Assessment of the measuring errors in tipping rain gauge located on a small mountain basin

2020 ◽  
Author(s):  
Leonor Rodriguez-Sinobas ◽  
Daniel Alberto Segovia-Cardozo ◽  
Sergio Zubelzu ◽  
Enrique Estefania ◽  
Andrés Díez-Herrero
Keyword(s):  
Measurement Errors ◽  
Rain Gauge ◽  
Sampling Procedure ◽  
Irrigation Scheduling ◽  
Time Interval ◽  
Manufacturing Cost ◽  
Calibration Data ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
High Precipitation

<p>Precipitation measurement has always been of human interest. Its estimation can guide the decisions concerning flooding prevention and irrigation scheduling in semi-arid regions.</p><p>Nowadays, manufactures offer several types of rain gauges. Among them, the tipping-bucket rain gauges (TBRs) is the most frequently used worldwide to collect rainfall data. It structure is simple and the manufacturing cost is reasonable. Also, the operating mechanical mechanism saves energy and can be easily automated. Its manufacture began in the seventeenth century although the recent models have improved their original characteristics.</p><p>Likewise, these gauges have some disadvantages such us: measurement errors, that can be significant during heavy rainfall or light drizzle; losses from evaporation and wind effects; time of onset; sampling procedure and rain residue in the bucket. Therefore, calibration is often needed.</p><p>This study assesses the data from a set of 12 TBRs spread in the small mountain basin “Venero Claro”, Avila (Spain). This is highly monitored due to its capacity to generate torrential flows and flash floods.  The data comprised a time interval of 14 years; the oldest TBRs were installed in 2006. The objective was quantified the errors, especially those caused by high precipitation intensities, which are common in the area. Thus, calibration curves for data analysis were estimated by a dynamic laboratory calibration for two different TBRs’ models.</p><p>The results from the calibration data have been statistically analysed in order to determine the errors and their significance along time and topography. A significant underestimation was observed in TBRs, especially in those located at higher areas.</p>

scholarly journals The Contribution of Rain Gauges in the Calibration of the IMERG Product: Results from the First Validation over Spain

2020 ◽  
Vol 21 (2) ◽  
pp. 161-182 ◽  
Author(s):  
Francisco J. Tapiador ◽  
Andrés Navarro ◽  
Eduardo García-Ortega ◽  
Andrés Merino ◽  
José Luis Sánchez ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Surface Precipitation ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Level 3 ◽  
Hydrologic Impacts ◽  
A Minor ◽  
Global Precipitation Measurement ◽  
Global Precipitation

AbstractAfter 5 years in orbit, the Global Precipitation Measurement (GPM) mission has produced enough quality-controlled data to allow the first validation of their precipitation estimates over Spain. High-quality gauge data from the meteorological network of the Spanish Meteorological Agency (AEMET) are used here to validate Integrated Multisatellite Retrievals for GPM (IMERG) level 3 estimates of surface precipitation. While aggregated values compare notably well, some differences are found in specific locations. The research investigates the sources of these discrepancies, which are found to be primarily related to the underestimation of orographic precipitation in the IMERG satellite products, as well as to the number of available gauges in the GPCC gauges used for calibrating IMERG. It is shown that IMERG provides suboptimal performance in poorly instrumented areas but that the estimate improves greatly when at least one rain gauge is available for the calibration process. A main, generally applicable conclusion from this research is that the IMERG satellite-derived estimates of precipitation are more useful (r2 > 0.80) for hydrology than interpolated fields of rain gauge measurements when at least one gauge is available for calibrating the satellite product. If no rain gauges were used, the results are still useful but with decreased mean performance (r2 ≈ 0.65). Such figures, however, are greatly improved if no coastal areas are included in the comparison. Removing them is a minor issue in terms of hydrologic impacts, as most rivers in Spain have their sources far from the coast.

scholarly journals Quality-Based Combination of Multi-Source Precipitation Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1709 ◽  
Author(s):  
Anna Jurczyk ◽  
Jan Szturc ◽  
Irena Otop ◽  
Katarzyna Ośródka ◽  
Piotr Struzik
Keyword(s):  
Spatial Resolution ◽  
Flash Flood ◽  
Radar Data ◽  
Rain Gauge ◽  
Quality Information ◽  
Precipitation Data ◽  
Spatial Error ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Temporal And Spatial

A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation.

scholarly journals Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus

2020 ◽  
Vol 12 (19) ◽  
pp. 3212
Author(s):  
Adrianos Retalis ◽  
Dimitris Katsanos ◽  
Filippos Tymvios ◽  
Silas Michaelides
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Monthly Basis ◽  
Rain Gauges ◽  
Annual Basis ◽  
Precipitation Measurement ◽  
Satellite Retrievals ◽  
Gauge Station ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) high-resolution product and Tropical Rainfall Measuring Mission (TRMM) 3B43 product are validated against rain gauges over the island of Cyprus for the period from April 2014 to June 2018. The comparison performed is twofold: firstly, the Satellite Precipitation (SP) estimates are compared with the gauge stations’ records on a monthly basis and, secondly, on an annual basis. The validation is based on ground data from a dense and well-maintained network of rain gauges, available in high temporal (hourly) resolution. The results show high correlation coefficient values, on average reaching 0.92 and 0.91 for monthly 3B43 and IMERG estimates, respectively, although both IMERG and TRMM tend to underestimate precipitation (Bias values of −1.6 and −3.0, respectively), especially during the rainy season. On an annual basis, both SP estimates are underestimating precipitation, although IMERG estimates records (R = 0.82) are slightly closer to that of the corresponding gauge station records than those of 3B43 (R = 0.81). Finally, the influence of elevation of both SP estimates was considered by grouping rain gauge stations in three categories, with respect to their elevation. Results indicated that both SP estimates underestimate precipitation with increasing elevation and overestimate it at lower elevations.

