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Topic Review
Tipping Bucket Rain Gauges
Tipping bucket rain gauges (TBRs) continue to be one of the most widely used pieces of equipment for rainfall monitoring; they are frequently used for the calibration, validation, and downscaling of radar and remote sensing data, due to their major advantages—low cost, simplicity and low-energy consumption.
  • 710
  • 20 Jun 2023
Topic Review
Corrosion Monitoring in Atmospheric Conditions
A variety of techniques are available for monitoring metal corrosion in electrolytes. However, only some of them can be applied in the atmosphere, in which case a thin discontinuous electrolyte film forms on a surface. Traditional and state-of-the-art real-time corrosion monitoring techniques include atmospheric corrosion monitor (ACM), electrochemical impedance spectroscopy (EIS), electrochemical noise (EN), electrical resistance (ER) probes, quartz crystal microbalance (QCM), radio-frequency identification sensors (RFID), fibre optic corrosion sensors (FOCS) and respirometry.
  • 678
  • 27 Jan 2022
Topic Review
Climate Change and Residential Buildings
Climate change is expected to influence cooling and heating energy demand of residential buildings and affect overall thermal comfort. Towards this end, the heating (HDD) and cooling (CDD) degree-days along with HDD + CDD were computed from an ensemble of seven high-resolution bias-corrected simulations attained from EURO-CORDEX under two Representative Concentration Pathways (RCP4.5 and RCP8.5) for mainland Portugal. 
  • 677
  • 14 Jul 2021
Topic Review
GaoFen-4 Images of Coastal Zones
Cloud-cover information is important for a wide range of scientific studies, such as the studies on water supply, climate change, earth energy budget, etc. In remote sensing, correct detection of clouds plays a crucial role in deriving the physical properties associated with clouds that exert a significant impact on the radiation budget of planet earth. Although the traditional cloud detection methods have generally performed well, these methods were usually developed specifically for particular sensors in a particular region with a particular underlying surface (e.g., land, water, vegetation, and man-made objects). Coastal regions are known to have a variety of underlying surfaces, which represent a major challenge in cloud detection. Therefore, there is an urgent requirement for developing a cloud detection method that could be applied to a variety of sensors, situations, and underlying surfaces. In the present study, a cloud detection method based on spatial and spectral uniformity of clouds was developed. In addition to having a spatially uniform texture, a spectrally approximate value was also present between the blue and green bands of the cloud region. The blue and green channel data appeared more uniform over the cloudy region, i.e., the entropy of the cloudy region was lower than that of the cloud-free region. On the basis of this difference in entropy, it would be possible to categorize the satellite images into cloud region images and cloud-free region images. Furthermore, the performance of the proposed method was validated by applying it to the data from various sensors across the coastal zone of the South China Sea. The experimental results demonstrated that compared to the existing operational algorithms, EN-clustering exhibited higher accuracy and scalability, and also performed robustly regardless of the spatial resolution of the different satellite images. It is concluded that the EN-clustering algorithm proposed in the present study is applicable to different sensors, different underlying surfaces, and different regions, with the support of NDSI and NDBI indices to remove the interference information from snow, ice, and man-made objects.
  • 672
  • 09 Nov 2020
Topic Review
Inversion (Meteorology)
In meteorology, an inversion is a deviation from the normal change of an atmospheric property with altitude. It almost always refers to an inversion of the air temperature lapse rate, in which case it is called a temperature inversion. Normally, air temperature decreases with an increase in altitude, but during an inversion warmer air is held above cooler air. An inversion traps air pollution, such as smog, close to the ground. An inversion can also suppress convection by acting as a "cap". If this cap is broken for any of several reasons, convection of any moisture present can then erupt into violent thunderstorms. Temperature inversion can notoriously result in freezing rain in cold climates.
  • 664
  • 11 Nov 2022
Topic Review
FluxNet
FluxNet is a global network of micrometeorological tower sites that use eddy covariance methods to measure the exchanges of carbon dioxide, water vapor, and energy between the biosphere and atmosphere. Fluxnet is a global 'network of regional networks' that serves to provide an infrastructure to compile, archive and distribute data for the scientific community. It works to ensure that different flux networks are calibrated to facilitate comparison between sites, and it provides a forum for the distribution of knowledge and data between scientists. As of April 2014, there are over 683 tower sites in continuous long-term operation. Researchers also collect data on site vegetation, soil, trace gas fluxes, hydrology, and meteorological characteristics at the tower sites.
