Improving GSMaP V06 Precipitation Products Over the Upper Tocantins River Basin in the Brazilian Cerrado, Based on Local Rain-Gauge Network

2021 ◽  
Author(s):  
Rodrigo Pereira ◽  
Vinícius Bof Bufon ◽  
Felipe Cardoso de Oliveira Maia
Keyword(s):  
Specific Area ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Network Data ◽  
Bias Analysis ◽  
Central Brazil ◽  
Influence Area ◽  
Daily Scale ◽  
Tocantins River ◽  
Rain Gauge Network

Abstract This study aimed to evaluate the performance of GSMaP (Global Satellite Mapping of Precipitation) in estimating rainfall in central Brazil, using the Upper Tocantins River sub-basin as a specific area of ​​analysis. GSMaP data were compared with data from a rain gauge network between 2000 and 2019. Evaluations were made at daily and monthly temporal scales. In general, GSMaP products show an overestimate bias for drizzle (0.1~1 mm day−1) and underestimate for rainfalls above 10 mm day−1. The use of monthly scale data significantly reduces the bias observed in the daily scale, but with an underestimation trend of -28.3% and -39.7% for the dry and rainy periods, respectively. Categorical indices showed that the GSMaP system had better hit rates for rain detection in the rainy season (October-April) than in the dry season (May-September). For the studied region, the use of GSMaP data on daily and monthly scales should be preceded by a bias analysis as a function of rain gauge network data. The use of bias coefficient corrected observed rainfall data underestimation on daily and monthly scales, improved correlation between GSMaP and observed rainfall data and reduced errors associated with rainfall network data within the basin influence area.

scholarly journals In search for relationships between lightning and rainfall with a rectangular grid-box methodology

2009 ◽  
Vol 20 ◽  
pp. 51-56 ◽  
Author(s):  
S. C. Michaelides ◽  
K. Savvidou ◽  
K. A. Nicolaides ◽  
M. Charalambous
Keyword(s):  
Rain Gauge ◽  
Rainfall Data ◽  
Lightning Activity ◽  
Time Lags ◽  
Data Filtering ◽  
National Observatory ◽  
Rectangular Grid ◽  
Rain Gauge Network

Abstract. The rainfall and lightning activity associated with three depressions which affected the area of Cyprus were studied in order to identify possible relationships between them. The lightning data were provided by the National Observatory of Athens, Greece, and were spatially and statistically related to the corresponding rainfall measurements from the rain gauge network of the Cyprus Meteorological Service. The study was carried out by using a rectangular grid-box methodology and various combinations of lightning and rainfall data filtering and time-lags were tested.

scholarly journals On Complex Network Construction of Rain Gauge Stations Considering Nonlinearity of Observed Daily Rainfall Data

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1578 ◽  
Author(s):  
Kyunghun Kim ◽  
Hongjun Joo ◽  
Daegun Han ◽  
Soojun Kim ◽  
Taewoo Lee ◽  
...  
Keyword(s):  
Network Analysis ◽  
Mutual Information ◽  
Complex Network ◽  
Pearson Correlation ◽  
Daily Rainfall ◽  
Analysis Data ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Relationship Of ◽  
Rain Gauge Network

Rainfall data is frequently used as input and analysis data in the field of hydrology. To obtain adequate rainfall data, there should be a rain gauge network that can cover the relevant region. Therefore, it is necessary to analyze and evaluate the adequacy of rain gauge networks. Currently, a complex network analysis is frequently used in network analysis and in the hydrology field, Pearson correlation is used as strength of link in constructing networks. However, Pearson correlation is used for analyzing the linear relationship of data. Therefore, it is now suitable for nonlinear hydrological data (such as rainfall and runoff). Thus, a possible solution to this problem is to apply mutual information that can consider nonlinearity of data. The present study used a method of statistical analysis known as the Brock–Dechert–Scheinkman (BDS) statistics to test the nonlinearity of rainfall data from 55 Automated Synoptic Observing System (ASOS) rain gauge stations in South Korea. Analysis results indicated that all rain gauge stations showed nonlinearity in the data. Complex networks of these rain gauge stations were constructed by applying Pearson correlation and mutual information. Then, they were compared by computing their centrality values. Comparing the centrality rankings according to different thresholds for correlation showed that the network based on mutual information yielded consistent results in the rankings, whereas the network, which based on Pearson correlation exhibited much variability in the results. Thus, it was found that using mutual information is appropriate when constructing a complex network utilizing rainfall data with nonlinear characteristics.

