Thursday, July 13, 2017

Spanish Fork HRRR terrain

If you live at the mouth of a canyon you know that it can get windy. One challenge in numerical weather prediction is the inability to resolve small scale features, such as terrain (well, we could run models at ultr-fine scale, but we don't have the computational capacity to do that). The HRRR model has a 3-km grid spacing, which is great, but still too coarse to simulate the actual depth of Spanish Fork Canyon. See the examples below:

Model terrain in the HRRR at Spanish Fork. You are looking south east towards Spansih Fork Canyon

Below is the actual terrain, from a 30 m Digital Elevation Model, is shown below. Again you are looking towards the southeast towards Spanish Fork Canyon.

Finally, a Google Earth image showing the area we are looking at...

Wednesday, July 5, 2017

HRRR "Hovmoller" Forecasts

Hovmoller diagrams have been used to show wave propagation over space and time. By changing the dimensions of these diagrams to a valid-time/forecast-time dimension, then these kind of plots can be useful to show an ensemble of model forecast results at a point.

For example, here is a "Hovmoller" diagram showing the HRRR forecasted temperature at the Salt Lake International Airport for two days. The observed air temperature is shown at the bottom and look almost like a bar code.

From this figure we can see how air temperature changes across the time period. Reading the figure from left to right and looking at all forecast hours (vertical columns), the temperature is generally cooler at night and warmer in the day with July 4th being forecasted as a hotter day than July 3rd.

A single model run can be read diagonally. For example, pick an analysis hour, then look at the 1-hour forecast by moving one box up and one box right. You can see that some model runs were cooler than other.

Next, we can look at how each model run differed from each other by reading a column from top to bottom, and see how the HRRR forecasts changed between successive model runs. This is useful for determining the likelihood for certain atmospheric conditions.

Now, in real-time operations you don't have all the HRRR data for the rest of the day. The Hovmoller plots instead look like this, with missing data because those HRRR models haven't run yet.
This figure shows forecasted wind speed at Antelope Island (station ID UFD09) for a 24 hour period on July 5th. The contours indicate stronger higher wind speeds forecasted within a 54x54 km box centered at UFD09. Again, the white is the missing information because those HRRR simulations have not run yet. 

You'll see that the latest HRRR run is showing strong winds for a period of time that were not previously forecasted. This may likely be a result of new data assimilated into the HRRR model leading to a greater chance of strong winds. In this case, this particular model run formed a thunderstorm in the area, causing a downdraft and stronger winds in the vicinity as shown in the Reflectivity Hovmoller...
No composite reflectivity was forecasted in any other model run except for the most recent two. There was higher reflectivity in the vicinity as shown by the contours. For this case, there was little warning of a possible storm activity at Antelope Island (at the time of my writing, there has not been a convective system develop over Antelope Island).

A Hovmoller for Spanish Fork (station ID UKBKB) the HRRR forecasts show a greater potential for storm activity while more successive runs are indicating some convective activity for the rest of the afternoon and evening.
There has been some storm activity near Spanish Fork at this time, but mostly in the surrounding mountain area.

Another example of using the HRRR Hovmoller forecast fires to determine the potential for storm activity is from the Burro Fire...

A storm never developed directly over the fire at 21:00 UTC, but there was some other convective activity in the surrounding area.

What we learn: Forecasting for convective outflows in the HRRR model is difficult, especially when you want to pin-point when and where convection will occur. Convective outflows are hard to forecast even an hour or two before they occur, because the time scale of these clouds form are short--less than an hour. A probabilistic approach is most useful for forecasting these events. The ensemble forecast approach should consider an offset  in storm location and timing for such events. In the above Hovmoller for reflectivity for the Burro fire, I would know that convective activity is possible, more so in the vicinity, but not certain. As a fire manager, I would be need to be more aware of convective situations that could potential make firefighting more difficult.

Thursday, March 9, 2017

The University of Utah Downtown Data Center

I finally got to see the computers I work on all the time. I took a trip to the University of Utah Downtown Data Center where the Center for High Performance Computing resources are located.

The building is an old Coca-Cola bottling plant built in 1938. It is as long as a city block. It is unmarked, for security purposes, and the windows are all fake. two inches on the other side of the window is about 8-12 inches of concrete and reinforced steel. The building can use up to 2.4 megawatts of power, but typically uses only 1. Power resources can be scaled up to 10 megawatts. There is quite a bit of redundant power sources. Everything runs on battery backup, which can sustain the building's computer resources for 15 minutes. But, it only takes about a second for diesel generators to kick on in a power outage. There are about 40 miles of copper in the building. Fans and sprinklers regulate the environmental temperature and humidity. Fine particulate's is also monitored and filtered.
Downtown Data Center, an old Coca Cola bottling plant

This reminded me of my own cable pulling days working for Nebo School District. Though, I never dealt with this much wire!

Fans and misters. It was really windy walking between the computer room and this room due to a large pressure difference.

Back up generators. They have enough fuel to run the place for a few weeks in an emergency.

Me with the CHPC PANDO archive. This is where my HRRR archive is located.

Kingspeak nodes. This is where I run my WRF simulations.

The back side of the Kingspeak computer.

A few computers used by researchers in our department.

Data is stored on these terabyte disks.

Computers I do my daily work on: gl1, gl2, meso1, meso2, meso3, and meso4.

I was so happy to finally met my computer.