Intervention Analysis of Urbanization Effect on Rainfall Data at the Seoul Rain Gauge Station

In this study, the Tropical Rainfall Measuring Mission (TRMM) version 7 satellite rainfall product, TRMM 3B42 (V7), was validated using rain gauge measurements in the Upper Huaihe Basin, China. This validation was carried out at monthly and annual temporal scales for an 11-year period using four selected grids with six, four, two and one rain gauge station (s) located within the TRMM grid respectively; the rain gage measurements for grids with more than one rain gauge were averaged. This study found that the validation of the TRMM dataset in grids where there were adequate rain gauge were present to capture the distributed and stochastic nature of rainfall with very good correlation (0.87-0.94) and with very little relative bias when the rain gage accumulations were compared with the TRMM estimates. From the study we found that the TRMM dataset can be used as precipitation input for hydrological modeling at monthly and annual scales for sustainable water resources management in the Upper Huaihe River and even in un-gaged or sparsely gaged basins in other parts of the world.

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Nigerian Rain Gauge Station Optimization and National Development: The importance of Head Count

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/413/1/012022 ◽

2018 ◽

Vol 413 ◽

pp. 012022 ◽

Cited By ~ 3

Author(s):

Ben U Ngene ◽

Njideka Obianigwe

Keyword(s):

National Development ◽

Rain Gauge ◽

Rain Gauge Station ◽

Gauge Station ◽

Head Count

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Contamination Vulnerability Analysis of Watershed for Water Quality Monitoring

Forum Geografi ◽

10.23917/forgeo.v20i1.1803 ◽

2016 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Widyastuti Widyastuti ◽

Slamet Suprayogi

Keyword(s):

Water Quality ◽

Land Surface ◽

Water Quality Monitoring ◽

Vulnerability Index ◽

Rain Gauge ◽

Vulnerability Analysis ◽

Quality Monitoring ◽

Neural Network Modeling ◽

Rain Gauge Station ◽

Gauge Station

This research is an early step to determine the location of rain gauge station for artificial neural network modeling. The implementation of this model is very useful for water quality monitoring. The objectives of this study are: 1) to study the distribution of watershed parameter, that are average annual precipitation, land use and land-surface slope, 2) to conduct vulnerability analysis of watershed contamination, 3) to determine the location of rain gauge station. The study was performed by weighing and rating method of watershed parameters. The vulnerability degree of watershedtocontaminationispresentedasvulnerabilityindex.Thisindexisdeterminedbyoverallsumofallmultiplication between score and weigh number of each parameter. All data manipulation and data analysis were performed by using Geographic Information System (ArcView version by 3.2). The vulnerability of watershed contamination map had been generated using overlay operation of parameters. The results show that vulnerability index are varies between 10 up to 40 intervals. Hence, the indexes were categorized into three levels of watershed vulnerability, namely low (10 – 20), moderate (20 – 30) and high (30 – 40). It is found that the study area covered more by high vulnerability of watershed to contamination. The zoning of watershed vulnerability meant to determine the rain gauge location. There are three rain gauge stations on the area that they are in a high vulnerability level, whereas the other vulnerability level area has one rain gauge station. Each level of vulnerability area is able to represent the source of contaminant that it maybe influence the water quality of Gajahwong river.

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Accuracy assessment and error cause analysis of GPM (V06) in Xiangjiang river catchment

Hydrology Research ◽

10.2166/nh.2021.188 ◽

2021 ◽

Author(s):

Bingru Tian ◽

Hua Chen ◽

Jialing Wang ◽

Chong-Yu Xu

Keyword(s):

Rain Gauge ◽

Precipitation Intensity ◽

Intensity Level ◽

Total Precipitation ◽

Rain Gauge Station ◽

Xiangjiang River ◽

River Catchment ◽

Satellite Precipitation ◽

Gauge Station ◽

The Impact

Abstract Application potential and development prospect of satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) have promising implications. This study discusses causes of spatiotemporal differences on GPM data through the following steps: Initially, calculate bias between satellite-based data and rain gauge data of Xiangjiang river catchment to assess the accuracy of GPM (06E, 06 L, and 06F) products. Second, total errors of satellite precipitation data are divided into hit bias (HBIAS: precipitation detected by both GPM and rain gauge station), missed precipitation (MBIAS: precipitation detected only by rain gauge station), and false precipitation (FBIAS: precipitation detected only by GPM). Third, evaluate the impact of precipitation intensity and total precipitation on accuracy of GPM data and their influence on three error components. Several conclusions are drawn from the results above: (1) Satellite-based precipitation measurements perform better on a larger temporal-spatial scale. (2) The accuracy of TRMM and GPM data displays significant variances on space and time. Season, precipitation intensity, and total precipitation are main factors influencing the accuracy of TRMM and GPM data. (3) The detection capability of satellite products change with seasonal variation and different precipitation intensity level.

