Explore the vast range of titles available.

The prediction accuracy of hourly air temperature is generally poor because of random changes, long time series, and the nonlinear relationship between temperature and other meteorological elements, such as air pressure, dew point, and wind speed. In this study, two deep-learning methods-a convolutional neural network (CNN) and long short-term memory (LSTM)-are integrated into a network model (CNN-LSTM) for hourly temperature prediction. The CNN reduces the dimensionality of the time-series data, while LSTM captures the long-term memory of the massive temperature time-series data. Training and validation sets are constructed using 60,133 hourly meteorological data (air temperature, dew point, air pressure, wind direction, wind speed, and cloud amount) obtained from January 2000 to October 2020 at the Yinchuan meteorological station in China. Mean absolute error (MAE), mean absolute percentage error (MAPE), and goodness of fit are used to compare the performances of the CNN, LSTM, and CNN-LSTM models. The results show that MAE, MAPE, RMSE, and PBIAS from the CNN-LSTM model for hourly temperature prediction are 0.82, 0.63, 2.05, and 2.18 in the training stage and 1.02, 0.8, 1.97, and −0.08 in the testing stage. Average goodness of fit from the CNN-LSTM model is 0.7258, higher than the CNN (0.5291), and LSTM (0.5949) models. The hourly temperatures predicted by the CNN-LSTM model are highly consistent with the measured values, especially for long time series of hourly temperature data.

Spatial climate datasets of 1981-2010 long-term mean monthly average dew point and minimum and maximum vapor pressure deficit were developed for the conterminous United States at 30-arcsec (~800m) resolution. Interpolation of long-term averages (twelve monthly values per variable) was performed using PRISM (Parameter-elevation Relationships on Independent Slopes Model). Surface stations available for analysis numbered only 4,000 for dew point and 3,500 for vapor pressure deficit, compared to 16,000 for previously-developed grids of 1981-2010 long-term mean monthly minimum and maximum temperature. Therefore, a form of Climatologically-Aided Interpolation (CAI) was used, in which the 1981-2010 temperature grids were used as predictor grids. For each grid cell, PRISM calculated a local regression function between the interpolated climate variable and the predictor grid. Nearby stations entering the regression were assigned weights based on the physiographic similarity of the station to the grid cell that included the effects of distance, elevation, coastal proximity, vertical atmospheric layer, and topographic position. Interpolation uncertainties were estimated using cross-validation exercises. Given that CAI interpolation was used, a new method was developed to allow uncertainties in predictor grids to be accounted for in estimating the total interpolation error. Local land use/land cover properties had noticeable effects on the spatial patterns of atmospheric moisture content and deficit. An example of this was relatively high dew points and low vapor pressure deficits at stations located in or near irrigated fields. The new grids, in combination with existing temperature grids, enable the user to derive a full suite of atmospheric moisture variables, such as minimum and maximum relative humidity, vapor pressure, and dew point depression, with accompanying assumptions. All of these grids are available online at http://prism.oregonstate.edu, and include 800-m and 4-km resolution data, images, metadata, pedigree information, and station inventory files.

The metal surfaces of a car exhibit favorable properties for the passive condensation of atmospheric water. Under certain nocturnal climatic conditions (high relative humidity, weak windspeed, and total nebulosity), dew is often observed on cars, and it is appropriate to ask the question of using a vehicle as a standard condenser for estimating the dew yield. In order to see whether cars can be used as reference dew condensers, we report a detailed study of radiative cooling and dew formation on cars in the presence of radiating obstacles and for various windspeeds. Measurements of temperature and condensed dew mass on different car parts (rooftop, front and back hoods, windshield, lateral and back windows, inside and outside air) are compared with the same data obtained on a horizontal, thermally isolated planar film. The paper concludes that heat transfer coefficients, evaluated from temperature and dew yield measurements, are found nearly independent of windspeed and tilt angles. Moreover, this work describes the relation between cooling and dew condensation with the presence or not of thermal isolation. This dependence varies with the surface tilt angle according to the angular dependence of the atmosphere radiation. This work also confirms that cars can be used to estimate the dew yields in a given site. A visual observation scale h = Kn, with h the dew yield (mm) and n = 0, 1 2, 3 an index, which depends whether dew forms or not on rooftop, windshield, and lateral windows, is successfully tested with 8 different cars in 5 sites with three different climates, using K = (0.067 ± 0.0036) mm·day−1.

Convection-permitting climate models (CPMs) are becoming increasingly used in climate change studies. These models show greatly improved convective rainfall statistics compared to parameterized-convection regional climate models (RCMs), but are they also more reliable in a climate change setting? Increases in rainfall extremes are generally considered to be caused by increases in absolute humidity, primarily following from the Clausius–Clapeyron relation, while the influence of relative humidity changes is uncertain and not systematically explored. Quantifying these humidity dependencies in the present-day climate may help the interpretation of future changes, which are driven by increases in absolute humidity but also decreases in relative humidity in most continental areas in summer. Here, we systematically analyse hourly rainfall extremes and their dependencies on 2 m dew point temperature (absolute humidity) and dew point depression (relative humidity) in seven RCM and five CPM simulations for the present-day climate. We compare these to observations from the Netherlands (a moderate moist climate) and southern France (a warmer and drier climate). We find that the RCMs display a large spread in outcomes, in particular in their relative humidity dependence, with a strong suppression of hourly rainfall extremes in low relative humidity conditions. CPMs produce better overall rainfall statistics, show less inter-model spread, and have absolute and relative humidity dependencies more consistent with the observations. In summary, our results provide evidence that future changes in convective rainfall extremes in CPMs are more reliable compared to RCMs, whereas the discussed dependencies also provide a metric to evaluate and further improve model performance as well as improving convection schemes.

