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UNIVERSITY OF ROME "TOR VERGATA" – SCHOOL OF ENGINEERING GRADUATED COURSE OF TELECOMMUNICATIONS GEOINFORMATION AREA NOWCASTING OF MESOSCALE CONVECTIVE SYSTEMS THROUGH METEOSAT DATA SUPERVISOR: PROF. FABIO DEL FRATE APPLICANT: VALERIO DE LUCA CORRELATOR: Dr. MICHELE DE ROSA

NOWCASTING OF MESOSCALE CONVECTIVE SYSTEMS … · nowcasting of mesoscale convective systems through meteosat data supervisor: prof. ... the algorithm was developed in esri arcgis

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Page 1: NOWCASTING OF MESOSCALE CONVECTIVE SYSTEMS … · nowcasting of mesoscale convective systems through meteosat data supervisor: prof. ... the algorithm was developed in esri arcgis

UNIVERSITY OF ROME "TOR VERGATA" – SCHOOL OF ENGINEERING

GRADUATED COURSE OF TELECOMMUNICATIONS

GEOINFORMATION AREA

NOWCASTING OF MESOSCALE CONVECTIVE SYSTEMS

THROUGH METEOSAT DATA

SUPERVISOR:

PROF. FABIO DEL FRATE

APPLICANT:VALERIO DE LUCA

CORRELATOR:

Dr. MICHELE DE ROSA

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Aims- Development of a forecasting algorithm of cloudiness scene.

- The predicion is NOWCASTING type, temporally near the REAL-TIME analysis, with results

inside 45 minutes ahead.

- The spatial extent of the forecasting is the Italian Peninsula, seas including. It is possible extend

the algorithm to other Mediterranean Bacin regions and out of Europe.

- The prediction time base is 15 minutes. In the sequence below it is shown the evolution of a

scene at 15, 30 and 45 minutes.

Progress of a severe storm (MCS – Mesoscale Convective System) over Tuscany and Cinque Terre

on September 19, 2014 from 0600 to 0815 UTC. Meteosat-10 "sandwich" product (IR10.8 and

HRV combined). Orange weather warnings for thunderstorms and rain were issued by Meteo

Alarm.

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Mesoscale Convective Systems – MCS

1, 2 Event over Hamburg on June 9, 2004 at 1200 UTC.

System phases:- Triggering of external nuclei (BASE),

- Growing step,

- Mature state and internal nuclei (TOP),

- Merging and splitting,

- Dissipating phase and evolution end.

Associated Risks:- Rear Inflow Jet, RIJ (severe wind),

- Mesovortexes,

- Intense Rainfall,

- Fasf Flooding,

- Mesoscale Convective Complex (MCC).

Physical characteristics:- Genesis: From two or more cumolonimbus

(CB), in condition of atmopherical instability.

- Mesoscale: from 3 km to 300 km,

- From 30 minutes to 24 hours,

- Observations by radar data and satellite data. Schema of a MCS.

1

2

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The NowcastingThe NOWCASTING describes the atmospherical situation and the prediction in the next hours,

through data, processing and device to provide the better service in standard situation and in

emergency cases.

Algorithm

Nefodina

Algorithm

CellTrack

APPSSMARTPHONES

HIGHWAY

READERBOARDS

MeteosatNOWCASTING

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Ellipses Vs. Pseudo Time Series - PTS

In scientific litterature, the ellipses approximation is a way for extracting the geometric

characteristics of MCS.

A PTS (Pseudo Time Series) is a sequence of distance values from outline points to centroid of

object.

Ellipses - Geometric Features:

- Coordinates of Centroid,

- Major Axis of Ellipse,

- Minor Axis of Ellipse,

- Orientation of Major Axis,

- Object Area.

The PTS allow a better reppresentation of objects and the extraction of less types of geometric

features. To reduce the computing load the outline is sampled (parameter).

PTS - Geometric Features:- Coordinates of Centroid,

- Distance of Outline points from Centroid.

PTS – Graph:- X-Axis: Radial Directions (from 0° to 360°),

- Y-Axis: Outline – Centroid Distance.

