April 2005

Technical Report Series on Global Modeling and 

Data Assimilation 

NASA/TM—2005–104606, Vol. 26

Max J. Suarez, Editor

Volume 26

Senior Authors:

S. Bloom, A. da Silva, and D. Dee

Contributing Authors:

M. Bosilovich, J-D. Chern, S. Pawson, S. Schubert, M. Sienkiewicz,

I. Stajner, W-W. Tan, and M-L. Wu

Documentation and Validation of the Goddard Earth Observing

System (GEOS) Data Assimilation System–Version 4
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April 2005

Technical Report Series on Global Modeling and 

Data Assimilation 

NASA/TM—2005–104606, Vol. 26

Max J. Suarez, Editor

NASA Goddard Space Flight Center, Greenbelt, MD

Volume 26

Senior Authors:

Arlindo da Silva and Dick Dee

NASA Goddard Space Flight Center, Greenbelt, MD

Stephen Bloom

Science Applications International Corporation, Beltsville, MD

Contributing Authors:

Michael Bosilovich, Steven Pawson, Siegfried Schubert, and Man-Li Wu

NASA Goddard Space Flight Center, Greenbelt, MD

Meta Sienkiewicz, Ivanka Stajner, and Wei-Wu Tan

Science Applications International Corporation, Beltsville, MD

Jiun-Dar Chern

University of Maryland Baltimore County (UMBC), Baltimore, MD

Documentation and Validation of the Goddard Earth Observing

System (GEOS) Data Assimilation System–Version 4

National Aeronautics and 

Space Administration

Goddard Space Flight Center 

Greenbelt, Maryland 20771
Available from:

NASA Center for AeroSpace Information National Technical Information Service

7121 Standard Drive 5285 Port Royal Road

Hanover, MD 21076–1320 Springfi eld, VA 22161

Price Code: A17 Price Code: A10
Abstract

This document describes the structure and validation of a frozen version of the Goddard Earth

Observing System Data Assimilation System (GEOS DAS): GEOS-4.0.3. Significant features of

GEOS-4 include: version 3 of the Community Climate Model (CCM3) with the addition of a finitevolume dynamical core; version two of the Community Land Model (CLM2); the Physical-space

Statistical Analysis System (PSAS); and an interactive retrieval system (iRET) for assimilating

TOVS radiance data.

Upon completion of the GEOS-4 validation in December 2003, GEOS-4 became operational on

15 January 2004. Products from GEOS-4 have been used in supporting field campaigns and for

reprocessing several years of data for CERES.

iii
This Technical Memorandum is the final documentation of the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) based on the Physical-space Statistical Analysis System (PSAS). The GEOS DAS was developed by the Data Assimilation

Office (DAO) over a period of 10 years, under the leadership first of Dr Richard Rood

and then of Dr Robert Atlas. Both model and analysis evolved substantially over these

years to the version, GEOS-4 DAS, documented here, a version which is now being used

for products for the CERES Project, for Instrument Teams of the EOS/Aura platform, and

for collaborative science projects. The results of the formal validation of the GEOS-4 DAS

are also documented here.

Many people have contributed to the various systems over time in many and varied

ways. They are acknowledged in Appendix A.

There are some whose leadership stands out who must be acknowledged here and whom

I thank for their careful and superior contributions to the GEOS systems to date. Stephen

Cohn started the PSAS project in 1992 and with Greg Gaspari developed the compactly

supported correlation functions used. Jim Pfaendtner developed the first coded version

of PSAS, thereby establishing its practical applicability. Lawrence Takacs was a major

contributor to the model and integration of prior versions of the GEOS-DAS. The GEOS-4

system would not exist without the development leadership of Arlindo da Silva. Shian-Jiann

Lin developed the finite volume dynamical core and worked very closely with Arlindo to

develop the model-analysis interface. Others must be recognized because of their long-lived

contributions: Dick Dee, Ricardo Todling, and Jing Guo for contributions to the analysis

system and the integrated DAS; Ron Gelaro and Lars Peter Riishojgaard for their leadership

in system development and testing; Joanna Joiner for the development of the DAOTOVS;

Man Li Wu for her diagnostics of the moisture fields and surface temperature analyses to

ensure their viability for CERES. The system would not be useful in applications without

the careful work of the system integration and test group led by Robert Lucchesi, and

the operations group led by Gi-Kong Kim. During the validation processes, the critical

insight and analyses by the lead and contributing authors, and the feedback from Bruce

Wieliecki and the CERES team were invaluable to the final improvements and approval of

the GEOS-4 system.

I especially thank Steve Bloom for his hard work and persistence in leading the effort

to assemble this comprehensive documentation and the validation results. This document

would not have been completed without him.

Michele Rienecker

Global Modeling and Assimilation Office

NASA/Goddard Space Flight Center

Greenbelt, Maryland

February 2005

iv
Contents

1 Introduction 1

2 Background 3

3 GEOS-4 Data Assimilation System 7

3.1 GEOS-4 GCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1.1 Hydrodynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1.2 Physics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1.2.1 CCM3 Parameterizations . . . . . . . . . . . . . . . . . . . 8

3.1.2.1.1 CCM3 precipitation processes. . . . . . . . . . . . 8

3.1.2.1.2 CCM3 clouds and radiation. . . . . . . . . . . . . 8

3.1.2.1.3 CCM3 turbulent mixing. . . . . . . . . . . . . . . 9

3.1.2.1.4 CCM3 surface processes. . . . . . . . . . . . . . . 9

3.1.2.2 fvGCM Modifications . . . . . . . . . . . . . . . . . . . . . 9

3.1.2.2.1 Modifications to initial and boundary data. . . . . 9

3.1.2.2.2 Changes to moist physics. . . . . . . . . . . . . . . 10

3.1.2.2.3 CLM2 land model. . . . . . . . . . . . . . . . . . . 10

3.1.3 Specific Implementation for GEOS-4.0.3 . . . . . . . . . . . . . . . . 11

3.2 GEOS-4 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2.1 PSAS Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2.2 Specification of Error Statistics . . . . . . . . . . . . . . . . . . . . . 16

3.2.2.1 Background Error Covariance . . . . . . . . . . . . . . . . 17

3.2.2.1.1 Moisture and height. . . . . . . . . . . . . . . . . 17

3.2.2.1.2 Wind. . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.2.2.2 Observation Error Covariance . . . . . . . . . . . . . . . . 19

v
3.2.2.2.1 Rawinsonde. . . . . . . . . . . . . . . . . . . . . . 19

3.2.2.2.2 TOVS. . . . . . . . . . . . . . . . . . . . . . . . . 19

3.2.3 Observing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2.3.1 Land Surface Observations . . . . . . . . . . . . . . . . . . 20

3.2.3.2 Ocean Surface Observations . . . . . . . . . . . . . . . . . 20

3.2.3.3 Conventional Upper-Air . . . . . . . . . . . . . . . . . . . . 21

3.2.3.4 Satellite Upper-Air . . . . . . . . . . . . . . . . . . . . . . 21

3.2.3.4.1 TOVS. . . . . . . . . . . . . . . . . . . . . . . . . 21

3.2.3.4.2 CTW. . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.2.3.4.3 TPW. . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.2.4 Skin Temperature Analysis . . . . . . . . . . . . . . . . . . . . . . . 23

3.2.5 Analysis Details for GEOS-4.0.3 . . . . . . . . . . . . . . . . . . . . 23

3.2.5.1 GEOS-4.0.3 analysis grid . . . . . . . . . . . . . . . . . . . 24

3.2.5.2 Background Error Covariance Model Parameters . . . . . . 25

3.2.5.3 Observation Error Covariance Model Parameters . . . . . . 30

3.3 Quality Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.3.1 Statistical Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.3.2 The Background Check . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.3.3 The Buddy Check . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.3.4 The Wind Check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.3.5 The Profile Check . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.3.6 Special Treatment of Moisture Observations . . . . . . . . . . . . . . 39

3.4 Interactive Retrievals – iRET . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.5 Model – Analysis Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.6 GEOS-4 Development History . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4 Validation Issues 45

4.1 GEOS-4.0.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.1.1 1.4 r1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.1.1.1 Modified Mass-Wind Balance . . . . . . . . . . . . . . . . . 46

4.1.1.2 Modified QC Gross-Check . . . . . . . . . . . . . . . . . . 47

4.1.1.3 CTW selection . . . . . . . . . . . . . . . . . . . . . . . . . 47

vi
4.1.2 1.4 r2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.1.2.1 CLM2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4.1.2.2 Modified Skin Temperature Analysis . . . . . . . . . . . . . 48

4.1.2.3 Near-ground Moisture Modification . . . . . . . . . . . . . 48

4.2 System Validation: Methodology and Resources . . . . . . . . . . . . . . . . 48

5 Validation Results 51

5.1 CERES Radiation Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . 51

5.1.1 CERES Concerns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

5.1.2 Tskin Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

5.1.3 Cloud-Clearing Issues . . . . . . . . . . . . . . . . . . . . . . . . . . 56

5.1.4 Clear Sky OLR Comparisons . . . . . . . . . . . . . . . . . . . . . . 57

5.1.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5.2 ISCCP and Station Observation Comparisons . . . . . . . . . . . . . . . . . 62

5.3 Stratospheric Circulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

5.3.1 Temperature and Zonal Wind . . . . . . . . . . . . . . . . . . . . . . 68

5.3.2 Vorticity and Winds . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

5.3.3 Equivalent Length . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

5.3.4 Rates of Isentropic Transport . . . . . . . . . . . . . . . . . . . . . . 72

5.3.5 The Residual Circulation . . . . . . . . . . . . . . . . . . . . . . . . 75

5.3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

5.4 Ozone Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

5.4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

5.4.2 Evaluation of Instantaneous 6-hourly Forecast Winds . . . . . . . . 79

5.4.3 Evaluation of 6-hourly Averages of Winds . . . . . . . . . . . . . . . 82

5.4.4 Evaluation of GEOS-4.0.3 Ozone Profiles . . . . . . . . . . . . . . . 86

5.5 Monthly Mean Diagnostics . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

5.5.1 AMIP (Climatology) Results . . . . . . . . . . . . . . . . . . . . . . 90

5.5.2 Monthly Precipitation in GEOS-4 DAS . . . . . . . . . . . . . . . . 96

5.5.3 Surface Wind Stress in GEOS-4 DAS . . . . . . . . . . . . . . . . . 104

5.6 Precipitation Variability: MJO . . . . . . . . . . . . . . . . . . . . . . . . . 108

5.6.1 Seasonal Mean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

vii
5.7 Data Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

5.8 Monitoring Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

5.8.1 Quality Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

5.8.2 Observing System Statistics . . . . . . . . . . . . . . . . . . . . . . . 130

5.8.3 Forecast Skill . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

5.8.4 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

5.8.4.1 QC example . . . . . . . . . . . . . . . . . . . . . . . . . . 139

5.8.4.2 RH Near Antarctic Topography . . . . . . . . . . . . . . . 143

5.8.4.3 Precipitation Issues . . . . . . . . . . . . . . . . . . . . . . 143

5.8.4.4 Synoptic Examples . . . . . . . . . . . . . . . . . . . . . . 145

6 Summary 149

A Acknowledgements 151

B Acronyms 153

References 157

viii
List of Tables

3.1 Layer edge pressures and the associated coefficients used to generate them. pk =

Ak + Bkps for k=1,...,56. The values of pk here are generated using a representative

value of ps = 1000 hPa. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2 Comparison of the vertical analysis levels, GEOS-4.0.3 and GEOS-4.0.2. An “x”

denotes an omitted level, quantities on these levels are obtained by interpolation

from the other levels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.3 GEOS-4.0.3 background error covariance model parameters, described in section 3.2.2.1.

The length scales are used by the Power Law function, equation 3.6. The moisture pseudo-relative humidity analysis uses a smaller “window” in equation 3.6, i.e.,

2c = 3000 km for moisture. The mass-coupled parameters are used in equations 3.11-

3.12. Note that the J(p),K(p), and L(p) terms in equations 3.11, 3.12, 3.13, and

3.14 are constant for all levels: K(p) = L(p) = 0.4 radians, and J(p) = 4 radians. 25

3.4 Vertical height background error correlations, ν

f

h

(m,n) in equation 3.4. . . . . . . 27

3.5 Background decoupled wind error (both χ and ψ): vertical correlations, ν

f

χ,ψ(m,n)

in equation 3.4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.6 Background moisture error vertical correlations, ν

f

q

(m,n) in equation 3.4. . . . . . 29

3.7 Observation error standard deviations for the two sources of vertical profile data

used in GEOS-4. Units are m for h and ms−1for wind (u and v components the

same). Also, the correlated and uncorrelated TOVS height error standard deviations

are the same; σ

o

h

(tot) is simply √

2 times σ

o

h

(u,c), and is included for comparison

with the rawinsonde entries for σh. . . . . . . . . . . . . . . . . . . . . . . . . 30

3.8 Observation error standard deviations for data from surface sources: ships, buoys,

QuikScat winds, and land stations. Units: m for heights, ms−1for wind. . . . . 31

3.9 Single-level upper-air wind observation error standard deviations, u and v components the same, units are ms−1

. . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.10 Rawinsonde height error vertical correlations, ν

o

h

(m,n) in equation 3.4. . . . . . . 32

3.11 TOVS uncorrelated height error vertical correlations, ν

o

h

(m,n) in equation 3.4. . . 33

3.12 TOVS correlated height error vertical correlations, ν

o

h

(m,n) in equation 3.4. . . . 34

4.1 Organization of the GEOS 4.0.3 validation effort. . . . . . . . . . . . . . . . 49

ix
5.1 CEOP observing stations for in situ comparisons. . . . . . . . . . . . . . . . . . 62

5.2 Precipitation averaged over the specified regions for January (July) of 2001. Units

are mm day−1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

5.3 Seasonal mean precipitation statistics, area averages (40◦S to 40◦N). See Figure 5.44. 108

5.4 Seasonal standard deviation precipitation statistics, area averages (40◦S to 40◦N).

See Figure 5.45. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

5.5 List of data withholding experiments and their identifying labels. . . . . . . . . . 114

x
List of Figures

3.1 D-grid layout in GEOS-4 GCM; “φ”-points denote mass locations. . . . . . . . . 11

3.2 Vertical distribution of GEOS-4 model layers for the case of ps = 1000 hPa, values

of A and B from Table 3.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.3 Finite Lagrangian control-volume and state variables for the GEOS-4 GCM. . . . 14

3.4 Background height error standard deviation, σ

o

h

(φ), with a focus primarily on the

troposphere. Units are m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.5 As in figure 3.4, only now showing the whole model domain. . . . . . . . . . . . 26

3.6 Illustration of the relationship between the rate at which the background check marks

observations as outliers and the prescribed error statistics, for normally distributed

errors. The yellow tails of the histograms correspond to observations marked as

outliers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.7 Quality control decisions for zonal wind observations at 200hPa on March 01 1998,

using a non-adaptive buddy check (top) and adaptive buddy check (bottom). Green

dots indicate observations that passed the background check, yellow dots indicate

suspect observations that were accepted by the buddy check, red dots indicate rejected observations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.8 After-analysis height and surface pressure. . . . . . . . . . . . . . . . . . . . . 43

5.1 Comparisons of GEOS-4.0.2 (“OLD GEOS”) and ECMWF clear sky OLR with

CERES OLR. The bottom panel is the difference of the absolute value of the fields

in the first two panels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

5.2 Scatter-plots of CERES retrieved Tskin (horizontal axis) versus GEOS-4.0.2 Tskin

(vertical axis) for the five regions on the panels. There are two colored groupings in

each scatter-plot, cooler (leftmost) are nighttime, warmer (rightmost) are daytime.

See text for additional details. . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5.3 Scatter plots of Tskin, comparing CERES data with model estimates from GEOS4.0.3 (C403), GEOS-4.0.2 (C402) and ECMWF for four months during 2001: (a) January, (b) April, (c) July, and (d) October. See text for discussion. . . . . . . . . 54

5.4 Global monthly mean Tskin comparisons, GEOS-4.0.3 and ECMWF vs CERES, for

four months during 2001: (a) January, (b) April, (c) July, and (d) October. . . . 55

xi
5.5 Tskin biases (differences between GEOS-4 and CERES retrieved Tskin) and cloud

parameters from ISCCP. Cold biases occur over areas having high cloud amount

with cold cloud top temperatures. . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.6 Cloud contamination example: Antarctica. a) Tskin comparisons between GEOS-4

(lower left) and CERES (upper left). Also shown are ISCCP cloud top temperatures

(upper right) and cloud amounts (lower right). b) Area averaged Tskin comparisons

among ECMWF (green), GEOS-4.0.3 (red) and CERES (black). Cloud top temperature shown in magenta. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

5.7 Cloud contamination example: Eurasia. Same conventions as 5.6. . . . . . . . . 59

5.8 Clear sky OLR comparisons. Statistics show that the GEOS-4 based clear sky OLR

is comparable to that from ECMWF. . . . . . . . . . . . . . . . . . . . . . . . 60

5.9 Clear sky OLR rms errors, showing a comparable behavior in clear sky OLR between

GEOS-4 and ECMWF. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

5.10 Monthly mean differences between C403 and ISCCP clear sky skin temperature

observations for July 2001 (top: daytime, middle: nighttime). The bottom panel

shows the differences between the TOVS retrieved Tskin and the ISCCP observations,

averaged only when both coexist in the time series. (Units: Kelvin). . . . . . . . 63

5.11 Mean diurnal cycle of several in situ reference site stations (data shown as dots)

and corresponding C403 grid point surface temperature (or Tskin, in red) and near

surface air temperature (T2m, in black) for July 2001. Units are degrees Kelvin. The

bars denote the fraction of available observations for each type of data. . . . . . 65

5.12 Mean diurnal cycle of radiation components at the surface from in situ references

sites and the nearest model grid point: downward shortwave (black); upward shortwave (red); downward longwave (blue); and upward longwave (green). Color bars indicate the percentage of available observations during the month. (Units are Wm−2

) 66

5.13 As in Figure 5.12, except for the surface energy flux components: net radiation

(black); turbulent heat flux (red); turbulent flux of latent heat (blue); and heat flux

from the ground (red). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

5.14 (a) Monthly average of zonal mean temperature and zonal wind for January 2001.

The last panel in each row shows the difference between GEOS-4.0.2 and GEOS-4.0.3. 69

5.14 (b) Monthly average of zonal mean temperature and zonal wind for August 2003. 69

5.15 (a) GEOS-4.0.2 meridional gradients of vorticity (left panels) and vorticity increments calculated from the analysis increments of the wind fields (right panel) at 72

hPa and 52 hPa. Negative values are depicted in shades of green and blue bounded

by the zero contours. Crosses indicate the locations of the sonde stations. . . . . . 71

5.15 (b) Same as (a) but for GEOS-4.0.3. . . . . . . . . . . . . . . . . . . . . . . . 72

5.16 (a) Equivalent length for January 2001. In the top two panels, values larger than 3

are shaded. The bottom panel shows the difference between GEOS-4.0.2 and GEOS4.0.3, with values larger than 1 shaded and negative values depicted with dashed

contours. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5.16 (b) Same as (a) but for August 2003. . . . . . . . . . . . . . . . . . . . . . . . 74

xii
5.17 Monthly rates of isentropic transport. The black solid curve depicts GEOS-4.0.2

entrainment rates calculated using 6-hourly instantaneous analyzed winds and the

black dash-dotted curve depicts GEOS-4.0.2 entrainment rates calculated using 6-

hourly averaged winds. Corresponding red curves show the entrainment rates for

GEOS-4.0.3. Left panel, January 2001; Right panel, August 2003. . . . . . . . . 75

5.18 Vertical residual velocity in January 2001 and August 2003 calculated using 6-hourly

instantaneous analyzed winds. . . . . . . . . . . . . . . . . . . . . . . . . . . 77

5.19 Vertical residual velocity in January 2001 and August 2003 calculated using 6-hourly

averaged winds. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

5.20 Total column ozone fields at 12Z on July 31, 2000 are shown (in DU) for two CTM

simulations that were driven by 6-hourly forecast winds from GEOS-4.0.3 (top),

GEOS-4.0.2 (middle). Independent TOMS total ozone column data are shown in

the bottom panel. Latitudinal biases are seen in both simulations, but they are

larger for GEOS-4.0.3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

5.21 Total column ozone fields at 12Z on July 31, 2000 are shown (in DU) for three ozone

assimilation experiments that were driven by 6-hourly forecast winds from GEOS4.0.3 (top), GEOS-4.0.2 (middle) and GEOS-3 (bottom). In all three figures there

is excessive variability in the tropical total ozone column, and it is the largest for

GEOS-4.0.3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

5.22 Time series of daily global root-mean-square of the total column O-F residuals for

TOMS data are shown for three ozone assimilation experiments driven by instantaneous 6-hourly forecast of the winds from GEOS-4.0.3 (cyan), GEOS-4.0.2 (orange),

and GEOS-3 (green). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

5.23 Total column ozone fields at 12Z on July 31, 2000 are shown (in DU) for two CTM

simulations that were driven by 6-hourly averages of the winds from GEOS-4.0.3

(top), GEOS-4.0.2 (middle). Independent TOMS total ozone column data are shown

in the bottom panel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

5.24 Total column ozone fields at 12Z on July 31, 2000 are shown (in DU) for two ozone

assimilation experiments that were driven by 6-hourly averages of the winds from

GEOS-4.0.3 (top), GEOS-4.0.2 (middle). Independent TOMS total ozone column

data are shown in the bottom panel. . . . . . . . . . . . . . . . . . . . . . . . 84

5.25 Time series of daily global root-mean-square of the total column O-F residuals for

TOMS data are shown for three ozone assimilation experiments driven by 6-hourly

averages of the winds from GEOS-4.0.3 (blue), GEOS-4.0.2 (black). For reference

the same quantities are shown for experiments driven by instantaneous 6-hourly

forecast winds GEOS-4.0.3 (cyan), GEOS-4.0.2 (orange). . . . . . . . . . . . . . 85

5.26 The RMS of O-F residuals for stratospheric SBUV layers (Umkehr layers) are shown

for GEOS-4.0.2 (red) and GEOS-4.0.3 (blue) ozone assimilation systems. Approximate pressure ranges for the SBUV layers are shown on the right-hand axis. The

residuals are for 912 SBUV profiles on December 31, 2003, which is one month after

the GEOS-4.0.3 ozone assimilation was initialized. . . . . . . . . . . . . . . . . 86

xiii
5.27 Mean POAM profiles (black) are compared with profiles of collocated GMAO ozone

analyses from GEOS-4.0.3 (blue) and GEOS-4.0.2 (red) assimilation systems. Comparisons are shown separately for the Northern (a and b) and Southern (d and e)

Hemisphere. The root-mean-square differences between POAM and GEOS-4.0.3

(red) and GEOS-4.0.2 (black) ozone analyses are shown for Northern Hemisphere

(c) and Southern Hemisphere (f). POAM data from January 1-14, 2004 are used,

except in a) and d) where POAM data for December 2003 are used. . . . . . . . 87

5.28 Ozone profiles from independent sondes (red solid line) that were launched from Neumayer, Antarctica, (70◦S, 8

◦W) and collocated GMAO ozone analyses from GEOS4.0.3 (blue dashed line) and GEOS-4.0.2 (black solid line) systems are shown for

December 24 and 31, 2003 and for January 7 and 14, 2004. . . . . . . . . . . . . 88

5.29 Mean profiles from MIPAS (solid) are compared with collocated assimilated ozone

from GEOS-4.0.2 (dotted) and GEOS-4.0.3 (dashed) in the left panel. The RMS

difference between MIPAS and collocated analyses are shown in the right panel for

GEOS-4.0.2 (solid) and GEOS-4.0.3 (dashed). Comparisons were done using over

2000 MIPAS observations for the period from January 1 to 7, 2004. . . . . . . . 89

5.30 December, January, February (DJF) mean total precipitation in mm day−1for the

six intercompared models. GPCP verification climatology for this period is shown

at the bottom. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

5.31 Same as 5.30, except the period is June, July, August (JJA) . . . . . . . . . . . . 92

5.32 December, January, February (DJF) precipitation differences in mm day−1for each

of the six models compared with the GPCP verification. . . . . . . . . . . . . . . 93

5.33 Same as 5.32, except the period is June, July, August (JJA) . . . . . . . . . . . . 94

5.34 Taylor plots of model precipitation anomaly behavior (see text for Taylor plot description): DJF: Dec-Feb, MAM: Mar-May, JJA: Jun-Aug, SON: Sep-Nov, ANN:

Annual. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

5.35 (a) January 2001 precipitation fields from C403 (top panel) and GPCP (middle

panel). The difference field (C403- GPCP) is shown in the bottom panel. Units are

mm day−1. (b) Same as (a) but for July 2001. . . . . . . . . . . . . . . . . . . 97

5.36 Monthly average precipitation comparisons for 30◦S − 30◦N, for January 2001 (top

set) and July 2001 (bottom set). Middle panels in both sets are the GPCP verification data for that time. Units are mm day−1

. . . . . . . . . . . . . . . . . . . . 99

5.37 Taylor plot showing global (excluding poles) precipitation comparison statistics

against GPCP for GEOS-4.0.3 (green), GEOS-4.0.2 (red) and NCEP (blue). NCEP

vs GEOS-4.0.3 (purple) added for reference. Also shown are daily time series of the

averages with and without the bias. (a) January 2001. (b) July 2001. Units are

mm day−1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

5.38 Taylor plots as in Figure 5.37, only for the tropics : 20◦S − 20◦N. . . . . . . . . 101

5.39 Taylor plots as in Figure 5.37, only for the tropical Western Pacific region: 15◦S −

15◦N, 120◦E − 180◦E. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

xiv
5.40 Monthly tropical precipitation averages for 2001, for GEOS-4.0.3 (cer), GEOS-4.0.3

(rp2), NCEP and GPCP verification. The land (green) and ocean (blue) contributions to the monthly averages (red) are also shown. Units are mm day−1

. . . . . 103

5.41 Monthly mean surface stresses, zonal component (left) and meridional component

(right column), for January 2001 (top set) and July 2001 (lower set). Compared

are the stresses from the ERA-40 reanalysis, GEOS-4.0.3 (“CERES”) and stresses

derived from SSM/I data. Units are dynes cm−2. . . . . . . . . . . . . . . . . . 105

5.42 Taylor plots comparing zonal wind surface stresses over oceans for January 2001

(top set) and July 2001 (lower set) for four regions: Global, Tropics, Northern Midlatitude and Southern Mid-latitude. Three comparisons on each plot: GEOS-4.0.3

vs ERA-40 (red); GEOS-4.0.3 vs SSM/I (green); ERA-40 vs SSM/I (blue). . . . . 106

5.43 Same as Figure 5.42, only for meridional stress component. . . . . . . . . . . . . 107

5.44 Seasonal mean precipitation for boreal summer (May to Oct.) and winter (Nov to

Apr.) Also shown are the area average (40◦S to 40◦N) seasonal means of precipitation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

5.45 Seasonal standard deviation precipitation for boreal summer (May to Oct.) and

winter (Nov to Apr.). Also shown are the area average (40◦S to 40◦N) seasonal

standard deviations of precipitation. . . . . . . . . . . . . . . . . . . . . . . . 111

5.46 Longitude-time cross sections (averaged 5◦S to 5◦N) of precipitation standard deviation. TRMM observations are the left-most plot. . . . . . . . . . . . . . . . . 112

5.47 Latitude-time cross sections (averaged 75◦E to 85◦E) of precipitation standard deviation. TRMM observations are the top-most plot. . . . . . . . . . . . . . . . 113

5.48 Top: analysis anomaly correlations, using NCEP analyses as verification, for the

Northern Hemisphere, January-February 2003. Experiments here are the first 5

entries in Table 5.5. Bottom: July-August 2003. . . . . . . . . . . . . . . . . . 116

5.49 Top: forecast anomaly correlations, using their own analyses as verification, for the

Northern Hemisphere, January-February 2003. Experiments here are the first 5

entries in Table 5.5. Bottom: July-August 2003. . . . . . . . . . . . . . . . . . 117

5.50 Top: analysis anomaly correlations, using NCEP analyses as verification, for the

Southern Hemisphere, January-February 2003. Experiments here are the first 5

entries in Table 5.5. Bottom: July-August 2003. . . . . . . . . . . . . . . . . . 118

5.51 Top: forecast anomaly correlations, using their own analyses as verification, for the

Southern Hemisphere, January-February 2003. Experiments here are the first 5

entries in Table 5.5. Bottom: July-August 2003. . . . . . . . . . . . . . . . . . 119

5.52 Forecast anomaly correlations, using their own analyses as verification, for experiments examining the relative impacts of cloud motion winds and QuikScat surface

winds, for the Southern Hemisphere, January-February 2003. . . . . . . . . . . . 120

5.53 Profiles of areal RMS bias of O-F for the control (red) and no-rawinsonde experiments (blue), January 2003. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

5.54 Profiles of areal RMS bias of O-F for the control (red) and no-TOVS experiments

(blue), January 2003. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

xv
5.55 Analysis anomaly correlation, GEOS-4.0.1 used as verifying analysis. No-wind assimilation (red), no-mass assimilation (green), control (black). . . . . . . . . . . 124

5.56 Anomaly correlation scores for January-February 2003, each experiment using its

own analysis as verification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

5.57 Observation counts during January 2003 for the control, no-mass and no-wind experiments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

5.58 Quality control flagging of suspect observations for TOVS heights for two runs:

GEOS-4.0.2 (blue) and GEOS-4.0.3 (red). Time period covered was September 2003. 126

5.59 TOVS QC statistics for 11 November 2003 for GEOS-4.0.2 (top) and GEOS-4.0.3

(bottom). Many fewer TOVS data are flagged in the newer system. . . . . . . . 127

5.60 Two examples of ineffective background checking in older versions of GEOS. Top:

inappropriate wind observations (green barbs, with each barb representing an O-F

of 10 m s−1) in GEOS-4.0.2. Bottom: mislocated sea level pressure observations in

GEOS-3 (white numerals, O-F units in hPa). . . . . . . . . . . . . . . . . . . . 128

5.61 QuikScat QC statistics for 13 June 2003 for two runs which used two different values

of τx (see section 3.3.2). Top panel used (old) τx = 1000, bottom panel used (new)

τx = 10. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

5.62 Time series of global RMS TOVS O-F for height (units: m); GEOS-4.0.3 (blue),

GEOS-4.0.2 (red). Time period ranges from 16 January 2000 to 30 April 2003. . 131

5.63 Time series of global RMS TOVS O-F (blue) and O-A (red) for height (units m);

(a) GEOS-4.0.3, (b) GEOS-4.0.2. Time period ranges from 1 January 2001 to 31

July 2001. Note the differing scales on the ordinates between the two sets of plots. 132

5.64 Time series of global RMS and global mean TOVS O-F for height (units are m);

GEOS-4.0.3 (blue), GEOS-4.0.2 (red). Time period ranges from 1 August 2003 to

24 November 2003. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

5.65 500 hPa geopotential height anomaly correlation forecast skill. Left panels using

GEOS (either 4.0.2 or 4.0.3) analyses as verification, right panels use NCEP analyses.

