RAPIDLY
DEVELOPING THUNDERSTORM - Convection Warning (PGE19 V4.0)
v2016
- Improvement
from previous version
- Goal
of the RDT product
- RDT Algorithm & characteristics
- The
detection of cloud systems
- The
tracking of cloud systems
- The
discrimination of convective
objects
- Forecasting cloud systems
- Additional attributes
- The output products
- Example
of visualization
- Visualization of additional attributes
- A
case study
- An
extended application
- The configuration file
- The Documentation
Improvement
from previous version
(v2013 to v2016)
- PGE19 (GEO-RDT-CW) is a continuation of CDOP-PGE11 (RDT). The
main changes implemented in v2016 concern:
- Adaptation to new v2016 NWCLIB
interfaces and structures
- More optional inputs: new CMIC product
for microphysics, HRW for movement analysis, NWP wind component as
additional inputs for NWP parameters.
- Processing of a 2D movement field as
guess field (from HRW and NWP winds) for initialization of cloud cell
motion
- Improvement of spatial and temporal
coherence of cloud cell motion, improvement of expansion rate processing
- Additional attributes related to CMIC
product (phase, microphysics parameters), basic related icing index at
high altitude, lightning trend, top
pressure trend, synthetic
multisource severity index
- Improvement of discrimination modules
that change a "No" convection diagnosis issued from statistical
discrimination step of algorithm (for example
in case of OT detection or, according to user’s configuration,
lightning activity or
high CRR). Product keeps memory of “forced” convection diagnosis too,
and this
characteristic is taken into account in the next slots. Improvement of
the
de-classification step.
- Optional parallax correction inserted
before product encoding
- The compliance with NetCDF format for
encoding SAFNWC v2016 outputs (BUFR output may additionally be produced
for non regression purposes,
depending on user’s configuration). NetCDF encoding refers to
bulletin-like
structure, but optional 2D map of type/phase of convective cells may be
available into the
output of analyzed cloud cells, depending on user’s configuration.
- The
development of a nowcast (+1h)
module (Convection Warning module), activated through user’s
configuration, and leading to maximum four forecast products (+15’,
+30’, +45’, +60')
Goal
of the RDT-CW product
The
RDT-CW (Rapid Development Thunderstorm) product has been developed by
Meteo-France
in the framework of the EUMETSAT SAF in support to Nowcasting. Using
mainly
geostationnary satellite data, it provides information on clouds
related to
significant convective systems, from meso-alpha scale (200 to
2000 km) down to
smaller scales (few pixels). It is provided to users in the form of
list of numerical data
stored in an output file.
The objectives of RDT-CW are :
- The identification, monitoring and tracking of intense
convective system clouds
- The detection of rapidly developing convective cells
- The forecast of the convective cells
The object-oriented approach
underlying the RDT product allows to add value to the satellite image
by
characterizing convective, spatially consistent, entities through
various
parameters of interest to the forecaster : motion vector, cooling and
expansion
rate, cloud top height … and their time series. It supports easy and
meaningful downstream data fusion (surface observations, NWP fields,
radar data...).
Thereby, RDT is a tool for
forecaster but can be used by research teams, and end-users like
aeronautical users.
RDT-CW Algorithm
The
RDT process includes four steps:
- The
detection of cloud systems
- The
tracking of cloud systems
- The
discrimination of convective cloud objects
- The advection of
convective cloud objects
The
detection of cloud systems
The
detection algorithm allows to define “cells” which represent the cloud
systems.
In the RDT-CW algorithm, “cells” are defined on infrared images
(channel
IR10.8) by applying a threshold which is specific to each cloud system,
depending on local brightness temperature pattern. But a pre-processing
is included prior to this step:
Processing
mask :
- Since v2009, RDT-CW may take advantage of
Cloud
Type as optional
input, to ignore cloud-free areas in the identification of
cloud cells. Even
if the impact on CPU is weak,
it forces RDT-CW to focus
on cloudy areas only. CT is used rather than CMa to take benefit
also from Cloud Type information and save a double file reading.
