1 of 141

sergio.masuelli@conae.gov.ar

Sergio Masuelli

November 2010 Fortaleza Brasil

System Engineer of SAC-D Geophysical Applications for MWR

Professor of Gulich Institute (CONAE-UNC)

2 of 141

Index

1) Activities of Gulich Institute

2) Aearte Master

3) Introduction to MWR

4) L1 to L2 project plan

5) Surface retrievals

6) Atmospheric Retrievals

7) Sea Ice Concentration

8) L2 Simulator

3 of 141

GULICH INSTITUTE

CETT

SAC-C

MOC

Instituto

Gulich

4 of 141

GULICH INSTITUTE

Instituto de Altos Estudios Espaciales Mario GulichCONAE-UNC

Natural Emergencies:

National Emergencies Services

•Images supply

•Users support

Courses:

•Non Qualified Users

•National Courses (INTA, 2004)

•International Charter (Regional PM, 2006)

Activities began in 2001

Health Applications:

•Scientific Support to Health Authorities

•Landscape study of vector dynamic.

•Spatial Modelling of vector dynamic.

•Regional Courses (Latinoamérica, 2006)

5 of 141

ρ = Densidad de mosquitos DR = tensor de DifusiónDW = Tensor de Rugosidad V = Velicidad Viento SuperficieKH = Tensor de Atracción H = Campo de Atracción = Tasa de nacimientos β = Tasa de muertes

Modelado espacio-temporal de la densidad de Culicidos en escenarios heterogéneos derivados de información de sensores remotos.

S. Masuelli, C. H. Rotela, M. Lamfri, C.M. Scavuzzo

,R W

H

P tD D V

tK H

OránOrán

La Ecuación representa el modelo básico de difusión. El primer término representa la difusión, el segundo el transporte por el viento y el tercero la atracción por mamíferos (humanos). La ultima corresponde a los términos Fuente y Sumidero.

6 of 141

AEARTE MASTER

Speciality branches:

• Applications on Natural Disasters

• Planning and Scheduling

• Applications on Human, Animal and Vegetal Epidemiology.

7 of 141

AEARTE MASTER

OBJECTIVES

To specialize professionals for the interdisciplinary managing of Emergencies by doing effective use of space technologies, geoprocessing and AI P&S technologies.

To promote research related to the factors originating natural Disasters including buds of agricultural, animal or human plagues. This would allow preparing strategies of Emergency prevention, monitoring, control and response .

To make possible the application of the most modern technologies to the aims of gathering, summarize, analysis and diffusion of data.

8 of 141

Introduction to MWR

9 of 141

•MWR swath width is ~380km, displaced 272km across-track (towards the right), wich overlaps the Aquarius instrument swath.

•MWR IFOV ~40km

Introduction to MWR

10 of 141

Conical Arc

Conical Arc

SUN

Introduction to MWR

11 of 141

Introduction to MWR

MWR beam overlapping along track

40 km

13 km

3 times

12 of 141

Objective:

To obtain the Geophysical Variables

WS: Wind Speed

Surface: WD: Wind Direction

IC: Ice Concentration

WV: Water Vapor

Atmosphere: LWC: Liquid Water Content

RW: Rain Water

Introduction to MWR

13 of 141

Introduction to MWR

Schematic Microwave Radiative Transfer Model [Thompson, 2004].

Up-welling Brightness

Atmospheric Emission

Surface Emission

Down-welling Brightness

Reflected Atmospheric Brightness

Microwave Antenna

Atmospheric Absorption

Radiative Transfer Model

14 of 141

Introduction to MWR

Pi Gi TB*i where TB*iP01,…, Pi ,…, P0

N

We have 6 Ps but only 4 TBs

TBj F Pj Forward problem

P G TB Inverse problem

Geophysical Variables P: WS, WD, IC, WV, LWC, RW

The Retrieval Problem

?

15 of 141

L1 to L2 project plan

ATBD

Simulators

Depuration of Algorithms

Application Prototype Calibration Prototype

General Scheme of the Development Plan

16 of 141

Surface retrievals

Insensitive on atmospheric

changes

Wind Retrieval. AVH Algorithm

A(WS,SST,f)TBH

TBV

F0(SST)

(ATBV − TBH ) - F(SST) = C0(WS)+C1(WS)COS(χ)+C2(WS)COS(2χ)

Does C’S

converge?

F(SST) = (ATBV − TBH) − [C0(WS)+C1(WS)COS(χ)+ C2(WS)COS(2χ)]

No

Yes

END

(ATBV − TBH ) - F(SST) - C0(WS)

=C1(WS)COS(χ)+C2(WS)COS(2χ)

17 of 141

Surface retrievals

WindSat EDR wind speed

Truth wind speed , m/s

EDR

win

d sp

eed,

m/s

0 5 10 15 200

2

4

6

8

10

12

14

16

18

20

ED

R W

ind

Spe

ed, m

/s

Wind Speed CFRSL Preliminary Results (AVH)

"AV-H" wind speed ret.

Truth wind speed, m/s

"AV-

H" w

ind

spee

d, m

/s

0 5 10 15 200

2

4

6

8

10

12

14

16

18

20

AV

H W

ind

Spe

ed, m

/s

18 of 141

WindSat wind direction ret.

Truth wind direction, degree

Win

dSat

win

d di

rect

ion,

deg

ree

0 50 100 150 200 250 300 3500

50

100

150

200

250

300

350

Surface retrievals

"AV-H" wind direction ret.

Truth wind direction, degree

"AV-

H" w

ind

dire

ctio

n, d

egre

e

0 50 100 150 200 250 300 3500

50

100

150

200

250

300

350

AV

H W

ind

Dir

ecti

on, d

egre

e

Wind Direction CFRSL Preliminary Results (AVH)

AV

H W

ind

Dir

ecti

on, d

egre

e

19 of 141

Atmospheric retrievals

The

Cloud

Problem

20 of 141

Atmospheric retrievals

Precipitation and LWC signal (CFRSL)

21 of 141

Atmospheric retrievals

Non Precipitative

Cloud

Rainy Cell

Rain and Cloud Analysis

22 of 141

Sea Ice Concentration

Bootstrap Algorithm

Nasa Team Algorithm

100 % FY ice boundary

100 % MY ice boundary

50% Ice Concboundary

100% Ice Concboundary

100 % FY ice boundary

100 % MY ice boundary

50% Ice Concboundary

100% Ice Concboundary

Sea Ice Algorithms

23 of 141

Sea Ice Concentration

WindSat: 19V & 37V GHz

MWR 24V & 37V GHz

Longitude, (deg.)

MWRSimulated

Latit

ude,

(de

g.)

Latit

ude,

(de

g.)

WindSat

First Year Ice Concentration using NT algorithm (CFRSL)

24 of 141

Sea Ice Concentration

37

(37) (37)

(37) (37)

V HB B

V HB B

T TPR

T T

24/37

(24) (37)

(24) (37)

H HB B

H HB B

T TGR

T T

CONAE Sea Ice Algorithm

25 of 141

Sea Ice Concentration

Obtaining Parameters from a Scatter Plot

26 of 141

L2 Simulator

External Data Preparation

GDAS

Internal Data Preparation

RTM

Data Base

IC/WS/WD

END

L2 Processor

It converges?No

Yes

WV/LWC/RW

Data Base

L1B1

RTM

27 of 141

Fin

We have a lot of work to do…..