scholarly journals Stochastic Reconstruction and Interpolation of Precipitation Fields Using Combined Information of Commercial Microwave Links and Rain Gauges

2017 ◽  
Vol 53 (12) ◽  
pp. 10740-10756 ◽  
Author(s):  
B. Haese ◽  
S. Hörning ◽  
C. Chwala ◽  
A. Bárdossy ◽  
B. Schalge ◽  
...  
Keyword(s):  
Rain Gauges ◽  
Stochastic Reconstruction ◽  
Commercial Microwave Links

Commercial microwave links instead of rain gauges: fiction or reality?

2014 ◽  
Vol 71 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Martin Fencl ◽  
Jörg Rieckermann ◽  
Petr Sýkora ◽  
David Stránský ◽  
Vojtěch Bareš
Keyword(s):  
Rainfall Variability ◽  
A Priori ◽  
Rain Gauges ◽  
Precipitation Estimates ◽  
Spatio Temporal ◽  
Areal Rainfall ◽  
Quantitative Precipitation Estimates ◽  
Commercial Microwave Links ◽  
Unique Dataset

Commercial microwave links (MWLs) were suggested about a decade ago as a new source for quantitative precipitation estimates (QPEs). Meanwhile, the theory is well understood and rainfall monitoring with MWLs is on its way to being a mature technology, with several well-documented case studies, which investigate QPEs from multiple MWLs on the mesoscale. However, the potential of MWLs to observe microscale rainfall variability, which is important for urban hydrology, has not been investigated yet. In this paper, we assess the potential of MWLs to capture the spatio-temporal rainfall dynamics over small catchments of a few square kilometres. Specifically, we investigate the influence of different MWL topologies on areal rainfall estimation, which is important for experimental design or to a priori check the feasibility of using MWLs. In a dedicated case study in Prague, Czech Republic, we collected a unique dataset of 14 MWL signals with a temporal resolution of a few seconds and compared the QPEs from the MWLs to reference rainfall from multiple rain gauges. Our results show that, although QPEs from most MWLs are probably positively biased, they capture spatio-temporal rainfall variability on the microscale very well. Thus, they have great potential to improve runoff predictions. This is especially beneficial for heavy rainfall, which is usually decisive for urban drainage design.

scholarly journals Gauge-adjusted rainfall estimates from commercial microwave links

2017 ◽  
Vol 21 (1) ◽  
pp. 617-634 ◽  
Author(s):  
Martin Fencl ◽  
Michal Dohnal ◽  
Jörg Rieckermann ◽  
Vojtěch Bareš
Keyword(s):  
Urban Areas ◽  
Ground Truth ◽  
Telecommunication Networks ◽  
Monitoring Networks ◽  
Mobile Telecommunication ◽  
Rain Gauges ◽  
Precipitation Estimates ◽  
Set Up ◽  
Quantitative Precipitation Estimates ◽  
Commercial Microwave Links

Abstract. Increasing urbanization makes it more and more important to have accurate stormwater runoff predictions, especially with potentially severe weather and climatic changes on the horizon. Such stormwater predictions in turn require reliable rainfall information. Especially for urban centres, the problem is that the spatial and temporal resolution of rainfall observations should be substantially higher than commonly provided by weather services with their standard rainfall monitoring networks. Commercial microwave links (CMLs) are non-traditional sensors, which have been proposed about a decade ago as a promising solution. CMLs are line-of-sight radio connections widely used by operators of mobile telecommunication networks. They are typically very dense in urban areas and can provide path-integrated rainfall observations at sub-minute resolution. Unfortunately, quantitative precipitation estimates (QPEs) from CMLs are often highly biased due to several epistemic uncertainties, which significantly limit their usability. In this manuscript we therefore suggest a novel method to reduce this bias by adjusting QPEs to existing rain gauges. The method has been specifically designed to produce reliable results even with comparably distant rain gauges or cumulative observations. This eliminates the need to install reference gauges and makes it possible to work with existing information. First, the method is tested on data from a dedicated experiment, where a CML has been specifically set up for rainfall monitoring experiments, as well as operational CMLs from an existing cellular network. Second, we assess the performance for several experimental layouts of ground truth from rain gauges (RGs) with different spatial and temporal resolutions. The results suggest that CMLs adjusted by RGs with a temporal aggregation of up to 1 h (i) provide precise high-resolution QPEs (relative error  < 7 %, Nash–Sutcliffe efficiency coefficient  >  0.75) and (ii) that the combination of both sensor types clearly outperforms each individual monitoring system. Unfortunately, adjusting CML observations to RGs with longer aggregation intervals of up to 24 h has drawbacks. Although it substantially reduces bias, it unfavourably smoothes out rainfall peaks of high intensities, which is undesirable for stormwater management. A similar, but less severe, effect occurs due to spatial averaging when CMLs are adjusted to remote RGs. Nevertheless, even here, adjusted CMLs perform better than RGs alone. Furthermore, we provide first evidence that the joint use of multiple CMLs together with RGs also reduces bias in their QPEs. In summary, we believe that our adjustment method has great potential to improve the space–time resolution of current urban rainfall monitoring networks. Nevertheless, future work should aim to better understand the reason for the observed systematic error in QPEs from CMLs.

