Impact of three decades of warming, increased nutrient availability, and increased cloudiness on the fluxes of greenhouse gases and biogenic volatile organic compounds in a subarctic tundra heath.

Climate change is exposing subarctic ecosystems to higher temperatures, increased nutrient availability, and increasing cloud cover. In this study, we assessed how these factors affect the fluxes of greenhouse gases (GHGs) (i.e., methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2)), and biogenic volatile organic compounds (BVOCs) in a subarctic mesic heath subjected to 34 years of climate change related manipulations of temperature, nutrient availability, and light. GHGs were sampled from static chambers and gases analyzed with gas chromatograph. BVOCs were measured using the push-pull method and gases analyzed with chromatography-mass spectrometry. The soil temperature and moisture content in the warmed and shaded plots did not differ significantly from that in the controls during GHG and BVOC measurements. Also, the enclosure temperatures during BVOC measurements in the warmed and shaded plots did not differ significantly from temperatures in the controls. Hence, this allowed for assessment of long-term effects of the climate treatment manipulations without interference of temperature and moisture differences at the time of measurements. Warming enhanced CH4 uptake and the emissions of CO2, N2O, and isoprene. Increased nutrient availability increased the emissions of CO2 and N2O but caused no significant changes in the fluxes of CH4 and BVOCs. Shading (simulating increased cloudiness) enhanced CH4 uptake but caused no significant changes in the fluxes of other gases compared to the controls. The results show that climate warming and increased cloudiness will enhance CH4 sink strength of subarctic mesic heath ecosystems, providing negative climate feedback, while climate warming and enhanced nutrient availability will provide positive climate feedback through increased emissions of CO2 and N2O. Climate warming will also indirectly, through vegetation changes, increase the amount of carbon lost as isoprene from subarctic ecosystems.

Associations of ambient temperature and total cloud cover during pregnancy with newborn vitamin D status.

OBJECTIVES: We aimed to estimate the effects of temperature and total cloud cover before birth on newborn vitamin D status. STUDY DESIGN: Prospective birth cohort. METHODS: This study included 2055 mother-newborn pairs in Wuhan, Hubei province, China. The data of temperature and total cloud cover from 30 days before birth were collected, and cord blood 25-hydroxyvitamin D [25(OH)D] were determined. Restricted cubic spline regression models, multiple linear regression models, and logistic regression models were applied to estimate the associations. RESULTS: A "J" shaped curve was observed between temperature and vitamin D status, and an inverse "J" shaped curve was observed between total cloud cover and vitamin D status. Compared to the fourth quartile (75-100th percentile, Q4) of average temperature (30 days before birth), the odds ratio (OR) for Q1 (0-25th percentile) associated with the vitamin D deficiency occurrence (<20 ng/mL) was 3.63 (95% CI, 1.54, 8.65). Compared to Q1 of the average total cloud cover (30 days before birth), the OR associated with the occurrence of vitamin D deficiency was 2.38 (95% CI, 1.63, 3.50) for the Q4. CONCLUSIONS: Low temperature and high cloud cover before delivery were significantly associated with an increased probability of vitamin D deficiency in newborns. The findings suggested that pregnancy women lacking sufficient sunlight exposure still need vitamin D supplement to overcome the potential vitamin D deficiency status.

A Combined Persistence and Physical Approach for Ultra-Short-Term Photovoltaic Power Forecasting Using Distributed Sensors.

This paper presents a novel method for forecasting the impact of cloud cover on photovoltaic (PV) fields in the nowcasting term, utilizing PV panels as sensors in a combination of physical and persistence models and integrating energy storage system control. The proposed approach entails simulating a power network consisting of a 22 kV renewable energy source and energy storage, enabling the evaluation of network behavior in comparison to the national grid. To optimize computational efficiency, the authors develop an equivalent model of the PV + energy storage module, accurately simulating system behavior while accounting for weather conditions, particularly cloud cover. Moreover, the authors introduce a control system model capable of responding effectively to network dynamics and providing comprehensive control of the energy storage system using PID controllers. Precise power forecasting is essential for maintaining power continuity, managing overall power-system ramp rates, and ensuring grid stability. The adaptability of our method to integrate with solar fencing systems serves as a testament to its innovative nature and its potential to contribute significantly to the renewable energy field. The authors also assess various scenarios against the grid to determine their impact on grid stability. The research findings indicate that the integration of energy storage and the proposed forecasting method, which combines physical and persistence models, offers a promising solution for effectively managing grid stability.