scholarly journals On CMORPH Rainfall for Streamflow Simulation in a Small, Hortonian Watershed

2011 ◽  
Vol 12 (3) ◽  
pp. 456-466 ◽  
Author(s):  
Dawit A. Zeweldi ◽  
Mekonnen Gebremichael ◽  
Charles W. Downer
Keyword(s):  
Peak Flow ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Rain Gauge Data ◽  
Small Watersheds ◽  
Hydrologic Analysis ◽  
Comparable Performance ◽  
Climate Prediction Center ◽  
Rain Gauge Network ◽  
Streamflow Simulation

Abstract The objective is to assess the use of the Climate Prediction Center morphing method (CMORPH) (~0.073° latitude–longitude, 30 min resolution) rainfall product as input to the physics-based fully distributed Gridded Surface–Subsurface Hydrologic Analysis (GSSHA) model for streamflow simulation in the small (21.4 km2) Hortonian watershed of the Goodwin Creek experimental watershed located in northern Mississippi. Calibration is performed in two different ways: using rainfall data from a dense network of 30 gauges as input, and using CMORPH rainfall data as input. The study period covers 4 years, during which there were 24 events, each with peak flow rate higher than 0.5 m3 s−1. Streamflow simulations using CMORPH rainfall are compared against observed streamflows and streamflow simulations using rainfall from a dense rain gauge network. Results show that the CMORPH simulations captured all 24 events. The CMORPH simulations have comparable performance with gauge simulations, which is striking given the significant differences in the spatial scale between the rain gauge network and CMORPH. This study concludes that CMORPH rainfall products have potential value for streamflow simulation in such small watersheds. Overall, the performance of CMORPH-driven simulations increases when the model is calibrated with CMORPH data than when the model is calibrated with rain gauge data.

scholarly journals Radar subpixel-scale rainfall variability and uncertainty: lessons learned from observations of a dense rain-gauge network

2013 ◽  
Vol 17 (6) ◽  
pp. 2195-2208 ◽  
Author(s):  
N. Peleg ◽  
M. Ben-Asher ◽  
E. Morin
Keyword(s):  
Temporal Variability ◽  
Rainfall Variability ◽  
Rain Gauge ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Daily Scale ◽  
Rain Gauge Network ◽  
Hydrological Applications

Abstract. Runoff and flash flood generation are very sensitive to rainfall's spatial and temporal variability. The increasing use of radar and satellite data in hydrological applications, due to the sparse distribution of rain gauges over most catchments worldwide, requires furthering our knowledge of the uncertainties of these data. In 2011, a new super-dense network of rain gauges containing 14 stations, each with two side-by-side gauges, was installed within a 4 km2 study area near Kibbutz Galed in northern Israel. This network was established for a detailed exploration of the uncertainties and errors regarding rainfall variability within a common pixel size of data obtained from remote sensing systems for timescales of 1 min to daily. In this paper, we present the analysis of the first year's record collected from this network and from the Shacham weather radar, located 63 km from the study area. The gauge–rainfall spatial correlation and uncertainty were examined along with the estimated radar error. The nugget parameter of the inter-gauge rainfall correlations was high (0.92 on the 1 min scale) and increased as the timescale increased. The variance reduction factor (VRF), representing the uncertainty from averaging a number of rain stations per pixel, ranged from 1.6% for the 1 min timescale to 0.07% for the daily scale. It was also found that at least three rain stations are needed to adequately represent the rainfall (VRF < 5%) on a typical radar pixel scale. The difference between radar and rain gauge rainfall was mainly attributed to radar estimation errors, while the gauge sampling error contributed up to 20% to the total difference. The ratio of radar rainfall to gauge-areal-averaged rainfall, expressed by the error distribution scatter parameter, decreased from 5.27 dB for 3 min timescale to 3.21 dB for the daily scale. The analysis of the radar errors and uncertainties suggest that a temporal scale of at least 10 min should be used for hydrological applications of the radar data. Rainfall measurements collected with this dense rain gauge network will be used for further examination of small-scale rainfall's spatial and temporal variability in the coming years.

