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2. Session Goals 2 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Understand use of the terms climatology and variability Characterize an area using rainfall climatology Combine time series, and variability to analyze implications in agriculture Know how to use the climatology knowledge base for an area of concern to develop an assumption 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 2. Evaluate current climate modes 3. Interpret available forecasts 4. Incorporate monitoring data from remote sensing and other sources After each step, develop assumptions about the start of season (SOS), early season progress, and seasonal outlook

4. Steps to developing agroclimatology assumptions 4 __________________________________________ 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)

5. Defining climatology 5 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Climatology: long term average of a weather variable. “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: the conditions of the atmosphere over a short period of time.

6. Understanding Characteristics of an Area of Concern 6 __________________________________________ 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

7. Climate variability 7 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Spatial variability: changes of a climate variable across a landscape Average rainfall, October to May. 1981- 2012

8. Climate Variability 8 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Temporal Variability: changes over time intra-annual refers to changes within a season. inter-annual refers to changes between years.

9. Rainfall Data Sources 9 __________________________________________ 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. TRMM RFE: 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.

10. Sources of Data and Tools to be Used 10 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK RFE Rainfall plots CHIRPS End of Season WRSI products

11. Sources of data and tools to be used 11 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Mapviewer (USGS) RFE Rainfall plots

12. Seasonal Average Rainfall 12 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Season: October to June Max dek: 40mm Cumulative rainfall 500mm

13. Seasonal Average Rainfall 13 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Season: October to June Max dek: 70mm Cumulative rainfall 500mm

14. Seasonal Average Rainfall 14 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Season: October to Jan and Mar – Jun. (bimodal) Max dek: 50mm Cumulative rainfall ~250mm for the first season

15. Seasonal Average Rainfall 15 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Season: ? Max dek: ? Cumulative rainfall ? m

16. Obtaining Data from Map Viewer 16 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK

17. Using CHIRPS data 17 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK

18. Temporal Variability 18 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK The plot shows the in??? variabilityHow do we measure variability?

19. 19 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK 19811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003200420052006200720082009201020112012 CHIRPS61544853090785651082169880650341372255047577585272810419439915366246956118297101029940677824713698 stdmeanCV 171.21721.0123.75 CV= (std / mean) * 100 Coefficient of variation is the ratio of the standard deviation to the mean CV allows the comparison between different magnitudes of variation, even if they have different means. Standard Deviation: a measure of variation or how spread the data are

20. How do we measure variability? 20 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK The time series plot is another way to show how variable rainfall has been through time. stdmeanCV Angola+Bie+maize69.321152.956.01 South Africa+Free State+maize95.95482.4419.89

21. How do we measure variability? 21 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Now who could tell us what these two maps mean? stdv CV

22. How do we measure variability? 22 __________________________________________ 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

23. Agriculture, water balance 23 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Water Requirement Satisfaction Index (WRSI) -Model to predict crop performance -Allows us to determine rainfall distribution over the season SOSEOS LGP Water Depth ( mm) PET WRSI = AET / WR WR AET Senay 2003

24. Rainfall Distribution, WRSI Timeseries 24 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK The time series plot shows the end of season WRSI values for each year WRSI is an indicator of how well the rainfall total was distributed during the growing season

25. Conclusion 25 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Understanding the general patterns of rainfall, such as average seasonal totals, variability through time and distribution during the season, allow you to make initial assumptions about the season.

26. InfraRed data 26 __________________________________________ 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

27. Tropical Precipitation Measuring Mission, (TRMM) PR- Precipitation Radar: - 3-D maps of storm structure. -Intensity and distribution of the rain -Rain type, storm depth and height TMI – TRMM Microwave Imager: - Measures microwave energy emitted by the earth and the atmosphere to quantify water vapor, cloud water and rainfall intensity in the atmosphere. VIRS – Visible and InfraRed Scanner: - senses radiation coming up from the Earth in five spectral regions, ranging from visible to infrared, -Ability to delineate rainfall. -serves as a transfer standard to other measurements made using POES or GOES LIS- Lighting and Imaging Sensor: detects and locates lightning over the tropical region of the globe. NASA http://pmm.nasa.gov/node/158 Video http://pmm.nasa.gov/education/videos/trmm-15-reign-rain back