scholarly journals Assessment of Level-3 Gridded Global Precipitation Mission (GPM) Products Over Oceans

2019 ◽  
Vol 11 (3) ◽  
pp. 255 ◽  
Author(s):  
Sana Khan ◽  
Viviana Maggioni
Keyword(s):  
Performance Metrics ◽  
Probability Of Detection ◽  
The Other ◽  
Coefficient Of Determination ◽  
Dual Frequency ◽  
Level 3 ◽  
The Times ◽  
Global Precipitation Mission ◽  
Global Precipitation ◽  
Better Than

The performance of Level-3 gridded Global Precipitation Mission (GPM)-based precipitation products (IMERG, Integrated Multi-satellite Retrievals for GPM) is assessed against two references over oceans: the OceanRAIN dataset, derived from oceanic shipboard disdrometers, and a satellite-based radar product (the Level-3 Dual-frequency Precipitation Radar, 3DPRD). Daily IMERG products (early, late, final) and microwave-only (MW) and Infrared-only (IR) precipitation components are evaluated at four different spatial resolutions (0.5°, 1°, 2°, and 3°) during a 3-year study period (March 2014–February 2017). Their performance is assessed based on both categorical and continuous performance metrics, including correlation coefficient, probability of detection, success ratio, bias, and root mean square error (RMSE). A triple collocation analysis (TCA) is also presented to further investigate the performance of these satellite-based products. Overall, the IMERG products show an underestimation with respect to OceanRAIN. Rain events in OceanRAIN are correctly detected by all IMERG products ~80% of the times. IR estimates show relatively large errors and low correlations with OceanRAIN compared to the other products. On the other hand, the MW component performs better than other products in terms of both categorical and continuous statistics. TCA reveals that 3DPRD performs consistently better than OceanRAIN in terms of RMSE and coefficient of determination at all spatial resolutions. This work is part of a larger effort to validate GPM products over nontraditional regions such as oceans.

A Dynamically Optimized Fluctuation Smoothing Rule for Scheduling Jobs in a Wafer Fabrication Factory

2011 ◽  
Vol 7 (4) ◽  
pp. 47-64 ◽  
Author(s):  
Toly Chen
Keyword(s):  
Standard Deviation ◽  
Test Data ◽  
Cycle Time ◽  
Performance Metrics ◽  
The Other ◽  
Wafer Fabrication ◽  
Fluctuation Smoothing ◽  
Time Standard ◽  
Nonlinear Fluctuation ◽  
Better Than

This paper presents a dynamically optimized fluctuation smoothing rule to improve the performance of scheduling jobs in a wafer fabrication factory. The rule has been modified from the four-factor bi-criteria nonlinear fluctuation smoothing (4f-biNFS) rule, by dynamically adjusting factors. Some properties of the dynamically optimized fluctuation smoothing rule were also discussed theoretically. In addition, production simulation was also applied to generate some test data for evaluating the effectiveness of the proposed methodology. According to the experimental results, the proposed methodology was better than some existing approaches to reduce the average cycle time and cycle time standard deviation. The results also showed that it was possible to improve the performance of one without sacrificing the other performance metrics.

scholarly journals Content Based Medical Image Retrieval for Accurate Disease Diagnosis

2021 ◽  
Vol 15 (1) ◽  
pp. 235-248
Author(s):  
Mayank R. Kapadia ◽  
Chirag N. Paunwala
Keyword(s):  
Image Retrieval ◽  
Performance Metrics ◽  
The Other ◽  
X Ray ◽  
Proposed Model ◽  
Chest Disease ◽  
Minimum Number ◽  
Medical Dataset ◽  
Precision Rate ◽  
Better Than