- Since v2010, RDT-CW takes into account the
deformation of pixels when on the edge of the spaceview domain. A mask
of stretched pixels (pixels more than 5 * nominal_area) is
automatically elaborated and backuped, and masked zones are ignored in
the detection
step, where gradients processing (used as predictor in discrimination
scheme) should be less reliable.
- Both approaches are merged before the
detection
step to focus on areas of interest
CT product
|
Non cloudy
pixels identification
|
mask for stretched pixels
(backup file)
|
stretched
pixels + non
cloudy pixels
|
IR10.8
image
|
Working image for detection step
|
Cells
identification :
The basic idea is to get the threshold
adapted to the topography of the
cloud top :
- In the case of simple
topography (like the simple, isolated, cloud associated to a single
convective cell in clear air, at development stage), the chosen
threshold corresponds to the outer limits of the cloudy zone
- In other cases,
the principle is to use the warmest temperature threshold which allows
to get one cell for each significant cloud "tower". A cloud tower is
here formally
defined as a local brightness temperature minima which is separated
from the other, nearby, minimas by a sufficiently warmer zone (6degC
warmer)
Hence,
the threshold used for a given cloud tower
depends on the temperature pattern in the vicinity, and may evolve just
because nearby towers do evolve (click
on figure for zooming: base of cloud towers defined by a red contour,
as soon as vertical extension reaches 6degC ).
Thus, the RDT-CW cells linked in time to form a trajectory do not
necessarily really
depict along time
the phenomena at the same threshold temperature. But the advantage
of the method
used (adaptative threshold) is to focus on
convective parts of the cloud systems, in order to perform
the
discrimination process (see below).
In order to
increase the relevancy of the cloud
contour corresponding to a given threshold, the vertical gradient of
contour surface is taken into account for a definitive choice of
representative level (base of cloud tower level). It is applied when
the vertical morphology
of the cloud presents particular shapes, with cloud systems defined by
only one tower, and when the chosen representative threshold,
automatically set to the warmest value, leads to a bottom contour much
larger than the horizontal extension of the top tower itself in its
coldest part. Moreover, as detailled later below, a secondary contour,
more representative of the top of tower, may complement the cloud
system representation.
The
tracking of cloud systems
The choice of an adaptative threshold algorithm makes
complex the cell comparability due to various phenomena depicted. This
method also induces numerous merge and split.
The tracking algorithm is mainly built
on the overlapping between cells in two successive images. Before the
cells overlap processing, the previous
cells are moved according to their (formerly analyzed) move and speed.
Nevertheless, correlation or neighboorhood methods are applied when
overlapping method doesn't succeed. Moreover, a guess movement field is
taken into account through NWP and HRW data for triggering cells
without any link with previous cells.
Then, the temporal links are processed as
follow:
- No match : the current
cell is a new one and begins a new trajectory
- Merge :
more than one former cell match with
one current cell. A main temporal link is established with one of the
"largest" former cell (continued trajectory) ;
the other ones, corresponding to ended trajectories, as considered as
secondary temporal links . Due to adaptative threshold
temperature use, the
largest former cell is not directly defined on its area attribute but
on a area defined at a common threshold.
- Split : One
former cell match
with several current cells. Here again, a main temporal link is established with one
of the "largest"
current cell for the continued trajectory. The
other current cells begin new trajectories.
- Merge and split :
Several
former cells match witch several current cells: In this case (less than
3% of trajectories), all trajectories are closed and the current cells
are processed
like
new cells.
The temporal links allow to
compute move, speed and trend of all cloud objects. The time
series of
cloud's characteristics (peripheral gradient, volume, cooling rate...)
are key input for the discrimination algorithm.
The
discrimination of convective
objects
As
was mentioned previously, the RDT-CW detection algorithm is able to
detect
cloud structure from meso-alpha scale down to pixel
scale. The goal of the discrimination method is to identify the
convective RDT-CW objects among all cloud cells, adding a strong
constraint
which
is that the discrimination should be effective as soon as
possible after the first detection by RDT-CW software.
