### Draft

63

Total stats bias abserr r2 ia rmse

Temperature (K) 0.00

- 0 .01 0.000 0.000

- 0.0029

Mixing Ratio (g/kg) 0.00 0.00

- 0.001 0.000

- 0.0009

Rel. Humidity (%)

- 0.05 0.01 0.000 0.000 0.0095

Wind Speed (m/s) 0.00

- 0.04

- 0.028

- 0.013

- 0.0470

Clouds (%) 0.05 0.01 0.001 0.000 0.0226

Wind stats bias abserr ue rr verr uverr

Wind (deg, m/s) 0.017

- 0.77

- 0.0373

- 0.0370

- 0.0525

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0002 0.0014 0.0000

- 0.0010

- 0.0006

- 0.0006

- 0.0008

- 0.0006

0.05

- 0.0001

- 0.0008

- 0.0005 0.0000

- 0.0003

- 0.0003

- 0.0001

- 0.0003

0.10 0.0005 0.0020 0.0023 0.0013 0.0023 0.0024 0.0017 0.0021

0.25 0.0001 0.0022 0.0015 0.0000 0.0010 0.0010 0.0001 0.0008

0.50

- 0.0001

- 0.0000

- 0.0003

- 0.0013

- 0.0010

- 0.0010

- 0.0012

- 0.0008

1.00 0.0000 0.0152

- 0.0025 0.0071 0.0028 0.0027 0. 0071 0.0028

Table 47. Episode 1 composite statistical comparison of sensitivity px_acm7 with base case px_acm2 for the US portion

of the 36

- km domain is shown.



### Total stats bias abserr r2 ia rmse

Temperature (K) 0.00 0.00 0.000 0.000 0.0019

Mix ing Ratio (g/kg)

- 0.01 0.00 0.000 0.000

- 0.0027

Rel. Humidity (%)

- 0.16 0.01 0.003 0.000 0.0066

Wind Speed (m/s)

- 0.01

- 0.01

- 0.004

- 0.004

- 0.0074

Clouds (%)

- 0.19 0.05 0.002 0.001 0.0657

Wind stats bias abserr uerr verr uverr

Wind (deg, m

/s)

- 0.013

- 0.17

- 0.0064

- 0.0081

- 0.0104

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0006

- 0.0033

- 0.0019 0.0007

- 0.0009

- 0.0014

- 0.0005

- 0.0012

0.05

- 0.0004

- 0.0021

- 0.0013 0.0002

- 0.0009

- 0.0011

- 0.0002

- 0.0009

0.10

- 0.0001 0.00 20 0.0004

- 0.0011

- 0.0005

- 0.0005

- 0.0010

- 0.0004

0.25

- 0.0005

- 0.0074

- 0.0006

- 0.0066

- 0.0052

- 0.0054

- 0.0065

- 0.0041

0.50 0.0003 0.0029 0.0023 0.0044 0.0043 0.0043 0.0045 0.0035

1.00

- 0.0001 0.0189

- 0.0088 0.0059 0.0003 0.0002 0.0058 0.0002

Tabl e 48. Like table 47, except for 12

- km VISTAS domain.

### Draft

64

Total stats bias abserr r2 ia rmse

Temperature (K) 0.00

- 0.01

- 0.001 0.000

- 0.0153

Mixing Ratio (g/kg) 0.06 0.01 0.003 0.000 0.0100

Rel. Humidity (%) 0.26 0.04 0.003 0.001 0.0765

Wind Speed

(m/s)

- 0.01

- 0.04

- 0.033

- 0.018

- 0.0484

Clouds (%)

- 0.18

- 0.13

- 0.002

- 0.002

- 0.1188

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s)

- 0.023

- 1.18

- 0.0404

- 0.0433

- 0.0592

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0 025

- 0.0088

- 0.0005

- 0.0071

- 0.0050

- 0.0045

- 0.0055

- 0.0053

0.05

- 0.0026 0.0117

- 0.0031

- 0.0103

- 0.0066

- 0.0065

- 0.0097

- 0.0080

0.10

- 0.0043 0.0104

- 0.0083

- 0.0152

- 0.0104

- 0.0108

- 0.0165

- 0.0139

0.25

- 0.0018 0.0062

- 0.0060

- 0.0114

- 0.0063

- 0.0064

- 0 .0119

- 0.0090

0.50

- 0.0012

- 0.0166

- 0.0047

- 0.0039

- 0.0033

- 0.0035

- 0.0050

- 0.0053

1.00

- 0.0002

- 0.0326 0.0014 0.0066 0.0018 0.0017 0.0062 0.0030

Table 49. Episode 2 composite statistical comparison of sensitivity px_acm7 with base case px_acm2 for the US portion

of the 36

- km domain is shown.



### Total stats bias abserr r2 ia rmse

Temperature (K)

- 0.01

- 0.01

- 0.003

- 0.002

- 0.0171

Mixing Ratio (g/kg) 0.04 0.01 0.000 0.001 0.0203

Rel. Humidity (%) 0.30 0.06 0.002 0.000 0.1136

Wind Speed (m/ s)

- 0.01 0.00

- 0.007

- 0.004

- 0.0082

Clouds (%)

- 0.15

- 0.14

- 0.004

- 0.003

- 0.1314

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.086

- 0.39

- 0.0154

- 0.0095

- 0.0174

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0013 0. 0000

- 0.0015

- 0.0012

- 0.0018

- 0.0025

- 0.0025

- 0.0025

0.05

- 0.0011 0.0012

- 0.0013

- 0.0021

- 0.0020

- 0.0023

- 0.0026

- 0.0024

0.10

- 0.0008 0.0025

- 0.0011

- 0.0028

- 0.0020

- 0.0021

- 0.0028

- 0.0025

0.25

- 0.0005 0.0070

- 0.0013

- 0.0049

- 0.0024

- 0.0024

- 0.0044

- 0.0030

0.50

- 0.0010

- 0.0050

- 0.0033

- 0.0034

- 0.0027

- 0.0028

- 0.0043

- 0.0041

1.00 0.0001

- 0.0088 0.0031 0.0065 0.0025 0.0025 0.0065 0.0043

Table 50. Like table 49, except for 12

- km VISTAS domain.

### Draft

65

Total stats bias abserr r2 ia rmse

Temperature (K ) 0.22 0.15 0.008 0.004 0.1800

Mixing Ratio (g/kg)

- 0.02 0.01 0.002 0.000 0.0076

Rel. Humidity (%) 0.35 0.32 0.021 0.010 0.4144

Wind Speed (m/s) 0.02 0.02 0.010 0.007 0.0180

Clouds (%) 0.03 0.05 0.003 0.001 0.1037

Wind stats bias abserr u err verr uverr

Wind (deg, m/s)

- 0.237 0.19 0.0098 0.0129 0.0161

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0043

- 0.0168

- 0.0107 0.0013

- 0.0059

- 0.0085

- 0.0054

- 0.0082

0.05

- 0.0015

- 0.0107

- 0.0059 0.0007

- 0.0036

- 0.0043

- 0.0012

- 0 .0039

0.10

- 0.0009

- 0.0110

- 0.0055 0.0010

- 0.0032

- 0.0037

- 0.0002

- 0.0032

0.25 0.0003 0.0225 0.0078

- 0.0078 0.0005 0.0008

- 0.0069 0.0006

0.50

- 0.0003

- 0.0312 0.0004

- 0.0220

- 0.0126

- 0.0126

- 0.0217

- 0.0111

1.00 0.0000

- 0.0233 0.0014

- 0.0132

- 0.0068

- 0.0068

- 0.0131

- 0.0069

Table 51. Episode 1 composite statistical comparison of sensitivity px_acm8 with base case px_acm2 for the US portion

of the 36

- km domain is shown.



### Total stats bias abserr r2 ia rmse

Temperature (K) 0.08 0.11 0.011 0.003 0 .1407

Mixing Ratio (g/kg) 0.01 0.01 0.002 0.001 0.0141

Rel. Humidity (%) 0.49 0.27 0.016 0.008 0.3229

Wind Speed (m/s)

- 0.01 0.01 0.009 0.004 0.0087

Clouds (%) 0.52 0.29 0.006 0.003 0.2435

Wind stats bias abserr uerr verr uverr

Wind (de g, m/s) 0.108 0.24 0.0045 0.0133 0.0127

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0021

- 0.0001

- 0.0031

- 0.0032

- 0.0044

- 0.0056

- 0.0047

- 0.0047

0.05

- 0.0003 0.0141 0.0030

- 0.0077

- 0.0029

- 0.0028

- 0.0061

- 0.0022

0.10

- 0.0016 0.019 4 0.0005

- 0.0147

- 0.0095

- 0.0100

- 0.0138

- 0.0077

0.25

- 0.0015

- 0.0226

- 0.0015

- 0.0198

- 0.0153

- 0.0159

- 0.0195

- 0.0121

0.50 0.0002

- 0.0344 0.0115

- 0.0150

- 0.0032

- 0.0031

- 0.0140

- 0.0026

1.00

- 0.0005

- 0.0239

- 0.0158

- 0.0287

- 0.0246

- 0.0246

- 0.0287

- 0.0 244

Table 52. Like table 51, except for 12

- km VISTAS domain.

### Draft

66

Total stats bias abserr r2 ia rmse

Temperature (K) 0.06 0.07 0.011 0.003 0.0879

Mixing Ratio (g/kg) 0.00 0.00 0.002 0.000 0.0121

Rel. Humidity (%)

- 0.13 0.23 0.014 0.005 0.3027

W ind Speed (m/s) 0.00 0.01 0.008 0.005 0.0113

Clouds (%) 0.33 0.19 0.004 0.002 0.2725

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s)

- 0.250 0.26 0.0091 0.0118 0.0148

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0040 0. 0122 0.0014 0.0104 0.0077 0.0074 0.0088 0.0086

0.05 0.0011

- 0.0235

- 0.0002 0.0136 0.0064 0.0052 0.0094 0.0063

0.10

- 0.0018

- 0.0272

- 0.0032 0.0094 0.0016 0.0004 0.0045 0.0005

0.25

- 0.0019

- 0.0089

- 0.0049

- 0.0044

- 0.0039

- 0.0042

- 0.0060

- 0.0059

0.50

- 0.0023

- 0.0344

- 0.0080

- 0.0054

- 0.0055

- 0.0058

- 0.0076

- 0.0088

1.00 0.0002

- 0.0058 0.0064 0.0109 0.0048 0.0048 0.0109 0.0082

Table 53. Episode 2 composite statistical comparison of sensitivity px_acm8 with base case px_acm2 for the US portion

of the 36

- km domain is shown.



### Total stats bias abserr r2 ia rmse

Temperature (K)

- 0.04 0.04 0.008 0.003 0.0389

Mixing Ratio (g/kg)

- 0.02 0.02 0.002 0.002 0.0233

Rel. Humidity (%) 0.07 0.17 0.012 0.004 0.2162

Wind Speed (m/s) 0.01 0.01 0.004 0.002 0.0100

Clouds (%) 0.49 0.02

- 0.002

- 0.002 0.1619

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.079 0.17 0.0072 0.0065 0.0098

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0027

- 0.0030 0.0043 0.0012 0.0033 0.0051 0.0 053 0.0054

0.05 0.0013

- 0.0017 0.0038

- 0.0036 0.0001 0.0015 0.0005 0.0017

0.10 0.0012 0.0188 0.0039

- 0.0074

- 0.0014 0.0000

- 0.0026

- 0.0001

0.25 0.0033 0.0476 0.0063

- 0.0144

- 0.0016 0.0002

- 0.0071 0.0003

0.50 0.0051 0.0777 0.0098

- 0.0111 0.0023 0.00 37

- 0.0043 0.0053

1.00 0.0055 0.2039 0.0188 0.0003 0.0086 0.0094 0.0059 0.0162

Table 54. Like table 53, except for 12

- km VISTAS domain.

### Draft

67

Total stats bias abserr r2 ia rmse

Temperature (K) 0.07 0.03 0.000 0.001 0.0466

Mixing Ratio (g/kg)

- 0.01 0 .00

- 0.001 0.000

- 0.0007

Rel. Humidity (%)

- 0.08

- 0.02 0.000 0.000

- 0.0138

Wind Speed (m/s) 0.01 0.01 0.001 0.001 0.0017

Clouds (%) 0.20 0.03 0.001 0.001 0.0607

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s)

- 0.040

- 0.01 0.0007 0.0 002 0.0007

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0014

- 0.0095

- 0.0044 0.0025

- 0.0012

- 0.0022

- 0.0005

- 0.0022

0.05

- 0.0009

- 0.0076

- 0.0038 0.0010

- 0.0021

- 0.0025

- 0.0004

- 0.0023

0.10

- 0.0003

- 0.0057

- 0.0020 0.0015

- 0.0005

- 0. 0007 0.0010

- 0.0006

0.25

- 0.0001

- 0.0003

- 0.0006

- 0.0005

- 0.0006

- 0.0006

- 0.0005

- 0.0006

0.50

- 0.0001

- 0.0013 0.0002

- 0.0016

- 0.0008

- 0.0008

- 0.0015

- 0.0007

1.00 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Table 55. Episode 1 composite statistical comparison of sensitivity s0px_acm with base case px_acm2 for the US portion

of the 36

- km domain is shown.



### Total stats bias abserr r2 ia rmse

Temperature (K) 0.01 0.01 0.001 0.000 0.0150

Mixing Ratio (g/kg) 0.00 0.00 0.000 0.000

- 0.0 005

Rel. Humidity (%)

- 0.01

- 0.01 0.000

- 0.001

- 0.0059

Wind Speed (m/s) 0.00 0.00 0.001 0.000

- 0.0002

Clouds (%)

- 0.05 0.04 0.001 0.001 0.0520

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.000 0.01 0.0001

- 0.0001

- 0.0001

Pc p Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0008

- 0.0032

- 0.0021 0.0004

- 0.0012

- 0.0018

- 0.0009

- 0.0016

0.05

- 0.0001

- 0.0013

- 0.0004 0.0005 0.0000

- 0.0001 0.0004

- 0.0001

0.10

- 0.0001

- 0.0007

- 0.0004 0.0002

- 0.0001

- 0.0002 0.0001

- 0.0001

0.25

- 0.0002

- 0.0001

- 0.0010

- 0.0010

- 0.0014

- 0.0015

- 0.0011

- 0.0012

0.50 0.0001

- 0.0030 0.0014

- 0.0009 0.0003 0.0002

- 0.0008 0.0002

1.00

- 0.0001 0.0021

- 0.0013 0.0004

- 0.0002

- 0.0002 0.0004

- 0.0002

Table 56. Like table 55, except for 12

- km VISTAS domain.

