No abstract available.

No abstract available.

No abstract available.

No abstract available.

One of the top priorities of the Surface Biology and Geology (SBG) Earth Observing System is the detection and retrieval of elevated temperature features (ETF) usually found in the vicinity of active fires and volcanic activity. We test the ability of currently proposed midwave (MIR: 3-5 μm) and thermal infrared (TIR: 8-12 μm) bands to detect ETF within the 400-1200 K range. Specifically, our investigation aims to compare and contrast the use of the 4 and 4.8 μm MIR bands. We use land surface temperature data obtained by the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) instrument over active fire and lava flows to model at-sensor SBG radiances in the 3-12 μm range. This is achieved using the Temperature Emissivity Uncertainty Simulator (TEUSim) with the designated/proposed SBG MIR and TIR band characteristics. For ETF detection, we applied the Normalized Thermal Index (NTI) and Enhanced Thermal Index (ETI) to determine a suitable threshold for a wide range of ETF sizes and temperatures. We find that combining an NTI threshold of -0.7 followed by an ETI threshold of 0.02 accurately identifies ETFs at a 97% rate. Sensor noise up to 0.5 K has negligible effects on ETF detection in the 400-1200 K range. The currently proposed SBG MIR and TIR bands are sufficient to detect unsaturated ETFs caused by wildfire and volcanic activities at a ~3 day revisit and subpixel ETF area of ~9 m^2 (at 500K) that is unattainable by current satellite TIR instruments.

No abstract available.

No abstract available.

Heatwaves in California manifest as both dry and humid events. While both forms have become more prevalent, recent studies have identified a shift towards more humid events. Understanding the complex interactions of each heatwave type with the urban heat island are crucial for impacts, but remain understudied. Here, we address this gap by contrasting how dry versus humid heatwaves shape the intra-urban heat of greater Los Angeles (LA) area. We used a consecutive 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 MERRA2 reanalysis data to compare mesoscale dynamics, followed by high-resolution Weather Research Forecast modeling over urbanized Southern California. We employ moist thermodynamic variables to quantify heat stress and perform spatial clustering analysis to characterize the spatiotemporal intra-urban variability. We find that despite temperatures being 10±3℃ 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 non-heatwave local climates. But both types of heatwaves diminish the intra-urban heat stress variability. Valley areas such as San Bernardino and Riverside experience the worst impacts with up to 6±0.5℃ 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.

Extreme heat continues to be a pressing challenge of the changing climate.The impacts of extreme heat manifest on two different spatio-temporal scales: (1) episodiccontinent-wide heatwaves (HW) and (2) the city-scale Urban Heat Island (UHI). As HWs arebecoming more frequent, longer, and severe, they pose serious implications of increased publichealth risks at a city scale, and have adverse impacts on agricultural and terrestrial/aquaticecosystems on the regional scale. Here we offer a fresh perspective of the HW as a forcing thatinvokes dynamic, heterogeneous, scale-dependent responses evident in inter and intra-urbanheat islets. A numerical simulation of the 2018 European heatwave including the surfaceand air temperature-based UHIs of six urban agglomerations, with a high-resolution focus onParis, serves as our case study. We find that the mean nighttime UHI intensities are reducedfor inland cities but increased for coastal cities. Our examination of the heat islets revealstwo major findings: (i) the HW homogenizes the intra-urban surface temperatures during thedaytime (reduces variance) (ii) the HW impacts are most significant on the scale of large,spatially discontiguous extreme heat islets during nighttime. These results underscore the needto move beyond the prevalent HW-mean UHI intensity characterization and toward intra-urbanheat islet analyses that aid targeted mitigation.