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The Effects of Weather on Daily Mood: A Multilevel Approach



The present study examines the effects of six weather parameters (temperature, wind power, sunlight, precipitation, air pressure, and photoperiod) on mood (positive affect, negative affect, and tiredness). Data were gathered from an online diary study (N = 1,233), linked to weather station data, and analyzed by means of multilevel analysis. Multivariate and univariate analyses enabled distinction between unique and shared effects. The results revealed main effects of temperature, wind power, and sunlight on negative affect. Sunlight had a main effect on tiredness and mediated the effects of precipitation and air pressure on tiredness. In terms of explained variance, however, the average effect of weather on mood was only small, though significant random variation was found across individuals, especially regarding the effect of photoperiod. However, these individual differences in weather sensitivity could not be explained by the Five Factor Model personality traits, gender, or age.
The Effects of Weather on Daily Mood: A Multilevel Approach
Jaap J. A. Denissen
Humboldt-University Berlin
Ligaya Butalid
Utrecht University
Lars Penke
University of Edinburgh
Marcel A. G. van Aken
Utrecht University
The present study examines the effects of six weather parameters (temperature, wind power, sunlight,
precipitation, air pressure, and photoperiod) on mood (positive affect, negative affect, and tiredness).
Data were gathered from an online diary study (N1,233), linked to weather station data, and analyzed
by means of multilevel analysis. Multivariate and univariate analyses enabled distinction between unique
and shared effects. The results revealed main effects of temperature, wind power, and sunlight on
negative affect. Sunlight had a main effect on tiredness and mediated the effects of precipitation and air
pressure on tiredness. In terms of explained variance, however, the average effect of weather on mood
was only small, though significant random variation was found across individuals, especially regarding
the effect of photoperiod. However, these individual differences in weather sensitivity could not be
explained by the Five Factor Model personality traits, gender, or age.
Keywords: weather, mood, multilevel modelling, personality
Weather is widely believed to influence people’s mood. For
example, the majority of people think they feel happier on days
with a lot of sunshine as compared to dark and rainy days.
Although this association seems to be common sense (Watson,
2000), it is striking to see that the number of studies on the
association between daily weather and mood is relatively small
(Keller et al., 2005). In studying such an association, seasonal
influences of weather must be distinguished from day-to-day in-
fluences. This distinction becomes clearest when studying the
effect of the amount of sunlight (i.e., hours per day were a shadow
can be detected) on mood: This variable both fluctuates between
days within a single season (e.g., a sunny vs. a cloudy summer
day), but also between seasons (in the northern hemisphere, pho-
toperiods are longest during summer and shortest during winter,
leading to more hours/day for potential unobstructed sunlight
accumulation in summer than in winter). This distinction is im-
portant since mood reactions to day-to-day weather fluctuations
may not generalize to reactions to seasonal weather fluctuations,
and vice versa. In studying the effects of weather on mood, several
studies have focused exclusively on individuals with seasonal
affective disorder (SAD), a condition that involves recurrent fall/
winter major depressive episodes that remit in the spring (e.g.,
Molin, Mellerup, Bolwig, Scheike, & Dam, 1996; Oren et al.,
1994; Young, Meaden, Fogg, Cherin, & Eastman, 1997). Such a
clinical condition may be an extreme manifestation of normal
variations in seasonality (defined as an individual’s degree of
seasonal variation in mood, energy level, sleep length, weight,
appetite, and social activities; Kasper, Wehr, Bartko, Garst, &
Rosenthal, 1989).
Previous studies on weather and its relation with psychological
constructs took only one or two weather parameters into account (e.g.,
Bushman, Wang, & Anderson, 2005; Keller et al., 2005). However,
it is important to examine a wide variety of weather parameters, to
be able to differentiate the effect of each parameter. For example,
temperature is often associated with sunlight. After controlling for
the parameter sunlight, the unique effect of temperature on mood
might decrease or even reverse in sign. The present study includes
a broad range of weather parameters simultaneously to study the
effects of daily weather changes. Specifically, we studied the
effect of temperature, wind power, sunlight, precipitation, air pres-
sure, and photoperiod on mood. Multivariate and univariate anal-
yses enable a distinction between unique and shared effects of
these parameters.
Mood has often been conceptualized within a circumplex struc-
ture of affect (Feldman Barrett, 1995). In this structure, the dimen-
sions of valence (unpleasantness vs. pleasantness) and arousal (low
vs. high) can be distinguished. In studying people’s mood, the
PANAS mood scale (Watson, Clark & Tellegen, 1988) is fre-
quently used, which provides a clear and reliable measurement of
positive and negative affect. However, the PANAS general dimen-
sion scales combine the dimension of valence with a high arousal
focus. To consider the low end of the arousal dimension, this study
also focused on tiredness as a dependent variable.
The effect of daily weather on people’s mood has been most
thoroughly investigated in two studies. First, Keller et al. (2005)
investigated the effect of temperature and barometric pressure on
single-occasion explicit and implicit mood valence (positive mood
Jaap J. A. Denissen, Personality Development, Humboldt-University
Berlin; Ligaya Butalid, Clinical & Health Psychology, Utrecht University;
Lars Penke, MRC Centre for Cognitive Ageing and Cognitive Epidemiol-
ogy, University of Edinburgh; Marcel A. G. van Aken, Developmental
Psychology, Utrecht University.
