Below will be a look at the variability due to seasonality in the EOF components. This look is based on the fact that through my event classification scheme the preponderance of events occur later in the year, late August through September, then seen in previous works, July through August.
Figure 1: Principal Component 1 for both seasons
Figure 2: Principal Component 2 for both seasons Figure 3: Variance of the first 10 components for both seasons
With PC #1 a well defined trough (lower heights region) is present over central Alberta that stretches southward into southern California. But, with a shift in the time frame from July/August to mid-August/September we see this feature become much more well defined. This makes physical sense (we are moving closer to a time of year where more mid-latitude 'action' is found further south) and it also falls in line with our findings of trough interactions being important components or drivers of surge events in the late Summer.
Comparing the first to the second principal component shows that the July/August season is picking up more of a trough signature than that of the later season. This trough feature is associated with roughly 20% of the variance for mid-Aug/September and roughly 14% for July/August. It is reassuring to see that this trough feature is the leading component for the mid-Aug/September season in that it lends more foundation to our findings of the importance of these features.
The one issue I am having qualms with is the absence of the monsoon ridge over the Four Corners region. I believe we could be seeing some indication of this feature in PC #1 for July/August, but it appears very weak in nature. The problem could be that we are simply looking slightly too high in the atmosphere to pick out a strong signal with this feature, or perhaps my domain is slightly too large to pick out what might be considered a subtle feature in context of the overall synoptic and planetary patterns.
Here is a composite of the 500 hPa height pattern associated with strong events. Though we were questioning whether the 500 hPa height anomalies were showing weakening of the monsoonal ridge or an actual trough, it appears quite clear from the actual height patterns that the anomaly feature is indeed associated with a strong trough moving in from the Pacific.
Ridging, though relatively weak in nature, does appear to be in place slightly east of the Four Corners (i.e., Four Corners' High) on Day -4 and -3 to the event. At this time we are already seeing signs of a deepening trough off Northern California. As we progress closer to the event day the trough deepens dramatically, nearly explosively, and progrades into regions where it could potentially affect the characteristics and development of the NAM system. The actual height patterns and the anomaly fields display a strong spatial relationship to one another, and thus, I feel confident say that the anomalies we are seeing are associated with a trough feature.
There are still quite a few questions regarding the origin of this trough feature though. More on that to come soon.
Following the same outline as laid-out in the most recent post regarding composite analysis, I will now show composites for large scale fields in relation to defined surge events.
Weak Surge
Weak Surge: Non-common with Strong Surges
Again, plots are shown in three day prior through 3 days after a surge event (middle column).
For these figures the top row is precipitation anomalies (0 to 5 mm/day), the second is 200 hPaisotachs (50 to 65 mph= Light Blue, 66 to 80 mph = Yellow), the third is 500 hPa height anomalies (-120 to 120 meters, blues = negative anomalies, yellows = positive anomalies) and the fourth row is 850 to 500 hPa lapse rate anomalies (-5 to 5 degrees Celsius, same color conventions as height anomalies).
Though I showed both for comparison, I will limit my discussion to only the non-common weak event plot since I feel it is more indicative of the true weak events. Isotachs show relatively zonal flow. From the 500 hPa height anomalies we notice pronounced ridging (positive anomalies) over the northern Pacific, and from event day on we notice troughing (negative anomalies) over the western United States. The ridging over the northern Pacific could potentially be a related to a slightly northwestward shifted North Pacific High. The ridging followed by troughing over the western United States is most likely related to surface warming then cooling (associated with precipitation) ahead and behind, respectively, of the surge event.
850 to 500 lapse rate anomalies show a clear, and persistent pattern throughout the composite. A couplet of positive and negative anomalies appear adjacent to one another over northern Mexico during the preceding days till the day after the event. This feature I am proposing is associated with some voriticty feature, either an easterly wave or some smaller scale vorticity lobe (TUTT?), that is causing upward and downward motions in the atmosphere. The upward (downward) motions are causing a stabilizing (destabilizing) of the atmosphere, i.e., negative (positive) lapse rate anomalies.
