Preliminary Investigation of the Potentialities of a Mesoscale Meteorological Model to Reproduce Experimental Statistics of Rain Attenuation on Earth-Space Links

Abstract

Current spatial resolutions achieved by mesoscale weather forecast models allow them to be used to generate the state of the lowest layers of the atmosphere over areas as small as a few square kilometers which corresponds to the typical size of the tropospheric area crossed by Earth-space links. Furthermore, they allow the evolution of the troposphere to be predicted with a time stamp of five minutes instead of every hour with large-scale weather forecast models which makes them attractive for radio propagation predictions for satellite communication applications. This paper aims at studying the capability of the Weather Research and Forecast (WRF) model coupled with an electromagnetic physical model to reproduce rain attenuation statistics for Earth-space paths at Ka-band. To this purpose, one year of propagation measurements collected at 20 GHz in different places at midlatitudes in Toulouse and Salon de Provence (France), Spino d’Adda (Italy), Aveiro (Portugal), and Madrid (Spain), at high latitudes in Svalbard (Norway) and at low latitudes in Kourou are used to make comparisons between simulations and measurements. Comparisons between the simulated and the experimental annual statistics considered in this paper provide encouraging results, with a similar accuracy as Recommendation ITU-R P.618–13 for midlatitude European locations and with better accuracy for a high latitude area in Svalbard and for an equatorial location in French Guiana.

Estimating Annual Temperate Rain Attenuation Distributions at 20/40 GHz With a High-Resolution Numerical Weather Prediction Model

“…the number of rain drops per unit mass of dry air. The details on how to use these variables to get to Λ or (𝑁 𝑅 , Λ), for a single or double-moment distribution respectively, are laid out in [12], [14] and [27] respectively.…”

“…It was generally observed that simulated rain attenuation time series correlate poorly with measurements (10-30%). However, acceptable agreements (within 1-5 dB) have been reported for the corresponding rain attenuation Complementary Cumulative Distribution Functions (CCDFs) [12], [15].…”

“…Numerical Weather Prediction (NWP) models are in principle ideal tools to reproduce past states of the troposphere, including precipitations when initiated by reanalysis databases such as ERA Interim [12]. Nowadays global archives such as ERA5 are available to the public at increasingly high resolutions in space (~30 km horizontally) and time (every hour).…”

Prediction of 3 Years of Annual Rain Attenuation Statistics at Ka‐Band in French Guiana Using the Numerical Weather Prediction Model WRF

“…The WRF‐EMM combined approach presented here is based on the Weather Research and Forecasting (WRF) model developed by NCAR/National Centers for Environmental Prediction (NCEP) in the US and on an ElectroMagnetic Module (EMM) developed by ONERA. It was used to compute annual statistics of attenuation in several regions (Castanet et al., 2022) such as Toulouse (Outeiral et al., 2013) (Quibus et al., 2022), the south of Europe (Fayon et al., 2017) as well as in Belgium and Italy (Quibus, 2020). It has also been tested in Kourou (Le Mire et al., 2020) where both annual and monthly statistics of rain attenuation have been compared to the measurements available on site (L’Hour et al., 2022).…”

“…The EMM physical propagation model converts the relevant WRF outputs concerning the propagation of radio waves to compute tropospheric attenuation due to oxygen, water vapor, clouds, and rain. More details on the EMM module can be found in LeMire et al (2020) andCastanet et al (2022).The parametrization of the WRF model to obtain an accurate simulation of the atmosphere is a critical aspect of the research studies surrounding this open-source research model. Due to the high number of input parameters as well as many different choices of parametrization, it is a complex exercise as can be shown in the literatureMourre et al (2016),Moya-Álvarez et al (2018), andRama Rao et al (2012).…”