Evaluation of the NASA Land Information System using the Rapid Prototyping Capabilities

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Evaluation of the NASA Land Information System using
the Rapid Prototyping Capabilities


1.0
Purpose


The goal of this proposed RPC experiment is to
evaluate the
cross
-
cutting
capabilities and utility of NASA land surface research results and resources

tow
ard
enabling applications of national priority in order to derive societal benefits.

The
award
-
winning

NASA Land Information System (LIS)

will be evaluated for its potential to
address the decision
-
making needs of at least two applications of national pri
ority,
including the USDA

Natural Resource Conservation Service (
NRCS
)

Soil Climate
Analysis Network (SCAN)

Decision Support Tool (DST)

for conservation planning
and management
.

LIS will be evaluated for

its potential for cross
-
c
utting applications
by

mea
ns of
:


a)

its
capabilities

to
enhance and
extend the SCAN DST to derive physically
consistent
soil moisture maps at a
wide
range of spatial resolutions, scaling
from 25x25 km
2

to 1
x1

km
2
;

b)

performing Observin
g

System Simulation Experiments (OSSE) for the
opti
mal extension of the SCAN DST via deployment of new sensor networks;


c)


its
effectiveness and
abilities to be coupled to the advanced Weather Research
and Fo
recasting (WRF) numerical model using the Earth Systems Modeling
Framework (ESMF); and

d)

to demonstrat
e the usefulness as a customizable NASA resource that can be
readily adopted and deployed for new applications.


2.0
Decision Support using t
he USDA
-
NRCS SCAN


The stated vision of NRCS is
“Harmony between people in the land.”

Toward
fulfilling this visi
on, NRCS has adopted the future goal to be
"A globally recognized
source for a top quality spatial snow, water, climate, and hydrologic network of
information and technology."

The USDA NRCS operates a network of nationwide
cooperative
soil moisture and cli
mate information system in order to support the
assessment and conservation of natural resources. The NRCS utilizes the SCAN
data in
conjunction with other sources information

in order to provide decision support for the

manage
ment of

irrigation, nutrient
s, animal waste, pests, salinity, and water quality.

Currently, the NRCS manages a network of 115 SCAN station in the United States (see
Fig. 1).


The USDA NRCS has
an

immediate identified need to provide high
-
resolution
analysis of soil moisture across t
he continental United States. The National Research
Council has also identified the national soil moisture monitoring network as an essential
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scientific and decision support tool to enhance our understanding of surface layer
processes. The National Integ
rated Drought Information System (NIDIS) is envisioned
to be a “drought early warning system”. The U.S.Group on Earth Observation (USGEO)
has identified the SCAN DST as an essential and significant component of NIDIS.
Hence, the evaluation of LIS to enha
nce the SCAN DST will serve to meet and an
immediate

societal need.



Figure 1:
The Soil Climate Analysis Network of observing stations


The current network of stations focuses primarily on the agricultural regions of the
nation. The expansion of
this

network requires careful planning to intelligently distribute
the future locations of the stations so that the network can be optimized to support the
requirements of NIDIS.

A set of Observing System Simulation Experiments (OSSE)
involving LIS can help id
entify, optimize, and prioritize the extension and continued
deployment of SCAN.

3
.0 Land Data Assimilation activities at NASA


Effective real
-
world decisions making in the areas of weather and climate prediction,
crop productivity estimation, water resou
rce management, air quality

monitoring and
prediction
, disaster management, and human health depend
s

upon a
proper

understanding
and knowledge of the water, energy, and carbon budgets in the land surface and their
changes over time across a range of spatia
l scales. NASA
continues to advance

the
scientific research in the study of the energy and water cycles
via

current and future

missions
(see Table 1)
and investigations involving
systematic observations and
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modeling of the eart
h’s atmosphere and land surf
ace

on
global,
continental and
regional

scale
s
. The Land Data Assimilation System (LDAS) project at NASA is a joint effort
involving a number of partner agencies and academia, including NOAA (NCEP &
NESDIS), Princeton, Rutgers, University of Washington, U
niversity of Maryland, and
the
GEWEX
international program
.

