Large Scale Patterns
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The Benefits
- 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.
The Drawbacks
- 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"
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The Benefits
- 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.;
The Drawbacks
- GEFS is under-dispersive
- EFI Temperatures are significant near 0 and 100 (cold/warm). The EFI "T" column will only show whichever percentile (cold/warm) is most significant
- A "-" symbol in the table indicated data is not available for that time.
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The Benefits
- Gathers several confidence tools (normalized spread, GFS deterministic versus GEFS ensemble, model climate QPF, ensemble IVT) in one place.
The Drawbacks
- Normalized spread is especially tricky to interpret, and can highlight strong gradients or pattern changes rather than low predictability.
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The Benefits
- 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.
The Drawbacks
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The Benefits
- RMOP in a given region indicates how much spread the GEFS emseble members have relative to spread over the last 30 days.
- Example... 90% RMOP: current GEFS at this forecast hour is more tightly clustered than 90% of forecasts at this hour over the past 30 days. 55% probability: 55% of GEFS forecasts at this hour with 90% RMOP have verified close to observations.
- High values of RMOP: ensemble has narrowed in on a solution. High values of probability: that solution might actually occur.
- High values of both RMOP and probability should increase confidence.
The Drawbacks
- Only the ensemble mean is plotted, which will wash out smaller scale features when there is timing/spatial uncertainty.
- High values of RMOP with low probabilities indicate that the GEFS is confident but you should not be!
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The Benefits
- 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.
The Drawbacks
- Low-resolution fields, no post-processing.
- Low spread does not always imply high skill.
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The Benefits
- 20-member CFS ensemble contains 00,06,12,18 UTC runs for the last 5 days.
- Some of the only operational guidance available beyond week 2.
The Drawbacks
- Limited verification data available. The CFS output is something to start looking at and evaluating, but be careful not to take these (or any) long-range forecasts at face value until we know more about the biases and skill of the forecast system.
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The Benefits
- Provides depictions of the GEFS ensemble mean and the GFS deterministic IWV transport for the western US
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Analogs and Surface Sensible WX
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The Benefits
- 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).
The Drawbacks
- Precipitation forecasts are subject to the quality of the NARR reanalysis precipitation (32-km resolution).
Also Check out:
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Also Check out:
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The Benefits
- 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.
The Drawbacks
- 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.
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The Benefits
- 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.
The Drawbacks
- Basic surface fields are not calibrated or bias-corrected. 16-km resolution is insufficient in complex terrain.
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Also Check Out:
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The Benefits
- 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.
The Drawbacks
- 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.
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The Benefits
- 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.
The Drawbacks
- 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)
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The Benefits
- 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
The Drawbacks
- This is an experimental product and may not update every model run
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The Benefits
- 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)
The Drawbacks
- 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
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The Benefits
- 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.
The Drawbacks
- 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
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