1. Global Observational Network and Data Sharing
Wassila Mamadou Thiaw
Climate Prediction Center
National Centers for Environmental Prediction
National Weather Service
National Oceanic and Atmospheric Administration

2. Importance of Data A strong Observational Network required to:
Assimilate data into numerical models that produce forecasts
Develop reliable historical data that can support high quality reanalysis datasets
Develop tools to monitor and assess the current state of the climate system in the context of present day or past climate
Climate Data Management Systems
Essential to improve data access and to use the information adequately
Observing system, Source WMO

3. NOAA Data Modeling and Prediction Monitoring
Provide access to real time weather and climate information to support decision making in various socio-economic sectors
Modeling and Prediction
Global Data Assimilation System
NCEP Global Forecasts System
NCEP Climate Forecast System
Monitoring
Station and gridded Precipitation and Temperature
Sea Surface Temperature
NCEP Reanalysis

4. NOAA Use of Observations
Assess the current state of the climate
Basic state
Variations associated with ENSO, MJO, other modes of variability
Predictions of weather and climate events
Floods
Drought
Heat waves
Calibration and verification of Forecasts
Model errors
Forecast skills

5. El Niño and Global Weather Patterns

6. MJO Influence on African rainfall and low level winds during MAM,
ARC2 and CDAS
( )
Ph1
Ph5
Ph2
Ph6 X
Convection and low level westerly wind anomaly associated with phases 2 to 4 of the MJO
Suppression occurs during phases 6 to 8 of the MJO
Ph3
Ph7 X
Ph4
Ph8

7. Regional Hazards Outlooks for Food Security
Integrating weather, climate, and land information to inform humanitarian response planning.
Regional hazards outlooks are combined with food security indicators to assess impacts of weather and climate on crops and pastures and to map countries that require food assistance.
Africa

8. Data Impact on Production
Stations Reporting into the GTS ( ) in Percent

9. Data Impact on Production
Stations Reporting at least 80% of the time ( )

10. Data Impact on Production
Stations Reporting less than 20% of the time ( )

11. Data Impact on Production
Rainfall Reporting and Analysis at a Station X
Erroneous Gauge measurement Transmission
Rainfall Evolution based on blended Gauge – Satellite

12. Tropical Cyclone Sagar
Strongest TC to make landfall in Djibouti-Somalia in recorded history
Dropped a year worth of rainfall about 200 mm
Caused flash floods
Thousands displaced
Damages estimated at over US$30 million

13. Tropical Cyclone Sagar – Land falling Djibouty-Somalia, 18 – 19 May 2018

14. TC Sagar Satellite and Rainfall Analysis
GPI
AMSU
RFE
Gauge+Sat
SSMI

15. Summary
Data is the backbone of climate services and early warning systems
Governments and policy makers need to understand the importance of data and the Met community needs to strongly advocate for the sustainability of the observational network and the preservation of the data
Need to work to improve the dissemination of the data and encourage data exchange and the creation of databases that will support collaborative multidisciplinary research and development
This data must be made to good use to support services through product development and research that feeds into improving services