Comparison of Tropospheric Ozone Columns Calculated from MLS, OMI, and Ozonesonde Data

This poster shows a comparison of three derived tropospheric ozone residual (TOR) products with integrated tropospheric ozone columns from ozonesonde profile: (1) the method of Ziemke et al. (2006), (2) a modified version of Fishman et al. (2003), and (3) a trajectory mapping approach. In each case, MLS ozone profiles are integrated to the tropopause and subtracted from OMI (TOMS retrieval) total column ozone. The effectiveness of each of these techniques is examined as a function of latitude, time, and geographic region. In general, we find good agreement between the derived products and the ozonesondes, with the Fishman et al. TOR (labeled “Amy”) generally high and the Schoeberl trajectory mapping (labeled “Mark”) product generally low as compared to the integrated ozonesonde profiles (labeled “Sonde”) as computed using the WMO tropopause definition. Differences in TOR results are due, at least in part, to non-uniform tropopause height definitions between the three approaches

SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations

Abstract. Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products suffer from space and time inconsistency due to the non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent "bottom-up" approach that exploits satellite soil moisture observations for estimating rainfall through the SM2RAIN (Soil Moisture to Rain) algorithm is suited to build a consistent rainfall data record as a single polar orbiting satellite sensor is used. Here we exploit the Advanced SCATterometer (ASCAT) on board three Meteorological Operational (MetOp) satellites, launched in 2006, 2012, and 2018, as part of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System programme. The continuity of the scatterometer sensor is ensured until the mid-2040s through the MetOp Second Generation Programme. Therefore, by applying the SM2RAIN algorithm to ASCAT soil moisture observations, a long-term rainfall data record will be obtained, starting in 2007 and lasting until the mid-2040s. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN–ASCAT quasi-global (only over land) daily rainfall data record at a 12.5 km spatial sampling from 2007 to 2018. The quality of the SM2RAIN–ASCAT data record is assessed on a regional scale through comparison with high-quality ground networks in Europe, the United States, India, and Australia. Moreover, an assessment on a global scale is provided by using the triple-collocation (TC) technique allowing us also to compare these data with the latest, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), the Early Run version of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), and the gauge-based Global Precipitation Climatology Centre (GPCC) products. Results show that the SM2RAIN–ASCAT rainfall data record performs relatively well at both a regional and global scale, mainly in terms of root mean square error (RMSE) when compared to other products. Specifically, the SM2RAIN–ASCAT data record provides performance better than IMERG and GPCC in data-scarce regions of the world, such as Africa and South America. In these areas, we expect larger benefits in using SM2RAIN–ASCAT for hydrological and agricultural applications. The limitations of the SM2RAIN–ASCAT data record consist of the underestimation of peak rainfall events and the presence of spurious rainfall events due to high-frequency soil moisture fluctuations that might be corrected in the future with more advanced bias correction techniques. The SM2RAIN–ASCAT data record is freely available at https://doi.org/10.5281/zenodo.3405563 (Brocca et al., 2019) (recently extended to the end of August 2019)

SM2RAIN-ASCAT (2007–2018): global daily satellite rainfallfrom ASCAT soil moisture

Abstract. Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products obtained from rain gauges, remote sensing and meteorological modelling suffer from space and time inconsistency due to non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent bottom up approach that uses satellite soil moisture observations for estimating rainfall through the SM2RAIN algorithm is suited to build long-term and consistent rainfall data record as a single polar orbiting satellite sensor is used. We exploit here the Advanced SCATterometer (ASCAT) on board three Metop satellites, launched in 2006, 2012 and 2018. The continuity of the scatterometer sensor on European operational weather satellites is ensured until mid-2040s through the Metop Second Generation Programme. By applying SM2RAIN algorithm to ASCAT soil moisture observations a long-term rainfall data record can be obtained, also operationally available in near real time. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN-ASCAT global daily rainfall dataset at 12.5 km sampling (2007–2018). The quality of SM2RAIN-ASCAT dataset is assessed on a regional scale through the comparison with high-quality ground networks in Europe, United States, India and Australia. Moreover, an assessment on a global scale is provided by using the Triple Collocation technique allowing us also the comparison with other global products such as the latest European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), the Global Precipitation Measurement (GPM) mission, and the gauge-based Global Precipitation Climatology Centre (GPCC) product. Results show that the SM2RAIN-ASCAT rainfall dataset performs relatively well both at regional and global scale, mainly in terms of root mean square error when compared to other datasets. Specifically, SM2RAIN-ASCAT dataset provides better performance better than GPM and GPCC in the data scarce regions of the world, such as Africa and South America. In these areas we expect the larger benefits in using SM2RAIN-ASCAT for hydrological and agricultural applications.The SM2RAIN-ASCAT dataset is freely available at https://doi.org/10.5281/zenodo.2591215

