Spectral Stratification of Botswana
5. COMPARISONS WITH ENS0 INDICATORS
As an indication of temporal evolution of NDVI precursor and drought patterns over the ENS0 indices suggest that teleconnections become weaker with distance from the Pacific basin. The evolution of the NDVI anomalies for TYPE I and II regions are shown in Figure areas especially northern and northwestern Zimbabwe and Mozambique the negative anomalies in observed in Type II region here in December - January are not surprising (see
eastern to central Pacific cannot be used as the sole indicator of the magnitude of impacts over Southern Africa. Although this is the largest ENS0 warm event this century, its impacts over Southern Africa were mild at least as seen from the evidence derived from NDVI analysis.
There is a need to examine ENS0 from a much more global perspective than the Pacific alone as we have seen from the correlations with the ENS0 indicators. Regional circulation mechanisms (for example, forced by anomalous SSTs in the Indian or Atlantic Ocean and/or interactions with other phenomena like the QBO) may modify the ENS0 signal transmitted from the Pacific either by amplification or by dampening its impacts. These are some of factors that should be taken into consideration in future forecasts and predictions of ENS0 warm effects over the region.
7. ACKNOWLEDGMENTS
This research is funded by NASA Post-Doctoral Fellowship NAS-56222 and is a contribution to the interagency program in support of United States Agency for International Development / Famine Early Warning System (USAID/FEWS).
Further information on this project can be found at:
http://www.clarklabs.or~/1Oapplic/assaf1/monitfront.htm 8. REFERENCES
Anyamba, A. (1997) Interannual Variations of NDVI over Africa and their relationship to ENSO:
1982-l 995. Ph.D. Dissertation, Clark University - Graduate School of Geography, Worcester, MA.
Anyamba, A J. R. Eastman. 1996. Interannual Variability of NDVI over Africa and its relation to El NitiolSouthern Oscillation. international Journal of Remote Sensing 7 7 (13): 2533-2548 Bell, G. D. and Halpert, M. S. (1998) Climate Assessment for 1997. National Centers for Environmental Prediction, Climate Prediction Center, URL: http:l/nic.fb4.noaa.gov:
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Eastman, J.R. and Anyamba, A. 1996 b, Prototypical Patterns of ENSO-related drought and drought precursors in Southern Africa. The Thirteenth Pecora Symposium Proceedings, August 20-22, 1996, Sioux Falls, South Dakota.
Eastman, J.R., Anyamba, A., Ramachandran, M., 1996a, The Spatial Manifestation of ENS0 in Southern Africa. Proceedings, Conference on the Application of Remote/y Sensed Data and Geographic information Systems in Environmental and Natural Resources Assessment in Africa, Harare, March 15-22, 269-281.
Eastman, J. R. and Fulk, M. A. (1993b) Long Sequence Time Series Evaluation Using Standardized Principal Components. Photogrammetric Engineering and Remote Sensing.
53(12): 1649-1658.
Eastman, J. R.. and McKendry, J. E (1992) Change and Time Series Analysis. Volume 1 : Explorations in Geographic Information Systems Technology. UNITAR, Geneva. 2nd. Edition.
Glantz H.M., 1994 “Usable Science: Food Security, Early Warning, and El-Nino” Environmental and Societal Impacts Group.National Center for Atmospheric Research. Colarado, USA.
Gordon, J. and Shortliffe, E. H. 1985, A Method for Managing Evidential Reasoning in a
Hierarchical Hypothesis Space, Artificial Intelligence, 26, 323-357.
Justice, C. 0, Holben, B. N. and Gwynne, M. D. (1986) Monitoring East African Vegetation Using AVHRR Data, international Journal of Remote Sensing. 7(11): 1453-1474.
Myneni, R. B., Los, S. & Tucker, C. J. (1996) Satellite-based identification of linked vegetation index and sea surface temperature anomaly areas from 1982-l 990 for Africa, Australia and South America, Geophysical Research Letters. 23, 729-732.
