scholarly journals Advanced Real-Time Monitoring of Rainfall Using Commercial Satellite Broadcasting Service: A Case Study

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 691
Author(s):  
Gian Luigi Gragnani ◽  
Matteo Colli ◽  
Emanuele Tavanti ◽  
Daniele D. Caviglia
Keyword(s):  
Real Time ◽  
Rain Gauge ◽  
Real Time Monitoring ◽  
The Sustainable Development ◽  
Rain Gauges ◽  
Weather Radars ◽  
Satellite Broadcasting ◽  
Fundamental Goal ◽  
Polarimetric Weather Radar ◽  
Broadcasting Service

Correct regulation of meteoric surface and subsurface flow waters is a fundamental goal for the sustainable development of the territories. A new system, aimed at real-time monitoring of the rainfall and of the cumulated rainfall, is introduced and discussed in the present paper. The system implements a Sensor Network based on the IoT paradigm and can cover safety-critical “hot spots” with a relatively small number of sensors, strategically placed, in areas not covered by traditional weather radars and rain gauges, and lowering the costs of deployment and maintenance with respects to these devices. A real application case, based on the implementation of the pilot plant at the Monte Scarpino landfill (Genoa, Italy), is presented and discussed. The system performances are assessed on the basis of comparisons with data provided by a polarimetric weather radar and by a traditional rain gauge.

scholarly journals Assesment of Satellite and Radar Quantitative Precipitation Estimates for Real Time Monitoring of  Meteorological Extremes Over the Southeast of Iberian Peninsula

2018 ◽  
Author(s):  
Fulgencio Cánovas-García ◽  
Sandra García-Galiano ◽  
Francisco Alonso-Sarría
Keyword(s):  
Iberian Peninsula ◽  
Real Time ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Real Time Monitoring ◽  
Rain Gauges ◽  
Extreme Precipitation Events ◽  
Empirical Calibration ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates

QPEs (Quantitative Precipitation Estimates) obtained from remote sensing or ground-based radars could complement or even be an alternative to rain gauge readings. However, to be used in operational applications, a validation process has to be carried out, usually by comparing their estimates with those of a rain gauges network. In this paper, the accuracy of two QPEs are evaluated for three extreme precipitation events in the last decade in the southeast of the Iberian Peninsula. The first QPE is PERSIANN-CCS, a satellite-based QPE. The second is a meteorological radar with Doppler capabilities that works in the C band. Pixel-to-point comparisons are made between the values offered by the QPEs and those obtained by two networks of rain gauges. The results obtained indicate that both QPEs were well below the rain gauge values, especially in extreme rainfall time slots. There seems to be a weak linear association between the value of the discrepancies and the precipitation value of the QPEs. It does not seem that radar is more accurate than PERSIANN-CCS, despite its larger spatial resolution and its commonly higher effectiveness. The main conclusion is that neither PERSIANN-CCS nor radar, without empirical calibration, are acceptable QPEs for the real-time monitoring of meteorological extremes in the southeast of the Iberian Peninsula.

scholarly journals Time-dependent <i>Z-R</i> relationships for estimating rainfall fields from radar measurements

2010 ◽  
Vol 10 (1) ◽  
pp. 149-158 ◽  
Author(s):  
L. Alfieri ◽  
P. Claps ◽  
F. Laio
Keyword(s):  
Time Estimation ◽  
Rain Gauge ◽  
Rainfall Rate ◽  
Time Step ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Weather Radars ◽  
The North ◽  
Real Time Estimation ◽  
Power Law Equation

Abstract. The operational use of weather radars has become a widespread and useful tool for estimating rainfall fields. The radar-gauge adjustment is a commonly adopted technique which allows one to reduce bias and dispersion between radar rainfall estimates and the corresponding ground measurements provided by rain gauges. This paper investigates a new methodology for estimating radar-based rainfall fields by recalibrating at each time step the reflectivity-rainfall rate (Z-R) relationship on the basis of ground measurements provided by a rain gauge network. The power-law equation for converting reflectivity measurements into rainfall rates is readjusted at each time step, by calibrating its parameters using hourly Z-R pairs collected in the proximity of the considered time step. Calibration windows with duration between 1 and 24 h are used for estimating the parameters of the Z-R relationship. A case study pertaining to 19 rainfall events occurred in the north-western Italy is considered, in an area located within 25 km from the radar site, with available measurements of rainfall rate at the ground and radar reflectivity aloft. Results obtained with the proposed method are compared to those of three other literature methods. Applications are described for a posteriori evaluation of rainfall fields and for real-time estimation. Results suggest that the use of a calibration window of 2–5 h yields the best performances, with improvements that reach the 28% of the standard error obtained by using the most accurate fixed (climatological) Z-R relationship.