The patterns seen in these figures fit well into the classical model of how a 'typical' NAM surge event should happen.
And now onto the interesting stuff...
Strong Surge
First, let's point out what appears to be similar. From the 850-500 hPa lapse rate anomalies we are still seeing the stabilizing of the atmosphere post surge. From the 500 hPa height anomalies we see the increased heights (surface warming) over the western United States associated with some thermal ridging. From that point on things seem different...very different.
200 hPaisotachs are now less zonal and are showing some signs of a trough progressing inland from the Pacific Ocean. Most striking off all are the results shown on the 500 hPa height anomalies that stand out from previous examples. The composite shows a dramatic and well-defined area of negative height anomalies (up to -120 meters!) moving in off the west coast. The feature is most certainly an indication of a strong trough feature, the exact type of extratropical control feature we have been hoping to see with our research. By comparing the precipitation anomalies with the 500 hPa height anomalies you can see that the precipitation stays just downwind adjacent to the incoming trough. Clearly, the trough feature is playing some role in progressing the NAM surge event system.
Here are some of my thoughts so far on how things are starting to look: (1) Weak events, as we have defined them, might end up being the ones tied to the tropical, e.g., easterly wave, controls. (2) Strong events where precipitation is seen further north into the Great Basin regions, i.e., Andy's work, might be the ones intimately tied to the extratropics.
As you can tell, I am more than a little excited by the results from the strong events.
To provide a prospective on the time-series of IWVF over the JJAS 'season,' a figure showing the day of maximum IWVF (daily average for 28 years) is shown. Days are broken down as follows: June: 1-30 July: 31-61 August: 62-92 September:93-122
The data shows that for the region most associated with NAM surge precipitation, i.e., AZ, western NM, southern UT and NV, and southwestern CO, the peak occurence of IWVF typically falls later in the season between mid-August and mid-September. Our data has been pointing towards this fact, but it is still striking to see how much of the region is dominated by later-season IWVF than most previous research has accounted for. What I am specifically revering to is that most NAM surge research is limited to only July and August analysis, thereby ignoring the vast majority of IWVF events, and most likely surge events, that are taking place.
To back up this point, draw your attention to the Yuma, AZ location (extreme southwestern AZ; Data point where most previous research bases 'surge event' definitions on). The peak for this region appears around day 70 (early August). So, if data analysis were limited to this region a season of only July and August observations might make sense. But, as data points out over the larger domain, this is merely an artifact of where you gather data, not actually physical manifestations for the region.
There are some swearly data areas where very early season peaks are shown directly adjacent to very late season peaks, e.g., northern Mexico. I believe this is an indication of regions with little variability in IWVF amounts during the period analyzed, therefore, resulting in drastic changes in date but not necesarrily amounts.
I believe there were some issues inherent to the way I was previously defining two day events in the IWVF field. For example, in a situation where there were four days straight above 90th percentile, then my code would have registered 3 events. To combat this flaw I went through and defined a two day event as:
2 consecutive days above 90th percentile for a zone where event day - 1 must not meet criteria
This appears to have helped out very well, and thus, I will show new and improved (and shiny, I might add) results.
Note: Since MATLAB disagrees with you about how large certain subplots should be when you start adding in colorbars, I will simply state data ranges here:
These restrictions are based on observed values in the data and which value range best described the data.
(1) Event for SAZ Zone The figure includes IWVF and anomalies of Dewpoint, Precipitable Water and Precipitation. Anomalies also helped to weed-out patterns present in these fields that were not easily decernded from the basic field values. We clear see a northward flux of dewpoint, precipitable water and precipitation in relation to IWVF events. Precipitable water shows the most concurrent signature with IWVF, which should be expected as these fields are based on similar data. We can now see the slightly delayed, by one or two days, flux of precipitation and dewpoint into the AZ, NM and UT domains. As described more fully in a previous post, this is a feature we expect to see. So, things are looking good.
(2) Event for SAZ and NAZ Zones
Features are similar in this figure as with (1) except that we are seeing a stronger signal in all fields compared to (1). This should be expected since we are further refining our events to cases where IWVF moved more northward, thus a strong flux.