### Draft

68

Total stats bias abserr r2 ia rmse

Temperature (K)

- 0.91

- 0.70

- 0.046

- 0.025

- 0.9534

Mixing Ratio (g/kg) 0.02

- 0.02

- 0.016

- 0.005

- 0.0415

Rel. Humidity (%)

- 0.03

- 0.33

- 0.048

- 0.021

- 0.5583

Wind Speed (m/s) 0.00

- 0.02

- 0.019

- 0.011

- 0.0332

Clouds (%)

- 0.13 0.17 0.004 0.002 0.1250

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.189

- 0.09

- 0.0204

- 0.0149

- 0.0249

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0035

- 0.0093 0.0109

- 0.0048 0.0037 0.0064 0.0023 0.0061

0.05 0.0017 0.0188 0.0078

- 0.0042 0.0029 0.0038

- 0.0014 0.0034

0.10 0.0011 0.0164 0.0069

- 0.0032 0.0031 0.0035

- 0.0016 0.0031

0.25 0.0004 0.0180 0.0077

- 0.0047 0.0023 0.0025

- 0.0039 0.0020

0.50

- 0.0001

- 0.0105 0.0016

- 0.0065

- 0.0029

- 0.0029

- 0.0064

- 0.0025

1.00 0.0000

- 0.0091 0.0006

- 0.0051

- 0.0025

- 0.0026

- 0.0050

- 0.0026

Table 57. Episode 1 composite statistical comparison of sensitivity s2px_acm with base case px_acm2 for the US portion

of the 36

- km domain is shown.



### Total st ats bias abserr r2 ia rmse

Temperature (K)

- 0.92

- 0.79

- 0.108

- 0.043

- 1.3572

Mixing Ratio (g/kg) 0.11

- 0.04

- 0.015

- 0.003

- 0.0642

Rel. Humidity (%)

- 1.17

- 1.13

- 0.085

- 0.045

- 1.5513

Wind Speed (m/s) 0.01

- 0.03

- 0.030

- 0.017

- 0.0378

Clouds ( %)

- 0.05 0.10 0.002 0.001 0.0779

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s)

- 0.138

- 0.33

- 0.0272

- 0.0155

- 0.0299

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0044 0.0137 0.0107 0.0001 0.0078 0.0110 0.0065 0.0090

0. 05 0.0029 0.0156 0.0109

- 0.0007 0.0075 0.0094 0.0032 0.0073

0.10 0.0011 0.0073 0.0056 0.0000 0.0040 0.0047 0.0014 0.0036

0.25 0.0002

- 0.0050 0.0029

- 0.0013 0.0010 0.0011

- 0.0009 0.0008

0.50 0.0000

- 0.0050 0.0012

- 0.0025

- 0.0008

- 0.0009

- 0.0023

- 0.000 7

1.00

- 0.0001

- 0.0041

- 0.0014

- 0.0039

- 0.0030

- 0.0030

- 0.0038

- 0.0029

Table 58. Like table 57, except for 12

- km VISTAS domain.

### Draft

69

Total stats bias abserr r2 ia rmse

Temperature (K)

- 0.42

- 0.12

- 0.009

- 0.004

- 0.1230

Mixing Ratio (g/kg)

- 0.08 0.01 0.000 0.000 0.0113

Rel. Humidity (%) 0.00

- 0.08

- 0.006

- 0.002

- 0.0670

Wind Speed (m/s)

- 0.01

- 0.01

- 0.005

- 0.005

- 0.0098

Clouds (%)

- 0.09

- 0.03

- 0.002 0.000

- 0.0983

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.139 0.05

- 0.0036

- 0.0039

- 0.0053

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0049

- 0.0160

- 0.0015

- 0.0132

- 0.0098

- 0.0084

- 0.0108

- 0.0105

0.05 0.0000

- 0.0220 0.0020

- 0.0125

- 0.0052

- 0.0033

- 0.0072

- 0.0043

0.10 0.0011 0.0376 0.0016

- 0.0163

- 0.0056

- 0.0040

- 0.0107

- 0.0052

0.25 0.0034 0.0449 0.0062

- 0.0062 0.0017 0.0027

- 0.0017 0.0038

0.50 0.0019 0.0502 0.0047

- 0.0031 0.0020 0.0024

- 0.0009 0.0036

1.00 0.0013 0.1032 0.0068 0.0036 0.0039 0.0040 0.0049 0.0071

Table 59. Episode 3 composite stati stical comparison of sensitivity px_acm9 with base case px_acm2 for the US portion

of the 36

- km domain is shown.



### Total stats bias abserr r2 ia rmse

Temperature (K)

- 0.57

- 0.23

- 0.063

- 0.021

- 0.3328

Mixing Ratio (g/kg)

- 0.07 0.02 0.005 0.002 0.032 5

Rel. Humidity (%)

- 0.70

- 0.25

- 0.014

- 0.008

- 0.2777

Wind Speed (m/s)

- 0.01

- 0.03

- 0.021

- 0.013

- 0.0265

Clouds (%)

- 0.06 0.11 0.003 0.002 0.1224

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.142 0.12

- 0.0143

- 0.0077

- 0.0154

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0054

- 0.0321 0.0023

- 0.0216

- 0.0133

- 0.0087

- 0.0114

- 0.0112

0.05

- 0.0005

- 0.0424 0.0047

- 0.0219

- 0.0098

- 0.0054

- 0.0105

- 0.0070

0.10 0.0014

- 0.0461 0.0048

- 0.0206

- 0.0078

- 0.0043

- 0.0108

- 0 .0057

0.25 0.0046 0.0522 0.0093

- 0.0123

- 0.0006 0.0017

- 0.0044 0.0023

0.50 0.0040 0.0653 0.0105

- 0.0079 0.0021 0.0033

- 0.0028 0.0050

1.00 0.0022 0.1121 0.0077

- 0.0058 0.0023 0.0027

- 0.0034 0.0044

Table 60. Like table 59, except for 12

- km VISTAS do main.

### Draft

70

Total stats bias abserr r2 ia rmse

Temperature (K) 0.09

- 0.07

- 0.010

- 0.002

- 0.0789

Mixing Ratio (g/kg)

- 0.08

- 0.09

- 0.021

- 0.007

- 0.1060

Rel. Humidity (%)

- 0.51

- 0.45

- 0.018

- 0.006

- 0.4458

Wind Speed (m/s) 0.00 0.00

- 0.002

- 0.001

- 0 .0039

Clouds (%) 0.04 0.00 0.000

- 0.001 0.0099

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s)

- 0.048

- 0.12

- 0.0029

- 0.0032

- 0.0043

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0030

- 0.0048

- 0.0022

- 0.0056

- 0.0049

- 0. 0052

- 0.0063

- 0.0061

0.05

- 0.0013 0.0071

- 0.0014

- 0.0057

- 0.0035

- 0.0033

- 0.0051

- 0.0041

0.10

- 0.0013 0.0051

- 0.0026

- 0.0056

- 0.0036

- 0.0036

- 0.0058

- 0.0046

0.25 0.0002 0.0279

- 0.0022

- 0.0130

- 0.0045

- 0.0041

- 0.0111

- 0.0058

0.50 0.0014 0.0497 0.000 6

- 0.0111

- 0.0019

- 0.0015

- 0.0090

- 0.0023

1.00 0.0009 0.0776 0.0014

- 0.0079

- 0.0008

- 0.0007

- 0.0071

- 0.0012

Table 61. Episode 2 composite statistical comparison of sensitivity n0px_acm2 with base case px_acm2 for the US

portion of the 36

- km domain is shown.



### As mentioned above instabilities can sometimes result when the thermodynamic variables (t, q) are nudged

in the boundary layer. In a test performed years ago using Blackadar mixing we saw checkerboard daytime PBL

height patterns that were obvious ly wrong. Heights varied in adjacent cells from ~2500 m to ~40 m to ~2500 m

and so on. The warning against nudging temperature/moisture in the boundary layer appears to be only passed

down by word of mouth; no warning appears to exist in the MM5 documentat ion. In fact, the sample deck in the

documentation has FDDA turned off completely, but if it were turned on the default would be for surface

analysis nudging of temperature/moisture to be enabled. Might the warnings against this be outdated? To test

this w e have conducted two sensitivities. Both tests have temperature nudging at the same strength as wind

nudging in the boundary layer. The first test (px_acm_n) has moisture nudged at 1.0E

- 4 for both 36

- km and 12

- km resolutions, while the second test (px_acm_ n2) reduces the moisture nudging strength to the default value of

1.0E

- 5 for both grids. Aloft we keep the nudging strengths the same as in the base case. Note that the low values

for moisture nudging aloft make sense given the discontinuous nature of mois ture aloft. There is no guarantee

that the moisture from an observed sounding is actually representative of the synoptic conditions. At the

surface, however, mixing ratio is a much more continuous quantity and theoretically could be nudged at a

higher stre ngth.

With the daytime checkerboard PBL pattern in mind, we checked the PBL heights from px_acm_n to see if

similar instabilities exist. We could find not find them. Tables 62

- 63 show the statistical difference results for

this sensitivity for the wintert ime episode 1. Note the general improvement in the surface statistics, especially

### Draft

71 the temperature bias for both grids. Precipitation skill is relatively unaffected. The episode 2 results (tables 64

- 65) show a different story. This sensitivity improves the error statistics for temperature, mixing ratio, and

relative humidity, and the winds are only slightly affected. But look at the precipitation statistics! The runs are

significantly biased high at all thresholds, especially in the 36

- km grid. Accordingly a ll of the precipitation skill

metrics, with the exception of course of probability of detection, show degraded performance. Apparently the

surface nudging is causing some sort of convective instabilities that are not evident to a significant extent in the

winter.

Perhaps the convective instabilities might go away if the moisture nudging coefficients are reduced to the

default values. Tables 66

- 67 show the results for the px_acm_n2 sensitivity for episode 2. The bias

overestimation improves over px_acm_n, bu t it still shows a significant degradation compared with our original

base case. It should be noted that if we did not consider precipitation statistics we would probably consider

these last two sensitivities to be the most appealing of all the sensitiviti es tested. Fortunately that is not the case

here. We should also note that the precipitation instabilities are relatively subtle and might not be found by

simply looking at a PAVE/RIP animation of model precipitation.

To further investigate the precipitati on patterns of all the sensitivities tested, we have developed a 4

- panel

episode

- composite approach. The upper left panel contains the total observed precipitation for the episode. The

upper right panel shows the total modeled precipitation for the sensiti vity being examined, while the lower left

panel shows the difference between the total modeled precipitation and the observed. Finally, for all non

- base

case runs, the lower right panel shows the difference between the sensitivity and the base case px_acm2 . Figure

38 shows the episode 1 36

- km precipitation composite plot for the base case. The general precipitation patterns

are captured nicely by the px_acm2 configuration, though there are places where the model underestimates total

precipitation (e.g. nort

hwest coastline) and places where the model overestimates total precipitation (e.g. eastern

South Carolina). Figure 39 shows the corresponding plot for the 12

- km grid. Again the model does a decent job

in the general placement and magnitude of the precipit ation, though certain precipitation bands are slightly

displaced.

Figures 40

- 41 show the px_acm total precipitation plots for the px_acm case. The results are similar to the

px_acm2 case, though less precipitation falls in this sensitivity, presumably due to the cold bias seen in this run.

Figures 42

- 43 show the episode 1 plots for noah_mrf. The results are different from the base case but not

particularly striking. Figures 44

- 45 show that the corresponding plots for the multi_blkdr runs exhibit large

area s of slightly increased precipitation compared with the base case. This is very similar to the noah_eta

- my

results shown in figures 46

- 47.

The base case results for episode 2 (figures 48

- 49) are less encouraging. The px_acm2 run shows a clear

positive accu

mulation bias at both grid resolutions. The bias is probably manageable, but it is certainly

something to consider when analyzing air quality runs driven by this meteorology. The px_acm (figures 50

- 51),

noah_mrf (figures 52

- 53), multi_blkdr (figures 54

- 55) , and noah_eta

- my (figures 56

- 57) results all are

qualitatively similar. Perhaps the model triggers convection a little too easily.

The Reisner 2 (px_acm5) plots are shown in figures 58

- 61. This sensitivity tends to produce slightly less

total precipitatio n than the base case, though the character of the run is essentially unchanged. The same can be

said for the px_acm8 results shown in figures 62

- 65. The wintertime plots for px_acm_n (figures 66

- 67) are

also very similar to the base case. The summertime px _acm_n plots (figures 68

- 69) are another matter entirely!

### Draft

72

Total stats bias abserr r2 ia rmse

Temperature (K) 0.27 0.47 0.039 0.012 0.5621

Mixing Ratio (g/kg)

- 0.07 0.17 0.065 0.017 0.2255

Rel. Humidity (%) 0.38 4.66 0.293 0.105 5.9568

Wind Speed (m/s)

- 0.05 0.04 0.025 0.014 0.0433

Clouds (%) 0.26

- 0.04

- 0.002 0.000

- 0.0581

Wind stats bias abserr Uerr verr uverr

Wind (deg, m/s)

- 0.586 0.07 0.0133 0.0093 0.0160

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0018

- 0.0310

- 0.0095 0.0126 0.0016

- 0.0007 0.0047

- 0.0007

0.05

- 0.0049

- 0.0512

- 0.0211 0.0102

- 0.0091

- 0.0117 0.0023

- 0.0106

0.10

- 0.0027

- 0.0503

- 0.0180 0.0122

- 0.0062

- 0.0077 0.0077

- 0.0068

0.25

- 0.0008

- 0.0434

- 0.0148 0.0141

- 0.0018

- 0.0023 0.0126

- 0.0 019

0.50

- 0.0003

- 0.0328

- 0.0101 0.0120 0.0009 0.0008 0.0115 0.0007

1.00

- 0.0002 0.0406

- 0.0171 0.0091

- 0.0027

- 0.0028 0.0089

- 0.0029

Table 62. Episode 1 composite statistical comparison of sensitivity px_acm_n with base case px_acm2 for the US port ion

of the 36

- km domain is shown.



### Total stats bias abserr r2 ia rmse

Temperature (K) 0.20 0.39 0.040 0.013 0.4709

Mixing Ratio (g/kg)

- 0.04 0.14 0.033 0.009 0.1789

Rel. Humidity (%)

- 0.06 3.25 0.225 0.080 4.2115

Wind Speed (m/s)

- 0.03 0.03 0.031 0.014 0.0386

Clouds (%)

- 0.55

- 0.21

- 0.005

- 0.002

- 0.1870

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s)

- 0.313 0.17 0.0263 0.0129 0.0273

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0012

- 0.0229

- 0.0081 0.0095 0.0005

- 0.0012 0.0033

- 0.0010

0.05

- 0.0020

- 0.0258

- 0.0109 0.0081

- 0.0030

- 0.0045 0.0035

- 0.0036

0.10

- 0.0028

- 0.0303

- 0.0163 0.0064

- 0.0077

- 0.0095 0.0019

- 0.0073

0.25

- 0.0026 0.0294

- 0.0237 0.0007

- 0.0152

- 0.0166

- 0.0022

- 0.0127

0.50

- 0.0001 0.0 099

- 0.0034 0.0042 0.0008 0.0006 0.0040 0.0005

1.00 0.0002 0.0750

- 0.0040 0.0483 0.0289 0.0288 0.0480 0.0275

Table 63. Like table 62, except for 12

- km VISTAS domain.

