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NAME Modeling and Data Assimilation “White Paper” June 2003

NAME Modeling and Data Assimilation “White Paper” June 2003. Provides a strategy for accelerating progress on the fundamental modeling issues pertaining to the NAME science objectives Unveiled at NAME Modeling and Data Assimilation Workshop (UMD, June 03)

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NAME Modeling and Data Assimilation “White Paper” June 2003

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  1. NAME Modeling and Data Assimilation “White Paper”June 2003 • Provides a strategy for accelerating progress on the fundamental modeling issues pertaining to the NAME science objectives • Unveiled at NAME Modeling and Data Assimilation Workshop (UMD, June 03) • Reviewed by the US CLIVAR Pan American Panel. • Emphasizes activities that bring observationalists, modelers and physical parameterization experts together to focus on key physical processes that are deficient in coupled models. NAME Modeling and Data Assimilation: A Strategic Overview NAME Science Working Group* June 2003

  2. IMPROVE warm season prediction • Improve understanding and prediction of the life cycle of the North American monsoon system and its variability. • warm season convective processes in complex terrain; (Tier 1) • intraseasonal variability of the monsoon;(Tier 2) • the response of warm season atmospheric circulation and precipitation patterns to slowly varying, potentially predictable oceanic and continental surface conditions (Tier 3)

  3. Strategy I. Multi-scale Model Development II. Multi-tier Synthesis and Data Assimilation III. Prediction and Global-scale Linkages

  4. GUIDING PRINCIPALS The strategy must takemaximum advantage of NAME enhanced observations, and should simultaneously provide model-basedguidance to the evolving multi-tiered NAME observing program. The modeling activities must maintain a multi-scale approachin which local processes are embedded in, and are fully coupled with, larger-scale dynamics.

  5. NAMAPModel Assessment for the North American Monsoon Experiment D.S. Gutzler H.-K. Kim University of New Mexico NOAA/NCEP/CPC gutzler@unm.edu hyun-kyung.kim@noaa.gov Thanks to: CPC for hosting DG’s visit, Spring 2003 NAMAP modeling participants UCAR/JOSS for archiving NAMAP output

  6. NAMAP Accomplishments • Establish the baseline simulations/forecasts To know what we do not know: • Position and structure of the GCLLJ • Diurnal cycle of the GCLLJ • Detailed structure and distribution of rainfall (both in space and time) d) Oceanic influence—local and remote

  7. No obs here! What is the “true” diurnal cycle? • All models show convective max between 21Z-04Z • Different diurnal max over different places

  8. use the NAME data • Understand the dynamical processes related to NAME • Better monitoring of the monsoon systems and the warm season precipitation regimes over North and Central America • Verify model forecasts • Improve modeling the physical processes related to the NAME Improve the operational forecasts and applications

  9. I. Multi-scale Model Development Premise of the NAME modeling strategy is that deficiencies in our ability to model "local" processes are among the leading factors limiting forecast skill in the NAME region. Requires:-improvements to the physical parameterizations -improvements to how we model interactionsbetween local processes and the larger scales

  10. I. Multi-scale Model Development • NAME Focus: Tier I • moist convection in the presence of complex terrain; • Diurnal cycle • land/atmosphere &ocean atmosphere interactions in the presence of complex terrain • We will have the NAME data as guide

  11. “Bottom-up” approaches: • Multi-scale modeling -> • M. Moncrieff Cloud-system-resolving models having computational domain(s) large enough to represent interaction/feedback with large scales Multiscale models explicitly represent convective cloud systems

  12. igure 6. The three-domain decomposition usedin the MM5 experiment: the horizontal resolutions are 81-km, 27-km and 9-km, respectively. Domain 4 is the CSRM domain (3-km grid spacing). Computational domains Cloud-resolving domain ( ) M. Moncrieff

  13. “top-down” approaches: 2. Global/regional models S.Schubert et al.; G. Zhang Use the observations to determine Resolution test the current parameterizations in the presence of complex terrain, and larger-scale organization E. g. Different convection schemes Radiation-cloud interaction

  14. II. Multi-tier Synthesis and Data Assimilation Data assimilation is critical to enhancing the value and extending the impact of the Tier I observations The specific objectives are: To better understand and simulate the various components of the NAM and their interactions with the larger-scales To quantify the impact of the NAME observations To identify model errors and attribute them to the underlying model deficiencies

