scholarly journals Evaluation of High-resolution Satellite Rainfall Data over Singapore

2016 ◽  
Vol 154 ◽  
pp. 158-167 ◽  
Author(s):  
Jina Hur ◽  
Srivatsan V. Raghavan ◽  
Ngoc Son Nguyen ◽  
Shie-Yui Liong
Keyword(s):  
High Resolution ◽  
Rainfall Data ◽  
Satellite Rainfall

Investigating Error Metrics for Satellite Rainfall Data at Hydrologically Relevant Scales

2008 ◽  
Vol 9 (3) ◽  
pp. 563-575 ◽  
Author(s):  
Faisal Hossain ◽  
George J. Huffman
Keyword(s):  
High Resolution ◽  
Spatial Scales ◽  
The United States ◽  
Space Time ◽  
Probability Of Detection ◽  
Rainfall Data ◽  
True Value ◽  
Error Metrics ◽  
Satellite Rainfall ◽  
New Generation

Abstract This paper addresses the following open question: What set of error metrics for satellite rainfall data can advance the hydrologic application of new-generation, high-resolution rainfall products over land? The authors’ primary aim is to initiate a framework for building metrics that are mutually interpretable by hydrologists (users) and algorithm developers (data producers) and to provide more insightful information on the quality of the satellite estimates. In addition, hydrologists can use the framework to develop a space–time error model for simulating stochastic realizations of satellite estimates for quantification of the implication on hydrologic simulation uncertainty. First, the authors conceptualize the error metrics in three general dimensions: 1) spatial (how does the error vary in space?); 2) retrieval (how “off” is each rainfall estimate from the true value over rainy areas?); and 3) temporal (how does the error vary in time?). They suggest formulations for error metrics specific to each dimension, in addition to ones that are already widely used by the community. They then investigate the behavior of these metrics as a function of spatial scale ranging from 0.04° to 1.0° for the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) geostationary infrared-based algorithm. It is observed that moving to finer space–time scales for satellite rainfall estimation requires explicitly probabilistic measures that are mathematically amenable to space–time stochastic simulation of satellite rainfall data. The probability of detection of rain as a function of ground validation rainfall magnitude is found to be most sensitive to scale followed by the correlation length for detection of rain. Conventional metrics such as the correlation coefficient, frequency bias, false alarm ratio, and equitable threat score are found to be modestly sensitive to scales smaller than 0.24° latitude/longitude. Error metrics that account for an algorithm’s ability to capture rainfall intermittency as a function of space appear useful in identifying the useful spatial scales of application for the hydrologist. It is shown that metrics evolving from the proposed conceptual framework can identify seasonal and regional differences in reliability of four global satellite rainfall products over the United States more clearly than conventional metrics. The proposed framework for building such error metrics can lay a foundation for better interaction between the data-producing community and hydrologists in shaping the new generation of satellite-based, high-resolution rainfall products, including those being developed for the planned Global Precipitation Measurement (GPM) mission.

scholarly journals How Has Malaysia Benefited from the High-Resolution Satellite Rainfall? Trends, Gaps and Further Research Opportunities

2019 ◽  
Vol 12 ◽  
pp. 1-11
Author(s):  
Mohd. Rizaludin Mahmud
Keyword(s):  
High Resolution ◽  
Environmental Sustainability ◽  
Rainfall Data ◽  
Tropical Country ◽  
Scientific Review ◽  
Scientific Databases ◽  
Rainfall Trends ◽  
Present Information ◽  
The Status ◽  
Satellite Rainfall

This paper presents a scientific review on how Malaysia has benefited from the high-resolution satellite rainfall since its first launch in 1998. As a tropical country in which the environment is highly characterised by rainfall dynamics, public domain access of high-resolution satellite rainfall data could be very useful to support the hydrologic and related environmental studies. The scope of this paper includes achievements, the trend of studies, as well as gaps and future opportunities for scientific research. Examining this element is crucial in determining the present information on the status quo of the applications of space-based technology to Malaysian hydrologic research. Furthermore, this information is critical to charter the future action for the policymakers and revision of respective disciplines, including climate change, environmental sustainability, disaster resilience, food security, and education. Based on the search throughout the largest scientific databases of Web of Science and Scopus, five major trends have been identified. Those trends were ranked based on the number of research, 1) Satellite rainfall data performance and quality evaluation (40%), 2) Satellite rainfall data as input to environmental modelling (27%), 3) Rain fade & telecommunication (16%), 4) Satellite rainfall data quality improvement (7%), and 5) Rainfall studies. These trends were identified about 11 years after the satellite rainfall project started in 1998. The major achievement till now is validating the accuracy of the satellite rainfall and also downscaling it for local application.