The
left picture below displays all RDT-CW detected cells. It
points out the detection and tracking efficiency of RDT-CW.
We
can notice the phenomena and scale diversity of RDT-CW objects.
The right image displays
convective
objects only. The ratio between no
convective and convective objects is about 100.
The discrimination method makes use of
discrimination parameters calculated from five MSG channels: IR 10.8µm, IR 8.7µm, IR 12.0µm, WV 6.2µm and WV
7.3µm.
Two kinds of discrimination
parameters are computed:
- Spatial
characteristics (peripheral gradient, surface...)
- Temporal characteristics (rate, extremum
on various past period )
The
discrimination scheme is a mix between
statistical models and empirical rules :
- The convective diagnosis
is the result of a statistical model applied to a growing or triggering
cloud system depending on its current and past parameters
- This convective characteristic is inherited as long as the cloud
system is developping as expected
- Empirical rules apply after a given period, to check , confirm or
declassify the convective characteristic of the cloud systems
Statistical models for each configuration
(processing modes, vertical categories, temporal depth) have been
previously tuned on a learning database over
widened France. The ground truth used for this data
base is
cloud to ground lightning occurrence.
The
discrimination scheme has then been validated over a
large Europe domain using the ground truth from the extended EUCLID
lightning network.
Since v2011 , NWP data is used for a
guidance of discrimination scheme when
available
- Convective indexes are computed from NWP to produce a convective
/ non convective mask to identify non convective areas
- Only cloud cells out of non convective areas are submitted to
discrimination scheme
- LI (Lifted Index) and gap to tropopause may be used as extra
predictors for discrimination scheme
NWP mask : 0 = non convective areas = union of 3 non convective indexes
The discrimination
scheme is described into ATBD document.
Forecasting
convective cloud systems
Convective cells have their own dynamic
and can have a trajectory that does not always follow the
environmental displacement fields. The object mode of RDT-CW analyses
the motion of each cell, and computes this speed. Speed estimate has
previously been improved for orphan and triggering cells through
movement guess
field. This guess field is derived
from current HRW winds when available, or in a lower extend from NWP
winds. A temporal coherence checking has been added to reduce
variability of speed estimate.
The forecast scheme
uses this speed estimate to forecast the successive position of each
cell. The Lagrangian method is proved to be
quite efficient up to 1 hour range. Thus the forecast is proposed
up to this limit. This leads, for MSG FDSS (15min update rate),
to an independant product for each lead range (+15', +30, +45 and
+60'). Corresponding products constitute the Convection-Warning part of
RDT-CW.
Analyzed (yellow contours) and 60min-forecast (magenta contours) RDT-CW
cells
External
attributes of convective
objects, and 3D description
- RDT-CW is able to take into
account lightning strokes
location. This optional additionnal data may also allow
to improve
discrimination skill, if requested. The object approach of RDT-CW
algorithm allows to
characterize the lighning activity associated to a convective cloud
object and to build its time serie. It is to note that the temporal
shift between satellite data and flash impact is taken into account.
- RDT-CW can take
advantage of cloud
top pressure information
as additional attribute, when CTTH
is requested as additional optional
data
- minimum value of pressure over cloud cell extension is
estimated to qualify the most representative and relevant value of
cloud top
- RDT-CW can take
advantage of cloud
type and cloud microphysics information
as additional attribute, when CT
and CMIC are requested as
additional optional
data
- Type of cloud of each pixel of cloud cell is collected and
the highest proportion is estimated
- Phase information of each pixel of cloud cell is collected and
the highest proportion is estimated, to qualify the cloud cell phase as
water / ice / mixed
- maximum values of each COT, Reff and CWP=LWP+IWP are identified
when available. Those parameters may be linked to a beta-index related
to High Altitude Icing Hazard
- RDT-CW can take
advantage of CRR
data as optional input, for additional attribute, when PGE05 is
requested as additional optional data
- CRR data are used to identify maximum convective rain rate over
cloud cell extension
- High CRR data may be used additionally to qualify a cloud cell
as significant and to encode this cell in BUFR output (for the last
BUFR version only)
- Very High CRR data may be used on request to force convective
diagnostic
- Depending on cloud morphology, a “second
information level”
may be
encoded for some cells. Practically, these cells are
encoded as additional cells whose contour corresponds to “top of tower”
(6degC warmer than minimum temperature). They are linked to the main
“tower-base” level through Identification number. They get their own
attributes, estimated over their own horizontal extension.
