scholarly journals Towards objective identification and tracking of convective outflow boundaries in next-generation geostationary satellite imagery

2020 ◽  
Vol 13 (3) ◽  
pp. 1593-1608 ◽  
Author(s):  
Jason M. Apke ◽  
Kyle A. Hilburn ◽  
Steven D. Miller ◽  
David A. Peterson
Keyword(s):  
Optical Flow ◽  
Satellite Imagery ◽  
Numerical Models ◽  
Deep Convection ◽  
Lower Troposphere ◽  
Primary Source ◽  
False Alarms ◽  
Identification Algorithm ◽  
Next Generation ◽  
Rapid Variation

Abstract. Sudden wind direction and speed shifts from outflow boundaries (OFBs) associated with deep convection significantly affect weather in the lower troposphere. Specific OFB impacts include rapid variation in wildfire spread rate and direction, the formation of convection, aviation hazards, and degradation of visibility and air quality due to mineral dust aerosol lofting. Despite their recognized importance to operational weather forecasters, OFB characterization (location, timing, intensity, etc.) in numerical models remains challenging. Thus, there remains a need for objective OFB identification algorithms to assist decision support services. With two operational next-generation geostationary satellites now providing coverage over North America, high-temporal- and high-spatial-resolution satellite imagery provides a unique resource for OFB identification. A system is conceptualized here designed around the new capabilities to objectively derive dense mesoscale motion flow fields in the Geostationary Operational Environmental Satellite 16 (GOES-16) imagery via optical flow. OFBs are identified here by isolating linear features in satellite imagery and backtracking them using optical flow to determine if they originated from a deep convection source. This “objective OFB identification” is tested with a case study of an OFB-triggered dust storm over southern Arizona. The results highlight the importance of motion discontinuity preservation, revealing that standard optical flow algorithms used with previous studies underestimate wind speeds when background pixels are included in the computation with cloud targets. The primary source of false alarms is the incorrect identification of line-like features in the initial satellite imagery. Future improvements to this process are described to ultimately provide a fully automated OFB identification algorithm.

scholarly journals Towards Objective Identification and Tracking of Convective Outflow Boundaries in Next-Generation Geostationary Satellite Imagery

2019 ◽  
Author(s):  
Jason M. Apke ◽  
Kyle A. Hilburn ◽  
Steven D. Miller ◽  
David A. Peterson
Keyword(s):  
Optical Flow ◽  
Satellite Imagery ◽  
Numerical Models ◽  
Deep Convection ◽  
Lower Troposphere ◽  
Primary Source ◽  
False Alarms ◽  
Identification Algorithm ◽  
Next Generation ◽  
Rapid Variation

Abstract. Sudden wind direction and speed shifts from outflow boundaries (OFBs) associated with deep convection significantly affect weather in the lower troposphere. Specific OFB impacts include rapid variation in wildfire spread rate and direction, the formation of convection, aviation hazards, and degradation of visibility and air quality due to mineral dust aerosol lofting. Despite their recognized importance to operational weather forecasters, OFB characterization (location, timing, intensity, etc.) in numerical models remains challenging. Thus, there remains a need for objective OFB identification algorithms to assist decision support services. With two operational next-generation geostationary satellites now providing coverage over North America, high-temporal and spatial resolution satellite imagery provides a unique resource for OFB identification. A system is conceptualized here designed around the new capabilities to objectively derive dense mesoscale motion flow fields in the Geostationary Operational Environmental Satellite (GOES)-16 imagery via optical flow. OFBs are identified here by isolating linear features in satellite imagery, and back-tracking them using optical flow to determine if they originated from a deep convection source. This objective OFB identification is tested with a case study of an OFB triggered dust storm over southern Arizona. Results highlight the importance of motion discontinuity preservation, revealing that standard optical flow algorithms used with previous studies underestimate wind speeds when background pixels are included in the computation with cloud targets. The primary source of false alarms is incorrect identification of line-like features in the initial satellite imagery. Future improvements to this process are described to ultimately provide a fully automated OFB identification algorithm.

scholarly journals Deep learning for bias correction of MJO prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
H. Kim ◽  
Y. G. Ham ◽  
Y. S. Joo ◽  
S. W. Son
Keyword(s):  
Deep Learning ◽  
Bias Correction ◽  
Numerical Models ◽  
Weather Prediction ◽  
Primary Source ◽  
Correction Method ◽  
Prediction Skill ◽  
Convective System ◽  
Forecast Errors ◽  
Potential Predictability