Towards the effective use of Artificial Neural Networks for accessing rainfall thresholds for rainfall-induced landslides, a study based on in-situ and satellite merged rainfall data

2021 ◽  
Author(s):  
Luísa Vieira Lucchese ◽  
Guilherme Garcia de Oliveira ◽  
Olavo Correa Pedrollo
Keyword(s):  
Neural Network ◽  
Rainfall Event ◽  
Rainfall Data ◽  
Extreme Rainfall Event ◽  
Ann Model ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Satellite Rainfall ◽  
Artificial Neural

<p>Rainfall-induced landslides have caused destruction and deaths in South America. Accessing its triggers can help researchers and policymakers to understand the nature of the events and to develop more effective warning systems. In this research, triggering rainfall for rainfall-induced landslides is evaluated. The soil moisture effect is indirectly represented by the antecedent rainfall, which is an input of the ANN model. The area of the Rolante river basin, in Rio Grande do Sul state, Brazil, is chosen for our analysis. On January 5<sup>th</sup>, 2017, an extreme rainfall event caused a series of landslides and debris flows in this basin. The landslide scars were mapped using satellite imagery. To calculate the rainfall that triggered the landslides, it was necessary to compute the antecedent rainfall that occurred within the given area. The use of satellite rainfall data is a useful tool, even more so if no gauges are available for the location and time of the rainfall event, which is the case. Remote sensing products that merge the data from in situ stations with satellite rainfall data are increasingly popular. For this research, we employ the data from MERGE (Rozante et al., 2010), that is one of these products, and is focused specifically on Brazilian gauges and territory. For each 12.5x12.5m raster pixel, the rainfall is interpolated to the points and the rainfall volume from the last 24h before the event is accumulated. This is added as training data in our Artificial Neural Network (ANN), along with 11 terrain attributes based on ALOS PALSAR (ASF DAAC, 2015) elevation data and generated by using SAGA GIS. These attributes were presented and analyzed in Lucchese et al. (2020). Sampling follows the procedure suggested in Lucchese et al. (2021, in press). The ANN model is a feedforward neural network with one hidden layer consisting of 20 neurons. The ANN is trained by backpropagation method and cross-validation is used to ensure the correct adjustment of the weights. Metrics are calculated on a separate sample, called verification sample, to avoid bias. After training, and provided with relevant information, the ANN model can estimate the 24h-rainfall thresholds in the region, based on the 2017 event only. The result is a discretized map of rainfall thresholds defined by the execution of the trained ANN. Each pixel of the resulting map should represent the volume of rainfall in 24h necessary to trigger a landslide in that point. As expected, lower thresholds (30 - 60 mm) are located in scarped slopes and the regions where the landslides occurred. However, lowlands and the plateau, which are areas known not to be prone to landslides, show higher rainfall thresholds, although not as high as expected (75 - 95 mm). Mean absolute error for this model is 16.18 mm. The inclusion of more variables and events to the ANN training should favor achieving more reliable outcomes, although, our results are able to show that this methodology has potential to be used for landslide monitoring and prediction.</p>

scholarly journals Sensitivity of point scale surface runoff predictions to rainfall resolution

2007 ◽  
Vol 11 (2) ◽  
pp. 965-982 ◽  
Author(s):  
A. J. Hearman ◽  
C. Hinz
Keyword(s):  
High Resolution ◽  
Western Australia ◽  
Surface Runoff ◽  
Rainfall Data ◽  
Soil Water Storage ◽  
Point Scale ◽  
Deep Drainage ◽  
Model Predictions ◽  
Infiltration Excess ◽  
Saturation Excess

