Spaceborne microwave radiometers represent an important component of the Global Precipitation Measurement (GPM) mission due to their frequent sampling of rain systems. Microwave radiometers measure microwave radiation (brightness temperatures Tb), which can be converted into precipitation estimates with appropriate assumptions. However, detecting shallow precipitation systems using spaceborne radiometers is challenging, especially over land, as their weak signals are hard to differentiate from those associated with dry conditions. This study uses a random forest (RF) model to classify microwave radiometer observations as dry, shallow, or nonshallow over the Netherlands}a regionwith varying surface conditions and frequent occurrence of shallow precipitation. The RF model is trained on five years of data (2016–20) and tested with two independent years (2015 and 2021). The observations are classified using ground-based weather radar echo top heights. Various RF models are assessed, such as using only GPM Microwave Imager (GMI) Tb values as input features or including spatially aligned ERA5 2-m temperature and freezing level reanalysis and/or Dual-Frequency Precipitation Radar (DPR) observations. Independent of the input features, the model performs best in summer and worst in winter. The model classifies observations from high-frequency channels ($85 GHz) with lower Tb values as nonshallow, higher values as dry, and those in between as shallow. Misclassified footprints exhibit radiometric characteristics corresponding to their assigned class. Case studies reveal dry observations misclassified as shallow are associated with lower Tb values, likely resulting from the presence of ice particles in nonprecipitating clouds. Shallow footprints misclassified as dry are likely related to the absence of ice particles. SIGNIFICANCE STATEMENT: Published research concerning rainfall retrieval algorithms from microwave radiometers is often focused on the accuracy of these algorithms. While shallow precipitation over land is often characterized as problematic in these studies, little progress has been made with these systems. In particular, precipitation formed by shallow clouds, where shallow refers to the clouds being close to Earth’s surface, is often missed. This study is focused on detecting shallow precipitation and its physical characteristics to further improve its detection from spaceborne sensors. As such, it contributes to understanding which shallow precipitation scenes are challenging to detect from microwave radiometers, suggesting possible ways for algorithm improvement.

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Terrestrial microwave links are increasingly being used to estimate path-averaged precipitation by determining the attenuation caused by rainfall along the link path, mostly with commercial microwave links from cellular telecommunication networks. However, the temporal resolution of these rainfall estimates and the method to derive them are often determined by the temporal sampling strategy that is employed by the mobile network operators. Currently, the links are most often sampled at a temporal resolution of 15 min with a recording of the minimum and maximum values, while more recently, a form of instantaneous sampling with possible intervals up to 1 s has also been set up. For rainfall research purposes, often high temporal resolutions in combination with averaged values are preferred. However, it is uncertain how these various temporal sampling strategies affect the estimated rainfall intensity. Here we aim to understand how temporal sampling strategies affect the measured rainfall intensities using microwave links. To do so, we use data from three collocated microwave links, two 38 GHz and one 26 GHz, sampled at 20 Hz and covering a 2.2 km path over the city of Wageningen, the Netherlands. We aggregate the microwave link power levels to multiple time intervals (1 s to 60 min) and use a mean, instantaneous, and minimum and maximum value to characterize the signal. Based on the aggregated data, we compute rainfall intensities and compare these with 20 Hz rainfall estimates, such that we isolate errors and uncertainties caused by the sampling strategies from instrumental effects, such as different biases between instruments and representativeness errors. In general, our results show that for all sampling strategies, an increase in sampling time interval reduces the performance of the rainfall estimates, which especially holds for the instantaneous sampling strategy. Even the mean sampling strategy, which generally performs best of all strategies, is sensitive to this reduction in temporal resolution and could lead to significant underestimations. This sensitivity of the mean sampling to the temporal resolution seems to be largely affected by the non-linear relation between attenuation and rainfall. The min–max sampling strategy is mostly prone to minor underestimations or large overestimations of the path-averaged rainfall intensities. Moreover, our results, including a comparison with theoretical events, show that the attenuation due to wet antennas not only affects the comparison between the rainfall estimates obtained with a microwave link and another reference instrument but also has a significant influence on the performance of the rainfall retrieval algorithm, especially for devices with relatively long duration of the wet-antenna attenuation combined with the longer sampling time intervals. Overall, this study demonstrates the effect a selected sampling strategy can have on rainfall intensity estimates using (commercial) microwave links.@en

