Update on COPC Action Item Presented by Dr. Bradley Ballish Co-Chair JAG/ODAA and Member of Dropout Team 17 May 2011 COPC Meeting AFWA

May 17, 2011 COPC Dropout Update 2 Outline COPC Action Item (AI) (real-time monitoring) Quad chart and status of work Forecast-Forecast correlations Extreme analysis differences

May 17, 2011 COPC Dropout Update 3 AI COPC Action Item : Develop a monitoring system to analyze differences between the NCEP and FNMOC global models and the ECMWF global model in real-time and make this real-time system available to OPCs as a daily tool. Initial Planned Deliverables NCEP will generate warnings on a restricted website when the GFS global analysis has extreme localized differences from ECMWF analyses on a 1x1 degree grid NCEP will develop a real-time warning system to alert us when the GFS global model 5-day forecast height correlations with the ECMWF exceed normal limits, which will give an early warning of a possible dropout NCEP website will have graphics to allow staff to study the divergence in forecasts in real time and show likely analysis problem areas

May 17, 2011 COPC Dropout Update 4 AI Status Project is getting close to initial operating capability, see quad chart on next page A number of slides will follow showing more on the status and usefulness of this work Recommend AI remain open

May 17, 2011 COPC Dropout Update 5 NCEP GFS Forecast Divergence vs. ECMWF - PMO124 Project Status as of 4/6/2011 Issues/Risks Issues: Other tasks and projects have higher priority causing the schedule to slip Risks: The correlations cannot be computed in real-time resulting in no viable information for the forecasters. Mitigation: Conduct testing at key points in the development to determine viability of the project and the results. G Finances Scheduling Project Information (Scope/Benefits) Lead: Scott Jacobs Scope: Provide a R/Y/G indication, on a web site, to the users when GFS global 5-day forecast height correlations, compared to the ECMWF forecasts, are beyond the given criteria Provide graphics on a web site to show the GFS global forecast correlations Create the criteria and the software to support the web site Create work flow process for when a significant difference in the forecasts occurs Expected Benefits: Better forecasts Better quality control Ability to predict low forecast correlations Training and action plan for the users Associated Costs: No hardware or software costs Labor: 4 person-months (625 person-hours) from personnel in NCO, for a duration of 6 calendar months Brad Ballish – 20% Krishna Kumar – 20% Shucai Guan – 10% Joe Carr / Kelly Kempisty – 10% Scott Jacobs – 5% Funding Sources: N/A Management Attention Required Potential Management Attention Needed On Target GY R v1.0 10/02//06 MilestoneDateStatus Project Start9/20/2010Complete Requirements/Design for support software 10/10/2010Complete Requirements/Design for displaying results 10/20/2010Complete Support software12/15/10Complete Testing of support software1/14/2011Complete Software to present the information1/21/2011Complete Sponsor briefing2/16/2011Complete Website available4/11/2011In Progress Testing of website4/15/2011 Training documentation and class outline4/15/2011 Project Close / Lessons Learned4/29/2011 Y G G

May 17, 2011 COPC Dropout Update 6 Status and Results from Work on COPC AI ECMWF-GFS forecast-forecast correlations are working very well GFS forecast-forecast correlation with previous GFS not quite ready Extreme analysis difference code and related plotting codes are proving useful Code to plot forecast divergence versus time is not ready Website to display results not quite ready

May 17, 2011 COPC Dropout Update 7 F-F Correlation Dec Predicting low GFS forecast skill valid dates 16 Dec Z and 3 Jan Z ECMWF-GFS

May 17, 2011 COPC Dropout Update 8 Anomaly Correlation Verification for 11 Dec Z GFS low-skill is consistent with low F-F correlation Extremes code shows important finding for this case in later slides

May 17, 2011 COPC Dropout Update 9 Anomaly Correlation Verification for New Year 2011 GFS dropout (< 0.7) predicted by F-F correlation

May 17, 2011 COPC Dropout Update 10 T700 Website will have a graphic like this to show where and at what levels and variables there were extreme differences There will be more website options below to view graphics

May 17, 2011 COPC Dropout Update 11 The extremes code diagnosed this large analysis minus guess change, that appears to lead to a forecast dropout This example is important as we never expected satellite mass-field data to have such large impact

