DEVELOPMENT OF
COMMON NOWCASTING TOOLS IN
CENTRAL EUROPEan
NATIONAL WEATHER SERVICES
V. Zwatz-Meise and A. Jann et. al. 1 , N. Strelec-Mahović and D. Drvar 2 ,
A. Horvath et. al. 3 , M. Jurasek and J. Kanak et. al. 4, A. Poredoš et. al. 5
presentors:
ales.poredos@gov.si
drvar@cirus.dhz.hr
1 Zentralanstalt fur Meteorogie und Geodynamik, Austria (ZAMG)
2 Meteorological and Hydrological Service of Croatia (MHS)
3 Hungarian Meteorological Service (HMS)
4 Slovak Hydrometeorological Institute (SHMI)
5 Environmental Agency of the Republic of Slovenia (EARS)
Content

INTRODUCTION - BACKGROUND OF COMMON PROJECTS

PROJECTS
2002-2004

Remote sensing based nowcasting techniques (satellite, radar):
– Convective Cell Detection
– Displacement Vectors
– Forecast Images




Validation and comparison of methods
Conceptual models of convective cell life cycle
Training
Operationalization of methods
2004-2006

Adaptation to MSG
– Nowcasting methods (above) to MSG
– Comparison of some methods with NWCSAF
– Fog and low clouds detection module


Integrated high resolution precipitation analysis
RESULTS
Introduction - background of common projects

missing nowc.tools at the beginning of the projects
– Austria

all radar-based nowcasting modules
– Slovenia and Slovakia

all satellite-based nowcasting modules
– Croatia

all remote-sensing nowcasting modules
– Hungary


some satellite-based Nowcasting modules
joining limited resources
of central european NWS
(Austria, Croatia, Hungary, Slovakia, Slovenia)
– sharing know-how
– establishing common software depository


exchanging software modules
modules then locally adapted & tuned
Projects

2002-2004
("CEI Nowcasting System")
CEI = Central European Inititative
– remote sensing, pattern recognition, extrapolation

2004-2006
("CONEX II")
COoperation and Networking for EXcellence
– adaptation to MSG, NWC-SAF, fog & low clouds, prec.analysis
resources of partner institutes
+
additionally funded by the
Austrian Federal Ministry for Education, Science and Culture.
Projects /
2002-2004
/ modules

Nowcasting of satellite / radar images (SatRad)

Validation & comparison of methods
Conceptual models of convective cell life cycle (CM of CC)
Training
Operationalization of methods



Projects /

2002-2004
/ modules
Nowcasting of satellite / radar images (SatRad)
– automatic Convective Cell Detection (CCD)







CC (Convective Cell)
FCC (Find Convective Cell)
TITAN (Ts Identification, Tracking, Analysis and Nowcasting)
Validation & comparison of methods
Conceptual models of convective cell life cycle (CM of CC)
Training
Operationalization of methods
Projects /

2002-2004
/ modules / SatRad / CCD
automatic Convective Cell Detection (sat ^ radar)
– CC
("Convective Cell" method)
=>
– Typical patterns : CC reach high levels  local IR T min;
CC have a circular or oval shape; diameter varies during the
life cycle.

Detection procedure: find the local minimum; investigate
pixels on 4 concentric circles; a sufficiently large
difference (center vs. surroundings)
– FCC ("Find CC" method)

Detection procedure: Detection of nodal points; Transfer
of nodal points into cells/gravity centers; Cells’ distance
and cells’ size tests; Output – Cells’coordinates
– TITAN method
(newly coded following parts of Dixon and Wiener, 1993)
 Detection procedure:
data evaluated & adapted to ellipses = "cells"
=>
Projects /

2002-2004
/ modules
Nowcasting of satellite / radar images (SatRad)
– automatic Convective Cell Detection (CCD)



CC (Convective Cell)
FCC (Find Convective Cell)
TITAN (Ts Identification, Tracking, Analysis and Nowcasting)
– Displacement Vectors (DV)








AMV (Atmospheric Motion Vectors)
RMV (Radar Motion Vectors)
TRACK (tracking)
TITAN
Validation & comparison of methods
Conceptual models of convective cell life cycle (CM of CC)
Training
Operationalization of methods
Projects /

2002-2004
/ modules / SatRad / DV / AMV
Nowcasting of satellite images
– AMV (Atmospheric Motion Vectors)

two succesive images

standard cross-correlation technique for rectangular targets
– backward tracking of features
Time t
Time t + T (10 min,...)
=>
Projects /

2002-2004
/ modules / SatRad / DV / RMV
Nowcasting of radar images
– RMV (Radar Motion Vectors)

two succesive images --> vectors (cross--correlation method TREC)
– average vector of entire image --> blank areas

