Crocodile23
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Posts posted by Crocodile23
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He does emphasise it was a minor SSW though, which by WMO definition, it was.
Can you give the link that contains the WMO definition?
Thanks.
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Wind is bound to the pressure gradient force (among some other stuff, but thats besides the point at the moment).
And that brings me to a specific point. I am on here for the third season, and even tho I am trying to make this specific point every year (and one page back actually
), some still dont get it.Write it down, print it, and put it on the fridge or a wall, as long as you see it enough times to remember it:
"Winds reflect the geopotential height pattern, NOT the temperature pattern."
Sure, on a first level, but on a deeper level, ultimately, everything are being driven by temperature(temperature gradients).
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The parallel GFS does run at a higher resolution, 13km. The output grids are tho different. You have "a","b" and "full" grid outputs in GRIB2 format.
a files are available in 0.25° (original), 0.5°, 1° and 2.5° grid outputs. The point is basically in the file size, with larger the grid spacing, smaller the file size, and higher speed, etc... Depends what details you need. For strat I use normally 1° grid or 2.5 if I am in a hurry.
Hi again and sorry for another off topic post.
I want to ask from where do you obtain these GRIB2 files you are talking about.
I know this page: http://www.nco.ncep.noaa.gov/pmb/products/gfs/index.shtml.upgrade
And from there, there is this page for example: http://www.ftp.ncep.noaa.gov/data/nccf/com/gfs/prod/
Where you can download what you want(seemingly). But i don't get it how exactly and where exactly to go and what means what.
So for example if i want the 0.25 grid files of the new GFS for a grid of 40° to 45° north, 10° to 15° east and for let's say geopotential heights of 500 hPa, where do i have to go to download them?
b files are available in the same output resolution, except there is no 2.5 grid output.
Now the difference between a and b are the vertical levels and parameters. a output has certain layers from surface to 10mb, and b output has the missing ones that are not in a, and the upper strat from 10mb to 1mb. The point is again in lower file size if you need only specific parameters (b has less parameters than a) and specific vertical layers.
Both a and b go to 384h in all degree grid outputs.
The "full" output goes from 0 to 240h, and has full vertical layering and max resolution (0.25°) and all parameters. But the file sizes are of course bigger and the loading is slower if you run it on your website, etc... Its not a big deal if you only download specific files, since one file (each forecast hour has its own file) of the full output is around 190MB.
Can you also explain from where i can obtain these 3 different formats (a,b full)?
Many thanks in advance!
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Isn't the 18z runs of both GFS and the new GFS(parallel) after +200, the pattern that OPI predicts for the winter?
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The MERRA Dataset is located here, they must be getting a good hit rate from this thread !
http://acdb-ext.gsfc.nasa.gov/Data_services/met/ann_data.html
Another quick way of looking at things is via the CPC page
http://www.cpc.ncep.noaa.gov/products/stratosphere/strat-trop/
Thanks but what about this:
But there is reanalysis data for the full profile up to 1mb and some in the mesosphere from ECMWF.
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But there is reanalysis data for the full profile up to 1mb and some in the mesosphere from ECMWF.
Link please?
You also have the great MERRA dataset or many others.
Links please?
I dont really see the point of looking at past forecasts, because each season is a bit different and "tricks" the models in their own way, so there isnt much you cant get out of it, as far as skill is concerned.
No i guess you misunderstood me. I'm not seeking for the forecasts archive, but for the actual(observed) archive of the atmosphere for a historical time e.g 03 February 1912 (12Z).
And this is for learning/comparing/statistics etc purposes so there is a point.
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I wonder the same thing. What do the seasonal models base their long term forecasts on ? All I see are anomaly maps for pressure and temps but no explanation as to how they reach their conclusions. Does anyone know ?
They are numerical prediction models. I.e they are just solvers of equations of motion of the atmosphere.
I want to ask if there is any archive of GFS or ECMWF about the stratosphere(multiple layers, 5,10,20,30 etc hPa)? Like the ones here but for the past years:
http://www.instantweathermaps.com/GFS-php/showmap-strat.php?var=HGT&lev=10mb
http://www.geo.fu-berlin.de/en/met/ag/strat/produkte/winterdiagnostics/index.html
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Without question an impressive set of charts and the starting point for this split isn't on D10 it begins around D7/D8.
I always wanted to ask this question to understand this wonderful thread even more. Perhaps it's a trivial or silly question but nevertheless i have to ask....