Red curves are GEOS-4.0.3, black curves are GEOS-4.0.2. Cases span SeptemberNovember 2003 (see Figure 5.66). . . . . . . . . . . . . . . . . . . . . . . . . . 136

5.66 Time series of individual 5-day 500 hPa geopotential height forecast skills in the

Northern Hemisphere. Top panel used NCEP analyses as verification, bottom panel

used GEOS (either 4.0.2 or 4.0.3) analyses as verification. Red lines are GEOS-4.0.3,

black are GEOS-4.0.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

5.67 Sea level pressure anomaly correlation comparison. Upper panel has cases from

September-November 2003, the red curve is GEOS-4.0.3 and the black curve for

GEOS-4.0.2. Lower panel has cases from June-August 2001, the green curve is

GEOS-4.0.3 and the red curve is GEOS-4.0.2. NCEP analyses are used for verification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

5.68 GEOS-4 1000 hPa analyzed wind field, with accepted wind observations (black) and

rejected wind observations (red). Each barb is 10 ms−1. Observations affected by

the change in QC are outlined in heavy blue. . . . . . . . . . . . . . . . . . . . 139

xvi
5.69 Comparison of 10 m winds from GEOS (top) and NCEP (bottom). QuikScat observations (black) superimposed on both. Each barb is 10 ms−1

. . . . . . . . . . 140

5.70 Comparison of the original QuikScat winds (left) with the inferred upper-air observations (accepted: black, rejected: red) used in GEOS-4 (right). Superimposed on

the upper-air observations are contours of background Tskin - T2m. Each barb is

10 ms−1, and the contour interval is 1 K. . . . . . . . . . . . . . . . . . . . . . 141

5.71 Impact of change to RH in GEOS-4.0.3 (bottom) compared to GEOS-4.0.2 (top).

Note, this is a monthly mean, and individual cases had RH greater than 1000 in

GEOS-4.0.2 near high topography over Antarctica. . . . . . . . . . . . . . . . . 142

5.72 Comparison of 6-hourly average convective precipitation rate for a case at 00Z 28

October 2003; GEOS-4.0.3 (left), GEOS-4.0.2 (right), with the 403 - 402 difference

(bottom). Units are in mmday−1. . . . . . . . . . . . . . . . . . . . . . . . . 143

5.73 IR image from GOES-12 for 00Z 28 October 2003. . . . . . . . . . . . . . . . . 144

5.74 Progression of forecast precipitation (shaded) and sea level (contours) from initial

assimilation values (DAS). Shown here is series of five 5-day forecasts over the Indian

Ocean region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

5.75 Comparisons of the 700 hPa temperature analysis at 06Z 10 November 2003. (a)

GEOS-4.0.3 (left), GEOS-4.0.2 (right), and 403 - 402 difference (bottom). (b)

GEOS-4.0.2 (left), NCEP analysis (right) and 402 - NCEP (bottom). Units are

in K. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

5.76 Example of change in mass-wind balance on wind analyses: “402” (top) is the 4.0.2

system using the old balance, “403” (bottom) is the 4.0.3 system using the revised

balance. In each, the GEOS analysis is on the left, the NCEP comparison analysis

on the right, with the difference plotted below. Units are in ms−1. . . . . . . . . 147

xvii

Chapter 1

Introduction

The assimilation system described in this document is a frozen version of the Goddard

Earth Observing System Data Assimilation System (GEOS DAS), significant features of

which include: version 3 of the Community Climate Model (CCM3) with the addition

of a finite-volume dynamical core; version two of the Community Land Model (CLM2);

the Physical-space Statistical Analysis System (PSAS); and an interactive retrieval system

(iRET) for assimilating TOVS radiance data.

The primary performance drivers for the production of GEOS DAS assimilation fields are

temperature and moisture fields suitable for the CERES instrument team radiation budget

calculations, and wind fields for the transport studies of the stratospheric and tropospheric

chemistry communities. Other significant concerns for GEOS DAS(throughout its existence)

have involved the provision of near real-time mission support for a number of chemistry

mission field campaigns as well as off-line support for other EOS instrument teams, notably

MODIS, AIRS and TRMM.

The validation process seeks to evaluate both the overall scientific behavior of a candidate upgrade to the GEOS DAS, as well as its capability to address specific issues (usually

issues that were drivers for changing/upgrading the DAS). Due to the breadth of topics

that require evaluation during the course of this validation, contributions are required from

science efforts across the entire GMAO organization. The specific validation objectives, as

well as the organization of this validation exercise are covered in Chapter 4.

This validation documentation is organized as follows:

• An overview of the development of GEOS-4 (the latest version of GEOS DAS), and

its context in the development of atmospheric assimilation systems at NASA/GSFC

is given in Chapter 2.

• The main characteristics of the climate model and the assimilation system are described in Chapter 3.

• The analysis and model changes that are the drivers for this validation effort, as well

as the approach for performing the validation, are summarized in Chapter 4.

• The results of the several validation evaluation efforts are detailed in Chapter 5.

• The final summary of the validation results is given in Chapeter 6.

1

Chapter 2

Background

GEOS DAS has evolved over a number of years, with the primary milestones driven by

major launches of the EOS series of satellites. It is reasonable to set the start of the current

GEOS DAS development process to GEOS-1, which combined a version of the Phoenix

4th order GCM with a mini-volume version of Optimum Interpolation (OI) analysis, and

used an Incremental Analysis Updating (IAU) framework (Schubert et al. 1993). That

system (model and analysis) had a horizontal resolution of 2 degrees latitude by 2.5 degrees

longitude, and it was used to produce one of the first multi-year reanalyses. More than

100 papers and presentations came from the GEOS-1 reanalysis effort. A workshop on the

GEOS-1 products was held in 1995, and improvements to the Phoenix model were made

as a consequence of results presented at that workshop. The modified GEOS-1 system,

with changes made both to GCM and analysis, was used to support many NASA-sponsored

aircraft missions to study stratospheric chemistry, as well as some tropospheric chemistry

missions.

With the advent of subsequent reanalyses by the National Centers for Environmental Prediction (NCEP) and the European Center for Medium-range Weather Forecasts

(ECMWF) that were significant improvements beyond the GEOS-1 reanalysis, an effort

to re-do the FGGE year (1979) with a modified version of GEOS-1 (based largely on the

changes made after the GEOS-1 reanalysis workshop) was planned. This modified version of

GEOS became instead GEOS-2, a testing platform for a new analysis: Physical-Space Statistical Analysis System (PSAS, Cohn et al. 1998). The main driver for GEOS-2 therefore

became the preparation for the upcoming launch of the EOS-AM (now TERRA) platform.

Along with the challenges of implementing a new analysis in a full assimilation system,

there were also a number of significant changes to other parts of the assimilation process

that complicated greatly the development process for GEOS-2. A brief list of the these

issues would include:

• Moist boundary layer (Schubert et al. 1993);

• Mosaic land surface model;

• On-line quality control (see section 3.3);

• Moisture bias adjustment (Dee and Todling 2000);

3
• Total precipitable water (TPW) assimilation (Hou et al. 2000a).

Ultimately a version of GEOS-2, which had a 1x1 degree GCM and 2x2.5 degree analysis,

was used in support of the CERES-TRMM mission in 1999.

As 2000, and the launch of TERRA approached, the CERES-TRMM version of GEOS

was subjected to an extensive overhaul to prepare it for operations; little of the scientific

behavior was affected by that work. This EOS-launch version, which produced 1x1 degree

output, became version 3.0 of GEOS DAS. All operational versions of the GEOS DAS have

been run in two modes:

First Look Assimilation. Atmospheric observations are grouped into six-hour data windows and processed by the atmospheric analysis four times each day. The first look

analysis runs 6 to 10 hours after the 4 analysis times (0Z, 6Z, 12Z, 18Z), and it uses

whatever conventional and satellite observations are available at the data cut-off time.

Late Look Assimilation. The software configuration is identical to the first look assimilation. The late look system is run with a delay of at least two weeks, which results in

a more complete set of input observations. Another advantage afforded the late look

system is the availability of fully analyzed boundary data sets (sea surface temperatures, ice). The majority of the DAS results presented in this report are from systems

running in late look mode.

Subsequent modifications that were incorporated in the GEOS-3.x series were:

• Interactive retrievals (iRET), for assimilating TOVS radiance data;

• Modified vertical interpolation, for better coupling model and analysis;

• Precipitation assimilation (Hou et al. 2000b, 2001).

The final operational version of GEOS-3.x was v3.3.9, and the final research version

with precipitation assimilation was v3.4.1. Even though the GEOS-3.x system had met basic validation requirements, there remained significant issues that needed to be addressed.

The system was slow, making reanalysis efforts with it an extremely cumbersome and expensive affair. The DAS fields had excessively noisy structures in the stratosphere, creating

significant problems when using GEOS-3 fields as forcing for transport modeling (especially

ozone). There was also a skin temperature bias that created problems for radiation budget

investigations (e.g., CERES).

In order to address these issues, a major effort to change the assimilation GCM was undertaken in 1999. The new model (fvGCM) combined the finite-volume dynamics developed

at the DAO (Lin and Rood 1996, 1998) with the physics developed for the NCAR Community Climate Model (CCM, see Kiehl et al. 1985, 1998). The DAS formed by combining the

fvGCM with the PSAS analysis became the “fvDAS” system, and this system then evolved

into the operational system referred to as GEOS-4. This approach marked a substantial

break with the previous 10 years of GEOS development; a whole new hydrodynamics as well

as physics was implemented, along with a substantially modified analysis-model interface,

and IAU was abandoned. A surface skin temperature assimilation was introduced to control

4
a significant skin temperature bias. The horizontal resolution was changed to 1x1.25 degree

(lat-lon) for fvGCM efficiency considerations. The vertical resolution was changed to 55

layers (in a hybrid sigma-pressure coordinate system) with the model top at 0.01 hPa.

The version of GEOS-4 that is the subject of this validation document has several

modifications beyond the original fvDAS system; these issues will be discussed in chapter

3 which describes GEOS-4 in some detail. This system went into operation, providing

datasets to EOS instrument teams as well as providing information for field campaigns, on

15 January 2004.

5

Chapter 3

GEOS-4 Data Assimilation System

In this Chapter, the salient features of the GEOS-4 Data Assimilation System (GEOS-4

DAS) will be described. For the purposes of this exposition, the DAS will be described in

four main sections:

• General Circulation Model (GCM)

• Analysis (PSAS)

• Quality Control (of observations)

• Model–Analysis Interface.

Each of these items will be covered in some detail in the sections that follow. The validation

effort that is the subject of this document was concerned with the third (and final) operational version of GEOS-4 DAS; this system was designated as 4.0.3, or “C403.” It was also

version 1.4r2 in the fvDAS development tree (which explains the occasional appearance of

this label in some of the results presented in Chapter 5).

3.1 GEOS-4 GCM

3.1.1 Hydrodynamics

The General Circulation Model used for the GEOS-4 DAS is the model jointly developed

by the Data Assimilation Office (DAO) and the Climate and Global Dynamics Division

(CGDD) at NCAR. The finite-volume dynamical core of this model was developed at the

DAO, and it has an extensive documentation in the open literature (cf. Lin 2004, and

references therein).

3.1.2 Physics

The operational version of fvGCM adopted the physics from the NCAR CCM3 (Community

Climate Model version 3) and WACCM (Whole Atmosphere Community Climate Model)

7
with several important modifications to make it more suitable for high-resolution weather

prediction applications and for coupling to a data assimilation system.

3.1.2.1 CCM3 Parameterizations

The NCAR CCM3 parameterizations are a well-balanced set of processes with a long history of development and documentation (Kiehl et al. 1985, 1998). The package includes four

major groups of physical processes: precipitation processes, clouds and radiation, turbulent

mixing, and surface processes. Each of these in turn is subdivided into various components. The precipitation processes are comprised of deep convection, shallow/middle moist

convection, and large-scale stable condensation. The clouds and radiation include cloud

parameterization, longwave radiation, and shortwave radiation. The turbulent mixing consists of the vertical diffusion, planetary boundary layer parameterization, and gravity wave

drag. The surface processes provide surface fluxes obtained from land, ocean and sea ice

models.

3.1.2.1.1 CCM3 precipitation processes. The process of moist penetrative convection is treated with a scheme developed by Zhang and McFarlane (1995). The scheme is

based on a plume ensemble approach where it is assumed that an ensemble of convective

updrafts and the associated downdrafts may exist whenever the atmosphere is conditionally

unstable in the lower troposphere. The cumulus plumes act to consume convective available

potential energy (CAPE) at an exponential rate using a specified adjustment time scale. The

treatment of shallow and middle-level convection processes is based on Hack (1994). The

cloud microphysics in fvGCM followed the simple diagnosed condensate parameterization

in the standard CCM3. Stratiform condensation takes place when a grid box is completely

saturated. The diagnosis of cloud fraction is a generalization of the scheme introduced by

Slingo (1987), with modifications described in Kiehl et al. (1998). Cloud fraction depends on

relative humidity, vertical velocity, atmospheric stability and convective mass fluxes. Three

types of cloud are diagnosed by the scheme: low-level marine stratus, convective cloud, and

layered cloud. Clouds are allowed to form in any model layer, except the layer nearest the

surface.

3.1.2.1.2 CCM3 clouds and radiation. The vertical distribution of in-cloud condensate used in the radiative transfer calculation is prescribed as a function of the vertical

integrated water vapor (Hack 1998). Clouds are assumed to be plane parallel and randomly

overlapped between layers. The parameterization of cloud droplet size and other cloud

optical properties is described in Kiehl et al. (1998). For the radiation package, the longwave radiative transfer is based on an absorptivity-emissivity formulation (Ramanathan and

Downey 1986) and the shortwave radiative parameterization used the δ-Eddington method

(Briegleb 1992) with 18 spectral bands. The model accounts for the radiative effects of water

vapor, O2, CO2, O3, and trace gases: CH4, N2O, CFC11, and CFC12. The distribution

of the trace gases is specified in terms of zonal-mean mixing ratios for each species (Kiehl

et al. 1985). A uniform background boundary layer aerosol is included in the shortwave

radiative parameterization. The aerosol is assumed well mixed in the bottom three layers

of the model.

8
3.1.2.1.3 CCM3 turbulent mixing. The free atmospheric turbulent diffusivities are

based on the gradient Richardson number. The atmospheric boundary-layer turbulence

parameterization utilizes the “nonlocal” formulation (Holtslag and Boville 1993). The eddy

diffusivity and the nonlocal terms are dependent on the PBL height that is estimated from a

bulk Richardson number with surface friction. The fvGCM incorporates a modified version

of the gravity wave drag parameterization used in the NCAR Whole Atmosphere Community Climate Model (WACCM). This parameterization includes an orographic gravity

wave drag formulation based on McFarlane (1987), as well as a modification for waves with

non-zero phase speeds (Garcia and Boville 1994) that are important in the stratosphere and

mesosphere.

3.1.2.1.4 CCM3 surface processes. The surface exchange of heat, moisture and momentum between the atmosphere and land, ocean or sea ice surfaces are treated with a bulk

exchange formulation based on Monin-Obukhov similarity theory. In fvGCM, the NCAR

Land Surface Model (LSM; Bonan 1998) in CCM3 was replaced by the Community Land

Model CLM2 (Bonan et al. 2002). A brief description of the land model will be provided

later. The fvGCM employs a specified distribution of sea surface temperatures and sea ice,

either from an observed weekly/monthly mean time series or annually repeating climatological mean. Sea ice is calculated via a multi-layer thermodynamic model.

3.1.2.2 fvGCM Modifications

Many important modifications to the CCM3 physics have been incorporated into the fvGCM

to make it more suitable for coupling to a data assimilation system as well as for highresolution weather prediction and climate simulation. When compared to CCM3, the

changes to the model physics fall into four major categories: modifications to initial and

boundary data; improvements and tunings of the existing schemes; addition and tuning of

a new land surface model, CLM2; and the addition of other optional physical packages.

3.1.2.2.1 Modifications to initial and boundary data. In its standard configuration the fvGCM employs a horizontal 1◦ × 1.25◦

resolution with 55 vertical levels from

surface to 1 Pascal. The initial and boundary data need to match the model resolution

and vertical domain. The new zonal mean climatological ozone dataset used in fvGCM

is a combination of AMIP (Atmospheric Model Intercomparison Project) ozone data, the

Free University Berlin (FUB) data, and the Upper Atmosphere Research Satellite (UARS)

ozone data. The surface orography and its sub-grid scale standard deviation were derived

from the 30-second US Geological Survey Global Topographic Data (GTOPO30). An areapreserving algorithm was used to map the high-resolution GTOPO30 dataset to model

resolutions. The sea surface temperature and sea ice concentrations in fvGCM are derived

from the weekly 1◦ × 1

◦ Reynolds’ optimum interpolation sea surface temperature (OISST)

version 2 (Reynolds et al. 2002). They are updated every time step using linear interpolation

in time. The fvGCM also employs an option to nudge the stratospheric water vapor to the

zonal mean climatological HALOE (Halogen Occultation Experiment) and MLS (Microwave

Limb Sounder) water vapor data (Randel et al. 1998).

9
3.1.2.2.2 Changes to moist physics. Changes to the moist physics include the incorporation of a convective rain re-evaporation scheme, the introduction of ice-phase latent

heating associated with freezing and melting in the calculation of large-scale stable condensation, and a revision to the diagnosed in-cloud water condensate. The fvGCM employs a

Sundqvist (1988) style evaporation of the convective precipitation as it makes its way to

the surface. In climate simulations, this evaporation scheme produces a very small moistening in the lower troposphere and reduction in the convective precipitation. The cloud

parameterization of the standard CCM3 assumes clouds in the near-surface layers always

have about the same water content (0.21 gm−3) everywhere. This assumption produces an

abundance of very bright, low cloud with very strong cloud top longwave radiation cooling

in the low troposphere, especially over the polar region. In the fvGCM, the in-cloud water content in the near-surface layers is limited to 5 percent of the saturated water vapor

mixing ratio. This change reduced the systematic polar cold bias in the lower troposphere

and prevented the extreme supersaturation over the Antarctic region in data assimilation

analyses.

The original physics package of the NCAR CCM3 has been tuned for T42 resolution.

With the use of substantially higher horizontal resolutions in the GEOS systems, along

with modifications to model algorithms and the use of new initial and boundary data,

a significant re-tuning of the fvGCM physics was required. The WACCM gravity wave

drag scheme was heavily tuned to produce a reasonable climate due to the introduction of

GTOPO30 high-resolution topography data and higher model horizontal resolution. Cloud

and radiation components were re-tuned to restore energy balance at the surface and top-ofatmosphere. Parameters that have been adjusted include: the relative humidity thresholds

for low and high clouds, the differential threshold at which clouds form over land versus

over ocean, the effective radius of the cloud droplets and ice crystals, and the background

aerosol formulation. As a result, the global mean absolute errors at the surface and topof-atmosphere are less than 1 Wm−2

, while maintaining very good agreement with global

observational estimates of cloud forcing.

3.1.2.2.3 CLM2 land model. The fvGCM incorporates version 2 of the Community

Land Model (CLM2) which provides for the comprehensive treatment of land surface processes. It was developed collaboratively by an open interagency/university group of scientists. CLM2 is a one-dimensional land surface model that includes comprehensive biogeophysics, hydrological and biogeochemical processes, and vegetation dynamics components.

Sub-grid scale tiles are used to represent the surface horizontal heterogeneity. It has one

vegetation layer with a realistic photosynthesis-conductance model based on Bonan (1996)

to depict evapotranspiration realistically. There are 10 unevenly spaced vertical layers for

soil with soil temperature, soil liquid water, and ice lens mass as model state variables

in each layer. The CLM2 features up to five snow layers depending on the snow depth,

with water flow, refreezing, compaction and aging allowed. In addition, the CLM2 utilizes

two-stream canopy radiative transfer, the Bonan lake model (1996), topographic enhanced

streamflow based on TOPMODEL (Beven and Kirkby 1979), and turbulence is considered

above, within, and below the canopy. Additional model details are provided in Zeng et al.

(2002), Bonan et al. (2002), and Dai et al. (2003).

A number of significant changes to the model formulations of CLM2 have been made at

NASA/GSFC. The drag coefficient under the canopy was newly formulated as a function of

10
the Leaf Area Index (LAI). This change had a substantial impact on reducing the warm and

dry bias in the model. The energy balance equation at the leaf surface was revised to include

the effect of leaf heat capacity. This modification improved the simulation of the diurnal

cycle with only marginal impact on the overall skin temperature error. A new efficient

implicit scheme was used to solve the water and energy balance of the vegetation canopy.

The incorporation of precipitation sub-grid scale variability into the canopy interception

scheme caused a decrease of interception loss and increase in canopy infiltration (Dickinson

et al. 2003). The positive impacts were an increase in the soil moisture and improved water

partitioning. With the modified interception scheme, the sub-surface runoff scheme was

turned off. This change corrected some overestimation of lateral sub-surface runoff, and

reduced warming caused by the revised interception. The thickness of the vertical layers in

the lake model was modified to prevent the Great Lakes from being perpetually frozen. The

interpolation scheme of the land boundary data was revised to avoid the faulty assignment

of lake points to wetlands in high-resolution applications. To compare with the observed

surface skin temperature from satellites, the calculation of model skin temperature was

modified to be based on the surface outgoing longwave radiation and the emissivities of

sub-grid tiles. Collectively these modifications substantially reduce systematic biases in the

CLM2. As a result, the CLM2 was found to be superior to the NCAR LSM in climate

simulation, numerical weather prediction and data assimilation.

3.1.3 Specific Implementation for GEOS-4.0.3

The GCM used in GEOS-4.0.3 has a 1◦latitude by 1.25◦longitude horizontal resolution,

with the prognostic variables discretized on a staggered D-grid (Figure 3.1). This system has

an effective time step of 30 minutes, which is the time step of its physics parameterization

(although the dynamics time step is considerably shorter).

Figure 3.1: D-grid layout in GEOS-4 GCM; “φ”-points denote mass locations.

The GEOS-4.0.3 model employs a generalized hybrid sigma-pressure vertical coordinate

system, where the pressures of the edges of the vertical layers are determined from the

11
surface pressure (ps) and two sets of coefficients, Ak and Bk, with “k” a vertical index

running from 1 to the number of model layers + 1. Table 3.1 shows the coefficients used

to generate the vertical grid for GEOS-4. Note that the pressures of the mid-points of the

55 layers in the GEOS-4 model are obtained by simply taking the average of the layer edge

pressures, and that the bounding edge pressures are surface pressure (bottom) and 0.01 hPa

(top). In addition, the hybrid nature of the vertical coordinate can be seen where the Bk

coefficients are all zero for values of k less than 43; for these values of k, the GEOS-4 model

is using a pure pressure-coordinate (mainly above 200 hPa). A sense of how the vertical

layers are distributed can be seen by plotting Ak and Bk, as shown in Figure 3.2.

Figure 3.3 is a schematic depiction of the state variables in the GEOS-4 model, and how

they are arranged in the vertical. The primary variables are: wind components (u,v); scaled

virtual potential temperature (θ = Tv/pκ); pressure thickness (of the Lagrangian control

volume, δp); and specific humidity (q). Although the pressure thickness is a prognostic

variable which is evolved by the finite-volume dynamical core, there is a mapping process

after each physics time-step which maps all fields from this Lagrangian control-volume

vertical coordinate back to the fixed Eulerian reference coordinates given in Table 3.1.

12
-25 0 25 50 75 100 125 150 175 200 225 250

A (hPa)

0.01

0.1

1

10

100

1000

PRESSURE (hPa)

-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

B (hPa)

0.01

0.1

1

10

100

1000

PRESSURE (hPa)

Figure 3.2: Vertical distribution of GEOS-4 model layers for the case ofp

s = 1000 hPa, values of

A and B from Table 3.1.

13
h

h

p

p

= p

1

p = p s K+1

 u, v

p

k

k

k+1

δ

θ, q

*

k+1

k

p

top

Figure 3.3: Finite Lagrangian control-volume and state variables for the GEOS-4 GCM.

Vertical Layer Distribution

k pk Ak Bk k pk Ak Bk k pk Ak Bk

1 0.01 0.01 0.00000 20 3.28 3.28 0.00000 39 108.66 108.66 0.00000

2 0.02 0.02 0.00000 21 4.08 4.08 0.00000 40 127.84 127.84 0.00000

3 0.03 0.03 0.00000 22 5.05 5.05 0.00000 41 150.39 150.39 0.00000

4 0.05 0.05 0.00000 23 6.22 6.22 0.00000 42 176.93 176.93 0.00000

5 0.07 0.07 0.00000 24 7.62 7.62 0.00000 43 208.15 201.19 0.00696

6 0.09 0.09 0.00000 25 9.29 9.29 0.00000 44 244.87 216.86 0.02801

7 0.12 0.12 0.00000 26 11.28 11.28 0.00000 45 288.08 224.36 0.06372

8 0.16 0.16 0.00000 27 13.64 13.64 0.00000 46 338.91 223.88 0.11503

9 0.21 0.21 0.00000 28 16.46 16.46 0.00000 47 398.72 215.42 0.18330

10 0.28 0.28 0.00000 29 19.79 19.79 0.00000 48 469.07 198.74 0.27033

11 0.37 0.37 0.00000 30 23.73 23.736 0.00000 49 551.84 173.40 0.37844

12 0.48 0.48 0.00000 31 28.37 28.374 0.00000 50 649.20 138.74 0.51046

13 0.62 0.62 0.00000 32 33.81 33.819 0.00000 51 744.38 101.67 0.64271

14 0.80 0.80 0.00000 33 40.18 40.183 0.00000 52 831.02 66.10 0.76492

15 1.02 1.02 0.00000 34 47.64 47.649 0.00000 53 903.30 35.47 0.86783

16 1.30 1.30 0.00000 35 56.39 56.396 0.00000 54 955.99 12.70 0.94329

17 1.65 1.65 0.00000 36 66.60 66.606 0.00000 55 985.11 0.00 0.98511

18 2.08 2.08 0.00000 37 78.51 78.518 0.00000 56 1000.00 0.00 1.00000

19 2.62 2.62 0.00000 38 92.37 92.372 0.00000

Table 3.1: Layer edge pressures and the associated coefficients used to generate them. pk =

Ak + Bkps for k=1,...,56. The values of pk here are generated using a representative value of ps =

1000 hPa.

14
3.2 GEOS-4 Analysis

An atmospheric analysis performed within a data assimilation context seeks to combine

in some “optimal” fashion the information from irregularly distributed atmospheric observations with a model state obtained from a forecast initialized from a previous analysis.

The analyzed variables are geopotential height, zonal and meridional wind components,

and pseudo-relative humidity (observation mixing ratio scaled by the background saturated

mixing ratio, Dee and da Silva 2003). A number of details concerning the GEOS-4 analysis

algorithm follow in section 3.2.1.

The types of observations used (the “observing system”) in the GEOS-4 analysis are

discussed in section 3.2.3. Information pertaining to the implementation of the analysis for

version 4.0.3 of GEOS-4 is given in section 3.2.5.