COTREC algorithm (variational techn. & shallow continuity equat.)
– smoothing of vectors ("COntinuity of TREC vectors")
=>
An example of RMV field obtained by TREC method (left), smoothed field
using COTREC method (right).
Projects
2002-2004
/ modules / SatRad / DV / TRACK
Nowcasting of radar ^ sat images

– TRACK




input = cells’coordinates (from FCC
module) and time sequence of images
cells’parrents detection
cells’trajectory construction
cells’trajectory time extrapolation
Input data:
- Time sequence of images:
T(-n)
T(-3)
T(-2)
=>
T(-1)
T(0)
T(+1)
T(+2)
…
Tracking of cells’ centers in time sequence of images Location of
cell centers
Time extrapolation of
cell centers
Projects

2002-2004
/ modules / SatRad / DV / TITAN
Nowcasting of radar images
– TITAN

coded following Dixon and Wiener, 1993
method for radar tracking and echo forecast
– cell detection at
t0 and t1
new
normalized eigenvectors, centroid position,
parameters like area of the storm, etc.
– cell pairing (parents --> no merging, splitting !)
– cell forecast
 extrapolation of DVs and trends of ellipses'
parameters


validation on case studies
– "diagnostic" mode OK
 for e.g. climatology of convective objects
– poor forecasts --> "prognostic" mode abandoned
10' TITANcast (above) missed cells
in an actual radar image (below)
Projects /

2002-2004
/ modules
Nowcasting of satellite / radar images (SatRad)
– automatic Convective Cell Detection (CCD)



CC (Convective Cell)
FCC (Find Convective Cell)
TITAN (Ts Identification, Tracking, Analysis and Nowcasting)
– Displacement Vectors (DV)




AMV (Atmospheric Motion Vectors)
RMV (Radar Motion Vectors)
TRACK (tracking)
TITAN
– Forecast Images (FI)







FCI (based on AMV)
dBcast (based on RMV)
TITAN
Validation & comparison of methods
Conceptual models of convective cell life cycle (CM of CC)
Training
Operationalization of methods
Projects /

2002-2004
/ modules / SatRad / FI / FCI
Nowcasting of sat ^ radar images
– FCI (ForeCast Image)

What
– computes forecast images from DV field and the second involved image
– for any kind of imagery
– under the assumption of a motion field remaining unchanged with time

How
– Computes for each pixel the trajectory
– repeatedly applying the displacement given in the DV file
 hence, forecasts for lead times = multiples of the DV interval
– considerable smoothing of the trajectory field at every time step
– determination of pixel values of the forecast image:
 weighted mean of all pixels which are forecast to overlap
– filling of gaps with averages of adjacent pixel values
Actual sat image and corresponding AMV field (left)
forecast sat images (right).
=>
Projects /

2002-2004
/ modules / SatRad / FI / dBcast
Nowcasting of radar images
– dBcast (forecast radar image)

What
– computes forecast images from RMV field and the second involved image
– for any kind of imagery
– under the assumption of a motion field remaining unchanged with time

How
– extrapolated echo patterns <-- backward-time integration of trajectories
– ... similar to FCI (see above) ...
=>
Originating radar image and corresponding AMV field
(left), 30' forecast radar image (middle), actual radar
image (right).
Error of dBcast (from bottom to top): stratiform case,
frontal convection, organized convection, air--mass
convection
Projects /
2002-2004
/ modules

Nowcasting of satellite / radar images (SatRad)

Validation & comparison of methods
Conceptual models of convective cell life cycle (CM of CC)
Training
Operationalization of methods



Projects /

2002-2004
/ modules / validation & comparison
nowcast of satellite image
– nowcast: +30,+60,+120 min; period: Sep-Jan 03, number of cases: > 4000

evaluation method:
– MeteoSat "B" image divided into squares; filled with 0/1 when > threshold
– for every square: contingency tables

evaluated method: FCI method (using AMV fields); e.g. +120 min:
– average / best / worst :

POD % ~ 88 / 94 /67;
FAR % ~ 11 / 6 / 30
nowcast of sat Conv.Cell position
– nowcast: +60 min; period: summer 2003 (nov&dec/2003), number of cases: 44 (600)

evaluation method:
– a Central European sector of the sat image divided into squares;
– for every square: contingency tables

compared 4 methods: FCC (or CC) + TRACK (or AMV); quite similar resuts:
– POD ~ 30%; FAR ~ 65%
– POD ~ 45%; FAR ~ 45%
– POD ~ 65%; FAR ~ 20%

