Transcript Convection Products NWC SAF 2015 Users’ Workshop 24-26 February 2015 - Madrid AEMET HQ, Madrid (Spain) Jean-Marc Moisselin, Frédéric Autones Météo-France –Nowcasting Department 42, av.

Convection Products
NWC SAF 2015 Users’ Workshop
24-26 February 2015 - Madrid
AEMET HQ, Madrid (Spain)
Jean-Marc Moisselin, Frédéric Autones
Météo-France –Nowcasting Department
42, av. Gaspard Coriolis 31057 Toulouse France
[email protected]
Convection Products at a glance
Two convection products: CI and RDT
CI = Convection Initiation
= probability for a cloudy pixel to become a
thunderstorm
RDT = Rapidly Developing Thunderstorm
= detection, tracking, description and
forecast of thunderstorms in object mode
PGE11
->RDT
Overview
1. RDT
2. CI
3.Links between Products
4. MTG Context
RDT: data fusion for description of
convection
INPUT DATA: MULTISOURCE
MSG data
(5 IR channels + VIS)
NWP data
Other
NWCSAF
products
PGE11
RDT
Lightning Data
OUTPUT DATA: MULTILEVEL DESCRIPTION OF CONVECTION
PGE11
->RDT
• Main description of cell: Yes/No convection
diagnosis,
cell-development
phase,
position,
surface, T, gap to tropopause, cloud type and
phase, cloud top pressure. Displacement Relevant
trends are calculated
• Overshooting Tops, Lightning Activity, Convective
Index, Rainfall Activity
5
3-steps algorithm of RDT
STEP1: Detection (in order to detect cells)
- Using vertical profile of 10.8µm BT
- Cells (towers) are detected at each slot
- Vertical extension: at least 6°C

STEP2: Tracking (in order to recognize each cell in the
previous slot)

- Analysis of cloud cells overlap: each cell of the previous slot is advected
- Merges and splits are taken into account
- Trends of various parameters are calculated
STEP3: Discrimination (in order to identify
convective cells). Statistical process
- Made complex by the unbalanced populations, the wide variety
of scales and evolution-phases of systems
- Highly improved by the use of a set of 5 IR-channels as
predictors, by the use of NWP data
- Very highly improved by the use of lightning data
STEP4: Forecast
4

6
Visualization of RDT through SYNERGIE
(Météo-France forecasters’ workstation)
Attributes:
Development-phases:
Yellow: First detection of convective system
Red: Developing system
Purple: Mature system
Blue: Decaying system
Orange: After a split of systems
Tracking:
- motion vector
- trajectory
Evolution of RDT product


Since IOP (2002-2007)
Pursued in CDOP, CDOP2, proposal for CDOP3

Recent evolutions
–
v2011: use of NWP data
–
v2012: main cloud phase of the cell, highest convective rain
rate inside the cell, second vertical level description
–
v2013: overshooting tops
–
v2016: advection scheme + change in NWCSAF Library +
new output format + CTRAJ
 CDOP3: CDOP2 continuation
GEO imager for MSG, MTG and a set of other meteorological
satellites
v2011: impact of NWP data
How:
CONVECTIVE INDEX calculated for each
pixel
- A mask is built at the beginning of RDT
process. Allows to focus on areas of interest
- Provides an additional predictor
Consequences:
- New attribute
- Strong reduction of the false alarms
during intermediate and winter seasons
- Improvement of early detection
v2010: without NWP data
EXAMPLE
25 May 2009, 12h15 UTC.
v2011 benefits from a better tuning
in warmer categories, with higher
early detection (cells over Italy
diagnosed 30 min when v2011
and v2010 releases are compared)
v2011: with NWP Nata
RECOMMENDATION: USE NWP DATA!
v2012: 2nd level description
When cell-extension is too large, it is interesting to have the depiction of
another level additionally to « Base of Tower » level.
An outline related to the « Top of Tower » has been added
main contour: general
attributes, including
tracking attributes
2nd contour:
specific attributes
v2013: OTD (Overshooting Tops Detection)
 OTD Inside each RDT cell
 Temperature of coldest pixel, BTD WV6.2-IR10.8, WBTD
WV6.2-WV7.3, reflectance VIS0.6, gap to NWP tropopause.
 Morphologic criteria to confirm a spot of cold temperatures
and to determine the pixels that belong to an OT
 HRV for tuning/validation
RDT: validation
 Subjective validation by Météo-France
various case studies, use of topical case for each release.
 Objective validation by Météo-France
Results fulfil the target accuracy requirements over a large domain
and for an extended period: detection is superior to 70% and 25% of
convective systems are diagnosed before lightning activity.
 Validation by users
any feedback is welcome
RDT: v2016 and following releases
 v2016: advection scheme + change in NWCSAF Library + new output format + CTRAJ + use
of CMIC
 v2017: ovelapping CDOP2 and CDOP3. Improved links between other products (Ci as
predictor). Himawari : impact of new channels and higher resolution. New tuning.
 CDOP3: CDOP2 continuation – v2017, then version ready for MTG Day1, then preparation of
a Day2 version
GEO imager for MSG, MTG and a set of other meteorological satellites
“A” cell has disappeared. Bad forecast
WNW displacement of
(False Detection)
“A”
20:10
A
21:10
v2016
B
WSW displacement
of cell “B”
“B” at the expected location. Even
if change in morphology is not
forecast
Overview
1. RDT
2. CI
3.Links between Products
4. MTG Context
14
Convection Initiation (CI)
New NWC SAF product released in v2016
Low probability
to develop into a
thunderstorm
High probability
to develop into a
thunderstorm
The convection probability for each pixel is based on:
 BT or BTD values or trends, e.g. BDT 6.2-10.8µm.
Some relevant Parameters of Interest in « Best Practice Document For EUMETSAT Convection Working
Group » Editors J. Mecikalski, K. Bedka, M. Marianne König
 NWCSAF products: Clear Air Products, Cloud Products, Wind Products
 NWP data
 Past positions and characteristics of pixel
CI – Main principles



