An IFPS Grid Editing Methodology

Slide 1: Title

Slide 2: Model grids will continue to improve

When the models are NOT forecasting correctly, is when we earn our
salaries

Slide 3: Emphasize the word "Editing". Editing grids sounds daunting --
but it is just the same as the objective analysis problem.

Slide 4: ADAS first guess fields

> ADAS is the ARPS Data Assimilation System.
>
> ARPS is the Advanced Regional Prediction System.
>
> This version of ADAS is run by the University of Utah and provided to
> all Western Region WFOs.
>
> The temperature color curve emphasizes 'decades' of temperature
> values, but still includes color variations within the decades. A
> useful color curve in areas of terrain -- since the daily variation is
> large.

Slide 5: ADAS analysis correction

The color curve emphasizes warming/cooling over the original first guess
with warm/cool colors.

Slide 6: Final ADAS analysis

Slide 7: Comparison of ADAS first guess and analysis

Note that differences are small -- but the color curve makes it easy to
see some of the changes -- especially in Oregon.

Slide 8: Objective Analysis relates to our editing task

Slide 9: You CAN do it any way you want. The point here is that
concentrating on the changes helps us MENTALLY stay on the right track.

Slide 10: This is a grid straight out of GFE initialization.

Note the topographic structure which is introduced by the GFE
initialization. This considers model soundings at every gridpoint -- so
where the model has an inversion -- the initialization will show it, and
variations in the model lapse rate will be shown in the initialized
grid.

Slide 11: A hypothetical meteorological situation where you need to edit
the grid.

Slide 12: First using a series of tools -- editing the MaxT field
directly.

The edit area is a little hard to see -- but it encompasses the western
part of the CWA.

Slide 13: A simple adjust_up edit (after setting the delta value)

Emphasize that the grid "looks" OK -- mainly because the topographic
structure is very large -- and has not yet been 'masked out' by any
edits.

Slide 14: However, the "look" of what we have done -- is pretty
simplistic.

Need a better word than 'cartoonish'

Slide 15: Our intent was to put more warming in strong downslope areas
-- and less warming in other areas.

Define another edit area where downslope flow is strong with southerly
winds. Drawn by hand here -- but could have been via queries -- or
stored edit areas.

Again, edit area is hard to see.

Slide 16: Do another adjust_up with edge effects.

You still cannot 'artifacts' of the editing - the final field still
looks OK because its spatial structure is controlled so much by
topography.

In flat terrain -- such edits would be much more obvious right away.

Slide 17: Look at the total "edits" again.

Still need a better word than "cartoonish"

Slide 18: It is doubtful that a forecaster would go much further.
Further edits would likely introduce visually obvious 'artifacts'

So if a forecaster stops here -- the edits we have done don't look at
all like they are from a person that understands meteorology.

Instead, what if we thought about the 'changes' only. We could make a
very detailed field -- which is meteorologically sound.

Slide 19: Start with zero change.

Using a special 'parm' called WG1 for "work grid one" -- you could name
it anything

Note 'change' color curve associated with the parm

Slide 20: Pick up the same edit area we used earlier for 'western part
of CWA'

Slide 21: Upslope/Downslope effects using Wind Effects tool.

You can pick a model wind (and model level) -- but here we have chosen
'constant' -- and have specified the constant wind as 180 degrees at 40
knots.

You can also choose the amount of topo smoothing, and scale the
magnitude of the results -- using the other options.

Slide 22: The result is a very detailed field of warmer / cooler.

Note how the sign is wrong. WindEffects tools can be used with PoPs to
INCREASE where wind is upslope. To be used for temperature where upslope
is cooling -- we need to do something else

Slide 23: Simple Multiply/Divide tool allows us to multiply by -1

Slide 24: Result now has the right sign.

Note color table has orange right at 0 -- so it looks like the values
changed -- but they didn't.

Slide 25: We want to chop off "cooling" values -- and only have warming.

Limit_Values tool takes any value outside the range -- and sets it to
the new limits.