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MCS Nowcasting Tool Algorithm – Schema and Data

The MCS Nowcasting Tool extracts the PTSs in multi-MCS scenes, forecast next scenes (Shape, Position and Temperature) and show it on screen with a symbology. The model implements a 15

minutes linear prediction and it can extends the forecasting considering the results like observed

data. The algorithm was developed in ESRI ArcGIS ModelBuilder (last release 2014).

The inputs are three scenes (Shapefile format) of subsequent times. The last one validate the

prediction. The inputs are the vectorial results (KML format) of CellTrack Algorithm of GEO-K,

that process the 12 spectral channels of Meteosat Second Generation (MSG).

Algorithm

CellTrack

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MCS Nowcasting Tool Algorithm – Radial Scanning (1/3)

The Radial Scanning is the technique to extract the PTSs. One shows it for a single vectorial object.

1 Centriod

calculation.

2 Vertices

extraction.

3 Radial

structure 1.4 Selection of major

ray and creation of the

circumference.

5 Increase of density

points of

circumference.

6 Radial structure 2

and re-centred of

the structure.

7 Clip and multi-part

to single-part

function.

8 Selection by location

between radial structure 2

and centroid.

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MCS Nowcasting Tool Algorithm – Radial Scanning (2/3)

The result is the PTS with the radial direction in X-axis starting from

East counterclockwise.

The Radial Scanning technique is applied automatically to every objects of two input scenes (Time 1, Time 2). The third scene is for

validation.

The steps 7. and 8. guarantee the correct PTS extraction in multi-

object scene.9 Final Radial Structure.

10 Related PTS.

Shape of convective

object.

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MCS Nowcasting Tool Algorithm – Radial Scanning (3/3)

Example of PTS extraction from a scene with four detected Mesoscale Convective Systems.

Scene on November 18, 2013 at 0000 UTC.

PTS

MCS 1

PTS

MCS 2

PTS

MCS 3

PTS

MCS 4

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MCS Nowcasting Tool Algorithm – Linear Prediction

The Linear Prediction

Module runs a linear

regression on values of

Temperature, Position

(Centroid) and Shape

(PTS).

For the PTSs one has sample

to sample regression, same

radial direction sample of

two subsequent PTSs (Same

object MCS).

CellTrack algorithm

provides the Parent_id

attribute for each object, it

links temporally the objects

in subsequent times.

In figures is highlighted a

single sample (radial

direction West) to see the

particular prediction sample.

OBSERVED PTS

on November 18, 2013

at 0000 UTC .

OBSERVED PTS

on November 18, 2013

at 0015 UTC .

PREDICTED PTS

on November 18, 2013

at 0030 UTC .

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MCS Nowcasting Tool Algorithm – Inverse Radial Scanning

After getting the predicted PTS, the model builds the objects according the following funciontions

sequence of Inverse Radial Scanning.

1 Predicted PTS.

The Smooting allows harmonizing the results of the PTS prediction. It is important run the smooting

before the Convex Shape to avoid getting oversized predicted objects.

3 From Points to Polygon. 4 Smooting. 5 Convex Shape.

2 Building of Vertices Layer.

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MCS Nowcasting Tool Algorithm – Synthesis (1/2)

Observed Data on

November 18, 2013 at

0000 UTC and its PTS.

The following figures show the overall results in summary form of Radial Scanning, Linear

Prediction and Inverse Radial Scanning with the compare between observed data and prediction.

Symbology: Observed data in light blue, predicted data in black grid, observed PTS in solid line,

predicted PTS in dot line.

T1

T2

T3

Observed Data on

November 18, 2013 at

0015 UTC and its PTS.

Compare between

Observed Data and

Prediction on November

18, 2013 at 0030 UTC and

its PTS.

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MCS Nowcasting Tool Algorithm – Synthesis (2/2)

MCS 1

The following figures show the tool results for a 15 minutes ahead forecasting, with multi-object

scene. It is reported the compare between observed data and prediction.

Symbology: Observed data in light blue, predicted data in black grid, observed PTS in solid line, predicted PTS in dot line.

MCS 2

MCS 3

MCS 4

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MCS Nowcasting Tool Algorithm – Validation

The algorithm was evaluated with two groups of Performance Indecis, one for Spatial Placement

and one for the Temperature. Data observed at time T3 is used for the indecis calculation.