AbstractProducing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

A Vehicle Identification Algorithm based on Optical Flow Location

2021 ◽  
Author(s):  
Daojin Nie ◽  
Yumin Gao ◽  
Xingcheng Li ◽  
Yangbo Zhang ◽  
Bangtian Xie
Keyword(s):  
Optical Flow ◽  
Identification Algorithm ◽  
Vehicle Identification

A Study of the Behaviour of a Single-Bladed Waste-Water Pump

2006 ◽  
Vol 220 (2) ◽  
pp. 79-87 ◽  
Author(s):  
J Keays ◽  
C Meskell
Keyword(s):  
Waste Water ◽  
Power Consumption ◽  
Turbulence Model ◽  
Numerical Models ◽  
Sewage Treatment ◽  
Computational Cost ◽  
Primary Source ◽  
Pressure Head ◽  
Primary Objective ◽  
Pump Efficiency

A single-vaned centrifugal pump, typical of the kind employed in waste-water applications (e.g. sewage treatment), has been investigated numerically. The primary objective was to identify a modelling approach that was accurate, but at an acceptable computational cost. A test program has been executed to provide data to validate the numerical models. The global performance of the pump was assessed in terms of the pressure head, the mass flowrate, the power consumption, and the pump efficiency. In addition, time-resolved surface-pressure measurements were made at the volute wall. Five combinations of three modelling approximations (two or 3D; k-ε or Reynolds stress model turbulence model; unsteady or quasi-steady) were investigated and compared with the experimental results. It was found that the choice of turbulence model did not have a significant effect on the predictions. In all cases, the head-discharge curve was well predicted. However, it was found that only the quasi-steady models could capture the trend of the power consumption curve, and hence that of the efficiency. Discrepancies in the magnitude of the power consumption can be accounted for by the lack of losses such as leakage in the numerical models. Qualitative analysis of the numerical results identifies the trailing edge of the impeller as the primary source of power loss, with the flow in the region of the cut water also contributing significantly to the poor overall efficiency of the design.

scholarly journals Assimilation of Lightning Data Using a Nudging Method Involving Low-Level Warming

2014 ◽  
Vol 142 (12) ◽  
pp. 4850-4871 ◽  
Author(s):  
Max R. Marchand ◽  
Henry E. Fuelberg
Keyword(s):  
Numerical Models ◽  
Deep Convection ◽  
Computational Cost ◽  
Wrf Model ◽  
Stage Iv ◽  
Simulated Precipitation ◽  
Synoptic Forcing ◽  
Total Lightning ◽  
Mu Method ◽  
Short Time

Abstract This study presents a new method for assimilating lightning data into numerical models that is suitable at convection-permitting scales. The authors utilized data from the Earth Networks Total Lightning Network at 9-km grid spacing to mimic the resolution of the Geostationary Lightning Mapper (GLM) that will be on the Geostationary Operational Environmental Satellite-R (GOES-R). The assimilation procedure utilizes the numerical Weather Research and Forecasting (WRF) Model. The method (denoted MU) warms the most unstable low levels of the atmosphere at locations where lightning was observed but deep convection was not simulated based on the absence of graupel. Simulation results are compared with those from a control simulation and a simulation employing the lightning assimilation method developed by Fierro et al. (denoted FO) that increases water vapor according to a nudging function that depends on the observed flash rate and simulated graupel mixing ratio. Results are presented for three severe storm days during 2011 and compared with hourly NCEP stage-IV precipitation observations. Compared to control simulations, both the MU and FO assimilation methods produce improved simulated precipitation fields during the assimilation period and a short time afterward based on subjective comparisons and objective statistical scores (~0.1, or 50%, improvement of equitable threat scores). The MU generally performs better at simulating isolated thunderstorms and other weakly forced deep convection, while FO performs better for the case having strong synoptic forcing. Results show that the newly developed MU method is a viable alternative to the FO method, exhibiting utility in producing thunderstorms where observed, and providing improved analyses at low computational cost.

scholarly journals Impacts of Shallow Convection on MJO Simulation: A Moist Static Energy and Moisture Budget Analysis