Stratosphere, troposphere, surface doesn't matter the models have their D+1, D+2, D+8, D+9..... etc predictions with some accuracy.
First question i have to ask is: Does the D+X prediction of a model of a stratospheric map(e.g 10 hPa heights) has more predictive skill than a D+X of a tropospheric variable like for example 500 hPa(mbar) heights?
Because if they have the same, then stratospheric-based forecasting has the same value as tropospheric one and has no actual advantage.
Or maybe it has anyway? Because since the stratospheric results in the space we live, low troposphere, are becoming apparent many days after(i mean a stratospheric pattern will lead to a tropospheric one some or many days later), then even IF D+6 for example has the same predictive skill for 50 hPa and 500 hPa, then forecasting(for long-range of course) via 50 hPa has the advantage to know the tropospheric synoptic end result(approximately of course) some 5-6 days after. Am i mistaken in the last conclusion?
Other than that, how does it really go in the aspect of stratosphere-troposphere interaction and about which "prevails"?
I.e: a certain stratospheric pattern at some time, will lead to a certain(with small probable diversities of course) synoptic tropospheric pattern. Right or this is not completely correct and it depends heavily on the tropospheric circulation also? What i mean more clearly:
Is looking at a map of stratosphere and forecasting the evolution of the troposphere, solid enough? Or we need to see the tropospheric map also and combine the stratospheric-tropospheric maps to forecast the synoptic evolution?
I.e: If we just look at a stratospheric map and ignore the troposphere, is there any possibility that a certain feature of the troposphere(e.g a extratropical cyclone, a surface anticyclone(that does not appear in stratospheric maps), a tropical cyclone, etc) to completely destroy our synoptic forecast and lead to a completely different synoptic result from our prediction?
With other words, are there sometimes features of the troposphere(like a surface low deepening too much over a warm sea) that will "lead" the stratosphere?
Long term(in months scale-climate) we know this happens with snow extent, sea ice cover, great mountain ranges like Rocky mountains and Himalayas, etc, that effect strongly the stratospheric circulation, but what about short-term(scales of days or weeks-weather)? Can a prediction of a map in the stratosphere that one says oh look this will definitely evolve this way after 10 days, be actually "destroyed" and a tropospheric event to lead the stratosphere to behave completely differently?
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CW event has a chance to develop in one of two windows but Recretos' possible double MT event has me very unsure now.
What is CV, double MT and single MT means?
Sorry if this had been explained earlier in this topic.
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Is this something similar to the OPI pattern? The one that predicts for the winter i mean.
Perhaps we start to see the precursors of winter's circulation pattern?
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No disrespect to anyone but this type of post makes me chuckle. Personally I'm inclined to side with the OPI teams hard work, experience and scientific methodology over anyone's 'gut feeling'.
Won't be until March/April before we really know who is right or wrong but until then its shaping up to be a fascinating winter either way.
More importantly, if someone's gut feeling proves to be correct over someone else's scientific work, it is still the scientific work that has won and deserves the credit. :-)
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According to their paper, all work is carried out using The NCEP archives which are available on Meteociel and Wetterzentrale - that goes back a long way but only 1976-2012 is used for the study.
Did they publish their paper in English somewhere?
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That tomorrow thing was there yesterday though indicating it should have been issued today.
I think yesterday, it wasn't saying tomorrow. It said soon, next week or something like that.
Anyway just be patient. Tomorrow we will have the prediction.
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Hmm....
http://www.accuweather.com/en/weather-news/europe-winter-2014-2015-forecast-snow-cold/36777733
Fewer Storms for Ireland, United Kingdom and FranceAnother aspect of the upcoming winter season is that large and widespread damaging wind events are expected to be less common than last winter, which featured several noteworthy storms that caused damage from the British Isles into northern Europe.
While occasional shots of cold air will send temperatures tumbling across Ireland, the United Kingdom and France early in the winter, a persistent southerly flow caused by storms tracking near and north of Scotland will often result in near- to above-normal temperatures.
Fewer storms tracking across the United Kingdom and Ireland into northern Europe will lead to below-normal precipitation overall for the winter season, following the wettest winter on record across the United Kingdom last year.
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Here's the charts for Feb, and the updated Mean and Median,
Better correlations for the mean and median and February comes in quite modestly.
What is median?