### Draft

73

Total stats bias abserr r2 ia rmse

Temperature (K)

- 0.02 0.21 0.029 0.009 0.2498

Mixing Ratio (g/kg)

- 0.45 0.34 0.080 0.018 0.4189

Rel. Humidity (%)

- 3.58 2.54 0.174 0.042 3.5250

Wind Speed (m/s) 0.06 0.01 0.002 0.009 0.0041

Clouds (%)

- 0.56

- 0.79

- 0.020

- 0.021

- 0.4014

Wind stats bias abserr uerr verr uverr

Wind (d eg, m/s) 0.106

- 0.41

- 0.0204

- 0.0090

- 0.0206

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0874

- 0.3281

- 0.1617 0.1888

- 0.0090

- 0.1196

- 0.1430

- 0.1542

0.05

- 0.1698

- 0.9034

- 0.1832 0.1689

- 0.0712

- 0.1464

- 0.1463

- 0.2021

0.10

- 0.1863

- 1.1667

- 0.1762 0.1491

- 0.0871

- 0.1379

- 0.1289

- 0.1980

0.25

- 0.1372

- 1.4409

- 0.1362 0.0971

- 0.0788

- 0.0992

- 0.0691

- 0.1501

0.50

- 0.0651

- 1.3678

- 0.0949 0.0436

- 0.0543

- 0.0616

- 0.0274

- 0.0987

1.00

- 0.0131

- 1.0558

- 0.0411 0.0305

- 0.0171

- 0.0187 0.0167

- 0.0327

Table 64. Episode 2 composite statistical comparison of sensitivity px_acm_n with base case px_acm2 for the US portion

of the 36

- km domain is shown.



### Total stats bias abserr r2 ia rmse

Temperature (K) 0.02 0.23 0.053 0.018 0.2624

Mixing Ratio (g/kg)

- 0.35 0.28 0.068 0.019 0.3339

Rel. Humidity (%)

- 2.14 1.89 0.167 0.044 2.6796

Wind Speed (m/s) 0.06 0.04 0.023 0.017 0.0440

Clouds (%)

- 1.97

- 2.41

- 0.056

- 0.050

- 1.9282

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0 .675 0.35 0.0223 0.0377 0.0428

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0568

- 0.1110

- 0.1403 0.1264

- 0.0155

- 0.0987

- 0.1078

- 0.1106

0.05

- 0.1082

- 0.4847

- 0.1632 0.0930

- 0.0680

- 0.1368

- 0.1294

- 0.1664

0.10

- 0.1177

- 0.5577

- 0.161 3 0.0774

- 0.0781

- 0.1313

- 0.1208

- 0.1681

0.25

- 0.0976

- 0.6405

- 0.1305 0.0488

- 0.0702

- 0.0963

- 0.0808

- 0.1335

0.50

- 0.0508

- 0.6025

- 0.0801 0.0248

- 0.0427

- 0.0517

- 0.0337

- 0.0779

1.00

- 0.0127

- 0.5768

- 0.0120 0.0516 0.0006

- 0.0012 0.0371

- 0.0020

Tabl e 65. Like table 64, except for 12

- km VISTAS domain.

### Draft

74

Total stats bias abserr r2 ia rmse

Temperature (K) 0.11 0.26 0.034 0.011 0.3233

Mixing Ratio (g/kg) 0.05 0.09 0.016 0.005 0.1248

Rel. Humidity (%)

- 0.94 1.85 0.114 0.036 2.6266

Wind Speed ( m/s) 0.08 0.01 0.004 0.012 0.0078

Clouds (%) 0.83

- 0.44

- 0.017

- 0.015

- 0.4841

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s)

- 0.162

- 0.51

- 0.0240

- 0.0119

- 0.0252

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0431

- 0.0 421

- 0.0993 0.0930

- 0.0051

- 0.0628

- 0.0705

- 0.0772

0.05

- 0.0849

- 0.4172

- 0.1192 0.0673

- 0.0485

- 0.0898

- 0.0829

- 0.1180

0.10

- 0.0867

- 0.4984

- 0.1138 0.0470

- 0.0584

- 0.0857

- 0.0763

- 0.1175

0.25

- 0.0545

- 0.5351

- 0.0804 0.0199

- 0.0470

- 0.0570

- 0.0437

- 0. 0831

0.50

- 0.0236

- 0.4618

- 0.0528

- 0.0009

- 0.0308

- 0.0341

- 0.0257

- 0.0533

1.00

- 0.0040

- 0.3357

- 0.0159 0.0138

- 0.0052

- 0.0059 0.0094

- 0.0101

Table 66. Episode 2 composite statistical comparison of sensitivity px_acm_n2 with base case px_acm2 for the US

portion of the 36

- km domain is shown.



### Total stats bias abserr r2 ia rmse

Temperature (K)

- 0.04 0.25 0.055 0.020 0.2906

Mixing Ratio (g/kg) 0.10 0.09 0.007 0.006 0.1162

Rel. Humidity (%) 0.17 1.70 0.138 0.048 2.3239

Wind Speed (m/s) 0.04 0.03 0.024 0.016 0.0392

Clouds (%)

- 0.16

- 1.09

- 0.029

- 0.022

- 0.9229

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.727 0.46 0.0204 0.0353 0.0398

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01

- 0.0338 0.1227

- 0.0763 0. 0330

- 0.0232

- 0.0612

- 0.0654

- 0.0666

0.05

- 0.0554

- 0.1760

- 0.0937 0.0115

- 0.0517

- 0.0840

- 0.0820

- 0.0979

0.10

- 0.0559

- 0.1935

- 0.0921 0.0010

- 0.0555

- 0.0798

- 0.0784

- 0.0978

0.25

- 0.0365

- 0.1856

- 0.0653

- 0.0087

- 0.0411

- 0.0511

- 0.0512

- 0.0681

0.50

- 0.0143

- 0.1517

- 0.0309

- 0.0022

- 0.0177

- 0.0204

- 0.0178

- 0.0300

1.00

- 0.0019

- 0.0956

- 0.0007 0.0117 0.0015 0.0011 0.0094 0.0020

Table 67. Like table 66, except for 12

- km VISTAS domain.

### Draft

75 Figure 38. Episode 1 (Jan 2

- 21, 2002) composite precipitation fo r the US land

- portion of the 36

- km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation

datasets. The top right panel shows the corresponding plot for the base case px _acm2 model configuration. The lower left

plot shows the difference plot of the top panels.

### Draft

76 Figure 39. Episode 1 (Jan 2

- 21, 2002) composite precipitation for the US land

- portion of the 12

- km domain is shown. The

top left panel displays the accumulate d precipitation generated from the Climate Prediction Center gridded precipitation

datasets. The top right panel shows the corresponding plot for the base case px_acm2 model configuration, and the lower

left plot shows the difference plot between the model and the observations.

### Draft

77 Figure 40. Episode 1 (Jan 2

- 21, 2002) composite precipitation for the US land

- portion of the 36

- km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridde d precipitation

datasets. The top right panel shows the corresponding plot for the px_acm model configuration, and the lower left plot

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the

px_acm sensitivity and the px_acm2 base case.

### Draft

78 Figure 41. Episode 1 (Jan 2

- 21, 2002) composite precipitation for the US land

- portion of the 12

- km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Pred iction Center gridded precipitation

datasets. The top right panel shows the corresponding plot for the px_acm model configuration, and the lower left plot

shows the difference plot between the model and the observations. The bottom right plot shows the dif ference between the

px_acm sensitivity and the px_acm2 base case.

### Draft

79 Figure 42. Episode 1 (Jan 2

- 21, 2002) composite precipitation for the US land

- portion of the 36

- km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation

datasets. The top right panel shows the corresponding plot for the noah_mrf model configuration, and the lower left plot

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the

noah_mrf sensitivity and the px_acm2 base case.

### Draft

80 Figure 43. Episode 1 (Jan 2

- 21, 2002) composite precipitation for the US land

- portion of the 12

- km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation

datasets. The top right panel shows the corresponding plot for the noah_mrf model configuration, and the lower left plot

shows the difference plot between the model and the o bservations. The bottom right plot shows the difference between the

noah_mrf sensitivity and the px_acm2 base case.

### Draft

81 Figure 44. Episode 1 (Jan 2

- 21, 2002) composite precipitation for the US land

- portion of the 36

- km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation

datasets. The top right panel shows the corresponding plot for the multi_blkdr model configuration, and the lower left plot

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the

multi_blkdr sensitivity and the px_acm2 base case.

### Draft

82 Figure 45. Episode 1 (Jan 2

- 21, 2002) composite precipitation for the US land

- portion of the 12

- km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation

datasets. The top right panel shows the corresponding plot for the multi_blkdr model configuration, and the lower left p lot

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the

multi_blkdr sensitivity and the px_acm2 base case.

### Draft

83 Figure 46. Episode 1 (Jan 2

- 21, 2002) composite precipitation for the US la nd

- portion of the 36

- km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation

datasets. The top right panel shows the corresponding plot for the noah_eta

- my model confi guration, and the lower left

plot shows the difference plot between the model and the observations. The bottom right plot shows the difference

between the noah_eta

- my sensitivity and the px_acm2 base case.

### Draft

84 Figure 47. Episode 1 (Jan 2

- 21, 2002) composi

te precipitation for the US land

- portion of the 12

- km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation

datasets. The top right panel shows the corresponding plot f or the noah_eta

- my model configuration, and the lower left

plot shows the difference plot between the model and the observations. The bottom right plot shows the difference

between the noah_eta

- my sensitivity and the px_acm2 base case.

### Draft

85 Figure 48. Epi

sode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 36

- km domain is shown.

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right pane l shows the corresponding plot for the base case px_acm2 model configuration.

The lower left plot shows the difference plot of the top panels.

### Draft

8 6 Figure 49. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 12

- km domain i s shown.

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the base case px_acm2 model configuration.

The lower left p

lot shows the difference plot of the top panels.

### Draft

87 Figure 50. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 36

- km domain is shown.

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm model configuration, and the

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows th e

difference between the px_acm sensitivity and the px_acm2 base case.

### Draft

88 Figure 51. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 12

- km domain is shown.

The top left panel displays the accumulated precipitation gene rated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm model configuration, and the

lower left plot shows the difference plot between the model and the observations. The bott om right plot shows the

difference between the px_acm sensitivity and the px_acm2 base case.

### Draft

89 Figure 52. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 36

- km domain is shown.

The top left panel displays the accumula ted precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the noah_mrf model configuration, and the

lower left plot shows the difference plot between the model and th e observations. The bottom right plot shows the

difference between the noah_mrf sensitivity and the px_acm2 base case.

### Draft

90 Figure 53. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 12

- km domain is shown.

The top left pa nel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the noah_mrf model configuration, and the

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the

difference between the noah_mrf sensitivity and the px_acm2 base case.

### Draft

91 Figure 54. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 36

- km domain is shown.

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the multi_blkdr model configuration, and the

lower left p

lot shows the difference plot between the model and the observations. The bottom right plot shows the

difference between the multi_blkdr sensitivity and the px_acm2 base case.

### Draft

92 Figure 55. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 12

- km domain is shown.

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the multi_blkdr model con figuration, and the

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the

difference between the multi_blkdr sensitivity and the px_acm2 base case.

### Draft

93 Figure 56. Episode 2 (Jul 13

- 28, 2001) comp osite precipitation for the US land

- portion of the 36

- km domain is shown.

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plo t for the noah_eta

- my model configuration, and the

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the

difference between the noah_eta

- my sensitivity and the px_acm2 base case.

### Draft

94 Figure 57. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 12

- km domain is shown.

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right p anel shows the corresponding plot for the noah_eta

- my model configuration, and the

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the

difference between the noah_eta

- my sensitivity and the px_a cm2 base case.

### Draft

95 Figure 58. Episode 1 (Jan 2

- 21, 2002) composite precipitation for the US land

- portion of the 36

- km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipi tation

datasets. The top right panel shows the corresponding plot for the px_acm5 model configuration, and the lower left plot

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the

px_acm5 sensitivity and the px_acm2 base case.

### Draft

96 Figure 59. Episode 1 (Jan 2

- 21, 2002) composite precipitation for the US land

- portion of the 12

- km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation

datasets. The top right panel shows the corresponding plot for the px_acm5 model configuration, and the lower left plot

shows the difference plot between the model and the observations. The bottom right plot shows the differenc e between the

px_acm5 sensitivity and the px_acm2 base case.

### Draft

97 Figure 60. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 36

- km domain is shown.

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm5 model configuration, and the

lower left plot shows the difference plot between the model and the observations. The bottom right p lot shows the

difference between the px_acm5 sensitivity and the px_acm2 base case.

### Draft

98 Figure 61. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 12

- km domain is shown.

The top left panel displays the accumulated precip itation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm5 model configuration, and the

lower left plot shows the difference plot between the model and the observati ons. The bottom right plot shows the

difference between the px_acm5 sensitivity and the px_acm2 base case.

### Draft

99 Figure 62. Episode 1 (Jan 2

- 21, 2002) composite precipitation for the US land

- portion of the 36

- km domain is shown. The

top left panel display

s the accumulated precipitation generated from the Climate Prediction Center gridded precipitation

datasets. The top right panel shows the corresponding plot for the px_acm8 model configuration, and the lower left plot

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the

px_acm8 sensitivity and the px_acm2 base case.

### Draft

100 Figure 63. Episode 1 (Jan 2

- 21, 2002) composite precipitation for the US land

- portion of the 12

- km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation

datasets. The top right panel shows the corresponding plot for the px_acm8 model configuration, and the lower left plot

shows the diff

erence plot between the model and the observations. The bottom right plot shows the difference between the

px_acm8 sensitivity and the px_acm2 base case.

### Draft

101 Figure 64. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 36

- km domain is shown.

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm8 model configuration, and the

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the

difference between the px_acm8 sensitivity and the px_acm2 base case.

### Draft

102 Figure 65. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the U S land

- portion of the 12

- km domain is shown.

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm8 model confi guration, and the

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the

difference between the px_acm8 sensitivity and the px_acm2 base case.

### Draft

103 Figure 66. Episode 1 (Jan 2

- 21, 2002) composite precipitation for the US land

- portion of the 36

- km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation

datasets. The top right panel shows the corresponding plot for the px_acm_n model configuration, and the lower left plot

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the

px_acm_n sensitivity and the px_acm2 base case.

### Draft

104 Figure 67. Episode 1 (Ja n 2

- 21, 2002) composite precipitation for the US land

- portion of the 12

- km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation

datasets. The top right panel shows the corresponding plot for the px_acm_n model configuration, and the lower left plot

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the

px_acm_n sensitivity and the px_acm2 base case.

