Improving real time observation and nowcasting RDT - SAWS RSMC

E de Coning, M Gijben, B Maseko and L van Hemert

Nowcasting and Very Short Range Forecasting

Improving real time

observation and nowcasting –

RDT

Introduction

• Satellite Application

Facilities (SAFs) are

centres for processing

satellite data

• The Nowcasting SAF

started in February

1997 aiming to produce

the software to deal

with the Nowcasting

and Very Short Range

Forecasting using the

characteristics of the

MSG SEVIRI data and

the NOAA and EPS

AVHRR data

(EUMETSAT

Satellites).

Nowcasting SAF products website

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Cloud Mask

Cloud type

Cloud top temperature and

height

Precipitation Clouds

Convective Rainfall Rate

Precipitation with microphysics

TPW

Layer PW

RDT

Stability indices

High Resolution wind

Satellite Image interpretation

Nowcasting SAF products in SA

• These products are operationally available in Europe,

but have not been operationally implemented and/or

extensively tested over regions in Africa.

• WRC Funded project in SA: Using MSG and the

local version of the UK Met Off Unified Model data as

NWP input to the algorithms

• Project started in 2013 and will end in 2015

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Aim of the WRC project

• To provide forecasters, aviation meteorologists

and hydrologists information about the

development, life cycle and dissipation of convection

in regions where radar systems do not provide

coverage (in between radars over South Africa) or no

radars systems are available (most of South Africa’s

neighbouring countries).

Rapidly Developing Thunderstorms - background

• The Rapid Developing Thunderstorm (RDT) combines

a cloud tracker and an algorithm to discriminate

convective and non-convective cloud objects.

• The cloud objects defined by the RDT are cloud towers

with a significant vertical extension (namely at least

6°C colder than the warmest pixels in its

surroundings)

• The major benefit of an automatic tool like the RDT is

the object and tracking approach.

Improved identification of convective cloud by the RDT product

Y. Guillou, F. Autones, S. Sénési 6

• The objectives of RDT are twofold:

• The identification, monitoring and tracking of

intense convective system clouds

• The detection of rapidly developing convective

cells

• There are 3 stages in the process:

• Detection

• Tracking

• Discrimination

RDT= Detection + Tracking

Phase 1: detection

– towers identification, based upon 10.8 m channel

– Adaptative threshold (reflectivity!)

– Tracking is done using consecutive images

Slide courtesy: Jean-Marc Moisselin

Météo-France, Toulouse

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Phase 2: The discrimination method makes use of discrimination

parameters calculated from MSG channels: IR 10.8, IR8.7, IR

12.0, WV 6.2 and WV 7.3 with NWP

• Two kinds of such discrimination parameters are considered:

• spatial characteristics

• temporal characteristics

• Discrimination is done using cloud top cooling rate and

expansion rate

• The discrimination scheme is a mix between empirical rules and

statistical models tuned on a learning database

• Phase of development is determined by:

• History (last few time steps)

• Temperature trend (cooling/warming)

• Vertical extent

• Expansion

• Convective or Non-Convective storm activity

• Mature: top temperature < -40°C for at least 45min

• Mature transition: crossing top temperature –40°C

• Cold transition: crossing top temperature –35°C or base of cloud tower –

25°C

• Warm2 transition: crossing top temperature –25°C or base of cloud tower

–15°C

• Warm1 transition: crossing top temperature –15°C or base of cloud tower

–5°C

• Warm : top temperature > -15° and base of cloud tower > –5°C,

preceding Warm1 crossing

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Discrimination of convective systems

The picture above displays all RDT

detected cells. This picture points out

the detection and tracking efficiency of

RDT.

The next image displays convective

objects only. The ratio between no

convective and convective objects is

about 100.

Convective mask (from NWP)

identifies stable, neutral/unclear

and unstable areas to remove

stable regions from being

processed, thus reducing

processing time and reducing

false alarms.



RDT uses….

• Mainly (and non-optional) satellite channel is IR10.8 μm (used

for detection, tracking and discrimination).

• Additionally WV6.2, WV7.3, IR8.7 and IR12.0 μm channels can

be used for convective discrimination.

• Other SAF-NWC products allow to establish a cloud mask (to

operate RDT detection only on cloudy areas) and to describe

RDT attributes (pressure and temperature at the cloud top,

cloud type, Convective Rain Rate)

• NWP data can be used as instability masks, improving the

detection of warm systems by RDT.

• Lightning data, if available in real time, greatly contribute to the

discrimination of convective systems.