(3) All Zones (1 Day Criteria)
So, the final figure is for an event that shows up in all three zones on a given day. This event is not restricted to 2 consecutive days criteria as with above examples. Most of the signatures appear before event day for this criteria. What we are pretty much looking at in this 'event day' is the height of activity, i.e., furthest northward flux. If we were defining events of 2 consecutive days for SAZ and NAZ then it would probably show up on Day-1 here. Again, signals are very amplified here, as we should expect.
Well, I am pretty to call events defined. So, Events = Defined:
Weak Event = 2 consecutive days of IWVF at or above 90th percentile for SAZ Moderate Event = 2 consecutive days of IWVF at or above 90th percentile for SAZ and NAZ Strong Event = 1 day of IWVF at or above 90thpercentile for SAZ, NAZ and SUT
I will be showing composites of three different surge criteria:
(1) SAZ Zone IWVF above 90th ( 342 Total: 12 per Year: 3 per month) (2) SAZ Zone IWVF above 90th for 2 consecutive days ( 146 Total: 5 per Year: 1.3 per month) (3) All Zones IWVF above 90th ( 47 Total: 1.7 per year: 0.4 per month)
These criteria were chosen empirically, and simply based on which results I found interesting or best told a story.
All composites will be shown for 3 Days Before through 3 Days After an event. ==================================================================== First, let's look at plot (1) I suppose my choosing this criteria was slightly less empirical than the other two simply because I felt as though looking at results for southern Arizona might provide some strong signals. The strongest signal composite appears to be between IWVF and precipitation. A clear northward flux of precipitation over the Great Basin can be seen to correlate temporally with Day -1 through Day +1. Precipitable Water values do appear larger during this same time period, but since the general spatial pattern of values for this parameter does not appear to be dynamic I am wondering if IWV results should be suspect. Having tried to use IWV in the past for defining surges and looking at other composites this issue has come up before. So, I believe that these values might be a little shifty in the model output. Again, there does seem to be a clear signal in the precipitation field in relation to these IWVF events.
==================================================================== Next, I wanted to look at events of two consecutive days of the above criteria. I felt that by further restricting my dataset, i.e., number of events, that I would get a clearer picture of patterns in the system. Patterns here are very similar to what was seen in the previous composite. There is still a strong temporal agreement between IWVF and precipitation. Something I found interesting in this plot though is that on Day +1 there seems to be a more intense region of precipitation further north into southern Utah and Colorado. I believe that this is indicative a stronger surge event, which would agree with our criteria of needing two consecutive days to reach or exceed the 90th percentile in SAZ.
==================================================================== Finally, I looked at events of all three zones meeting or exceeding their 90th percentile on the same day. This criteria should allow us to look at the very strong surge events, and hopefully show us heightened signals compared to the previous two plots. Indeed, we are seeing signals that one would suspect to be associated with strong surges of the NAM. Precipitation appears to be moving quite far northward now, and it also seems augmented compared to previous plots. At first, I felt as if my calculation might have been off based on the fact that precipitation seemed so heavy in Arizona for Day -1, and even Day -2. I don't believe this is an error, but is simply the first stages of the northward flux of moisture. If we looked at events for SAZ we would see that the majority of them are centered around Day -1 for this criteria, in place of the event day. This is shown in Figure 1 from 5 Sept 2009.
Again, the IWV values seem rather questionable. They do show some sign of being heigthened on Day -3 through Day -1, but I still have my reservations about trusting this data. One feature I have noticed in all plots is the elevated values of IWV in southeastern Arizona. I realized after looking at numerous plots that this region of elevated values of IWV is spatial correlated to a region of very low, for the region, precipitation values. This only makes me question this data more as I feel like this just does not make solid physical sense. The only connect I could make between the two is that the model is representing moisture that has not been precipitated out yet in this region. I am baffled.
