Henri Laurent

Marielle Gosset (Benin)

Christian Depraetere

Thierry Lebel (Niger)

IRD/LTHE, Grenoble, France

Abou Amani

Abdou Ali

Agrhymet, Niger

Rainfall data for validating satellite rainfall estimates - Precipitation network

set up in Africa for AMMA

L ET H

- time sampling effect

-“ground truth”: estimation of areal rainfalland accuracy

Need for a good knowledge of rain fields

raingauge networks

radar

Observations and studies in the AMMA framework(African Monsoon Multidisciplinary Analyses)

- regional scale – daily rainfall observations

- meso scale – high resolution observations

Ground validation issues

Time sampling problem

 

 

 

  1°x1° 5°x5°

  annual Jul/Aug Jun/Sep annual

SSM/I 40 73 101 29

TRMM 36 76 92 23

MT 25 40 65 18

Rainfall estimate error (%)

Using the dense raingauge network in Niamey (1°x1°), it has been shown that the time sampling error is reduced by 15-25 % for MT compared to TRMM

Convective cloud cover – 1 month

Reference: all Meteosat images

Sampling: SSM/I

Sampling: TRMM

Sampling: MT

Rainfall estimation at regional scale(from daily rainfall)

Raingauge network - Sahel• CILSS : 600-650 + CRA : 280 O Synop : 85

Raingauge network - SahelSummary on the kriging method used to create the areal rainfall for

validation of satellite rainfall estimates

 Regression Kriging method used to estimate the mean areal rainfall

(grid: 0.5°x0.5°, 1°x1° or 2.5°x2.5°)

cumulated oved 10-day, monthly or annual periods

 

Anisotropy of rainfall fields: the drift has to be taken into account

 For details on the kriging method, see:

Ali et al., J. Appl. Meteo, 2005 (in press)

3 krigging methods taking the drift into account3 krigging methods taking the drift into account

Interpolated values are close,Interpolated values are close,

but very different estimations of theoretical errorbut very different estimations of theoretical error

UK 54,11 33,69SC 54,62 37,15RK 53,92 52,33

Observed error Theor. error

Comparison of different methods for areal rainfall estimateComparison of different methods for areal rainfall estimate

Monthly rainfall, 2.5°x2.5° Monthly rainfall, 2.5°x2.5° Sahel - 1990-1999Sahel - 1990-1999

Intercomparison of different satellite productsIntercomparison of different satellite products

Reference : Regression

Kriging

Reference:

Kriging without drift

CMAP 15.9 21

GPCP 18 25

GPCC 16 27

GPI 42 45

SYN 14.4 24.5

– CMAP (Sat+gauges)CMAP (Sat+gauges)

– GPCP (Sat+gauges)GPCP (Sat+gauges)

– GPCC  (gauges)GPCC  (gauges)

– GPI (Sat)GPI (Sat)

– SYNSYN (gauges from synoptic (gauges from synoptic network)network)

RMSE (%)

Kriging from CILSS daily rain gauges

Tracking from METEOSAT infrared channel

19 July 1994

Rainfall at regional scale: daily estimation?

AMMA Long term experimental set up3 meso-scale sites – 2 of which are already well equiped for precipitation measurements.

Upper OUEME valley • 15 000 km2

• Soudanian

• rain : 1200-1300 mm/year

• specialized in water budget / hydrological processes.

• data since 1997

Gourma • 30 000 km2

• Sahelian to Saharian

• Rain : 200 - 400 mm/year

• site specialized in vegetation + satellite validation.

• Few rain gages / Possibility to densify.

Niamey square degree • 10 000 km2

• Sahelian

• rain : 450-600 mm/year

• Specialized in hydrology and the study of land / rain intreactions.

