High Temporal Resolution Path-Average Rain Gauge with 50-GHz Band Microwave

Abstract Rain radar measures instantaneous spatial-average rainfall, while conventional rain gauges directly measure point rainfall with low temporal resolution. Thus differences in the resolution of the sensors create difficulties for rain radar validation, especially for spaceborne rain radar. Accordingly, rainfall measurement by microwave link has been proposed for several decades, as it estimates instantaneous path-average rainfall. Thus it is expected that the microwave link rain gauge will overcome, at least partly, the problems in the rain radar validation, toward which a 50-GHz band microwave link [the path-averaged rain gauge (PRG)] was developed that has been in operation since September 2000. In this paper, the authors show the potential of the PRG system by a simple model and rainfall comparison with a disdrometer and a tipping-bucket rain gauge. Differences observed by the instruments were within 15% (within 10% in half of the cases) during actual rain events in 2003. This confirmed that the PRG system displayed good performance as a rain gauge. Finally, the possibility of the PRG system being applied for spaceborne rain radar validation is considered.

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Five years of commercial microwave link network derived rainfall research in Sweden

10.5194/egusphere-egu2020-7969 ◽

2020 ◽

Author(s):

Remco (C.Z.) van de Beek ◽

Jafet Andersson ◽

Jonas Olsson ◽

Jonas Hansryd

Keyword(s):

Spatial Resolution ◽

Temporal Resolution ◽

Rain Rate ◽

Ground Level ◽

Rain Gauge ◽

Professional Services ◽

High Temporal Resolution ◽

Rain Gauges ◽

Microwave Link ◽

Rain Rates

Accurate rainfall measurements are very important in hydrology, meteorology, agriculture and other fields. Traditionally rain gauges combined with radar have been used to measure rain rates. However, these instruments are not always available. Also combining point measurements at the ground with measured reflectivities of volumes in the air to an accurate rain rate map at ground level poses challenges. Commercial microwave link networks can help in these areas as these can provide measurements at a high temporal resolution and tend to be available wherever people live, with highest network densities where most people are. They also measure precipitation along a path near ground level and offer a way to close the gap between rain gauge measurements and radar.In this study we highlight the work SMHI has performed on deriving rain rates from commercial microwave links since 2015. This started with a pilot study in Gothenburg. The signal strengths of 364 microwave links were sampled every ten seconds and were used to create rainfall maps at a one-minute temporal resolution and 500m spatial resolution. These rain maps were then applied in a hydrological experiment and compared to rain gauge and radar measurements. The results were very promising, not only due to the high temporal and spatial resolution, but also with the accuracy of the actual measurements. The correlation was found to be equal to those of the rain gauges, while links were found to overestimate rainfall volumes on average. A demo site was created showing the one-minute rain rate maps and can be found at: https://www.smhi.se/en/services/professional-services/microweather/. Since then the methodology has been further improved and also applied within Stockholm in a new hydrological experiment. Currently new regions are being considered, as well as novel ways to merge data sources to create high quality precipitation maps. This contribution summarizes the progress to date.

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High temporal resolution rainfall rate estimation from rain gauge measurements

Journal of Hydroinformatics ◽

10.2166/hydro.2017.054 ◽

2017 ◽

Vol 19 (6) ◽

pp. 930-941 ◽

Cited By ~ 6

Author(s):

Yang Song ◽

Dawei Han ◽

Miguel A. Rico-Ramirez

Keyword(s):

Temporal Resolution ◽

Interpolation Method ◽

Linear Interpolation ◽

Rain Gauge ◽

Rainfall Rate ◽

High Temporal Resolution ◽

Feed Forward Neural Network ◽

Rate Estimation ◽

Rain Gauges ◽

Nonlinear Relationships

Abstract Rainfall rates derived from tipping bucket rain gauges generally ignore the detailed variation at a finer temporal scale that particularly occurs in light rainfall events. This study extends the exploration of using artificial neural networks (ANNs), in comparison with the conventional linear interpolation method (LIM) and the cubic spline algorithm (CSA) for rainfall rate estimation at fine temporal resolution using rain gauge data based on a case study at Chilbolton and Sparsholt observatories, UK. A supervised feed-forward neural network integrated with the backpropagation algorithm is used to identify the complex nonlinear relationships between input and target variables. The results indicate that the ANN considerably outperforms the CSA and LIM with higher Nash–Sutcliffe efficiency, lower root mean square error and lower rainfall amount differences when compared to the disdrometer observations when the model is trained within a broad span of input values. Consistent stability in accurately estimating rainfall rate in different sites shows the intrinsic advantage of ANNs in learning and self-adaptive abilities in modelling complex nonlinear relationships between the inputs and target variables.

