Agenda

1
Agenda

• Opening Remarks M. Cleave
• Program Overview J. Kaye
• Earth Science Applications R. Birk
• Science Research and Products for CCRI
• Carbon, Ecosystem, Land Cover/Use Sciences
  D. Wickland
• Water Cycle J. Entin
• Climate Variability W. Abdalati
• Atmospheric Composition P. DeCola
• Computational Earth System Modeling R. Rood
• Summary J. Kaye

2
Science Questions from the Research Strategy
Variability
Forcing
Response
Consequence
Prediction
Precipitation, evaporation cycling of water
changing?
Atmospheric constituents solar radiation on
climate?
Clouds surface hydrological processes on
climate?
Weather variation related to climate variation?
Weather forecasting improvement?
Global ocean circulation varying?
Changes in land cover land use?
Consequences in land cover land use?
Transient climate variations?
Ecosystem responses affects on global carbon
cycle?
Surface transformation?
Changes in global ocean circulation?
Coastal region change?
Trends in long-term climate?
Global ecosystems changing?
Stratospheric ozone changing?
Stratospheric trace constituent responses?
Future atmospheric chemical impacts?
Ice cover mass changing?
Sea level affected by climate change?
Future concentrations of carbon dioxide and
methane?
Motions of Earth interior processes?
Pollution effects?
Critical to Water Cycle
Related to Water Cycle
Other Elements of our Program
3
ESE National Applications
Carbon Management
Aviation Safety
Energy Forecasting
Public Health
Disaster Preparedness
Coastal Management
Homeland Security
Water Management
Agricultural Competitiveness
Air Quality
Community Growth
Invasive Species
4
Research Challenges

• What is the mean state of the global water cycle?
• What is its variability?
• Is the global water cycle accelerating?
• What are the key processes involved in
  precipitation processes and patterns? How might
  these be influenced in the future by climate
  change?

5
Research Foci

• Integrated Water Cycle Research
• Global Observations
• Precipitation
• Soil Moisture
• Snow Depth
• Water Storage
• Boundary Layer Connections

6
Observation Strategy
Prospective Mission
7
Major Advances Thus Far

• Tropical Rainfall Measuring Mission (TRMM) has
  led to major advancements
• Improved algorithms for precipitation
  acquisition (of other Satellites)
• Assimilation of TRMM data has lead to better
  precipitation products in global models
• Hurricane/Typhoon forecasting
• Enhanced understanding of cloud processes

8
TRMM
Improved Ocean Rainfall Estimation due to
algorithm improvement from pre-TRMM (e.g. SSM/I)
era Uncertainties in Tropical Rainfall Estimates
Reduced from 50 to 25 using TRMM
TRMM Products
Pre-TRMM Microwave Ocean Rainfall
Estimates (Zonal Mean)
9
Improved Hurricane Track Forecasts Using TRMM
Rainfall
Assimilation of TRMM rainfall location, intensity
and vertical structure into hurricane forecast
models leads to improvements in forecasts of
future position
Hurricane Bonnie, Atlantic, Aug 1998
Typhoon Paka, Pacific, Dec 1997
5 Day Forecast Official Without
TRMM With TRMM
Error decreases with time
3 Day Forecast Without TRMM
With TRMM
Dr. X. Pu, NASA GSFC
Dr. A. Hou, NASA DAO
Reduced track errors can save money (600,000 per
mile of coast evacuated) and save lives by more
precise prediction of eye location at landfall
10
Alteration of Precipitation Processes
TRMMs Precipitation Radar confirms that clouds
in areas 1, 2, and 3 all have sufficient water
for precipitation, however there is only
precipitation occurring in areas 1 and 3. The
yellow coloring of area 2 depicts pollution
tracks, as detected by TRMMs Visible Infrared
Radiometer (VIRS) instrument.
Thus the integrated picture delivered by TRMM
suggests a suppression of rain and snow by smoke
and air pollution due to the shifting of droplet
size.
Prof. Daniel Rosenfeld
11
Major Advances Thus Far

• Resolving the Land Surface
• Precipitation predictability increases when
  knowledge of land surface is added to a GCM.
• Improvement of soil moisture monitoring
  techniques field experiments (with USDA in 97,
  99, 02, 03). Also, ancillary studies of land
  surface dynamics, scaling, and the boundary
  layer.
• Increase usability of Satellite and other data
  sets for scientific research and education.
• International Satellite Land-Surface Climatology
  Project (ISLSCP) data sets constructed to assess
  and improve land surface modeling. Also used by
  the secondary and collegiate education community.
• Land Data Assimilation System (LDAS), constructed
  along with NOAA, to create initialization fields
  of soil moisture for modeling. Also involved
  with advances in data assimilation and the Common
  Land Model (w/NCAR). Products usable by the
  water resources community.