(allowing app. 30km of CC distances)
(allowing app. 45km of CC distances)
(allowing app. 100km of CC distances)
nowcast of radar Conv.Cell position
– nowcast: +30 min; period: summer 2002&2003, number of cases: > 800

evaluation method:
– Radar image divided into squares;
– for every square: contingency tables

evaluated method: FCC + TRACK
– POD ~ 37%; FAR ~ 39%
– POD ~ 62%; FAR ~ 20%
(allowing app. 5km of CC distances)
(allowing app. 15km of CC distances)
Projects /

2002-2004
/ modules / validation & comparison 2
nowcast of precipitation areas

diagnosed QUALITATIVELY (yes/no)
– interpolation (weather reports, IR sat > threshold, radar) to sat_grid

then extrapolated with FCI method (using AMV fields)
– nowcast: +30,+60,+120 min; period: Jan-Sep 03, 3 hourly,
– every pixel within Austria


all weather situations/ all pixels : no signal - comparable to persistence
"nowcast" situations/ "interesting" pixel = starts/stops/keeps raining
– % of correct pixels = app. 70% (30') to 60 % (120') --- 10% better than persistence

"nowcast" situations/ "changing" pixel
= starts/stops
raining
– % of correct pixels = app. 30% (30') to 40 % (120')
– precipitation areas (manually selected, automatic counting of pixels)
– % of correct pixels = app. 50% (120') for MCS to 70 % (120') for fronts
Projects /

2002-2004
/ modules / validation & comparison 3
PODs, FARs ... and what now?
– Q: are such methods really useful?
– A: a useful nowcasting product must:

lead to better results than persistence :
– some of the above comparisons indeed confirmed that methods (e.g. nowcast of
convective cells) can be better than persistence up to three times,

assist at subjective nowcasting :
– some of the investigations among operational forecasters showed that methods are
accepted reasonably well in the subjective operational practice:
 example answers to the investigation (G: general fcast, A: airport)
Which products do you use in
the operational process?
How useful you find each product?
Very
Mostly
Useful
Sometimes
1
2
5
3
FCI
5
4
2
5/11
VIS+IR
5
4
2
1
6/11
AMVCC
5
3
3
1
8/11
FCICC
4
4
3
G
A
Together
AMV
4
2
6/11
AMV
FCI
5
3
8/11
VIS+IR
3
2
AMVCC
5
FCICC
7
The satellite based nowcasting products give
better overview of atmospheric evolution
(agreed by 8).
Not
Q: In which situations are sat. nowcasting products valuable?
A: “nice advection”; frontal zone with intensive Cb; stratiform process
(WF better, CF "also good"); time of frontal passage; determination of
high/low clouds; tracking of storm cells;
Projects /
2002-2004
/ modules

Nowcasting of satellite / radar images (SatRad)

Validation & comparison of methods
Conceptual models of convective cell life cycle (CM of CC)
Training
Operationalization of methods



Projects /

2002-2004
/ modules /
CM of CC
Conceptual models of life cycles of convective cells
– using time series of remote sensing data
– using LAM models (Aladin - MM5) --> no firm results
Projects /
2002-2004
/ modules

Nowcasting of satellite / radar images (SatRad)

Validation & comparison of methods
Conceptual models of convective cell life cycle (CM of CC)
Training
Operationalization of methods



Projects /



2002-2004
/ modules /
training
brochure, posters, etc.
Computer Aided Learning (CAL) - Training CD
Joined Training Workshop (sponsored by EUMETSAT)
Projects /
/ modules

Nowcasting of satellite / radar images (SatRad)

Validation & comparison of methods
Conceptual models of convective cell life cycle (CM of CC)
Training
Operationalization of methods



wsn05 - 7.24
2002-2004
Projects /

2002-2004
/ modules /
operationalization
commonly developed methods --> operational use
– examples of "national" operational visualization
of "common" nowcast products:
Projects /

2004-2006
/ modules
new data source - MSG
– adaptation of the developed CEI nowcasting methods to MSG
– comparison of some CEI methods with NWC SAF products
– fog and low clouds detection module

integrated high resolution precipitation analysis

adaptation of NWP output
Projects /

2004-2006
/ modules / MSG / AMV, FCI, RGB
Adaptation to MSG
– AMV, FCI
Example of 120 min forecast out of 15 min interval
between two successive IR 10.9 images; grey shades
for current image, vectors for AMVs, isolines for some
values of nowcast image
– common RGB composites

detection/dyagnosis of certain features
– combinations of channels (e.g. 139, 321, etc.)
– difference of channels (e.g. 4-9 for fog, etc.)
MSG-1, 5 June 2003, 11:30 UTC, RGB 01-03-09
Projects /

