Hasil untuk "Meteorology. Climatology"

Amar Rouabhi

Abstract The study presents a comprehensive analysis of long-term climate trends in Sétif, Algeria, a representative semiarid highland station, using a 125-yr dataset (1900–2024) of monthly and annual precipitation and maximum and minimum temperature. Given the discontinuities in the historical dataset, two neighboring reference stations (Batna and Constantine) and three imputation methods were used: multiple linear regression (MLR), seasonal imputation, and multiple imputation by chained equations (MICE). MICE demonstrated superior performance for all variables. The imputed dataset revealed statistically significant climate changes consistent with Mediterranean aridification patterns. Trend analysis showed precipitation declining at −6.5 mm decade −1 , while maximum temperature and minimum temperature increased at 0.02° and 0.29°C decade −1 , respectively. The asymmetric warming pattern, with minimum temperature rising faster than maximum temperature, is confirmed. However, Pettitt’s changepoint test identifies several breakpoints in the time series 1900–2024; the precipitation pattern breakpoint was in 1975 with a decrease of −77.5 mm. The minimum temperature breakpoint was in 1973 (+2.1°C), and maximum temperature breakpoint was in 1987 (+1.44°C). Recent decades (1980–2024) show accelerated trends, with precipitation decreasing by −13 mm decade −1 and a maximum temperature warming of +0.5°C decade −1 . These findings confirm North Africa as a climate change hotspot, with Sétif experiencing rates of change exceeding global averages. The successful reconstruction of this century-scale dataset provides crucial baseline information for climate adaptation strategies in semiarid regions facing increasing water stress and agricultural challenges.

Thomas Savary, François Rozet, Gilles Louppe

Data assimilation is widely used in many disciplines such as meteorology, oceanography, and robotics to estimate the state of a dynamical system from noisy observations. In this work, we propose a lightweight and general method to perform data assimilation using diffusion models pre-trained for emulating dynamical systems. Our method builds on particle filters, a class of data assimilation algorithms, and does not require any further training. As a guiding example throughout this work, we illustrate our methodology on GenCast, a diffusion-based model that generates global ensemble weather forecasts.

Janssens Jan, Delouille Véronique, Clette Frédéric et al.

In December 2019, the Space Weather Prediction Center (SWPC) started using the GOES1-16 satellite as its primary input for solar X-ray flux monitoring. As such, it stopped applying a scaling factor that had been applied since the GOES-8 came into operation. This has an important impact on the number of flares that can be expected and on the flare rates associated with the McIntosh classifications, which are often used to help forecast flare activity. To quantify the effects, the flare intensities for the period covering 1976–2019 have all been recalculated. An increase of respectively 55% and 52% in the total number of M-class and X-class events has been observed. Also, for the same period, McIntosh classifications have been redone by visually evaluating 4720 Kanzelhöhe solar drawings (about 1 drawing every 3 days) and determining the McIntosh type of 22232 sunspot regions. There is an excellent agreement with the values originally reported by McIntosh (1990), but some deviations from the SWPC data are found. For a majority of the McIntosh classes, an increase in the flare rates is observed, which translates into increased flare probabilities assuming a Poisson distribution for the flare occurrence. This is an important given for space weather forecasters when making solar flare forecasts. The McIntosh classification is successful in distinguishing flare active from flare inactive regions: Considering only the “p” and “c” components of the McIntosh classification and linking them to the number of flares associated with the corresponding sunspot groups, we find that 48% of all M- and X-class flares in our study are produced by only 8% of all sunspot groups, belonging to the McIntosh subclasses -ai, -kc, and -ki corresponding to sunspot groups with an asymmetric main spot and a more complex (intermediate or compact) internal sunspot distribution. About 57% of all classified sunspot groups produce only 12% of all M- and X-class flares. They belong to the McIntosh subclasses -so, -sx, -xo, and -xx, which correspond to the smallest and simplest sunspot regions and the phases marking the emergence and final decay of sunspot groups (Axx, Bxo, Hsx). Though the McIntosh classification is a great tool to forecast flare activity, there remain large differences in the actual flare behaviour of individual sunspot groups within the same McIntosh class.

Ibrahim H. Elsebaie, Atef Q. Kawara, Raied Alharbi et al.

Accurate rainfall data with good spatial–temporal distribution remain a challenge worldwide, particularly in arid regions such as western Saudi Arabia, where variability critically influences water resource management and flood mitigation. This study evaluates five satellite-based rainfall products—GPM, GPCP, CHIRPS, PERSIANN-CDR and PERSIANN—against observed monthly rainfall at 28-gauge stations, using the correlation coefficient (CC), root mean square error (RMSE), relative bias (RB) and mean absolute error (MAE). Among uncorrected products, GPM achieved the highest mean CC (0.52), and lowest RMSE (17.0 mm) and MAE (9.18 mm) compared with CC = 0.39 (RMSE 19.9 mm) for GPCP, CC = 0.20 (RMSE 21.6 mm) for CHIRPS, CC = 0.43 (RMSE 19.2 mm) for PERSIANN-CDR and CC = 0.26 (RMSE 57.3 mm) for PERSIANN. Four bias correction methods—linear scaling, nonlinear adjustment, quantile mapping and artificial neural networks (ANN)—were applied. The ANN reduced GPM’s RMSE by 19% to 13.8 mm, increased CC to 0.59, lowered RB to 2.5% and achieved an MAE of 6.89 mm. These results demonstrate that GPM, particularly when bias-corrected via ANN, provides a dependable rainfall dataset for hydrological modeling and flood risk assessment in arid environments.

