scholarly journals Bias correction of satellite rainfall estimates using a radar-gauge product – a case study in Oklahoma (USA)

2011 ◽  
Vol 15 (8) ◽  
pp. 2631-2647 ◽  
Author(s):  
K. Tesfagiorgis ◽  
S. E. Mahani ◽  
N. Y. Krakauer ◽  
R. Khanbilvardi
Keyword(s):  
Bias Correction ◽  
Flash Flood ◽  
Stage Iv ◽  
Bias Field ◽  
Daily Basis ◽  
Performance Criteria ◽  
Absolute Difference ◽  
Random Errors ◽  
Flood Prediction ◽  
Uncertainty Sources

Abstract. Hourly Satellite Precipitation Estimates (SPEs) may be the only available source of information for operational hydrologic and flash flood prediction due to spatial limitations of radar and gauge products. SPEs are prone to larger systematic errors and more uncertainty sources in comparison with ground based radar and gauge precipitation products. The present work develops an approach to seamlessly blend satellite, radar and gauge products to fill gaps in ground-based data. To mix different rainfall products, the bias of any of the products relative to each other should be removed. The study presents and tests a proposed ensemble-based method which aims to estimate spatially varying multiplicative biases in hourly SPEs using a radar-gauge rainfall product and compare it with previously used bias correction methods. Bias factors were calculated for a randomly selected sample of rainy pixels in the study area. Spatial fields of estimated bias were generated taking into account spatial variation and random errors in the sampled values. Bias field parameters were determined on a daily basis using the shuffled complex evolution optimization algorithm. To include more error sources, ensembles of bias factors were generated and applied before bias field generation. We demonstrate this method using two satellite-based products, CPC Morphing (CMORPH) and Hydro-Estimator (HE), and a radar-gauge rainfall Stage-IV (ST-IV) dataset for several rain events in 2006 over Oklahoma. The method was compared with 3 simpler methods for bias correction: mean ratio, maximum ratio and spatial interpolation without ensembles. Bias ratio, correlation coefficient, root mean square error and mean absolute difference are used to evaluate the performance of the different methods. Results show that: (a) the methods of maximum ratio and mean ratio performed variably and did not improve the overall correlation with the ST-IV in any of the rainy events; (b) the method of interpolation was consistently able to improve all the performance criteria; (c) the method of ensembles outperformed the other 3 methods.

scholarly journals Bias correction of satellite rainfall estimation using a radar-gauge product

2010 ◽  
Vol 7 (6) ◽  
pp. 8913-8945 ◽  
Author(s):  
K. Tesfagiorgis ◽  
S. E. Mahani ◽  
R. Khanbilvardi
Keyword(s):  
Bias Correction ◽  
Bias Field ◽  
Systematic Errors ◽  
Daily Basis ◽  
Random Errors ◽  
Field Generation ◽  
Shuffled Complex Evolution ◽  
Hydrologic Prediction ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract. Satellite rainfall estimates can be used in operational hydrologic prediction, but are prone to systematic errors. The goal of this study is to seamlessly blend a radar-gauge product with a corrected satellite product that fills gaps in radar coverage. To blend different rainfall products, they should have similar bias features. The paper presents a pixel by pixel method, which aims to correct biases in hourly satellite rainfall products using a radar-gauge rainfall product. Bias factors are calculated for corresponding rainy pixels, and a desired number of them are randomly selected for the analysis. Bias fields are generated using the selected bias factors. The method takes into account spatial variation and random errors in biases. Bias field parameters were determined on a daily basis using the Shuffled Complex Evolution optimization algorithm. To include more sources of errors, ensembles of bias factors were generated and applied before bias field generation. The procedure of the method was demonstrated using a satellite and a radar-gauge rainfall data for several rainy events in 2006 for the Oklahoma region. The method was compared with bias corrections using interpolation without ensembles, the ratio of mean and maximum ratio. Results show the method outperformed the other techniques such as mean ratio, maximum ratio and bias field generation by interpolation.

Flash Flood Prediction on Shoal Creek: or Why Radar Rainfall?