scholarly journals Analisis Spasial Pengaruh Dinamika Suhu Muka Laut Terhadap Distribusi Curah Hujan di Sulawesi Utara

Jurnal MIPA ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. 25
Author(s):  
Febriany Florence Rey ◽  
Seni H. J. Tongkukut ◽  
Wandayantolis .
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Correlation Method ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Sea Surface ◽  
North Sulawesi ◽  
Rain Gauge Network

Telah dilakukan analisis terhadap distribusi curah hujan yang dipengaruhi oleh dinamika suhu muka laut untuk mengetahui hubungan distribusi curah hujan dan dinamika suhu muka laut di Sulawesi Utara, serta telah dibuat peta spasial hubungan distribusi curah hujan dengan dinamika suhu muka laut menggunakan ArcMap 9.3. Analisis yang dilakukan menggunakan data curah hujan bulanan selama 10 tahun dari 5 stasiun BMKG dan 10 pos hujan kerjasama di Sulawesi Utara, dan data dinamika suhu muka laut berdasarkan nilai Indeks Osilasi Selatan dengan metode korelasi. Hasil penelitian diperoleh nilai korelasi positif antara 0,50 hingga 0,90 terjadi pada periode tiga bulanan yaitu Agustus-September-Oktober pada seluruh wilayah pengamatan.Analysis of the precipitation that caused by the dynamics of sea surface temperature has been made to find the correlation between the precipitation and the dynamics of sea surface temperature, with its spatial map of the correlation between precipitation and the dynamics of sea surface temperature using ArcMap 9.3. The analysis use the monthly rainfall data for 10 years from 5 BMKG Stations and 10 rain-gauge network in North Sulawesi, and Southern Oscillation Index monthly value using correlation method. The result of this research is the positive correlation between 1,50 to 0,90 occur in the period of August-September-October.

scholarly journals Citizen rain gauge improves hourly radar rainfall bias correction using a two-step Kalman filter

2021 ◽  
Author(s):  
Punpim Puttaraksa Mapiam ◽  
Monton Methaprayun ◽  
Thom Bogaard ◽  
Gerrit Schoups ◽  
Marie-Claire Ten Veldhuis
Keyword(s):  
Kalman Filter ◽  
Bias Correction ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Spatial Density ◽  
Limiting Factor ◽  
Rainfall Time Series ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Rain Gauge Network

Abstract. Low density of conventional rain gauge networks is often a limiting factor for radar rainfall bias correction. Citizen rain gauges offer a promising opportunity to collect rainfall data at higher spatial density. In this paper hourly radar rainfall bias adjustment was applied using two different rain gauge networks consisting of tipping buckets (measured by Thailand Meteorological Department, TMD) and daily citizen rain gauges in a two-step Kalman Filter approach. Radar reflectivity data of Sattahip radar station and gauge rainfall data from the TMD and citizen rain gauges located in Tubma basin, Thailand were used in the analysis. Daily data from the citizen rain gauge network were downscaled to hourly resolution based on temporal distribution patterns obtained from radar rainfall time series and the TMD gauge network. The radar rainfall bias correction factor was sequentially updated based on TMD and citizen rain gauge data using a Kalman Filter. Results show that an improvement of radar rainfall estimates was achieved by including the downscaled citizen observations compared to bias correction based on the conventional rain gauge network only. These outcomes emphasize the value of citizen rainfall observations for radar bias correction, in particular in regions where conventional rain gauge networks are sparse.

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