درود خدا بر شما محسن عزیز
من هم نظر امیر محسنو دارم
به نظر امسال شرایط متفاوت از سال گذشتس و بهتره
آخرین ویرایش:
-
توجه: در صورتی که از کاربران قدیمی ایران انجمن هستید و امکان ورود به سایت را ندارید، میتوانید با آیدی altin_admin@ در تلگرام تماس حاصل نمایید.
مباحث عمومی هواشناسی تابستان 1393
- شروع کننده موضوع heaven1
- تاریخ شروع
- وضعیت
من هم نظر امیر محسنو دارم
به نظر امسال شرایط متفاوت از سال گذشتس و بهتره
El Nino and Positive PDO in Winter
Back at the end of July, reader Tracy asked if I could look at the relationship between Alaska winter climate patterns and El Niño or the PDO phase. I've been very remiss in not getting to this earlier, but now with the change of seasons the topic is more immediately relevant. Earlier in the year I posted some maps illustrating the El Niño connection to summer and spring conditions, so we can do the same thing for winter. For the purposes of this post, I've defined winter as November through February: the season that is both cold and dark.
First, the maps below show the distribution of temperature and precipitation anomalies in the top 10 El Niño winters from the period 1951-2013. The colored columns represent the fraction of years falling in the three climatological terciles, i.e. below-normal, near-normal, and above-normal. We see that strong El Niño winters most often bring unusual warmth to southeastern and south-central Alaska, but temperatures are most often close to normal overall in the interior and west. Below-normal precipitation is a common occurrence from Barrow and Bettles down to Cold Bay in these winters.
http://ak-wx.blogspot.com/2014/09/el-nino-and-positive-pdo-in-winter.html
Back at the end of July, reader Tracy asked if I could look at the relationship between Alaska winter climate patterns and El Niño or the PDO phase. I've been very remiss in not getting to this earlier, but now with the change of seasons the topic is more immediately relevant. Earlier in the year I posted some maps illustrating the El Niño connection to summer and spring conditions, so we can do the same thing for winter. For the purposes of this post, I've defined winter as November through February: the season that is both cold and dark.
First, the maps below show the distribution of temperature and precipitation anomalies in the top 10 El Niño winters from the period 1951-2013. The colored columns represent the fraction of years falling in the three climatological terciles, i.e. below-normal, near-normal, and above-normal. We see that strong El Niño winters most often bring unusual warmth to southeastern and south-central Alaska, but temperatures are most often close to normal overall in the interior and west. Below-normal precipitation is a common occurrence from Barrow and Bettles down to Cold Bay in these winters.
http://ak-wx.blogspot.com/2014/09/el-nino-and-positive-pdo-in-winter.html
درود بر محسن جان
انشاء ا... که سال زراعی جدید بر خلاف آنچه که در ذهن ما موج می زند و باعث نگرنی ماست طبق گفته امیر محسن سالی با برکت و اتمام خشکسالی های ایران عزیز ما باشدانشاء الله
ولی دلم خیلی برای ابرهای شمالی با اون رعدو برقهایش تنگ شده :iranjoman::گل:
Amir Mohsen
متخصص بخش هواشناسی
شک نداشته باشید که فاز مثبت نوسان اقیانوس اطلس در شهریور ماه همبستگی مثبت با بارش ایران داره.
ببینید
http://up.iranjoman.com/images/lmgqatoh5j903na4xlx.png
http://up.iranjoman.com/images/qgmyw202dhy7dg8di88k.png
ببینید
http://up.iranjoman.com/images/lmgqatoh5j903na4xlx.png
http://up.iranjoman.com/images/qgmyw202dhy7dg8di88k.png
Amir Mohsen
متخصص بخش هواشناسی
درود فرهاد عزیز
انشا ء الله اون خاطرات بد بزودی پشت سر گذاشته میشه و روزهای خوب آغاز میشه
Amir Mohsen
متخصص بخش هواشناسی
درود بر سامان عزیز
آرزو میکنم روزهای خوبمون هر چه زودتر آغاز بشه
درود استاد بيشتر توضيح مى دهيد ؟
و اينكه فرموديد از نوامبر امسال دوران جديدى شروع ميشه ، ممنون ميشم بيشتر توضيح بدهيد درضمن منتظر پيش بينى فصلى شما هستيم
لازم بذكر است كه استاد اميرمحسن فقط روى خطوط را نمى خوانند بلكه لا به لاى خطوط را هم مى خوانند:گل:
Amir Mohsen
متخصص بخش هواشناسی
درود امیر کوروش عزیز
خیلی عذر خواهی میکنم فعلا بیرون هستم و موبایل انشاء الله سر فرصت در این مورد صحبت میکنیم
راستی فراموش کردم.سپاس از لطف تون
درود وتشکر فراوان
Amir Mohsen
متخصص بخش هواشناسی
مشهد باد خیلی باحالی داره میاد. زنده باد پاییز
Amir Mohsen
متخصص بخش هواشناسی
خبرهای خوبی دارم..................
هادی جان پیش بینی MJo رو لطفا در انجمن بذار.
