Conference Report 2nd European Nowcasting Conference

The 2nd European Nowcasting Conference took place in Offenbach, Germany, on 3–5 May 2017. The conference was structured into four thematic sessions i) observations as basis for nowcasting, ii) nowcasting techniques and systems, iii) application, user aspects and verification, and iv) combination of numerical weather prediction and nowcasting. This report summarises the scientific contributions presented and the open scientific questions discussed at the conference

Preliminary results of SMOS data analysis over the ocean during commissioning phase

International audienceThe SMOS (Soil Moisture and Ocean Salinity) satellite was successfully launched on 2nd November 2009. The first 6 months after launch is dedicated to the commissioning phase during which the ground segment processing chains are validated. SMOS is the first interferometric radiometer in orbit. In the first ground processing (Level 1) an image reconstruction algorithm is applied which yields measured brightness temperatures (TB). Preliminary studies have shown that this processing is critical and likely to introduce biases that affect subsequent processing. Therefore, a comparison of modeled to reconstructed TB is essential. Homogenous ocean surfaces far from land masses are ideal for this task as the TB variation with the satellite geometry (incidence angle) is relatively well known. In this presentation we will focus on the following analysis: 1. Extensive comparisons between SMOS Level 1c TB and forward model simulated TB using ECMWF (European Centre for Medium-Range Weather Forecast) forcings. Comparisons will be performed with one of the forward models implemented in the L2 ocean salinity processor (2-scale model, Dinnat et al. 2003). Statistics of the TB differences will be presented. They may be related to flaws in the level 1c Tb related to image reconstruction or to instrumental imperfections, in the ECMWF forcings, or in the forward model. In order to discriminate between these various sources of uncertainties, the statistics of the Tb differences will be analysed in various reference frame: - the antenna reference frame (director cosines) - the earth reference frame and the correlation of the differences with the geometry of the measurement (incidence angle, distance to the center of the swath, ascending or descending orbit…) and with respect to the auxiliary geophysical parameters (wind speed, sea surface temperature…) will be looked at. 2. The pseudo-dielectric parameter Acard (Waldteufel et al., 2004) is used to synthesize information contained in both the real and the imaginary parts of dielectric constant of medium, such as sea water. The principle for retrieving sea surface salinity (SSS) from radiometric measurements is that the dielectric constant of sea water depends on several physical parameters, including SSS and sea surface temperature (SST). Given the relation between dielectric constant and Acard, retrieving Acard with the whole direct model is in a way equivalent to retrieve dielectric constant, which represents the property of sea water more directly than SSS and SST. The consistency between Acard_retrieved and Acard_modeled will be another tool for testing and assessing the validity of SMOS measurements and retrieved parameters. The consistency between retrieved Acard (Acard_retrieved) and Acard from known values of dielectric constants (Acard_modeled) will be investigated using similar methods as the ones described in 1. Perspectives about the accuracy on sea surface salinity retrieved from SMOS measurements will be given based on the above comparisons and analysis

Preliminary results of SMOS data analysis over the ocean during commissioning phase

International audienceThe SMOS (Soil Moisture and Ocean Salinity) satellite was successfully launched on 2nd November 2009. The first 6 months after launch is dedicated to the commissioning phase during which the ground segment processing chains are validated. SMOS is the first interferometric radiometer in orbit. In the first ground processing (Level 1) an image reconstruction algorithm is applied which yields measured brightness temperatures (TB). Preliminary studies have shown that this processing is critical and likely to introduce biases that affect subsequent processing. Therefore, a comparison of modeled to reconstructed TB is essential. Homogenous ocean surfaces far from land masses are ideal for this task as the TB variation with the satellite geometry (incidence angle) is relatively well known. In this presentation we will focus on the following analysis: 1. Extensive comparisons between SMOS Level 1c TB and forward model simulated TB using ECMWF (European Centre for Medium-Range Weather Forecast) forcings. Comparisons will be performed with one of the forward models implemented in the L2 ocean salinity processor (2-scale model, Dinnat et al. 2003). Statistics of the TB differences will be presented. They may be related to flaws in the level 1c Tb related to image reconstruction or to instrumental imperfections, in the ECMWF forcings, or in the forward model. In order to discriminate between these various sources of uncertainties, the statistics of the Tb differences will be analysed in various reference frame: - the antenna reference frame (director cosines) - the earth reference frame and the correlation of the differences with the geometry of the measurement (incidence angle, distance to the center of the swath, ascending or descending orbit…) and with respect to the auxiliary geophysical parameters (wind speed, sea surface temperature…) will be looked at. 2. The pseudo-dielectric parameter Acard (Waldteufel et al., 2004) is used to synthesize information contained in both the real and the imaginary parts of dielectric constant of medium, such as sea water. The principle for retrieving sea surface salinity (SSS) from radiometric measurements is that the dielectric constant of sea water depends on several physical parameters, including SSS and sea surface temperature (SST). Given the relation between dielectric constant and Acard, retrieving Acard with the whole direct model is in a way equivalent to retrieve dielectric constant, which represents the property of sea water more directly than SSS and SST. The consistency between Acard_retrieved and Acard_modeled will be another tool for testing and assessing the validity of SMOS measurements and retrieved parameters. The consistency between retrieved Acard (Acard_retrieved) and Acard from known values of dielectric constants (Acard_modeled) will be investigated using similar methods as the ones described in 1. Perspectives about the accuracy on sea surface salinity retrieved from SMOS measurements will be given based on the above comparisons and analysis