2004-2006
/ modules / MSG / CEI vs. NWCSAF
Adaptation to MSG
– Conceptual models of convective cells

comparison of conv.cell detection algorithms
CEI (CC, FCC)
vs.
"Rapidly Developing Thunderstorm" (Nowcasting-SAF)

inclusion of MSG channels for cloud phase and life cycle

e.g.
3 (1.6 µm), 4 (3.9 µm), HRVIS
– eventually improved "cell detection" algorithm
 e.g. ch10 - ch9 filter to distinguish
non-convective phenomena (such as lee clouds)
from actual convection.
Projects /

2004-2006
/ modules / MSG / fog & low clouds
Adaptation to MSG
– fog and low cloud detection

Qualitative analysis of low clouds
– thresholds --> binarized image (yes/no)
– day- & night-time algorithm
 difference (e.g. ch4-ch9)
 combination (e.g. ch9, ch4, diff.)
 dawn/dusk problems
Example of "fog and low clouds" product.
Red areas indicate appearance of this phenomena
(according to the algorithm). Overlayed is MSG IR 10.9
image and ww from SYNOPs.
Area left of the tilted line (purple) illustrates the
dawn/dusk sensitivity of the algorithm.
Projects /

2004-2006
/ modules / MSG / fog & low clouds 2
Adaptation to MSG
– fog and low cloud detection
(more info) --> P 4.30: A. Wirth, ZAMG, Austria. Day/night-time low cloud
detection using different spectral bands from meteosat second generation

Qualitative analysis
+ surface.temperature
-->
lower fog boundary
– model (or objective analysis) T
vs.
cloud top T (ch 10.8)
 in certain range -- > high probability for fog reaching the ground

Q: ? to use NWCSAF Cloud Type product
instead ?
– classes „low“ and „very low“
– although often confusing low clouds and snow ...
– ... it is still developing !
Projects /

2004-2006
/ modules / MSG / Precip. analysis
Integrated high-resolution precipitation analysis
– Quantitative analysis of precipitation

Production of (national) integrated prec.fields
– from surface observations, radar data, NWC-SAF products

Synthesis of the (national) prec.fields --> supranational
– exchanging national products
– with common software
– Semiquantitative analysis

where the quantitative analyses are missing
--> beyond the common region
– including e.g. foreign countries / sea / mountains

intensity in e.g. 3 categories (light/medium/strong)
– from an available combination of NWC-SAF products/radar
data/conventional precipitation measurements
Projects /

2004-2006
/ modules / MSG / NWP
Adaptation of NWP output
– in :
–
local observations, remote sensing data, high resolution topography
between: objective analysis, adaptation of DMO, assimilation of local data, ...
– out:

analysis & nowcasting fields ;
frequent update ...
operational national examples:
– MEANDER (H)
(more info) --> O 6.14: A. Horvath
Nowcasting system of the hungarian meteorological service
– INCA (A)
(more info) --> O 2.13: T. Haiden
Prediction of convective cell initiation in mountainous terrain
using a high-resolution analysis system
– exchange of know-how, collaboration, ...., --> common orientation

remote-sensing &
NWP &
operational nowcasting
specialists
Results

nothing revolutionary ...
– way of work -- common for projects
– scientifically -- well-known, relatively simple methods
– ...

... but noteworthy anyway
– operationally:


improved or even first operational nowc.applications
in partner NWServices
resources were "optimized":
– used for adaptation and local tuning
instead for re-coding of algorithms

"... spirit of international collaboration is enhanced ..."
– operational exchange of nowc.data became more possible
– and so, as such:
" ... an example of possible fruitful cross-border meteo-collaboration ..."
:-)
wsn05 - 7.24
Brown symbols: cells
failing the VIS test
<=
Demonstration of the FCC detection process
~
<=
wsn05 - 7.24
<=
AMVs + CCs zoom
29 August 2003
~
<=
AMVs + ccs zoom 29 August 2003/ 17:00
IR nowc + ccs zoom 29 August 2003 /17:00 + 0030
IR nowc + ccs zoom 29 August 2003 /17:00 + 0100
IR nowc + ccs zoom 29 August 2003 /17:00 + 0130
IR nowc + ccs zoom 29 August 2003 /17:00 + 0200
~
<=
Radar 05 October 2003 09:20
Radar 05 October 2003 09:30
Radar 05 October 2003 09:40
Radar 05 October 2003 09:50
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Radar 05 October 2003 09.50 + 10 Min
Radar 05 October 2003 09.50 + 20 Min
Radar 05 October 2003 09.50 + 30 Min
Radar 05 October 2003 10:20
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