TIME OPTIMAL CONTROL OF HIGH-DIMENSIONAL SYSTEMS

For time optimal control of high-dimensional systems, we use time stages of varying length, and iterative dynamic programming (IDP) to search simultaneously for the switching times and the stage lengths. The procedure is evaluated with a twenty plate gas absorber having two control variables. To obtain convergence in reliable manner, the use of continuation as shown in this paper is very effective. The accurate switching times yield better results than have been reported in the literature. The computational procedure is straightforward and the computations can be readily carried out on a personal computer

Benefits of the EUMETSAT Polar System – Second Generation (EPS-SG) for Arctic and Northern Regions Monitoring and Applications

The EUMETSAT Polar System – Second Generation (EPS-SG) is under development to follow the first generation EPS from 2022 onwards. EPS-SG will provide 9 observation missions in a dual satellite system in support of operational meteorology, climate monitoring and environmental services covering the ocean, atmosphere, land, cryosphere, and biosphere, to the extent they interact with, drive, or are driven by meteorology and climate. The benefits of EPS-SG for the Arctic and Northern regions cover a wide range of monitoring and user applications

A new method for the validation of the GOMOS high resolution temperature profiles products

This article proposes a new validation method for GOMOS HRTP atmospheric temperature and density profiles, with the aim of detecting and removing 0.2 to 5 km scale vertical structures in order to minimise the impact of atmospheric artefacts in the comparison exercises. The proposed approach is based on the use of the “Morlet” Continuous Wavelet Transformation (CWT), for the characterisation and removal of non-stationary and localised vertical structures, in order to produce wave-free profiles of atmospheric temperature and density. Comparison of wave-free temperature/density profiles and wavy structures profiles with those estimated from a limited number of collocated SHADOZ soundings for the years of 2003, 2004 and 2008, is discussed in detail. First results suggest that the proposed approach could lead to a significantly improved HRTP validation scheme, in terms of reduced uncertainties in the estimated biases. Furthermore, this method may be adopted for the study of the vertical component of gravity waves from high spatial/temporal resolution data

The Atmospheric Composition Validation and Evolution Workshop (ACVE2013) - Recommendations

During the last 18 years, the European Space Agency (ESA) has provided the scientific community with a large amount of valuable atmospheric composition data produced by a series of instruments, starting with GOME [Burrows et al., 1999], on-board the ERS-2 satellite (1995-2011), and followed by the GOMOS [Kyrola et al., 2004], MIPAS [Fischer et al., 2008], and the SCIAMACHY [Bovensmann et al., 1999], all flying on-board the Envisat satellite (2002-2012). These observations will be continued by the Sentinel-5 Precursor, Sentinel-4 and Sentinel-5 and extended the EarthCARE and ADM missions for aerosols and clouds. […

A Spectral Unmixing Model for the Integration of Multi-Sensor Imagery: A Tool to Generate Consistent Time Series Data

The Sentinel missions have been designed to support the operational services of the Copernicus program, ensuring long-term availability of data for a wide range of spectral, spatial and temporal resolutions. In particular, Sentinel-2 (S-2) data with improved high spatial resolution and higher revisit frequency (five days with the pair of satellites in operation) will play a fundamental role in recording land cover types and monitoring land cover changes at regular intervals. Nevertheless, cloud coverage usually hinders the time series availability and consequently the continuous land surface monitoring. In an attempt to alleviate this limitation, the synergistic use of instruments with different features is investigated, aiming at the future synergy of the S-2 MultiSpectral Instrument (MSI) and Sentinel-3 (S-3) Ocean and Land Colour Instrument (OLCI). To that end, an unmixing model is proposed with the intention of integrating the benefits of the two Sentinel missions, when both in orbit, in one composite image. The main goal is to fill the data gaps in the S-2 record, based on the more frequent information of the S-3 time series. The proposed fusion model has been applied on MODIS (MOD09GA L2G) and SPOT4 (Take 5) data and the experimental results have demonstrated that the approach has high potential. However, the different acquisition characteristics of the sensors, i.e. illumination and viewing geometry, should be taken into consideration and bidirectional effects correction has to be performed in order to reduce noise in the reflectance time series