Introduction: Content Based Image Retrieval (CBIR) system is an innovative technology to retrieve images from various media types. One of the CBIR applications is Content Based Medical Image Retrieval (CBMIR). The image retrieval system retrieves the most similar images from the historical cases, and such systems can only support the physician's decision to diagnose a disease. To extract the useful features from the query image for linking similar types of images is the major challenge in the CBIR domain. The Convolution Neural Network (CNN) can overcome the drawbacks of traditional algorithms, dependent on the low-level feature extraction technique. Objective: The objective of the study is to develop a CNN model with a minimum number of convolution layers and to get the maximum possible accuracy for the CBMIR system. The minimum number of convolution layers reduces the number of mathematical operations and the time for the model's training. It also reduces the number of training parameters, like weights and bias. Thus, it reduces the memory requirement for the model storage. This work mainly focused on developing an optimized CNN model for the CBMIR system. Such systems can only support the physicians' decision to diagnose a disease from the images and retrieve the relevant cases to help the doctor decide the precise treatment. Methods: The deep learning-based model is proposed in this paper. The experiment is done with several convolution layers and various optimizers to get the maximum accuracy with a minimum number of convolution layers. Thus, the ten-layer CNN model is developed from scratch and used to derive the training and testing images' features and classify the test image. Once the image class is identified, the most relevant images are determined based on the Euclidean distance between the query features and database features of the identified class. Based on this distance, the most relevant images are displayed from the respective class of images. The general dataset CIFAR10, which has 60,000 images of 10 different classes, and the medical dataset IRMA, which has 2508 images of 9 various classes, have been used to analyze the proposed method. The proposed model is also applied for the medical x-ray image dataset of chest disease and compared with the other pre-trained models. Results: The accuracy and the average precision rate are the measurement parameters utilized to compare the proposed model with different machine learning techniques. The accuracy of the proposed model for the CIFAR10 dataset is 93.9%, which is better than the state-of-the-art methods. After the success for the general dataset, the model is also tested for the medical dataset. For the x-ray images of the IRMA dataset, it is 86.53%, which is better than the different pre-trained model results. The model is also tested for the other x-ray dataset, which is utilized to identify chest-related disease. The average precision rate for such a dataset is 97.25%. Also, the proposed model fulfills the major challenge of the semantic gap. The semantic gap of the proposed model for the chest disease dataset is 2.75%, and for the IRMA dataset, it is 13.47%. Also, only ten convolution layers are utilized in the proposed model, which is very small in number compared to the other pre-trained models. Conclusion: The proposed technique shows remarkable improvement in performance metrics over CNN-based state-of-the-art methods. It also offers a significant improvement in performance metrics over different pre-trained models for the two different medical x-ray image datasets.

scholarly journals The Response of Performance Metrics for Binary Forecasts to Hedging that Approaches Random Change

2010 ◽  
Vol 25 (4) ◽  
pp. 1307-1314 ◽  
Author(s):  
Keith F. Brill ◽  
Matthew Pyle
Keyword(s):  
Performance Metrics ◽  
Performance Measure ◽  
Probability Of Detection ◽  
Performance Ratio ◽  
Equitable Threat Score ◽  
Threat Score ◽  
Detection Failure ◽  
Random Change ◽  
Better Than ◽  
A Performance

Abstract Critical performance ratio (CPR) expressions for the eight conditional probabilities associated with the 2 × 2 contingency table of outcomes for binary (dichotomous “yes” or “no”) forecasts are derived. Two are shown to be useful in evaluating the effects of hedging as it approaches random change. The CPR quantifies how the probability of detection (POD) must change as frequency bias changes, so that a performance measure (or conditional probability) indicates an improved forecast for a given value of frequency bias. If yes forecasts were to be increased randomly, the probability of additional correct forecasts (hits) is given by the detection failure ratio (DFR). If the DFR for a performance measure is greater than the CPR, the forecast is likely to be improved by the random increase in yes forecasts. Thus, the DFR provides a benchmark for the CPR in the case of frequency bias inflation. If yes forecasts are decreased randomly, the probability of removing a hit is given by the frequency of hits (FOH). If the FOH for a performance measure is less than the CPR, the forecast is likely to be improved by the random decrease in yes forecasts. Therefore, the FOH serves as a benchmark for the CPR if the frequency bias is decreased. The closer the FOH (DFR) is to being less (greater) than or equal to the CPR, the more likely it may be to enhance the performance measure by decreasing (increasing) the frequency bias. It is shown that randomly increasing yes forecasts for a forecast that is itself better than a randomly generated forecast can improve the threat score but is not likely to improve the equitable threat score. The equitable threat score is recommended instead of the threat score whenever possible.

