References

References (Forecast Method)

[1] Lorenz, EN 1965: A study of the predictability of a 28-variable model. Tellus, 17, 321–333.
[2] Lupo, AR, Li, YC, Feng, ZC, Fox, NI, Rabinowitz, JL, and Simpson, MA 2016: Sensitive versus rough dependence in Initial conditions in atmospheric flow regimes. Atmosphere, 7, 157; doi:10.3390/atmos7120157.
[3] Birk, K, Lupo, AR, Guinan, PE, and Barbieri, CE 2010: The interannual variability of midwestern temperatures and precipitation as related to the ENSO and PDO. Atmofera, 23, 95 - 128.
[4] Wallace, JM, and Gutzler, BS 1981: Teleconnections in the geopotential height field during the northern hemisphere winter. Monthy Weather Review, 109, 784-812
[5] Walker, GT and Bliss, EW 1932: World Weather V. Memoirs of the Royal Meteorological Society, 4, 53-84.
[7] Kung, EC, and Chern, JG 1995: Prevailing anomaly patterns of the Global Sea Surface temperatures and tropospheric responses. Atmósfera, 8, 99-114.
[9] Newberry, RG, Lupo, AR, Jensen, AD, and Rodgriges – Zalipynis, RA 2016: An analysis of the spring-to-summer transition in the West Central Plains for application to long range forecasting. Weather and Climate Science, 6, 373-393.
[10] Lupo, AR, Mokhov, II, Chendev, YG, Lebedeva, MG, Akperov, MG, and Hubbart, JA 2014: Studying summer season drought in western Russia. Advances in Meteorology, Special Issue: Large-scale Atmospheric Science, 9 pp. Article ID 942027.
[12] Lupo, AR, and Market, PS 2002: The verification of weather forecasts in Central Missouri and seasonal variations in forecast accuracy. Weather and Forecasting, 8, 891 - 897.
[13] Lupo, AR, Kelsey, EP, Weitlich, DK, Davis, NA, and Market, PS 2008: Using the monthly classification of global SSTs and 500 hPa height anomalies to predict temperature and precipitation regimes one to two seasons in advance for the mid-Mississippi region. National Weather Digest, 32:1, 11-33.
[15] Dymnikov, VP, Kazantsev, YV, Kharin, VV 1992: Information entropy and local Lyapunov exponents of barotropic atmospheric circulation. Izvestia, Atmospheric and Oceanic Physics, 28, 425–432.
[16] Jensen, AD, Lupo, AR, Mokhov, II, Akperov, MG and Reynolds, DD 2017: Integrated regional enstrophy and block intensity as a measure of Kolmogorov Entropy, Atmosphere, 8 (12), 237.


References (Produced by this research)

[6] Jensen, AD, Lupo, AR, Mokhov, II, Akperov, MG, and Sun, F 2018: The dynamic character of Northern Hemisphere flow regimes in a near term climate change projection. Atmosphere, 9(1), 27.
[8] Henson, CB, Lupo, AR, Market, PS, and Guinan, PE 2016: ENSO and PDO-related climate variability impacts on Midwestern United States crop yields International Journal of Biometeorology doi:10.1007/s00484-016-1263-3.
[11] Lebedeva, MG, Lupo, AR, Henson, CB, Solovyov, AB, Chendev, YG, and Market, PS 2017: A comparison of bioclimatic potential of two global regions during the late 20th century and early 21st century. International Journal of Biometeorology, 14pp doi:10.1007/s00484-017-1470-6
[14] Renken, JD, Herman, JJ, Bradshaw, TR, Market, PS, and Lupo, AR 2017: The utility of the Bering Sea and East Asian Rules in long range forecasting. Advances in Meteorology, 2017, 14 pp. doi:10.1155/2017/1765428.
[17] Kastman, JS, Ganetis, S, Lamberson, W, Bodner, M, Market, PS, and Lupo, AR 2018: The use of integrated enstrophy in blocking and flow regime transition for operational forecasting. The 29th Weather Analysis and Forecasting Meeting of the American Meteorological Society, 4-8 June, 2018, Denver, CO.