RAPID DEVELOPMENT THUNDERSTORM (RDT)

J.-M. Moisselin1, P. Brovelli1, F. Autonès1

Météo-France, Nowcasting Department

RDT–PGE 11

Detection

+

Discrimination

Cloud Products

SAF/NWC NWP indicesFoudre

Brightness

temperature

BUFR/HDF5

Rapidly Developing Thunderstorms Product v2012



RDT: Examples from case studies comparing to radar data

Case 1: 09 November 2012

1130 UTC 1130 UTC

Slide courtesy Bathobile Maseko

Case 2: 17 October 2012

1615 UTC1615 UTC


RDT: Examples from case studies comparing to TRMM

rainfall data

Case 3: 28 December 2013

120012001200

ZIMBABWE


Case 3: 28 December 201312001200 1200


RDT: Recent (v2013) examples

Case 3: 5 June 2014

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Case 3: Compared to radar at

1630 UTC

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Case 4: 20 September 2014 –Afternoon thunderstorms and hail in JhB

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Case 5: 10 August 2014

from 1200 to 1445 UTC

Case 6: 28 Sep 2014

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Radar 1045 UTC

Case 7: 6 Oct 2014

0600-1100 UTC - Namibia

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Case 8: Yesterday

Hail/tornado?

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Validation of RDT by the developers (in France)

• “RDT provides an accurate depiction of convective

phenomena, from triggering phase to mature

stage.

• The RDT object allows pointing out some areas of

interest of a satellite image.

• It provides relevant information on triggering and

development clouds and on mature systems.

• The subjective evaluation confirmed the

usefulness of the RDT with moderate lightning

activity.

• Thanks to these good results the status of RDT

has been set up to “operational” by EUMETSAT in

2012.”

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Validation of RDT by Hungarian Weather Service (v2009)

• It detects the majority of the mature phase convective

clouds.

• The small and/or warm cells are often missed

• Better performance in „pure’ convective situation than

in frontal situation. Sometimes a huge part of a front

is detected as convective.

• We have verified RDT without the optional lightning

input. If we used lightning as input, we would get

better results.

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Validation of RDT in SA

• 24 Lightning detection sensors over SA domain which measures

CLOUD-GROUND

Advantages:

• Complete coverage of SA

• 90% or more of Cloud-to-ground (CG) lightning strokes can be

detected.

Disadvantages:

• Only CG lightning detected – Intra-cloud lightning (IC) not detected.

This can negatively impact on the evaluations since some lightning is

not observed.

• Lightning occurs in all thunderstorms, not just rapidly developing

thunderstorms. This can negatively impact the statistics unless one

distinguishes between the intensities of the strokes.

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Slide courtesy Morne Gijben

Validation methodology

• Used Mature and growing phase storms

(Most CG lightning)

• Considered lightning intensities (similar to

developers of RDT)

• Lightning intensities defined as:

• Low: >3 strokes (1 flash x 3)

• Moderate: >15 strokes (5 flashes x 3)

• Severe: >60 strokes (15 flashes x 3)


Results for 10 cases over SA


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0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

TIME (UTC)

Hanssen-Kuipers Discriminant - Average of 10 Cases

3 Strokes 15 Strokes 60 Strokes

Validation results

• Statistics show:

• Fair amount of lightning occurs inside the RDT

polygons.

• POD, POFD and HKD are good

• FAR are too high – due to grid-box-based validation

methods. Object orientated methodology is better to

use. This is future work.

• Visual and statistical evaluations show that RDT

polygons correctly identify the storms which

produce lightning.

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Validation – future plans

• We are looking into improving our evaluation

methodology by using an object-orientated

evaluation technique on both the RDT polygons

and lightning.

• Include lightning data as input into RDT

• Test the 2013 version of NowSAF software

(operational)

• The NOWSAF products are also updated regularly,

so we can look forward to even better products in

the future.

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Back to 16 October 2014…

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WIND

ARROWS

INDICATE

DIRECION AND

SPEED OF

MOVEMENT –

NOWCASTING!

Tornado time +- 0900 to 1000 UTC

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Conclusion

• For the initial work, the v2012 of the software was used, the

latest version of the software (v2013) is now operational in SA.

Improvements in the software algorithms will possibly have even

better validation results

• RDT showed very promising results to be used in addition to

other observations such as radar and lightning detection in SA,

where these are available

• In regions without radar systems and/or lightning detection

networks, these satellite and NWP products will certainly benefit

nowcasting procedures.

• RDT - Images for SADC and SA regions on RSMC for past 2

hours

RDT Summary and Future work

• The RDT can provide useful information on the

development and phase of the intense parts of

thunderstorms over data sparse regions such as

Africa.

• If we upgrade to UM on 4 or 1.5 km we will also see

improvement to RDT (Sfc Press/925 hPa included)

• Nowcasting applications in South Africa – to

complement the radar data (where available)

• Nowcasting applications in southern Africa – where

very few radar systems are operationally available

• Validation against lightning data over SA domain

showed promising results. Methodology will be

improved with time.

• Nowcasting on direction/speed of identified storms! 45

Improving real time observation and nowcasting RDT - SAWS RSMC

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