scholarly journals Retrieving Rain Drop Size Distribution Moments from GPM Dual-Frequency Precipitation Radar

2021 ◽  
Vol 13 (22) ◽  
pp. 4690
Author(s):  
Merhala Thurai ◽  
Viswanathan Bringi ◽  
David Wolff ◽  
David A. Marks ◽  
Patrick N. Gatlin ◽  
...  
Keyword(s):  
Size Distribution ◽  
Drop Size ◽  
Drop Size Distribution ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Distribution Moments ◽  
The Us ◽  
Rain Drop ◽  
Global Precipitation Mission ◽  
Global Precipitation

A novel method for retrieving the moments of rain drop size distribution (DSD) from the dual-frequency precipitation radar (DPR) onboard the global precipitation mission satellite (GPM) is presented. The method involves the estimation of two chosen reference moments from two specific DPR products, namely the attenuation-corrected Ku-band radar reflectivity and (if made available) the specific attenuation at Ka-band. The reference moments are then combined with a function representing the underlying shape of the DSD based on the generalized gamma model. Simulations are performed to quantify the algorithm errors. The performance of methodology is assessed with two GPM-DPR overpass cases over disdrometer sites, one in Huntsville, Alabama and one in Delmarva peninsula, Virginia, both in the US. Results are promising and indicate that it is feasible to estimate DSD moments directly from DPR-based quantities.

scholarly journals Ground Validation of Surface Snowfall Algorithm in GPM Dual-Frequency Precipitation Radar

2019 ◽  
Vol 36 (4) ◽  
pp. 607-619 ◽  
Author(s):  
Minda Le ◽  
V. Chandrasekar
Keyword(s):  
Ground Truth ◽  
Identification Algorithm ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Ground Validation ◽  
Potential Improvement ◽  
Global Precipitation Mission ◽  
Global Precipitation ◽  
Level 2 ◽  
Precipitation Events

AbstractExtensive evaluations have been performed on the dual-frequency classification module in the Global Precipitation Mission (GPM) Dual-Frequency Precipitation Radar (DPR) level-2 algorithm. Both rain type classification and melting-layer detection continue to show promising results in the validations. Surface snowfall identification is a feature newly added in the classification module to the recently released version to provide a surface snowfall flag for each qualified vertical profile. This algorithm is developed upon vertical features of Ku- and Ka-band reflectivity and dual-frequency ratio from DPR. In this paper, we validate this surface snowfall identification algorithm with ground radars including NEXRAD, NASA Polarimetric Radar (NPOL), and CSU–CHILL radar during concurrent precipitation events and GPM validation campaign Olympic Mountain Experiment (OLYMPEX). Other ground truth such as Precipitation Imaging Package (PIP) and ground report is also included in the validation. Based on 16 validation cases in the years 2014–18, the average match ratio between surface snowfall flag from space radar and ground radar is around 87.8%. Promising agreements are achieved with different validation sources. Algorithm limitation and potential improvement are discussed.

scholarly journals GPM-Based Multitemporal Weighted Precipitation Analysis Using GPM_IMERGDF Product and ASTER DEM in EDBF Algorithm

2020 ◽  
Vol 12 (19) ◽  
pp. 3162 ◽  
Author(s):  
Sana Ullah ◽  
Zhengkang Zuo ◽  
Feizhou Zhang ◽  
Jianghua Zheng ◽  
Shifeng Huang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Thermal Emission ◽  
Empirical Distribution ◽  
Mainland China ◽  
Annual Precipitation ◽  
Coefficient Of Determination ◽  
Precipitation Variable ◽  
Precipitation Analysis ◽  
Global Precipitation Mission ◽  
Global Precipitation