  • 656
  • 22 Nov 2022
Topic Review
Low-Level Jets in Heavy Rainfall in Taiwan
During the early summer rainy season over Taiwan, three types of low-level jets are observed, including a synoptic low-level jet (SLLJ) situated in the 850–700 hPa layer in the frontal zone, a marine boundary layer jet (MBLJ) embedded within the southwesterly monsoon flow over the northern South China Sea at approximately the 925 hPa level, and an orographically induced jet at approximately the 1 km level off the northwestern Taiwan coast (e.g., barrier jet (BJ)).
  • 652
  • 31 Mar 2022
Topic Review
Assimilation of Key Data in Land Surface Models
The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature (BT) from passive microwave sensors, and retrieved SM from active, passive, or combined active–passive sensor products have been used as two critical inputs in improvements of the LSM.
  • 648
  • 22 Feb 2022
Topic Review
Diurnal Extrema Timing—A New Climatological Parameter
Diurnal Extrema Timing (DET) are daily occurrence times of air temperature minimum and maximum. Although unrecognized and unrecorded as a meteorological variable, the exact timing of daily temperature extrema plays a crucial role in the characterization of air temperature variability. The results reveal the timing of daily air temperature maximum as the most vulnerable to climate change among temperature and timing extrema indices.
  • 646
  • 12 Jan 2022
Topic Review
ICON Single-Column Mode
The ICON (ICOsahedral Nonhydrostatic) SCM (single-column mode) is a single-column configuration of the ICON modeling framework. The primary purpose of the ICON SCM is to use it as a tool for research, model evaluation and development. Thanks to the simplified geometry of the ICON SCM, various aspects of the ICON model, in particular the model physics, can be studied in a well-controlled environment. Additionally, the ICON SCM has a reduced computational cost and a low data storage demand. The ICON SCM can be utilized for idealized cases—several well-established cases are already included—or for semi-realistic cases based on analyses or model forecasts.
  • 630
  • 05 Aug 2021
Topic Review
Controlling Factors of California Precipitation
Using observational data covering 1948–2020, the environmental factors controlling the winter precipitation in California were investigated. Empirical orthogonal function (EOF) analysis was applied to identify the dominant climate regimes contributing to the precipitation. The first EOF mode described a consistent change, with 70.1% variance contribution, and the second modeexhibited a south–east dipole change, with 11.7% contribution. For EOF1, the relationship was positive between PC1(principal component) and SST (sea surface temperature) in the central Pacific Ocean, while it was negative with SST in the southeast Indian Ocean. The Pacific–North America mode, induced by the positive SST and precipitation in the central Pacific Ocean, leads to Californiabeing occupied by southwesterlies, which would transport warm and wet flow from the ocean, beneficial for precipitation. As for the negative relationship, California is controlled by biotrophically high pressure, representing part of the Rossby wave train induced by the positive SST in the Indian ocean, which is unfavorable for the precipitation. For EOF2, California is controlled by positivevorticity at the upper level, whereas at the lower level, there is positive vorticity to the south and negative vorticity to the north, the combination of which leads to the dipole mode change in the precipitation.
  • 628
  • 20 Aug 2021
Topic Review Peer Reviewed
A Methodology for Air Temperature Extrema Characterization Pertinent to Improving the Accuracy of Climatological Analyses
The suggested methodology for the characterization of temperature extrema presents a multistep preprocessing procedure intended to derive extrema time series of correctly identified and thermally defined daily air temperature extrema pairs. The underlying conceptual framework for this approach was developed in response to the existing gaps in the current state of daily extrema identification and the development of extrema-based synthetic air temperature time series. A code consisting of a series of algorithms was developed to establish four-parameter criteria for a more accurate representation of daily variability that allows easy replication of temperature distribution based on the correct characterization of daily temperature patterns. The first preprocessing step consists of subjecting the high-frequency temperature time series to a theoretical diurnal observing window that imposes latitudinally and seasonally crafted limits for the individual identification of daily minima and maxima. The following pre-processing step involves the supplementation of air temperature extrema with the information on the occurrence of extrema timing deemed as vital information for the reconstruction of the temperature time series. The subsequent step involves the application of an innovative temperature pattern recognition algorithm that identifies physically homogeneous air temperature populations based on the information obtained in previous steps. The last step involves the use of a metric for the assessment of extrema temperature and timing parameters’ susceptibility to climate change. The application of the presented procedure to high-frequency temperature data yields two strains of physically homogeneous extrema time series with the preserved characteristics of the overall temperature variability. In the present form, individual elements of this methodology are applicable for correcting historical sampling and air temperature averaging biases, improving the reproducibility of daily air temperature variation, and enhancing the performance of temperature index formulae based on daily temperature extrema. The objective of this analysis is the eventual implementation of the presented methodology into the practice of systematic temperature extrema identification and preprocessing of temperature time series for the configuration of physically homogeneous air temperature subpopulations.