You may have noticed that I left out dewpoint from this figures. Plots of dewpoint for all figures were ineffective at showing any association with the larger pattern or with IWVF events. I assumed, again, that my calculations were off, but after looking back through codes I found that dewpoint was calculated no different than any other parameter, and thus should not be showing signs of bad data manipulation. Once again, I am baffled.
I feel that the strong composites between IWVF and Precipitation is good, and expected, sign. This is showing that we are not only picking up events of IWVF, but that those events are associated with elevated precipitation patterns for the desert southwest. Seeing further northward progression of precipitation appear as we further restricted IWVF events to tougher criteria is also a good sign as it shows we are able to classify the stronger surges through IWVF values.
Building onto work that was shown on my last blog post, I will present my current work on describing poleward moisture surges of the NAM system.
I am still working with the sub-domains, or zones, that were defined in the previous post. First, I will show a figure of IWVF values (blue line) from 1984 for each of the three zones. Again, these values are the aggregated value for each zone for each day of the monsoon season (1 June through 30 Sept). On top of each zones IWVF will be plotted the 95th (magenta), 90th (cyan) and 80th (red) percentile values for each specific zone. The quantitative value for each threshold level will be indicated in the title of each subplot.
Interesting to note is the fact that the 95th percentile is larger for Southern Utah (SUT) compared to Northern Arizona (NAZ), 10.5 and 10.2 respectively. The percent difference is only 2.9% which is a neglagiable difference and doesn't necessarily constitute or reflect bad data or bad calculations.
Event Correlation Between Zones:
Lead/Lag correlations between zones will provide an understand of northward progression of moisture surges, as well as a hint at surge intensity. My calculation is based on events in Southern Arizona (SAZ) and looks at the correlation between the other two zones for Days -2 through Days +2 relative to the event in SAZ. Therefore, a 0.44 value for NAZ at Day +1 would mean that 45 percent of the events in SAZ had an event in NAZ the next day. Below will be three figures: The first will be 80th percentile threshold calculation for events, the second will be 90th and the third will be 95th.
There appears to be quite good correlation between all through zones on event days for SAZ. All figures also appear to have higher percentage values for post-event days, which makes physical sense when thinking about the typical northward progression of these surge events. At first I was a little surprised to see the left side of each probability curve having such high values. Once I spent a little time thinking about this I came to the conclusion that given the close temporal proximity of many of the surge events during active years, some of these high values could be attributed to surge events on previous days and not the one defined for the correlation.
Event Classification Scheme:
Onwards to defining what a surge event 'is' for this study. Based on these results, I propose the following classification algorithm for surge events in this study.
Foundation to this Criteria: 90th percentile threshold for each zone
Further refinements: As has been shown in many recent papers, there is a push towards defining not only 'surge events' but also surge intensity, i.e., weak, moderate and strong.
Weak Surge: Only SAZ shows an event for a given day Moderate Surge: SAZ and NAZ both show an event for a given day Strong Surge:tSAZ, NAZ and SUT all show an event for a given day
With this criteria, I get the following numbers: Weak Surge: 342 events over 28 yrs; avg 12.2 per yr; avg 3.1 per month Moderate Surge: 217 events over 28 yrs; avg 7.8 per yr; avg 2.0 per month Strong Surge: 150 events over 28 yrs: avg 5.4 per yr; avg 1.4 per month
Therefore, 63% of surges are 'moderate' and 44% are classified 'strong.'
I feel confident about this scheme, or as confident as I will ever feel about defining such dynamic weather phenomena.
Below are plots of aggregated IWVF, blue line, for three different domains within the NAM for 1983. The red line on each plot is the daily 80th percentile value for the specific domain. Domains are as follow:
Southern Arizona: 31 to 33 N Northern Arizona: 33 to 35.5 N Southern Utah: 35.5 to 38 N
Some of the events do show a northward progression, e.g., Day 55. But, overall, most events appear to show up on all domains on the same day, e.g., Day 100 and 78. The later is the event that I have used most frequently for case studies because it was shown to be one of the most substantial northward surge events in our study domain. Here, it appears to all happen on the same day but with a lessening intensity further north. An event on roughly Day 28 seems to work the reverse to this in that it is stronger the further north you go. Since this event is in June it could possible be more synoptically influenced rather than monsoon related. These subtle differences might be a good way to further classify events as "Strong" or "Weak."
