Matching radiative transfer models and radiosonde data from the EPS/MetOp Sodankylä campaign to IASI measurements

Abstract

Abstract. Radiances observed from IASI are compared to calculated ones. Calculated radiances are obtained using several radiative transfer models (OSS, LBLRTM v11.3 and v11.6) on best estimates of the atmospheric state vectors. The atmospheric state vectors are derived from cryogenic frost point hygrometer and humidity dry bias corrected RS92 measurements flown on sondes launched 1 h and 5 min before IASI overpass time. The temperature and humidity profiles are finally obtained by interpolating or extrapolating these measurements to IASI overpass time. The IASI observed and calculated radiances match to within one sigma IASI instrument noise in the wavenumber, ν, range of 1500 ≤ ν ≤ 1570 and 1615 ≤ ν ≤ 1800 cm−1 .

Validation practices for satellite‐based Earth observation data across communities

“…Within most communities, detailed validation protocols have been established, tailored to the specific products and validation aims. Some examples can be found in the early work on SAGE II ozone profile validation using ozonesondes by Cunnold et al [], the recent work on IASI radiance validation, from temperature and water vapor profiles, developed by Calbet et al [, ], and the detailed processing model for the future QA/Validation service developed in QA4ECV [ Compernolle et al , ].…”

“…Various strategies have been used (with some success) in recent years for the validation of satellite sounding observations, including comparison with collocated airborne radiometers flown as part of dedicated Cal/Val campaigns [ Newman et al , ] and collocated radiosondes [e.g., Calbet et al , , ].…”

“…Several strategies have been devised to overcome this issue, which can be summarized as follows: Taking more than one reference point measurement. For example, by using two consecutive radiosonde measurements, separated by ∼1 h, it is possible to make a time interpolation [ Tobin et al , ], providing an atmospheric state best estimate which has proven to be well collocated with satellite observations giving a nearly negligible collocation uncertainty [ Calbet et al , ]. It is possible to envisage other measurement strategies, involving, for example, ground‐based remote sensing equipment such as lidar systems, which can achieve a similar goal. Estimate the collocation errors (and resulting uncertainties) when comparing single‐reference measurements with satellite data using, e.g., Numerical Weather Prediction (NWP) model fields.…”

Horizontal small-scale variability of water vapor in the atmosphere: implications for intercomparison of data from different measuring systems

“…The radiosonde measurement data are from the EUMETSAT EPS/MetOp campaigns made in 2007 and 2008 at Lindenberg (Germany) and Sodankylä (Finland) observatories (Calbet et al, 2011). In these campaigns, two consecutive radiosondes were launched from the same site separated by about 50 min in time.…”