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NASA Technical Memorandum 104606, Vol. 6

Technical Report Series on

Global Modeling and Data Assimilation

Max J. Suarez, Editor

Goddard Space Flight Center

Greenbelt, Maryland

Volume 6

A Multiyear Assimilation With the

GEOS-1 System: Overview and Results

Siegfried Schubert

Richard Rood

Goddard Space Flight Center

Greenbelt, Maryland

Chung-Kyu Park

Joint Center for Earth System Science

University of Maryland

College Park, Maryland

Chung-Yu Wu

Yelena Kondratyeva

Andrea Molod

Lawrence Takacs

Michael Seablom

General Sciences Corporation

Laurel, Maryland

Wayne Higgins

Climate Analysis Center

NMC/NWS/NOAA

Washington, D. C.

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland

1995
Ii

This publication is available from the NASA Center for AeroSpace Information,

800 Elkridge Landing Road, Linthicum Heights, MD 21090-2934, (301) 621-0390.
Abstract

The Data Assimilation Office (DAO) at Goddard Space Flight Center has produced a

multi-year global assimilated data set with version 1 of the Goddard Earth Observing

System Data Assimilation System (GEOS-1 DAS). One of the main goals of this project,

in addition to benchmarking the GEOS-1 system, was to produce a research quality

data set suitable for the study of short-term climate variability. The output, which

is global and gridded, includes all prognostic fields and a large number of diagnostic

quantities such as precipitation, latent heating, and surface fluxes. Output is provided

four times daily with selected quantities available eight times per day. Information

about the observations input to the GEOS-1 DAS is provided in terms of maps of

spatial coverage, bargraphs of data counts and tables of all time periods with significant

data gaps.

The purpose of this document is to serve as a users guide to NASA's first multiyear assimilated data set and to provide an early look at the quality of the output.

Documentation is provided on all the data archives, including sample read programs

and methods of data access. Extensive comparisons are made with the corresponding

operational European Center for Medium-Range Weather Forecasts analyses, as well

as, various in situ and satellite observations. This document is also intended to alert

users of the data about potential limitations of assimilated data in general, and the

GEOS-1 data in particular. Results are presented for the period March 1985-Feb t990.

°°°

111

Contents

List of Figures

1 Introduction

2 Assimilated Data: The Role of the Model

3 Overview of GEOS-1 DAS

3.1 The OI Scheme ..................................

3.2 The GEOS-1 Model ...............................

3.3 The Processing System ..............................

vii

4

Results 12

4.1 Comparison Datasets ............................... 12

4.2 Climate Mean Quantities ............................ 14

4.2.1 Zonal Mean Prognostic ......................... 14

4.2.2 Global Prognostic ............................ 27

4.2.3 Surface Energy Balance ......................... 46

4.2.4 Precipitation and the U.S. Moisture Budget .............. 63

4.3 Intraseasonal Variability ............................. 80

4.3.1 Global RMS Differences ......................... 80

4.3.2 The Tropics ................................ 85

4.3.3 Correlations ................................ 92

4.4 Interannual Variability .............................. 97

4.4.1 Space-Time ................................ 97

PRECEDING PAGE BLANK NOT FILMED
4.4.2 Global Distribution (1988-1987) .................... 102

4.4.3 Precipitation Anomalies ......................... 112

4.5 Concluding Remarks ............................... 125

5 Input Observations 127

5.1 Spatial Coverage ................................. 127

5.2 Inventory ..................................... 132

5.2.1 Sample Bargraphs of Data Counts ................... 132

5.2.2 Tables of Significant Data Gaps ..................... 137

6 Datasets and Data Access 145

6.1 NCCS Datasets .................................. 146

6.1.1 Overview ................................. 146

6.1.2 Details of Assimilation Output ..................... 149

6.1.3 NCCS Unitree Directory Structure ................... 163

6.1.4 Sample Read Program (Phoenix Format) ............... 164

6.2 GDAAC Time Series Datasets .......................... 167

6.2.1 Description of the Datasets ....................... 168

6.2.2 Menu of Available Quantities ...................... 170

6.3 Datasets Available from DAO Server ...................... 174

Appendix: History of Output Problems 177

References 179

vi
List of Figures

2

3

4

5

6

A schematic of the variability found in operational analyses. The spurious

climate signals produced by changes in the data assimilation systems are

found primarily at the seasonal and longer time scales .............

The main elements of the processing system and data flow of the GEOS-1

DAS .........................................

(a) Seasonal cycle of the 200 mb zonal mean u-wind from the GEOS-DAS

and (b) the difference fields (GEOS-ECMWF). Contour interval is 5.0 m/sec

in (a) and 1.0 m/sec in (b). Negative regions are shaded ...........

(a) Seasonal cycle of the 200 mb zonal mean v-wind from the GEOS-DAS and

(b) the difference fields (GEOS-ECMWF). Contour interval is 0.5 m/sec.

Negative regions are shaded ............................

(a) Seasonal cycle of the 300 mb zonal mean height from the GEOS-DAS

and (b) the difference fields (GEOS-ECMWF). Contour interval is 100.0 m

in (a) and 10.0 m in (b). The values greater than 9700.0 m are shaded in (a)

and negative regions are shaded in (b) ......................

(a) DJF climatology of zonal mean u-wind from the GEOS-DAS and (b) the

difference fields (GEOS-ECMWF). Contour interval is 5.0 m/sec in (a) and

1.0 m/sec in (b). Negative regions are shaded. The top level is 30 mb . . .

(a) JJA climatology of zonal mean u-wind from the GEOS-DAS and (b) the

difference fields (GEOS-ECMWF). Contour interval is 5.0 m/sec in (a) and

1.0 m/sec in (b). Negative regions are shaded. The top level is 30 mb . . .

(a) DJF climatology of zonal mean v-wind from the GEOS-DAS and (b)

the ECMWF analysis. Contour interval is 0.5 m/sec. Negative regions are

shaded. The top level is 30 mb .........................

(a) JJA climatology of zonal mean v-wind from the GEOS-DAS and (b)

the ECMWF analysis. Contour interval is 0.5 m/sec. Negative regions are

shaded. The top level is 30 mb .........................

10 (a) DJF climatology of zonal mean temperature from the GEOS-DAS and

(b) the difference fields (GEOS-ECMWF). Contour interval is 5.0 °C in (a)

and 1.0 °C in (b). Negative regions are shaded. The top level is 30 mb . . .

2

11

16

17

18

19

2O

21

22

23

vii
11 (a) JJA climatologyof zonalmeantemperaturefrom the GEOS-DASand

(b) thedifferencefields(GEOS-ECMWF).Contourintervalis 5.0°C in (a)

and1.0°C in (b). Negativeregionsareshaded.The toplevelis 30mb . . . 24

12 DJF climatologyof zonalmeanvertical-componentof wind from (a) the

GEOS-DASand (h) from the ECMWFanalysis.Contourintervalis 40.0

nb/sec. Negative regions are shaded ....................... 25

13 JJA climatology of zonal mean vertical-component of wind from (a) the

GEOS-DAS and (b) from the ECMWF analysis. Contour interval is 40.0

nb/sec. Negative regions are shaded. Negative regions are shaded ......

26

14 (a) DJF climatology of 200 mb u-wind from the GEOS-DAS and (b) the

difference fields (GEOS-ECMWF). Contour interval is 5.0 m/sec in (a) and

2.0 m/sec in (b). Negative regions are shaded .................

28

15 (a) DJF climatology of 850 mb u-wind from the GEOS-DAS and (b) the

difference fields (GEOS-ECMWF). Contour interval is 3.0 m/sec in (a) and

2.0 m/sec in (b). Negative regions are shaded ................. 29

16 (a) JJA climatology of 200 mb u-wind from the GEOS-DAS and (b) the

difference fields (GEOS-ECMWF). Contour interval is 5.0 m/sec in (a) and

2.0 m/sec in (b). Negative regmns are shaded .................

3O

17 (a) JJA climatology of 850 mb u-wind from the GEOS-DAS and (b) the

difference fields (GEOS-ECMWF). Contour interval is 3.0 m/sec in (a) and

2.0 m/sec in (b). Negative regions are shaded .................

31

18 (a) DJF climatology of 200 mb v-wind from the GEOS-DAS and (b) the

difference fields (GEOS-ECMWF). Contour interval is 2.0 m/sec in (a) and

1.0 m/sec in (b). Negative regions are shaded .................

32

19 (a) DJF climatology of 850 mb v-wind from the GEOS-DAS and (b) the

difference fields (GEOS-ECMWF). Contour interval is 2.0 m/sec in (a) and

1.0 m/sec in (b). Negative regions are shaded ................. 33

2O (a) JJA climatology of 200 mb v-wind from the GEOS-DAS and (b) the

difference fields (GEOS-ECMWF). Contour interval is 2.0 m/sec in (a) and

1.0 m/sec in (b). Negative regions are shaded .................

34

21 (a) JJA climatology of 850 mb v-wind from the GEOS-DAS and (b) the

difference fields (GEOS-ECMWF). Contour interval is 2.0 m/sec in (a) and

1.0 m/sec in (b). Negative regions are shaded ................. 35

viii
22 (a) DJF climatologyof 200mb eddystreamfunctionfrom the GEOS-DAS

and(b) thedifferencefields(GEOS-ECMWF).Contourintervalis 5.0x 106

m2/sec in (a) and 2.0 x 10 6 m2/sec in (b). The values greater than 10.0 x 10 6

m2/sec and less than -10.0 x 106 m2/sec are shaded in (a) and the values

greater than 2.0 x 106 m2/sec and less than -2.0 x 106 m2/sec are shaded

in (b) ........................................

36

23 (a) JJA climatology of 200 mb eddy streamfunction from the GEOS-DAS

and (b) the difference fields (GEOS-ECMWF). Contour interval is 5.0 x 106

m2/sec in (a) and 2.0 x 10 6 m2/sec in (b). The values greater than 10.0 x 10 6

m2/sec and less than -10.0 x 106 m2/sec are shaded in (a) and the values

greater than 2.0 x 106 m2/sec and less than -2.0 x 10 6 m2/sec are shaded

in (b) ........................................

37

24 (a) DJF climatology of 200 mb velocity potential from the GEOS-DAS and

(b) the difference fields (GEOS-ECMWF). Contour interval is 1.0 x 106

m2/sec. The values greater than 5.0 x 106 m2/sec and less than -5.0 × 106

rn2/sec are shaded in (a) and the values greater than 3.0 × 106 m2/sec and

less than -3.0 x 106 m2/sec are shaded in (b) .................

38

25 (a) DJF climatology of 850 mb velocity potential from the GEOS-DAS and

(b) the difference fields (GEOS-ECMWF). Contour interval is 1.0 x 106

m2/sec. The values greater than 5.0 × 106 m2/sec and less than -5.0 x 106

m2/sec are shaded in (a) and the values greater than 2.0 × 106 m2/sec and

less than -2.0 × 106 m2/sec are shaded in (b) ................. 39

26 (a) JJA climatology of 200 mb velocity potential from the GEOS-DAS and

(b) the difference fields (GEOS-ECMWF). Contour interval is 1.0 x 106

m2/sec. The values greater than 5.0 x 106 m2/sec and less than -5.0 × 106

m2/scc are shaded in (a) and the values greater than 3.0 x 106 m2/sec and

less than -3.0 x 106 m2/sec are shaded in (b) .................

4O

27 (a) JJA climatology of 850 mb velocity potential from the GEOS-DAS and

(b) the difference fields (GEOS-ECMWF). Contour interval is 1.0 x 106

m2/sec. The values greater than 5.0 x 106 m2/sec and less than -5.0 x 106

m2/sec are shaded in (a) and the values greater than 2.0 x 106 m2/sec and

less than -2.0 x 106 m2/sec are shaded in (b) ................. 41

28 (a) DJF climatology of sea-level pressure from the GEOS-DAS and (b) the

difference fields (GEOS-ECMWF). Contour intervals are 4.0 mb. Values

greater than 1020 mb are shaded in (a). Negative regions are shaded in (b). 42

ix
29 (a) JJA climatology of sea-level pressure from the GEOS-DAS and (b)

the difference fields (GEOS-ECMWF). Contour interval is 4.0 rob. Values greater than 1020 mb are shaded in (a). Negative regions are shaded in

(b) ..........................................

43

3O (a) DJF climatology (Dec 1987-Feb 1990) of total precipitable water from the

GEOS-DAS and (b) the difference (GEOS-SSM/I) over the oceans. Contour

interval is 0.5 gm/cm 2. The values greater than 4.0 gm/cm 2 are shaded in

(a) and negative values are shaded in (b) .................... 44

31 (a) JJA climatology of total precipitable water (Jul 1987-Aug 1989) from the

GEOS-DAS and (b) the difference (GEOS-SSM/I) over the oceans. Contour

interval is 0.5 gm/cm 2. The values greater than 4.0 gm/cm 2 are shaded in

(a) and negative values are shaded in (b) .................... 45

32 The latent heat flux for DJF for a) GEOS-DAS, and b) GEOS-DAS minus

COADS. Contour interval is 30 W/m 2 ...................... 5O

33 The latent heat flux for JJA for a) GEOS-DAS, and b) GEOS-DAS minus

COADS. Contour interval is 30 W/m 2...................... 51

34 The sensible heat flux for DJF for a) GEOS-DAS, and b) GEOS-DAS minus

COADS. Units:W/m 2. Contour intervals are -5 0 5 10 20 30 60 90 120 150

180 in (a) and every 5 in (b) ........................... 52

35 The sensible heat flux for JJA for a) GEOS-DAS, and b) GEOS-DAS minus

COADS. Units:W/ra 2. Contour intervals are -5 0 5 10 20 30 60 90 120 150

180 in (a) and every 5 in (b) ........................... 53

36 The net surface shortwave radiation for DJF for a) GEOS-DAS, and b)

GEOS-DAS minus COADS. Contour interval is 30 W/m 2........... 54

37 The net surface shortwave radiation for JJA for a) GEOS-DAS, and b)

GEOS-DAS minus COADS. Contour interval is 30 W/m 2........... 55

38 The net surface longwave radiation for DJF for a) GEOS-DAS, and b)

GEOS-DAS minus COADS. Units:W/m 2. Contour interval is 15 in (a) and

10 in (b) ......................................

56

39 The net surface longwave radiation for JJA for a) GEOS-DAS, and b) GEOSDAS minus COADS. Units:W/m 2. Contour interval is 15 in (a) and 10 in

(b) .......................................... 57
40 The net surfaceheatflux for DJF for a) GEOS-DAS,and b) GEOS-DAS

minusCOADS.Units:W/m2. Contourintervalis 60in (a) and30in (b)... 58

41 The net surfaceheatflux for JJA for a) GEOS-DAS,and b) GEOS-DAS

minusCOADS.Units:W/m2. Contourintervalis 60in (a) and30in (b)... 59

42 The zonalmean(oceansonly) sensibleandlatent heatflux for the GEOSDAS,GEOS-GCM,and COADS.Umts:W/m " 2................. 6O

43 The zonalmean(oceansonly) surfaceshortwaveandlongwaveflux for the

GEOS-DAS,GEOS-GCM,andCOADS.Units:W/m2............. 61

44 Thezonalmean(oceansonly) netsurfaceradiativeflux andnetsurfaceheat

flux for the GEOS-DAS,GEOS-GCM,andCOADS.Units:W/ra2...... 62

45 TheGEOS-DASprecipitationclimatologiesfor(a) DJF and(b) JJA. Units:

mm/day ...................................... 64

46 The climatological mean difference (GEOS-DAS minus station observations)

of precipitation for (a) DJF and (b) JJA. The contour interval is 2 mm/day.

Negative values are dashed. The values greater than 1 ramday and less than

-1 ramday are shaded .............................. 65

47 The boxes contain the land areas over which the averages are done for the

precipitation line plots ............................... 66

48 The seasonal cycle of the regional mean precipitation over land from GEOS

and the station data. Units: mm/day ...................... 67

49 The seasonal cycle of the regional mean precipitation over land from GEOS

and the station data. Units: mm/day ...................... 68

5O Components of the vertically-integrated moisture budget from the GEOS1 DAS. a) precipitation, b) precipitation verification from climate division

observations, c) evaporation, d) moisture convergence, e) the analysis increments,and f) the Shapiro filter (a-c-d-e). Contour intervals are -10 -8

-6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute values

greater than 2 are shaded .......................... : . . 71

xi
51 Components of the vertically-integrated moisture budget from the GEOS1 DAS. a) precipitation, b) precipitation verification from climate division

observations, c) evaporation, d) moisture convergence, e) the analysis increments,and f) the Shapiro filter (a-c-d-e). Contour intervals are -10 -8

-6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 ram/day. Absolute values

greater than 2 are shaded ............................. 72

52 Components of the vertically-integrated moisture budget from the GEOS1 DAS. a) precipitation, b) precipitation verification from climate division

observations, c) evaporation, d) moisture convergence, e) the analysis increments,and f) the Shapiro filter (a-c-d-e). Contour intervals are -10 -8

-6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 ram/day. Absolute values

greater than 2 are shaded ............................. 73

53 Components of the vertically-integrated moisture budget from the GEOS1 DAS. a) precipitation, b) precipitation verification from climate division

observations, c) evaporation, d) moisture convergence, e) the analysis increments,and f) the Shapiro filter (a-c-d-e). Contour intervals are -10 -8

-6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute values

greater than 2 are shaded ............................. 74

54 Components of the vertically-integrated moisture budget from the GEOS1 DAS. a) precipitation, b) precipitation verification from climate division

observations, c) evaporation, d) moisture convergence, e) the analysis increments,and f) the Shapiro filter (a-c-d-e). Contour intervals are -10 -8

-6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute values

greater than 2 are shaded ............................. 75

55 Components of the vertically-integrated moisture budget from the GEOS1 DAS. a) precipitation, b) precipitation verification from climate division

observations, c) evaporation, d) moisture convergence, e) the analysis increments,and f) the Shapiro filter (a-c-d-e). Contour intervals are -10 -8

-6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute values

greater than 2 are shaded ............................. 76

56 Annual cycle of vertically-integrated moisture budget terms averaged over

the continental United States (30°N-50°N). The CD refers to the climate

division precipitation. Units are ramday .................... 77

57 Moisture transport into the continental United States for January through

June. LLJ (Low Level Jet) refers to the region between 92.5°W and 102.5°W,

and below about 850 rob. Units are 106 kg/sec. To obtain units of cm/raonth

divide by 29 × 106 ................................. 78

xii
58 Moisture transportintothe continentalUnited StatesforJuly through December. LLJ (Low Level Jet) refersto the region between 92.5°W and

102.5°W, and below about 850 rob. Unitsare 108 kg/sec.To obtain unitsof

cm/month divideby 29 × 106.......................... 79

59 (a) DJF and (b) JJA 200 mb zonal wind rms (non-systematic)difference

between the GEOS-DAS and the ECMWF analysisbased on dailytimeseries.Contour intervalsare 1 m/sec. Values greaterthan 6 m/scc are

shaded....................................... 82

6O (a) DJF and (b) JJA 200 mb v-wind rms (non-systematic)differencebetween the GEOS-DAS and the ECMWF analysisbased on dailytime-series.

Contour intervalsare 1 m/sec. Values greaterthan 5 m/sec are shaded... 83

61 (a)DJF and (b) JJA 500 mb heightrms (non-systematic)differencebetween

GEOS-DAS and ECMWF analysisbased on dailytime-series.Contour intervalsare 10 m. Values greaterthan 40 m are shaded............ 84

62 Longitude-time section of the 200 mb velocity potential anomalies from (a)

the GEOS-DAS and (b) the ECMWF analysis averaged in the tropics [10S10N]. The anomalies are departures from the seasonal cycle filtered to retain

periods of 20-70 days (1986). Contour interval is 2 × l0 s m2/sec. The

negative values are shaded ............................

86

63 Longitude-time section of the 200 mb velocity potential anomalies from (a)

the GEOS-DAS and (b) the ECMWF analysis averaged in the tropics [10S10N]. The anomalies are departures from the seasonal cycle filtered to retain

periods of 20-70 days (1987). Contour interval is 2 × l0 s m2/sec. The

negative values are shaded ............................

87

64 Longitude-time section of the 200 mb velocity potential anomalies from (a)

the GEOS-DAS and (b) the ECMWF analysis averaged in the tropics [10S10N]. The anomalies are departures from the seasonal cycle filtered to retain

periods of 20-70 days (1988). Contour interval is 2 × l0 s m2/sec. The

negative values are shaded ............................

88

65 Longitude-time section of the 200 mb velocity potential anomalies from (a)

the GEOS-DAS and (b) the ECMWF analysis averaged in the tropics [10S10N]. The anomalies are departures from the seasonal cycle filtered to retain

periods of 20-70 days (1989). Contour interval is 2 × l0 s m2/sec. The

negative values are shaded ............................

89

,o.

XUl
66 Longitude-time section of the 200 mb velocity potential anomalies from (a)

the GEOS-DAS and (b) the ECMWF analysis averaged in the tropics [10S10N]. The anomalies are departures from the seasonal cycle filtered to retain

periods of 10-20 days (1987). Contour interval is 1 × 106 m2/sec. The

negative values are shaded ............................ 9O

67 Longitude-time section of the 200 mb velocity potential anomalies from (a)

the GEOS-DAS and (b) the ECMWF analysis at 10N. The anomalies are

departures from the seasonal cycle filtered to retain periods of 2-10 days.

Contour interval is 1 × 10 6 m2/sec and contours between -1 × 10 6 m2/sec

and 1 × 106 m2/sec are not shown ........................ 91

68 Correlation between the 200 mb velocity potential anomalies from the GEOSDAS and the ECMWF analysis (Mar 1986-Feb 1990), filtered for the periods

of (a) 2-10 days, (b) 10-20 days, and (c) 20-70 days. Contour intervals are

0.1. The shading represents the correlations greater than 0.7 in (a), 0.8 in

(b), and 0.9 in (c) ................................. 93

69 Correlation between the 200 mb velocity potential anomalies from the GEOSDAS and the ECMWF analysis (Mar 1986-Feb 1990), filtered for the periods

of 2-10 days, 10-20 days, and 20-70 days at (a) 40N, (b) the equator, and

(c) 40S ....................................... 94

7O Correlation between the 500 mb height anomalies from the GEOS-DAS and

the ECMWF analysis, filtered for the periods of (a) 2-10 days, (b) 10-20

days, and (c) 20-70 days. Contour intervals are 0.1. The shading represents

the correlations greater than 0.9 ........................ 95

71 Correlation between the 500 mb height anomalies, filtered for the periods of

2-10 days, 10-20 days, from the GEOS-DAS and the ECMWF analysis and

20-70 days at (a) 40N, (b) the equator, and (c) 40S .............. 96

72 Departure from the seasonal cycle for the 200 mb zonal mean u-wind from

(a) the GEOS-DAS and (b) the ECMWF analysis. Contour interval is 2.0

m/sec. Zero contours are not drawn ....................... 98

73 Departure from the seasonal cycle for the 200 mb zonal mean v-wind from

(a) the GEOS-DAS and (b) the ECMWF analysis. Contour interval is 0.2

m/sec. Zero contours are not drawn. The values greater than 0.4 m/sec and

less than -0.4 m/sec are shaded ......................... 99

xiv
74 Departure from the seasonal cycle for the 300 mb zonal mean height from

(a) the GEOS-DAS and (b) the ECMWF analysis. Contour interval is 20.0

m. Zero contours are not drawn. The values greater than 20 m and less than

-20 m are shaded .................................. 100

75 Longitude-time section of the outgoing longwave radiation anomaly [5S-5N]

from NOAA satellite measurements, the GEOS-DAS and the GEOS-GCM

simulation. Units: W/m 2 ............................ 101

76 The 200 mb streamfunction difference (1988-87) for (a) May, (b) June, and

(c) July from the GEOS-DAS. Contour intervals are 5.0 × 106 m2/see. The

values greater than 10.0 × 106 m2/sec and less than -10.0 × 106 m2/sec are

shaded ....................................... 104

77 The 200 mb streamfunction difference (1988-87) for (a) May, (b) June, and

(c) July from the ECMWF analysis. Contour intervals are 5.0 × 106 m2/sec.

The values greater than 10.0 × 106 m2/sec and less than -10.0 × 106 m2/sec

are shaded ..................................... 105

78 The 200 mb velocity potential difference (1988-87) for (a) May, (b) June, and

(c) July from the GEOS-DAS. Contour intervals are 1.0 × 106 m2/sec. The

values greater than 5.0 × 106 m2/sec and less than -5.0 × 106 m2/sec are

shaded ....................................... 106

79 The 200 mb velocity potential difference (1988-87) for (a) May, (b) June, and

(c) July from the ECMWF analysis. Contour intervals are 1.0 × 106 rn2/see.

The values greater than 5.0 x 106 m2/sec and less than -5.0 × 106 m2/sec

are shaded ..................................... 107

8O The total precipitable water difference (88-87) between July 1987 and July

1988 from (a) the GEOS-DAS and (b) SSM/I. Contour intervals are 0.5

gm/cm 2. The zero contours are not drawn. The values greater than 0.5

gm/cm 2 and less than -0.5 gm/cm 2 are shaded ................. 108

81 The outgoing longwave radiation difference (1988-87) for May, June, July,

and August from the GEOS-DAS. Units: W/m 2 ............... 109

82 The outgoing longwave radiation difference (1988-87) for May, June, July,

and August from the NOAA satellite observations. Units: W/m 2 ...... 110

83 The outgoing longwave radiation difference (1988-87) for May, June, July,

and August from the GEOS-GCM simulation. Units: W/m 2 ......... 111

xv
84 The regional precipitation anomalies over land from the GEOS analysis and

the station observations. The anomalies are departure from the seasonal

cycle. Units are mm/day .............................

113

85 The regional precipitation anomalies over land from the GEOS analysis and

the station observations. The anomalies are departure from the seasonal

cycle. Units are ramday ............................. 114

86 The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for April, June and August of 1985. Contour intervals

are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute

values greater than 2 are shaded ......................... 115

87 The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for October, December of 1985, and February 1986.

Contour intervals are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10

mm/day. Absolute values greater than 2 are shaded .............. 116

88 The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for April, June and August of 1986. Contour intervals

are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute

values greater than 2 are shaded ......................... 117

89 The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for October, December of 1986, and February 1987.

Contour intervals are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10

ramday. Absolute values greater than 2 are shaded .............. 118

9O The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for April, June and August of 1987. Contour intervals

are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 ramday. Absolute

values greater than 2 are shaded ......................... 119

91 The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for October, December of 1987, and February 1988.

Contour intervals are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10

mm/day. Absolute values greater than 2 are shaded .............. 120

92 The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for April, June and August of 1988. Contour intervals

are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute

values greater than 2 are shaded ......................... 121

xvi
93 TheprecipitationanomaliesfromtheGEOS-DAScomparedwith theclimate

divisionprecipitationfor October,Decemberof 1988,and February1989.

Contourintervalsare-10 -8 -6, -4, -2, -1, -0.5, 0, 0.5,1,2, 4, 6, 8, 10

ramday. Absolute values greater than 2 are shaded .............. 122

94 The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for April, June and August of 1989. Contour intervals

are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute

values greater than 2 are shaded ......................... 123

95 The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for October, December of 1989, and February 1990.

Contour intervals are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10

mm/day. Absolute values greater than 2 are shaded .............. 124

96 A summary of known deficiencies in the GEOS-1 DAS output based on the

analysis of the first 5 years (March 1985-February 1990) ........... 126

97 Locations of rawinsonde (upper panel) and special upper air (lower panel)

observations for May 5th, 00Z. The latter include pilot balloon, dropwinsonde

and reconnaissance aircraft and radar winds .................. 128

98 Locations of land (upper panel) and ship (lower panel) reports for May 5th,

00Z ......................................... 129

99 Locations of buoy (upper panel) and aircraft (lower panel) reports for May

5th, 00Z ....................................... 130

100 Locations of satellite wind (upper panel) and NESDIS temperature retrievals

(lower panel) under clear (path A), partly cloudy (path B) and cloudy (path

C) conditions for May 5th, 00Z .......................... 131

101 Number of TOVS clear (path A, upper panel) and TOVS partly cloudy (path

B, lower panel) reports for May 1985. Stratospheric only soundings are defined as occurring above the 100 mb level .................... 133

102 Number of TOVS cloudy (path C, upper panel) and surface reports (lower

panel) for May 1985. In (a) stratospheric only soundings are defined as

occurring above the 100 mb level ......................... 134

103 Number of rawinsonde (moisture only, upper panel) and rawinsonde (all,

lower panel) reports for May 1985 ........................ 135

xvii
104 Numberof upperair reports(upperpanel),andsatellitewindreports(lower

panel)for May 1985................................ 136

xviii
1 Introduction

The Data Assimilation Office (DAO) at Goddard Space Flight Center has produced a multiyear global assimilated data set with version 1 of the Goddard Earth Observing System

Data Assimilation System (GEOS-1 DAS; Schubert et al., 1993). One of the main goals

of this project was to produce a research quality data set suitable for the study of general

Earth science problems such as climate variability, atmospheric chemistry, stratospheretroposphere exchange, and surface processes.