The Goddard Profiling algorithm (GPROF) converts radiometer observations from Global Precipitation Measurement (GPM) constellation satellites into precipitation estimates. Typically, high-quality ground-based estimates serve as reference to evaluate GPROF's performance. To provide a fair comparison, the ground-based estimates are often spatially aligned to GPROF. However, GPROF combines observations from various sensors and channels, each associated with a distinct footprint. Consequently, uncertainties related to the representativeness of the sampled areas are introduced in addition to the uncertainty when converting brightness temperatures into precipitation intensities. The exact contribution of resampling precipitation estimates, required to spatially and temporally align different resolutions when combining or comparing precipitation observations, to the overall uncertainty remains unknown. Here, we analyze the current performance of GPROF over the Netherlands during a 4-year period (2017-2020) while investigating the uncertainty related to sampling. The latter is done by simulating the reference precipitation as satellite footprints that vary in size, geometry, and applied weighting technique. Only GPROF estimates based on observations from the conical-scanning radiometers of the GPM constellation are used. The reference estimates are gauge-adjusted radar precipitation estimates from two ground-based weather radars from the Royal Netherlands Meteorological Institute (KNMI). Echo top heights (ETHs) retrieved from the same radars are used to classify the precipitation as shallow, medium, or deep. Spatial averaging methods (Gaussian weighting vs. arithmetic mean) minimally affect the magnitude of the precipitation estimates. Footprint size has a higher impact but cannot explain all discrepancies between the ground- and satellite-based estimates. Additionally, the discrepancies between GPROF and the reference are largest for low ETHs, while the relative bias between the different footprint sizes and implemented weighting methods increase with increasing ETHs. Lastly, our results do not show a clear difference between coastal and land simulations. We conclude that the uncertainty introduced by merging different channels and sensors cannot fully explain the discrepancies between satellite- and ground-based precipitation estimates. Hence, uncertainties related to the retrieval algorithm and environmental conditions are found to be more prominent than resampling uncertainties, in particular for shallow and light precipitation.

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This chapter reviews the state-of-the-art of land surface rainfall estimation using measurements from weather radars, personal weather stations, and commercial microwave links, including comparisons to rain gauge measurements. These studies are related to recently emerging field of opportunistic weather sensing using the existing communications infrastructure.@en

During the last decade, rainfall monitoring using signal-level data from commercial microwave links (CMLs) in cellular communication networks has been proposed as a complementary way to estimate rainfall for large areas. Path-Averaged rainfall is retrieved between the transmitting and receiving cellular antennas of a CML. One rainfall estimation algorithm for CMLs is RAINLINK, which has been employed in different regions (e.g., Brazil, Italy, the Netherlands, and Pakistan) with satisfactory results. However, the RAINLINK parameters have been calibrated for a unique optimum solution, which is inconsistent with the fact that multiple similar or equivalent solutions may exist due to uncertainties in algorithm structure, input data, and parameters. Here, we show how CML rainfall estimates can be improved by calibrating all parameters of the algorithm systematically and simultaneously with the stochastic particle swarm optimization method, which is used for the numerical maximization of the objective function. An open dataset of approximately 2800 sub-links of minimum and maximum received signal levels over 15gmin intervals covering the Netherlands (g1/4g35g500gkm2) is employed: 12gd are used for calibration and 3 months for validation. A gauge-Adjusted radar rainfall dataset is utilized as a reference. Verification of path-Average daily rainfall shows a reasonable improvement for the stochastically calibrated parameters with respect to RAINLINK's default parameter settings. Results further improve when averaged over the Netherlands. Moreover, the method provides a better underpinning of the chosen parameter values and is therefore of general interest for calibration of RAINLINK's parameters for other climates and cellular communication networks.

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Commercial microwave links (CMLs) from cellular telecommunication networks can provide a valuable 'opportunistic' source of high-resolution space-time rainfall information, complementing traditional in-situ measurement devices (rain gauges, disdrometers) and remote sensors (weather radars, satellites). Their greatest potential lies in areas with low gauge densities and lack of weather radars, often in developing countries with a subtropical or tropical climate and generally large spatial rainfall variability. Here, the open-source R package RAINLINK is employed to retrieve CML rainfall maps covering the majority of Sri Lanka for a 3.5 month period based on CML data from on average 1140 link paths. These are compared locally to hourly and daily rain gauge data, as well as to rainfall maps from the dual-frequency precipitation radar on board the global precipitation measurement core observatory satellite. The potential of CMLs for real-time tropical rainfall monitoring is demonstrated.

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This study presents the first evaluation of using commercial microwave link (CML) data for rainfall measurements in Australia, with the test site being the greater Melbourne Metropolitan area. More than 100 CMLs with microwave frequency ranging between 10 and 40 GHz have been used for the rainfall retrieval. The 15-minute received signal levels (RSLs) for each CML based on two sampling strategies (average and minimum/maximum) collected for 2 years provided a unique dataset to compare performances of rainfall retrievals. The open source algorithm RAINLINK was used for deriving rainfall from the 15-minute RSL data. From two years of data, a subset of 30 rainy days distributed across this period were used for calibrating the RAINLINK parameters, with the remaining data used for validation. For this study, only path-averaged rainfall intensities were validated based on a gauge-adjusted radar product serving as the reference. The result of the wet-dry classification showed that the minimum and maximum RSL data performed better, with lower probability of false detection and higher Matthews correlation coefficient than average RSL data. For the rainfall retrieval, both datasets showed similar correlation with the gauge adjusted radar product. However, based on other statistics (RMSE, bias and CV) minimum and maximum RSL data outperformed average for the rainfall retrieval. Overall, this study highlights the robust accuracy of commercial microwave links for rainfall retrieval while using only minimum and maximum RSL data.