Correspondence concerning this article should be addressed to Jaap J. A.
Denissen, Unter den Linden 6, 10099, Berlin, Germany. E-mail: jjadenissen@
Emotion Copyright 2008 by the American Psychological Association
2008, Vol. 8, No. 5, 662–667 1528-3542/08/$12.00 DOI: 10.1037/a0013497
subtracted by negative mood) and cognition (memory and cogni-
tive style) in three different samples of (mostly) North American
participants. They found no consistent main effects of weather on
mood, though they found a moderator effect of both season and the
time participants’ spent in the open air: On spring days when
people spent a lot of time outside, mood was positively associated
with air temperature. On summer days, however, spending more
time outside on warm days was associated with decreased mood In
addition, on spring days, barometric pressure was positively asso-
ciated with mood (no main or interaction effects were reported for
the other seasons).
Watson (2000) collected diary reports by eight different samples
of students from Texas (total N478) between 1985 and 1993,
during either fall or spring. Participants reported their mood on an
average of 43.6 occasions), using the 11 subscales of the
PANAS-X (Watson & Clark, 1994). Watson (2000) focused his
analyses on the amount of sunshine and rain, but found no con-
sistent effects on any of the daily mood variables. To investigate
whether mood would be associated with weather on days with
extreme weather conditions, Watson (2000) also compared days
with 0% sunshine with days with 100% sunshine but found that
sunshine only influenced the overall intensity of participants’
mood reports, not the valence of these reports (i.e., participants
reported more extreme scores on both positive and negative mood
To summarize, both the Keller et al. (2005) and the Watson
(2000) study found no consistent main effects of weather on mood.
However, the conclusions of both studies are limited by a number
of factors. First, they almost exclusively focused on participants
from North America, so it is unclear whether effects can be
generalized to other regions. Second, the study by Keller et al.
(2005; though not the one by Watson, 2000) investigated only the
effects of two weather parameters and one mood valence variable.
To extend these findings, the present study used a broader ap-
proach in studying the relation between weather and mood by
including a wider variety of weather parameters and including
three aspects of mood. Going beyond both the Keller et al. (2005)
and the Watson (2000) study, the current investigation also studies
the effect of wind power and photoperiod on mood. Third, the
present study also extends previous research by examining indi-
vidual differences in sensitivity to weather fluctuations. It is ex-
pected that the effects of weather on mood differ across individ-
uals. Although individual differences in sensitivity to daily
weather have not been studied previously, the results of some
studies suggest a link between seasonality and personality, espe-
cially concerning the trait of neuroticism (e.g., Jang, Lam, Lives-
ley, & Vernon, 1997; Murray, Hay, & Armstrong, 1995). There-
fore, it is interesting to examine whether also a link between
sensitivity to daily weather and personality exists.
Personality and demographic characteristics will be examined to
see if individual differences in sensitivity to weather changes can
be predicted (Ennis & McConville, 2004). To adopt an exploratory
approach of personality as a moderator between daily weather and
mood in the current study, personality will be assessed at the broad
level of the Five Factor Model. In addition, gender and age will be
included in the analysis. Some studies (e.g., Rosen et al., 1990)
have found a decrease in seasonality and SAD with increasing age,
though it may also require some degree of exposure to develop a
sensitivity to the local climate and photoperiod. In addition, sea-
sonal affective disorder is found to have a higher prevalence rate
in women than in men (e.g., Lucht & Kasper, 1999; Rosen et al.,
1990). Finally, the present study investigated the moderating role
of season to replicate the finding by Keller et al. (2005) that
temperature only affects mood during the spring.
Since the present study examines within-person associations
between daily psychological states and daily weather variables,
data have to be collected across a series of days (diary method).
Diary methods reduce retrospective bias because of minimizing
the time between experiences and the report of these experiences
(Bolger, Davis, & Rafaeli, 2003). In this study, the Internet was
used to conduct a diary study. The use of the Internet might result
in large sample sizes and eases data collection for a longer period
of time (Michalak, 1998). Data was gathered for an uninterrupted
sequence of months that include different seasons of the year.
To summarize, this study investigates the effects of daily
weather on people’s mood, while taking individual differences into
account. The effects of six different weather variables on three
separate mood variables are examined. Furthermore, to examine
what might account for individual differences, the Big Five per-
sonality traits, gender, and age are included in the study. In
addition, we investigated the possible moderating role of season.
The aim of this exploratory approach is to get a further under-
standing of the possible relation between weather, personality, and
Participants and Design
Initially, 1,668 individuals signed up for the current study. Only
the 1,233 respondents (73.9%) who provided a German postal zip
code and an email address that indicated residence in Germany
were included in the study, so that we were able to match respon-
dents with the available weather information. Participants had a
mean age of 27.67 (SD 9.77), with a range from 13 to 68 years.
A majority of 88.6% (1092) of the respondents were women.
Participants most often started their participation in autumn (N
449), followed by summer (N336), spring (N233), and
winter (N215).