The DAO project is one of several efforts (see also Bengtsson and Shukla, 1988; Kalnay and

Jenne, 1991; US Naval Research Laboratory) to re-analyze historical data with an unvarying analysis system. These "reanalyses" are motivated by the fact that operational data

assimilation systems undergo frequent updates which introduce spurious climate signals in

the analysis output. Figure 1 illustrates the dominant climate signals found in operational

analyses at various frequencies. At weekly and shorter time scales the variability is dominated by synoptic-scale (weather) fluctuations. At somewhat longer time scales one finds

the signature of middle latitude planetary wave fluctuations and blocking events. In the

tropics, the Madden-Julian Oscillation is the dominant mode of intraseasonal variation;

operational analyses appear to capture at least some aspects of this oscillation fairly well

(see e.g., section 4.3.2). The seasonal time scale is dominanted in most regions by the

annual cycle. Various teleconnections such as the Pacific/North American (PNA) pattern

are also important components of the variability at this and longer time scales. It is at

the seasonal time scale that spurious signals due to changes in the analysis system become

quite evident. At interannual and longer time scales the problem gets worse, such that

the signature of even the dominant climate events (e.g., the E1 Nifio/Southern Oscillation,

ENSO) is often masked by the spurious signals produced by analysis system changes. The

bottom panel of Fig. 1 provides a somewhat subjective assessment of the quality of the

analyses in each of the frequency bands. The strong analysis signal at the longest time

scales makes them poorly suited for studying climate change. As we shall see in section

4.4, the signal of the analysis system changes is very strong in some quantities, while it is

almost undetectable in others. Unfortunately, quantities tied to the hydrological cycle (e.g.,

convection, divergence, precipitation) are especially sensitive to analysis system changes.

Section 4.4 provides examples of the ablility of the GEOS-DAS to estimate interannual

variations in such key phenomena as the Hadley cell and tropical precipitation.

The current assimilation experiment is also considered a critical benchmark for further

development of the GEOS system, which must become operational in time for the first

launch of the Earth Observing System (EOS) platform scheduled for 1998. Thus it is

important that a thorough evaluation of the quality and usefulness of the data be done

in a timely manner. While an important part of the evaluation is carried out "in-house',

the potential applications of the data are quite broad, and feedback from the general Earth

Science community is deemed vital. This feedback is critical to the DAO development effort

since, unlike data assimilation for Numerical Weather Prediction (NWP), which has as its
Variability found in operational analyses

Weather M-J Oscillation

Blocking

Seasonal Cycle Analysis System

PNA QBO

Analysis System El Nino

I I v

1 day 1 week 1 season 1 year 1 decade

Quality of analysis products

Fair

I Very Good I Good

day 1 week

Poor

v

1 1 season 1 year 1 decade

Figure 1: A schematic of the variability found in operational analyses. The spurious climate

signals produced by changes in the data assimilation systems are found primarily at the

seasonal and longer time scales.
central goal to produce initial conditions that maximize forecast skill, there is no clear cut

measure of quality for climate data assimilation.

It is expected that at the turn of the century the GEOS-DAS will provide one of the primary,

high-level data products from the Earth Observing System (EOS) program for the study

of the Earth (atmosphere, ocean, and land) system. The quality and usefulness of these

data products will depend very much on the veracity of the modeled physical processes and

the extent to which the assimilation system encompasses the climatologically important

components of the climate system. It should be kept in mind that the current version of the

GEOS-DAS has a strong NWP heritage (and thus atmospheric focus); the scientific studies

employing this first multi-year assimilated data set will help set priorities for increased

emphasis in other areas.

The greatest potential benefit of assimilation systems for climate studies is that they can

provide essentially time continuous global estimates of all the relevant parameters at the

full resolution of the assimilating geophysical model. The danger is, of course, that users

treat the assimilated data as truth, while in fact the estimates may not be very good. This

is particularly true for highly derived quantities, which are only weakly constrained by the

observations. Thus climate applications place new demands on the quality of the parameterized physical processes in the assimilating geophysical models. For example, accurate

and consistent estimates of such quantities as precipitation, cloudiness, and surface fluxes

require a degree of veracity in the physical parameterizations and a level of sophisticated

in the analysis techniques that current systems are just beginning to achieve.

The purpose of this document is to provide potential users of the data a convenient guide

to NASA's first multi-year assimilated data set and an early look at the quality of the data

for studying the climate and its variability. This document is also intended to alert users to

potential limitations of the GEOS-1 data, in particular, and of assimilated data, in general.

Section 2 discusses the possible impact of model deficiencies on the assimilated data. Section

3 gives an overview of the GEOS-1 DAS. Selected results from the assimilation are presented in Section 4. The results are organized as follows: Section 4.2 shows various climate

mean fields, including differences from the operational European Center for Medium-Range

Weather Forecasts (ECMWF) analyses and from various observational data sets described

in Section 4.1. The surface energy balance is presented in section 4.2.3. Global and regional

precipitation estimates, including the full vertically-integrated U.S. moisture budget, are

presented in section 4.2.4. Sections 4.3-4.4 give examples of intraseasonal and interannual variability, respectively. Section 4.5 gives some general conclusions, and includes a

list of known problems with the assimilated data. Section 5 provides information about

the input observational data in the form of data coverage maps, data count histograms,

and summaries of the major occurrences of unexpected/unusual data volumes. Section 6

provides information about the organization of the various data sets from the multi-year

assimilation available from the NASA Center for Computational Science (NCCS), from the
GoddardDistributedActiveArchiveCenter(GDAAC),andfrom alocal(DAO) servervia

anonymousftp.

2 Assimilated Data: The Role of the Model

The key advantage of modern global data assimilation systems is the incorporation of a

geophysical model as an integral part of the data processing scheme. Currently these are

primarily atmospheric general circulation models (AGCMs). It is the model which, in principle, provides a global and physically consistent framework for combining and interpreting

the observations. An important goal of climate data assimilation is to increase the utility of assimilated data products by making available accurate estimates, of not just the

prognostic fields, but of a host of derived fields based on the model's physical parameterizations. These estimates included climate processes and forcing functions (e.g., precipitation

and heating rates), which should allow a better understanding of the mechanisms driving

climate variability.

Data assimilation is basically a synthesis of two different estimates of nature: one provided

by the observations and the other provided by a short forecast from a geophysical model.

The accuracy and distribution of the observations, the veracity of the geophysical model,

and the ability of the analysis technique to optimally combine the observations and the

model forecast (the first guess) all influence the accuracy of the final assimilated fields.

Molod et al. (1995) have performed an extensive study of the impact of the model on the

quality of the GEOS-1 assimilation, and the reader is referred to that study for specific

examples. We present here a general overview of what users should be aware of when

working with assimilated data.

Assimilated data products can be approximately grouped into two catagories: those (primarily prognostic) fields that are directly assimilated (e.g. winds and specific humidity) and

those (primarily diagnostic) fields that are generated from the various physical parameterizations. The former are the quantities which are strongly constrained by the observations

and, where these are available, are only marginally impacted by errors in the model (examples are shown in the section 4). The quality of the latter fields depends strongly both

on the accuracy of the physical parameterizations and the quality of the observations. Of

course, in regions where observations are sparse, all estimates will be dominated by the

model's first guess field.

While the hope is that by constraining the prognostic fields with the observations the

geophysical model will generate consistent and accurate estimates of the various diagnostic

fields, there are two basic reasons why this has generally not been true. One reason is

that the process of inserting observations into the model tends to generate imbalances in
the model;for example,convective precipitation tends to show very unrealistic fluctuations

during the times at which data are inserted. It is for this reason that the operational centers

currently provide precipitation estimates based on short term forecasts. Fortunately, much

progress has been made over the past few years to dramatically reduce the severity of

this problem. In the GEOS-1 DAS this problem has been virtually eliminated by the

introduction of an Incremental Analysis Update (IAU) procedure (Bloom et al. 1995; see

section 3), and the precipitation fields are those generated during the assimilation.

The other main reason for inconsistencies between the prognostic and diagnostic fields is

that the physical parameterizations have deficiencies which result in significant model bias.

Thus, for example, if the model prefers to have a drier tropical atmosphere than nature, the

observations will systematically add water during the assimilation. This will be reflected in

budget equations for the assimilation, which then include a mean moisture source from the

observations. The general budget equation for the assimilated quantities is thus:

= dynamics + physics + Ax, (I)

where the dynamics terms depends primarily on directly assimilated quantities (e.g. winds,

temperature, moisture), and the physics terms are determined from the physical parameterizations, which depend only indirectly on the data. The Ax is a non-physical source/sink

coming from the observations. Note that not all terms in the dynamics are strongly constrained by the observations. An obvious exception is the vertical motion field, which in

general, is poorly observed.

Many applications of assimilated data have focused on the dynamics terms. The physics

fields and data increments have rarely been made available, so that the total "forcing"

is determined as a residual of the dynamics and tendency terms. This is an obvious and

useful application of existing analyses since most of the effort has been directed at improving

the estimates of the prognostic (and thus dynamics) fields. As the physics fields improve,

assimilated data will provide a more complete picture of the processes driving the climate

system. Thus the residual approach will be replaced with the direct calculation (from the

GCM parameterizations) of all the relevant forcing (physics) terms.

It is as yet unclear whether current assimilation systems are of sufficient quality that the

residual approach to estimating the forcing terms can give way to the direct approach of

using the physical parameterizations. Some light can be shed on this by examing the mean

increments (Ax), which provide an estimate of the model bias. Averaging (1) over a time

period v one obtains

,--"T

x = dynamics T + phys--si_cJ + A-_. (2)
The size of the bias must be considered relative to the physical forcing terms. For example,

when employing such a budget to study nature's balance, one would like to have the ratio

AZ

physics r

(3)

be much less than 1. If this is not the case, the budget terms are ambiguous, since a

substantial part of the forcing cannot be attributed to real physical processes. For large %

(and removing any systematic tendency such as that associated with the seasonal cycle) (2)

may be written as

physics 4- _ = -dynamics . r , (4)

which illustrates the traditional approach to estimating the forcing as a residual of the

dynamics. Section 4.2.4 shows examples of the contributions of the mean analysis increments

to the budget of the vertically-integrated moisture over the continental United States. The

results show the increments are a substantial component of the mean budget, suggesting

that considerable improvements still need to be made to the physical parameterizations to

reduce the model bias. The results of section 4.4 suggest, however, that despite the mean

climate bias, estimates of interannual forcing anomalies may be quite reasonable. This is

apparently the result of the bias cancelling when the budgets (4) for, say, two different years

are subtracted.

3 Overview of GEOS-1 DAS

The two primary components of the GEOS-1 DAS are the GEOS-1 GCM and an optimal

interpolation (OI) analysis scheme. The GEOS-1 GCM is described in Takacs et al. (1994),

Suarez and Wakacs (1995) and Molod et al. (1995). The analysis scheme is described in

Pfaendtner et al. (1995). For convenience, the GEOS-1 DAS is summarized below.

3.1 The OI Scheme

The GEOS-1 DAS employs an optimal interpolation (OI) analysis scheme, which for the

multi-year assimilation described here, was carried out at a horizontal resolution of 2 °

latitude by 2.5 ° longitude at 14 upper-air pressure levels (20, 30, 50, 70,100, 150,200,250,

300, 400, 500, 700, 850, 1000 mb) and at sea-level. The analysis increments are computed

every 6 hours using observations from a 4-/- 3 hour data window centered on the analysis

6
times (00, 06, 12,and 18 UTC). The innovationvector(observationminusbackground

forecast)usedasinput to the OI is computedusinga singleforecastvalid at the analysis

time.

The upper-air analysesof height, wind, and moisture incorporate the data from rawinsondes, dropwindsondes, rocketsondes, aircraft winds, cloud tracked winds, and thicknesses

from the historical TOVS soundings produced by NOAA NESDIS. The satellite heights

are computed using a reference level that depends on the analyzed sea-level pressure. The

only bogus data used are 1000 mb height observations, which are generated above pressure

reports from ships. These serve to further couple the surface and upper-air analyses.

The OI scheme is multivariate in geopotential height and winds and employs a damped

cosine function for the horizontal correlation of model prediction error. The height-wind

cross-correlation model is geostrophic and scaled to zero at the equator. The multivariate

surface analysis scheme over the oceans adopts an Ekman balance for the pressure-wind

analysis. The moisture analysis for mixing ratio employs only rawinsonde moisture data.

All grid point analyses are done using up to 75 nearby observations fl'om within a circular

data-selection cylinder of 1600 km radius.

The assimilation system does not include an initialization scheme and relies on the damping

properties of a Matsuno time differencing scheme to control initial imbalances generated

by the insertion of observations. However, the initial imbalances and spinup have been

greatly reduced over earlier versions by the introduction of an Incremental Analysis Update

(IAU) procedure (Bloom et al., 1991, 1995). In the IAU procedure, standard OI analysis

increments are computed at the analysis times; the increments are then inserted gradually

into the AGCM by rerunning the forecast and adding a fraction of the increment at each

model time step over the 6 hour period centered on the analysis time (see Pfaendtner et al.

1995 and Bloom et al. 1995 for details of the implementation and the statistical properties

of the IAU approach). The assimilation thus effectively consists of a continuous AGCM

forecast with additional heat, momentum, moisture and mass source terms updated every

6 hours from observations (see section 2).

3.2 The GEOS-1 Model

The tropospheric version of the GEOS-1 GCM uses the potential enstrophy and energy/-

conserving horizontal differencing scheme on a C-grid developed by Sadourny (1975), and

further described by Burridge and Haseler (1977). An explicit leapfrog scheme is used for

the time differencing, applying an Asselin (1972) time filter to damp out the computational

mode. An 8th-order Shapiro filter (Shapiro, 1970) is applied to the wind, potential temperature, and specific humidity to avoid non-linear computational instability. The filter is

applied at every step in such a way that the amplitude of the two-grid interval wave would

be reduced by half in two hours. Applying the filter weakly at each time step eliminates the
shockthat occurredin earlierassimilationsby intermittentapplicationof filter. Themodel

alsousesa polar Fourierfilter to avoidlinearinstability dueto violationof the CFL condition for the Lambwaveandinternalgravitywaves.This polarfilter, however,is applied

only to the tendenciesof the winds,potentiMtemperature,specifichumidity,andsurface

pressure.The model'sverticalfinite differencingschemeis that of ArakawaandSuarez

(1983).Theabovedynamicsroutinesareorganizedinto a plug-compatiblemodulecalled

theARIES/GEOS"dynamicalcore"(SuarezandTakacs1995).

Theinfraredandsolarradiationparameterizationsfollowcloselythosedescribedby Harshvardhanet al. (1987).In thelongwave,watervaporabsorptionisparameterizedasin Chou

(1984),the 15micronbandof CO2asin Chouet al. (1983),andozoneabsorptionasin

Rodgers(1968)with themodificationssuggestedby Rosenfieldet al. (1987).Theshortwave

followsDavies(1982),asdescribedin Harshvardhanet al. (1987).Shortwaveabsorptionby

watervaporusesa k-distribution approachasin LacisandHansen(1974).Cloudalbedo

andtransmissivityfor themodellayersareobtainedfrom specifiedsingle-scatteringalbedo

and cloudoptical thicknessby usingthe delta-Eddingtonapproximation(Josephet M.,

1976;King and Harshvardhan,1986).

The penetrativeconvectionoriginatingin the boundarylayeris parameterizedusingthe

RelaxedArakawa-Schubert(RAS) scheme(Moorthi andSuarez,1992),whichis a simple

andefficientimplementationof theArakawa-Schubert(1974)scheme.UnliketheArakawaSchubertscheme,whichsolvesan adjustmentproblemby consideringsimultaneousinteraction amongall possiblecloudtypes,RASconsidersonly onecloudat a time,and rather

than adjustingfully everyhour or two, it doesa seriesof partial adjustmentsthat tend

to relax the state towardequilibrium. The AGCM Msoincludesa parameterizationthat

modelsthe evaporationof falling convectiverain as describedin SudandMolod (1988).

Negativevaluesof specifichumidityproducedby thefinite-differencedadvectionarefilled

by borrowingfrom below.

The planetaryboundarylayer(PBL) isexplicitly resolvedin a 2 to 4 layerregion.Wind,

temperature,andhumidityprofilesin an"extended"surfacelayer(whichcanbeupto 150m

thick), andtheturbulentfluxesofheat,moisture,andmomentumat thesurfaceareobtained

from Monin-Obukovsimilarity theoryby selectingsimilarity functionsthat approachthe

convectivelimit for unstableprofilesandthat agreewithobservationsfor verystableprofiles.

Surfaceroughnesslengthsaretakenas functionsof vegetationtype overland and as a

functionof surfacestressoverwater.Turbulentfluxesabovethe"extended"surfacedlayer

arecomputedusingthesecondorderclosuremodelof HelfandandLabraga(1988).In this -

scheme,theturbulentkineticenergyis aprognosticvariableandtheremainingsecondorder

momentsarediagnosedfrom it andfromthe atmosphericsounding.

Forthe multi-yearassimilation,GEOS-1wasintegratedona2° latitudeby 2.5° longitude

grid with 20sigmalevels.Thesigmalevelsaredistributedto provideenhancedresolution

in the planetaryboundary layer and at upper levels. The topography used in GEOS-1
waspreparedfrom the 10minute topographyobtainedfrom the NavyFleet Numerical

OceanographyCenterin Monterey.The2° latitude by 2.5° elevationvalueswereobtained

by averagingthe high resolutionvalues(areaswith morethan 60%waterwereconsidered

waterpoints),andthenapplyinga Lanczos(1966)filter. TheLanczosfilter wasdesigned

to removesmallscalestructure(it completelyremoves2AX waves)whileminimizingthe

Gibbsphenomena.

This versionof the AGCM is run without a land surfacemodel (notethat currentdevelopmenteffortsincludethe incorporationof a land surfacemodel). Forthe assimilation

describedhere,soilmoistureis computedoff-line,basedonasimplebucketmodelthat used

monthly meanobservedsurfaceair temperatureand precipitation(Schemmet al., 1992).

Thesnowlineandsurfacealbedoareprescribedandvarywith theseason(seeTakacset al.

1994).The sea-surfacetemperatureis updatedaccordingto the observedmonthlymean

valuesprovidedby the ClimateAnalysisCenterat NMC and the Centerfor Ocean,Land

andAtmosphere(COLA).

3.3 The Processing System

Figure 2 presents an overview of the main processing elements of the GEOS-1 DAS. The primary computations are carried out at the Nasa Center for Computational Sciences (NCCS).

All output is stored on the NCCS Unitree mass storage system which currently has a capacity of about 19.2 terabytes (TB) of near-online storage consisting of robotically-controlled

silos.

The processing was begun on a Cray YMP with 6 processors and then moved to the Cray

C90 (also with 6 processors) when it became available in August of 1993. The new computer

provided a much improved turn around and allowed the completion of the first five years

in just over one year (March 1993-May 1994). A substantial fraction of that time was

devoted to trouble shooting, dealing with mass storage problems, and filling gaps in the

observational database.

The processing is now fully automated. It is carried out in 5-10 day segments. After each

segment is completed the output is temporarily placed on a silo attached directly to the

Cray (current capacity of 2.4TB). After one month is completed, various post processing .

activities are carried out. These include the generation of monthly means, quality control

statistics to look for outliers, and a re-organization of the data into a time series format

(described in section 6). The time series are then copied to 8mm tape and given to the

Goddard DAAC for ingest into their system.

Local workstations are used for pre-processing the conventional and satellite observations.

This processing includes: reformatting, data thinning, consistency checks, and documenting

the observational data coverage. Examples of the data coverage are shown in section 5.
A quick-lookcapabilitywasdevelopedto helpkeeptrack of the processingandto lookfor

anyobviousproblemsthat mightariseduringproduction.Thequick-lookfieldsconsistof a

smallsubsetof theoutput,whichis automaticallysentto the Craysiloto alloweasyaccess

andviewingonlocalworkstations.

All visualizationandmuchof the post-processingis carriedout onlocalworkstations.The

GRADSdisplayprogram(developedat the Centerfor Ocean-Land-Atmosphere, COLA),

is usedextensivelyfor muchof thein-housevisualizationand scientificanalysis;the time

seriesoutput hasbeenorganizedto facilitatetheuseof this software(seesection6.2).

10
Local Workstations

I Process (Conventional+satellite) observations

I Inputobserv_

NCCS CRAY- C90

GEOS-1 ASSIMILATION SYSTEM

Analysis

O-F,QC

Restarts

87 MB/day

Assimilation

Prog/Diag

(pressure)

59 MB/day

Assimilation

Prognostics

(sigma)

27 MB/day

Assimilation

Diagnostics

(sigma)

82 MB/day

Assimilation

Increments

(sigma)

34 MB/day

NCCS UNITREE

(Central File Server)

19.2 TB

L

All Ouput

355 MB/day

POST PRODUCTION

1) Generate monthly means

2) Generate monthly QC statistics

3) Generate time series

4) Quicklook

Local workstations

9

Time Series

(30 GB/yr)

Goddard DAAC

Figure 2: The main elements of the processing system and data flow of the GEOS-1 DAS.

11
4 Results

We present here some selected results from the first five years of the assimilation (March,

1985-February, 1990). Comparisons are made with ECMWF analyses, a parallel GEOS-1

GCM simulation forced by the same boundary conditions used in the assimilation, and

other observational data sets described below. It is important to note that the ECMWF

data are an operational product and therefore are impacted by changes introduced to the

system during this time period (see e.g. Trenberth and Olson 1988; Arpe 1990). We hope

in future reports to make further comparisons with the reanalysis products from both NMC

and ECMWF. The assimilation currently extends to the end of 1993; it is planned that, by

the end of 1995, the assimilation will cover the period (1979-1994).

Note that an output field generated from an assimilation is tradionally referred to as an

analysis. This terminology is all right for those fields that are actually analyzed (e.g.,

with an optimal interpolation scheme), however, it is misleading for a diagnostic field (e.g.,

surface sensible heat flux), which is generated by the GCM during the assimilation but

is never analyzed and, therefore, is not directly influenced by the observations. In this

document we have attempted to be consistent and refer to the results as the "output from

the assimilation" or simply as coming from the GEOS-DAS, though when comparing the

prognostic fields with ECMWF results, we have also referred to the results as analyses.

The results presented here are not meant to be comprehensive, and the reader is referred

to Takacs and Suarez (1995) and Molod et al. (1995) for further results on the circulation

statistics and diagnostic fields. Extensive comparisons between the surface wind products

from the GEOS-1 DAS and several other sources have been carried out over the North

Pacific by Rienecker et al. (1995).

4.1 Comparison Datasets

ECMWF Analyses

The European Center for Medium-Range Weather Forecasts (ECMWF) analyses used for

comparison consist of the operational "ECMWF/TOGA basic level III" data (ECMWF,

1989). The data were received in a packed format from ECMWF and consist of 00Z and

12Z uninitialized analyses on a 2.5 ° X 2.5 ° latitude/longitude grid beginning January 1,

1985. The data were unpacked and interpolated to a 2.0 ° X 2.5 ° latitude/longitude grid for

comparison with the GEOS results. Periods with missing analyses (Schubert et. al., 1991)

were avoided in the processing.

12
GEOS-1 GCM Simulation

A companion simulation was performed with the GEOS-1 model for the same five year

period as the assimilation. The model simulation was forced by the same SST and soil

moisture boundary conditions as the assimilation. The model is identical to that used

in the assimilation, except that in simulation mode the model employs a leapfrog timedifferencing scheme, while in assimilation mode it employs a Matsuno scheme (see Takacs

et al. 1994).

NOAA OLR

The outgoing longwave radiation (OLR) data (kindly provide by K.-M. Lau) are from

NOAA polar orbiting satelites (Gruber and Krueger, 1984). The data were received on a

2.5 ° X 2.5 ° latitude/longitude grid. The nighttime and daytime OLR values were averaged

to obtain 5-day means. No data are available for March 1978-December 1978, most of

November 1988, and March-April, 1989. The impact of satellite changes and data processing

techniques on the quality of the data are discussed in Hurrel and Campbell (1992) and

references therein.

SSM/I Total Precipitable Water

The Special Sensor Microwave Imager (SSM/I) total precipitable water is that generated

by Wentz (1992). The radiative transfer algorithm uses three channels of microwave measurements (22V, 37V, 37H) and a model that accounts for absorption and emission in the

atmosphere. The model uses a surface emissivity value over oceans appropriate for a windroughened sea surface. The model does not account for scattering by raindrops or by frozen

hydrometers and is, therefore inaccurate for high rain rates. No calculation is done over

land or sea ice, because of the complexity of the surface emissivity. These data are available

starting in June 1987.

Comprehensive Ocean-Atmosphere Data Set

Estimates of the surface energy budget were obtained from the comprehensive OceanAtmosphere Data Set (COADS) processed by da Silva et al. (1995). The observations were

corrected for systematic biases, and the derived quantities were interpolated to a 1° × 1°

grid, filling-in data gaps with a sinusoidal interpolation. Data quality control included

the conversion of all wind measurements to an improved Beaufort Equivalent Scale and

the rejection of nighttime cloud observations if the sky is too dark. The surface fluxes were

calculated using a similarity theory parameterization that calculates the transfer coefficients

13
accordingto LargeandPond(1981).Surfaceradiationestimatesweremadeusingempirical

formulasto approximatecloudattenuation,surfacealbedo,andatmospherictransmissivity.

The objectiveanalysisschemeusedis essentiallythe sameschemedescribedby Levitus

(1982).This is a sucessive-correctionscheme(Cressman1959,Daley1991)with a weight

functiondevelopedby Barnes(1964).

Gridded Precipitation Station Data

The global monthly mean precipitation data are from the world monthly surface station

climatology data received from W. Spangler and R. Jenne at the National Center for Atmospheric Research (NCAR). Most of the data are fromthe National Climate Data Center

(NCDC). See Spangler and Jenne (1990) for additional information on data sources and

characteristics. The station values were interpolated to a 2.0 ° × 2.5 ° latitude/longitude

grid by averaging station values within a 300 km radius of each grid point. The averaging

weights are proportional to the inverse of the square of the distance to the station. The

value at a gridpoint was set to undefined if there were no stations within the 300 km radius.

Details of the processing may be found in Schemm et al. (1992)

The U.S. climate division precipitation data used here are a combination of about 300

National Weather Service (NWS) stations and approximately 2500 cooperative stations

over the continental United States. These station data were averaged over each of the 344

United States climate divisions (see Cayan et. al., 1986). The data were then gridded as

described above to a 2.0 ° × 2.5 ° latitude/longitude grid. The data were kindly provided by

Jae Schemm and Suranjana Saha of the National Meteorological Center.

4.2 Climate Mean Quantities

4.2.1 Zonal Mean Prognostic

The top panels of Figs. 3-5 show the seasonal cycles of the GEOS-DAS zonal mean

zonal (u) and meridional (v) winds at 200 mb and the zonal mean 300 mb height field.

The cycle is based on monthly data and is an average over the five years. The difference

(GEOS-ECMWF) is shown in the bottom panels. The differences in the zonal (u) wind at

200 mb are generally less than 1 m/sec everywhere except over Antarctica. These differences are small compared to the magnitude of the u-wind itself. This is in contrast to the

v-wind, which shows that the ECMWF boreal winter Hadley cell is almost twice as strong

as that in the GEOS-DAS at this level. The differences in the height field are generally

small except for much of the Antarctic, where the difference exceeds 100 meters.

Figures 6-13 show the five-year mean zonal mean cross sections of various quantities for

14
thetwoextremeseasonsJune-July-August(JJA) andDecember-January-February (DJF).

Thelayout of the figuresis suchthat the GEOS-DASresultsarein the top panelandthe

differenceswith the ECMWFanalyses(GEOS-ECMWF)arein the bottom panel.

Differences in the u-wind during DJF (Fig. 6) are largest in the tropics and Southern

Hemisphere at upper levels. The GEOS-DAS tends to produce somewhat stronger westerlies

(or weaker easterlies) at upper levels. Similar results are found for JJA (Fig. 7), except in

the tropics above 100 mb where the GEOS analysis shows substantially stronger easterlies.

The peculiar shape of the region of weak westerlies at the equator in the GEOS-DAS results

during DJF appears to be the systematic signature of a problem with the GEOS-1 analysis

scheme due to an improper treatment of the wind-wind correlations near the equator (see

Appendix C.4 in Pfaendtner et al. 1995).

The differences in the DJF Hadley cell mentioned earlier are quite evident in Fig. 8. The

ECMWF zonal mean meridional wind reaches a maximum of 3 m/sec at 200 mb, while the

maximum in the GEOS-DAS is less than 2 m/sec and is placed somewhat higher, above

200 mb. In contrast, the JJA Hadley cells (Fig. 9) are more similar. The zonal mean temperature field (Figs. 10-11) show that the GEOS analyses are warmer than the ECMWF

analyses in the tropical upper troposphere and cooler in the stratosphere during both seasons. The large differences near the surface are likely due to interpolation/extrapolation

problems in regions of high topography. The zonal mean vertical velocity (Figs. 12-13) is

remarkably similar in the two analyses. The largest differences occur near the South Pole.

