scholarly journals Comparing satellite rainfall estimates with rain gauge data: Optimal strategies suggested by a spectral model

2003 ◽  
Vol 108 (D3) ◽  
pp. n/a-n/a ◽  
Author(s):  
Thomas L. Bell ◽  
Prasun K. Kundu
Keyword(s):  
Optimal Strategies ◽  
Rain Gauge ◽  
Spectral Model ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall ◽  
Rainfall Estimates

scholarly journals Experiences in using the TMPA-3B42R satellite data to complement rain gauge measurements in the Ecuadorian coastal foothills

2013 ◽  
Vol 17 (7) ◽  
pp. 2905-2915 ◽  
Author(s):  
M. Arias-Hidalgo ◽  
B. Bhattacharya ◽  
A. E. Mynett ◽  
A. van Griensven
Keyword(s):  
Satellite Data ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
New Technologies ◽  
Simple Procedure ◽  
Rain Gauge ◽  
Experimental Methodology ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall

Abstract. At present, new technologies are becoming available to extend the coverage of conventional meteorological datasets. An example is the TMPA-3B42R dataset (research – v6). The usefulness of this satellite rainfall product has been investigated in the hydrological modeling of the Vinces River catchment (Ecuadorian lowlands). The initial TMPA-3B42R information exhibited some features of the precipitation spatial pattern (e.g., decreasing southwards and westwards). It showed a remarkable bias compared to the ground-based rainfall values. Several time scales (annual, seasonal, monthly, etc.) were considered for bias correction. High correlations between the TMPA-3B42R and the rain gauge data were still found for the monthly resolution, and accordingly a bias correction at that level was performed. Bias correction factors were calculated, and, adopting a simple procedure, they were spatially distributed to enhance the satellite data. By means of rain gauge hyetographs, the bias-corrected monthly TMPA-3B42R data were disaggregated to daily resolution. These synthetic time series were inserted in a hydrological model to complement the available rain gauge data to assess the model performance. The results were quite comparable with those using only the rain gauge data. Although the model outcomes did not improve remarkably, the contribution of this experimental methodology was that, despite a high bias, the satellite rainfall data could still be corrected for use in rainfall-runoff modeling at catchment and daily level. In absence of rain gauge data, the approach may have the potential to provide useful data at scales larger than the present modeling resolution (e.g., monthly/basin).

scholarly journals Validation of Satellite and Merged Rainfall Data over Ghana, West Africa

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 859 ◽  
Author(s):  
Winifred Ayinpogbilla Atiah ◽  
Leonard Kofitse Amekudzi ◽  
Jeffrey Nii Armah Aryee ◽  
Kwasi Preko ◽  
Sylvester Kojo Danuor
Keyword(s):  
Grid Cell ◽  
Rain Gauge ◽  
Climate Impact ◽  
Rain Gauge Data ◽  
Ecological Zones ◽  
Transition Zones ◽  
Impact Studies ◽  
Gauge Data ◽  
Satellite Rainfall ◽  
Agro Ecological Zones

In regions of sparse gauge networks, satellite rainfall products are mostly used as surrogate measurements for various rainfall impact studies. Their potential to complement rain gauge measurements is influenced by the uncertainties associated with them. This study evaluates the performance of satellites and merged rainfall products over Ghana in order to provide information on the consistency and reliability of such products. Satellite products were validated with gridded rain gauge data from the Ghana Meteorological Agency (GMet) on various time scales. It was observed that the performance of the products in the country are mostly scale and location dependent. In addition, most of the products showed relatively good skills on the seasonal scale (r > 0.90) rather than the annual, and, after removal of seasonality from the datasets, except ARC2 that had larger biases in most cases. Again, all products captured the onsets, cessations, and spells countrywide and in the four agro-ecological zones. However, CHIRPS particularly revealed a better skill on both seasonal and annual scales countrywide. The products were not affected by the number of gauge stations within a grid cell in the Forest and Transition zones. This study, therefore, recommends all products except ARC2 for climate impact studies over the region.

