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

Active flow control in wind turbines - integration technologies and assessment of economic effects

Synthetic Jet Actuators (SJAs), as subgroup of active flow control devices are used to influence aerodynamic parameters by introducing energy into the airflow (e.g. by suction and blowing). For the core material of fiber-reinforced wind turbines, several designs of SJA and integration concepts are developed. In regard to the thin-walled structure of the wind turbines, a flat design of the actuators was developed so that they can be embedded into the sandwich material. In order to become widely accepted, those fluidic actuators have to be technologically as well as economically advantageous. Therefore, a method has been conceptualized that allows to compare the additional costs arising from actuator integration with the additional revenues considering the entire life cycle

An overview of the ACE-2 CLOUDYCOLUMN closure experiment

International audienceCLOUDYCOLUMN is one of the 6 ACE-2 projects which took place in June-July 1997, between Portugal and the Canary Islands. It was specifically dedicated to the study of changes of cloud radiative properties resulting from changes in the properties of those aerosols which act as cloud condensation nuclei. This process is also refered to as the aerosol indirect effect on climate. CLOUDYCOLUMN is focused on the contribution of stratocumulus clouds to that process. In addition to the basic aerosol measurements performed at the ground stations of the ACE-2 project, 5 instrumented aircraft carried out in situ characterization of aerosol physical, chemical and nucleation properties and cloud dynamical and microphysical properties. Cloud radiative properties were also measured remotely with radiometers and a lidar. 11 case studies have been documented, from pure marine to significantly polluted air masses. The simultaneity of the measurements with the multi-aircraft approach provides a unique data set for closure experiments on the aerosol indirect effect. In particular CLOUDYCOLUMN provided the 1st experimental evidence of the existence of the indirect effect in boundary layer clouds forming in polluted continental outbreacks. This paper describes the objectives of the project, the instrumental setup and the sampling strategy. Preliminary results published in additional papers are briefly summarized

Surface Albedo Retrieval from 40-Years of Earth Observations through the EUMETSAT/LSA SAF and EU/C3S Programmes: The Versatile Algorithm of PYALUS

Land surface albedo quantifies the fraction of the sunlight reflected by the surface of the Earth. This article presents the algorithm concepts for the remote sensing of this variable based on the heritage of several developments which were performed at Méteo France over the last decade and described in several papers by Carrer et al. The scientific algorithm comprises four steps: an atmospheric correction, a sensor harmonisation (optional), a BRDF (Bidirectional Reflectance Distribution Function) inversion, and the albedo calculation. At the time being, the method has been applied to 11 sensors in the framework of two European initiatives (Satellite Application Facility on Land Surface Analysis—LSA SAF, and Copernicus Climate Change Service—C3S): NOAA-7-9-11-14-16-17/AVHRR2-3, SPOT/VGT1-2, Metop/AVHRR-3, PROBA-V, and MSG/SEVIRI. This work leads to a consistent archive of almost 40 years of satellite-derived albedo data (available in 2020). From a single sensor, up to three different albedo products with different characteristics have been developed to address the requirements of both, near real-time (NRT) (weather prediction with a demand of timeliness of 1 h) and climate communities. The evaluation of the algorithm applied to different platforms was recently made by Lellouch et al. and Sánchez Zapero et al. in 2020 which can be considered as companion papers. After a summary of the method for the retrieval of these surface albedos, this article describes the specificities of each retrieval, lists the differences, and discusses the limitations. The plan of continuity with the next European satellite missions and perspectives of improvements are introduced. For example, Metop/AVHRR-3 albedo will soon become the medium resolution sensor product with the longest NRT data record, since MODIS is approaching the end of its life-cycle. Additionally, Metop-SG/METimage will ensure its continuity thanks to consistent production of data sets guaranteed till 2050 by the member states of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). In the end, the common strategy which we proposed through the different programmes may offer an unprecedented opportunity to study the temporal trends affecting surface properties and to analyse human-induced climate change. Finally, the access to the source code (called PYALUS) is provided through an open access platform in order to share with the community the expertise on the satellite retrieval of this variable

Surface Albedo Retrieval from 40-Years of Earth Observations through the EUMETSAT/LSA SAF and EU/C3S Programmes: The Versatile Algorithm of PYALUS