Symbology: Observed data in light blue, predicted data in black grid, observed PTS in solid line,

predicted PTS in dot line. Daily averages of indecis at (a) 15, (b) 30 and (c) 45 minutes.

Shape and Position Indecis

POD: Probability of Detection

FAR: False Alarm Rate

CSI: Critical Success Index

Temperature Indecis

ME: Mean Error

MAE: Mean Absolute Error

CC: Correlation Coefficient

RMSE: Root Mean Square Error

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Conclusions- MCS Nowcasting Tool is fully functional in this trial with linear predicted PTS.

- The PTS is a valid alternative to the ellipsis approximation, getting prediction with less geometric

parameters.

- Extraction of Geometric Signature (PTS) for Multi-MCS scene.

- Acceptable prediction of Temperature, Shape and Position up to 30 minutes in ahead.

- Processing Time sufficiently low. Improvements in next optimizations.

- Getting Python code of the model.

Future Developments :

- Prediction from 30 minutes up to 90 minutes in ahead with Artificial Neural Networks (ANN) like

Multi-Layer Perceptron (MLP), Support Vector Machine (SVM) and Nonlinear Autoregressive with

Exogenous Input (NARX).

- Extraction of other PTS such as Outlinee Curvature, Complex Coordinates, to get a Multi-

Characteristic Prediction model, for each MCS.

Paper: A novel multispectral algorithm based on the Meteosat Second Generation satellite for the detection, the tracking and the nowcasting of the thunderstorms.

Authors : M. de Rosa2, M. Picchiani1,2, D. Biron3, D. Melfi3, F. Del Frate1, A. Vocino3, E.

Gasbarri2, V. De Luca1

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UNIVERSITA' DEGLI STUDI DI ROMA"TOR VERGATA" - FACOLTA' DI INGEGNERIA

CORSO DI LAUREA MAGISTRALE IN INGEGNERIA DELLE TELECOMUNICAZIONI

INDIRIZZO GEOINFORMAZIONE

NOWCASTING DI SISTEMI CONVETTIVI ALLA MESOSCALA

TRAMITE DATI METEOSAT

RELATORE:

PROF. FABIO DEL FRATE

CANDIDATO:VALERIO DE LUCA

CORRELATORE:

DOTT. MICHELE DE ROSA

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Obiettivi

- Sviluppo di un algoritmo di previsione dello scenario della nuvolosità.

- La previsione è di tipo NOWCASTING, ovvero temporalmente prossima all'analisi

REAL-TIME, con risultati entro 45 minuti in avanti.

- L'estensione spaziale della previsione è tutta la penisola italiana inclusi i mari. E' possibile

estendere l'algoritmo ad altre aree del bacino del Mediterraneo e regioni extra Europee.

- La base temporale della previsione è 15 minuti. Nella sequenza sottostante è mostrata

l'evoluzione di uno scenario a 15, 30 e 45 minuti.

Sviluppo di un fenomeno temporalesco MCS su Toscana e Cinque Terre del 19 settembre 2014

0600 – 0815 UTC. Rappresentazione multilivello Meteosat, canale Infrarosso IR10.8 µm e canale

ad alta risoluzione HRV sottostante. Secondo livello massimo di allerta assegnato da Meteo

Alarm.

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Sistemi Convettivi a Mesoscala – MCS

1, 2 Evento su Amburgo del 09/06/2004 1200 UTC.

Fasi del sistema:- Nascita (triggering) nuclei esterni (BASE),

- Periodo di crescita (growing),

- Stato maturo e nuclei interni (TOP),

- Unione e divisione (merging, splitting),

- Dissipazione e termine (dissipating).

Rischi connessi:- Rear Inflow Jet, RIJ (venti molto intensi),

- Mesovortici,

- Intense Precipitazioni,

- Rapide Inondazioni,

- Sistemi Convettivi Complessi a Mesoscala.

Caratteristiche fisiche del fenomeno:- Generati da due o più cumulonembi in

condizioni di instabilità atmosferica,

- Mesoscala: 3 km – 300 km,

- Da 30 minuti a 24 ore,

- Osservazioni con dati radar, satellite.Schematizzazione struttura MCS.

