Reprint

Satellite Data Application, Validation and Calibration for Atmospheric Observation

Edited by
October 2021
472 pages
  • ISBN978-3-0365-2138-1 (Hardback)
  • ISBN978-3-0365-2137-4 (PDF)

This book is a reprint of the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation that was published in

Engineering
Environmental & Earth Sciences
Summary

This Special Issue focuses on the calibration/validation (cal/val) of advanced passive sensors (IR and/or MW) essential for Earth (atmospheric/oceanic) observation onboard both operational and experimental environmental satellites. Featured topics range from sensor (SDR) calibration and algorithm/retrieval (EDR) validation, to the subsequent improvements, impacts and applications of derived products.

Format
  • Hardback
License
© by the authors
Keywords
GEMS; UV; VIS; hyperspectral data; deep convective cloud; vicarious calibration; OMI; TROPOMI; remote sensing; joint polar satellite system; advanced technology microwave sounder; COSMIC-1; GNSS radio occultation; satellite instrument performance monitoring and anomaly detection; data quality tracking; surface diffuse solar radiation; temporal trend; spatial pattern; atmospheric factor; infrared sounder; calibration; moon; surface; diurnal variation; upper tropospheric humidity; homogenized radiances; GEO weather satellites; evaluation of reanalysis; ERA5; evaluation; sea surface skin temperature; M-AERI; AOD; total ozone; NO2; Validation; GEMS; Infrared; hyperspectral; climate; satellite reprocessing; satellite recalibration; suomi NPP and JPSS satellite instruments; fundamental climate data records; climate change monitoring; Metop-C advanced microwave sounding unit-A; radiometry; calibration and validation; inter-sensor calibration among Metop-A to -C; simultaneous nadir overpass (SNO); inter satellite calibration; microwave radiometry; passive microwave remote sensing; AMSU-A; methane; proxy method; GOSAT; TCCON; machine learning; neural network; bias correction; MiRS; atmospheric infrared sounder (AIRS); breaks for additive season and Trend (BFAST) algorithm; methane (CH4); multitemporal data; Zoige wetland; China; satellite cal/val; error analysis; greenhouse gases; carbon monoxide; methane; carbon dioxide; trace gas; remote sensing; retrieval algorithms; satellite applications; VIIRS; S-NPP; NOAA-20; JPSS-2; spectral response; out-of-band; in-band; hyperspectral; SLSTR; evaluation; thermal bands; ABI; SEVIRI; NUCAPS; satellite soundings; weather forecasting; operational applications; retrievals; infrared; CrIS; severe weather; fire weather; tropical weather; stratospheric intrusions; tropical cyclone; climatology; wind shear; storm motion; satellite measurement; brightness temperature; community radiative transfer model (CRTM); deep learning; fully connected “deep” neural network (FCDN); radiative transfer; artificial neural network (ANN); batch normalization (BN); real time; the visible infrared imaging radiometer suite (VIIRS); radiosondes; satellite; upper tropospheric humidity; infrared radiances; radiative transfer