2013 ◽  
Vol 26 (8) ◽  
pp. 2417-2431 ◽  
Author(s):  
Qiongqiong Cai ◽  
Guang J. Zhang ◽  
Tianjun Zhou
Keyword(s):  
Boundary Layer ◽  
Deep Convection ◽  
Lower Troposphere ◽  
Longwave Radiation ◽  
Reanalysis Data ◽  
Specific Humidity ◽  
Moist Static Energy ◽  
Shallow Convection ◽  
Budget Analysis ◽  
Static Energy

Abstract The role of shallow convection in Madden–Julian oscillation (MJO) simulation is examined in terms of the moist static energy (MSE) and moisture budgets. Two experiments are carried out using the NCAR Community Atmosphere Model, version 3.0 (CAM3.0): a “CTL” run and an “NSC” run that is the same as the CTL except with shallow convection disabled below 700 hPa between 20°S and 20°N. Although the major features in the mean state of outgoing longwave radiation, 850-hPa winds, and vertical structure of specific humidity are reasonably reproduced in both simulations, moisture and clouds are more confined to the planetary boundary layer in the NSC run. While the CTL run gives a better simulation of the MJO life cycle when compared with the reanalysis data, the NSC shows a substantially weaker MJO signal. Both the reanalysis data and simulations show a recharge–discharge mechanism in the MSE evolution that is dominated by the moisture anomalies. However, in the NSC the development of MSE and moisture anomalies is weaker and confined to a shallow layer at the developing phases, which may prevent further development of deep convection. By conducting the budget analysis on both the MSE and moisture, it is found that the major biases in the NSC run are largely attributed to the vertical and horizontal advection. Without shallow convection, the lack of gradual deepening of upward motion during the developing stage of MJO prevents the lower troposphere above the boundary layer from being preconditioned for deep convection.

scholarly journals Land–atmosphere interactions in the tropics

2019 ◽  
Author(s):  
Pierre Gentine ◽  
Adam Massmann ◽  
Benjamin R. Lintner ◽  
Sayed Hamed Alemohammad ◽  
Rong Fu ◽  
...  
Keyword(s):  
Land Surface ◽  
Deep Convection ◽  
Lower Troposphere ◽  
Surface Fluxes ◽  
Surface Radiation ◽  
Spatial And Temporal Scales ◽  
Wide Range ◽  
Shallow Clouds ◽  
The Tropics ◽  
The Impact

Abstract. The continental tropics play a leading role in the terrestrial water and carbon cycles. Land–atmosphere interactions are integral in the regulation of surface energy, water and carbon fluxes across multiple spatial and temporal scales over tropical continents. We review here some of the important characteristics of tropical continental climates and how land–atmosphere interactions regulate them. Along with a wide range of climates, the tropics manifest a diverse array of land–atmosphere interactions. Broadly speaking, in tropical rainforests, light and energy are typically more limiting than precipitation and water supply for photosynthesis and evapotranspiration; whereas in savanna and semi-arid regions water is the critical regulator of surface fluxes and land–atmosphere interactions. We discuss the impact of the land surface, how it affects shallow clouds and how these clouds can feedback to the surface by modulating surface radiation. Some results from recent research suggest that shallow clouds may be especially critical to land–atmosphere interactions as these regulate the energy budget and moisture transport to the lower troposphere, which in turn affects deep convection. On the other hand, the impact of land surface conditions on deep convection appear to occur over larger, non-local, scales and might be critically affected by transitional regions between the climatologically dry and wet tropics.

scholarly journals DENSE 3D OBJECT RECONSTRUCTION USING STRUCTURED-LIGHT SCANNER AND DEEP LEARNING

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 777-783
Author(s):  
V. V. Kniaz ◽  
V. A. Mizginov ◽  
L. V. Grodzitkiy ◽  
N. A. Fomin ◽  
V. A. Knyaz
Keyword(s):  
Optical Flow ◽  
3D Model ◽  
Stereo Matching ◽  
Structured Light ◽  
Primary Source ◽  
Ground Truth ◽  
Complex Surface ◽  
Systems Model ◽  
Structured Light Scanner ◽  
Starting Point