3.2.1 PSAS Algorithm

The PSAS algorithm solves the analysis equations (3.1) and (3.2):

w

f

k = ak−1



w

a

k−1



yk =

h

FkIkP

f

k

I

T

k F

T

k + Rk

i−1 

w

o

k − f(w

f

k

)



=

h

HkP

f

k HT

k + Rk

i−1 

w

o

k − f(w

f

k

)



(3.1)

w

a

k = w

f

k + P

f

k

I

T

k F

T

k

yk

= w

f

k + P

f

k HT

k

yk, (3.2)

with

n number of gridpoints × variables n ∼ 106

p number of observations p ∼ 105

w

a gridded analysis state vector ∈ IRn

w

f gridded forecast state vector ∈ IRn

w

o observation vector ∈ IRp

I interpolation operator I : IRn → IRp

(interpolates from model grid to observation locations)

f non-linear observation operator f : IRp → IRp

F tangent linear version of f, F = ∂f/∂w F : IRp → IRp

H tangent linear version of h, H = FI, H : IRp → IRn

a non-linear forecast model a : IRn → IRn

A tangent linear version of a A : IRn → IRn

P

f

forecast error covariance P

f

: IRn → IRn

R observation error covariance R : IRp → IRp.

The innovation covariance matrix,

M ≡ HPfHT + R, (3.3)

15
appearing in (3.1) is symmetric positive definite, making a standard pre-conditioned conjugate gradient (CG) algorithm (Golub and van Loan 1989) the method of choice for solving

the large linear system (3.1). For the current observing system, setting up and solving the

linear system (3.1) represents about half the computational effort of PSAS, and involves

computation in observation space: M ∈ IRp×p and y ∈ IRp. The other half of the computational expense is taken by step (3.2) which transfers the solution y to the state space:

P

fHT y ∈ IRn

. Additional technical details on the implementation of the PSAS solver are

given in da Silva and Guo (1996), Guo and da Silva (1997), Guo et al. (1998), Larson et al.

(1998).

3.2.2 Specification of Error Statistics

A crucial part of the implementation of the PSAS analysis is the specification of the background error covariance, P

f

, and the observation error covariance, R, needed to solve

Equations 3.1 and 3.2. As the dimensionality of these multivariate covariances is extremely

large (essentially the number of variables times the number of gridpoints for the rank of

the background covariance), it is necessary to parameterize them with covariance models

having a limited number of parameters.

The main characteristics of the covariance models used in GEOS-4 are:

• Compactly supported spline functions are used for modeling all single-level univariate

correlations. The modeled horizontal correlations are exactly zero beyond a certain

finite distance (6000 km for GEOS-4). These univariate correlations are horizontally isotropic. Three-dimensional covariances are constructed in terms of single-level

isotropic covariances.

• Geopotential height and mixing ratio errors are uncorrelated with each other.

• Wind-mass covariances are modeled according to a linear friction balance which ensures the geostrophic balance of the analysis increments in the extra-tropics, and

cross-isobar flow near the surface and in the tropics.

• Wind errors possess a unbalanced component which results in height-decoupled wind

analysis increments in the tropics.

• Error covariance parameters are obtained from a maximum-likelihood procedure (Dee

and da Silva 1999).

The background and observation correlation models used in GEOS-4 are expressed as

isotropic functions of the separation distance between two points on a sphere: kri − rjk. A

chord-length approximation is used for this distance: sij ≈ 2a sin(

θ

2

), where θ is the angular

separation between points i and j, and a is the Earth’s radius (6371 km). The following

subsections describe the design of the covariance models used in GEOS-4, with the specifics

of how the models have been implemented given in section 3.2.5.

16
3.2.2.1 Background Error Covariance

The GEOS-4 analysis system contains a univariate moisture analysis and a multivariate

height-wind analysis. The foundation of the height-wind covariance is the univariate background height error covariance, the remaining wind and wind-height covariances are developed upon this foundation.

3.2.2.1.1 Moisture and height. The basic 3-dimensional univariate covariance model

between horizontal locations (i, j) and vertical levels (m, n) in GEOS-4 can be expressed

as:

P

f

ij (x; m, n) = σ

f

i

(x; m) σ

f

j

(x; n) ν

f

x

(m, n) B

f

ij (x; m, n) C

f

ij (x), (3.4)

where x is a label denoting either moisture (x = q) or height (x = h). The function B is a

power-law correlation function:

B

f

ij (m, n) =

√

LmLn

Lmn

1

1 + 1

2



sij

Lmn 2

, (3.5)

Lmn =

Lm + Ln

2

,

where Lm is a level-dependent length scale parameter. Note that this formulation of the

correlation function is non-separable, since the length scale of the horizontal correlation

varies with height; a separable form could be recovered by having all the length parameters

Lm set to one constant.

The function C is a compactly supported window correlation function (see Gaspari and

Cohn 1999, for details). This function serves the purpose of causing the overall correlation

to vanish beyond a set separation distance, but doing so in a manner that retains positivedefiniteness, a crucial property for global analysis schemes. If the cutoff distance is 2c =

6000 km, and a non-dimensional distance variable is defined, Z =

sij

c

, then C takes the

form:

C

f

ij =







−

1

4

Z

5 +

1

2

Z

4 +

5

8

Z

3 −

5

3

Z

2 + 1 0 ≤ Z ≤ 1

1

12Z

5 −

1

2

Z

4 +

5

8

Z

3 +

5

3

Z

2 − 5Z + 4 −

2

3

Z

−1 1 ≤ Z ≤ 2

0 2 ≤ Z .

(3.6)

The remaining terms in equation 3.4, the standard deviations of background moisture

and geopotential height errors as well as the vertical correlation structures for moisture and

height, are shown as tables in section 3.2.5.

3.2.2.1.2 Wind. The upper-air analysis in GEOS-4 is multivariate in geopotential height

and wind components. This leads to an immediate complication in equation 3.4, as now covariances among all the interrelated background variables need to be specified. In principle

17
one could simply recast equation 3.4 for the multivariate case as a 3×3 matrix,













P

f

HH P

f

HU P

f

HV

P

f

UH P

f

UU P

f

UV

P

f

V H P

f

V U P

f

V V













, (3.7)

with each of its elements having the form of equation 3.4. In practice, however, there is

insufficient independent data to obtain all the required parameters for such an approach.

In order to reduce the number of independent parameters needed for the multivariate

statistics, some constraints are imposed on the nature of the wind errors. First, the wind

component errors are decomposed into two parts:

"

u

v

#

=

"

uh

vh

#

+

"

ud

vd

#

, (3.8)

where the subscripts h and d indicate wind component errors that are derived from height

errors and wind component errors that are independent from height errors respectively.

The coupled wind errors are then assumed to have the following dependence on the height

errors:

"

uh

vh

#

=

g

2Ω "

a11(φ, p) a12(φ, p)

a21(φ, p) a22(φ, p)

# " ∂λh

∂φh

#

, (3.9)

where g is Earth’s gravitational acceleration, 2Ω is the Earth’s rotation rate, φ is latitude,

λ is longitude, ∂φ =

∂

a∂φ, and ∂λ =

1

a cos(φ)

∂

∂λ. The decoupled wind component errors are

expressed in terms of streamfunction (ψ) and velocity potential (χ) errors:

"

ud

vd

#

=

"

−∂φ ψ + ∂λ χ

∂λ ψ + ∂φ χ

#

. (3.10)

The statistics for ψ and χ have the same univariate form as equation 3.4 (i.e. now x

ranges over q, h, ψ and χ). Using the shorthand notation hUUi = P

f

UU , and assuming that

hψχi = hhχi = hhψi = 0, it follows from equations 3.8 - 3.10 that the height-wind and

wind-wind terms in equation 3.7 are various combinations of the h, ψ and χ covariances

and their φ− and λ− derivatives. For example, the height-u component (P

f

HU ) element of

3.7 is expressed as:

hHUi =

g

2Ω

Da11(j)hi∂λjh + a12(j)hi∂φjhE

=

g

2Ω



a11(j)∂λj

hHHi

ij + a12(j)∂φj

hHHi

ij

.

The wind-wind covariance elements, while straightforward to derive, are cumbersome in

detail and will not be shown here. See Guo et al. (1998) or Pfaendtner et al. (1995) for

more detailed examples of such wind-mass and wind-wind covariance elements.

There are some additional approximations employed in the modeling of the background

error covariance statistics:

18
• A “homogeneity” approximation for the background height error standard deviations

is used. Although there is a latitudinal variation in these standard deviations (cf.

figures 3.4 and 3.5 in section 3.2.5), the derivatives of these quantities are ignored in

the height-wind and wind-wind covariance modeling.

• The height-wind coupling constants are modeled as follows:

a11 = a22 = A(p) + B(p) exp h− (φ/L(p))2

i

(3.11)

−a12 = a21 = b(p)



1 − exp h− (φ/K(p))2

i / sin φ. (3.12)

The values of A(p), B(p), b(p), K(p), and L(p) are given in Table 3.3 in section 3.2.5.

• The error standard deviations of velocity potential and streamfunction also follow the

homogeneity approximation, and they have a latitudinal structure:

σψ = σs exp h− (φ/J(p))2

i

(3.13)

σχ = σv exp h− (φ/J(p))2

i

. (3.14)

The values of σs, σv, and J(p) are given in Table 3.3 in section 3.2.5.

It should be noted that PSAS employs an implicit implementation of the covariances in

equation 3.7 (see Guo et al. 1998).

3.2.2.2 Observation Error Covariance

The observation error covariances are all univariate for the observing system used in GEOS4. Although in principle the covariance model described by equation 3.4 applies to observations, in practice the situation is much simpler. For all observations except heights and

moisture from rawinsondes and heights from iRET, the correlation terms in equation 3.4

are delta-functions, leaving only the standard deviations. Global constants (usually varying by level) for the observation error standard deviations used in GEOS-4 are given in

section 3.2.5.

3.2.2.2.1 Rawinsonde. Rawinsonde moisture and height observations are assumed to

have no horizontally correlated errors. Rawinsonde height observations are assumed to have

a vertically correlated error structure within the same profile. The table for ν

obs

h

(m, n) used

in GEOS-4.0.3 is given in section 3.2.5.

3.2.2.2.2 TOVS. The observation height error covariance for TOVS retrievals contains

two parts, one having horizontally correlated errors and one with the errors having no

horizontal correlation (here δi,j is equal to 1 if i = j, and 0 otherwise):

P

sat

ij (h; m, n) = σ

oc

i

(m) σ

oc

j

(n) ν

oc(m, n) Bo

ij (m, n) C

o

ij (sat)

+ σ

ou

i

(m) σ

ou

j

(n) ν

ou(m, n) δi,j . (3.15)

Tables of the parameters in 3.15 are given in section 3.2.5. The moisture observations from

TOVS retrievals are currently assumed to have no spatially correlated errors.

19
3.2.3 Observing System

GEOS-4 assimilates observations from a diverse, heterogeneous observing system:

• Land Surface

• Ocean Surface

• Upper-air “conventional” (or in situ)

• Upper-air “satellite” (or remotely sensed).

Often, these data occur at spatial resolutions considerably denser than the analysis grid

used in GEOS-4.0.3 (see 3.2.5.1). In order to eliminate costly and unnecessary averaging, a

thinning procedure is applied to several of these observation types. For the most part, the

observations are thinned using to an equal-area grid with resolution of approximately 1.4

◦

latitude by 1.76◦longitude (at the Equator).

3.2.3.1 Land Surface Observations

Two sources of land surface information are used in GEOS-4. One is surface pressure (reduced to sea level pressure) from the hourly observations taken at land stations having

elevations less than 1000 m above mean sea level. There is a diverse array of land station

observations. GEOS-4 treats two broad categories somewhat differently. The “standard”

land surface observations are limited to a 3-hour window centered on the analysis time;

automated or METAR data are limited to a 1-hour window. These observations are then

converted to geopotential height observations at a pressure level corresponding to the background estimate of the surface pressure. This “recast” observation is obtained by using the

hydrostatic relation in concert with assumptions involving synthetic (“underground”) lapse

rates for grid points above sea level and estimates of the near-surface temperatures taken

from the GCM 6 hour forecast. These data are thinned to the 1.4

◦

latitude by 1.76◦ grid.

A second source of land information is an estimate of Tskin obtained in 1-hour windows

every 3 hours. Although these data are not assimilated, off-line analyses incorporating these

data do influence output diagnostic fields which are of considerable importance to customers

of GMAO products (e.g., CERES). Tskin “observations” are obtained as a byproduct of the

iRET process (see section 3.4).

3.2.3.2 Ocean Surface Observations

GEOS-4 is capable of assimilating sea level pressure and winds from ships, buoys, and ocean

platforms. Observed sea level pressures are recast as geopotential heights, as described in the

previous section, for assimilation in the upper air analysis. Ocean surface wind observations

are modified prior to assimilation to represent layer-mean wind vectors for the lowest model

layer, using a stability-dependent similarity theory for the boundary layer.

In addition to the above sources of conventional sea surface observations, there is a

significant additional source of sea-level wind vectors which are inferred from the backscatter

20
return from space-borne radars: ERS-2 and QuikScat. These data are also converted to

layer-mean winds for the lowest model layer prior to assimilation. These data are thinned

before use in the analysis.

3.2.3.3 Conventional Upper-Air

The predominant source of conventional upper-air data in GEOS-4 is rawinsondes, which

supply height, temperature, wind and moisture information at mandatory levels. Rawinsondes also report “significant” level information, but these data are not assimilated in

GEOS-4. The PSAS analysis in GEOS-4 assimilates heights (not temperatures) and wind

components; the moisture information is assimilated independently of temperature and

wind.

While rawinsonde data are quite valuable, providing profiles of mass and wind information, they are very anisotropically distributed, favoring land over ocean and the Northern

Hemisphere over the Southern Hemisphere. The rawinsonde network is augmented to a

limited degree by dropwinsondes (from aircraft) and pilot balloons (which provide low-level

wind profiles of lesser accuracy).

Another significant source of conventional upper-air wind data is aircraft winds. These

data are also highly anisotropic, and they have the additional characteristic of being singlelevel data. As these data are extremely dense over North America and Europe, they are

thinned to the 1.4

◦

latitude by 1.75◦ grid.

3.2.3.4 Satellite Upper-Air

Remotely sensed information from satellites typically offers much greater and more isotropic

coverage than that from conventional in situ sources. GEOS-4 utilizes three major types of

satellite data: height and moisture profiles obtained from layer mean retrievals using TOVS

radiance data; single level cloud motion vector winds obtained from geostationary satellite

images; and column Total Precipitable Water (TPW) obtained from the SSM/I instrument

on board the DMSP series of satellites.

3.2.3.4.1 TOVS. TOVS consists of three separate sounding instruments:

• High-resolution Infrared Radiation Sounder 2 (HIRS2), later HIRS3;

• Microwave Sounding Unit (MSU), later the Advanced Microwave Sounding Unit

(AMSU);

• Stratospheric Sounding Unit (SSU), also replaced by AMSU.

TOVS has flown on the TIROS-N satellite and on National Oceanic and Atmospheric Administration (NOAA) operational polar-orbiting environmental satellites (POES) 6-12 and

14. NOAA 10 and 12 did not have an SSU instrument. The Advanced TOVS instrument

(ATOVS), consisting of HIRS3 and AMSU, has been launched on the latest series of NOAA

satellites; NOAA-15, 16 and 17.

21
The TOVS instruments measure the radiance from Earth passively in spectral elements

or channels. The measured radiance includes thermal emission in the microwave and infrared channels and reflected solar radiation in the visible and shorter-wavelength infrared

channels. Radiance is commonly expressed in terms of equivalent blackbody temperature (brightness temperature), as brightness temperature behaves more linearly with atmospheric temperature and other parameters than does the radiance. HIRS2 has 19 infrared

channels with center frequencies ranging from approximately 670 to 2660 cm−1 along with

a single visible channel. MSU has 4 channels centered near the 57 GHz oxygen cluster. SSU

employs the pressure modulation technique to measure stratospheric emission in 3 channels

of the 15 μm CO2 band.

GEOS-4 employs an interactive retrieval process (iRET, see section 3.4) that produces

layer mean temperatures and moisture, as well as an estimate of the skin temperature

(Tskin, used in the off-line surface analysis mentioned in section 3.2.3.1). The GEOS-4

analysis requires that temperature observation profiles need to be converted to geopotential

height profiles (using the hydrostatic relation, and “anchoring” the profile with either the

background surface pressure, or the background 250 hPa height for microwave-only retrieved

profiles above the cloud-clearing cut-off level).

3.2.3.4.2 CTW. Cloud track winds (also known as cloud-drift winds, as well as atmospheric motion vectors, AMV) are observations derived from sequences of images observed

by satellites. The winds are calculated by an objective procedure that selects targets, assigns pressure altitude, and calculates atmospheric motion from the motion of the selected

targets in successive images.

Currently the GEOS-4 assimilation system uses cloud-track winds from geostationary

satellites produced by NESDIS, JMA, and EUMETSAT; data from these sources provides

near global coverage of AMV winds, equatorward of 60 degrees. The geostationary satellite

winds from two layers (surface-700 hPa and above 400 hPa) are used, and are thinned using

an equal-area grid with resolution of approximately 1.4

◦

latitude by 1.75◦longitude (at the

Equator).

The GEOS-4 system has enabled the use of winds from the MODIS instruments on

the Terra and Aqua platforms. MODIS winds from all levels are used, except for the

winds within 2 degrees of the poles, and they should provide coverage that complements

that provided by the geostationary satellite winds. These winds are also thinned to the

1.4

◦ by 1.75◦

resolution. The specific impact of the MODIS winds is not a major issue in

this validation, and it will be addressed in future testing (beyond the scope of the current

validation effort).

3.2.3.4.3 TPW. The GEOS-4 assimilation system uses total precipitable water (TPW)

data from the SSM/I (Special Sensor Microwave / Imager) instrument on the DMSP series

of satellites. The 22.235 GHz channel on the SSM/I instrument is sensitive to atmospheric

water vapor and can be used along with the other channels on the instrument to derive the

water vapor in the column between the sea surface and the satellite.

The SSM/I TPW data used in GEOS-4 comes from two sources. The first-look assimilation uses TPW data obtained from NCEP. The NCEP TPW observations are derived

22
using the Neural Net 3 algorithm (Krasnopolsky et al. 2000). The SSM/I TPW retrievals

are superobbed by NCEP to a 1◦ × 1

◦

resolution. A website with information about the

Neural Net 3 algorithm is

http://polar.ncep.noaa.gov/winds/NNs/OMBNN3.html .

The late-look assimilation (see Chapter 2) uses SSM/I TPW data produced by Remote

Sensing Systems using the Wentz algorithm (Wentz 1997). The SSM/I retrievals are currently produced using Version 5 of the algorithm. For the late-look assimilation we use

the interim product files (file name with “rt”) that are produced in near real time. Other

assimilations (such as the CERES reanalysis) use the SSM/I final product (file name with

“v5”). Information about the Remote Sensing Systems Wentz SSM/I products can be found

at their website:

http://www.ssmi.com/ssmi/ssmi description.html .

The TPW retrievals are thinned on input to GEOS-4.0.3 to the 1.4

◦

latitude by 1.75◦

longitude grid. These data are analyzed in GEOS-4 separately from all other data, as follows. First a two-dimensional statistical analysis of TPW is performed, which combines the

data with an estimate of TPW obtained from the background moisture field. This produces

a global estimate of TPW. A three-dimensional moisture increment is then constructed

which, when added to the background moisture field, corresponds exactly to this global

TPW estimate. The vertical distribution of the moisture increment is determined on the

basis of the background error covariance for moisture; i.e., moisture is added where the

uncertainty is presumed to be greatest. Typically the moisture increments due to TPW

data peak around the 850 hPa level, due to the fact that the uncertainties are largest there

and that the prescribed vertical correlation functions for moisture (shown by Table 3.6 in

section 3.2.5) are rather sharp. The TPW analysis has provisions for limiting the impact

of TPW moisture information on stratospheric moisture.

3.2.4 Skin Temperature Analysis

Tskin plays a very important role in radiation budget calculations. It has been determined

over the course of the development from fvDAS to GEOS-4 that the upper-air data are

insufficient to constrain biases in Tskin which arise from inaccuracies in the surface parameterizations. A two-pronged approach has been taken to address this issue for the final

version of GEOS-4: an improved land surface model (see 3.1.2.2.3); and an off-line Tskin

analysis utilizing the data described in 3.2.3.1.

This is an off-line analysis, i.e., it only generates a diagnostic output field that does not

feed back into the assimilation process.

3.2.5 Analysis Details for GEOS-4.0.3

There are a large number of parameters that govern the behavior of the GEOS-4.0.3 analysis.

The tables of error standard deviations, correlation coefficients and other tuning parameters

largely reflect the estimation processes used their construction. Values needed at other levels

(e.g. 925 hPa) are obtained by interpolating the relevant table entries.

23
3.2.5.1 GEOS-4.0.3 analysis grid

The cost of the “matrix-multiply” step of the analysis (equation 3.2) is roughly comparable

to the “matrix-solve” step (equation 3.1). Since the cost of the latter step scales as the

number of output grid-points, and since the scales of the analysis increments are fairly

broad (as can be seen by looking at the values of L in Table 3.3), it follows that the analysis

can be output to a horizontal grid having a resolution of 2◦ × 2.5

◦

, thereby improving the

overall cost of the analysis without a large penalty in accuracy. The analysis increments

are then interpolated to 1◦ × 1.25◦to prepare the initial state for the forecast model.

Table 3.2.5.1 shows the vertical analysis grid for GEOS-4.0.3 as well as the one from

the previous GEOS-4.0.2 version. The current version of GEOS performs the step in equation 3.2 to all of the model levels below roughly 30 hPa. Quantities at all of the other levels

are obtained via vertical interpolation.

GEOS-4.0.3 GEOS-4.0.2 GEOS-4.0.3 GEOS-4.0.2

LAYER MEAN LAYER MEAN LAYER MEAN LAYER MEAN

ID PRESS ID PRESS ID PRESS ID PRESS

[hPa] [hPa] [hPa] [hPa]

1 0.015 1 0.015 29 22. 29 22.

x 2 0.026 x 2 0.026 x 30 26. x 30 26.

x 3 0.040 x 3 0.040 31 31. 31 31.

x 4 0.057 x 4 0.057 32 37. x 32 37.

x 5 0.078 x 5 0.078 33 44. x 33 44.

6 0.10 6 0.10 34 52. 34 52.

x 7 0.14 x 7 0.14 35 61. x 35 61.

x 8 0.19 x 8 0.19 36 73. 36 73.

x 9 0.25 x 9 0.25 37 85. x 37 85.

x 10 0.32 x 10 0.32 38 100. 38 100.

11 0.42 11 0.42 39 118. x 39 118.

x 12 0.55 x 12 0.55 40 139. 40 139.

x 13 0.71 x 13 0.71 41 163. x 41 163.

14 0.91 14 0.91 42 192. 42 192.

x 15 1.2 x 15 1.2 43 225. x 43 225.

x 16 1.5 x 16 1.5 44 265. 44 265.

17 1.9 17 1.9 45 310. 45 310.

x 18 2.4 x 18 2.4 46 365. x 46 365.

19 2.9 x 19 2.9 47 429. 47 429.

20 3.7 20 3.7 48 504. 48 504.

x 21 4.6 x 21 4.6 49 592. x 49 592.

22 5.6 22 5.6 50 687. 50 687.

23 6.9 x 23 6.9 51 776. x 51 776.

x 24 8.5 x 24 8.5 52 854. 52 854.

25 10. 25 10. 53 914. 53 914.

x 26 12. x 26 12. 54 955. 54 955.

27 15. 27 15. 55 977. 55 977.

x 28 18. x 28 18.

Table 3.2: Comparison of the vertical analysis levels, GEOS-4.0.3 and GEOS-4.0.2. An “x” denotes

an omitted level, quantities on these levels are obtained by interpolation from the other levels.

24
3.2.5.2 Background Error Covariance Model Parameters

Table 3.3 contains the length scales, standard deviations and coupling coefficients required

for the background error covariance models. Note, the values of σh in this table are only

representative of the background height error standard deviations; Figures 3.4 and 3.5 show

the background error standard deviation structure, σ

f

h

(φ), used in GEOS-4.0.3. In the

troposphere (Figure 3.4) the largest values occur at the extra-tropical jetstream levels, with

a pronounced minimum in the tropics. In the upper levels of the model (Figure 3.5), the

largest values occur in the equatorial region.

mass-coupled parms standard deviations length scales [km]

p [hPa] A(p) B(p) b(p) σh σs σv σq Lh Lψ Lχ Lq

0.01 0. 0. 0.30 77.208 40. 10. 0.147 7026.5 1100. 1000. 287.

0.2 0. 0. 0.30 62.109 40. 10. 0.147 7026.5 1100. 1000. 287.

0.4 0. 0. 0.30 50.544 40. 10. 0.147 7026.5 1100. 1000. 287.

0.5 0. 0. 0.30 47.907 40. 10. 0.147 5267.9 1100. 1000. 287.

0.7 0. 0. 0.30 42.228 40. 10. 0.147 4025.7 1100. 1000. 287.

1. 0. 0. 0.30 38.455 40. 10. 0.147 3303.1 1100. 1000. 287.

2. 0. 0. 0.30 37.368 40. 10. 0.147 2503.7 1100. 1000. 287.

3. 0. 0. 0.30 34.783 40. 10. 0.147 2201.1 1100. 1000. 287.

5. 0. 0. 0.30 33.538 40. 10. 0.147 1907.3 1100. 1000. 287.

7. 0. 0. 0.30 32.973 40. 10. 0.147 1749.1 1100. 1000. 287.

10. 0. 0. 0.30 27.872 35.1 11.7 0.147 1603.1 1100. 1000. 287.

20. 0. 0. 0.30 25.498 28.3 11.9 0.147 1365.1 1000. 1000. 287.

30. 0. 0. 0.30 23.964 21.3 10.6 0.147 1246.3 920. 1000. 287.

50. 0. 0. 0.40 22.902 19.1 12.5 0.147 1112.4 860. 1000. 287.

70. 0. 0. 0.50 21.204 18.3 14.9 0.147 1031.9 800. 1000. 287.

100. 0. 0. 0.60 20.315 19.3 19.3 0.147 952.0 750. 1000. 287.

150. 0. 0. 0.70 21.306 23.8 23.8 0.147 866.9 690. 1000. 287.

200. 0. 0. 0.80 23.274 24.4 24.4 0.147 809.6 640. 1000. 287.

250. 0. 0.1 0.80 24.088 20.4 20.4 0.147 766.8 610. 1000. 287.

300. 0. 0.25 0.80 23.241 16.9 16.9 0.147 732.8 600. 1000. 287.

400. 0. 0.4 0.80 19.088 16.6 16.6 0.147 680.6 610. 1000. 287.

500. 0. 0.45 0.80 15.758 15.8 15.8 0.147 641.3 620. 1000. 287.

700. 0. 0.5 0.80 10.168 15.2 15.2 0.135 583.8 640. 1000. 287.

850. 0.07 0.6 0.80 7.510 13.9 15.4 0.122 551.5 660. 1000. 287.

925. 0.12 0.7 0.66 7.183 12.6 15.5 0.110 537.6 680. 1000. 287.

1000. 0.20 0.8 0.45 7.494 12. 14.7 0.110 524.9 700. 1000. 287.

1040. n.a n.a n.a 7.878 n.a n.a 0.073 n.a n.a n.a n.a

Table 3.3: GEOS-4.0.3 background error covariance model parameters, described in section 3.2.2.1.

The length scales are used by the Power Law function, equation 3.6. The moisture pseudo-relative

humidity analysis uses a smaller “window” in equation 3.6, i.e., 2c = 3000 km for moisture. The

mass-coupled parameters are used in equations 3.11- 3.12. Note that the J(p),K(p), and L(p) terms

in equations 3.11, 3.12, 3.13, and 3.14 are constant for all levels: K(p) = L(p) = 0.4 radians, and

J(p) = 4 radians.

25
Figure 3.4: Background height error standard deviation, σ

o

h

(φ), with a focus primarily on the

troposphere. Units are m.

Figure 3.5: As in figure 3.4, only now showing the whole model domain.