Also, the DJF Hadley cell appears to be stronger and better organized in the ECMWF

analyses.

15
(o)

90N _

60N

3ONEQ

30S

60S.

90S

APR MAY JUN JUL AUG SEP OCT NOV DEC JA_N FEB

90N

60N

3ON'

60S

90S

APR

Cb)

i ' . i

I ....

MAY JUN JUL AUG SEP OCT NOV DEC JAN. FEB

Figure 3: (a) Seasonal cycle of the 200 mb zonal mean u-wind from the GEOS-DAS and

(b) the difference fields (GEOS-ECMWF). Contour interval is 5.0 m/sec in (a) and 1.0

m/sec in (b). Negative regions are shaded.

16
90N

6ON'

3ON'

(o)

90N

(b)

60N

30N

EO

30S

60S

90S

APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB

Figure 4: (a) Seasonal cycle of the 200 mb zonal mean v-wind from the GEOS-DAS and

(b) the difference fields (GEOS-ECMWF). Contour interval is 0.5 m/sec. Negative regions

are shaded.

17
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(o)

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o

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,_._N

._'_o

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o

v

r_

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o
(0)

100"

200"

300'

400'

500'

600'

700'

1

90N 60N 30N EQ 30S 60S 90S

100"

(b)

200"

300

400

500

600

700

90N 60N 30N EQ 30S 60S 90S

Figure S: (a) DJF climatology of zonal mean v-wind from the GEOS-DAS and (b) the

ECMWF analysis. Contour interval is 0.5 m/sec. Negative regions are shaded. The top

level is 30 mb

21
(0)

I00'

200"

300-

400-

500-

600-

700-

1

ION 60N 3ON EQ 30S 60S 90S

IO0

(b)

200

300"

400.

500'

600'

700'

90N 60N 30N EQ 30S 60S 90S

Figure 9: (a) JJA climatology of zonal mean v-wind from the GEOS-DAS and (b) the

ECMWF analysis. Contour interval is 0.5 m/sec. Negative regions are shaded. The top

level is 30 mb

22
(0)

100"

90N 60N 30N EQ 30S 60S 90S

(b)

100'

200"

300 ........... _.......... i ...........................

400-

500-

600-

700-

1

90N 60N 30N EQ 30S 60S 90S

Figure 10: (a) DJF climatology of zonal mean temperature from the GEOS-DAS and (b)

the difference fields (GEOS-ECMWF). Contour interval is 5.0 °C in (a) and 1.0 °C in (b).

Negative regions are shaded. The top level is 30 mb

23
100 .........

200

300.

400"

500"

600"

700"

(o)

90N 60N 30N EQ 30S 60S 90S

(b)

100"

200

300

400

500

600

700

90N 60N 30N EQ 30S 60S 90S

Figure 11: (a) JJA climatology of zonal mean temperature from the GEOS-DAS and (b)

the difference fields (GEOS-ECMWF). Contour interval is 5.0 °C in (a) and 1.0 °C in (b).

Negative regions are shaded. The top level is 30 mb

24
o

v

0

o_

-0

r-_

_r_

©.o

o_

c_ b0

_z0

c0 I

a_

0

,° •_

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4.2.2 Global Prognostic

Figures 14-31 show the global distribution of selected GEOS-DAS quantities along with

difference maps with ECMWF analyses, satellite and in situ observations.

The general layout for these figures is the same as for the zonal means, with the GEOS

results on the top panels and difference fields on the bottom panels, The upper-level GEOS

and ECMWF mean zonal winds are quite similar, with differences of less than 2 m/sec in

most places. The largest differences occur during JJA (Fig. 16) in the tropics and at high

latitudes. At 850 mb the largest difference in the zonal wind occur over the tropics, with

the GEOS results showing larger westerlies over the western Pacific during DJF (Fig. 15)

and larger easterlies over the eastern Pacific during JJA (Fig. 17). Considerable differences

also exist over the tropical land masses.

The meridional wind differences (GEOS-ECMWF) show more complicated patterns than

the zonal wind. During DJF the GEOS 200 mb meridional winds (Fig. 18) show a more

northerly tendency just north of the equator, while at 850 mb (Fig. 19) the winds have a

more southerly tendency in that latitude band; this is consistent with a weaker Hadley cell

during this season (see Fig. 8). During JJA the differences at 200 mb (Fig. 20) are primarily

at high latitudes and appear to be associated with problems in the ECMWF winds near

the poles. At low levels, besides the large differences near the poles, the differences tend to

be primarily tied to local topographic features.

Both the stream function and velocity potential fields (Figs. 22-27) show the largest differences (GEOS-ECMWF) over the tropics. Figure 24 shows clearly the signature of the

weaker DJF Hadley cell in the GEOS results. The JJA velocity potential differences suggest

the GEOS results tend to produce stronger convection over the western tropical Pacific and

the Caribbean and stronger subsidence over the eastern tropical oceans. This is consistent

with more intense tropical east-west (Walker) circulations in the GEOS results during JJA.

The sea-level pressure differences (Figs. 28-29) largely follow the regions of high orography.

This must be primarily due to differences in the algorithm used to reduces the surface

pressure to sea-level. The strong signature of the topography in the GEOS results suggests

a problem with that algorithm. There are also very large scale (but small amplitude)

differences, with the GEOS sea-level pressure being higher than that from ECMWF over

much of the Pacific and Indian Oceans during both seasons.

The GEOS-DAS total precipitable water (TPW) over water surfaces is compared with

SSM/I values in Figs. 30-31. The GEOS assimilation is drier over most of the oceans, except

in middle and high latitudes. The largest differences occur in the tropics and subtropics

where magnitudes often exceed 0.5g/cm 2. The differences show a clear seasonal shift, with

the largest regions of significant dry bias (with respect to the SSM/I values) moving from

the boreal subtropics during JJA to the tropics during DJF.

27
90N

(o)

60N

30N

30S'

60S"

90S

0 60E 120E 180 120W 60W 0

90N

Cb)

60N

30N

EQ,

30S60S"

90S

0 60E 120E 180 120W 60W 0

Figure 14: (a) DJF climatology of 200 mb u-wind from the GEOS-DAS and (b) the difference fields (GEOS-ECMWF). Contour interval is 5.0 m/sec in (a) and 2.0 m/sec in (b).

Negative regions are shaded

28
90N

60N

30N

30S60S

90S

0 60E 120E 180 120W 60W

90N

6ON30N

EQ

30S.

60S'

(b)

90S •

0 60E 120E 180 120W 60W 0

Figure 15: (a) DJF climatology of 850 mb u-wind from the GEOS-DAS and (b) the difference fields (GEOS-ECMWF). Contour interval is 3.0 m/see in (a) and 2.0 resee in (b).

Negative regions are shaded.

29
9ON'

(o)

60N •

3ON'

30S"

60S"

90S

0 60E 120E 180 120W 60W 0

90N

6ON.

3ON.

EQ,

30S60S"

90S

0

(b)

:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: !__ ._.:_.:.::::::::::::::::.:.:.:.'.'.'.'.'.' ..... ..-..:.

:::" .._::i!_:_._iii

60E 120E 180 120W 60W 0

Figure 16: (a) JJA climatology of 200 mb u-wind from the GEOS-DAS and (b) the difference fields (GEOS-ECMWF). Contour interval is 5.0 m/sec in (a) and 2.0 m/sec in (b).

Negative regions are shaded.

3O
90N

6ON"

(o)

3ON"

30S"

60S90S

0 60E 120E 180 120W 60W 0

90N

(b)

60N"

30N

EQ

30S'

60S'

90S' r T T

0 60E 120E 180 120W 60W 0

Figure 17: (a) JJA climatology of 850 mb u-wind from the GEOS-DAS and (b) the difference fields (GEOS-ECMWF). Contour interval is 3.0 m/sec in (_) and 2.0 m/sec in (b).

Negative regions are shaded.

31
90N

60N

30N

EQ"

30S

60S

W

l

f

(o)

90S

0 60E 120E 180 120W 60W 0

90N

(b)

60N

30N

EQ

30S

60S.

90S

0 60E 120E 180 120W 60W 0

Figure lS: (a) DJF climatology of 200 mb v-wind from the GEOS-DAS and (b) the difference fields (GEOS-ECMWF). Contour interval is 2.0 m/sec in (a) and 1.0 m/sec in (b).

Negative regions are shaded.

32
9ON'

(o)

6ON'

3ON'

EQ'

30S'

60S'

]

90S

0 60E 120E 180 120W 6()W 0

90N ,

(b)

6ON'

30N

30S"

60S"

90S • • r

0 60E 120E 180 120W 60W 0

Figure 19: (a) DJF climatology of 850 mb v-wind from the GEOS-DAS and (b) the difference fields (GEOS-ECMWF). Contour interval is 2.0 m/sec in (a) and 1.0 m/sec in (b).

Negative regions are shaded.

33
90N

(o)

60N

3ON'

30S

60S

90S

0 60E 120E 180 120W 60W 0

90N"

(b)

6ON"

3ON"

EQ

30S

60S'

90S

0 60E 120E 180 120W 60W 0

Figure 20: (a) 33A climatology of 200 mb v-wind from the GEOS-DAS and (b) the difference fields (GEOS-ECMWF). Contour interval is 2.0 m/sec in (a) and 1.0 m/sec in (b).

Negative regions are shaded.

34
(o)

6ON'

3ON'

30S

60S

90S

0 60E 12'0E 180 120W 60W 0

90N

(b)

6ON"

30N -

30S'

60S"

90S T , T

0 60E 120E 180 120W 60W 0

Figure 21: (a) JJA climatology of 850 mb v-wind fi'om the GEOS-DAS and (b) the difference fields (GEOS-ECMWF). Contour interval is 2.0 resee in (a) and 1.0 m/see in (b).

Negative regions are shaded.

35
9ON'

Ca)

6ON'

3ON"

30S

60S

90S

0 60E 1201" 180 120W 60W 0

9ON'

(b)

6ON'

3ON"

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 22: (a) DJF climatology of 200 mb eddy streamfunction from the GEOS-DAS and

(b) the difference fields (GEOS-ECMWF). Contour interval is 5.0 × 106 m2/sec in (a)

and 2.0 × 106 m2/sec in (b). The values greater than 10.0 × 106 m2/sec and less than

-10.0 × 106 m2/sec are shaded in (a) and the values greater than 2.0 × 106 m2/sec and less

than -2.0 × 106 m2/scc are shaded in (b).

36
9ON'

(o)

6ON'

3ON'

EQ'

30S'

60S'

90S o 66E 120E 180 120W 60W 0

90N

(b)

6ON'

,_.:.:.:.:+:.:.:.:.:::: :....: ...................

3ON'

EQ'

30S"

60S"

90S" T

0 60E

T f T

120E 180 120W 60W 0

Figure 23: (a) JJA climatology of 200 mb eddy streamfunction from the GEOS-DAS and

(b) the difference fields (GEOS-ECMWF). Contour interval is 5.0 x 106 m2/sec in (a)

and 2.0 x 106 m2/sec in (b). The values greater than 10.0 x 106 m2/sec and less than

-10.0 x 106 m2/sec are shaded in (a) and the values greater than 2.0 x 106 m2/sec and less

than -2.0 x 106 m2/sec are shaded in (b).

37
90N

(o)

60N

3ON,

30S60S

90S

0 60E 12'0E 180 12()W 60W 0

90N

(b)

6ON- "_, ...... \

3ON"

EQ

30S

60S'

.... b .........

°"F:

90S _ r T •

0 60E 120E 180 120W 60W 0

Figure 24: (a) DJF climatology of 200 mb velocity potential from the GEOS-DAS and (b)

the difference fields (GEOS-ECMWF). Contour interval is 1.0 × 106 m2/sec. The values

greater than 5.0 × 106 m2/sec and less than -5.0 × 106 m2/sec are shaded in (a) and the

values greater than 3.0 × 106 m2/sec and less than -3.0 × 106 m2/scc are shaded in (b).

38
90N

(o)

60N

30N

EQ

30S

60S

90S

0 60E 12'0E 180 12()W 60W 0

90N

Cb)

60N

._._. ..... _-:_.; .................................................... ;-............

30N

EQ

30S.

60S.

90So 60E 120E "_ 180I. 120W, 60WT

Figure 25: (a) DJF climatology of 850 mb velocity potential from the GEOS-DAS and (b)

the difference fields (GEOS-ECMWF). Contour interval is 1.0 × l06 m2/sec. The values

greater than 5.0 × 106 m2/sec and less than -5.0 × 106 m2/sec are shaded in (a) and the

values greater than 2.0 × 106 m2/sec and less than -2.0 x 106 m2/sec are shaded in (b).

39
9ON'

(o)

6ON'

,5ON

EQ

30S

60S'

90S

0 60E 1;_OE 180 120W 60W 0

90N

(b)

60N

30N

30S ,

60S"

T T r

90S0 60E 120E 180 12"0W 60W 0

Figure 26: (a) JJA climatology of 200 mb velocity potential from the GEOS-DAS and (b)

the difference fields (GEOS-ECMWF). Contour interval is 1.0 × 106 m2/sec. The values

greater than 5.0 × 106 m2/sec and less than -5.0 × 106 m2/sec are shaded in (a) and the

values greater than 3.0 × 106 m2/sec and less than -3.0 × 106 m2/sec are shaded in (b).

4O
90N

(o)

60N

30S"

60S"

90S

0 60E 12'0E 180 12()W 6()W

90N

Cb)

6ON"

3ON30S"

Figure 27: (a) JJA climatology of 850 mb velocity potential from the GEOS-DAS and (b)

the difference fields (GEOS-ECMWF). Contour interval is 1.0 x 106 m2/sec. The values

greater than 5.0 x 106 m2/sec and less than -5.0 × 106 m2/sec are shaded in (a) and the

values greater than 2.0 x 106 m2/sec and less than -2.0 x 106 m2/sec are shaded in (b).

41
90N

(o)

60N

30N

30S.

90S'

0 60E 120E 180 120W 60W 0

90N

(b)

6ON'

30N"

EQ"

30S"

60S"

90S T

0 60E 120E 180 120W 60W 0

Figure 28: (a) DJF climatology of sea-level pressure from the GEOS-DAS and (b) the

difference fields (GEOS-ECMWF). Contour intervals are 4.0 mb. Values greater than 1020

mb are shaded in (a). Negative regions are shaded in (b).

42
90N

(o)

6ON"

3ON"

30S

60S

90S

0 60E 120E 180 120W 60W 0

9ON'

(b)

6ON"

3ON'

EQ

30S'

60S"

90S

0 60E 120E 180 120W 60W 0

Figure 29: (a) JJA climatology of sea-level pressure from the GEOS-DAS and (b) the

difference fields (GEOS-ECMWF). Contour interval is 4.0 mb. Values greater than 1020

mb are shaded in (a). Negative regions are shaded in (b).

43
9ON'

(o)

3ON'

30S"

60S"

90S

0 180 120W 0

90N

(b)

6ON"

3ON"

30S"

60S

90S T 7 T

0 60E 120E 180 120W 60W 0

Figure 30: (a) DJF climatology (Dec 1987-Feb 1990) of total precipitable water from the

GEOS-DAS and (b) the difference (GEOS-SSM/I) over the oceans. Contour interval is 0.5

gm/cm 2. The values greater than 4.0 gm/cm 2 are shaded in (a) and negative values are

shaded in (b).

44
(0)

90N

60N -

3ON"

30S"

60S"

90S

0 60E 120E 180 120W 60W 0

90N

(b)

60N

30N

EQ

30S60S. 7 ...... -i ........... i .......... i ..................... ..........

90S0 60E 12_0E " .r r 180 120W 60W 0

Figure 31: (a) JJA climatology of total precipitable water (Jul 1987-Aug 1989) from the

GEOS-DAS and (b) the difference (GEOS-SSM/I) over the oceans. Contour interval is 0.5

gm/cm 2. The values greater than 4.0 gm/cm 2 are shaded in (a) and negative values are

shaded in (b).

45
4.2.3 Surface Energy Balance

This section presents the global distribution of the components of the surface energy balance

from the GEOS-DAS and the differences between the GEOS-DAS and COADS results

(oceans only). Further comparisons are made with the parallel GEOS GCM simulation for

the zonal means. Section 4.1 describes the COADS data and the parallel model run. The

net surface energy balance can be expressed as

Fsw I - Fsw T - (Flw T - Flwl ) - S H - L H = Qn_t (5)

where Fs_ terms are the upward and downward shortwave fluxes, Fz_ terms are the longwave

fluxes, SH is the sensible heat flux, LH is the latent heat flux, and Qn_t is the net surface

heat flux. Since the sea-surface temperature (SST) is specified from observed monthly mean

values, the net surface heating over the oceans serves mainly as a bulk diagnostic indicator

of the interaction at the ocean-atmosphere interface. Note that, since the GEOS-1 DAS

does not perform a surface temperature analysis, there is no explicit forcing term in (5)

coming from the analysis (see section 2).

The latent heat flux is assumed to depend chiefly on the surface wind magnitude, the air-sea

moisture gradient, and the surface layer stability. Because of the similarity in technique used

to estimate evaporation by the GEOS GCM and by the COADS algorithm (see section 4.1),

any differences between the GEOS-DAS and COADS results reflects differences in the nearsurface gradients of humidity or winds and in surface roughness. Both seasons (Figs. 32-33)

show maxima over the subtropical oceans in the regions of the subtropical highs. There is a

strong seasonal shift associated with large evaporation rates over the summer continents and

boreal winter east coasts of the two major continents. The GEOS-DAS results are generally

close to the COADS results but tend to underestimate the magnitude, especially over the

Kuroshio and Gulf currents during winter, and in the Northern Hemisphere (NH) over the

east tropical Pacific during summer. Molod et al. (1995) provide a further discussion of

these results and comparisons with the parallel GCM simulation (see also the zonal mean

results below).

The flux of sensible heat at the surface is an important mechanism for energy transfer from

the surface to the atmosphere, although over the oceans somewhat less so than the latent

heat flux. The most important determinants of the sensible heat flux over the oceans are

the air-sea temperature gradient and the ocean surface roughness. Oceanic sensible heat

flux is generally from the surface to the atmosphere, but can be downward over sea ice

or cold ocean currents. The global distribution of the sensible heat flux (Figs. 34-35),

shows that, over the oceans, the regions of maximum flux occur over the Gulf Stream and

Kuroshio current during winter, when the temperature gradients are strongest. Over land,

the largest fluxes occur over the summer desert regions. The difference plot between the

DAS and COADS, indicates an underestimation of the Gulf Stream sensible heat flux. As

46
was the case for the latent heat flux, the similarity in the parameterizations used to calculate

sensible heat flux in the GEOS-DAS and by the COADS algorithm implies that differences

in resultant fluxes must be due to differences in the air-sea temperature gradient and slight

differences in the determination of surface roughness.

The major energy source term for the earth's surface, providing energy for sensible and latent heat fluxes as well as the surface heating and terrestrial emissions, is the net shortwave

radiation at the surface. The major factors influencing the distribution of net surface shortwave flux are the incoming shortwave at the top of the atmosphere, the surface albedo,

and the absorption and reflection by clouds. The net shortwave and difference with the

COADS results are shown in Figs. 36 and 37 for the two extreme seasons. The largest values occur over the summer hemisphere subtropics, corresponding to the incident shortwave

on the atmosphere, with a local minimum over the intertropical convergence zone (ITCZ)

corresponding to reflection by clouds, and a final minimum over the dark winter pole. The

seasonal differences in the tropics are strongly tied to the movement of the ITCZ. Compared

with COADS, the GEOS-DAS results tend to overestimate the net surface shortwave in

the extratropics during both seasons, with a greater disparity in the summer. There is an

underprediction of net surface shortwave in the tropics, especially during boreal summer

over the western Pacific and the Caribbean Sea. These differences are related to an underestimation of cloud cover in the extratropics, and an overestimation of the cloud cover in

the tropics by the DAS (see Molod et al. 1995).

The longwave radiation at the surface is the difference between the upward surface emission

and the downward emission by clouds and the moist atmosphere. The net surface longwave

radiation (upward minus downward flux) is shown in Figs. 38 and 39. The net longwave is

a smaller component of the surface heat budget than the net surface shortwave and exhibits

a smaller latitudinal and seasonal variability, particularly over the oceans. The largest

seasonal variations occur over the extratropical storm track regions of both hemispheres,

the polar regions, and over the desert land areas. The smaller values during summer over

the storm tracks suggest that the influence of clouds dominates any differences due to the

seasonal variations in sea-surface temperatures. The GEOS-DAS overpredicts (compared

with COADS) the net surface longwave during both seasons, especially over the boreal

winter storm tracks. The SST values used by COADS and GEOS-DAS are essentially the

same, so the discrepancy in surface longwave must be in an underestimation of the downward

component, either due to clouds or due to water vapor emissions. In the extratropics, Molod

et al. (1995) found that the TOA longwave cloud radiative forcing was underestimated by

the DAS, which implies a low estimate of cloud-related downward emissions as well. In the

tropics, the low downward longwave emissions are probably related to the dry bias (Figs.

30-31).

The net surface heat flux, Qnet, represents the residual heating available to raise the ground

temperature over land or the local sea surface temperature. Figures 40 and 41 show a

positive maximum in the summer hemisphere throughout the extratropics and subtropics,

47
correspondingto the maximumsolarirradianceandthe relativeminimumof evaporative

flux in the extratropics.Thepositivevaluesextendto the equatorovertheeasternoceans,

correspondingto localminimain evaporativeandlongwaveflux. Theborealwinterfluxes

aredominatedby thestrongnegativefluxesovertheKuroshioandGulf streams.The main

differencewith theCOADSresultsoccursin thesummerhemispherewheretheGEOS-DAS

tendsto overestimatethe netheatflux into theoceans,correspondingto the overestimate

of net surfaceradiativeflux. LargedifferencesMsooccuroverthe tropicalandsubtropical

IndianandPacificOceansandtheCaribbeanSeaduringJJA;theseareassociatedwith the

differencesin the netlongwaveflux mentionedearlier(seeFig. 39).

Thefollowingzonalmeanplotsarecomputedoveroceansonly to allowcomparisonswith

the COADSdata. Thefiguresincludetheresultsfromthe parallelGEOSGCMsimulation

with the sameboundaryconditionsusedin the GEOS-DAS(seesection4.1).

The zonalmeansensibleheatflux (Fig. 42) showsa maximumin the winter hemisphere

extratropicsthroughoutthe year,correspondingto thelargestair-seatemperaturegradients.Thereexistsa fairly goodcorrespondencebetweenGCMsimulation,assimilationand

COADS,with a tendencyfor the GCM to overpredictthe flux in the extratropics.The

sparcityof COADSdata in the SouthernOceanminimizesthe significanceof the differencesin that region.The zonalmeanlatent heatflux clearlyshowsthe maximaoverthe

subtropicaloceans,whereprecipitationislowandtheair abovethesurfaceisrelativelydry

dueto the subsidenceassociatedwith the subtropicalhighs.The winterhemispherestend

to havea greaterlatentheatflux, presumablyassociatedwith thehigherwintertimesurface

winds. The COADSandGEOS-DASresultsagreequite well,thoughthereis a tendency

for the DASfluxesto beweaker.TheGCM resultsshowconsiderablystrongerfluxes;this

appearsto be primarily dueto the strongersimulatedsurfacewinds,whicharecorrected

in the GEOS-DAS(seeMolodet al. 1995).

Thezonalmeannet shortwaveandlongwaveradiationat the surfaceareshownin Fig. 43.

The GEOS-DASandGEOS-GCMfluxesarequitesimilar,exceptin the NH extratropics

during JJA, wherethe GEOS-DAShassignificantlylargerfluxes,apparentlyassociated

with reducedcloudinessin the DAS.In this latitude bandtheGCM simulationis actually

closerto the COADSresults.The largedifferencesbetweenthe DASand COADSin the

summerhemispherenet surfaceshortwaveflux notedearlieris quiteevidentin the zonal

averages.Notethesubstantialpolewardshift in themaximacomparedwith COADS.In all

threeestimates,thezonalmeannetsurfacelongwavefluxshowslittle latitudinal orseasonal

variation,but the COADSresultsshowconsistentlylowervalues.

The zonalmeannet surfaceradiation(Fig. 44)is dominatedby the shortwavecomponent

and,therefore,exhibitsthesamelatitudinalandseasonal behavior,asseenin theshortwave

flux (Fig. 43). The DAS and GCM estimatesof the net surfaceradiationare closerto

the COADSdatathantheindividualcomponentsdueto the compensatingoverprediction

errorsin both longwaveandshortwave.The zonalmeannet heatflux (Fig. 44),showsa

48
positivemaximumin thesummerhemispherethroughouttheextratropics.Thezonalmean

flux hasa slightlocalmaximumnearthe equator,correspondingto the localminimumin

the zonalmeanevaporativeflux. Theimplied oceanfluxes,therefore,arefrom summer

to winter hemisphere,with a slightly greaterhemisphericdisparityduringthe southern

summer;however,the knownsensitivityof theimpliedoceanfluxesto smallchangesin the

zonalaveragesrequiresa morethoroughanalysisof thequality of theseresults.

In the tropics the DAS and GCM agreerelativelywell with the COADSresults. In the

summerhemisphereboth the GCM and DAStend to overestimatethe net heatflux into

the oceans,correspondingto the overestimateof net surfaceradiativeflux. The implication is that the parameterizationof the net surfaceradiation,probablyin the simulation

of cloud-radiativeinteraction,is biasedevenwhengivenamoreaccurateatmospherictemperatureand moistureprofileby the DAS. In the NH winterthe GCM showsa stronger

negativesurfaceheatflux than eitherthe DASor COADSdata,duein largepart to the

overestimationof the latentheatflux.

49
9ON"

6ON'

3ON'

EQ30S"

60S

gos

0 60E 0

90N

6ON.

3ON"

EQ

30S ..........

60S .......... _........ ; ....

....... 2 ..........

90S" , _ _ I T

0 60E 120E 180 120W 60W 0

 iii!iiiliiii!iiiiiiiiiiii!!ii!iiiiiiiiiiii!iiiiiiil Iiiiiiiiiiiltiiiiiiiiiiiiiiii;iiiiiiiiiiiiii!i 

-60 -30 30 60

Figure 32: The latent heat flux for DJF for a) GEOS-DAS, and b) GEOS-DAS minus

COADS. Contour interval is 30 W/m 2.

5O
(o)

6ON"

3ON'

30S'

60S"

90S

90N

0 60E 12'0E 180 120W 60W 0

l_i_!_i_,!_i_!_i_!_!_!_!_i_i_i_i!!ii_!_!_!_!_i_!_!_,!_lmm _-

60 120 240

(b)

60N

30N

EQ

30S'

60S'

90S T f T

0 60E 120E 180 120W 60W 0

"-=_l_ii!i!iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii!iil Iiiiiiiiiiiiiiiiiiiiiiiiiiiiii!iiiil!iiiii!iiiiiiiii_

-60 -30 30 60

Figure 33: The latent heat flux for JJA for a) GEOS-DAS, and b) GEOS-DAS minus

COADS. Contour interval is 30 W/m 2.

51
90N

6ON"

30N

EQ

30S

60S .........................

90S

0 60E 12'0E

 = Wiiii iii iiiiiiiiii!iiiiiiiiiiiiii iiiiiiiiiiiil

-60 -30

9ON'

180 12'0W 60W

liii ii!iiii iiiii ,!iiiiiiiiiiiiiiiiiiii iiiiii! iitl lt = ,--

30 60 120

6ON'

3ON'

30S"

60S"

90S v ,

0 60E 120E 180 120W 60W 0

iiiiiiiiiiiiiiiii!Iii!i!iii!ii ii!iii i 

-15 15 30

Figure 34: The sensible heat flux for DJF for a) GEOS-DAS, and b) GEOS-DAS minus

COADS. Units:W/m 2. Contour intervals are -5 0 5 10 20 30 60 90 120 150 180 in (a) and

every 5 in (b).

52
90N

6ON3ON"

30S"

60S"

90S

90N

60E 120E

-60 -30

180 120W 60W

30 60 120

0

6ON"

3ON"

30S"

60S"

90S f _ T T

0 60E 120E 180 120W 60W 0

-15 15 30

Figure 35: The sensible heat flux for JJA for a) GEOS-DAS, and b) GEOS-DAS minus

COADS. Units:Wire 2. Contour intervals are -5 0 5 10 20 30 60 90 120 150 180 in (a) and

every 5 in (b).

53
90N

60N

30N

30S.

60S,

90S

90N

0 60E 120E 180 120W 60W 0

60N

30N

30S'

60S

90S , T

0 60E 120E 180 120W 60W 0

Iiiiiiiiiiiiiiiiiiii!!iiiiiii!i!iiiiiiiiiiiiiii 

-60 60 120

Figure 36: The net surface shortwave radiation for DJF ior a) GEOS-DAS, and b) GEOSDAS minus COADS. Contour interval is 30 W/m 2.

