1. Thirty-year Time Series of Merged Raingauge-Satellite Rainfall Data over Ethiopia
Tufa Dinku1, Stephen Connor1, David Grimes2, Kinfe Hailemariam3, Ross Maidement2, and Elena Tarnavsky2
1. International Research Institute for Climate and Society,
the Earth Institute at Columbia University
2. Department of Meteorology, University of Reading, UK
3. National Meteorological Agency, Addis Ababa, Ethiopia
Long-term, temporally homogeneous time series of rainfall data are of great importance in a number of applications. The conventional source of climate data is weather stations. However, reliable climate information, particularly throughout rural Africa, is very limited. The available weather stations are unevenly distributed, with most of the stations located along the main roads in cities and towns. The alternative has been satellite rainfall estimates with the main advantage of excellent spatial converge. But satellite rainfall estimates also suffer from a number of critical shortcomings that include heterogeneous time series, short time period, and poor accuracy particularly at higher temporal and spatial resolutions. Thus, it makes a lot of sense to combine the point accuracy of the raingauge measurements with the better spatial coverage of the satellite estimates. This poster describes the use of raingauges and satellite rainfall estimates to produce 30-year rainfall time series over Ethiopia at spatial resolution of 10km and ten-daily time scale.
1. Required
Complete, homogenous, and hi-resolution(10km) time series of ten-daily rainfall data going back 30-years.
2. Problems
2.1 Raingauge data
Sparse station network
Complex terrain
A lot of missing data
Complex terrain and the sparse
station network makes interpolation
of raingauge data unreliable.
For instance, the grid point G in Fig.1
is more correlated to station S2
than the closer station S1.
2.2 Satellite rainfall estimates
Accuracy not good particularly over
mountainous regions like the current
study region
Time series not homogenous because
of different sensor inputs
Low spatial/temporal resolutions
Short time series for products
with relatively higher temporal
and spatial resolutions
3. Approach to overcome the problem
Obtain and process raw satellite TIR data going back 30 years
Organize and quality control ALL available raingauge data
Calibrate a simple satellite rainfall retrieval algorithm over the area of interest
Generate satellite rainfall estimates
Remove satellite rainfall bias using raingauge data
Grid raingauge data using spatial correlation information obtained from the the bias-adjusted satellite estimates
Optimally combine the gauge measurements with bias-adjusted satellite estimates
4.Outputs
30-year time series of gridded rainfall data
30-year time series of satellite rainfall estimate
30-year time series of merged raingauge-satellite rainfall
5. Sample outputs
Fig.3: Sample outputs
(A) Mean gauge rainfall Jul 2nd dekad( )
(B) Interpolated gauge
(C) As in (B) but bias-adjusted satellite data used as background
(D) Satellite rainfall estimate
(E) Bias-adjusted satellite rainfall estimate
7.Acknowledgment
This project has been funded by Google.org G S2 S1 A E D C B
Fig.1. Distribution of raingauge stations with 20 or more years of data
Figure 2: Comparison of some satellite Rainfall products over Ethiopia at ten-daily time scale and spatial resolution of 0.25 deg