26
1040 1.000

1000 0.990 1.000

925 0.911 0.952 1.000

850 0.764 0.819 0.944 1.000

700 0.452 0.495 0.662 0.835 1.000

500 0.270 0.265 0.369 0.527 0.797 1.000

400 0.222 0.200 0.280 0.424 0.681 0.947 1.000

300 0.182 0.152 0.221 0.355 0.583 0.834 0.940 1.000

250 0.176 0.146 0.216 0.348 0.562 0.786 0.887 0.975 1.000

200 0.182 0.158 0.229 0.356 0.558 0.751 0.828 0.908 0.962 1.000

150 0.199 0.186 0.254 0.366 0.547 0.707 0.748 0.793 0.849 0.932 1.000

100 0.209 0.211 0.264 0.338 0.466 0.573 0.571 0.577 0.617 0.701 0.840 1.000

70 0.200 0.212 0.251 0.293 0.371 0.430 0.403 0.396 0.436 0.521 0.656 0.875 1.000

50 0.185 0.203 0.232 0.253 0.297 0.321 0.278 0.262 0.304 0.397 0.539 0.740 0.903 1.000

40 0.177 0.198 0.222 0.234 0.262 0.270 0.219 0.198 0.240 0.336 0.487 0.690 0.834 0.962

30 0.166 0.189 0.208 0.211 0.226 0.219 0.162 0.135 0.175 0.271 0.428 0.643 0.770 0.875

20 0.152 0.176 0.190 0.185 0.186 0.167 0.106 0.076 0.112 0.204 0.357 0.581 0.713 0.790

10 0.128 0.152 0.160 0.148 0.137 0.108 0.048 0.018 0.050 0.131 0.269 0.475 0.607 0.691

7 0.126 0.150 0.158 0.143 0.127 0.095 0.033 0.001 0.032 0.114 0.254 0.462 0.595 0.681

5 0.122 0.146 0.152 0.135 0.117 0.082 0.020 -0.012 0.018 0.098 0.236 0.442 0.574 0.658

3 0.113 0.136 0.141 0.123 0.102 0.066 0.005 -0.026 0.002 0.078 0.209 0.407 0.535 0.615

2 0.110 0.133 0.137 0.118 0.095 0.056 -0.004 -0.036 -0.008 0.066 0.196 0.393 0.520 0.600

1 0.100 0.122 0.125 0.105 0.080 0.041 -0.017 -0.047 -0.022 0.048 0.169 0.354 0.473 0.549

0.400 0.088 0.109 0.110 0.090 0.064 0.026 -0.028 -0.057 -0.034 0.029 0.139 0.308 0.417 0.487

0.200 0.084 0.104 0.104 0.083 0.056 0.017 -0.036 -0.064 -0.043 0.018 0.124 0.289 0.396 0.463

0.050 0.068 0.085 0.085 0.065 0.039 0.002 -0.043 -0.068 -0.050 0.000 0.090 0.230 0.322 0.380

0.010 0.055 0.069 0.068 0.050 0.025 -0.008 -0.048 -0.069 -0.055 -0.013 0.063 0.181 0.259 0.308

40 1.000

30 0.944 1.000

20 0.833 0.905 1.000

10 0.725 0.754 0.797 1.000

7 0.719 0.753 0.790 0.951 1.000

5 0.695 0.731 0.773 0.904 0.959 1.000

3 0.650 0.684 0.725 0.857 0.882 0.918 1.000

2 0.635 0.668 0.708 0.839 0.865 0.886 0.951 1.000

1 0.582 0.613 0.650 0.767 0.795 0.820 0.861 0.881 1.000

0.400 0.517 0.545 0.579 0.683 0.706 0.727 0.768 0.791 0.839 1.000

0.200 0.492 0.520 0.553 0.651 0.674 0.694 0.732 0.753 0.806 0.919 1.000

0.050 0.405 0.428 0.456 0.536 0.555 0.571 0.603 0.620 0.661 0.761 0.787 1.000

0.010 0.329 0.349 0.372 0.437 0.453 0.466 0.491 0.505 0.537 0.618 0.637 0.824 1.000

Table 3.4: Vertical height background error correlations, νfh(m,n) in equation 3.4.

27
1000 1.00

925 0.91 1.00

850 0.66 0.89 1.00

700 0.13 0.29 0.55 1.00

500 0.00 0.00 0.01 0.16 1.00

400 0.00 0.00 0.00 0.01 0.45 1.00

300 0.00 0.00 0.00 0.00 0.02 0.27 1.00

250 0.00 0.00 0.00 0.00 0.00 0.03 0.59 1.00

200 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.45 1.00

150 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.27 1.00

100 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 1.00

70 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 1.00

50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.16 1.00

40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.45 1.00

30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.27 1.00

20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07

15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

7 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.700 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.500 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.400 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.200 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.050 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.010 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

20 1.00

15 0.27 1.00

10 0.00 0.07 1.00

7 0.00 0.00 0.13 1.00

5 0.00 0.00 0.00 0.16 1.00

3 0.00 0.00 0.00 0.00 0.02 1.00

2 0.00 0.00 0.00 0.00 0.00 0.07 1.00

1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

0.700 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 1.00

0.500 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.16 1.00

0.400 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.45 1.00

0.200 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

0.050 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

0.010 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

Table 3.5: Background decoupled wind error (both χ and ψ): vertical correlations, νfχ,ψ(m,n) in equation 3.4.

28
1000 1.00

925 0.23 1.00

850 0.00 0.37 1.00

700 0.00 0.16 0.32 1.00

500 0.00 0.00 0.00 0.28 1.00

400 0.00 0.00 0.00 0.00 0.42 1.00

300 0.00 0.00 0.00 0.00 0.14 0.44 1.00

250 0.00 0.00 0.00 0.00 0.00 0.22 0.37 1.00

200 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

150 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

100 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

70 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

7 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.700 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.500 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.400 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.200 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.050 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.010 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

20 1.00

15 0.00 1.00

10 0.00 0.00 1.00

7 0.00 0.00 0.00 1.00

5 0.00 0.00 0.00 0.00 1.00

3 0.00 0.00 0.00 0.00 0.00 1.00

2 0.00 0.00 0.00 0.00 0.00 0.00 1.00

1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

0.700 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

0.500 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

0.400 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 1.00

0.200 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

0.050 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

0.010 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

Table 3.6: Background moisture error vertical correlations, νfq (m,n) in equation 3.4.

29
Tables 3.4 - 3.6 contain the vertical correlations used in the background error covariances.

Note that the vertical correlation structure for the decoupled wind components and moisture

have a scale of roughly 1 km in the vertical; heights (and thus the coupled wind components)

have a considerably broader vertical correlation structure. The covariance parameters for

levels not specified in the tables are obtained through interpolation.

3.2.5.3 Observation Error Covariance Model Parameters

The observation error standard deviations used in GEOS-4.0.3 are grouped into three tables: upper-air profile data from TOVS and rawinsondes in Table 3.7; observations from

surface locations (ships, QuikScat, land stations) in Table 3.8; and single-level upper-air

wind observations from cloud tracked winds and various aircraft reports in Table 3.9. One

additional item, not in the tables, is the observation error standard deviation assigned to

the SSM/I TPW observations: σ

TPW

q = 0.06.

Tables 3.10, 3.11 and 3.12 contain the vertical error correlations for the profile observations. Note that the (horizontally) uncorrelated TOVS errors have a slightly different

vertical correlation than do the correlated TOVS errors (3.11 vs 3.12).

TOVS Rawinsonde

p [hPa] σ

o

q σ

o

h

(u, c) σ

o

h

(tot) σ

o

h

σ

o

u σ

o

q

0.4 . 43.3 (61.2) . . .

1. . 25.8 (36.5) . . .

2. . 22.8 (32.2) 100.0 2.7 .

5. . 20.6 (29.1) 60.0 2.7 .

10. . 15.5 (21.9) 35.8 2.7 .

30. . 12.3 (17.4) 27.7 2.7 .

50. . 10.0 (14.1) 23.5 2.7 .

70. . 10.2 (14.4) 21.9 2.7 .

100. 0.12 10.5 (14.8) 19.3 2.7 0.19

150. 0.12 10.9 (15.4) 16.3 2.7 0.18

200. 0.12 10.3 (14.6) 14.5 3.3 0.17

250. 0.12 9.5 (13.4) 13.5 3.4 0.16

300. 0.12 9.1 (12.9) 12.8 3.4 0.15

400. 0.11 7.6 (10.8) 10.8 3.2 0.14

500. 0.10 6.1 ( 8.6) 8.6 2.7 0.13

700. 0.09 4.4 ( 6.2) 6.2 2.3 0.12

850. 0.09 4.0 ( 5.7) 5.6 2.2 0.11

1000. 0.06 3.8 ( 5.4) 5.4 2.0 0.10

1040. 0.06 3.7 ( 5.2) 5.2 2.0 0.10

Table 3.7: Observation error standard deviations for the two sources of vertical profile data used in

GEOS-4. Units are m for h and ms−1for wind (u and v components the same). Also, the correlated

and uncorrelated TOVS height error standard deviations are the same; σ

o

h

(tot) is simply √

2 times

σ

o

h

(u,c), and is included for comparison with the rawinsonde entries for σh.

30
p [hPa] σ

o

h

σ

o

u σ

o

v σ

o

q

400 . 3.2 3.4 .

500 8.6 2.7 3.2 0.10

700 6.2 2.3 2.7 0.09

850 5.6 2.2 2.3 0.09

1000 5.4 2.0 2.2 0.06

1040 5.2 2.0 2.2 0.06

Table 3.8: Observation error standard deviations for data from surface sources: ships, buoys,

QuikScat winds, and land stations. Units: m for heights, ms−1for wind.

p [hPa] Pibal ASDAR Airep ACARS CTW

10 3.0 2.3 4.0 2.3 2.70

30 3.0 2.3 4.0 2.3 2.70

50 3.0 2.3 4.0 2.3 2.70

70 3.0 2.3 4.0 2.3 2.70

100 3.0 2.3 4.0 2.3 2.70

150 2.9 2.3 4.0 2.3 2.70

200 2.8 2.3 4.0 2.3 2.70

250 2.7 2.3 4.0 2.3 2.45

300 2.6 2.3 4.0 2.3 2.20

400 2.6 2.4 4.0 2.4 1.90

500 2.6 2.5 3.9 2.5 1.70

700 2.6 2.5 3.8 2.5 1.60

850 2.6 2.5 3.6 2.5 1.50

1000 2.6 2.5 3.5 2.5 1.50

1040 2.6 2.5 3.5 2.5 1.50

Table 3.9: Single-level upper-air wind observation error standard deviations, u and v components

the same, units are ms−1.

31
1040 1.00

1000 0.77 1.00

925 0.54 0.77 1.00

850 0.38 0.54 0.77 1.00

700 0.34 0.38 0.53 0.67 1.00

500 0.29 0.34 0.37 0.39 0.57 1.00

400 0.20 0.29 0.30 0.31 0.43 0.76 1.00

300 0.15 0.20 0.21 0.22 0.31 0.58 0.82 1.00

250 0.11 0.15 0.16 0.17 0.25 0.48 0.69 0.88 1.00

200 0.06 0.11 0.12 0.12 0.19 0.39 0.56 0.76 0.87 1.00

150 0.00 0.06 0.07 0.08 0.13 0.30 0.44 0.59 0.67 0.83 1.00

100 0.00 0.00 0.00 0.03 0.07 0.18 0.31 0.42 0.47 0.61 0.80 1.00

70 0.00 0.00 0.00 0.00 0.04 0.12 0.21 0.31 0.35 0.47 0.68 0.76 1.00

50 0.00 0.00 0.00 0.00 0.00 0.07 0.15 0.22 0.25 0.38 0.52 0.61 0.79 1.00

40 0.00 0.00 0.00 0.00 0.00 0.05 0.12 0.17 0.20 0.31 0.44 0.48 0.65 0.85 1.00

30 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.13 0.14 0.23 0.36 0.36 0.52 0.69 0.89 1.00

20 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.08 0.09 0.15 0.24 0.24 0.39 0.54 0.72 0.82 1.00

10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.08 0.09 0.15 0.24 0.24 0.39 0.54 0.72 0.82 1.00

Table 3.10: Rawinsonde height error vertical correlations, νoh(m,n) in equation 3.4.

32
1000 1.00

925 0.85 1.00

850 0.71 0.85 1.00

700 0.54 0.68 0.82 1.00

500 0.37 0.47 0.58 0.83 1.00

400 0.29 0.37 0.45 0.68 0.86 1.00

300 0.20 0.26 0.32 0.52 0.63 0.81 1.00

250 0.16 0.21 0.26 0.42 0.52 0.71 0.83 1.00

200 0.12 0.16 0.20 0.32 0.41 0.57 0.66 0.80 1.00

150 0.08 0.11 0.14 0.22 0.29 0.42 0.49 0.59 0.75 1.00

100 0.03 0.04 0.07 0.12 0.18 0.27 0.32 0.39 0.49 0.66 1.00

70 0.00 0.00 0.03 0.06 0.11 0.18 0.22 0.26 0.34 0.46 0.70 1.00

50 0.00 0.00 0.00 0.02 0.06 0.12 0.15 0.18 0.24 0.32 0.50 0.72 1.00

40 0.00 0.00 0.00 0.00 0.03 0.09 0.11 0.14 0.19 0.25 0.39 0.57 0.80 1.00

30 0.00 0.00 0.00 0.00 0.00 0.06 0.08 0.10 0.14 0.19 0.29 0.43 0.60 0.80

20 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.05 0.09 0.12 0.19 0.29 0.40 0.53

15 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.03 0.05 0.08 0.14 0.22 0.30 0.40

10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.09 0.15 0.20 0.26

7 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.10 0.14 0.19

5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.08 0.10 0.13

3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.05 0.08

2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05

1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03

0.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

30 1.00

20 0.66 1.00

15 0.50 0.75 1.00

10 0.33 0.50 0.67 1.00

7 0.23 0.35 0.47 0.70 1.00

5 0.16 0.25 0.33 0.50 0.71 1.00

3 0.09 0.15 0.20 0.31 0.43 0.63 1.00

2 0.06 0.10 0.13 0.21 0.29 0.44 0.70 1.00

1 0.00 0.03 0.07 0.11 0.15 0.25 0.41 0.57 1.00

0.70 0.00 0.00 0.05 0.08 0.10 0.20 0.32 0.44 0.76 1.00

0.50 0.00 0.00 0.03 0.06 0.08 0.16 0.26 0.36 0.60 0.78 1.00

0.40 0.00 0.00 0.03 0.05 0.06 0.14 0.23 0.32 0.52 0.66 0.85 1.00

Table 3.11: TOVS uncorrelated height error vertical correlations, νoh(m,n) in equation 3.4.

33
1000 1.00

925 0.84 1.00

850 0.68 0.84 1.00

700 0.53 0.67 0.81 1.00

500 0.35 0.46 0.56 0.81 1.00

400 0.27 0.35 0.44 0.64 0.79 1.00

300 0.18 0.25 0.32 0.46 0.58 0.75 1.00

250 0.13 0.19 0.25 0.38 0.48 0.62 0.83 1.00

200 0.09 0.14 0.19 0.29 0.38 0.49 0.66 0.80 1.00

150 0.06 0.09 0.13 0.20 0.28 0.36 0.49 0.59 0.75 1.00

100 0.00 0.03 0.06 0.10 0.17 0.23 0.32 0.39 0.50 0.75 1.00

70 0.00 0.00 0.03 0.04 0.11 0.16 0.22 0.27 0.34 0.52 0.79 1.00

50 0.00 0.00 0.00 0.00 0.07 0.11 0.15 0.19 0.24 0.37 0.56 0.71 1.00

40 0.00 0.00 0.00 0.00 0.05 0.08 0.11 0.15 0.19 0.29 0.44 0.57 0.80 1.00

30 0.00 0.00 0.00 0.00 0.03 0.05 0.08 0.11 0.14 0.22 0.33 0.43 0.60 0.80

20 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.07 0.09 0.14 0.22 0.28 0.40 0.53

15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.07 0.10 0.16 0.21 0.30 0.40

10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.07 0.10 0.14 0.20 0.27

7 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.06 0.10 0.14 0.19

5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.03 0.07 0.10 0.13

3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.05 0.08

2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.03 0.05

1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03

0.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

30 1.00

20 0.67 1.00

15 0.50 0.75 1.00

10 0.34 0.50 0.67 1.00

7 0.24 0.35 0.47 0.70 1.00

5 0.17 0.25 0.34 0.50 0.72 1.00

3 0.10 0.15 0.20 0.31 0.44 0.62 1.00

2 0.07 0.10 0.14 0.21 0.30 0.43 0.69 1.00

1 0.00 0.04 0.07 0.11 0.16 0.23 0.39 0.55 1.00

0.70 0.00 0.00 0.05 0.08 0.12 0.18 0.29 0.42 0.74 1.00

0.50 0.00 0.00 0.04 0.06 0.09 0.14 0.23 0.33 0.57 0.78 1.00

0.40 0.00 0.00 0.03 0.05 0.08 0.12 0.20 0.28 0.48 0.67 0.86 1.00

Table 3.12: TOVS correlated height error vertical correlations, νoh(m,n) in equation 3.4.

34
3.3 Quality Control

The PSAS algorithm (like other analyses based on Estimation Theory constructs) makes

strong assumptions about the statistical nature of the errors in both the forecast fields and

in the observations. Observations that clearly violate these statistical assumptions (i.e.,

“outliers”) must be identified and removed from the analysis process; this is done through

a Quality Control (QC) process.

GEOS-4 employs an on-line Statistical Quality Control (SQC) system (Dee et al. 2001)

that seeks to identify observations that are likely to be contaminated by gross errors. Its algorithms involve statistical tests of the actual data against assumptions about their expected

errors and about GCM forecast errors. Essentially, a local statistical analysis is performed

for each outlier observation, i.e., for each observation that differs significantly from the

short-term forecast produced by the GCM. If this analysis indicates that the observation is

inconsistent with surrounding data, then that observation is marked for rejection.

The SQC encompasses a background check, a buddy check, a wind check, and a profile

check, each of which is described below. All checks are formulated in terms of the observedminus-forecast residuals (O-F) rather than the observations themselves. All checks potentially modify the quality control marks associated with the observations, but leave all other

data attributes unchanged. The background check and buddy check involve the forecast

and observation error variances for the quantities being tested, which are prescribed in the

global analysis system.

3.3.1 Statistical Aspects

The SQC algorithms operate on the vector of observed-minus-forecast residuals, v, defined

by

v = w

o − f(Iwf

), (3.16)

where w

o

, w

f

, f, and I are defined in 3.2.1. The observation operator, f, maps model variables to observables. For remotely sensed radiances, for example, the function f represents

a radiative transfer model. It is simply the identity for conventional, in situ observations of

model variables.

The SQC attempts to identify corrupt data based on statistical expectations. This

requires knowledge of the covariance S of the observed-minus-forecast residuals, defined by

Sij = hvivj i, (3.17)

with i, j indicating location. In general these covariances are poorly known, but a rough

estimate is available from the global analysis system. It follows from (3.16) that

S ≈ FIP

f

I

TFT + R, (3.18)

where F is the linearized observation operator,

F =

∂f

∂w









w=wf

, (3.19)

35
and P

f

, R are the covariances of forecast and observation errors, respectively. Equation (3.18) would be exact if forecast and observation errors were entirely independent

(they are not, since both types of errors depend on the true state) and if all observation

operators were linear.

If the prescribed error statistics in the global analysis system are reasonably accurate,

then the right-hand side of (3.18) can be presumed to provide some useful information about

the residual error covariances. Accordingly, prescribed error statistics are used to define

tolerances for the background check, whose main purpose is to mark outlier observations for

subsequent reexamination in the buddy check. However, since actual errors depend on many

unknown model defects and other intangibles, covariance specifications in operational data

assimilation systems cannot be relied upon to accurately describe error characteristics in all

situations at all times. In particular, during extreme events—when quality control decisions

become especially important—the covariances as prescribed by the global analysis system

are almost certainly inadequate. Thus, a key aspect of the SQC is the attempt to adjust

the prescribed error statistics based on actual data. This adjustment takes place during the

buddy check, before a final accept/reject decision is reached for an outlier observation.

3.3.2 The Background Check

The background check tests each single observation against a background estimate, which

is simply the 6-hour model forecast interpolated to the time and location of the observation. If the discrepancy is extremely large then the observation is rejected outright. If the

discrepancy is large, but within some specified rejection tolerance, then the observation is

marked as “suspect” or a potential outlier, to be reexamined in the buddy check. The

tolerances for the background check are defined in terms of standard deviations obtained

from the error statistics as prescribed by the global analysis system.

The algorithm is as follows:

For each observation w

o

i

:

mark w

o

i

as an outlier if |vi| > τoσi,

mark w

o

i

as excluded if |vi| > τxσi.

Here σi =

√

Sii, and τs, τx are prescribed non-dimensional tolerance parameters. Typically

we take τo = 2, τx = 10.

The rate at which the background check produces suspect marks presents a useful check

on the accuracy of the prescribed error statistics. If the forecast and observation error

variances are correctly tuned, and if the errors are roughly normally distributed, then the

suspect rate can be predicted. For example, when τo = 2, the rate should be about 4.5%.

If the actual suspect rate is larger (smaller), then the prescribed error variance is too small

(large). This is illustrated in Fig. 3.6. Monitoring the background check failure rates for

specific instruments has, in a number of cases, led to adjustments of observation error

statistics in GEOS DAS.

36
−4 −3 −2 −1 0 1 2 3 4

0

200

400

600

Prescribed errors too small

Clear 

Suspect (13.2%) 

−4 −3 −2 −1 0 1 2 3 4

0

200

400

600

Prescribed errors just right

Clear 

Suspect (4.51%) 

−4 −3 −2 −1 0 1 2 3 4

0

200

400

600

Prescribed errors too large

Clear 

Suspect (1.18%) 

Figure 3.6: Illustration of the relationship between the rate at which the background check marks

observations as outliers and the prescribed error statistics, for normally distributed errors. The

yellow tails of the histograms correspond to observations marked as outliers.

3.3.3 The Buddy Check

The buddy check is applied to a subset of observations which are considered suspect, either

because they were identified as outliers by the background check, or because they were

marked as suspect during the preprocessing stage. The buddy check attempts to predict

the value of a suspect observation from nearby non-suspect observations (the buddies.) If

the predicted value is in reasonable agreement (defined below) with the observation, then

the observation is no longer considered suspect. If a sufficient number of buddies is available,

then the tolerance for the buddy check is adjusted based on a local estimate of O-F standard

deviations. Once all suspect observations have been tested, the entire process is repeated

for all observations that are still considered suspect. The process stops when the set of

suspects no longer changes: all remaining suspects are then rejected.

The buddy check initially labels observations as suspect based on their quality control

history. A single iteration of the algorithm proceeds as follows:

For each suspect observation w

o

j

:

37
1. Define the set of buddies:

Nearby non-suspect observations of the same data type as w

o

j

are ranked according to

the scalar weight that each would receive in an optimal univariate statistical analysis

at the location of w

o

j

. The buddies are simply the n highest ranking of these, where

n is a configuration parameter. Typically n = 50.

2. Predict the value of the suspect observation based on its buddies:

Using the weights determined in the previous step, the weighted average v

⋆

j

of the vi

associated with the buddies provides the optimal univariate analysis of the buddies

at the location of w

o

j

.

3. Adjust the prescribed estimate of the local O-F standard deviation:

If σb

2

j

is the sample variance of the vi associated with the buddies, the prescribed

variance σ

2

j

is adjusted according to

(σ

⋆

j

)

2 = (n⋆σ2

j + nσb

2

j

)/(n

⋆ + n) (3.20)

where n

⋆

is a configuration parameter. Typically we take n

⋆ = 25.

4. Re-evaluate the status of w

o

j

:

Change the status of w

o

j

to non-suspect if

|vj − v

⋆

j

| < τbσ

⋆

j

(3.21)

where τb is a prescribed non-dimensional tolerance parameter. Typically τb = 3.

These steps are repeated until no further observations change status. At that point, any

remaining suspect observations are marked for rejection.

The adaptive nature of the buddy check has two important consequences. First, the

final quality control decisions are not very sensitive to the prescribed error statistics in

the global analysis system. This has been verified experimentally by varying the tolerance

parameter, τo, of the background check. It was found that the final accept/reject status

of observations is not very sensitive to the background check failure rate, as long as this

rate is roughly between 1% and 10%. This insensitivity to the prescribed statistics is a

major practical advantage, since (1) these statistics are not very reliable and (2) the SQC

algorithms do not require retuning each time the prescribed statistics in the global analysis

change.

The second consequence of adjusting rejection limits on the fly based on the local variability of surrounding data is that the buddy check becomes increasingly tolerant in synoptically active situations (and, conversely, more stringent when the flow is smooth). This

is best illustrated by an example, in which we contrast the results of a nonadaptive buddy

check against those of the adaptive buddy check. Figure 3.7 shows two maps with quality

control marks for zonal wind observations (obtained from aircraft and rawinsonde reports)

over North America at or near 200hPa, on March 01 1998. The top panel shows rejections

(indicated by red marks) by a non-adaptive buddy check, based on tolerances derived from

prescribed statistics. Yellow marks indicate data that were marked as outliers by the background check, but which passed the buddy check. The lower panel shows rejections by the

38
adaptive buddy check. Tolerances are increased due to greater variability than implied by

the prescribed statistics, resulting in the acceptance of several additional outlier observations. The effect on the wind analysis (not shown) is to increase wind speeds by about

3m/s in some places.

3.3.4 The Wind Check

This check is applied to all u-wind and v-wind data to make sure that wind components

pass the quality control in pairs. The algorithm determines whether two wind components

are paired (i.e., whether they originate from the same report) by matching their location

attributes, instrument type, and sounding index.

3.3.5 The Profile Check

This check eliminates an entire vertical sounding in case any of the data from that sounding

are marked for exclusion. It is applied to selected data types only. Currently the profile

check is used for TOVS height retrievals only. For example, if the buddy check rejects a

TOVS height observation at 10hPa, then the entire sounding is marked for rejection.

3.3.6 Special Treatment of Moisture Observations

The analyses moisture field in GEOS DAS is water vapor mixing ratio, which is highly

variable in space and time. This causes difficulties for the buddy check, which presumes that

the field is spatially coherent on the scales resolved by the observing network. Experience has

shown that a buddy check applied to water vapor mixing ratio observations (or, equivalently,

specific humidity) tends to reject too many of them, unless the tolerances are relaxed to a

point where the quality control becomes almost completely inactive. This is obviously not

acceptable, unless preprocessing quality control is completely reliable.

To remedy this situation, the statistical tests (background check and buddy check) in

the SQC are applied to relative humidity residuals. These residuals are computed in two

ways: first, using observed mixing ratios and observed temperatures, and second, using

observed mixing ratios and model-predicted temperatures. This prevents the situation in

which a relative humidity looks good even though both mixing ratio and temperature are

corrupt. The tests are applied in sequence to both types of residuals, and an observation

passes QC only if none of the tests fails.

39
Buddy check with prescribed tolerances

−140 −130 −120 −110 −100 −90 −80 −70 −60 −50

20

30

40

50

60

70

Buddy check with adaptive tolerances

−140 −130 −120 −110 −100 −90 −80 −70 −60 −50

20

30

40

50

60

70

Figure 3.7: Quality control decisions for zonal wind observations at 200hPa on March 01 1998,

using a non-adaptive buddy check (top) and adaptive buddy check (bottom). Green dots indicate

observations that passed the background check, yellow dots indicate suspect observations that were

accepted by the buddy check, red dots indicate rejected observations.

40
3.4 Interactive Retrievals – iRET

The interactive retrieval process in GEOS-4 uses a variational approach (1DVAR) to extract

layer mean temperature, moisture and surface properties from observed remotely sensed radiances in a number of channels. What makes iRET “interactive” is that the 1DVAR process

uses GEOS-4 background fields in the retrieval process, and the retrieved information is in

turn assimilated into the GEOS-4 system. The salient features of the iRET 1DVAR system

are:

• Use of raw data (level 1b radiances);

• Variational cloud clearing (Joiner and Rokke 2000);

• Physically-based systematic error correction (tuning);

• GLATOVS forward model (Susskind et al. 1997; Sienkiewicz 1996);

• Runs in assimilation, future retrievals affected by information from prior retrievals;

• Use of both cloud and land affected data;

• Rawinsonde shadowing, TOVS data in the vicinity of rawinsondes not assimilated;

• Tuning using collocated rawinsondes (not the background), updated daily.

Joiner and Rokke (2000) provides considerable additional detail on a number of the above

topics, especially on the issues of cloud-clearing and tuning, although the system described

in that paper was non-interactive. One key aspect of the tuning process in iRET is the identification of cloud-contaminated channels, and the generation (and assimilation) of retrieved

information for vertical levels above any levels influenced by clouds. In practice iRET identifies cloudy regions, and only generates retrieval profiles above some pre-set “cut-off” level.

In GEOS-4.0.3, this level was set to 250 hPa (as can be seen in figure 5.63).

3.5 Model – Analysis Interface

A number of steps are required to move between the “world” (defined by the state variables

and their spatial discretization) of the GEOS-4 GCM and the GEOS-4 analysis. Some are

relatively minor, such as interpolating wind components back and forth from the GCM

D-grid (Figure 3.3) to the analysis A-grid (essentially the “φ” points in Figure 3.3).

A significant amount of care needs to be taken with communicating the mass variable

information between model and analysis. The principal issue is that while the model state

variable is (scaled, potential) temperature, the analysis works with geopotential height. The

hydrostatic relation is the mechanism for the interchange:

θk = −

g

Cp

·

hk − hk+1

p

κ

k − p

κ

k+1

, κ = R/Cp. (3.22)

Given a set of θ ’s and pressures, one can generate a consistent set of heights to serve as

background fields for the analysis. The reverse process requires some extra consideration,

41
since the analysis changes both the geopotential heights and the surface pressure (and thus

the definition of all the vertical levels).