Long-term remote sensing-based methods for monitoring air pollution and cloud cover in the Balkan countries.

The use of remote sensing and GIS methodology has accelerated the processing of data on pollution, but has also raised a question about the accuracy of the same. The research focuses on four main air pollutants (CO, NO, SO2, O3), the data on which were obtained from satellite images of Landsat 8 and Landsat 9, for the period 2000-2020. The data on relative cloudiness were obtained from the database CHELSA (Climatologies at high resolution for the earth's land surface areas) for the period 1980-2010. All the data were further processed and analyzed through the procedures of numerical GIS analysis, multi-criteria analysis, supervised and unsupervised satellite classification, and pixel analysis. The results of the analysis of cloud cover in the Balkan region showed that the month with the highest cloud cover in this period was February, with the maximum of (93.18%), whereas the lowest cloud cover was in July (0.19%). The analyzed period (2000-2010) was in the middle range for the pollutants NO and SO2 and in the lower range for CO; O3. In the period 2010-2020, there were high concentrations of NO, SO2, and CO and low concentrations of O3. The most polluted cities in the last twenty years are Ordu (Turkey), Sarajevo (Bosnia and Herzegovina), and Bor (Serbia). Finally, two most extreme air pollutants in the territory of Balkan countries were SO2 and NO (2000-2020).

Acclimation of subarctic vegetation to warming and increased cloudiness.

Subarctic ecosystems are exposed to elevated temperatures and increased cloudiness in a changing climate with potentially important effects on vegetation structure, composition, and ecosystem functioning. We investigated the individual and combined effects of warming and increased cloudiness on vegetation greenness and cover in mesocosms from two tundra and one palsa mire ecosystems kept under strict environmental control in climate chambers. We also investigated leaf anatomical and biochemical traits of four dominant vascular plant species (Empetrum hermaphroditum, Vaccinium myrtillus, Vaccinium vitis-idaea, and Rubus chamaemorus). Vegetation greenness increased in response to warming in all sites and in response to increased cloudiness in the tundra sites but without associated increases in vegetation cover or biomass, except that E. hermaphroditum biomass increased under warming. The combined warming and increased cloudiness treatment had an additive effect on vegetation greenness in all sites. It also increased the cover of graminoids and forbs in one of the tundra sites. Warming increased leaf dry mass per area of V. myrtillus and R. chamaemorus, and glandular trichome density of V. myrtillus and decreased spongy intercellular space of E. hermaphroditum and V. vitis-idaea. Increased cloudiness decreased leaf dry mass per area of V. myrtillus, palisade thickness of E. hermaphroditum, and stomata density of E. hermaphroditum and V. vitis-idaea, and increased leaf area and epidermis thickness of V. myrtillus, leaf shape index and nitrogen of E. hermaphroditum, and palisade intercellular space of V. vitis-idaea. The combined treatment caused thinner leaves and decreased leaf carbon for V. myrtillus, and increased leaf chlorophyll of E. hermaphroditum. We show that under future warmer increased cloudiness conditions in the Subarctic (as simulated in our experiment), vegetation composition and distribution will change, mostly dominated by graminoids and forbs. These changes will depend on the responses of leaf anatomical and biochemical traits and will likely impact carbon gain and primary productivity and abiotic and biotic stress tolerance.

Impact of climate change on non-communicable diseases caused by altered UV radiation.

Background: UV radiation can cause serious skin and eye diseases, especially cancers. UV-related skin cancer incidences have been increasing for decades. The determining factor for this development is the individual UV exposure. Climate change-induced changes in atmospheric factors can influence individual UV exposure. Methods: On the basis of a topic-specific literature research, a review paper was prepared and supplemented by as yet unpublished results of the authors' own studies. The need for scientific research and development is formulated as well as primary prevention recommendations. Results: Climate change alters the factors influencing UV irradiance and annual UV dose in Germany. First evaluations of satellite data for Germany show an increase in mean peak UV irradiance and annual UV dose for the last decade compared to the last three decades. Conclusions: The climate change-related influences on individual UV exposure and the associated individual disease incidence cannot yet be reliably predicted due to considerable uncertainties. However, the current UV-related burden of disease already requires primary preventive measures to prevent UV-related diseases.

The effect of climate on melioidosis incidence in Townsville, Australia: a dry tropical region.

