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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 uerr 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 

Mixing 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 0.0020 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 

Table 48. Like table 47, except for 12-km VISTAS domain.

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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 -0.0025 -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.

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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 uerr 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 (deg, 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 0.0194 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 -0.0244 

Table 52. Like table 51, except for 12-km VISTAS domain.

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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 

Wind 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 0.0053 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 0.0037 -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.

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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 0.0002 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 -0.0005 

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 

Pcp 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.

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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 stats 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 -0.0007 

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.

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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 statistical 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 0.0325 

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 domain.

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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 0.0006 -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 obviously 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 documentation. 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 we 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 moisture 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 strength. 

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 wintertime episode 1. Note the general improvement in the surface statistics, especially 

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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 all 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, but 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 sensitivities 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 precipitation 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 sensitivity 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. northwest 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 precipitation, 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 

areas 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 accumulation 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 precipitation 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! 

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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 -0.0019 

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 portion 

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 0.0099 -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.

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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 (deg, 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 -0.1613 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 

Table 65. Like table 64, except for 12-km VISTAS domain.

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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 -0.0421 -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.

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Figure 38. 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 base case px_acm2 model configuration. The lower left 

plot shows the difference plot of the top panels. 

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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 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, and the lower 

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

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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 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 difference between the 

px_acm sensitivity and the px_acm2 base case. 

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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 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 the difference between the 

px_acm sensitivity and the px_acm2 base case. 

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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. 

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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 observations. The bottom right plot shows the difference between the 

noah_mrf sensitivity and the px_acm2 base case. 

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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. 

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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 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. 

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Figure 46. 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_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. 

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Figure 47. 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_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. 

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Figure 48. 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 base case px_acm2 model configuration. 

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

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Figure 49. 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 base case px_acm2 model configuration. 

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

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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 the 

difference between the px_acm sensitivity and the px_acm2 base case. 

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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 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 the 

difference between the px_acm sensitivity and the px_acm2 base case. 

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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 the observations. The bottom right plot shows the 

difference between the noah_mrf sensitivity and the px_acm2 base case. 

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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 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. 

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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 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. 

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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 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. 

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Figure 56. 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 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. 

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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 panel 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_acm2 base case. 

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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 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 difference between the 

px_acm5 sensitivity and the px_acm2 base case. 

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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 difference between the 

px_acm5 sensitivity and the px_acm2 base case. 

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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 plot shows the 

difference between the px_acm5 sensitivity and the px_acm2 base case. 

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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 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 

difference between the px_acm5 sensitivity and the px_acm2 base case. 

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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 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. 

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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 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. 

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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. 

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Figure 65. 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_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. 

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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. 

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Figure 67. 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_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. 

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Note 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 nudging 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 plot shows the 

difference between the px_acm_n sensitivity and the px_acm2 base case. 

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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 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. 

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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 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 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. 

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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 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. 

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6 Conclusions from Sensitivity Modeling

Considering only the original four (five, including 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 winds 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, 

including 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_eta-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 setup produces the best results of the credible configurations, the cold bias still remains. The problem seems 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 are unavoidable. 

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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 we 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 notably 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 notes, 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 ice 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 effort 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 MM5 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 another 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:

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Date tag Duration 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 jul05_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 Jan 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 Jan 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_02 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 Jun 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

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B. Model Execution

To prepare the MM5 input files we execute the MM5v3.6.2 preprocessor system. The general 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) Translates 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) global 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 exact 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 monthly 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 interpolated 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 

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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 preprocessing 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 upper-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 “improve” 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 executed 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 the 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 included in Appendix 9G.

MM5

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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 decreased 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-Fritsch 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 shows 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 is 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, inclusive)

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 March 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 transmitted 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 summary of the evaluation 

products that will be available on that web site:

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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 Statistical 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 RPO, 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)

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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 intervals. 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 busy).

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 precipitation, 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)

Surface 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 derive from the 

Climate Prediction Center.

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

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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 0.82-0.80, 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 Products (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 (Profiler 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 Agreement (IA, in blue), the coefficient of 

determination (r2, 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 skill 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 define 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” 

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means the model failed to predict measurable precipitation 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 the 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 of 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 to 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) Mixing 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 the 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 (Figure 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 precipitation (0.50 in threshold)

15) 24-hr precipitation (1.00 in threshold)

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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 wind 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)

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Labels in first part of table:

1) obsmean: Average of observations

2) modmean: Average of model predictions

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 its 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 of 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 first 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 metric, 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 “TOTAL” does not appear in the variable name, then an animation of 

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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 spatial 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 root 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 meteorological 

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 temperature 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 right) as the x-axis, as illustrated in figure 73. 

