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2. Session Goals 2 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Become familiar with the available data sources for climatology information Understand use of the terms climatology and variability Characterize an area using rainfall climatology Know how to use the climatology knowledge base for an area of concern to develop assumptions about rainfall performance in advance of forecast information

3. Steps to developing agroclimatology assumptions 3 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK 1. Understand the climatology for the area of concern (well in advance of SOS and as necessary) 2. Evaluate current climate modes (~3 months before SOS and until EOS) 3. Interpret available forecasts (~2 months before SOS and through EOS) 4. Incorporate monitoring data from remote sensing and other sources (SOS through EOS)

4. DATA SOURCES 4 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK

5. Rainfall Data Sources 5 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK CHIRPS: USGS/UCSB-CHG, InfraRed unbiased by climatology, added stations. 1981-present, 5Km, global 50N-50S, 5 days total.InfraRed TRMM: NASA InfraRed, microwave, radar, stations data. 2000- present, 25Km, global 50N-50S,tmp 3hr. RFE2: NOAA CPC, InfraRed, microwave, GTS stations. 2001- present, 10Km, Africa, Central Asia, daily. ARC2: NOAA CPC, InfraRed, GTS stations. 1982-present, 10Km, Africa, Central Asia, daily.

6. Sources of data and tools to be used 6 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Mapviewer (USGS) RFE Rainfall plots http://earlywarning.usgs.gov/fews/mapv iewer/ CHIRPS - Monthly

7. Mapviewer -Select region of interest -Select the years of interest and click on the area to obtain plots -Select the layer to be summarized with time series Sources of Data and Tools to be Used

8. CORE CONCEPTS IN UNDERSTANDING CLIMATOLOGY 8 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK

9. Defining climatology 9 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Climatology: long-term average of a weather variable; the observational record, or range of variability in rainfall intensity, spatial distribution, and temporal distribution, that are historically possible for a region. “Climate is what you expect, weather is what you get” Climate: how the atmosphere behaves over a long period of time; average weather over a long period of time. Weather: conditions of the atmosphere over a short period of time.

10. Climate variability 10 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Spatial variability: changes of a climate variable across a landscape Average rainfall, October to May, 1981-2012

11. Climate Variability 11 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Temporal Variability: changes over time Intra-annual: changes within a season Inter-annual: changes between years

12. UNDERSTANDING CLIMATOLOGY IN PRACTICE 12 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK

13. Developing an agroclimatology knowledge base 13 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Review seasonal calendar and understand seasonal context and patterns Review the key aspects of climatology in your area of concern: Spatial and temporal distribution of rains Changes in temperature Growing season (start and end) Water requirements for staple crops Winds Understand elevation and other key parameters impacting agriculture in the area of concern

14. Seasonal Average Rainfall 14 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Season: October to June Maximum dekadal rainfall: 40 mm Cumulative rainfall 500mm

15. Seasonal Average Rainfall 15 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Season: October to June Max dek: 70 mm Cumulative rainfall 500mm

16. Seasonal Average Rainfall 16 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Season: October to Jan and Mar – June (bimodal) Max dek: 50mm Cumulative rainfall: ~250mm for the first season

17. Building blocks for an assumption 17 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Based on climatology, rainfall during the first rainy season will begin in October and end in January, with average cumulative rainfall of approximately 250 mm. Most rainfall is received in November and December.

18. Seasonal Average Rainfall 18 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Season: ? Max dek: ? Cumulative rainfall ? Assumption? m

19. Assessing Temporal Variability 19 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK The plot shows the in??? variabilityHow do we measure variability?

20. Inter-annual variability using RFE data 20 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK

21. 21 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK In Zimbabwe, Mashonaland East maize cropping areas over the 1981-2012 period, the wettest year was 1998 with slightly more than 1000 mm of rainfall during the season. The driest year was 1991 with 400 mm. Using CHIRPS data, seasonal rainfall was >800 mm 12 times in 32 years, or 37% of the time. Seasonal rainfall was 600-800 mm 11 times in 32 years, or 34% of the time. Rainfall was <600 mm 9 of 32 times, or 28% of the time. Building Blocks for an Assumption

22. Measuring Variability: Standard deviation 22 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK 19811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003200420052006200720082009201020112012 CHIRPS61544853090785651082169880650341372255047577585272810419439915366246956118297101029940677824713698 SDMeanCV 17172124 CV= (std / mean) * 100 Coefficient of variation (CV): the ratio of the standard deviation to the mean CV allows you to make a comparison between different magnitudes of variation, even if they have different means Standard Deviation (SD): a measure of variation or how spread out the data are

23. Measuring Variability: time series plot 23 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK The area in south Africa is more variable than the area in Angola. SDMeanCV Angola, Bie region6911536 South Africa, Free State9548220

24. Building blocks for an assumption 24 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK stdv CV Using the coefficient of variation, we see that rainfall variability is much higher in Free State (South Africa) than in Bie (Angola).

25. Measuring Variability: Cumulative rainfall plots 25 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK The cumulative rainfall plots is a way to show the inter-annual variability. The plots show that there more spread on the South African area than that of Angola meanMAXMINRANGE(RAN/Mean)*100 Angola1153128798130627 S. Africa48267129937177

26. Conclusion 26 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Understanding the general patterns of rainfall, such as average seasonal totals and temporal variability, allow you to make initial assumptions about the season.

27. InfraRed data 27 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK A region of the electromagnetic spectrum that has slightly longer wavelengths and lower frequencies than visible light, but is not visible to the human eye. Infrared light can be detected as the heat from a fire or a light bulb. (back)back