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Toward a practical approach for ergodicity analysis

Nonlinear Processes in Geophysics Discussions ◽

10.5194/npgd-2-1425-2015 ◽

2015 ◽

Vol 2 (5) ◽

pp. 1425-1446 ◽

Cited By ~ 1

Author(s):

H. Wang ◽

C. Wang ◽

Y. Zhao ◽

X. Lin ◽

C. Yu

Keyword(s):

Time Series ◽

Yellow River ◽

Rain Gauge ◽

Ergodic Property ◽

Practical Approach ◽

Function Evaluation ◽

Rain Gauge Station ◽

Gauge Station ◽

Mean Ergodicity ◽

The Mean

Abstract. It is of importance to perform hydrological forecast using a finite hydrological time series. Most time series analysis approaches presume a data series to be ergodic without justifying this assumption. This paper presents a practical approach to analyze the mean ergodic property of hydrological processes by means of autocorrelation function evaluation and Augmented Dickey Fuller test, a radial basis function neural network, and the definition of mean ergodicity. The mean ergodicity of precipitation processes at the Lanzhou Rain Gauge Station in the Yellow River basin, the Ankang Rain Gauge Station in Han River, both in China, and at Newberry, MI, USA are analyzed using the proposed approach. The results indicate that the precipitations of March, July, and August in Lanzhou, and of May, June, and August in Ankang have mean ergodicity, whereas, the precipitation of any other calendar month in these two rain gauge stations do not have mean ergodicity. The precipitation of February, May, July, and December in Newberry show ergodic property, although the precipitation of each month shows a clear increasing or decreasing trend.

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Does More Moisture in the Atmosphere Lead to More Intense Rains?

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-21-0117.1 ◽

2021 ◽

Author(s):

Agostino Manzato

Keyword(s):

Precipitable Water ◽

Rain Gauge ◽

Rain Gauge Station ◽

Scatter Plot ◽

North East ◽

Station Data ◽

Gauge Station ◽

Friuli Venezia Giulia

Abstract It is typically interpreted that more moisture in the atmosphere leads to more intense rains. This notion may be supported, for example, by taking a scatter plot between rain and column precipitable water. The present paper suggests, however, that the main consequence of intense rains with more moistures in the atmosphere is that there is a more chance to happen, rather than of an increase in the expected magnitude. This tendency equally applies to any rains above 1 mm/6h to a lesser extent. The result is derived from an analysis of 33 local rain–gauge station data and a shared sounding over Friuli Venezia Giulia, North–East Italy.

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Development of Rain Gauge Station in SGI Campus

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2018.3569 ◽

2018 ◽

Vol 6 (3) ◽

pp. 2504-2510

Author(s):

Anuradha Kamble

Keyword(s):

Rain Gauge ◽

Rain Gauge Station ◽

Gauge Station

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Point process models of rainfall: developments for fine-scale structure

Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rspa.2007.1889 ◽

2007 ◽

Vol 463 (2086) ◽

pp. 2569-2587 ◽

Cited By ~ 62

Author(s):

Paul Cowpertwait ◽

Valerie Isham ◽

Christian Onof

Keyword(s):

Poisson Process ◽

Time Scales ◽

Rain Gauge ◽

Historical Record ◽

Rainfall Data ◽

Process Models ◽

Third Order ◽

Time Intervals ◽

Historical Series ◽

Derived Properties

A conceptual stochastic model of rainfall is proposed in which storm origins occur in a Poisson process, where each storm has a random lifetime during which rain cell origins occur in a secondary Poisson process. In addition, each cell has a random lifetime during which instantaneous random depths (or ‘pulses’) of rain occur in a further Poisson process. A key motivation behind the model formulation is to account for the variability in rainfall data over small (e.g. 5 min) and larger time intervals. Time-series properties are derived to enable the model to be fitted to aggregated rain gauge data. These properties include moments up to third order, the probability that an interval is dry, and the autocovariance function. To allow for distinct storm types (e.g. convective and stratiform), several processes may be superposed. Using the derived properties, a model consisting of two storm types is fitted to 60 years of 5 min rainfall data taken from a site near Wellington, New Zealand, using sample estimates taken at 5 min, 1 hour, 6 hours and daily levels of aggregation. The model is found to fit moments of the depth distribution up to third order very well at these time scales. Using the fitted model, 5 min series are simulated, and annual maxima are extracted and compared with equivalent values taken from the historical record. A good fit in the extremes is found at both 1 and 24 hour levels of aggregation, although at the 5 min level there is some underestimation of the historical values. Proportions of time intervals with depths below various low thresholds are extracted from the simulated and historical series and compared. A tendency for underestimation of the historical values is evident at some time scales, with a close fit being obtained as the threshold is increased.

