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1 (Global) Data Assimilation Priorities Presented By: John. Derber (NWS/NCEP) Contributors: Jeff Whitaker (ESRL/PSD), EMC DA team.

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Presentation on theme: "1 (Global) Data Assimilation Priorities Presented By: John. Derber (NWS/NCEP) Contributors: Jeff Whitaker (ESRL/PSD), EMC DA team."— Presentation transcript:

1 1 (Global) Data Assimilation Priorities Presented By: John. Derber (NWS/NCEP) Contributors: Jeff Whitaker (ESRL/PSD), EMC DA team

2 Community analysis system –GMAO, DTC, JCSDA, AFWA?, etc. Regional/Global –EMC applications GFS/GDAS, NAM, RAP, HRRR, RTMA, URMA, CFS 3-4D Hybrid Enkf/Variational assimilation system 2 Operational System Attribute(s)

3 3 Why System(s) are Operational  Primary stakeholders and requirement drivers Users of NWP forecasts, Quality of NWP forecasts  What products are the models contributing to? GDAS/GFS, NAM, HRRR, RAP, CFS, RTMA, URMA analyses and forecasts  What product aspects are you trying to improve with your development plans? Quality of analyses Additional analysis variables (clouds, trace gases, aerosols, etc.) Use of additional observations and improved use of current observation  Top 3 System Performance Strengths Unified system across NCEP atmospheric modeling system Major driver of improved forecast skill over last 20 years Community cod  Top 3 System Performance Challenges Keeping up with many new observing systems Balance between analysis variables (e.g., clouds, u,v, T, q, SST, etc.) Computational resources for testing Simplified model suite so that ensembles do not have to run for each model Community code

4 4 System Evolution Over the Next 5 Years  Major forcing factors Application to smaller scales and different balance between variables Requirement for additional analysis variables (clouds, aerosols, trace gases, etc.) New observing platforms Changes in models or model infrastructures  Science and development priorities Clouds and cloudy observations (balanced with other variables) Improved background and observational error characteristics Improved ensembles accounting for the model error better Use of additional information from observations  What are you top challenges to evolving the system(s) to meet stakeholder requirements? Insufficient, unstable and uncoordinated resources for development  Potential opportunities for simplification going forward Elimination of support for some models and data formats Refactoring and restructuring of analysis system (major project)

5 5 Top 3 Things You Need From the UMAC 1.Stable and sufficient support focused on operational DA projects Improved techniques Use of observations 2.“Research” using operational systems and within community infrastructure Little R2O despite major effort and DTC set up for this purpose 3.Balanced DA development focused on quality of analysis and observing system experiments


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