Nicholson, S. E., Davenport, M. L., and Malo, A. R. (1990) A comparison of vegetation response to rainfall in the Sahel and East Africa using the Normalized Difference Vegetation Index from NOAA AVHRR. Climate Change 17: 209-241.
Ropelewski,, C. F. and Halpert, M. S., 1987, Global and regional scale precipitation patterns associated with El Nitio/Southern Oscillation. Monthly Weather Review, 115: 1606-I 626 Tucker, C. J. Dregne, H. W. and Newcomb, W. W. (1991) Expansion and contraction of the Sahara desert from 1980 to 1990. Science, 233: 299-301
Tucker, C. J. (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of the Environment, 8: 127-l 50.
64
--____---- -... _ .II
Veg%t - a real-time early warning satellite system for drought and flood Dietrich Bannert and Wolfgang Kruck,
Federal Institute for Geosciences and Natural Resources (BGR), Hannover, Germany
Within the framework of the IDNDR The World Conference on Natural Disaster Reduction (Yokohama, Japan, 23-27 May, 1994) calls for the ready access to global, regional, national and local warning information. The installation.of a medium resolution application-oriented satellite system, suitable for early warning of natural hazards in a few days interval is an essential tool towards this goal.
Such systems have not been realised, because over the past 25 years of satellite earth observation two developments have characterised satellite design:
improved spatial and spectral resolution and all-weather capabilities commercialisation of earth observation data.
Concentration on urgent problems like e.g. early warning of natural hazards, clearing of tropical forests and processes of desertification, which are considerably affecting the quality of live and the economy of the Third World, are almost being placed up against the above mentioned development.
The main weaknesses of the existing earth observations systems are:
low repetition rate between 16 and 35 days small field of view
- high spatial resolution high costs for the data slow delivery time
Therefore, it is imperative to develop a satellite system that offers:
0 cost-efficiency
l long range repetition
l real time availability of regional data at the researcher’s disposal
l optical data in a limited number of bands within the VIS/NIR spectral range To fill this gap the VegSat concept is presented; its characteristics are
low cost of the space segment
approved technology in data reception and processing ready access in real-time through small receiving stations It comprises three segments:
l the satellite
l the local data reception
l the data processing
The satellite seqment
The satellite covers a swath of 600 km. It transmits the data continuously like the previous LANDSAT 1 - 3 . Any antenna in view of the satellite can receive the data, if necessary.
The satellite segment can be based on off-the-shelf technology which has been successfully operated in the past. It can be equipped with a minimum of two channels in the visible and
mission profile, the pixel size can be 100 m X 100 m, allowing a local application. The width of the swath is defined by the number of pushbroom scanner-elements, which have usually 6000 pixels in a line, and the altitude and orbit parameters of the satellite. On-board pre- processing of the satellite data could be used, if necessary, to reduce the rate of data to be transmitted to earth. The satellite falls into the group of dedicated, low-costs satellites. Costs could be in the magnitude of 50 million US$.
Ground seqment and data processinq:
The data reception must be cost-effective, simple and easy to maintain. The data processing should be based on reliable processing steps and should avoid a universal flexibility, which is expensive and complicated. The small amount of data prevents ,,data graveyards“ and
guarantees immediate processing by the national user organisation.
For the data reception, a one to two meter dish-antenna similar to those used in NOAA reception is acceptable. Data will be temporarily stored on a hard disc. Image display will be on screen and colour printer. The costs for data reception and downstream processing hard and software are estimated in the range of 100.000 US$.
To provide real-time early warning data, the classified data should be transferred and incorporated into a GIS data base, including data on population density, food and water requirements for livestock, soils, transportation network and others.
Depending on the final satellite swaths one station per country might be adequate.
Conclusion
The system philosophy derives from the need to provide continuously problem oriented earth observation data. It is necessary, to employ local, decentralised, inexpensive data reception and processing technology within the individual countries. Data processing and interpretation should be with national organisations. It would strengthen the organisation’s capabilities and would create independence from commercial data suppliers and interpreters. Thus, VegSat could become a component for drought and flood early warning system. It could become the base for an drought related early warning network in Africa.
