SHARPpy Preview (AMS Presentation)
Last July I wrote about software I was developing for displaying forecast soundings. Unfortunately, after discussing what I already had done in preparation for last year’s Hazardous Weather Testbed (HWT) Experimental Forecast Program (EFP), my schedule prevented me from devoting any time toward this project.
In the days before Christmas I realized that I needed to revisit SHARPpy (SkewT and Hodograph Analysis and Research Program in Python) if I was going to have anything for my presentation at the American Meteorological Society’s Annual Meeting in New Orleans, LA. So, the last two weeks has been devoted to frantic code writing to put together some form of SHARPpy in time for my presentation. When I sat down and looked at my old work, I couldn’t understand, nor could I remember, what I had been doing. I decided to throw out my old work and begin anew.
SHARPpy has been completely overhauled. The visual aesthetics are modeled after the Storm Prediction Center’s sounding analysis tool, NSHARP, and the underlying numerical routines are based on SHARP95. SHARPpy is written completely in pure Python — no Numpy, Scipy, or Matplotlib. In other words, once Python is installed on a computer, you can install and run SHARPpy — there are absolutely no additional dependencies to install! The motivation for sticking with pure Python, and sacrificing the speed Numpy, Scipy, and Matplotlib offer, was to allow for simple integration into the National Weather Service’s data visualization software package (Advanced Weather Information Processing System II — AWIPSII), which is currently under development. (Note, SHARPpy 2.0 will most likely be refactored to make use of Numpy, Scipy, and Matplotlib.)
SHARPpy is written in such a manner that the file handing and data management, graphical displays, and numerics are all separate. This greatly increases SHARPpy’s utility. Inside SHARPpy, all calculations are done on a custom data structure, called a Profile Object. The Profile Object consists of 6 data arrays: Pressure, Height, Temperature, Dewpoint, U-component of wind, and V-component of the wind, as well as some meta-data and helper functions to identify things such as the index of the surface layer. (Alternatively, one could provide the Wind Direction in degrees and Wind Speed and the Profile Object will convert these to the U-, V-components on the fly.) The benefit of using the Profile Object is that SHARPpy knows the structure of the data on which it will operate and/or draw. Thus, in order to add support for additional data types (observational, BUFKIT format, raw models, etc) all one has to do is create a wrapper to put the data into the Profile Object. (The Profile Object has helper functions to create itself. All one does is pass the 6 arrays!) Also, since the drawing is separate from the numerics, SHARPpy can be used to compute thermodynamic and kinematic parameters for model output — without having to actually draw individual soundings!
Below are a smattering of sample images created this evening.
The first image is tonight’s sounding from Miami, FL. The temperature trace is in red, the dewpoint trace is in green. The blue trace corresponds to the wet-bulb temperature. The yellow-traces (there are more than one, they just overlap!) are the parcel trajectories for a Surface-Based Parcel, 100-hPa Mixed Layer Parcel, and the Effective-Inflow-Layer Mixed Parcel. In the upper-right, the hodograph is displayed with white dots indicating each 1km AGL interval. (Note, the program goes out to the web and downloads the data, lifts all the parcels, and draws the display in about 1-1.5 seconds!)
In addition to computing the visual SkewT and Hodograph, SHARPpy can compute kinematic variables and parameters. Below are just a sample of the fields that can be computed. Wind information is displayed in a format of U-, V-component, Wind Direction @ Wind Speed. Helicity information is provided positive+negative helicity, positive helicity, and negative-helicity. Again, this takes less than 0.5 seconds to compute and display. (These are for the Miami, FL sounding displayed above.)
Below is a small sample of the thermodynamic variables and parameters that can be computed. All five parcels (Surface, Mixed-Layer, Most-Unstable, Forecast Surface, and Effective Inflow Layer) are computed. This routine takes about 0.5 seconds to run. (These are for the Miami, FL sounding displayed above.)
Lastly, I’ve incorporated preliminary support for ensemble soundings. Below are five, 4-km storm-scale ensemble member forecasts for Birmingham, Alabama. These model simulations were created in support of last year’s HWT EFP. They were initialized at 00 UTC 27 April 2011 and are valid for 21 UTC 27 April 2011. Each forecast member has over 1100 sounding locations, with 37 forecast soundings at each location. These data are stored in a text file that is approximately 150MB per member! SHARPpy can read these text files, parse out the correct soundings, compute all the parameters, and draw the sounding in less than 5 seconds!
What is displayed are the temperature, dewpoint, wet-bulb temperature, and hodograph for each of the 5 members. The thicker lines are from the “control member” and the other lines are from various perturbations. I should also point at that the wind barbs plotted on the right of the skewt are from the control member, as well.