To obtain the high-resolution multitemporal precipitation using spatial downscaling technique on a precipitation dataset may provide a better representation of the spatial variability of precipitation to be used for different purposes. In this research, a new downscaling methodology such as the global precipitation mission (GPM)-based multitemporal weighted precipitation analysis (GMWPA) at 0.05° resolution is developed and applied in the humid region of Mainland China by employing the GPM dataset at 0.1° and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m DEM-based geospatial predictors, i.e., elevation, longitude, and latitude in empirical distribution-based framework (EDBF) algorithm. The proposed methodology is a two-stepped process in which a scale-dependent regression analysis between each individual precipitation variable and the EDBF-based weighted precipitation with geospatial predictor(s), and to downscale the predicted multitemporal weighted precipitation at a refined scale is developed for the downscaling of GMWPA. While comparing results, it shows that the weighted precipitation outperformed all precipitation variables in terms of the coefficient of determination (R2) value, whereas they outperformed the annual precipitation variables and underperformed as compared to the seasonal and the monthly variables in terms of the calculated root mean square error (RMSE) value. Based on the achieved results, the weighted precipitation at the low-resolution (e.g., at 0.75° resolution) along-with the original resolution (e.g., at 0.1° resolution) is employed in the downscaling process to predict the average multitemporal precipitation, the annual total precipitation for the year 2001 and 2004, and the average annual precipitation (2001–2015) at 0.05° resolution, respectively. The downscaling approach resulting through proposed methodology captured the spatial patterns with greater accuracy at higher spatial resolution. This work showed that it is feasible to increase the spatial resolution of a precipitation variable(s) with greater accuracy on an annual basis or as an average from the multitemporal precipitation dataset using a geospatial predictor as the proxy of precipitation through the weighted precipitation in EDBF environment.

Validation of GPM IMERG V05 and V06 Precipitation Products over Iran

2020 ◽  
Vol 21 (5) ◽  
pp. 1011-1037 ◽  
Author(s):  
Seyed-Mohammad Hosseini-Moghari ◽  
Qiuhong Tang
Keyword(s):  
Probability Of Detection ◽  
Validation Metrics ◽  
Precipitation Measurement ◽  
Retrieval Algorithms ◽  
Satellite Sensors ◽  
Daily Scale ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Early Late ◽  
Better Than

AbstractThis study attempts to assess the validity of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) products across Iran. Six IMERG precipitation products (IPPs) including early, late, and final runs for versions 05 and 06 were compared with precipitation data from 76 synoptic stations on a daily scale for the period from June 2014 to June 2018. According to the results, V05 performed better than V06, particularly in early and late runs. The IPPs overestimate precipitation ranging from 5% to 32%; however, IPPs tended to underestimate (overestimate) the amount of precipitation for wet (dry) areas and precipitation classes higher than 5 mm day−1 (less than 5 mm day−1). The probability of detection (POD) in IPPs was almost similar (with a median equal to 0.60), whereas other categorical validation metrics like false alarm ratio (FAR) improved in the final run. Our assessments revealed that the dependency of IPPs to the elevation was low, while the error characteristics of IPPs were strongly dependent on the climate and precipitation intensity. For instance, the systematic error varied between less than 12% in dry regions to more than 60% in wet regions. Also, according to modified Kling–Gupta efficiency (KGE) and relative bias (RBias), the performance of IPPs in winter with the highest KGE (ranging from 0.47 to 0.63) and lowest RBias (ranging from 0% to 16%) was better than other seasons. Further improvement is recommended in the satellite sensors and the precipitation retrieval algorithms to achieve a reliable precipitation source.

scholarly journals Intercomparison of Attenuation Correction Methods for the GPM Dual-Frequency Precipitation Radar