54
9ON'

6ON'

30S"

60S ............ : ........... : ........... , ...................................

90S

0 60E 12'0E 180 120W 60W

120 240 360

0

90N

60N

30N

J

30S.

60S'

90S' r T w

0 60E 120E 180 120W 60W 0

Iiiiiiiiiii!iiiiiiiiiiiii!!iiiiiiiiiiiiiii!iiiiiiii 

-60 60 120

Figure 37: The net surface shortwave radiation for JJA for a) GEOS-DAS, and b) GEOSDAS minus COADS. Contour interval is 30 W/m 2.

55
90N

6ON'

3ON'

EQ'

30S"

60S

90S

90N

0 60E 120E 180 120W 60W 0

60 120

(b)

6ON'

3ON"

30S

90S T • r

0 60E 120E 180 120W 60W 0

Iii!iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiif 

-30 30 60

Figure 38: The net surface longwave radiation for DJF for a) GEOS-DAS, and b) GEOSDAS minus COADS. Units:W/m 2. Contour interval is 15 in (a) and 10 in (b).

56
9ON'

6ON"

30N

EQ

30S60S"

90S I

0

9ON'

60E 120E 180 120W

• F,i i i !ii ii!iiiiiiiiiiiiiiiiii i',iii ! iiiii if lEm ===-

60 120

60W

6ON"

3ON"

30S

60S'

90S

0 60E 120E 180 120W 60W 0

Iiiiiiiiiiiiiiiiiiiii!i!iiiii!iiiiiiiiiiiiiiiiiiiii 

-30 30 60

Figure 39: The net surface longwave radiation for JJA for a) GEOS-DAS, and b) GEOSDAS minus COADS. Units:W/m 2. Contour interval is 15 in (a) and 10 in (b).

57
90N (o)

60N

3ON"

30S"

60S

90S

9ON"

0 60E 12'0E 180

-360 -240 -120

II,L i

120W 60W

liiiiiii!i!iiiiii!i!i!!!!!!iiliiiiiiiiiiiiiiiiiiiiil m 

120 24-0

0

6ON"

EQ

30S"

60S"

90S

0 60E 60W 0

I 1'

120E 180 120W

-60 60 120

Figure 40: The net surface heat flux for DJF for a) GEOS-DAS, and b) GEOS-DAS minus

COADS. Units:W/m 2. Contour interval is 60 in (a) and 30 in (b).

58
9ON'

6ON'

3ON'

30S"

60S"

90S

90N

0

6ON"

3ON"

30S

60S

90S

0 60E 12'0E 180 12'0W 60W 0

Iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii!iiiiiiil t 

-60 60 120

Figure 41: The net surface heat flux for JJA for a) GEOS-DAS, and b) GEOS-DAS minus

COADS. Units:Wire 2. Contour interval is 60 in (a) and 30 in (b).

59
_c_"

"°

©

¢D

¢D

0

©

r.ao

I

8O

80

40

20

0

-20

IC$

Sensible Heat Flux for DJF

11_

140.

120,

I_l.

I0,

_l,

0

-lO

lif

/ .;. I

+, / E";"_-I

'<_ VV t_i,i./

.-" "i-. - .:

_" ( -.2:

Sensible Heat Flux for JJA

DAS

GCM

...... COADS

/\_-'fT-"\., ..... .- -.

_s _os go _ m

1W

120

8O

!0

40

gg

0

g_

180

180

140

12'0

100

8O

40.

20.

O.

-20,

gO5

Lotent Heot Flux for DJF

\

/ J""\ ,"" -..._[

/"'///_'J t" \'_

/ "y ':. . ..'..I

...... ,

........• - ,_

Latent Heot Flux for JJA

/

/ .-'"-. _

/ ..- -.._ /-.--_

/ .:/'_.. _ / \

II :"__.. ,t'..""-. ',

/ .-': "" ;':.

/

/ •

._ ro 3_ m_4
_o_

o_=

I

o_

>2

_o

o.

©

#D

©

Oq

9_

#D

0

0

Surface Shortwove Flux for DJF

""-"'" "-....._ _-.__

Surfoce Shortwove Flux for JJA

Surfoce LongWove Flux for DJF

IIIG

Surface Longwove Flux for JJA

DAS

GCM

...... COADS
b_

_0_"

X

=-_.

I ¢0

_o

©

mb

¢b

¢o

co

200,

lllO,

0

-100

-lll_

o

--215o,

Net Surface Radiative Flux for DJF

...... COADS

._s Eo _ a

Net Surface Radiative Flux for JJA

2_0

2OO

150

IO0

5O

0

-100

_l;,n

-301

_0.

I00,

IW,

0'

-50

-100

-150

-2Q0

-250

-30

Net Surface Heat Flux for DJF

_.' ......%

'...

\ "°'..

\ _"...

__. ... --.._

\ I

\ !

\_.AAI

Net Surface Heat Flux for JJA

.°.. .."'""*"" ""'"i

.. /t
4.2.4 Precipitation and the U.S. Moisture Budget

Global Precipitation

The global precipitation fields and the corresponding difference fields (GEOS-station observations) are shown in Figs. 45 and 46, respectively. The precipitation patterns show

a clear seasonal shift, with the heaviest tropical precipitation moving northward with the

sun. The heavy summer monsoon rainfall over India is also evident. Other features of note

are the precipitation associated with the boreal winter middle latitude storm tracks and the

boreal summer increase in rainfall over land. The largest differences with station observations occur over the tropics in the regions of heavy rainfall. The JJA GEOS precipitation

appears to be too heavy over Southeast Asia, northern South America, and much of the

NH middle and high latitude land masses. The DJF GEOS precipitation appears to be

somewhat too weak over parts of the United States and Europe. The differences in the

tropics (Africa and South America) suggest the ITCZ in the GEOS-DAS may be shifted

too far north during DJF.

The seasonal cycle of precipitation from the GEOS-DAS and station observations averaged

over selected regions is given in Figs. 48-49. The regions consist of the land areas encompassed by the rectangles in Fig. 47. The seasonal cycle is computed from monthly means

averaged over the five years (Mar 1985-Feb 1990).

The station observations show that the general behavior of the seasonal cycle varies substantially between the eight regions. For example, there is a pronounced annual cycle over

India associated with the monsoons, while there is very little seasonal variation in the areaaveraged precipitation over the Aleutian region. The general behavior in each of the regions

is reproduced in the GEOS-DAS; however, the GEOS values are generally larger than the

station observations, particularly during boreal summer. There is also a tendency for GEOS

to underestimate the winter time precipitation over North America (including the Aleution

region) and Europe. A more detailed look at the precipitation over the continental United

States is provided in the next section. The deviations from the seasonal cycle are shown in

section 4.4.3.

63
90N

Ca)

6ON"

3ON"

EQ

30S

60S

90S

0 60E 120E 180 120W O

9ON'

(b)

6ON'

3ON"

EQ"

30S

60S

90S

0

I" I'

60E 120E 180 120W 60W 0

ii!iiiiiiiiiiiiiiiiiiiiiiiiiii!iiiiiii illii/ii_jil_l Iti!ili

2 4. 6 8 10 14 18

Figure 45: The GEOS-DAS precipitation climatologies for (a) DJF and (b) JJA. Units:

ramday.

64
9ON'

Co)

6ON'

3ON"

30S"

o

60S ............................................................. ' ...........

90S

0 60E 120E 180 120W 60W

90N

(b)

EQ

3ON

30S

60S ............................................................ " ...........

90S , f ,

0 80E 120[ 180 120W 60W

Figure 46: The climatological mean difference (GEOS-DAS minus station observations) of

precipitation for (a) DJF and (b) JJA. The contour interval is 2 ramdaB. Negative values

are dashed. The values greater than 1 mm/da y and less than -1 mm/da y are shaded.

65
0 M09 M0_ I, 09 I, 'q0_I. "409

0 S06

.....................,.......................... S0£

qlJON

03

NO£

N09

N06

o

c_V>>ooX

o

o_
6.71 North America

5.6:

4.5:

231

1.2 " ...

°, ,,,o, ,,,,.

O-0

5.5 ,, 0 ..... 0 .....

4.51

3._ ,." .0" '

!I ....

"0 ..... 0 ..... _'

0.501 South Amerlco

5.6:

4.5:

4.

3,5'

2,531

0._:

Aleutian

• .' 0 ,,, ,.0 ..... 0 ..... '0 ..... 0 ..... 0.. °o,

6.7: Europe

2.5-

I.?: _. o.."'o .... .o ..... ,

0.5:

OMAR AI_R MAY JUN JUL AUG SI_P OCT N{)V DEC J_ FER

I ............ GEOS Sf.oflon anolysis observotions I

Figure 48: The seasonal cycle of the regional mean precipitation over land from GEOS and

the station data. Units: mm/day.

67
4.45'

3.53:

2.5:

0.5

East Asia

14

12

11

1

8_

India

6.7.

5,561

4,51

4'

2,5.

0.5'

0

• ,0 b'°

,,° ,,°., o J°,o o° ,°,

Indonesia/Australia

4.5:

3

2,5

0.5

0

Africa

o .....

AI:_R MAY JI)N JUL ^UG SI_P OCT N(}V DISC ,J_ FEB

I ............ GEOS _tot_ononolysis observotions

Figure 49: The seasonal cycle of the regional mean precipitation over land from GEOS and

the station data. Units: mm/day.

68
U.S. Moisture Budget

The vertically integrated moisture budget in the GEOS-DAS may be written as

Ot " = -(V" qTr?) - e + E + (Aq_r) + (Fq), (6)

where _" is the surface pressure minus the constant prescribed pressure at the top of the

model atmosphere (10 mb), P is the precipitation, E the evaporation, and Aq_r the massweighted analysis increment. The last term includes the effects of the Shapiro filter and any

filling of moisture to remove negative specific humidities (see section 3.2). The angle brackets

denote a vertical integral. The filter and filling terms were not saved in the assimilation;

however, off-line tests showed that the residual is dominated by the Shapiro filter. In the

results shown here, the filter term is estimated as a residual of the other terms. As shown

below, the filter contributes significantly only to the local budget: in the area average this

term is very small. The moisture increments will contribute to the time mean budgets if

the first guess is biased.

Figures 50-58 show the components of the vertically-integrated moisture budget over the

continental United States from the GEOS-DAS averaged over two month intervals. Comparisons are made with the observed precipitation from the U.S. climate division data set

described in section 4.1.

The GEOS-DAS substantially overestimates the summertime precipitation over the eastern

United States, while it tends to underestimate the winter precipitation over the southeast.

The precipitation is closest to the observations during the transition seasons. Both the

analysis increments and the filter contribute significantly to the local budgets throughout

the year. The analysis increments are generally negative (water is being removed, suggesting

a wet bias in the GCM first guess) especially over the western United States, except during

the summer when much of the eastern part of the continent has postive analysis increments

(water is being added, suggesting the first guess is too dry). Some caution must be used in

interpreting the analysis increments, especially during the summer months when spurious

feedbacks may occur with the convection (see Molod et al. 1995).

The area averages of each of the terms in the vertically-integrated moisture budget are

shown in Fig. 56. The area averages are computed over all the continental land points

between latitudes 30°N and 50°N. The overestimate of the summer precipitation and the

underestimate of the winter precipitation is clearly evident. The analysis increments--

unlike the filter, which does not contribute to the area averaged budget--make a substantial

contribution. The mean contribution of the analysis increment is comparable in magnitude

to the convergence term and tends to act in the opposite sense. The apparent connection

between the analysis increments and the convergence is perplexing since a diagnostic study

of the analysis increments suggests that (during late spring) the first guess bias is primarily

69
associatedwith errorsin the precipitationand evaporationfields(Schubertand Chang,

1995).

The moisturefluxesenteringand leavingthe region30°N- 50°N and 80°W-120°Ware

shownin Figs.57-58.Notethis regionincludessomeoceanpointsnotincludedin Fig. 56.

Theresultsshowthat mostofthemoistureentersthecontinentfromthewestandsouthand

leavesacrosstheeastcoastbelow500mb. DuringthespringandsummermonthstheGreat

Plainslow leveljet (LLJ) contributessubstantiallyto the moisturetransport,accounting

for about30%of themoistureenteringthecontinent(seealsoHelfandandSchubert1995).

7O
(o) PRECIP Jan/Feb 86-90 (b) PRECIP(CD) Jan/Feb 86-90

_] :l_t_I..,=-_{_:li::......I___I....::.....::......::..... :.. ' ...._liltiiiliii...-

(c) EVAP Jan/Feb 86-90 (d) VQCON Jan/Feb 86-90

4_

4_

le N

3IN

301(

27N

(e) ANALQ Jan/Feb 86-90 (f) FILTER Jan/Feb 86-90

Figure 50: Components of the vertically-integrated moisture budget from the GEOS-1

DAS. a) precipitation, b) precipitation verification from climate division observations, c)

evaporation, d) moisture convergence, e) the analysis increments,and f) the Shapiro filter

(a-c-d-e). Contour intervals are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10

mm/day. Absolute values greater than 2 are shaded.

71
(a) PREClP Mar/Apr 85-89

: ..... i .....

(b) PRECIP(CD) Mar/Apr 85-89

"_iii,i iY _i...... ...... '_ 51N"1 ..............................

: i+_ _iiiti++._ ....._....'.................... _i....•........

'+' illl

54N

51N

48,N

43N

,_N

30N27N13

(c) EVAP Mor/Apr 85-89 (d) VQCON Mar/Apr 85-89

iiiii_'i__+++++i+++"+:+_ .......+.... i+:_+::i:: ::".......:t..... _y:i_ii_"__

54H.

SIN,

48N.

4,_.

42N,

,1_N+

33N

ZiON

2714

(e) ANALQ Mor/Apr 85-89 (f) FILTER Mar/Apr 85-89

_"_,_, :\_ ..... _k_, :-_ - ............................................

::iii:iiiii_'(:_;_'__<<iiiiiiiiiiiiiiiiiii

Figure 51: Components of the vertically-integrated moisture budget from the GEOS-1

DAS. a) precipitation, b) precipitation verification from climate division observations, c)

evaporation, d) moisture convergence, e) the analysis increments,and f) the Shapiro filter

(a-c-d-e). Contour intervMs are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10

mm/day. Absolute values greater than 2 are shaded.

72
PRECIP Moy/Jun 85-89

i ..... i......i.....

,._ i......].... i.....

(b)

54M'J ..... _ ..................

51M4

4eM.I

4,5M4

42t4-1

30144

_14"1

3314]

,SON4 .... i ..... ..

27141 .... :_..... i ....

PRECIP(CD) Uoy/Jun 85-89

EVAP May/Jun 85-89 (d) VQCON Moy/Jun 85-89

l-i1" _] '++,r+'+++(+__-! ++

51N.

4_N,

46N.

36N,

_4

(e) ANALQ Moy/Jun 85-89 (f) FILTER Moy/Jun 85-89

-,, _++p_ .+ _C'V ................ .!!..._ -.... :_: -_:_>__, .......... <--

+ 51N • " : ........... _ _ + _@,-0,5"0_ _ • .........• , ...... " "•

+_.+........ ,,I+. _.+ .....

!] ,+ + +

i ...... +. .._.+:.... :=....i... I

Figure 52: Components of the vertically-integrated moisture budget from the GEOS-1

DAS. a) precipitation, b) precipitation verification from climate division observations, c)

evaporation, d) moisture convergence, e) the analysis increments,and f) the Shapiro filter

(a-c-d-e). Contour intervals are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10

mm/day. Absolute values greater than 2 are shaded.

73
(a) PRECIP Jul/Aug 85-89 (b)

s_m-i.....!....!.......

§lN-I

48N-I

48N-I

42N_1

_N-I

27N-i ..... i ....

l_Oq

PRECIP(CD) Jul/Aug 85-89

GIN

4.8N

46N

4211

39N

_ON

27N

13

EVAP

120W 115W IIOW IOGW tO01

Jut/Aug 85-89 (d) VQCON Jut/Aug 85-89

_. •......;...... 27W_ __' _- ' ! '

(e) ANALQ Jul/Aug 85-89 (f) FILTER Jut/Aug 85-89

=

I_[_V 12_W $20W 11SW II0_ I_ IOOW S)_ 9_ _W 8_W 7_W 76W _,_

Figure 53: Components of the vertically-integrated moisture budget from the GEOS-1

DAS. a) precipitation, b) precipitation verification from climate division observations, c)

evaporation, d) moisture convergence, e) the analysis increments,and f) the Shapiro filter

(a-c-d-e). Contour intervals are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10

ram/day. Absolute values greater th_n 2 _re shaded.

74
Sep/Oct 85-89

i ...... i..... _, .i .-i 'Ai .... i...... : z_ii

_N ..! .... ion: ........ i iii:_iitW_ii

_ " "....... : ...... :..... 27

IO_lf 10OW 95W

54#(.

51N'

48N'

4_N,

4_'

_N'

3494.

33N'

30_'

27N.

(c) EVAP Sep/Oct 85-89 (d) VQCON Sep/Oct 85-89

...._.....: ..... _.. _.. _.. .::,,,, 0_,__,_u_,_:_:.. ,,,,,,,#...,,,__.__ :_.,__.._

51H"

48N'

45N'

42J4'

Zd;N.

34_'

3,1N'

.10N.

2714-

13

(e) ANALQ Sep/Oct 85-89 (f) FILTER Sep/Oct 85-89

...._ ............... _o., --_.,_I ..... '_:\_ "__'_":'_ ..................................... " ....

Figure 54: Components of the vertically-integrated moisture budget from the GEOS-1

DAS. a) precipitation, b) precipitation verification from climate division observations, c)

evaporation, d) moisture convergence, e) the analysis increments,and f) the Shapiro filter

(a-c-d-e). Contour intervals are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10

mm/day. Absolute values greater than 2 are shaded.

75
(o) PRECIP Nov/Dec 85-89 (b) PRECIP(CD) Nov/Dec 85-89

(c) EVAP Nov/Dec 85-89 (d) VQCON Nov/Dec 85-89

_4N_

51N"

4_.

,I_N42N.

3ON.

._.

3,._.

3ON.

27N.

'1,,t

(e) ANALQ Nov/Dec 85-89 (f) FILTER Nov/Dec 85-89

48N .....

= ; iiiiiiiiiiiiiill

)W 1 1_ 11 tll lOftY lO_lf _ _ 8_ _" 7_ff 7_W _ 41 I: rif 125W 120W 11511 11011 IGSff 1)Oil SI_ Si_ 8_ 8_ _ 7t_W

Figure 55: Components of the vertically-integrated moisture budget from the GEOS-1

DAS. a) precipitation, b) precipitation verification from climate division observations, c)

evaporation, d) moisture convergence, e) the analysis increments,and f) the Shapiro filter

(a-c-d-e). Contour intervals are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10

ram/day. Absolute values greater than 2 are shaded.

76
-,q

-q

==

>.

r..f3

O

o

,.4_

I=-'oq

_o

¢.,p

0

gg

_° _

_,,_o

_._ O

_._-_

O _

Annuol Cycle

,

,

0

-1

-2

-SF

o s" -....

• . ° . • , o ." °"

i......... . -" " " /'1" " x " " "--, I'''''''''" I

_-" _ ."

• - ...

• • .,..,.. °

• . ,,.,,.....

"i"

•-.,,.,,..

• " "i • . . /

I

PRECIP

PRECIP(CD)

......... EVAP

...... VQCON

.... ANALQ

I

MA

I

MJ

I

JA

I

SO ND
Jan/Feb _xdg.el Mar/Apr

Evap: 93.33 259.78

Analq: -85.71 _ ._" J Analq: -128.65

Precip: 91.15 _ J I Precip: 149.72

_, Oiv: 81.82 __" v _.0.73 41.1 JJ Div: 27.20

I b I v1313 1-5°°rob

J, 18.66 _ 700rob _ ! _"_4.7 I 2_i_bh,= 119.63

J_'_l 900mb 152.0

120W _" 80W 130 N _

50 120 60W

54.09 (LLJ: 30.26) 60.26 (LLJ: 48.18)

May/Jun

_9.12

120W J 80W

90.61 (LLJ: 71.33)

g.udg._

Storage: 11.86

Evap: 373.35

Analq: -47.69

J Precip: 258.50

J Div: -53.42

._. osj

• 500rob

_ - 600rob - 700rob

-- 800rob 144.64

900rob

gON

_94.73

Figure 57: Moisture transport into the continental United States for January through June.

LLJ (Low Level Jet) refers to the region between 92.5°W and 102.5°W, and below about

850 mb. Units are 10 6 kg/sec. To obtain units of cm/month divide by 29 × 10 6.

78
JuVAug

31.3,,,_, 38.25_

120W f 80W

85.33 (LLJ: 64.73)

30N

Budget Sep/Oct

Storage: -1.08

Evap: 288.44

Analq: 54.07

Precip: 246.14

Div: -94.93

- 500rob I _23.27

600mb 700mb

800mb 157.43

• 900mb

50N _ 107.34

F,-

5O

4o_ _

Storage: -14.32

Evap: 184.82

Analq: -30.18

Precip: 158.27

Div: -9.56

- 500mb

600mb

700mb

- - 800mb

5to_ _ .900mb 136.48

50N J -_4702

120W 45.47 (LLJ: 39.67) 80w 30N_

Nov/Dec

34.41

J

.53

61.5

Storage: -9.38

Evap: 81.71

Analq: -70.29

Precip: 99.52

Div: 75.81

_'I__ _- - 800mb

=" 900mb

120 ul80 "

62.39 (LW: 34.41)

500mb

600mb

- 700mb

"157.91

Figure 58: Moisture transport into the continental United States for July through December.

LLJ (Low Level Jet) refers to the region between 92.5°W and 102.5°W, and below about

850 mb. Units are 106 kg/sec. To obtain units of cm/month divide by 29 × 10 6.

79
4.3 Intraseasonal Variability

In this section we examine the intraseasonal variations in the GEOS-1 assimilated data.

We first examine the root-mean-square differences between GEOS-1 and ECMWF analyses.

These fields are based on daily averaged anomalies (the respective seasonal cycles have been

removed from both for each year separately) and thus reflect the non-systematic differences

(on time scales shorter than one year).

Further comparisons are made by filtering the data to isolate various time scales of interest.

The Madden-Julian oscillation is compared in terms of the velocity potential at the equator

filtered to retain time scales between 20 and 70 days. Shorter time-scale phenomena are

examined with filters that retain time scales 10 to 20 days and 2 to 10 days.

4.3.1 Global RMS Differences

Figures 59-61 show the global distribution of the non-systematic root mean square (rms)

differences in the GEOS and ECMWF wind and height fields. The rms differences were

computed as follows: The fields were first averaged to produce daily mean values. Next, the

seasonal cycle (computed separately for each analysis) was removed to produce anomalies.

The seasonal cycle was computed separately for each year by performing a least squares fit

to the data of the first two harmonics (annual and semi-annual). The squared differences

of the anomalies were then averaged for each season and, finally, a square root was taken.

During both seasons the largest rms differences (> 6m/s) in the 200 mb zonal wind (Fig.

59) are concentrated over the equatorial oceans. Values are also relatively large at high

latitudes during JJA. The large values at the equator may be partly the result of a problem

with the GEOS-1 analysis scheme (see Appendix C.4 in Pfaendtner et al. 1995), which

can produce unrealistic divergence when pairs of wind observations straddle the equator.

The 200 mb meridional wind rms differences (Fig. 60) show latitudinally somewhat broader

regions of large values (> 5m/s) over the wintertime subtropics and the high latitude austral

winter. In the polar regions, the large rms differences during JJA appear to be the result

of unrealistic ECMWF winds.

The 500 mb rms non-systematic height differences (Fig. 61) highlight the large uncertainties

in the Southern Hemisphere (SH) middle and high latitudes. In the NH middle latitudes

the rms is largest over the oceans (> 20) and smallest (< 20) over the land areas. That

the smallest differences occur over the NH land areas is consistent with the distribution of

rawinsonde stations (Fig. 97). While satellite retrievals (Fig. 100) provide temperature

information on a more global basis, these are less reliable than the station observations.

Further errors enter into the height field analyis as a result of errors in the first guess surface

pressure field, which is needed to convert the retrieved thickness values to geopotential

heights. These factors may help to explain the larger SH rms differences especially at high

8O
latitudes.OverAntarcticathesteepterrainandassociatedinterpolationproblemsmayalso

contributeto thelargedifferences.

81
90N

6ON,

(o)

3ON"

EQ

30S

60S ........... : .......

90S

0 60E 120E 180 120W 60W 0

90N

(b)

60N

30N

30S,

60S90S

0 60E 120E 180 120W 60W 0

Figure 59: (a) DJF and (b) JJA 200 mb zonal wind rms (non-systematic) difference between

the GEOS-DAS and the ECMWF analysis based on daily time-series. Contour intervals

are 1 resee. Values greater than 6 m/sec are shaded.

82
9ON'

(o)

6ON'

3ON'

EQ'

30S'

60S'

90S

0 60E 12'0E 180 120W 60W 0

9ON'

(b)

6ON'

3ON'

30S'

60S'

905

0 60E 120E 180 120W 60W 0

Figure 60: (a) DJF and (b) JJA 200 mb v-wind rms (non-systematic) difference between

the GEOS-DAS and the ECMWF analysis based on daily time-series. Contour intervals

are 1 m/sec. Values greater than 5 m/sec are shaded.

83
90N'

(o)

6ON'

3ON"

30S"

sos.... <_ ....... _

90S

0 60E 12'0E 180 12()W 60W 0

90N

Cb)

60N

30N

30S.

60S'

90S

0 60E 120E 180 120W 60W 0

Figure 61: (a) DJF and (b) JJA 500 mb height rms (non-systematic) difference between

GEOS-DAS and ECMWF analysis based on daily time-series. Contour intervals are 10 m.

Values greater than 40 m are shaded.

84
4.3.2 The Tropics

In this sectionthevelocitypotentiMat 200mbisusedto lookin moredetailat thetropical

variability. The velocitypotentiM anomaliesareproducedasdescribedin the previous

section.Next threedifferenttime filters areappliedto isolatetime scalesof 20- 70days,

10-20days,and2-10days.Thealgorithmfor generatingthefiltersis describein Hamming

(1989).

Figures62-65showtime-longitudeplotsat theequatorfor 1986-89for the20-70day time

scales.The GEOSandECMWFvaluesshowverysimilarfeaturesin this frequencyband,

which is dominatedby the eastwardpropagatingMadden-JulianOscillation(MJO). In

particular,the substantialinterannualvariabilityof theMJOis capturedin bothanalyses:

the E1Nifio year(1987)showsa veryrobustMJO, whilethe La Nifia year(1988)showa

veryweakand disorganizedoscillation.

Examplesof thevariationsat theshortertimescalesareshownin Fig. 66forthe 10-20day

filter, and Fig. 67for the2-10dayfilter. Notethat the2-10dayfieldsareshownat 10°N,

whichisnearthelatitude oftheITCZ. The10-20daysignalshowsmoredifferencesbetween

the analysesthanat thelowerfrequencies,thoughthe generallyeastwardpropagationand

apparentcouplingwith the MJO areevidentin both. At the synoptictime scales(2-10

days)the fields beginto showverylittle resemblance,exceptfor the overallpatternsof

weakand strongactivity and someof the moreprominentevents.Thesedifferencesare

quantifiedin the nextsectionin termsof correlations.

85
MAR1986

(o) (b)

APR1986

MAY1986"

JUN1986"

JUL1986'

AUG1986'

SEP1986-

NOV1986

DEC1986

JAN1987

FEB1987'

0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0

Figure 62: Longitude-time section of the 200 mb velocity potential anomalies from (a)

the GEOS-DAS and (b) the ECMWF analysis averaged in the tropics [10S-10N]. The

anomalies are departures from the seasonal cycle filtered to retain periods of 20-70 days

(1986). Contour interval is 2 × 106 m2/sec. The negative values are shaded.

86
(o) (b)

MAR1987"

APR1987'

MAY1987'

JUN1987-

JUL1987-

AUG1987

SEP1987

0CT1987"

NOV1987'

DEC1987'

JAN1988-

FEB1988

0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0

Figure 63: Longitude-time section of the 200 mb velocity potential anomalies from (a)

the GEOS-DAS and (b) the ECMWF analysis averaged in the tropics [10S-10N]. The

anomalies are departures from the seasonal cycle filtered to retain periods of 20-70 days

(1987). Contour interval is 2 × 106 m2/sec. The negative values are shaded.

87
MAR1988-

APR1988-

MAY1988-

JUN1988

JUL1988

(o) (b)

AUG1988

SEP1988

0CT1988

NOV1988

DEC1988

JAN1989

FEB1989

0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0

Figure 64: Longitude-time section of the 200 mb velocity potential anomalies from (a)

the GEOS-DAS and (b) the ECMWF analysis averaged in the tropics [10S-10N]. The

anomalies are departures from the seasonal cycle filtered to retain periods of 20-70 days

(1988). Contour interval is 2 × 106 m2/sec. The negative values are shaded.

88
Co) (b)

MARl989.

APR1989.

MAY1989.