Accurate initialization of land surface water and
energy stores is critical in environmental prediction because of their regulation of land
-
atmosphere fluxes over a variety of
spatial and tempora
l

scales. Errors in land surface
forcing and parameterization accumulate in these integrated land stores leading to
incorrect surface water and energy partitioning. However, many
relatively
new land
surface observations

from current (or future) remote sens
ing and other sources,
based on

AMSR
-
E, ASTER, GOES, GOES
-
R, GPM, MODIS, NPOESS, and TRMM
,

are
becoming
(or will become)
available
. These observations

can

be used to constrain the
dynamics of land surface states. These
constraints can be imposed by (a
) fo
rcing the land
surface primarily by observations, thereby avoiding the often severe
model

biases, a
nd
(b
) using data assimilation techniques to constrain unrealistic storage dynamics.

The

LDAS

conceptual framework aims to develop the best estimation of the

current state of
land surfaces through a best possible integration of
these
land surface
and atmospheric
observation
s
.



Table 1.

List of NASA data products that have potential
application

using

LIS




Several LDAS systems have been implemented in near r
eal time and at high spatial
resolution for North American, European, and global domains. These
systems
are forced
Class

Observation

Technique

Example Platform

Tempora
l

Spatial

Land
Parameters

Leaf area and greenness

optical/IR

AVHRR, MODIS, NPOESS

weekly

1km

Albedo

optical/IR

MODIS, NPOESS

weekly

1km

Emissivity

optical/IR

MODIS, NPOESS

weekly

1km

Vegetation structure

Lidar

ICESAT, ESSP lidar
mission

weekly
-
monthly

100m

Topography

in
-
situ

survey, radar

GTOPO30, SRTM

episodic

30m

1km

Land
Forcings

Wind profile

radar




Air Humidity and
temperature

IR, MW

TOVS, GOES, AVHRR, MODIS,
AMSR

hourly
-
weekly

5 km

Near
-

surface radia
tion

optical/IR

GOES, MODIS, CERES, ERBS, etc.

hourly
-
weekly

1km

Precipitation

microwave/IR

TRMM, GPM, SSMI, GEO
-
IR, etc.

hourly
-
monthly

10km

Land
States

Temperature

IR, in
-
situ

IR
-
GEO, MODIS, AVHRR, TOVS

hourly
-
monthly

10m
-
4km

Thermal

anomalies

IR, NIR, optical

AVHRR, MODIS, TRMM

daily
-
weekly

250m

1km

Snow cover and water

optical, microwave

SSMI, TM, MODIS, AMSR, AVHRR,
etc.

weekly
-
monthly

1km

Freeze/thaw

radar

Quickscat, HYDROS, IceSAT,
CryoSAT

weekly

3km

Total wa
ter storage

gravity

GRACE

monthly

1000km

Soil moisture

active/passive
microwave

SSMI, AMSR, HYDROS, SMOS, etc.

3
-
30 day

10
-
100
km

Land
Fluxes

Evapotranspiration

optical/IR, in
-
situ

MODIS, GOES

hourly
-
weekly

10m
-
4km

Solar radiation

opt
ical, IR

MODIS, GOES, CERES, ERBS

hourly
-
monthly


Longwave radiation

optical, IR

MODIS, GOES

hourly
-
monthly

10m
-
4km

Sensible heat flux

IR

MODIS, ASTER, GOES

hourly
-
monthly

10m
-
4km

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with real time output from numerical prediction models, satellite
and other
in
-
situ

data,
and ra
dar precipitation measurements. Various

land

state observations
can be
incorporated
as a constraint to the model dynamics using hydrol
ogic data assimilation
methods. The National Land Data Assimilation System (
NLDAS
)

has developed and
incorporated

land data assimilation schemes to provide continual
ly updated, 1/8 degree
fields of land
-
surface states over central North America

(Cosgrove et

al., 2003
; Mitchell
et al., 2004
)
.
The Global Land Data Assimilation System (
GLDAS
) extends the NLDAS
concepts to
the global scale

(Rodell et al., 2004)
.

They
op
timally
integrate

multiple

observation based data products
,

and use

them to parameterize, force,
and
constrain (via
data assimilation)

the model
,
as well as

validat
e results
.

