Challenges in Diurnal Humidity Analysis from Cellular Microwave Links (CML) over Germany

Abstract

Near-surface humidity is a crucial variable in many atmospheric processes, mostly related to the development of clouds and rain. The humidity at the height of a few tens of meters above ground level is highly influenced by surface characteristics. Measuring the near-surface humidity at high resolution, where most of the humidity’s sinks and sources are found, is a challenging task using classical tools. A novel approach for measuring the humidity is based on commercial microwave links (CML), which provide a large part of the cellular networks backhaul. This study focuses on employing humidity measurements with high spatio–temporal resolution in Germany. One major goal is to assess the errors and the environmental influence by comparing the CML-derived humidity to in-situ humidity measurements at weather stations and reanalysis (COSMO-Rea6) products. The method of retrieving humidity from the CML has been improved as compared to previous studies due to the use of new data at high temporal resolution. The results show a similar correlation on average and generally good agreement between both the CML retrievals and the reanalysis, and 32 weather stations near Siegen, West Germany (CML—0.84, Rea6—0.85). Higher correlations are observed for CML-derived humidity during the daytime (0.85), especially between 9–17 LT (0.87) and a maximum at 12 LT (0.90). During the night, the correlations are lower on average (0.81), with a minimum at 3 LT (0.74). These results are discussed with attention to the diurnal boundary layer (BL) height variation which has a strong effect on the BL humidity temporal profile. Further metrics including root mean square errors, mean values and standard deviations, were also calculated.

High-Resolution Humidity Observations Based on Commercial Microwave Links (CML) Data—Case of Tel Aviv Metropolitan Area

“…The first subsection focuses on the quality of the CMLs observations. A comparison between the CMLs' humidity fields and the weather stations over the study region was made in order to assess the CMLs' performances in a similar manner to pioneering works in the field [19,45]. The second and third subsections focus on the way the LCs over the study region influenced the near-surface humidity.…”

“…When compared with errors caused by the minimal interval of the RSL measurements (i.e., the quantization error, QE) [19,26,44], an approximation of T and p caused smaller errors in humidity calculations. The error range can be explained primarily by the QE and CML length [45]. This error comes from dividing the WV attenuation by the link's length in order to obtain the normalized attenuation in dB/km, resulting in the average value of the true humidity along the link's path.…”

“…When there was a substantial height difference between two stations, such as over complex terrains, the nearest station to a CML may exhibit different humidity patterns than the CML itself. Consequently, in [19] and [45] and in this study, we used a different method. First, the median of the WS-HO, WS-HO m , from each weather station over the study area was calculated for a two-week period.…”

A Statistical Approach to Retrieve Rainfall Intensity by Attenuation Measurements From Commercial Microwave Links

“…Another possible reason for the worse results in January to March might be the influence of the local meteorological elements. Studies showed that the humidity could cause extra attenuation, thus higher humidity may result in a worse rainfall retrieving (David et al., 2011; Rubin et al., 2022). Although the absolute humidity during January and March are lower than other months, the influence caused by it might be significant since the rain‐induced attenuation is smaller than other periods.…”

Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives

“…Pu et al [105] achieved high temporal resolution (5-min) water vapor retrieval based on E-band CMLs using an LSTM deep learning model. More studies using CMLs for water vapor estimation can be found in the literature [106][107][108][109]. In addition to these, CMLs have been shown to be valid in recognizing and measuring fog [110][111][112] and dew [113].…”