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

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1) Temperature (deg K)

2) Mixing Ratio (g/kg)

3) Relative Humidity (%)

4) Cloud cover – alt (%)

5) Wind Speed (m/s)

6) Wind direction (degrees), 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 set to the 

default white background.

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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.

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Monthly 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 Accumulated 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 anticipate 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 cluster, 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

Revised 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 Annual 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., and 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.

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125

Emery, C., E. Tai, and G. Yarwood, 2001. “Enhanced Meteorological Modeling 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-generation 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. Fritsch, 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 correlated-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. Available 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 Association)] 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 liquid 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 Meteorology, 40, 192-209.

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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, 221, 

 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, 

 VSPLOT = .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

 NFUGBOX = 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. 

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 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_GLOBAL ',

 'Data/DEM_30S_GLOBAL ',

 LNDNAME = 'Data/LANDUSE.60 ',

 'Data/LANDUSE.30 ',

 'Data/LANDUSE.10 ',

 ' ',

 ' ',

 ' ',

 LWNAME = '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.10 ',

 '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 $DataDir) 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-formatted NCEP GDAS analyses

# set SRC3D = NCEP # Newer GRIB-formatted NCEP GDAS analyses

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 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 analyses (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 specify 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 InSoil 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 )

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 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

 # which 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_Namelist

#

# 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 VT3D 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 = ( grib.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 )

 set 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

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if ( ! $?InFiles ) then

 set InFiles = ()

endif

if ( ! $?InSST ) then

 set InSST = ()

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 source 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

#

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# Build the Vtable:

#

 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 $file ON84FILE${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

 endif

 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 file ( 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

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

Pre-Hearing Draft

Regional Haze SIP for TN Class I Areas

Appendix E.2 - 134

January 18, 2008
Draft

7

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

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

 ( ( $SourceType == 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 there 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) then

 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.namelist

 ./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.

#

Pre-Hearing Draft

Regional Haze SIP for TN Class I Areas

Appendix E.2 - 135

January 18, 2008
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 files 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 whatever 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

Pre-Hearing Draft

Regional Haze SIP for TN Class I Areas

Appendix E.2 - 136

January 18, 2008
Draft

9

 rm -f Vtable

 rm -f pregrid.namelist

#

# Build 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 ) 

 @ 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

 endif

#

Pre-Hearing Draft

Regional Haze SIP for TN Class I Areas

Appendix E.2 - 137

January 18, 2008
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 = 01

 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. ,

Pre-Hearing Draft

Regional Haze SIP for TN Class I Areas

Appendix E.2 - 138

January 18, 2008
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

Pre-Hearing Draft

Regional Haze SIP for TN Class I Areas

Appendix E.2 - 139

January 18, 2008
Draft

12

 start_year = 2002

 start_month = 01

 start_day = 01

 start_hour = 00

 end_year = 2002

 end_month = 01

 end_day = 07

 end_hour = 00

 interval = 10800/

&record2

 fg_filename = '../regridder/REGRID_DOMAIN1'

 obs_filename = '../../FETCH/adp_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/jan02/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/adp_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/adp_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'

Pre-Hearing Draft

Regional Haze SIP for TN Class I Areas

Appendix E.2 - 140

January 18, 2008
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_obs_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/surface_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'

 '../../FETCH/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_extend_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.

Pre-Hearing Draft

Regional Haze SIP for TN Class I Areas

Appendix E.2 - 141

January 18, 2008
Draft

14

 print_oa = .FALSE./

&record7

 use_first_guess = .TRUE.

 f4d = .TRUE.

 intf4d = 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

 usesfc = .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

 ifdatim = -1 / ! # of IC time periods to output

Pre-Hearing Draft

Regional Haze SIP for TN Class I Areas

Appendix E.2 - 142

January 18, 2008
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., 

 MASCHK = 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, 

 IFGROW = 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 

 &NPARAM

 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, 

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Appendix E.2 - 143

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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, 

 IMOVEJ = 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,

 I4DI =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

Pre-Hearing Draft

Regional Haze SIP for TN Class I Areas

Appendix E.2 - 144

January 18, 2008