  15. Regional CDAS (R-CDAS) and NAME Data Impact and Prediction Experiments Kingtse Mo and Wayne Higgins –CPC/NCEP, Fedor Mesinger--- UCAR/EMC,Hugo Berbery--- University of Maryland • Real time monitoringof hydro-meteorological conditions during NAME 2004 based on regional reanalysis and RCDAS; • Data impact studies • With data into the GTS system , data assimilation • will be done using CDAS (T62), GDAS( GFS T256) and R_CDAS relatively quickly • b) Same as (a) but without data from NAME • c) After 12 to 18 months, all data are collected including rain gauges, a final sets of data assimilation will be done using GDAS and RCDAS • d) forecasts (1-90 days) every 6h using GFS T126

  16. All PIs, please help us Please give me a list of • A) station WMO ID • B) lat-lon position • C) Data type and time For all data entering the GTS network before the cutoff time h+16Z Thanks

  17. An Assessment and Analysis of the Warm Season Diurnal Cycle over the Continental US/N. Mexico in Global AGCM’S Siegfried Schubert, Max Suarez, Myong-In Lee -NASA/GSFC Isaac Held-GFDL Arun Kumar, Hyun-Kyung Kim, Wayne Higgins – NCEP/CPC OBJECTIVES 1) Assess / analyze the diurnal cycle in three different AGCMs (NASA, NCEP and GFDL), • 2) Improve understanding of the important physical processes that drive • the diurnal cycle, • 3) Provide guidance for the development of physical parameterizations • aimed at improving the simulation of the warm season hydrological cycle • over the US / N. Mexico http://janus.gsfc.nasa.gov/~milee/diurnal

  18. III. Prediction and Global-Scale Linkages Once we have a reliable model: we are able to • determine the predictability and prediction skill over the NAMS region associated with the leading patterns of climate variability; • Extend to examine the precipitation regimes over North and Central America • determine the predictability and prediction skill associated with anomalous land surface conditions in the NAME region (e.g. soil moisture) • assess the relative influences of local and remote SST’s

  19. Predictability and Forecast Skill In Global Models Jae-Kyung E. Schemm et al. CPC/NCEP/NWS/NOAA • Objectives: • 1) To examine the predictability of warm season precipitation over • the NAM region; • 2) To quantify error growth due to model errors versus that due to • uncertainties in analyses and boundary conditions; • 3) To assess the value of NAME observations for prediction; • 4)To help define field campaigns to follow NAME 2004. • Key Questions(ultimately critical for climate prediction): • How is the life cycle of the monsoon related to the evolution of oceanic and continental boundary conditions? • Can models reproduce the observed summertime precipitation in average years and years with strong SST influence? • Models • On board: NSIPP, NCEP/GFS; Possible: GFDL, NCAR

  20. Different stages of modeling • Regional model simulations Convection, diurnal cycle, rainfall distribution regional features • Observed SSTs– Global forecasts-> regional Model nesting • Two tier prediction system Predicted SSTs – global model forecasts • Coupled model prediction

  21. NAME DELIVERABLES • Observing system design for monitoring and predicting the North American monsoon system. • More comprehensive understanding of North American summer climate variability and predictability. • Strengthened multinational scientific collaboration across Pan-America. • Measurably improved climate models that predict North American monsoon variability months to seasons in advance.

  22. NAME ROADMAP Pre-NAME 2004 Activities: * Diagnostics and Analysis - Model (e.g. NAMAP; Warm Season Diurnal Cycle in AGCM’s) - Reanalysis (global, regional) * NAME FOC Practice Forecasting * Workshops - NASA/CLIVAR Subseasonal Workshop / NAME Modeling Workshop - NAME SWG-5 / NAME Special Session (Puerto Vallarta) NAME 2004 Activities: * NAME EOP Forecaster Support - Forecast Discussions / Operational Assessments * Real-time Monitoring, Analysis and Forecast Products

  23. NAME ROADMAP Post-NAME 2004 Activities * Model and Forecast System Development - NAME CPT activities (simulation of convective precipitation) - Multi-scale modeling / CRM * Experimental Prediction - NAME 2004 case studies / hindcasts - Sensitivity to SST and soil moisture (operational centers) - Subseasonal prediction (e.g. TISO.MJO) * Diagnostics and Analysis - Reanalysis (global, regional, NAME data impact) - Model diagnostics (NAMAP 2) * Applications and Product Development - Assessments (Hazards, North American drought monitor) - Forecasts (North American seasonal and subseasonal) - Applications (Agriculture, Fire WX, Water Resource) * Research and Dataset Development - PACS-GAPP warm season precipitation initiative

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