•Data since 1990 (Hapex –Sahel, Epsat-Niger)

African Monsoon Multidisciplinary Analysis

Since 1989: between 30 and 109 raingauges

Many works, e.g.:

Lebel et al., Water Res. Res., 1992 – Amani et al., Water Res. Res., 1996 – Lebel et al., J. Hydro, 1997 – LeBarbé and Lebel, J. Hydro, 1997 – Amani and Lebel, J. Hydro, 1997 – Amani and Lebel, Sto. Hydro., 1998 – Lebel and Amani, J. Appl. Meteo, 1999 – Mathon et al., J. Appl. Meteo, 2002 – Ali et al., J. Hydromet., 2003

See www.lthe.hmg.inpg.fr/catch/

Very good knowledge of Sahelian rain fields

- modelisation, downscaling issues

- interpolation and estimation (kriging)

- estimation error

Not simply transposable in another region

First it is needed to study precipitation fields

Niamey square degree

Raingauge network – Mesoscale sites Available data sets for 2004

Niamey square degree (Niger)

33 recording rain gauges

Raw data 5 min, ~ from May to September (depends on the station)

Validated data:

• 10-day periods - station and grid (kriging 5 km x5 km)

• rainfall events (i.e. >30% rainy stations) - station and grid (kriging 5 km x5 km)

Oueme (North Benin)

35 recording rain gauges

Raw data 5 min, all over the year (with some missing data)

Validated data: daily rainfall (stations)

Gridded estimates are not available (yet) by lack of climatological knowledge

Mesoscale Site:-Raingage network, river flow, ground water.- Surface, monitoring of vegetation dynamics.- Meteo/climate stations

“Super Site”: Donga watershed• Denser network (20 gages / 600 km2)• Meteorological radar Xport + optical disdrometer

Bassin de la Donga4km

(carroyage UTM 31N)

380000 3840001096000

1092000

Ara Kolo

Koua

Nek

Don

puits avec lecteurPluviographe limite de bassin versant

rivièrePuits avec enregistreurLimnigraphe

Upper OUEME valley

L ET H

Optical Spectrogranulometer, recording data every 1 min on the rain drop size distributions observed at ground level.

X port Radardeveloped at LTHEX band – 9.4 GHzdiameter 1.8 m – 100 kWpolarisation H and Vdoppler

Details: www.lthe.hmg.inpg.fr/catch/xport/

2D - Structure

3 D structure

multiparameters

Which measurement ?

-Reflectivity (power returned to the radar by the precipitation) Amount of precipitation

-Polarimetric variables (difference between Horizontal and Vertical signal)

median diameter of the drop size distribution

attenuation correction

-Doppler velocities of hydrometeors.

X-port : Data and objectives

Donga / Bénin, 2004-2007 - EOP AMMA

Vertically pointing mode: vertical structure of precipitation

(images VPR McGill)

•Derive statistics of vertical structure in a given climatic region

•observe amount of convective vs stratiform rain

•quantify occurrence of evaporation

improve parameterization

feed data base for satellite remote sensing algo.

•(+ FFT analysis of Doppler spectrum -> evolution of DSD with height)

Radar - vertical profiles analysis

Radar + disdrometer data - Application

- High resolution 2D fields of precipitation – Homogeneity ? Propagation at ground ?- Down scaling issues- Observation of vertical profile of reflectivity within rain storm – gather profil types and assess variability, useful for inversion of satellite data- DSD analysis + radar polarimetric product : Analyze the time/space

variability of Drop size distributions at the ground level.

L ET H

Ongoing studies on the characterisation of the rainfall events in North Benin using rain gauge data

•Development of a rainfall model valid for Sahel and sub-Sahel rain fields. Based on a modelisation of convective cells

Rainfall types during the rainy season (1999-2003)

•Identification of Mesocale Convective Systems (rainfall) and determination of their propagation (speed and direction)

Depraetere et al., EGU Conference, 24-29 April 2005, Vienna, Austria

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0,00 0,50 1,00 1,50 2,00 2,50 3,00 3,50 4,00 4,50 5,00 5,50 6,00

Pseudo-time (Hours)

Rai

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(mm

/h)

02468101214161820222426283032

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Computation of optimal direction and speed of the rainfall event

Directional chronogram of the rainfall event

Meso scale hyetogram derived from the pseudo-chronogram

Directional pseudo-chronogram of the rainfall event

rainfall ground truth – estimation and estimation error - regional scale - meso scale possibilities in the West African, sub-Sahelian zone : - Data from the Upper Ouémé meso-scale site- Rainfield modelling /Down scaling issues.- Use of a light X-Band, polarimetric radar for field observation of the precipitating systems

Summary

Possible collaborations on ground validation issues