Instruments and Procedure
Pretest. Data was gathered by means of an online diary, which
focused on the determinants of individual daily well-being. The
data was collected between July 2005 and February 2007. Public-
ity for this study was generated through links on websites dedi-
cated to psychological research as well as postings on online
forums. Before taking part in the diary study, participants com-
pleted an extensive online pretest questionnaire (including some
measures irrelevant to the current study). The Five Factor Model
personality traits were assessed using the Big Five Inventory (BFI;
John & Srivastava, 1999). This scale contained 42 items measuring
Extraversion (eight items), Neuroticism (seven items), Openness
to Experiences (10 items), Conscientiousness (nine items), and
Agreeableness (eight items). Reliability analysis revealed a Cron-
bach’s alpha of .90 for Extraversion, .85 for Neuroticism, .83 for
Openness, .84 for Conscientiousness, and .74 for Agreeableness.
Internet-based diary study. Upon completing the pretest ques-
tionnaire, participants filled out daily questionnaires including
measures of positive affect, negative affect, and tiredness. Daily
positive and negative affect were assessed by means of the PA-
NAS mood scale (Watson & Clark, 1994). Positive affect was
measured with the items “active,” “alert,” “attentive,” “deter-
mined,” “enthusiastic,” “excited,” “inspired,” “interested,”
“proud,” and “strong,” whereas the scale of negative affect con-
tained the items “afraid,” “scared,” “nervous,” “jittery,” “irritable,”
“hostile,” “guilty,” “ashamed,” “upset,” and “distressed.” The
items “sleepy,” “tired,” “sluggish,” and “drowsy” from the
PANAS-fatigue scale (Watson & Clark, 1994) loaded on the same
factor as the items “quiet” and “still” that tap into the arousal
dimension of the mood circumplex (Feldman Barrett, 1995), so
they were combined into a single scale of daily tiredness. Items
were presented with scales from 1 (“not at all”) to 5 (“very much”).
The mean score on positive affect in the sample was 2.85 (SD
0.79), negative affect had a mean of 1.83 (SD 0.75), and the
mean score on tiredness was 2.23 (SD 0.88).
The different scales of the daily questionnaires were presented
in randomized order to avoid the development of automatic re-
sponse sets. The questionnaire was only accessible between 9 p.m.
and 4 a.m. Participants were asked to complete 25 daily question-
naires within 30 days. However, not all participants completed the
full 25 questionnaires needed for feedback. On average, partici-
pants contributed 13.75 daily reports (SD 10.30). As an incen-
tive, participants received feedback regarding the extent to which
a number of factors affected their mood during the course of the
study (e.g., amount of sleep, number of social interactions) after
the last daily report.
Objective weather data. Data from the German Weather In-
stitute (Deutscher Wetterdienst; was used to
obtain weather data from all German weather stations. The daily
weather variables were matched to the diary data of the respon-
dents by date and ZIP code. The data from the weather stations
contained variables that were highly correlated, such as minimum
temperature, maximum temperature, and mean temperature.
Therefore, a factor analysis with oblique (oblimin) rotation was
conducted. This resulted in three factors, which were labeled
‘temperature,’ ‘sunlight,’ and ‘wind power.’ The variables mean
temperature in degrees Celsius, hours of unobstructed sunlight
(i.e., the number of hours in which a shadow can be detected), and
mean wind power on the Beaufort scale (Bft) were used to repre-
sent these factors in the analysis. The variables precipitation in
millimeters and mean air pressure measured in hectopascal (hPa)
did not load on one of the three defined factors and were therefore
considered as separate variables. The daily mean temperature
ranged from 17.80 to 28.40 °C (0.04 to 83.12 °F), with a mean
of 11.28 (SD 6.62) degrees Celsius (M52.30, SD 43.92
°F). The mean wind power ranged from 0 to 7 Bft (M2.56,
SD 0.80), sunlight ranged from 0 to 16.50 hours (M4.76,
SD 4.15), precipitation had a range from 0 to 47.10 mm (M
1.93, SD 3.84), and air pressure had a range from 895.90 to
1042.30 hPa (M990.86, SD 22.26).
In addition, photoperiod was calculated by subtracting the time of
sunrise from the time of sunset for the various days that were studied
(using the geographical center of Germany as the reference point
on The result-
ing variable had a range from 7.87 to 16.60 (M12.33, SD
2.66). Although this variable is obviously confounded with hours
of sunlight (see below), photoperiod is completely determined by
calendar date and latitude (e.g., shortest and longest day length at
the winter and summer solstice, respectively, in the northern
hemisphere), whereas the amount of unobstructed sunlight also
taps into day-to-day fluctuations (e.g., a clouded vs. a sunny
summer day).
Data Analysis
Because data from diary studies are nested data (repeated mea-
sures within an individual) and missings often occurred because
not all respondents participated in the study for the full length of
25 days, multilevel analysis (linear mixed-model using SPSS) was
the method of choice to analyze this dataset. Because of the large
sample size, only effects that are significant at the p.01 level
will be reported.
First, a multivariate linear mixed-model analysis with fixed
effects of the six weather parameters (Level 1) was conducted to
identify main effects of weather data on mood, while controlling
for the other weather parameters. Second, to test whether the
effects of weather on mood differed across individuals, univariate
linear mixed-model analyses were conducted including random
effects (slopes). Finally, personality and demographic characteris-
tics (age, gender; Level 2), as well as season (Level 1) were added
in the linear mixed-models for each weather parameter separately
to explain the expected variance in random effects.
