Fog Monitor User Training Mike Churma Jason Taylor May 2008.

Slides:



Advertisements
Similar presentations
5th Intensive Course on Soil Micromorphology Naples th - 14th September Image Analysis Lecture 5 Thresholding/Segmentation.
Advertisements

5th Intensive Course on Soil Micromorphology Naples th - 14th September Image Analysis Lecture 5 Thresholding/Segmentation.
AVIATION WEATHER DEBBIE SCHAUM.
Image Interpretation for Weather Analysis Part I 29 October 2009 Dr. Steve Decker.
© Paradigm Publishing, Inc Excel 2013 Level 2 Unit 1Advanced Formatting, Formulas, and Data Management Chapter 1Advanced Formatting Techniques.
Photoshop Lab colorspace A quick and easy 26 step process for enhancing your photos.
Accessing and Interpreting Web-based Weather Data Clinton Rockey National Weather Service Portland, Oregon.
Visible and Infrared (IR) Weather Satellite Interpretation 1. Visible satellite images are coded from black to white according to the amount of reflected.
VIIRS Cloud Phase Validation. The VIIRS cloud phase algorithm was validated using a 24-hour period on November 10, Validation was performed using.
Intermediate Level Course. Text Format The text styles, bold, italics, underlining, superscript and subscript, can be easily added to selected text. Text.
Satellites and Radar – A primer ATMO 203. Satellites Two main types of satellite orbits – Geostationary Earth Orbiting Satellite is 35,786 km (22,236.
Chapter 1 Ways of Seeing. Ways of Seeing the Atmosphere The behavior of the atmosphere is very complex. Different ways of displaying the characteristics.
Image Interpretation for Weather Analysis Part I 21 October 2010 Dr. Steve Decker.
MOBILE THREAT NET When you need to know whether to go, you need weather to go. Use the single down-arrow on your scroll bar  to advance slides.
Reading Weather Maps. How to Read Surface Weather Maps On surface weather maps you will often see station weather plots. On surface weather maps you will.
Image Interpretation for Weather Analysis Part I 11 November 2008 Dr. Steve Decker.
How do we measure and forecast the weather?. Radar images can detect areas of rainfall and how heavy it is. RAINFALL.
Weather Station Models Meteorologists use a system of assignment and coding to report a variety of weather conditions at a single location on a weather.
Weather Forecasting - II. Review The forecasting of weather by high-speed computers is known as numerical weather prediction. Mathematical models that.
Motivation Many GOES products are not directly used in NWP but may help in diagnosing problems in forecasted fields. One example is the GOES cloud classification.
Quick Start Guide: Filters Advanced Learn about: 1.What filters are and their functionality 2.How to create a filter using Samples, Equipment & Labels.
SC.912.E.7.5 Predict future weather conditions based on present observations and conceptual models and recognize limitations and uncertainties of such.
Modelling and Simulation Types of Texture Mapping.
STEP 1: Determining the exact image width STEP 1: Determining the exact image width Position of X-ray Filter Position of X-ray Filter STEP 5: Crop Extra.
Met Alert Tool (MAT). Introduction What is MAT? –Met Alert Tool (MAT) monitors and alerts the user to weather conditions exceeding thresholds (for example,
Using BUFKIT to Display and Analyze Meteorological Data Prepared by: Sean Nolan¹ and Scott Jackson² ¹Pennsylvania Department of Environmental Protection.
Station Models How Meteorologist can look at a lot of cities’ data at once!
Creating a PowerPoint Presentation
20.5 Forecasting Weather Objectives
Microsoft ® Office Excel 2007 Working with Charts.
CREATING A POWERPOINT PRESENTATION. Planning a presentation Create a presentation Rearrange and delete text and slides Add animations Add transitions.
What is an image? What is an image and which image bands are “best” for visual interpretation?
SAFESEAS Workshop Wednesday, August 04 and Thursday, August National Weather Service Headquarters Silver Spring, Maryland.
Classroom use of web- sourced weather maps Mark Powers Vergennes Union High School
XP. Objectives Sort data and filter data Summarize an Excel table Insert subtotals into a range of data Outline buttons to show or hide details Create.
11/23/2015On Camera Flash1 Basic Photography Using Flash.
Characteristics of Fog/Low Stratus Clouds are composed mainly of liquid water with a low cloud base Cloud layers are highly spatially uniform in both temperature.
More digital reading explaining LUT RT 244 Perry Sprawls, Ph.D. Professor Emeritus Department of Radiology Emory University School of.
Pinnacle Pro Painting Program User Manual Created by: David Kwasny Chris Schulz W. Scott DePouw.
Lighting a 3D Scene Guilford County Sci Vis V part 1.
SAFESEAS Overview July, 2006 Michael E. Churma Jason Taylor NOAA National Weather Service Office of Science and Technology Meteorological Development Laboratory.
Station Models : Symbols used to represent weather conditions in a select location.
Shade & Shadow Figure 1-3, Page 11
AWIPS DATA VISUALIZATION AND MONITORING SYSTEM FOR OPERATIONAL RECORDS ADVISOR AWIPS DATA VISUALIZATION AND MONITORING SYSTEM FOR OPERATIONAL RECORDS September.
31-01: Demand Planning Overview Supply Chain Platform Training Presentation Updated April 2011.
Web Interface Design Basic GUI and Web layouts Fig H.12: Basic Layouts of Web and Graphic User Interfaces.
AIRS/AMSU-A/HSB Data Subsetting and Visualization Services at GES DAAC Sunmi Cho, Jason Li, Donglian Sun, Jianchun Qin and Carrie Phelps, Code 902, NASA.
More digital 244 wk 12 Perry Sprawls, Ph.D. Professor Emeritus Department of Radiology Emory University School of Medicine Atlanta, GA,
Applied Meteorology Unit 1 Observation Denial and Performance of a Local Mesoscale Model Leela R. Watson William H. Bauman.
Cloud Detection: Optical Depth Thresholds and FOV Considerations Steven A. Ackerman, Richard A. Frey, Edwin Eloranta, and Robert Holz Cloud Detection Issues.
Station lists and bias corrections Jemma Davie, Colin Parrett, Richard Renshaw, Peter Jermey © Crown Copyright 2012 Source: Met Office© Crown copyright.
Station Model Eric Angat Teacher. Station Model 1.What is a station model? 2.What is the weather data in the center circle? 3.How do you determine the.
Over the recent years, computer vision has started to play a significant role in the Human Computer Interaction (HCI). With efficient object tracking.
Chapter 20 Section 5 Forecasting Weather Objectives: -Compare and contrast the different technologies used to gather weather data -Analyze weather symbols,
OVERVIEW S9k Home Page Review. Home Page The presentation will dissect each section of the Home Page.
Working with Data Blocks and Frames
COMMANDtrack Configuration Overview
Creating, formatting, and editing graphs using Google Sheets
A Probabilistic Nighttime Fog/Low Stratus Detection Algorithm
FLIPPED CLASS ROOM ACTIVITY CONSTRUCTOR-USING EXISTING CONTENT
Friday, August 26, 2016 Russia’s Climate
GOES visible (or “sun-lit”) image
Alabama Marine Resources Division Aquaculture Siting Tool
How to Start This PowerPoint® Tutorial
Weather Symbols Meteorologists have developed a system of symbols to help them communicate weather conditions quickly and efficiently Half-circle picture.
“Keep looking up…that’s the secret of life.”
Chapter 3 Creating and Editing Sketched Features
How to Start This PowerPoint® Tutorial
Geographic Features in Satellite Imagery
Microsoft Excel 2007 – Level 2
Presentation transcript:

Fog Monitor User Training Mike Churma Jason Taylor May 2008

Fog Monitor User Training Fog Monitor User Training Training Objectives l Introduction and Background l Reviewing Station Observation Functionality l Reviewing Fog Monitor’s presence in Guardian l Explaining Satellite Algorithms l Describing Satellite Algorithm Limitations

The Fog Monitor is an AWIPS application that: l l Monitors and displays station observation data, to alert forecasters of low visibility conditions. l l Uses GOES satellite imagery to identify and highlight potential areas of fog. l l Allows users to configure alert thresholds, algorithm settings, and the application’s monitoring area. Fog Monitor User Training Background

Fog Monitor User Training Fog Monitor User Training Fog Monitor Display Features in D-2D D-2D fog threat level image Interactive Table Station Plot Guardian Icon

Fog Monitor User Training The Fog Monitor Zone/County Table l l Displays worst-case scenarios for counties and marine zones. l l Sorts data by column to reflect worst-case threat level. l l Displays colors (green-yellow-red) based on user-selectable thresholds l l Similar “buttonology” to SCAN and FFMP. l l Clicking on a zone/county name will reveal station information for that area

Fog Monitor User Training Fog Monitor User Training Fog Monitor Display Features in D-2D “Configure Thresholds” button activates an interface with which to modify color threshold s in the table.