12
Why Soil Moisture Is Important
Randy Koster NASA-GSFC
13
Soil Moisture from SSMI
Dotted line is satellite soil moisture for an
area in Illinois are soil
moisture observations from three different
stations in that area
14
A First Look at Output from the Global Land Data
Assimilation System
Vegetated fraction (a static parameter).
Soil moisture in the top 1m (mm), 0Z 26 March
2001.
Fraction of sand in soil (a static parameter).
Average surface temperature (K), instantaneous,
1Z 02 March 2001.
15
Soil Moisture Experiments (SMEX02 and SMEX03)

• Science
• Water Cycle
• Algorithms
• Validation
• Technology

• Aircraft Instruments
• PALS
• PSR
• ESTAR/2DSTAR
• GPS
• Aircraft flux
• VIS/IR

• Satellite Instruments
• AMSR-E
• SSM/I
• TMI
• Envisat, ERS-2
• Radarsat, Quiksat
• MODIS, ASTER
• TM
• GOES, AVHRR

• Sites (June-July)
• Iowa (Story Co.)
• Algorithms
• Water cycle
• SGP (LW and CF)
• Validation
• New Inst.
• Georgia (Little River)
• Validation
• Alabama (NALMNET)
• Validation

• Ground Investigations
• Soil moisture
• Soil temperature
• Surface flux and
  atmospheric boundary layer
• Vegetation
• Surface roughness
• Ground based radiometry
• Insitu calibration
• Insitu scaling

16
The Fourth Convection And Moisture EXperiment
Sept 10, 2001 observations of Hurricane Erin
NASA led science team with collaborative
partners from NOAA, USWRP, and Air Force Reserve
DC-8 Flight Track
Region of Interest
Unprecedented sampling of tropical Cycles, a
landfalling tropical storm, Hurricanes and an eye
of a hurricane. Made possible by high and
medium Altitude aircrafts, Unpiloted Aerial
Vehicles, weather balloons, ground-based
radars, and EOS satellites
QuikSCAT
Contributed to development of climatic data
base needed to study long-term hurricane
trends Provided long-endurance sampling of
western Atlantic Ocean
17
Interagency Linkages
Science

• USGCRP IWG
• NOAA, NSF, USDA, DOE, USGS
• Co-chair provided by NASA
• Dept. of Commerce (NOAA)
• Cold land seasons processes
• GAPP (NOAA-OGP)
• Dept. of Agriculture (USDA)
• Soil moisture field experiments
• National Science Foundation
• Atmospheric water vapor field experiment

18
Interagency Linkages
Applications

• Water Resources Research Group
• BoR(DOI), NOAA, USDA, ACE, USGS, etc.
• USDA
• Improving Agriculture Competitiveness
• Bureau of Reclamation
• Water Supply Demand
• (DSS RiverWare)
• EPA
• Total Maximum Daily Load (of Pollutants)
• (DSS Basins 3)

19
Major Contributions to Come

• Global coverage of precipitation at a three
  hourly time step enabling many science and
  application possibilities
• Global soil moisture data sets necessary to
  provide accurate initializations for weather and
  climate predictions
• Remote sensing techniques capable of retrieving
  liquid water content of snow packs, able to be
  implemented from a satellite platform
• Estimates of the changes in the Earths water
  storages.
• Quantification of the global water cycles
• Mean State
• Variability
• Possible responses to various climate change
  scenarios
• End to end connections

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Global Precipitation Measurement (GPM)

• Global Precipitation Processing Center
• Capable of Producing Global Precip. Data Products
  as Defined by GPM Partners

• Precipitation Validation Sites
• Global Ground Based Rain Measurement

• Core Satellite
• Dual Frequency Radar
• Multi-frequency Radiometer
• TRMM-like Spacecraft
• 4 km Horizontal
• Resolution (Maximum)
• 250 m Vertical Resolution

• Constellation Satellites
• Multiple Satellites with Microwave Radiometers
• Aggregate Revisit Time,
• 3 Hour goal

CORE SATELLITE OBJECTIVE Understand the
Horizontal and Vertical Structure of Rainfall and
Its Microphysical Element. Provide Training for
Constellation Radiometers.
CONSTELLATION OBJECTIVE Provide Enough Sampling
to Reduce Uncertainty in Short-term Rainfall
Accumulations.
MISSION OBJECTIVE Provide sufficient data to
understand the diurnal cycle of precipitation
globally. Extend the quantum leap of global,
complete day precipitation data sets to
scientific and societal applications. Continue
TRMMs excellent achievement level of extending
uses of the satellite network to areas of
observation other than precipitation processes.
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Cold Land Seasons Process Experiment
To Validate the AQUA Satellite
Also to gain a better understanding of Snow
Accumulation and Melting Snowpack Properties and
Dynamics Freeze/Thaw Processes
Increase our abilities at Liquid Water
Assessment Modeling Snow
22
Products for Decision-Makers
Use of sustained observations to support
decision-making in water management,
agricultural competitiveness and homeland security
Decision Support
Value benefits to citizens and society
Science Models Data Assimilation
Assessments of surface and sub-surface water
storage and transport. Improved water supply and
use, resulting in improved crop selection and
performance Improved capability to modify
decision based on short term predictions (lt12
hours) of nominal and severe weather
events. Identify downstream exposure to
waterborne contaminants
Distribution and Forecasts of Precipitation
Fields Soil Moisture, Snow state and Runoff
forecasts Modes and Predictions of the Water
Cycle
Improved ability to manage water use,
consumption, production, recreation Improved
crop production, market competitiveness Reduce
health effects and exposure to contaminants
Data
Observations Measurements

