A Brief Guide to MDL's SREF Winter Guidance

A Brief Guide to MDL's SREF Winter Guidance (SWinG)Version 1.0January 2013

What's this all about?An innovative way to view and understand SREF output

Calibrated probabilistic forecast guidance, based on NCEP's Short Range Ensemble Forecast (SREF) system--SREF Winter Guidance (SWinG)

Prototype includes weather elements that focus on rain/snow/freezing rain forecast decisions

Available on the web for all SREF forecast cycles and time projections at a limited number of stations

Why use calibrated probabilities?Ensembles are often overconfident (underdispersed).

Too frequently the verification falls outside the spread of the ensemble members.

SWinG forecasts are calibrated.True measure of forecast confidence.Statistically reliable spread.

How do I use it?If precipitation type is a question, and

You expect SREF to be skillful

Assess the meteograms for your stations

http://www.mdl.nws.noaa.gov/~BMA-SREF/xml/meteoform_sref.php

SREF Winter Guidance--Full Page

SREF Winter GuidanceTop Half

Higher confidenceLower confidenceSREF Winter GuidanceBottom Half

How to Assess MeteogramsTime series forecasts of weather elements related to precipitation typeBlack line is 50th percentile

How to Assess MeteogramsGrey areas show spread of the distribution (10th, 30th, 70th, and 90th percentiles)

10%90%70%30%How to Assess MeteogramsRed lines show the station specific climatological boundary for rain/snow, if available

How to Assess MeteogramsTri-color lines show rule of thumb values for rain/freezing/frozenRain/freezing frozen thresholdFreezing/snow lineAll-snow threshold

Up CloseClose up of 850 mb Temperature

Up CloseCompare spread at 0300 and 1500 UTC. Forecast is more confident at 0300.

Up CloseAt 0300, guidance indicates ~70% chance of 850 mb temperature below key value (-1.5 C)

Up CloseAt 1500, SWinG indicates ~20% chance of 850 mb temperature below key value (-1.5 C)

Which weather elements?Current2-m Temperature850 mb Temperature1000-500 mb Thickness850-700 mb Thickness1000-850 mb Thickness1000-700 mb ThicknessFutureDendritic Growth Zone DepthOmegaFreezing LevelPositive/Negative EnergySnow Liquid EquivalentWhy these weather elements? There are better parameters for winter weather!

Why these weather elements?We have a very short sample. SREF began running in this configuration 21 Aug 2012.We are using a modified form of Bayesian Model Averaging (BMA). This technique can only forecast weather parameters that are observed daily.Currently, SREF vertical velocities for NMM and NMMB have problems.It's a new technique. We started with the easiest weather elements.

Vertical velocities are brokenWhich Stations?On NCEP's Central Computing System, we generate SWinG for more than 3000 stationsAdapted from the BUFR station list used at NCEPBUFR station list is source for BUFKIT applicationOn our web page, we generate images for ~400 stationsAll upper air stations in CONUS and AlaskaAdditional stations to support WFO LWX Winter Weather Pilot Project.We can, and will, add stations to the web pageContact us if you want us to add stations

How do we make SWinG? Using most recent verification...Correct bias of each member Weight the bias-corrected members (ARW, NMM, NMMB members)Correct forecast spreadCompute probabilitiesWe have named this technique Decaying Average Bayesian Model Averaging (DABMA).

Previous ForecastsToday's ObservationUpdate Bias CorrectionsBias correction for each model core ... Latest SREF ForecastCorrect Bias of Each MemberWe track and remove the bias of each member. We update this bias correction daily with the most recent verification.Previous Bias CorrectionsNew Estimate = 0.95 x Previous Estimate + 0.05 x Today's EstimateLatest SREF ForecastCorrect the Bias of Each Member

i.e., 1 bias correction value each for ARW, NMM, NMMB, which is applied to each of their respective members moreToday's estimate contributes 5% to the total correction. As time passes, the influence of today's value asymptotically approaches zero. Model verification over the past ~20 days accounts for 50% of the total correction, and the past ~40 days accounts for 95% of the total correction.Previous Bias-Corrected ForecastsToday's ObservationUpdate Relative WeightsRelative weights for each model coreUsing the most recent verification, we compute relative weights for bias-corrected ARW, NMM, NMMB membersPrevious Weights

Using most recent verification, correct forecast spreadPrevious ForecastsToday's ObservationCompute optimal spreadOptimal spreadRawSpreadCorrected

We compute probabilities using a Normal Mixture Model to combine member forecasts.

For simplicity we are only showing 3 members (in blue) contributing to the final probability distribution (in black). However, when we create the SWinG, we use 21 members.

The relative model weights set the height of each blue curve. The optimal spread determines the spread of each blue curve. The bias-corrected SREF forecasts set the position of each blue curve on the x-axis.We compute probabilities using a Normal Mixture Model to combine member forecasts. Illustration: Three members (blue) contrib-ute to final probability distribution (black)

For SREF, we use all 21 members.For simplicity we are only showing 3 members (in blue) contributing to the final probability distribution (in black). However, when we create the SWinG, we use 21 members.

The relative model weights set the height of each blue curve. The optimal spread determines the spread of each blue curve. The bias-corrected SREF forecasts set the position of each blue curve on the x-axis.We compute probabilities using a Normal Mixture Model to combine member forecasts. Relative model weights set height of each blue curve

For simplicity we are only showing 3 members (in blue) contributing to the final probability distribution (in black). However, when we create the SWinG, we use 21 members.

The relative model weights set the height of each blue curve. The optimal spread determines the spread of each blue curve. The bias-corrected SREF forecasts set the position of each blue curve on the x-axis.We compute probabilities using a Normal Mixture Model to combine member forecasts. Bias-corrected SREF forecasts set position of each blue curve on X-axis

For simplicity we are only showing 3 members (in blue) contributing to the final probability distribution (in black). However, when we create the SWinG, we use 21 members.

The relative model weights set the height of each blue curve. The optimal spread determines the spread of each blue curve. The bias-corrected SREF forecasts set the position of each blue curve on the x-axis.We compute probabilities using a Normal Mixture Model to combine member forecasts. The optimal spread deter-mines the spread of each blue curve.

For simplicity we are only showing 3 members (in blue) contributing to the final probability distribution (in black). However, when we create the SWinG, we use 21 members.

The relative model weights set the height of each blue curve. The optimal spread determines the spread of each blue curve. The bias-corrected SREF forecasts set the position of each blue curve on the x-axis.Join the conversation!We are using the NWS Innovation Web Portal (IWP) to gather feedback from forecasters.

https://nws.weather.gov/innovate/group/guest/communities

You will findAdditional documentation and case studiesForum where you can submit questions and commentsFor accessFollow the URL and login with NOAA e-mail credentialsSelect Available Communities tabFind SREF Winter Guidance and Join