Will do work on NARR and OBS DWPT correlation next.
From a previous post you might remember that I found that by defining a surge through criteria of IWVF, aggregated over a domain, above the 90th percentile threshold for two consecutive days gave me quite decent results. The domain I am currently using is the same as that shown on the 26 July 2009 "Surge Classification" post. Some quick numbers before I shown the figures:
Average number of events in a given July or August: 5.6/month
This number is higher than what one should expect, but having a high number now is what I want. This is because I will also be agumenting this criteria with dewpoint thresholds which will bring this number down to are more reasonable number. And, actually....
Criteria: 2 days of IWVF >= 90th and 1 day of DWPT >= 90th Average number of events in a given July or August: 1.1/month
This seems low, but plotting up the events for 1984 (the year that I have most been using during my studies so far) shows a very good agreement with what days I suspect are surge events. Below I will show those days (I based this on empirical evidence from Hovmoller diagrams which I am sure there are some on previous posts) and the plot for 1984 under the latest current surge criteria.
Suspected Event Days: ~38, ~50, ~70, ~78 and ~120. Even though the monthly average over the 28 years is low it still gives a good representation of the surge events of this year. It could stand to reason that this low number is an artifact of the rather inactive monsoon years of the 1990's. I am basing my 'normal' per month surge events on Stensrud's word which is based on a time period of mid-70's through late 80's, typically.
Below is a composite of IWVF for a period 2 Days Before through 2 Days After an event. Map 3 corresponds to the event day, Map 1 and 2 correspond to 2 and 1 Days Before, respectively, and Map 4 and 5 correspond to 1 and 2 Days After, respectively. From this data it seems clear that our IWVF is concentrated over regions adjacent, at least longitudionally, to the GoC. We can also see that as the surge happens parts of southern Nevada see very heightened concentrations relative to it's pre-surge values. Now, let's look at a composite of IWVF based on events of 1 Day of DWPT >= 90th percentile. Well, this is a very interesting and pretty cool figure. Again, Map 3 is the event, in this case DWPT, day. Based on this figure, it appears to me that IWVF is a precursor to DWPT events. We see in the preceding days, i.e., Map 1 and 2, that the IWVF field looks similar to the the post-surge event days, i.e., Map 4 and 5, from the IWVF event figure above. This does make physical sense in that we would expect a large northward shift of winds before an increase of dewpoint at the surface. I believe next I will use look at composites of precipitable water. Since winds are not part of this variable, we will get a better idea of exactly how the atmospheric moisture is moving pre- and post-surge events.
After much effort and little success with classifying easterly waves with the methodologies of Knippertz (2003) I have decided to classify these features with the criteria of Stensrud et al. (1997). I think the issue with using Knippertz methods is that the easterly waves typically have shorter wavelengths and are often tilted horizontally at angles that could prove to be difficult for identification with his strictly zonal calculations. Probably a more important factor is that these waves are identified at a lower level, usually 600 or 700 hPa, which makes topography an issue. Either way, I plan on coming back to this issue at a later time. For now, let's see what results I have found with Stensrud et al. (1997).
This identification methodology uses time-dependent changes in 600 or 700 hPa meridional winds to classifty easterly waves. They classified passage of an easterly wave when a northerly wind on Day 1 was followed by a southerly wind on Day 2, as reference to trough axis passage, at 110 W. They used Hovmoller diagrams to show how this methodology 'looked.' So, below is an example of 600 hPa for 1 June through 24 Aug 1987. Winds are at 22.5 N and 145 to 60 W. Warm (cool) colors correspond to southerly (northerly) flow. Their study used winds that were averaged over 10 to 22.5 N. Douglas and Leal (2003) showed that high amplitude waves over western Mexico typically migrate across the region between 10 and 30 N. Therefore, I chose to limit my domain to 20 (~Mexico City, MX) to 25 N (~Brownsville, TX) so that I would capture features inline with their findings as well as ones more associated spatially with the central and northern portions of the NAM. Many recent papers have started to use winds at 600 hPa in lue of 700 hPa so that an influences from the topography of Mexico does not affect the signatures of these wave features. I performed analysis on both levels for comparison and my own interest in their correlation.