  • 626
  • 29 Mar 2023
Topic Review
Climate Change with Everest Region
The Himalayas, especially the Everest region, are highly sensitive to climate change. Although there are research works on this region related to cryospheric work, the ecological understandings of the alpine zone and climate impacts are limited. This study aimed to assess the changes in surface water including glacier lake and streamflow and the spatial and temporal changes in alpine vegetation and examine their relationships with climatic factors (temperature and precipitation) during 1995–2019 in the Everest region and the Dudh Koshi river basin. In this study, Landsat time-series data, European Commission’s Joint Research Center (JRC) surface water data, ECMWF Reanalysis 5th Generation (ERA5) reanalysis temperature data, and meteorological station data were used. It was found that the glacial lake area and volume are expanding at the rates of 0.0676 and 0.0198 km3/year, respectively; the average annual streamflow is decreasing at the rate of 2.73 m3/s/year. Similarly, the alpine vegetation greening as indicated by normalized difference vegetation index (NDVI) is increasing at the rate of 0.00352 units/year. On the other hand, the annual mean temperature shows an increasing trend of 0.0329 °C/year, and the annual precipitation also shows a significant negative monotonic trend. It was also found that annual NDVI is significantly correlated with annual temperature. Likewise, the glacial lake area expansion is strongly correlated with annual minimum temperature and annual precipitation. Overall, we found a significant alteration in the alpine ecosystem of the Everest region that could impact on the water–energy–food nexus of the Dudh Koshi river basin. 
  • 606
  • 06 Aug 2021
Topic Review
Ground Level Ozone
Ground level ozone (O3), also known as tropospheric ozone, is a trace gas of the troposphere (the lowest level of the Earth's atmosphere), with an average concentration of 20–30 parts per billion by volume (ppbv), with close to 100 ppbv in polluted areas. Ozone is also an important constituent of the stratosphere, where the ozone layer exists which is located between 10 and 50 kilometers above the Earth's surface. The troposphere extends from the ground up to a variable height of approximately 14 kilometers above sea level. Ozone is least concentrated in the ground layer (or planetary boundary layer) of the troposphere. Ground level or tropospheric ozone is created by chemical reactions between oxides of nitrogen (NOx gases) and volatile organic compounds (VOCs). The combination of these chemicals in the presence of sunlight form ozone. Its concentration increases as height above sea level increases, with a maximum concentration at the tropopause. About 90% of total ozone in the atmosphere is in the stratosphere, and 10% is in the troposphere. Although tropospheric ozone is less concentrated than stratospheric ozone, it is of concern because of its health effects. Ozone in the troposphere is considered a greenhouse gas, and may contribute to global warming. Photochemical and chemical reactions involving ozone drive many of the chemical processes that occur in the troposphere by day and by night. At abnormally high concentrations (the largest source being emissions from combustion of fossil fuels), it is a pollutant, and a constituent of smog. Its levels have increased significantly since the industrial revolution, as NOx gasses and VOCs are some of the byproducts of combustion. With more heat and sunlight in the summer months, more ozone is formed which is why regions often experience higher levels of pollution in the summer months. Although the same molecule, ground level ozone can be harmful to our health, unlike stratospheric ozone that protects the earth from excess UV radiation. Photolysis of ozone occurs at wavelengths below approximately 310–320 nanometres. This reaction initiates the chain of chemical reactions that remove carbon monoxide, methane, and other hydrocarbons from the atmosphere via oxidation. Therefore, the concentration of tropospheric ozone affects how long these compounds remain in the air. If the oxidation of carbon monoxide or methane occur in the presence of nitrogen monoxide (NO), this chain of reactions has a net product of ozone added to the system.