scholarly journals Rainfall estimate using Commercial Microwave Links (CML): first outcomes of the MOPRAM project

2020 ◽  
Author(s):  
Roberto Nebuloni ◽  
Michele D'Amico ◽  
Greta Cazzaniga ◽  
Carlo De Michele ◽  
Cristina Deidda
Keyword(s):  
Water Resource Management ◽  
Rain Rate ◽  
Power Level ◽  
Rain Attenuation ◽  
Propagation Path ◽  
Precipitation Event ◽  
Rain Gauges ◽  
Received Power ◽  
Electromagnetic Models ◽  
Commercial Microwave Links

&lt;p&gt;The measurement of space-time rainfall fields is of great importance for several purposes including weather forecast, water resource management, evaluation of hydrological risk and monitoring of hydrologic extremes. Conventional methods for rainfall measurement include rain gauges and weather radars, both types of sensor having their own advantages and limitations. A different and not fully tested methodology exploits the power loss (namely, the attenuation) experienced by microwave radio signals when travelling across rain along either terrestrial or ground-to-satellite links. Indeed, it is well known that microwave attenuation due to rain can be calculated from the rain rate along the propagation path. The inverse problem can be solved once the disturbances affecting the radio signal and not induced by rain have been identified and removed.&lt;/p&gt;&lt;p&gt;In this contribution, we present the first results of the experimental campaign carried out in the framework of MOPRAM (MOnitoring Precipitation through a Network of RAdio links at Microwaves). MOPRAM is a scientific project funded by Fondazione Cariplo, which aims at assessing the potential of Commercial Microwave Links (CML) for rainfall estimates and rainfall field retrieval in areas of hydrological interest located in Northern Italy. A network of CMLs, owned by a major mobile operator in Italy, has been exploited to estimate the average rain rate along each link. The available data are the minimum and maximum values of the transmitted and received signal power across each link (two-ways), measured during 15-min time slots.&lt;/p&gt;&lt;p&gt;We start by describing the procedure adopted for the estimation of the baseline, i.e. the received power level immediately before and after a precipitation event. Rain attenuation is subsequently calculated by subtracting the received power during the event from the baseline level. To this aim, every 15-min slot of each link is classified in advance as either wet or dry by taking advantage of the spatial correlation of rain. Path-averaged rainfall intensity can be retrieved from rain attenuation by formulas based on well-known electromagnetic models. Finally, CML-based rainfall is compared with the one obtained from co-located rain gauges and disdrometers.&lt;/p&gt;&lt;p&gt;Despite the CML setup is not optimized for rainfall measurements, preliminary results highlight a good correlation between the occurrence of wet periods detected by CMLs on one side and by rain gauges and disdrometers on the other, as well as a fair agreement between the corresponding time series of accumulated precipitation.&lt;/p&gt;

scholarly journals Gauge-Adjusted Rainfall Estimates from Commercial Microwave Links

2016 ◽  
Author(s):  
Martin Fencl ◽  
Michal Dohnal ◽  
Jörg Rieckermann ◽  
Vojtěch Bareš
Keyword(s):  
Urban Areas ◽  
Ground Truth ◽  
Telecommunication Networks ◽  
Monitoring Networks ◽  
Mobile Telecommunication ◽  
Urban Centers ◽  
Rain Gauges ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates ◽  
Commercial Microwave Links

