Water and Energy Cycle Simulation andPrediction Studies (WESP)•J. Roads, GAPP, co-chair•J. Marengo, LBA, co-chair•K. Szeto, MAGS
•C. Fortelius, BALTEX•B. Rockel, BALTEX•M. Kooiti, GAME
•T. Lebel, CATCH• D. Lettenmaier, GAPP, NLDAS•G. Takle, GAPP/PIRCS•H. Berberry, La Plata•M. Rodell, NASA GLDAS•C. Peters Lidard, NASA GLDAS•M. Bosilovich, NASA DAO•K. Mitchell, NCEP, NLDAS•TBD, representatives from someother other NWP centers

2002: WESPCEOP (An element of WCRP)• To use enhanced observations to better
document and simulate water and energy fluxes
and reservoirs over land on diurnal to annual
scales and to better predict these up to seasonal
for water resource applications (WESP portion of
CEOP)• To document the seasonal march of the monsoon
systems, and their driving mechanisms, andinvestigate their possible physical connections(Monsoon Systems Studies)
2002: WESP

Research Plans: WESP portion of CEOP
• Conduct a pilot 2-year synoptic climatological casestudy of regional CSE and global water and energy
budgets as a guide to the interpretation of longer-term
past and future global and regional analyses and data
sets.
• Starting with the current efforts to close simplified
vertically integrated water and energy budgets withobservations and analysis and models, CEOP will begin
the effort to transfer this knowledge to global scales,
include more water and energy cycle processes, andbegin to examine the vertical and diurnal landatmosphere-ocean hydroclimatological characteristics
in observations and models.
2002: WESPWESP Activities
•
–
•
–
•
–

•
–

Water and Energy Budget Studies (WEBS)
Characterize and attempt to close atmospheric and land
water and energy budgets
Land Data Assimilation studies
Develop global and regional (NLDAS & ELDAS) land data
assimilation
Transferability Studies
Transfer modelling techniques for water and energy fluxes
and budgets between various continental-scale areas(GAPP, BALTEX, La Plata
Develop data requirements from in situ, satellite, models
Sampling frequency, variables, budgets, model requirements,
composite data sets

2002: WESPWESP Activities
•
–
•
–
•
–
•
–

Water and Energy Budget Studies (WEBS)
Balance water and energy budgets over land areas and, as
appropriate, over associated oceanic areas
Land Data Assimilation studies
Develop regional (NLDAS & ELDAS) land data assimilation
Transferability Studies
Transfer modelling techniques for water and energy fluxes
and budgets between various land areas (BALTEX, La Plata)
Develop data requirements from in situ, satellite, models
Sampling frequency, variables, budgets, model requirements,
composite data sets

2002: WESPPre-CEOP Water and Energy Budgets
Atmospheric Mass∂π
= −∇ • πv∂t

Atmospheric Water Vapor∂{q}= −∇ • {vq} + E − P + {RSQ}'∂t
Surface Water∂(m + s)= P − E − N + RSW '∂t
Atmospheric Energy∂{C T + KE + φ }ps= −∇ • {v(C T + φ + KE)} + SH + LP + {QR} − {QF} + RST'p∂t
Surface Energy∂TsC= QRS − SH − LE − L C + QF + G'
v ∂t
f sm
s
2002: WESPPre-CEOP Water & Energy Budget Studies (WEBS)
•

•

•

•

•

•

GCIP/GAPP– Roads et al. (2002b) are developing a GCIP WEBS from representative models,available observations and various reanalyses. Maurer et al. (2002) aredeveloping a corresponding 50-year LDAS legacy data set.
MAGS
– Budget studies using regional climate models show general agreement withavailable measurements for some parameters but problems with others (such as
orographic precipitation).
LBA– Marengo et al (2002) used a combination ob station rainfall and the NCEP reanalysis to characterize the annual cycle of critical water budget parameters andtheir variations for the northern and southern sections of the Amazon River
Basin.
BALTEX– Omstedt and Rutgersson (2000), Omstedt et al. (2000) provided WEBS for the
BALTIC Sea. Jacob (2001; Jacob et al. (2001) described regional model
simulations of WEBS.
GAME– GAME is developing a comprehensive dataset through a special re-analysis
effort by JMA and the bringing together of many observational measurements
taken over many regions of Asia. Model studies are using operational and reanalysis products
GHP– A global synthesis is being developed that will make use of pre-CEOP data. This
will help to set the stage for global syntheses developed from CEOP data
2002: WESP

J., 30 coauthors, 2002: GCIP Water and Energy Budget Synthesis (WEBS).
GCIP Roads,CD-ROM(available from GEWEX office)J., 30 coauthors, 2002: GCIP Water and Energy Budget Synthesis (WEBS). J.
WEBS Roads,Geophys.
Res. (submitted to, and almost in press, GCIP3 special issue)

Precipitation
DJF

Evaporation

MAM

JJA

DJF

SON

OBS

OBS

REAN2

REAN2

GSM

GSM

RSM

RSM

ETA

ETA

NEXRAD

VIC

0

1

2

3

mm/day

4

5

6

MAM

0

1

JJA

2002:
WESP
2
3
4
mm/daySON

5

GHP TP Roads, J., M. Kanamitsu, R.
Stewart, 2002: CSE Water and Energy Budgets
in the NCEP-DOE Reanalysis II. J.
Hydrometeorology , Vol. 3, Issue 3, p. 227-248.

