1. Cyberinfrastructure Needs for African Weather and Climate Arlene Laing 1, Tom Hopson 2, Arnaud Dumont 2, Mary Hayden 2, Raj Pandya 3, Mukul Tewari 2, Tom Yoksas 4, Vanja Dukic 5 1 UCAR/COMET, 2 NCAR/RAL 3 UCAR/Spark, 4 UCAR/Unidata 5 University of Colorado-Boulder

2. Motivation Africa: Major heat source that drives global atmospheric circulation, tropical cyclone origin, primary source of mineral dust, most intense thunderstorms on Earth. Society vulnerable to environmental hazards and climate change. Need to share weather & climate information (observations, models, etc…) to serve society through : – Research – Education – Applications

3. NatCatSERVICE Natural catastrophes in Africa 1980 – 2009 Number of events Climatological events (Extreme temperature, drought, forest fire) Hydrological events (Flood, mass movement) Meteorological events (Storm) Geophysical events (Earthquake, tsunami, volcanic eruption) Number MunichRE

4. Science Challenges/Critical Needs Data Access and Dissemination – Access to observations, numerical weather prediction models, and climate models – Ability to share data & improve analysis and prediction Knowledge advance through research, education, and training – Collaborative research (atmosphere is everywhere) – Unidata (real-time data access, tools to analyze and integrate data) – COMET (interactive multimedia modules, virtual courses) Application of meteorological and climatological information to societal needs, e.g., – Food Security (famine early warning systems), Public Health, Water Resource Management Effective engagement of end-users – Guide research priorities, give feedback on data usage, collect and share data, and results

5. UCAR Africa Initiative (AI) Context: Managing Meningitis in the Sahel Periodic epidemics occur in the dry season Current vaccination strategy is reactive (i.e. contain epidemics, don’t prevent them) World Health Organization (WHO) decides where to send emergency vaccines. Even with this strategy, often less vaccines available than needed

6. Weather-meningitis link? Adapted from Greenwood, 1999 Nm. meningitidis epidemics are observed to occur in the dust season and end with the onset of the rainy season –Can humidity forecasts help identify regions where the epidemic will end naturally, so that scarce vaccines can be moved elsewhere? Nm. meningitidis epidemics observed in dust season and end with onset of rainy season –Can humidity forecasts help identify regions where epidemic will end naturally, so that scarce vaccines can be moved elsewhere?

7. UCAR AI Objectives and Strategies 1.Predict epidemic end by: – Verifying Greenwood hypothesis linking meningitis season end and humidity – Leveraging existing meteorological forecasts 2.Characterize risk factor by: – Surveying 222 households for knowledge, attitudes and practices – Testing disease models against atmospheric, demographic, and epidemiological data 3.Characterize economic impact by: – Surveying 74 households for Cost of Illness 4.Inform reactive vaccination campaigns by: – Developing a useful Decision Information System that includes archived and real-time data and analysis tools

8. Relative Humidity Impact on Meningococcal Meningitis Risk = f(Relative Humidity) Probability of crossing alert threshold Low Risk when Wet High Epidemic Risk when Dry Hopson and Dukic found that knowing the RH two weeks ago improves accuracy in predicting an epidemic by ~25% 1 Coupled with a two week forecast, this indicates an improved ability to anticipate a roll-off in epidemic 4 weeks in advance

9. Relative Humidity Impact on Meningococcal Meningitis 16-Day ensemble RH forecasts Meningitis Belt Converted to probability of a meningitis alert 3 weeks in advance

10. Africa Decision Information System (ADIS) for Meningitis WHO-initiated pilot project participants: – Benin, Nigeria, Tchad, Togo – WHO, Columbia/IRI, Lancaster U. (UK), UCAR Web-based interface provides: – Ensemble forecast RH fields – Map of districts colored & sized by meningitis attack rate – Interactive display of district- level information including district-specific time series plots of ensemble RH forecasts – Access limited to project participants (privacy concerns)

11. UCAR AI Next Steps Refine forecast products and web-based end-user interface from feedback from WHO pilot project participants Finalize data processing workflow at UCAR Transfer technology to African Centre of Meteorological Application for Development (ACMAD) – agreement in principle in-place Train ACMAD personnel in use of technology transferred Assist ACMAD personnel in use of freely-available data access and visualization tools from Unidata

12. Challenges in Technology Transfer ACMAD computing infrastructure (important) – Scheduled for upgrade in near to mid-term Consistently available, “clean” power (critical) High-speed access to global Internet resources (critical); current capabilities (768 Kbps down, 256 Kbps up) limit ability to: – Access to high-volume TIGGE ensemble model forecasts – Access to global observational data – Serve relevant datasets – Provide products online