Fog Monitor User Training Fog Monitor Station Table l Invoked by clicking on the county/zone ID in the zone table. D-2D map will zoom to the corresponding area. l Displays attributes for individual stations within the area. l Sorts data by column l Additional graphing and tabular features

Fog Monitor User Training Fog Monitor User Training Fog Monitor Display Features in D-2D Visibility – in statute miles; rankable with an emphasis on lower values. Ceiling – for METARS only – in hundreds of feet, ranked with emphasis on lower values; “CLR” and “SKC” will be presented as necessary. Present Weather – not rankable; “NM” (not monitored) at the zone level, text info available at the station level. Wind Direction – rankable according to user-specified azimuths (“NM” if no angle is specified.) Wind Speed, Gust, Peak -- in knots; rankable with an emphasis on higher values. Temperature, Dewpoint, Dewpoint Depression– in degrees Fahrenheit; rankable with an emphasis on higher values. Relative Humidity – In percent; rankable with an emphasis on higher values. Algorithm – Green/Yellow/Red worst-case thresholds in the zone; available at the zone level, but not at station level.

Fog Monitor User Training Fog Monitor Display Features in D-2D 24-Hour trend graphs are available for most parameters. Color levels correspond to those in the table.

Fog Monitor User Training Fog Monitor Display Features in D-2D Wind direction trend is represented by a hodograph

Fog Monitor User Training Fog Monitor Display Features in D-2D Observation History Table. The parameters in the table will change depending on whether the site is land or sea-based, and also by how the user configures it.

Fog Monitor User Training Station Plots in Fog Monitor Fog Monitor provides a conventional observation display, differentiated in color by station type. METARS Fixed Buoys Ships and Moving Buoys Mesonets MAROBS

Fog Monitor User Training Fog Monitor Display Features in D-2D Monitoring Area Configuration Tool (Zone Mode) Found under the Fog Monitor selection in the Apps Launcher. Add and remove counties/zones from the monitoring area. Associate stations with zones/counties. Set a time window to determine the duration of time to be monitored. Set a distance from the nearest zone than a ship can be included. Set the Fog Monitor algorithm status in Guardian.

Fog Monitor User Training Fog Monitor Display Features in D-2D Monitoring Area Configuration Tool (Station Mode) Add and remove stations from the monitoring area. Associate zones/counties to the stations. Manually add a new station to the monitoring area

Fog Monitor User Training Fog Monitor Display Features in D-2D Monitoring Area Configuration tool -- Additional Features Time Window Ship Distance Fog Monitor/Guardian toggle.

Fog Monitor User Training Fog Monitor contribution to Guardian l l Fog Monitor’s fog icon in Guardian is colored according to worst-case threat level in the monitoring area. l l Scrolling the cursor over the icon reveals worst case visibility conditions, present weather obscurations, and satellite algorithm worst-case conditions. l l Users can control the visibility thresholds for this icon with a tool invoked from the Fog Monitor section of the Apps Launcher.

Fog Monitor User Training Fog Monitor Display Features in D-2D black= this location is not within the Monitored Area. gray= insufficient data available (usually due to clouds). green= there is probably no fog at this location. yellow= there may be fog at this location. red= there is probably fog at this location.

Fog Monitor User Training View Blocked by Opaque Higher Clouds Opaque high clouds can block the Fog Monitor’s view.

Fog Monitor User Training Algorithm Scenarios Nighttime Use 3.9 and 10.7  m infra-red images to produce night time fog product. l l Fog areas based on night time fog product threshold Filter out high clouds using 10.7  m IR data. Apply a filter to eliminate small-sized areas as noise. (Optional) Apply a filter to eliminate rough-edged detected area (Optional) Daytime l Use the visible image to identify areas of brightness consistent with fog. l Apply smoothness filter to eliminate noise. (optional) l Apply a filter to eliminate snow/ice (optional) Twilight No Detection

Fog Monitor User Training Fog Monitor Image from Default Thresholds Settings

Fog Monitor User Training Customizing Your Monitor Thresholds The Fog Monitor GUI enables offices to customize the range of: l l IR brightness temperature differences - fog in fog product images. l l Normalized visible brightness values - fog in visible images.

Fog Monitor User Training Decreasing Maximum Cloud Temperature Decreasing the Maximum Cloud Temperature Threshold will increase the potential areas considers as fog.