• Water storage changes (GRACE)
• Snow Cover Aqua, Terra
• Soil Moisture Aqua, (LDAS)
• Precipitation TRMM, GPM
• Atm. Temp, Humidity Aqua,GIFTS

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Fulfilling CCRI Program Goals

• Enhance the science base
• Global cycle of water GWEC Program incorporating
  numerous satellites striving to understand the
  system as a whole
• Natural climatic variability GPM and modeling
  efforts providing necessary realization of the
  global water cycle
• Enhance observing monitoring systems
• Observation systems for crucial parameters
• TRMM, Aqua, GPM, GIFTS, etc.
• Develop new monitoring systems Leading the
  effort to incorporate satellite data with data
  assimilation
• Improve Decision Support Tools
• Improved Indicators forAgriculture and Water
  Links with USDA, USGS, BoR, EPA
• Enhance exploratory research
• Development of approaches to strengthen future
  analyses GWEC Program exploring End to End
  Research
• Development new global observing techniques and
  systems Soil Moisture, Snow quantity, River
  Floodplains

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Agenda

• Opening Remarks M. Cleave
• Program Overview J. Kaye
• Earth Science Applications R. Birk
• Science Research and Products for CCRI
• Carbon, Ecosystem, Land Cover/Use Sciences
  D. Wickland
• Water Cycle J. Entin
• Climate Variability W. Abdalati
• Atmospheric Composition P. DeCola
• Computational Earth System Modeling R. Rood
• Summary J. Kaye

25
Back up slides follow
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Why study the water cycle?... Earth is a water
planet! Water sustains Earths vitality and
societys prosperity
Ranking of Publics Environmental Concerns (based
on Gallup Poll)
Variations in greenhouse gases, aerosols, and
solar activity force changes in climate
but the consequences of climate change are
delivered through the water cycle.
Majority of concerns about water
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Integrating Research Strategy
Existing Climate Models
Integrated Global Observations Ground- and
Space-Based Observing Programs
Advance Understanding and Model Physics
Next-generation Global Water-Cycle Prediction
System
Improve Initialization Assimilation
Diagnose and Identify Predictable Changes
Water-cycle Prediction
28
Global Water Cycle Advance Understanding and
Model Physics
Climate models grid-box representation of
Earths processes...
However, controlling processes of the water cycle
(e.g. precipitation) vary over much smaller
areas.
Each grid-box can only represent the average
conditions of its area.

• How can climate models effectively represent the
  controlling processes
• of the global water cycle?
• Conventional approach make the model
  grid-boxes smaller (increase resolution)
• Slow progress may take 50 years to be
  computationally feasible
• Breakthrough approach Simulate a sample of the
  small-scale physics and dynamics using high
  resolution process-resolving models within each
  climate model grid-box
• Short-cut the conventional approach (10 years
  to implement)

29
TRMM Lightning Imaging Sensor (LIS)
Merged Climatology of Lightning Flashes - LIS and
OTD Cross-Calibrated
Preliminary climatologies - annualized (0.5 deg),
daily (2.5 deg), diurnal (2.5 deg)
Christian, Blakeslee, Goodman, Mach
30
Skin Temperature Data Assimilation

DAO-PSAS Assimilation of ISCCP (IR based) Surface
Skin Temperature into a global 2 degree uncoupled
land model.
Assimilation with Bias Correction
Observation
No Assimilation
Assimilation
Surface temperature has little memory or inertia,
so without a continuous correction, it tends to
quickly drift toward the control case.
31
3) Summary

• NASAs ability to participate in end-to-end
  research is greatly coincident to the
  requirements of water cycle Research.
• Accurate assessment and prediction of the water
  cycle and the ability to communicate this
  information to the users community provides a
  wealth of benefits for society and the
  environment.

32
Improving Climate Analysis
GEOS assimilation without rain data
GEOS with TMISSM/I rain rates
Precipitation Verification GPCP
IR Cloud Forcing Verification CERES

• TRMM rainfall assimilation
• Improves hydrological fields and related climate
  parameters such as clouds and radiation in global
  analysis (as verified against CERES TOA radiative
  flux measurements)
• Improves tropical latent heating, associated
  large-scale circulation tropospheric humidity
  fields (as verified against TOVS moisture
  channels)

Principal Investigator Arthur Y. Hou / NASA GSFC
33
Data Assimilation
Data Assimilation merges observations model
predictions to provide a superior state
estimate. Remotely-sensed hydrologic state or
storage observations (temperature, snow, soil
moisture) are integrated into a hydrologic model
to improve prediction, produce research-quality
data sets, and to enhance understanding of
complex hydrologic phenomenon.
Data Assimilation Example
Current Uses Snow Extent, Skin Temperature,
Precipitation
Future Uses Soil Moisture, Grace Measurements
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Modeling Precipitation Backtracking
TX
Provides detailed information about where
precipitation comes from and thus the land and
ocean features that influence precipitation
year to year.