(I actually know what it is, but i can't understand its usefulness here and perhaps you mean something different)
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151020142930251.pnghttp://weerwoord.be/includes/forum_read.php?id=2026137&tid=2026137
According to the creator of this graphic, Rutger, the SAI 2014 will be around 2,4. Significant higher than 2009 and 2012 with 1,5-7. Amazing!.
What do you mean by 1.5-7 in the "Significant higher than 2009 and 2012 with 1,5-7." ?
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Without meaning to stir anything up, I find it surprising how astoundingly rude and dismissive people can be just because some one's work isn't giving them a cast-iron guarantee of a record breaking cold winter.
I don't think this is the main reason. Actually i'm not speaking specifically about any post here, but only speaking generally, but the main reason for mocking words about any such try like OPI for example, is that people don't understand how science works.
OPI may be a complete bust(hopefully it isn't). But it opens new roads of research. And anyway it's a scientific try based on the scientific method that may or may not work after all, so it's a respectful one.
The problem is and always was that, ignorant about science people, ONLY SEE THE END RESULT. They could care less about the scientific in-between theory and research and the method and the probabilities and the margins of error etc of the method and they don't be amazed even for a second about the science any method has, they only see the white-black of the final result.
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Why is the UK never coloured in? Is this as we are often on the edge of cold weather so harder to forecast?
From what i understand from the paper, they have selected this area as they've found a strong correlation of the winter's geopotential heights there, with October's OPI. Here is the google translation of what it says in the paper(that was written in Italian):
Google translation:
Just about to central-western, it was possible validate, even in numerically, the predictive power of OPI, referring to the parameter of the fault geopotential average calculated on the winter quarter. The latter, for each year was calculated again using the maps available for download from the archives NCEP reanalysis using software Telemappa NG. The reference sample is always including the years 1976 to 2012, while the European area subject to analysis is shown in the following figure:
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Which begs the question- why?
Being 'selective' with the input data wouldnt be very scientific!
Of course, but this is just for the useless in-between results we get every day(which are just for the fun of it).
The actual final OPI value in November 1st, will be based on input of real values of the atmosphere for the 31 days of October, so nothing unscientific is going on at this aspect.
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Croc23 - great post, backs up a theory that a sub -1.50 OPI leads to a much colder winter perhaps??!
However, where exactly are these figures for 2013/14 from? Surely it was warmer than that!
As i've said it's from NCEP's(of NOAA) reanalysis project.
Here are the complete temperature statistics for the 50° N to 55° N , 5° W to 0°E (Greenwich meridian), area**:
**The area:
Monthly average 850 hPa temperatures in degrees Celcius of the above area:
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec1948 -1.703 -2.286 2.857 0.303 2.981 4.046 6.110 5.970 6.456 3.316 4.646 1.2001949 -0.064 -0.148 -1.788 2.247 1.679 6.478 7.831 7.794 7.674 4.519 -0.097 -1.2871950 -0.151 -1.811 1.296 -1.948 3.037 7.128 6.450 6.303 4.570 3.111 -1.170 -5.1711951 -2.048 -3.699 -2.868 -1.976 1.050 4.783 7.396 5.386 6.802 4.106 1.427 -0.1041952 -3.832 -2.590 -0.649 0.786 4.500 4.581 6.918 6.482 1.662 1.438 -2.481 -1.9431953 -1.077 -2.124 1.382 -1.410 4.040 4.874 5.793 6.758 6.048 3.709 3.197 2.0391954 -3.292 -3.707 -0.819 -0.973 1.599 4.189 4.282 5.683 3.488 4.391 0.260 0.2311955 -1.722 -5.744 -4.092 1.593 -0.086 4.863 9.357 8.761 5.510 2.432 1.698 0.1171956 -1.807 -7.034 -0.263 -1.850 2.656 3.637 6.846 4.332 7.247 2.971 0.151 -0.1001957 -0.578 -2.006 3.277 0.188 1.304 6.432 7.089 6.826 4.528 4.702 1.377 -0.3811958 -1.882 -1.767 -3.226 -1.850 1.596 4.481 6.369 6.793 7.064 4.858 1.384 -1.2381959 -2.566 1.489 0.092 0.188 4.489 5.704 7.810 8.344 7.407 5.089 0.394 -0.1641960 -2.594 -2.537 0.506 0.027 3.971 7.022 4.797 5.371 5.164 2.522 0.189 -2.2201961 -2.328 1.381 1.767 1.292 1.774 5.136 5.380 6.222 7.433 3.313 -0.076 -0.6771962 -2.021 -3.442 -5.491 -1.038 0.010 3.734 4.646 4.927 4.538 5.257 -0.571 -1.8971963 -6.757 -6.320 -0.968 -0.441 0.421 5.036 5.048 4.418 4.612 4.927 0.691 -1.9341964 0.142 -2.376 -3.209 -1.096 3.878 4.202 6.516 6.054 6.551 2.577 1.748 -3.0511965 -3.298 -3.939 -1.608 -1.206 1.998 4.528 3.679 4.800 4.044 6.364 -2.406 -1.7731966 -2.247 -1.138 -2.280 -0.964 1.624 5.498 4.641 5.008 7.321 1.819 -2.958 -1.7491967 -1.912 -1.954 -1.652 -1.334 0.647 5.430 7.498 6.194 4.936 2.877 1.367 -1.2971968 -1.274 -4.069 -1.409 -1.061 -0.047 5.248 5.583 7.509 4.939 5.758 1.253 -1.6281969 -0.581 -6.667 -1.923 -1.567 1.938 4.906 8.756 7.413 7.106 7.591 -2.218 -3.2441970 -1.749 -5.399 -5.057 -3.316 4.520 7.346 5.639 7.110 7.471 3.652 0.883 -1.8661971 -1.070 -0.489 -3.512 0.322 2.959 3.437 7.806 6.856 7.191 5.876 -0.388 1.7571972 -3.459 -2.497 -1.021 -0.862 0.153 1.730 7.390 6.792 4.133 4.773 -0.067 1.1341973 0.403 -2.702 -0.624 -2.758 2.367 6.360 6.397 8.817 7.054 3.457 0.067 -1.4931974 0.212 -1.858 -1.099 1.026 1.392 5.101 5.473 5.920 3.690 -1.003 -0.431 0.1491975 -0.213 1.463 -3.631 -0.654 1.292 6.099 8.006 9.878 4.682 4.157 -0.161 -0.4261976 -1.031 -1.369 -1.907 -0.927 2.209 8.079 8.759 7.772 4.184 2.800 -0.287 -3.8581977 -3.707 -2.378 -0.856 -2.980 1.284 4.636 7.064 6.147 5.578 4.990 -1.724 1.0701978 -2.656 -4.100 -1.061 -2.071 3.147 3.904 5.772 6.319 6.316 5.861 1.626 -1.1931979 -5.073 -2.982 -3.756 -1.669 0.894 5.600 6.748 5.670 6.013 4.841 0.567 -1.2731980 -3.267 -0.578 -2.998 -0.514 2.384 4.187 5.213 7.374 6.719 1.536 -0.681 -1.3381981 -1.831 -2.994 0.238 0.528 2.361 4.271 6.796 8.987 6.058 0.036 1.293 -3.5901982 -0.944 -0.774 -1.899 0.059 1.853 6.118 8.487 6.547 7.313 3.412 1.053 -1.2321983 -0.099 -4.202 -0.081 -2.039 0.567 6.027 11.769 9.552 6.938 3.982 2.862 1.4611984 -3.659 -2.341 -3.847 0.777 1.420 5.879 8.191 8.576 4.696 4.089 1.451 0.1341985 -5.540 -1.151 -3.010 -0.151 2.287 3.574 7.532 5.774 7.910 6.422 -3.370 0.4281986 -3.328 -7.016 -1.776 -3.334 2.114 7.490 6.613 4.213 5.167 4.366 1.233 -1.2221987 -3.388 -2.517 -2.728 2.481 1.554 3.351 7.126 7.820 5.331 2.740 0.903 1.5001988 -1.373 -3.080 -2.586 0.404 3.153 6.934 5.523 6.892 6.818 4.427 1.487 1.3611989 1.470 -1.368 -0.277 -2.667 5.129 5.209 9.891 7.632 6.549 5.448 2.592 0.5061990 -0.176 -0.143 0.953 -0.701 3.908 3.993 8.616 8.986 4.470 4.943 0.281 -1.4491991 -1.797 -4.582 1.041 -0.722 2.616 2.832 8.656 9.361 7.283 2.842 0.486 2.6121992 1.998 0.353 -0.551 -0.558 5.692 7.233 7.346 6.561 5.362 0.069 1.480 0.2941993 -0.414 0.101 -0.386 1.894 3.391 6.956 5.936 5.586 4.211 1.683 0.692 -1.9691994 -1.928 -2.517 -0.672 -1.041 1.918 6.319 9.477 6.572 4.709 4.704 4.460 0.3111995 -2.050 -0.777 -2.377 1.877 2.031 5.892 9.999 10.348 4.578 6.333 2.151 -2.3681996 0.410 -4.191 -2.208 -0.010 -0.461 6.370 7.820 6.932 5.504 4.047 -0.941 -1.4591997 -0.628 -0.977 1.903 1.084 3.880 4.622 7.317 10.556 7.918 5.392 2.571 -0.5061998 -0.350 1.794 1.041 -1.409 5.198 5.067 6.433 8.361 7.171 2.368 -0.673 -0.1171999 -1.298 -1.750 -0.559 0.997 4.086 4.136 9.122 7.277 7.710 3.764 0.421 -2.1942000 -0.850 -1.333 -0.039 -1.051 3.789 6.373 6.384 7.821 6.793 2.640 -0.346 -0.3992001 -2.043 -1.347 -2.090 -1.769 4.086 4.724 7.641 8.186 4.594 6.072 1.153 -1.2592002 0.674 -0.869 0.420 0.797 2.663 5.338 7.139 8.361 6.178 2.819 1.680 -0.0042003 -2.342 -1.347 2.000 1.710 2.212 7.530 8.431 10.322 6.638 2.171 2.173 1.0892004 -0.922 -2.026 -1.781 0.266 3.209 6.047 6.060 8.484 7.589 2.596 1.119 0.8022005 -0.996 -4.526 -0.213 0.329 1.910 7.648 8.101 7.287 7.708 6.372 0.749 -0.8822006 -0.252 -2.172 -2.449 -0.967 3.088 7.260 11.123 6.884 9.377 6.189 1.831 1.7932007 -0.473 0.096 -0.860 4.778 3.353 6.194 5.828 7.453 5.750 5.938 1.040 0.9422008 0.200 1.554 -2.881 -1.047 4.727 4.754 7.366 7.977 5.148 2.548 0.657 0.0742009 -1.142 -2.031 -0.172 1.920 3.212 5.344 6.394 8.166 7.831 5.058 1.167 -2.7122010 -4.618 -4.324 -1.389 0.957 1.177 6.758 8.019 6.103 5.978 4.298 -1.632 -3.9382011 -1.872 0.106 1.032 4.970 2.781 5.109 5.797 6.393 7.173 5.701 4.617 -1.0022012 -0.653 -0.892 3.509 -1.547 3.761 4.679 6.327 7.824 6.243 2.926 0.554 -1.2092013 -1.334 -3.146 -3.838 -1.710 1.869 5.798 11.291 7.914 7.704 4.923 0.509 0.8182014 -0.884 -1.530 1.948 2.400 3.259 5.779 8.813 5.506 7.959 850mb Pressure Level Air Temperature (C)Latitude Range used: 55.0 to 50.0Longitude Range used: 355.0 to 0.0
So if you take:
December 2013 = 0.818
January 2014 = -0.884
February = -1.530
.....and do (0.818-0.884-1.53)/3 you get -0.532 ~= -0.53 as i've used in my original post.
And anyway why do you think it was warmer? Warmer from what?
As you can see from the below it(the aforementioned area) was a warmer winter than your climatological average(1980-2010) but overall was below 0 °C.
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Temperature is a measure chosen for another purpose, if the october CET anomaly correlated with the winter CET anomaly then it would be interesting to measure the significance.
However if I took the last 24 months of CET data and combined it in such a way that it would have a 80% correlation with the next winter CET anomaly calibrated over the last 30 years. a significance test wouldn't really tell me much only a testing going forward (or using historical data its not calibrated to) would determine whether there was something there. The second situation is closer to the OPI in my opinion.
And combined them in such a way??
I don't really understand what you are saying.
Here is what i've done:
The OPI team tried to create an index(OPI) that would correlate well with winter AO(another index).
I took these OPI values(ALL of them, i haven't selected them with any special criteria) for the Octobers and have tried to see if they correlate with the temperatures(in 850 hPa) of a specific part of the earth(your area, UK).
I have not picked special data, i have not selected anything, i have taken all years that OPI has been calculated(from 1976 till 2013) and calculated the correlations.
And i have found that OPI and your winter's temperatures are correlated. I have not calibrated anything, i have not picked any specific data to improve the fitting of the correlations, nothing like that at all! I have chosen ALL the OPI values.
