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

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

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

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

El Niño and Global Weather Patterns

MJO Influence on African rainfall and low level winds during MAM, ARC2 and CDAS (1983-2010) 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

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

Data Impact on Production Stations Reporting into the GTS (1983-2018) in Percent

Data Impact on Production Stations Reporting at least 80% of the time (1983-2018)

Data Impact on Production Stations Reporting less than 20% of the time (1983-2018)

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

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

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

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

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