a) Data availability (b) Satellite imagery (c) GFS model (d) Precipitation detection Figure 1: (a) The availability of input data: full field of view of the Meteosat-8 satellite, the currently processed area inside it and the coverage of Roshydromet radars. (b) IR-097 (infrared channel) from the Meteosat-8 satellite imagery. (c) Total cloud water (cloud liquid water + cloud ice) from the GFS model of the atmosphere. (d) Our reconstruction of the precipitation field.ABSTRACT Precipitation nowcasting is a short-range forecast of rain/snow (up to 2 hours), often displayed on top of the geographical map by the weather service. Modern precipitation nowcasting algorithms rely on the extrapolation of observations by ground-based radars via optical flow techniques or neural network models. Dependent on these radars, typical nowcasting is limited to the regions around their locations. We have developed a method for precipitation nowcasting based on geostationary satellite imagery and incorporated the resulting data into the Yandex.Weather precipitation map (including an alerting service with push notifications for products in the Yandex ecosystem), thus expanding its coverage and paving the way to a truly global nowcasting service.
In the present paper we discuss the setup and the results of series of numerical experiments aiming to recover the
plasma drift and neutral wind velocities using the Ensemble Square Root Filter together with the ionospheric numerical model. One of the objectives of the current research was assessing the performance of the upper atmosphere state and parameter ensemble estimation technique in the framework of the Observational System Simulation Experiment (OSSE). The other purpose was to improve calculation accuracy for the major driving forces in the ionosphere and to increase modeling reliability in real‐data operational cases. In the current paper we describe the setup of the modeling system used to obtain the presented results. In the first section we introduce the background physics‐based model used in the simulations and discuss its main assumptions along with
drift and the neutral wind velocity calculation algorithms. Further we present the observations simulation system and describe the data used for assimilation and parameter estimation. We also provide a brief description of the Ensemble Square Root Filter and its application in the current study. In the last few sections the results of the numerical experiments are presented and discussed.
Severe geomagnetic storms have a strong impact on space communication and satellite navigation systems. Forecasting the appearance of geomagnetically induced disturbances in the ionosphere is one of the urgent goals of the space weather community. The challenge is that the processes governing the distribution of the crucial ionospheric parameters have a rather poor quantitative description, and the models, built using the empirical parameterizations, have limited capabilities for operational purposes. On the other hand, data assimilation techniques are becoming more and more popular for nowcasting the state of the large-scale geophysical systems. We present an example of an ionospheric data assimilation system performance assessment during a strong geomagnetic event, which took place on 26 September 2011. The first-principle model has assimilated slant total electron content measurements from a dense network of ground stations, provided by the Norwegian Mapping Authority. The results have shown satisfactory agreement with independent data and demonstrate that the assimilation model is accurate to about 2-4 total electron content units and can be used for operational purposes in high-latitude regions. The operational system performance assessment is the subject of future work.
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