JUN1989

JUL1989

AUG1989

SEP1989

0CT1989

NOV1989

DEC1989

JAN1990-

EB1990-

0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0

Figure 65: Longitude-time section of the 200 mb velocity potential anomalies from (a)

the GEOS-DAS and (b) the ECMWF analysis averaged in the tropics [10S-10N]. The

anomalies are departures from the seasonal cycle filtered to retain periods of 20-70 days

(1989). Contour interval is 2 x 106 m2/sec. The negative values are shaded.

89
MAR1987

Co) (b)

APR1987

MAY1987

JUN1987

JUL1987

AUG1987

SEP1987"

0CT1987"

NOV1987"

DEC1987,

JAN1988,

FEB1988,

0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0

Figure 66: Longitude-time section of the 200 mb velocity potential anomalies from (a)

the GEOS-DAS and (b) the ECMWF analysis averaged in the tropics [10S-10N]. The

anomalies are departures from the seasonal cycle filtered to retain periods of 10-20 days

(1987). Contour interval is 1 × 106 m2/sec. The negative values are shaded.

9O
MAR1987'

APR1987,

MAY1987

JUN1987-

JUL1987-

AUG1987

SEP1987

0CT1987

NOV1987

DEC1987.

JAN1988.

FEB1988

Co) (b)

._.-., _L, _ _

. _--.._,_ _- ,_,=_-.

0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0

Figure 67: Longitude-time section of the 200 mb velocity potential anomalies from (a) the

GEOS-DAS and (b) the ECMWF analysis at 10N. The anomalies are departures from the

seasonal cycle filtered to retain periods of 2-10 days. Contour interval is 1 × 10 6 m2/sec

and contours between -1 × 10 6 m2/sec and 1 × 10 6 m2/sec are not shown.

91
4.3.3 Correlations

Figures 68-71 show various correlations between the GEOS and ECMWF fields. The correlations are computed as follows: The fields were first averaged to produce daily mean

values. Next, the seasonal cycle (computed separately for each analysis) was removed. The

seasonal cycle was computed separately for each year by performing a least squares fit to

the data of the first two harmonics (annual and semi-annual). The deviations from the

seasonal cycle were filtered to produce three new time series with time scales of 2-10, 10-20

and 20-70 days. The quadratic terms (cross-products and variances) were then computed

for each of the filtered fields over the period March 1986-February 1990. These were then

used to produce the correlations.

Figures 68-69 show the correlations for the velocity potential field. The global distributions

show that for the longest time scales the values are > 0.8 over most of the globe. This

reflects the large scale and smoothed nature of the velocity potential and the ability of both

analyses to capture these long period, planetary scale fluctuations in the divergence. At the

shorter time scales, the largest correlations (> 0.7) occur over the storm track regions in

both hemispheres. The line plots (Fig. 69) highlight the variations of the correlation with

longitude and latitude. Most surprising is the similarity in magnitude of the correlations in

the Northern and Southern Hemispheres. Note also that, for the shorter time scale tropical

variations, the smallest correlations tend to occur over the Indian Ocean.

Figures 70-71 show the correlations for the 500 mb height field. The correlations are

substantially higher than for the velocity potential field, with values exceeding 0.9 over

most of the globe at all time scales. The largest variation is with latitude, especially for

the shorter time scales, where the correlations drop to 0.7 and less; this is likely due to the

weak signature of the height field in the tropics. The line plots show that the correlations

are extremely high (exceeding 0.99) in middle latitudes for the intermediate and longer

time scales. The drop in correlations near 100°E longitude at 40°N is likely associated with

interpolation problems over the northern reaches of the Tibetan plateau.

92
(0)

30N

20N i

1ON

EO

10S

20S'

30S,

40S ...........................

0 60E 120E 180 120W 60W 0

(b)

60N,

i, ....":.

40N4•.... , ........ , :_'_ii!!!!_ii_ii_ii: .... _ ......... [

r . "%::i::i!i::iii::ii!iliiii::i!::i:_::_i_:_.

30N]

20N -4..................... :... ,_. . .:.........

1ONr.. ,, _:

EQ.... _¢ .............

10S.

505'

605

0 6()E 120E 180 120W 60W 0

60N "l,_'- _ . •

40N .- "...... .

30N 20N -- .=. " -i ....... _- ......

ION

EQ

10S

20S

30S

40S

50S

60S _-_ '"

0 60E 12'0E 180 120w 60w 0

Figure 68: Correlation between the 200 mb velocity potential anomalies from the GEOSDAS and the ECMWF analysis (Mar 1986-Feb 1990), filtered for the periods of (a) 2-10

days, (b) 10-20 days, and (c) 20-70 days. Contour intervals are 0.1. The shading represents

the correlations greater than 0.7 in (a), 0.8 in (b), and 0.9 in (c).

93
,

0.9'

0°6 '

0.7'

0.6'

0.5

0.4

0.3

0.2

0.1

0

0

(o)

.................................. .................. .............................

6(':)E 12"0E 180 12()W GOW

(b)

(c)

0.9

0.8

0.7.

0.6.

0.5 '

0.4.

0.3'

0.2'

0.1

0

0

2-- 10 d_ys

10--20 doys

20--70 days

6()E 12"0E 180 120W 60W 0

Figure 69: Correlation between the 200 mb velocity potential anomalies from the GEOSDAS and the ECMWF analysis (Mar 1986-Feb 1990), filtered for the periods of 2-10 days,

10-20 days, and 20-70 days at (a) 40N, (b) the equator, _tnd (c) 40S.

94
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60E 12E 180 120W 60W 0

(b)

0.99

0.98

0.97

0.96

0.95

0.94"

0.93'

0.92'

0.91

0.9

0

I 2- I 0 days

I 0--20 days

----=-----==== 20---70 days

66E 12oE 180 120W 60W o

Figure 71: Correlation between the 500 mb height anomalies, filtered for the periods of

2-10 days, 10-20 days, from the GEOS-DAS and the ECMWF analysis and 20-70 days at

(a) 40N, (b) the equator, and (c) 40S.

96
4.4 Interannual Variability

In this section we examine various aspects of the interannual variations that occurred during

the 5-year period March 1985-Feb 1990. The emphasis is primarily on the intercomparisons with other estimates, including those from the operational ECMWF analyses, station

observations, and satellite measurements.

4.4.1 Space-Time

Figures 72-74 compare the variations in the monthly mean zonal mean winds and height

field over the five-year period for the DAO and ECMWF analyses. The variations are the

anomalies from the seasonal cycle (computed separately for each analysis as a five-year

average for each month). The 200 mb zonal mean zonal wind and 300 mb zonal mean

height variations show a remarkable degree of similarity, suggesting that the changes made

to the ECMWF operational system during this time period (see e.g. Arpe 1990) had little

effect on these quantities. This is in contrast to the variations in the 200 mb zonal mean

merional winds (Fig. 73), which show almost no correspondence in the two analyses. This

quantity is a measure of the strength of the Hadley cell, which is poorly observed and

strongly dependent on the convection scheme of the model. Arpe(1990) lists numerous

changes to the ECMWF system during the period examined here, suggesting that many

of the anomalies in the ECMWF 200 mb zonal mean meridional wind may be artifacts of

these changes.

Figure 75 compares the outgoing longwave radiation at the equator over the Pacific region

from NOAA satellite measurements, the GEOS-DAS, and the GEOS GCM simulation. All

three estimates show relatively large negative anomalies (indicating enhanced deep convection) during 1987 and positive anomalies during most of 1988. The GEOS-DAS and

NOAA comparison shows generally good agreement; however, the DAS results appear to

overestimate the magnitude of the deep convection. Surprisingly, the GEOS-GCM results

are similar in many of the main features, suggesting that much of the signal is in the SST

anomalies. Some information does appear to come from the assimilated observations; for example, the anomalies in early 1986 are in both the NOAA and GEOS-DAS results, but not

in the GEOS- GCM simulation. A further look at the spatial patterns of these anomalies

is left for the next section.

97
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_ :.-...,..;, . : _.-.._.,.,

' _ L".::_. ,.:::::::.:.;_,

Z Z Z (:_ (/1 (/1 (/I

0 0 0 "' 0 0 0

U)

Figure 72: Departure from the seasonal cycle for the 200 mb zonal mean u-wind from (a) the

GEOS-DAS and (b) the ECMWF analysis. Contour interval is 2.0 m/sec. Zero contours

are not drawn.

98
'7-

........ ,_2 / :_:i.:_::_-,

Figure 73: Departure from the seasonal cycle for the 200 mb zonal mean v-wind from (a) the

GEOS-DAS and (b) the ECMWF analysis. Contour interval is 0.2 m/sec. Zero contours

are not drawn. The values greater than 0.4 m/sec and less than -0.4 ra/sec are shaded.

99
Figure 74: Departure from the seasonal cycle for the 300 mb zonal mean height from (a)

the GEOS-DAS and (h) the ECMWF analysis. Contour interval is 20.0 m. Zero contours

are not drawn. The values greater than 20 m and less than -20 m are shaded.

100
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N

©

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=

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°.

°.

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1987

1988

1989'

1990 •

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NOAh, OLR Anomaly

•._ ._:::.%_.:::::::::__" .._::: :_! " _.__:.:

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GEOS-1 DAS OLR Anomaly

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150 180 210 240 270

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GEOS-1 GCM OLR Anomaly

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_ _.

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i

150

10 20 30 40

180 210 240 2_'0
4.4.2 Global Distribution (1988-1987)

Figures 76-83 show maps of differences between 1987 and 1988 for various quantities from

the GEOS-DAS, ECMWF analyses, SSM/I data, and the GEOS-GCM simulation. These

two years were characterized by a transition from an E1 Nifio to a La Nifia event in 1988,

and therefore provide good examples of the type of interannual variations that assimilated

data must be able to capture. Figures 76-77 show the 200 mb streamfunction differences

for May, June and July for the GEOS-DAS and ECMWF analyses, respectively. The fields

are quite similar in the two analyses with both showing, for example, anomalous wave

trains apparently emanating from the Indian Ocean (propagating southward) and from the

SH middle latitudes (propagating equatorward) during May. Comparisons with Fig. 81

suggests these may be associated with anomalous convection in these regions. In general,

all of the main features of the streamfunction differences are quite similar in both analyses.

The velocity potential differences (Figures 78-79) for the GEOS-DAS and ECMWF analyses

are also in general agreement in the main features. For example, in July both analyses show

a strong negative anomaly over the Indian Ocean and a strong postive anomaly over the

eastern Pacific and Central America. However, unlike for the streamfunction differences, a

closer inspection of the velocity potential anomalies reveals significant differences between

the GEOS-DAS and ECMWF analyses in the detailed shape, location and magnitude of the

features. These differences between the two results reflect the sensitivity of the divergence

field to the convection schemes employed in the assimilation systems, as well as the fact

that the observations are of insufficient quality to adequately constrain the divergence field.

Figure 80 shows the July 1987/88 differences in the total precipitable water from the GEOSDAS and the SSM/I. The SSM/I results are only available over water surfaces (see section

4.1). Both estimates show similar features with much reduce TPW over the central and

eastern tropical Pacific, and a region of enhanced TPW over the Indian Ocean extending

northward over the North Pacific, and southward in the region of the South Pacific convergence zone. The SSM/I anomalies are, however, significantly stronger than the GEOS-DAS

values, especially over the tropics and northern middle latitudes of the Pacific.

The OLR differences (1988-87) estimated from the GEOS-DAS (Fig. 81), NOAA satellite

measurements (Fig. 82), and the GEOS-GCM simulation (Fig. 83) all show the basic

La Nifia (El Nifio) signature consisting of enhanced OLR over the eastern tropical Pacific,

flanked by a v-shaped region of reduced OLR to the west, north and south. The GEOS-DAS

and NOAA OLR also show large negative anomalies over India during July associated with

the strong 1988 monsoon: this feature is absent from the GEOS-GCM simulation. Other

features absent from the GEOS simulation include the signature of the drought over the

U.S. (June), and the large negative anomaly over the eastern North Pacific in July. The

simulation also shows an apparently unrealistic tendency for a double maximum in the OLR

over the central and eastern tropical Pacific. These results show that while a substantial

amount of information about the interannual variations comes from the SST anomalies, the

102
influenceis primarily local. An accurateestimateof anomaliesawayfrom the immediate

influenceof the SSTsrequiresthe additionalinformationprovidedby the assimilationof

atmosphericobservations.The lackof a doublemaximumin the DAS resultsoverthe

centralandeasterntropicalPacificsuggeststhat theassimilationof observationsalsohelps

in providinga betterestimateof the localresponse.

103
(o)

if-"

., . i_ii_ii$:::.:_ ,,- ;i::i..:ili_i::. ,

20N

20S

30S

40S

50S

60S

0 60E 120E 180 120W 60W 0

60N

50N

40N

30N

20N

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EO

10S.

20S"

30S.

40S.

50S'

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0

(b)

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EO.

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0

(c)

:. : .... ,.. _. ,,. _:_ • . ; .... ,_

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%:: ._; _ O "

i::_::" _'_:::;" . / ....

i_ ........ .. .... .......

60E 120E 180 120W

___ ,:. _::i::iii_:: ,::.

60W 0

Figure 76: The 200 mb streamfunction difference (1988-87) for (a) May, (b) June, and (c)

July from the GEOS-DAS. Contour intervals are 5.0 x 106 m2/sec. The values greater than

10.0 x 106 m2/sec and less than -10.0 x 106 m2/sec are shaded.

104
(o)

60N - ,

50N ,_j,, U..llljr _ \ .--"" j -.---.,....,n14, -, - •_:_:_:_:_:_m-,.._:::::,

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20N _.1 --

EO i

105,

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30S. __.

40S.

50S.

60S

0 60E 120E 180 120W 60W 0

60N ,

50N t

40N 1

30N 1

20N 1

1ON

EQ

10S

20S

30S

40S

50S

60S

0

(b)

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.. .T ,, l - ,.... ,'._'...

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•.................... , "'" , , • 1 f" I

60E 120E 180 120W 60W 0

(c)

60N . : .............. _,_.::_:_;._.... . '_, '._:_:_:_::

40N , " " " ",""_-'.;-_'. -

30N__.L_# :._k.: __,. :-._':__

e_._ _, _ ,,.,_._ __:-.LS :"__7 _.' 20N _ - _ _/( '1 -_ - _ ..... ,;. _::_::#_:::?..,,._.,. ___ __-,_

ION "" II .... "........ "":'" :':':" .... "" .......

EQ "".......... " ""

IOS _-:_, ........ _"'-".... _...:"..-:....

....:...::-:_:,:_._:_ o" ,..:: :::::_._,. .'.+.¥_: 20S {__

3os[ !

60S

0 60E: 1flOE: 180 120W 60W 0

Figure 77: The 200 mb streamfunction difference (19SS-ST) for (a) May, (b) June, and (c)

July from the ECMWF analysis. Contour intervals are 5.0 × 106 m2/sec. The values greater

than 10.0 × 106 m2/sec and less than -10.0 × 106 m2/sec are shaded.

105
(0)

60N

20N1__'__JfJ'_

"b:_'.;'-

3ost __,-y-_../_ _ ......

6osI _ _¢J _ ---_ - "

0 60E 120E 180 120W 6ow o

8oN. (b)

4ON' 5ON.. ........ "_t: "

3ON"

2ON'

1ON'

EQ'

10S'

20S'

30S'

40S.

SOS.

60S0 60E 120_ 180 120W 60W 0

(c)

40N __ '_J, __ _'_) IE:_

20N .:::_.... _.... ._.... • : ....

10N1EQ i ...... _, ""

3os4_XXX_ '_ -:-:- _ :)1111ii: :"

._:_ _ _ ',,, ,,, ,,

40St _)b_,"

_os .............. ..;......:'-_/:",'-i ..... ".;",

60S "" "'" ' ............... "'" " '

0 60E 120E 180 120W 60W 0

Figure 78: The 200 mb velocity potential difference (1988-87) for (a) May, (b) June, and

(c) July from the GEOS-DAS. Contour intervals are 1.0 x 106 m2/sec. The values greater

than 5.0 x 106 m2/sec and less than -5.0 x 106 m2/sec are shaded.

106
60N (0)

4ON' " ."/- ..-,.,.:'-.:--.:.-................ ...... "-',,-............... ,.]ON' • .','.-'--,'.n " , '

1ON' '.::i::._;_:", : ,,,, .

_os.___.-Cd,_ _, __.7::::; _.

30S' "'J, .-- ...........

60S' " "

o eoF 12o_- leo 12ow 6ow o

N (b)

60 - ; - ... -, _

....

40S ....

60S . ,

0 60E 120E 180 120W 60W 0

60N

50N

40N

,]ON

20N

1ON

EQ

10S

20S

30S

40S

505

60S

(c)

;..'.':__!.'+-:..+;;_..+..'..t."::>-'., ,.?.:.".':.".) ;, ," I

• _::0..::'..:.::_:::..._;;-'._:.:.'.-_:::.:.:.:.:_, * •

'__ :,

.',',';A'_'L:L.I:"_'_. : ,- - .,'-,': ......

. :::::::_t_:" :-",'_',', ',

,,:,,,_ ... , ....

i. - ",_'

o 6_E 12'oE 180 12ow 6_w o

Figure 79: The 200 mb velocity potential difference (1988-87) for (a) May, (b) June, and

(c) July from the ECMWF analysis. Contour intervals are 1.0 × 106 m2/sec. The values

greater than 5.0 × 106 m2/sec and less than -5.0 × 106 m2/sec are shaded.

107
90N

(o)

60N

,ION

EO

30S

60S

90S

,._:E!:!i:.] ' ,:-::::_:_:!__:!!:::::: .::':_?.:, 'S::::8_:,_

.... "_:'_;:::::::" _ ._:i:i:i::_

............. _: _-:-.'-._;..:.:::::?_ .....

"T I--'-'--

0 6_)r 120r 1_0 120w 6dw 0

9ON"

(b)

0 60E 120E 180 120W 60W 0

Figure 80: The total precipitable water difference (88-87) between July 1987 and July 1988

from (a) the GEOS-DAS and (b) SSM/I. Contour intervals are 0.5 gm/cm 2. The zero

contours are not drawn. The values greater than 0.5 gm/cm _ and less than -0.5 gm/cm _

are shaded.

108
40N

30N

20N

1ON

EQ

10S

20S

30S

40S

40N

3ON'

2ON,

1ON

10S t

20S_ I

3os_

40S

40N

30N -

20NIONEQ"

10S"

20S"

30S"

40S

40N J,':-;_L,_.,,_

3ONk ,"

2oN 3

30S- --_:.,/'

40S

0

'e _ °

GEOS-1 DAS OLR (1988-1987)

.._ _ t_,:_ -'_;';" _:i:!:!_:_.'_'.:_.::=:_ J._t_k_,_':f _:_:1_,

,..__° _..:.i_-_- '___

• ,,,2_

_t .....

_.'._::_.,. '_,

__ _;-:_"_ "_1_._', _:_:_

.--..:.:.._-.-%_.. ,._ _.,

.. _.i ___..,_:_ _;' '> ';_'_ ':i_._. _ "_'_ "-:-::::::::::'.'.:....-. " ":':':::::"'':':" ":'::_:::... '_'_

60E 12'0E 180 120W 60W 0

MiiiMiiiiiiiiiiiiiiiiiiiiiiii! iii i i ii iii!i iii!i!iiiiiiiiiiiiiiii

-60 -4.0 -30 -20 -10 10 20 30 4-0 50 60

Figure 81: The outgoing longw_ve r_diation difference (1988-87) for M_y, June, July, _nd

August from the GEOS-DAS. Units: W/m 2

109
NOAA (4x5) OLR (1988-1987)

40N --,,w_ -,_::::_-,._._:::._.= _:_:_........_:_:_:_.,::_:_:_:_:_:_:_:_:_:_:_:_:_:_:_:_, _.,:.::_:_:_;_: ,_, _ .... _:_:_:_:_:_:_:_ ..............

,__ "_ , _ _.:._.__ "i:i:..- "'"" ._ _......, _"::2::::::" . . . <_'_. ..",,================================== ":i_iiiiiiiiiiiiiiiiiiiiiiii::

. .'.:.N._:.:._':':':.:':_ .. '"':" '" ":_ .,¢• ::::.%

•_ _:::.:::::..::.:.,',._.*" :::::_ ..:::::_ _ ..':.::: .:.'::_:_:_._ .

20N'] _\\ __ ,._:__I'! _ .. __.,..., . ..LoJ

•:: :$_. ' ,.;_,_._ e ..::._! _.

'f •_ ' ,_:4::_:_:_ '.: .:::::..._ ,.::: 1ON.... "" -'

EQ'

10S'

20S'

30S'

40S

0 60E 12'0E 180 120W 60W 0

:::::::::::::::::::::::::::::::::::::::::: _ !:.':_ _

-60 -4-0 -30 -20 -10 10 20 30 40 50 60

Figure 82: The outgoing longwave r_di_tion difference (1988-87) for May, June, July, _nd

August from the NOAA satellite observations. Units: W/m 2

110
GEOS-1 GCM OLR (1988-1987)

40N - "_ _ _ _:_:_:_:_:_ b,,_ " "__:_:'*" " ":"

•- V_/.:i:i:i:i:i:i-.. , .....::_:_:_:_:_:_::, :::

30N ...... ..._iiiiiiii_:_.-, _ _ _:i- _ .... "

2ON. "J:.iii_!i '__i__:_:_ " o '_, -- :_.......... i.

1ON.

EQ.

10S'_" " "....

20S.]._U. __ 30S.

40S "_

40N i- ""_ '_ ,,a,, ,,.._:_, _.-_i::."............... "...... __ :_:_................. .... _i_i_: ....... _ _

• ; ...- _ _: • ; _" .:_::.. ".._,, ,

20N

m -. . i_. o

i

_;' ":_

10S" _ " "": "_'__

20S..... _::_"*'_::'_ ......... '

i "_:_"_ _ :.:i_i_i_i#:_ -

30S

405 ........

._, _::.:_:_._ _':6_::,_, "" ._ _:','. :_ ..... _':_':'_: I .._ .... .,:

IOSI_ _ -- _ .....

I -

40S _ - -

• " _ 9 ,c

....., _'_ ,._,,_ ..,.._ .... _:_:_ ):! .... . ..:...#..........

3ON_____,.:: - "-_,. _..:,::.,_ .... __: :_.......... :::::::::)::::::::::: ......6 20N _" _ ' _'__'_°-....... '": ''":*_:_*_::"..... '

........ __..-._ .._. .. ,_

EQt L_ __ ._.dl_,

2°si ___....'...:i:_:i....._ _',d

3os-I__"_::":_::"

40S+ ..........................

0 60E 120E 180 120W 60W 0

-60 -4-0 -30 -20 -10 10 20 30 40 50 60

Figure 83: The outgoing longwave radiation difference (1988-87) for May, June, July, and

August from the GEOS-GCM simulation. Units: W/m _

111
4.4.3 Precipitation Anomalies

Area-Averages

Figures 84 and 85 compare the area-averaged precipitation from the GEOS-DAS and the

world monthly station observations (see section 4.1) for the land regions outlined in Fig. 47.

The comparison shows generally good agreement over North America. Over other regions

there are substantial differences, with a tendency for the GEOS-DAS results to underestimate the station anomalies. It is unclear how much of these differences are the result of

deficiencies in the GEOS-DAS or problems with the station observations (e.g., erroneous

observations, sampling problems, errors of representativeness, etc.). These differences point

to the need for further comparisons with other analyses and independent estimates of preciptation.

United States

The relatively good comparison between the monthly precipitation anomalies over North

America found in the previous section warrants a closer look at these anomalies. This section

presents the spatial maps of the U.S. precipitation anomalies from the GEOS-DAS and the

observations based on the comprehensive U.S. climate division precipitation data set (see

section 4.1). The results are shown in Figs. 86-95 for every other month over the entire

five-year period. The comparison provides rather mixed results, with some months showing

fairly good agreement while other others show very poor agreement. The assimilation tends

to produce a more realistic precipitation climatology during the transition seasons (see

section 4.2.4) and there is some indication that the anomalies are better reproduced during

these months as well. The rather coarse resolution of the GEOS-DAS is likely one of the

factors responsible for the differences, though further work needs to be done to try to assess

any systematic problems in these results.

112
0.8

0.6

0.4

0.2

0

-0.2

-0.4

-0.6

-0.8

-1

North America

J.5 _ :.

0.3 ,'. . :; , :: : - - .:- .-

0 " " " ..'" ". ". :" " ". : . ..

-0.3 " " " : " ":

-0.6

0.6 -J

0.4 4 " . :.

0.2 . " . .....,.. i'..:. :.'" ".. :'. ,./

o . : "" ":;...

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0.3

0.2 -. ,

o.1 :".:: .': .... .. ""

-o._ ;: :- "'"" "" '": ' " :" "

-0.3

-0"5

1986 1987 1988 1989

GEOS analysis

............ Station observations

1990

Figure 84: The regional precipitation anomalies over land from the GEOS analysis and

the station observations. The anomalies are departure from the seasonal cycle. Units are

mm/day.

113
1_

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o.s4-A /_ :

• .'- ,- .

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-3

21

I .'. -'. :- V0.5 ."- -:" " " " "'.

0 " " " "'. " " "" "" "-

-0.5 " " "-

-1

- 1.5 Indonesia/Australia

-2

•

0.8] _" :i Africa

0.6 : : ::

0.4 :: i: :'. ,

o_ : . " _.. .. .: ...........

-0.2 " "..; '-

-0.4 " '" :: :

-o.e "

-0.8

!986 1987 1988 1989 1990

.......... OEOS Station analysis observations 1

Figure 85: The regional precipitation anomalies over land from the GEOS analysis and

the station observations. The anomalies are departure from the seasonal cycle. Units are

ramday.

114
DAO 8504 CD 8504

i _ ! ............ ....... :...... .................. "_, "

..:_ ..... :_...._..._...o...! ...... _ !...... i...... _...... i .....

54N

51N

4eN

4_

42N

"il

DAO 8506 CD 8506

_ 'i_ : :'__!_!_;'l?_",.._._".i'_l:_.._,,:_._ i_i_ _ ' : ........................:................._

,,,li !/ii i.... i..... i...........

-I..... i\_..._-_/_._'_....

........................ii ......

1301_ 12_il 120W II_W llOql 10_ IOOW g_/ _V 8_ 80_ 7$W 70W _ I

DAO 8508 CD 8508

_H

Figure 86: The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for April, June and August of 1985. Contour intervals are -10 -8

-6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute values greater than 2 are

shaded.

115
54N,

51H

48N

42N

_N

36N

27H

DAO

!!

DAO 8602

-- ,_l_,_:& ............... _-:

" :'#............. :1 ::_..iiii;;;;i_:.:. _.

" "'"! 'i .....;_ 'io_"N! ......i..i_

1

CD 8602

",.,,,. __\.;ii iiiiiiiii;ii:iill i_,i ......... _"_ :'_.iiii_' _ii__i_2 _' ...... _' .....

i :tttll_i,_:_..'_ _ (i-r_"-:: ',

2/N

I;

Figure 87: The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for October, December of 1985, and February 1986. Contour intervals

are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute values greater

than 2 are shaded.

116
DAO 8604

_,N ........_• _...... •; ..... =_;;Iil;I; _.._.. _..::_il;iiii;_i=....i. _,N

CD 8604.

DAO 8606 CD 8606

s,_ • _._!"_. _.. .......... -... _ .. :.... : .._._...::i_i._i_i_.._!i_i._i!i_i_ili_ ._ s_ ............. .. -_ • •

_-,:=_ _ oxen,, _ii:............ i\_ ...... ...... ',i_!iiiiiiii:::,,i:ii

_4N.

=IN'

48/4'

4_.

42N.

_14N.

33N.

_i0N.

27N

DAO 8608 CD 8608

Figure 88: The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for April, June and August of 1986. Contour intervals are -10 -8

-6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 ramday. Absolute values greater than 2 are

shaded.

117
! i.....! i .....i....i.... ! i_j

L__I i........ it i....i i!:!:!:_i_i!'i::!!!!!! _.......

o

00

!!!

o

I'_

0o

i!i!!!i

t_l

o

o<

¢d

.._

o ¢d

oM

0 • _0_

_% I-S

_'_ e

_._ _

_ 0 ''_ Cxl

_'_ I _

_'_ _=

oo
DAO 8704 CD 8704

_":::_:!iIi_:i i i i : :_:_: ::k_/_ .......... v_

DAO

o._-i i : i

2:_ .... l_Wi _i : .... ]]]iil' 4::]

8706

_ ....iliiiiii_ii : i

........ .:............. ......==

CD 8706

DAO 8708 CD 8708

_'"'_.... ..... :....:_"_....... -_ ........1 "'k. _,_-_-"¢..........

,_, ,., ,_, ,,_, ,,_ ,_, ,_, ,_,, _, _, . / ' ,

Figure 90: The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for April, June and August of 1987. Contour intervals are -10 -8

-6, "4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute values greater than 2 are

shaded.