13. Meteorology Research and Education Advances require access to variety of data: – Satellite Products (exponential increase in volume) – Global numerical weather prediction products Initialize customized regional models Apply to societal needs (e.g., meningitis vaccine guidance) – Regional numerical weather prediction products Tailor to regional/local needs – AMDAR (observations from commercial flights) Aid aviation forecasting, improve aviation safety record – Air quality sensor data Regional/local data to assimilate into numerical models

14. Advances with new data, data sharing, and use in numerical models New upper air soundings Resuscitated stations Data shared with European Center for Medium-range Weather Forecasting (ECMWF) Improve temperature, wind predictions Fink et al. 2011

15. NWP Forecast skill scores continue to improve Most of Africa not yet benefitting because of lack of capacity

16. Biomass Burning – Open burning – Cooking Dust Health Climate Interactions Aerosols in Africa Emissions  Climate Climate  Emissions Christine Wiedinmyer, NCAR

17. Climate  Fire: Future Fire Krawchuk et al. PLoSONE, (2009)

18. Rainwatch: Climate Analysis & Food Security in Niger Climate studies applied to disaster mitigation U of Oklahoma & Niger Ongoing updates of rainfall anomaly to Niger’s government 2011 Cumulative Daily Rainfall and Percentiles for Niamey Airport Station 13.483N - 2.167E Courtesy: Peter Lamb

19. Why Satellite Remote Sensing? EUMETSAT funded EUMETCast satellite downlinks in all African Weather Service offices

20. The Impending Data Deluge More Data, New Data Sources Environmental Satellites: – US: Both GOES-R and JPSS will have data rates 30-60 times the current – Europe: MSG 3 rd generation and METOP Raw data rate: 3 terabytes per day Global, coupled models at a grid spacing of 1-5 km, integrated for multi-decades NCAR Global WRF model for use in Weather and Climate research TIGGE New initiatives…

21. At Unidata: Tools and Support Are Central Enhance and distribute software developed by others Meteorological display and analysis tools from UW-Madison (McIDAS-X), National Weather Service/NCEP (GEMPAK, AWIPS-II), etc. Remote access technologies: OPeNDAP (U of RI, NASA, and others), ADDE (UW-Madison) Develop software in-house Widely used tools for managing scientific data (e.g., LDM, netCDF, UDUNITS, data decoders, etc.) Java-based tools (IDV Framework built on top of VisAD) for 2D and 3D visualization and next-generation, collaborative data analyses Build systems from software we support Internet Data Distribution (IDD) system THematic Realtime Environmental Data Distributed Services (THREDDS) Support software use via training, consultation, bug fixes, and upgrades

22. COMET: Education and Training via Distance Learning & Residence Courses Interactive, multimedia training using case scenarios, based on sound science and guided by innovative instructional design Provide modern conceptual framework for analyzing and forecasting major atmospheric features (e.g., tropical waves, jet streams, monsoon onset/migration) Web-based – train large numbers of people; similar learning outcomes as residence at less cost Virtual international courses for specialized training, requires high capacity band width for animations & interactive visualization tools

23. Think Globally, Model Locally Personal tiles are subsets of larger, high-resolution data sets that have been packaged specifically for EMS real-time modeling Provides the highest resolution initialization data tailored to a user’s domain Only fields necessary for model initialization are provided Dedicated data servers with restricted access As much as 99% reduction in file size and bandwidth usage! Process is entirely dynamic – no user configuration necessary Personal Tiles Robert Rozumalski, NWS Weather Research & Forecasting (WRF) model EMS: WRF on a desktop http://strc.comet.ucar.edu/software/newrems/

24. WRF EMS Personal tile for model initialization Think Globally, Model Locally EMS Personal tile size ~1.47mb at full 0.5 degree resolution! A single global 0.5 deg GFS file size ~55.5mb WRF Domain WRF EMS Global data set WRF Domain Global data set Robert Rozumalski, NWS

25. Implications for leveraging CI Enhancing connections to user communities – For input into research priorities – For application of research results – For data collection Supporting interdisciplinary, data-intensive research via data integration systems Enabling modeling with bandwidth and hardware Supporting training via Distance Learning Facilitating collaboration via long-distance communications

26. Acknowledgements NCAR is supported by the National Science Foundation COMET is primarily funded by NOAA Unidata is primarily funded by the National Science Foundation Contact Information Arlene Laing, laing@ucar.edulaing@ucar.edu Tom Hopson, hopson@ucar.eduhopson@ucar.edu Arnaud Dumont, dumont@ucar.edudumont@ucar.edu Mary Hayden, mhayden@ucar.edumhayden@ucar.edu Raj Pandya, pandya@ucar.edupandya@ucar.edu Mukul Tewari, mukul@rap.ucar.edumukul@rap.ucar.edu Tom Yoksas, yoksas@unidata.ucar.eduyoksas@unidata.ucar.edu Vanja Dukic, Vanja.Dukic@colorado.eduVanja.Dukic@colorado.edu