NAME Modeling and Data Assimilation: A Strategic Overview

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NAME Modeling and Data Assimilation A Strategic
Overview

• NAME Science Working Group
• September
• 2003

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The NAME Science Working Group Jorge Amador1,
E. Hugo Berbery2 , Rit Carbone3, Miguel
Cortez-Vazquez4, Art Douglas5, Michael Douglas6,
Dave Gochis3, Dave Gutzler7, Wayne Higgins8,
Richard Johnson9, Dennis Lettenmaier10, Rene
Lobato11, Robert Maddox12, Jose Meitin6, Kingtse
Mo8, Mitchell Moncrieff3, Erik Pytlak13,
Francisco OCampo-Torres14, Chester Ropelewski15,
Jae Schemm8, Jim Shuttleworth12, Siegfried
Schubert16, David Stensrud6,Chidong Zhang17
1University of Costa Rica, San Jose, Costa Rica
2Dept. Of Meteorology, University of Maryland,
College Park, MD 3National Center for Atmospheric
Research, Boulder, CO 4Servicio Meteorologico
Nacional, México 5Atmospheric Sciences Dept.,
Creighton University, Omaha, NE 6National Severe
Storms Laboratory, NOAA, Norman, OK 7Earth
Planetary Sciences Dept., University of New
Mexico, Albuquerque, NM 8Climate Prediction
Center, NCEP/NWS/NOAA, Camp Springs, MD 9Colorado
State University, Fort Collins, CO 10University
Of Washington, Seattle, WA 11Instituto Mexicano
de Tecnología del Agua, Jiutepec, Morelos, México
12University of Arizona, Tucson, AZ 13National
Weather Service, Tucson, AZ 14 Centro de
Investigación Científica y de Educación
Superior de Ensenada Ensenada, Baja California,
México 15IRI for Climate Prediction, LDEO of
Columbia University, Palisades, NY 16Data
Assimilation Office, NASA/GSFC, Greenbelt,
MD 17RSMAS, University of Miami, Miami, FL
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NORTH AMERICAN MONSOON EXPERIMENT (NAME)
HYPOTHESIS The NAMS provides a physical basis for
determining the degree of predictability of warm
season precipitation over the region.
Topographic and Sea-Land Influence

• OBJECTIVES
• Better understanding and
• simulation of
• warm season convective
• processes in complex terrain
• (TIER I)
• intraseasonal variability of
• the monsoon (TIER II)
• response of warm season
• circulation and precipitation
• to slowly varying boundary
• conditions (SST, soil
• moisture) (TIER III)
• monsoon evolution and
• variability (TIER I, II, III).

Intraseasonal Variability
Boundary Forcing?
Develop a strategy for accelerating progress on
the fundamental modeling issues pertaining to the
NAME science objectives
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• Multi-scale Model Development
• Multi-tier Synthesis and Data Assimilation
• 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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Role of Observations in Model Development and
Assessment
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I. Multi-scale Model Development
The underlying 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.
will require both improvements to the physical
parameterizations and improvements to how we
model the interactions between the local
processes and regional and larger scale
variability
Specifically moist convection in the presence
of complex terrain and land/sea contrasts
land/atmosphere interactions in the presence of
complex terrain and land/sea contrasts
ocean/atmosphere interactions in coastal regions
with complex terrain.
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Bottom-up and top-down approaches
1. 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
2. Global/regional models -gt NAMAP S. Schubert
et al. G. Zhang
Examine impact of resolution, diagnose behavior
of parameterizations in the presence of complex
terrain, and larger-scale organization Understand
behavior and limitations of current
parameterizations at higher resolutions, pursue
improved parameterizations
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Computational domains
Cloud-resolving domain ( )
M. Moncrieff
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Sequences of precipitation
Kain-Fritsch
Grell
Carbone et al. (2002)
Betts-Miller
Betts-Miller
2-D CRM
16 m/s
10 m/s
14 m/s
10 days
16 m/s
14 m/s
14 m/s
6 m/s
14 m/s
5 m/s
5 m/s
Grell
CSRM-derived precipitation travels at the
observed speed (14 m/s) shear the key quantity
M. Moncrieff
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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 analysis goals

• Motivate a set of baseline control simulations
  for more focused research by each group
• Identify and describe inter-model consistencies
  and differences tentatively suggest physical
  explanations for differences
• Provide measurement targets for NAME 2004 field
  campaign
• Examine effects of core monsoon (Tier I)
  convection differences on larger-scale (Tier II)
  circulation

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NAMAP participating models/groups
Regional
Global
Lateral boundary conditions Reanalysis SST
NOAA OIv2 1?1 weekly analysis Land surface
treatments vary
Summer 1990 simulations
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• No obs here! What is the true diurnal cycle?
• All models show convective max between 21Z-04Z
• How much nocturnal rain should be falling?

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PACS/GAPP funded proposal An Assessment and
Analysis of the Warm Season Diurnal Cycle over
the Continental United States and Northern Mexico
in Global Atmospheric General Circulation Models-
Siegfried Schubert, Isaac Held, Arun Kumar, Max
Suarez, Myong-In Lee, Hyun-Kyung Kim

• 1) assess and analyze the diurnal cycle in three
  different AGCMs
• (NASA, NCEP and GFDL)
• 2) improve our 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 United States and northern Mexico.

http//janus.gsfc.nasa.gov/milee/diurnal
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Snapshot of water vapor (white) and
precipitation (orange) from a simulation with the
NASA Seasonal-to-Interannual Prediction Project
(NSIPP) AGCM run at 1/2 degree lat/lon resolution.
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NAME FIELD CAMPAIGN (JJAS 2004)
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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
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

• Provide near real time monitoring of
  hydro-meteorological conditions during the NAME
  field campaign based on regional reanalysis and
  RCDAS
• Regional data assimilation with/without data from
  the NAME and SREF Forecast experiments (62h) to
  study the data impact
• Global data assimilation with /without the
  additional data from the NAME and forecasts (1-90
  days) every 6h

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Data Assimilation and Model Development
Regress analysis increments
where
Schubert and Chang (1996)
Leith 1978, Klinker and Sardeshmukh 1992, DelSole
and Hou 1999
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III. Prediction and Global-Scale Linkages
One of the measures of success of the NAME
program will be the extent to which predictions
of the NAMS are improved
The key issue to be addressed is to determine the
extent to which model improvements (and improved
boundary and initial conditions) translate into
improved dynamical predictions
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• Specific objectives include
• determining the predictability and prediction
  skill over the NAMS region associated with the
  leading patterns of climate variability
• ? determining the predictability and prediction
  skill associated with anomalous land surface
  conditions in the NAME region
• ? assessing the relative influences of local and
  remote SST
• ? assessing the advantage for NAME region
  predictions of increased resolution

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PREDICTABILITY AND FORECAST SKILL IN GLOBAL MODELS
Jae-Kyung E. Schemm 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
  ENSO influence?
• Models
• On board NSIPP, NCEP/GFS Possible GFDL,
  NCAR

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Challenges
Strengthening linkages between modeling, data
assimilation and observational activities/programs
relevancy - timing is everything doesnt
happened naturally - requires programmatic
nudging/support
Developing CPT-like efforts - e.g. focus on
diurnal cycle