Associations Between Weather Variables
To inspect the degree of interdependence between the six
weather variables, these parameters were correlated across the 585
days that were studied. The correlations between the six weather
variables ranged between |.12| and |.76| and were all statistically
significant, with large correlations (|.5|) between temperature
and photoperiod, sunlight and photoperiod, and temperature and
sunlight (see Table 1). The strength of these associations illustrates
the need to distinguish between univariate (i.e., potentially con-
founded) and multivariate (i.e., unique) effects of weather param-
eters on mood.
Main Effects of Weather Parameters
Positive affect. Multivariate and univariate linear mixed-
model analyses revealed no significant ( p.01) main effects of
temperature, wind power, sunlight, precipitation, air pressure, and
photoperiod on positive affect (see Table 2 for standardized re-
gression coefficients). In addition, it was checked whether tem-
perature had a curvilinear effect on mood. However, including a
quadratic term of temperature did not result in better model fit of
the data for any of the three dependent variables. Thus, in further
analyses of the data, the effect of temperature was treated to be
The random-effects analysis indicated that the variance between
individuals in the effects of weather on positive affect was significant
(p.01) for all variables except precipitation ( p.013). The largest
random effect was found for photoperiod. Overall, these findings
suggest that the direction and the strength of the association between
weather and positive affect differed between individuals.
Negative affect. A significantly ( p.01) positive main effect
of temperature (␤⫽0.035) and negative main effects of wind
power (␤⫽⫺0.023) and sunlight (␤⫽⫺0.023) on negative affect
were found to be significant in the multivariate linear mixed-model
analysis with fixed effects. Interestingly, these main effects were
not found in the univariate linear mixed-model analyses, indicating
that the positive effect of temperature initially suppressed the
negative effect of sunlight.
The variance between individuals in the effects of weather on
negative affect was found to be significant ( p.01) for all variables
except for precipitation. Thus, the effects of all weather parameters on
negative affect differed between individuals. Interestingly, the random
variance around the slope of the (nonsignificant) photoperiod effect
.166) was more than three times higher than the random
variance around the other slopes (
Tiredness. In the multivariate mixed-model analysis with
fixed effects, a significantly ( p.01) negative main effect of
sunlight on tiredness was found (␤⫽⫺0.063). The results of the
univariate mixed-model analyses with random effects also yield a
significant main effect of sunlight (␤⫽⫺0.065). In addition,
univariate mixed-model analyses showed main effects of precipi-
tation (␤⫽0.032) and air pressure (␤⫽⫺0.071).
The absence of these main effects in the multivariate analysis
suggests that the effects of precipitation and air pressure were
partly dependent on the effect of sunlight. To test whether the
effects of precipitation and air pressure were mediated by the
variable sunlight, a mediation analysis was conducted (Baron &
Kenny, 1986). First, the unique effects of precipitation and air
pressure on tiredness were found to be significant, but disappeared
when controlling for sunlight (see Table 2). Second, the associa-
tion between sunlight and tiredness was significant (see Table 2),
with more sunlight being associated with less tiredness. Finally,
the negative association between precipitation and sunlight and the
positive association between air pressure and sunlight was signif-
icant (see Table 1). Therefore, it can be concluded that sunlight
significantly mediated the effects of precipitation and air pressure
on tiredness.
The random-effects analyses revealed significant ( p.01)
random slope variance between individuals in the effects of all
weather variables on tiredness, except for wind power. These
findings suggest that the effects of weather on tiredness differ
between individuals. Notably, the between-person variance around
the slope of photoperiod was again much higher than the variance
around the slope of the other weather variables.
Including Variables on the Person Level in the Models
The random-effects analysis on Level 1 (weather variables)
revealed that the effects of weather variables on people’s mood
differed between individuals. To examine whether person charac-
teristics such as personality traits, age, and gender could account
for these individual differences, these variables were included as
predictors of the slope between each weather parameter and mood
outcome combination. In addition, the effect of weather on mood
was allowed to vary according to season (with separate dummies
for spring, summer, and autumn). After Bonferroni correction to
decrease the possibility of capitalization by chance (z3.58, p
.0003 when applying an alpha level of .05), the interaction effects
of the weather parameters and the Big Five, age, and gender were
not found to be significant. These variables were thus unable to
Table 1
Pearson Correlations Between Weather and Mood Variables Across 585 Days
Temperature Wind power Sunlight Rain Air pressure
Wind power .171
Sunlight .580
Precipitation .119
Air pressure .107
Photoperiod .762
Table 2
Regression Coefficients of the Main Effects of Weather Parameters on Positive Affect, Negative Affect, and Tiredness
Positive affect Negative affect Tiredness
Multivar. Univar. Multivar. Univar. Multivar. Univar.
Temperature .016 .015 .063
.026 .044
.019 .007 .050
Wind power .006 .012 .009
.016 .008
.001 .017 .004
Sunlight .012 .017 .012
.013 .009
Precipitation .002 .011 .004 .001 .001 .002 .011 .032
Air pressure .023 .031 .047
.010 .018 .048
.032 .071
Photoperiod .011 .006 .105
.000 .019 .166
.042 .043 .206
Note.␤⫽regression weight based on standardized variables;
random variance; Multivariate analyses are with fixed effects, univariate analyses are
with random effects.
explain the individual differences in the effect of weather on mood.