Land surface albedo quantifies the fraction of the sunlight reflected by the surface of the Earth. This article presents the algorithm concepts for the remote sensing of this variable based on the heritage of several developments which were performed at Méteo France over the last decade and described in several papers by Carrer et al. The scientific algorithm comprises four steps: an atmospheric correction, a sensor harmonisation (optional), a BRDF (Bidirectional Reflectance Distribution Function) inversion, and the albedo calculation. At the time being, the method has been applied to 11 sensors in the framework of two European initiatives (Satellite Application Facility on Land Surface Analysis—LSA SAF, and Copernicus Climate Change Service—C3S): NOAA-7-9-11-14-16-17/AVHRR2-3, SPOT/VGT1-2, Metop/AVHRR-3, PROBA-V, and MSG/SEVIRI. This work leads to a consistent archive of almost 40 years of satellite-derived albedo data (available in 2020). From a single sensor, up to three different albedo products with different characteristics have been developed to address the requirements of both, near real-time (NRT) (weather prediction with a demand of timeliness of 1 h) and climate communities. The evaluation of the algorithm applied to different platforms was recently made by Lellouch et al. and Sánchez Zapero et al. in 2020 which can be considered as companion papers. After a summary of the method for the retrieval of these surface albedos, this article describes the specificities of each retrieval, lists the differences, and discusses the limitations. The plan of continuity with the next European satellite missions and perspectives of improvements are introduced. For example, Metop/AVHRR-3 albedo will soon become the medium resolution sensor product with the longest NRT data record, since MODIS is approaching the end of its life-cycle. Additionally, Metop-SG/METimage will ensure its continuity thanks to consistent production of data sets guaranteed till 2050 by the member states of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). In the end, the common strategy which we proposed through the different programmes may offer an unprecedented opportunity to study the temporal trends affecting surface properties and to analyse human-induced climate change. Finally, the access to the source code (called PYALUS) is provided through an open access platform in order to share with the community the expertise on the satellite retrieval of this variable

Evaluating aerosol/cloud/radiation process parameterizations with single-column models and Second Aerosol Characterization Experiment (ACE-2) cloudy column observations: Evaluating aerosol/cloud/radiation process parameterizations withsingle-column models and Second Aerosol Characterization Experiment (ACE-2) cloudy column observations

The Second Aerosol Characterization Experiment (ACE-2) data set along with ECMWF reanalysis meteorological fields provided the basis for the single column model (SCM) simulations, performed as part of the PACE (Parameterization of the Aerosol Indirect Climatic Effect) project. Six different SCMs were used to simulate ACE-2 case studies of clean and polluted cloudy boundary layers, with the objective being to identify limitations of the aerosol/cloud/radiation interaction schemes within the range of uncertainty in in situ, reanalysis and satellite retrieved data. The exercise proceeds in three steps. First, SCMs are configured with the same fine vertical resolution as the ACE-2 in situ data base to evaluate the numerical schemes for prediction of aerosol activation, radiative transfer and precipitation formation. Second, the same test is performed at the coarser vertical resolution of GCMs to evaluate its impact on the performance of the parameterizations. Finally, SCMs are run for a 24–48 hr period to examine predictions of boundary layer clouds when initialized with large-scale meteorological fields. Several schemes were tested for the prediction of cloud droplet number concentration (N). Physically based activation schemes using vertical velocity show noticeable discrepancies compared to empirical schemes due to biases in the diagnosed cloud base vertical velocity. Prognostic schemes exhibit a larger variability than the diagnostic ones, due to a coupling between aerosol activation and drizzle scavenging in the calculation of N. When SCMs are initialized at a fine vertical resolution with locally observed vertical profiles of liquid water, predicted optical properties are comparable to observations. Predictions however degrade at coarser vertical resolution and are more sensitive to the mean liquid water path than to its spatial heterogeneity. Predicted precipitation fluxes are severely underestimated and improve when accounting for sub-grid liquid water variability. Results from the 24–48 hr runs suggest that most models have problems in simulating boundary layer cloud morphology, since the large-scale initialization fields do not accurately reproduce observed meteorological conditions. As a result, models significantly overestimate optical properties. Improved cloud morphologies were obtained for models with subgrid inversions and subgrid cloud thickness schemes. This may be a result of representing subgrid scale effects though we do not rule out the possibility that better large-forcing data may also improve cloud morphology predictions