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

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

<p>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 –CMLs– 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.</p><p>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.</p><p> </p>

Propagation of precipitation measurement biases into the hydraulic modelling of urban drainage systems – A case study of the Parco D’Orleans sub-urban catchment

2020 ◽  
Author(s):  
Angela Candela ◽  
Antonio Francipane ◽  
Mattia Stagnaro ◽  
Arianna Cauteruccio ◽  
Luca Giovanni Lanza
Keyword(s):  
Rain Gauge ◽  
Urban Drainage ◽  
Drainage Network ◽  
Rapid Response System ◽  
Urban Catchment ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Measurement Biases ◽  
The University

<p>Aim of this study is to evaluate the impact of Precipitation Measurement Biases (PMBs) of tipping-bucket rain gauges onto the hydraulic modelling of urban drainage networks.  As a case study, the monitored experimental suburban catchment of Parco d’Orleans located in the University Campus of Palermo, Italy and managed since 1987 by the Department of Engineering of the University of Palermo is considered. . Two tipping-bucket rain gauges provide a good spatial coverage of the catchment area and an acoustic level gauge is installed at the outlet of the drainage network for flow mesaurements. Contemporary high temporal resolution rainfall and runoff data series are available between 1993 to 1998, and are used for the calibration of the hydraulic model in terms of roughness of the urban surfaces. The total drainage area is 12.8 ha with 68% of impervious areas; the drainage network is composed of circular and egg-shaped concrete conduits. In the present work, the sensitivity of this rapid response system to the accuracy of the rainfall input is studied, with reference to drainage failures and urban flooding issues. In order to quantify the instrumental mechanical error of the two available Tipping Bucket Rain-gauges, these were calibrated at the rain gauge laboratory of the WMO Lead Centre on Precipitation Intensity “B. Castelli” following the procedure described in the recent EN 17277:2019 standard on precipitation measurements. For each gauge a calibration curve was provided in order to quantify the measurement bias and the associated calibration uncertainty.</p><p>For rainfall-runoff transformation in the urban drainage system, a conceptual model for urban catchment, which incorporates semi-distributed modelling concepts has been used. The urban basin is divided in external sub-catchments connected to the drainage network. Each external sub-catchment is modelled as two separate conceptual linear elements, a reservoir and a channel, one for the pervious part, the other for the impervious part of the investigated area. The drainage network is schematized as a cascade of non-linear cells and the flood routing is simplified in the form of kinematic wave and represented as a flux transfer between adjacent cells. The sensitivity of this rapid response system to the accuracy of the rainfall input has been studied with reference to drainage failures and urban flooding issues.</p><p>To examine the effects due to PMBs on the catchment response, a number of simulations were carried out using raw rainfall data and corrected data obtained after the application of the calibration curve for each rain gauge. Results, expressed in terms of comparisons between the hydrographs at catchment outlet, show a significant influence of the PMB on the peak flow and the total hydrograph volume.</p>

scholarly journals Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution

2018 ◽  
Vol 10 (9) ◽  
pp. 3209 ◽  
Author(s):  
Zhaokai Yin ◽  
Weihong Liao ◽  
Xiaohui Lei ◽  
Hao Wang ◽  
Ruojia Wang
Keyword(s):  
Hydrological Modeling ◽  
Eastern China ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Distributed Model ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Average Accuracy ◽  
Reliability And Robustness ◽  
Model Structures

Precipitation provides the most crucial input for hydrological modeling. However, rain gauge networks, the most common precipitation measurement mechanisms, are sometimes sparse and inadequately distributed in practice, resulting in an imperfect representation of rainfall spatial variability. The objective of this study is to analyze the sensitivity of different model structures to the different density and distribution of rain gauges and evaluate their reliability and robustness. Based on a rain gauge network of 20 gauges in the Jinjiang River Basin, south-eastern China, this study compared the performance of two conceptual models (the hydrologic model (HYMOD) and Xinanjiang) and one process-based distributed model (the water and energy transfer between soil, plants and atmosphere model (WetSpa)) with different rain gauge distributions. The results show that the average accuracy for the three models is generally stable as the number of rain gauges decreases but is sensitive to changes in the network distribution. HYMOD has the highest calibration uncertainty, followed by Xinanjiang and WetSpa. Differing model responses are consistent with changes in network distribution, while calibration uncertainties are more related to model structures.

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 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.