Information content of millimeter observations for hydrometeor properties in mid-latitudes

For future remote sensing applications the potential of the millimeter wavelength range for precipitation observations from geostationary orbits is investigated. Therefore, a database consisting of hydrometeor profiles from various mid-latitude precipitation cases over Europe and corresponding simulated brightness temperatures at 18 microwave frequencies was built using the cloud resolving model Meso-NH and the radiative transfer model micro wave model. The information content of the database was investigated by applying simple statistical methods, as well as developing first-order retrieval approaches. The results show that, particularly for snow and graupel, the total column content can be retrieved accurately with relative errors smaller than 25% in dominantly stratiform precipitation cases over land and ocean surfaces. The performance for rain-water path is similar to the one for graupel and snow in light precipitation cases. For the cases with higher precipitation amounts, the relative errors for rain-water path are larger particularly over land. The same behavior can be seen in the surface rain rate retrieval with the difference that the relative errors are doubled in comparison to the rain-water path. Algorithms with reduced number of frequencies show that window channels at higher frequencies are important for the surface rain rate retrieval because these are sensitive to the scattering in the ice phase related to the rain below. For the frozen hydrometeor retrieval, good results can be achieved by retrieval algorithms based only on frequencies at 150 GHz and above which are suitable for geostationary applications due to their reduced demands concerning the antenna size

Preliminary results of SMOS data analysis over the ocean during commissioning phase

International audienceThe SMOS (Soil Moisture and Ocean Salinity) satellite was successfully launched on 2nd November 2009. The first 6 months after launch is dedicated to the commissioning phase during which the ground segment processing chains are validated. SMOS is the first interferometric radiometer in orbit. In the first ground processing (Level 1) an image reconstruction algorithm is applied which yields measured brightness temperatures (TB). Preliminary studies have shown that this processing is critical and likely to introduce biases that affect subsequent processing. Therefore, a comparison of modeled to reconstructed TB is essential. Homogenous ocean surfaces far from land masses are ideal for this task as the TB variation with the satellite geometry (incidence angle) is relatively well known. In this presentation we will focus on the following analysis: 1. Extensive comparisons between SMOS Level 1c TB and forward model simulated TB using ECMWF (European Centre for Medium-Range Weather Forecast) forcings. Comparisons will be performed with one of the forward models implemented in the L2 ocean salinity processor (2-scale model, Dinnat et al. 2003). Statistics of the TB differences will be presented. They may be related to flaws in the level 1c Tb related to image reconstruction or to instrumental imperfections, in the ECMWF forcings, or in the forward model. In order to discriminate between these various sources of uncertainties, the statistics of the Tb differences will be analysed in various reference frame: - the antenna reference frame (director cosines) - the earth reference frame and the correlation of the differences with the geometry of the measurement (incidence angle, distance to the center of the swath, ascending or descending orbit…) and with respect to the auxiliary geophysical parameters (wind speed, sea surface temperature…) will be looked at. 2. The pseudo-dielectric parameter Acard (Waldteufel et al., 2004) is used to synthesize information contained in both the real and the imaginary parts of dielectric constant of medium, such as sea water. The principle for retrieving sea surface salinity (SSS) from radiometric measurements is that the dielectric constant of sea water depends on several physical parameters, including SSS and sea surface temperature (SST). Given the relation between dielectric constant and Acard, retrieving Acard with the whole direct model is in a way equivalent to retrieve dielectric constant, which represents the property of sea water more directly than SSS and SST. The consistency between Acard_retrieved and Acard_modeled will be another tool for testing and assessing the validity of SMOS measurements and retrieved parameters. The consistency between retrieved Acard (Acard_retrieved) and Acard from known values of dielectric constants (Acard_modeled) will be investigated using similar methods as the ones described in 1. Perspectives about the accuracy on sea surface salinity retrieved from SMOS measurements will be given based on the above comparisons and analysis

Preliminary results of SMOS data analysis over the ocean during commissioning phase

