Rainfall monitoring based on commercial terrestrial microwave links is tested for the first time in Burkina Faso, in Sahelian West Africa. In collaboration with one national cellular phone operator, Telecel Faso, the attenuation on a 29 km long microwave link operating at 7 GHz was monitored at 1 s time rate for the monsoon season 2012. The time series of attenuation is transformed into rain rates and compared with rain gauge data. The method is successful in quantifying rainfall: 95% of the rainy days are detected. The correlation with the daily rain gauge series is 0.8, and the season bias is 6%. The correlation at the 5 min time step within each event is also high. These results demonstrate the potential interest of exploiting national and regional wireless telecommunication networks for monitoring rainfall in Africa, where operational rain gauge networks are degrading and the hydrometeorological risk increasing.

WHAT: Eighty-seven participants from 18 countries met to discuss the prospect for rainfall measurement and high-resolution mapping based on commercial microwave links in Africa. Experts from Europe and Israel provided training to African students, scientists, and meteorologists on this innovative method.

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<p>Commercial Microwave Link (CML) data can provide important rainfall information, in particular in regions with low density of rain gauges and with no radar coverage. We have set up and operate a CML data acquisition (DAQ) system for Burkina Faso and report on the first larger scale analysis of the derived rainfall information.</p><p>Our real-time DAQ system started as a pilot project covering only eight CMLs and was gradually extended. For the monsoon season 2020 and 2021 it collected data for more than 1000 CMLs in Burkina Faso with a temporal resolution of one minute. Our first analysis is focusing on the 300 CMLs which operate in the frequency range between 11 GHz and 13 GHz in and around the city of Ouagadougou, the capital of Burkina Faso. We carry out a comparison with official daily rain gauge data, both for individual CMLs as well as for CML-derived rainfall maps. Our results for the period of the 2019, 2020 and 2021 rainy season indicate good performance of the CML rainfall information, with a Pearson correlation coefficient of 0.8 and higher. </p><p>The processing of the longer CMLs in the frequency range between 7 GHz and 9 GHz, which connect the urban centers in Burkina Faso, currently is in progress. To tackle the challenge of noisy dry periods we are investigating the use of cloud cover and cloud type information from MSG SEVIRI data.</p>

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Several factors can attenuate radio signal between transmitting and receiving antenna. One can cite: vegetation, atmospheric gases, fog, water vapor, transmission instruments, rain, temperature, etc... The sources of attenuation differ according to the climate and the relief of each continent or even each country. In this work we aim to show that there is link between microwave signal attenuation and weather visibility in the presence of dust. Weather visibility is a very important factor for the safety of road, sea, rail and air transportation. In the presence of dust, the visibility is strongly reduced and there is also a strong attenuation of the microwave signal propagating between two antennas. By performing a linear regression on the attenuation-visibility scatter plot, we propose a method for real-time estimation of the visibility knowing the microwave signals attenuation. A correlation measurement between the visibility estimated by our method from the real attenuation data of the mobile phone operator Telecel Faso SA (Burkina Faso) and the visibility measured by the National Meteorological Agency of Burkina Faso (ANAM) gave a correlation coefficient of 0.86.

Accurately measuring meteorological visibility is an important factor in road, sea, rail, and air transportation safety, especially under visibility-reducing weather events. This paper deals with the application of Machine Learning methods to estimate meteorological visibility in dusty conditions, from the power levels of commercial microwave links and weather data including temperature, dew point, wind speed, wind direction, and atmospheric pressure. Three well-known Machine Learning methods are investigated: Decision Trees, Random Forest, and Support Vector Machines. The correlation coefficient and the mean square error, between the visibility distances estimated by Machine Learning methods and those provided by Burkina Faso weather services are computed. Except for the SVM method, all the other methods give a correlation coefficient greater than 0.90. The Random Forest method presents the best result both in terms of correlation coefficient (0.97) and means square error (0.60). For this last method, the best variables that explain the model are selected by evaluating the weight of each variable in the model. The best performance is obtained by considering the attenuation of the microwave signal and the dew point.

Sahelian countries are confronted with a lack of reliable data on water and climate allowing them to understand the effects of climate variability. To address this situation, with the support of Water Aid, we have collected rainfall data and groundwater level in wells from 2012 to 2018 with help of local populations. Their contribution made it possible to cover a wider geographical area and to obtain the data necessary to analyze the climate variability on a small and large scale in the sub-basin of the Nouhao. The data collected are well correlated with those collected from the rain gauges of the national meteorological agency in the region of Fada N'Gourma. From 2012 to 2018, August appears to be the rainiest month. It recorded, alone, 1/3 of the average annual rainfall. The depletion of surface water tables is faster after the rainy season. The static level of the water table in the crystalline subsoil also depletes and replenishes at night after the peak water collection time, which is between noon and 8 p.m. These few years of measuring rainfall and groundwater fluctuations have shown that the correlation between rainfall and groundwater level is clearly established. Nevertheless this needs to be more investigated during a longer period to confirm the robustness of the method. On the other hand, the approach to securing water resources based on community monitoring of water resources gives good results in accordance with the National Meteorological Agency and the Directorate of water resources, however, caution recommends continuing measurements over a few decades to confirm its robustness for this aspect too.