CEOP Molts Locations with 1988-2000 Annual Mean Temp & Precip
90 N

35

1
2

32

60 N

28
29
27
24

31 30

3

22 23
25
26

30 N

84
11
12

34
33
0

6
10
5 9
7
13

14

19
17
18
16
20 21

15

30 S

60 S

90 S
180

120 W

60 W

0

1

0

2

3

4

60 E

5

6

7

120 E

180

8

Precip (mm/day)
9
9

8
8

7
7

6
6

5
5

4
4

3
3

2
2

1
1

-7.28

-2.91

-2.4

4.63

10.32

13.59

23.14

23.34

26.83

L

K

E

B

M

H

A

T

C

-14.8
-13.3
-10
-8.9
-3.1
-1.2
0.2
0.3
0.7
0.7
5.4
6.3
9.3
10.5
11.5
12.5
14.6
14.7
15.3
18.1
18.2
21
22.9
23.1
24.1
24.6
24.8
24.9
25.1
25.3
25.8
26.9
28.1
28.9
29.7

0
0

1 35 2

48 32 3 292728 6 243023312510 5 2226 9

2002: WESP

7 1121121417191816203334131 5

WESP Activities
•
–
•
–
•
–
•
–

Water and Energy Budget Studies (WEBS)
Balance water and energy budgets over land areas and, as
appropriate, over associated oceanic areas
Land Data Assimilation studies
Develop regional (NLDAS & ELDAS) land data assimilation
Transferability Studies
Transfer modelling techniques for water and energy fluxes
and budgets between various land areas (BALTEX, La Plata)
Develop data requirements from in situ, satellite, models
Sampling frequency, variables, budgets, model requirements,
composite data sets

2002: WESP

LDAS
PILPS
– The GCIP regional PILPS-2C experiment (Wood et al., 1998) and the ISLSCPGlobal Soil Wetness Project (Dirmeyer et al., 1999) demonstrated the viability of
executing physically based, distributed, and, uncoupled land-surface models
over large spatial domains and multi-year periods, provided that moderately
dense observations of precipitation were available. CEOP will thus be importantfor future PILPS.
GLOBAL LDAS
– The goal is to develop a 1/4 degree resolution, near-real time Global Land Data
Assimilation Scheme (GLDAS), which makes use of various new satellite- andground-based observation systems within a land data assimilation framework, inorder to produce optimal output fields of land surface states and fluxes.
US LDAS– A number of NOAA, NASA and university partners are developing a US Land
Data Assimilation System (N-LDAS) -- a real-time, hourly, distributed, uncoupled,
land-surface simulation and assimilation system executing on a horizontal grid
spanning the continental portion of the USA (CONUS) at 0.125 degree resolution(Mitchell et al (2001). Following the PILPS-2C and GSWP approach of landsurface model (LSM) intercomparisons over large spatial domains, the N-LDAS
project is applying four different LSMs: NOAH (Mitchell et al., 2001), MOSAIC(Koster and Suarez, 1996) VIC (Liang et al. 1994);SAC-SWA (Sacramento Model)
2002: WESPWESP Activities
•
–
•
–
•
–
•
–

Water and Energy Budget Studies (WEBS)
Balance water and energy budgets over land areas and, as
appropriate, over associated oceanic areas
Land Data Assimilation studies
Develop regional (NLDAS & ELDAS) land data assimilation
Transferability Studies
Transfer modelling techniques for water and energy fluxes
and budgets between various land areas (BALTEX, La Plata)
Develop data requirements from in situ, satellite, models
Sampling frequency, variables, budgets, model requirements,
composite data sets

2002: WESPTransferability
Studies
What is the performance of coupled hydrologic/atmospheric models in different geographical andclimate regions as well as over the same region at different temporal and spatial scales. Can
CEOP data help to compare the models?
•BALTEX– BALTEX has recently invited global participation in a model intercomparison as part ofthe BALTEX PIDCAP experiment. Comparisons will be made at 18 km resolution.
•PIRCS– PIRCS developed one of the original regional model intercomparison and transferability
experiments They are now planning on developing a followon covering the period June
1986-end of CEOP. Comparisons will be made at 50 km resolution.
•The La Plata River-basin Model Evaluation and Research Applications– The objective is to carry out a demonstration test of the degree to which ourunderstanding and model capability that has been developed in GCIP is sufficient to also
account for the La Plata basin's climate system. This initiative would link the GEWEXinterests on hydrological issues with CLIVAR/VAMOS concerns in monsoon circulations.
So far only the Eta Model is participating.
•Others?
– In a sense, GLDAS is a global transferability experiment, Also, the global model
2002: WESP
analyses are another transferability experiment.

