Monday, October 7, 2024Animation of daily wind speed measurements from the Compact Ocean Wind Vector Radiometer (COWVR) instrument during its first year of operation. COWVR was developed at the Jet Propulsion Laboratory and is installed on the International Space Station, where it has been collecting measurements since January 2022. COWVR aims to demonstrate new low-cost microwave sensor technologies for weather applications. This animation uses the first public release of the data which includes wind speed, wind direction, and moisture parameters and can be found on NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) at https://doi.org/10.5067/COWVR-STPH8-EDR100. In the future these data may be retired and replaced with updated versions in which case please see https://podaac.jpl.nasa.gov/COWVR-TEMPEST for a list of all data sets including the most recent public versions, as well as information on the COWVR-TEMPEST project.

via PO.DAAC https://podaac.jpl.nasa.gov/animations/COWVR-Level-2-Wind-Speed-First-Year-of-Operation

Wednesday, October 9, 2024The PO.DAAC is pleased to announce the first public release of the COWVR-TEMPEST Temperature Sensor Data Records (TSDRs) and Environmental Data Record (EDR), produced by the Jet Propulsion Laboratory. The COWVR (Compact Ocean Wind Vector Radiometer) and TEMPEST (Temporal Experiment for Storms and Tropical Systems) instruments are passive microwave radiometers installed on the International Space Station as part of the Space Test Program - Houston 8 (STP-H8) technology demonstration mission. The project aims to demonstrate a lower-cost, lighter-weight sensor architecture for providing microwave data, with the primary objective of ocean surface vector wind products and tropical cyclone intensity tracking for the Department of Defense. More information regarding the project can be found at PO.DAAC’s project page.An animation of daily global wind speed from COWVR during its first year of operation can be found here .The data sets include Level 1 brightness temperatures (TSDRs) from both instruments, and Level 2 wind vector, column liquid water, and column precipitable water vapor from COWVR (EDR). Data records span January 2022 to the present, with forward streaming planned at least until August of 2025. Both Level 1 and Level 2 data provide data over the satellite tracks/swaths in HDF5 format, with roughly one file per hour (the orbital period of the International Space Station is ~90 minutes). Version 10.0 is the first un-restricted public release, and is named as such to be consistent with the internal version numbering of the project team prior to release. More information can be found in the EDR User Guide and the Data Product Development Documents, linked to on the landing pages.The data sets are described and discoverable via the PO.DAAC data portal.DOI:
COWVR_STPH8_L2_EDR_V10.0 (10.5067/COWVR-STPH8-EDR100)
COWVR_STPH8_L1_TSDR_V10.0 (10.5067/COWVR-STPH8-TSDR100)
TEMPEST_STPH8_L1_TSDR_V10.0 (10.5067/TEMPEST-STPH8-TSDR100)Due to the format of these data files, services such as OPeNDAP and Level 2 Subsetter are not available. However, data can be accessed/downloaded via the virtual directory, Earthdata Search, the podaac-data-subscriber tool, or using s3 endpoints in an AWS cloud environment. Data files for period covering January 2022 - present are actively being reprocessed by the COWVR-TEMPEST Project Team, and are ingested by PO.DAAC as they become available. Therefore not all files are available as of this release announcement, but will be over the next few weeks. Related PO.DAAC Animation:COWVR Level 2 Wind Speed - First Year of Operation (https://podaac.jpl.nasa.gov/animations/COWVR-Level-2-Wind-Speed-First-Year-of-Operation) Citations:Brown, Shannon, Paolo Focardi, Amarit Kitiyakara, Frank Maiwald, Lance Milligan, Oliver Montes, Sharmila Padmanabhan et al. "The COWVR Mission: Demonstrating the capability of a new generation of small satellite weather sensors." In 2017 IEEE Aerospace Conference, pp. 1-7. IEEE, 2017.Brown, Shannon, Paolo Focardi, Amarit Kitiyakara, Frank Maiwald, Oliver Montes, Sharmila Padmanabhan, Richard Redick, D. Russel, and James Wincentsen. "The compact ocean wind vector radiometer: A new class of low-cost conically scanning satellite microwave radiometer system." In Proc. IEEE Geosci. Remote Sens. Soc.(IGRSS), 35th Can. Remote Sens. Soc.(CSRS), pp. 1-3. 2014.Farrar, Spencer, Steven Swadley, Shannon Brown, Eric Simon, Sayak Biswas, David Kunkee, and Kieran Smith. "An Initial on-Orbit Performance Assessment of the Compact Ocean Wind Vector Radiometer (COWVR)." In IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium, pp. 6277-6281. IEEE, 2024. 

via PO.DAAC https://podaac.jpl.nasa.gov/announcements/2024-10-09-First-Public-Release-V10.0-Microwave-Brightness-Temperatures-Ocean-Wind-Vectors-and-Atmospheric-Water-Products-from-COWVR-TEMPEST-STP-H8-Project

FrontpageCOWVR Level 2 Wind Speed - First Year of OperationLink: https://podaac.jpl.nasa.gov/animations/COWVR-Level-2-Wind-Speed-First-Year-of-Op…This graphic illustrates the daily wind speed measurements from the Compact Ocean Wind Vector Radiometer (COWVR) instrument during its first year of operation.