scholarly journals Application of Satellite Rainfall Products for Flood Inundation Modelling in Kelantan River Basin, Malaysia

Hydrology ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 95 ◽  
Author(s):  
Tam ◽  
Abd Rahman ◽  
Harun ◽  
Hanapi ◽  
Kaoje
Keyword(s):  
Rain Gauge ◽  
Flood Inundation ◽  
Primary Input ◽  
Rain Gauge Data ◽  
Flood Level ◽  
Gauge Data ◽  
Flood Inundation Mapping ◽  
Precipitation Estimation ◽  
Inundation Modelling ◽  
Satellite Rainfall

The advent of satellite rainfall products can provide a solution to the scarcity of observed rainfall data. The present study aims to evaluate the performance of high spatial-temporal resolution satellite rainfall products (SRPs) and rain gauge data in hydrological modelling and flood inundation mapping. Four SRPs, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) - Early, - Late (IMERG-E, IMERG-L), Global Satellite Mapping of Precipitation-Near Real Time (GSMaP-NRT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Cloud Classification System (PERSIANN-CCS) and rain gauge data were used as the primary input to a hydrological model, Rainfall-Runoff-Inundation (RRI) and the simulated flood level and runoff were compared with the observed data using statistical metrics. GSMaP showed the best performance in simulating hourly runoff with the lowest relative bias (RB) and the highest Nash-Sutcliffe efficiency (NSE) of 4.9% and 0.79, respectively. Meanwhile, the rain gauge data was able to produce runoff with −12.2% and 0.71 for RB and NSE, respectively. The other three SRPs showed acceptable results in daily discharge simulation (NSE value between 0.42 and 0.49, and RB value between −23.3% and −31.2%). The generated flood map also agreed with the published information. In general, the SRPs, particularly the GSMaP, showed their ability to support rapid flood forecasting required for early warning of floods.

scholarly journals Intercomparison of improved satellite rainfall estimation with CHIRPS gridded product and rain gauge data over Venezuela

Atmósfera ◽  
2016 ◽  
Vol 29 (4) ◽  
pp. 323-342 ◽  
Author(s):  
Franklin Javier Paredes Trejo ◽  
◽  
Humberto Álvarez Barbosa ◽  
Marcos A. Peñaloza-Murillo ◽  
Maria Alejandra Moreno ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Rainfall Estimation ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall

scholarly journals Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)

2014 ◽  
Vol 18 (7) ◽  
pp. 2493-2502 ◽  
Author(s):  
D. Kneis ◽  
C. Chatterjee ◽  
R. Singh
Keyword(s):  
Real Time ◽  
Stream Flow ◽  
Rain Gauge ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Rainfall Estimates

Abstract. The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall–runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day-1). At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash–Sutcliffe index of 0.76–0.88 at gauges not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gauge-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.

scholarly journals Study on quantification of areal mean precipitation using satellite-gauge merging precipitation

2018 ◽  
Vol 12 (5) ◽  
pp. 117-126
Author(s):  
Bui Thi Hieu
Keyword(s):  
Design Method ◽  
Rain Gauge ◽  
Bias Reduction ◽  
Rainfall Runoff ◽  
Sample Design ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Precipitation Product ◽  
Gauge Data ◽  
Satellite Rainfall

Satellite based precipitation product (GSMaP-MVK) can be reliably used to estimate the Areal Mean Precipitation error based on “Sample Design method” (Esdd) with the effort to mitigate the problem of sparse data, especially severe in poorly gauged river basins. In addition, the satellite-gauge merging precipitation would reduce significantly the magnitude gaps between the satellite rainfall estimations and the rain gauge data. In this study, the capability of satellite-gauge merging precipitation using GSMaP-MVK and local dense rain gauge data with bias reduction approach to evaluate the AMP is investigated. The main finding is that satellite-gauge blending data which incorporates a dense rain gauge measurements shows the better capability to evaluate AMP using Esdd index than the original satellite only precipitation estimations. However, Esdd quantification performances of satellite-gauge blending precipitation are inferior to the original satellite only precipitation product GSMaP-MVK when the number of blended rain gauges is not large enough. Keywords: areal mean precipitation; remote sensed precipitation product; satellite-gauge merging; rainfall runoff simulations.