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e investigate the spatiotemporal structure of rainfall at spatial scales from 7 m to over 200 km in the Netherlands. We used data from two networks of laser disdrometers with complementary interstation distances in two Dutch cities (comprising five and six disdrometers, respectively) and a Dutch nationwide network of 31 automatic rain gauges. The smallest aggregation interval for which raindrop size distributions were collected by the disdrometers was 30 s, while the automatic rain gauges provided 10-min rainfall sums. This study aims to supplement other micro-γ investigations (usually performed in the context of spatial rainfall variability within a weather radar pixel) with new data, while characterizing the correlation structure across an extended range of scales. To quantify the spatiotemporal variability, we employ a two-parameter exponential model fitted to the spatial correlograms and characterize the parameters of the model as a function of the temporal aggregation interval. This widely used method allows for a meaningful comparison with seven other studies across contrasting climatic settings all around the world. We also separately analyzed the intermittency of the rainfall observations. We show that a single parameterization, consisting of a two-parameter exponential spatial model as a function of interstation distance combined with a power-law model for decorrelation distance as a function of aggregation interval, can coherently describe rainfall variability (both spatial correlation and intermittency) across a wide range of scales. Limiting the range of scales to those typically found in micro-γ variability studies (including four of the seven studies to which we compare our results) skews the parameterization and reduces its applicability to larger scales.@en

The Royal Netherlands Meteorological Institute (KNMI) operates two operational dual-polarization C-band weather radars providing 2D radar rainfall products. Attenuation can result in severe underestimation of rainfall amounts, particularly in convective situations that are known to have high impact on society. To improve the radar-based precipitation estimates, two attenuation correction methods are evaluated and compared: 1) modified Kraemer (MK) method, i.e., Hitschfeld–Bordan where parameters of the power-law Z_h–k_h relation are adjusted such that reflectivities in the entire dataset do not exceed 59 dBZ_h and attenuation correction is limited to 10 dB; and 2) using two-way path-inte-grated attenuation computed from the dual-polarization moment specific differential phase K_dp (Kdp method). In both cases the open-source Python library wradlib is employed for the actual attenuation correction. A radar voxel only con-tributes to the computed path-integrated attenuation if its height is below the forecasted freezing-level height from the numerical weather prediction model HARMONIE-AROME. The methods are effective in improving hourly and daily quantitative precipitation estimation (QPE), where the Kdp method performs best. The verification against rain gauge data shows that the underestimation diminishes from 55% to 37% for hourly rainfall for the Kdp method when the gauge indicates more than 10 mm of rain in that hour. The improvement for the MK method is less pronounced, with a resulting underestimation of 40%. The stability of the MK method holds a promise for application to data archives from single-polarization radars.

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Several opportunistic sensors (private weather stations, commercial microwave links and smartphones) are employed to obtain weather information and successfully monitor urban weather events. The ongoing urbanisation and climate change urges further understanding and monitoring of weather in cities. Two case studies during a 17-day period over the Amsterdam metropolitan area, the Netherlands, are used to illustrate the potential and limitations of hydrometeorological monitoring using non-traditional and opportunistic sensors. We employ three types of opportunistic sensing networks to monitor six important environmental variables: (1) air temperature estimates from smartphone batteries and personal weather stations; (2) rainfall from commercial microwave links and personal weather stations; (3) solar radiation from smartphones; (4) wind speed from personal weather stations; (5) air pressure from smartphones and personal weather stations; (6) humidity from personal weather stations. These observations are compared to dedicated, traditional observations where possible, although such networks are typically sparse in urban areas. First we show that the passage of a front can be successfully monitored using data from several types of non-traditional sensors in a complementary fashion. Also we demonstrate the added value of opportunistic measurements in quantifying the Urban Heat Island (UHI) effect during a hot episode. The UHI can be clearly determined from personal weather stations, though UHI values tend to be high compared to records from a traditional network. Overall, this study illustrates the enormous potential for hydrometeorological monitoring in urban areas using non-traditional and opportunistic sensing networks.@en

Microwave backhaul links from cellular communication networks provide a valuable “opportunistic” source of high-resolution space–time rainfall information, comple-menting traditional in situ measurement devices (rain gauges, disdrometers) and remote sensors (weather radars, satellites). Over the past decade, a growing community of researchers has, in close collaboration with cellular communication compa-nies, developed retrieval algorithms to convert the raw microwave link signals, stored operationally by their network management systems, to hydrometeorologically useful rainfall estimates. Operational meteorological and hydrological services as well as private consulting firms are showing an increased interest in using this complemen-tary source of rainfall information to improve the products and services they provide to end users from different sectors, from water management and weather prediction to agriculture and traffic control. The greatest potential of these opportunistic environmental sensors lies in those geographical areas over the land surface of the Earth where the densities of traditional rainfall measurement devices are low: mountainous and urban areas and the developing world. This article provides a nonexpert summary of the history, theory, challenges, and opportunities toward continental-scale rainfall monitoring using microwave links from cellular communication networks. This article is categorized under: Science of Water > Methods Science of Water > Hydrological Processes.

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