Results: Fuller and Stensrud (2000) using Stensrud et al. (1997)methods -- 85 easterly waves over 14 years (only July and August) => 6.07/year and 3.03/month My work -- 260 (243) easterly waves at 600 (700) hPa over 29 years (only July and August) => 8.9/year and 4.4/month(600 hPa) => 8.4/year and 4.2/month(700 hPa)
How did the two levels compare with one another? Well, 161 waves (62% and 66% of waves at 600 and 700 hPa, respectively) were identified at both levels on the same day. I am happy with these results in the context of how they compare with previous work, but I believe we can definitely improve the validaty of these results by using a more robust identification scheme like those in Knippertz. In the case where unforeseen events come into play, at least we have results to use in our reserach that are in line with previous research.
After talking with Mike last week I decided to go back to my surge classification work. Along with thresholds for dewpoint and winds, I have now included precipitable water. Before I get to how I have taken into account precipitable water, I first want to mention how I have come to my threshold values for dewpoint and winds.
Many of the Stensrud papers have used wx station data from Yuma, AZ to define surges. Since this is the present benchmark for surge classification, I have worked to judge my results to theirs, i.e., roughly the same average monthly surge events. All of the papers that I have read so far have found an average of ~3.0 surge events per month for July and August (Note: Almost all of these papers only look at data for these two months). Mike, though admitting that describing an average for something as dynamic and variable as the NAM can be tricky, said that this value seemed quite legitimate. As a simple test of the methods of Stensrud, I ran his threshold algorithm on our SAO data for the Yuma station and got very similar results. After this, I used data from the closest NARR grid point to Yuma. With some trial and error I found that a 70th percentile threshold level for dewpoint, wind speeds and precipitable water gave me comparable results. From this successful result I moved forward and defined a larger domain of interest to test these thresholds over a larger, and more appropriate, area.
I am using a domain of 31 to 35N and 108 to 115W. An example of this domain is shown below for precipitable water, in mm, on 19 August 1983. I calculated an aggregated daily mean for each variable, as well as a daily 70th percentile value for each variable. Events of each are defined when the daily value is equal to or greater than the 70th threshold. I defined 4 different combinations of these criteria:
(1) Wind event on Day 1; PW event on Day1 or Day 2; DWP event on Day 1 (2) Wind event on Day 1; PW event on Day1 or Day 2; 2 consecutive days of DWP event from Day 1 (3) Wind event on Day 1; PW event on Day1 or Day 2; 3 consecutive days of DWP event from Day 1 (4) Wind event on Day 1; PW event on Day1 or Day 2; DWP event on Day 1 or Day 2
The average monthly number of surge events in a given July or August for each criteria were 2.85 (1), 2.79 (2), 2.2 (3) and 3.86 (4). Next, I wanted to compare these results with the results from using the Stensrud surge criteria for the NARRgrid point near Yuma. I did this by finding days were an event was classified by a specific criteria about, i.e., 1 through 4, and also classified by the one grid pointStensrudcriteria for Day-1, Day or Day+1. The results for percentage of agreement is below for each criteria:
(1) 59.8% (2) 64.7% (3) 70.7% (4) 56.9%
There does appear to be rather good agreement between these criteria and Stensrud's results, but it is not perfect. The criteria with the lowest average monthly surge events, (3) with 2.2, showed the highest correlation with Yuma. In general, I believe there is much room for improvement over the classification scheme of Stensrud since he has used such a limited data source of only one station. Branching out from what is tried and true could raise a lot of questions of our work, but I imagine that is typical of anything new within research.
Below is a graph of the average monthly occurrences of troughs, left column, and ridges, right column, over the rough extent of the United States. Our dominate monsoonal ridge is well represented in the June-Aug data. July and August also show a well defined separation between troughs off the east and west coasts of the US. A large blocking pattern, like the one associated with the monsoonal ridge, would produce a pattern very similar to this one.