Abstract. Increasing urbanization makes it more and more important to have accurate stormwater runoff predictions, especially with potentially severe weather and climatic changes on the horizon. Such stormwater predictions in turn require reliable rainfall information. Especially for urban centers, the problem is that the spatial and temporal resolution of rainfall observations should be substantially higher than commonly provided by weather services with their standard rainfall monitoring networks. Commercial microwave links (CMLs) are non-traditional sensors, which have been proposed about a decade ago as a promising solution. CMLs are line-of-sight radio connections widely used by operators of mobile telecommunication networks. They are typically very dense in urban areas and can provide path-integrated rainfall observations at sub-minute resolution. Unfortunately, quantitative precipitation estimates from CMLs (QPEs) are often highly biased due to several epistemic uncertainties, which significantly limit their usability. In this manuscript we therefore suggest a novel method to reduce this bias by adjusting QPEs to existing rain gauges. The method has been specifically designed to produce reliable results even with comparably distant rain gauges or cumulative observations. This eliminates the need to install reference gauges and makes it possible to work with existing information. First, the method is tested on data from a dedicated experiment, where a CML has been specifically set up for rainfall monitoring experiments, as well as many operational CMLs from an existing cellular network. Second, we assess the performance for several experimental layouts of "ground truth" from RGs with different spatial and temporal resolutions. The results suggest that CMLs adjusted by RGs with a temporal aggregation of up to one hour i) provide precise high-resolution QPEs (rel. error 0.75) and ii) that the combination of both sensor types clearly outperforms each individual monitoring system. Unfortunately, adjusting CML observations to RGs with longer aggregation intervals of up to 24 h has drawbacks. Although it also substantially reduce bias, it unfavourably smoothes out rainfall peaks of high intensities, which is undesirable for stormwater management. A similar, but less severe, effect occurs due to spatial averaging when CMLs are adjusted to remote RGs. Nevertheless, even here, adjusted CMLs perform better than RGs alone. Furthermore, we provide first evidence that the joint use of multiple CMLs together with RGs also reduces bias in their QPEs. In summary, we believe that our adjustment method has great potential to improve the space-time resolution of current urban rainfall monitoring networks. Nevertheless, future work should aim to better understand the reason for the observed systematic error in QPEs from CMLs.

The Prediction of Rainfall and the Direction of Rainfall by a Narrow Area Radar Rain Gauge

1993 ◽  
Vol 28 (11-12) ◽  
pp. 79-85
Author(s):  
Shinichi Kondo
Keyword(s):  
Small Area ◽  
Arrival Time ◽  
Rain Gauge ◽  
Rain Gauges ◽  
Sewage Plant ◽  
Future Prediction ◽  
Near Future ◽  
Narrow Area

Narrow area radar rain gauges are currently used for measuring rainfall. These radar gauges can measure rainfall accurately in a small area. In sewage plants it is important to predict stormwater. To calculate predicted stormwater the results of rainfall and a prediction of the near future are necessary. Recently urbanization has made the arrival time of flooding to the sewage plant much shorter. This paper deals with system technologies for the near future prediction of radar rain gauge rainfall. The method of prediction of rainfall, calculation of results and other considerations are described.

scholarly journals A Comprehensive Evaluation of Latest GPM IMERG V06 Early, Late and Final Precipitation Products across China

2021 ◽  
Vol 13 (6) ◽  
pp. 1208
Author(s):  
Linfei Yu ◽  
Guoyong Leng ◽  
Andre Python ◽  
Jian Peng
Keyword(s):  
Comprehensive Evaluation ◽  
Probability Of Detection ◽  
Light Rain ◽  
Spatial And Temporal Scales ◽  
Rain Gauges ◽  
Precipitation Estimation ◽  
Major River ◽  
Low Levels ◽  
Rain Events ◽  
Early Late

This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.

scholarly journals Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

2021 ◽  
Vol 13 (15) ◽  
pp. 2922
Author(s):  
Yang Song ◽  
Patrick D. Broxton ◽  
Mohammad Reza Ehsani ◽  
Ali Behrangi
Keyword(s):  
Snow Water Equivalent ◽  
Stage Iv ◽  
Microwave Remote Sensing ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
Correction Factors ◽  
Rainfall Analysis ◽  
Rain Gauges ◽  
Snow Properties ◽  
Precipitation Estimates

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

A hybrid process-based and stochastic reconstruction method of porous media

2008 ◽  
Vol 110 (1) ◽  
pp. 92-99 ◽  
Author(s):  
M.G. Politis ◽  
E.S. Kikkinides ◽  
M.E. Kainourgiakis ◽  
A.K. Stubos
Keyword(s):  
Porous Media ◽  
Hybrid Process ◽  
Reconstruction Method ◽  
Stochastic Reconstruction
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