### Draft

105

No te the explosion of precipitation that nudging temperature/moisture in the boundary layer produces in

figures 68

- 69. The problem is lessoned somewhat by reducing the nudging strength of moisture (px_acm_n2,

figures 70

- 71), but the accumulation bias already evident in the base case is exacerbated nonetheless. We might

be able to get away with a px_acm_n or px_acm_n2 configuration in the winter, but definitely not in the

summer. To avoid these potential precipitation artifacts altogether, we recommend not nud ging temperature or

moisture in the boundary layer at all.

Figure 68. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 36

- km domain is shown.

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm_n model configuration, and the

lower left plot shows the difference plot between the model and the observations. The bottom right plo t shows the

difference between the px_acm_n sensitivity and the px_acm2 base case.

### Draft

106 Figure 69. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 12

- km domain is shown.

The top left panel displays the accumulated precip itation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm_n model configuration, and the

lower left plot shows the difference plot between the model and the observat ions. The bottom right plot shows the

difference between the px_acm_n sensitivity and the px_acm2 base case.

### Draft

107 Figure 70. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 36

- km domain is shown.

The top left panel displa ys the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm_n2 model configuration, and the

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the

difference between the px_acm_n2 sensitivity and the px_acm2 base case.

### Draft

108 Figure 71. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 12

- km domain is shown

. The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm_n2 model configuration, and the

lower left plot shows t

he difference plot between the model and the observations. The bottom right plot shows the

difference between the px_acm_n2 sensitivity and the px_acm2 base case.

### Draft

109 

## 6 Conclusions from Sensitivity Modeling

Considering only the original four (five, inc luding px_acm2) sensitivity runs mandated under VISTAS

Met task 2d, the px_acm2 run performs best overall. This conclusion is the result of a rather exhaustive

quantitative and qualitative assessment of these sensitivity runs over three episodes. While the statistical

performance for px_acm2 for certain quantities might be surpassed by other model configurations (e.g.

noah_eta

- my winds), it seldom performs the poorest of the five original sensitivities tested.

A few general conclusions can be drawn about these initial sensitivity tests. They are:

• PBL heights are consistently highest in the noah_mrf simulations, while the noah_eta

- my runs

consistently produce the lowest daytime PBL heights. At night the px_acm(2) PBL heights are

usually lowest.

• Surface win ds are consistently best represented by the noah_eta

- my runs, in large part because that

configuration appropriately produces the calmest winds at night.

• The px_acm2 temperatures are modeled better than all other configurations for the winter episode,

inc luding px_acm. The noah_eta

- my runs consistently show the most extreme low temperature

bias of all configurations for all episodes.

• All configurations other than noah_eta

- my exhibit similar performance in modeling measurable

24

- h precipitation. The noah_e ta

- my runs produce more precipitation coverage and are generally

slightly less skilled than the other sensitivities, especially for the winter episode. The runs all seem

to produce too much accumulated precipitation in the summer.

The auxiliary tests show

that using LITTLE_R output to drive INTERPF (px_acm8) produces slightly

beneficial results. In essence px_acm8 is the “best and final” configuration. Our recommendation for the annual

modeling effort, therefore, is to implement the px_acm8 configuration. These tests also reveal that MM5 seems

to suffer from a wintertime cold bias. All of our configurations yielded that signature. While the px_acm8 set

up produces the best results of the credible configurations, the cold bias still remains. The problem seem s to be

most acute whenever the temperature changes significantly over the course of a 5

- 5 day segment. More effort

from the MM5 community at large needs to be made to correct this bias, but at present it appears that these

transient cold bias signatures a re unavoidable.

### Draft

110 

## 7 Methodology for Annual Modeling Effort

With the sensitivity runs completed, we can establish the procedures we will use to execute/analyze MM5

for the annual modeling study period.



## A. Modeling Approach

For the sensitivity modeling w e used the MPP implementation of MM5 version 3.6.1. It is important to

note that considerable effort was expended to port EPA’s MPP version of the Pleim

- Xiu LSM into the 3.6.1

MM5 code. A few other modifications were made to the code to allow MPP, most not ably modifications to the

Kain

- Fritsch 2 cumulus parameterization module. This version was kept frozen throughout the course of the

sensitivity testing. Since then a new minor

- release version of MM5 (v3.6.2) has been released. According to

NCAR’s release n

otes, the only changes in the actual MM5 code that could possibly affect our modeling results

involve seasonal vegetative adjustments and sea ice treatments. The vegetative adjustments have already been

implemented in our version of the code, and the sea i ce issue is likely to have negligible effects on our results.

Considering the resources required to test the v3.6.2 code with all of our modifications added, and considering

the expected negligible effects of these modifications, for the annual modeling ef fort we will implement the

v3.6.1 code used in the sensitivity modeling. The preprocessor code, however, could affect results positively.

Therefore we will use the latest v3.6.2 releases of those codes.

In the sensitivity modeling we generally executed M M5 in 5.5

- day segments starting at 00Z, and we will

continue this approach for the annual modeling. Though our modeling period is all of 2002, we will begin our

simulations at 00Z December 17, 2001. This allows the air quality team to “spin

- up” CMAQ (or an other air

quality model) for up to two weeks before our period of interest. It requires 76 5.5

- day segments to process the

entire year, assuming a 12

- hr overlap between runs. Realizing that new runs may at some point be needed, we

will assign a “case” tag to each file associated with a particular segment. This tag includes the start date/year of

the segment, as well as “v02_aaa” (“v” for VISTAS, “02” for 2002, and “aaa” indicating the first

meteorological run). The segments align as follows:

### Draft

111 Date tag Dura tion Date Tag Duration

dec17_01 00Z Dec 17, 2001

– 12Z Dec 22, 2001 jun25_02 00Z Jun 25, 2002

– 12Z Jun 30, 2002

dec22_01 00Z Dec 22, 2001

– 12Z Dec 27, 2001 jun30_02 00Z Jun 30, 2002

– 12Z Jul 5, 2002

dec27_01 00Z Dec 27, 2001

– 12Z Jan 1, 2002 jul 05_02 00Z Jul 5, 2002

– 12Z Jul 10, 2002

jan01_02 00Z Jan 1, 2002

– 12Z Jan 6, 2002 jul10_02 00Z Jul 10, 2002

– 12Z Jul 15, 2002

jan06_02 00Z Jan 6, 2002

– 12Z Jan 11, 2002 jul15_02 00Z Jul 15, 2002

– 12Z Jul 20, 2002

jan11_02 00Z Jan 11, 2002

– 12Z J an 16, 2002 jul20_02 00Z Jul 20, 2002

– 12Z Jul 25, 2002

jan16_02 00Z Jan 16, 2002

– 12Z Jan 21, 2002 jul25_02 00Z Jul 25, 2002

– 12Z Jul 30, 2002

jan21_02 00Z Jan 21, 2002

– 12Z Jan 26, 2002 jul30_02 00Z Jul 30, 2002

– 12Z Aug 4, 2002

jan26_02 00Z J an 26, 2002

– 12Z Jan 31, 2002 aul04_02 00Z Aug 4, 2002

– 12Z Aug 9, 2002

jan31_02 00Z Jan 31, 2002

– 12Z Feb 5, 2002 aug09_02 00Z Aug 9, 2002

– 12Z Aug 14, 2002

feb05_02 00Z Feb 5, 2002

– 12Z Feb 10, 2002 aug14_02 00Z Aug 14, 2002

– 12Z Aug 19, 2002

feb10_02 00Z Feb 10, 2002

– 12Z Feb 15, 2002 aug19_02 00Z Aug 19, 2002

– 12Z Aug 24, 2002

feb15_02 00Z Feb 15, 2002

– 12Z Feb 20, 2002 aug24_02 00Z Aug 24, 2002

– 12Z Aug 29, 2002

feb20_02 00Z Feb 20, 2002

– 12Z Feb 25, 2002 aug29_02 00Z Aug 29, 2002

– 12Z Sep 3, 2002

feb25_02 00Z Feb 25, 2002

– 12Z Mar 2, 2002 sep03_02 00Z Sep 3, 2002

– 12Z Sep 8, 2002

mar02_02 00Z Mar 2, 2002

– 12Z Mar 7, 2002 sep08_02 00Z Sep 8, 2002

– 12Z Sep 13, 2002

mar07_02 00Z Mar 7, 2002

– 12Z Mar 12, 2002 sep13_02 00Z Sep 13, 2002

– 12Z Sep 18, 2002

mar12_02 00Z Mar 12, 2002

– 12Z Mar 17, 2002 sep18_02 00Z Sep 18, 2002

– 12Z Sep 23, 2002

mar17_02 00Z Mar 17, 2002

– 12Z Mar 22, 2002 sep23_02 00Z Sep 23, 2002

– 12Z Sep 28, 2002

mar22_02 00Z Mar 22, 2002

– 12Z Mar 27, 2002 sep28_02 00Z Sep 28, 2002

– 12Z Oct 2, 2002

mar27_02 00Z Mar 27, 2002

– 12Z Apr 1, 2002 oct03_02 00Z Oct 3, 2002

– 12Z Oct 8, 2002

apr01_02 00Z Apr 1, 2002

– 12Z Apr 6, 2002 oct08_02 00Z Oct 8, 2002

– 12Z Oct 13, 2002

apr06_02 00Z Apr 6, 2002

– 12Z Apr 11, 2002 oct13_02 00Z Oct 13, 2002

– 12Z Oct 18, 2002

apr11_02 00Z Apr 11, 2002

– 12Z Apr 16, 2002 oct18_02 00Z Oct 18, 2002

– 12Z Oct 23, 2002

apr16_02 00Z Apr 16, 2002

– 12Z Apr 21, 2002 oct23_02 00Z Oct 23, 2002

– 12Z Oct 27, 2002

apr21_0 2 00Z Apr 21, 2002

– 12Z Apr 26, 2002 oct28_02 00Z Oct 28, 2002

– 12Z Nov 2, 2002

apr26_02 00Z Apr 26, 2002

– 12Z May 1, 2002 nov02_02 00Z Nov 2, 2002

– 12Z Nov 7, 2002

may01_02 00Z May 1, 2002

– 12Z May 6, 2002 nov07_02 00Z Nov 7, 2002

– 12Z Nov 12, 2002

may06_02 00Z May 6, 2002

– 12Z May 11, 2002 nov12_02 00Z Nov 12, 2002

– 12Z Nov 17, 2002

may11_02 00Z May 11, 2002

– 12Z May 16, 2002 nov17_02 00Z Nov 17, 2002

– 12Z Nov 22, 2002

may16_02 00Z May 16, 2002

– 12Z May 21, 2002 nov22_02 00Z Nov 22, 2002

– 12Z Nov 27, 2002

may21_02 00Z May 21, 2002

– 12Z May 26, 2002 nov27_02 00Z Nov 27, 2002

– 12Z Dec 2, 2002

may26_02 00Z May 26, 2002

– 12Z May 31, 2002 dec02_02 00Z Dec 2, 2002

– 12Z Dec 7, 2002

may31_02 00Z May 31, 2002

– 12Z Jun 5, 2002 dec07 _02 00Z Dec 7, 2002

– 12Z Dec 12, 2002

jun05_02 00Z Jun 5, 2002

– 12Z Jun 10, 2002 dec12_02 00Z Dec 12, 2002

– 12Z Dec 17, 2002

jun10_02 00Z Jun 10, 2002

– 12Z Jun 15, 2002 dec17_02 00Z Dec 17, 2002

– 12Z Dec 22, 2002

jun15_02 00Z Jun 15, 2002

– 12Z J un 20, 2002 dec22_02 00Z Dec 22, 2002

– 12Z Dec 27, 2002

jun20_02 00Z Jun 20, 2002

– 12Z Jun 25, 2002 dec27_02 00Z Dec 27, 2002

– 12Z Jan 1, 2003

### Draft

112 

## B. Model Execution

To prepare the MM5 input files we execute the MM5v3.6.2 preprocessor system. The gene ral flow stream

is as follows:

Processor Function Execution Schedule

TERRAIN Prepare time

- independent grid information Once

REGRID (Pregrid) Extract files from EDAS analyses at 3

- hr (sfc)

and 12

- hr (3D) temporal resolution Monthly

REGRID (Regridder) T ranslates pregrid output onto TERRAIN grids 6 days

FETCH (adp_sfc) Creates surface/ship data at 3

- hr resolution Monthly (nominal 3

- day chunks)

FETCH (adp_upa) Creates upper air data (cat with sfc data) at 3

- hr

resolution Monthly (nominal 15

- day chunks)

LITTLE_R Uses FETCH output to “improve” REGRID

output 6 days

INTERPF Translates LITTLE_R output to MM5 vertical

coordinate system 6 days

MM5 Creates hourly gridded meteorological files 5.5 days

Terrain

Terrain is processed using the 2 min (~4 km) glo bal land

- use data set for both the 36

- km and 12

- km

domains. The 36

- km grid is the national RPO domain, and the 12

- km domain is identical to what was used in

the sensitivity modeling. Note that we have selected the “BotSoil” option per EPA’s example. The ex act details

are found in the terrain.namelist file that is included in the Appendix 9A.

REGRID (pregrid)

Since the EDAS files (ds609.2 from NCAR) are archived in month chunks (half month 3D analyses files),

we process these files through pregrid at monthl y intervals. We use the standard GRID vtables to process four

types of files: 1) FILE*, 2) SNOW*, 3) SOIL*, and 4) SST*, each at 3

- hourly resolution. An example pregrid

namelist file is shown in Appendix 9B.

REGRID (regridder)

The output of pregrid is in

terpolated to the MM5 grids for each modeled segment by regridder. Note that

these input grids cover 6 days rather than 5.5 days due to an INTERPF requirement. Separate regridder runs are

made for both the 36

- km and 12

- km grids, and the temporal resolution is 3 hours. An example regridder

namelist file is shown in Appendix 9C. We have implemented one minor change from the methodology used in

### Draft

113 the sensitivity tests, that being that the climatological albedo file (used by the NOAH LSM) is not processed for

the annual run.

FETCH (adp_sfc)

The adp_sfc preprocessor converts surface and ship data files (ds464.0) into a format that can be read by

LITTLE_R (Objective Analysis). The surface data are stored in five or six day chunks so that a month of data is

always included in six data sets, while the ship data are in monthly chunks. We have discovered that adp_sfc

might not work correctly if we try to process too long a time span, so we limit our processing to 3 or 4 day

periods. So we execute adp_sfc ten times for both the surface data and the ship data. Hourly output files are

produced, though we only use the 3

- hourly files. The surface and ship data are concatenated, and eventually the

upper air data are also concatenated at the end of the data file. The preproce ssing for the sensitivity runs did not

include upper air data in the surface_obs files. An example namelist for adp_sfc is shown in Appendix 9D.