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Performance of TRMM TMPA 3B42 V7 in Replicating Daily Rainfall and Regional Rainfall Regimes in the Amazon Basin (1998–2013)

Remote Sensing ◽

10.3390/rs10121879 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1879 ◽

Cited By ~ 7

Author(s):

Véronique Michot ◽

Daniel Vila ◽

Damien Arvor ◽

Thomas Corpetti ◽

Josyane Ronchail ◽

...

Keyword(s):

Amazon Basin ◽

Daily Rainfall ◽

Rain Gauge ◽

Rainfall Data ◽

Annual Rainfall ◽

Spatial Coverage ◽

Rainfall Regimes ◽

Trmm 3B42 ◽

Good Agreement

Knowledge and studies on precipitation in the Amazon Basin (AB) are determinant for environmental aspects such as hydrology, ecology, as well as for social aspects like agriculture, food security, or health issues. Availability of rainfall data at high spatio-temporal resolution is thus crucial for these purposes. Remote sensing techniques provide extensive spatial coverage compared to ground-based rainfall data but it is imperative to assess the quality of the estimates. Previous studies underline at regional scale in the AB, and for some years, the efficiency of the Tropical Rainfall Measurement Mission (TRMM) 3B42 Version 7 (V7) (hereafter 3B42) daily product data, to provide a good view of the rainfall time variability which is important to understand the impacts of El Nino Southern Oscilation. Then our study aims to enhance the knowledge about the quality of this product on the entire AB and provide a useful understanding about his capacity to reproduce the annual rainfall regimes. For that purpose we compared 3B42 against 205 quality-controlled rain gauge measurements for the period from March 1998 to July 2013, with the aim to know whether 3B42 is reliable for climate studies. Analysis of quantitative (Bias, Relative RMSE) and categorical statistics (POD, FAR) for the whole period show a more accurate spatial distribution of mean daily rainfall estimations in the lowlands than in the Andean regions. In the latter, the location of a rain gauge and its exposure seem to be more relevant to explain mismatches with 3B42 rather than its elevation. In general, a good agreement is observed between rain gauge derived regimes and those from 3B42; however, performance is better in the rainy period. Finally, an original way to validate the estimations is by taking into account the interannual variability of rainfall regimes (i.e., the presence of sub-regimes): four sub-regimes in the northeast AB defined from rain gauges and 3B42 were found to be in good agreement. Furthermore, this work examined whether TRMM 3B42 V7 rainfall estimates for all the grid points in the AB, outgoing longwave radiation (OLR) and water vapor flux patterns are consistent in the northeast of AB.

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Comparative rainfall data analysis from two vertically pointing radars, an optical disdrometer, and a rain gauge

Nonlinear Processes in Geophysics ◽

10.5194/npg-15-987-2008 ◽

2008 ◽

Vol 15 (6) ◽

pp. 987-997 ◽

Cited By ~ 6

Author(s):

E. I. Nikolopoulos ◽

A. Kruger ◽

W. F. Krajewski ◽

C. R. Williams ◽

K. S. Gage

Keyword(s):

Comparative Analysis ◽

Temporal Variability ◽

Rain Gauge ◽

Iowa City ◽

Rainfall Data ◽

Instrumental Effects ◽

Remote Sensors ◽

X Band ◽

Derived Properties ◽

Instrumental Effect

Abstract. The authors present results of a comparative analysis of rainfall data from several ground-based instruments. The instruments include two vertically pointing Doppler radars, S-band and X-band, an optical disdrometer, and a tipping-bucket rain gauge. All instruments were collocated at the Iowa City Municipal Airport in Iowa City, Iowa, for a period of several months. The authors used the rainfall data derived from the four instruments to first study the temporal variability and scaling characteristics of rainfall and subsequently assess the instrumental effects on these derived properties. The results revealed obvious correspondence between the ground and remote sensors, which indicates the significance of the instrumental effect on the derived properties.

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