3 Works

SEV06-CLD : A MODIS-like SEVIRI cloud products dataset

G. Wind, S. Platnick, J. Riedi, A. Heidinger, S. Ackerman & B. Baum
The SEV06-CLD dataset provides cloud properties derived from the SEVIRI instrument series using algorithms that have been historically developped for the processing of MODIS data. SEV06-CLD uses MODIS-like algorithms for cloud optical and microphysical property retrievals, and a modified GOES-R Algorithm Working Group (AWG) algorithm for cloud-top temperature and pressure. Cloud masking is obtained from the Météo France Satellite Application Facility for supporting NoWCasting (SAFNWC) software package. The SEV06-CLD datasets share a common retrieval core...

SOFT-IO: SOft attribution using FlexparT and carbon monoxide emission inventories for In-situ Observation database

B. Sauvage, A. Auby & A. Fontaine
SOFT-IO is a tool based on the FLEXPART particle dispersion model (Stohl et al., 2005) coupled on emission inventories provided in the scientific community. It has been developed to quantify source/receptor links for atmospheric trace gases. SOFT-IO simulates the contributions of anthropogenic and biomass burning emissions from the ECCAD emission inventory database for all locations and times corresponding to the measured carbon monoxide mixing ratio along each flight measurements. The main goal is to supply...

IAGOS ancillary data (L4) - CO contributions to the aircraft measurements

Bastien Sauvage, Philippe Nédélec & Damien Boulanger
In order to help analyzing the IAGOS carbon monoxide (CO) observations and understanding the processes driving their evolutions, we provide ancillary parameters calculated with SOFT-IO (Sauvage et al., 2017; http://dx.doi.org/10.25326/2) along aircraft observations. CO contribution is defined as follow. CO mixing ratio is calculated for each IAGOS observations (every 0.5° in latitude or longitude at cruising altitude; every 10hPa during ascent or descent of the plane) and sorted by source origin (biomass burning and anthropogenic...

Registration Year

  • 2017

Resource Types

  • Dataset
  • Software