scholarly journals Joint Exploitation of SAR and GNSS for Atmospheric Phase Screens Retrieval Aimed at Numerical Weather Prediction Model Ingestion

2020 ◽  
Vol 12 (4) ◽  
pp. 654 ◽  
Author(s):  
Marco Manzoni ◽  
Andrea Virgilio Monti-Guarnieri ◽  
Eugenio Realini ◽  
Giovanna Venuti
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Fast Method ◽  
Total Delay ◽  
Dense Area ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Phase Screens

This paper proposes a simple and fast method to estimate Atmospheric Phase Screens (APSs) by jointly exploit a stack of Synthetic Aperture Radar (SAR) images and a dataset of GNSS-derived atmospheric product. The output of this processing is conceived to be ingested by Numerical Weather Prediction Models (NWPMs) to improve weather forecasts. In order to provide wide and dense area coverage and to respect requirements in terms of spatial resolution of ingestion products in NWPMs, both Permanent Scatterers (PSs) and Distributed Scatterers (DSs) are jointly exploited. While the formers are by definition stable targets, but unevenly distributed, the latter are ubiquitous but stable only within a certain temporal baseline that can vary depending on the operational frequency of the radar. The proposed method is thus particularly suited for C, L, and P band missions with low temporal baseline between two consecutive acquisitions of the same scene: these conditions, that are both necessary to provide the dense space-time coverage required by meteorologists, allow for a reliable and robust estimation of APSs thanks to the intrinsic limitation of temporal decorrelation. The proposed technique integrates Zenith Total Delay (ZTD) products computed on a very sparse grid from a network of GNSS stations to correct for SAR orbital errors and to provide the missing phase constant from the derived APS map. In this paper, the complete workflow is explained, and a comparison of the derived APSs is performed with phase screens derived from state-of-the-art SAR processing workflow (SqueeSAR®).

scholarly journals Operational Assimilation of GPS Zenith Total Delay Observations into the Met Office Numerical Weather Prediction Models

2012 ◽  
Vol 140 (8) ◽  
pp. 2706-2719 ◽  
Author(s):  
Gemma V. Bennitt ◽  
Adrian Jupp
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Total Delay ◽  
Numerical Weather ◽  
Zenith Total Delay ◽  
Nwp Model ◽  
The Impact ◽  
Numerical Weather Prediction Models

Abstract Zenith total delay (ZTD) observations derived from ground-based GPS receivers have been assimilated operationally into the Met Office North Atlantic and European (NAE) numerical weather prediction (NWP) model since 2007. Assimilation trials were performed using the Met Office NAE NWP model at both 12- and 24-km resolution to assess the impact of ZTDs on forecasts. ZTDs were found generally to increase relative humidity in the analysis, increasing the humidity bias compared to radiosonde observations, which persisted through the forecasts at some vertical levels. Improvements to cloud forecasts were also identified. Assimilation of ZTDs using both three-dimensional and four-dimensional variational data assimilation (3D-Var/4D-Var) was investigated, and it is found that assimilation at 4D-Var does not deliver any clear benefit over 3D-Var in the periods studied with the NAE model. This paper summarizes the methods used to assimilate ZTDs at the Met Office and presents the results of impact trials performed prior to operational assimilation. Future improvements to the assimilation methods are discussed.

scholarly journals Estimation of GNSS tropospheric products and their meteorological exploitation in Slovakia

2020 ◽  
Vol 50 (1) ◽  
pp. 83-111
Author(s):  
Martin Imrišek ◽  
Mária Derková ◽  
Juraj Janák
Keyword(s):  
Prediction Model ◽  
Numerical Weather Prediction ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Precipitation Forecast ◽  
Total Delay ◽  
Numerical Weather ◽  
Zenith Total Delay ◽  
The Impact

This paper discusses the in near–real time processing of Global Navigation Satellite System observations at the Department of Theoretical Geodesy at the Slovak University of Technology in Bratislava. Hourly observations from Central Europe are processed with 30 minutes delay to provide tropospheric products. The time series and maps of tropospheric products over Slovakia are published online. Zenith total delay is the most important tropospheric parameter. Its comparison with zenith total delays from IGS and E–GVAP solutions and the validation of estimated zenith total delay error over year 2018 have been made. Zenith total delays are used to improve initial conditions of numerical weather prediction model by the means of the three–dimensional variational analysis at Slovak Hydrometeorological Institute. The impact of assimilation of different observation types into numerical weather prediction model is discussed. The case study was performed to illustrate the impact of zenith total delay assimilation on the precipitation forecast.

