Beschreibung: Our focus is the way various scales of motion in the tropical ocean are linked through mixing and its modification by larger scales. Enhanced mixing caused by small vertical scale features (SVSs) in the equatorial thermocline is known to impact the state of the ocean and its interaction with the atmosphere, in particular the sea surface temperature of the Pacific cold tongue and ENSO variability. The SVSs are produced by wind variability and instabilities, with an equatorial enhancement caused by a combination of factors including the characteristics of the forcing and propagation of internal waves and near-equator inertial and sub-harmonic parametric instabilities. Numerous scale interactions are at play. For instance, an eastward extension of the warm(fresh) pool in the western tropical Pacific, typical under El Niño conditions, stratifies the upper ocean. This stratification can produce a dramatic decrease in the downward propagation of wind-generated inertia-gravity waves and a decrease in the mixing in the main thermocline. The associated changes to the thermocline are advected to the east and impact the eastern cold tongue and hence the coupling with the atmosphere. Using a combination of observations and models we investigate the properties of SVS activity, its impact on mixing, and interaction with larger scales. Of particular interest is the dependency on stratification, the spatial and temporal variability of wind forcing, the impact on larger scales, and the resolution of both observations and models.

Beschreibung: Climate-induced natural hazards are thought to be happening more frequent and more intense under an increasingly warmer Earth. Earth-observing satellites including the ones operate in constellations and deliver accurate and sub-daily high-resolution/stereo multispectral satellite imagery, provide an opportunity to rapidly monitoring and quantifying the magnitude of disasters. Planet PBC’s Dove/SuperDove, SkySat, RapidEye (retired in March 2020) satellite constellations have at present over 150 CubeSats providing daily/sub-daily sampled 0.5-5-meter resolution images globally. Here, we use the available multispectral images from Planet CubeSat constellations, other geodetic and remote sensing data with the objective to conduct a feasibility study to monitor hazards. In particular, our goal is to identify, track, and quantify the scopes of active wildfires and rapid flood events even at relatively small scales and relatively short-durations worldwide, to study the feasibility of using timely and multi-platform satellite observations to complement informed disaster responses. In this study, we provide case studies and demonstrations of example brush fire and flood hazards to examine the feasibility of a satellite observation-based decision-support disaster response and management tool.

Beschreibung: The economic and population growth in India accompanied by increasingly frequent and severe disasters, such as floods and forest fires, raises concerns about the future. In recent decades, satellite technology has become an important tool for monitoring the Earth's surface, including natural hazards and land surface changes. Our USAID (United States Agency for International Development) project, REmote Sensing for Forest Renewal, Ecosystem Services, and Sustainable Hydrological Management (REFRESH), aims to improve forest sustainability in India with the help of satellite geodetic and remote sensing data. Here we report the progress for the physical science component of the REFRESH Project. Our team has developed a deep-learning model for classifying 10 types of Land Use/Land Cover (LULC) to detect long-term changes in forest areas over India. With collaboration from Indian partners, we validate our classification results using their in-situ land cover data. We used PlanetScope daily images at 3–5 m spatial resolution to monitor the evolutions of floods and forest fires over India. Other satellite data products include in-land water level virtual stations over rivers, dams, and lakes using three decades of satellite radar altimetry, daily-sampled GRACE/GRACE-FO Level 1B gravimetry data to monitor monsoonal floods and seasonal droughts, spaceborne lidar and GEDI for mapping canopy changes, and deep-learning downscaled satellite gravimetry measured total water storage anomalies over entire India. Finally, we plan to generate spaceborne GNSS-Reflectometry and NASA’s CYGNSS for water/land classification data products over India. The data products can be retrieved, visualized, and analyzed on an elaborate observation portal.

Beschreibung: An exponentially increasing number of scientific and commercial Earth orbiting satellites are delivering global and timely sensing of the Earth from space, on its surface or from inside the Earth. The onset of climate change and its dire consequences has been exacerbating the adverse impacts on Earth’s environments and its inhabitants. Timely satellite-based Big Earth observations at adequate spatiotemporal resolution provide a means to monitor the evolutions of more frequent and abrupt climate induced and enhanced hazards. These observations could contribute towards the elucidation of their respective governing climatic processes, and enable improved hazards forecasting, water resources monitoring, and informed hazards management and response. Example satellite geodetic and other observations include satellite gravimetry, altimetry, GNSS, GNSS bistatic altimetry, SAR/InSAR, and Planet PBC's high spatiotemporal (subdaily and 3-5 m) resolution multispectral imageries. We illustrate that the use of deep machine learning analytics can effectively integrating hydrometeorological model and other data, and downscaling the satellite geodetic observations, towards enabling timely monitoring of abrupt weather episode evolutions, including floods, groundwater depletions, cyclone landfall, snowstorms, and meteotsunamis.