In addition, three-way interaction effects of weather, personality
traits, and gender were examined, but no significant effect was
found. However, we did find a significantly negative interaction
effect between season and wind power in explaining fluctuations
in positive affect, indicating that the experience of a windy day had
a more negative effect on mood in summers and springs than
during winters and autumns.
The aim of the study was to investigate the effect of daily
weather changes on people’s mood. It was expected that individual
differences in sensitivity to weather changes exist. The results of
the current study showed no significant main effects of daily
weather on positive affect. This result is consistent with findings
by Keller et al. (2005) and Watson (2000), who also did not find
effects of weather on overall mood valence. Going against com-
monly held conceptions (Watson, 2000), it is interesting to note
that none of the six weather parameters had any significant main
effects on positive mood. Accordingly, the idea that pleasant
weather increases people’s positive mood in general is not sup-
ported by the findings of this study, although the finding of
significant between-person variance suggests that such a link may
still exist for some individuals (while simultaneously a reverse
effect holds for others).
Significant main effects of temperature, wind power, and sun-
light on negative affect were found. The difference between the
multivariate and univariate results showed that it was very infor-
mative to include different weather variables to identify the unique
effect of each variable. The multivariate analysis showed unique
effects of temperature, wind power, and sunlight, whereas the effects
of temperature and sunlight were not visible in the univariate analyses
because of confounds. Because univariate analyses do not control
for other variables, positive and negative effects might cancel each
other out.
Sunlight was found to have a significant main effect on tiredness
and mediated the effect of precipitation and air pressure on tired-
ness. Vitamin D
, which is produced in skin exposed to the
hormone of sunlight, has been found to change serotonin levels in
the brain, which could account for changes in mood (Lansdowne
& Provost, 1998). Therefore, lower levels of vitamin D
could be
responsible for increases in negative affect and tiredness.
As indicated by the relatively small regression weights (|.071|),
weather fluctuations accounted for very little variance in peo-
ple’s day-to-day mood. This result may be unexpected given the
existence of commonly held conceptions that weather exerts a
strong influence on mood (Watson, 2000), though it replicates
findings by Watson (2000) and Keller et al. (2005), who also
failed to report main effects. A number of factors may explain
the discrepancy between empirical results and widely held
beliefs (see also Watson, 2000). For example, it may be that
these beliefs are a reflection of our historical (and possibly
culturally transmitted) past, when people were much more
dependent on weather-related phenomena (e.g., for shelter and
food). It may also be that the discrepancy is because of a small
number of extreme cases (e.g., individuals with SAD) who
indeed report a strong association between weather and mood.
Such cases may leave a very vivid impression on others, but the
existence of significant between-person variance against the
background of a null effect in the general population suggests
that these extreme cases are offset by individuals with strikingly
different weather dependencies (e.g., people who become sad-
der during summers instead of winters).
The random effects analyses indicated that the effects of
weather on positive affect, negative affect, and tiredness varied
significantly across individuals. This was especially true for the
effect of photoperiod. Even though the within-subject range of this
variable (M0.83, SD 0.73) was vastly reduced when com-
pared to the range of the amount of hours with unobstructed
sunlight (M7.56, SD 4.64), the random slope variance
between persons was between 9 (positive affect) and 23 (tiredness)
times greater in the former when compared to the latter. On
average, the random effect for photoperiod was no less than 21
times greater than the average random effect of the other five
weather variables, indicating that individual differences in reac-
tions to changes in photoperiod are much more extreme than
reactions to day-to-day weather fluctuations. This means that
whereas some individuals may react to the shortening of daylight
time with a relatively strong darkening of their mood, the emotions
of other individuals may actually improve equally strongly as a
result of this seasonal change. The former individuals may be the
ones who fall prey to SAD during the dark season, though more
research is needed to substantiate this conclusion.
Inclusion of the Five Factor Model personality traits, gender and
age, did not reveal more moderation effects of the person-level
variables than expected by chance. In other words, people seem to
differ in the effects of weather on mood in a way that cannot be
explained by the Five Factor Model personality traits, age, or
gender. This might suggest that weather sensitivity is an individual
difference variable by itself. Research on diurnal types (e.g.,
Jackson & Gerard, 1996) and seasonality (Reid, Towell, & Gold-
ing, 2000) also found individual differences that were independent
to other personality traits. The findings of the present study suggest
that people can also differ in weather sensitivity, independent from
other personality traits.
The current study also investigated the moderating effect of
season on the association between weather and mood in an attempt
to replicate the finding by Keller et al. (2005) that weather fluc-
tuations have an especially strong effect in spring. Consistent with
this, we found that wind power had a more negative effect on
positive mood during spring and summer. This might be because
of the fact that people spend more of their leisure time outside
during these periods, so experiencing strong winds represents more
of a hassle, though this conclusion is highly speculative and needs
to be backed up by future research.
