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