BACKGROUND: Townsville is in the dry tropics in Northern Australia and an endemic region for melioidosis. Melioidosis is an infectious disease caused by Burkholderia pseudomallei, a soil dwelling organism. The incidence of melioidosis is associated with high levels of rainfall and has been linked to multiple weather variables in other melioidosis endemic regions such as in Darwin. In contrast to Townsville, Darwin is in the wet-dry tropics in Northern Australia and receives 40% more rainfall. We assessed the relationship between melioidosis incidence and weather conditions in Townsville and compared the patterns to the findings in Darwin and other melioidosis endemic regions. METHOD: Performing a time series analysis from 1996 to 2020, we applied a negative binomial regression model to evaluate the link between the incidence of melioidosis in Townsville and various weather variables. Akaike's information criterion was used to assess the most parsimonious model with best predictive performance. Fourier terms and lagged deviance residuals were included to control long term seasonal trends and temporal autocorrelation. RESULTS: Humidity is the strongest predictor for melioidosis incidence in Townsville. Furthermore, the incidence of melioidosis showed a three-times rise in the Townsville region when >200 mm of rain fell within the fortnight. Prolonged rainfall had more impact than a heavy downpour on the overall melioidosis incident rate. There was no statistically significant increase in incidence with cloud cover in the multivariable model. CONCLUSION: Consistent with other reports, melioidosis incidence can be attributed to humidity and rainfall in Townsville. In contrast to Darwin, there was no strong link between melioidosis cases and cloud cover and nor single large rainfall events.

Atmospheric pressure predicts probability of departure for migratory songbirds.

BACKGROUND: Weather can have both delayed and immediate impacts on animal populations, and species have evolved behavioral adaptions to respond to weather conditions. Weather has long been hypothesized to affect the timing and intensity of avian migration, and radar studies have demonstrated strong correlations between weather and broad-scale migration patterns. How weather affects individual decisions about the initiation of migratory flights, particularly at the beginning of migration, remains uncertain. METHODS: Here, we combine automated radio telemetry data from four species of songbirds collected at five breeding and wintering sites in North America with hourly weather data from a global weather model. We use these data to determine how wind profit, atmospheric pressure, precipitation, and cloud cover affect probability of departure from breeding and wintering sites. RESULTS: We found that the probability of departure was related to changes in atmospheric pressure, almost completely regardless of species, season, or location. Individuals were more likely to depart on nights when atmospheric pressure had been rising over the past 24 h, which is predictive of fair weather over the next several days. By contrast, wind profit, precipitation, and cloud cover were each only informative predictors of departure probability in a single species. CONCLUSIONS: Our results suggest that individual birds actively use weather information to inform decision-making regarding the initiation of departure from the breeding and wintering grounds. We propose that birds likely choose which date to depart on migration in a hierarchical fashion with weather not influencing decision-making until after the departure window has already been narrowed down by other ultimate and proximate factors.

Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand.

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98 °C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand.

Reduced diurnal temperature range mitigates drought impacts on larch tree growth in North China.

Forests are facing climate changes such as warmer temperatures, accelerated snowmelt, increased drought, as well as changing diurnal temperature ranges (DTR) and cloud cover regimes. How tree growth is influenced by the changes in daily to monthly temperatures and its associations with droughts has been extensively investigated, however, few studies have focused on how changes in sub-daily temperatures i.e., DTR, influence tree growth during drought events. Here, we used a network of Larix principis-rupprechtii tree-ring data from 1989 to 2018, covering most of the distribution of planted larch across North China, to investigate how DTR, cloud cover and their interactions influence the relationship between drought stress and tree growth. DTR showed a negative correlation with larch growth in 95 % of sites (rmean = -0.30, significant in 42 % of sites). Cloud cover was positively correlated with growth in 87 % of sites (rmean = 0.13, significant in 5 % of sites). Enhanced tree growth was found at lower DTR in the absence of severe drought. Our findings highlight that in the absence of severe droughts, reduced DTR benefits tree growth, while increased cloud cover tended to benefit tree growth only during severe drought periods. Given how DTR influences drought impacts on tree growth, net tree growth was found to be larger in regions with smaller DTR.

A sandstorm extreme event from the Yellow River Basin in March 2021: Accurate identification and driving cause.

