Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. AMESD eStation users’...

Nairobi, 25th/Oct. – 18 Dec 2010 AMESD eStation users’ training.

AMESD eStation users’ training

N 08. Precipitations products from RS

Marco Clerici, JRC/IES/GEM


• TAMSAT product (UK)

• FEWSNET product (USA)

• MPE product (EUMETSAT)

Contents


TAMSAT Operational Rainfall Monitoring for Africa

David Grimes

TAMSAT*Dept of MeteorologyUniversity of Reading

U.K.

TAMSAT = Tropical Applications of Meteorology using SATellite data


• Use Meteosat TIR imagery

• Identify cloud top temperature threshold Tt distinguishing between rain and no rain

• Calculate Cold Cloud Duration (CCD) for each pixel (length of time cloud top is colder than Tt )

• Estimate rain amount as rain = a0 + a1 CCD

• a0, a1, Tt are calibrated against local gauges using historic data

• Calibration parameters vary in space and time

TAMSAT algorithm


TIR image 18:00, 23/07/08


21/04/23

Comparison of Satellite rainfall estimates and gauge data over the Sahel July dekad 2 2004

Met office – Raingauge anomaly

TAMSAT – Raingauge anomaly

Met office – TAMSAT anomaly


TAMSAT Calibration zones

August

Calibration zones vary slightly from month to monthDifferently shaded areas show the different zones for August


TAMSAT operational estimates

-40

-30

-20

-10

0

10

20

30

40

-25 -5 15 35 55

longitude/degrees

lati

tud

e/d

eg

ree

s

No of gauges for calibration = 4569Format: idrisi or geotiffProjection = lat longNominal resolution = 0.03750


9

Dekad 2August, 2009


Validation of rainfall estimates

Question: How do we know if rainfall estimates are any good?

Answer: compare against independent data set.

For Africa, this usually means comparison against raingauge data


+

Use of gauge measurements for validation

•Validation in Africa is problematic because of the lack of ground-based observations•JRC in collaboration with IPWG have produced a set of guidelines for African validation•Main recommendations:– use geostatistical methods to convert gauge data to pixel (or larger)

scale– specify minimum number of gauges per grid square – take account of uncertainty of gauge data as areal estimator

gauges = +

+

++ +


Improved calibration

Calibration currently being updated and extended as part of the MARSOP3 project

Additional raingauge data provided by Meteoconsult

Old calibration New calibration


Continental product


TAMSAT: Conclusions

• TAMSAT algorithm provides good quality rainfall estimates for most of Africa

• The approach is successful because of careful calibration against local gauge data

• Current calibrations are being extended to cover all Africa + Arabian peninsula for all months

• Operational products being used by JRC, Agrhymet, Uganda, Sudan, Ethiopia

• Current research includes– 30 year time climatology and time series– improved algorithm using all MSG channels– ensemble estimation of uncertainty– applications to crop yield and hydrology


The Climate Prediction CenterRainfall Estimation Algorithm Version 2

Tim Love -- RSIS/CPC


RFE 2.0 Overview

• Run daily at NOAA CPC for Africa, southern Asia, Afghanistan area domains

• The Overall schema is:

1. Use satellite IR temperature data (MSG) for the GOES Precipitation Estimate (GPI)

2. gauge fields (via GTS)3. Use microwave precip estimates (SSM/I & AMSU-B), 4. Combine the above products into RFE 2.0


Meteosat Data

– Resultant field = cold cloud duration (CCD) @ 0.1° resolution (about 10 km)

– CCD used for GOES Precipitation Index (GPI) calculation

hrshr

mm

T

TGPI #

3235

– GPI tends to overestimate spatial distribution but underestimates convective precipitation


GPI Estimate

CPC RFE 2.0


GTS Data

• 2534 stations available daily• Only 400-800 report daily• Few reports from Nigeria, none from Liberia,

Sierra Leone• Data ingested from GTS line, Quality Controlled,

fed to operational machine, then gridded to 0.1° resolution file

• Other station data may be readily used as input to algorithm via changing 2 tables in base code

• Requirements for RFE processing:– GPI and GTS inputs

GTS = Global Telecommunication System


GTS Inputs


GTS vs GPI


SSM/I: definition

Special Sensor Microwave/Imager

• a seven-channel, four-frequency, linearly polarized passive microwave radiometric system

• The instrument is flown onboard the US Air Force DMSP spacecraft


SSM/I Inputs

• 2 instruments estimate precip twice daily

~6 hourly data frequency• Fails to catch other rainfall in temporal

gaps• Data needs only small conversion in

preparation for input to algorithm


AMSU-B definition

• Advanced Microwave Sounding Unit

• 15-channel microwave radiometer installed on NOAA polar orbiting satellites.


• As with SSM/I, data is available 4 times daily, staggered temporally

• Tends to overestimate most precip, but does well with highly convective systems

• Data sent in HDF format, thus needs to be deciphered before input to RFE algorithm

• Preprocessing straightforward

AMSU-B Data


AMSU-B Estimate


TAMSAT – FEWSNET2nd dekad Nov 2009


Multi-sensor Precipitation Estimate (MPE): An operational real-time rain-rate product

Thomas HeinemannMeteorological Operations DivisionEUMETSAT

[email protected]


Geostationary satellite data:METEOSAT IR - data from the operational METEOSAT satellites:0° (currently MET-9) and 57° East (currently MET-7) and RSS (currently MET-8)

Polar orbiting satellite data:SSM/I and SSMIS passive microwave data from currently 2-3 of the American DMSP satellites on a sun-synchronous orbit:

DMSP13: (SSM/I) DMSP15: (SSM/I), when undisturbedDMSP16: (SSMIS), pre-processed with SSMISPP

MPE Algorithm : used data


SSM/I data

Rain Rate (mm/h)

METEOSATdata

IR Brightness Temperature

Longitude

Lat

itu

de

Store in the corresponding geographical box during a certain period of accumulation

2IR Brightness Temperature(METEOSAT)

Rai

n rate

(m

m/h

)

LUTsbuild3

Create LUT

(RR,TIR)

Co-located data

1

Temporal and spatial co-location

Derive the product on IR- pixel level

4

Algorithm overview


MPE: a real-time precipitation algorithm


Validation against radar


Precipitation intensity & soil erosion / degradation


Impact of rain

• It is estimated that the erosion impact on the global scale is between 15 to 30 t/ha/yr, which equals 1 to 2 mm/yr soil loss.

• As a reference:

Introduction à la gestion conservatoire de l'eau, de la biomasse » (http://www.fao.org/docrep/T1765F/t1765f0d.htm)


Relation between precipitation rates and Ek


Influence of season, max intensity in 30 minutes, and rains during preceding dekad (h 10 jours = soil humidity index) on erosion and water flow due by similar rain amounts on bare and vegetation covered soils(Roose, 1973)


• Indonesia: I=86.517*D–0.408

I: intensity P (mm/day), D: rain duration (nb days)

Problems if P > 80 mm/j for 3-5 days sustained rains

• Azores: I=144.06*D–0.5551 problems if 78 > P > 144 mm/j for 1-3 days

Relations between Intensity and Duration


Rainfall erosion power index

Disaster Prevention System Department, Fukken Co, Japan

72 h1.5h

Nairobi, 25 th /Oct. – 18 Dec 2010AMESD eStation users’ training. AMESD eStation users’...

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