# Presentation: 708351

## 4th GPM International Workshop15-17, June 2004Greenbelt, Maryland, U.S.A

- GPM Research Actions in Brazil
- Presentation: Roberto Vicente Calheiros, IPMet/UNESP
- Contributions:	Augusto J. Pereira Filho 1
- 		Carlos A. Morales 2
- 		Edson Eiji Sano 3
- 		Juan Ceballos 4
- 		Luiz Augusto Machado 5
- 		Roberto V. Calheiros 6
- 1,2 Institute of Astronomy, Geophysics and Atmospheric Sciences – IAG/USP
- 3    Brazilian Agriculture Research Organization – EMBRAPA
- 4,5 Environmental Satellite Division – DSA/CPTEC/INPE
- 6     Meteorological Research Institute – IPMet/UNESP

## BRAZILIAN SPACE AGENCY (AEB)BRAZILIAN NUCLEUS OF THE INTERNATIONAL PROGRAM FOR PRECIPITATION MEASUREMENT(GPM-BRAZIL)

- A decision was reached to stablish the Brazilian participation in the GPM, AEB acting as the national coordinator and link institution
- GPM-Brazil nucleus coordinates activities from Brazilian institutions and estimulates the use of available data
- Lines of action:
- a) validation of modeling
- b) data availability
- c) research
- d) development of sensors
- e)  release
- Instruments of action: specific projects, under the responsibility of one or more national institutions
- Structure:
- a) Coordination Comittee (CCO)
- b) Management
- c) Participating institutions
- d) Assessors
- e) Users

## Gauge, Radar and Satellite Rainfall(IAG/USP) (ONE)

*Map of Eastern São Paulo State, Brazil. The SPWR is at 23**o **36' 00'' South and 45**o **58' 20" West. Inner and outer radii are 120 km and 240 km, respectively. The DAEE rain gauge network is also shown. Data sets are compared within the 300 km**2 **area. *
* *

-  *Scatter diagram of gauge spatial mean rainfall against radar and satellite ones (left) as well as satellite against radar (right). It is indicated the respective variance coefficients. *

- (From: Pereira Filho et al, 2003)

## Gauge, Radar and Satellite Rainfall(IAG/USP) (two)

*Maps of satellite (left) and radar (right) total rainfall accumulation for the entire data set (Table 1). Color scale indicates total rainfall (mm). Numbers in the radar map (right) identifies areas where the total rainfall was underestimated by ground clutter suppression (1, 2 and 3) and contamination (4), range effect (e.g., 5) and signal processing (e.g., 7).*

- (From: Augusto J. Pereira Filho, Andrew J. Negri, and P.T. Nakayama: “An Intercomparison of Gauge, Radar and Satellite Rainfall in the Tropics”, IPWG/CGMS, 1st Workshop, Madrid, Spain, 23-27 September, 2003, 8pp)

## IWVC FROM HIGH MICROWAVE CHANNELS (HSB)(DAS/CPTEC) (ONE)

- Comparison of total IVW as estimated from HSB channels and radiosonde measurements

- Brightness temperature (K) as a function of IWLC in a CB  cloud for a) 150 GHz, b) 183 +/- 1 GHz, c) 183 +/-3 GHz and d) 183 +/-7GHz. Curves refer to rainfall rates of 0, 5 and 10 Kg/m2 respectively

- 150 GHz Channel

- 183+/- 1GHz Channel

- 183+/-3GHz Channel

-  183+/-7GHz Channel

- Estimated IVW(Kg/m2))

- Radiosonde IVW (Kg/m2)

- (From: Wagner Flauber Araújo Lima and Luiz A. T. Machado: “Analysis of the HSB sensor in estimating the ILWC of WV: a study applied to the RACCI/LBA”, submitted, XIII Congremet, Fortaleza, CE, Brazil, August 2004, in Portuguese)

- (From:  W.F. Araújo Lima, L.A T.Machado and C. A. Morales: “Analysis of the HSB Channel’s potential to estimate precipitation”, submited,  XIII Congremet, Fortaleza, CE, Brazil, August 2004, in Portuguese)

## MULTI-SENSOR VALIDATION (DSA/CPTEC) (TWO)

- Validation – Based on Radar-Satellite – DCP (Data Collection Platform) Measurements

- From: (L. A. T. Machado, personal communication)

## RAINFALL FROM HIGH MICROWAVE CHANNELS (HSB) (DAS/CPTEC) (THREE)

- Modeling – Radiative Transfer
- Microwave & Hydrometeors Interactions

- 150 GHz

- 184+-1 GHz

- 183 +- 3 GHz

- 183 +- 7 GHz

- CAPPI 7 km

- CAPPI 3 km

- 150 GHz Channel

- Rainfall  rate (mm/h)

- From: (L. A. T. Machado, personal communication)

## MICROWAVE IMAGER PLATFORM(DAS/CPTEC) (FOUR)

- Technological Development (in house) – Satellite in Tropical Orbit –
-   Passive Microwavve Sensor. Second GPM Phase (around 2010).