A specific approximation in the GEOS-4 system is to localize the effects of the analysis

change to the surface pressure to the lowest model control volume. The first step in this

approach is the estimation of the analyzed surface pressure (p

a

s

) from the upper-air analyzed

heights. Figure 3.8 shows the arrangement of variables in this process. For the lowest

atmospheric layer, the analyzed height is:

hˆa

K+1 = hs + δhˆaK+1 (3.23)

where hs is the topographic height; it then follows that adjustments must be made to both

ps and the layer-mean virtual temperature θ∗ at the lowest control volume, so that the

after-analysis bottom edge height corresponds to the surface height (hs). A straightforward

rearrangement of 3.22 for the bottom layers (using the variables shown in Figure 3.8) yields

the following estimate for p

a

s

:

p

a

s = p

f

s

·

"

1 +

gδhˆa

K+1

Cpθ∗(p

f

s )

κ

#1/κ

. (3.24)

Notice that θ∗ enters this equation in the denominator, and therefore the final value of

p

a

s

is not very sensitive to the precise value of θ∗. For typical values, p

f

s ∼ 1000 hPa,

δhˆa

K+1 ∼ 100 m, T∗ = (p

f

s

)

κ

θ∗ ∼ 300 K, one can estimate that an error of 5 K in T∗

corresponds to less than 0.2 hPa error in p

a

s

. For the calculation in (3.24), θ∗ is approximated

as a layer-mean virtual temperature at the lowest control-volume associated with p

f

s

:

θ∗ ≈ −

g

Cp

·

hˆa

K+1 − hˆaK

(p

f

K+1)

κ − (p

f

K)

κ

. (3.25)

Once an estimate of the after-analysis surface pressure has been obtained, the question

then arises of what should be used for the after-analysis pressure-thickness, δpa, for each

finite control-volume. The vertical integral of δpa is constrained by the after-analysis surface

pressure:

p

a

s = p

a

K = ptop +

k

X=K

k=1

δpa

k

, (3.26)

where ptop is the top pressure, typically 0.01 hPa. When p

a

s > pfs

, mass is added to the

lowest model layer by setting

δpa

K = δpf

K +



p

a

s − p

f

s



(3.27)

keeping δpa

k = δpf

k

, for k = 1, ..., K − 1. The volume-mean values of all quantities are not

altered by this expansion of the lowest control volume.

However, when p

a

s < pfs

, (3.27) can lead to very small or even negative values of δpa

K,

requiring some special handling. In some cases, it is necessary to remove one or more of the

model lowest layers in order to accommodate the new value of the surface pressure p

a

s

. (In

practice, model layers are removed by assigning an extremely small mass to them.)

42
The main advantage of this shaving method is that no mapping or interpolation is necessary except for those one or two lowest model layers affected. However, the resulting

Lagrangian control volume could be very different from the fixed Eulerian reference coordinate (3.1). For this reason, this method has output deferred until the completion of

the Finite-volume dynamical core and physics modules, during which a remapping to the

fixed Eulerian reference coordinate system is performed, along with any necessary physical

adjustments.

f

a

a

s s

s K+1

p , h

θ

δ

^

^

h a

*

p , h

Figure 3.8: After-analysis height and surface pressure.

43
3.6 GEOS-4 Development History

The antecedents to the current GEOS-4 system were described in Chapter 2. This validation

effort is concerned with the evaluation of the third version of GEOS-4, labeled “4.0.3.” The

prior two operational GEOS-4 versions were:

GEOS-4.0.1 First operational version of fvDAS

• validation: December 2001

• entered operations: 1 October 2002

GEOS-4.0.2 Used for MODIS reprocessing run

• needed to address Tskin feedback problem

• entered operations: 16 April 2003

• also used for MODIS reprocessing period

Issues involving the changes for GEOS-4.0.3 are discussed in Chapter 4. The validation

process addresses the entire GEOS-4.0.3 system, not just the changes from GEOS-4.0.2 to

GEOS-4.0.3.

44
Chapter 4

Validation Issues

The GMAO (and formerly the DAO), has always worked from the premise that short to

medium range forecast skill is not the absolute measure of the quality of the assimilation

state. In fact, it is possible to improve forecast skill at the expense of analysis accuracy.

This situation arises because there may be compensating errors in the model-analysis-data

combination during the assimilation process. Furthermore, many of the problems of interest to customers of the GMAO are not well represented by the forecast skill metrics (e.g.,

forecasts of 500 hPa geopotential heights, or of sea level pressure). A more general framework of validation, which includes forecast metrics, is required to ensure that the needs of

customers of the GEOS-4 products are met.

If the assimilation system is focused at generalized applications, the number of possible

problems requiring metrics to measure improvement becomes very large. Often conflicts

develop because efforts to improve performance in one area result in degraded performance

in another area. The temptation to fix a problem with an ad hoc specification of a system

parameter is high. Inevitably such fixes haunt future development because typically they

are “shortcuts” for a more involved nonlinear tuning process. As a result, engineering fixes

tend to short-circuit feedback loops that are overlooked, unanticipated or poorly understood.

In this regard, the validation process acts as a “safety net” for the development process.

However it is greatly to be preferred that such tuning problems be caught by aggressive

system testing. Problems that result in a candidate system failing validation really should

lead to changes in the testing process as well as modifications to the system that failed to

meet minimum validation criteria.

The effort described in this document is a System Validation, a process by which a

“candidate” data assimilation system, which has significant modifications beyond a currently running system, is evaluated and judged to be scientifically suitable to be put into

operations for the generation of products for GMAO customers. System Validation efforts

tend to be demanding of resources across the organization, as they must bring to bear the

results of the relevant scientific investigations, new verification data, and the diagnostic

tools which are best suited for evaluating the system modifications at hand.

The following section describes the changes that went into the candidate system for

GEOS-4.0.3. The final section in this chapter outlines how this System Validation effort

was organized. Results from this effort will follow in the next chapter.

45
4.1 GEOS-4.0.3

The validation effort for GEOS-4.0.3 was unusual in that it proceeded in two stages, with a

preliminary evaluation effort in October 2003, followed by a final evaluation effort (which is

the subject of this document) in December 2003. For the sake of clarity, the two stages in

the development of GEOS-4.0.3 will be referred to by their CVS development tags: 1.4 r1

and 1.4 r2. While the evaluation of 1.4 r1 yielded satisfactory results, it became clear

during the following months that there remained some customer-driven issues that had to

be addressed before the candidate system could be considered ready for operations. The

changes implemented to address these problems (described in 4.1.2) resulted in the 1.4 r2

system, which in turn was evaluated. A significant constraint on the evaluation of 1.4 r2

was that this system had to retain the positive validated behavior obtained for 1.4 r1.

The following two sections give an overview of the changes implemented for the two

stages of GEOS-4.0.3 development. This material, with considerable additional details,

resides on the following Monitoring web page:

http://gmao.gsfc.nasa.gov/science/dolms/validation/ .

4.1.1 1.4 r1

The following list summarizes the major changes in the 1.4 r1 system:

• Modified mass-wind balance

• QC changes

• CTW selection

• Increased iRET coverage over ice

• 25 → 36 levels in analysis

• MPI PSAS (efficiency increase)

The first three items will be discussed in more detail below. The increased coverage of

interactive retrievals (see 3.4) over ice was an attempt to address a serious lack of data

coverage in GEOS over the Southern Oceans. The enhanced vertical resolution of the

analysis output grid helped to reduce the impact of interpolation from the analysis to

model grids. The implementation of message-passing interface code (MPI) in the PSAS

greatly improves the computational efficiency of the analysis; while this should not directly

influence the scientific behavior of the system, this is still a greatly desirable change as it

allows for many more tuning and testing runs with limited resources.

4.1.1.1 Modified Mass-Wind Balance

Of the three analysis changes made in GEOS 4.0.3, the most profound was the modification

made to the analysis mass-wind balance by means of an extensive retuning of the masswind coupling statistics statistics (see the description in section 3.2.2.1; the specific tuning

46
parameters used in GEOS-4.0.3 are shown in section 3.2.5.2). This change actually addresses

problems that have existed in the PSAS analysis going back as far as the GEOS-2 system.

4.1.1.2 Modified QC Gross-Check

It was noted in the weekly monitoring meetings that the Quality Control in previous GEOS4 versions appeared on occasion to be overly “generous” in its treatment of grossly bad

data. Eventually the problem was traced to a mistakenly set parameter (τx = 1000 in

section 3.3.2) within the Quality Control algorithm that basically had the result of outright

outlier observations never being flagged for rejection. For this version of GEOS-4, this

parameter was reset to a more reasonable value (τx = 10). Some of the previous bad cases

were rerun with this value, with far more satisfactory results.

The objective of validation here is to examine the overall QC statistics, as well as to

examine some case studies, to insure that the new settings are behaving properly.

4.1.1.3 CTW selection

Problems with mid-level (i.e., 700-400 hPa) cloud motion vector wind data (or “CTW” for

short) have been evident in monitoring throughout the entire operational life of GEOS, with

the worst behavior in the Tropics. As these data typically occur in isolation from other wind

data, they pose a stiff challenge to the Quality Control implemented in GEOS (see 3.3 and

subsections therein). An alternative approach (followed at NCEP) is to restrict the usage

of these problematic data; so this version of GEOS summarily eliminates the participation

of mid-level CTW data.

4.1.2 1.4 r2

The following list summarizes the major changes in the 1.4 r2 system:

• CLM2 land surface model

• Modified Tskin analysis

• RH fix over high topography

It should be noted that there are other changes in the GEOS-4.0.3 system (listed on the

monitoring web page) that are not playing a role in the current validation process: the

use of NOAA-17 retrievals; the use of MODIS cloud-track wind data; and the output file

structures added for the study of transports in the stratosphere. These changes were largely

implemented and examined after the validation exercise described in this document. The

NOAA-17 and MODIS CTW data are not used in the historical reprocessed data sets

provided to CERES.

47
4.1.2.1 CLM2

In order to address significant problems with skin temperature biases in the earlier versions

of GEOS-4, a new land surface model (CLM2) was incorporated into the system. A brief

overview of this module is given in section 3.1.2.2.3. This change has the greater significance

for validation as it directly affects all the model surface fluxes which in turn can participate

in a number of feedback loops in the system.

4.1.2.2 Modified Skin Temperature Analysis

It was found that the rapid changes in Tskin at certain times of day put strong constraints

on the time-windowing allowed for using observational estimates of Tskin in an analysis.

To address this issue, the off-line analysis was modified to run every 3 hours (instead of 6

hours), with the data windowed in 1 hour intervals (instead of 6 hour intervals). It was

also determined that the data from polar orbiting satellites were inadequately sampling the

strong diurnal cycle in Tskin, so that the Tskin bias estimation/correction process should be

removed in the new version.

The objective of validation in this case is to check that Tskin and associated radiation

diagnostics (e.g., Outgoing Longwave Radiation) retain their desired behavior in the full

assimilation system.

4.1.2.3 Near-ground Moisture Modification

A supersaturation problem in the GCM low-level moisture near high topography in polar

regions was noticed during the course of the weekly monitoring activity. The modification

described in the first paragraph of section 3.1.2.2.2 was introduced to address this issue. As

this problem was of a highly localized and intermittent nature, its modification is less of an

issue for the overall system behavior. Validation’s role here is to check that the process of

removing this problem did not lead to other unintended consequences.

4.2 System Validation: Methodology and Resources

There are intersecting broad themes in this validation effort:

• Does the modified system meet customer requirements?

• Do the system changes retain their intended benefits?

– Tskin and radiation (from both analysis and model)

– Antarctic moisture anomaly correction

– Mass-wind issues

– Gross-check parameter modification

• Overall scientific behavior of modified system?

48
This validation effort has been accomplished by the distribution of the evaluation effort

among several investigators, each focusing on specific areas/metrics that, taken as a whole,

encompass all of the above validation themes. Table 4.1 displays the investigators and their

topics.

Validation Topics

Theme Topic Investigator Metrics

CUS, CHG clear sky OLR, M.-L. Wu Comparisons with ECMWF and CERES

tskin

CUS, CHG surface fields M. Bosilovich ISSCP Comparisons

(esp tskin) J. Radakovich Station Data

CUS, SYS Stratosphere S. Pawson Residual Circulation

W. Tan

CUS ozone I. Stajner Comparisons with NOAA-16 SBUV, TOMS

L.-P. Chang Rawinsondes

K. Wargan

SYS precipitation S. Schubert ERA-40, GPCP, SSM/I comparisons

surface stress D. VanPelt Monthly Means, Taylor Plots

SYS precipitation TRMM comparisons

variability on M.-L. Wu

MJO time scale

SYS data impact M. Sienkiewicz Data Withholding, Forecast Skills

CHG, SYS monitoring A. Conaty O-F, O-A, QC Stats; Case Studies

S. Bloom

CHG: verify system change

CUS: customer requirement

SYS: diagnose system behavior

Table 4.1: Organization of the GEOS 4.0.3 validation effort.

The other major consideration for System Validation, given the themes to be examined,

is the choice of assimilation runs of the candidate system to serve as the “raw material” for

the evaluation process. Ideally, long multi-seasonal runs with sampling from different years

would be used for the evaluations. However, there are very real constraints on resources,

both for executing the assimilation runs as well as for having a diverse group of investigators

budget their time for completing their validation commitments.

49
For this validation effort, output from two separate runs were used:

• CERES RUN

– May 2000 - December 2001

– long run, providing monthly means from different seasons

• PARALLEL RUN

– 2003

– behavior with current observing system

In addition to the wide array of diagnostic quantities to be examined, there are also other offline processing steps for this work: radiation calculations for CERES; transport calculations

using GEOS-4 winds for Ozone.

50
Chapter 5

Validation Results

This chapter presents the results of the several validation evaluation efforts listed in table

4.1. Each entry in the table has its own section in this chapter. The results given here were

presented in the GMAO System Validation meeting held on 12 December 2003.

5.1 CERES Radiation Comparisons

The radiation calculations of the CERES Instrument Team place tight tolerances on the

temperature and moisture profiles, as well as on Tskin, that are produced by GEOS. The

results in this section are organized as follows:

• CERES concerns with previous GEOS-4 Tskin products

• Improvements in Tskin with candidate system

• Cloud-clearing issues

• Clear Sky OLR comparisons

5.1.1 CERES Concerns

The first figure (Figure 5.1) shows a clear sky OLR (CLR in figures) computation using a

prior version of GEOS-4 (GEOS-4.0.2) for May 2001. Comparisons with CLR from ECMWF

fields and the corresponding CERES satellite results show the GEOS4 CLR bias against

CERES to be in general larger than the ECMWF bias. The GEOS-4 biases over land are

mainly due to Tskin biases which are presented in Figure 5.2.

Figure 5.2 contains scatter-plots of Tskin for five regions: Australia, North Africa, Saudi

Arabia, Eastern and Western US. In all cases, the CERES retrieved Tskin is on the horizontal

axis, and GEOS-4.0.2 Tskin is on the vertical axis. Perfect correspondence between the two

sets of temperatures would align the points along the dashed line. In all these cases, the

actual alignment shows the GEOS-4.0.2 Tskin to be too warm during the nighttime and too

cold during the daytime. The likely sources of these biases are:

51
Figure 5.1: Comparisons of GEOS-4.0.2 (“OLD GEOS”) and ECMWF clear sky OLR with CERES

OLR. The bottom panel is the difference of the absolute value of the fields in the first two panels.

• Deficiencies in the land-surface model used for GEOS-4.0.2;

• The use of 6-hourly analyses of Tskin, although the temperatures (and thus the CERES

estimates) can be rapidly varying over that time interval;

• A Tskin bias adjustment process in GEOS-4.0.2 that had problems with using inadequately sampled data from polar orbiting satellites.

5.1.2 Tskin Improvements

Figure 5.3 (a-d) shows the impact of the changes described in section 4.1.2 . The four parts

of this figure show groups of scatter-plots of Tskin for four months (January, April, July and

October) in 2001. Each grouping is arranged as follows:

ECMWF vs CERES

C403 vs CERES C402 vs CERES

C403 vs ECMWF C402 vs ECMWF

52
Figure 5.2: Scatter-plots of CERES retrieved Tskin (horizontal axis) versus GEOS-4.0.2 Tskin

(vertical axis) for the five regions on the panels. There are two colored groupings in each scatterplot, cooler (leftmost) are nighttime, warmer (rightmost) are daytime. See text for additional details.

where “C402” and “C403” are shorthand labels for GEOS-4.0.2 and GEOS-4.0.3 respectively. The different colors represent nighttime (orange) and daytime (blue) Tskin estimates.

Comparing the top left and middle left plots with the top right plots in Figure 5.3 (a-d),

it is apparent that GEOS-4.0.3 has superior agreement to the CERES data than does the

prior version of GEOS, and that this agreement is comparable with the ECMWF agreement

with CERES. The bottom plots reinforce this point by showing how GEOS-4.0.3 is in much

better agreement with ECMWF than was GEOS-4.0.2. Scatter plots for other regions (not

shown) show similar behavior to those in Figure 5.3.

Global comparisons of monthly means of Tskin are shown in Figure 5.4 (a-d), for the

same time periods as in Figure 5.3 (a-d). In this figure, each of the panels consists of three

plots: GEOS-4.0.3 minus CERES (top); ECMWF minus CERES (middle); and |(GEOS -

4.0.3 − CERES)| − |(ECMWF − CERES)| (bottom). The plots with the difference of

absolute values allow one a ready view of which of the GEOS or ECMWF Tskin products agrees better with the CERES retrieved Tskin values. In the absolute difference plots

“warm” colors (reds, yellows) indicate better ECMWF agreement with CERES, while “cool”

colors (blue, green) indicate better GEOS agreement with CERES. Inspection of the bottom plots in Figures 5.3 (a-d) shows a general preponderance of the “cool” colors over land,

53
(b)

(d)

(a)

(c)

Figure 5.3: Scatter plots of Tskin, comparing CERES data with model estimates from GEOS-4.0.3

(C403), GEOS-4.0.2 (C402) and ECMWF for four months during 2001: (a) January, (b) April,

(c) July, and (d) October. See text for discussion.

54
(b)

(d)

(a)

(c)

Figure 5.4: Global monthly mean Tskin comparisons, GEOS-4.0.3 and ECMWF vs CERES, for

four months during 2001: (a) January, (b) April, (c) July, and (d) October.

55
indicating that for these time periods GEOS-4.0.3 had a lower Tskin bias than ECMWF

when compared to CERES. This conclusion is borne out quantitatively by the bias and

RMS numbers included on the plots in Figure 5.3.

5.1.3 Cloud-Clearing Issues

As retrievals of Tskin from CERES are being used as a verification standard, it is important

to be aware of any potential problems with the use of these data for this purpose. Figure 5.5

shows an example of a potential problem. The left two panels show ECMWF and GEOS-4

having similar biases vs CERES retrieved Tskin for January 2001. The right two panels

show that an independent estimate of cloud cover (ISCCP) has a large amount of clouds in

the Northern high latitudes, where the ECMWF and GEOS-4 biases are the largest. The

problem here is that a failure to recognize the presence of clouds in a scene could lead to

an anomalously low retrieved value of Tskin. Figures 5.6 and 5.7 provide more detailed

examples of possible cloud issues with the CERES estimates of Tskin, for a period in early

January 2001. Figure 5.6 focuses on an area over the Weddell Sea off the coast of Antarctica,

while Figure 5.7 examines an area north of the Black and Caspian Seas in Central Eurasia.

Figure 5.6 (a) compares the GEOS-4 Tskin estimate to that from CERES (lower left

and upper left plots, respectively) and also shows the corresponding ISCCP cloud coverage

and cloud top temperatures (lower and upper right plots) for January 5, 2001. While the

GEOS-4 Tskin field is relatively featureless over the Weddell Sea, there is a pronounced

(less than 260 K) minimum in the CERES field, which does not appear to be a reasonable

estimate for an ocean or ocean-ice region. The CERES feature does correlate well with

an area having an extensive coverage of clouds with cloud tops. It should be noted that

these plots represent nighttime conditions (03Z for the Tskin estimates, 06Z for the ISCCP

data). The implication here is that the nighttime clouds are not identified as such, and thus

end up making an inappropriate contribution to the CERES Tskin estimate. Figure 5.6 (b)

tends to confirm this hypothesis, by examining the behavior of area-averages of Tskin (for

ECMWF, GEOS-4 and CERES) as well as ISCCP cloud top temperatures over the course

of a week in January 2001. The nighttime data points are encircled for clarity. The striking

feature of this figure is how the ECMWF, GEOS-4 and the daytime CERES values tend to

cluster together, while the nighttime CERES estimates tend toward the ISCCP cloud top

temperatures.

Figure 5.7 examines the behavior of the Tskin estimates over Eurasia. While the results

in this figure are not as dramatic as those in the previous figure, the strong disagreement

between GEOS-4 and CERES north of the Caspian Sea again occurs when a strong CERES

minimum correlates with a maximum in ISCCP cold cloud top coverage. In addition, the

clustering behavior of areal averages of Tskin is repeated for this region, with the nighttime

CERES estimates tending to follow the ISCCP cloud top temperatures.

56
Figure 5.5: Tskin biases (differences between GEOS-4 and CERES retrieved Tskin) and cloud

parameters from ISCCP. Cold biases occur over areas having high cloud amount with cold cloud top

temperatures.

5.1.4 Clear Sky OLR Comparisons

Figures 5.8 and 5.9 show that the positive Tskin results shown above are reflected in the

clear sky OLR computations. Monthly bias results against CERES (for January, April, July

and October 2001) shown in Figure 5.8 show the GEOS-4.0.3 clear sky OLR biases against

CERES to be very comparable to those from ECMWF, with the global bias and standard

deviation figures somewhat better for GEOS-4.0.3. Figure 5.9 shows clear sky OLR RMS

comparisons of GEOS-4 and ECMWF against CERES over three types of surface: Ocean,

Desert and Crop-land. Clear sky OLR comparisons over desert should have Tskin as the

dominant signal; clear sky OLR differences over oceans should be dominated by upper-air

moisture and temperature profiles; the crop-land signal should behave somewhat between

the desert and ocean signals. The desert and crop-land RMS results indicate that there is

very little difference between GEOS-4 and ECMWF. There is a consistently larger RMS

for GEOS-4 over oceans, which is a consequence of biases in upper tropospheric moisture

in GEOS-4.

57
(a)

(b)

Figure 5.6: Cloud contamination example: Antarctica. a) Tskin comparisons between GEOS-4

(lower left) and CERES (upper left). Also shown are ISCCP cloud top temperatures (upper right)

and cloud amounts (lower right). b) Area averaged Tskin comparisons among ECMWF (green),

GEOS-4.0.3 (red) and CERES (black). Cloud top temperature shown in magenta.

58
(a)

(b)

30E to 45E ; 50N to 65N

45E to 60E ; 50N to 65N

Figure 5.7: Cloud contamination example: Eurasia. Same conventions as 5.6.

59
Figure 5.8: Clear sky OLR comparisons. Statistics show that the GEOS-4 based clear sky OLR is

comparable to that from ECMWF.

Figure 5.9: Clear sky OLR rms errors, showing a comparable behavior in clear sky OLR between

GEOS-4 and ECMWF.

60
5.1.5 Summary

• Previous version of GEOS-4 (4.0.2) had significant Tskin problems

• Current version of GEOS-4 (4.0.3) now comparable to ECMWF

– new LSM, improved surface albedos

– Tskin analysis at 3 hr intervals

– 1 hr data window

• Clear-sky OLR comparisons

– GEOS-4 and ECMWF statistics (bias, RMS) over land are comparable

– over ocean, GEOS-4 upper-troposphere moisture bias results in somewhat poorer

performance compared to ECMWF

• Common biases (ECMWF, GEOS-4) indicate possible cloud clearing problems in

CERES retrievals.

61
5.2 ISCCP and Station Observation Comparisons

ISCCP DX data provides 30 km 3-hourly surface temperature fields for clear sky conditions.

Figure 5.10 shows the daytime and nighttime differences of surface temperature averaged

for July 2001. In day and night, GEOS-4.0.3 analysis skin temperature is generally warmer

than the ISCCP data. Some regions do exhibit diurnal variations in the mean bias, but

it is difficult to discern a systematic diurnal bias. The difference between ISCCP surface

temperatures and the TOVS retrievals that are being analyzed is also provided (Figure 5.10,

bottom). Since the TOVS data are warm compared to ISCCP, any further improvements

to the TOVS analysis will not make this difference much smaller. It has not been ruled out

that ISCCP may be biased cold, but this issue needs further study.

Several in situ stations, available from many different providers through the Coordinated

Enhanced Observing Period (CEOP), include surface skin and air temperature as well as

the radiation components and turbulent fluxes of heat. There are many issues involved in

comparing surface site observations to grid point data, such as the heterogeneity of the real

world is not adequately represented, but systematic problems may be identified. The sites

used in this analysis were taken from a number of Continental Scale Experiments (CSE),

and they are listed in Table 5.1:

CEOP Stations

CSE station lat lon

BALTEX Cabauw 51.97 4.93

BALTEX Lindenberg 52.17 14.12

CAMP Mongolia (330) 46.13 106.37

GAPP Bondville 40.01 -88.29

GAPP Ft Peck 48.31 -105.10

GAPP SGP 36.60 -97.49

LBA Manaus -2.61 -60.21

LBA Rondonia -10.08 -61.93

MAGS BERMS 53.99 -105.12

ARM NSA (Atqasuk) 70.47 -157.41

ARM NSA (Barrow) 71.32 -156.61

Table 5.1: CEOP observing stations for in situ comparisons.

Note that the ISCCP Tskincomparison (Figure 5.10) shows a significant daytime cold

bias extending from Texas through the Northern Plains states. This feature can be seen as

well in two panels of Figure 5.11 (“Ft Peck” and “Lamont”). The panels in this figure depict

monthly mean surface temperature and near-surface air temperature for July 2001 (the black

and red bars denote the fraction of available observations, shown as dots). In contrast to the

Fort Peck and Lamont sites, there is a marked warm bias in the daytime temperatures at

the Bondville (Illinois) site. The remaining panels of Figure 5.11 show a better agreement

between model and observations. At Atqasuk, on the North Slope of Alaska (NSA), the

mean diurnal cycle is reasonably represented. The amplitude of the diurnal cycle for Barrow

conforms well to the observations; the bias of the model results at this location may be

related to a difference of altitude between the model grid point and station elevation (the

62
Figure 5.10: Monthly mean differences between C403 and ISCCP clear sky skin temperature observations for July 2001 (top: daytime, middle: nighttime). The bottom panel shows the differences

between the TOVS retrieved Tskin and the ISCCP observations, averaged only when both coexist

in the time series. (Units: Kelvin).

63
model point would be at a lower altitude). Rondonia, near complicated rainforest terrain,

exhibits some differences between the simulated and observed surface temperatures. The

mean diurnal cycle of the Mongolia site is reasonably represented, though when evaluating

the time series, the analysis system does have difficulty regularly reaching the observed

daytime maximum.

Figure 5.12 shows the monthly mean diurnal cycle of radiation at several reference sites.

In a general sense, the incoming shortwave radiation (black curves) is somewhat overestimated (at some stations, a daytime average difference can be as much as +100 Wm−2

).

The upward shortwave (red curves) is also generally larger than the observations, but it

and the longwave radiation components do not appear to balance the discrepancy in incoming shortwave radiation. Figure 5.13 shows the net radiation, ground and turbulent

energy fluxes (at the corresponding stations for the radiation data). The availability of the

turbulent and ground heat flux observations is not as regular as the radiation observations.

In many of the regions the net radiation exceeds observations, following the downwelling

shortwave radiation bias. In some, but not all stations, this can lead to excessive turbulent

heat transport. Not all the stations have reliable ground heat to attempt to close the budget, however, there are biases in the diurnal cycle of ground heat that are typical of most

land parameterizations (e.g. Bondville and Fort Peck). Care must be taken when evaluating the surface fluxes against station observations. For example the high latent heat that

occurs in Lamont, is a result of an overactive precipitation event produced by the analysis

system, that was observed as mostly clouds with little precipitation. The high latent heat

flux followed this erroneous precipitation forcing.

In general, the model simulations and analyses including the CLM showed some warming

and drying over land, compared to the previous version with NCAR LSM. This result

represents an improvement to the system, as compared with observations and analyses

(ECMWF and NCAR). These results closely resemble those published by Zeng et al. (2002),

obtained in their testing of the CLM at NCAR.

64
Figure 5.11: Mean diurnal cycle of several in situ reference site stations (data shown as dots) and

corresponding C403 grid point surface temperature (or Tskin, in red) and near surface air temperature

(T2m, in black) for July 2001. Units are degrees Kelvin. The bars denote the fraction of available

observations for each type of data.

65
Figure 5.12: Mean diurnal cycle of radiation components at the surface from in situ references sites

and the nearest model grid point: downward shortwave (black); upward shortwave (red); downward

longwave (blue); and upward longwave (green). Color bars indicate the percentage of available

observations during the month. (Units are Wm−2)

66
Figure 5.13: As in Figure 5.12, except for the surface energy flux components: net radiation (black);

turbulent heat flux (red); turbulent flux of latent heat (blue); and heat flux from the ground (red).

67
5.3 Stratospheric Circulation

The stratospheric circulations of GEOS-4.0.2 and GEOS-4.0.3 were compared, with an

emphasis on those derived variables that are important indicators for realistic transport.

The following discussion examines:

• latitude-height cross sections of zonal-mean temperatures

• latitude-height cross sections of zonal-mean zonal wind

• the relation between the meridional gradient of vorticity and zonal wind

• equivalent length

• rates of isentropic transport

• the residual circulation

• the difference between 6-hourly instantaneous background winds and 6-hourly averaged winds.