Evaluation of Global Forecast System (GFS) Medium-Range Precipitation Forecasts in the Nile River Basin

2021 ◽  
Author(s):  
Haowen Yue ◽  
Mekonnen Gebremichael ◽  
Vahid Nourani
Keyword(s):  
Temporal Dynamics ◽  
Disease Outbreaks ◽  
Rain Gauge ◽  
Global Forecast System ◽  
Forecast System ◽  
Forecast Performance ◽  
Weather Forecasts ◽  
Rain Gauges ◽  
Basin Scale ◽  
Medium Range

Abstract Reliable weather forecasts are valuable in a number of applications, such as, agriculture, hydropower, and weather-related disease outbreaks. Global weather forecasts are widely used, but detailed evaluation over specific regions is paramount for users and operational centers to enhance the usability of forecasts and improve their accuracy. This study presents evaluation of the Global Forecast System (GFS) medium-range (1 day – 15 day) precipitation forecasts in the nine sub-basins of the Nile basin using NASA’s Integrated Multi-satellitE Retrievals (IMERG) “Final Run” satellite-gauge merged rainfall observations. The GFS products are available at a temporal resolution of 3-6 hours, spatial resolution of 0.25°, and its version-15 products are available since 12 June 2019. GFS forecasts are evaluated at a temporal scale of 1-15 days, spatial scale of 0.25° to all the way to the sub-basin scale, and for a period of one year (15 June 2019 – 15 June 2020). The results show that performance of the 1-day lead daily basin-averaged GFS forecast performance, as measured through the modified Kling-Gupta Efficiency (KGE), is poor (0 < KGE < 0.5) for most of the sub-basins. The factors contributing to the low performance are: (1) large overestimation bias in watersheds located in wet climate regimes in the northern hemispheres (Millennium watershed, Upper Atbara & Setit watershed, and Khashm El Gibra watershed), and (2) lower ability in capturing the temporal dynamics of watershed-averaged rainfall that have smaller watershed areas (Roseires at 14,110 sq. km and Sennar at 13,895 sq. km). GFS has better bias for watersheds located in the dry parts of the northern hemisphere or wet parts of the southern hemisphere, and better ability in capturing the temporal dynamics of watershed-average rainfall for large watershed areas. IMERG Early has better bias than GFS forecast for the Millennium watershed but still comparable and worse bias for the Upper Atbara & Setit, and Khashm El Gibra watersheds. The variation in the performance of the IMERG Early could be partly explained by the number of rain gauges used in the reference IMERG Final product, as 16 rain gauges were used for the Millennium watershed but only one rain gauge over each Upper Atbara & Setit, and Khashm El Gibra watershed. A simple climatological bias-correction of IMERG Early reduces in the bias in IMERG Early over most watersheds, but not all watersheds. We recommend exploring methods to increase the performance of GFS forecasts, including post-processing techniques through the use of both near-real-time and research-version satellite rainfall products.

scholarly journals Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data

2021 ◽  
Vol 13 (21) ◽  
pp. 4243
Author(s):  
Mona Morsy ◽  
Ruhollah Taghizadeh-Mehrjardi ◽  
Silas Michaelides ◽  
Thomas Scholten ◽  
Peter Dietrich ◽  
...  
Keyword(s):  
Arid Regions ◽  
Flash Flood ◽  
Primary Source ◽  
Remote Sensing Data ◽  
Test Site ◽  
Rain Gauge ◽  
Cumulative Distribution ◽  
Climatic Data ◽  
Empirical Cumulative Distribution Function ◽  
Rain Gauges

Water depletion is a growing problem in the world’s arid and semi-arid areas, where groundwater is the primary source of fresh water. Accurate climatic data must be obtained to protect municipal water budgets. Unfortunately, the majority of these arid regions have a sparsely distributed number of rain gauges, which reduces the reliability of the spatio-temporal fields generated. The current research proposes a series of measures to address the problem of data scarcity, in particular regarding in-situ measurements of precipitation. Once the issue of improving the network of ground precipitation measurements is settled, this may pave the way for much-needed hydrological research on topics such as the spatiotemporal distribution of precipitation, flash flood prevention, and soil erosion reduction. In this study, a k-means cluster analysis is used to determine new locations for the rain gauge network at the Eastern side of the Gulf of Suez in Sinai. The clustering procedure adopted is based on integrating a digital elevation model obtained from The Shuttle Radar Topography Mission (SRTM 90 × 90 m) and Integrated Multi-Satellite Retrievals for GPM (IMERG) for four rainy events. This procedure enabled the determination of the potential centroids for three different cluster sizes (3, 6, and 9). Subsequently, each number was tested using the Empirical Cumulative Distribution Function (ECDF) in an effort to determine the optimal one. However, all the tested centroids exhibited gaps in covering the whole range of elevations and precipitation of the test site. The nine centroids with the five existing rain gauges were used as a basis to calculate the error kriging. This procedure enabled decreasing the error by increasing the number of the proposed gauges. The resulting points were tested again by ECDF and this confirmed the optimum of thirty-one suggested additional gauges in covering the whole range of elevations and precipitation records at the study site.

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