Crocodile23

Much weaker.

Can you give the link that contains the WMO definition? Thanks.

Sure, on a first level, but on a deeper level, ultimately, everything are being driven by temperature(temperature gradients).

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? Can you also explain from where i can obtain these 3 different formats (a,b full)? Many thanks in advance!

Isn't the 18z runs of both GFS and the new GFS(parallel) after +200, the pattern that OPI predicts for the winter?

Thanks but what about this:

Link please? Links please? 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.

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

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?

What is CV, double MT and single MT means? Sorry if this had been explained earlier in this topic.

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?

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. :-)

Did they publish their paper in English somewhere?

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.

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.

What is median? (I actually know what it is, but i can't understand its usefulness here and perhaps you mean something different)

What do you mean by 1.5-7 in the "Significant higher than 2009 and 2012 with 1,5-7." ?

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.

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:

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.

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.

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. That would be interesting to see the correlation from 1948 to 2013 for example.

What? Why not? 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.

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

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.