FETCH (adp_upa)

The adp_upa preprocessor converts upper air data files (ds353.4) into a format that can be read by

LITTLE_R. The upper air data are stored in monthly segments, and we process the data in half

- month intervals

one after the other. The output interval is typically 6

- hourly. We concatenate the surface/ship data into these

data sets, so the output up per

- air_obs files are actually at 3

- hour intervals. An example namelist for adp_upa is

shown in Appendix 9E.

LITTLE_R (Objective Analysis)

LITTLE_R performs internal quality control on the observational data produced by FETCH and uses these

data to “impr

ove” the REGRID output fields. Our auxiliary sensitivity tests indicate that this provides a

discernable improvement in the MM5 predictions, so unlike the base sensitivity runs we will use the LITTLE_R

output as input to INTERPF. LITTLE_R needs to be execu ted for both the 36

- km and 12

- km grids. This

processor produces SFCFDDA files that directly feed into MM5. An example LITTLE_R namelist file is shown

in Appendix 9F.

INTERPF

INTERPF converts the LITTLE_R output into the 34

- sigma layers that have become t he RPO standard.

Because the LOWBDY files require a full day (00Z

- 21Z) to produce output, this preprocessor, as well as

REGRID and LITTLE_R, needs to be run for a full six days to allow MM5 to execute the desired 5.5 days. An

example INTERPF namelist is in cluded in Appendix 9G.

MM5

### Draft

114

The sensitivity tests described earlier in this document enables us to define the likely optimal model

configuration we will implement for the annual run. One expected minor change from px_acm8 is that the

RADFRQ interval is de creased from 30 minutes to 15. In summary here are the key parameters:

36

- km domain: 129x165x34 (N

- S dot cells, W

- E dot cells, vertical levels)

12

- km domain: 190x181x34 (N

- S dot cells, W

- E dot cells, vertical levels)

2

- way nesting, no feedback, 90

- second outer grid time step, 7920 minutes total, hourly output

LSM: Pleim

- Xiu (independent segments initiated from EDAS soil quantities)

PBL: Asymmetric Convective Model (ACM)

Radiation scheme: Rapid Radiative Transfer Model (RRTM)

Cumulus scheme: Kain

- Frit sch 2

Snow: Snow effects on (IFSNOW=1)

SST: Varying (ISSTVAR=1)

Analysis nudging: 2.5E

- 4 (36

- km sfc/aloft winds, temps aloft only)

1.0E

- 4 (12

- km sfc/aloft winds, temps aloft only)

1.0E

- 5 (36

- km/12

- km mixing ratio, aloft only)

Appendix 9H show

s an example mmlif file.

The MM5 output files are broken down into daily chunks to keep the output files from becoming too

large. The default is for MM5 to name the outer 36

- km grid as domain “1”, while the 12

- km grid is referred to

as domain “2”. This i

s what the 36

- km files are named by default:

MMOUT_DOMAIN1_00 (initial hour 0 only)

MMOUT_DOMAIN1_01 (hours 1

- 24, inclusive)

MMOUT_DOMAIN1_02 (hours 25

- 48, inclusive)

MMOUT_DOMAIN1_03 (hours 49

- 72, inclusive)

MMOUT_DOMAIN1_04 (hours 73

- 96, inclus ive)

MMOUT_DOMAIN1_05 (hours 97

- 120, inclusive)

MMOUT_DOMAIN1_06 (hours 121

- 132, inclusive)

When the model files are sent to the air quality team they will have the date/case tag added as previously

described. So for the model run that initiates on Ma rch 17, 2002, a file might be named

MMOUT_DOMAIN2_04.mar17_02.v02_aaa. This file contains 12

- km MM5 output data for the time period

01Z March 20, 2002 through 00Z March 21, 2002 from the run that started at 00Z March 17, 2002.



## C. Model Evaluation Procedures

A variety of evaluation procedures have been established for this project (see

http://www.baronams.com/projects/VISTAS/reports/VISTAS_TASK1.pdf ). The results will be transm itted to

the TAWG and other interested parties via the VISTAS meteorological web page

( http://www.baronams.com/projects/VISTAS/select_annual_product.html ). Here is a summar y of the evaluation

products that will be available on that web site:

### Draft

115



## 1) Surface Products

a. Spatial Plots

b. Timeseries Plots

c. Combination Plots



## 2) Aloft Products

a. Spatial Plots

b. Sounding Plots

c. Profiler Plots



## 3) Statistical Products

a. Surface Statistical Timeseries

b. Aloft S tatistical Timeseries

c. Statistical Tables



## 4) Monthly Products

a. Spatial Statistical Plots

b. Spatial Summary Plots

c. Monthly “Bakergram” Plots

d. Annual “Bakergram” Plots

e. Monthly Summary Statistics

In many of the web forms the user is asked to select options from a few drop

- down menus. Here is a summary

of the most common menus:

1) 

## Region , or areal extent of results

a. VISTAS (VISTAS RPO, valid for both 36

- km and 12

- km grids)

b. Full (Entire grid, valid for both grids)

c. US (All US stations, 36

- km stats only)

d. MANE

- VU (MANE

- VU R PO, 36

- km only)

e. MIDWEST (MIDWEST RPO, 36

- km only)

f. CENRAP (CENRAP RPO, 36

- km only)

i. CENRAP_N (Northern portion of CENRAP)

ii. CENRAP_S (Southern portion of CENRAP)

g. WRAP (WRAP RPO, 36

- km only)

i. WRAP_N (Northern portion of WRAP)

ii. WRAP_S (Southern portion of WRAP)



## 2) Sensitivity

a. v02_aaa (for Vistas ’02, initial met/emis/air_quality)



## 3) Scale

a. 12km (12

- km grid)

b. 36km (36

- km grid)

4) 

## 5

- day segment (Segment identifier, MonDD_YY)

a. dec17_01 (Segment from Dec 17, 2001, 12Z to Dec 22, 2001, 12Z)

b. dec22_01 (Segment from Dec 22, 2001 , 12Z to Dec 27, 2001, 12Z)

### Draft

116

c.

... d. dec27_02 (Last segment, Dec 27, 2002, 12Z to Jan 1, 2003, 12Z)

A brief summary of each product type is given below.



## Surface Products (Spatial Plots)

Spatial plots of key surface variables are available at 6

- hrly interval s. Variables include:

1) Temperature (1.5 m), with TDL obs overlaid.

2) Mixing Ratio, with TDL obs overlaid.

3) Winds, vectors (with a very light wind speed background) at an MCIP2

- derived 10 meter

(observational) height. Not available for 36

- km Full region (too b usy).

4) Cloud fraction (CFRAC in MCIP2.1). The overlaid obs come from the TDL surface reports and

represent the maximum of the low

- , middle

- , and high

- observed cloud coverage. The black and

white color scale is designed to mimic (sort of) satellite imagery.

5) Cloud fraction (alt). Like the above cloud fraction, except we take the maximum of the MCIP2.1

low, middle, and high clouds to represent the total model cloud fraction. This is the preferred

variable from a meteorological perspective.

6) 6

- hrly accumulated p

recipitation, with TDL obs overlaid. Be warned that in the TDL obs it is

impossible to differentiate “missing” precipitation values from “No precip” reports. So use these

images qualitatively with that realization.

7) Relative humidity, with TDL obs overlaid

. 8) Temperature (layer 1), with TDL obs overlaid.

9) PBL heights.



## Surface Products (Timeseries Plots)

Time series plots of key meteorological variables are available for a number of important sites. Note

that no 12

- km plots are available for stations outside of the 12

- km grid. Plots are available for the

following scales:

1) 12KM: 12

- km grid (includes aloft trace where applicable)

2) 36KM: 36

- km grid (includes aloft trace where applicable)

3) 36_12KM: 36

- km/12

- km results co

- plotted. (no aloft traces)



## Surf ace Products (Combination Plots)

Model/observed (or analyzed) fields are plotted next to each other. These animations are region

independent. Here are the available fields:

1) pcp24: 24

- hr accumulated precipitation ending at 12Z daily. Observed values d erive from the

Climate Prediction Center.

2) ir_sat: Model cfrac (alt) is compared with GOES 8 infrared imagery at 00Z and 12Z.

### Draft

117

3) slp: The Unisys surface analyses are compared with a RIP

- generated analyses. Note that the

plotted precipitation is qpr, the total precipitation hydrometeor mixing ratio. The scale for

this variable does not

match the analyzed precipitation, and in fact there are times,

especially in the summer, in which actual modeled precipitation falls below the lower

range of the qpr scale.

4) vis_sat: Model cfrac (alt) is compared with GOES 8 visible imagery daily at 18Z.



## Aloft Products (Spatial Plots)

This product shows spatial plots for the basic meteorological variables at layers 9 (sigma 0.94

- 0.93,

midpoint ~500m), 17 (sigma 0.82

- 0.8 0, midpoint ~1600m), and 22 (sigma 0.65

- 0.60, midpoint

~3400m). The observations are integrated through the depth of the sigma layers. Note that only full

grid images are produced. Here are the available variables:

1) Temperature

2) Mixing ratio

3) Winds



## Aloft Pr oducts (Sounding Plots)

This product shows sounding plot animations for selected upper air observing sites. Observations are

co

- plotted, and soundings are available from the surface to 100 mb as well as from the surface to 500

mb.



## Aloft Products (Profil er Plots)

This product shows profiler plot wind animations for selected profiler sites in the southeastern US up

to a height of 2500 AGL. Individual images show 12 hours of data, from 00

- 11Z or 12

- 23Z.



## Statistical Products (Surface Statistical Timeseries )

Statistical metrics are plotted on a three

- panel image. For most variables the top panel shows the

mean obs (blue) and the mean model (red), the second panel shows the bias (blue) and absolute error

(red), while the third panel shows the Index of Agreem ent (IA, in blue), the coefficient of

determination (r

2 

### , in red), and the number of valid obs/model pairs (#, thin black, right axis). Wind

direction plots differ from the above paradigm in that the number of valid obs/model pairs appears in

the top panel, and more importantly in that the third panel shows the U/V wind component absolute

error and bias. The three

- panel precipitation plots are vastly different in description from the other

variable. The top panel shows precipitation accuracy (“Acc”, in blue) , false alarm ratio (“FAR”, in

red), probability of detection (“POD”, in black), and bias (“Bias”, right axis, in magenta). A thin

green line marks the 1.0 level (right axis), the “perfect” precipitation bias level. The second panel

shows the following ski

ll measures: Threat score (“Threat”, in blue), the Equitable Threat Score

(“ETS”, in red), the True Skill Score (or Hanssen and Kuipers score, “TSS”, in black), and Heidke

Skill Score (“HSS”, in magenta). Before describing the third panel, we need to defin e a few terms.

Measurable precipitation, by definition, matches or exceeds 0.01 inches. A “Hit” means that the

model predicted measurable precipitation and measurable precipitation actually occurred. A “Miss”

### Draft

118

means the model failed to predict measurable pr ecipitation when measurable precipitation occurred.

A “False” means that the model predicted measurable precipitation when none actually occurred.

Finally, a “Zero” means that the model and observations both indicated no measurable precipitation.

The third panel thus shows the number of “Hits” (blue), “Misses” (red), and “Falses” (black) plotted

using the left axis, and the number of “Zeroes” (magenta) plotted using the right axis. All of the

metrics plotted in the first two panels are calculated based on t he numbers plotted in this third panel.

A final note about precipitation metrics needs to be made. It is possible to calculate these metrics

using a threshold other than 0.01, which we indeed will do. A higher threshold can yield insight into

the ability o f the model to accurately handle more significant precipitation events. The downside of

using higher thresholds is that some of the metrics (especially “Bias”) may end up being calculated

with smallish numbers in its denominator, thus enabling the metric t o at times reach very high

numbers, essentially blowing the scale for other time periods. The third panel plot then becomes a

useful surrogate to gauge model performance.

Here are the available statistical time series variables:

1) Temperature (1.5m)

2) Mixin g Ratio

3) Wind Speed (10m)

4) Wind Direction

5) Cloud Fraction (orig): (From MCIP2.1 CFRAC)

6) Cloud Fraction (alt): (preferred)

7) Relative Humidity

8) Temperature (layer 1)

9) Wind Speed (layer 1)

10) 24

- hr precipitation (0.01 in threshold):

i. These stats are calculated using th e Climate Prediction Center (CPC) gridded

precipitation fields (0.25x0.25 degree resolution) regridded to match our 36km and 12km

grids. The stats are then calculated on a cell by cell basis. Since the CPC fields are mainly

for the US, we applied a mask (F igure 72) to only consider grid points near the US, and

another mask to only consider land points in the calculations.

11) 24

- hr precipitation (0.05 in threshold)

12) 24

- hr precipitation (0.10 in threshold)

13) 24

- hr precipitation (0.25 in threshold)

14) 24

- hr precipitati

on (0.50 in threshold)

15) 24

- hr precipitation (1.00 in threshold)

### Draft

119 Figure 72. Mask for precipitation stats (36km).



## Statistical Products (Aloft Statistical Timeseries)

This product shows the 3

- panel statistical time series plots for our three aloft sigma layers (09, 17,

and 22). Here are the available variables:

1) Temperature (K)

2) Mixing Ratio (g/kg)

3) Wind Speed (m/s)

4) Wind Direction (degrees)



## Statistical Products (Statistical Tables)

This product shows region

- specific statistical tables for all applicable surface variables other than

precipitation. The data are parsed temporally such that:

1) All hours are considered

2) Only 00Z

- 11Z (“nighttime”) hours are considered

3) Only 12Z

- 23Z (“daytime”) hours are considered

The variable/stat labels are very cryptic. Here’s how to interpret them:

Variables in first part of table:

1) TMP

- 1.5m: Temperature at 1.5 m (deg K)

2) QV: Mixing ratio (g/kg)

3) RH: Relative humidity (%)

4) WSPD

- 10m: Wind speed reduced by MCIP2 to 10m observational height (m/s)

5) SPD

- lyr1: Layer 1 wi

nd speed (m/s)

6) CLD: Cloud cover

–original (%) (use CLD2 instead)

7) CLD2: Cloud cover

–alternate (%)

8) TMP

- lyr1: Layer 1 temperature (deg K) (use TMP

- 1.5m instead)

### Draft

120

Labels in first part of table:

1) obsmean: Average of observations

2) modmean: Average of model pr edictions

3) bias: Simple bias

4) abserr: Absolute error

5) r2: Coefficient of determination

6) ia: Index of Agreement

7) rmse: Root Mean Square Error

8) nbias: Normalized bias

9) jtot: Number of valid obs/model pairs for stats

The WDIR (wind direction, degrees) metric has it s own label structure:

1) obsmean: Average of observations

2) modmean: Average of model predictions

3) bias: Simple bias (Should be ignored for this discontinuous measure)

4) abserr: Absolute error

5) ubias: Simple bias for U

- wind component (m/s)

6) vbias: Simple bias for V

- wind component (m/s)

7) uerr: Absolute error of U

- wind component (m/s)

8) verr: Absolute error of V

- wind component (m/s)

9) newtot: Number of valid obs/model pairs for wind direction

10) dbias: Correct direction bias (Use instead of bias!)