scholarly journals Precipitation Analysis over the French Alps Using a Variational Approach and Study of Potential Added Value of Ground-Based Radar Observations

2017 ◽  
Vol 18 (5) ◽  
pp. 1425-1451 ◽  
Author(s):  
Camille Birman ◽  
Fatima Karbou ◽  
Jean-François Mahfouf ◽  
Matthieu Lafaysse ◽  
Yves Durand ◽  
...  
Keyword(s):  
Snow Depth ◽  
Positive Impact ◽  
Weather Prediction ◽  
Rain Gauge ◽  
Added Value ◽  
Background Information ◽  
Time Step ◽  
Rain Gauges ◽  
Weather Radars ◽  
Precipitation Estimates

Abstract A one-dimensional variational data assimilation (1DVar) method to retrieve profiles of precipitation in mountainous terrain is described. The method combines observations from the French Alpine region rain gauges and precipitation estimates from weather radars with background information from short-range numerical weather prediction forecasts in an optimal way. The performance of this technique is evaluated using measurements of precipitation and of snow depth during two years (2012/13 and 2013/14). It is shown that the 1DVar model allows an effective assimilation of measurements of different types, including rain gauge and radar-derived precipitation. The use of radar-derived precipitation rates over mountains to force the numerical snowpack model Crocus significantly reduces the bias and standard deviation with respect to independent snow depth observations. The improvement is particularly significant for large rainfall or snowfall events, which are decisive for avalanche hazard forecasting. The use of radar-derived precipitation rates at an hourly time step improves the time series of precipitation analyses and has a positive impact on simulated snow depths.

scholarly journals Statistical and neural classifiers in estimating rain rate from weather radar measurements

2007 ◽  
Vol 10 ◽  
pp. 111-115
Author(s):  
C. I. Christodoulou ◽  
S. C. Michaelides
Keyword(s):  
Electromagnetic Radiation ◽  
Rain Rate ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Meteorological Conditions ◽  
Swiss Alps ◽  
Rain Gauges ◽  
Weather Radars ◽  
Radar Measurements

Abstract. Weather radars are used to measure the electromagnetic radiation backscattered by cloud raindrops. Clouds that backscatter more electromagnetic radiation consist of larger droplets of rain and therefore they produce more rain. The idea is to estimate rain rate by using weather radar as an alternative to rain-gauges measuring rainfall on the ground. In an experiment during two days in June and August 1997 over the Italian-Swiss Alps, data from weather radar and surrounding rain-gauges were collected at the same time. The statistical KNN and the neural SOM classifiers were implemented for the classification task using the radar data as input and the rain-gauge measurements as output. The proposed system managed to identify matching pattern waveforms and the rainfall rate on the ground was estimated based on the radar reflectivities with a satisfactory error rate, outperforming the traditional Z/R relationship. It is anticipated that more data, representing a variety of possible meteorological conditions, will lead to improved results. The results in this work show that an estimation of rain rate based on weather radar measurements treated with statistical and neural classifiers is possible.

scholarly journals Precipitation observation using microwave backhaul links in the alpine and pre-alpine region of Southern Germany

2012 ◽  
Vol 9 (1) ◽  
pp. 741-776 ◽  
Author(s):  
C. Chwala ◽  
A. Gmeiner ◽  
W. Qiu ◽  
S. Hipp ◽  
D. Nienaber ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Alpine Region ◽  
New Method ◽  
Small Data ◽  
Near Surface ◽  
Southern Germany ◽  
Rain Gauges ◽  
Weather Radars ◽  
Data Loggers ◽  
Rain Rates

Abstract. Measuring rain rates over complex terrain is afflicted with large uncertainties because rain gauges are influenced by orography and weather radars are mostly not able to look into mountain valleys. We apply a new method to estimate near surface rain rates exploiting attenuation data from commercial microwave links in the alpine region of Southern Germany. Received signal level (RSL) data is recorded minutely with small data loggers at the towers and then sent to a database server via GSM. Due to the large RSL fluctuations in periods without rain, the determination of attenuation caused by precipitation is not straightforward. To be able to continuously process the RSL data from July 2010 to October 2010, we introduce a new method to detect wet and dry periods using spectral time series analysis. We show the performance and limitations of the method and analyse the derived rain rates compared to rain gauge and weather radar measurements. The resulting correlations differ for different links and reach values of R2 = 0.80 for the link-gauge comparison and R2 = 0.84 for the link-radar comparison.

