Table 5 Scaling of the overall test accuracy for prediction at lead day 1, lead day 3, and lead day 5 with sample size (N) for each of the methods (CNN4 and Log-Reg) for summer and winter.

From: Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data

Lead days

Sample Size

CNN4 Summer

CNN4 Winter

Log-Reg Summer

Log-Reg Winter

Lead day 1

N = 12000

92.1% ± 0.4%

93.3% ± 0.2%

65.3% ± 0.2%

66.7% ± 0.3%

N = 8000

88.3% ± 0.4%

90.4% ± 0.4%

60.3% ± 0.3%

62.7% ± 0.5%

N = 4000

87.6% ± 0.4%

86.5% ± 0.2%

52.3% ± 0.4%

54.6% ± 0.7%

Lead day 3

N = 12000

83.4% ± 0.2%

87.2% ± 0.2%

59.7% ± 0.2%

63.1% ± 0.3%

N = 8000

81.3% ± 0.3%

86.4% ± 0.0%

52.3% ± 0.3%

57.7% ± 0.5%

N = 4000

78.6% ± 0.5%

83.4% ± 0.3%

48.3% ± 0.5%

50.6% ± 0.4%

Lead day 5

N = 12000

76.4% ± 0.4%

80.3% ± 0.2%

55.4% ± 0.2%

61.7% ± 0.3%

N = 8000

74.2% ± 0.2%

78.6% ± 0.5%

51.3% ± 0.3%

55.7% ± 0.5%

N = 4000

71.1% ± 0.5%

74.4% ± 0.3%

45.3% ± 0.3%

49.6% ± 0.2%