1

2

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Il Nowcasting

Il NOWCASTING descrive lo stato atmosferico e la previsione nelle successive ore, tramite dati,

elaborazioni, dispositivi per offrire il migliore servizio possibile in situazioni normali e di emergenza.

Algoritmo

Nefodina

Algoritmo

CellTrack

APPSSMARTPHONES

SEGNALETICA

DINAMICA

MeteosatNOWCASTING

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Ellissi Vs. Pseudo Time Series - PTS

In letteratura, uno degli approcci all'estrazione delle caratteristiche geometriche dei sistemi MCS è

l'approssimazione tramite ellissi.

Una PTS rappresenta, in una sequenza ordinata, i valori della distanza dei punti del contorno dal

baricentro dell'oggetto di interesse.

Ellissi - Geometrie Estratte:

- Coordinate del Baricentro,

- Asse Maggiore Ellisse,

- Asse Minore Ellisse,

- Inclinazione Asse Maggiore,

- Area oggetto convettivo.

Le PTS permettono una migliore rappresentazione degli oggetti ed estrazione di meno tipi di

parametri. Per ridurre il carico computazionale il contorno viene campionato (parametro).

PTS - Geometrie Estratte:- Coordinate del Baricentro,

- Distanza dei punti contorno dal baricentro.

PTS – Diagramma:- Ascissa: direzioni radiali (da 0° a 360°),

- Ordinata: distanze contorno – baricentro.

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Algoritmo MCS Nowcasting Tool – Schema e Dati

MCS Nowcasting Tool estrae le PTS in scenari con muti-MCS, prevede lo scenario futuro (Forma,

Posizione e Temperatura) e lo presenta a video con una simbologia. Il modello implementa una previsione lineare di 15 minuti, che può essere estesa con iterazioni del tool, assumendo dati

osservati le previsioni ottenute. L'algoritmo è sviluppato nell'ambiente ModelBuilder di ESRI

ArcGIS all'ultima versione disponibile (2014).

Gli ingresso sono tre scenari (SHP) relativi a istanti successivi. L'ultimo valida la previsione. Essi

sono i risultati vettoriali (KML) di CellTrack di GEO-K che elabora i 12 canali spettrali MSG.

Algoritmo

CellTrack

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Algoritmo MCS Nowcasting Tool – Scansione Radiale (1/3)

La Scansione Radiale è la tecnica per estrarre le PTS. Viene illustrata per singolo oggetto vettoriale.

1 Calcolo del

baricentro.

2 Estrazione

dei vertici.

3 Sturttura

radiale 1.4 Selezinoe del raggio

maggiore e creazione

della circonferenza.

5 Aumento della

densità dei punti

della circonferenza.

6 Struttura radiale 2

e centratura della

struttura.

7 Clip e funzione da

multi-parte a parte

singola.

8 Selezione per posizione

tra struttura e baricentro.

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Algoritmo MCS Nowcasting Tool – Scansione Radiale (2/3)

Infine si ottiene la PTS con in ascissa le direzioni radiali a partire da

Est proseguendo in senso antiorario.

La tecnica è applicata in automatico a tutti gli oggetti di tutti e due gli

scenari di ingresso. Il terzo scenario è di validazione.

I passi 7. e 8. mirano a garantire la corretta estrazione delle PTS in

uno scenario multi-oggetto.9 Struttura radiale finale.

10 PTS associata.

Forma iniziale dell'oggetto

convettivo.

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Algoritmo MCS Nowcasting Tool – Scansione Radiale (3/3)

Esempio di estrazione di PTS da uno scenario con quattro Sistemi Convettivi a Mesoscala rilevati.

Scenario del 18 Novembre 2013 0000 UTC.

PTS

MCS 1

PTS

MCS 2

PTS

MCS 3

PTS

MCS 4

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Algoritmo MCS Nowcasting Tool – Previsione Lineare

Il blocco di Previsione

Lineare esegue una

regressione lineare sui valori

di Temperatura, Posizione

(baricentro) e Forma (PTS).

Per le PTS la regressione è

campione a campione di due

PTS di due istanti temporali

successivi. Entrambi i

campioni si riferiscono alla

stessa direzione radiale.