Strengths. Usually, causal effects can only be identified in
experimental designs. However, weather is an external variable
that cannot be influenced by individuals’ mood or any other third
variable. Although our naturalistic design did not allow us to
establish causality in the strict (experimental) sense, we believe
that are findings are highly consistent with a causal model that
flows from weather to mood (although we acknowledge that our
results remain silent regarding the processes that mediate associ-
ations between weather and mood, such as physiological processes
or daily activities). Furthermore, the data collection for the current
study ran over an 18-month period, spanning all seasons, which
provided a robust examination of the association between weather
and mood and the moderating effect of season. Finally, the Web-
based data collection ensured access to a large population, which
resulted in a large and heterogeneous sample from all across
Limitations. Response to the questionnaire was based on vol-
untary participation of the study, which likely resulted in selection
bias (e.g., unequal gender distribution). Furthermore, the relation
between daily weather and mood were found in a moderate mar-
itime/continental climate and might not generalize to other climate
types. In addition, we failed to assess the time that participants
spent outside, which may have emerged as an important moderator
of the effect of weather on mood (Keller et al., 2005). A final
limitation is that we relied on self-reported mood that may be
biased by implicit theories regarding the association between
weather and mood. This might especially be the case given that the
framing of the study (focusing on the determinants of well-being)
may have primed some subjects to reflect about the types of
external factors affecting their mood. Nevertheless, we think this
concern is somewhat assuaged by a number of factors. For one
thing, our study was not advertised as being about the link between
weather and mood, mostly assessed unrelated variables (e.g., so-
cial interaction quantity and quality), and included a personalized
feedback that should have encouraged accurate responding. Most
importantly, however, our results, if anything, go against popular
stereotypes about the link between weather and mood. For exam-
ple, we did not find any weather effects on positive mood, whereas
most people assume that sunlight induces positive emotions
(Watson, 2000). Against this background, some of the null results
of the current study are all the more remarkable.
The present study contributes to the understanding of the rela-
tion between daily weather and people’s mood. It extends the work
by Keller et al. (2005) and Watson (2000) with two novel features.
First, six different weather variables were included, which enabled
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Received October 19, 2007
Revision received July 21, 2008
Accepted July 25, 2008
... Weather is widely known to influence people's affective states (Denissen et al. 2008;Govind et al. 2020;Howarth and Hoffman 1984;Kööts, Realo, and Allik 2011;Persinger and Levesque 1983;Sanders and Brizzolara 1982). One study found that weather (changes) explained up to 60% of participants' daily mood variations (Persinger and Levesque 1983). ...
... Govind et al. (2020) established that weather had a causal influence on individuals' mood through changes in positive affect: relative to a typical day, participants reported significantly higher positive affect for a sunny day, but significantly lower positive affect for a rainy or snowy day. Since positive affect includes feelings of excitement, enthusiasm, strength, pride, and activity (Watson, Clark, and Tellegen 1988), the results of Govind et al. (2020) help to explain why individuals are relatively more optimistic (Howarth and Hoffman 1984) and less tired (Denissen et al. 2008) on sunny days. On the flip side, people feel relatively less optimistic and less energized on days of rain and snow. ...
This empirical study investigates whether unpleasant weather—a prominent aspect of a consumer’s offline environment—influences online review provision and content. It uses a unique dataset that combines 12 years of data on hotel bookings and reviews, with weather condition information at a consumer’s home and hotel address. The results show that bad weather increases review provision and reduces rating scores for past consumption experiences. Moreover, 6.5% more reviews are written on rainy days and that these reviews are 0.1 points lower, accounting for 59% of the difference in average rating scores between four- and five-star hotels in our data. These results are consistent with a scenario in which bad weather (i) induces negative consumer mood, lowering rating scores, and (ii) makes consumers less time-constrained, which increases review provision. Additional analyses with various automated sentiment measures for almost 300,000 review texts support this scenario: reviews on rainy days show a significant reduction in reviewer positivity and happiness, yet are longer and more detailed. This study demonstrates that offline context influences online reviews, and discusses how platforms and businesses should include contextual information in their review management approaches.
... Accurate weather forecasting is beneficial to the agriculture sector, tourism, and disaster preparedness in the event of natural disasters such as floods and droughts [2]. Also, the mood of a person might shift from positive to negative or fatigue when the weather changes, as weather has enormous impact on a person's subconscious [3]. ...
Conference Paper
In this paper, descriptive and diagnostic analysis of the National Aeronautics and Space Administration (NASA) weather data predictions and the Nigerian Meteorological Agency (NiMet) ground truth weather data is presented. Also, the prediction accuracy of the NASA weather data with respect to the NiMet weather data is evaluated. This research was based on ninety-two months of daily weather data from NiMet (January 1, 2010 to August 14, 2017) alongside corresponding NASA data, with specific consideration on minimum and maximum temperature meteorological parameters. Using time series plots, descriptive analysis was implemented to identify patterns and trends, while the diagnostic analysis shows the basis for the patterns and trends. The time series plots show that the patterns and trends can be attributed to seasonal weather changes in the study area. Furthermore, statistical evaluation of the NASA predictions with respect to the NiMet ground truth data resulted in a root mean square error (RMSE) of 1.2, and correlation coefficient value of 0.6, for minimum temperature, then, RMSE of 2.6, and correlation coefficient value of 0.7, for maximum temperature. Evidently, the NASA prediction was able to better predict the study area minimum temperature compared to the maximum temperature. However, the correlation analysis clearly establishes a good correlation between the NASA and the NiMet weather data, which indicates that a model can be developed to predict more accurate ground truth weather data, using the NASA data as input feature.
... Weather can aect a consumer's mood and personality [1][2][3][4], attitude toward products [5], and purchasing behavior [6]. Weather conditions are always salient issues for businesses because they are uncontrollable and unpredictable factors, which can have a positive or negative inuence on demand. ...