2010 ◽  
Vol 2010 (6) ◽  
pp. 193-200
Author(s):  
Baxter Vieux ◽  
Jean Vieux ◽  
Susan Janek ◽  
Janna Renfro
Keyword(s):  
Flash Flood ◽  
Radar Rainfall ◽  
Flood Prediction

Forecasting excessive rainfall with random forests and a deterministic convection-allowing model

2021 ◽  
Author(s):  
Aaron J. Hill ◽  
Russ S. Schumacher
Keyword(s):  
Great Plains ◽  
Flash Flood ◽  
Weather Prediction ◽  
Stage Iv ◽  
Careful Consideration ◽  
Forecast Skill ◽  
Northern Great Plains ◽  
Training Models ◽  
Early Fall ◽  
Recurrence Intervals

AbstractApproximately seven years of daily initializations from the convection-allowing National Severe Storms Laboratory Weather Research and Forecasting model are used as inputs to train random forest (RF) machine learning models to probabilistically predict instances of excessive rainfall. Unlike other hazards, excessive rainfall does not have an accepted definition, so multiple definitions of excessive rainfall and flash flooding – including flash flood reports and 24-hr average recurrence intervals (ARIs) – are used to explore RF configuration forecast sensitivities. RF forecasts are analogous to operational Weather Prediction Center (WPC) day-1 Excessive Rainfall Outlooks (EROs) and their resolution, reliability, and skill are strongly influenced by rainfall definitions and how inputs are assembled for training. Models trained with 1-y ARI exceedances defined by the Stage-IV (ST4) precipitation analysis perform poorly in the northern Great Plains and southwest U.S., in part due to a high bias in the number of training events in these regions. Increasing the ARI threshold to 2 years or removing ST4 data from training, optimizing forecast skill geographically, and spatially averaging meteorological inputs for training generally results in improved CONUS-wide RF forecast skill. Both EROs and RF forecasts have seasonal skill – poor forecasts in the late fall and winter and skillful forecasts in the summer and early fall. However, the EROs are consistently and significantly better than their RF counterparts, regardless of RF configuration, particularly in the summer months. The results suggest careful consideration should be made when developing ML-based probabilistic precipitation forecasts with convection-allowing model inputs, and further development is necessary to consider these forecast products for operational implementation.

Towards flash-flood prediction in the dry Dead Sea region utilizing radar rainfall information

2009 ◽  
Vol 32 (7) ◽  
pp. 1066-1076 ◽  
Author(s):  
Efrat Morin ◽  
Yael Jacoby ◽  
Shilo Navon ◽  
Erez Bet-Halachmi
Keyword(s):  
Flash Flood ◽  
Dead Sea ◽  
Radar Rainfall ◽  
Flood Prediction

scholarly journals Spending for Advanced Cancer Diagnoses: Comparing Recurrent Versus De Novo Stage IV Disease

2019 ◽  
Vol 15 (7) ◽  
pp. e616-e627 ◽  
Author(s):  
Michael J. Hassett ◽  
Matthew Banegas ◽  
Hajime Uno ◽  
Shicheng Weng ◽  
Angel M. Cronin ◽  
...  
Keyword(s):  
Advanced Cancer ◽  
Cancer Diagnosis ◽  
De Novo ◽  
Stage Iv ◽  
Absolute Difference ◽  
Medicare Data ◽  
Stage Iv Disease ◽  
De Novo Cancer ◽  
Payment Models ◽  
Diagnosis Age