## Monthly Products (Spatial Statistical Plots)

This page shows basic statistical metrics (bias, absolute error) plotted on a per station basis. The idea

is to graphically display these metrics so that the analyst can see the spatial pattern of model

bias/error. There are two forms o f these plots. The string “TOTAL” in the variable name designates

the first of the forms. The resultant plot shows the metric calculated over all valid times. If the string

“TOTAL” does not appear in the variable name, then a 24

- hr animation appears. The f irst plot (hour

0) in the animation shows the metric calculated over all valid 00Z times, the second plot (hour 1)

shows the metric calculated over all valid 01Z times, and so on through 23Z. This enables one to see

the average diurnal/spatial variation of the metrics. Since only the hour field has meaning in these

plots (and not even the hour has meaning in the “TOTAL” plots), we’ve arbitrarily set the Julian date

for display to be January 1, 0000, and the “TOTAL” plots have an arbitrarily defined hour of 0.

These are only placeholders so that we can use PAVE to visualize the results.

The naming convention for the variables that can be displayed has already partially been described.

In review, variable names that have “BIAS” in them display the bias metri c, while variable names

that have “ERR” in them display the absolute error metric. If “TOTAL” appears in the variable

name, a single plot showing the metric for all valid hours will be displayed at the arbitrarily defined

time of Jan 1, 0000, 00Z. If “TOTA L” does not appear in the variable name, then an animation of

### Draft

121

the metric will appear cycling from 00Z

- 23Z, Jan 1, 0000. The actual variable to be plotted is listed

first in the variable name string. They are:

1) CLD: Cloud cover (orig), as defined in spa tial plots (%).

2) CLD2: Cloud cover (alt)

3) DIR: Wind direction (degrees)

4) Q: Water vapor mixing ratio (g/kg)

5) RH: Relative humidity (%)

6) SPD...10M: Wind speed reduced by MCIP2 to 10m obs height (m/s)

7) SPD...LYR1: Layer 1 wind speed (m/s)

8) T ...1P5M: Temperature reduced by MCIP2 to 1.5m obs height (K)

9) T...LYR1: Layer 1 temperature (K)

10) UV: This is a unique variable in that only “ERR” is plotted. UV_ERR can be interpreted as the

average magnitude of the error vector, defined as the square ro ot of (U_ERR^2+V_ERR^2).

This metric essentially combines wind speed error and wind direction error into one quantifiable

metric.



## Monthly Products (Spatial Summary Plots)

This product is designed to show daily summary information for a few of the key met eorological

variables. Whenever possible the observations are overlaid onto the MM5 predicted fields. Generally

speaking for each variable a 24

- hr average (01Z

- 00Z), a “daytime” average (18Z

- 21Z) and a

“nighttime” average (07Z

- 10Z) are generated. For tempe rature the “daytime” average is replaced by

a daily maximum, and the “nighttime” average is replaced with the daily minimum. For precipitation

a 24

- hr accumulation ending at 00Z is created, as well as a “daytime” accumulation that we define to

be 18Z

- 00Z. Since observations may include missing data, we require at least 3 (out of 4) valid

observations per “daytime”/”nighttime” block, and 22 valid observations for the daily averages.

These fields are generated for all regions. Here are the variables available for the three output

averages:

1) Temperature (deg K)

2) Mixing Ratio (g/kg)

3) Precipitation (in)

4) Relative Humidity (%)

5) PBL height (m), with no obs overlay.

6) Wind Vectors with a wind speed (m/s) background

7) Cloud cover (alt)



## Monthly Products (Monthly “Bakergram” Plots)

This product shows key statistical quantities summarized in a tile plot following the paradigm Kirk

Baker developed for his “Mosaic” plots. This particular product has hours (00Z

- 23Z, top to bottom)

as the y

- axis and day of month (1

- 31, left to ri ght) as the x

- axis, as illustrated in figure 73.

We produce bias, error, and index of agreement plots for the following variables:

### Draft

122

1) Temperature (deg K)

2) Mixing Ratio (g/kg)

3) Relative Humidity (%)

4) Cloud cover

–alt (%)

5) Wind Speed (m/s)

6) Wind direction (degre es), bias and error only

Additionally we produce a plot showing the magnitude of error vector (m/s).

Figure 73. “Bakergram” prototype plot for February 2002 is shown. Hourly temperature biases (K) are plotted for

the 12

- km VISTAS region. The upper left tile represents the average bias in the selected region for

00Z, February 1, 2002. Hours increase from 00Z at the top to 23Z at the bottom, while the day of the

month increases from 1 on the left to 31 on the right. Missing periods (e.g. Feb 29

- 31) are se t to the

default white background.

### Draft

123 

## Monthly Products (Annual “Bakergram” Plots)

This page shows key statistical quantities summarized in a tile plot following the paradigm Kirk

Baker developed for his “Mosaic” plots. This particular product has days (1

- 31, top to bottom) as the

y

- axis and month (1

- 12, left to right) as the x

- axis, creating a calendar effect as illustrated in figure

3. (This particular example only shows the first six months of the year.) The available variables

exactly match the variable list from the Monthly “Bakergram” Plot list.

Figure 74. “Bakergram” prototype plot for February 2002 is shown. Daily temperature biases (K) are plotted

for the 12

- km VISTAS region. The upper left tile represents the average bias in the selected

region for January 1, 2002. Days of month increase from 1 at the top to 31 at the bottom, while

the month increases from 1 (January) on the left to 12 (December) on the right. Missing periods

(e.g. Feb 29

- 31, Apr 31) are set to the default white background.

### Draft

124 

## Mont hly Products (Monthly Summary Statistical Tables)

This product is analogous to the 5

- day segment statistical tables, except it covers a full month. These

also include precipitation performance metrics when applicable.



## Monthly Products (Monthly Accumulat ed Precipitation)

This product is analogous to the precipitation combination plots, except precipitation will be

accumulated over monthly intervals.



## D. Computing Resources

Except for the TERRAIN preprocessor and a few miscellaneous analysis routines, we a nticipate using

MCNC

- GTEC’s Linux cluster, which is a 64

- node IBM X335's with dual 2.8Ghz Xeon processors, 4GB of

memory, 40GB online storage. The actual MM5 runs will be run in MPP mode using 32 processors. Since

TERRAIN did not readily work on the cluste r, it was easier to run that processor on a 300 MHZ SGI machine.

Once runs are completed, we will archive the model inputs/outputs onto 100/200 GB LTO tapes.

Additionally we will make triplicate copies of those files onto 250 GB FireWire/USB 2.0 external drives.



## E. Project Schedule

Here are some key deliverable dates in the project:

Draft Protocol Approved: Dec 12, 2003

Annual Modeling Begins: Dec 15, 2003

First 6 Months MM5 Completed: Jan 30, 2004

Website for Annual Products: Feb 5, 2004

Re vised Protocol: Feb 11, 2004

First 6 Months MM5 Results to Air Quality Team: Feb 19, 2004

MM5 Runs Completed: Mar 1, 2004

MM5 Evaluation Completed: Mar 19, 2004

Delivery of Second 6 Months to Air Quality Team: Mar 19, 2004

Documentation of Annu al Run: Jun 30, 2004



## 8 References

Chen, F., and J. Dudhia, 2001: Coupling an advanced land

- surface/hydrology model with the Penn State/NCAR

MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569

- 585.

Chen, F., an

d J. Dudhia, 2001: Coupling an advanced land

- surface/hydrology model with the Penn State/NCAR

MM5 modeling system. Part II: Preliminary Model validation. Mon. Wea. Rev., 129, 587

- 604.

### Draft

125 Emery, C., E. Tai, and G. Yarwood, 2001. “Enhanced Meteorological Modeli ng and Performance Evaluation

for Two Texas Episodes”, report to the Texas Natural Resources Conservation Commission, prepared by

ENVIRON, International Corp, Novato, CA.

Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of the fifth

- generat ion Penn State/NCAR

Mesoscale Model (MM5). NCAR Tech. Note, NCAR/TN

- 398+STR, 122 pp.

Hong, S.

- Y., and H.

- L. Pan, 1996: Nocturnal boundary layer vertical diffusion in a medium range forecast

model, Mon. Wea. Rev,, 124 , 2322

- 2339.

Kain, J. S. and J. M. F ritsch, 1993: Convective parameterization for mesoscale models: The Kain

- Fritsch

scheme, The Representation of Cumulus Convection in Mesoscale Models, K. A. Emanuel and D. J.

Raymond, Eds., American Meteorological Society, Boston, 246 pp.

Kain, John S., 2002: The Kain

- Fristch convective parameterization: An update. To be submitted to J. of Appl.

Meteoro.

Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for

inhomogeneous atmospheres: RRTM, a validated c orrelated

- k model for the longwave. J. Geophys.

Res., 102(D14), 16,663

- 16,682.

Olerud, D. T., 2003: Summation of Relevant MM5 Sensitivity Modeling in Support of VISTAS (Visibility

Improvement

- State and Tribal Association), VISTAS task 2a deliverable. A vailable from Mike

Abraczinskas, Meteorologist, NC Division of Air Quality, 1641 Mail Service Center, Raleigh, NC

27699

- 1641

Olerud, D. T., 2003: Recommended MM5 Sensitivity Modeling in Support of VISTAS (Visibility Improvement

- State and Tribal Associat ion)] VISTAS Task 2b deliverable. Available from Mike Abraczinskas,

Meteorologist, NC Division of Air Quality, 1641 Mail Service Center, Raleigh, NC 27699

- 1641

Reisner, J., R. M. Rasmussen, and R. T. Bruintjes, 1998: Explicit forecasting of supercooled li quid water in

winter storms using the MM5 mesoscale model. Quart. J. Roy. Metero. Soc., 124 B, 1071

- 1107.

Xiu, A., and J.E. Pleim, 2000: Development of a land surface model. Part I: Application in a mesoscale

meteorology model. Journal of Applied Meteoro logy, 40, 192

- 209.

### Draft

1 

## 9 Appendices

A. Terrain namelist

&MAPBG

PHIC = 40.0,

XLONC =

- 97.0,

IEXP = .F.,

AEXP = 144.,

IPROJ = 'LAMCON',

&END

&DOMAINS

MAXNES = 2,

NESTIX = 129, 190, 136, 181, 211, 2 21,

NESTJX = 165, 181, 181, 196, 211, 221,

DIS = 36., 12., 9., 3.0, 1.0, 1.0,

NUMNC = 1, 1, 2, 3, 4, 5,

NESTI = 1, 18, 28, 35, 45, 50,

NESTJ = 1, 84, 25, 65, 55, 50,

RID = 1.5 , 1.5, 1.5, 3.1, 2.3, 2.3,

NTYPE = 5, 5, 4, 6, 6, 6,

NSTTYP= 1, 2, 2, 2, 2, 2,

&END

&OPTN

IFTER = .TRUE.,

IFANAL = .F.,

ISMTHTR = 2 ,

IFEZFUG = .F.,

IFTFUG = .F.,

IFFUDG = .F.,

IPRNTD = .F.,

IPRTHT = .F.,

IPRINT = 0,

FIN = 200., 100., 100., 100., 100., 100.,

TRUELAT1=33.,

TRUELAT2=45.,

IFILL = .TRUE.,

LSMDATA = .TRUE.,

VEGTYPE = 1,

VSPL OT = .TRUE.,

IEXTRA = .TRUE.,

&END

&FUDGE

IFFUG = .F.,.F.,

NDFUG = 0,0,

IFUG(1,1)= 200*0,

IFUG(1,2)= 200*0,

JFUG(1,1)= 200*0,

JFUG(1,2)= 200*0,

LNDFUG(1,1)= 200*0,

LNDFUG(1,2)= 200*0,

&END

&FUDGET

NF UGBOX = 2

STARTLAT=45.0,44.0,

ENDLAT =46.5,45.0,

STARTLON=

- 95.0,

- 79.8,

ENDLON =

- 92.6,

- 78.5,

&END

&EZFUDGE

HTPS(441) =

- .001

HTPS(550) = 183.

HTPS(587) = 177.

HTPS(618) = 176.

HTPS(613) = 174.

HTPS(645) = 75.

### Draft

2 HTPS(480) = 1897.

HTPS(500) = 1281.

&END

&DATANAME

TERNAME = 'Data/DEM_60M_GLOBAL ',

'Data/DEM_30M_GLOBAL ',

'Data/DEM_10M_GLOBAL ',

'Data/DEM_05M_GLOBAL ',

'Data/DEM_02M_GLOB AL ',

'Data/DEM_30S_GLOBAL ',

LNDNAME = 'Data/LANDUSE.60 ',

'Data/LANDUSE.30 ',

'Data/LANDUSE.10 ',

' ',

' ',

' ',

LWNA ME = 'Data/LWMASK

- USGS.60 ',

'Data/LWMASK

- USGS.30 ',

'Data/LWMASK

- USGS.10 ',

'Data/LWMASK

- USGS.05 ',

'Data/LWMASK

- USGS.02 ',

'Data/LWMASK

- USGS.30s',

VGNAME = 'Data/VEG

- USGS.60 ',

'Data

/VEG

- USGS.30 ',

'Data/VEG

- USGS.10 ',

'Data/VEG

- USGS.05 ',

'Data/VEG

- USGS.02 ',

'Data/VEG

- USGS.30s ',

SONAME = 'Data/SOILCATB.60 ',

'Data/SOILCATB.30 ',

'Data/SOILCATB.1 0 ',

'Data/SOILCATB.05 ',

'Data/SOILCATB.02 ',

'Data/SOILCATB.30s ',

VFNAME = 'Data/VEG

- FRACTION.10',

TSNAME = 'Data/SOILTEMP.60 ',

&END



### B. Pregrid script

#!/bin/csh

- f

set echo

set MON_PROC = jan02

set OLD_PROC = dec01

set OYR = 2001

set OMM_PROC = 12

#

# Put your input files for pregrid into the directory you specify as DataDir:

#

#set DataDir = /usr/tmp/username/REGRID

set DataDir = /scratch/olerud/pregrid/${MON_PROC}

if (!