Sandstorm is a natural meteorological disaster that can appear suddenly and is often extremely destructive. In areas with small number of meteorological observation stations, it is difficult to effectively monitor sandstorm. Moderate Resolution Imaging Spectroradiometer (MODIS) data have the characteristics of high resolution and wide coverage, making it possible to monitor dynamic weather changes in a large area over time, and such data are widely used in sandstorm monitoring. The purpose of our research was to achieve a more accurate identification of sandstorm according to the differences in reflectance and brightness temperature between sandstorm and other phenomena, and to better understand the formation, movement track and driving cause of sandstorm extreme event. Taking the intense sandstorm event that occurred in the Yellow River Basin from March 13th to 18th, 2021 as an example, sandstorm process was analyzed based on MODIS data and meteorological monitoring data. The threshold of Normalized Difference Dust Index (NDDI) and Normalized Brightness Temperature Dust Index (NBTDI) realized accurate sandstorm monitoring and quantification of the sandstorm coverage areas. Sandstorm covered 32.89 % and 37.23 % of the total areas of the Yellow River Basin on March 15th and 16th, 2021, respectively. In addition, observation data from 22 meteorological stations also provided an important reference for further understanding of sandstorm weather. The intense sandstorm event in China on March 15th, 2021 originated from the dust in Mongolia. This sandstorm event caused great damage to the ecological environment and caused serious losses to people's lives and properties. This study improved the monitoring of sandstorm by remote sensing technology, and the results had importance for the long-term monitoring and prevention of sandstorm.

Estimated cloud-top entrainment index explains positive low-cloud-cover feedback.

How subtropical marine low cloud cover (LCC) will respond to global warming is a major source of uncertainty in future climate change. Although the estimated inversion strength (EIS) is a good predictive index of LCC, it has a serious limitation when applied to evaluate LCC changes due to warming: The LCC decreases despite increases in EIS in future climate simulations of global climate models (GCMs). In this work, using state-of-the-art GCMs, we show that the recently proposed estimated cloud-top entrainment index (ECTEI) decreases consistently with LCC in warmer sea surface temperature (SST) climates. For the patterned SST warming predicted by coupled GCMs, ECTEI can constrain the subtropical marine LCC feedback to -0.41 ± 0.28% K-1 (90% CI), implying virtually certain positive feedback. ECTEI physically explains the heuristic model for LCC changes based on a linear combination of EIS and SST changes in previous studies in terms of cloud-top entrainment processes.

Phenology and production of Hassaoui grapevines as affected by climate anomalies in Al Ahsa region.

Climate change is a dramatic crisis that has left severe impacts on viticulture. Phenological events over 41 years and annual climatic anomalies' data over these years in Al Ahsa region were procured. Annual temperature and wind speed anomalies had the strongest influence on all phenological events of the varieties White and Red Hassaoui, starting from the beginning of budburst until harvest. Moreover, the average yield of both varieties decreased significantly by 319.4 and 317 kg ha-1 respectively between 1997 and 2019 in comparison with the interval of years 1979-1996. Earlier phenological events were positively correlated with annual temperature anomaly and negatively correlated with annual wind speed anomaly. The latter shortened the dates of occurrence of beginning and full veraison. Yield decreased with higher annual temperature, wind speed and total cloud cover anomalies, and lower annual total precipitation anomaly. Higher annual temperature and wind speed anomalies were correlated with a shorter period between beginning of budburst to beginning of veraison (P3). Shorter periods between beginning and full veraison (P6) and beginning of veraison and harvest (P7) of Red Hassaoui were positively correlated with annual precipitable water anomaly. Results suggest a high level of adaptation of both tested varieties to changing climate conditions in Al Ahsa, though irrigating vines after harvest on a weekly basis would help overcoming the minimal reduction in yield which was caused by the shortage in precipitation.

Deep Learning Based Cloud Cover Parameterization for ICON.

A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm-resolving model (SRM) simulations. The ICOsahedral Non-hydrostatic (ICON) modeling framework permits simulations ranging from numerical weather prediction to climate projections, making it an ideal target to develop neural network (NN) based parameterizations for sub-grid scale processes. Within the ICON framework, we train NN based cloud cover parameterizations with coarse-grained data based on realistic regional and global ICON SRM simulations. We set up three different types of NNs that differ in the degree of vertical locality they assume for diagnosing cloud cover from coarse-grained atmospheric state variables. The NNs accurately estimate sub-grid scale cloud cover from coarse-grained data that has similar geographical characteristics as their training data. Additionally, globally trained NNs can reproduce sub-grid scale cloud cover of the regional SRM simulation. Using the game-theory based interpretability library SHapley Additive exPlanations, we identify an overemphasis on specific humidity and cloud ice as the reason why our column-based NN cannot perfectly generalize from the global to the regional coarse-grained SRM data. The interpretability tool also helps visualize similarities and differences in feature importance between regionally and globally trained column-based NNs, and reveals a local relationship between their cloud cover predictions and the thermodynamic environment. Our results show the potential of deep learning to derive accurate yet interpretable cloud cover parameterizations from global SRMs, and suggest that neighborhood-based models may be a good compromise between accuracy and generalizability.

