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Found 7 results

  1. So we are wrapping up a month that say many highs (Early month Blizzard) and lows (late month TORCH) but we have survived and are in the midst of our stormiest period of the season historically. The pattern looks to be quite active as we enter the month with the PAC reigning supreme. We have a small threat later this week to start the month that could be a refresher of sorts for some and wet for others. After that we look towards 5-7th for a more significant storm coming up the coast. Details still to be ironed out on that one. Lets see where we go from here.....
  2. NAM cross-section for Thursday afternoon from just south of Philly to about Greenville, Maine
  3. Ngwips is now in open beta. Please check it out! www.ngwips.com Some features of this system include: * Ability to overlay/compare model outputs across many different models, and levels at the same time. * Fluid and simplified looping/stepping mechanism. * ECMWF, NAM, NAM4km, GFS 0.5 degree models. (HRRR, RAP, UKMET, and NOGAPS to be added soon) * Ability to view past runs. * Extremely quick updating of model data. * Ability to drill down to subregions. Some Recently added features: * Kuchera snowfall Algorithm plots (12hr, 24hr, 72hr, 120hr, Total) * Precipitation (12hr, 24hr, Total) * Catagorized Simulated reflectivity (snow/rain/mix) * Loopable/zoomable Radar Application overlaid on google maps. * High resolution 2m temperature overlays. * Ability to save/export images, and share links to friends. * Best of all, it's free! It's in Beta, so expect bugs. Official release is scheduled for August. Also, model images won't update as quickly as some other sites initially. After the open beta I will be migrating the data processing cluster to Linode, so the images will generate much faster. The images will generate faster than any alternative. Thanks! NOTE: Data will be unavailable on Tuesdays and Sundays during open beta. Please follow @Ngwips https://twitter.com/Ngwips on twitter for future updates! Thanks again to everyone that is trying it out!
  4. Hi Everyone, I have been working on a tool to allow global point extractions from various models. Right now the page allows you to pull surface variables from the following models, GFS,GEM,NAM and the GFS and CMC ensemble means globally. The page pulls the points you request not predefined cities or airports. I would like to get some feedback so please check out the page. The page URL is http://weather-globe.com. Let me know what you think!
  5. Hi Everyone, I have been putting together a WRF modeling page for a while now and would like to get some feedback. Any opinions or suggestions are welcomed. If there is something you think would be interesting to see just leave me a comment. It currently runs a 50 member ensemble at 00 UTC using the GFS and NAM for initial conditions along with 4 high resolution runs just for the Northeast. If you have any question just let me know! http://wrf-modeling.com/ -Tim
  6. Hi, Everyone here is a page I am currently working on getting up and running. Have a look and if you have any suggestions I would be happy to hear them! www.wrf-modeling.com
  7. http://www.nws.noaa....s_hybridaab.htm This is pretty big news thats been in the making for a while, potentially bigger than the upgrades that we saw with the GFS in previous years. While the model itself is not being modified, its data assimilation scheme is set for a big upgrade. As many of us know, its current data assimilation scheme (3DVAR) can sometimes cause the model to lag behind in the predicability standings. With this upgrade though, the GFS shifts from using a pure 3DVAR scheme to a hybrid 3DVAR-Kalman scheme. The main difference between 3DVAR and Kalman is that error covariances of observations is no longer static but rather are based on an ensemble spread of error. A good way to think of it is that values relation to one another near strong gradients (such as temperature near a frontal boundary) is likely to go up. In 3DVAR, we would assume that the error value between two observations at two specific locations is static regardless of the environmental regime. In Ensemble Kalman filtering, we can better account the error in relation between two observations depending on the synoptic environment. This is possible through gfs ensemble spread. Look for significant improvements in model error scores in both the mid-latitudes and tropics, although there might be a slight degradation in the predicability of precipitation in the summer.
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