119
DAO 8710 CD 8710

:1___::.......... _,!__-_,o_1

DAO 8712 CD 8712

DAO 8802 CD 8802

Figure 91: The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for October, December of 1987, and February 1988. Contour intervals

are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute values greater

than 2 are shaded.

120
DAO 8804 C0 8804

OAO 8806

51N_

ii21 ..... .ii.2, i

DAO 8808 CD 8808

,,.........,_............. _-_..-

1 125W 120W lily I10rll IO_W lOOql 94_W 9(_W 8_ 8(_ 7_il 7(_ _ 0

Figure 92: The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for April, June and August of 1988. Contour intervals are -10 -8

-6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute values greater than 2 are

shaded.

121
DAO 8810

:=_i_i' i_ii_ii_iiii'i_ i...... ___iii,i'_ _'i,__= .i.,_._'_'_

.iiiii\i ilfiiil)71111111111111111

54N

51N

48N

48N

42N

39N

_N

3,1N

,ION

27N

C0 8810

..,.....,,......._.....i...i....._ iiii.iiiiiiiiill ......'_,!:::,i

........ ..... ...... ....

DAO 8812 CO 8812

DAO 8902

•..=l_=_.\\.o...................... _, _.I .........\

CD 8902

iiI_fii.,_ ..... iiii,:.... __' iiiii....iiii.:_ .....':..i__i__.:: ........"II_.._.___:_. _,.,.._-_. •_;;i;;ili!il ..... : "

_,,f___._,,, ..:,. _'¢ ::._...::._i. ' ..,_ti/_lii_::.. _.... :......i......:.....

:' ,_i"_-°"_',o.i_: _..... ",..,'..... ...._..."-:

Figure 93: The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for October, December of 1988, and February 1989. Contour intervals

are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute values greater

than 2 are shaded.

122
DAO 8904 CO 8904

DAO 8906 CD 8906

51N

48N

42N

59N

27N

DAO 8908

/._i _i___,_ ............ ,

_ ....#:@::iii : i i

...... ............... .

CD 8908

i_iiilliiiii

. .. :........__=, • .. ..

42N ..... i . .: ..... : .... L .... i..... :.... i._'..: ._l.. :...... i .....

_,..! ....: ._L\! : ; i......( i i

Figure 94: The precipitation anomalies from the GEOS DAS compared with the climate

division precipitation for April, June and August of 1989. Contour intervals are -10 -8

-6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute values greater than 2 are

shaded.

123
G4N

51N

419(

46N

42N

_N

DAO 8910 CD 8910

GIN

51N

48N

'i6N

49.N

,._N

_N

,T]N

27N

DAO 8912 CD 8912

::t f,i /i 5

54.14

_IN

4e_

4GN

3_N

3(_N

33N

3014

27N

DAO 9002 CD 9002

Figure 95: The precipitation anomalies from the GEOS-DAS compared with the climate

division precipitation for October, December of 1989, and February 1990. Contour intervals

are -10 -8 -6, -4, -2, -1, -0.5, 0, 0.5, 1, 2, 4, 6, 8, 10 mm/day. Absolute values greater

than 2 are shaded.

124
4.5 Concluding Remarks

The resultspresentedin this documentare intendedto give a generaloverviewof the

quality and consistencyof the GEOS-1DASoutput. The resultsshowthat manyof the

time-meanprognosticfieldsarequite similarto thoseof the ECMWF analyses,though

substantialsystematicdifferencesdooccurin someregions.It is unclearhowmanyof these

differencesarethe resultof modelbias,or whetherthey reflectdifferencesin the input

observations,or howcloselyeachsystemdrawsto thesameobservations.Otherquantities,

suchasthe zonalmeanmeridionalwind, showsubstantialdifferences.Thesequantities

areonly weaklyconstrainedby the observationsandappearto reflectmodelbiases.The

impactof modelbiasis clearestin the hydrologicalcycle. This is reflectedin the list of

knowndeficiencies(summarizedin Fig. 96,seealsothe Appendix),whicharedominated

by biasesin themoisturefield, clouds,andprecipitation.

Perhapsthe mostpromisingnewproductsprovidedby the currentassimilationarethe estimatesof interannualvariability. This includesboth the prognosticfieldsand diagnostic

fieldssuchasprecipitationandlatentheating.It is for theseproductsthat the useof an

unvaryingassimilationsystemhasclearlyresultedin majorimprovementsoveroperational

products,thoughit mustbekeptin mind that any inhomogeneitiesin the input observations canalsoleadto spurioussignals.The E1Nifio and La Nifia events,the variability

in the Indian summermonsoon,andthe droughtsareclearlyevidentin the assimilated

fieldsduring this period(1985-90).Theavailabilityof consistentforcinganomalies(e.g.

precipitation)associatedwith theseeventsshouldmakethis datasetanextremelyvaluable

resourcefor studyingshort-termclimatevariabilty.

125
Time scale

Time mean

Winter mean

Summer mean

Time mean

Time mean

Time mean

Time mean

Diurnal

model time

step

Quantity

T s over land

precipitation over U.S.

precipitation over U.S.

lower troposphere

q in tropics

upper troposphere

q over oceans

tropical clouds

middle latitude clouds

precipitation over U.S.

noise in divergence

and vertical motion

near poles

winds at equator

Remarks

Too cold during winter

Weakly constrained by

observations. Model bias

underestimate

overestimate

too dry, suggest need for

down drafts

too wet, improve/tune

convective scheme, reduce

contention with analysis,

analyze RH

Over estimate cloud forcing

Feedback from analysis

Improve cloud optical

properties and cloud amount

Reduce contention between

convection scheme and analysis

underestimate cloud forcing

improve cloud optical

properties and cloud amount

Diurnal cycle too strong,

improve interaction between

convection scheme and PBL,

currently no land surface model

also evident in other quantities,

new version of GCM allows

pole rotation

excessive divergence due to

problem with OI wind-wind

correlations (probably confined

to high frequency, also appears

to have a mean component)

Figure 96: A summary of known deficiencies in the GEOS-1 DAS output based on the

analysis of the first 5 years (March 1985-February 1990).

126
5 Input Observations

This section summarizes the basic characteristics (spatial and temporal coverage) of the

observations that were used as input to the assimilation. Details about the analysis scheme,

including the quality control of the observations are described in Pfaendtner et al. (1995).

5.1 Spatial Coverage

An example of the spatial distribution of the various data sources for May 5th, 00Z is given in

Figures 97-100. The GEOS-1 DAS incorporates the rawinsonde reports (upper panel Fig.

97) of horizontal winds, temperature (converted to heights), and dew point temperature

(converted to mixing ratio). Horizontal winds are also obtained from a number of special

wind observations (lower panel Fig. 97), commercial aircraft reports (lower panel Fig.

99), and satellite cloud-tracks (upper panel Fig. 100). Sea-level pressure information is

incorporated from land surface (upper panel Fig. 98), ship (lower panel Fig. 98), and buoy

(upper panel Fig. 99) observations. Surface winds are obtained from the ship and buoy

reports. An example of the coverage of the NESDIS temperature retrievals (also converted

to heights in the analysis) is shown in the lower panel of Fig. 100.

127
Location of rawinsonde observations for May 5, 1985,0z

gON i i i i i i

' *" _x ' zx zx zxA

60. _---i--:r_-----_--_'__

' .............. i.. _ _ " _ PE-,,T_ .... • ....

............. i ........... .,, ......... : _ ¢_ : tx -_ i

............. i ............. i ................... a_............ i..... _ ............ _-! ......... _

A

60S

gOS

180W 120W 60W 0 60E 120E 180E

Location of special upper air observations for May 5, 1985,0z

gON i i i i i

............ :_.................. :: _ : _,_ _'_ ..... -,_i _ -!:.........

60S

90S

180W 120W 60W 0 60E 120E 180E

Figure 97: Locations of rawinsonde (upper panel) and special upper air (lower panel) observations for May 5th, 00Z. The latter include pilot balloon, dropwinsonde and reconnaissance

aircraft and radar winds.

128
Location of land observations for May 5, 1985, Oz

90N

60N

z_

i..........

EQ

60S

............ ; .....'

A

.Z& ...... A..

90S

180W 120W 6OW 0 60E 120E 180E

90N

60N

EQ

60S

Location of ship observations for May 5, 1985, Oz

90S

180W 120W 60W

60E 120E 180E

Figure 98: Locations of land (upper panel) and ship (lower panel) reports for May 5th, 00Z.

129
90N

60N

30N

EQ

90S

180W

Location of buoy observations for May 5, 1985, Oz

i z_ i& i i

..... i .......... ?,

120W 60W 60E 120E 180E

90N

60N

30N

EQ

30S

60S

90S

180W

Location of conventional aircraft observations for May 5, 1985, Oz

l.i .............

120W 60W 0 60E 120E 18QE

Figure 99: Locations of buoy (upper panel) and aircraft (lower panel) reports for May 5th,

00Z.

130
90N

60N

30N

EQ

30S

60S

90S

180W

Location of satellite wind observations for May 5, 1985, Oz

120W 60W 0 60E 120E 180E

A TOVS poth A

O TOMS poth B

+ TOMS pdth C

gON i

++

Location of NESDIS TOVS retrievals for May 5, 1985 0z

30N

30S

60S

90S L + !

- +++-

180W 120W 60W 0 60E 120E 180E

Figure 100: Locations of satellite wind (upper panel) and NESDIS temperature retrievals

(lower panel) under clear (path A), partly cloudy (path B) and cloudy (path C) conditions

for May 5th, 00Z.

131
5.2 Inventory

This section lists (in the form of tables) all significant gaps in the DAO input observational

database for the period (1985-1990). These represent periods for which the tapes received

by the DAO contained no data and initial efforts to fill these gaps from other sources were

unsuccessful. Data for times prior to July 1987 were mostly acquired from the National

Center for Atmospheric Research (NCAR). For later times, the data have been obtained

directly from NMC and do not include data that came in after the cut-off time for the operational NMC system. In addition to these two sources, some TOVS temperature soundings

have come directly from the National Oceanic and Atmospheric Administration (NOAA)

National Environmental Satellite, Data and Information Service (NESDIS), and gaps have

been filled with data from the National Climatic Data Center (NCDC) in Ashville, NC.

For the globM sea-level pressure and near surface wind analysis over the oceans, data from

surface land synoptic reports (sea-level pressure only), ships and buoys are used. The

upper-air analyses of height, wind, and moisture incorporate the data from rawinsondes,

dropwindsondes, rocketsondes, aircraft winds, cloud tracked winds, and thicknesses from

the historical TOVS soundings produced by NOAA NESDIS.

The actual counts for each of the observational types is also available in the form of bargraphs, via anonymous ftp to the machine: hera.gsfc.nasa.gov

The directory is: pub/assimilation/e0054A/observations

5.2.1 Sample Bargraphs of Data Counts

An example of data counts for May, 1985 is given in Figs. 101-104.

132
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5.2.2 Tables of Significant Data Gaps

1985

TIME PERIOD MISSING DATA

January 4 (18Z)

February 6 - February 25

March 16 (18Z)

March 17

April 1 (0Z)

April 3 (18Z)

April 4 (0Z)

May 2 (18Z)

May 4 (0Z-12Z)

May 10 (18Z)

May 11 - May 12

May 13 (0Z)

May 29 (0Z)

May 30 (18Z)

May 30 (0Z,6Z, 18Z )

July 16 (0Z)

July 20 (12Z)

July 29 (18Z)

July 30 (0Z)

August 11 (6Z) - August 17

September 27 (6Z)

October 3 (12Z)

October 9 (18Z)

About 1/2 Land data

No A and B TOVS data

No TOVS data

No TOVS data

About 1/2 Land data

No TOVS data

No TOVS data

No Rawins,No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

Almost No Land,Ships and Buoys, No

Rawins,Aircrafts, No Upper Air,No

Sat.Wind

No Aircrafts,Upper Air,Sat.Wind

No TOVS data

No Land,Ships and Buoys, No Rawins,Aircraftss,Upper Air,Sat.Wind

About 1/2 Land data

No TOVS data

No TOVS data

No TOVS

No TOVS

About 1/2 Land data

Almost No Land data

137
1986

TIME PERIOD MISSING DATA

January 2 (18Z)

January 3 (0Z-12Z)

February 13 (18Z)

February 24 (6Z)

February 25 (18Z)

February 27 (18Z)

March 14 (6Z-18Z)

March 15

April 14 (0Z)

April 15 (18Z)

April 21 (6Z)

April 27 (18Z)

April 30 (6Z)

May 4 (OZ)

May 11 (OZ)

May 23 (18Z)

June 2 (6Z-18Z)

June 3 (OZ-6Z)

June 5 - 8

June 11 (6Z-18Z)

June 12 (OZ-12Z)

June 14 (18Z)

June 14 (18Z)

June 14 (18Z)

June 16 (6Z-18Z)

June 17 (OZ-6Z)

June 20

June 21 (18Z)

June 21 (18Z)

June 21 (18Z)

June 22 (OZ)

June 26 (18Z)

No TOVS data

No TOVS data

No Sat.Wind

No Sat.Wind

Almost No Land

No Rawins,Upper Air,Sat.Wind

No TOVS data

No TOVS data

Almost No TOVS data (only 6 reports)

No TOVS data

No TOVS data

Almost No Rawins

No TOVS data

No Land, Ships, Buoys (almost), No

Rawins, Aircrafts,Sat.Wind, almost

no Upper Air

No TOVS data

No Sat.Wind

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No Land,No Ships,No Buoys data

No Rawins data

No Sat.Wind data

No TOVS data

No TOVS data

No Aircrafts data

No Land,No Ships,No Buoys data

No Rawins data

No Sat.Wind data

No TOVS data

No TOVS data

138
1986

TIME PERIOD MISSING DATA

July 19 (18Z) No Sat.Wind

August 1 - August 5(6Z) No TOVS data

August 11 (6Z - 12Z) No TOVS data

August 21 (18Z) - August 23 No TOVS data

August 25 (6Z)- August 30(12Z) No TOVS data

September i (6Z-18Z) No TOVS data

September 2 - September 3 No TOVS data

September 7 (6Z) No TOVS data

September 12 No TOVS data

September 13 (0Z - 12Z) No TOVS data

September 14 (6Z-18Z) No TOVS data

September 21 (6Z-18Z) No TOVS data

September 25 (12Z-18Z) No TOVS data

September 26 - 28 No TOVS data

October 2 (18Z) No Sat.Wind data

October 4 Almost No Buoys data

October 13 No TOVS data

October 14 (0Z) No TOVS data

November 2 - 8 No AIRCRAFT data

November 2 - 8 No Sat.Wind data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No Sat.Wind data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

November 3 (6Z)

November 10 (6Z, 12Z )

November 17 (6Z, 12Z )

November 24 (6Z)

December 1 (18Z)

December 1 (6Z)

December 8 (6Z)

December 15 (6Z, 12Z )

December 22 (6Z, 12Z )

December 29 (6Z)

139
1987

TIME PERIOD MISSING DATA

January2 (6Z) No TOVS data

January5 (6Z- 12Z) No TOVS data

January9 (6Z- 18Z) No TOVS data

January 12 (6Z) No TOVS data

January 19 (6Z- 18Z) No TOVS data

January 20 (0Z- 12Z) No TOVS data

January 26 (6Z- 18Z) No TOVS data

February3 (6Z) No TOVS data

February 10 (6Z) No TOVS data

February 17 (6Z) No Ships and Buoys data

February 17 (6Z) No TOVS data

March3 (6Z- 18Z) No TOVS data

March4 No TOVS data

March8 No TOVS data

March9 (0Z- 6Z) No TOVS data

March 12 (6Z) No TOVS data

March 17 (6Z) No TOVS data

March 24 (6Z) No TOVS data

March 31 (6Z) No TOVS data

April7 (6Z- 18Z) No TOVS data

April 22 (0Z) No Sat.Wind data

April 27 (6Z-12Z) No TOVS data

April 28 (6Z-18Z) No TOVS data

May2 (6Z-18Z) No TOVS data

May4 (6Z-12Z) No TOVS data

May 11 (6Z-12Z) No TOVS data

May 24 (6Z) No TOVS data

May 30 (6Z-18Z) No Land ,Ships

May 30 (6Z) No TOVS data

June 20 (18Z) No TOVS data

June 21 (0Z-6Z) No TOVS data

June 28 (6Z) No TOVS data

July 14 (6Z) No Rawins data

July 18 (6Z) No TOVS data

July 24 (6Z- 18Z) No TOVS data

July 25- July 26 No TOVS data

July 27 (12Z- 18Z) No TOVS data

July 28 (6Z- 18Z) No TOVS data

July 29 (0Z) No TOVS data

140

data
1987

TIME PERIOD MISSING data

August 1 (6Z)

August 1 (12Z)

August 1 (12Z- 18Z)

August 2 (6Z- 18Z)

August 3 (0Z- 6Z)

August 9 (6Z)

August 16 (6Z)

August 31 (6Z)

August 31 (12Z)

August 31 (18Z)

September 6 (6Z-18Z)

September 7

September 12 (18Z)

September 22 (6Z)

September 24

September 29 (0Z-6Z)

October 6 (6Z)

October 11 (6Z,18Z)

October 12 (6Z,18Z)

October 13 (6Z,18Z)

October 13 (6Z-12Z)

October 16 (18Z)

October 17 (18Z)

October 20 (6Z)

October 27 (6Z)

November 3 (6Z)

November 10 (6Z)

November 17 (6Z)

November 18 (6Z-18Z)

November 24 (6Z)

December 1 (6Z)

December 8 (6Z-12Z)

December 15 (6Z)

December 23 (6Z)

December 29 (6Z)

No Sat.Wind

No Aircrafts

No Land,No Ships,No Buoys,No Rawins, No Sat.Wind,Upper Air

No TOVS

No TOVS

No TOVS

No TOVS

No Sat.Wind

No Land,No Sat.Wind,Upper Air

No Land,Ships,Buoys,Rawins, No

Sat.Wind,Upper Air

No TOVS

No TOVS

No Sat.Wind

No TOVS

No TOVS

No TOVS

No TOVS

No Land,No Ships,No Buoys,No Rawins, No Sat.Wind

No Land,No Ships,No Buoys,No Rawins, No Sat.Wind

No Land,No Ships,No Buoys,No Rawins, No Sat.Wind

No TOVS

No TOVS

No Sat.Wind

No TOVS

No TOVS

No TOVS

No TOVS

No TOVS

No Rawins,No Aircrafts, No Upper

Air, No Sat.Wind

No TOVS

No TOVS

No TOVS

No TOVS

No TOVS

No TOVS

141
1988

TIME PERIOD MISSING DATA

January 2 (6Z)

January 5 (6Z- 12Z)

January 12 (6Z- 12Z)

January 19 (6Z- 12Z)

January 26 (6Z)

February 2 (6Z)

February 9 (6Z- 12Z)

February 13 (0Z- 6Z)

February 14 (18Z)

February 15 (18Z)

February 15 (6Z- 18Z)

February 16 (6Z)

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

Almost No Rawins data

No Rawins data

No Land, No Ships, No Buoys data

Almost No Rawins data

No TOVS data

February 23 (6Z)

March 1 (6Z)

March 8 (6Z-12Z)

March 15 (6Z)

March 22 (6Z)

March 29 (6Z)-18Z

March 30 (0Z- 6Z)

April 5 (6Z-18Z)

April 6 (0Z- 6Z)

April 12 (6Z)

April 19 (6Z- 12Z)

April 26 (6Z-18Z)

April 27 (0Z- 6Z)

May3 (6Z)

May 5

May 10 (6Z)

May 18 (OZ- 6Z)

May 24 (6Z)

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

No TOVS data

142
1988

TIME PERIOD MISSING DATA

June 10 (0Z- 6Z) No TOVS data

June 21 (12Z-18Z) No TOVS data

June 29 (0Z- 6Z) No TOVS data

July 5 (6Z) No TOVS data

July 11 (6Z) No TOVS data

July 19 (6Z) No TOVS data

July 26 (6Z-12Z) No TOVS data

August 2 (6Z-18Z) No TOVS data

August 3 No TOVS data

August 4 (0Z- 6Z) No TOVS data

August 9 (6Z) No TOVS data

August 16 (0Z- 6Z) No TOVS data

August 23 (6Z) No TOVS data

August 30 (6Z) No TOVS data

September 6 (6Z) No TOVS data

September 13 (6Z) No TOVS data

September 20 (6Z) No TOVS data

September 27 (6Z) No TOVS data

October 4 (6Z) No TOVS data

October 5 No TOVS data

October 11 (6Z) No TOVS data

October 18 (0Z) No TOVS data

October 25 (6Z) No TOVS data

October 30 (0Z) No TOVS data

November 1 (6Z) No TOVS data

November 8 (6Z) No TOVS data

November 15 (0Z) No TOVS data

November 22 (6Z) No TOVS data

November 29 (6Z) No TOVS data

143
1989

TIME PERIOD MISSING DATA

January 29 (18Z) Almost No TOVS data

February 27 No Land

March 18 (18Z) about 1/2 Land

APRIL 16 (12Z) about 1/2 Land

April 21 No Ships, No Buoys

May 3 (18Z) No Ships, No Buoys

June 28 (6Z, 18Z) Almost No Rawins (Moisture)

July 26 (18Z) about 1/2 Land

July 27 (6Z- 12Z) about 1/2 Land

August 2 (6Z) No Rawins data

August 3 (6Z) No Rawins data

August 4 (6Z) No Rawins data

August 5 (6Z) No Rawins data

August 6 (6Z) No Rawins data

August 7 (6Z) No Rawins data

October 9 (6Z-18Z) No TOVS data

November 5 (6Z) No TOVS data

November 9 (18Z) No Rawins

December 14 (6Z-18Z) No TOVS data

1990

TIME PERIOD MISSING DATA

February 7 (18Z)

May 18 (18Z)

June 2 (12Z-18Z)

June 6 (lSZ)

June 10

July 13 (6Z)

July 21 (18Z)

July 9 (6Z) - July 15

August 20 (12Z-18Z)

September 21 - 30 (12Z)

September 3 (6Z) - September 11

September 17 (6Z- 12Z)

September 24 (12Z-18Z)

October 2 (12Z)

October 13 (12Z)- October 14

November 26 - 31 (except 30,0Z)

December 2 (6Z-18Z)

No Rawins

No Rawins

No TOVS

No Rawins

No TOVS

No Rawins,No Upper Air,less Aircrafts Land, Buoys, and Sat.Wind

No Land,Ships,Buoys

No TOVS

No TOVS

about 1/2 of Rawins

No TOVS

No TOVS

No TOVS

about 1/2 of Rawins

No TOVS

No TOVS

No TOVS

144
6 Datasets and Data Access

All output from the assimilation is sent to the local Unitree mass storage system of the

NASA Center for Computational Sciences (NCCS). A subset of the data stored in a time series format are also available from the Goddard Distributed Active Archive Center (GDAAC).

Access to the data for outside users will be primarily through the GDAAC. Some processed

fields (e.g., monthly means) are available via anonymous ftp from a local (DAO) server. Details of the archives are presented below. Further information about methods of accessing

the assimilated data may be obtained by sending e-mail to

data@dao.gsfc.nasa.gov

The primary products are the assimilated data (A-D below) which consist of the model

output generated during the assimilation cycle and the analysis increments. The assimilation consists of the GCM integration forced by the analysis increments (the increments are

recomputed every 6 hours as part of the OI analysis). This assimilation method is called

the Incremental Analysis Update (IAU) procedure (Bloom et al. 1991, 1995). All upper

air fields are saved at the sigma levels of the assimilating GCM. The prognostic fields are

saved every 6 hours as instantaneous quantities. All the upper air diagnostics are saved 4

times daily as 6 hour averages centered on the output time. This is consistent with the IAU

procedure in which the increments forcing the model are applied over the 6 hour window

spanning the analysis times (0Z, 6Z, 12Z, 18Z). The single level and vertically-integrated

fields are saved every 3 hours accumulated over the previous 3 hours.

Separate pressure-level data sets are generated for selected quantities. These are currently

stored in two forms. The standard format groups many quantities together, and should be

most useful for case studies and multivariable analysis. The time series format groups one

month of a single upper-level quantity into one data set; these data sets should be convenient

for longer term (multi-year) statistical analyses of selected quantities. We believe the time

series data sets will initially be the most popular and these are currently available through

the GDAAC.

We also save many of the analysis quantities (E-G below). These include the standard OI

analysis and first guess fields produced every 6 hours on the standard pressure levels. They

also include information about the input data (first guess-observations at the observation

location), quality control information, and the files needed to restart the assimilation or

perform model forecasts.

145
6.1 NCCS Datasets

6.1.1 Overview

Generally,all quantitiesaresavedin IEEE format and on an A- grid. The exceptions

arethe restart ("r" files,seebelow)and the analysisincrement("iau" files,seebelow)

fileswhicharesavedin full-precisionCraywordsandon the GCM'sC-grid. Savingthe

incrementsin this wayallowsusto reproducethe assimilationexactlyby runningthe GCM

with theincrementsasanadditionalmodelforcing.Also,thedelfiles(seebelow)consistof

ungriddedobservationaldata.In thefollowing,the dateis givenasyymmdd(e.g.,850305,

is March5, 1985).

A. Sigma Level Output (4X daily and one day/file)

prognostic

primary diagnostic

secondary diagnostic

e0054A.prg.tyymmdd

e0054A.pri.tyymmdd

e0054A.sec.tyymmdd

27061584 (27MB)

38597280 (39MB)

26012016 (26MB)

The prognostic files consist of the state variables and various lower boundary fields. The

primary diagnostics are all the source/sink terms necessary to compute three-dimensional

heat, momentum, and moisture budgets. The secondary diagnostics are additional derived

quantities, such as clear sky radiances (e.g., for comparing with ERBE data), cloud fraction,

and cloud mass flux needed for computing tropospheric transports of tracers.

B. Surface/Vertically-Integrated (8X daily and one day/file)

e0054A.sfc.tyymmdd 17639328 (18MB)

These are various surface and vertically-integrated fields. Most have a strong diurnal signal

and thus need to be saved at least 8 times a day. The vertically-integrated moisture flux

is computed "on the fly" at each time step to minimize sampling and interpolation errors.

This is important for computing accurate moisture budgets.

146
C. Analysis Increments (4X daily; one day/file)

e0054A.iau.tyymmdd 34045952 (34MB)

These are the forcing functions (increments) computed by the OI at each analysis time

(00,06,12,18UTC). The assimilation consists of the GCM run with these increments. The

assimilation can be reproduced using a single restart file and a history of the increments.

D. Pressure Level Output

1. Standard Format (4X daily; one day/file)

prognostic

primary diagnostic

e0054A.prog.prs.tyymmdd 24544624 (25MB)

e0054A.pri.prs.tyymmdd 34821856 (35MB)

2. Time Series Format (4X daily)

Includes D1 and B 80000000 (80MB)

Each upper level field is in a separate monthly data set. Also includes the estimated

height analysis errors. These data are available from the Goddard DAAC.

E. Analysis Fields (4X daily; one day/file)

first guess/analysis e0054A.prs.tyymmdd 61673600 (62MB)

F. First Guess - Observations (4X daily; one day/file)

Also QC info e0054A.del.tyymmdd (approx.) 10000000 (10MB)

147
G. Restarts (one/file; every 2 days at 03Z)

e0054A.rs.tyymmdd 29941760/2 (15MB)

H. Monthly Means Size (5 years)

Selected fields 4.33 GB

Total output per assimilated day is 370365072 (370 MB) or 0.676 Terabytes/5 years

148
6.1.2 Details of Assimilation Output

DATA DIMENSION: (144, 91)

-180.,-177.5, ....,177.5 (longitude)

-90.,-88., ..... ,90. (latitude)

A. Sigma level output:

Definition: sigma=(p-pt)/(ps-pt) where pt=pressure at top (=I0 mb), and

ps=surface pressure.

The quantities are saved at the following 20 sigma levels. We also show

below the 21 sigma edges.

Sigma levels (20):0.9939,

0.6571, 0.5784, 0.5005, 0

0.1001, 0.0730, 0.0498, 0

0.9713, 0.9299, 0.8741, 0.8078, 0.7345,

.4248, 0.3520, 0.2838, 0.2228, 0.1722, 0.1322,

.0290, 0.0095

Sigma edges (21): 1.0000, 0.9879, 0.9547, 0.9051, 0.8432, 0.7725,

0.6964, 0.6178, 0.5390, 0.4620, 0.3875, 0.3165, 0.2510, 0.1945, 0.1498,

0.1146, 0.0855, 0.0605, 0.0390, 0.0190, 0.0000

1. Prognostic

eOO54A.prg.tyymmdd 27,061,584 BYTES

Each prognostic field is an instantaneous (snap shot) quantity.

Each file contains one day: OOZ, 06Z, 12Z, 18Z.