June and September both show a larger extent of both trough and ridge occurrences. Since these months can be are more closely related to the more dynamical Spring and Fall weather patterns of the northern hemisphere than July and August, we could expect to see more transient features and thus a larger area experiencing these features during June and September.
Following Knippertz (2003), I ran calculations on 500 hPa height data for 1 Jun through 30 Sept from 1980 to 2008.
This identification scheme uses calculated zonal geopotential height gradient data to detect occurrences of troughs. For an in-depth methodology refer to the above paper. My domain covers 10 to 60N and 145 to 65W. Setting these domain boundaries, specifically the longitudinal boundaries, produces data for trough occurrences located between 130 to 80W, which is of importance because of its influence on the North American monsoon system.
I ran a moving-grid box calculation across the domain for each day of each year in order to locate troughs within the region. The grid-box calculation consisted of three boxes. Z2, the central of the three, was a 3x4 grid domain. Z1 and Z3, the boxes westward and eastward adjacent, respectively, were comprised of 3x5 grid domains. For a given day the mean of the averaged Z1 and Z3 was compared to the mean of Z2. The resulting value, P, comparable to the zonal geopotential height gradient, was then assigned to a central point within Z2. Values of P greater than 25 are indicative of a location experiencingtroughiness. Values of 100 or greater relate to more extreme, a.k.a. deep, troughs.
Below is an example of results for one day, 1 Jun 2001, from these calculations. The 500 hPa height field is shown in black, solid lines. We can see a clear pattern of troughiness off the western coast of the United States and the Mississippi River Delta. A well defined ridge is located between these two locations. The results from the P calculations are colored-coded, values ranging from 25 to 100, and plotted under the 500 hPa height field. The location of the P values that are associated with troughs are indeed co-located along the trough axis. After running loops of one week to one month, it seems clear that the calculations for P have worked quite well, and can be considered accurate, at least empirically. Next, ridges will also be defined. This will be a simple task though, as I will use the same P calculations that have already been done, expect I will look for large negative values to indicate ridge occurrences.
As referenced in an earlier post, I have been working towards quantitatively describing surges with IWV and IWVF. I have tried many different approaches to this problem, such as:
(1) the aggregated value of the domain being greater than 95th, 90th, 80th, 70th or 50th percentiles (2) sustained values of (1) for 2 and 3 consecutive days
Below are the 12 combinations of the above two critera for the year 1983. Titles for each plot are given above the respective plot. Note: You can click on any graph to open up the full resolution version of the image in a new tab.
To test these 12 thresholds for accuracy, I compared them to a Hovmoller for precipitation (averaged over -116 to -107 longitude) for the same domain and same year (This figure will be shown below). From the precipitation Hovmoller, I chose 5 suspected events on days ~38, ~50, ~70, ~78 and ~120. I compared the above plots to see which, if any, produced events on roughly these same dates. From that, I observed that the IWVF thresholds produced better results than the IWV, note plots 1 and 2 (1 day of >=90th percentile for IWVF and IWV, respectively). Overall, it appeared that 1 day of IWVF >=95th percentile, plot 3, and two consecutive days of IWVF >= 90th percentile, plot 5, produced the best results. These two plots will be highlighted below and compared to the Hovmoller of precipitation mentioned earlier. It is my judgment, that for this year the two consecutive days of >=90th percentile threshold produced better results. To provide a better view of exactly what this threshold is describing, below is a comparison of 'events' in this criteria to a Hovmoller of IWVF for the same time period and domain.
It seems clear that this threshold limit is picking-out the more statistically significant periods of IWVF. Now, to show the relationship between IWVF and precipitation, below are IWVF and precipitation Hovmollers for same temporal and spatial extents.
There does seem to be a rather nice agreement between this two parameters (precipitation surges seem to be associated with high values of IWVF). So, I got that going for me...which is nice.
Hopefully, this will be an accurate and clean way of describing this "Gulf Surges" in the North American monsoon.