... Howarth and Hoffman (1984) identified a negative effect of humidity on concentration and a positive effect on tiredness, as well as a positive correlation between temperature and skepticism and a positive effect of sunshine on optimism. Recent studies by Denissen et al. (2008) and Kööts et al. (2011) have shown similar results and identified a negative impact of sunshine on tiredness. Schwarz and Clore (1983) observed that study participants generally thought more positively about their life on sunny days. ...
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This paper examines the impact of weather phenomena on the German stock market, evaluating cloud cover, humidity, air pressure, precipitation, temperature, and wind speed as weather variables. We use stock market data (returns, trading volume, and volatility) from the DAX, MDAX, SDAX, and TecDAX for the period from 2003 to 2017 and show, with modern time-series (GARCH) models that air pressure is the only weather variable that exerts a potentially consistent effect on the stock market. Air pressure reduces the trading volume on the SDAX and TecDAX, and changes in air pressure lead to increases in returns on the DAX, MDAX and SDAX. The effects of the other weather variables show no clear pattern and are critically discussed. In addition, this article contains an overview of the historical research results on the effects of weather on stock markets.
The influences of environmental factors such as weather on the human brain are still largely unknown. A few neuroimaging studies have demonstrated seasonal effects, but were limited by their cross-sectional design or sample sizes. Most importantly, the stability of the MRI scanner has not been taken into account, which may also be affected by environments. In the current study, we analyzed longitudinal resting-state functional MRI (fMRI) data from eight individuals, where they were scanned over months to years. We applied machine learning regression to use different resting-state parameters, including the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity matrix, to predict different weather and environmental parameters. For careful control, the raw EPI and the anatomical images were also used for predictions. We first found that daylight length and air temperatures could be reliably predicted with cross-validation using the resting-state parameters. However, similar prediction accuracies could also be achieved by using one frame of EPI image, and even higher accuracies could be achieved by using the segmented or raw anatomical images. Finally, the signals outside of the brain in the anatomical images and signals in phantom scans could also achieve higher prediction accuracies, suggesting that the predictability may be due to the baseline signals of the MRI scanner. After all, we did not identify detectable influences of weather on brain functions other than the influences on the baseline signals of MRI scanners. The results highlight the difficulty of studying long-term effects using MRI.
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Hintergrund. Licht ist die Quelle des Lebens. Dies gilt gleichermaßen für die Tier- und Pflanzenwelt sowie den Menschen. Im Hinblick auf unseren Alltag stellen soziale Interaktion, Produktivität oder das emotionale Befinden nur einen kleinen Ausschnitt der Aspekte dar, auf die das Licht einen positiven Einfluss hat. Ziel der Studie. Der Zusammenhang zwischen Sonnenexposition und dem individuellen Glücksempfinden wird im internationalen Vergleich untersucht. Material und Methoden. Für die Analyse wurden Daten der Sonnenscheindauer (Anzahl der Sonnenstunden) und der geo grafischen Lage der betrachteten Regionen (Breitengrade) vom Deutschen Wetterdienst manuell zu den Daten des International Social Survey Programme (2011) Health and Health Care ergänzt und eine multivariate lineare Mehrebenenanalyse durchgeführt. Ergebnisse. Sonnenstunden haben einen positiven und statistisch signifikanten Einfluss auf das Glücksempfinden. Interessant ist, dass Personen, die dem Einfluss wechselnder Jahreszeiten ausgesetzt sind, sich statistisch signifikant in gleichem Maße schlechter in ihrem Glücksempfinden einschätzen als Personen, die so nah am Äquator wohnen, dass sie keinen saisonbedingten Klimaveränderungen ausgesetzt sind. Schlussfolgerung. Sonnenstunden wirken sich statistisch signifikant positiv auf das Glücksempfinden aus. Ein Mix an weiteren Einflüssen zur Steigerung des Glücksempfindens setzt sich zusammen aus Vertrauen in das nationale Gesundheitswesen, hoher Bildung, Rauchabstinenz, sportlicher Aktivität und Arbeitsmarktbeteiligung. Diese Mechanismen wirken sich im Durchschnitt in allen berücksichtigten Länderkontexten gleichermaßen positiv auf das Glücksempfinden der Menschen aus.
By using 64,270 daily observations from a large hospital in Guangzhou between 2017 and 2019, we analyzed the impact of extreme temperature on the health of newborns via OLS regression with time fixed effect. Given that the short-term temperature change can be regarded as exogenous and random, solving the potential endogenous problem is critical. We find that extreme temperature negatively affects the health of newborns. The Apgar score, an index for evaluating neonatal health, decreases by 0.008 (0.029%) when the duration of extreme temperature events increases by a day. A series of robustness checks verify the reliability of this negative effect. Extreme temperature also has a particularly serious effect on the health of newborns whose mothers have poor education. By gradually extending the observation period, we find that the effect of extreme temperature on neonatal health is mainly concentrated 1–6 weeks before delivery, whereas the effect of extreme temperature on hospitalization cost is mainly concentrated 4–8 weeks before delivery. This paper provides a valuable reference for evaluating the health and social costs of extreme weather, and our findings are conducive to the construction of climate-resilient health systems, especially in Guangzhou.