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A New Parameter to Assess Impact of Rain Gauge Density on Uncertainty in the Estimate of Monthly Rainfall over India

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0161.1 ◽

2019 ◽

Vol 20 (5) ◽

pp. 821-832 ◽

Cited By ~ 12

Author(s):

Satya Prakash ◽

Ashwin Seshadri ◽

J. Srinivasan ◽

D. S. Pai

Keyword(s):

Theoretical Model ◽

Central India ◽

Ground Truth ◽

Accurate Method ◽

Rain Gauge ◽

Monthly Rainfall ◽

Spatial Density ◽

Spatial Average ◽

Rain Gauges ◽

Rainfall Estimates

Abstract Rain gauges are considered the most accurate method to estimate rainfall and are used as the “ground truth” for a wide variety of applications. The spatial density of rain gauges varies substantially and hence influences the accuracy of gridded gauge-based rainfall products. The temporal changes in rain gauge density over a region introduce considerable biases in the historical trends in mean rainfall and its extremes. An estimate of uncertainty in gauge-based rainfall estimates associated with the nonuniform layout and placement pattern of the rain gauge network is vital for national decisions and policy planning in India, which considers a rather tight threshold of rainfall anomaly. This study examines uncertainty in the estimation of monthly mean monsoon rainfall due to variations in gauge density across India. Since not all rain gauges provide measurements perpetually, we consider the ensemble uncertainty in spatial average estimation owing to randomly leaving out rain gauges from the estimate. A recently developed theoretical model shows that the uncertainty in the spatially averaged rainfall is directly proportional to the spatial standard deviation and inversely proportional to the square root of the total number of available gauges. On this basis, a new parameter called the “averaging error factor” has been proposed that identifies the regions with large ensemble uncertainties. Comparison of the theoretical model with Monte Carlo simulations at a monthly time scale using rain gauge observations shows good agreement with each other at all-India and subregional scales. The uncertainty in monthly mean rainfall estimates due to omission of rain gauges is largest for northeast India (~4% uncertainty for omission of 10% gauges) and smallest for central India. Estimates of spatial average rainfall should always be accompanied by a measure of uncertainty, and this paper provides such a measure for gauge-based monthly rainfall estimates. This study can be further extended to determine the minimum number of rain gauges necessary for any given region to estimate rainfall at a certain level of uncertainty.

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

Remote Sensing ◽

10.3390/rs13163274 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3274

Author(s):

Kingsley K. Kumah ◽

Joost C. B. Hoedjes ◽

Noam David ◽

Ben H. P. Maathuis ◽

H. Oliver Gao ◽

...

Keyword(s):

Second Generation ◽

Rainfall Intensity ◽

Rain Gauge ◽

Conventional Technique ◽

Rain Area ◽

Evaluation Scores ◽

Rain Events ◽

Spatially Varying ◽

Microwave Link ◽

Rainfall Estimates

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.

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One year of attenuation data from a commercial dual-polarized duplex microwave link with concurrent disdrometer, rain gauge and weather observations

10.5194/essd-2021-3 ◽

2021 ◽

Author(s):

Anna Špačková ◽

Vojtěch Bareš ◽

Martin Fencl ◽

Marc Schleiss ◽

Joël Jaffrain ◽

...

Keyword(s):

Remote Sensing ◽

Relevant Information ◽

Rain Gauge ◽

Telecommunication Networks ◽

High Temporal Resolution ◽

Potential Applications ◽

Dual Polarized ◽

One Year ◽

Microwave Link ◽

Commercial Microwave Links

Abstract. Commercial microwave links (CML) in telecommunication networks can provide relevant information for remote sensing of precipitation and other environmental variables, such as path-averaged drop size distribution, evaporation or humidity. To address this issue, the CoMMon field experiment (COmmercial Microwave links for urban rainfall MONitoring) monitored a 38-GHz dual-polarized CML of 1.85 km at a high temporal resolution (4 s), as well as a collocated array of five disdrometers and three rain gauges over one year. The dataset is complemented with observations from five nearby weather stations. Raw and pre-processed data, which can be explored effortlessly with a custom static HTML viewer, are available at https://doi.org/10.5281/zenodo.4524632 (Špačková et al., 2020). The data quality is generally satisfactory and potentially problematic measurements are flagged to help the analyst identify relevant periods for specific study purposes. Finally, we encourage potential applications and discuss open issues regarding future remote sensing with CMLs.