Since most of the variables examined are indicators of global-scale processes that are not

observed directly, there are no independent observations available for the validation process.

In this situation the validation process then becomes an exercise in the subjective evaluation

of features and behaviors in analysis fields.

5.3.1 Temperature and Zonal Wind

Figure 5.14 shows the monthly averages of zonal mean temperature and zonal wind, for

GEOS-4.0.2 and GEOS-4.0.3, between 1000 hPa and 0.2 hPa for 2001 (Figure 5.14a) and

2003 (Figure 5.14b). The shaded areas in this figure identify regions of temperatures lower

than 210 K, negative values in the zonal wind and negative differences. The global structures

of temperature and wind are very similar in the two systems. In January 2001, apart from

the equatorial lower stratosphere near 50 hPa, and the summer polar region near 100 hPa,

the temperature differences are small below 10 hPa. Above 10 hPa, the differences in

temperature generally increase with height, with the largest values occurring in the winter

extratropics. At the top of the domain, the temperature differences show a pronounced

meridional pattern with alternating colder and warmer regions. The zonal wind differences

behave similarly to the temperature differences in that they are relatively small below 10

hPa. Between 10 hPa and 1 hPa, the tropical winds in GEOS-4.0.3 are much more easterly

compared to those in GEOS-4.0.2, with a difference of 20 m s−1 at the equator. Large

differences between the wind fields can also be found above 1 hPa. Since there are no wind

observations above 10 hPa, these differences could likely be due to changes in the wind-mass

relationship introduced into GEOS-4.0.3.

In August 2003, both the temperature and zonal wind exhibit large differences above 2

hPa. In contrast to January 2001, the GEOS-4.0.3 temperatures above 2 hPa are warmer

than those in GEOS-4.0.2 for all latitudes, with the exception of the polar area near 0.3

hPa. The meridional pattern of the August 2003 differences in zonal wind is similar to

68
Figure 5.14: (a) Monthly average of zonal mean temperature and zonal wind for January 2001.

The last panel in each row shows the difference between GEOS-4.0.2 and GEOS-4.0.3.

Figure 5.14: (b) Monthly average of zonal mean temperature and zonal wind for August 2003.

69
January 2001, apart from the top of the domain in the northern high latitudes. However,

the magnitude of the August 2003 differences is smaller than that in January 2001.

Since the top analysis level is at 0.4 hPa, it is not surprising that the largest differences

are found above 1 hPa. With no constraint from observations, two runs could drift apart

significantly at the top levels of the model.

5.3.2 Vorticity and Winds

Previous studies have found that, in certain cases, analysis increments in the wind fields

could generate regions with a negative vorticity gradient in the subtropics. These unstable

regions lead to excessive subtropical transport and mixing in comparison to results obtained from running the GCM in simulation mode (Tan et al. 2004). Figure 5.15 shows the

meridional gradients of vorticity (left panels) and vorticity increments calculated from the

analysis increments of the wind fields (right panels). The plots of meridional gradient of

vorticity are at 72 hPa and 52 hPa, and depict negative values in shades of green and blue

bounded by the zero contours. The corresponding panels for the meridional gradient of

vorticity increments use the same color scheme; crosses indicate the locations of the sonde

stations.

Note that the subtropics in GEOS-4.0.2 are replete with patches of negative vorticity

gradients. Close examination shows that some of these regions directly mirror similar structures in the meridional gradient of the vorticity analysis increments. In GEOS-4.0.3, the

magnitudes of the analysis increments are greatly reduced compared to those in GEOS4.0.2, and there is a corresponding reduction in regions with negative vorticity gradient. As

a result of the reduction in these unstable regions, subtropical transport and mixing are

less excessive in GEOS-4.0.3, as shown in a following subsection.

5.3.3 Equivalent Length

Equivalent length and effective diffusivity are measures of isentropic transport and mixing

(cf. Nakamura 1995). In regions where transport and mixing are inhibited, the isopleths

of potential vorticity (PV) or a long-lived constituent field are relatively undisturbed or

stretched. On the other hand, in regions where transport is strong, the isopleths will be

stretched and folded into complex shapes, with abundant filamentary structures. A suitably

defined metric based on the gradient and geometry of these isopleths can be used to compare

isentropic transport and mixing between two systems. Equivalent length calculated using

PV on theta surfaces is one such metric. Figure 5.16 shows the equivalent length for

January 2001 and August 2003. The ordinate is potential temperature and the abscissa is

equivalent latitude. The calculations were performed on the 450 K, 550 K, 700 K, 850 K,

and 1000 K theta surfaces. This selection encompasses pressure levels from 70 hPa to 7 hPa

in the tropics. A value of one indicates that an isopleth is completely undisturbed, with an

effective length equal to the length of the equivalent latitude circle. In the top two panels,

contours with values larger than 3 are shaded. The bottom panel shows the difference

between GEOS-4.0.2 and GEOS-4.0.3.

For January 2001, the two data sets exhibit similar structures. Around the Equator, a

minimum extends from the bottom of the domain to around 700 K while a maximum can

70
Figure 5.15: (a) GEOS-4.0.2 meridional gradients of vorticity (left panels) and vorticity increments

calculated from the analysis increments of the wind fields (right panel) at 72 hPa and 52 hPa.

Negative values are depicted in shades of green and blue bounded by the zero contours. Crosses

indicate the locations of the sonde stations.

be found in the top half of the domain. In the subtropics of both hemispheres, there is

a minimum near 20◦, extending from 550 K to the top of the domain. In comparison to

GEOS-4.0.2, the minima in GEOS-4.0.3 are more elongated in the vertical direction. While

the basic features are similar, the amplitudes are generally much larger in GEOS-4.0.2,

except above 800 K in the northern tropics and midlatitudes.

For August 2003, a minimum extends from the bottom to the top of the domain at the

equator. There is a minimum at around 20◦N, below 700 K. In the Southern Hemisphere,

there is a minimum near 20◦S at 550 K. It tilts toward the equator and extends upward to

700 K. The overall features in the two systems are similar, again with the amplitudes being

larger in GEOS-4.0.2 nearly everywhere.

For both time periods, these results show that isentropic stirring is more vigorous in the

subtropics of GEOS-4.0.2. This is in general agreement with the result of unstable regions

shown in the previous section and the calculations of the rates of isentropic transport

described in the following subsection.

71
Figure 5.15: (b) Same as (a) but for GEOS-4.0.3.

5.3.4 Rates of Isentropic Transport

The rates of isentropic transport were examined using the trajectory method. On the

11th, 21st, and 31st day of the month, a group of parcels were initialized on a 0.5

◦ × 0.5◦

grid between 30◦S and 30◦N, on the 450 K, 550 K, 700 K, 850 K, and 1000 K isentropes.

These parcels were advected backward for 10 days using horizontal winds interpolated to

the isentropes. The fraction of parcels within a latitude band around the equator that

came from higher latitudes 10 days before the initialization was defined as the entrainment

rate. In these calculations, the equatorial band was defined to be the region between 10◦S

and 10◦N. Parcels that came from locations poleward of each boundary were counted as

entrained into the band from higher latitudes. Note that a more rigorous calculation should

define the subtropical transport barrier or a proxy of the subtropical transport barrier, and

use that as the reference latitude to set the band boundaries. However, a simple definition

as described above is adequate to provide a means to compare isentropic transport and

mixing in the subtropics.

Figure 5.17 shows the monthly rates of isentropic transport obtained by summing the

results of three 10-day calculations. In both January 2001 and August 2003, the rates

in GEOS-4.0.2 are much larger than those in GEOS-4.0.3. In all cases, the minima of

the rates are located at 550 K. When the bounding latitudes were widened to 20◦, the

entrainment rates were reduced substantially in all cases, indicating that a large percentage

of the entrained parcels originated within the subtropics; in other words, mixing occurred

72
Figure 5.16: (a) Equivalent length for January 2001. In the top two panels, values larger than 3 are

shaded. The bottom panel shows the difference between GEOS-4.0.2 and GEOS-4.0.3, with values

larger than 1 shaded and negative values depicted with dashed contours.

73
Figure 5.16: (b) Same as (a) but for August 2003.

mainly within the same dynamical zone. Nevertheless, the entrainment rates in GEOS4.0.2 remain significantly larger than those in GEOS-4.0.3, even with the use of bounding

latitudes poleward of 20◦.

A comparison of the solid and dash-dotted curves in Figure 5.17 shows that the use

of 6-hourly averaged winds reduces the entrainment rate by up to 40% over that obtained

by using the 6-hourly instantaneous analyzed winds. These results are consistent with

the equivalent length results presented above. The vast reduction in entrainment rates in

GEOS-4.0.3 is related to the reduction in the unstable regions with negative meridional

gradient of vorticity.

74
5.3.5 The Residual Circulation

The known climatology of the vertical residual velocity of the middle atmosphere in the

solstices exhibits a simple structure, with upwelling in the tropics and most of the summer

hemisphere, and downwelling in the summer high latitudes below the stratopause and in

the winter hemisphere. Monthly mean vertical residual velocity calculated from GEOS4.0.2 analyses exhibits a similar structure, with some small scattered cells of anomalies.

In contrast, the results in GEOS-4.0.3 exhibit a very fragmented structure, with a string

of alternating upwelling and downwelling cells extending from the stratosphere to the top

levels of the data set.

Figure 5.18 shows the vertical residual velocity in January 2001 and August 2003. The

fragmented structures in GEOS-4.0.3 arise mainly from similar structures in the zonal mean

vertical velocity field, which is in turn a result of a large gradient in the meridional wind.

When wind fields plagued with such structures are used in offline transport models or in

offline ozone assimilations, spurious transport processes result, leading in turn to very noisy

constituent fields.

Further investigations showed that changes in the observation error covariance of the

decoupled wind below 150 hPa are the main source of these structures while changes in

the wind-mass relationship are the secondary source. This is not surprising since these are

Figure 5.17: Monthly rates of isentropic transport. The black solid curve depicts GEOS-4.0.2 entrainment rates calculated using 6-hourly instantaneous analyzed winds and the black dash-dotted

curve depicts GEOS-4.0.2 entrainment rates calculated using 6-hourly averaged winds. Corresponding red curves show the entrainment rates for GEOS-4.0.3. Left panel, January 2001; Right panel,

August 2003.

75
the two main mechanisms that account for the zeroth order changes in the wind analysis.

Results from an intermediate version of the assimilation system using the same error covariance statistics as GEOS-4.0.3 showed that meridional wind fields in the stratosphere

are replete with noise with 6-hourly period. When 6-hourly averaged winds are used to

calculate the residual circulation, the multiple-cell structures in GEOS-4.0.3 vanish. The

results become almost identical with GEOS-4.0.2 results calculated using time-averaged

winds. This is shown in Figure 5.19. In fact, when 6-hourly averaged winds are used in

offline transport model or in ozone assimilation, the results are significantly better than

those using 6-hourly instantaneous winds.

5.3.6 Summary

At pressures of 0.4 hPa and higher, where data are available to assimilate, the structures

of the zonal-mean temperature and zonal wind fields are similar between GEOS-4.0.2 and

GEOS-4.0.3. This lack of sensitivity of these basic fields to the parameters used in the

assimilation is encouraging. There are, however, substantial differences between the two

analyses when more highly derived quantities are examined. Calculations of isentropic

mixing into the equatorial region (after Tan et al. 2004) reveal that large overestimates

present in GEOS-4.0.2 are reduced substantially in GEOS-4.0.3. The reduction in the

excessive mixing in GEOS-4.0.3 is related to a decrease in the dynamically unstable regions,

which is a consequence of reducing the magnitude of the wind analysis increments.

The instantaneous wind fields in GEOS-4.0.3 contain excessive noise that leads to a

multiple-cell structure in the vertical component of the residual circulation. The source of

this noise is not fully understood and is the focus of ongoing research. Using instantaneous

winds in offline transport calculations leads to unrealistic structures in constituent distributions. A practical solution to this problem is to use six-hour averages of the GEOS-4.0.3

winds for diagnostic study of the residual circulation and for offline transport calculations.

Noise is substantially reduced in the six-hour averages, so that substantial aliasing is eliminated from the calculations. While the use of the time-averaged winds is beneficial to

features such as the basic pattern of the residual circulation, it does not mean that the

strength of the transport is correct: results from the ozone assimilation (section 5.4) suggest that the tropical upwelling remains too strong. Quantitative examination of these

properties remains a research question.

76
Figure 5.18: Vertical residual velocity in January 2001 and August 2003 calculated using 6-hourly

instantaneous analyzed winds.

77
Figure 5.19: Vertical residual velocity in January 2001 and August 2003 calculated using 6-hourly

averaged winds.

78
5.4 Ozone Validation

The ozone assimilation system developed at GMAO, along with a parameterized ozone

chemistry and transport model, was used to evaluate the quality of GEOS-4 assimilated

winds. The 6-hourly wind averages from GEOS-4.0.3 performed well. However, instantaneous 6-hourly forecast winds from GEOS-4.0.3 had undesirable features for the transport of

ozone in the stratosphere, and their use for driving chemistry and transport models (CTMs)

is not recommended.

Assimilated ozone in GEOS-4.0.3 was evaluated using O-F statistics and comparisons

with independent high quality observations. Improvements over GEOS-4.0.2 ozone products

are seen mainly at pressure levels between 20 and 70 hPa globally, and in the total ozone

columns in the tropics. The key to these improvements is the use of 6-hourly wind averages

to drive the transport in the GEOS-4.0.3 ozone assimilation system.

5.4.1 Background

The GMAO ozone assimilation system (Riishøjgaard et al. 2000; Stajner et al. 2001) includes a global three-dimensional parameterized chemistry and transport model (CTM).

The transport uses a flux-form semi-Lagrangian advection scheme by Lin and Rood (1996).

The chemistry scheme uses parameterized ozone production and loss rates (Fleming et al.

2001), where production rates were adjusted so that the quotient of production and loss

rates in the upper stratosphere agrees with an ozone climatology constructed by Langematz

(2000). Total column ozone data from the Total Ozone Mapping Spectrometer (TOMS)

(McPeters et al. 1998) and stratospheric ozone profiles from the NOAA 14 Solar Backscatter

Ultraviolet/2 (SBUV/2) instrument (Bhartia et al. 1996) are assimilated during the validation period in year 2000. The configuration that is run in near real-time since January

15, 2004 uses NOAA 16 SBUV/2 total ozone column and stratospheric ozone profiles. This

system was used successfully for monitoring of satellite ozone data characteristics (Stajner

et al. 2004).

The ozone assimilation system provides a convenient in-house tool for evaluation of

stratospheric winds produced by the GMAO. The main metric used in this evaluation is

the size of the ozone observed-minus-forecast (O-F) residuals, i.e. the differences between

incoming ozone observations and the short-term CTM forecast. Smaller ozone O-F residuals

imply a better agreement between the model forecast and the incoming observations. In

the lower stratosphere chemical processes are slow, and the transport processes are the

dominant cause of the ozone variability. Most of the total ozone column is contained in

the lower stratosphere. Thus, total ozone column O-F residual statistics are used as the

primary metric for the evaluation of the wind quality in the lower stratosphere.

5.4.2 Evaluation of Instantaneous 6-hourly Forecast Winds

Two CTM ozone simulations of several months duration were driven by instantaneous 6-

hourly forecast winds taken from GEOS-4.0.2 and GEOS-4.0.3. The winds used in these

runs were taken from the model-level output files in order to minimize vertical interpolation.

The total column ozone fields are shown in Figure 5.20. In comparison with the TOMS

79
Figure 5.20: Total column ozone fields at 12Z on July 31, 2000 are shown (in DU) for two CTM

simulations that were driven by 6-hourly forecast winds from GEOS-4.0.3 (top), GEOS-4.0.2 (middle). Independent TOMS total ozone column data are shown in the bottom panel. Latitudinal

biases are seen in both simulations, but they are larger for GEOS-4.0.3.

80
Figure 5.21: Total column ozone fields at 12Z on July 31, 2000 are shown (in DU) for three ozone

assimilation experiments that were driven by 6-hourly forecast winds from GEOS-4.0.3 (top), GEOS4.0.2 (middle) and GEOS-3 (bottom). In all three figures there is excessive variability in the tropical

total ozone column, and it is the largest for GEOS-4.0.3.

81
Figure 5.22: Time series of daily global root-mean-square of the total column O-F residuals for

TOMS data are shown for three ozone assimilation experiments driven by instantaneous 6-hourly

forecast of the winds from GEOS-4.0.3 (cyan), GEOS-4.0.2 (orange), and GEOS-3 (green).

data both exhibit too low ozone in the tropics and too high ozone in the middle latitudes,

especially in the Southern (winter) Hemisphere. This slowly developing latitudinal bias in

the total ozone is often seen in CTMs due to an excessive residual circulation that transports

air from the tropical stratosphere upward and towards the winter midlatitudes.

The disagreements between GEOS-4.0.3 and TOMS are larger. The latitudinal bias

is stronger. There are two belts of low total ozone in GEOS-4.0.3: around the equator

and near 30◦N. Only one low-ozone belt is seen in the TOMS data, somewhat south of

the Equator. These results appear to be consistent with issues seen in the stratospheric

dynamical behavior of GEOS-4.0.3 (see the summary in section 5.3.6).

5.4.3 Evaluation of 6-hourly Averages of Winds

Ozone assimilation experiments driven by instantaneous 6-hourly forecast winds were performed using GEOS-4.0.3 and GEOS-4.0.2 winds and compared with those driven by GEOS3 winds. Total column ozone fields from these experiments are shown in Figure 5.21. All

three fields can be compared with TOMS total columns in Figure 5.20. Assimilated ozone

fields are constrained by the TOMS and SBUV data, which prevent development of large

latitudinal biases like those in CTM fields in Figure 5.20. Thus, the properties of the

transport can be evaluated on shorter time scales, especially within 24 hours, which is the

typical time between two ozone observations over the same location. A qualitative shortcoming of 6-hourly forecast winds from GEOS-4.0.3 is seen from the excessive variability

82
Figure 5.23: Total column ozone fields at 12Z on July 31, 2000 are shown (in DU) for two CTM

simulations that were driven by 6-hourly averages of the winds from GEOS-4.0.3 (top), GEOS-4.0.2

(middle). Independent TOMS total ozone column data are shown in the bottom panel.

83
Figure 5.24: Total column ozone fields at 12Z on July 31, 2000 are shown (in DU) for two ozone

assimilation experiments that were driven by 6-hourly averages of the winds from GEOS-4.0.3 (top),

GEOS-4.0.2 (middle). Independent TOMS total ozone column data are shown in the bottom panel.

84
Figure 5.25: Time series of daily global root-mean-square of the total column O-F residuals for

TOMS data are shown for three ozone assimilation experiments driven by 6-hourly averages of the

winds from GEOS-4.0.3 (blue), GEOS-4.0.2 (black). For reference the same quantities are shown

for experiments driven by instantaneous 6-hourly forecast winds GEOS-4.0.3 (cyan), GEOS-4.0.2

(orange).

in the tropical total ozone at small spatial scales. Even though this problem is seen with

GEOS-4.0.2 and GEOS-3 winds, it is manifested more strongly with GEOS-4.0.3 winds. A

quantitative measure of the quality of 6-hourly forecasts from three systems is seen in Figure 5.22, where time series of daily global RMS of TOMS total ozone column O-F residuals

are shown for June and July 2000. These residuals average about 12 Dobson units (DU) for

GEOS-3, about 13 DU for GEOS-4.0.2, and about 18 DU for GEOS-4.0.3. These results

indicate that the instantaneous 6-hourly forecast winds from GEOS-4.0.3 are not suitable

for driving CTMs or for ozone assimilation.

The above experiments were repeated using time averages of winds centered at the synoptic times (0, 6, 12, and 18Z) to drive the CTM and the ozone assimilation system. The

averaging intervals were 6 hours for GEOS-4.0.3 winds and 12 hours for GEOS-4.0.2 winds.

After two months of CTM simulations the total ozone fields develop latitudinal biases (Figure 5.23), but they are smaller than with instantaneous winds (Figure 5.20). Qualitatively,

the zonal symmetry in the tropical ozone increases, which is also an improvement over the

CTM fields in Figure 5.20. However, in the CTM driven by GEOS-4.0.3 winds the belt of

lowest total ozone is positioned in the northern tropics, rather than in the southern tropics

as in TOMS data.

Maps of total column ozone from assimilation experiments driven by time-averaged

winds are shown in Figure 5.24. Tropical total ozone from these experiments is less vari85
able and in better qualitative agreement with the TOMS total ozone than the fields in

Figure 5.21. A quantitative comparison is shown in Figure 5.25. The daily global RMS of

TOMS O-F residuals is about 10 DU for both GEOS-4.0.3 and GEOS-4.0.2 time averaged

winds, a value significantly lower than that obtained using the instantaneous 6-hourly forecast winds. An assimilation experiment was also performed, driven by GEOS-4.0.3 winds

that were averaged over 12 hours. The longer averaging period resulted in an increased size

of total ozone O-F residuals. Thus the use of 6-hourly averages of GEOS-4.0.3 winds for

driving CTMs is recommended.

5.4.4 Evaluation of GEOS-4.0.3 Ozone Profiles

The ozone assimilation system in GEOS-4.0.3 (ozone007) includes a CTM driven by 6-

hourly averages of winds. In the GEOS-4.0.2 ozone assimilation (ozone005) the transport

was driven by instantaneous 6-hourly wind forecasts. Total ozone columns and stratospheric

profiles from NOAA 16 SBUV/2 instrument are assimilated in both versions of the system.

The GEOS-4.0.3 system became operational on January 15, 2004. This section summarizes

the comparisons of parallel runs of GEOS-4.0.3 and GEOS-4.0.2 ozone assimilation for

December 2003 and the first half of January 2004. The emphasis is on the quality of

ozone profiles that is evaluated through comparison with high quality independent ozone

observations.

Figure 5.26: The RMS of O-F residuals for stratospheric SBUV layers (Umkehr layers) are shown

for GEOS-4.0.2 (red) and GEOS-4.0.3 (blue) ozone assimilation systems. Approximate pressure

ranges for the SBUV layers are shown on the right-hand axis. The residuals are for 912 SBUV

profiles on December 31, 2003, which is one month after the GEOS-4.0.3 ozone assimilation was

initialized.

86
The RMS of O-F residuals for stratospheric SBUV layers (Umkehr layers) are compared

for GEOS-4.0.2 and GEOS-4.0.3 in Figure 5.26. The residuals are from December 31,

2003, which is one month after the GEOS-4.0.3 ozone assimilation was initialized. The O-F

residuals for GEOS-4.0.3 have a smaller RMS throughout the stratosphere (Umkehr layers

3-9) and they are very close to those of GEOS-4.0.2 in the mesosphere (Umkehr layers

10-12). The largest relative improvement in the O-F residuals that exceeds 30% is seen for

layers 4 and 5, i.e., between 16 and 64 hPa. This is a region with sharp vertical gradients

in ozone, and the quality of ozone fields is sensitive to details of vertical transport. Zonal

means of O-F statistics indicate that the largest improvements are near 20◦N and 20◦S.

The remaining figures show comparisons of GMAO ozone analyses against independent

ozone profile data. High quality ozone profiles are available from the Polar Ozone and

a

b

d

c

e f

Figure 5.27: Mean POAM profiles (black) are compared with profiles of collocated GMAO ozone

analyses from GEOS-4.0.3 (blue) and GEOS-4.0.2 (red) assimilation systems. Comparisons are

shown separately for the Northern (a and b) and Southern (d and e) Hemisphere. The root-meansquare differences between POAM and GEOS-4.0.3 (red) and GEOS-4.0.2 (black) ozone analyses

are shown for Northern Hemisphere (c) and Southern Hemisphere (f). POAM data from January

1-14, 2004 are used, except in a) and d) where POAM data for December 2003 are used.

87
Aerosol Measurement III (POAM) occultation instrument (Lucke et al. 1999). More than

600 POAM profiles are available during the validation period, however the coverage of

POAM is limited to northern and southern high latitudes (between about 60◦ and 65◦

in each hemisphere). Differences between analyses and POAM are shown in Figure 5.27,

and they are generally small. Mean differences are largest around 10 hPa in the Southern

Hemisphere and around 2 hPa in the Northern Hemisphere. Differences between GEOS4.0.2 and GEOS-4.0.3 ozone means are hardly visible. However, the RMS difference between

GEOS-4.0.3 ozone and POAM is smaller than between GEOS-4.0.2 ozone and POAM at

all pressure levels between 20 and 70 hPa.

Comparisons against ozone sondes launched from Neumayer, Antarctica, near 70◦S and

8

◦W (G. Koenig-Langlo personal communication 2004) are shown in Figure 5.28. Both

analyses agree well with sonde profiles. The largest differences in profile shapes of the

analyses are seen in the pressure layer between 20 and 50 hPa. In this layer the laminar

feature in the GEOS-4.0.2 analysis is too strong, and the smoother shape of the GEOS-4.0.3

analysis agrees better with the shape of sonde profiles.

The limb sounding Michelson Interferometer for Passive Atmospheric Sounding (MIFigure 5.28: Ozone profiles from independent sondes (red solid line) that were launched from

Neumayer, Antarctica, (70◦S, 8

◦W) and collocated GMAO ozone analyses from GEOS-4.0.3 (blue

dashed line) and GEOS-4.0.2 (black solid line) systems are shown for December 24 and 31, 2003 and

for January 7 and 14, 2004.

88
PAS) on board ESA’s Environmental Satellite provides ozone profiles over a wide range of

latitudes (Ridolfi et al. 2000). A total of 2238 MIPAS ozone profiles from the first week

of January 2004 were used to evaluate GEOS ozone analyses. About 600 of these profiles

are in the tropics, more than 350 in southern midlatitudes, and more than 400 in northern

midlatitudes. A good agreement is seen between MIPAS and analyses means throughout

the stratosphere in Figure 5.29. The means from two analyses are indistinguishable. However, in the RMS differences the GEOS-4.0.3 agrees better with MIPAS in the stratosphere

between 1 and 70 hPa. The largest improvement from GEOS-4.0.2 to GEOS-4.0.3 is seen

near 20 hPa.

Figure 5.29: Mean profiles from MIPAS (solid) are compared with collocated assimilated ozone

from GEOS-4.0.2 (dotted) and GEOS-4.0.3 (dashed) in the left panel. The RMS difference between

MIPAS and collocated analyses are shown in the right panel for GEOS-4.0.2 (solid) and GEOS-4.0.3

(dashed). Comparisons were done using over 2000 MIPAS observations for the period from January

1 to 7, 2004.

The above comparisons show very small changes in mean profiles between GEOS-4.0.2

and GEOS-4.0.3 ozone. The representation of ozone variability, which is quantified by RMS

differences, improved in GEOS-4.0.3 in the stratosphere at pressure levels between about

20 and 70 hPa. Qualitative improvement to the total ozone column maps is the largest in

the tropics, where the fields are smoother and more zonally symmetric. This is consistent

with a decrease in the total column O-F residuals from about 14 DU to about 10 DU.

In summary, the ozone product in GEOS-4.0.3 is better than in GEOS-4.0.2. The key

to this improvement is the use of time averaged winds, rather than instantaneous winds, to

drive the ozone transport.

89
5.5 Monthly Mean Diagnostics

Examples of the longer time-scale behavior of the GEOS-4.0.3 system are shown here with

diagnostics of monthly means of precipitation and surface stresses over oceans. This section complements the short-term system diagnostics of GEOS-4.0.3 shown in a subsequent

section on monitoring results (5.8).

5.5.1 AMIP (Climatology) Results

Periodically, Atmospheric Model Intercomparison Project (AMIP) runs are performed at

the GMAO to provide benchmarks with existing models as a first step in the process of

developing or implementing new atmospheric GCMs for a new DAS effort. Of particular interest in this validation effort is the precipitation climatology of the GEOS-4 GCM,

especially when compared with the precipitation behavior of GEOS-4 DAS.

The fvGCM (the model in the GEOS-4 DAS) was examined in such an AMIP context;

this effort consisted of a base version of fvGCM, some versions of fvGCM having variations

in physics, the model used in the GEOS-3 DAS, and the models used and being developed

by the NASA Seasonal-to-Interannual Prediction Project (NSIPP). The following is a list

of the GCMs evaluated:

Model #1) GEOS-3 (2x2.5 x 48 Levels, Aries Dynamical Core, Goddard Physics)

Model #2) fvGCM (1x1.25 x 55 Levels, Finite Volume Core, NCAR Physics)

Model #3) fvGCM w/McRAS Moist Physics

Model #4) fvCSU (fvGCM w/CSU Moist Physics)

Model #5) NSIPP-1 (1x1.25 x 34 Levels, Aries Dynamical Core, NSIPP-1 Physics)

Model #6) NSIPP-2 (2x2.5 x 40 Levels, Aries Dynamical Core, NSIPP-2 Physics).

The models were run from January 1982 through December 1998 using the Hadley

Centre SST and Sea-Ice datasets. Detailed results from the intercomparisons are available

from the GMAO Web home page:

http://gmao.gsfc.nasa.gov/research/modeling/models/private/amip/index.php

.

