Measurement and interpolation uncertainties in rainfall maps from cellular communication networks

Abstract

Abstract. Accurate measurements of rainfall are important in many hydrological and meteorological applications, for instance, flash-flood early-warning systems, hydraulic structures design, irrigation, weather forecasting, and climate modelling. Whenever possible, link networks measure and store the received power of the electromagnetic signal at regular intervals. The decrease in power can be converted to rainfall intensity, and is largely due to the attenuation by raindrops along the link paths. Such an alternative technique fulfils the continuous effort to obtain measurements of rainfall in time and space at higher resolutions, especially in places where traditional rain gauge networks are scarce or poorly maintained.Rainfall maps from microwave link networks have recently been introduced at country-wide scales. Despite their potential in rainfall estimation at high spatiotemporal resolutions, the uncertainties present in rainfall maps from link networks are not yet fully comprehended. The aim of this work is to identify and quantify the sources of uncertainty present in interpolated rainfall maps from link rainfall depths. In order to disentangle these sources of uncertainty, we classified them into two categories: (1) those associated with the individual microwave link measurements, i.e. the errors involved in link rainfall retrievals, such as wet antenna attenuation, sampling interval of measurements, wet/dry period classification, dry weather baseline attenuation, quantization of the received power, drop size distribution (DSD), and multi-path propagation; and (2) those associated with mapping, i.e. the combined effect of the interpolation methodology and the spatial density of link measurements.We computed ∼ 3500 rainfall maps from real and simulated link rainfall depths for 12 days for the land surface of the Netherlands. Simulated link rainfall depths refer to path-averaged rainfall depths obtained from radar data. The ∼ 3500 real and simulated rainfall maps were compared against quality-controlled gauge-adjusted radar rainfall fields (assumed to be the ground truth). Thus, we were able to not only identify and quantify the sources of uncertainty in such rainfall maps, but also test the actual and optimal performance of one commercial microwave network from one of the cellular providers in the Netherlands. Errors in microwave link measurements were found to be the source that contributes most to the overall uncertainty.

Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

“…by treating the rainfall values as line observations instead of as point observations, which is expected to have the largest impact at local scales for areas with high link densities or at areas with long links. Using data from the same 12 days as employed in Overeem et al (2013), Rios Gaona et al (2015 find that link rainfall retrieval errors themselves are the source of error that contributes most to the overall uncertainty in rainfall maps from a commercial link network. Errors due to mapping, i.e.…”

“…This data set is from June, August, and September 2011. Overeem et al (2013) andRios Gaona et al (2015) provide more information on the characteristics of microwave links from this 12-day data set. All link data are from an independent validation data set; i.e.…”

“…The user can choose to apply ordinary kriging (OK) employing a spherical variogram model (Overeem et al, 2013;Rios Gaona et al, 2015) or to apply inverse distance weighted (IDW) interpolation on link rainfall data. OK and IDW are well suited for dealing with heterogeneously distributed data locations.…”

The effect of differences between rainfall measurement techniques on groundwater and discharge simulations in a lowland catchment

“…The severity of these errors depends on the link length, frequency, and the rainfall intensity (Leijnse et al ., ); Spatial sampling : because link networks have a variable spatial density and distribution of link lengths, the effective resolution of the resulting precipitation maps is also variable. This may lead to local smoothing of precipitation peaks, resulting in local mapping errors (Zinevich et al ., ; Rios Gaona et al ., ); Precipitation types : microwave links are suitable for rainfall estimation, but it is difficult to distinguish between rain and wet snow (Cherkassky et al ., ). The attenuation caused by dry snow is negligible.…”

An urban microwave link rainfall measurement campaign

“…Error sources can generally be divided into errors due to the mapping of the rainfall estimates from the microwave links, and errors in the individual measurements and the rainfall retrieval algorithm. It is in this last category where the largest remaining sources of error reside and not in the mapping (Rios Gaona, et al, 2015). Therefore further research is needed into the microphysical aspects of the retrieval 25 algorithm.…”