سپاس
هادی جان پیش بینی MJo رو لطفا در انجمن بذار.
سپاس
Amir Mohsen
متخصص بخش هواشناسی
از دانشگاه آلبانی اتمسفر.
نموداریکه مثل پیش بینی انسو هست رو منظورمه.
ممنون
نموداریکه مثل پیش بینی انسو هست رو منظورمه.
ممنون
Amir Mohsen
متخصص بخش هواشناسی
خیلی ممنون از محبتت. پیش بینی بلند مدت NAO- AO , PNA رو هم اگه لطف کنید خیلی ممنون میشم.
Waves and Statistics
[h=2]What is an atmospheric wave?Before we can understand equatorial waves, we need to understand what an atmospheric wave is in the first place. At its core, an atmospheric wave is an oscillation in some atmospheric field(s). Most meteorologists are used to seeing Rossby waves in the midlatitudes which show up really well on isobaric geopotential height maps. These Rossby waves are oscillating patterns in the pressure (and therefore geopotential height) fields which propagate around the globe.
[h=2]Basic Wave PropertiesJust like the sinusoidal waves taught in math and physics classes, atmopsheric waves have very important properties: amplitude, wavelength, wavenumber, frequency, period, phase velocity and group velocity.
Amplitude: the amplitude of a wave is the amount that a wave deviates from its base state.
Wavelength: the wavelength of a wave in the atmosphere is the horizontal or vertical extent of the wave, measured from ridge-to-ridge or trough-to-trough.
Wavenumber: wavenumber is simply the number of waves you could fit around the globe, almost always measured in a longitudinal sense. For example, you can fit one oscillation (one ridge and one trough) of a wavenumber 1 disturbance around the globe. You can fit 2 oscillations (two ridges and two troughs) of a wavenumber 2 disturbance around the globe, etc.
Frequency: the frequency of a wave is the number of times a wave oscillates in a given amount of time. Frequency and wavelength are inversely related (for a given phase speed) – longer wavelengths are associated with higher frequencies and vice-versa.
Period: The period of wave is the amount of time it takes to complete one cycle. It is the recipirocal of frequency, so low frequency waves have long periods and vice-versa.
Phase velocity: the phase velocity of a wave is the speed and direction at which the wave propagates. Positive phase velocities usually refer to eastward moving waves and negative phase velocities refer to westward moving waves. The phase speed is just the magnitude of the phase velocity.
Group velocity: the group velocity of a wave is the speed and direction at which the wave envelope propagates. When the group speed and the phase speed are equal, we say that the wave is non-dispersive. Also of note is that energy (information) about the wave packet moves at the group velocity, not the phase velocity.
[h=2]Fourier Decomposition/FilteringFourier decomposition is one of the best techniques to separate wave types in the atmosphere. Every well-behaved (math term, we don’t need to worry about it so much) pattern can be separated entirely into a combination of sine/cosine waves with different properties. In meteorology we usually decompose atmospheric flows into waves that have separate wavenumbers and frequencies. This is called filtering. An important property of fourier decomposition is that when the sines and cosines are added back together, the original field is re-created, exactly as it was.
The take home point behind filtering is that it allows us to extract the parts of a field, such as OLR, that have the same wavenumber and frequency properties as a particular type of wave. This allows us to focus only on the relevant parts of the field.
[h=2]Empirical Orthogonal Function (EOF) AnalysisEOF (a.k.a. Principal Component Analysis [PCA]) is a statistical technique for extracting the spatial structure which represents the most variance in a dataset. For example, the mode representing the most variance in a total-field (not anomaly) dataset is usually the seasonal cycle. EOF analysis is almost always performed on anomalies for just this reason. The mode representing the most variance of SSTs in the equatorial Pacific is ENSO.
The EOF is the spatial structure and the Principal Component (PC) is the timeseries that corresponds to the EOF. Imagine that we have the first EOF of SSTs. We can compare that spatial structure to the spatial structure that has occured every day for which we have recorded SST data. The more similar to the EOF that the actual data looks, the greater the amplitude of its PC will be. One could use the first EOF of SSTs as an ENSO timeseries.
EOF analysis is very complicated because it is full of caveats and subtleties which can make it difficult to interpret. Below is a list of some of them.
This is a popular method of tracking the MJO because it is easy to visualize and simple to make. However, since we do not know what the MJO actually is, we need to be careful to interpret the RMM index simply as a convenient method of tracking the MJO. It does not necessarily represent the true structure of the MJO, and often it is confused by other equatorial waves.
The RMM index is created like this:
[h=2]What is an atmospheric wave?Before we can understand equatorial waves, we need to understand what an atmospheric wave is in the first place. At its core, an atmospheric wave is an oscillation in some atmospheric field(s). Most meteorologists are used to seeing Rossby waves in the midlatitudes which show up really well on isobaric geopotential height maps. These Rossby waves are oscillating patterns in the pressure (and therefore geopotential height) fields which propagate around the globe.
[h=2]Basic Wave PropertiesJust like the sinusoidal waves taught in math and physics classes, atmopsheric waves have very important properties: amplitude, wavelength, wavenumber, frequency, period, phase velocity and group velocity.