Recommendation
On a regional level discussion should be organise the installation of VegSat. An African institution or the World Bank could become the leader to organise, finance and control the satellite.
66
THE CONTRIBUTION OF EXTRA-TROPICAL SEA-SURFACE
TEMPERATURE ANOMALIES TO THE 1996/97 RAINFALL SIMULATIONS OVER THE SOUTH AFRICAN SUMMER RAINFALL REGION
F A ENGELBRECHT and C J dew RAUTENBACH*
South African Weather Bureau, Private Bag X097, Pretoria, South Africa, 0001
*Department of Earth Science, Faculty of Science, University of Pretoria, Pretoria, South Africa, 0002
Introduction
Research on numerical seasonal forecasting schemes using climate Atmospheric General Circulation Models (AGCMs) has become increasingly appropriate. Shukla (198 1) emphasised the necessity to replace the problem of dynamical predictability by predictability due to external forcing. Monthly observed or predicted Sea Surface Temperature (SST) perturbations are usually prescribed as the only non-climatic boundary forcing contributing to seasonal atmospheric fluctuations. Monthly SST anomalies are predicted by various ocean models (Zebiak and Cane, 1987; Ji et al., 1996).
Many observational and statistical studies have revealed noticeable links between tropical Pacific Ocean SST variations, such as those associated with the El NiiiolSouthem Oscillation (ENSO) phenomenon, and prevalent atmospheric circulation and rainfall patterns over Southern Africa (Lindesay, 1988; Van Heerden et al., 1988; Halpert and Ropelewski, 1992; Allan et al., 1996). The occasional faltering of the association between the ENS0 and South African rainfall may be a consequence of the fact that the Southern African sub-continent is also exposed to SST anomaly fluctuations located in the sub- and extra-tropical Indian and Atlantic Oceans (Walker, 1990; Mason, 1995). However, forcing from non-climatic seasonal SST fluctuations in extra-tropical regions are often ignored in AGCM forecasting schemes (Harrison, 1996; Hunt, 1997, 1994).
The reason for investigating the inter-seasonal contribution of extra-tropical ocean forcing on model simulated atmospheric fields over South Africa, is to assess the shortcomings, or possible advantages, which might result from including extra-tropical SST anomalies in AGCM forecasting schemes.
This paper therefore aims to investigate the contribution of extra-tropical (>30° north and south) SST forcing on the inter-seasonal variability of model simulated rainfall over South Africa. Only a limited period (1996/97) has been considered. Output from two separate AGCM simulations is
compared. The first climate AGCM simulation results from globally prescribed observed SST fields whilst equatorial only (>30° north and south) SST fields, over the same time interval, are prescribed for the second simulation. During the latter model simulation the ocean climatology has been retained at higher latitudes.
Climate model and data
The numerical model used in this study is the T63 version of the CSIRO-9 (Mark II) AGCM developed at the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Atmospheric Research in Australia. The model utilises a sigma (CT = p/ps) co-ordinate system over 9 vertical atmospheric levels. Here p and ps represent pressure and surface pressure respectively. A triangular spectral truncation at wave number 63 (T63) is applied which implies a horizontal resolution of 192 zonal and 96 meridional (gaussian) model grid points. This provides a global grid resolution of approximately 1.87’ by 1.87’ (Fig. 1). For more details, see Gordon (198 1).
Monthly observed rainfall fields from approximately 2500 point rain gauges for the years 1996 and 1997 were acquired from the South African Weather Bureau (SAWB). The observed rainfall for each model grid has been calculated from grid box area averages. The observed rainfall climatology, which has also been acquired from the SAWB, was calculated in a similar way from a 30-year climatology consisting of approximately 3000 point values. To calculate the initial monthly model SST input fields, the high-resolution ( loxlo) monthly mean observed optimum interpolation SST analysed fields (Reynolds and Smith, 1994) from NCEP were used. See Engelbrecht and Rautenbach (1999) for more details.