NSFLD = 9 NUFLD = 6 NDFLD = 0 NDUM = 2 NLEV = 20 PTOP = 10.000

I PHIS

2 ALBD

3 GWET

4 PS-PTOP

5 GTMP

6 SLP

7 LWI

8 UBAR

9 VBAR

SURFACE GEOPOTENTIAL HEIGHTS (M/S)**2

SURFACE ALBEDO (0-I)

SURFACE GROUND WETNESS (0-1)

SURFACE PRESSURE - PTOP (MB)

SURFACE GROUND TEMPERATURE (K)

SEA LEVEL PRESSURE (MB)

LAND_(2), WATER_(1), PERMANENT ICE_(3) SEA ICE_(4) FLAGS2

VERTICALLY INTEGRATED (BAROTROPIC) UWND

VERTICALLY INTEGRATED (BAROTROPIC) VWND

1UWND

2 VWND

3 HGHT

4 TMPU

U-WIND (M/S)

V-WIND (M/S)

PERTURBATION GEOPOTENTIAL HEIGHT (M)

TEMPERATURE (E)

149
5 SPHU

6 qq

SPECIFIC HUMIDITY (G/KG)

TURBULENT KINETIC ENERGY (M*.2/S*.2)

UNDEFINED FILL VALUE (REAL) 1.0E+15

2. Primary diagnostic

eOO54A.pri.tyymmdd 38,597,280 BYTES

Each diagnostic field is an average over six hours centered on the

output time. The exceptions to this are the first 3 fields which are

simply copies from the prognostic file (PS-PTOP is an instantaneous

field, PHIS and currently LWI are specified).

Each file contains one day: OOZ, 06Z, 12Z, 18Z.

NSFLD = 3 NUFLD = 0 NDFLD = I0 NDUM = 2 NLEV = 20 PTOP = 10.000

1PHIS

2 PS-PTOP

3 LWI

SURFACE GEOPOTENTIAL HEIGHTS (M/S)**2

SURFACE PRESSURE - PTOP (MB)

LAND_(2), WATER_(1), PERMANENT ICE_(3) SEA ICE_(4) FLAGS

1TURBU

2 TURBV

3 TURBT

4 TURBq

5 MOISTT

6 MOISTQ

7 RADLW

8 RADSW

9 OMEGA

I0 PAVE

U-MOMENTUM CHNGS DUE TO TURB (M/S/DAY) 20

V-MOMENTUM CHNGS DUE TO TURB (M/S/DAY) 20

TEMPERATURE CHNGS DUE TO TURB (DEG/DAY) 20

MOISTURE CHNGS DUE TO TURB (G/KG/DAY) 20

TEMPERATURE CHNGS DUE TO MOIST (DEG/DAY) 20

MOISTURE CHANGES DUE TO MOIST (G/KG/DAY) 20

TEMPERATURE CHNGS DUE TO LW RAD (DEG/DAY) 20

TEMPERATURE CHNGS DUE TO SW RAD (DEG/DAY) 20

VERTICAL VELOCITY OMEGA (MB/DAY) 20

TIME-AVERAGED PS-PTOP (MB) 1

UNDEFINED FILL VALUE (REAL) 1.0E+15

3. Secondary diagnostic

eOO54A.sec.tyymmdd 26,012,016 BYTES

Each diagnostic field is an average over six hours centered on the

output time. The exceptions to this are the first 3 fields which are

simply copies from the prognostic file (PS-PTOP is an instantaneous

field, PHIS and currently LWI are specified).

Each file contains one day: OOZ, 06Z, 12Z, 18Z.

NSFLD = 3 NUFLD = 0 NDFLD = 7 NDUM = 2 NLEV = 20 PTOP = 10.000

150
I PHIS

2 PS-PTOP

3 LWI

I LWCLR

2 CLMOLW

3 CLROLW

4 SWCLR

5 CLDMAS

6 PAVE

7 DTRAIN

SURFACE GEOPOTENTIAL HEIGHTS (M/S)**2

SURFACE PRESSURE - PTOP (MB)

LAND_(2), WATER_(1), PERMANENT ICE_(3) SEA ICE_(4) FLAGS

CLEAR SKY LW HEATING RATES (DEC/DAY) 20

MAXIMUM OVERLAP CLOUD FRACTION FOR LW 20

RANDOM OVERLAP CLOUD FRACTION FOR LW 20

CLEAR SKY SW HEATING RATES (DEC/DAY) 20

CLOUD MASS FLUX (KG M/SEC**2) 20

TIME-AVERAGED PS-PTOP (MB) 1

CONVECTIVE CLOUD DETRAINMENT (KG*M/S) 20

UNDEFINED FILL VALUE (REAL) I.OE+I5

B. Surface and vertically-integrated quantities

eOO54A.sfc.tyymmdd 17,639,328 BYTES

Each file contains I day (Eight time periods:averaged over the previous

3 hours: OOZ, 03Z, 06Z, 09Z, 12Z, 15Z, 18Z, 21Z). The exceptions to

this are the first 3 fields which are simply copies from the prognostic

file (PS-PTOP is an instantaneous field, PHIS and currently LWI are

specified).

NSFLD = 3 NUFLD = 0 NDFLD = 39 NDUM = 2 NLEV = I PTOP = I0.000

I PHIS

2 PS-PTOP

3 LWI

I UFLUX

2 VFLUX

3 HFLUX

4 RADLWG

5 RADSWG

6 CT

7 CU

8 PREACC

9 PRECON

I0 WINDS

II TG

12 TS

13 qS

14 OLR

15 OLRCLR

16 LWGCLR

SURFACE GEOPOTENTIAL HEIGHTS (M/S)**2

SURFACE PRESSURE - PTOP (MB)

LAND_(2), WATER_(1), PERMANENT ICE_(3) SEA

U-MOMENTUM SURFACE STRESS (N/M**2) 1

V-MOMENTUM SURFACE STRESS (N/M**2) 1

SRFCE FLX OF SENSIBLE HEAT (W/M**2) I

NET UPWARD LW RAD. AT GRND (W/M**2) 1

NET DOWNWARD SW RAD. AT GRND (W/M**2) 1

SURFACE DRAG COEF. FOR T AND Q (M/S) I

SURFACE DRAG COEF. FOR U AND V (M/S) I

TOTAL PRECIPITATION (MM/DAY) I

CONVECTIVE PRECIPITATION (MM/DAY) 1

SURFACE WIND SPEED (M/S) 1

GROUND TEMPERATURE (DEC) 1

SURFACE AIR TEMPERATURE (DEC) 1

SATURATION SURFACE SPEC. HUM. (G/KG) I

OUTGOING LONGWAVE RADIATION (W/M**2) 1

OUTGOING LONGWAVE RAD CLEAR SKY (W/M**2)1

SURFACE LONGWAVE FLUX CLEAR SKY (W/M**2)1

ICE_(4) FLAGS

151
17 RADSWT INCIDENT SW RAD. AT TOP OF ATM. (W/M**2)I

18 EVAP SURFACE EVAPORATION (MM/DAY) I

19 DPDT SURFACE PRESSURE TENDENCY (MB/DAY) I

20 USTAR USTAR (M/SEC) i

21ZO SURFACE ROUGHNESS ZO (M) i

22 PBL PBL DEPTH (MB) i

23 OSR OUTGOING SHORTWAVE RADIATION (W/M**2) I

24 OSRCLR OUTGOING SHORTWAVE RAD CLEAR (W/M**2) I

25 SWGCLR SURF. SHORTWAVE FLUX CLEAR SKY (W/M**2) I

26 VINTUQ VERTICALLY INTEGRATED U*Q (M/SEC G/KG)I

27 VINTVQ VERTICALLY INTEGRATED V*Q (M/SEC G/KG)I

28 VINTUT VERTICALLY INTEGRATED U*T (M/SEC DEG)I

29 VINTVT VERTICALLY INTEGRATED V*T (M/SEC DEG)i

30 CLDFRC 2-DIMENSIONAL TOTAL CLOUD FRACTION (0-I)I

31QINT PRECIPITABLE WATER (GM/CM**2) I

32 U2M U AT 2 METERS (M/SEC) I

33 V2M V AT 2 METERS (M/SEC) I

34 T2M T AT 2 METERS (DEG) i

35 Q2M Q AT 2 METERS (KG/KG) i

36 UIOM U AT iO METERS (M/SEC) 1

37 VIOM V AT I0 METERS (M/SEC) i

38 TIOM r AT I0 METERS (DEG) I

39 QIOM Q AT I0 METERS (KG/KG) I

UNDEFINED FILL VALUE (REAL) 1.0E+15

C. Analysis Increments

eOO54A.iau.tyymmdd 34045952 BYTES

These are the analysis fields minus the first guess fields divided by

the interval between analysis times (six hours). The quantities are

saved in the form required by the GCM prognostic equations on the c-grid

in full precision Cray words. PI is the surface pressure minus the

pressure at the top (currently 10 mb). Each file contains one day.

NSFLD = i NUFLD = 4 NDFLD = 0 NDUM = 2 NLEV = 20 PTOP = iO.O00

I ANALP ANALYSIS INCREMENT OF PSURF (MB/SEC)

1ANALU

2 ANALV

3 ANALT

4 ANALQ

ANALYSIS INCREMENT OF U-WIND (M/SEC/SEC)

ANALYSIS INCREMENT OF V-WIND (M/SEC/SEC)

ANALYSIS INCREMENT OF PI*(T/pk) (MB*DEG/(MB)k/SEC)

ANALYSIS INCREMENT OF PI*SPHU (MB*G/G/SEC)

UNDEFINED FILL VALUE (REAL) I.OE+I5

152
D. Pressure Level Data

1. Prognostic (standard format; all quantities together in one file)

eO054A.prog.prs.tyymmdd 49,089,248 BYTES

Each prognostic field is an instantaneous (snap shot) quantity.

Each file contains one day: OOZ, 06Z, 12Z, 18Z.

NSFLD = 9 NUFLD = 6 NDFLD = 0 NDUM = 2 NLEV = 18 PTOP = 10.000

ZLEV: 1000., 950., 900., 850., 800., 700., 600., 500., 400., 300., 250.,

200., 150., 100., 70., 50., 30., 20.

1PHIS

2 ALBD

3 GWET

4 PS-PTOP

5 GTMP

6 SLP

7 LWI

8 UBAR

9 VBAR

SURFACE GEOPOTENTIAL HEIGHTS (M/S)**2

SURFACE ALBEDO (0-1)

SURFACE GROUND WETNESS (0-1)

SURFACE PRESSURE - PTOP (MB)

SURFACE GROUND TEMPERATURE (K)

SEA LEVEL PRESSURE (MB)

LAND_(2), WATER_(1), PERMANENT ICE_(3) SEA ICE_(4) FLAGS

VERTICALLY INTEGRATED (BAROTROPIC) UWND

VERTICALLY INTEGRATED (BAROTROPIC) VWND

1UWND

2 VWND

3 HGHT

4 TMPU

5 SPHU

6 QQ

U-WIND (M/S)

V-WIND (M/S)

GEOPOTENTIAL HEIGHT (M)

TEMPERATURE (K)

SPECIFIC HUMIDITY (G/KG)

TURBULENT KINETIC ENERGY (M*.2/S*.2)

UNDEFINED FILL VALUE (REAL) 1.0E+15

2. Primary diagnostics (standard format)

eO054A.pri.prs.tyymmdd 69,643,712 BYTES

Each diagnostic field is an average over six hours centered on the

output time. The exceptions to this are the first 3 fields which are

simply copies from the prognostic file (PS-PTOP is an instantaneous

field, PHIS and currently LWI are specified).

Each file contains one day: OOZ, 06Z, 12Z, 18Z.

NSFLD = 3 NUFLD = 0 NDFLD = 10 NDUM = 2 NLEV = 18 PTOP = 10.000

153
ZLEV: 1000., 950., 900., 850., 800., 700., 600., 500., 400., 300., 250.,

200., 150., 100., 70., 50., 30., 20.

1 PHIS

2 PS-PTOP

3 LWI

SURFACE GEOPOTENTIAL HEIGHTS (M/S)**2

SURFACE PRESSURE - PTOP (MB)

LAND_(2), WATER_(1), PERMANENT ICE_(3) SEA ICE_(4) FLAGS

1TURBU

2 TURBV

3 TURBT

4 TURBO

5 MOISTT

6 MOISTQ

7 RADLW

8 RADSW

9 OMEGA

I0 PAVE

U-MOMENTUM CHNGS DUE TO TURB (M/S/DAY) 18

V-MOMENTUM CHNGS DUE TO TURB (M/S/DAY) 18

TEMPERATURE CHNGS DUE TO TURB (DEG/DAY) 18

MOISTURE CHNGS DUE TO TURB (G/KG/DAY) 18

TEMPERATURE CHNGS DUE TO MOIST (DEG/DAY) 18

MOISTURE CHANGES DUE TO MOIST (G/KG/DAY) 18

TEMPERATURE CHNGS DUE TO LW RAD (DEG/DAY) 18

TEMPERATURE CHNGS DUE TO SW RAD (DEG/DAY) 18

VERTICAL VELOCITY OMEGA (MB/DAY) 18

TIME-AVERAGED PS-PTOP (MB) 1

UNDEFINED FILL VALUE (REAL) 1.OE+15

3. Time Series data

Same quantities as (B) and (D.1) above (excluding pave), except the data

have been put in time series format, and the estimated height analysis

errors (HGHTE from E below) have been included. This data set will

likely be the preferred data set for many investigators, since the

fields are stored in a time series format. That is, each upper level

quantity is in a separate file (by month), making it easier to perform

statistical calculations requiring long time histories of selected

fields. These are the data provided to the DAAC at Goddard.

Analysis Output

E. Analysis and first guess fields

eOO54A.prs.tyymmdd 61,673,600 BYTES

This file cantains two sets of the quantities listed below. The first guess

fields are obtained by setting the record flag to -i. The analysis is

obtained by setting the record flag to O. (see sample read program)

NSFLD = 6 NUFLD = 6 NDFLD = 5 NDUM = 1NLEV = 14 PTOP = I0.000

ZLEV: 1000., 850., 700., 500., 400., 300., 250., 200., 150., 100., 70.,

154
50., 30., 20.

1PHIS

2 TVSFC

3 PSFC

4 SLP

5 SLU

6 SLV

surface geopotential heights (m/s)**2

surface virtual temperatures(K)

surface pressure(mb)

sea--level pressure (mb)

sea--level u-wind (m/s)

sea--level v-wind (m/s)

1UWND u-wind (m/s)

2 VWND v-wind (m/s)

3 HGHT geopotential height (m)

4 TMPU temperature (k)

5 MIXR mixing ratio (g/kg)

6 RH relative humidity

1PRSE estimate of analysis error in SLP (mb) 1

2 UWNDE estimate of analysis error in u-wind (m/s) 14

3 VWNDE estimate of analysis error in v-wind (m/s) 14

4 HGHTE estimate of analysis error in height (m) 14

5 MIXRE estimate of analysis error in mixing ratio (g/kg) 14

l(i) record type flag -1, O.

Sample time periods (NTYPE =-i indicates a first guess field)

CURRENT

CURRENT

CURRENT

CURRENT

CURRENT

CURRENT

CURRENT

CURRENT

DATE: 850305 0

DATE: 850305 0

DATE: 850305 60000

DATE: 850305 60000

DATE: 850305 120000

DATE: 850305 120000

DATE: 850305 180000

DATE: 850305 180000

BEGINNING DATE: 850304 180000 NTYPE: -1

BEGINNING DATE: 850305 0 NTYPE: 0

BEGINNING DATE: 850305 0 NTYPE: -I

BEGINNING DATE: 850305 60000 NTYPE: 0

BEGINNING DATE: 850305 60000 NTYPE: -i

BEGINNING DATE: 850305 120000 NTYPE: 0

BEGINNING DATE: 850305 120000 NTYPE: -I

BEGINNING DATE: 850305 180000 NTYPE: 0

F. First guess - observations

eOO54A.del.tyymmdd about 10000000 BYTES

These files contain ungridded information about the data which was

incorporated into the assimilation. This includes the first guessobservation at the observation location, the geographical location of

the observations and quality control information. These data sets are

written after the quality control for each of the three analysis

segments (sea-level analysis, moisture analysis, and upper air analysis)

is performed. The data sets are writtem using the format described

below.

155
HEADER RECORD containing:

num_obs (integer) Number of observations for synoptic time

nymd (integer) Date in format yymmdd

nhms (integer) Time in format hhmmss

obs_type (char*3) Observation type (SLP,MIX, or ZUV)

NUM_OBS RECORDS containing

lat (real) Latitude of observation

Ion (real) Longitude of observation

quality (real) quality of observation, where 0 indicates a useless

report, I indicates only the mass, moisture, or

height data passed quality control (see value of

"obs_type"), 2 indicates only the wind data

passed, and 3 indicates all quantities passed. For

a "MIX" observation type, only values of I and 0

are possible.

level (real) Pressure level of observation (unused for "SLP"

observation type)

inst_type (real) Instrument type

uwind (real) u-wind report (unused for "MIX" observation type)

vwind (real) v-wind report (unused for "MIX" observation type)

quan_3 (real) Sea-level pressure report, mixing ratio report, or

height report (see value of "obs type").

For the system used in eOO54A, there are four synoptic times and three

analysis segments per data set. Please note that references to "reports"

and "observations" refer here to the "deltas", that is, the difference

between the measured quantity and the model first guess. The full

observed data can be reconstructed by interpolating the first guess

fields (see data sets eOO54A.prs.tyymmdd in E above) to the observation

locations, and adding the "deltas".

G. Restarts

eOO54A.rs.tyymmdd 29941760 BYTES

Each file contains a 03Z model restart. The restarts are saved every

second day. These are intended only for restarting the model or

assimilation.

156
Selected Monthly Means

The monthly mean files are organized the same way as the corresponding

daily files, except for the addition of quadratic (second moment)

quantities and omega in the prognostic files. The sigma level averages

are done without mass weighting. The full path name for each set of

monthly mean files is /u2/ctmss/eOO54A/assim_monthly/monyy_mean/

followed by the file names given below (see section describing the

unitree structure).

i. Prognostic fields averaged on sigma levels

eOO54A.prog.sig.monyy.mean23545684 BYTES/MONTH

1 PHIS

2 ALBD

3 GWET

4 PS-PTOP

5 GTMP

6 SLP

7 LWI

8 UBAR

9 VBAR

SURFACE GEOPOTENTIAL HEIGHTS (M/S)**2

SURFACE ALBEDO (O-I)

SURFACE GROUND WETNESS (0-I)

SURFACE PRESSURE - PTOP (MB)

SURFACE GROUND TEMPERATURE (K)

SEA LEVEL PRESSURE (MB)

WATER(1) LAND(2) GLACIER(3) SEA-ICE(4)

VERTICALLY INTEGRATED (BAROTROPIC) UWND

VERTICALLY INTEGRATED (BAROTROPIC) VWND

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

UWND

VWND

HGHT

TMPU

SPHU

QQ

OMEGA

UU

VV

TT

ZZ

MM

UV

UT

VT

UZ

VZ

uQ

vq

WW

WT

wQ

U-WIND (M/S)

V-WIND (M/S)

PERTURBATION GEOPOTENTIAL HEIGHT (M)

TEMPERATURE (K)

SPECIFIC HUMIDITY (G/KG)

TURBULENT KINETIC ENERGY (M*.2/S*.2)

OMEGA VERTICAL VELOCITY (MB/DAY)

UWND * UWND

VWND * VWND

TMPU * TMPU

HGHT * HGHT

SPHU * SPHU

UWND * VWND

UWND * TMPU

VWND * TMPU

UWND * HGHT

VWND * HGHT

UWND * SPHU

VWND * SPHU

OMEGA*OMEGA

OMEGA*TMPU

OMEGA*SPHU

157
2. Primary diagnostics averaged on sigma levels

eOO54A.diag.pri.sig.monyy.mean 9649320 BYTES/MONTH

I PHIS

2 PS-PTOP

3 LWI

SURFACE GEOPOTENTIAL HEIGHTS (M/S)**2

SURFACE PRESSURE - PTOP (MB)

LAND_(2), WATER_(1), PERMANENT ICE_(3) SEA

1TURBU

2 TURBV

3 TURBT

4 TURBQ

5 MOISTT

6 MOISTQ

7 RADLW

8 RADSW

9 OMEGA

10 PAVE

U-MOMENTUM CHNGS DUE TO TURB (M/S/DAY) 20

V-MOMENTUM CHNGS DUE TO TURB (M/S/DAY) 20

TEMPERATURE CHNGS DUE TO TURB (DEG/DAY) 20

MOISTURE CHNGS DUE TO TURB (G/KG/DAY)20

TEMPERATURE CHNGS DUE TO MOIST (DEG/DAY)20

MOISTURE CHANGES DUE TO MOIST (G/KG/DAY)20

TEMPERATURE CHNGS DUE TO LW RAD DEG/DAY20

TEMPERATURE CHNGS DUE TO SW RAD DEG/DAY20

VERTICAL VELOCITY OMEGA (MB/DAY) 20

TIME-AVERAGED PS-PTOP (MB) 1

UNDEFINED FILL VALUE (REAL) 1.0E+15

ICE_(4) FLAGS

3. Secondary diagnostics averaged on sigma levels

eOO54A.diag.sec.sig.monyy.mean 6503004 BYTES/MONTH

I PHIS

2 PS-PTOP

3 LWI

SURFACE GEOPOTENTIAL HEIGHTS (M/S)**2

SURFACE PRESSURE - PTOP (MB)

LAND_(2), WATER_(i), PERMANENT ICE_(3) SEA ICE_(4) FLAGS

I LWCLR

2 CLMOLW

3 CLROLW

4 SWCLR

5 CLDMAS

6 PAVE

7 DTRAIN

CLEAR SKY LW HEATING RATES (DEG/DAY) 20

MAXIMUM OVERLAP CLOUD FRACTION FOR LW 20

RANDOM OVERLAP CLOUD FRACTION FOR LW 20

CLEAR SKY SW HEATING RATES (DEG/DAY) 20

CLOUD MASS FLUX (KG M/SEC**2) 20

TIME-AVERAGED PS-PTOP (MB) I

CONVECTIVE CLOUD DETRAINMENT (KG*M/S) 20

UNDEFINED FILL VALUE (REAL) 1.0E+15

4. Surface diagnostics

eOO54A.sfc.monyy.mean 2204916 BYTES/MONTH

i PHIS SURFACE GEOPOTENTIAL HEIGHTS (M/S)**2

158
2 PS-PTOP

3 LWI

1UFLUX

2 VFLUX

3 HFLUX

4 RADLWG

5 RADSWG

6 CT

7 CU

8 PREACC

9 PRECON

10 WINDS

ii TG

12 TS

13 QS

14 OLR

15 OLRCLR

16 LWGCLR

17 RADSWT

18 EVAP

19 DPDT

20 USTAR

21ZO

22 PBL

23 OSR

24 OSRCLR

25 SWGCLR

26 VINTUQ

27 VINTVQ

28 VINTUT

29 VINTVT

30 CLDFRC

31 qINT

32 U2M

33 V2M

34 TRM

35 O2M

36 UIOM

37 VIOM

38 TIOM

39 QIOM

SURFACE PRESSURE - PTOP (MB)

LAND_(2), WATER_(1), PERMANENT ICE_(3) SEA ICE_(4) FLAGS

U-MOMENTUM SURFACE STRESS (N/M**2) 1

V-MOMENTUM SURFACE STRESS (N/M**2) 1

SRFCE FLX OF SENSIBLE HEAT (W/M**2) 1

NET UPWARD LW RAD. AT GRND (W/M**2) I

NET DOWNWARD SW RAD. AT GRND (W/M**2) 1

SURFACE DRAG COEF. FOR T AND q (M/S) I

SURFACE DRAG COEF. FOR U AND V (M/S) 1

TOTAL PRECIPITATION (MM/DAY) 1

CONVECTIVE PRECIPITATION (MM/DAY) 1

SURFACE WIND SPEED (M/S) 1

GROUND TEMPERATURE (DEG) 1

SURFACE AIR TEMPERATURE (DEG) 1

SATURATION SURFACE SPEC. HUM. (G/KG) 1

OUTGOING LONGWAVE RADIATION (W/M**2) I

OUTGOING LONGWAVE RAD CLEAR SKY (W/M**2)I

SURFACE LONGWAVE FLUX CLEAR SKY (W/M**2)l

INCIDENT SW RAD. AT TOP OF ATM. (W/M**2)1

SURFACE EVAPORATION (MM/DAY) 1

SURFACE PRESSURE TENDENCY (MB/DAY) 1

USTAR (M/SEC) 1

SURFACE ROUGHNESS ZO (M) 1

PBL DEPTH (MB) 1

OUTGOING SHORTWAVE RADIATION (W/M**2) I

OUTGOING SHORTWAVE RAD CLEAR (W/M**2) i

SURF. SHORTWAVE FLUX CLEAR SKY (W/M**2) 1

VERTICALLY INTEGRATED U*Q (M/SEC G/KG)I

VERTICALLY INTEGRATED V*Q (M/SEC G/KG)I

VERTICALLY INTEGRATED U*T (M/SEC DEG)I

VERTICALLY INTEGRATED V*T (M/SEC DEG)I

2-DIMENSIONAL TOTAL CLOUD FRACTION (0-i)I

PRECIPITABLE WATER (GM/CM**2) 1

U AT 2 METERS (M/SEC) 1

V AT 2 METERS (M/SEC) 1

T AT 2 METERS (DEG) 1

Q AT 2 METERS (KG/KG) 1

U AT lO METERS (M/SEC) I

V AT 10 METERS (M/SEC) i

T AT 10 METERS (DEG) 1

O AT 10 METERS (KG/KG) 1

UNDEFINED FILL VALUE (REAL) 1.0E+15

5. IAU increments averaged on sigma levels

These are the analysis fields minus the first guess fields divided

159
by the interval between analysis times (six hours). The quantities are

saved on the c-grid. The monthly means are converted to ieee format

(this is different from the unaveraged IAU quantities which are saved in

full-precision Cray words). PI is the surface pressure minus the

pressure at the top (currently I0 mb).

eOO54A.iau.monyy.mean4247988 BYTES/MONTH

i ANALP ANALYSIS INCREMENT OF PSURF (MB/SEC)

1ANALU

2 ANALV

3 ANALT

4 ANALQ

ANALYSIS INCREMENT OF U-WIND (M/SEC/SEC)

ANALYSIS INCREMENT OF V-WIND (M/SEC/SEC)

ANALYSIS INCREMENT OF PI*(T/pk) (MB*DEG/(MB)k/SEC)

ANALYSIS INCREMENT OF PI*SPHU (MB*G/G/SEC)

UNDEFINED FILL VALUE (REAL) 1.0E+15

6. Prognostic fields averaged on pressure levels

eOO54A.prog.prs.monyy.mean 21238492 BYTES/MONTH

ZLEV: 1000.00 950.000 900.000 850.000 800.000

700.000 600.000 500.000 400.000 300.000

250.000 200.000 150.000 I00.000 70.0000

50.0000 30.0000 20.0000

PHIS

ALBD

GWET

PS-PTOP

GTMP

SLP

LWI

UBAR

VBAR

SURFACE GEOPOTENTIAL HEIGHTS (M/S)**2

SURFACE ALBEDO (0-1)

SURFACE GROUND WETNESS (0-1)

SURFACE PRESSURE - PTOP (MB)

SURFACE GROUND TEMPERATURE (K)

SEA LEVEL PRESSURE (MS)

WATER(1) LAND(2) GLACIER(3) SEA-ICE(4)

VERTICALLY INTEGRATED (BAROTROPIC) UWND

VERTICALLY INTEGRATED (BAROTROPIC) VWND

1

2

3

4

5

6

7

8

9

10

11

UWND

VWND

HGHT

TMPU

SPHU

qQ

OMEGA

UU

VV

TT

ZZ

U-WIND (M/S)

V-WIND (M/S)

GEOPOTENTIAL HEIGHT (M)

TEMPERATURE (K)

SPECIFIC HUMIDITY (G/KG)

TURBULENT KINETIC ENERGY (M*.2/S*.2)

OMEGA VERTICAL VELOCITY (MB/DAY)

UWND * UWND

VWND * VWND

TMPU * TMPU

HGHT * HGHT

160
12 MM SPHU * SPHU

13 UV UWND * VWND

14 UT UWND * TMPU

15 VT VWND * TMPU

16 UZ UWND * HGHT

17 VZ VWND * HGHT

18 UQ UWND * SPHU

19 VQ VWND * SPHU

20 WW OMEGA*OMEGA

21 WT OMEGA*TMPU

22 WQ OMEGA*SPHU

7. Primary diagnostics averaged on pressure levels

eOO54A.diag.pri.prs.monyy.mean 8705464 BYTES/MONTH

ZLEV: 1000.00 950.000 900.000 850.000 800.000

700.000 600.000 500.000 400.000 300.000

250.000 200.000 150.000 lO0.O00 70.0000

50.0000 30.0000 20.0000

PHIS

PS-PTOP

LWI

SURFACE GEOPOTENTIAL HEIGHTS (M/S)**2

SURFACE PRESSURE - PTOP (MB)

LAND_(2), WATER_(1), PERMANENT ICE_(3) SEA ICE_(4) FLAGS

1TURBU

2 TURBV

3 TURBT

4 TURBO

5 MOISTT

6 MOISTQ

7 RADLW

8 RADSW

9 OMEGA

10 PAVE

U-MOMENTUM CHNGS DUE TO TURB (M/S/DAY) 18

V-MOMENTUM CHNGS DUE TO TURB (M/S/DAY) 18

TEMPERATURE CHNGS DUE TO TURB (DEG/DAY) 18

MOISTURE CHNGS DUE TO TURB (G/KG/DAY)18

TEMPERATURE CHNGS DUE TO MOIST (DEG/DAY)18

MOISTURE CHANGES DUE TO MOIST (G/KG/DAY)I8

TEMPERATURE CHNGS DUE TO LW RAD DEG/DAY18

TEMPERATURE CHNGS DUE TO SW RAD DEG/DAY18

VERTICAL VELOCITY OMEGA (MB/DAY) 18

TIME-AVERAGED PS-PTOP (MB) 1

UNDEFINED FILL VALUE (REAL) 1.0E+15

8. First guess and analysis averaged on pressure levels

eOO54A.analysis.prs.monyy.mean 15418512 BYTES/MONTH

This file cantains two sets of the quantities listed below. The first guess

fields are obtained by setting the record flag to -1. The analysis is

obtained by setting the record flag to O. (see sample read program)

161
ZLEV: 1000., 850., 700., 500., 400., 300., 250., 200., 150., I00., 70.,

50., 30., 20.

