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Henri Laurent Marielle Gosset (Benin) Christian Depraetere Thierry Lebel (Niger) IRD/LTHE, Grenoble, France Abou Amani Abdou Ali Agrhymet, Niger Rainfall.

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Presentation on theme: "Henri Laurent Marielle Gosset (Benin) Christian Depraetere Thierry Lebel (Niger) IRD/LTHE, Grenoble, France Abou Amani Abdou Ali Agrhymet, Niger Rainfall."— Presentation transcript:

1 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

2 - time sampling effect -“ground truth”: estimation of areal rainfall and 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

3 Time sampling problem 1°x1°5°x5° annualJul/AugJun/Sepannual SSM/I TRMM MT Rainfall estimate error (%) Using the dense raingauge network in Niamey (1°x1°), it has been shown that the time sampling error is reduced by % for MT compared to TRMM Convective cloud cover – 1 month Reference: all Meteosat images Sampling: SSM/I Sampling: TRMM Sampling: MT

4 Rainfall estimation at regional scale ( from daily rainfall) Raingauge network - Sahel CILSS : CRA : 280 O Synop : 85

5 Raingauge network - Sahel Summary 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)

6 3 krigging methods taking the drift into account Interpolated values are close, but very different estimations of theoretical error Observed error Theor. error Comparison of different methods for areal rainfall estimate

7 Monthly rainfall, 2.5°x2.5° Sahel Intercomparison of different satellite products –CMAP (Sat+gauges) –GPCP (Sat+gauges) –GPCC (gauges) –GPI (Sat) –SYN (gauges from synoptic network) RMSE (%)

8 Kriging from CILSS daily rain gauges Tracking from METEOSAT infrared channel 19 July 1994 Rainfall at regional scale: daily estimation?

9 AMMA Long term experimental set up 3 meso-scale sites – 2 of which are already well equiped for precipitation measurements. Upper OUEME valley km 2 Soudanian rain : mm/year specialized in water budget / hydrological processes. data since 1997 Gourma km 2 Sahelian to Saharian Rain : mm/year site specialized in vegetation + satellite validation. Few rain gages / Possibility to densify. Niamey square degree km 2 Sahelian rain : mm/year Specialized in hydrology and the study of land / rain intreactions. Data since 1990 (Hapex –Sahel, Epsat- Niger) African Monsoon Multidisciplinary Analysis

10 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 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

11 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

12 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 Upper OUEME valley

13 Optical Spectrogranulometer, recording data every 1 min on the rain drop size distributions observed at ground level. X port Radar developed at LTHE X band – 9.4 GHz diameter 1.8 m – 100 kW polarisation H and V doppler Details:

14 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, EOP AMMA

15 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

16 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.

17 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 ( ) Identification of Mesocale Convective Systems (rainfall) and determination of their propagation (speed and direction) Depraetere et al., EGU Conference, April 2005, Vienna, Austria

18 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

19 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

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