Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
by
Holden, Lucas
, Choy, Suelynn
, Kuleshov, Yuriy
, Wild, Ashley
in
Bias
/ Buoys
/ Collocation
/ Coral reefs
/ Cyclones
/ CYGNSS
/ Datasets
/ Estimates
/ Global navigation satellite system
/ GNSS-R
/ instrument validation
/ Navigation systems
/ near-surface ocean winds
/ Remote sensing
/ Remote sensing systems
/ Satellite observation
/ Satellites
/ Soil moisture
/ Synthetic aperture radar
/ triple collocation analysis
/ Tropical cyclones
/ Wind
/ Wind speed
2024
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
by
Holden, Lucas
, Choy, Suelynn
, Kuleshov, Yuriy
, Wild, Ashley
in
Bias
/ Buoys
/ Collocation
/ Coral reefs
/ Cyclones
/ CYGNSS
/ Datasets
/ Estimates
/ Global navigation satellite system
/ GNSS-R
/ instrument validation
/ Navigation systems
/ near-surface ocean winds
/ Remote sensing
/ Remote sensing systems
/ Satellite observation
/ Satellites
/ Soil moisture
/ Synthetic aperture radar
/ triple collocation analysis
/ Tropical cyclones
/ Wind
/ Wind speed
2024
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
by
Holden, Lucas
, Choy, Suelynn
, Kuleshov, Yuriy
, Wild, Ashley
in
Bias
/ Buoys
/ Collocation
/ Coral reefs
/ Cyclones
/ CYGNSS
/ Datasets
/ Estimates
/ Global navigation satellite system
/ GNSS-R
/ instrument validation
/ Navigation systems
/ near-surface ocean winds
/ Remote sensing
/ Remote sensing systems
/ Satellite observation
/ Satellites
/ Soil moisture
/ Synthetic aperture radar
/ triple collocation analysis
/ Tropical cyclones
/ Wind
/ Wind speed
2024
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
Journal Article
Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
2024
Request Book From Autostore
and Choose the Collection Method
Overview
Existing validation of mean wind speed estimates via reflectometry from global navigation systems of satellites (GNSS-R)—has been largely limited in spatial coverage to equatorial buoys or tropical cyclone events near continental United States. Two alternative validation techniques are presented for the Cyclone GNSS (CYGNSS) mission using surface-based observations along coasts and coral reefs instead of buoys, and triple collocation analysis (TCA) instead of a 1:1 gridded comparison for tropical cyclone (TC) events. For the surface-based analysis, Fully Developed Seas (FDS) v3.2 and NOAA v1.2 were compared to anemometer data provided by the Australian Bureau of Meteorology across the Australia and Pacific regions. Overall, the products performed similarly to previous studies with NOAA having higher correlations and lower errors than FDS, though FDS performed better than NOAA over the Australian dataset for high wind speed events. TCA was used to validate NOAA v1.2 and Merged v3.2 datasets with other satellite remotely sensed products from the Soil Moisture Active Passive (SMAP) mission and Synthetic Aperture Radar (SAR). Both additive and multiplicative error models for TCA were applied. The performance overall was similar between the two products, with NOAA producing higher errors. NOAA performed better than Merged for mean winds above 17 m/s as the large temporal averaging reduced sensitivity to high winds. For SMAP winds above 17 m/s, NOAA’s average bias (−2.1 m/s) was significantly smaller than the average bias in Merged (−4.4 m/s). Future ideas for rapid intensification detection and constellation design are discussed.
This website uses cookies to ensure you get the best experience on our website.