Abstract. Structured light scanners are intensively exploited in various applications such as non-destructive quality control at an assembly line, optical metrology, and cultural heritage documentation. While more than 20 companies develop commercially available structured light scanners, structured light technology accuracy has limitations for fast systems. Model surface discrepancies often present if the texture of the object has severe changes in brightness or reflective properties of its texture. The primary source of such discrepancies is errors in the stereo matching caused by complex surface texture. These errors result in ridge-like structures on the surface of the reconstructed 3D model. This paper is focused on the development of a deep neural network LineMatchGAN for error reduction in 3D models produced by a structured light scanner. We use the pix2pix model as a starting point for our research. The aim of our LineMatchGAN is a refinement of the rough optical flow A and generation of an error-free optical flow B̂. We collected a dataset (which we term ZebraScan) consisting of 500 samples to train our LineMatchGAN model. Each sample includes image sequences (Sl, Sr), ground-truth optical flow B and a ground-truth 3D model. We evaluate our LineMatchGAN on a test split of our ZebraScan dataset that includes 50 samples. The evaluation proves that our LineMatchGAN improves the stereo matching accuracy (optical flow end point error, EPE) from 0.05 pixels to 0.01 pixels.

scholarly journals Liveness Measurements Using Optical Flow for Biometric Person Authentication

2012 ◽  
Vol 19 (2) ◽  
pp. 257-268 ◽  
Author(s):  
Maciej Smiatacz
Keyword(s):  
Optical Flow ◽  
Video Sequences ◽  
Support Vector ◽  
Biometric Identification ◽  
Behavioral Characteristics ◽  
Security Level ◽  
Identification Algorithm ◽  
Face Region ◽  
The Face ◽  
Consumer Devices

Liveness Measurements Using Optical Flow for Biometric Person Authentication Biometric identification systems, i.e. the systems that are able to recognize humans by analyzing their physiological or behavioral characteristics, have gained a lot of interest in recent years. They can be used to raise the security level in certain institutions or can be treated as a convenient replacement for PINs and passwords for regular users. Automatic face recognition is one of the most popular biometric technologies, widely used even by many low-end consumer devices such as netbooks. However, even the most accurate face identification algorithm would be useless if it could be cheated by presenting a photograph of a person instead of the real face. Therefore, the proper liveness measurement is extremely important. In this paper we present a method that differentiates between video sequences showing real persons and their photographs. First we calculate the optical flow of the face region using the Farnebäck algorithm. Then we convert the motion information into images and perform the initial data selection. Finally, we apply the Support Vector Machine to distinguish between real faces and photographs. The experimental results confirm that the proposed approach could be successfully applied in practice.

On the Origin of Rotation Derived from Super Rapid Scan Satellite Imagery at the Cloud-Tops of Severe Deep Convection

2021 ◽  
Author(s):  
Jason M. Apke ◽  
John R. Mecikalski
Keyword(s):  
Quality Control ◽  
Numerical Model ◽  
Flow Field ◽  
Optical Flow ◽  
Case Studies ◽  
Model Simulation ◽  
Deep Convection ◽  
Vertical Wind Shear ◽  
Future Research ◽  
Severe Thunderstorms

AbstractSevere thunderstorms routinely exhibit adjacent maxima and minima in cloud-top vertical vorticity (CTV) downstream of overshooting tops within flow fields retrieved using sequences of fine-temporal resolution (1-min) geostationary operational environmental satellite (GOES)-R series imagery. Little is known about the origin of this so-called “CTV couplet” signature, and whether the signature is the result of flow field derivational artifacts. Thus, the CTV signature’s relevance to research and operations is currently ambiguous. Within this study, we explore the origin of near-cloud-top rotation using an idealized supercell numerical model simulation. Employing an advanced dense optical flow algorithm, image stereoscopy, and numerical model background wind approximations, the artifacts common with cloud-top flow field derivation are removed from two supercell case studies sampled by GOES-R imagers. It is demonstrated that the CTV couplet originates from tilted and converged horizontal vorticity that is baroclinically generated in the upper levels (above 10 km) immediately downstream of the overshooting top. This baroclinic generation would not be possible without a strong and sustained updraft, implying an indirect relationship to rotationally-maintained supercells. Furthermore, it is demonstrated that CTV couplets derived with optical flow algorithms originate from actual rotation within the storm anvils in the case studies explored here, though supercells with opaque above anvil cirrus plumes and strong anvil-level negative vertical wind shear may produce rotation signals as an artifact without quality control. Artifact identification and quality control is discussed further here for future research and operations use.

Sign in / Sign up

Export Citation Format

Share Document