Th
e

output fields provide more
accurate land surface states than is currently ava
ilable
. H
ence
,

the incorporation of these
NASA research results and
validated data products

should increase
the

accuracy

of
weather forecast models (such as WRF), augment water management decision
capabilities (using
decision support systems and tools

suc
h as
RiverWare

and
BASINS
)
,
and enhance and extend the continental s
cale soil moisture analysis of
the USDA S
CAN

DST
.



The Land Information System has its lineage in NLDAS and GLDAS. LIS

is a
high
performance
and terrascale

extension of LDAS,
overcoming
the limitations and
enhancing

the capabilities of
G
LDAS to perform

1
x1
k
m
2

global

land data assimilation

(Kumar et al., 2004)
.
LIS incorporates a suite of land
surface
models

(LSM)

of various
level of sophistication encapsulating various approaches for ph
ysical solution. The
default set of
LSMs

include the Noah, CLM, and Vic models.
It has
a
user
-
friendly
web
-
based user interface for
the
configuration

of models

and visualization

of the output
results
. LIS
also
incorporates

community standards and conven
tions

such as
the
Earth System Modeling Framework (ESMF)

to en
able coupling with other ESMF
-
enabled model
s (that include

WRF and COAMPS
), and the Assi
stance for Land
Modeling (ALMA)
, a
n internal

data exchange structure,

to
facilitate the
generic
coupling
o
f the

LSMs

via the
specialized
ESMF super
-
structure

(Hill et al., 2004)
.

The high
-
resolution capabilities of LIS facilitate the evaluation and implementation of decision
support solutions at the same fine spatial scales of physical processes that are impo
rtant
in the application domain (such as the atmospheric boundary layer and cumulus cloud
development); and thereby improving the surface layer parameter and flux estimates.

It is
also developed and implemented using advanced software
and systems
engineeri
ng
concepts and
interoperable
design principles. Hence,
the components of
LIS
can be
readily integrated

into the relevant systems components of the Rapid Prototyping
Node(s).


4
.0 LIS Evaluations using the RPC


The RPC LIS
evaluations are structured

to cl
early establish

the suitability and
capabilities of NASA research results and
data

to adequately meet
the identified
operational requirements
and
needs of the partner agencies.
The
specified

needs and
operational goals of the USDA SCAN DST are being
analy
zed

to define the RPC
experiment.
Further, a coupled WRF
-
LIS evaluation experiment, in conjunction
with a

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MRC funded ISS project, will be conducted to demonstrate the cross
-
cutting nature of
potential LIS applications to serve national priorities for soci
etal benefits.
The coupled
WRF
-
LIS experiment will also serve to evaluate the ESMF coupling capabilities of LIS
as well as evaluate the outcomes of the LIS enhanced SCAN DST.
The experiments will
also utilize the high
-
performance computing facilities avai
lable at NASA’s Project
Columbia Supercomputing Center and at the Mississippi State University

(MSU)



High
Performance Computing Collaboratory

(HPC
2
)
.


The general approach for the evaluation of LIS will involve the identification and
evaluation of (1) SC
AN
DST requirements
, (2)
relevant NASA observations
, (3)
relevant
AMSR
-
E soil moisture data
, (4)
relevant active microwave data sources
, (5)
future soil
mo
isture observation

capabilities
, (6)
relevant LIS modeling components
, (7)
NASA
generated land surfac
e forcing
, (8)
NASA data assimilation resources
, and (9)
relevant
radiation transfer mode
l(s)
.
The following
categories of
evaluation activities are
envisioned:




LIS Performance A
nalysis:

The suite of
LSMs

in LIS will be exercised to
study

the performance

requirements of the partner
applications
, specifically
the
USDA SCAN DST and the NASA SPoRT research to operations activities
to support NOAA via the couple WRF
-
LIS experiment evaluations.

A small
region of interest that encompasses the states of Mississ
ippi and Alabama has
been selected due to the relatively dense network of SCAN stations. Further,
independent ground observations, acquired by agriculture producers, are also
available seasonally in this region. The experiments will be performed for a
ra
nge of spatial resolutions
,

and the uncertainties involved will be quantified.
The computational performance and the storage requirements will also be
evaluated and
documented.




