Large Scale Patterns

Ensemble Situational Awareness Table

• Provides a framework for quickly identifying significant events in the forecast.
• NAEFS ensemble mean fields are compared to the 1979-2009 CFSR reanalysis climatology to highlight potentially significant features in the forecast.
• The NAEFS ensemble consists of a control run and 20 perturbed members from each of the Canadian and GEFS models.

• Only presents the ensemble mean-- no explicit confidence information. 
• Large anomalies in an ensemble mean at long lead times suggest a higher likelihood of significant events, but not if the model is underdispersive or "overconfident"

Probabilistic QPF and EFI

• The PQPF represent high resolution (1/8 degree) probabilistic forecasts at various thresholds. Raw (and frequently biased) ensemble forecasts from the GEFS are transformed into reliable predictive probability distributions for various precipitation accumulations.
• The Extreme Forecast Index (EFI) is an index designed to identify situations where the GEFS mean forecast is predicting an extreme solution relative to a reference climatology. The percentile shown represents where the current forecast value falls within quantiles created from the reforecast database climatology. Values toward 0 and 100 are very near or beyond the reference climate distribution.;

GEFS Ensemble Graphics

• Gathers several confidence tools (normalized spread, GFS deterministic versus GEFS ensemble, model climate QPF, ensemble IVT) in one place.

GFS Comparison Graphics 

• Easy to use web page that compares the GFS to the Parallel GFS.
• Plots standard levels and several precip fields.
• Includes a dProg/dt function to see how the guidance has changed over the prior few runs.

Relative Measures of Predictability (RMOP)

FSU Confidence in Surface Fields

• Can help to establish confidence in localized sensible weather from a large-scale ensemble.
• Compares de-biased climatological GEFS spread to spread in the current GEFS forecast
• Similar to normalized spread plots, but spread is compared to climatology rather than last 30 days of forecasts.
• Plan-view and point-based plots for 2-m temperature, wind speed and other surface variables.

• Low-resolution fields, no post-processing.
• Low spread does not always imply high skill.

Climate Forecast System Ensemble

Atmospheric River Plots

• Provides depictions of the GEFS ensemble mean and the GFS deterministic IWV transport for the western US

Analogs and Surface Sensible WX

GEFS Reforecast Analogs

• Reforecast analogs are used to calibrate the real-time GEFS forecast, which shows considerable skill over raw ensemble probabilies.
• Uses a GEFS reforecast dataset covering 1984 to 2012 at ~50km (~70 km) resolution for days 1-8 (8-16).

• Precipitation forecasts are subject to the quality of the NARR reanalysis precipitation (32-km resolution).

CIPS Analogs

• Identifies 15 past NARR analyses that are similar to the current NAM and GFS forecast.
• Initial page shows the list of dates sorted by how well they match the forecast pattern.
  
• Plots show the probabilties of exceeding various precipitation, snowfall and temperature thresholds.  Example: 50% probability of >12" snowfall at a point means that on more than half of the 15 past dates with similar patterns, at least 12" of snow fell at that point (based on COOP data).
• Western US domain moves based on the pattern.

• Based on deterministic NAM/GFS solutions.  Builds confidence in the impacts, but only assuming that the model forecast is good.
• Precipitation/snowfall probabilities use smoothed COOP data, not PRISM.  Terrain is not well represented.

SPC SREF Probability Plots

• SPC does some post-processing of the 21 SREF members here.
• Useful fields: calibrated thunderstorm probability, Fire Weather joint probabilities, CAPE/Shear and other combined convective probabilities, PQPF.
• Click on the forecast hour for d(prog)/dt graphics.

• Basic surface fields are not calibrated or bias-corrected.  16-km resolution is insufficient in complex terrain.

SPC Interactive SREF Plume Diagrams

• CFS 45-Day Plumes

• Time series plots of all SREF ensemble members at a point
  
• Useful fields: 10m winds, precip/snow accumulations
• The SREF has 21 members that are all variations of the WRF model.  3 different cores, 7 initial condition perturbations each.  16 km resolution.

• This is not MOS and is not bias-corrected.  As such, WRF configurations typically overpredict wind speed and underpredict the diurnal range of temperature, although this varies from event to event.

NOMADS Probability Tool

• Show the raw probability of exceeding various thresholds (% of 21 GEFS members).
• Point-based one-stop shop for raw ensemble mean/spread data and probabilities.

• Use raw ensemble probabilities at your peril. Particularly for high precipitation thresholds, these uncalibrated values are unreliable in complex terrain.
• Tool takes a short time to run (30 seconds or more)

CFS Severe Weather Guidance Dashboard

• Helps identify potential severe weather outbreaks well in advance 
• Based on the CFS (Coupled Forecast System) 
• The colored blocks in the table show the number of grid point where the daily-averaged Supercell Composite Parameter (SCP) is > = 1
• SCP components are found here: http://www.spc.noaa.gov/exper/mesoanalysis/help/help_scp.html
• Vertical continuity in the grids suggest run to run consistency in the forecast

• This is an experimental product and may not update every model run

UCAR High Resolution Ensemble

• Provides ensemble based guidance using high resolution models (mainly WRF). 
• 10 Member, 48 Hour forecast at 3 km run at 00Z.
• Shows skill in both identifying precipitation locations and magnitudes (has a tendency to overforecast high precipitation rates and underforecast low precipitation rate events)

• This is an experimental quasi-operational product
• Subjective analysis suggests this system has difficulty maintaining forward-propagating mesoscale convective systems (MCSs) that develop in weakly forced environments

Anomaly Composite Maps

• Creates map composites of any number of dates (within a one to three month window) and outputs anomaly maps in terms of Standard Deviation (Standardized Anomalies). 
• Output presents composite anomalies for 500 mb height, PWAT, 850 mb air temp, SLP, 700 mb wind speed and 850 mb wind speed.
• By compositing using one or three month windows, the output shows anomalies relative to seasonal climatology.  This helps to draw more direct comparisons to the WR Situational Awareness Table.

• Current iteration only shows standard anomalies and not output such as return frequencies or percentiles.
• Output is image based so there is little direct interaction with the data.
• Plot regions are limited.
• Users MUST ensure they enter dates