Analysis of the seasonal mean precipitation fields show that the operational GEOS-4

GCM (Model #2 described above) produced simulations that were comparable to the other

models and quite realistic when compared to GPCP data for the same time period. Figures

5.30 and 5.31 display the mean seasonal DJF and JJA, respectively) precipitation fields

from the six runs and a GPCP verification. Figures 5.32 and 5.33 show the pattern of

differences of the six precipitation patterns with the verification.

It can be seen that while specific subtle differences exist between models, the dominant

seasonal mean errors are quite similar when compared to the GPCP data. This result

90
Figure 5.30: December, January, February (DJF) mean total precipitation in mm day−1

for the

six intercompared models. GPCP verification climatology for this period is shown at the bottom.

91
Figure 5.31: Same as 5.30, except the period is June, July, August (JJA)

92
Figure 5.32: December, January, February (DJF) precipitation differences in mm day−1

for each

of the six models compared with the GPCP verification.

93
Figure 5.33: Same as 5.32, except the period is June, July, August (JJA)

94
Figure 5.34: Taylor plots of model precipitation anomaly behavior (see text for Taylor plot description): DJF: Dec-Feb, MAM: Mar-May, JJA: Jun-Aug, SON: Sep-Nov, ANN: Annual.

95
is significant in light of the DAS results presented in Chapter 5, where the DAS-forced

precipitation differs strongly from climatology.

A sense of the differences in variability (anomalies with respect to the above means)

can be seen through the use of Taylor diagrams. A point on a Taylor diagram represents

a polar-coordinate point: the radius is a model’s anomaly standard deviation divided by

the verification anomaly standard deviation; the angle from the x-axis is the arc-cosine of

the correlation between a model and the verification anomalies. Thus a “perfect” agreement between a model’s anomaly pattern and a verification anomaly pattern would be

represented by a circle at “1.00” on the x-axis. The anomaly pattern correlation and ratio

of anomaly standard deviations in Figure 5.34 indicate that the GEOS-4 GCM produces

results comparable to the other model runs.

5.5.2 Monthly Precipitation in GEOS-4 DAS

The primary focus was on the January and July 2001 monthly mean precipitation fields

(although some comparisons were carried out for all months of 2001). Intercomparisons

were performed on precipitation products from:

• C403 cer 01 - the current system being validated (GEOS-4.0.3);

• C402 rp 02 - previous version of GEOS-4 (GEOS-4.0.2);

• NCEP GDAS - the NCEP operational system;

• GPCP - an observational product used as verification.

These comparisons included maps of monthly means and their differences, and time series of

daily values for selected area averages. The comparisons were summarized with bar charts

(area means), and Taylor plots (points on the Taylor plots show the correlations and relative

variances of any two time series; also discussed in section 5.5.1).

Both the NCEP GDAS and GEOS data were first regridded to match the 1-degree GPCP

grid. Also, the 6-hourly GEOS and NCEP products were averaged to create daily means

in order to compare with the daily GPCP product. The 1-degree daily GPCP product is

described at:

http://precip.gsfc.nasa.gov/gpcp daily comb.html .

Figure 5.35(a,b) shows the monthly mean differences of the GEOS-4.0.3 precipitation

(in mm day−1) compared to GPCP for January and July 2001. The precipitation is overestimated over the tropical oceans during all months of the year. The bias is largest during

boreal summer (the tropical ocean average is a factor of 2 too large), especially over the

western tropical Pacific (equator to 30◦N) and the Caribbean. During the Boreal winter

the bias is largest over the South Pacific Convergence Zone (SPCZ). On the other hand, the

precipitation is underestimated throughout most of the extratropical oceans (poleward of

30◦) especially in the North Pacific, and throughout the Southern Hemisphere extratropics.

Area averages for these monthly statistics, and their differences, are given in Table 5.2.

Note that the results in Figure 5.35(a,b) should be compared with their AMIP counterparts

(Figures 5.30, 5.31, 5.32, and 5.33; the panels labeled “fvGCM” in each), with the caveat

96
a) b)

Figure 5.35: (a) January 2001 precipitation fields from C403 (top panel) and GPCP (middle panel).

The difference field (C403- GPCP) is shown in the bottom panel. Units are mm day−1. (b) Same

as (a) but for July 2001.

97
that the GPCP climatology in the AMIP comparisons spans more years than just the

year 2001 used for the DAS precipitation comparisons. A close examination of the two

(DAS, AMIP) sets of results does indicate that the DAS precipitation results tend to have

a 50% to 100% greater disagreement with the corresponding GPCP verification, with the

preponderance of the disagreement occurring in the tropics.

January (July) Average Precipitation

System Global Tr. Ocean Tr. Land (30N-60N) (30S-60S)

C403 2.90 (3.50) 4.6 (5.7) 3.30 (3.30) 2.00 (2.10) 1.30 (2.20)

GPCP 2.60 (2.70) 2.8 (2.9) 3.10 (2.80) 2.70 (2.40) 2.60 (3.30)

C403-GPCP 0.32 (0.86) 1.8 (2.9) 0.25 (0.52) -0.69 (-0.33) -1.30 (-1.10)

Table 5.2: Precipitation averaged over the specified regions for January (July) of 2001. Units are

mm day−1.

Figure 5.36 shows the January and July 2001 precipitation results in the tropical band

30N - 30S for three DAS systems: GEOS-4.0.3, GEOS-4.0.2 and NCEP. The GEOS-4.0.3

bias is somewhat improved (reduced by 5-10% over tropical oceans) compared to the previous version of the system (GEOS-4.0.2). The bias is nevertheless still quite a large. For

comparison, the precipitation from the NCEP operational system has only one half to one

third of the precipitation bias compared to GPCP.

A sense of the variability of the DAS precipitation results is given in Figures 5.37,

5.38, and 5.39. These results should be compared with the AMIP results shown earlier

(Figure 5.34, the blue “dots” depict fvGCM). Even for the global result (Figure 5.37), the

July variability for GEOS-4.0.3 (green dot) is considerably less than its AMIP counterpart.

As the region of interest is focused to the areas of greatest precipitation (first the Tropics,

20◦S − 20◦N; then the Western Pacific, 15◦S − 15◦N and 120◦E − 180◦E) the clustering

so evident in the AMIP results largely disappears. The amplitude in the daily fluctuations

of precipitation shows some improvement in GEOS-4.0.3 compared with those from GEOS4.0.2. Correlations with the daily GPCP values tend to be low throughout the tropics

(generally less than 0.4), with somewhat larger values in the extratropics (in some places

exceeding 0.8). These values (while low) are similar if not somewhat better than those

based on a comparison between GPCP and NCEP GDAS values.

Figure 5.40 shows how the 30◦S − 30◦N average monthly mean precipitation varies

by month during 2001 for GEOS-4.0.3, GEOS-4.0.2, NCEP and GPCP. In addition, the

land and oceanic contributions to the averages are shown. The tropical ocean precipitation

exhibits an unrealistic seasonal cycle with a substantial increase in precipitation during

boreal summer that is not seen in the GPCP values. There is a uniform improvement in

the bias and RMS statistics of the GEOS-4.0.3 precipitation over that from GEOS-4.0.2 in

the monthly comparisons against GPCP for 2001.

In summary, a significant deficiency in GEOS-4.0.3 is its tendency to overestimate precipitation over the tropical oceans during all months of the year. The tropical ocean bias is

largest during Boreal summer (area averaged precipitation is a factor of two too large), especially over the western tropical Pacific (equator to 30◦N) and the Caribbean. In contrast,

GEOS-4.0.3 underestimates precipitation over the extratropical oceans, particularly in the

98
Figure 5.36: Monthly average precipitation comparisons for 30◦S − 30◦N, for January 2001 (top

set) and July 2001 (bottom set). Middle panels in both sets are the GPCP verification data for that

time. Units are mm day−1.

99
a)

b)

Figure 5.37: Taylor plot showing global (excluding poles) precipitation comparison statistics against

GPCP for GEOS-4.0.3 (green), GEOS-4.0.2 (red) and NCEP (blue). NCEP vs GEOS-4.0.3 (purple)

added for reference. Also shown are daily time series of the averages with and without the bias. (a)

January 2001. (b) July 2001. Units are mm day−1.

100
a)

b)

Figure 5.38: Taylor plots as in Figure 5.37, only for the tropics : 20◦S − 20◦N.

101
a)

b)

Figure 5.39: Taylor plots as in Figure 5.37, only for the tropical Western Pacific region: 15◦S−15◦N,

120◦E − 180◦E.

102
Figure 5.40: Monthly tropical precipitation averages for 2001, for GEOS-4.0.3 (cer), GEOS-4.0.3

(rp2), NCEP and GPCP verification. The land (green) and ocean (blue) contributions to the monthly

averages (red) are also shown. Units are mm day−1.

103
North Pacific, and in the Southern Hemisphere during January where the area-averaged

values are underestimated by a factor of two.

5.5.3 Surface Wind Stress in GEOS-4 DAS

Figure 5.41 compares the monthly mean (January and July) zonal and meridional components of the surface stresses in GEOS-4.0.3 with those from ERA-40 and values inferred

from SSM/I data (using methods based on those described in Atlas et al. (1996)). The

bottom pair of plots in the January and July panels in Figure 5.41 are the full zonal and

meridional stresses from GEOS-4.0.3; the remaining plots in this figure are all difference

plots of stresses versus their SSM/I estimated counterpart.

In general, the GEOS values compare quite well with the stresses estimated from SSM/I.

In fact, for both months and for both components of the stress, the biases (with respect

to SSM/I) tend to be smaller than those from ERA-40. Notable exceptions are the larger

regions of westerly bias for GEOS in the tropical Pacific and southern Indian Oceans. The

comparison is quantified in several Taylor plots (Figures 5.42 and 5.43). These plots show

that the monthly mean GEOS fields have consistently higher (compared with ERA-40)

spatial correlations with the SSM/I fields and also tend to have spatial variances that are

closer to SSM/I. The meridional component of the stress, in particular, is remarkably similar

to the meridional stress derived from SSM/I data.

104
Figure 5.41: Monthly mean surface stresses, zonal component (left) and meridional component

(right column), for January 2001 (top set) and July 2001 (lower set). Compared are the stresses

from the ERA-40 reanalysis, GEOS-4.0.3 (“CERES”) and stresses derived from SSM/I data. Units

are dynes cm−2.

105
Figure 5.42: Taylor plots comparing zonal wind surface stresses over oceans for January 2001 (top

set) and July 2001 (lower set) for four regions: Global, Tropics, Northern Mid-latitude and Southern

Mid-latitude. Three comparisons on each plot: GEOS-4.0.3 vs ERA-40 (red); GEOS-4.0.3 vs SSM/I

(green); ERA-40 vs SSM/I (blue).

106
Figure 5.43: Same as Figure 5.42, only for meridional stress component.

107
5.6 Precipitation Variability: MJO

This section examines the precipitation variability on the MJO/ISO time scale in the GEOS4 system. Precipitation data were produced from the c403 cer 01 run of this system (basically the current validation version of GEOS-4, run in earlier time periods to support the

CERES reprocessing). Currently, very few GCM or assimilation systems are capable of

producing reasonable precipitation variability on the MJO/ISO time scale, especially with

the known MJO/ISO features during both hemispheric summer seasons. Previous results

(e.g., Sections 5.1, 5.2) have shown that this system produces reasonable temperature and

moisture profiles and surface skin temperature for the clear sky OLR computations. The

issue here is how well the GEOS-4 system handles a specific type of organized atmospheric

process that operates on a longer time scale.

5.6.1 Seasonal Mean

Figure 5.44 presents seasonal mean precipitation for boreal summer (May through October)

and winter (November through April) of 2000 and 2001. Four months of analysis data from

four different assimilation systems are compared with TRMM observational data. The

assimilation systems compared are: fv141 val 01 (described in 4.1.1), C403 cer 01, GDAS

(NCEP reanalysis), and ERA-40 (ECMWF 40 years reanalysis).

Seasonal Precipitation Mean (mm day−1)

System Summer Winter

TRMM 2.57 2.59

fv141 val 01 4.48 3.94

c403 cer 01 4.18 3.64

GDAS 3.63 3.59

ERA-40 2.29 2.32

Table 5.3: Seasonal mean precipitation statistics, area averages (40◦S to 40◦N). See Figure 5.44.

The comparisons are between 40◦S to 40◦N. In order to compare the patterns among all of

these runs in Figure 5.44, the two GEOS precipitation results have had a scale factor of

0.6 applied. Figure 5.44 shows that all the four analyses produce reasonable patterns, the

two versions of the GEOS-4 overestimate precipitation over the western Pacific warm pool

region. For clarity, the mean seasonal precipitation for each run (on the left hand of the

figure) between 40◦S to 40◦N are summarized in Table 5.3. Among the four runs, ERA-40

shows a closer mean to observations than the other three, with the two versions of GEOS-4

overestimating precipitation from 70% to as much as 100%.

108
Figure 5.44: Seasonal mean precipitation for boreal summer (May to Oct.) and winter (Nov to

Apr.) Also shown are the area average (40◦S to 40◦N) seasonal means of precipitation.

109
The variability of precipitation on the MJO/ISO time scale for these runs is examined by

comparing their standard deviations in Figure 5.45. This figure shows that all four analyses

generally underestimate precipitation variability. As was done for the previous figure, the

seasonal standard deviations are summarized in Table 5.4.

Seasonal Precipitation Std. Dev. (mm day−1)

System Summer Winter

TRMM 1.85 1.93

fv141 val 01 1.44 1.48

c403 cer 01 1.39 1.42

GDAS 1.40 1.42

ERA-40 1.02 1.02

Table 5.4: Seasonal standard deviation precipitation statistics, area averages (40◦S to 40◦N). See

Figure 5.45.

Examining Figure 5.45, the areas of greatest disagreement between observations and the

model-based precipitation variability occur over the equatorial region (especially the central

Pacific), and over the summer monsoon regions of both hemispheres.

A more detailed view of the precipitation processes on these time scales can be obtained

by examining longitude-time (Figure 5.46) and latitude-time (Figure 5.47) cross sections.

Figure 5.46 shows averages of the precipitation between 5◦S and 5◦N for the different runs

along with the TRMM observations. Eastward propagating convection associated with the

MJO/ISO activity, which starts in the Indian Ocean and extends to the western Pacific

warm pool region, is clearly shown in the TRMM data. In comparison with TRMM, the

two GEOS-4 runs perform somewhat better than GDAS, though not as well as ERA-40.

Figure 5.47 presents latitude-time cross sections, averaged from 75◦E to 85◦E. This figure

shows the northward propagation of precipitation associated with the tropical MJO/ISO

activity. All systems examined here show a weak northward propagation during the summer

season, which affects the monsoon variability over the Indian subcontinent.

110
Figure 5.45: Seasonal standard deviation precipitation for boreal summer (May to Oct.) and

winter (Nov to Apr.). Also shown are the area average (40◦S to 40◦N) seasonal standard deviations

of precipitation.

111
Figure 5.46: Longitude-time cross sections (averaged 5◦S to 5◦N) of precipitation standard deviation. TRMM observations are the left-most plot.

112
Figure 5.47: Latitude-time cross sections (averaged 75◦E to 85◦E) of precipitation standard deviation. TRMM observations are the top-most plot.

113
5.7 Data Impact

A series of data withholding experiments was conducted with GEOS-4.0.3 with the purpose

of determining the impact of various observation types on the GEOS-4 assimilation system.

The initial experiments involved “data denial” from major observing systems (rawinsondes,

TOVS retrievals, satellite winds). Further experiments were also conducted which excluded

all wind data and all mass data; additionally, a pair of experiments were run which excluded

upper-level satellite winds and QuikScat winds respectively.

The experiment framework consisted of a two-week “spinup” period for the assimilations

followed by a two month evaluation period. During the evaluation period, 5-day forecasts

were run every other day for a total of 30 forecasts. The experiment periods used were

January-February 2003 and July-August 2003. The initial conditions for the assimilations

were taken from the CERES reanalysis assimilation run. The experiment setup is similar

to the CERES reanalysis except that the NOAA-17 radiance data is being used in these

experiments.

The following experiments were performed:

Data Impact Experiment Summary

Experiment Description

r14 ctrl control

r14 nraw no rawinsondes

r14 nstw no satellite winds (no CTW, no QuikScat)

r14 ntov no DAOTOVS

r14 nups no “uppersat” (= no sat wind, no DAOTOVS)

r14 wind no height observations

r14 mass no wind observations

r14 nctw no cloud motion winds

r14 nqks no QuikScat winds

Table 5.5: List of data withholding experiments and their identifying labels.

The results of these experiments were assessed using a variety of methods:

Assimilation assessment

• Assimilation anomaly correlation, RMS difference from control

• Bias and standard deviation of O-F (Observations minus Forecast)

• Quality control marks and QC rejection rates

Forecast skill assessment

• anomaly correlation

• RMS difference

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The first four withholding experiments indicate that rawinsonde data is the primary

source of information utilized by GEOS-4 in the Northern Hemisphere (NH). This was particularly true for the NH winter season, but applied to the NH summer season as well.

This conclusion is drawn from the no-raob experiment having the largest impact in producing differences in the winter assimilation (vs the control, see Figure 5.48(top)), with lesser

differences from the other experiments for the summer run (Figure 5.48(bottom)). The

no-raob experiment showed the largest reduction in forecast skill of the four withholding

experiments for both winter (Figure 5.49(top)) and summer (Figure 5.49(bottom)).

The “no-uppersat” (removing all satellite-derived data) had the largest impact in the

Southern Hemisphere, during both experiment periods (see Figures 5.50-5.51). Note that

the removal of satellite winds (QuikScat and upper-level cloud cloud motion) had a larger

impact than the withholding of satellite temperature retrieval data (compare the blue and

green curves in Figures 5.50-5.51). Additional experiments were performed to determine the

impact of removing QuikScat data and cloud-track wind data individually in the Southern

Hemisphere. Figure 5.52 shows that withholding the QuikScat ocean surface wind data

had a greater impact than withholding the upper-level cloud motion winds. This disparity

may be a testament to the far greater and more persistent coverage of the QuikScat data

in regions not receiving much coverage from the other components of the observing system.

A key aspect of the global observing system in 2003 was the presence of greatly improved

satellite temperature sounding instruments, specifically ATOVS which has the Advanced

Microwave Sounding Unit (AMSU). The ATOVS data had considerably less influence in

the GEOS-4 assimilation system than is the case for the other NWP centers. That this is

the case can be inferred by comparing the GEOS-4 impacts above with the position stated

in the summary of the Third WMO Workshop on Impact of Observing systems on NWP

(Alpbach, Austria March 2004, see

http://www.wmo.int/web/www/GOS/Alpbach2004/Agenda-index.html):

It is confirmed from all global data impact studies that satellite data, in particular ATOVS data, are the major source of information in NWP systems.

The consensus from other centers is that the impact of these data is on the order of 12-24

hours in forecast skill in the Northern Hemisphere extratropics, which is equivalent to or

better than rawinsondes. It thus appears that the ATOVS data appear to be under-utilized

in the GEOS-4 system.

115
Figure 5.48: Top: analysis anomaly correlations, using NCEP analyses as verification, for the

Northern Hemisphere, January-February 2003. Experiments here are the first 5 entries in Table 5.5.

Bottom: July-August 2003.

116
Figure 5.49: Top: forecast anomaly correlations, using their own analyses as verification, for the

Northern Hemisphere, January-February 2003. Experiments here are the first 5 entries in Table 5.5.

Bottom: July-August 2003.

117
Figure 5.50: Top: analysis anomaly correlations, using NCEP analyses as verification, for the

Southern Hemisphere, January-February 2003. Experiments here are the first 5 entries in Table 5.5.

Bottom: July-August 2003.

118
Figure 5.51: Top: forecast anomaly correlations, using their own analyses as verification, for the

Southern Hemisphere, January-February 2003. Experiments here are the first 5 entries in Table 5.5.

Bottom: July-August 2003.

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Figure 5.52: Forecast anomaly correlations, using their own analyses as verification, for experiments

examining the relative impacts of cloud motion winds and QuikScat surface winds, for the Southern

Hemisphere, January-February 2003.

Profiles of (O-F) residual RMS bias for rawinsonde data for January and February 2003

were generated; only the January results will be shown here. The (O-F) RMS bias values

for the no-raob experiment (Figure 5.53) were calculated as a post-processing step using

the same quality controlled rawinsondes as used in the control experiment. The no-raob

experiment had larger (O-F) biases than the control (with raobs) in the Northern Hemisphere near 200 hPa, which is consistent with known disagreements between rawinsondes

and TOVS retrievals. The no-raob (O-F) biases were smaller than the control in the tropics

at levels above 400 hPa. The Southern Hemisphere results generally agree with the Northern Hemisphere results, though care must be taken in interpreting Southern Hemisphere

rawinsonde statistics due to their limited sampling of rawinsondes in that region. Overall,

the system appears to be very sensitive to the availability of rawinsonde data, with these

data having a positive effect outside the tropics, and a negative effect at upper levels within

the tropics.

The no-TOVS results are shown in Figure 5.54. For the most part, the differences

between no-TOVS and the control (O-F) RMS biases against rawinsondes are significantly

smaller than the no-raob differences against the control. This result indicates a reduced

sensitivity to TOVS retrievals compared to rawinsonde data in the GEOS-4 system.

120
Figure 5.53: Profiles of areal RMS bias of O-F for the control (red) and no-rawinsonde experiments

(blue), January 2003.

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Figure 5.54: Profiles of areal RMS bias of O-F for the control (red) and no-TOVS experiments

(blue), January 2003.

122
Complexities can arise in interpreting data withholding results, as can be seen from

the no-wind versus no-mass experiments. Figure 5.55 shows that the no-mass assimilation

(labeled “wind” in the figure) had a lower analysis anomaly correlation in the Northern

Hemisphere than that from the no-wind experiment (labeled “mass” in the figure). The

closeness of the control to the verifying analyses (from GEOS-4.0.1 in this case) is shown by

the black curve in Figure 5.55. However, Figure 5.56 shows that the resulting forecasts from

the above analyses had the opposite behavior; the no-mass experiment displayed greater

forecast skill than did the no-wind experiment. Figure 5.57 shows that the no-wind assimilations consistently used more observations than did the no-mass assimilations (likely a

result of the large number of TOVS height profiles in the no-wind assimilation). It should

be noted that the forecast skill results discussed here were generated using their own analyses as verification. The results for the July-August 2003 no-mass and no-wind experiments

behaved in a similar manner.

In summary, rawinsonde data in the GEOS-4 system have the largest impact on the

Northern Hemisphere forecast skill, and the satellite winds (particularly QuikScat winds)

have the largest impact on Southern Hemisphere forecast skill. Data withholding experiments with an earlier version of GEOS-4 (performed with data from 1998; results not

presented here) indicated that the height data obtained from the interactive retrieval processing of TOVS radiances (see section 3.4) had only a modest impact on the GEOS-4

assimilation results. Although the impact of TOVS data on forecast skill is greater in

GEOS-4.0.3 than in the prior versions of GEOS-4, the TOVS impact is still significantly

less than that obtained by other centers. There is a concomitant over-sensitivity of the

GEOS-4 system to rawinsonde data. Experiments with withholding mass and wind data

show that a “better” analysis does not necessarily translate into better forecast skill.

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Figure 5.55: Analysis anomaly correlation, GEOS-4.0.1 used as verifying analysis. No-wind assimilation (red), no-mass assimilation (green), control (black).

Figure 5.56: Anomaly correlation scores for January-February 2003, each experiment using its own

analysis as verification.

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Figure 5.57: Observation counts during January 2003 for the control, no-mass and no-wind experiments.

125
5.8 Monitoring Results

One of the major components of the validation process is the assessment of the behavior

of the candidate system while it is running in a “parallel” operational mode alongside

of the previous operational version of the system. In this section, the behavior of the

parallel system will be presented from the viewpoint of a number of broad monitoring

categories: quality control statistics; observing system statistics (e.g., O-F and O-A time

series); forecast skill scores; and individual case studies highlighting particular issues.

5.8.1 Quality Control

The overall behavior of the quality control process (described in section 3.3) is influenced

by a number of factors, which can include both the errors in the observations as well as

the assumptions about the model and observation error statistics. Figure 5.58 shows how

the flagging of suspect observations for TOVS heights (for the month of September, 2003)

changed from GEOS-4.0.2 to GEOS-4.0.3. The figure shows that the many changes made

to obtain GEOS-4.0.3 result in a markedly different TOVS QC suspect observation flagging

pattern. The older system had an unusual bimodal pattern, with a significant flagging of

suspect observations in the middle troposphere, next to no flagging in the lower to middle

stratosphere, and a very steep suspect flagging rate in the upper stratosphere. The newer

system flattens out this pattern, with the overall suspect observation flagging rates tending

to be lower, except for the lower to middle stratosphere. Some perspective on the numbers

in Figure 5.58 can be found by noting that the “ideal” rate at which suspect data should

be flagged is roughly 4% (cf. Figure 3.6).

Figure 5.58: Quality control flagging of suspect observations for TOVS heights for two runs: GEOS4.0.2 (blue) and GEOS-4.0.3 (red). Time period covered was September 2003.

126
Figure 5.59: TOVS QC statistics for 11 November 2003 for GEOS-4.0.2 (top) and GEOS-4.0.3

(bottom). Many fewer TOVS data are flagged in the newer system.

127
Figure 5.60: Two examples of ineffective background checking in older versions of GEOS. Top:

inappropriate wind observations (green barbs, with each barb representing an O-F of 10 m s−1) in

GEOS-4.0.2. Bottom: mislocated sea level pressure observations in GEOS-3 (white numerals, O-F

units in hPa).

128
Figure 5.61: QuikScat QC statistics for 13 June 2003 for two runs which used two different values

of τx (see section 3.3.2). Top panel used (old) τx = 1000, bottom panel used (new) τx = 10.

129
Another view of the difference in TOVS QC between GEOS-4.0.2 and GEOS-4.0.3

is shown in Figure 5.59. There is a large amount of information in each of the panels

in this figure; the most striking aspect is the greatly reduced amount of data that were

flagged/rejected in the newer system (compare the number of yellow or red dots between

the top and bottom panels in Figure 5.59). The pressure distribution of the QC decisions

(bar charts, top left in each panel) also shows a significantly different behavior between the

two systems, as there is something clearly strange occurring in the stratosphere in GEOS4.0.2, which will be shown in a different form in section 5.8.2. The difference in the level

chosen as the cutoff for cloud-clearing in iRET (see section 3.4) between the two systems

is marked by the change in where the abrupt “jump” in observation counts occurs in the

pressure distribution charts (clearly showing the 200 hPa level chosen for GEOS-4.0.3).

A problem with the QC background check parameter (τx, see section 3.3.2) was identified

in prior versions of GEOS. Figure 5.60 shows two extreme examples of poor QC behavior

with τx = 1000. In both cases, the failure of the background check to flag these extremely

unrealistic data resulted in poor analyses; note the 50 m s−1 O-F winds at 850 hPa over

Africa (top), and the 47 hPa sea level pressure O-F off the coast of Antarctica (bottom).

Although the highly suspect data were flagged in each case, the buddy check was effectively

swamped by the vast amount of suspect data and was therefore rendered ineffective.

Figure 5.61 shows an example of the impact on the QC decisions made for QuikScat

winds by changing the background check parameter from 1000 to 10 (the value used for

GEOS-4.0.3). In this case, 13 June 2003 over the South Atlantic, the change resulted in

three additional QuikScat observations being rejected prior to the Buddy Check process.

This case will be described in further detail in section 5.8.4.

5.8.2 Observing System Statistics

The most effective tools to date for monitoring the ongoing behavior of GEOS DAS systems

have been time series of means or RMS for Observation minus Forecast (“OMF”) or Observation minus Analysis (“OMA”), for specified regions and specific observation types. These

diagnostics readily identify such observing system problems as data-dropout or instrument

drift. In addition, some of the more subtle aspects of how a DAS is handling different

aspects of the observing system can be inferred by examining how the OMF time series

compares to the OMA time series.

Figure 5.62 shows a multi-year time series of OMF for GEOS-4.0.2 (red) and GEOS4.0.3 (blue). There were two significant changes in the observing system over this time

period: NOAA-15 HIRS (see section 3.2.3.4.1) was removed from iRET processing due

to excessive noisiness starting 31 May 2000; NOAA-16 data began to be used in iRET

processing starting 2 March 2001. GEOS-4.0.2 had an immediate tropospheric response in

March 2000, with the TOVS OMF global time series becoming much noisier (and larger).

The stratospheric OMF response in the older system was slower to appear, yet grew to very

large amplitudes by November 2000. The GEOS-4.0.3 response to the loss of NOAA-15 was

considerably milder, with smaller and less noisy values of TOVS OMF throughout. The

response of both systems to the introduction of NOAA-16 data also had GEOS-4.0.2 having

abrupt transitions in behavior, with a far more modest change in the behavior of OMF in

GEOS-4.0.3. Note the divergent nature of the impact on 1 hPa OMF in the two systems,

130
Figure 5.62: Time series of global RMS TOVS O-F for height (units: m); GEOS-4.0.3 (blue),

GEOS-4.0.2 (red). Time period ranges from 16 January 2000 to 30 April 2003.