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A Novel Electromagnetic Wave Rain Gauge and its Average Rainfall Estimation Method

Remote Sensing ◽

10.3390/rs12213528 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3528

Author(s):

S. Lim

Keyword(s):

Electromagnetic Wave ◽

Short Range ◽

Estimation Method ◽

Rain Gauge ◽

Observation Data ◽

Dual Polarization ◽

Rainfall Events ◽

Rain Gauges ◽

Average Rainfall ◽

Average Observation

It is essential to accurately estimate rainfall to predict and prevent hydrological disasters such as floods. In this paper, an electromagnetic wave rain gauge system and a method to estimate average rainfall using the system’s multiple elevation observation data are presented. The compact electromagnetic wave rain gauge is a small-sized radar that performs very short-range observations using K-band dual-polarization technology. The method to estimate average rainfall is based on the concept of an average observation derived from multiple elevation scans with very short range and dual-polarization information. The proposed method was evaluated by comparing it with ground instruments, including a pit-gauge, tipping-bucket rain gauges, and a Parsivel disdrometer. The evaluation results demonstrated that the new methodology worked fairly well for various rainfall events.

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A Variational Approach to Retrieve Rain Rate by Combining Information from Rain Gauges, Radars, and Microwave Links

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-094.1 ◽

2013 ◽

Vol 14 (6) ◽

pp. 1897-1909 ◽

Cited By ~ 25

Author(s):

Blandine Bianchi ◽

Peter Jan van Leeuwen ◽

Robin J. Hogan ◽

Alexis Berne

Keyword(s):

Variational Approach ◽

Rain Rate ◽

Rain Gauge ◽

Sources Of Information ◽

Rain Gauges ◽

Strongly Nonlinear ◽

Using Data ◽

The Cost ◽

Microwave Link ◽

Link Information

Abstract Accurate and reliable rain rate estimates are important for various hydrometeorological applications. Consequently, rain sensors of different types have been deployed in many regions. In this work, measurements from different instruments, namely, rain gauge, weather radar, and microwave link, are combined for the first time to estimate with greater accuracy the spatial distribution and intensity of rainfall. The objective is to retrieve the rain rate that is consistent with all these measurements while incorporating the uncertainty associated with the different sources of information. Assuming the problem is not strongly nonlinear, a variational approach is implemented and the Gauss–Newton method is used to minimize the cost function containing proper error estimates from all sensors. Furthermore, the method can be flexibly adapted to additional data sources. The proposed approach is tested using data from 14 rain gauges and 14 operational microwave links located in the Zürich area (Switzerland) to correct the prior rain rate provided by the operational radar rain product from the Swiss meteorological service (MeteoSwiss). A cross-validation approach demonstrates the improvement of rain rate estimates when assimilating rain gauge and microwave link information.

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A Study of the Influence of the Spatial Distribution of Rain Gauge Networks on Areal Average Rainfall Calculation

Water ◽

10.3390/w10111635 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1635 ◽

Cited By ~ 4

Author(s):

Jiho Lee ◽

Soojun Kim ◽

Hwandon Jun

Keyword(s):

Spatial Distribution ◽

Estimation Error ◽

Case Analysis ◽

Rain Gauge ◽

Spatial Characteristic ◽

Rain Gauges ◽

First Case ◽

Average Rainfall ◽

Major River ◽

Rain Gauge Network

Estimating the AAR (Areal Average Rainfall) is an essential process when determining the accurate amount of available water resources and building the input data which is integral to the Rainfall-Runoff Analysis. To estimate the AAR, using rain gauge networks that are spatially well distributed is ideal. In this study, the spatial characteristics of the rain gauge networks for the five major river basins in South Korea are considered and the amount of influence the spatial distribution has on the estimation of the AAR is evaluated. For this purpose, the estimation error for AAR is calculated for two cases. The first case (Analysis 1) compares the value of the estimation error of the AAR from two different basins where one has well distributed rain gauges while the other does not. The second case (Analysis 2) estimates the estimation error of two different rain gauge distributions for the same basin. The spatial characteristic of the rain gauge network is evaluated by using the NNI (Nearest Neighbour Index), while the Arithmetic Mean Method, Thiessen Method and the Estimation Theory are applied to calculate the AAR. From Analysis 1, we are able to prove that the estimation error of the AAR is relatively small in the basins with that have spatially well distributed rain gauge networks whereas the estimation error is relatively large when the spatial distribution of the rain gauge network is clustered. Also, results from Analysis 2 showed that not only is the spatial distribution of the rain gauge networks important, but that the density has a significant influence on accurately calculating the AAR. The results from this study can be applied towards the ideal establishment of the rain gauge networks.