- Overshooting Top Detection (OTD)
is attempted for each cloud satisfying some criteria
- Static and morphological analysis of cloud cell allows a
pre-detection of OT candidates
- Stricter and additional criteria, added with NWP tropopause,
allow a confirmation of the relevant OT
The output products
The final products
concerning RDT-CW
are
- Current living cloud
cells : numerical data which depict infrared characteristics
(spatial and time)
and move
information associated to RDT-CW cells. Numerical data are provided in
a bulletin-like NetCDF
format. Depending on user's configuration, it may also content a map of cloud cells, whose numerical
values correspond to the
phase of development of each cloud system.
- Forecast cloud cells
: numerical data provided in
a bulletin-like NetCDF
format, focusing on localization and a
limited set of attributes for the next
15, 30, 45 or 60min (one product for each forecast range)
- Achieved cloud
trajectories : all the life of a cloud system is described once
completed, from its triggering phase to its dissipation. This
information is encoded in ASCII
format.
Example of RDT-CW analyzed map. For a correspondance between encoded
values and Cloud type and phase, see DOF documentation
Optimal Visualization the
RDT-CW should need
development of a dedicated post-processing, depending on given
specifications :
access to current cell's localizations and attributes, or access to the
recent current
trajectories (with historical past series) from a single RDT-CW
product, or to the
whole trajectory through a serie of RDT-CW
products, the latter case implying to manage the temporal links between
cloud cells
The
Current Object-Netcdf and Trajectory-ASCII formats are described in the
Document Output Format (DOF) of
SAFNWC. Trajectory file is produced only on user's request (see
configuration file).
For non-regression purposes, RDT-CW may still offer the possibility to
encode RDT in BUFR format (preferentially the latest fourth
version including OTD characteristics). But Netcdf output offers more
flexible possibilities to display the product.
The case study below gives an example
of a simple RDT-CW operating tool. Because RDT-CW output is a
convective
object approach allowing downstream data fusion, RDT-CW objects
could
also be integrated into more complex systems.
Example
of visualization
=>RDT-CW may be visualized at Meteo-France on several platforms:
- a prototype for intranet / internet
browsers
- SYNERGIE Meteo_France forecaster tool
- SYNOPSIS new Meteo_France forecaster tool
All visualization tools process current
and previous RDT-CW products
to
display whole cloud trajectories. But Netcdf format already content a
recent history of the cloud systems, allowing a direct access to the
close trajectory.
Those tools usually combine RDT-CW product and MSG satellite data
(either infra-red, visible or RGB), and
rely
three levels of visualization :
- The localization
corresponds to the superposition of
graphical attributes on the corresponding infrared image. These graphical attributes are:
- A colored contour which defines cloud system edge.
The color of this(those) contour(s) is usually related to the
life-cycle
stage of
the
system
yellow => triggering = warmest categories, i.e.
minimum temperature > -25degC |
red => growing = minimum
temperature > -40degC |
magenta => mature categories, i.e. minimum
temperature <= -40deg |
orange => split cases (inherited convective
diagnostic) |
- A line (blue in
the image below) shows the
trajectory of the system (all previous locations of the centre of
gravity of
the system in past images).
- An arrow (magenta in the image below)
shows the expected
move of the
gravity center of the system for the next hour.
- A symbol
(green
diamond or triangle) identifies an overshooting top detection (OTD)
when it occurs
- The attributes
allows to access numerical values of
some characteristics of the RDT-CW object.
These values are displayed into a flying window. Its
visualization is activated by moving the mouse over the
contour
of the corresponding cloud system (in the image below the interest
cell's contour becomes green).
