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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/I407310129 TRMM36769223 MT25406518 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

4 Rainfall estimation at regional scale ( from daily rainfall) Raingauge network - Sahel CILSS : 600-650 + 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 - 1990-1999 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 15 000 km 2 Soudanian rain : 1200-1300 mm/year specialized in water budget / hydrological processes. data since 1997 Gourma 30 000 km 2 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 km 2 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

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 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

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

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, 2004-2007 - 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 (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

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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