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Using Ensembles

Using Ensembles. CHAOS. The idea that a system is very sensitive to the initial conditions – small changes to the initial state end up being big differences at a later time

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Using Ensembles

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  1. Using Ensembles

  2. CHAOS • The idea that a system is very sensitive to the initial conditions – small changes to the initial state end up being big differences at a later time • This concept is fundamental to weather forecasting – it basically says that if we can’t observe the current state of the atmosphere perfectly, our ability to forecast the future weather will be limited (hint: we can’t observe the current state perfectly!)

  3. Chaos • What we now do to account for this is make one numerical forecast, tweak the initial conditions slightly, run the model again, and so forth. • This is called an ensemble forecast • We know that we can’t know the exact state of the atmosphere in, say, 2 weeks. But an ensemble can give an idea of the range of possibilities

  4. Stochastic (Probabalistic) vs. Deterministic • Deterministic forecasting – one model run, one answer • Ensembles – many model runs, many answers • The issue is making the model runs different, called perturbations • Two main ways • Perturb the initial conditions • Perturb the Physics • Or a little of both…

  5. Perturbing the Initial Conditions • Have slightly different starting conditions based on the same observation and forcing sets, but within the error of those observations, etc. • That is, nothing wacky, just reasonable variations in the analysis conditions based on the data • Run the model multiple times and look at the “ensemble” of results. • Obviously, you can’t usually run a full resolution dynamical model 32 times, so usually you scale back the resolution/levels, etc.

  6. Perturb the physics • Use different schemes, such as for convective parameterization • Or, use different models • Guess what, you are already comfortable with using a 2-member perturbed physics ensemble: • GFS and NAM • When you see them agree, how do you “feel” about the forecast? • Can have multi-model ensembles – can be very powerful

  7. Traditional Approach:Enhancing the Long Range • Looking for agreement, disparity, and multiple solutions • In general, intuitively you know that good agreement means high confidence, and… • Spaghetti Plots and Postage Stamps • http://hdwx.tamu.edu/product.php?productID=90 • http://hdwx.tamu.edu/product.php?productID=92 • http://www.emc.ncep.noaa.gov/gmb/ens/fcsts/ensframe.html • Multiple Solutions – A new challenge for the meteorologist, especially in conveying information/uncertainty • The atmosphere might have a reasonable plan “B” • Ensemble mean can be very powerful, but sometimes misleading • White board

  8. A new wave of Ensemble Usage:Short Range • Very Helpful for QPF • HDWX SREF: • https://hdwx.tamu.edu/product.php?productID=110 • SREF – SPC • Heavily used for Severe Weather Risk (“Slight Risk” areas, etc.) • http://www.spc.noaa.gov/exper/sref/cmm_sref.php • Ewall SREF: http://www.meteo.psu.edu/ewall/ewallsref.html

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