To show the relationship between precipitation and southerly winds over our domain of study, I present the following figure:
I first noticed that the occurrence of southerly winds on days with precipitation (>=0.1mm/day) is amplified around topographic features. This makes physical sense, however, because these features will work to channel winds more northward as a result of up-slope orographic lifting.
Also important to note is the seemingly lack of a positive relationship between these variables along the western portions of our domain. This could be a sign of the main, north-to-south gulf surge corridor being further east, or simply an artifact of my data processing and calculations. Both are possibilities, but based on my knowledge thus far of the characteristics associated with surges, I believe some confidence can be given to the first assumption.
Before I get to the EOF-talk, here is a plot of climatology vs the 21-day moving mean for a pixel, chosen at random. I think this looks pretty good, or at least close to what I expected to see, i.e., close to climo but with some deviations.
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Now on to the EOF fun
EOF Procedures, same for IWVF and IWV, thus far:
(1) 28yr mean for each day for each pixel (2) Calculated 21-day moving mean for each day from 1 July to 31 August, a.k.a. "Tha Meat" (3) Perform EOF calculations on Step (2) data
Here are the plots of pattern variance for IWVF (top) and IWV (bottom).
These plots show that most of the variance is associated with the first pattern. This is a good sign, I believe, since it shows that we have a dominate pattern in both fields. Next, will be the contour plot of this first pattern for IWVF (top) and IWV (bottom).
These plots are hot off the presses, so I haven't come to any profound understanding of their deeper meaning as of yet. Any thoughts?
After reading over Simona Bordoni again, I felt compelled to take a quick look at 10m winds over our domain.
I first plotted a Hovmoller of only V component winds averaged over -116 to -107W (Lat = 24 to 26N) for the whole of 1983. After looking at this data, I noticed a lot of noise in the northern portions of the graph. As a means to loose that noise and help pick-out patterns associated with surges I restricted the domain to 24 to roughly 33N (longitude held same). An example of this domain is shown below. Looking at the first Hovmoller plot for this new domain, I noticed that the prevailing southerly winds were not allowing any clear patterns to show up. To compensate for the abundance of southerly, or positive V winds, I calculated the mean-wind for the year to provide a dividing point in the data. On the next Hovmoller, warm colors, i.e., white to red, will signify above average winds and cool colors, i.e., white to blue, will signify below average winds. A black contour interval is added to provide reference to the 0 m/s2 value. It can be seen by comparing the winds and precipitation, Hovmoller over the same year and domain as V winds, that the two variables show quite close temporal agreement with one another. Though these results should be expected, it is of importance to note this agreement now so that later analysis can use winds as a parameter with confidence.
Welcome to the wild world of classifying northward surges in the North America monsoon!
Currently, I am working with surge characteristics of IWV and IWVF. My domain of study is, for now, 29 to 33N and -116 to -107W. Here is an example of the domain (plotted is precipitation on 19 Aug 1983). For both IWV and IWVF a daily, domain-aggregated value was calculated from the daily-mean value of each pixel. This provided a daily value (1 June - 30 Sept) for each year of study (1980-2007). From this data the 95th, 90th, 80th, 70th and 50th percentile values were calculated for each day. This data provided the threshold from which events could be characterized for IWV and IWVF.
An event was first classified by one of five ways: The daily-aggregated value for each day was greater than or equal to the (1) 95th, (2) 90th, (3) 80th, (4) 70th or (5) 50th percentile for the specific given day. Events were further restricted to (A) two consecutive days or (B) three consecutive days of 1 through 5. Here are examples of events for IWVF and IWV, 3 consecutive days of equal to or greater than 90th percentile (2B), and a Hovmoller of precipitation for 1983:
Though the criteria for IWVF and IWV seem to be picking-up on some of the events displayed in the precipitation Hovmoller, it is clear that this classification is not yet perfected.
As for tomorrow, further work will be done to refine this classification scheme. I will also begin work using EOF analysis to better understand IWVF and IWV patterns for July and August.
Figure 2: Principal Component 2 for both seasons
Figure 3: Variance of the first 10 components for both seasons