- e $DataDi r) mkdir

- p $DataDir

rm

- rf $DataDir/*AWIP*

cp /scratch/olerud/ds609.2/${MON_PROC}/*_3Danal/*AWIP* $DataDir/

cp /scratch/olerud/ds609.2/${MON_PROC}/*_SFanal/*AWIP* $DataDir/

#

# Specify the source of 3

- d analyses

#

# set SRC3D = ON84 # Old ON84

- form atted NCEP GDAS analyses

# set SRC3D = NCEP # Newer GRIB

- formatted NCEP GDAS analyses

### Draft

3 set SRC3D = GRIB # Many GRIB

- format datasets

# InFiles: Tell the program where you have put the analysis files,

# and what you have called them. If SRC3D has the value "GRIB",

# then the Vtables you specify below in the script variable VT3D will

# be used to interpret the files you specify in the ${InFiles} variable.

# set InFiles = ( ${DataDir}/NCEP* )

set InFiles = ( ${DataDir}/*AWIP* )

#

# Specify the source of SST analyses

#

# set SRCSST = ON84

# set SRCSST = NCEP

# set SRCSST = NAVY

set SRCSST = $SRC3D

#

# InSST: Tell the program where the files with SST analyses are. Do

# this only if SST analyses are coming from files not named above in

# InFiles. If SRCSST has the value "GRIB", then the Vtables you

# specify below in the script variable VTSST will be used to interpret

# the files you specify in the ${InSST} variable.

#

set InSST = ()

#

# Select the source of snow

- cover anal yses (entirely optional)

#

set SRCSNOW = $SRC3D

# set SRCSNOW = ON84

# set SRCSNOW = GRIB

# InSnow: Set InSnow only if the snow

- cover analyses are from files

# not listed in InFiles. If SRCSNOW has the value "GRIB", then the

# Vtables you s pecify below in the script variable VTSNOW will be used

# to interpret the files you specify in the ${InSnow} variable.

set InSnow = ()

#

# Select the source of soil model analyses (entirely optional)

#

set SRCSOIL = $SRC3D

# InSoil: Set In Soil only if the soil analyses are from files

# not listed in InFiles. If SRCSOIL has the value "GRIB", then the

# Vtables you specify below in the script variable VTSOIL will be

# used to interpret the files you specify in the ${InSoil} variable.

set InSoil = ()

#

# Build the Namelist

#

if (

- e ./pregrid.namelist ) then

rm ./pregrid.namelist

endif cat << End_Of_Namelist | sed

- e 's/#.*//; s/ *$//' > ./pregrid.namelist

&record1

#

# Set the starting date of the time period you want to process :

#

START_YEAR = 2001 # Year (Four digits)

START_MONTH = 12 # Month ( 01

- 12 )

START_DAY = 31 # Day ( 01

- 31 )

### Draft

4 START_HOUR = 15 # Hour ( 00

- 23 )

END_YEAR = 2002 # Year (Four digits)

END_MONTH = 01 # Month ( 01

- 12 )

END _DAY = 31 # Day ( 01

- 31 )

END_HOUR = 21 # Hour ( 00

- 23 )

#

# Define the time interval to process.

#

INTERVAL = 10800 # Time interval (seconds) to process.

# This is most sanely the same as the time interval for

# w hich the analyses were archived, but you can really

# set this to just about anything, and pregrid will

# interpolate in time and/or skip over time periods for

# your regridding pleasure.

/

End_Of_N amelist

#

# Tell the pregrid programs which Vtables to use. Do this only

# if you have selected GRIB

- formatted input using SRC___ = GRIB above.

# The directories referenced here are relative to REGRID/pregrid/.

#

# The Vtable files specified in VT 3D will be applied to the files

# specified in the InFiles variable. Similarly, the Vtable files

# specified in VTSST, VTSNOW, and VTSOIL will be applied to the files

# listed above in InSST, InSNOW, and InSoil, respectively.

#

# set VT3D = ( gri b.misc/Vtable.NNRP3D )

# set VTSST = ( grib.misc/Vtable.NNRPSST )

# set VTSNOW = ( grib.misc/Vtable.xxxxSNOW )

# set VTSOIL = ( grib.misc/Vtable.xxxxSOIL )

set VT3D = ( grib.misc/Vtable.AWIP3D )

set VTSST = ( grib.misc/Vtable.AWIPSST )

s et VTSNOW = ( grib.misc/Vtable.AWIPSNOW )

set VTSOIL = ( grib.misc/Vtable.AWIPSOIL )

########################################################################

########################################################################

###### ######

###### END USER MODIFICATION ######

###### ######

########################################################## ##############

########################################################################

if ( ! $?SRC3D ) then

set SRC3D

endif

if ( ! $?SRCSST ) then

set SRCSST

endif

if ( ! $?SRCSNOW ) then

set SRCSNOW

endif

if ( ! $?SRCSOIL ) then

set SRCSOIL

endif

if ( ! $?VTSOIL ) then

set VTSOIL

endif

if ( ! $?VTSNOW ) then

set VTSNOW

endif

if ( ! $?VTSST ) then

set VTSST

endif

if ( ! $?VT3D ) then

set VT3D

endif

### Draft

5 if ( ! $?InFiles ) then

set InFiles = ()

endif

if ( ! $?InSST ) then

set InSS T = ()

endif

if ( ! $?InSnow ) then

set InSnow = ()

endif

if ( ! $?InSoil ) then

set InSoil = ()

endif

if ( $SRCSST == $SRC3D) then

if ( $#InSST == 0 ) then

set InSST = ( ${InFiles} )

endif

endif

if ( $SRCSNOW == $SRC3D) then

if ( $# InSnow == 0 ) then

set InSnow = ( ${InFiles} )

endif

endif

if ( $SRCSOIL == $SRC3D) then

if ( $#InSoil == 0 ) then

set InSoil = ( ${InFiles} )

endif

endif

set LETTERS = ( A B C D E F G H I J K L M N O P Q R S T U V W X Y Z )

foreach SourceType ( 3D SST SNOW SOIL)

printf " \ nProcessing for SourceType = %s \ n \ n" $SourceType

if ( ( $SourceType == SOIL ) && ( $SRCSOIL == ON84) ) then

printf " \ n \ nSoil fields not available in ON84 Dataset. \ n"

printf "Do not request soil fields or select another source for soil fields. \ n \ n"

exit (1)

endif

if ( ( $SourceType == SOIL ) && ( $SRCSOIL == NCEP) ) then

printf " \ n \ nSoil fields not available in NCEP GDAS Dataset."

printf "Do not request soil fields or select another sou rce for soil fields. \ n \ n"

exit (1)

endif

###############################################################################

if ( ( ( $SourceType == 3D ) && ( $SRC3D == ON84) ) || \

( ( $SourceType == SST ) && ( $SRCSST == ON84) ) || \

( ( $SourceType == SNOW ) && ( $SRCSNOW == ON84) ) ) then

printf " \ n \ nStarting ON84 processing for type %s \ n \ n" $SourceType

#

# Go down to the "on84" directory.

#

printf "cd %s \ n \ n" `pwd`/on84

cd on84

#

# Remove whatever files may be leftover from a prior job. Redirect

# printout to supress warnings if there is nothing to remove.

#

rm ON84FILE* >&! /dev/null

rm PSST:* >&! /dev/null

rm PSNOW:* >&! /dev/null

rm

- f pregrid.namelist

rm

- f Vtable

#

### Draft

6 # Build the V table:

#

touch Vtable

if ($SourceType == 3D) then

cat Vtable.ON84 >> Vtable

else if ($SourceType == SST) then

cat Vtable.SST >> Vtable

else if ($SourceType == SNOW) then

cat Vtable.SNOW >> Vtable

endif

#

# Link the requested input files to "ON84FILE.A", "ON84FILE.B", etc.

#

set Num = 0

if ($SourceType == 3D) then

foreach file ( $InFiles )

@ Num ++

printf " ln

- s %s %s \ n" $file ON84FILE${LETTERS[$Num]}

ln

- s $file ON84F ILE${LETTERS[$Num]}

end

endif

if ($SourceType == SST) then

foreach file ( $InSST )

@ Num ++

printf " ln

- s %s %s \ n" $file ON84FILE${LETTERS[$Num]}

ln

- s $file ON84FILE${LETTERS[$Num]}

end

e ndif

if ($SourceType == SNOW) then

foreach file ( $InSnow )

@ Num ++

printf " ln

- s %s %s \ n" $file ON84FILE${LETTERS[$Num]}

ln

- s $file ON84FILE${LETTERS[$Num]}

end

endif

#

# Link the pregrid.namelist file and run the program.

#

ln

- s ../pregrid.namelist pregrid.namelist

./pregrid_on84.exe

#

# Move the output up to the "pregrid" directory.

#

if ($SourceType == 3D) then

foreach file ( ON84:* )

printf " mv %s ../%s \ n" $file $ file

mv $file ..

end

else if ($SourceType == SST) then

foreach file ( SST:* )

printf " mv %s ../ON84_%s \ n" $file $file

mv $file ../ON84_$file

end

else if ($SourceType == SNOW) then

foreach fi le ( SNOW:* )

printf " mv %s ../ON84_%s \ n" $file $file

mv $file ../ON84_$file

end

endif

#

# Go back up to the "pregrid" directory.

#

printf " \ ncd %s \ n" `pwd`/..

cd ..

printf " \ nDone with ON84 processing for type % s \ n \ n" $SourceType

endif

###############################################################################

### Draft

7 if ( ( ( $SourceType == 3D ) && ( $SRC3D == NCEP) ) || \

( ( $SourceType == SST ) && ( $SRCSST == NCEP) ) || \

( ( $SourceTy pe == SNOW ) && ( $SRCSNOW == NCEP) ) ) then

#

# Go down to the "ncep.grib" directory.

#

printf " \ ncd %s \ n" `pwd`/ncep.grib

cd ncep.grib

#

# Remove whatever files may be leftover from a prior job. Redirect

# printout to supress warnings if the re is nothing to remove.

#

rm GRIBFILE* >&! /dev/null

rm

- f pregrid.namelist

rm

- f Vtable

#

# Build the Vtable:

#

touch Vtable

if ($SourceType == 3D) then

cat Vtable.NCEP >> Vtable

else if ($SourceType == SST) th en

cat Vtable.SST >> Vtable

else if ($SourceType == SNOW) then

cat Vtable.SNOW >> Vtable

endif

#

# Link the requested input files to "GRIBFILE.A", "GRIBFILE.B", etc.

#

set Num = 0

if ( $SourceType == 3D ) then

foreach file ( $InFiles )

@ Num ++

ln

- s $file GRIBFILE${LETTERS[$Num]}

end

endif

if ($SourceType == SST) then

foreach file ( $InSST )

@ Num ++

ln

- s $file GRIBFILE${LETTERS[$Num]}

end

endif

if ($SourceType == SNOW) then

foreach file ( $InSnow )

@ Num ++

ln

- s $file GRIBFILE${LETTERS[$Num]}

end

endif

#

# Link the pregrid.namelist file and run the program.

#

ln

- s ../pregrid.namelist pregrid.namelis t

./pregrid_ncep.exe

#

# Move the output up to the "pregrid" directory.

#

if ($SourceType == 3D) then

mv NCEP:* ..

else if ($SourceType == SST) then

foreach file ( SST:* )

mv $file ../NCEP_$file

end

else if ( $SourceType == SNOW) then

foreach file ( SNOW:* )

mv $file ../NCEP_$file

end

endif

#

# Go back up to the "pregrid" directory.

#

### Draft

8 echo "cd `pwd`/.."

cd ..

endif

######################################################## #######################

if ( ($SourceType == SST) && ( $SRCSST == NAVY ) ) then

printf " \ n \ nStarting NAVYSST processing. \ n \ n"

#

# Go down to the "navysst" directory.

#

echo "cd `pwd`/navysst"

cd navysst

#

# Remove whatever files may be leftover from a prior job. Redirect

# printout to supress warnings if there is nothing to remove.

#

rm

- f pregrid.namelist

rm NAVYFILE* >&! /dev/null

#

# Link the requested files to "NAVYFILE.A", "NAVYFILE.B", etc.

#

set Num = 0

foreach file ( $InSST )

@ Num ++

ln

- s ${file} NAVYFILE${LETTERS[$Num]}

end

#

# Link the pregrid.namelist file and run the program.

#

ln

- s ../pregrid.namelist pregrid.namelist

./pregrid_navy.exe

#

# Move the output fil es up to the "pregrid" directory.

#

foreach file ( SST:* )

mv $file ../NAVY_$file

end

#

# Go back up to the "pregrid" directory.

#

echo "cd `pwd`/.."

cd ..

printf " \ n \ nDone with NAVYSST processing. \ n \ n"

endif

######### ######################################################################

if ( ( ( $SourceType == 3D ) && ( $SRC3D == GRIB) ) || \

( ( $SourceType == SST ) && ( $SRCSST == GRIB) ) || \

( ( $SourceType == SNOW ) && ( $SRCSNOW == GRIB) ) || \

( ( $SourceType == SOIL ) && ( $SRCSOIL == GRIB) ) ) then

printf " \ n \ nStarting GRIB processing for type %s \ n \ n" $SourceType

#

# Go down to the "grib.misc" directory.

#

echo "cd `pwd`/grib.misc"

cd grib.misc

#

# Remove whatev er files may be leftover from a prior job. Redirect

# printout to supress warnings if there is nothing to remove.

#

rm FILE:* >&! /dev/null

rm GRIBFILE* >&! /dev/null

### Draft

9 rm

- f Vtable

rm

- f pregrid.namelist

#

# Bu ild the Vtable:

#

touch Vtable

if ( $SourceType == 3D ) then

foreach file ( $VT3D )

cat ../$file >> Vtable

end

else if ( $SourceType == SST ) then

foreach file ( $VTSST )

cat ../$file >> Vtable

end

else if ( $SourceType == SNOW ) then

foreach file ( $VTSNOW )

cat ../$file >> Vtable

end

else if ( $SourceType == SOIL ) then

foreach file ( $VTSOIL )

cat ../$file >> Vtable

end

endif

#

# Link the requested files to "GRIBFILE.AA", "GRIBFILE.AB", etc.

#

set NUM = 0

set num = 1

if ( $SourceType == 3D ) then

foreach file ( $InFiles )

@ NUM ++

if ( $NUM == 27 ) then

set NUM = 1

@ num ++

endif

printf " ln

- s %s %s \ n" $file GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

ln

- s ${file} GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

end

else if ( $SourceType == SST ) then

foreach file ( $InSST )

@ NUM ++

if ( $NUM == 27 ) then

set NUM = 1

@ num ++

endif

printf " ln

- s %s %s \ n" $file GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

ln

- s ${ file} GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

end

else if ( $SourceType == SNOW ) then

foreach file ( $InSnow )

@ NUM ++

if ( $NUM == 27 ) then

set NUM = 1

@ num ++

endif

printf " ln

- s %s %s \ n" $file GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

ln

- s ${file} GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

end

else if ( $SourceType == SOIL ) then

foreach file ( $InSoil )

@ NU M ++

if ( $NUM == 27 ) then

set NUM = 1

@ num ++

endif

printf " ln

- s %s %s \ n" $file GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

ln

- s ${file} GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM] }

end

endif

#

### Draft

10 # Link the pregrid.namelist file and run the program.