**GMI – GPM Microwave Imager**

- From: (L. A. T. Machado, personal communication)

## Gabor FilteringIPMet/UNESP (ONE)

**The filtered images in the frequency domain, ( real  part)**

**CAPPI de 3,5 km (Bauru), do dia 11JAN03, aproximadamente às 01:30H local**

- Proceed to classification through a NNW varying the “   ” parameter set

- (From: R.V. Calheiros, R.Machado, C..A.Thompson, Zildene P. Emidio, submited to the XIII Congremet, Fortaleza, CE, Brazil, August 2004 in Portuguese)

## Brightness temperature – Reflectivity (Z-R) relationshipsIPMet/UNESP (TWO)Tb x Z

**Tb-Z relationship ****curves: solid/red-16 Sept-night, dashed/blue-01 Oct-day, dash/dot/blue-21 Oct-day, solid/green-30 Oct-night , dash/red-07 Sept-night, solid/blue-12 Oct-day, all from 2002.**

- (From: R.V.Calheiros, R. Machado and M.A. Lima: “Cell Detection at Far Ranges: Supporting Tb-Z relationships”, Sixth International Symposium on Hydrological Applications of Weather Radar, Melbourne, Australia, 2-4 February 2004)

## PRELIMINARY  DATA  ANALYSESValidation of Satellite Observations  

**(Falcon DIAL) 04/03/04 ****(THREE)**

**Explanation of Next Slide: **
-  Impact of smoothing imposed by the sensor’s Field Of View (FOV) -  a,b,c
-  Vertical cut through horizontal pixel 289 - Fig. d (orig.) and Fig. e (filtered)
-  Horizontal profile at 12,7km height – Figs f, g, h, without filtering, for a
-   13km FOV and 50km FOV, respectively

**(Courtesy: DLR)**

- Gerhard Held and Roberto Vicente Calheiros, general and scientific coordinators - TroCCiBras, TROCCINOX e HIBISCUS Teams** **

- From: TroCCiBras PROGRESS REPORT: January to May 2004

## Validation of Satellite Observations: TroCCiBras  04/03/04 (FOUR)

- (R.Calheiros &
- R. Machado)

## (FIVE)

## USE OF AIRS OBSERVATIONS TO SIMULATE HSB(DSA/CPTEC)

- 	(From: Rodrigo Augusto Ferreira deSouza, “Simulations of Brigthness Temperature for the HSB Channels from AIRS Information and Performance Analysis of Aqua Soundings over Brazil”, PhD Thesis /INPE, 2003,adviser: Juan Ceballos/DAS).
- Partial compensation for HSB loss was sought: use made of AIRS data from cloud-free pixels
- AIRS WV channels were correlated with the 4 HSB channels over oceans for September (actual HSB data); highest correlated  AIRS channels were selected for analysis.
- Similarity with HSB Jacobian criterion was applied to evaluate AIRS simulation ability: a linear combination of a reduced number of AIRS channels was found to closely assess brightness temperatures in tropical continental environment with a m.s.e, within about 1 K
- Atmospheric profiles for clear sky pixels with HSB-simulated and AMSU-A channnels input were retrieved over Rondonia (September-October 2002, Dry-to-Wet LBA Campaign): the profiles follow, in general, the radiosonde profiles and were within sensor specifications, i.e., 1.5 K for temperature and ~1,5g/Kg for humidity, a behaviour not attained in the region by the NASA retrieval procedures (versions b-30 and c-01)

## BRAZILIAN GEOESTATIONARY SATELLITE – (SGB) (ONE)(~2009)

- Implementation of the Communications Navigation & Surveillance (CSN)/Air Traffic Management (ATM) Concept

## BRAZILIAN GEOESTATIONARY SATELLITE – (SGB) (TWO)

## GPM: APPLICATION IN AGRICULTURE (one)

**Embrapa (Brazilian Agriculture Research Organization)**
**is interested in the precip data from GPM to improve its **
**annual crop yield prediction.**
**The accuracy of crop yield prediction depends strongly on **
**the availability of spatially-based rainfall data.**
**The country ́s raingage distribution is poor in some regions,**
**including most of the Cerrado ́s biome, the most important **
**region in Brazil for grain production. **
**We expect that GPM can provide accurate precipitation**
**data in order to improve our water balance models.**

_**The Embrapa ́s Interest in GPM Data**_

**1. **_**Crop yield prediction**_

## GPM: APPLICATION IN AGRICULTURE (two)

- Source:EMBRAPA