Paul M. DiGiacomo

Paul M. DiGiacomo


Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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9 April 2016 in Remote Sensing
Verified
Journal Article
Open access
PDF file
AIAlexander Ignatov
Alexander Ignatov
XZxinjia zhou
xinjia zhou
BPBoris Petrenko
Boris Petrenko
XLXingming Liang
Xingming Liang
YKYury Kihai
Yury Kihai
PDPaul M. DiGiacomo
Paul M. DiGiacomo
In response to its users’ needs, the National Oceanic and Atmospheric Administration (NOAA) initiated reanalysis (RAN) of the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km) sea surface temperature (SST) data employing its Advanced Clear Sky Processor for Oceans (ACSPO) retrieval system. Initially, AVHRR/3 data from five NOAA and two Metop satellites from 2002 to 2015 have been reprocessed. The derived SSTs have been matched up with two reference SSTs—the quality controlled in situ SSTs from the NOAA in situ Quality Monitor (iQuam) and the Canadian Meteorological Centre (CMC) L4 SST analysis—and analyzed in the NOAA SST Quality Monitor (SQUAM) online system. The corresponding clear-sky ocean brightness temperatures (BT) in AVHRR bands 3b, 4 and 5 (centered at 3.7, 11, and 12 µm, respectively) have been compared with the Community Radiative Transfer Model simulations in another NOAA online system, Monitoring of Infrared Clear-sky Radiances over Ocean for SST (MICROS). For some AVHRRs, the time series of “AVHRR minus reference” SSTs and “observed minus model” BTs are unstable and inconsistent, with artifacts in the SSTs and BTs strongly correlated. In the official “Reanalysis version 1” (RAN1), data from only five platforms—two midmorning (NOAA-17 and Metop-A) and three afternoon (NOAA-16, -18 and -19)—were included during the most stable periods of their operations. The stability of the SST time series was further improved using variable regression SST coefficients, similarly to how it was done in the NOAA/NASA Pathfinder version 5.2 (PFV5.2) dataset. For data assimilation applications, especially those blending satellite and in situ SSTs, we recommend bias-correcting the RAN1 SSTs using the newly developed sensor-specific error statistics (SSES), which are reported in the product files. Relative performance of RAN1 and PFV5.2 SSTs is discussed. Work is underway to improve the calibration of AVHRR/3s and extend RAN time series, initially back to the mid-1990s and later to the early 1980s.

26 May 2014 in Natural Resources Forum
Verified
Journal Article
Closed access
PDF file
TMThomas Malone
Thomas Malone
PDPaul M. DiGiacomo
Paul M. DiGiacomo
EGEmanuel Gonçalves
Emanuel Gonçalves
AKAnthony H. Knap
Anthony H. Knap
LTLiana Talaue‐McManus
Liana Talaue‐McManus
JMJose H Muelbert
Jose H Muelbert

20 July 2024 in Remote Sensing
Verified
Journal Article
Open access
PDF file
EBEric Bayler
Eric Bayler
PCPaul S. Chang
Paul S. Chang
Jacqueline De La CourJacqueline De La Cour
Jacqueline De La Cour
SHSean Helfrich
Sean Helfrich
AIAlexander Ignatov
Alexander Ignatov
PDPaul M. DiGiacomo
Paul M. DiGiacomo
The National Oceanic and Atmospheric Administration’s (NOAA) Center for Satellite Applications and Research (STAR) facilitates and enables societal benefits from satellite oceanography, supporting operational and experimental satellite missions, developing new and improved ocean observing capabilities, engaging users by developing and distributing fit-for-purpose data, applications, tools, and services, and curating, translating, and integrating diverse data products into information that supports informed decision making. STAR research, development, and application efforts span from passive visible, infrared, and microwave observations to active altimetry, scatterometry, and synthetic aperture radar (SAR) observations. These efforts directly support NOAA’s operational geostationary (GEO) and low Earth orbit (LEO) missions with calibration/validation and retrieval algorithm development, implementation, maintenance, and anomaly resolution, as well as leverage the broader international constellation of environmental satellites for NOAA’s benefit. STAR’s satellite data products and services enable research, assessments, applications, and, ultimately, decision making for understanding, predicting, managing, and protecting ocean and coastal resources, as well as assessing impacts of change on the environment, ecosystems, and climate. STAR leads the NOAA Coral Reef Watch and CoastWatch/OceanWatch/PolarWatch Programs, helping people access and utilize global and regional satellite data for ocean, coastal, and ecosystem applications.