1. Early warning Systems in Sudan Meteorological Authority Ahmed M Abdel Karim Sudan Meteorological Authority Ahmed@ersad.gov.sd Crop and RAngeland Monitoring Regional Centre for Mapping of Resources for Development 26-30 September 2011 Nairobi Kenya

2. Climate Climate and weather over Sudan is controlled by the following weather system: Azores anticyclone (Sahara) St. Helena anticyclone Siberian anticyclone Mascarene anticyclone The monsoon Sudan Thermal low The ITCZ The Easterly Jet Stream

3. Winter Season (NDJF)Advancing Monsoon Season (MAM)

4. Rainy Season (JAS) Retreating Monsoon Season (Oct)

5. Seasons Four Season are identified: 1. Winter Season (NDJF) 2.Advancing Monsoon Season (MAM) 3.Rainy Season (JJAS) 4.Retreating Monsoon Season (Oct) 5.This lead to the rainfall distribution which is the most important element in monitoring and forecasting. JJAS is main rainy season

6. Monthly rainfall distribution over Sudan

7. July normal rainfall distribution during the base period (1971-2000) [in mm] 1.Rainy Season (JJAS)

8. Natural Hazards and its impact in Africa

9. SOURCE: Global results of the WMO Country-Level DPM Survey: Maryam Golnaraghi, Chief of DPM Programme, December 4, 2006 Patterns of casualties by natural hazards in WMO Regions

10. SOURCE: Global results of the WMO Country-Level DPM Survey: Maryam Golnaraghi, Chief of DPM Programme, December 4, 2006

11. 11 Patterns of economic losses by natural hazards in WMO Regions

13. Monitoring Different types of report are be issued on different time scale

14. Agrometeorological products Monthly Bulletin Dekadal Bulletin Pentad Bulletin Climate bulletin SAMIS (Sudan Agro-meteorological information System)

15. Monthly Bulletin

17. Dekadal Bulletin

22. Dekadal Bulletin (WSI)

23. Pentad Bulletin

25. 25 The Dekad rainfall report

28. S udan A gro- M eteorological I nformation S ystem (SAMIS ): Is an operational system for the production of agro-meteorological information from meteorological station and satellite data, installed at the Sudan Meteorological Authority. SAMIS plays a fundamental role as the provider of core information for early warning and vulnerability assessment and mapping activities. It also enables the SMA to fulfill its role of providing agro-meteorological information for users involved in the monitoring and management of agricultural, environmental and hydrological resources.

29. SAMIS products Dekadal Rainfall Amounts Dekadal Rainfall Anomaly (ratio against climatology) Cumulative Rainfall from March to current dekadal Cumulative Rainfall Anomaly (ratio against climatology) Dekadal Number of Rain Days Length of Currently Active Dry Spell (maximum over 30days) Vegetation Index Vegetation Index Difference from long term mean Monthly Rainfall Monthly Rainfall Anomaly Monthly Number of Rain Days

30. SAMIS report

31. 31 Rainfall Analysis – 10 Day Amounts 10 day rainfall amounts produced by SAMIS at SMA are based on a combination of METEOSAT satellite and synoptic gauge data. Rainfall climatology is similarly derived from a combination of historical data from the two sources.

32. 32 Rainfall Analysis – Cumulative Amounts Cumulative amounts are obtained by summing the dekadal estimates starting from Dekad 1 of March until present. The climatological cumulative are likewise derived by summing the dekadal climatological estimates over the same period of time.

33. 33 The cumulative rainfall amounts display the usual organization in latitude bands (as the rainfall moves north following the ITCZ). In relative terms, significant above average departures

34. 34 The rainfall total for the dekadal and its departure for the Same period SAMIS is at http://ersad.gov.sd

35. Rainfall AS Parentage of Average

36. Cumulative Rainfall as % of Average

37. Total Rainfall

38. Dekads Since Start of Growing Season

39. Number of Wet Dekads

40. Moisture Index

41. End of Growing Season

42. Location Analysis: RVT Plots

43. Rainfall and Vegetation Plots Start of Green Up End of Rainfall Season Planting Rains

44. Rainfall and Vegetation Plots

45. Seasonal Forecast Prediction

46. Numerical Weather Prediction Models

47. 22 August 2010

48. 23 August 2010

49. 24 August 2010

50. 25 August 2010

51. 26 August 2010

52. The monthly seasonal rainfall outlook

53. Seasonal forecast outlook fro the rainfall for JJAS 2010 ANBANB

54. Three stations is understudy to predict the PET. Kosti, Fashir and Damzine The following graph show the inverse relations with the PET and the rainfall. The months for the PET is taken (July, Aug and September) The month of the SST is April When the PET is positive ==== Dry When the PET is negative ==== Wet On the study to predict Drought using the SST

55. The monthly average PET for Kosti

56. PET Anomalies for Kosti

58. Tricle Table

59. NDVI prediction using SST

60. Methodology NDVI images from 1982-2003 were processed, by extracting the maximum NDVI for each of the years This maximum NDVI is an indicator of vegetation seasonal development Values were extracted at the synoptic station locations generating an NDVI time series (22 elements) for each Anomalies were then computed for each station

61. RainfallNDVI max1984( dry)

63. RainfallNDVI max1990( Dry)

65. Rainfall year 1999 (wet year) NDVI

67. RainfallNDVI max2003( wet)

69. Recommendations Forecasting rainfall in different time scales (monthly, dekadal). Prediction element other than rainfall must be introduced to be used as indicator as (NDVI and PET) to predict drought. Joint research