PURPOSE: Spending for patients with advanced cancer is substantial. Past efforts to characterize this spending usually have not included patients with recurrence (who may differ from those with de novo stage IV disease) or described which services drive spending. METHODS: Using SEER-Medicare data from 2008 to 2013, we identified patients with breast, colorectal, and lung cancer with either de novo stage IV or recurrent advanced cancer. Mean spending/patient/month (2012 US dollars) was estimated from 12 months before to 11 months after diagnosis for all services and by the type of service. We describe the absolute difference in mean monthly spending for de novo versus recurrent patients, and we estimate differences after controlling for type of advanced cancer, year of diagnosis, age, sex, comorbidity, and other factors. RESULTS: We identified 54,982 patients with advanced cancer. Before diagnosis, mean monthly spending was higher for recurrent patients (absolute difference: breast, $1,412; colorectal, $3,002; lung, $2,805; all P < .001), whereas after the diagnosis, it was higher for de novo patients (absolute difference: breast, $2,443; colorectal, $4,844; lung, $2,356; all P < .001). Spending differences were driven by inpatient, physician, and hospice services. Across the 2-year period around the advanced cancer diagnosis, adjusted mean monthly spending was higher for de novo versus recurrent patients (spending ratio: breast, 2.39 [95% CI, 2.05 to 2.77]; colorectal, 2.64 [95% CI, 2.31 to 3.01]; lung, 1.46 [95% CI, 1.30 to 1.65]). CONCLUSION: Spending for de novo cancer was greater than spending for recurrent advanced cancer. Understanding the patterns and drivers of spending is necessary to design alternative payment models and to improve value.

scholarly journals Verification Results from the 2017 HMT–WPC Flash Flood and Intense Rainfall Experiment

2019 ◽  
Vol 58 (12) ◽  
pp. 2591-2604 ◽  
Author(s):  
Michael J. Erickson ◽  
Joshua S. Kastman ◽  
Benjamin Albright ◽  
Sarah Perfater ◽  
James A. Nelson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flash Flood ◽  
Weather Prediction ◽  
Stage Iv ◽  
Skill Score ◽  
Machine Learning Techniques ◽  
Intense Rainfall ◽  
Deterministic Models ◽  
Relative Operating Characteristic ◽  
Risk Categories

AbstractThe Flash Flood and Intense Rainfall (FFaIR) Experiment developed within the Hydrometeorology Testbed (HMT) of the Weather Prediction Center (WPC) is a pseudo-operational platform for participants from across the weather enterprise to test emerging flash flood forecasting tools and issue experimental forecast products. This study presents the objective verification portion of the 2017 edition of the experiment, which examines the performance from a variety of guidance tools (deterministic models, ensembles, and machine-learning techniques) and the participants’ forecasts, with occasional reference to the participants’ subjective ratings. The skill of the model guidance used in the FFaIR Experiment is evaluated using performance diagrams verified against the Stage IV analysis. The operational and FFaIR Experiment versions of the excessive rainfall outlook (ERO) are evaluated by assessing the frequency of issuances, probabilistic calibration, Brier skill score (BSS), and area under relative operating characteristic (AuROC). An ERO first-guess field called the Colorado State University Machine-Learning Probabilities method (CSU-MLP) is also evaluated in the FFaIR Experiment. Among convection-allowing models, the Met Office Unified Model generally performed optimally throughout the FFaIR Experiment when using performance diagrams (at the 0.5- and 1-in. thresholds; 1 in. = 25.4 mm), whereas the High-Resolution Rapid Refresh (HRRR), version 3, performed best subjectively. In terms of subjective and objective ensemble scores, the HRRR ensemble scored optimally. The CSU-MLP overpredicted lower risk categories and underpredicted higher risk categories, but it shows future promise as an ERO first-guess field. The EROs issued by the FFaIR Experiment forecasters had improved BSS and AuROC relative to the operational ERO, suggesting that the experimental guidance may have aided forecasters.

scholarly journals Bevacizumab for Newly Diagnosed Ovarian Cancers: Best Candidates Among High-Risk Disease Patients (ICON-7)

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Olivier Colomban ◽  
Michel Tod ◽  
Julien Peron ◽  
Timothy J Perren ◽  
Alexandra Leary ◽  
...  
Keyword(s):  
High Risk ◽  
Elimination Rate ◽  
Stage Iv ◽  
Progression Free Survival ◽  
Phase Iii ◽  
Stage Iii ◽  
Absolute Difference ◽  
Ca 125 ◽  
Ovarian Cancers ◽  
High Risk Disease