Observation

S
ensitivity

Experiments (OSE):

LIS has the capabilities to
assimi
late or otherwise incorpora
te observations and products fro
m a suite of
NASA assets, models as well as from other sources of partner agencies.
Normally, the OSEs are conducted as part of the verification and validation
process. Since the assimilation of
AMSR
-
E data is an essential component of
the evaluations, it is necessary to understand its impacts using OSEs. Hence,
a

candidate set of OSEs will be identified and defined to evaluate the value
added by
AMSR
-
E

observations.

[
The exhaustive set of OSEs
will be
deferred till
a

future potential ISS process.
]

The results will be analyzed to
determine the optimal configuration
,

and
iterated

using additional experiments
as necessary. During the process, the uncertainties associated with the
individual obser
vations will also be characterized.

For this task, we will
evaluate the use of the
Ensemble Kalman Filter

(
EnKF
)

to assimilate the
available remote sensing and
in
-
situ

(SCAN) observations

(Reichle et al.,
2002)
. The same EnKF data assimilation method can
also be used to
assimilate surface

temperature observations from MODIS, GOES,
NPP,
or
NPOESS sensors, if needed.


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Observing System Simulation Experiments (OSSE):
The SCAN DST
network of stations
is

currently not optimally distributed in the continental
Uni
ted States. It is essential that the future deployment and expansion of the
network be designed optimally in order to maximize the benefits of the DST.

A set of “twin experiments” to be conducted to evaluate the potential of LIS
OSSEs to identify and pri
oritize the future deployment and enhancement of
SCAN DST.




Model Coupling Evaluation Experiments:
The ESMF
-
enabled capability of
LIS is a technically valuable feature to facilitate coupled modeling
experiments and analyses. The ESMF coupling capabilities

will be evaluated
using coupled WRF
-
LIS experiments to study the impact of LIS feedback in
the atmospheric model. A case study in our region of interest will be
identified and developed to study the genesis and propagation of a severe
weather event. The

impact and value of model coupling will be evaluated
along with the utility of the LIS enhanced SCAN DST. This activity will be
coordinated with NASA SPoRT.




Cross
-
cutting Demonstrations:
LIS can be adapted and extended to meet the
needs
of a diverse set

of national applications. One of the MRC funded ISS
project
s

to support the USDA FAS PECAD utilizes meteorological and land
information from the AFWA AGRMET system.

However, there
are
ongoing
activities at NASA GSFC to enhance the AGRMET system using LI
S.

The
set of AGRMET compatible data from LIS will be made available to the ISS
team for applications of agricultural efficiency
, if suitable agreements can be
negotiated with the Air Force Weather Agency (AFWA), who has sponsored
this project
.

Similarly
, the MRC ISS project to support the USFS for fire risk
estimation (disaster management) will also evaluate the utility of LIS products
at 12x12 km
2

resolutions in Mississippi forest areas.




Evaluation Tools for PMW:
The LIS evaluation team will contribute

to the
development of the necessary evaluation tools for the RPC Performance
Metrics Workbench (PMW). Any useful software will be contributed for
integration into RPC in exchange for the availability of evaluations tools for
integration into the LIS V&V
package.


5
.0 Anticipated societal benefits via
a
future ISS implementation


There are a wide range of expected
societal
beneficial results from the proposed re
al
time integration of USDA

SCAN
information and

assimilation of
NASA remote sensing
data

using

LIS in order to derive a high resolution analysis of soil moisture information
across the continental United States
, as follows: (1) provides

critical information to
support drought monitoring and mitigation, (2) provides critical information for
predicti
ng

droughts based on weather and climate predictions, (3) supports irrigation
water management, (4) supports

fire risk assessment, (5) supports water supply
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forecasting and NWS flood forecasting, (6) supplies a critical

missing component to
assist with sno
w, climate and associated hydrometeorological data analysis, (7)

supports
climate change assessment, (8) enables water quality monitoring, (9) supports soil survey

interruption and mapping, and (10) supports a wide variety of natural resource
management &
research

activities such as NASA remote sensing activities of soil
moisture and ARS watershed studies. The primary

contribution of NASA data
, research
results and models
will be
validated high
-
quality
satellite data
products,
such as
capa
bilities to be pro
vided by
current and/or f
uture sensors and missions including
AMSR,
TRMM,

and
GPM
,
and water availability
and surface energy
parameters from
LIS
.