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ESOLIP – estimate of solid and liquid precipitation at sub-daily time resolution by combining snow height and rain gauge measurements

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-8683-2013 ◽

2013 ◽

Vol 10 (7) ◽

pp. 8683-8714 ◽

Cited By ~ 4

Author(s):

E. Mair ◽

G. Bertoldi ◽

G. Leitinger ◽

S. Della Chiesa ◽

G. Niedrist ◽

...

Keyword(s):

Time Resolution ◽

Hydrological Cycle ◽

Winter Precipitation ◽

Rain Gauge ◽

High Temporal Resolution ◽

Precipitation Data ◽

Rain Gauges ◽

Alpine Catchments ◽

Liquid Precipitation ◽

Daily Time

Abstract. Measuring precipitation in mountain areas is a demanding task, but essential for hydrological and environmental themes. Especially in small Alpine catchments with short hydrological response, precipitation data with high temporal resolution are required for a better understanding of the hydrological cycle. Since most climate/meteorological stations are situated at the easily accessible bottom of valleys, and the few heated rain gauges installed at higher elevation sites are problematic in winter conditions, an accurate quantification of winter (snow) precipitation at high elevations remains difficult. However, there are an increasing number of micro-meteorological stations and snow height sensors at high elevation locations in Alpine catchments. To benefit from data of such stations, an improved approach to estimate solid and liquid precipitation (ESOLIP) is proposed. ESOLIP allows gathering hourly precipitation data throughout the year by using unheated rain gauge data, careful filtering of snow height sensors as well as standard meteorological data (air temperature, relative humidity, global shortwave radiation, wind speed). ESOLIP was validated at a well-equipped test site in Stubai Valley (Tyrol, Austria), comparing results to winter precipitation measured with a snow pillow and a heated rain gauge. The snow height filtering routine and indicators for possible precipitation were tested at a field site in Matsch Valley (South Tyrol, Italy). Results show a good match with measured data because variable snow density is taken into account, which is important when working with freshly fallen snow. Furthermore, the results show the need for accurate filtering of the noise of the snow height signal and they confirm the unreliability of heated rain gauges for estimating winter precipitation. The described improved precipitation estimate ESOLIP at sub-daily time resolution is helpful for precipitation analysis and for several hydrological applications like monitoring systems and rainfall-runoff models.

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Measuring tropical rainfall with a dense Commercial Microwave Link network in Nigeria

10.5194/ems2021-239 ◽

2021 ◽

Author(s):

Arjan Droste ◽

Aart Overeem ◽

Jan Priebe ◽

Daniele Tricarico ◽

Linda Bogerd ◽

...

Keyword(s):

R Package ◽

Developed Countries ◽

Rain Gauge ◽

Local Network ◽

Dense Network ◽

Large Cities ◽

Rain Gauges ◽

Precipitation Measurement ◽

Physically Based ◽

Microwave Link

Accurate, global rainfall estimates are crucial for many fields, e.g. agriculture or disaster management. While developed countries typically enjoy a dense network of rain gauges and radar, in many less developed areas across the globe, precipitation measurement networks are sparse. To obtain rainfall data for these regions, opportunistic sensing techniques are especially valuable: the use of unconventional sources to extract valuable data that can allow us to estimate precipitation. One of the more prominent data sources is the use of Commercial Microwave Links &#8211;CMLs&#8211; to measure rainfall, by making use of the signal attenuation between cell phone towers. This method of estimating rainfall has been mostly tested and applied in developed countries that already have reasonable coverage of conventional precipitation measurements. However, the strongest benefits are to be gained in developing regions lacking such measurement networks, where CML data can make a big difference. Only few studies address this, generally using relatively small datasets.This research focuses on tropical CML rainfall estimation in Nigeria. Nigeria has a dense network of CMLs and relatively few official measurement stations, making it an interesting area to study the effectiveness of CML precipitation measurements. Our dataset spans 4 regions within Nigeria, from the coast to inland, with several large cities (Lagos; Ibadan) as well as areas with less dense CML networks to investigate the influence. We employ the open-source R package RAINLINK to obtain 15-min rainfall maps based on data from several thousand CMLs during the rainy season. We optimise the most important RAINLINK parameters by comparing to rain gauge data, considering local network and environmental conditions. In addition, disdrometer data from Nigeria (or similar climates) are used to compute the values of the physically-based coefficients relating specific attenuation to rainfall rate.&#160;

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