International audienceThe SMOS (Soil Moisture and Ocean Salinity) satellite was successfully launched on 2nd November 2009. The first 6 months after launch is dedicated to the commissioning phase during which the ground segment processing chains are validated. SMOS is the first interferometric radiometer in orbit. In the first ground processing (Level 1) an image reconstruction algorithm is applied which yields measured brightness temperatures (TB). Preliminary studies have shown that this processing is critical and likely to introduce biases that affect subsequent processing. Therefore, a comparison of modeled to reconstructed TB is essential. Homogenous ocean surfaces far from land masses are ideal for this task as the TB variation with the satellite geometry (incidence angle) is relatively well known. In this presentation we will focus on the following analysis: 1. Extensive comparisons between SMOS Level 1c TB and forward model simulated TB using ECMWF (European Centre for Medium-Range Weather Forecast) forcings. Comparisons will be performed with one of the forward models implemented in the L2 ocean salinity processor (2-scale model, Dinnat et al. 2003). Statistics of the TB differences will be presented. They may be related to flaws in the level 1c Tb related to image reconstruction or to instrumental imperfections, in the ECMWF forcings, or in the forward model. In order to discriminate between these various sources of uncertainties, the statistics of the Tb differences will be analysed in various reference frame: - the antenna reference frame (director cosines) - the earth reference frame and the correlation of the differences with the geometry of the measurement (incidence angle, distance to the center of the swath, ascending or descending orbit…) and with respect to the auxiliary geophysical parameters (wind speed, sea surface temperature…) will be looked at. 2. The pseudo-dielectric parameter Acard (Waldteufel et al., 2004) is used to synthesize information contained in both the real and the imaginary parts of dielectric constant of medium, such as sea water. The principle for retrieving sea surface salinity (SSS) from radiometric measurements is that the dielectric constant of sea water depends on several physical parameters, including SSS and sea surface temperature (SST). Given the relation between dielectric constant and Acard, retrieving Acard with the whole direct model is in a way equivalent to retrieve dielectric constant, which represents the property of sea water more directly than SSS and SST. The consistency between Acard_retrieved and Acard_modeled will be another tool for testing and assessing the validity of SMOS measurements and retrieved parameters. The consistency between retrieved Acard (Acard_retrieved) and Acard from known values of dielectric constants (Acard_modeled) will be investigated using similar methods as the ones described in 1. Perspectives about the accuracy on sea surface salinity retrieved from SMOS measurements will be given based on the above comparisons and analysis

Radiative transfer simulations using mesoscale cloud model outputs: comparisons with passive microwave and infrared satellite observations for mid-latitudes

Real midlatitude meteorological cases are simulated over western Europe with the cloud mesoscale model Méso-NH, and the outputs are used to calculate brightness temperatures at microwave frequencies with the Atmospheric Transmission at Microwave (ATM) radiative transfer model. Satellite-observed brightness temperatures (TBs) from the Advanced Microwave Scanning Unit B (AMSU-B) and the Special Sensor Microwave Imager (SSM/I) are compared to the simulated ones. In this paper, one specific situation is examined in detail. The infrared responses have also been calculated and compared to the Meteosat coincident observations. Overall agreement is obtained between the simulated and the observed brightness temperatures in the microwave and in the infrared. The large-scale dynamical structure of the cloud system is well captured by Méso-NH. However, in regions with large quantities of frozen hydrometeors, the comparison shows that the simulated microwave TBs are higher than the measured ones in the window channels at high frequencies, indicating that the calculation does not predict enough scattering. The factors responsible for the scattering (frozen particle distribution, calculation of particle dielectric properties, and nonsphericity of the particles) are analyzed. To assess the quality of the cloud and precipitation simulations by Méso-NH, the microphysical fields predicted by the German Lokal-Modell are also considered. Results show that in these midlatitude situations, the treatment of the snow category has a high impact on the simulated brightness temperatures. The snow scattering parameters are tuned to match the discrete dipole approximation calculations and to obtain a good agreement between simulations and observations even in the areas with significant frozen particles. Analysis of the other meteorological simulations confirms these results. Comparing simulations and observations in the microwave provides a powerful evaluation of resolved clouds in mesoscale models, especially the precipitating ice phase. [Completion of this field awaiting access to full-text paper] Peer reviewe

A midlatitude precipitating cloud database validated with satellite observations

The simulations of five midlatitude precipitating events by the nonhydrostatic mesoscale model Méso-NH are analyzed. These cases cover contrasted precipitation situations from 30° to 60°N, which are typical of midlatitudes. They include a frontal case with light precipitation over the Rhine River area (10 February 2000), a long-lasting precipitation event at Hoek van Holland, Netherlands (19 September 2001), a moderate rain case over the Elbe (12 August 2002), an intense rain case over Algiers (10 November 2001), and the “millennium storm” in the United Kingdom (30 October 2000). The physically consistent hydrometeor and thermodynamic outputs are used to generate a database for cloud and precipitation retrievals. The hydrometeor vertical profiles that were generated vary mostly with the 0°C isotherm, located between 1 and 3 km in height depending on the case. The characteristics of this midlatitude database are complementary to the GPROF database, which mostly concentrates on tropical situations. The realism of the simulations is evaluated against satellite observations by comparing synthetic brightness temperatures (BTs) with Advanced Microwave Sounding Unit (AMSU), Special Sensor Microwave Imager (SSM/I), and Meteosat observations. The good reproduction of the BT distributions by the model is exploited by calculating categorical scores for verification purposes. The comparison with 3-hourly Meteosat observations demonstrates the ability of the model to forecast the time evolution of the cloud cover, the latter being better predicted for the stratiform cases than for others. The comparison with AMSU-B measurements shows the skill of the model to predict rainfall at the correct location