6.0
Identified Risks


The following categories of risks are identified:




System incompatibilities



Security is
sues



Intellectual property



Quality, availability, suitability of data



AMSR
-
E,
in
-
situ

data



Validation data



Technical and scientific issues



Data assimilation, inconsistencies



Personnel, political, funding risks


The LIS Performance Analysis phase will inclu
de more comprehensive analysis of risks.


7
.0 LIS Evaluation Team


The
LIS evaluation

team
for this RPC experiment
consists of research scientists and
students from Mississippi State University
,

George Mason University

(GMU)
, and
NASA GSFC
.
The interface
with this science team wil
l be via Valentine Anantharaj, the

single point of contact for the RPC

LIS activities
. The
RPC LIS
evaluation
efforts

will
be closely coordinated with the
RPC
node
development

activities
.
External science
consultations
or

collabo
rations are anticipated: (a) with
Dr. Christa Peters
-
Lidard of
NASA GSFC for the
technical and scientific guidance

and application of
LIS, the design
and evaluation of experiments, the coupling of models, liaison with external partners as
necessary and pro
vision of necessary data
, and for the development of PMW
; (b) with
Prof. Paul Houser at the
GMU
Center for Research on Environment and Water (CREW)
for scientific
leadership and
guidance,
data assimilation activities
, and liaison with USDA
NRCS
, and for th
e development of PMW
;
(c) with NASA Global Modeling and
Assimilation Office (GMAO) regarding the design and analysis of OSSEs;
(d) NASA
SPoRT regarding the coupled WRF
-
LIS case study;
and
(e
)
with National Center for
Atmospheric Research for the implementa
tion of the WRF model. Support for GSFC
,
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GMU,

and NCAR are programmed via t
he rapid
-
prototyping proposal.

The team at
Mississippi State University will set
-
up LIS and conduct the evaluation experiments,
develop and/or incorporate the necessary validation

in coordination with the RPC
Performance Metric Workbench (PMW) team.


7.0 References


Cosgrove, B.A., and 14 Coauthors. 2003. Real
-
time and retrospective forcing in the
North American Land Data Assimilation System (NLDAS) project.
J. Geophys. Res.,

108(D
42): 8842.


Hill, C., DeLuca, C., Balaji, V., Suarez, M. and da Silva, A. (2004). The architecture of
the Earth System Modeling Framework.
Computing in Science and Engineering
, 6(1).


Houser, P. R., W. J. Shuttleworth, H. V. Gupta, J. S. Famiglietti, K. H.

Syed, and D. C.
Goodrich, 1998: Integration of Soil Moisture Remote Sensing and Hydrologic
Modeling using Data Assimilation.
Water Resources Research
, 34(12):3405
-
3420


Kumar, S.V., C.D. Peters
-
Lidard, Y. Tian, P.R. Houser, J. Geiger, S. Olden, L. Lighty,

J.L. Eastman, B. Doty, P. Dirmeyer, J. Adams, K. Mitchell, E.F. Wood, and J.
Sheffield. 2004. Land Information System


An interoperable framework for high
resolution land surface modeling, submitted to
Environ. Modelling and Software.


Mitchell, K.E., an
d 21 co
-
authors (2004). The Multi
-
institution North American Land
Data Assimilation System (NLDAS): Utilization of multiple GCIP products and
partners in a continental distributed hydrological modeling system.
Journal of
Geophysical Research,

109: doi:10.1
029/2003JD003823.


NRC "Confronting the Nation's Water Problems: The Role of Research" (2004);
Committee on the Assessment of Water Resources, Water Science and Technology
Board, National Academies Press, Washington, DC; prepublication version available
at
:
http://www.nap.edu


Reichle, R.H., McLaughlin D.H, and Entekhabi D., 2002, Hydrologic data assimilation
using the Ensemble Kalman filter,
Mon. Wea. Rev.,

130: 103
-
115.


Rodell, M., P. R. Houser, U. Jambor, J. Gottschal
ck, K. Mitchell, C.
-
J. Meng, K.
Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J. K. Entin, J. P. Walker, D.
Lohmann, and D. Toll, The Global Land Data Assimilation System,
Bull. Amer.
Meteor. Soc.,

85 (3), 381

394, 2004.