On the Power of Microwave Communication Data to Monitor Rain for Agricultural Needs in Africa

Design of a Microwave Heating and Permittivity Measurement System Based on Oblique Aperture Ridge Waveguide

In this paper, an oblique aperture ridge waveguide operating at 2450 MHz is proposed, and, using the ridge waveguide, a permittivity measurement system is constructed which can measure the permittivity of materials during microwave heating. The system calculates the amplitudes of the scattering parameters by using the forward, reflected and transmitted powers of the power meters, and it reconstructs the permittivity of the material by combining the scattering parameters with an artificial neural network. The system is used to measure the complex permittivity of mixed solutions of methanol and ethanol with different ratios at room temperature, and the permittivity of methanol and ethanol with increasing temperature, from room temperature to 50 °C. The measured results are in good agreement with the reference data. The system allows simultaneous measurement of the permittivity with microwave heating and provides real-time, rapid changes in the permittivity during heating, avoiding thermal runaway and providing a reference for applications of microwave energy in the chemical industry.

The MSG Technique: Improving Commercial Microwave Link Rainfall Intensity by Using Rain Area Detection from Meteosat Second Generation

Commercial microwave link (MWL) used by mobile telecom operators for data transmission can provide hydro-meteorologically valid rainfall estimates according to studies in the past decade. For the first time, this study investigated a new method, the MSG technique, that uses Meteosat Second Generation (MSG) satellite data to improve MWL rainfall estimates. The investigation, conducted during daytime, used MSG optical (VIS0.6) and near IR (NIR1.6) data to estimate rain areas along a 15 GHz, 9.88 km MWL for classifying the MWL signal into wet–dry periods and estimate the baseline level. Additionally, the MSG technique estimated a new parameter, wet path length, representing the length of the MWL that was wet during wet periods. Finally, MWL rainfall intensity estimates from this new MSG and conventional techniques were compared to rain gauge estimates. The results show that the MSG technique is robust and can estimate gauge comparable rainfall estimates. The evaluation scores every three hours of RMSD, relative bias, and r2 based on the entire evaluation period results of the MSG technique were 2.61 mm h−1, 0.47, and 0.81, compared to 2.09 mm h−1, 0.04, and 0.84 of the conventional technique, respectively. For convective rain events with high intensity spatially varying rainfall, the results show that the MSG technique may approximate the actual mean rainfall estimates better than the conventional technique.

Rain Area Detection in South-Western Kenya by Using Multispectral Satellite Data from Meteosat Second Generation

This study presents a rain area detection scheme that uses a gradient based adaptive technique for daytime and nighttime rain area detection and correction from reflectance and infrared (IR) brightness temperatures data of the Meteosat Second Generation (MSG) satellite. First, multiple parametric rain detection models developed from MSG’s reflectance and IR data were calibrated and validated with rainfall data from a dense network of rain gauge stations and investigated to determine the best model parameters. The models were based on a conceptual assumption that clouds characterised by the top properties, e.g., high optical thickness and effective radius, have high rain probabilities and intensities. Next, a gradient based adaptive correction technique that relies on rain area-specific parameters was developed to reduce the number and sizes of the detected rain areas. The daytime detection with optical (VIS0.6) and near IR (NIR1.6) reflectance data achieved the best detection skill. For nighttime, detection with thermal IR brightness temperature differences of IR3.9-IR10.8, IR3.9-WV73 and IR108-WV62 showed the best detection skill based on general categorical statistics. Compared to the Global Precipitation Measurement (GPM) Integrated Mult-isatellitE Retrievals for GPM (IMERG) and the gauge station data from the southwest of Kenya, the model showed good agreement in the spatial dynamics of the detected rain area and rain rate.