The CI product will be elaborated in two main steps.
The first step concerns the selection of pixels of interest. We propose to
exclude the pixels that are too cold or are already thunderstorm (using RDT
tracks): “too much” mask. We also exclude cloud-free pixels (using NWCSAF
cloud products): “not enough” mask. Masks are combined
Then cloud tower are identified having at least 3°C of vertical extension and a
surface lower than 10000 km²
The second step concerns the probability calculation. Accordingly to literature
on the subject three categories of predictors will be considered:
– Vertical extension of the cloud,
– Ice presence,
– Cloud growing rate

In order to compute the trends we take into account cloud-displacement and
calculate the past position of the cloud. For that purpose the HRW (High
Resolution Wind) product of NWCSAF will be use additionally for an objectbased movement detection.

Verification/tuning: RDT / radar / lightning data
Pixels of Interest selection – Preliminary results
Overview
1. RDT
2. CI
3.Links
between Products
4. MTG Context
Links between products: RDT case
 RDT product:
– Cloud products:
• To operate RDT on cloudy areas
• Use of CMIC for v2016
– For the advection scheme. Complementary or additionally to cell speed
estimate HRW/AMV can provide useful information: for new cells without
speed estimate, in case of uncertainty in speed estimate, in case of merge
or split. Foreseen for v2016
– RDT diagnosis: to validate RDT forecast. Foreseen for v2016
– Lightning data: to validate RDT diagnosis (operated in full configuration but
without lightning data).
– RDT diagnosis (motion vector, cooling rate): to contribute to RDT forecast
– CRR: an attribute for RDT, a possibility to set the convection diagnosis to
Yes if CRR above a threshold. Since v2012
– CI: predictor for RDT discrimination scheme. If a pixel has a high probability
to become a thunderstorm it increase the chance of the corresponding cell to
be defined as convective. Foreseen for v2017
Links between products: CI case
 CI product
–
–
–
–
CI [0-60’] to be coherent with CI [0-30’] (p0-90’>p0-60’>p0-30’)
Cloud products: to operate CI on cloudy areas (mask)
RDT (diagnosis): to operate CI on non-thunderstorm areas (mask)
RDT (diagnosis - full configuration but without CI): to validate CI
(additionally to radar and lightning data)
– Some predictors of this new pixel-based product are based on trends. These
trends have to be calculated following the cloud track. For that we will use
the HRW/AMV in the low layers of the atmosphere.
Overview
1. RDT
2. CI
3.Links between Products
4. MTG Context
MTG Context
LI is eagerly expected
LI instrument is eagerly expected to improve many
components of RDT:
 Statistical scheme,
 Real time mode,
 Enhancement of characteristics for a more complete
description of convection,
 Monitoring.
image credit: ESA
MTG Context
FCI is eagerly expected
Number of channels:
FCI
MSG SEVIRI
Meteosat7
In previous years, RDT algorithm has always taken a lot of
advantages from the increase of the number of channels
e.g. for FCI: 0.91µm (total column precipitable water)
Resolution:
Better estimate of morphological parameters and small scale phenomena
RDT Cell with OT
Spectral accuracy: better estimate of BT input data of RDT
RSS Challenge
The lack of channels in RSS would mean for RDT a lack of predictors and a lower
quality
Conclusion
CI and RDT product
– Algorithm: RDT algorithm has been developed before CI
algorithm
– Meteorology / conceptual model: CI occurs before.
– Two products different in terms of presentation
• CI: image mode
• RDT: object mode
– Strong links between these two products: input data,
exclusion mask, validation
– Strong links with other NWCSAF products: cloud
products, CRRR, HRW/AMV
– Together they offer a complete description of convective
systems in various phases