Rainfall monitoring in Sri Lanka employing commercial microwave links

2021 ◽  
Author(s):  
Aart Overeem ◽  
Hidde Leijnse ◽  
Thomas van Leth ◽  
Linda Bogerd ◽  
Jan Priebe ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Land Surface ◽  
Urban Areas ◽  
Weather Prediction ◽  
Rain Gauge ◽  
Cellular Communication ◽  
Dual Frequency ◽  
Middle Income ◽  
Rain Gauges ◽  
Weather Radars

&lt;p&gt;Microwave backhaul links from cellular communication networks provide a valuable &amp;#8220;opportunistic&amp;#8221; source of high-resolution space&amp;#8211;time rainfall information, complementing 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 companies, 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 complementary 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 with few rain gauges and no weather radars: often mountainous and urban areas, but especially low- to middle-income regions, which are generally in (sub)tropical climates.&amp;#160;&lt;/p&gt;&lt;p&gt;Here, the open-source R package RAINLINK is employed to retrieve CML rainfall maps covering the majority of Sri Lanka, a middle-income country having a tropical climate. This is performed for a 3.5-month period based on CML data from on average 1140 link paths. CML rainfall maps 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 results confirm the potential of CMLs for real-time tropical rainfall monitoring. This holds a promise for, e.g., ground validation of or merging with satellite precipitation products.&lt;/p&gt;

scholarly journals Validation of satellite OPEMW precipitation product with ground-based weather radar and rain gauge networks

2013 ◽  
Vol 6 (11) ◽  
pp. 3181-3196 ◽  
Author(s):  
D. Cimini ◽  
F. Romano ◽  
E. Ricciardelli ◽  
F. Di Paola ◽  
M. Viggiano ◽  
...  
Keyword(s):  
Winter Season ◽  
National Research Council ◽  
Weather Radar ◽  
Temporal Analysis ◽  
Skill Score ◽  
Rain Gauge ◽  
Set Covering ◽  
Data Set ◽  
Rain Gauges ◽  
Weather Radars

Abstract. The Precipitation Estimation at Microwave Frequencies (PEMW) algorithm was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) for inferring surface rain intensity (sri) from satellite passive microwave observations in the range from 89 to 190 GHz. The operational version of PEMW (OPEMW) has been running continuously at IMAA-CNR for two years. The OPEMW sri estimates, together with other precipitation products, are used as input to an operational hydrological model for flood alert forecast. This paper presents the validation of OPEMW against simultaneous ground-based observations from a network of 20 weather radar systems and a network of more than 3000 rain gauges distributed over the Italian Peninsula and main islands. The validation effort uses a data set covering one year (July 2011–June 2012). The effort evaluates dichotomous and continuous scores for the assessment of rain detection and quantitative estimate, respectively, investigating both spatial and temporal features. The analysis demonstrates 98% accuracy in correctly identifying rainy and non-rainy areas; it also quantifies the increased ability (with respect to random chance) to detect rainy and non-rainy areas (0.42–0.45 Heidke skill score) or rainy areas only (0.27–0.29 equitable threat score). Performances are better than average during summer, fall, and spring, while worse than average in the winter season. The spatial–temporal analysis does not show seasonal dependence except over the Alps and northern Apennines during winter. A binned analysis in the 0–15 mm h−1 range suggests that OPEMW tends to slightly overestimate sri values below 6–7 mm h−1 and underestimate sri above those values. With respect to rain gauges (weather radars), the correlation coefficient is larger than 0.8 (0.9). The monthly mean difference and standard deviation remain within ±1 and 2 mm h−1 with respect to rain gauges (respectively −2–0 and 4 mm h−1 with respect to weather radars).

scholarly journals The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison

2020 ◽  
Vol 12 (3) ◽  
pp. 481 ◽  
Author(s):  
Thierry Pellarin ◽  
Carlos Román-Cascón ◽  
Christian Baron ◽  
Rajat Bindlish ◽  
Luca Brocca ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Central Africa ◽  
Tropical Rainfall Measuring Mission ◽  
Product Evaluation ◽  
Rain Gauge ◽  
Satellite Precipitation ◽  
Weather Radars ◽  
Global Precipitation Climatology Centre ◽  
Almost All