via PO.DAAC https://podaac.jpl.nasa.gov/node/1994

Parade of tropical cyclones across the western Pacific

via Himawari-9 – CIMSS Satellite Blog (author: Scott Lindstrom)

Man-Yi moves through the Marianas Islands

via Himawari-9 – CIMSS Satellite Blog (author: Scott Lindstrom)

#Typhoon #Man-yi
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#Typhoon #Toraji
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#Typhoon #Usagi
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#Typhoon #Yinxing
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Super Typhoon Man-Yi makes landfall on the Philippines island of Luzon

via Himawari-9 – CIMSS Satellite Blog (author: Scott Bachmeier)

Eruption of Kanlaon Volcano in the Philippines

JMA Himawari-9 Air Mass RGB images, from 0650-1500 UTC on 9th December [click to play animated gif]
The Kanlaon Volcano in the Philippines erupted at 0703 UTC on 9th December 2024. JMA Himawari-9 Air Mass RGB images from 0650-1500 UTC (above) — created using Geo2Grid — showed the volcanic cloud as it drifted westward across the Sulu Sea. This volcanic cloud was primarily composed of SO2 (along with some ash), and exhibited shades of orange to pink in the RGB images.

via Himawari-9 – CIMSS Satellite Blog (author: Scott Bachmeier)

Animation Caption: Sea Surface Height Anomaly (SSHA) from -30 cm (blue) to 30 cm (red) over the global ocean, arrows on top represent the ocean currents. The circular shapes of those anomalies are mesoscale eddies. The panels present a side-by-side comparison between the NeurOST method and the conventional method widely used. First, the SSHA, then the derived ocean current speed and finally the vorticity are shown to illustrate NeurOST’s capability to retrieve fine details of the ocean eddies. Mesoscale eddies, circular currents of water with diameters spanning from 50 to 300 kilometers, transport freshwater, heat, carbon, nutrients, etc. around the world and within the ocean, profoundly impacting marine ecosystems and the Earth's climate. They also account for over 80% of the ocean’s kinetic energy, the energy created by the movement of ocean water. Mesoscale eddies are therefore critical components of the global ocean circulation and climate, and need to be better characterized and understood.Satellite altimetry measuring Sea Surface Height (SSH), from which ocean currents can be deduced, has been a valuable tool in tracking these ocean mesoscale eddies over the past three decades1. However, tracking eddies from SSH observations poses significant challenges due to the limitations of conventional nadir altimeters, such as Sentinel-6 Michael Freilich. These instruments measure sea level along the nadir track, which leaves large gaps between tracks. Therefore, it is necessary to interpolate these observations using an a-priori estimation of the relationship between different measurements in space and in time. This results in global grids of SSH with spatial and temporal resolutions of ~100km and 10 days, respectively, which limits the two-dimensional eddy reconstruction and tracking. Researchers are starting to leverage the recent advancement of machine learning to overcome those limitations. The recently published SSH gridded product, NeurOST, on PO.DAAC is a notable example.The novel method combines Sea Surface Temperature anomaly (SSTA) and along-track nadir altimeter SSH anomaly (SSHA) data to create a new daily higher resolution SSHA and ocean current dataset2. This artificial intelligence (AI) method called deep learning teaches computers to recognize patterns and connections in space and time in a combination of data, here SSHA and SSTA. The resulting animation above, spanning from January 2018 to December 2023, displays NeurOST SSHA estimates with ocean currents on top as arrows. The height of the sea surface has highs (red) and lows (blue) indicating ocean eddies and currents. The animation then zooms in a very energetic area in the western Pacific Ocean and shows some comparisons between this new artificial intelligence method and the observations from conventional gridded altimetry products (Data Unification and Altimeter Combination System; DUACS). First, the SSHA are displayed, then the ocean current speed computed from both products and finally, the vorticity, that represents the rotation of the ocean water mass. We can see the improved ability of this new AI product to capture features such as ocean eddies. This suggests that AI methods such as deep learning can be powerful tools to study our oceans.The machine learning methodology and data product were developed by an Ocean Surface Topography Science Team at the University of Washington and sponsored by NASA Physical Oceanography program (PO). The method was published in two publications2,3 and welcomes further investigations. The project is emerging as an essential component of the newly established NASA Ocean AI Working Group, an important step forward by NASA PO teams to advance modern satellite data synthesis. By leveraging cutting-edge machine learning techniques, researchers can explore how to effectively process and analyze vast amounts of satellite data to enhance understanding of the Earth's oceans and ultimately improve climate predictions.

via PO.DAAC https://podaac.jpl.nasa.gov/DataAction-2024…

Pyrocumulonimbus cloud in southeast Australia

via Himawari-9 – CIMSS Satellite Blog (author: Scott Bachmeier)

#Typhoon #Pabuk
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A record wet day on Guam

via Himawari-9 – CIMSS Satellite Blog (author: Scott Lindstrom)

Significant Flooding in Northeast Australia

via Himawari-9 – CIMSS Satellite Blog (author: Tim Wagner)

Grampians bushfire in southeast Australia produces a pyrocumulonimbus cloud

via Himawari-9 – CIMSS Satellite Blog (author: Scott Bachmeier)

Severe Tropical Cyclone Zelia Hits Australia

via Himawari-9 – CIMSS Satellite Blog (author: Tim Wagner)