Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci)

Thomas Popp; Gerrit de Leeuw; Christine Bingen; Christoph Brühl; Virginie Capelle; Alain Chedin; Lieven Clarisse; Oleg Dubovik; Roy Grainger; Jan Griesfeller; Andreas Heckel; Stefan Kinne; Lars Klüser; Miriam Kosmale; Pekka Kolmonen; Luca Lelli; Pavel Litvinov; Linlu Mei; Peter North; Simon Pinnock; Adam Povey; Charles Robert; Michael Schulz; Larisa Sogacheva; Kerstin Stebel; Deborah Stein Zweers; Gareth Thomas; Lieuwe Tilstra; Sophie Vandenbussche; Pepijn Veefkind; Marco Vountas; Yong Xue

doi:10.3390/rs8050421

Development of a microwave-based precipitation climate data record for the Copernicus Climate Change Service

10.5194/egusphere-egu2020-18636 ◽

2020 ◽

Author(s):

Giulia Panegrossi ◽

Paolo Sanò ◽

Leonardo Bagaglini ◽

Daniele Casella ◽

Elsa Cattani ◽

...

Keyword(s):

Neural Network ◽

Climate Change ◽

Satellite Observations ◽

Passive Microwave ◽

Monthly Precipitation ◽

Climate Data ◽

Data Record ◽

Level 3 ◽

Climate Data Record ◽

Level 2

Within the Copernicus Climate Change Service (C3S), the Climate Data Store (CDS) built by ECMWF will provide open and free access to global and regional products of Essential Climate Variables (ECV) based on satellite observations spanning several decades, amongst other things. Given its significance in the Earth system and particularly for human life, the ECV precipitation will be of major interest for users of the CDS.C3S strives to include as many established, high-quality data sets as possible in the CDS. However, it also intends to offer new products dedicated for first-hand publication in the CDS. One of these products is a climate data record based on merging satellite observations of daily and monthly precipitation by both passive microwave (MW) sounders (AMSU-B/MHS) and imagers (SSMI/SSMIS) on a 1&#176;x1&#176; spatial grid in order to improve spatiotemporal satellite coverage of the globe.The MW sounder observations will be obtained using, as input data, the FIDUCEO Fundamental Climate data Record (FCDR) for AMSU-B/MHS in a new global algorithm developed specifically for the project based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR; San&#242; et al., 2015), adapted for climate applications (PNPR-CLIM). The algorithm consists of two Artificial Neural Network-based modules, one for precipitation detection, and one for precipitation rate estimate, trained on a global observational database built from Global Precipitation Measurement-Core Observatory (GPM-CO) measurements. The MW imager observations by SSM/I and SSMIS will be adopted from the Hamburg Ocean Atmosphere Fluxes and Parameters from Satellite data (HOAPS; Andersson et al., 2017), based on the CM SAF SSM/I and SSMIS FCDR (Fennig et al., 2017). The Level 2 precipitation rate estimates from MW sounders and imagers are combined through a newly developed merging module to obtain Level 3 daily and monthly precipitation and generate the 18-year precipitation CDR (2000-2017).Here, we present the status of the Level 2 product&#8217;s development. We carry out a Level-2 comparison and present first results of the merged Level-3 precipitation fields. Based on this, we assess the product&#8217;s expected plausibility, coverage, and the added value of merging the MW sounder and imager observations.ReferencesAnderssonet al., 2017, DOI:10.5676/EUM_SAF_CM/HOAPS/V002Fennig, et al., 2017, DOI:10.5676/EUM_SAF_CM/FCDR_MWI/V003San&#242;, P., et al., 2015, DOI: 10.5194/amt-8-837-2015

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Systems approach to developing a climate data record from satellite observations

10.1117/12.862073 ◽

2010 ◽

Author(s):

Steve E. Broberg ◽

Thomas S. Pagano ◽

Hartmut H. Aumann ◽

Denis A. Elliott ◽

Fred O'Callaghan

Keyword(s):

Systems Approach ◽

Satellite Observations ◽

Climate Data ◽

Data Record ◽

Climate Data Record

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Detecting Ecosystem Performance Anomalies for Land Management in the Upper Colorado River Basin Using Satellite Observations, Climate Data, and Ecosystem Models

Remote Sensing ◽

10.3390/rs2081880 ◽

2010 ◽

Vol 2 (8) ◽

pp. 1880-1891 ◽

Cited By ~ 21

Author(s):

Yingxin Gu ◽

Bruce K. Wylie

Keyword(s):

River Basin ◽

Land Management ◽

Colorado River ◽

Satellite Observations ◽

Colorado River Basin ◽

Climate Data ◽

Ecosystem Models ◽

Upper Colorado River Basin

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Radiometrically Consistent Climate Fingerprinting Using CrIS and AIRS Hyperspectral Observations

Remote Sensing ◽

10.3390/rs12081291 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1291

Author(s):

Wan Wu ◽

Xu Liu ◽

Qiguang Yang ◽

Daniel K. Zhou ◽

Allen M. Larar

Keyword(s):

Water Vapor ◽

Atmospheric Temperature ◽

Satellite Observations ◽

Radiative Transfer Model ◽

Retrieval Algorithm ◽

Transfer Model ◽

Climate Data ◽

Fingerprinting Method ◽

Level 2

We introduce a novel spectral fingerprinting scheme that can be used to derive long-term atmospheric temperature and water vapor anomalies from hyperspectral infrared sounders such as Cross-track Infrared Sounder (CrIS) and Atmospheric Infrared Sounder (AIRS). It is a challenging task to derive climate trends from real satellite observations due to the difficulty of carrying out accurate cloudy radiance simulations and constructing radiometrically consistent radiative kernels. To address these issues, we use a principal component based radiative transfer model (PCRTM) to perform multiple scattering calculations of clouds and a PCRTM-based physical retrieval algorithm to derive radiometrically consistent radiative kernels from real satellite observations. The capability of including the cloud scattering calculations in the retrieval process allows the establishment of a rigorous radiometric fitting to satellite-observed radiances under all-sky conditions. The fingerprinting solution is directly obtained via an inverse relationship between the atmospheric anomalies and the corresponding spatiotemporally averaged radiance anomalies. Since there is no need to perform Level 2 retrievals on each individual satellite footprint for the fingerprinting approach, it is much more computationally efficient than the traditional way of producing climate data records from spatiotemporally averaged Level 2 products. We have applied the spectral fingerprinting method to six years of CrIS and 16 years of AIRS data to derive long-term anomaly time series for atmospheric temperature and water vapor profiles. The CrIS and AIRS temperature and water vapor anomalies derived from our spectral fingerprinting method have been validated using results from the PCRTM-based physical retrieval algorithm and the AIRS operational retrieval algorithm, respectively.

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Results of Latitude Observations with the Floating Zenith Telescope at Mizusawa: Comparisons between the Results Obtained from the Astronomical and Doppler Satellite Observations

International Astronomical Union Colloquium ◽

10.1017/s0252921100051241 ◽

1975 ◽

Vol 26 ◽

pp. 461-468

Author(s):

S. Takagi

Keyword(s):

Photographic Plate ◽

Satellite Observations ◽

Pole Motion

In this article, we intended to see whether we can obtain the same pole motion from two kinds of telescopes: the floating zenith telescope (PZT) and the ILS zenith telescope (VZT). The observations with the PZT have been pursued since 1967.0 with a star list whose star places are taken from the PK4 and its supplement. We revised the method of reduction of the observations with the PZT by adopting a variable scale value for the photographic plate (Takagi et al., 1974).

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