South African rainfall regions
According to model grid locations and prevalent rainfall patterns, two South African rainfall regions have been selected.
25’
20” E 25’ E 30’ E
Figure I : The T63 resolution summer grids (all shaded), prominent summer grids (darker shaded) and isohyets representing the December, January, February (DJF) observed rainfall climate expressed in mm/d. The extra- tropics are defined at latitudes south of 3O”S.
68
The summer grids (all shaded in Fig. 1) are defined as those model grid boxes that accumulatively receive more rain during December, January and February (DJF) than during June, July and August (JJA). The prominent summer grids (darker shaded in Fig. 1) cover the areas capturing the combined observed and model simulated leading summer and winter rainfall patterns respectively (Rautenbach and Smith, 1999). The observed rainfall climatology for DJF is depicted by isohyets in Fig. 1.
Model simulations
A four-year seasonal cycle model simulation (control run) was performed using monthly fixed observed SST climate fields (model SST climatology) as model ocean forcing. Each year was started with slightly different initial conditions. The four-member ensemble mean of the monthly rainfall output from this model simulation was regarded as model climate. Two perturbation runs, using the following SST fields, were subsequently completed:
1)
2)
Global run: Globally observed monthly SST input fields for the years 1996 and 1997.
Tropical run: Tropical only (>30° north and south) observed monthly SST input fields for the years 1996 and 1997. Observed SSTs were spatially smoothed towards the SST climatology from latitudes 30’ to 35.6’ north and south. SST climatology has been retained in the remaining extra-tropical latitudes.
For each case, a four-year seasonal cycle model simulation was performed, and the four-member ensemble mean of the monthly rainfall and MSLP output was calculated. Each year from the perturbation runs was started with slightly different initial conditions.
Statistical techniques
To obtain a quantitative measure of the ability of a model to simulate inter-seasonal variability the following skill-score (ER-score), based on the reduction of variance (Stanski et al., 1989), is defined:
N
C( Fi - Oi)*
ER _ 1 _ i=l i(Ci -oi)*
(1)
i=l
Here Oi, Fi and Ci denote the observed mean value, forecasted (model simulated) mean value and model climatology respectively of the quantity for month i.
The ER-score is a “skill” measure in the sense that it compares the mean squared error of the forecast (here based on observed SST forcing) with the mean square error of the unskilled model climatology. The ER-score ranges from - QO to +l. ER = 1 implies a perfect forecast (model simulation = observations). ER = 0 indicates that a model relaxes to its own climate, implying that the forecast is without any skill. A positive ER-score (O<ER<l) suggest that a model performs better than model climatology.
It is often more meaningful to assess the ability of a model to simulate anomalous events. A statistical measure for this may be obtained by replacing each term in Eq. (1) with the appropriate anomalous expression. In this way, the ER anomaly-score (ERa-score) is obtained:
~[(Fi -Ci)-(Oi -Mi)]
ERa=l- i=’
i(M, -Oi)*
i=l
(2)
where Mi is the observed climatology for month i. The ERa-score has the same characteristics as the ER-score (Eq. (I)), except that the ERa-score measures the ability of a model to simulate the variability in the anomalous behaviour of a variable.
Climate model validation
Results of the rainfall climatologies over the two selected rainfall regions are displayed in Fig. 2.
Although the annual rainfall cycle is well represented, rainfall totals for the summer (thick dashed lines in Fig. 2) and prominent summer (thin dashed lines in Fig. 2) grids are obviously overestimated by model simulations. In agreement with Joubert (1997) more extreme differences between observed and model simulated rainfall totals appear during the summer months.
Differences as high as 90 mm are calculated for the months January, March and October. This overestimation of rainfall may be a consequence of the underestimation of MSLP over the eastern interior of the country (not shown).
180 fl60
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