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Heatwaves in California manifest as both dry and humid events. While both forms have become more prev-alent, recent studies have identified a shift toward more humid events. Understanding the complex interactions of each heatwave type with the urban heat island is crucial for impacts but remains understudied. Here, we address this gap by contrasting how dry versus humid heatwaves shape the intraurban heat of the greater Los Angeles area. We used a consecu-tive contrasting set of heatwaves from 2020 as a case study: a prolonged humid heatwave in August and an extremely dry heatwave in September. We used MERRA-2 reanalysis data to compare mesoscale dynamics, followed by high-resolution Weather Research and Forecasting modeling over urbanized Southern California. We employ moist thermodynamic varia-bles to quantify heat stress and perform spatial clustering analysis to characterize the spatiotemporal intraurban variability. We find that, despite temperatures being 108 6 38C hotter in the September heatwave, the wet-bulb temperature, closely related to the risk of human heat stroke, was higher in August. While dry and humid heat display different spatial patterns, three distinct spatial clusters emerge based on nonheatwave local climates. Both types of heatwaves diminish the intraur-ban heat stress variability. Valley areas such as San Bernardino and Riverside experience the worst impacts, with up to 68 6 0.58C of additional heat stress during heatwave nights. Our results highlight the need to account for the disparity in small-scale heatwave patterns across urban neighborhoods in designing policies for equitable climate action.

The Landsat global consolidated data archive now exceeds 50 years. In recognition of the need for consistently processed data across the Landsat satellite series, the U.S. Geological Survey (USGS) initiated collection-based processing of the entire archive that was processed as Collection 1 in 2016. In preparation for the data from the now successfully launched Landsat 9, the USGS reprocessed the Landsat archive as Collection 2 in 2020. This paper describes the rationale for, and the contents and advancements provided by Collection 2, and highlights the differences between the Collection 1 and Collection 2 products. Notably, the Collection 2 products have improved geolocation and, for the first time, the USGS provides a global inventory of Level 2 surface reflectance and surface temperature products. Also for the first time, the USGS used a commercial cloud computing architecture to efficiently process the archive and enable direct cloud access of the Landsat products. The paper concludes with discussion of likely improvements expected in Collection 3 in preparation for the Landsat Next mission that is planned for launch in the early 2030s.

In recent decades, Earth Observation (EO) satellite missions have improved in spatial resolution and revisit times. These missions, traditionally government-funded, utilize state-of-the-art technology and rigorous instrument calibration, with each mission costing millions of dollars. Recently, nano-satellites known as CubeSats are presenting a cost-effective option for EO; their capacity of working as a constellation has brought an unprecedented opportunity for EO in terms of achievable spatial and temporal resolutions, albeit at the cost of decreased accuracy and cross-sensor consistency. As such, CubeSat datasets often require post-calibration approaches before using them for scientific applications. K-sharp is a relatively simple, data-agnostic machine learning approach that combines K-means and partial least squares regression to derive relationships between two sets of images for normalization. This study used Planet's four-band CubeSat imagery to sharpen day-coincident Landsat 8 normalized difference vegetation index, albedo, and the first short-wave infrared (SWIR) band from 30 m to 3 m spatial resolution (it should be noted that the four-band CubeSat product does not include the first SWIR band, and that the calculation of albedo is not directly possible from this product). K-sharp was tested over agricultural, savanna, rainforest, and tundra sites with and without atmospheric correction. Our model reproduced surface conditions with an average r2 of 0.88 (rMAE = 11.39%) across all study sites and target variables when compared against the original Landsat 8 data. These results showcase the promising potential of K-sharp in generating precise, CubeSat-derived datasets with high radiometric quality, which can be incorporated into agricultural or ecological applications to enhance their decision-making process at fine spatial scales.

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Stormwater quality management is essential for the protection of aquatic life of receiving waterbodies. This study presents a unique case study, whereby a stormwater management model (SWMM) was developed to assess the performances of street sweeping and infiltration trenches to reduce total suspended solids (TSS) loads in the Town of Jasper located in Jasper National Park, AB, Canada. Two catchment models were developed for the Town using long-term meteorological data, as well as watershed and drainage system data provided by the Municipality of Jasper. The quality parameters were calibrated so that TSS loads at the outfalls were within a 20% error of monitoring data. The performance of proposed best management practices was assessed using single-event and continuous data simulations. The results indicated that a combined sweeping/trench control scenario would generate the highest TSS removal efficiency of up to 51%. Key words: best management practices, stormwater quality management, SWMM model, TSS modelling, 1. Introduction Stormwater management has been a long-standing engineering practice that has primarily focused on flood control (Guo and Adams 1998). In recent years, there has been a move towards [...]