Dew is ubiquitous but also the least studied hydrological component in most ecosystems. Dew can serve as a supplementary water source for sustaining plant growth and potentially play an important role in broader ecosystem functions, especially in drylands. However, there is still no unified comprehension of the ecological effects of dew on plants. In this review, we focus on vascular plants and summarize recent advances in the understanding of the effects of dew across diverse vegetation types and climatic regions, including the distribution of dew formation and key dew characteristics, the mechanisms by which dew influences ecosystem water, carbon, and energy fluxes, and dew quantification and modeling. We also provide a detailed synthesis of its ecological roles across scales from the leaf to the plant and ecosystem levels. This comprehensive overview synthesizes current evidence on the connections between dew formation patterns, their ecological mechanisms, and consequences, and outlines future research directions to advance understanding of dew‐ecosystem interactions.

The Maisotsenko cycle-based coolers have gained increasing attention in recent years due to their advantages of low energy consumption and environmental friendliness. Optimizing the model structure and operating conditions is the primary approach for enhancing the cooling performance of dew-point evaporation systems. In this paper, a novel mathematical model of the perforated dew-point evaporative cooler was developed to investigate its cooling performance. The key findings that emerged from this investigation were: (1) Both perforated and non-perforated dew-point evaporative cooling systems exhibited similar trends in relation to the impact of model size and inlet air parameters. (2) The performance of the dew-point evaporative cooler could be enhanced by implementing the perforation method when the total supply air ratio is below 0.5, and optimal performance was achieved with a single-perforation design. (3) The outlet temperature of the dry channel initially showed a downward trend when the supply air ratio was 0.3, and subsequently gradually increased with an elevated supply air ratio. It is worth noting that within the range of 0.5 to 0.6 for the supply air ratio, the minimum outlet air temperature was achieved.

To understand the expected changes of extreme rainfalls due to climate change, the sensitivity of rainfall to surface temperature is often calculated. However, as surface temperatures may not be a good indicator of atmospheric moisture, an alternative is to use atmospheric temperatures, but the use of atmospheric temperatures lacks precedent. Using radiosonde atmospheric temperature data at a range of geopotential heights from 34 weather stations across Australia and its territories, we examine whether atmospheric temperature can improve our understanding of rainfall-temperature sensitivities. There is considerable variability in the calculated sensitivity when using atmospheric air temperature, while atmospheric dew point temperature showed robust positive sensitivities, similar to when surface dew point temperature measurements were used. We conclude atmospheric dew point temperature may be a promising candidate for future investigations of empirically calculated sensitivities of rainfall to temperature but does not appear superior to the use of surface dew point temperature measurements.

Hydrocarbon has important influence on the safe operation of natural gas pipeline. Iso and Chinese standards clearly specify the technical requirements for hydrocarbon dew point in pipeline natural gas. This paper developed a natural gas online Chilled-Mirror Hydrocarbon Dew-Point Meter (CMHDP) by adopting an integrated metal mirror. The optical metal mirror is directly coupled with the cold finger of the refrigerator, which bases on Stirling thermodynamic cycle and optimized for specified work condition. The double-light-path photoelectric system is designed to reduce the influence of external factors and enlarge the differential voltage signal. The precise mechanical structure, material matching and stress treatment are used to solve the problems of high-pressure low-temperature sealing and chilled mirror micro-vibration. The intelligent program is also developed to determine the hydrocarbon dew point. Finally, the work range of CHMDP prototype is −50 ∼ +30 C, the accuracy is 0.5°C, and the pressure range is 1∼12MPa.

The present study, carried out in Gujarat (India) between 2005 and 2021, aims to prepare dew and rain maps of Gujarat over a long period (17 years, from 2005 to 2021) in order to evaluate the evolution of the potential for dew and rain in the state. The ratio of dew to precipitation is also determined, which is an important metric that quantifies the contribution of dew to the overall water resources. Global warming leads, in general, to a reduction in precipitation and non-rainfall water contributions such as dew. The study shows, however, a rare increase in the rainfall and dew condensation, with the latter related to an increase in relative humidity and a decrease in wind amplitudes. Rain primarily occurs during the monsoon months, while dew forms during the dry season. Although dew alone cannot resolve water scarcity, it nonetheless may provide an exigent and unignorable contribution to the water balance in time to come. According to the site, the dew–rain ratios, which are also, in general, well correlated with dew yields, can represent between 4.6% (Ahmedabad) and 37.2% (Jamnagar). The positive trend, observed since 2015–2017, is expected to continue into the future.