#

ln

- s ../pregrid.namelist pregrid.namelist

./pregrid_grib.exe

#

# Move the output files up to the "pregrid" directory.

#

if ( $SourceType == 3D ) then

mv FILE:* ..

else

foreach file ( FILE:* )

printf "mv %s %s \ n" $file ../${SourceType}_${file}

mv $file ../${SourceType}_${file}

end

endif

#

# Go back to the "pregrid" directory.

#

echo "cd `pwd`/.."

cd ..

printf " \ n \ nDone with GRIB processing for type %s \ n \ n" $SourceType

endif

#

# Print out five lines of # as a delimiter between ${SourceType}s

#

repeat 5 printf \

"################################################################################ \ n"

end

printf " \ n"

mv *FILE:${OYR}

- ${OMM_PROC}

- * $DataDir/../${OLD_PROC}

mv *FILE* $DataDir/



### C. Regridder 36

- km sample namelist

&record1

start_year = 2002

start_month = 01

start_day = 0 1

start_hour = 00

end_year = 2002

end_month = 01

end_day = 07

end_hour = 00

interval = 10800 /

&record2

ptop_in_Pa = 10000

new_levels_in_Pa = 95000 , 92500 , 90000 ,

80000 ,

75000 ,

65000 , 60000 ,

55000 ,

45000 ,

35000

sst_to_ice_threshold =

- 9999

linear_interpolation = .FALSE. /

&record3

root = '../pregrid/FILE' '../pregrid/SOIL_FILE' '../pregrid/SST_FILE' '../pregrid/SNOW_FILE'

terrain_file_name = '../../TERRAIN/TERRAIN_DOMAIN1' /

constants_full_name = './ALMX_FILE' /

&record4

print_echo = .FALSE. ,

### Draft

11 print_debug = .FALSE. ,

print_mask = .FALSE. ,

print_interp = .FALSE. ,

print_link_list_store = .FALSE. ,

print_array_store = .FALSE. ,

print_header = .FALSE.

, print_output = .FALSE. ,

print_file = .FALSE. ,

print_tc = .FALSE. ,

print_f77_info = .TRUE. /

&record5

insert_bogus_storm = .FALSE.

num_storm = 1

latc_loc = 36.0

lonc_loc =

- 35.0

vmax_meters_per_second = 35.0

rmax = 90000.0

vmax_ratio = 0.75 /



### D. FETCH (adp_sfc) sample namelist

&LATLON

XLONE =

- 40

XLONW =

- 140

XLATS = 15

XLATN = 60

/

&DATE

ISTARTYR = 2002

ISTARTMO = 01

ISTARTDY = 01

ISTARTHR = 00

IENDYR = 2002

IENDMO = 01

IENDDY = 04

IENDHR = 00

/



### E. FETCH (adp_upa) sample namelist

&LATLON

XLONE =

- 50

XLONW =

- 150

XLATS = 15

XLATN = 62

/

&DATE

ISTARTYR = 2002

ISTARTMO = 01

ISTARTDY = 01

ISTARTHR = 00

IENDYR = 2002

IENDMO = 01

IENDDY = 15

IENDHR = 23

/



### F. LITTLE_R sample 36

- km namelist

&record1

### Draft

12 start_year = 2002

start_month = 01

start_day = 01

start_hour = 00

end_year = 2002

end_mont h = 01

end_day = 07

end_hour = 00

interval = 10800/

&record2

fg_filename = '../regridder/REGRID_DOMAIN1'

obs_filename = '../../FETCH/a dp_upa/jan02/upper

- air_obs_r:2002

- 01

- 01_00'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 01_03'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 01_06'

'. ./../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 01_09'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 01_12'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 01_15'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 01_18'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 01_21'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 02_00'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 02_03'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 02_06'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 02_09'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 02_12'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 02_15'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 02_18'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 02_21'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 03_00'

'../../FETCH/adp_upa/jan02/upper

- air _obs_r:2002

- 01

- 03_03'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 03_06'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 03_09'

'../../FETCH/adp_upa/jan0 2/upper

- air_obs_r:2002

- 01

- 03_12'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 03_15'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 03_18'

'../../FETCH/a dp_upa/jan02/upper

- air_obs_r:2002

- 01

- 03_21'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 04_00'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 04_03'

'.. /../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 04_06'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 04_09'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 04_12'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 04_15'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 04_18'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 04_21'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 05_00'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 05_03'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 05_06'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 05_09'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 05_12'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 05_15'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 05_18'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 05_21'

'../../FETCH/adp_upa/jan02/upper

- air_ obs_r:2002

- 01

- 06_00'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 06_03'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 06_06'

'../../FETCH/adp_upa/jan02 /upper

- air_obs_r:2002

- 01

- 06_09'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 06_12'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 06_15'

'../../FETCH/ad p_upa/jan02/upper

- air_obs_r:2002

- 01

- 06_18'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 06_21'

'../../FETCH/adp_upa/jan02/upper

- air_obs_r:2002

- 01

- 07_00'

sfc_obs_filename = '../ ../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 01_00'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 01_03'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 01_06'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 01_09'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 01_12'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 01_15'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 01_18'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 01_21'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 02_00'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 02_03'

### Draft

13 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 02_06'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 02_09'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 02_12'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 02_15'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 02_18'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 02_21'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 03_00'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 03_03'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 03_06'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 03_09'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 03_12'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 03_15'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 03_18'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 03 _21'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 04_00'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 04_03'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 04_06'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 04_09'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 04_12'

'../../FETCH/adp_sfc/jan02/surface_obs_r: 2002

- 01

- 04_15'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 04_18'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 04_21'

'../../FETCH/adp_sfc/jan02/surface_o bs_r:2002

- 01

- 05_00'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 05_03'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 05_06'

'../../FETCH/adp_sfc/jan02/surf ace_obs_r:2002

- 01

- 05_09'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 05_12'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 05_15'

'../../FETCH/adp_sfc/jan02 /surface_obs_r:2002

- 01

- 05_18'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 05_21'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 06_00'

'../../FETCH/adp_sfc/ jan02/surface_obs_r:2002

- 01

- 06_03'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 06_06'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 06_09'

'../../FETCH/adp _sfc/jan02/surface_obs_r:2002

- 01

- 06_12'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 06_15'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 06_18'

'../../FETC H/adp_sfc/jan02/surface_obs_r:2002

- 01

- 06_21'

'../../FETCH/adp_sfc/jan02/surface_obs_r:2002

- 01

- 07_00' /

&record3

max_number_of_obs = 10000

fatal_if_exceed_max_obs = .TRUE./

&record4

qc_test_error_max = .TRUE.

qc_test_buddy = .TRUE.

qc_test_vert_consistency = .FALSE.

qc_test_convective_adj = .FALSE.

max_error_t = 10

max_error_uv = 13

max_error_z = 8

max_error_rh = 50

max_error_p = 600

max_buddy_t = 8

max_buddy_uv = 8

max_buddy_z = 8

max_buddy_rh = 40

max_buddy_p = 800

buddy_weight = 1.0

max_p_exte nd_t = 1300

max_p_extend_w = 1300/

&record5

print_obs_files = .FALSE.

print_found_obs = .FALSE.

print_header = .FALSE.

print_analysis = .FALSE.

print_qc_vert = .FALSE.

print_qc_dry = .FALSE.

print_error_max = .FALSE.

print_buddy = .FALSE.

### Draft

14 print_oa = .FALSE./

&record7

use_first_guess = .TRUE.

f4d = .TRUE.

i ntf4d = 10800

lagtem = .FALSE. /

&record8

smooth_type = 1

smooth_sfc_wind = 0

smooth_sfc_temp = 0

smooth_sfc_rh = 0

smooth_sfc_slp = 0

smooth_upper_wind = 0

smooth_upper_temp = 0

smooth_upper_rh = 0/

&record9

oa_type = 'MQD'

mqd_minimum_num_obs = 30

mqd_maximum_num_obs = 1000

radius_influence = 5,4, 3,2

oa_min_switch = .TRUE.

oa_max_switch = .TRUE./



### G. INTERPF sample 36

- km namelist

&record0

input_file = '../LITTLE_R/LITTLE_R_DOMAIN1' / ! pressure

- level data file name

&record1

start_year = 2002 ! The starting and

start_month = 01 ! ending dates to

start_day = 01 ! process

start_hour = 00

end_year = 2002

end_month = 01

end_day = 07

end_hour = 00

interval = 10800 ! time difference (s)

less_than_24h = .FALSE. / ! if input is less than 24 h

&record2

sigma_f_bu = 1.000,0.995,0.990,0.985,0.980,0.970,0.960 , ! full sigma, bottom

- up,

0.950,0.940,0.930,0.920,0.910,0.900,0.880, ! end

0.860,0.840,0.820,0.800,0.770,0.740,0.700, ! with 0.0

0.650,0.600,0.550,0.500,0.450,0.400,0.350,

0.300,0.250,0.200,0.150,0.100,0.050,0.000

ptop = 10000 ! top pressure if need to be redefined

isfc = 0 / ! # sigma levels to spread

! surface information

&record3

p0 = 1.e5 ! base state sea

- level pres (Pa)

tlp = 50. ! base state lapse rate d(T)/d(ln P)

ts0 = 275. ! base state sea

- level temp (K)

tiso = 0./ ! base state isothermal stratospheric temp (K)

&record4

removediv = .TRUE. ! T/F remove integrated mean divergence

use sfc = .TRUE. ! T/F use surface data

wrth2o = .TRUE. ! T/F specific humidity wrt H2O

psfc_method = 0 / ! T/F sfc temperature from diurnal avg

&record5

i fdatim =

- 1 / ! # of IC time periods to output

### Draft

15

H. MM5 sample mmlif

&OPARAM

TIMAX = 7920.,

TISTEP = 90.,

IFREST = .FALSE.,

IXTIMR = 0,

IFSAVE = .TRUE.,

SVLAST = .TRUE.,

SAVFRQ = 360.,

IFTAPE = 1,

TAPFRQ = 60.,

BUFFRQ = 1440.,

INCTAP = 1,1,1,1,1,1,1,1,1,1,

IFSKIP = .FALSE.,

CDATEST = '2002

- 10

- 28_00:00:00',

IFPRT = 0,

PRTFRQ = 720.,

MASC HK = 99999,

IFTSOUT = .FALSE.,

TSLAT = 0.0,0.0,0.0,0.0,0.0,

TSLON = 0.0,0.0,0.0,0.0,0.0,

&END

&LPARAM

RADFRQ = 15.,

IMVDIF = 1,

IVQADV = 1,

IVTADV = 1,

ITHADV = 1,

ITPDIF = 1,

ICOR3D = 1,

IEXSI = 0,

IFUPR = 1,

IBOUDY = 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,

IFDRY = 0,

ISSTVAR= 1,

IMOIAV = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

IZ0TOPT= 0,

IFSNOW = 1, 1, 0, 0, 0, 0, 0 , 0, 0, 0,

ISFFLX = 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,

ITGFLG = 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,

ISFPAR = 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,

ICLOUD = 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,

IEVAP = 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,

ISMRD = 2,

NUDGE = 1,

I FGROW = 2,

RDMAXALB=.FALSE.

RDBRDALB=.FALSE.

IFRAD = 4,

ICUPA = 8,8,8,8,8,8,8,8,8,8,

IMPHYS = 5,5,4,4,4,4,4,4,4,4 ,

IBLTYP = 7,7,7,7,7,7,7,7,7,7,

ISHALLO = 0,0,0,0,0,0,0,0,0,0,

IPOLAR = 0,

ISOIL = 3,

&END

&NPARA M

LEVIDN = 0,1,2,1,1,1,1,1,1,1,

NUMNC = 1,1,2,1,1,1,1,1,1,1,

NESTIX = 129, 190, 31, 46, 46, 46, 46, 46, 46, 46,

NESTJX = 165, 181, 31, 61, 61, 61, 61, 61, 61, 61,

NESTI = 1, 18, 8, 1, 1, 1, 1, 1, 1, 1,

NESTJ = 1, 84, 9, 1, 1, 1, 1, 1, 1, 1,

XSTNES = 0., 0.,900., 0., 0., 0., 0., 0., 0., 0.,

XENNES =7920.,7920.,1440.,720.,720.,720.,720.,720.,720.,720.

IOVERW = 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,

IACTIV = 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,

### Draft

16 IMOVE = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

IMOVCO = 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,

IMOVEI = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

IM OVEJ = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

IMOVET = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

IFEED = 0,

&END

&PPARAM

ZZLND = 0.1,

ZZWTR = 0.0001,

ALBLND = 0.15,

THINLD = 0.04,

XMAVA = 0.3,

CONF = 1.0,

&END

&FPARAM

FDASTA=0.,0.,0.,0.,0.,0.,0.,0.,0.,0.

FDAEND=7920.,7920.,0.,0.,0.,0.,0.,0.,0.,0.,

I4D= 1,1,0,0,0,0,0,0,0,0,

1,1,0,0,0,0,0,0,0,0,

DIFTIM=180.,180.,0.,0.,0.,0.,0.,0.,0.,0.,

180.,180.,0.,0.,0.,0.,0.,0., 0.,0.,

IWIND=1,1,0,0,0,0,0,0,0,0,

1,1,0,0,0,0,0,0,0,0,

GV=2.5E

- 4,1.0E

- 4,0.,0.,0.,0.,0.,0.,0.,0.,

2.5E

- 4,1.0E

- 4,0.,0.,0.,0.,0.,0.,0.,0.,

ITEMP=1,1,0,0,0,0,0,0,0,0,

0,0,0,0,0,0,0,0,0,0,

GT=2.5E

- 4,1.0E

- 4,0.,0.,0.,0.,0.,0.,0.,0.,

2.5E

- 4,1.0E

- 4,0.,0.,0.,0.,0.,0.,0.,0.,

IMOIS=1,1,0,0,0,0,0,0,0,0,

0,0,0,0,0,0,0,0,0,0,

GQ=1.E

- 5,1.E

- 5,0.,0.,0.,0.,0.,0.,0.,0.,

1.E

- 5,1.E

- 5,0.,0.,0.,0.,0.,0.,0.,0.,

IROT=0,0 ,0,0,0,0,0,0,0,0,

GR=5.E6,5.E6,0.,0.,0.,0.,0.,0.,0.,0.,

INONBL =0,0,0,0,0,0,0,0,0,0,

0,0,0,0,0,0,0,0,0,0,

1,1,1,1,1,1,1,1,1,1,

1,1,1,1,1,1,1,1,1,1,

RINBLW=250.,

NPFG=50,

I4D I =0,0,0,0,0,0,0,0,0,0,

ISWIND =1,0,0,0,0,0,0,0,0,0,

GIV =4.E

- 4,4.E

- 4,0.,0.,0.,0.,0.,0.,0.,0.,

ISTEMP=1,0,0,0,0,0,0,0,0,0,

GIT =4.E

- 4,4.E

- 4,0.,0.,0.,0.,0.,0.,0.,0.,

ISMOIS=1,0,0,0,0,0,0,0,0,0,

GIQ =4.E

- 4,4.E

- 4,0.,0.,0.,0.,0.,0.,0.,0.,

RINXY=240

., RINSIG=0.001,

TWINDO=40.0,

NPFI=20,

IONF=2,

IDYNIN=0,

DTRAMP=60.,

&END