NAME Modeling and Data Assimilation

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NAME Modeling and Data Assimilation White
PaperJune 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
 
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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)

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Strategy

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

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GUIDING PRINCIPALS The strategy must take
maximum advantage of NAME enhanced observations,
and should simultaneously provide model-based
guidance to the evolving multi-tiered NAME
observing program. The modeling activities must
maintain a multi-scale approach in which local
processes are embedded in, and are fully coupled
with, larger-scale dynamics.
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NAMAPModel Assessment for the North American
Monsoon Experiment

• D.S. Gutzler H.-K. Kim
• University of New Mexico NOAA/NCEP/CPC
• gutzler_at_unm.edu hyun-kyung.kim_at_noaa.gov

Thanks to CPC for hosting DGs visit, Spring
2003 NAMAP modeling participants UCAR/JOSS for
archiving NAMAP output
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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 influencelocal and remote

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

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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

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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 interactions
between local processes and the larger scales
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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

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Bottom-up approaches

• Multi-scale modeling -gt
• 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
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Computational domains
Cloud-resolving domain ( )
M. Moncrieff
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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

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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
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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 monitoring of 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

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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 h16Z
• Thanks

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An Assessment and Analysis of the Warm Season
Diurnal Cycle over the Continental US/N. Mexico
in Global AGCMS 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
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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 SSTs

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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

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Different stages of modeling

• Regional model simulations
• Convection, diurnal cycle, rainfall
  distribution
• regional features
• Observed SSTs Global forecasts-gt regional
• Model nesting
• Two tier prediction system
• Predicted SSTs global model forecasts
• Coupled model prediction

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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.

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NAME ROADMAP
Pre-NAME 2004 Activities Diagnostics and
Analysis - Model (e.g. NAMAP Warm Season
Diurnal Cycle in AGCMs) - 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
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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