Pre-CEOP IRI/ARCS Regional Model
Intercomparisons
Model

Reanalysis

Scripps RSM

FSUNRSM

GISS RCM

IRI
RegCM2
Levels
28
28
27
16
14
Type
spectral
spectral
spectralgrid
grid
DaviesSW. Rad. Lacis andChou and Lee CCM 3.6 (Kiehl
CCM 3.3(1982)
Hansen(1996)
et al. 1998)(Kiehl et(1974)
al. 1998)
Harshvardhan CCM 3.6
LW Rad.
GFDL
GFDL
CCM 3.6 (Kiehl
and Corsetti(Schwartzkopf (Schwartzkopf et al. 1998)(Kiehl et(1984)
et al. 1994)
et al. 1994)
al. 1998)
Convective SAS (Kalnay SAS* (HongZhang-McFarlane modified Kuo Grell(Krishnamurti (1993)
Param.
et al. 1996)
and Pan 1996) (1995)
et al., 1990)
Krishnamurti Holtslag et
PBLLouis et al.
Hong and Pan Holtslag andet al. (1990)(1982)(1996)
Boville (1993)
al. 1990
Land
NOAA
NOAA* (see FSU (Cocke and Fulakeza et
BATS
al. (2002)
Surface(Kalnay et al. Chen and
LaRow 2000)(Dickinson
1996)
Roads 2002)
et al.
1993)
2002: WESPPre-CEOP IRI/ARCS Regional Model
Intercomparisons

2002: WESPWESP Activities
•
–
•
–
•
–
•
–

Water and Energy Budget Studies (WEBS)
Balance water and energy budgets over land areas and, as
appropriate, over associated oceanic areas
Land Data Assimilation studies
Develop regional (NLDAS & ELDAS) land data assimilation
Transferability Studies
Transfer modelling techniques for water and energy fluxes
and budgets between various land areas (BALTEX, La Plata)
Develop data requirements from in situ, satellite, models
variables, sampling frequency, composite data sets

2002: WESPA number of NWP Centers are being asked foranalyses and short term forecast output••

There have previously been a number of extensive modelintercomparisons that have helped to improve the models.
– global atmospheric model comparisons (AMIP)– regional model comparisons (PIRCS)– land surface model comparisons (PILPS)– ocean, coupled ocean, seasonal forecast models (etc.)
comparisons
By contrast, there has not been any systematic comparison
made for analyses.
– Can we trust these analyses?
– What are their intrinsic differences?
– What are their errors in comparison to observations?
– Is the error small enough that we can use them in place ofsparse observations for many variables?
– What is the difference between the analyses and short term
forecasts?
2002: WESPWESP Data Requirements•••

Spatial Scales– At local scales, in situ data from several international tower and first order stations alongwith level II and level III satellite data, and numerical model output for these same sites
need to be consolidated into useful data sets for studying water and energy budgets.
– Regional and global networks of more standard observations and model simulations arealso needed for developing WEBS and understanding monsoon interactions.
Two types of basic output have been requested from major numerical weather
prediction centers:– MOLTS (water and energy budget processes in vertical model columns at 3-hourly or
more frequent intervals.) at 15 international reference sites, which will have correspondingin situ tower and surface station measurements and satellite data.
– 3D gridded output processed as synoptic snapshots. Gridded global data should have aresolution of 2.5 deg. or higher. Higher resolution for specific regions is desirable.
Sampling frequency– Both molts and gridded data are requested every 3 hours or less.
• Given that most analysis cycles are every 6 hours, this output will include pure
forecasts as well as analyses.
– In addition, we request two day forecasts every 2 days. Such data will further help todiagnose model errors, which contribute to analyses errors.
2002: WESPWESP 3-D
Atmospheric Mass∂π∂π σÝ= −∇ • vπ −∂t∂σAtmospheric Water Vapor∂π q∂π σÝq ∂Fq= −∇ • πvq −+− πCQ + RSQ'∂t∂σ∂σ
Cloud Water∂π qc∂π σÝqc= −∇ • πvq c −+ π CQ − πCP∂t∂σSoil Moisture∂m= P − E − N + Csm + RSW '
∂t
Snow
∂(s)
= Ps − E s − Csm∂t

