that addresses these issues. CoCoMET simplifies the process of running multiple tracking algorithms simultaneously and analyzing objects in both model and observational datasets by specifying parameters in a single configuration file. It standardizes input data from different sources into a consistent format and unifies the tracking output across algorithms. CoCoMET enhances the functionality of existing tracking methods by calculating additional properties such as cell growth and dissipation rates, perimeter, surface area, convexity, and irregularity. In addition, CoCoMET includes a novel method for identifying mergers and splits in 2D and 3D tracks and supports the integration of Eulerian/stationary datasets external to the tracking data for process studies. Its potential utility is demonstrated through examples of model intercomparison, model evaluation against observations, and comparisons between tracking algorithms. Designed for open-source environments, CoCoMET will continue to expand with future releases, incorporating more input data types and tracking algorithms.