- The history
is available through time series of past
evolution of the following characteristics. The visualization of these series is interactive with
the corresponding RDT-CW object
- Number of positive / negative
/ intracloud lightning impacts registered below or in the convective
system (in this example, Cloud-to-ground
lightning time serie associated to the green-focused cell).
- Threshold temperature and minimum temperature of
cells
- .../...
=>In NWCSAF real-time production chain, other choices for the
vizualisation have been made : the cells are distinguished by their
cooling rates.
Example of RDT-CW visualization
through SYNERGIE forecaster's tool (left) , through SYNOPSIS
forecaster's tool (right)
Other SAFNWC PGEs attributes , 3D description
CT, CRR and second level
The Figure below gives a full
description of a convective cloud cell over Italy. This cell is
described at two levels.
- Some attributes are affected to the main (BT "Base of Tower")
contour level
only : tracking parameters (speed, duration), expansion rate
- Few attributes are common to both levels : minimum temperature,
cooling rate, ...
- Other parameters may be different at each level :
threshold, area, position, external characteristics (top pressure,
cloud type and phase, rain rate)
Characteristics from external input
data are computed over the horizontal cell
extension (except for lightning data, associated when possible to
the whole cloud system). For that reason, large cells should offer very
different parameter's value between main and second level.
But this can be the case even for small cells : in the figure below
main cell is associated with maximum CRR value of 7mm/h, where second
level limits to 3mm/h.
Main and second level attributes of RDT-CW cloud cell over Italy.
Concerned contour highlighted in black
Overshooting top detection (OTD)
Example below illustrate the
visualisation of OTDs for a given cloud cell, around 13h UTC on the
25th of May , 2009. This topical case show a several hours lasting
convective cell, regenerating on the south-eastern edge and moving
eastwards. This cell is exceptionnally associated with 2 OTDs. Their
BTD signature is quite high and relevant, as the minimum temperatures.
They rise 3-4 deg above NWP tropopause.
The visualization is allowed through punctual localization on the IR
image and limited characteritics in the jauge
- The main points (minimum temperature, maximum of BTD ...) of
overshooting top are pointed with a green diamond.
- Some of their characteristics may be displayed in the jauge
: temperature, BTD=WV6.2-IR10.8, temperature gap over NWP
tropopause
One can note below the U-shape like of the seconf level contour
A
case study (25/05/2009)
The
following case study deals with diurnal
convection triggering over Adriatic edges, and heavy convection
developing in a convergence line in the afternoon over France.
This case study is also detailled for previous releases in SAFNWC Topical
Image Gallery or in RDT-CW web pages
On this visualization tool, yellow lines represent convective cloud trajectories, and black arrows a 30min
move of gravity centers. The contour's colors are the same as
mentionned above.
Second level contour appears sometimes depending on cloud morphology,
and allow to identify Cloud Cap
Ovsershooting tops appear as green diamond
An
extended application
The lack of ground truth over
Africa didn't allow to
evaluate discrimination and precocity skill of RDT-CW on such an area.
Nevertheless, the final result seems to be fine for the users
involved (take note of better ratio between convective and no
convective systems and a
stronger cooling rates compared to European cloud systems).
The RDT-CW is now processed
over
the
whole African continent, and displayed through forecaster tool SYNERGIE
for French Army, but also for Aeronautical purposes. Below is presented
a 3h-loop of "observed" RDT-CW, extended with 1h nowcasts, based
on dilatation and
advection of diagnosed motion.
On this example, RDT-CW manages small rapid developing convective
cells (with deep cooling up to 20-30degC in 15min) , but also large
MCSs
lasting several hours (displayed here without additionnal level, full
trajectory does not appear in this example
because limited to 3 hours).
The loop shows good tracking of MCS and good depiction of small cells.
