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Robert Davies-Jones

identify environments that support various types of severe weather (e.g., Rasmussen 2003 ; Thompson et al. 2003 ) and that may factor in the forecast likelihood that a thunderstorm will produce a significant tornado in probabilistic models (e.g., Hamill and Church 2000 ). These parameters all require the computation of adiabatic wet-bulb temperature, T w , along water-saturation pseudoadiabats. They should be calculated as accurately as possible because errors affect statistical measures of their

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Matthias Mauder, R. L. Desjardins, Zhiling Gao, and Ronald van Haarlem

1. Introduction Horizontal homogeneity of the air temperature field is considered a prerequisite for tower-based micrometeorological techniques to accurately measure the turbulent heat exchange between the surface and the atmosphere ( Kaimal and Finnigan 1994 ). In reality, such conditions are probably rarely met. Measuring the spatial distribution of air temperature allows checking whether spatial differences are small enough that this basic requirement can be assumed. If not, it allows

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Mariana Adam

1. Introduction While the total cross sections in Raman spectra are expected to be temperature independent, the individual rotational line strengths are temperature dependent and this must be taken into consideration when narrowband interference filters used for signal detection allow the transmission of only a portion of the pure rotational (PR) or vibrational rotational (VR) Raman signals. Not considering this dependence may lead to significant errors in retrieving water vapor mixing ratios

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Robert Davies-Jones

1. Introduction There are several formulas for equivalent potential temperature (EPT) in the literature ( Bolton 1980 ; Bryan 2008 ). Rossby (1932) devised the first such formula by dropping an intractable term from the equation governing pseudoadiabatic water-saturation processes. This term is part of the entropy of water vapor. Its omission is equivalent to setting the specific heat of water to zero. Owing to the missing entropy, the Rossby formula underestimates the EPT by several degrees

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Yukitomo Tsutsumi

1. Introduction The quality of the evidence of increasing temperature is a long-standing issue for studies of global warming. Precise and consistent long-term measurements of temperature are not easy, even on a regional scale. Temperature data may include errors in calibrations and visual reading, especially in old data. Measurements at ground sites may be influenced by environmental changes such as urbanization effects and alterations in wind ventilation (exposure) as shown by Klotzbach et al

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G. Reverdin, J. Boutin, N. Martin, A. Lourenco, P. Bouruet-Aubertot, A. Lavin, J. Mader, P. Blouch, J. Rolland, F. Gaillard, and P. Lazure

1. Introduction The temperatures measured by surface drifters play a key role in the establishment of bulk sea surface temperature (SST) maps in blended products, as they are used to calibrate or validate satellite retrievals ( Reynolds et al. 2007 ; O’Carroll et al. 2008 ). They also contribute a significant share of all in situ SST data in the last two decades (close to 40% of the data since 2000; Rayner et al. 2009 ). Compared to other in situ SST observations from ships, it appears that

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Lucie A. Vincent, William A. van Wijngaarden, and Ron Hopkinson

1. Introduction An important feedback for the warming predicted by climate models due to an increase in greenhouse gas concentration is an increase in atmospheric water vapor ( Philipona et al. 2005 ). As temperatures rise, the atmosphere’s capacity to hold water increases. Knowledge about changes in water vapor in the upper troposphere and lower stratosphere is important because it can result in strong alterations in radiative forcing. Changes in the surface air temperature and humidity are

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Mingyue Chen, Wanqiu Wang, and Arun Kumar

1. Introduction The short- to medium-range numerical prediction of day-to-day weather relies on atmospheric initialization: the accurate specification of atmospheric pressures, temperatures, winds, and humidity at the beginning of the forecast. For the time scales of seasonal or longer, on the other hand, a primary source of atmospheric prediction skill is the lower boundary conditions such as the variability associated with the tropical Pacific sea surface temperatures (SSTs) related to El

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Ryoko Oda and Manabu Kanda

1. Introduction Sea surface temperature (SST) is one of the key parameters for understanding air–sea interaction processes. In the field of global climatology, air–sea interactions at time scales of longer than a day have been thoroughly studied, and the impact of SST on the seasonal or annual climate is now widely accepted (e.g., Bjerknes 1969 ; Horel and Wallace 1981 ). However, in regional meteorology, the importance of air–sea interaction is less recognized. This is because the diurnal

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Jeffrey C. Rogers, Sheng-Hung Wang, and Jill S. M. Coleman

1. Introduction The use of surface air temperature data is one of the cornerstones in the analysis of global change (e.g., Mann and Jones 2003 ; Jones and Moberg 2003 ). Air temperature, however, only measures the atmospheric dry static energy and is not a complete measure of the total surface energy content. Moist static energy is potentially a more comprehensive variable for analysis of global warming as it represents the total surface warming and energy content ( Pielke et al. 2004

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