Abstract Bevacizumab is approved as a maintenance treatment in first-line setting in advanced-stage III-IV ovarian cancers, because GOG-0218 and ICON-7 phase III trials demonstrated progression-free survival benefits. However, only the subgroup of patients with high-risk diseases (stage IV, and incompletely resected stage III) derived an overall survival (OS) gain in the ICON-7 trial (4.8 months). The modeled CA-125 elimination rate constant K (KELIM) parameter, based on the longitudinal CA-125 kinetics during the first 100 days of chemotherapy, is a potential indicator of the tumor primary chemo-sensitivity. In the ICON-7 trial dataset, the OS of patients within the low- and high-risk disease groups was assessed according to treatment arms and KELIM. Among the patients with high-risk diseases, those with favorable standardized KELIM of at least 1.0 (n = 214, 46.7%) had no survival benefit from bevacizumab, whereas those with unfavorable KELIM less than 1.0 (n = 244, 53.2%) derived the highest OS benefit (absolute difference = 9.1 months, 2-sided log-rank P = .10; Cox hazard ratio = 0.78, 95% confidence interval = 0.58 to 1.04, 2-sided P = .09).

scholarly journals Prediction of Flash Flood using Rainfall by MLP Classifier

2020 ◽  
Vol 9 (1) ◽  
pp. 425-429
Keyword(s):  
Prediction Model ◽  
Nearest Neighbor ◽  
Weather Forecasting ◽  
Flash Flood ◽  
Human Life ◽  
Cost Effective ◽  
Rainfall Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Flood Prediction

Flood are one of the unfavorable natural disasters. A flood can result in a huge loss of human lives and properties. It can also affect agricultural lands and destroy cultivated crops and trees. The flood can occur as a result of surface-runoff formed from melting snow, long-drawn-out rains, and derisory drainage of rainwater or collapse of dams. Today people have destroyed the rivers and lakes and have turned the natural water storage pools to buildings and construction lands. Flash floods can develop quickly within a few hours when compared with a regular flood. Research in prediction of flood has improved to reduce the loss of human life, property damages, and various problems related to the flood. Machine learning methods are widely used in building an efficient prediction model for weather forecasting. This advancement of the prediction system provides cost-effective solutions and better performance. In this paper, a prediction model is constructed using rainfall data to predict the occurrence of floods due to rainfall. The model predicts whether “flood may happen or not” based on the rainfall range for particular locations. Indian district rainfall data is used to build the prediction model. The dataset is trained with various algorithms like Linear Regression, K- Nearest Neighbor, Support Vector Machine, and Multilayer Perceptron. Among this, MLP algorithm performed efficiently with the highest accuracy of 97.40%. The MLP flash flood prediction model can be useful for the climate scientist to predict the flood during a heavy downpour with the highest accuracy.

scholarly journals Multilinear approach to the precipitation–lightning relationship: a case study of summer local electrical storms in the northern part of Spain during 2002–2009 period

2013 ◽  
Vol 1 (6) ◽  
pp. 6467-6498
Author(s):  
I. Herrero ◽  
A. Ezcurra ◽  
J. Areitio ◽  
J. Diaz-Argandoña ◽  
G. Ibarra-Berastegi ◽  
...  
Keyword(s):  
Spatial Structure ◽  
Geographical Distribution ◽  
Flash Flood ◽  
Daily Basis ◽  
Strongly Correlated ◽  
Local Instability ◽  
Maximum Precipitation ◽  
Pca Method ◽  
The Difference

Abstract. Storms developed under local instability conditions are studied in the Spanish Basque region with the aim of establishing precipitation–lightning relationships. Those situations may produce, in some cases, flash flood. Data used correspond to daily rain depth (mm) and the number of CG flashes in the area. Rain and lightning are found to be weakly correlated on a daily basis, a fact that seems related to the existence of opposite gradients in their geographical distribution. Rain anomalies, defined as the difference between observed and estimated rain depth based on CG flashes, are analysed by PCA method. Results show a first EOF explaining 50% of the variability that linearly relates the rain anomalies observed each day and that confirms their spatial structure. Based on those results, a multilinear expression has been developed to estimate the rain accumulated daily in the network based on the CG flashes registered in the area. Moreover, accumulates and maximum values of rain are found to be strongly correlated, therefore making the multilinear expression a useful tool to estimate maximum precipitation during those kind of storms.

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