I PHIS

2 TVSFC

3 PSFC

4 SLP

5 SLU

6 SLV

surface geopotential heights (m/s)**2

surface virtual temperatures(K)

surface pressure(mb)

sea--level pressure (mb)

sea--level u-wind (m/s)

sea--level v-wind (m/s)

I UWND

2 VWND

3 HGHT

4 TMPU

5 MIXR

6 RH

u-wind (m/s)

v-wind (m/s)

geopotential height (m)

temperature (k)

mixing ratio (g/kg)

relative humidity

1PRSE

2 UWNDE

3 VWNDE

4 HGHTE

5 MIXRE

estimate of analysis error in SLP (mb) 1

estimate of analysis error in u-wind (m/s) 14

estimate of analysis error in v-wind (m/s) 14

estimate of analysis error in height (m) 14

estimate of analysis error in mixing ratio (g/kg) 14

162
6.1.3 NCCS Unitree Directory Structure

/u2/ctmss/e0054A

/assim__sigma

/monyy

/assim_sfc

/monyy

/assim_incr

/monyy

/assim_press

/monyy

/monyy_ts

/assimJnonthly

/analysis

/restarts

e0054A.prg.tyymmdd

e0054A.pri.tyymmdd

e0054A.sec.tyymmdd

e0054A.sfc.tyymmdd

e0054A.iau.byymmdd.eyymmdd

e0054A.prog.prs.byymmdd.eyymmdd

e0054A.pri.prs.byymmdd.eyymmdd

e0054A.prs.$$$$.byymmdd.eyymmdd

e0054A.tabl.$$$$.byymmdd.eyymmdd

/monyy_mean

e0054A.prog.sig.monyy.mean

e0054A.diag.pri.sig.monyy.mean

e0054A.diag.sec.sig.monyy.mean

e0054A.diag.sfc.monyy.mean

e0054A.iau.monyy.mean

e0054A.prog.prs.monyy.mean

e0054A.diag.pri.prs.monyy.mean

e0054A.analysis.prs.monyy.mean

/monyy

e0054A.prs.tyymmdd

e0054A.del.tyymmdd

/monyy

e0054A.rs.tyymmdd

163
6.1.4 Sample Read Program (Phoenix Format)

All data sets on unitree (accept for the time series files) are written in our local "Phoenix"

format. Sample read programs are given below. The time series format is described in

section 6.2.1.

Assign statements (for Cray)

1) cray data (iau and restart files)

assign -a $datal fort.21

2) ieee data (all other files)

assign -a Sdatal -N ieee -F f77 fort.21

Fortran Program

SAMPLE READ PROGRAM

************************************************************************ C

C PURPOSE

C Routine to read STANDARD Forecast/Analysis output

C

C READ DESCRIPTION

C JOB ...... CHARACTER*8 Experiment job identifier

C NYMD ..... INTEGER Current Date (YYMMDD) of data record

C NHMS ..... INTEGER Current Time (HHMMSS) of data record

C NYMDO .... INTEGER Beginning Date (YYMMDD) of forecast

C NHMSO .... INTEGER Beginning Time (HHMMSS) OF forecast

C

C XLABEL ... CHARACTER*80 Character description of experiment

C IM ....... INTEGER Longitudinal dimension of data

C JNP ...... INTEGER Latitudinal dimension of data

C NSFLD .... INTEGER Number of Surface Fields

C NUFLD .... INTEGER Number of Upper-air Fields

C NDFLD .... INTEGER Number of Diagnostic Fields

C

C PTOP .....

C NULEV ....

C ZLEV .....

C NLAY .....

C SIGE .....

C

C NDUM ..... INTEGER Number of user-defined

REAL Model top pressure

INTEGER Number of Upper-air Levels

REAL Upper-air Level Values (Pressure or Sigma Level)

INTEGER Number of Model Levels

REAL Model Sigma edge values

Header variables

164
C DDUM .....

C IDUM .....

C RDUM .....

C CDUM .....

C

C NAMES ....

C DESCS ....

C NAMEU ....

C DESCU ....

C NAMED ....

C DESCD ....

C NDLEV ....

C

C ZLEVID ...

CHARACTER*40 User-defined Description of Header variable

INTEGER User-defined Integer Header variable

REAL User-defined Real Header variable

CHARACTER*8 User-defined Character Header variable

CHARACTER*8 Surface Field Name

CHARACTER*40 Surface Field Description

CHARACTER*8 Upper-air Field Name

CHARACTER*40 Upper-air FIELD Description

CHARACTER*8 Diagnostic FIELD Name

CHARACTER*40 Diagnostic FIELD Description

INTEGER Number of levels defined for diagnostic field

REAL Level Identifier (Pressure or Sigma Level)

C NAME ..... CHARACTER*8 Character Name of quantity being read

C Q ........ REAL Two-dimensional quantity being read

C

C NOTE:

C For the Datasets produced by GEOS-DAS, the following User-Defined

C Header information has been used:

C

C NDUM =

C DDUM(1) =

C IDUM(1) =

C

C

2

'OUTPUT RECORD TYPE (INTEGER)'

-I FIRST GUESS

0 AFTER ANALYSIS (or STRAIGHT FORECAST)

1 INITIALIZED ANALYSIS

C RDUM(1) = 0.I00000E+16

C CDUM(1) = 'LOG8R J

C

C DDUM(2) = 'UNDEFINED FILL VALUE (REAL)'

C IDUM(2) = 0

C RDUM(2) = 0.100000E+16

C CDUM(2) = 'UNDEF'

C

C* GODDARD LABORATORY FOR ATMOSPHERES

C

PARAMETER ( IDIM =144 )

PARAMETER ( JDIM = 91 )

DIMENSION Q( IDIM,JDIM )

CHARACTER*8 XLABEL(IO) , JOB, NAME

DIMENSION

DIMENSION

CHARACTER*8

CHARACTER*40

DATA KU /21/

10 CONTINUE

SIGE (50) , NDLEV(IO0), ZLEV(50)

IDUM(IO0), RDUM(IO0)

NAMES(IO0), NAMEU(IO0), NAMED(IO0), CDUM(IO0)

DESCS(IO0), DESCU(IO0), DESCD(IO0), DDUM(IO0)

165
READ(KU,END=500) JOB, NYMD, NHMS, NYMDO, NHMSO,

XLABEL, IM, JNP,

NSFLD, NUFLD, NDFLD,

PTOP, NULEV, (ZLEV(K),K=I,NULEV),

NLAY, (SIGE(K),K=I,NLAY+I),

NDUM, (DDUM(N),IDUM(N),RDUM(N),CDUM(N),N=I,NDUM)

if( ndum.eq.2 ) then

undef = rdum(2)

else

undef = -999.9

endif

READ(KU) (NAMES(N), DESCS(N), N=I,NSFLD ),

(NAMEU(N), DESCU(N), N=I,NUFLD ),

(NAMED(N), DESCD(N), NDLEV(N), N=I,NDFLD )

C _##_#_*_#_#_*#__#_#_**#*_#_**#_#_*_#_#_

C **** READ SURFACE FIELDS ****

C $$$$$$*$$$*$*$#$$$$$*$*$$#$$$$$$$$$*$*$$$$$$$$$#$*$***$#$$#$_#_*$#$

DO 20 N=I,NSFLD

READ(KU) ZLEVID,NAME,Q

20 CONTINUE

C *_*_#_*_#_#_#_#_#**_#_#_#*_*_#_##_#__*_##_

C **** READ UPPER-AIR FIELDS  

DO 30 L=I,NULEV

DO 30 N=I,NUFLD

READ(KU) ZLEVID,NAME,Q

30 CONTINUE

C ** ** READ DIAGNOSTICS ****

C _*$_$$$#$$$$$$$$$$$$#$$$_#$#_$$$$#$_$$$$$$$$#$$$$#*$$$_#$$$*$$$$$$_

DO 40 N=I,NDFLD

DO 40 L=I,NDLEV(N)

READ(KU) ZLEVID,NAME,Q

40 CONTINUE

GOTO I0

500 CONTINUE

STOP

END

166
6.2 GDAAC Time Series Datasets

The Goddard DAAC (GDAAC) holds the time series subset of the full data set (see above;

a copy of these data sets is also on the NCCS Unitree system). The files are organized to

allow them to be easily read and displayed with the GRADS graphics and display program

(available via anonymous ftp from grads.iges.org) developed at the Center for Ocean-LandAtmosphere (COLA).

The GDAAC has an online Information Management System. This menu-driven interactive

data system is open to the public, but access to certain restricted data products require

special authorization. To connect to the system via Telnet follow the instuctions below.

Node name: daac.gsfc.nasa.gov

Node number: 192.107.190.139

Example: telnet daac.gsfc.nasa.gov

Username: daacims

Password: gsfcdaac

Upon connecting to the GSFC DAAC host computer you will be asked to register your

name and address and will be offered a tutorial to help orient you to the system. For more

information on access to the 4-D Assimilated Data Set, contact the GSFC DAAC User

Services Office at (301)286-3209, or via email at daacuso@daac.gsfc.nasa.gov

The DAAC also has a data guide for the time series available via MOSAIC

(http://daac.gsfc.nasa.gov/DATASET_DOCS/dao_data set.html)

Information about these data sets is found in the following four sections:

1. Description of the data sets in timeseries format.

2. Contents of the data sets.

3. Contents of the data descriptor (table) file.

4. Menu of available quantities.

167
6.2.1 Description of the Datasets

The data representation is IEEE 32 bit floating point and the record blocking is fortran

block control words (in a Cray assign the data sets have ieee f77 format). The data sets are

stored in one month segments. The directory structure of the timeseries data sets and the

associated table files (which describe the contents of the timeseries data sets) is as follows:

/u2/ctmss/eOO54A/assim_press/monyy_ts

eOOS4A.prs.$$$$.byymmdd.eyymmdd

e0054A.tabl.$$$$.byymmdd.eyymmdd

where:

monyy = month and year (e.g. mar85)

$$$$ = name of the desired upper level field (see menu in section 4) or name

given to the surface prognostic/diagnostic data set. The surface prognostic and

diagnostic data sets have multiple fields. The naming convention for the surface

prognostics is sfcprog. The naming convention for the diagnostics is diag#

(where #=1,2,3 or 4; see the menu of available quantities in section 4)

byymmdd= beginning year, month and date (e.g. 850301)

eyymmdd= ending year, month and date

For example, the zonal wind for March of 1985 is in the following directory:

/u2/ctmss/eOO54A/assim_press/mar85_ts/eOO54A.prs.uwnd.b85030I.e850331

The associated data descriptor (hereafter table) file (which describes the

contents of eOO54A.prs.uwnd.b850301.e850331) is

/u2/ctmss/eOO54A/assim_press/mar85_ts/eOO54A.tabl.uwnd.b85030I.e850331

Contents of the data sets

Each data set consists of a series of records. Each record contains one level of a field. The

field at one level is read, then the next, and so on. Upper air fields (both prognostic and

diagnostic) are available at 18 levels; see the table file (described in Section 3) for specific

information about the pressure levels, the resolution, the date and time stamp, etc.

With the exception of the height error data sets (HGHTE), all data sets were obtained

by interpolating assimilated data from 20 sigma levels to 18 pressure levels (14 mandatory

levels given by 1000 mb, 850 mb, 700 mb, 500 mb, 400 mb, 300 mb, 250 mb, 200 mb, 150

mb, 100 mb, 70 mb, 50 mb, 30 mb, and 20 mb plus 4 additional levels given by 950 mb, 900

168
mb, 800 mb and 600 mb). In the case of tIGHTE, the assimilated data was interpolated

from 14 mandatory pressure levels to 18 pressure levels (as listed above).

The upper air fields on pressure levels are read with the bottom level (highest pressure)

first, i.e.

u wind at 1000 mb

u wind at 950 mb

$

u wind at 30 mb

u wind at 20 mb

A sample READ program for data sets containing upper air quantities is:

PARAMETER (IM=I44,JNP=91)

DIMENSION FIELD(IM,JNP)

DO I ITIMES=I,NTIMES

DO 2 ILEVS=I,NLEVS

READ(8) FIELD

2 CONTINUE

1 CONTINUE

where

NTIMES(=TDEF in table file) is the number of times data was written

NLEVS (=ZDEF in table file) is the number of pressure levels

IM(=XDEF in table file) is the number of gridpoints in longitude

JNP(=YDEF in table file) is the number of gridpoints in latitude

Some data sets contain a number of different fields at a single level; the table

file is used to determine how many fields are in the data set. A sample READ

program for these data sets is

PARAMETER (IM=I44,JNP=91)

DIMENSION FIELD(IM,JNP)

DO I ITIMES=I,NTIMES

DO 2 IqTYS=I,NQTYS

READ(8) FIELD

2 CONTINUE

I CONTINUE

where

NQTYS(=VARS in table file) is the number of different fields in the

169
data set. The order of the fields is given by the list of field descriptors

located below the VARS entry in the table file. The other parameters in

the READ program are as described above.

Contents of the Data Descriptor (Table) File

The table file contains all information necessary to READ a given data set. Each monthly

data set has an associated table file. Thus, prior to accessing a given data set, the user

should study the associated table file. The following is an example of a table file for a single

upper air prognostic quantity:

DSET

FORMAT sequential

TITLE OII.3/vcSb20 Version 1.3.0

UNDEF 1.0E+15

XDEF 144 LINEAR -180.0 2.500

YDEF 91 LINEAR -90.0 2.000

ZDEF 18 LEVELS 1000.0 950.0 900.0

400.0 300.0 250.0 200.0 150.0

TDEF 112 LINEAR O0:OOZOIMAR85

VARS 1

UWND 18 0 U-WIND (M/S)

ENDVARS

850.0 800.0 700.0 600.0

100.0 70.0 50.0 30.0 20.0

6hr

500.0

In this example, the first two lines are relevant only for users of the Grid Analysis and Display System (GRADS). Next, the title of the experiment is given followed by the undefined,

or missing, value 1.0E+15. There are 144 grid points in the x-direction (longitude) with

the first grid point at the dateline and with a grid spacing of 2.5 degrees. There are 91

grid points in the y-direction (latitude) with the first grid point at the south pole and with

a grid spacing of 2.0 degrees. There are 18 pressure levels with the bottom level (highest

pressure) first. Data was written 112 times starting at 00Z on March 1, 1985 and every six

hours thereafter. There is 1 variable in this data set, namely the zonal wind UWND.

6.2.2 Menu of Available Quantities

The following is a list of quantities that are currently available in time series format.

170
PROGNOSTIC MENU

..... sfcprog .....

NAME LEVELS DESCRIPTION

PHIS I SURFACE GEOPOTENTIAL HEIGHTS (M/S)**2

ALBD 1 SURFACE ALBEDO (0-1)

GWET 1 SURFACE GROUND WETNESS (0-1)

PS-PTOP I SURFACE PRESSURE - PTOP (MB)

GTMP 1 SURFACE GROUND TEMPERATURE (K)

SLP 1 SEA LEVEL PRESSURE (MB)

LWI 1 WATER (1) LAND_(2) PERMANENT ICE_(3) SEA ICE_(4) FLAGS

UBAR 1 VERTICALLY INTEGRATED (BAROTROPIC) UWND (M/S)

VBAR 1 VERTICALLY INTEGRATED (BAROTROPIC) VWND (M/S)

..... upper air

NAME LEVELS DESCRIPTION

UWND 18 U-WIND (M/S)

VWND 18 V-WIND (M/S)

HGHT 18 GEOPOTENTIAL HEIGHT (M)

TMPU 18 TEMPERATURE (K)

SPHU 18 SPECIFIC HUMIDITY (G/KG)

QQ 18 TURBULENT KINETIC ENERGY (M*.2/S*_2)

HGHTE 18 STANDARD DEVIATION OF THE HEIGHT ERROR (M)

DIAGNOSTIC MENU

171
...... diagl

NAME LEVELS DESCRIPTION

PS-PTOP 1 SURFACE PRESSURE - PTOP (MB)

PREACC I TOTAL PRECIPITATION (MM/DAY)

PRECON

EVAP 1

VINTUQ

VINTVQ

VINTUT

VINTVT

QINT I

1 CONVECTIVE PRECIPITATION (MM/DAY)

SURFACE EVAPORATION (MM/DAY)

1 VERTICALLY INTEGRATED U*Q (M/S G/KG)

i VERTICALLY INTEGRATED V*Q (M/S G/HG)

I VERTICALLY INTEGRATED U*T (M/S DEG)

1 VERTICALLY INTEGRATED V*T (M/S DEG)

PRECIPITABLE WATER (GM/CM**2)

...... diag2 ......

NAME LEVELS DESCRIPTION

PS-PTOP I SURFACE PRESSURE - PTOP (MB)

UFLUX I U-MOMENTUM SURFACE STRESS (N/M**2)

VFLUK I V-MOMENTUM SURFACE STRESS (N/M**2)

HFLUX I SURFACE FLUX OF SENSIBLE HEAT (W/M**2)

CT 1 SURFACE DRAG COEFFICIENT FOR T AND Q (M/S)

CU 1 SURFACE DRAG COEFFICIENT FOR U AND V (M/S)

WINDS i SURFACE WIND SPEED (M/S)

USTAR I USTAR (M/S)

ZO I SURFACE ROUGHNESS (S)

PBL 1PBL DEPTH (MB)

...... diag3 ......

NAME LEVELS DESCRIPTION

PS-PTOP 1 SURFACE PRESSURE - PTOP (MB)

RADLWG 1 NET UPWARD LW RADIATION AT GROUND (W/H**2)

RADSWG i NET DOWNWARD SW RADIATION AT GROUND (W/M**2)

OLR I OUTGOING LONGWAVE RADIATION (W/M**2)

OLRCLR I OUTGOING LONGWAVE RADIATION CLEAR SKY (W/M**2)

LWGCLR i SURFACE LONGWAVE FLUX CLEAR SKY (W/M**2)

172
RADSWT I INCIDENT SW RADIATION AT TOP OF ATMOS (W/M**2)

OSR i OUTGOING SW RADIATION (W/M**2)

OSRCLR I OUTGOING SHORTWAVE RADIATION CLEAR SKY (W/M**2)

SWGCLR I SURFACE SHORTWAVE FLUX CLEAR SKY (W/M*$2)

CLDFRC 1 2-DIMENSIONAL TOTAL CLOUD FRACTION CO-l)

...... diag4 ......

NAME LEVELS DESCRIPTION

PS-PTOP I SURFACE PRESSURE - PTOP (MB)

TG 1 GROUND TEMPERATURE (DEG)

TS I SURFACE AIR TEMPERATURE (DEG)

QS I SATURATION SURFACE SPECIFIC HUMIDITY (G/KG)

DPDT I SURFACE PRESSURE TENDENCY (MB/DAY)

U2M 1U AT 2 METERS (M/S)

V2M 1V AT 2 METERS (M/S)

T2M 1T AT 2 METERS (DEG)

Q2M 1 Q AT 2 METERS (KG/KG)

UIOM 1 U AT 10 METERS (M/S)

VIOM 1V AT 10 METERS (M/S)

TIOM I T AT 10 METERS (DEG)

QIOM I Q AT I0 METERS (KG/KG)

..... upper air .....

NAME LEVELS DESCRIPTION

TURBU 18 U-MOMENTUM CHANGES DUE TO TURBULENCE (M/S/DAY)

TURBV 18 V-MOMENTUM CHANGES DUE TO TURBULENCE (M/S/DAY)

TURBT 18 TEMPERATURE CHANGES DUE TO TURBULENCE (DEG/DAY)

TURBQ 18 MOISTURE CHANGES DUE TO TURBULENCE (G/KG/DAY)

MOISTT 18 TEMPERATURE CHANGES DUE TO MOIST PROC (DEG/DAY)

MOISTQ 18 MOISTURE CHANGES DUE TO MOIST PROC (G/KG/DAY)

RADLW 18 TEMPERATURE CHANGES DUE TO LW RAD (DEG/DAY)

RADSW 18 TEMPERATURE CHANGES DUE TO SW RAD (DEG/DAY)

OMEGA 18 VERTICAL VELOCITY (MB/DAY)

173
6.3 Datasets Available from DAO Server

Several files reside on an anonymous ftp account on a local workstation in the DAO to allow

outside users to obtain 1) the latest documentation of the data sets, 2) statistics about the

observations going into the assimilation, 3) a detailed description of the diagnostics, 4)

selected monthly mean and time series files, and 5) a subset of the data with surface marine

fluxes. The anonymous ftp account is on hera.gscf.nasa.gov. Here you will find the following

directories:

/pub/assimilation/e0054A/documentation/

assim_files.doc.ascii

This is a plain text version of most of section 6.

assim_files.doc.ps

This is a postscript version of most of section 6.

assim_files.doc.nofonts.ps

This is a postscript version of most of section 6

without the embedded fonts.

data_problems

Contains a summary of problems discovered with the data. This

will be updated periodically (see Appendix).

suspic.tyymm (e.g. suspic.t8503)

These are monthly summaries of outlier counts (and locations) based

on the variance at each grid point for the assimilated prognostic

fields. These are not necessarily bad data since extreme

(many standard deviations) values do occur in nature, especially

for specific humidity at upper levels. This is meant to be

another source of information for judging the quality of the

assimilated fields.

/pub/gem/

geosl.0_gcm.doc.ps

Details of the diagnostics (how they were computed, etc.) and

further information about the GEOS-1 GCM used in the

assimilation may be found in this postscript file.

/pub/assimilation/e0054A/observations

174
bar_obscountyymm.ps

Thesearepostscriptfiles(bargraphsof datacounts).

Therearealsotext filessummarizingthe datagoing

into theassimilation.(seesection5.2)

/pub/assimilatlon/e0054A/data/monthly_means

selected monthly mean files in GRADS format

/pub/assimilation/e0054A/data/time_series

selected single level time series in GRADS format

/pub/assimilation/e0054A/dat a/ocean_fluxes

The surface marine ocean flux data sets are individual files

with fluxes of heat, momentum and fresh water over the ocean

surface, along with basic quantities necessary for recalculation

of these fluxes. The files are in NetCDF. There is very easy to

use FORTRAN access software and a "point-and-click" IDL utility

for browsing of the data.

175
Acknowledgements We would like to thank Arlindo da Silva for reviewing a draft version

of this document. The assimilation experiment and research described in this report were

supported by the EOS Interdisciplinary Science Program and by the NASA Research and

Applications Program. Computer resources and funding were provided by the EOS Project

through the Scientific Computational Facility of the Data Assimilation Office.

176
Appendix: History of Output Problems

The following list of output problems is updated on an irregular basis and may be obtained

via anonymous ftp from the machine: hera.gsfc.nasa.gov (/pub/assimilation/e0054A/documentation).

The list presented below was last updated on 1/10/95.

Specifics: (reporting date:problem)

1/10/95: All segments inadvertantly run with the wrong (the previous year's) SST and

soil moisture boundary conditions have been replaced (see 9/15/94 entry below). For

anyone that used the data with the incorrect boundary conditions it appears that the

system adjusts very quickly (within a few days) so that error does not influence results

much beyond the dates given below. We did, however, find an unexpected impact in

the temperature (and probably other) fields at upper levels-this appears to be due to

occasional differences in quality control decisions made (e.g. data rejection) with the

incorrect bcs (upper levels are linked by the sea-level pressure analysis).

10/6/94: The diagnostic fields TURBU and TURBV have unrealistic values for 06Z, April

11,1986

10/6/94: The diagnostic field OMEGA is computed incorrectly at the international dateline (I=1).

9/15/94: The following segments were inadvertantly run with the wrong (the previous

year's) SST and soil moisture boundary conditions . We are currently in the process

of rerunning these segments. We will then replace the January time series data sets

in the DAAC, and the monthly means on hera. This will probably take several weeks.

When this is completed we will update this file so please check back in a few weeks. We

will also try to provide some indication of the impact on other fields. This problem,

of course, impacts the near surface quantities and fluxes directly. We suspect that the

impact on most upper level fields will be small.

860101- 860104

870101- 870104

880101- 880104

890101- 890102

900101- 900103

9/15/94: There is also a minor problem with the interpolation used for determining the

SST and soil moisture fields during the last half of every December, resulting in a

slight discontinuity in these fields at the start of a new year.

177
9/15/94: Note: All clear sky radiative diagnostics are sample every 3 hours. This causes

a signature of the diurnal cycle in the daily means purely due to the coarse sampling

frequency. This is not true for the full (cloudy) radiative diagnostics, since they are

accumulated at each model time step (2.5 minutes).

6/94: Semi-permanent unrealistic high pressure anomaly at (0E, 82S)

6/94: Semi-permanent temperature spike at (72N, 125W). Appears to be due to incorrect

LWI flag for the years 1985-88 during which it is always considered a water point.

(Should be ice during cold season).

2/94: Unrealistically cold surface and near surface temperatures over land during the cold

season. This appears to occur somewhat randomly primarily at gridpoints in regions

of steep topography.

8/17/93: Beginning 06Z Nov 16, 1985 the LWI output field was updated to include

SEAJCE (4).

6/18/93: March 15, 1985, 12Z and 15Z, unrealistic precip maximum (> 200 mm/day)

over gridpoint in Arctic.

178
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182
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

183

REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

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April 1995 Technical Memorandum

4. TITLE AND SUBTITLE 5. FUNDING NUMBERS

Technical Report Series on Global Modeling and Data Assimilation

Volume 6 - A Multiyear Assimilation With the GEOS-1 System:

Overview and Results

6. AUTHOR(S)

Siegfried Schubert, Chung-Kyu Park, Chung-Yu Wu, Wayne Higgins,

Yelena Kondratyeva, Andrea Molod, Lawrence Takacs,

Michael Seablom, and Richard Rood

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS (ES)

Laboratory for Atmospheres

Data Assimilation Office

Goddard Space Flight Center

Greenbelt, Maryland 20771

9. SPONSORING / MONITORING ADGENCY NAME(S) AND ADDRESS (ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

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95B00079

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TM-104606, Vol. 6

11. SUPPLEMENTARY NOTES

Schubert and Rood: Goddard S _ace Flight Ctr., Greenbelt, MD; Wu, Kondratyeve, Molod, Takacs, and

Seablom: General Sciences Cor 5., Laurel, MD; Park: Joint Center for Earth System Science, Univ. of

Maryland, College Park, MD; Iq iggins: Climate Analysis Ctr, NMC/NWS/NOAA, Washington, D.C.

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13. ABSTRACT (Maximum 200 words)

The Data Assimilation Office (DAO) at Goddard Space Flight Center has produced a multiyear

global assimilated data set with version 1 of the Goddard Earth Observing System Data Assimilation

System (GEOS-1 DAS). One of the main goals of this project, in addition to benchmarking the

GEOS-1 system, was to produce a research quality data set suitable for the study of short-term

climate variability. The output, which is global and gridded, includes all prognostic fields and a

large number of diagnostic quantities such as precipitation, latent heating, and surface fluxes. Output is provided four times daily with selected quantities available eight times per day. Information

about the observations input to the GEOS- 1 DAS is provided in terms of maps of spatial coverage,

bar graphs of data counts, and tables of all time periods with significant data gaps.

The purpose of this document is to serve as a users' guide to NASA's first multiyear assimilated data

set and to provide an early look at the quality of the output. Documentation is provided on all the

data archives, including sample read programs and methods of data access. Extensive comparisons

are made with the corresponding operational European Center for Medium-Range Weather Forecasts

analyses, as well as various in situ and satellite observations. This document is also intended to alert

users of the data about potential limitations of assimilated data, in general, and the GEOS- 1 data, in

particular. Results are presented for the period March 1985-February 1990.

14. SUBJECT TERMS

Data assimilation, GEOS-1, reanalysis, multiyear assimilated data set

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OF REPORT OF THIS PAGE OF ABSTRACT

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2O7

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