NWCSAF/High Resolution Winds AMV Software for Geostationary and Polar satellites Status in 2023

Ponencias presentadas en: 16th International Winds Workshop celebradas Montreal, Canadá, del 8-12 de mayo de 2023.The High Resolution Winds (NWC/GEO-HRW) software is developed by the EUMETSAT Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting (NWCSAF). It is part of a stand-alone software package for the calculation of meteorological products with geostationary satellite data (NWC/GEO). NWCSAF High Resolution Winds provides a detailed calculation of Atmospheric Motion Vectors (AMVs) and Trajectories, locally and in near real time, using as input geostationary satellite image data, NWP model data, and OSTIA sea surface temperature data. The whole NWC/GEO software package can be obtained after registration at the NWCSAF Helpdesk, www.nwcsaf.org, where users also find support and help for its use. NWC/GEO v2018.1 software version, available since autumn 2019, is able to process MSG, Himawari-8/9, GOES-N, and GOES-R satellite series images, so that AMVs and trajectories can be calculated all throughout the planet Earth with the same algorithm and quality. Considering other equivalent meteorological products, in the ‘2014 and 2018 AMV Intercomparison Studies’ NWCSAF High Resolution Winds compared very positively with six other AMV algorithms for both MSG and Himawari-8/9 satellites. Finally, the Coordination Group for Meteorological Satellites (CGMS) recognized in its ‘2012 Meeting Report’: (1) NWCSAF High Resolution Winds fulfills the requirements to be a portable stand-alone AMV calculation software due to its easy installation and usability. (2) It has been successfully adopted by some CGMS members and serves as an important tool for development. It is modular, well documented, and well suited as stand-alone AMV software. (3) Although alternatives exist as portable stand-alone AMV calculation software, they are not as advanced in terms of documentation and do not have an existing Helpdesk

Neural network cloud top pressure and height for MODIS

Cloud top height retrieval from imager instruments is important for nowcasting and for satellite climate data records. A neural network approach for cloud top height retrieval from the imager instrument MODIS (Moderate Resolution Imaging Spectroradiometer) is presented. The neural networks are trained using cloud top layer pressure data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) dataset. <br><br> Results are compared with two operational reference algorithms for cloud top height: the MODIS Collection 6 Level 2 height product and the cloud top temperature and height algorithm in the 2014 version of the NWC SAF (EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting) PPS (Polar Platform System). All three techniques are evaluated using both CALIOP and CPR (Cloud Profiling Radar for CloudSat (CLOUD SATellite)) height. <br><br> Instruments like AVHRR (Advanced Very High Resolution Radiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) contain fewer channels useful for cloud top height retrievals than MODIS, therefore several different neural networks are investigated to test how infrared channel selection influences retrieval performance. Also a network with only channels available for the AVHRR1 instrument is trained and evaluated. To examine the contribution of different variables, networks with fewer variables are trained. It is shown that variables containing imager information for neighboring pixels are very important. <br><br> The error distributions of the involved cloud top height algorithms are found to be non-Gaussian. Different descriptive statistic measures are presented and it is exemplified that bias and SD (standard deviation) can be misleading for non-Gaussian distributions. The median and mode are found to better describe the tendency of the error distributions and IQR (interquartile range) and MAE (mean absolute error) are found to give the most useful information of the spread of the errors. <br><br> For all descriptive statistics presented MAE, IQR, RMSE (root mean square error), SD, mode, median, bias and percentage of absolute errors above 0.25, 0.5, 1 and 2 km the neural network perform better than the reference algorithms both validated with CALIOP and CPR (CloudSat). The neural networks using the brightness temperatures at 11 and 12 µm show at least 32 % (or 623 m) lower MAE compared to the two operational reference algorithms when validating with CALIOP height. Validation with CPR (CloudSat) height gives at least 25 % (or 430 m) reduction of MAE

Three-dimensional variational data assimilation for a limited area model Part I : General formulation and the background error constraint

A 3-dimensional variational data assimilation (3D-Var) scheme for the HIgh Resolution Limited Area Model (HIRLAM) forecasting system is described. The HIRLAM 3D-Var is based on the minimization of a cost function that consists of one term J(b). which measures the distance between the resulting analysis and a background field, in general a short-range forecast. and another term J(o). which measures the distance between the analysis and the observations. This paper is concerned with the general formulation of the HIRLAM 3D-Var and with Jb. while the companion paper by Lindskog and co-workers is concerned with the handling of observations, including the J(o) term, and with validation of the 3D-Var through extended parallel assimilation and forecast experiments. The 3D-Var minimization requires a pre-conditioning that is achieved by a transformation of the minimization control variable. This change of variable is designed as an operator approximating an inverse square root of the forecast error covariance matrix in the model space. The main transformations are the Subtraction of the geostrophic wind increment, the bi-Fourier transform, and the projection on vertical eigenvectors. The spectral bi-Fourier approach allows one to derive non-separable structure functions in a limited area model. in the form of vertically dependent horizontal spectra and scale-dependent vertical correlations. Statistics have been accumulated from differences between +24 h and +48 h HIRLAM forecasts valid at the same time. Results from single observation impact studies as well as results from assimilation cycles using operational observations are presented. It is shown that the HIRLAM 3D-Var produces assimilation increments in accordance with the applied analysis structure functions, that the fit of the analysis to the observations is in agreement with the assumed error statistics. and that assimilation increments are well balanced. It is also shown that the particular problems associated with the limited area formulation have been solved. These results, together with the results of the companion paper, indicate that the 3D-Var scheme performs significantly better than the statistical interpolation scheme