Evaluation of high-resolution satellite rainfall products using rain gauge data over complex terrain in southwest China

2014 ◽  
Vol 119 (1-2) ◽  
pp. 203-219 ◽  
Author(s):  
Meixian Liu ◽  
Xianli Xu ◽  
Alexander Y. Sun ◽  
Kelin Wang ◽  
Yuemin Yue ◽  
...  
Keyword(s):  
High Resolution ◽  
Complex Terrain ◽  
Southwest China ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall

Combined rainfall estimates from personal weather station and commercial microwave link data in Germany

2021 ◽  
Author(s):  
Maximilian Graf ◽  
Abbas El Hachem ◽  
Micha Eisele ◽  
Jochen Seidel ◽  
Christian Chwala ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Hydrological Cycle ◽  
Spatial Scales ◽  
Rainfall Variability ◽  
Rain Gauge ◽  
Data Sets ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Gauge Data ◽  
Rainfall Estimates

&lt;p&gt;Rain gauges and weather radars are the default sources of rainfall information. Rainfall estimates from these sensors improve our understanding of the hydrological cycle and are vital for water-resource management, agriculture, urban planning, as well as for weather, climate, and hydrological modelling. Still, due to the high spatio-temporal variability of rainfall and the specific drawbacks of the individual rainfall sensors, the rainfall variability cannot be captured completely. In the last decade, the number and availability of opportunistic rainfall sensors increased rapidly. These sensors are initially not meant to measure rainfall for scientific or operational purposes, but, if processed carefully, can be used for these cases . Here we present an analysis of two years of data from two opportunistic rainfall sensors, namely personal weather stations (PWS) and commercial microwave links (CMLs). We evaluate the performance of rainfall maps derived from these sensors on different spatial and temporal scales in Germany.&lt;/p&gt;&lt;p&gt;The data from around 15000 PWS tipping bucket-style rain gauges from the Netatmo network were accessed via Netatmos API. The data from around 4000 CMLs, which can be used to derive rainfall estimates from the rain-induced attenuation of the CMLs&amp;#8217; signal, were obtained from Ericsson. As both, PWS and CML data, can suffer from various error sources e.g. from unfavourable positioning and poor maintenance of PWS and from non-rain induced attenuation of the CMLs signal, we used a strict filtering routine. A total of seven gridded rainfall products were derived from different combinations of PWS, CML, and rain gauge data from the German Weather Service (DWD) with a geostatistical interpolation approach. This approach incorporates the uncertainty of the opportunistic sensors and the path-averaging characteristic of the CML observations.&lt;/p&gt;&lt;p&gt;To evaluate the resulting rainfall maps, we used three rain gauge data sets with different temporal and spatial scales covering the whole of Germany, the state of Rhineland-Palatinate and the city of Reutlingen, respectively. For all three reference data sets, rainfall maps from opportunistic sensors provided good agreement, with best results being derived from the combinations with PWS. Rainfall maps including CML data had the lowest bias. In a comparison with gauge adjusted radar products from the DWD, the radar products yielded better results than the rainfall maps from opportunistic sensors for the country-wide comparison of daily rainfall sums, which was carried out using the DWD&amp;#8217;s independent network of manual rain gauges. But for the hourly references covering Rhineland-Palatinate and Reutlingen, the rainfall maps derived from opportunistic sensors outperformed the radar products. These results highlight the capabilities of opportunistic rainfall sensors which could be used in many hydrometeorological applications.&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document