131
GEOS−4.0.2 GEOS−4.0.3

(a (b

Figure 5.63: Time series of global RMS TOVS O-F (blue) and O-A (red) for height (units m); (a)

GEOS-4.0.3, (b) GEOS-4.0.2. Time period ranges from 1 January 2001 to 31 July 2001. Note the

differing scales on the ordinates between the two sets of plots.

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RMS MEAN

Figure 5.64: Time series of global RMS and global mean TOVS O-F for height (units are m);

GEOS-4.0.3 (blue), GEOS-4.0.2 (red). Time period ranges from 1 August 2003 to 24 November

2003.

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with the GEOS-4.0.2 OMF nearly doubling while the OMF from GEOS-4.0.3 is reduced by

nearly half.

Figure 5.63 focuses on the NOAA-16 transition period, showing both OMF and OMA

time series for GEOS-4.0.2 and GEOS-4.0.3. Of particular interest are the differences in

OMA behavior between the two systems. In the stratosphere, the OMA behavior is nearly

the same for the two systems (note the differing ordinate scales in this figure), despite the

very large differences in OMF. In the troposphere, the TOVS OMA from GEOS-4.0.2 are

significantly noisier and have greater magnitudes than those from GEOS-4.0.3 both before

and after the NOAA-16 transition. In addition, the pre-transition OMA for GEOS-4.0.2 is

larger than OMF for most of the troposphere, to a much greater degree than is the case

for GEOS-4.0.3. This suggests that even with a significantly reduced TOVS data coverage

(roughly half, pre-transition), the contributions from other data types lead to analyses that

better agree with the remaining TOVS data (i.e., smaller TOVS OMA) for GEOS-4.0.3.

The post-transition reduction of OMA for GEOS-4.0.2 simply reflects the greater number

of TOVS observations affecting the analysis. Finally, note that the transient effect of the

observing system change lasts for nearly two months for GEOS-4.0.2, while it is nearly

instantaneous for GEOS-4.0.3.

A more disturbing aspect of the behavior of GEOS-4.0.2 is shown in Figure 5.64, for

a time period (August-November 2003) during which there was no significant change to

the observing system. The RMS TOVS OMF for GEOS-4.0.2 grew nearly four-fold in the

stratosphere over a two month period, during which the RMS TOVS OMF from GEOS-4.0.3

remained level. The mean global TOVS OMF for GEOS-4.0.2 displayed a peculiar 3-4 day

oscillation that grew in amplitude throughout the period. Although never fully diagnosed,

this oscillation seemed to be a result of some feedback process involving the model, the

analysis and the interactive retrieval system. It is not in evidence in GEOS-4.0.3.

In summary, previous versions of GEOS-4 (of which GEOS-4.0.2 is an example) were

overly sensitive to perturbations in the observing system, and this sensitivity appears to

have been reduced to reasonable levels in GEOS-4.0.3. In addition, the handling of TOVS

data appears to be more consistent with other observations in GEOS-4.0.3 than in GEOS4.0.2.

5.8.3 Forecast Skill

Forecast skill has long been (and remains, at operational centers) a crucial measure of

performance for an assimilation system. Although the provision of forecast products is not

the central mission of the GMAO, the forecast behavior of GEOS nevertheless provides a

realistic quantitative context for judging the overall system performance. The tool of choice

here is the anomaly correlation, which is the pattern correlation between an anomaly (i.e.,

difference from climatology) of a forecast field against an anomaly of a verification analysis

field. The principal fields examined by this approach are 500 hPa geopotential height and

sea-level pressure; occasionally upper-air wind components are also tested.

For the results presented here, the anomaly correlations of 500 hPa heights and sea-level

pressure were computed at 2◦×2.5

◦ degrees, using both the NCEP operational analysis and

GEOS analyses as verification; forecast and verification fields were smoothed to the 2◦×2.5

◦

resolution. The climatology used for these calculations is a 10-year (1987-1998) average of a

134
2

◦ × 2.5◦ gridded ECMWF product. The region covered in each hemisphere range from 20◦

to 80◦, so that the polar regions and the tropics are excluded from the anomaly correlation

calculation. Three time periods were examined: December 2000 through February 2001;

June 2001 through August 2001; and 1 September 2003 through 19 November 2003. For

the runs in 2001, 5-day forecasts were generated every third day, for a sample size of about

30 members. The 2003 runs generated a 5-day forecast every day, for a sample size of 70-80

(there were some drop-outs due to availability of verification data sets).

Figure 5.65 shows the 500 hPa anomaly correlation results from the 2003 period. In this

figure, “SELF” indicates that each forecast used analyses from its own assimilation system as

verification, while “NCEP” indicates the use of NCEP operational analyses for verification.

There is a clear signal of a positive impact on forecast skill in going from GEOS-4.0.2 to

GEOS-4.0.3. The signal is in both hemispheres, and it is robust with respect to choice

of verification. The 2003 forecasting period corresponds to the period where the feedback

problems evident in OMF and OMA (described in section 5.8.2) became very evident.

Very similar results (not shown) were obtained from the sea-level pressure anomaly

correlation. Figure 5.66 shows the day by day variability in the Northern Hemisphere

pattern correlation at 5 days, the average of which ends up being one point on a curve in

Figure 5.65. Gaps in Figure 5.66 indicate missing verification data.

The results from the two time periods in 2001 are less definitive. In general, the 2001

anomaly correlations are lower than their counterparts in 2003. Figure 5.67 shows an example for sea-level pressure in the Northern Hemisphere. The GEOS-4.0.3 anomaly correlation

(red curve in each figure) has nearly one day less skill for the 2001 cases (June-August) than

for the 2003 cases (September-November). In addition, the impact of changing systems for

the 2001 periods is greatly reduced. It appears that the observing system problems discussed in section 5.8.2 continued to affect the performance of both of the GEOS systems,

even after the OMF signatures had quieted down during the Summer months of 2001.

5.8.4 Case Studies

A number of issues were examined via case studies during the parallel validation run of

GEOS-4.0.3, examples of which include:

• a detailed examination of the QC process;

• an assessment of the improvement in low-level RH behavior over Antarctica;

• an examination of the precipitation behavior;

• a number of synoptic cases, mainly to assess the impact of the modification of the

wind-mass balance covariance model.

135
SELF

SELF

NCEPNCEP

Figure 5.65: 500 hPa geopotential height anomaly correlation forecast skill. Left panels using

GEOS (either 4.0.2 or 4.0.3) analyses as verification, right panels use NCEP analyses. Red curves are

GEOS-4.0.3, black curves are GEOS-4.0.2. Cases span September-November 2003 (see Figure 5.66).

136
NCEP

SELF

Figure 5.66: Time series of individual 5-day 500 hPa geopotential height forecast skills in the

Northern Hemisphere. Top panel used NCEP analyses as verification, bottom panel used GEOS

(either 4.0.2 or 4.0.3) analyses as verification. Red lines are GEOS-4.0.3, black are GEOS-4.0.2.

137
Figure 5.67: Sea level pressure anomaly correlation comparison. Upper panel has cases from

September-November 2003, the red curve is GEOS-4.0.3 and the black curve for GEOS-4.0.2. Lower

panel has cases from June-August 2001, the green curve is GEOS-4.0.3 and the red curve is GEOS4.0.2. NCEP analyses are used for verification.

138
5.8.4.1 QC example

The situation shown in Figure 5.61, 13 June 2003, merits further discussion. Figure 5.68

revisits this situation, showing the GEOS-4 analyzed winds at 1000 hPa, with the QC

accepted wind observations (black) and QC rejected wind observations (heavy red) superimposed. The three observations affected by the QC change are outlined by heavy blue

contours. The two observations near 12◦W and (especially) 8◦W definitely appear to be

outliers, while the circled observation near 3◦E is in a region having a sharp discontinuity

in wind direction.

Note that the analysis in GEOS-4 does not use surface data directly, but rather it uses

boundary layer similarity theory to infer values for the observations at levels roughly between 1000 to 925 hPa, depending on the surface pressure of the model (see section 3.2.3.2).

A sense of a potential problem with this procedure can be obtained from Figure 5.69, which

compares the original QuikScat winds with 10 m winds taken from GEOS and NCEP.

Figure 5.68: GEOS-4 1000 hPa analyzed wind field, with accepted wind observations (black) and

rejected wind observations (red). Each barb is 10 ms−1. Observations affected by the change in QC

are outlined in heavy blue.

139
Figure 5.69: Comparison of 10 m winds from GEOS (top) and NCEP (bottom). QuikScat observations (black) superimposed on both. Each barb is 10 ms−1

.

140
A close inspection of the QuikScat winds in Figure 5.69 reveals that the large values (especially the 70 ms−1 “observation” near 9◦W and 1000 hPa in Figures 5.68 and 5.70) are not

present in the actual surface observations. In addition, the directional discontinuity has a

much better agreement between the NCEP analysis and QuikScat, especially in the vicinity

of 3◦E. Thus, the QC example shown in Figure 5.61 is actually pointing to more than just

rejection of three observations; it leads to at least two broader issues affecting GEOS-4.0.3.

The first is an apparent difficulty with the current QC process to handle situations involving

strong discontinuities; this issue has appeared a number of times in the monitoring of the

operational system. The second issue is the apparent occasional “mangling” of perfectly

acceptable observations by the inference process that puts them in a form acceptable for

the current analysis process.

Figure 5.70 provides some additional information on the last point, as it compares the

original QuikScat observations with the inferred upper-air (near 1000 hPa) observations

used for the analysis. The left panel shows the high-density QuikScat observations, while

the right panel shows the thinned (to roughly one degree resolution) upper-air observations;

also shown on the right panel is the background Tskin-T2m difference. The correspondence

between problematic inferred values and regions of high stability (i.e., areas of negative

Tskin-T2m) is striking. It appears that the use of similarity theory to infer winds through

any significant vertical distance in the presence of high stability runs the risk of unacceptable

extrapolation; in such situations such inferred data should be marked as highly questionable

and treated with great caution.

Figure 5.70: Comparison of the original QuikScat winds (left) with the inferred upper-air observations (accepted: black, rejected: red) used in GEOS-4 (right). Superimposed on the upper-air

observations are contours of background Tskin - T2m. Each barb is 10 ms−1, and the contour

interval is 1 K.

141
Figure 5.71: Impact of change to RH in GEOS-4.0.3 (bottom) compared to GEOS-4.0.2 (top).

Note, this is a monthly mean, and individual cases had RH greater than 1000 in GEOS-4.0.2 near

high topography over Antarctica.

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5.8.4.2 RH Near Antarctic Topography

A problem with extremely unphysical values of RH near the ground in Antarctica was noted

in prior versions of GEOS-4. Figure 5.71 shows how GEOS-4.0.3 has improved considerably

with respect to this problem as a result of a modification to the GCM radiation parameterization near the ground. This figure shows a monthly mean of global RH at 650 hPa, and

even with a monthly average the prior system was producing RH greater than 100% in the

Antarctic region. For individual cases, the older system occasionally had RH’s greater than

1000%; GEOS-4.0.3 is vastly improved over the older system in those cases.

5.8.4.3 Precipitation Issues

Section 5.5 discussed the behavior of precipitation in GEOS-4 on monthly-mean time scales.

The Monitoring activity had many opportunities to see the precipitation behavior of GEOS4 on a case-by-case basis. Figure 5.72 shows an example of a positive impact on precipitation

by using GEOS-4.0.3. The Caribbean area has often been an area of excessive precipitation

in prior GEOS-4 versions, and this case shows GEOS-4.0.3 with significant decrease in

precipitation.

Figure 5.72: Comparison of 6-hourly average convective precipitation rate for a case at 00Z 28

October 2003; GEOS-4.0.3 (left), GEOS-4.0.2 (right), with the 403 - 402 difference (bottom). Units

are in mmday−1.

143
By way of comparison, Figure 5.73 shows a CONUS IR view from the GOES-12 geostationary satellite for this time period. The nearly uniform distribution of convective

precipitation in GEOS-4.0.2 is not confirmed by the satellite image, though the intense

precipitation zone off the Yucatan coast does seem to verify. The “patchier” appearance of

GEOS-4.0.3 appears to conform better to the satellite image, notably for the convection in

southern Guatemala and the general minimum of convection over the central part of the

area.

Figure 5.73: IR image from GOES-12 for 00Z 28 October 2003.

Figure 5.74 addresses a different issue: how does the excessive precipitation evident

in the GEOS-4 assimilation system transition to the AMIP-like behavior of the GEOS-4

GCM (described in section 5.5.1)? In this figure, precipitation over the Indian Ocean region

immediately following an analysis is shown in the leftmost and rightmost columns of panels

(labelled “DAS”); the panels in between are day 1 through day 5 forecasts starting from

the DAS states on the left. Each row is a separate 5-day forecast. Contours of sea level

pressure are also displayed in all of the plots in the figure.

The most striking feature of this figure is the rapidity in how the GEOS-4 GCM transforms the large-scale and somewhat amorphous DAS precipitation patterns into more realistic smaller scale organized patterns that are more consistent with ITCZ structures in the

tropics. It is also instructive to see how similar the forecasted structures are (by following

the plots diagonally, lower right to upper left) and how dissimilar they are to the nearest

DAS plots (22Z being “close” enough to 00Z for these comparisons).

144
Figure 5.74: Progression of forecast precipitation (shaded) and sea level (contours) from initial

assimilation values (DAS). Shown here is series of five 5-day forecasts over the Indian Ocean region.

5.8.4.4 Synoptic Examples

Two examples of the impact of the changes in the covariance modeling are presented here.

Figure 5.75(a) shows the change in a broad thermal trough at 700 hPa off the California

coast at 06Z 10 November 2003. The GEOS-4.0.3 analysis has a much larger scale with

much sharper temperature gradients. Figure 5.75(b) shows that the GEOS-4.0.3 analysis

is in far better agreement with the corresponding NCEP analysis.

Figure 5.76 shows comparisons of the 500 hPa wind field over Northwest Africa: GEOS4.0.2 versus NCEP in the top three panels (“402”); GEOS-4.0.3 versus NCEP in the bottom

three panels (“403”). The GEOS-4.0.3 winds are in far better agreement with the NCEP

analysis. This has been an area where the previous versions of GEOS were having ongoing

problems due to problems with the wind-mass covariance modeling.

145
a)

b)

Figure 5.75: Comparisons of the 700 hPa temperature analysis at 06Z 10 November 2003. (a)

GEOS-4.0.3 (left), GEOS-4.0.2 (right), and 403 - 402 difference (bottom). (b) GEOS-4.0.2 (left),

NCEP analysis (right) and 402 - NCEP (bottom). Units are in K.

146
402

403

Figure 5.76: Example of change in mass-wind balance on wind analyses: “402” (top) is the 4.0.2

system using the old balance, “403” (bottom) is the 4.0.3 system using the revised balance. In each,

the GEOS analysis is on the left, the NCEP comparison analysis on the right, with the difference

plotted below. Units are in ms−1.

147

Chapter 6

Summary

The validation themes discussed in section 4.2 were addressed by the results in section 5:

Customer requirements: GEOS-4.0.3 meets the requirements for CERES (section 5.1)

and the chemical transport community (sections 5.3 and 5.4).

Success of intended changes:

• Tskin – sections 5.1 and 5.2

• mass-wind covariance modeling changes – sections 5.3, 5.4 and 5.8

• QC, moisture and CTW changes – section 5.8

Overall scientific behavior: The full range of scientific behavior of GEOS-4.0.3 was covered by the investigations in section 5, including land surface properties (5.1, 5.2),

stratospheric structure and ozone transport (5.3, 5.4), monthly mean statistics (5.5),

tropical dynamics (5.6), data impact (5.7), and synoptic behavior (5.8).

These validation results definitely indicate that GEOS-4.0.3 is a significant improvement

over GEOS-4.0.2. The changes to the mass-wind covariance modeling (section 3.2.2.1.2)

had profound positive effects throughout the entire assimilation atmosphere, affecting both

stratospheric and tropospheric results. The other major set of changes, involving land

surface modeling and Tskin, also resulted in significant improvements in GEOS-4.0.3 over

GEOS-4.0.2. The other changes made for GEOS-4.0.3 (Quality Control tuning, RH fixes

and CTW changes) were verified as improvements during the monitoring process.

There remain a number of problematic issues with GEOS-4.0.3. The most troubling is

the tendency for GEOS-4 systems to produce excessive precipitation, both in monthly means

(5.5), as well as in synoptic cases (5.8). This limits the value of GEOS-4 systems for use in

general reanalysis projects or for forcing ocean or land surface models. The instantaneous

states in the stratosphere of GEOS-4.0.3 are excessively noisy (5.3, 5.4), although 6-hour

averages of GEOS-4.0.3 stratospheric winds do have utility for transport studies. Finally,

while improved in GEOS-4.0.3, the forecast skill of the GEOS-4 systems remains generally

substandard in comparison to the current forecasting capabilities at other centers. The

data impact studies (5.7) performed for GEOS-4.0.3 tend to indicate that this system is not

making full use of radiance data (compared to other centers), and this may be a significant

factor in the poorer forecast skill results for GEOS-4.0.3.

149

Appendix A

Acknowledgements

The support of Dr. Michael King, EOS Senior Project Scientist, program managers and

NASA/HQ, the NASA Center for Computational Sciences at the Goddard Space Flight

Center and the NASA Advanced Supercomputing Division at Ames Research Center over

the lifetime of the development of the GEOS-DAS are gratefully acknowledged. The continual support and challenges proffered by Dr. Franco Einaudi have always guided the DAO,

and now GMAO, to advance the use of satellite data in atmospheric data assimilation.

Contributors to the GEOS-DAS and the validation of the GEOS-4 system:

Manina Almeida Joanna Joiner John Rosenfelder

Joseph Ardizzone Mahendra Karki Al Ruddick

Robert Atlas Gi-Kong Kim Leonid Rukhovets

Stephen Bloom Thomas King Siegfried Schubert

Michael Bosilovich Dan Kokron Meta Sienkiewicz

Eugenia Brin Yelena Kondratyeva Ivanka Stajner

L.P. Chang David Lamich Max Suarez

Jiun-Dar Chern Shian-Jiann Lin King-Sheng Tai

Stephen Cohn Hui-Chun (Emily) Liu Lawrence Takacs

Douglas Collins Guang Ping Lou Wei Tan

Austin Conaty Robert Lucchesi Ricardo Todling

Arlindo da Silva Ann Melton Derek Van Pelt

Dick Dee Thomas Owens Frances Verter

Don Frank Gary Partyka Chung-Yu (John) Wu

Gregory Gaspari Steven Pawson Man-Li Wu

Ron Gelaro James Pfaendtner Runhua Yang

Wei Gu William Putman Eueng-Nan Yeh

Jing Guo Harper Pryor Baoyu Yin

Hiroo Hayashi Jon Radakovich Joon Yoon

Arthur Hou Chris Redder Banglin Zhang

Xiaochuan Huang Lars Peter Riishojgaard Sara Zhang

Sarah Jackson Richard Rood Yanqiu Zhu

151

Appendix B

Acronyms

1DVAR One-dimensional (vertical column) Variational retrieval

AIRS Advanced Infrared Sounder (on Aqua)

AMIP Atmospheric Model Intercomparison Project

AMSU Advanced Microwave Sounding Unit (on later TIROS)

AMV Atmospheric Motion Vectors (also called CTW)

AQUA EOS PM satellite

ARM Atmospheric Radiation Measurement

AURA EOS CHEM satellite

ARM Atmospheric Radiation Measurement program (DOE)

ATOVS Advanced TOVS

BALTEX Baltic Sea Experiment

CAMP CEOP Asian-Australian Monsoon Project

CAPE Convective Available Potential Energy

CCM3 Community Climate Model, version 3

CEOP Coordinated Enhanced Observing Period

CERES Clouds and the Earth’s Radiant Energy System (TERRA,AQUA)

CERES-TRMM CERES instrument on TRMM platform

CGDD Climate and Global Dynamics Division (NCAR)

CLM, CLM2 Community Land Model

CONUS Continental United States

CSE Continental Scale Experiment

CTM Chemical Transport Model

CTW Cloud Track Wind (now called AMV)

CVS Concurrent Versions System

DAO Data Assimilation Office (GSFC)

DAS Data Assimilation System

DMSP Defense Military Satellite Program

DU Dobson Unit (Ozone amount in atmospheric column)

ECMWF European Centre for Medium Range Weather Forecasting

EOS Earth Observing System

ERS-1,2 Environmental Research Satellite (surface winds obtained

153
using the AMI, Active Microwave Unit)

EUMETSAT European Organisation for the Exploitation of Meteorological Satellites

FGGE First GARP (Global Atmospheric Research Program) Global Experiment

fvDAS finite-volume Data Assimilation System (GEOS-4)

fvGCM finite-volume General Circulation Model

GAPP GEWEX Americas Prediction Project

GCM General Circulation Model (Atmospheric)

GDAS Global Data Assimilation System (NCEP)

GEOS Goddard Earth Observing System

GLATOVS Goddard Laboratory for Atmospheres TOVS retrievals

GMAO Global Modeling and Assimilation Office (GSFC)

GOES Geosynchronous Operational Environmental Satellite

GPCP Global Precipitation Climatology Project

GSFC (NASA) Goddard Space Flight Center

GTOPO30 Global Digital Elevation Model (DEM), 30 arc second resolution

HALOE Halogen Occultation Experiment

HIRS2,3 High-resolution Infrared Spectrometer

IAU Incremental Analysis Updating (GEOS-3)

iRET Interactive Retrieval system

ISCCP International Satellite Cloud Climatology Project

ITCZ Intertropical Convergence Zone

JMA Japan Meteorological Agency

LAI Leaf Area Index

LBA Large-scale Biosphere Atmosphere Experiment in Amazonia

MAGS MacKenzie GEWEX Study

MIPAS Michelson Interferometer for Passive Atmospheric Sounding

MJO(/ISO) Madden-Julian Oscillation (/Intraseasonal Oscillation)

MLS Microwave Limb Sounder

MODIS Moderate Resolution Imaging Spectroradiometer

MPI Message Passing Interface

MSU Microwave Sounding Unit (part of TOVS)

NAS North Slope of Alaska

NASA National Aeronautics and Space Administration

NCAR National Center for Atmospheric Research

NCEP National Centers for Environmental Prediction

NESDIS National Environmental Satellite, Data, and Information Service

NOAA National Oceanic and Atmospheric Administration

NSIPP NASA Seasonal-to-Interannual Prediction Project

NWP Numerical Weather Prediction

OI Optimum Interpolation

OISST Optimum Interpolation applied to ocean Sea Surface Temperatures

OLR Outgoing Longwave Radiation

OMA(O-A) Observation Minus Analysis

OMF(O-F) Observation Minus Background (or First Guess)

154
POAM Polar Ozone and Aerosol Measurement

POES Polar Operational Environment Satellite

PSAS Physical-space Statistical Analysis System

QC Quality Control

RH Relative Humidity

RMS Root Mean Square

SBUV/2 Solar Backscatter Ultraviolet Spectral Radiometer-2

SGP Southern Great Plains

SQC Statistical Quality Control

SSM/I Special Sensor Microwave/Imager (on DMSP satellites)

SSU Stratospheric Sounding Unit (superseded by AMSU)

T2M Atmospheric Temperature at 2 meters above the ground

TERRA EOS AM Satellite

TIROS-N Television and Infrared Observatory Spacecraft

TOMS Total Ozone Mapping Spectrometer

TOVS TIROS Operational Vertical Sounder

TPW Total Precipitable Water

TRMM Tropical Rainfall Measurement Mission

UARS Upper Atmosphere Research Satellite

WACCM Whole-Atmosphere Community Climate Model

WMO World Meteorological Organization

155

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161

Previous Volumes in This Series

Volume 1

September 1994

Documentation of the Goddard Earth Observing System

(GEOS) general circulation model - Version 1

L.L. Takacs, A. Molod, and T. Wang

Volume 2

October 1994

Direct solution of the implicit formulation of fourth order

horizontal diffusion for gridpoint models on the sphere

Y. Li, S. Moorthi, and J.R. Bates

Volume 3

December 1994

An efficient thermal infrared radiation parameterization for

use in general circulation models

M.-D. Chou and M.J. Suarez

Volume 4

January 1995

Documentation of the Goddard Earth Observing System

(GEOS) Data Assimilation System - Version 1

James Pfaendtner, Stephen Bloom, David Lamich,

Michael Seablom, Meta Sienkiewicz, James Stobie,

and Arlindo da Silva

Volume 5

April 1995

Documentation of the Aries-GEOS dynamical core: Version 2

Max J. Suarez and Lawrence L. Takacs

Volume 6

April 1995

A Multiyear Assimilation with the GEOS-1 System:

Overview and Results

Siegfried Schubert, Chung-Kyu Park, Chung-Yu Wu,

Wayne Higgins, Yelena Kondratyeva, Andrea Molod,

Lawrence Takacs, Michael Seablom, and Richard

Rood

Volume 7

September 1995

Proceedings of the Workshop on the GEOS-1 Five-Year Assimilation

Siegfried D. Schubert and Richard B. Rood

Volume 8

March 1996

Documentation of the Tangent Linear Model and Its Adjoint

of the Adiabatic Version of the NASA GEOS-1 C-Grid GCM:

Version 5.2

Weiyu Yang and I. Michael Navon

163
Volume 9

March 1996

Energy and Water Balance Calculations in the Mosaic LSM

Randal D. Koster and Max J. Suarez

Volume 10

April 1996

Dynamical Aspects of Climate Simulations Using the GEOS

General Circulation Model

Lawrence L. Takacs and Max J. Suarez

Volume 11

May 1997

Documentation of the Tangent Linear and its Adjoint Models

of the Relaxed Arakawa-Schubert Moisture Parameterization

Package of the NASA GEOS-1 GCM (Version 5.2)

Weiyu Yang I. Michael Navon, and Ricardo Todling

Volume 12

August 1997

Comparison of Satellite Global Rainfall Algorithms

Alfred T.C. Chang and Long S. Chiu

Volume 13

December 1997

Interannual Variability and Potential Predictability in Reanalysis Products

Wie Ming and Siegfried D. Schubert

Volume 14

August 1998

A Comparison of GEOS Assimilated Data with FIFE Observations

Michael G. Bosilovich and Siegfried D. Schubert

Volume 15

June 1999

A Solar Radiation Parameterization for Atmospheric Studies

Ming-Dah Chou and Max J. Suarez

Volume 16

November 1999

Filtering Techniques on a Stretched Grid General Circulation

Model

Lawrence Takacs, William Sawyer, Max J. Suarez,

and Michael S. Fox-Rabinowitz

Volume 17

July 2000

Atlas of Seasonal Means Simulated by the NSIPP-1 Atmospheric GCM

Julio T. Bacmeister, Philip J. Pegion, Siegfried D.

Schubert, and Max J. Suarez

Volume 18

December 2000

An Assessment of the Predictability of Northern Winter Seasonal Means with the NSIPP 1 AGCM

Philip J. Pegion, Siegfried D. Schubert, and Max J.

Suarez

164
Volume 19

July 2001

A Thermal Infrared Radiation Parameterization for Atmospheric Studies

Ming-Dah Chou, Max J. Suarez, Xin-Zhong, Michael

M.-H. Yan

Volume 20

August 2001

The Climate of the FVCCM-3 Model

Yehui Chang, Siegfried D. Schubert, Shian-Jiann Lin,

Sharon Nebuda, Bo-Wen Shen

Volume 21

September 2001

Design and Implementation of a Parallel Multivariate Ensemble Kalman Filter for the Poseidon Ocean General Circulation Model

Christian L. Keppenne and Michele M. Rienecker

Volume 22

August 2002

A Coupled Ocean-Atmosphere Radiative Model for Global

Ocean Biogeochemical Models

Watson W. Gregg

Volume 23

November 2002

Prospects for Improved Forecasts of Weather and Short-term

Climate Variability on Subseasonal (2-Week to 2-Month)

Time Scales

Siegfried D. Schubert, Randall Dole, Huang van den

Dool, Ma J. Suarez, and Duane Waliser

Volume 24

July 2003

Temperature Data Assimilation with Salinity Corrections:

Validation for the NSIPP Ocean Data Assimilation System

in the Tropical Pacific Ocean, 1993–1998

Alberto Troccoli, Michele M. Reinecker, Christian L.

Keppenne, and Gregory C. Johnson

Volume 25

December 2003

Modeling, Simulation, and Forecasting of Subseasonal Variability

Duane Waliser, Siegfried D. Schubert, Arun Kumar,

Klaus Weickmann, and Randall Dole

165
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Observing System Data Assimilation System (GEOS DAS): GEOS-4.0.3. Significant features of

GEOS-4 include: version 3 of the Community Climate Model (CCM3) with the addition of a finitevolume dynamical core; version two of the Community Land Model (CLM2); the Physical-space

Statistical Analysis System (PSAS); and an interactive retrieval system (iRET) for assimilating

TOVS radiance data.

Upon completion of the GEOS-4 validation in December 2003, GEOS-4 became operational on

15 January 2004. Products from GEOS-4 have been used in supporting field campaigns and for

reprocessing several years of data for CERES.

This document describes the structure and validation of a frozen version of the Goddard Earth