Transcript Observed Convective initiation

CI VERIFICATION
METHODOLOGY &
PRELIMINARY RESULTS
[email protected]
In short:
1.
2.
3.
4.
5.
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
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
2. Comparing Observed to Forecast
By finding distance between centroids
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
3. Scoring
Two ways: Hungarian match and distance
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)


Time Window:


13 km^2
-15 min to +45 min
Distance threshold:

Hits have to be within 100 km
4. Preliminary results
Real time images and daily scores
Real time


Can see ObservedCI, ObservedCIv2, UAH and
UWCI algorithms at:
http://wdssii.nssl.noaa.gov/web/wdss2/products/r
adar/civer.shtml
Example
Verification dataset

Dataset of centroids over Spring experiment is
available at:
 ftp://ftp.nssl.noaa.gov/users/lakshman/civerification.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