2015 ◽  
Vol 32 (5) ◽  
pp. 915-926 ◽  
Author(s):  
Shinta Seto ◽  
Toshio Iguchi
Keyword(s):  
Attenuation Correction ◽  
Heavy Precipitation ◽  
Frequency Measurement ◽  
Correction Method ◽  
New Method ◽  
Single Frequency ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Global Precipitation ◽  
Better Than

AbstractA new attenuation correction method has been developed for the dual-frequency precipitation radar (DPR) on the core satellite of the Global Precipitation Measurement (GPM) mission. The new method is based on Hitschfeld and Bordan’s attenuation correction method (HB method), but the relationship between the specific attenuation k and the effective radar reflectivity factor Ze (k–Ze relationship) is modified by using the dual-frequency ratio (DFR) of Ze and the surface reference technique (SRT). Therefore, the new method is called the HB-DFR-SRT method (H-D-S method). The previous attenuation correction method, called the HB-DFR method (H-D method), results in an underestimation of precipitation rates for heavy precipitation, but the H-D-S method mitigates the negative bias by means of the SRT. When only a single-frequency measurement is available, the H-D-S method is identical to the HB-SRT method (H-S method).The attenuation correction methods were tested with a simple synthetic DPR dataset. As long as the SRT gives perfect estimates of path-integrated attenuation and the adjustment factor of the k–Ze relationship (denoted by ε) is vertically constant, the H-S method is much better than the dual-frequency methods. Tests with SRT error and vertical variation in ε showed that the H-D method was better than the H-S method for weak precipitation, whereas the H-S method was better than the H-D method for heavy precipitation. The H-D-S method did not produce the best results for both weak and heavy precipitation, but the results are stable. Quantitative evaluation should be done with real DPR measurement datasets.

scholarly journals Comprehensive Comparisons of State-of-the-Art Gridded Precipitation Estimates for Hydrological Applications over Southern China

2020 ◽  
Vol 12 (23) ◽  
pp. 3997
Author(s):  
Zhen Gao ◽  
Bensheng Huang ◽  
Ziqiang Ma ◽  
Xiaohong Chen ◽  
Jing Qiu ◽  
...  
Keyword(s):  
Southern China ◽  
Temporal Distribution ◽  
Rain Gauge ◽  
Vital Role ◽  
Probability Of Detection ◽  
Precipitation Estimates ◽  
Accumulated Rainfall ◽  
Daily Scale ◽  
Global Precipitation ◽  
Better Than

Satellite-based precipitation estimates with high quality and spatial-temporal resolutions play a vital role in forcing global or regional meteorological, hydrological, and agricultural models, which are especially useful over large poorly gauged regions. In this study, we apply various statistical indicators to comprehensively analyze the quality and compare the performance of five newly released satellite and reanalysis precipitation products against China Merged Precipitation Analysis (CMPA) rain gauge data, respectively, with 0.1° × 0.1° spatial resolution and two temporal scales (daily and hourly) over southern China from June to August in 2019. These include Precipitation Estimates from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS), European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5-Land), Fengyun-4 (FY-4A), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). Results indicate that: (1) all five products overestimate the accumulated rainfall in the summer, with FY-4A being the most severe; additionally, FY-4A cannot capture the spatial and temporal distribution characteristics of precipitation over southern China. (2) IMERG and GSMaP perform better than the other three datasets at both daily and hourly scales; IMERG correlates slightly better than GSMaP against CMPA data, while it performs worse than GSMaP in terms of probability of detection (POD). (3) ERA5-Land performs better than PERSIANN-CCS and FY-4A at daily scale but shows the worst correlation coefficient (CC), false alarm ratio (FAR), and equitable threat score (ETS) of all precipitation products at hourly scale. (4) The rankings of overall performance on precipitation estimations for this region are IMERG, GSMaP, ERA5-Land, PERSIANN-CCS, and FY-4A at daily scale; and IMERG, GSMaP, PERSIANN-CCS, FY-4A, and ERA5-Land at hourly scale. These findings will provide valuable feedback for improving the current satellite-based precipitation retrieval algorithms and also provide preliminary references for flood forecasting and natural disaster early warning.

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