Broad-UNet: Multi-scale feature learning for nowcasting tasks.

Weather nowcasting consists of predicting meteorological components in the short term at high spatial resolutions. Due to its influence in many human activities, accurate nowcasting has recently gained plenty of attention. In this paper, we treat the nowcasting problem as an image-to-image translation problem using satellite imagery. We introduce Broad-UNet, a novel architecture based on the core UNet model, to efficiently address this problem. In particular, the proposed Broad-UNet is equipped with asymmetric parallel convolutions as well as Atrous Spatial Pyramid Pooling (ASPP) module. In this way, the Broad-UNet model learns more complex patterns by combining multi-scale features while using fewer parameters than the core UNet model. The proposed model is applied on two different nowcasting tasks, i.e. precipitation maps and cloud cover nowcasting. The obtained numerical results show that the introduced Broad-UNet model performs more accurate predictions compared to the other examined architectures.

Cloudiness reduces the bleaching response of coral reefs exposed to heat stress.

Climate change and warming ocean temperatures are a threat to coral reef ecosystems. Since the 1980s, there has been an increase in mass coral bleaching and associated coral mortality due to more frequent and severe thermal stress. Although most research has focused on the role of temperature, coral bleaching is a product of the interacting effects of temperature and other environmental variables such as solar radiation. High light exacerbates the effects of thermal stress on corals, whereas reductions in light can reduce sensitivity to thermal stress. Here, we use an updated global dataset of coral bleaching observations (n = 35,769) from 1985 to 2017 and satellite-derived datasets of SST and clouds to examine for the first time at a global scale the influence of cloudiness on the likelihood of bleaching from thermal stress. We find that among coral reefs exposed to severe bleaching-level heat stress (Degree Heating Weeks >8°Cˑweek), bleaching severity is inversely correlated with the interaction of heat stress and cloud fraction anomalies (p < 0.05), such that higher cloudiness implies reduced bleaching response. A Random Forest model analysis employing different set of environmental variables shows that a model employing Degree Heating Weeks and the 30-day cloud fraction anomaly most accurately predicts bleaching severity (Accuracy = 0.834; Cohen's Kappa = 0.769). Based on these results and global warm-season cloudiness patterns, we develop a 'cloudy refugia' index which identifies the central equatorial Pacific and French Polynesia as regions where cloudiness is most likely to protect corals from bleaching. Our findings suggest that incorporating cloudiness into prediction models can help delineate bleaching responses and identify reefs which may be more resilient to climate change.

Cloud cover amplifies the sleep-suppressing effect of artificial light at night in geese.

In modern society the night sky is lit up not only by the moon but also by artificial light devices. Both of these light sources can have a major impact on wildlife physiology and behaviour. For example, a number of bird species were found to sleep several hours less under full moon compared to new moon and a similar sleep-suppressing effect has been reported for artificial light at night (ALAN). Cloud cover at night can modulate the light levels perceived by wildlife, yet, in opposite directions for ALAN and moon. While clouds will block moon light, it may reflect and amplify ALAN levels and increases the night glow in urbanized areas. As a consequence, cloud cover may also modulate the sleep-suppressing effects of moon and ALAN in different directions. In this study we therefore measured sleep in barnacle geese (Branta leucopsis) under semi-natural conditions in relation to moon phase, ALAN and cloud cover. Our analysis shows that, during new moon nights stronger cloud cover was indeed associated with increased ALAN levels at our study site. In contrast, light levels during full moon nights were fairly constant, presumably because of moonlight on clear nights or because of reflected artificial light on cloudy nights. Importantly, cloud cover caused an estimated 24.8% reduction in the amount of night-time NREM sleep from nights with medium to full cloud cover, particularly during new moon when sleep was unaffected by moon light. In conclusion, our findings suggest that cloud cover can, in a rather dramatic way, amplify the immediate effects of ALAN on wildlife. Sleep appears to be highly sensitive to ALAN and may therefore be a good indicator of its biological effects.