Fog Monitor Thresholds Increasing Maximum Cloud Temperature Increasing the Maximum Cloud Temperature Threshold will decrease the potential areas considered as fog.

Fog Monitor User Training Daytime Ice/Snow vs. Fog Threshold

Fog Monitor Thresholds Cool Fog vs. Warm Surface Threshold

Fog Monitor User Training Decreasing the Daytime Smoothness Threshold Decreasing the Daytime Smoothness threshold will increase the bright areas of depicted fog.

Fog Monitor User Training Increasing the Daytime Smoothness Threshold Increasing the Daytime Smoothness Threshold will help to filter out bright areas which may not be fog.

Fog Monitor User Training Adjacency Threshold The Adjacency Threshold determines a minimum pixel size for the potential fog areas.

Fog Monitor User Training Increasing Adjacency Threshold Increasing the Adjacency Threshold will increase the minimum size at which an area of fog will be flagged.

Fog Monitor User Training Increasing the Twilight Angle Threshold Increasing the Twilight Angle Threshold during twilight hours will allow Fog Monitor to apply an “unknown” determination to more areas illuminated by low angle sunlight.

Fog Monitor User Training Decreasing the Twilight Angle Threshold Decreasing the Twilight Angle Threshold during twilight hours will allow Fog Monitor to apply an “unknown” determination to less areas illuminated by low angle sunlight.

Fog Monitor User Training Default Twilight Angle Settings and Image

Fog Monitor User Training Increasing Fractional Dimension Threshold Increasing the Fractional Dimension Threshold will allow for shapes with more jagged edges.

Fog Monitor User Training Decreasing Fractional Dimension Threshold Decreasing the Fractional Dimension Threshold will allow Fog Monitor to consider brightness areas with relatively straight edges.

Fog Monitor User Training Algorithm & Display Limitations Low sun conditions/Twilight Brightness Averaging at Feature Boundaries Brightness averaging between clouds and surface Brightness averaging of cloud shadows Sun Glint

Fog Monitor User Training Low Sun Conditions/Twilight Decreasing sunlight at lower angles affects Fog Monitor’s ability to properly normalize the visible satellite images.

Fog Monitor User Training Brightness Averaging at Feature Boundaries Satellite’s View (with pixel boundaries superimposed) fog altostratus snow ground fog ground Actual Situation (with pixel boundaries superimposed) (A)(B) (C) Fog Threat Image (with pixel boundaries superimposed) (A)Boundary between fog and dark ground. Top row of green pixels is too dark to recognize as fog. The fog area looks smaller. (B)Boundary between dark ground and snow. Column of red pixels is false fog. (C)Boundary between fog and bright higher cloud. Left column of green pixels is too bright to recognize as fog. The fog area looks smaller.

Fog Monitor User Training Brightness averaging between clouds and surface Small mid-level or translucent high-level clouds ground fog Actual Situation Averaged brightness matches fog. Averaged brightness too high for fog. Satellite’s View (A) (B) Small bright clouds or high translucent clouds makes the ground below look brighter to the satellite, causing the Fog Monitor to: (A) incorrectly identify the ground below the cloud deck as fog. (B) fail to recognize the fog below the cloud deck.

Fog Monitor User Training Brightness Averaging of Cloud Shadows Satellite’s View Fog Threat Image snow-covered ground fog ground (A)(B) The red area is the ground directly under the cloud. The medium gray area is the ground obscured from the satellite’s view by the cloud. The blue area is the cloud’s shadow. The area outlined in red is the ground directly under the cloud. The medium gray area is the area that is obscured from the satellite’s view by cloud. The area outlined in blue is the cloud’s shadow. lenticular cumulus Satellite’s View Fog Threat Image

Sun glint can cause the Fog Monitor to wrongly identify an area as having fog. Sun Glint Sun glint as it might be interpreted by the Fog Monitor -- Fog Monitor User Training Limitations -- Sun Glint

Fog Monitor User Training Web Site l Users Guides l Technical Briefs l Troubleshooting Tips l SAFESEAS/Fog Monitor listserve information

Fog Monitor User Training Training Summary During this training the user has been: l l Introduced to the Fog Monitor. l l Informed about the Fog Monitor surface observation functionality. l l Informed about the Fog Monitor information available in Guardian. l l Informed about the Fog Monitor’s satellite algorithms. l l Informed about the limitations of the Fog Monitor’s satellite algorithms.

Fog Monitor User Training Michael Churma Jason Taylor