Near real-time precipitation is essential to many applications. In Africa, the lack of dense rain-gauge networks and ground weather radars makes the use of satellite precipitation products unavoidable. Despite major progresses in estimating precipitation rate from remote sensing measurements over the past decades, satellite precipitation products still suffer from quantitative uncertainties and biases compared to ground data. Consequently, almost all precipitation products are provided in two modes: a real-time mode (also called early-run or raw product) and a corrected mode (also called final-run, adjusted or post-processed product) in which ground precipitation measurements are integrated in algorithms to correct for bias, generally at a monthly timescale. This paper describes a new methodology to provide a near-real-time precipitation product based on satellite precipitation and soil moisture measurements. Recent studies have shown that soil moisture intrinsically contains information on past precipitation and can be used to correct precipitation uncertainties. The PrISM (Precipitation inferred from Soil Moisture) methodology is presented and its performance is assessed for five in situ rainfall measurement networks located in Africa in semi-arid to wet areas: Niger, Benin, Burkina Faso, Central Africa, and East Africa. Results show that the use of SMOS (Soil Moisture and Ocean Salinity) satellite soil moisture measurements in the PrISM algorithm most often improves the real-time satellite precipitation products, and provides results comparable to existing adjusted products, such as TRMM (Tropical Rainfall Measuring Mission), GPCC (Global Precipitation Climatology Centre) and IMERG (Integrated Multi-satellitE Retrievals for GPM), which are available a few weeks or months after their detection.

scholarly journals Uncertainties in rainfall retrievals from ground-based weather radar: overview, case study, and simulation experiment

2006 ◽  
Vol 3 (4) ◽  
pp. 2385-2436
Author(s):  
R. Uijlenhoet ◽  
S. H. van der Wielen ◽  
A. Berne
Keyword(s):  
Simulation Experiment ◽  
Temporal Distribution ◽  
Weather Radar ◽  
Rain Gauge ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Weather Radars ◽  
Wide Range ◽  
Rainfall Retrieval

Abstract. Because rainfall constitutes the main source of water for the terrestrial hydrological processes, accurate and reliable measurement and prediction of its spatial and temporal distribution over a wide range of scales is an important goal for hydrology. We investigate the potential of ground-based weather radar to provide such measurements through a detailed analysis of the associated observation uncertainties. First, a historical perspective on measuring the space-time distribution of rainfall, from the rain gauge to the radar era, is presented. Subsequently, we provide an overview of the various errors and uncertainties affecting radar rainfall retrievals. As an example, we present a case study of the relation between measurements from an operational C-band weather radar and a network of tipping bucket rain gauges as a function of range. Finally, a recently developed stochastic model of range profiles of rainfall microstructure is employed in a simulation experiment designed to investigate the rainfall retrieval uncertainties associated with weather radars operating in different widely used frequency bands.

Complementing urban rainfall/flood monitoring using low-cost citizen observatories: first result and challenges

2020 ◽  
Author(s):  
Boud Verbeiren ◽  
Kim Tondeur ◽  
Solomon Seyoum ◽  
David Pireaux
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Open Data ◽  
Rain Gauge ◽  
Added Value ◽  
Intense Rainfall ◽  
Direct Evaluation ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Pluvial Flooding

&lt;p&gt;Internet-of-Things (IoT) technology is evolving rapidly and within the frame of the FloodCitiSense.eu project we are exploring the potential of low-cost citizen observatories for the monitoring of intense rainfall and pluvial flooding in three pilot cities: Brussels, Rotterdam and Birmingham. In this presentation we focus on the Brussels pilot in which we evaluate the added value of low-cost rainfall sensors (developed by Disdrometrics, Delft &amp;#8211; The Netherlands) to complement the existing network with 16 professional rain gauge (Flowbru.be &amp;#8211; Open data). The main objective is to obtain a higher density of rainfall measurements enabling to capture, in near real-time, intense rainfall events. Due to the high degree of imperviousness of the city landscape intense rainfall is often the trigger for a fast hydrological response, sometimes causing pluvial flooding in Brussels. The low-cost rainfall sensors are disdrometers, counting the number and estimating the size of raindrops. The low-cost sensors make use of LoRa technology to send their data in near real-time to the central database. In Brussels 20 low-cost sensors were installed with help of citizens, mainly aiming at filling the &amp;#8220;gaps&amp;#8221; of the existing rain gauge network. To enable direct evaluation some of the low-cost sensors where installed next to professional rain gauges. We evaluate the performance of the low-cost sensor by (1) direct comparison (intensity and volumes) with the professional rain gauges of the Flowbru.be network, (2) comparing the spatial pattern of measured rainfall intensities, with and without low-cost sensors, to radar rainfall maps and (3) the reliability of the low-cost measurements. In this contribution we will focus on the first results from the test phase (October 2019 &amp;#8211; January 2020). Next we also elaborate on the challenges involved in the deployment of a network of low-cost sensors. The FloodCitiSense.eu project is a close collaboration with TU Delft, Imperial College London, IIASA, Disdrometrics, VUB SMIT-imec, LGiU, EGEB and is funded within the ERA-NET Smart Urban Future programme (Urban Europe ENSUF).&lt;/p&gt;

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