Surface Energy∂Ts= QRS − SH − LE − L C + QF + G'
Cv
f sm
s
∂t

Atmospheric Energy∂π (C T + KE + φ )∂π σÝ(C T + φ + KE )psp= −∇ • πv(C T + φ + KE) −
p
∂t
∂σ
∂F
∂F
T
+
+ π CT + R − QF + RST'∂σ∂σ

2002: WESP

Vertical Water and Energy Budgets,
Roads, J. O., S-C., Chen, M. Kanamitsu, and H. Juang, 1998: Vertical structure of humidity andtemperature budget residuals over the Mississippi River basin. J. Geophys. Res., 103, 3741-3759.

2002: WESPCEOP Variables•Top of atmosphere–radiation fluxes (3)•Atmosphere–Standard variables (sufficient to initialize regional and global scalemodels.
–Large-scale and turbulent water and energy fluxes and divergences,diabatic heating–clouds•Surface–radiation fluxes (4), turbulent fluxes, albedo, land cover, snowfall,snow lwe, total pcp.
•Subsurface–Temperature, moisture, subsurface processes•Miscellaneous–Cloud cover, vegetation,

2002: WESPNCEP surface variables
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•

sea level and surface pressure
skin and lowest model lvl temperature
total soil moisturesnow water equivalent
1-hr accumulated total precip
1-hr accum. convective precip
1-hr average flux of latent heat
1-hr avg potential flux of latent heat
1-hr average flux of sensible heat
1-hr average flux of sub-surface heat
1-hr avg flux of snow phase change heat
1-hr average shortwave downward flux
1-hr average shortwave upward flux
1-hr average longwave downward flux
1-hr average longwave upward flux
1-hr average net longwave flux at top
1-hr average net shortwave flux at top
1-hr accumulated snow fall
1-hr accum. snow melt
1-hr accum. surface runoff
1-hr accum. baseflow-groundwater runoff

•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•

bottom soil temperatureroughness lengthu and v component at 10 m
potential temperature at 10 m
specific humidity at 10m
2-meter temperature
2-meter specific humidity
surface exchange coefficientgreen vegetation covercanopy waterlayer volumetric soil moisturelayer soil temperature
station land/sea mask
total cloud coversnow ratio from explicit cloud schemesnow precip typeice pellet precip typefreezing rain precip typerain precip typeu-component of storm motion
v_component of storm motion
2002: WESP

3-D variables
NCEP Standard output• pressure• Virtual temperature• u wind• v wind• omega velocity• Specific humidity• cloud water mixing ratio• cloud layer fractional cover• convective latent heating rate
• stable latent heating rate
• short-wave heating rate
long-wave heating rate
turbulent kin. energy

Additional requested variables• geopotential (gZ)
• Kinetic energy (KE)• 3-d water vapor flux
• water vapor flux divergence• 3-d dry energy (CpT+gZ+KE) flux
• dry static energy flux divergence• planetary boundary layer height• Moisture analysis increment• Energy analysis increment• Surface analysis increment

2002: WESPData Archive Centers
•
•
•
•
•

UCAR
– in situ and MOLTS
Univ. Tokyo– Remote sensing products, level II, and MOLTSGLDAS (Goddard)– Land surface products and model output and MOLTS
MPI– Global model output and MOLTS
Others– Each individual center responsible for archiving own data
before transmitting to archive center– Regional analyses archived locally within CSEs

2002: WESPWhat do we still need to do?
••

ASAP we need to:– Fully define the diagnostic CEOP equations– Fully define the needed model output molts and gridded data.
– Define what in situ and remotely sensed variables can be compared.
– Set up functioning data centers to begin archiving and compositing
CEOP data and distributing this data to research community.
– Develop a time line of activities, with achievable milestones
During CEOP we need to:– Develop studies utilizing in situ, remotely sensed, and CEOP model
output– Clarify and organize specific research efforts• WEBS• Transferability studies• Analyses comparisons• Prediction studies– Better articulate CEOP WESP’s scientific challenges
2002: WESPWhy is CEOP important?
•
•

•
•
•
•
•

A natural extension to current CSE efforts, which are
working with past data to develop a global WEBS.
Incorporates global models and analyses, which have been
underemphasized by the CSE regional experiments, whichhave focused on regional models
Brings together all the other GEWEX projects, which havenot been adequately coordinated with the CSEs.
Brings together GEWEX and CLIVAR projects (monsoon
studies)
Begins pilot global hydroclimatological data sets, which
could be continued, depending upon CEOP success.
Provides new global research opportunities for otherongoing regional and global research efforts.
Will develop an improved understanding of global waterand energy components, which should lead to improved
climate prediction.
2002: WESP