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CI Verification methodology & preliminary results. lakshman@ou.edu. In short:. Find observed CI using radar echoes aloft Compare to CI forecasts from UAH and UW Find hits, misses, false alarms Preliminary results Discussion. 1. How observed CI was determined. From radar data aloft. - PowerPoint PPT Presentation
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CI VERIFICATIONMETHODOLOGY & PRELIMINARY RESULTSlakshman@ou.edu
In short:1. Find observed CI using radar echoes
aloft2. Compare to CI forecasts from UAH and
UW3. Find hits, misses, false alarms4. Preliminary results5. Discussion
From radar data aloft
1. How observed CI was determined
Observed CI For verification purposes, need a “truth”
field Independent of way in which CI is detected Not tied to “objects”
Based on multi-radar reflectivity at -10C isotherm Reflectivity aloft, associated with graupel
formation Good indication on convection Less contaminated by clutter, biological
echoes The multi-radar reflectivity is QC’ed, but QC is
not perfect
Reflectivity at -10C on 4/4/2011 Approx. 1km resolution over CONUS
Classifying CI Define convection as:
Reflectivity at -10C exceeds 35 dBZ New convection:
Was below 35 dBZ in previous image Images are 5 minutes apart
Done on a pixel-by-pixel basis But allow for growth of ongoing convection
Model verification The CI detection algorithm is now
running realtime Being used to verify NSSL-WRF model
forecasts of CI
Aside: model verification Probability of CI in one hour very similar
But time evolution different
Real time: Image at t0
Real time: Image at t1
Real time: Observed CI
Methodology Take image at t0 and warp it to align it
with the image at t1 Warping limited to a 5 pixel movement Determined by cross-correlation with a
smoothness constraint imposed on it 5 pixels in 5 min 60kmph maximum
movement Then, do a neighborhood search
Pixels above 35 dBZ with no pixel above 35 dBZ within 3km of aligned image is “New Convection”
Example: Image at t0
Example: Image at t1
Example: Image at t0 aligned to t1
Classification
Definition of Observed CI Computed CI using 4 different distance
thresholds: 3 km (as described) 5 km 15 km 25 km
The 15 km threshold means that a new CI pixel would have to be at least 15 km from existing convection to considered new In the HWT, this is what forecasters tended to like What I will use for scoring
Significant cells? One possible problem is that even one pixel
counts as CI So, also tried to look for at least 13 km^2 cells
This will be called ObservedCIv2 Tends to find only significant cells (or cells after
they have grown a little bit). Started doing this after some feedback on this
point Not available for all days Can go back and recompute, but doesn’t seem to
make much difference to final scores
By finding distance between centroids
2. Comparing Observed to Forecast
Computing distance Take the ObservedCI, SatCast and UWCI
grid points Find contiguous pixels and call it an object Find centroid of those objects
Use storm motion derived from radar echoes and model 500mb wind field
Compute distance between each ObservedCI centroid and each forecast CI centroid
Distance computation Distance is computed as follows:
If observed CI is outside time window of forecast CI (-15 to +45 min), then dist=MAXDIST
Project forecast CI to time of observed CI Using storm motion field
Compute Euclidean distance in lat-lon degrees
MAXDIST was set to be 100 km Pretty generous
Two ways: Hungarian match and distance
3. Scoring
Scoring: Hungarian Match Create cost matrix of distance between
each pair Observed CI to forecast CI
Find best association for each centroid to minimize global sum-of-distances
Any associated pair is a hit Any unassociated observed CI is a miss Any unassociated forecast CI is a false
alarm
Scoring: Neighborhood Match Consider each observed CI
If there is any forecast CI within MAXDIST, then it is a hit
Otherwise, it is a miss Consider each forecast CI
If there is no observed CI within MAXDIST, then it is a miss
More generous than the Hungarian Match Since multiple forecasts can be verified by
a single observation
Summary of numbers that matter Observed CI:
35 dBZ 5 pixel warp in 5 minutes 15 pixel isolation for new CI
Significant cells area threshold (ObservedCIv2) 13 km^2
Time Window: -15 min to +45 min
Distance threshold: Hits have to be within 100 km
Real time images and daily scores
4. Preliminary results
Real time Can see ObservedCI, ObservedCIv2, UAH
and UWCI algorithms at: http://wdssii.nssl.noaa.gov/web/wdss2/pr
oducts/radar/civer.shtml
Example
Verification dataset Dataset of centroids over Spring
experiment is available at: ftp://ftp.nssl.noaa.gov/users/lakshman/civerifica
tion.tgz
Contains: All ObservedCI, SatCast and UWCI centroids ObservedCIv2 for when we started creating
them Results of matching and skill scores by day
Example result for June 10, 2011 UAH
UWCI
These scores are typical
Only significant cells (ObservedCIv2)
UAH
UWCI
5. Discussion
Possible reason for low values Could be a factor of the cirrus mask
Computing scores without taking the mask into account is problematic Because mask is so widespread, most
radar-based CI happens under the mask
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