Can trim both the top/bottom at the same time -- but here we are only
trimming the bottom of the range.

Slide 26: The results of the trimming of negative values.

Slide 27: Could also remove "cooling" by raising all values such that
lowest value is now zero. This keeps more of the smooth spatial
distribution, without creating artificial areas of "flat" changes.

ShrinkStretch tool can also stretch the spatial pattern -- or change
both ends of the range -- but here we are only 'squeezing' up the bottom
of the range.

Slide 28: Results of the shrink/stretch

Slide 29: Might want to further edit using a variety of freehand tools
like Pencil, Smooth, etc.

Not as 'critical' that you get things right as when using these tools on
the field itself -- because you are only editing a change grid. Mistakes
with pencil tool (wrong size, etc.) are not as devastating.

Slide 30: Now need to add this 'change grid' back onto our original
field.

Add_change tool does this.

When you are editing the real grid (i.e., MaxT) you can run Add_change.
It grabs "change grids" in this time range -- and adds them to the
current field. It puts the result back into the current field.

Emphasize on grid manager the sequence: take field, add change grid, put
result back into field.

Slide 31: Results of Add_change

Slide 32: Comparison of using change grid methodology -- versus editing
MaxT alone.

Grids are not THAT different.

Slide 33: However, comparison of the changes that were applied:

Very different. Change grids helped to make a very complex changes
possible -- without ruining the field, or making artifacts of the
editing process obvious.

Slide 34: The main point.

Why are you changing the grid? How much do you want to change it? Where
do you want to change it?

Slide 35: Real example:

Visible satellite image the morning after a small mesoscale snow band
dropped a lot of snow in central Oregon. Up to 12 inch snowfall at some
locations -- while standard reporting stations had just a few hundredths
of liquid water. Northerly flow created upslope conditions in the areas
where snow fell.

However clear skies now means that temps will be warmer than current
forecast -- over the rest of the area, but cooler than current forecast
over the area with new snow cover.

Slide 36: Change grid that forecaster derived.

Very complex. Warming over much of Oregon, but cooling over two areas
with snow.

Would be VERY difficult to accomplish this using a sequence of tools
while operating on the MaxT field directly.

Slide 37: Any tool can be used to edit the 'change grid'

However, a few have special purpose to help derive some useful change
grids.

Slide 38: Change_over_Time tool

While editing a 'change grid' -- specifiy the parm, the model, and the
time period.

Use grid manager to emphasize the sequence: The current grid, minus the
the grid from earlier.

Could be done on Fcst grids -- but here we calculated the change in the
model grids.

Slide 39: MesoEta has interesting 24-hour changes. Cooling in valleys,
Warming aloft.

Would be very difficult to create such changes yourself.

MesoEta may have the temporal changes well depicted -- but be off with
the actual temperature values.

Slide 40: So...we can use the Add_Change_over_Time to take the change
grid we got from the model -- and apply it to our forecast grids.

Used when editing the final grid.

Use the grid manager to emphasize the sequence: previous grid, plus the
change grid, put result in current grid.

Slide 41: The result -- a very nice looking grid.

Slide 42: Another specialized tool to calculate a change grid.

Used when editing 'change grid'.

Use the grid manager to emphasize the sequence: Fcst grid minus model
grid -- put result in change grid.

Could be called 'Difference_from_Model' tool.

Slide 43: The difference grid.

Wow -- this is ugly. Our current forecast has LOTS of differences from
the model

Slide 44: Perhaps we want to be 'less detailed' in our differences from
the model. Smooth the field a few times and you get the large-scale
pattern of how our forecast is different than the model.

We could edit this further.

Slide 45: Then add the change back onto the grid with the
Add_Change_over_Model tool.

Use grid manager to emphasize the sequence: model grid, plus change grid
-- put result in current grid.

Slide 46: Many tools can be useful -- introduce serp tool

Slide 47: Sample points indicate values we want (color table is turned
off so that field is invisible)

Slide 48: Field that serp creates. A smooth field that matches all the
points.