Abstract. This paper investigates the effects of using non-linear, high resolution rainfall, compared to time averaged rainfall on the triggering of hydrologic thresholds and therefore model predictions of infiltration excess and saturation excess runoff at the point scale. The bounded random cascade model, parameterized to three locations in Western Australia, was used to scale rainfall intensities at various time resolutions ranging from 1.875 min to 2 h. A one dimensional, conceptual rainfall partitioning model was used that instantaneously partitioned water into infiltration excess, infiltration, storage, deep drainage, saturation excess and surface runoff, where the fluxes into and out of the soil store were controlled by thresholds. The results of the numerical modelling were scaled by relating soil infiltration properties to soil draining properties, and in turn, relating these to average storm intensities. For all soil types, we related maximum infiltration capacities to average storm intensities (k*) and were able to show where model predictions of infiltration excess were most sensitive to rainfall resolution (ln k*=0.4) and where using time averaged rainfall data can lead to an under prediction of infiltration excess and an over prediction of the amount of water entering the soil (ln k*>2) for all three rainfall locations tested. For soils susceptible to both infiltration excess and saturation excess, total runoff sensitivity was scaled by relating drainage coefficients to average storm intensities (g*) and parameter ranges where predicted runoff was dominated by infiltration excess or saturation excess depending on the resolution of rainfall data were determined (ln g*<2). Infiltration excess predicted from high resolution rainfall was short and intense, whereas saturation excess produced from low resolution rainfall was more constant and less intense. This has important implications for the accuracy of current hydrological models that use time averaged rainfall under these soil and rainfall conditions and predictions of larger scale phenomena such as hillslope runoff and runon. It offers insight into how rainfall resolution can affect predicted amounts of water entering the soil and thus soil water storage and drainage, possibly changing our understanding of the ecological functioning of the system or predictions of agri-chemical leaching. The application of this sensitivity analysis to different rainfall regions in Western Australia showed that locations in the tropics with higher intensity rainfalls are more likely to have differences in infiltration excess predictions with different rainfall resolutions and that a general understanding of the prevailing rainfall conditions and the soil's infiltration capacity can help in deciding whether high rainfall resolutions (below 1 h) are required for accurate surface runoff predictions.

scholarly journals Validation of Daily Rainfall Based on Global Satellite Mapping of Precipitation (GSMAP) Data of Bali and Nusa Tenggara Region

2018 ◽  
Vol 19 (1) ◽  
pp. 12
Author(s):  
Sanjaya Natadiredja ◽  
I Ketut Sukarasa ◽  
Gusti Ngurah Sutapa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Satellite Data ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Observation Data ◽  
Similar Data ◽  
Strong Positive Correlation ◽  
Cause Analysis ◽  
Satellite Rainfall

Limitations of observation data cause analysis and prediction of precipitation is difficult. One way to overcome such limitations is the use of satellite data such as GSMaP, but satellite data needs to be validated before use. This study aims to validate GSMaP rainfall data on observation data in Bali and Nusa Tenggara. Through monthly time series analysis, GSMaP rainfall data tend to have smaller value than observation data, but it has similar data pattern in each region with rain pattern that occurs in November to March (NDJFM). While validation between GSMaP satellite rainfall data and observation using Pearson and RMSE correlation and MBE at each location showed strong positive correlation value (> 0.5), correlation value obtained from each location from 0.82 to 0.93 with RMSE value from 2.08 to 5.51 and MBE values ??from 0.23 to 0.89, this indicates that GSMaP satellite data is valid and can be used to fill in empty data especially in 5 observation areas ie Denpasar, Ampenan, Sumbawa Besar, Bima and Kupang.

scholarly journals Evaluation of CMORPH Precipitation Products at Fine Space–Time Scales

2009 ◽  
Vol 10 (1) ◽  
pp. 300-307 ◽  
Author(s):  
Dawit A. Zeweldi ◽  
Mekonnen Gebremichael
Keyword(s):  
High Resolution ◽  
Time Scales ◽  
Spatial Patterns ◽  
Time Scale ◽  
Statistical Techniques ◽  
Spatial Fields ◽  
Satellite Rainfall ◽  
Performance Statistics ◽  
Averaging Time ◽  
Temporally Correlated

Abstract In this study, a comparison of the spatial patterns of high-resolution precipitation products obtained from the Climate Prediction Center’s morphing technique (CMORPH), which is a satellite-only product, and gauge-adjusted Next Generation Weather Radar (NEXRAD) rainfall observations is performed using a variety of statistical techniques for the Little Washita watershed region in Oklahoma for a 3-yr period. Results show that 1) the performance statistics of CMORPH show tremendous variability from one hour to the next, suggesting that the performance statistics are dynamic in time, and therefore each satellite rainfall product should be accompanied by an error product to make it more meaningful; 2) CMORPH is positively biased in summer and negatively biased in winter, consistent with the findings of previous studies; 3) CMORPH spatial fields tend to be smoother than NEXRAD output; 4) the errors are temporally correlated, in particular within the range from 1 to 6 accumulation hours, implying that averaging CMORPH products over these time scales does not reduce the errors significantly; and 5) the errors become less correlated in time as the averaging time scale increases to the range from 6 to 24 h.

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