numerical model: mm5
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MUM Architecture and Data Flow. Global Archive. MM5 Data Extractor. Satellite. Numerical Model: MM5. Stoplight Servlet. Stoplight Servlet. Global Verification Stoplights. MM5 Uncertainty Stoplights. Stoplight Table Tag. Stoplight Table Tag. SREF Stoplight. MM5 Data - PowerPoint PPT PresentationTRANSCRIPT
Numerical Model: MM5 Satellite
The MURI* Uncertainty Monitor (MUM) David W. Jones, Applied Physics Laboratory, University of Washington, Seattle, Washington and Susan Joslyn, Department of Psychology, University of Washington, Seattle, Washington
Introduction
UW researchers asked the question: Would Navy forecasters use additional uncertainty information? To answer the question UW researchers conducted two studies:
Study 1 Results: Forecasters used few sources of information, mostly models, and already evaluated model uncertainty on every forecast, but relied on rules of thumb to avoid the computationally intensive procedure of model comparisons.
Study 2 - Post-TAF Questionnaire Forecasters filled out questionnaires after their TAF indicating their sources of information and rating the numerical models’ performance. Researchers wanted to find out if they increased their evaluation techniques or used additional information sources when the models were judged to be less reliable.
Study 2 results: Forecasters’ routine varied little from one forecast to the next. There was no correlation between model ratings and forecasters’ evaluation strategies or their use of other information sources such as satellites or buoys.
Major Findings:
Naval forecasters are concerned about model uncertainty, but tend to avoid computationally intensive procedures such as
Examination and comparison of multiple models Comparisons to multiple sources Head-to-head comparisons between models Adjustments to the forecasting process to the
perceived model uncertainty Evaluation of model performance over previous
few days
Forecasting tasks that are working memory intensive are either offloaded to long term memory or avoided
Why? Human information processing tells us that people have large long-term memory capacity, but limited working memory capacity, which is aggravated under time pressure and the task switching demands common in the Navy.
Prototype Solution: MUM (MURI* Uncertainty Monitor)A software prototype that assembles, processes, and visualizes uncertainty information for weather forecasters.
Frees forecasters from the computational demands on their working memory.
Lets forecasters use models and techniques appropriate to specific location and situation.
Alerts forecasters to problems and encourages thorough evaluation.
Allows quick assessment of model uncertainty.
MUM Architecture – Based on Java Server Page technology that
only requires a browser to read and interact with the system.
Model data is produced at the lowest tier, post-processing at the second, and user interaction at the top tier.
MUM Interface – Information presented in past-present-future on left
control panel, bottom center and right control panels. Users select information needed on the control panels
and a visualized representation appears in the center. Task analysis revealed forecasters spent most of their
time reviewing model initialization. Thus, MUM’s default is the current model initialization field overlaid on top of the most current satellite picture.
MUM with ensemble spread meteogram
The ensemble spread meteogram displays information about the MM5 ensemble performance for a single geographic location and parameter over a four day period. The most recent 00 hour prediction lies at the center, marked by a bright vertical line.
* This research is supported by the DOD Multidisciplinary University Research Initiative (MURI) program administered by the Office of Naval Research under Grant N00014-01-10745. The support of the sponsor is gratefully appreciated. The MUM system is based on Java Server Page (JSP) and servlet technology. It is hosted at the Applied Physics Laboratory (APL-UW) on a Linux system running a Tomcat server. The model data used in the system comes from the UW SREF. This includes the global fields used for the SREF boundary conditions and the individual ensemble members of the SREF. This data is stored and archived on the APL-UW server.
Approach: University of Washington researchers studied the forecasting task and the users who perform it, then built a tool to increase the forecasters’ ability to evaluate uncertainty.
Study 1 - Think aloud Verbal Protocol Analysis of Terminal Aerodrome Forecast (TAF) Forecasters thought aloud through the generation of their forecast to explain what they were doing and how they did it. They functioned under severe time pressure and often had to do other tasks simultaneously while producing their TAF.
Rules of thumbSet routines
Knowledge about model biases & strengths
Mental model of the atmosphere• Goals and steps to reach e.g.Make comparisons Models to satellite Models to models Avoid• Calculate differences• Maintain results of calculations & comparisons• Make decisions Sources of information Evaluation techniques Forecast
Long Term Memory Working Memory
GlobalVerificationStoplights
MM5 Uncertainty Stoplights
RMSEVerification Plots
SREFStoplight
ProbabilisticEnsemble forecasts
Meteograms
StoplightServlet
GlobalArchive
StoplightTable Tag
PlotImageGenServlet
StoplightServlet
MM5 Data Extractor
StoplightTable Tag
MeteogramPlotServlet
Zero-Hour Analysis /Satellite Overlays
External Link to AtmosphericSciences
External Link to At.Sci.
MM5 Data Extractor
Verification Portal
Uncertainty Portal
Link to Verification Portal
Link to Uncertainty Portal
MUM Architecture and
Data Flow
Mesoscale Error Plots
MVIRServletMesoscale ImageArchive
For more information, contact: David Jones, APL-UW (206-543-3236 [email protected])
Problem: Navy weather forecasters rely on numerical models to produce forecasts, but also have to consider the level of uncertainty in the models, which can be crucial in the tactical decisions the military has to make.
ConclusionMUM will continue to be used to test methods of presentation and user interactivity toward the goal of improving forecast quality, timeliness, and usefulness. Forecasters can now use probabilistic information in new and innovative ways.