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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.

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.

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 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.

This paper presents an overview of recent observational studies on the Clausius-Clapeyron precipitation-temperature (P-T) scaling in midlatitudes. As the capacity of air to hold moisture increases in connection with increasing temperature, extreme precipitation events may become more abundant and intense. The capacity of air to hold moisture is governed by the Clausius-Clapeyron (CC) relation, approximately 7% per °C. Departures from this, so called super-CC scaling and sub-CC scaling, are consequences of different factors (moisture availability, type of precipitation, annual cycle, the percentile of precipitation intensity and regional weather patterns). Since the moisture availability and enhanced convection were considered as the most important drivers governing the P-T scaling, dew point temperature as a scaling variable is discussed in detail and methods of disaggregation of precipitation events into convective and non-convective are also reviewed.

Soil–water characteristic curve (SWCC) is an important unsaturated soil property relating the water content of a soil to soil suction and it is conventionally measured using Tempe cell, pressure plate and salt solution methods. However, these tests are tedious and time consuming. The SWCC measurements using fast and efficient methods are required for engineering designs such as excavation, slope protection, retaining wall and landfill cover designs. This paper describes the testing procedures and apparatuses associated with rapid measurements of a complete SWCC of a residual soil as obtained from combined measurements using a small-scale centrifuge and dew point methods. The SWCC test results obtained using these alternative methods were compared with the SWCC data from Tempe cell, pressure plate and salt solution methods. Shrinkage tests were carried out in this study to incorporate the volume change of soil into SWCC. The experimental data from all SWCC tests were evaluated using first order analysis with 95% confidence interval for determination of upper and lower bounds of SWCC. The analysis results showed that the SWCC data obtained from tests using small-scale centrifuge and dew point methods were in good agreement with those obtained from Tempe cell, pressure plate and salt solution methods. This indicates that the combination of small-scale centrifuge and dew point methods can be used to generate a complete curve of SWCC for the residual soil. In addition, the time required to perform SWCC tests using the alternative methods is shorter than the SWCC tests using the conventional methods.

Extensive research has delved into the impact of environmental circumstances on the pacing and performance of professional marathon runners. However, the effects of environmental conditions on the pacing strategies employed by marathon participants in general remain relatively unexplored. This study aimed to examine the potential associations between various environmental factors, encompassing temperature, barometric pressure, humidity, precipitation, sunshine, cloud cover, wind speed, and dew point, and the pacing behavior of men and women. The retrospective analysis involved a comprehensive dataset comprising records from a total of 668,509 runners (520,521 men and 147,988 women) who participated in the ’Berlin Marathon’ events between the years 1999 and 2019. Through correlations, Ordinary Least Squares (OLS) regression, and machine learning (ML) methods, we investigated the relationships between adjusted average temperature values, barometric pressure, humidity, precipitation, sunshine, cloud cover, wind speed, and dew point, and their impact on race times and paces. This analysis was conducted across distinct performance groups, segmented by 30-minute intervals, for race durations between 2 hours and 30 minutes to 6 hours. The results revealed a noteworthy negative correlation between rising temperatures and declining humidity throughout the day and the running speed of marathon participants in the ’Berlin Marathon.’ This effect was more pronounced among men than women. The average pace for the full race showed positive correlations with temperature and minutes of sunshine for both men and women. However, it is important to note that the predictive capacity of our model, utilizing weather variables as predictors, was limited, accounting for only 10% of the variance in race pace. The susceptibility to temperature and humidity fluctuations exhibited a discernible increase as the marathon progressed. While weather conditions exerted discernible influences on running speeds and outcomes, they did not emerge as significant predictors of pacing.

Although crustose and fruticosea lichens were shown to efficiently use dew and fog, the link between their expansion and the occurrence of dew and fog has never been shown experimentally. This is also the case for the Negev Desert Highlands, where (i) dewless habitats were not inhabited by lichens and (ii) an increase in fruticose lichens with high-altitude fog-prone areas was noted, leading us to hypothesize that the expansion of crustose and fruticose lichens is mainly linked to dew and fog, respectively. Experiments aiming to compare the non-rainfall water (NRW) were conducted. We used cloths attached to 7 cm-high cobbles to mimic crustose lichens (MCL), cloths placed horizontally aboveground to evaluate the amount of NRW without the presence of the cobble (CoP), cloths attached to a wire scaffold mimicking fruticose lichens (MFL), and cloths attached to glass plates (CPM) that served as a reference. Substrate temperatures were compared to the dew point temperature. In addition, sprinkling experiments, which mimicked fog under variable wind speeds (0.9, 1.4, 3.3 and 5.7 m s−1), were also conducted. NRW followed the pattern: MCL ≈ CPM > CoP > > MFL. While MCL yielded substantially higher amounts of NRW (0.09 mm) in comparison to MFL (0.04 mm) during dew events, similar amounts were obtained by both substrates (0.15–0.16 mm) following fog. However, fog interception increased substantially with wind speed. The findings may explain the expansion of crustose lichens in extreme deserts benefiting mainly from dew (but also fog), and the proliferation of fruticose lichens in fog-prone areas, especially when accompanied by high-speed winds. While (mainly) high proliferation of crustose lichens may serve as bioindicators for dew in extreme deserts, fruticose lichens may serve as bioindicators for fog.