The Role of Clouds in Global Radiation Changes Measured in Israel during the Last Sixty Years

Paudel, Indira; Cohen, Shabtai; Stanhill, G.

doi:10.4236/ajcc.2019.81004

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…Month-to-month pyranometer measurements during the study period showed a statistically significant increasing trend of 20 W m −2 decade −1 in surface solar radiation (Figure 4e,f). This increasing trend in solar radiation can be explained by a decrease in cloud cover, in accordance with Paudel et al [27].…”

Section: Long-term Water Temperature Trends Based On In-situ Measurements For All Monthssupporting

confidence: 90%

Absence of Surface Water Temperature Trends in Lake Kinneret despite Present Atmospheric Warming: Comparisons with Dead Sea Trends

et al. 2021

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This study was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km × 1 km resolution records on board Terra and Aqua satellites and in-situ measurements during the period (2003–2019). In spite of the presence of increasing atmospheric warming, in summer when evaporation is maximal, in fresh-water Lake Kinneret, satellite data revealed the absence of surface water temperature (SWT) trends. The absence of SWT trends in the presence of increasing atmospheric warming is an indication of the influence of increasing evaporation on SWT trends. The increasing water cooling, due to the above-mentioned increasing evaporation, compensated for increasing heating of surface water by regional atmospheric warming, resulting in the absence of SWT trends. In contrast to fresh-water Lake Kinneret, in the hypersaline Dead Sea, located ~100 km apart, MODIS records showed an increasing trend of 0.8 °C decade−1 in summer SWT during the same study period. The presence of increasing SWT trends in the presence of increasing atmospheric warming is an indication of the absence of steadily increasing evaporation in the Dead Sea. This is supported by a constant drop in Dead Sea water level at the rate of ~1 m/year from year to year during the last 25-year period (1995–2020). In summer, in contrast to satellite measurements, in-situ measurements of near-surface water temperature in Lake Kinneret showed an increasing trend of 0.7 °C decade−1.

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Section: Long-term Water Temperature Trends Based On In-situ Measurements For All Monthssupporting

confidence: 90%

Absence of Surface Water Temperature Trends in Lake Kinneret despite Present Atmospheric Warming: Comparisons with Dead Sea Trends

et al. 2021

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“…Aerosol–cloud interactions cause considerable uncertainty in climate change predictions (Seinfeld et al., 2016). Aerosols can act as effective cloud condensation nuclei (CCN) under certain temperature supersaturation conditions (Dusek et al., 2006; Gunthe et al., 2009), affecting cloud formation and distribution, which in turn influence radiative forcing and climate change (Paudel et al., 2019; Ursem, 2016). The proclivity of aerosols toward CCN is dependent on their chemical composition.…”

Section: Introductionmentioning

confidence: 99%

Sulfate Formation Driven by Wintertime Fog Processing and a Hydroxymethanesulfonate Complex With Iron: Observations From Single‐Particle Measurements in Hong Kong

Zhang,

Han,

Sun

et al. 2024

JGR Atmospheres

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Fog processing has a significant impact on sulfur chemistry in the atmosphere. This study analyzed three winter fog events in Hong Kong using single‐particle aerosol mass spectrometry (SPAMS) and a Monitor for AeRosols and GAses in ambient air (MARGA). Black carbon (BC)‐related carbonaceous particles with substantial sulfate amounts comprised the largest particle number fraction (56.7%). Sulfate mass concentration decreased during fog due to the cloud's effective scavenging, but fog processing notably promoted sulfate formation at the single‐particle level (average peak area increases of 31.2%). Hydroxymethanesulfonate (HMS), an important S(IV) compound and fog tracer, was identified accounting for up to 12% by particle number fraction. Although pH showed a positive correlation (r = 0.53–0.69) with HMS particles in each fog scenario, a negative overall correlation (r = −0.51) was observed. Further analysis revealed that the higher aerosol acidity (pH 0.65–3.11), promoted Fe dissolution, leading to 49% of HMS particles being mixed with Fe, which potentially facilitated sulfate formation via the Fenton reaction. Additionally, around 40% of HMS‐Fe particles are mixed with oxalate, thereby warranting further attention for their potential to cause more intricate sulfur oxidation processes. This study reveals the initial identification of a high mixed‐state of HMS‐Fe, which could potentially serve as a crucial avenue for the formation of sulfate on individual particulate matter. Considering the persistent augmentation of aerosol acidity in the Asian region, this phenomenon necessitates further investigation and attention.

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“…Solar radiation reaching the ground is scattered, absorbed, or transmitted by intervening atmospheric variables such as ozone, ambient pressure, and precipitable water [3], but in particular, the most prominent effect is due to clouds [4]. A moving cloud with varying shading strength has been shown to change irradiance value by over 300 W/m 2 in 0.1 s, implying that irradiance transitions produced by overpassing cloud shadows can be significant and severe [5].…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment of Global Horizontal Irradiance Models in All-Sky Conditions

2021

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Solar irradiance models contribute to mitigating the lack of measurement data at a ground station. Conventionally, the models relied on physical calculations or empirical correlations. Recently, machine learning as a sophisticated statistical method has gained popularity due to its accuracy and potential. While some studies compared machine learning models with other models, a study has not yet been performed that compares them side-by-side to assess their performance using the same datasets in different locations. Therefore, this study aims to evaluate the accuracy of three representative models for estimating solar irradiance using atmospheric variables measurement and cloud amount derived from satellite images as the input parameters. Based on its applicability and performance, this study selected the fast all-sky radiation model for solar applications (FARMS) derived from the radiative transfer approach, the Hammer model that simplified atmospheric correlation, and the long short-term memory (LSTM) model specialized in sequential datasets. Global horizontal irradiance (GHI) data were modeled for five distinct locations in South Korea and compared with hourly measurement data of two years to yield the error metrics. When identical input parameters were used, LSTM outperformed the FARMS and the Hammer model in terms of relative root mean square difference (rRMSD) and relative mean bias difference (rMBD). Training an LSTM model using the input parameters of FARMS, such as ozone, nitrogen, and precipitable water, yielded more accurate results than using the Hammer model. The result shows unbiased and accurate estimation with an rRMSD and rMBD of 23.72% and 0.14%, respectively. Conversely, the FARMS has a faster processing speed and does not require significant data to make a fair estimation.

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The Role of Clouds in Global Radiation Changes Measured in Israel during the Last Sixty Years

Cited by 5 publications

References 34 publications

Absence of Surface Water Temperature Trends in Lake Kinneret despite Present Atmospheric Warming: Comparisons with Dead Sea Trends

Absence of Surface Water Temperature Trends in Lake Kinneret despite Present Atmospheric Warming: Comparisons with Dead Sea Trends

Sulfate Formation Driven by Wintertime Fog Processing and a Hydroxymethanesulfonate Complex With Iron: Observations From Single‐Particle Measurements in Hong Kong

Performance Assessment of Global Horizontal Irradiance Models in All-Sky Conditions

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