A Methodology for Optimizing Numerical Weather Prediction Models

2020 ◽  
Author(s):  
Rafaella - Eleni Sotiropoulou ◽  
Ioannis Stergiou ◽  
Efthimios Tagaris
Keyword(s):  
Numerical Weather Prediction ◽  
Land Surface ◽  
Prediction Models ◽  
Carbon Sink ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Numerical Weather ◽  
Temperature And Precipitation ◽  
Statistical Measures ◽  
Numerical Weather Prediction Models

<p>Optimizing the performance of numerical weather prediction models is a very complicated process due to the numerous parameterization choices provided to the user. In addition, improving the predictability of one model’s variable (e.g., temperature) does not necessarily imply the improvement of another (e.g., precipitation). In this work the Technique of Preference by Similarity to the Ideal Solution (TOPSIS) is suggested as a method to optimize the performance of a numerical weather prediction model. TOPSIS provides the ability of using multiple statistical measures as ranking criteria for multiple forecasting variables. The Weather Research and Forecasting model (WRF) is used here for application of TOPSIS in order to optimize the model’s performance by the combined assessment of temperature and precipitation over Europe. Six ensembles optimize model’s physics performance (i.e., microphysics, planetary boundary layer, cumulus scheme, Long–and Short– wave and Land Surface schemes). The best performing option for each ensemble is selected by using multiple statistical criteria as input for the TOPSIS method, based on the integration of entropy weights. The method adopted here illustrates the importance of an integrated evaluation of weather prediction models’ performance and suggests a pathway for its improvement.</p><p>Acknowledgments LIFE CLIMATREE project “A novel approach for accounting & monitoring carbon sequestration of tree crops and their potential as carbon sink areas” (LIFE14 CCM/GR/000635).</p>

scholarly journals Computational Efficiency Examination of a Regional Numerical Weather Prediction Model using KISTI Supercomputer NURION

2021 ◽  
Vol 12 (6) ◽  
pp. 743-749
Author(s):  
Ji-Sun Kang Et.al
Keyword(s):  
Prediction Model ◽  
Numerical Weather Prediction ◽  
Computational Efficiency ◽  
Prediction Models ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Extreme Weather Events ◽  
Numerical Weather ◽  
Computational Performance ◽  
The Impact

For well-resolving extreme weather events, running numerical weather prediction model with high resolution in time and space is essential. We explore how efficiently such modeling could be, using NURION. We have examined one of community numerical weather prediction models, WRF, and KISTI’s 5th supercomputer NURION of national HPC. Scalability of the model has been tested at first, and we have compared the computational efficiency of hybrid openMP + MPI runs with pure MPI runs. In addition to those parallel computing experiments, we have tested a new storage layer called burst buffer to see whether it can accelerate frequent I/O. We found that there are significant differences between the computational environments for running WRF model. First of all, we have tested a sensitivity of computational efficiency to the number of cores per node. The sensitivity experiments certainly tell us that using all cores per node does not guarantee the best results, rather leaving several cores per node could give more stable and efficient computation. For the current experimental configuration of WRF, moreover, pure MPI runs gives much better computational performance than any hybrid openMP + MPI runs. Lastly, we have tested burst buffer storage layer that is expected to accelerate frequent I/O. However, our experiments show that its impact is not consistently positive. We clearly confirm the positive impact with relatively smaller problem size experiments while the impact was not seen with bigger problem experiments. Significant sensitivity to the different computational configurations shown this paper strongly suggests that HPC users should find out the best computing environment before massive use of their applications

scholarly journals Parametrizations of Liquid and Ice Clouds’ Optical Properties in Operational Numerical Weather Prediction Models

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 89
Author(s):  
Harel. B. Muskatel ◽  
Ulrich Blahak ◽  
Pavel Khain ◽  
Yoav Levi ◽  
Qiang Fu
Keyword(s):  
Optical Properties ◽  
Numerical Weather Prediction ◽  
Radiation Transfer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Effective Size ◽  
Asymmetry Factor ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models ◽  
Spectral Averaging

Parametrization of radiation transfer through clouds is an important factor in the ability of Numerical Weather Prediction models to correctly describe the weather evolution. Here we present a practical parameterization of both liquid droplets and ice optical properties in the longwave and shortwave radiation. An advanced spectral averaging method is used to calculate the extinction coefficient, single scattering albedo, forward scattered fraction and asymmetry factor (bext, v, f, g), taking into account the nonlinear effects of light attenuation in the spectral averaging. An ensemble of particle size distributions was used for the ice optical properties calculations, which enables the effective size range to be extended up to 570 μm and thus be applicable for larger hydrometeor categories such as snow, graupel, and rain. The new parameterization was applied both in the COSMO limited-area model and in ICON global model and was evaluated by using the COSMO model to simulate stratiform ice and water clouds. Numerical weather prediction models usually determine the asymmetry factor as a function of effective size. For the first time in an operational numerical weather prediction (NWP) model, the asymmetry factor is parametrized as a function of aspect ratio. The method is generalized and is available on-line to be readily applied to any optical properties dataset and spectral intervals of a wide range of radiation transfer models and applications.

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