CellTrack fornisce l'attributo

Parent_id per ogni oggetto

che consente di stabilire il

legame temporale tra essi.

Si indica un singolo

campione per farne

comprendere la previsione

particolare.

PTS OSSERVATA

del 18 Novembre 2013

0000 UTC .

PTS OSSERVATAdel 18 Novembre 2013

0015 UTC .

PTS PREVISTA

del 18 Novembre 2013

0030 UTC .

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Algoritmo MCS Nowcasting Tool – Scansione Radiale Inversa

Ottenuta la previsione della PTS, il modello ricostruisce ogni oggetto secondo la sequenza di

operazioni della Scansione Radiale Inversa.

1 PTS Prevista

Lo Smussamento permette di armonizzare i risultati della previsione sulle PTS. E' importante

eseguirlo prima della Forma Convessa per evitare di avere oggetti previsti sovradimensionati.

3 Da punti a poligono. 4 Smussamento. 5 Forma convessa.

2 Costruzione del livello dei vertici.

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Algoritmo MCS Nowcasting Tool – Sintesi (1/2)

Dato osservato del 18

Novembre 2013 0000 UTC

e relativa PTS.

Si riportano di seguito in forma sintetica i risultati complessivi di Scansione Radiale, Previsione

Lineare e Scansione Radiale Inversa con il confronto tra dato osservato e previsione.

Simbologia: dato osservato in celeste, dato previsto in griglia nera.

PTS osservata in linea continua, PTS prevista in linea trattegiata.

T1

T2

T3

Dato osservato del 18

Novembre 2013 0015 UTC

e relativa PTS.

Confronto tra Dato

osservato e Previsione del

18 Novembre 2013 0030

UTC e relativa PTS.

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Algoritmo MCS Nowcasting Tool – Sintesi (2/2)

MCS 1

Di seguito è mostrato il risultato del tool per una previsione a 15 minuti in avanti, per uno Scenario

Multi-Oggetto. E' riportato il confronto tra dato osservato e previsione.

Simbologia: dato osservato in celeste, dato previsto in griglia nera.

MCS 2

MCS 3

MCS 4

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Algoritmo MCS Nowcasting Tool – Validazione

L'algoritmo è stato validato con due gruppi di indici di prestazione, uno relativo all'occupazione

spaziale e l'altro gruppo relativo alla Temperatura. Viene preso il dato osservato per il calcolo.

Simbologia: dato osservato in celeste, dato previsto in griglia nera. Medie giornaliere degli

indici a (a) 15, (b) 30 e (c) 45 minuti.

Indici per Forma e Posizione

POD: Probabilità di Rilevazione

FAR: Frequenza di Falso Allarme

CSI: Indice Critico di Successo

Indici per la Temperatura

ME: Errore Medio

MAE: Errore Medio Assoluto

CC: Coefficiente di Correlazione

RMSE: Errore Quadratico Medio

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Conclusioni

- MCS Nowcasting Tool è pienamente funzionante in questa sperimentazione con PTS lineari.

- PTS valida alternativa all'approssimazione con Ellissi. Previsioni con forme meno geometriche.

- Estrazione delle Firme Geometriche (PTS) per scenario multi-sistema MCS.

- Prestazioni di Temperatura, Forma e Posizione accettabili fino a 30 minuti in avanti.

- Tempi di Elaborazione sufficentemente contenuti. Miglioramenti da successive ottimizzazioni.

- Estrazione codice Python del modello.

Sviluppi Futuri:- Previsione dopo la mezzora fino a 90 minuti con Reti Neurali Multi-Layer Perceptron, Support

Vector Machine e NARX (Nonlinear Autoregressive with Exogenous Input).

- Estrazioni di altre PTS (Curvatura del contorno, Coordinate complesse) per un modello di previsione multi-caratteristica per ogni oggetto MCS.

Paper: A novel multispectral algorithm based on the Meteosat Second Generation satellite for the detection, the tracking and the nowcasting of the thunderstorms.

Authors : M. de Rosa2, M. Picchiani1,2, D. Biron3, D. Melfi3, F. Del Frate1, A. Vocino3, E.

Gasbarri2, V. De Luca1