Slide 49: Serp is a 'distance weighted' scheme -- but unlike some
objective analysis schemes -- it will match every point EXACTLY.

Slide 50: Again, color table is turned off to emphasize what the values
are at points. Note that some points -- with a lot of variability -- are
concentrated in one area

Slide 51: Note that serp puts a lot of detail where there is a lot of
detail in the observed data.

Some objective analysis schemes would not do this -- they would limit
the detail to be reasonable for the average grid spacing.

Slide 52: If all values are the same -- a good objective analysis system
sees that a consistent bias exists

Slide 53: An objective analysis that makes four bulls-eyes is not
sensitive to any consistent bias

Slide 54: Serp sees that the bias is consistent and makes a flat
adjustment

Slide 55: Where 2 points specified -- a gradient is introduced. Note
that the influence reduces to zero at infinite distance.

Slide 56: The points are 'curve fit' with serpentine curves. This shows
how a 'two-point' curve would fit.

Slide 57: The distance between two points controls the scale of the
response.

Two points far away create a very large spatial correction -- while two
points very close create a very small spatial correction.

Slide 58: Topography is important in most locations

This is a picture of topography in our area. Note large flat Snake River
plain in center of grid -- and small river canyons in other areas.

Slide 59: Zoomed in a bit on one of our difficult deep river canyons.
Note nearly 5000 foot elevation difference between two points very close
together spatially.

Slide 60: serp without elevation effect. Distance is only influence. A
point at 8000 feet -- but close to the 3000 foot control point -- is
more influenced by the 3000ft control point than the 8000 ft control
point.

Slide 61: Equate 12 ft to 1km of horizontal distance. This gives good
results with points at nearby elevations getting similar changes.

Can change the elevation effect. 12ft to 1km works well for our CWA. In
a flat CWA it would have little effect. Would be interested to hear if
people think other values are more appropriate.

Slide 62: OK -- serp intro is over. Serp used to keep spatial detail --
but match values at certain points.

Slide 63: Be careful of serp! Dialog box -- show using 'actual values'
option (and elevation effect is on).

Note that you can specify pre-deterimined sets of points, or the current
sample points on the screen.

Note that current values at sample points will be read.

Slide 64: Dialog shows current values -- and then user can change them
as they like.

Slide 65: Result now matches all the new values.

Everyone is happy right?

Slide 66: But this is the change that was introduced.

Is that the kind of pattern you were envisioning when you decided to
raise some stations by x degrees, and cool others by y degrees?

Do you possibly have more meteorological info about where the
warming/cooling will take place?

Slide 67: Serp option for 'changes' rather than 'actual values'

Slide 68: Note that all changes start at 'zero'.

We can set the changes EXACTLY as before.

This is a bit better -- but still dangerous.

Slide 69: The changes are exactly the same!

Slide 70: Use this as a starting point. Then refine it using other
tools.

Here I modified the area of the cooling in Oregon, the zero line along
Oregon/Idaho border, and included the warming all along the river valley
-- well further west than in the blindly 'serped' field.

Slide 71: Sometimes you DON'T have any idea though. Perhaps the point
values come from a statistical scheme.

Great. Use that as a starting point -- then make further changes.

Slide 72: If you want to use MOS -- you don't have to decode the
bulletins yourself. Tools are already available

Slide 73: Example of MRF unadjusted and adjusted at day 7.

Note new model on grid manager (ADJMRF). This is output from
MatchMOSAll. Others (ADJAVN, ADJETA, etc.) are also available.

Slide 74: Conclusion

Slide 75: Thanks to Brian Motta / Shannon White

Thanks to FSL

Thanks to BOI staff

Slide 76: Summarize tools discussed here.

URL to smart tool respository.

Emphasize registering for tool -- so you get notified of updates

Slide 77: Common question about how to add 'change grid' to your IFP
database.

Two lines in your localConfig file.

Note that only one parms= statement is allowed. If you have any other
fields in parms= statement, you need to include them here)