GLOBE Observer Data: 2016-2019.

This technical report summarizes the GLOBE Observer data set from 1 April 2016 to 1 December 2019. GLOBE Observer is an ongoing NASA-sponsored international citizen science project that is part of the larger Global Learning and Observations to Benefit the Environment (GLOBE) Program, which has been in operation since 1995. GLOBE Observer has the greatest number of participants and geographic coverage of the citizen science projects in the Earth Science Division at NASA. Participants use the GLOBE Observer mobile app (launched in 2016) to collect atmospheric, hydrologic, and terrestrial observations. The app connects participants to satellite observations from Aqua, Terra, CALIPSO, GOES, Himawari, and Meteosat. Thirty-eight thousand participants have contributed 320,000 observations worldwide, including 1,000,000 georeferenced photographs. It would take an individual more than 13 years to replicate this effort. The GLOBE Observer app has substantially increased the spatial extent and sampling density of GLOBE measurements and more than doubled the number of measurements collected through the GLOBE Program. GLOBE Observer data are publicly available (at observer.globe.gov).

Impact of meteorological parameters on suicide mortality rates: A case-crossover analysis in Southern Germany (1990-2006).

BACKGROUND: There is evidence for a seasonal pattern of suicides with peaks in spring and early summer; however, only a limited number of studies has investigated whether daily changes in meteorological variables may trigger suicides. METHODS: Daily fatal suicide (N = 10,595) and meteorological data were available for four Bavarian cities and ten counties (Germany) for 1990-2006. City/county-specific immediate, delayed and cumulative effects of air temperature, sunshine duration, and cloud cover on suicides were analyzed using a time-stratified case-crossover approach; city/county-specific effects were then combined using random effects meta-analysis. Potential effect modifiers were specific weather conditions, personal or regional characteristics, and season. RESULTS: A 5 °C increase in air temperature on the day before a suicide compared to the control days was associated with a 5.7% (95% confidence interval (CI): 0.6; 11.0) higher suicide risk. Further, the suicide risk was 6.5% (95% CI: 0.2; 13.3) higher on days with low/medium cloud cover (0-6 oktas) compared to days with high cloud cover (7-8 oktas). While daily changes in temperature were not associated with suicides in spring, we found a higher suicide risk in summer, autumn, and winter in association with temperature increases. The effects of cloud cover were strongest in summer and autumn and on days with temperature above the median (>8.8 °C). Sunshine duration was not associated with suicides. CONCLUSION: We found a higher risk for suicides in association with short-term increases in air temperature on the day before the event compared to the control days and on days with low to medium cloud cover. This may highlight times when people are more likely to commit suicide.

Impacts of snow and cloud covers on satellite-derived PM2.5 levels.

Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM2.5) levels, whereas snow/cloud covers often lead to a large proportion of non-random missing AOD values. As a result, the fully covered and unbiased PM2.5 estimates will be hard to generate. Among the current approaches to deal with the data gap issue, few have considered the cloud-AOD relationship and none of them have considered the snow-AOD relationship. This study examined the impacts of snow and cloud covers on AOD and PM2.5 and made full- coverage PM2.5 predictions by considering these impacts. To estimate missing AOD values, daily gap-filling models with snow/cloud fractions and meteorological covariates were developed using the random forest algorithm. By using these models in New York State, a daily AOD data set with a 1-km resolution was generated with a complete coverage. The "out-of-bag" R2 of the gap-filling models averaged 0.93 with an interquartile range from 0.90 to 0.95. Subsequently, a random forest-based PM2.5 prediction model with the gap-filled AOD and covariates was built to predict fully covered PM2.5 estimates. A ten-fold cross-validation for the prediction model showed a good performance with an R2 of 0.82. In the gap-filling models, the snow fraction was of higher significance to the snow season compared with the rest of the year. The prediction models fitted with/without the snow fraction also suggested the discernible changes in PM2.5 patterns, further confirming the significance of this parameter. Compared with the methods without considering snow and cloud covers, our PM2.5 prediction surfaces showed more spatial details and reflected small-scale terrain-driven PM2.5 patterns. The proposed methods can be generalized to the areas with extensive snow/cloud covers and large proportions of missing satellite AOD data for predicting PM2.5 levels with high resolutions and complete coverage.