HelioWebDiego Loyola
flowchart
A[Diego Loyola]
AC["Associated Concepts (23)"]
AW["Authored Works (15)"]
CA["Linked Co-Authors (22)"]
CI["Linked Collaborating Institutions (23)"]
A== dcterms:relation ==>AC
AW== author ==>A
AW== author ==>CA
AW== affil ==>CI
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click AW "#authored-works"
click CA "#co-authors"
click CI "#collaborating-institutions"
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- ORCiD
- https://orcid.org/0000-0002-8547-9350
- OpenAlex ID
- https://openalex.org/A5003866050 (API record)
Associated Concepts [?]
- Physics
- Geography
- Environmental science
- Geology
- Meteorology
- Chemistry
- Atmospheric sciences
- Organic chemistry
- Engineering
- Astronomy
- Aerospace engineering
- Remote sensing
- Climatology
- Satellite
- Computer science
- Ozone
- Troposphere
- Quantum mechanics
- Mathematics
- Optics
- Biology
- Aerosol
- Geometry
Authored Works
sorted by decreasing year, and then by display-name
- Uncertainty quantification in the retrieval of cloud properties for Sentinel-4 and TROPOMI / Sentinel-5 Precursor (S5P) using deep neural networks
- S5P-BASCOE compared OMPS-MERRA2 tropospheric ozone and the influence of the total columns
- Mapping the Hunga Tonga-Hunga Ha'apai Volcanic Cloud with Satellite Observations of Backscattered Ultraviolet (BUV) Radiation below 300 nm
- InPULS: Sentinel Based Air Pollution Monitoring and Forecasting Service for Germany
- Improved Inter-Band Co-Registration for the TROPOMI Cloud Parameters using VIIRS Cloud Information
- Global and Regional Analysis of Total and Height-Resolved Ozone Trends 1995-2021 from the ESA-CCI GTO-ECV and GOP-ECV Climate Data Records
- Development of multi-satellite nitrogen dioxide product: Application to GOME-2 on MetOP-A and -B
- Volcanic SO2 height retrieval from UV satellite measurements
- Using Machine Learning Techniques for the Retrieval of Cloud Information from Polar, Geostationary and Deep Space UV/VIS/NIR Instruments
- Tropospheric ozone column data records based on total columns from GOME, SCIAMACHY, GOME-2, OMI and TROPOMI using CCD algorithm or in combination with BASCOE/MLS
- Retrieval of Formaldehyde from GEMS in Preparation of the Geostationary Sentinel-4 Mission.
- Innovative Sentinel 5P Level 3 Atmosphere Products: Will Become Accessible Through Innovative Technologies and Interfaces at DLR's Geoservice
- Improved Lightning NOx Production Estimates Using TROPOMI and GLM Data
- Harmonised Satellite Glyoxal Data Records from TROPOMI, OMI and GOME-2 and Initial Retrievals from GEMS
- General framework for accelerating and integrating machine learning retrieval algorithms for environmental satellites into operational processing systems
Linked Co-Authors
- Andreas Richter
- Barry G. Latter
- Can Li
- D. Balis
- D. J. Allen
- D. P. Haffner
- Dmitry Efremenko
- Hitoshi Irie
- Jean-François Müller
- Jeroen van Gent
- Joanna Joiner
- Kenneth Pickering
- Klaus-Peter Heue
- L. N. Lamsal
- Melanie Coldewey‐Egbers
- Michel Van Roozendaël
- Nickolay A. Krotkov
- Omar Torres
- Pascal Hedelt
- Víctor Molina García
- William J. Koshak
- Zachary Fasnacht
Linked Collaborating Institutions
- Aristotle University of Thessaloniki, Department of Physics
- Aristotle University of Thessaloniki, Greece
- Belgian Institute for Space Aeronomy
- CAS, Institute of Atmospheric Physics, Beijing
- Chiba University, Japan
- German Aerospace Center, Wessling
- Max-Planck-Institute for Chemistry, Mainz
- Munich University of Technology, Germany
- NASA Goddard Space Flight Center, Maryland
- NASA Marshall Space Flight Center
- National Institute of Environmental Research, Korea
- Pukyong National University, Korea
- Royal Dutch Meteor Society
- Royal Observatory of Belgium
- Rutherford Appleton Laboratory
- Science Systems and Applications Inc., Maryland
- Seoul National University, Korea
- Stanford Research Institute, California
- University of Bremen, Department of Physics
- University of Bremen, Germany
- University of Maryland, Baltimore County
- University of Maryland, College Park
- University of Natural Resources and Life Sciences, Vienna, Austria
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