Amplitude: the amplitude of a wave is the amount that a wave deviates from its base state.
Wavelength: the wavelength of a wave in the atmosphere is the horizontal or vertical extent of the wave, measured from ridge-to-ridge or trough-to-trough.
Wavenumber: wavenumber is simply the number of waves you could fit around the globe, almost always measured in a longitudinal sense. For example, you can fit one oscillation (one ridge and one trough) of a wavenumber 1 disturbance around the globe. You can fit 2 oscillations (two ridges and two troughs) of a wavenumber 2 disturbance around the globe, etc.
Frequency: the frequency of a wave is the number of times a wave oscillates in a given amount of time. Frequency and wavelength are inversely related (for a given phase speed) – longer wavelengths are associated with higher frequencies and vice-versa.
Period: The period of wave is the amount of time it takes to complete one cycle. It is the recipirocal of frequency, so low frequency waves have long periods and vice-versa.
Phase velocity: the phase velocity of a wave is the speed and direction at which the wave propagates. Positive phase velocities usually refer to eastward moving waves and negative phase velocities refer to westward moving waves. The phase speed is just the magnitude of the phase velocity.
Group velocity: the group velocity of a wave is the speed and direction at which the wave envelope propagates. When the group speed and the phase speed are equal, we say that the wave is non-dispersive. Also of note is that energy (information) about the wave packet moves at the group velocity, not the phase velocity.
[h=2]Fourier Decomposition/FilteringFourier decomposition is one of the best techniques to separate wave types in the atmosphere. Every well-behaved (math term, we don’t need to worry about it so much) pattern can be separated entirely into a combination of sine/cosine waves with different properties. In meteorology we usually decompose atmospheric flows into waves that have separate wavenumbers and frequencies. This is called filtering. An important property of fourier decomposition is that when the sines and cosines are added back together, the original field is re-created, exactly as it was.
The take home point behind filtering is that it allows us to extract the parts of a field, such as OLR, that have the same wavenumber and frequency properties as a particular type of wave. This allows us to focus only on the relevant parts of the field.
[h=2]Empirical Orthogonal Function (EOF) AnalysisEOF (a.k.a. Principal Component Analysis [PCA]) is a statistical technique for extracting the spatial structure which represents the most variance in a dataset. For example, the mode representing the most variance in a total-field (not anomaly) dataset is usually the seasonal cycle. EOF analysis is almost always performed on anomalies for just this reason. The mode representing the most variance of SSTs in the equatorial Pacific is ENSO.
The EOF is the spatial structure and the Principal Component (PC) is the timeseries that corresponds to the EOF. Imagine that we have the first EOF of SSTs. We can compare that spatial structure to the spatial structure that has occured every day for which we have recorded SST data. The more similar to the EOF that the actual data looks, the greater the amplitude of its PC will be. One could use the first EOF of SSTs as an ENSO timeseries.
EOF analysis is very complicated because it is full of caveats and subtleties which can make it difficult to interpret. Below is a list of some of them.
- EOFs are mathematical constructs. They are the eigenvectors of the covariance matrix of the dataset. They do not have to represent physical modes. All because an EOF looks physical does not mean that it is. Extreme caution needs to be used when relating EOFs to physical modes.
- EOFs are orthogonal (uncorrelated) with each other. This means that the first EOF represents the spatial pattern that explains the most variance, but the second EOF represents the spatial pattern that explains the second most varianceconstrained to being orthogonal to the first EOF. This is important because EOFs are forced to be independent of each other even though in the real world we know that physical modes are almost always correlated. If the first EOF changes, the subsequent EOFs change also. The principal components associated with each EOF are also completely uncorrelated.
- Because EOFs are orthogonal (90 degree phase shift) to each other, two EOFs can be used to describe propagating patterns. This is analogous to plotting a graph of sin(x) vs. cos(x), you end up with a circular pattern.
This is a popular method of tracking the MJO because it is easy to visualize and simple to make. However, since we do not know what the MJO actually is, we need to be careful to interpret the RMM index simply as a convenient method of tracking the MJO. It does not necessarily represent the true structure of the MJO, and often it is confused by other equatorial waves.
The RMM index is created like this:
- Start with OLR and zonal wind anomalies at 850 mb and 200 mb averaged over the Equator from 15S-15N.
- Subract the linear relationship that these fields have with ENSO.
- Perform an EOF analysis of all three fields, combined.
- The first and second EOFs are orthogonal to each other and show a propagating pattern, as described above.
- Plot the principal components (RMMs) associated with the first two EOFs on a polar coordinate plot.
- Arbitrarily split the circle into 8 phases. There is nothing magical about 8 phases. It could be as many, or as few, phases as you’d like.
- The amplitude of the MJO is shown by the distance of the plot from the center of the circle. This is equivalent to the modulus of the PCs [that is: amplitude = sqrt(PC1.^2+PC2.^2)].
- When the MJO amplitude is greater than 1 (outside the smaller circle), we consider the MJO to be active. When it is less than 1 we consider it to be inactive. Again, the number 1 is completely arbitrary.
- وضعیت