I still have a lot of work left ahead of me (such as fixing up some of the displays and incorporating the text output on the main graphical display), but SHARPpy is coming along nicely. If you will be attending the AMS Annual Meeting later this month, please be sure to stop by my talk! It’s in the Python Symposium and will take place Tuesday morning at 11:15 AM. After my presentation, I hope to release SHARPpy to the open-source community. This will give people the ability to download and test SHARPpy while it is still under development, provide feedback, and even help develop new features! Some features that I’m interested in including are time-height cross-sections, more winter weather support, and whatever else might come to mind! It is my hope that SHARPpy can become a community supported sounding analysis package that the meteorological community can coalesce around!
And, for my international friends, if you aren’t fond of SkewTs, SHARPpy can also make STUVEs!
Please let me know what you think!
Tornado Warnings: The Map
One of the most frequently asked maps is for all plot of all the tornado warnings. Here are the tornado warnings issued by year. Each warning was color-filled red with an alpha value of 0.1. Thus, it would take 10 overlaps for the color to display as true red. Thus, the colors are shaded from light pink (no overlaps) to red (at least 10 overlaps). 1 October 2007 the tornado warnings transitioned from county-based warnings to storm-based warnings.
As you can see, the number of warnings increased up through the start of storm-based warnings. After that time period, the number of warnings increased, but the overlap has decreased. One thing that is very apparent is that the number of warnings across the southeast United States has certainly been on the increase…
Also, one visual aspect that should be more disturbing that it is, notice the number of county borders that stand-out in the storm-based warning maps of 2008-2010.
Corrections to Previous Posts
It is with regret I am forced to announce corrections to two previous posts:
Weather Ready Nation: Counties Warned for Tornadoes By CWAs
and
Weather Ready Nation: Tornado Warnings By CWAs.
For the post, Weather Ready Nation: Counties Warned for Tornadoes By CWAs, the only update has been to color white all CWA areas with 0 counties warned for tornadoes. Previously these CWAs were colored cyan making it impossible to differentiate between WFOs that warned no county for a tornado and WFOs that did warn counties for tornadoes.
For the post, Weather Ready Nation: Tornado Warnings By CWAs, the updates were a little more serious. I first applied the previous correction so that CWAs with no tornado warnings would be colored white. After doing this correction, I noticed that some of the newer CWAs were consistently white in the early years. I realized this was because the tornado warnings had been issued by different WFOs, some of which no longer exist. To correct for this, I took the county UGC (Universal Geographic Code) of each tornado warning and converted it to the FIPS code. I then took the FIPS code and converted that to one of the current (2011) WFOs. Thus, the maps now depict the number of tornado warnings issued for each geographic region, marked by the current CWA boundaries.
As is always the case, I strive to keep my posts factual and will post corrections as soon as errors are brought to my attention.
Caption This: Me at the Weather Ready Nation Conversation
Those who know me well know that I absolutely love to tease those with whom I am friends. To this end, below is a rather unflattering picture of me taken this week at the Weather Ready Workshop. I encourage everyone to take a moment and create a caption for this photograph. Please post your caption in the comments! (And, please, try and keep the captions somewhat clean!)
Weather Ready Nation: Tornado Warnings by CWAs
Tonight’s blog post depicts the number of tornado warnings issued by year by CWA. I should add a disclaimer: I am using the current CWA boundaries for all prior years. Thus, it one runs the risk of making incorrect assessments if one directly compares the specific increase in CWA tornado warnings counts. Instead, the idea is to look at the general trend in the number of warnings issued by forecast offices.
Data for 2007 and later years need to be understood in the context that WFOs have switched from county-based warnings to storm-based warnings. Thus, it is possible to have situations where multiple storm-based warnings are in effect when in the past a single county-based warning would have been issued. The flip side to that is, a single storm-based polygon can now capture 2 or more counties, which would have required multiple county-based warnings in the old system.
As I mentioned last night, the number of warnings issued can be thought of as a cumulative measure of the number of times the NOAA Weather Radios would have sounded in a specific CWA. Thus, the increasing number of warnings issued can be thought of in terms of the National Weather Service requesting more responses from the residents in their CWAs.
About Patrick
Patrick Marsh is a Ph.D. student at the University of Oklahoma's School of Meteorology. He earned a Bachelors of Science in Mathematics and Physics (cum laude) from the University of Arkansas in 2005 and a Masters of Science in Meteorology in 2007. In addition to working on his doctorate, Patrick serves as the National Severe Storms Laboratory Liaison to the Hazardous Weather Testbed where his research interests lie in working with operational forecasters, data mining, data visualization, and all things high-impact weather. For more about Patrick's work, check out his complete CV!
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