  • 575
  • 28 Sep 2022
Topic Review
Assessment System and Observation Network
We propose to build on and link with the existing research activities and observational networks and infrastructures to specifically address the key North Atlantic challenges that encompass a range of policy areas. This will strengthen the institutional response to weather, climate, environmental and ecological threats and reduce societal risk. 
  • 566
  • 16 Aug 2021
Topic Review
Indigenous Kinabatangan Perspectives on Climate Change Adaptations
Indigenous perspectives on the effects of climate change are frequently elicited through surveys and interviews, and the responses are compared to meteorological data. However, there remains a limited approach to examining the underlying predictors that best determine Indigenous support for adaptation strategies. Some Indigenous communities do not draw a causal link between the effects and responses to climate hazards. Coping strategies such as the inclusion of Indigenous livelihoods, a bottom-up approach, and transparent communication are suggested to cultivate Indigenous support for climate change adaptation. 
  • 566
  • 17 Jun 2022
Topic Review
Forecasting Pollution in Urban Area
Particulate air pollution has aggravated cardiovascular and lung diseases. Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions.  Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions. CAMS provides 4-day-ahead regional (EU) forecasts in a 10 km spatial resolution, adding value to the Copernicus EO and delivering open-access consistent air quality forecasts. In this work, we evaluate the CAMS PM forecasts at a local scale against in-situ measurements, spanning 2 years, obtained from a network of stations located in an urban coastal Mediterranean city in Greece. Moreover, we investigate the potential of modelling techniques to accurately forecast the spatiotemporal pattern of particulate pollution using only open data from CAMS and calibrated low-cost sensors. Specifically, we compare the performance of the Analog Ensemble (AnEn) technique and the Long Short-Term Memory (LSTM) network in forecasting PM2.5 and PM10 concentrations for the next four days, at 6 h increments, at a station level. The results show an underestimation of PM2.5 and PM10 concentrations by a factor of 2 in CAMS forecasts during winter, indicating a misrepresentation of anthropogenic particulate emissions such as wood-burning, while overestimation is evident for the other seasons. Both AnEn and LSTM models provide bias-calibrated forecasts and capture adequately the spatial and temporal variations of the ground-level observations reducing the RMSE of CAMS by roughly 50% for PM2.5 and 60% for PM10. AnEn marginally outperforms the LSTM using annual verification statistics. The most profound difference in the predictive skill of the models occurs in winter, when PM is elevated, where AnEn is significantly more efficient. Moreover, the predictive skill of AnEn degrades more slowly as the forecast interval increases. Both AnEn and LSTM techniques are proven to be reliable tools for air pollution forecasting, and they could be used in other regions with small modifications.
  • 560
  • 16 Jul 2021
Topic Review
Climate Change and Its Impact on Crops
Plants are a highly advanced kingdom of living organisms on the earth. They survive under all climatic and weather variabilities, including low and high temperature, rainfall, radiation, less nutrients, and high salinity. Even though they are adapted to various environmental factors, which are variable, the performance of a crop will be compensated under sub/supra optimal conditions. 
  • 533
  • 22 Dec 2022
Topic Review
Molecular to Meteorological
The path from molecular to meteorological scales begins with the persistence of molecular velocity after collision inducing symmetry breaking, from continuous translational to scale invariant, associated with the emergence of hydrodynamic behaviour in a Maxwellian (randomised) population undergoing an anisotropic flux. The statistical multifractal formulation of observed atmospheric variability enables the examination of turbulence. The unexpected correlation between the intermittency of temperature and the ozone photodissociation rate in the lower Arctic stratosphere led to an analysis of the role of Gibbs free energy in the general circulation of the atmosphere, and a suggestion to solve the persistent cold bias in its siumlation by free running numerical models.
  • 530
  • 12 Apr 2022
Topic Review
Machine Learning Development
ML models can be classified into several types depending on the task objectives, such as regression, classification, reinforcement learning, generative models, and so on.  Regarding ML models available for regression prediction, all ML models in the collected research were classified into 4 categories: traditional convex optimization-based models (TCOB models), tree models, linear regression (LR), and modern deep-learning structure models (modern DL structure). 
  • 518
  • 30 Dec 2021
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