May 17, 2011 COPC Dropout Update 12 These large analysis wind changes were diagnosed by the extremes code Radiosonde had deep very suspect winds that would have been rejected by profile QC as done at the ECMWF

May 17, 2011 COPC Dropout Update 13 This is a case with large amplitude wind differences over a large area for a period of time – case is still being analyzed We are analyzing Mars, with ECMWF analyzing Venus

May 17, 2011 COPC Dropout Update 14 Background Slides

May 17, 2011 COPC Dropout Update 15 00Z 5-day AC Scores in March/April 2011 Example of low score in NH, which has not happened often with the latest GFS model Dropouts in SH are still too frequent

May 17, 2011 COPC Dropout Update 16 New adjoint code for impact of observations on the analysis showed the GPSRO data was having large impact

May 17, 2011 COPC Dropout Update 17 Dropout Related project areas Data QC and data usage Dropout analysis Analysis and model changes

May 17, 2011 COPC Dropout Update 18 Dropout Related Projects ProjectStatus Using Quickscat QC FlagsImplemented 2009 Aircraft Track-CheckingImplementation of NRL code Q Major Dictionary CorrectionsUpper air changes in operations 2009 North American surface changes implemented in June 2010 NWSTG waypoint updates in Feb 2011 Observational Data Impact TestsContinuous testing at NCEP plus Langland has started tests Adjoint Estimates of Observation Impact Langland has been running tests and NCEP has adjoint of GSI analysis running with help from NASA/Goddard Surface Pressure Bias CorrectionsPreliminary testing Data QC and Data Usage

May 17, 2011 COPC Dropout Update 19 Dropout Related Projects ProjectStatus Aircraft Temperature Bias CorrectionsWaiting for NRL aircraft QC upgrade - Satellite Wind QC UpgradesBoth speed dependent QC and ECMWF filtering rules showed positive impact - NCEP implementation TBD Profile QCPreliminary investigation Improved Reject-listPreliminary investigation Use-list for Conventional ObsPreliminary testing Data QC and Data Usage

May 17, 2011 COPC Dropout Update 20 Dropout Related Projects (cont) Establish Dropout Investigation Procedures Is done and performed for dropout cases Dropout Relation to Data CountsRTDMS extended to 30 days with January 2011 implementation Dropout Climatology StudiesCommon origin areas found – Langland has shown preferred synoptic patterns ECM RunsJournal publication in old hold– High resolution ECMWF input being tested Improved Diagnostics for Analysis Differences for Dropouts Preliminary investigation at both NCEP and FNMOC Dropout Analysis

May 17, 2011 COPC Dropout Update 21 Dropout Related Projects (cont) GSI Analysis UpgradesOperational GSI updates Q implementation GFS T574 Model BundleImplemented on 27 July 2010 Analysis and Model Changes

May 17, 2011 COPC Dropout Update 22 Interagency Participants NCEP/EMC –Jordan Alpert –Yangrong Ling –DaNa Carlis NCEP/NCO –Bradley Ballish –Krishna Kumar –Joe Carr NCEP/HPC –James Cisco NRL –Rolf Langland FNMOC –Chuck Skupniewicz NESDIS –Kathy Kelly –Tom Renkevens The NCEP dropout team meets weekly with the EMC director, John Derber and sometimes with the JCSDA director and reports quarterly to the NCEP director

May 17, 2011 COPC Dropout Update 23 Latest Dropout Team Findings (continued) For more information on dropout research, see the reports presented at the AMS meetings:

May 17, 2011 COPC Dropout Update 24 Based on the code from the NASA/Goddard/GMAO GSI, the dropout team has the GSI analysis adjoint code running Further work is needed to develop an adjoint of the NCEP global model that with the analysis adjoint will produce estimates of how every observation helps or hurts the forecast skill We will determine if smart statistics from these promising new tools will give us more precision on how to better use observations, such as: –Satellite radiance data impact estimates by type, channel, surface conditions (water, ice, etc.) and more –Possibly better rules on filtering of satellite winds –Improved reject-list criterion –Possibly better analysis of dropout cases Adjoint Codes