BackgroundVest Prevention of Early Sudden Death Trial did not demonstrate a significant reduction in arrhythmic death with the wearable cardioverter-defibrillator (WCD), but compliance with the device may have substantially affected the results. ThePletcher influence of WCD compliance on outcomes has not yet been fully evaluated.MethodsUsing linear and pooled logistic models, we performed as-treated analyses omitting person-time in the hospital and adjusted for correlates of WCD compliance. To assess the impact of early stopping of WCD, we performed a per-protocol Kaplan-Meier analysis, censoring after the last day the WCD was worn. Interactions of potential effect modifiers with treatment assignment and WCD compliance on outcomes were investigated. Finally, we used linear models to identify predictors of WCD compliance.ResultsA per-protocol analysis demonstrated a significant reduction in total (P

IntroductionFor decades, functional primary human osteocyte cultures have been crucially needed for understanding their role in bone anabolic processes and in endocrine phosphate regulation via the bone-kidney axis. Mature osteocyte proteins (sclerostin, DMP1, Phex and FGF23) play a key role in various systemic diseases and are targeted by successful bone anabolic drugs (anti-sclerostin antibody and teriparatide (PTH1-34)). However, cell lines available to study osteocytes produce very little sclerostin and low levels of mature osteocyte markers. We have developed a primary human 3D organotypic culture system that replicates the formation of mature osteocytes in bone.MethodsPrimary human osteoblasts were seeded in a fibrinogen / thrombin gel around 3D-printed hanging posts. Following contraction of the gel around the posts, cells were cultured in osteogenic media and conditioned media was collected for analysis of secreted markers of osteocyte formation.ResultsThe organoids were viable for at least 6 months, allowing co-culture with different cell types and testing of bone anabolic drugs. Bulk RNAseq data displayed the developing marker trajectory of ossification and human primary osteocyte formation in vitro over an initial 8- week period. Vitamin D3 supplementation increased mineralization and sclerostin secretion, while hypoxia and PTH1-34 modulated sclerostin. Our culture system also secreted FGF23, enabling the future development of a bone-kidney-parathyroid-vascular multi-organoid or organ-on-a-chip system to study disease processes and drug effects using purely human cells.DiscussionThis 3D organotypic culture system provides a stable, long-lived, and regulated population of mature human primary osteocytes for a variety of research applications.

High spatial resolution global land surface emissivity datasets are valuable for various applications. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is the only accessible global emissivity data with a high spatial resolution (∼100 m). Since the launch of the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) in June 2018, another high spatial resolution GED can be generated. A comprehensive comparison is needed to investigate the consistency between the ASTER and ECOSTRESS GEDs before subsequent applications and the release of the ECOSTRESS GED. We conducted a first-ever comparison between the ASTER and ECOSTRESS GEDs in two ways: 1) global spatial comparison and 2) validation using emissivity measurements at 9 sand dune and 2 vegetated sites. The results reveal that the ECOSTRESS emissivities in bands 2 and 4 are in good agreement with ASTER GED, with mean absolute biases (MAB) of ∼ 1 % averaged over the globe. The emissivity difference is the largest for snow and ice in these two bands. For band 5, the discrepancy is relatively larger with a MAB of 1.7 %. The largest emissivity difference is found over evergreen broadleaf forests in humid regions. Comparison with measurements reveals an average root-mean-square error (RMSE) of ECOSTRESS emissivity of ∼ 3 % among all the nine sand sites for all the 3 bands. At the two vegetated sites, the average RMSE is ∼ 1.5 %. The absolute biases are below 3 % in all the bands. The average RMSE between ASTER and ECOSTRESS GEDs among all sand dune sites is 1.29 %. The evaluation results demonstrate a good agreement between these two emissivity retrievals overall and high accuracy of the ECOSTRESS GED.