This Paper provides an overview on the first results of the Metop-C satellite, third and last part of the series of three Metop-satellites of the EUMETSAT Polar System (EPS). EPS is the European contribution to the Polar Meteorological Satellite Observing System. It forms a part of the Initial Joint Polar System (IJPS), formed with NOAA (National Oceanic and Atmospheric Administration). The Metop-C satellite, launched on the 7 November 2018 from the Guyana Space Centre in Kourou, and is finalizing its commissioning activities. The Metop satellites were developed in co-operation with the European Space Agency (ESA). Seven meteorological instruments (among 10) are embarked on Metop-C satellites (eight on Metop-A and –B where the HIRS/4 instrument was embarked as well). These are the IASI (Infrared Atmospheric Sounding Interferometer), developed by CNES in co-operation with EUMETSAT, the AVHRR (Advanced Very High Resolution Radiometer) and AMSU-A (Advanced Microwave Sounding Unit-A) instruments, provided by NOAA, the Microwave Humidity Sounder (MHS), developed by EUMETSAT and the GRAS (GNSS (Global Navigation Satellite System) Receiver for Atmospheric Sounding) instrument, the GOME-2 (Global Ozone Monitoring .-2) instrument and ASCAT (Advanced Scatterometer), developed by ESA as part of the space segment. Metop instrument data – in particular the sounding instruments - provide an essential contribution to global operational Numerical Weather Prediction (NWP). Climate monitoring and atmospheric composition monitoring and ocean and cryosphere observations are further application areas supported by Metop instrument data. Results from the commissioning phase and first application impacts will be presented. After its successful commissioning, there will be three Metop-satellites in orbit for about three years.
Principal Component (PC) compression is the method of choice to achieve band-width reduction for dissemination of hyper spectral (HS) satellite measurements and will become increasingly important with the advent of future HS missions (such as IASI-NG and MTG-IRS) with ever higher data-rates. It is a linear transformation defined by a truncated set of the leading eigenvectors of the covariance of the measurements as well as the mean of the measurements. We discuss the strategy for generation of the eigenvectors, based on the operational experience made with IASI. To compute the covariance and mean, a so-called training set of measurements is needed, which ideally should include all relevant spectral features. For the dissemination of IASI PC scores a global static training set consisting of a large sample of measured spectra covering all seasons and all regions is used. This training set was updated once after the start of the dissemination of IASI PC scores in April 2010 by adding spectra from the 2010 Russian wildfires, in which spectral features not captured by the previous training set were identified. An alternative approach, which has sometimes been proposed, is to compute the eigenvectors on the fly from a local training set, for example consisting of all measurements in the current processing granule. It might naively be thought that this local approach would improve the compression rate by reducing the number of PC scores needed to represent the measurements within each granule. This false belief is apparently confirmed, if the reconstruction scores (root mean square of the reconstruction residuals) is used as the sole criteria for choosing the number of PC scores to retain, which would overlook the fact that the decrease in reconstruction score (for the same number of PCs) is achieved only by the retention of an increased amount of random noise. We demonstrate that the local eigenvectors retain a higher amount of noise and a lower amount of atmospheric signal than global eigenvectors. Local eigenvectors do not increase the compression rate, but increase the amount of atmospheric loss and should be avoided. Only extremely rare situations, resulting in spectra with features which have not been observed previously, can lead to problems for the global approach. To cope with such situations we investigate a hybrid approach, which first apply the global eigenvectors and then apply local compression to the residuals in order to identify and disseminate in addition any directions in the local signal, which are orthogonal to the subspace spanned by the global eigenvectors.
IASI has 4 different detectors, CrIS has 9, IASI-NG will have 16 and MTG-IRS 25600. There is a clear interest to harmonise the sensor data originating from different detectors, if it can be done be removing the parts of the instrument artefacts, which are not common to all detectors.
When IASI spectra are analysed in principal component (PC) score space, differences between the four detectors are clearly observed. These differences are caused by different characteristics and different strengths of the ghost effect among the detectors and although they are small when analysed in radiance space, they can have a distinct negative impact on the use of the data. Considering that a large part of the operationally disseminated IASI PC scores are dominated by instrument artefacts, the partial removal of instrument artefacts is also of interest for data compression purposes.
The instrument artefacts can be partly removed by projection onto a subspace common to all detectors. We show how the techniques of canonical angles can be used to compute a set of orthogonal vectors capturing only directions which are close to directions found in the signal spaces of all detectors. This principle can also be applied to detectors on-board different satellites, as we demonstrate with the example of IASI-A and IASI-B.
The danger of the method is that a single deficient detector, ’blind’ to one or more directions of the atmo- spheric signal, could potentially ’contaminate’ the data from the other detectors. We discuss how to detect and avoid this problem and check it in practice with CrIS data.
The METOP-A satellite Infrared Atmospheric Sounding Interferometer (IASI) Level 2 products comprise
retrievals of vertical profiles of temperature and water vapor. The L2 data were validated through
assessment of their error covariances and biases using radiosonde data for the reference. The radiosonde
data set includes dedicated launches as well as the ones performed at regular synoptic times at Lindenberg
station (Germany). For optimal error estimate the linear statistical Validation Assessment Model (VAM)
was used. The model establishes relation between the compared satellite and reference measurements based
on their relations to the true atmospheric state. The VAM utilizes IASI averaging kernels and statistical
characteristics of the ensembles of the reference data to allow for finite vertical resolution of the retrievals
and spatial and temporal non-coincidence. For temperature retrievals expected and assessed errors are in
good agreement; error variances/rms of a single FOV retrieval are 1K between 800 - 300 mb with an
increase to ~1K in tropopause and ~2K at the surface, possibly due to wrong surface parameters and
undetected clouds/haze. Bias against radiosondes oscillates within ±0 5K . between 950 - 100 mb. As for
water vapor, its highly variable complex spatial structure does not allow assessment of retrieval errors with
the same degree of accuracy as for temperature. Error variances/rms of a single FOV relative humidity
retrieval are between 10 - 13% RH in the 800 - 300 mb range.