So what you say is completely mistaken.
Which brings something to mind/ Is it possible to reconstruct OPI figures (and winter AOs) from earlier dates than the 30years the index was developed on. Isn't there reanalysis that goes back for a much longer period?
That would be interesting to see the correlation from 1948 to 2013 for example.
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Interesting post however the independence tests don't really work when you have selected specific data and combined them in a particular way to match the historical series which is what has been done here.
What? Why not?
Its not like the OPI is say a relatively simple temperature or pressure measurement at one point.
Well temperature is not simple either. It's a combination of billions of measurements of the kinetic energy of billions and billions molecules.
OPI is a specific value, temperature is a specific value and that's what matters when we do correlation tests. The validity of the results and the hidden variables behind is another issue.
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I have made a simple calculation about the correlation between October's OPI and the average temperature of following winter in the 850 hPa level for a big portion of UK. (I provide the values i used in the end of this post)
So i took the October's OPI value and the following winter's average temperatures in degrees Celcius in the 850 hPa level for december+january+february, for the area that is defined by 5° W to 0° E and 50° N to 55° N (that contains most of England and Wales) and have calculated Pearson's r correlation coefficient and Kendall's tau.
The values of temperatures have been acquired from NOAA and the values for OPI from a post here.
So the calculated values between October's OPI compared to following December+January+February average temperature in 850 hPa were:Pearson's r = 0.76
Kendall's tau = 0.55
2-sided p-value = 0.0000014
Pearson's r value is relatively high and shows that something might going on with this OPI thing.Even more, in the Kendall's calculation, we see a smaller correlation but a very interesting p-value, since the almost zero 2-sided p-value shows that assuming(the null hypothesis) October OPI and average temperature of DJF are independent, we have an extremely small chance of obtaining this 0.55 correlation if October OPI and average temperature of DJF are independent.
So we must assume they are dependent with a +0.55 (positive) correlation.
Positiv emeans that when the value of October OPI is large(get's larger) the average temperature of DJF is large(get's larger) also and when the values of October OPI decrease, the value of average temperature of DJF decrease also.
So all in all there is something interesting with this OPI.
And it seems to suggest that a correlation with your winter's temperature lies between them. A positive OPI brings warmer winter and a negative colder. With the aforementioned correlations of course. This is NOT a strict 1 to 1 rule of course.
The data:
(For example for year 1980:
OPI value is for October 1980
Average temperature(in 850 hPa) is for December 1980 + January 1981 + February 1981)
YEAR OPI AVG1976 -1,75 -3,311977 -0,95 -1,901978 -1,80 -3,081979 -0,50 -1,711980 -0,05 -2,051981 -0,40 -1,771982 -1,10 -1,841983 -0,30 -1,511984 -1,80 -2,191985 -1,90 -3,311986 -1,30 -2,381987 -0,45 -0,981988 +1,45 +0,491989 +0,25 +0,061990 +0,55 -2,611991 +1,10 +1,651992 +1,75 -0,011993 -0,15 -2,141994 +0,70 -0,841995 -0,65 -2,051996 -0,75 -1,021997 -0,75 +0,311998 +0,10 -1,061999 -0,20 -1,462000 -0,75 -1,262001 +0,45 -0,482002 -0,90 -1,232003 -0,70 -0,622004 +0,30 -1,572005 -0,70 -1,102006 +0,85 +0,472007 +0,75 +0,902008 +0,25 -1,032009 -3,15 -3,882010 -0,85 -1,902011 +0,65 -0,852012 -1,65 -1,902013 +1,60 -0,53
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No, hindcast is using past data to test your mathematical theory. However, you have the benefit of the statistical data to build your theory - in fact you may have played around a number of times to modify your theory so that the backtest is improved.
This is not necessarily a mistake. If the procedure is done correctly and you succeed in a high correlation of your prediciton then nothing is wrong of course and you have done a good job. But of course the question is always if you haven't ovedone it with your model and your number of inputs is larger compared to the observation and the phenomenon you want to explain. I think you(English dudes) call it overfitting or something right?
And the ultimate and perhaps only test is of course the FUTURE. If your model can explain the next new "values",- in our case the next AO-, with similar correlation as the hindcasted data, then you are ok and your model really works.
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), some still dont get it.
Stratosphere Temperature Watch 2014/2015
in Spring Weather Discussion
Posted
Much weaker.