Background Abusive head trauma (AHT) is a leading cause of death and disability in children and one of the most lethal forms of child abuse. Most known risk factors for AHT pertain to the infant’s caregiver and limited research has assessed external influences beyond the familial or caregiver/infant dyad. Objective Our primary objective was to determine if temperature patterns are associated with AHT events. Secondary outcomes included associations between AHT and specific days of the week, months or seasons. Methods This was a retrospective review of 198 patients under 2 years old diagnosed with AHT at Saint Louis Children’s Hospital. Demographic information was obtained from the medical record for each patient. For each AHT incident, the date and zip code of the incident were recorded. Temperature on the date of incident was identified using the Midwestern Regional Climate Center (MRCC). Chi square tests were utilized to calculate differences in cases per year as well as temperature and seasonal variation. Results Temperature was not associated with a statistically significant increase in cases of AHT. There was an increase in cases as temperatures rose, but no statistically significant associations between incidence of AHT and day of the week, month, or season. Conclusion Our study suggests no significant association between AHT incidence and temperature or temporal patterns in this hospital.
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In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators. (46 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
In recent studies of the structure of affect, positive and negative affect have consistently emerged as two dominant and relatively independent dimensions. A number of mood scales have been created to measure these factors; however, many existing measures are inadequate, showing low reliability or poor convergent or discriminant validity. To fill the need for reliable and valid Positive Affect and Negative Affect scales that are also brief and easy to administer, we developed two 10-item mood scales that comprise the Positive and Negative Affect Schedule (PANAS). The scales are shown to be highly internally consistent, largely uncorrelated, and stable at appropriate levels over a 2-month time period. Normative data and factorial and external evidence of convergent and discriminant validity for the scales are also presented. (PsycINFO Database Record (c) 2010 APA, all rights reserved)
The structure of affect is well represented as a circumplex. The results of a within-subject longitudinal study of self-reported mood indicate individual differences in the circumplex structure of affective experience. These differences can be captured by two constructs: valence focus and arousal focus. Valence focus is the degree to which individuals attend the hedonic component of their affective experience; arousal focus is the degree to which individuals attend the arousal component of their affective experience. In this study, differences in individuals' attention to the hedonic and arousal components of affective experience were related to observed correlations between specific affective elements, such as (a) ratings of anxious and depressed mood and (b) "Negative Affect" and "Positive Affect." (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Differences between morning and evening types (i.e., diurnal types) on the "Big Five" personality factors and on the personal characteristics of self-esteem, body-esteem, and locus of control were examined. 360 college students participated. Consistent with previous research (e.g., M. J. Blake and D. W. J. Corcoran, 1972), evening types were marginally more extroverted than morning types. More pronounced were differences between diurnal types on the conscientiousness factor of the Big Five, and in self-esteem and locus of control: Morning types were more conscientious, higher in self-esteem, and more internal in locus of control than evening types. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
It has recently been shown that those individuals who experience more profound seasonal disturbances in mood and behavior have increased levels of neurotic personality traits (Jang, Lam, Livesley, & Vernon, 1997; Kane & Lowis, 1999; Murray, Hay, & Armstrong, 1995). The present study however proposes that the development and nature of seasonal depressions may be better explained through consideration of the combined effects of neurotic and extraverted personality traits. Using the EPQ and the Seasonal Pattern Assessment Questionnaire, personality and levels of seasonal disturbance were measured in 77 adults (16 males and 61 females). As predicted, increased levels of neurotic personality traits were associated with more profound seasonal disturbances in mood and behavior but the degree of seasonal variation in mood and behavior was equally well explained in terms of “impulsivity” as reflecting the activity of the Behavioral Activation System. Overall, it is concluded that a more integrated approach to personality could be adopted to aid the understanding of seasonal depressions.
146 women and 44 men (out- and inpatients; treatment sample) with Seasonal Affective Disorder (SAD; winter type) were tested for gender differences in demographic, clinical and seasonal characteristics. Sex ratio in prevalence was (women : men) 3.6 : 1 in unipolar depressives and 2.4 : 1 in bipolars (I and II). Sex ratios varied also between different birth cohorts and men seemed to underreport symptoms. There was no significant difference in symptom-profiles in both genders, however a preponderance of increased eating and different food selection on a trend level occured in women. The female group suffered significantly more often from thyroid disorders and from greater mood variations because of dark and cloudy weather. Women referred themselves to our clinic significantly more frequently as compared to men. In summary gender differences in SAD were similar to those of non-seasonal depression: the extent of gender differences in the prevalence of affective disorders appears to depend on case criteria such as diagnosis (unipolar vs. bipolar), birth cohort and number of symptoms as minimum threshold for diagnosis. We support the idea of applying sex-specific diagnostic criteria for diagnosing depression on the basis of our data and of the literature.
Seasonal Affective Disorder (SAD) is a recently-defined variant of recurrent major depression which is understood as having a uniquely biological aetiology. Notwithstanding the appealing simplicity of light's putative role in the aetiology and treatment of SAD, there are a priori grounds for expecting that a wider range of variables might be relevant. Based on the accepted description of SAD as a continuum of mood vulnerability and the clinical overlap between SAD and neurotic depression, it was hypothesized that the trait construct of neuroticism might prove relevant to SAD. Results suggested that the tendency to report seasonal variation in SAD symptoms covaries with neuroticism. Furthermore, reports of seasonality were found to covary with a tendency to attribute moods to non-seasonal environmental factors beyond the individual's control.