The configuration file
Input
Data
SEV_BANDS
channel bands to be used by PGE
VIS06 WV62
WV73 IR87 IR108 IR120 IR134
CT
0(default) or 1 if using CT product for
additional attributes Cloud Type and Cloud Phase
CMa
0(default) or 1 if using CT product for
masking non cloud pixels
CMIC
0(default) or 1 if using CMIC product for
microphysics characteristics
CTTH
0(default) or 1 if adding
top cloud attribute via CTTH product
CRR
0(default) or 1 if using CRR product
for
additional attribute
HRW
0(default) or 1 if using HRW product as
guess data for movement estimation
NWP
data parameters
NWP_PARAM NWP_SP
Surface
pressure : resolution(1) and interpolation method(BLI)
NWP_PARAM NWP_2T
2m
temperature
NWP_PARAM NWP_2D
2m dew
point temperature
NWP_PARAM NWP_2RH 2m
relative humidity (when
available)
NWP_PARAM NWP_SGEOP
Surface
geopotential height
NWP_PARAM NWP_ALTM altitude
of ground (when available)
NWP_PARAM NWP_T
temperature at pressure levels specified in nwp_conf_file
NWP_PARAM NWP_RH
relative humidity at
pressure levels specified in nwp_conf_file
NWP_PARAM NWP_GEOP geopotential height at
pressure levels specified in nwp_conf_file
NWP_PARAM NWP_UW
U component of wind at
pressure levels specified in nwp_conf_file
NWP_PARAM NWP_VW
V
component of wind at
pressure levels specified in nwp_conf_file
NWP_PARAM NWP_TT
tropopause
temperature (when available)
NWP_PARAM NWP_TP
tropopause
pressure (when available)
NWP_PARAM NWP_LI Lifted
index (when available)
NWP_PARAM NWP_KI K index (when available)
NWP_PARAM NWP_SHW Showalter index
(when available)
NWPMVTLVL
Pressure level of U/V NWP data as guess data
for movement estimation (default 700hPa)
Lightning
data parameters
LGH
0(default) or N (-1 or N>0) if
using lightning data for data blending and additional attribute
0 (default) :
no association
-1 :
association without forcing discrimination result
N :
association with impact forcing convective diagnostic from Nth impact
LGHDTANT
time limit (sec)
before current slot for considering lightning data
LGHDTPOST
time limit (sec)
after current slot for considering lightning data
LGHTLR
spatial tolerance (nb of pixels - default 3) for pairing lightning
stroke with cloud cell
LGHPROXI
maximum
distance (nb of pixels - default 0) for processing distance between
lightning stroke and cloud cell
Detection
parameters
TCOLD
cold temperature threshold when
multiple thresholding, deg Celsius. Recommended
temperature is -75 degC
TWARM
warm temperature threshold when multiple
thresholding, deg Celsius. Recommended temperature is
5degC
DELTATEMPE
temperature step between Tcold and Twarm, deg, default 1
SMIN
min detection area , km2,
default
1. Recommended value is 60
SMAX
max detection area , km2, default
no
limit. Recommended value is 200 000
Discrimination options
CRRDISCRI
0(default) or >=1 if
using high values of CRR for forcing convective
diagnostic
TROPICALDISCRI 0(default) or
value(degC) if
using colder values of BT for forcing convective
diagnostic
Additional options
DBG
More precise Informations on program running,
default 0 (no information)
Output
Data
FCST
0(default) or
maximum range
value (min) for forecast products - nb of products = FCST/update rate
recommanded value = 60min for FDSS, 20min for RSS (4 additional
forecast products)
PARALLAX
0(default) or 1 for activation of parallax correction
SMOOTHPTS
1(default) or >1 for nb of pts taken into account for smoothing
contours
NCMAPINCLD
0(default) or 1 if map of convective cells included in Netcdf product
INT_PRODUCT Production of the ended
trajectories
file (default NO )
TRAJ
Format specification of the ended
trajectories
file when requested (ex: TISXOLH)
TRAJPROD
production
frequency of ended trajectories file : 0=> monthly 1
=> daily
2 => each slot(default)
The Documentation
SAFNWC
user
documentation for PGE19:
- Product
User Manual
- Algorithm
Theoretical Basis Document
- Validation
Report