Matthieu Vernay, M. Lafaysse, D. Monteiro et al.

Abstract. This work introduces the S2M (SAFRAN–SURFEX/ISBA–Crocus–MEPRA) meteorological and snow cover reanalysis in the French Alps, Pyrenees and Corsica, spanning the time period from 1958 to 2021. The simulations are made over elementary areas, referred to as massifs, designed to represent the main drivers of the spatial variability observed in mountain ranges (elevation, slope and aspect). The meteorological reanalysis is performed by the SAFRAN system, which combines information from numerical weather prediction models (ERA-40 reanalysis from 1958 to 2002, ARPEGE from 2002 to 2021) and the best possible set of available in situ meteorological observations. SAFRAN outputs are used to drive the Crocus detailed snow cover model, which is part of the land surface scheme SURFEX/ISBA. This model chain provides simulations of the evolution of the snow cover, underlying ground and the associated avalanche hazard using the MEPRA model. This contribution describes and discusses the main climatological characteristics (climatology, variability and trends) and the main limitations of this dataset. We provide a short overview of the scientific applications using this reanalysis in various scientific fields related to meteorological conditions and the snow cover in mountain areas. An evaluation of the skill of S2M is also displayed, in particular through comparison to 665 independent in situ snow depth observations. Further, we describe the technical handling of this open-access dataset, available at https://doi.org/10.25326/37#v2020.2. The S2M data are provided by Météo-France – CNRS, CNRM, Centre d'Études de la Neige, through AERIS (Vernay et al., 2022).

Yared A. Bayissa, T. Tadesse, G. Demisse et al.

Drought is a recurring phenomenon in Ethiopia that significantly impacts the socioeconomic sector and various components of the environment. The overarching goal of this study is to assess the spatial and temporal patterns of meteorological drought using a satellite-derived rainfall product for the Upper Blue Nile Basin (UBN). The satellite rainfall product used in this study was selected through evaluation of five high-resolution products (Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) v2.0, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Climatology and Time-series (TARCAT) v2.0, Tropical Rainfall Measuring Mission (TRMM) and Africa Rainfall Estimate Climatology version 2 [ARC 2.0]). The statistical performance measuring techniques (i.e., Pearson correlation coefficient (r), mean error (ME), root mean square error (RMSE), and Bias) were used to evaluate the satellite rainfall products with the corresponding ground observation data at ten independent weather stations. The evaluation was carried out for 1998–2015 at dekadal, monthly, and seasonal time scales. The evaluation results of these satellite-derived rainfall products show there is a good agreement (r > 0.7) of CHIRPS and TARCAT rainfall products with ground observations in majority of the weather stations for all time steps. TARCAT showed a greater correlation coefficient (r > 0.70) in seven weather stations at a dekadal time scale whereas CHIRPS showed a greater correlation coefficient (r > 0.84) in nine weather stations at a monthly time scale. An excellent score of Bias (close to one) and mean error was observed in CHIRPS at dekadal, monthly and seasonal time scales in a majority of the stations. TARCAT performed well next to CHIRPS whereas PERSSIAN presented a weak performance under all the criteria. Thus, the CHIRPS rainfall product was selected and used to assess the spatial and temporal variability of meteorological drought in this study. The 3-month Z-Score values were calculated for each grid and used to assess the spatial and temporal patterns of drought. The result shows that the known historic drought years (2014–2015, 2009–2010, 1994–1995 and 1983–1984) were successfully indicated. Moreover, severe drought conditions were observed in the drought prone parts of the basin (i.e., central, eastern and southeastern). Hence, the CHIRPS rainfall product can be used as an alternative source of information in developing the grid-based drought monitoring tools for the basin that could help in developing early warning systems.

Benjamin Davis, Elinor R. Martin, Bradley G. Illston

Abstract Extreme heat such as that seen in the United States and Europe in summer 2022 can have significant impacts on human health and infrastructure. The Occupational Safety and Health Administration (OSHA) and the U.S. Army use wet-bulb globe temperature (WBGT) to quantify the impact of heat on workers and inform decisions on workload. WBGT is a weighted average of air temperature, natural wet-bulb temperature, and black globe temperature. A local hourly, daily, and monthly WBGT climatology will allow those planning outdoor work to minimize the likelihood of heat-related disruptions. In this study, WBGT is calculated from the ERA5 reanalysis and is validated by the Oklahoma Mesonet and found to be adequate. Two common methods of calculating WBGT from meteorological observations are compared. The Liljegren method has a larger diurnal cycle than the Dimiceli method, with a peak WBGT about 1°F higher. The high- and extreme-risk categories in the southern U.S. Great Plains (USGP) have increased from 5 days per year to 15 days from 1960 to 2020. Additionally, the largest increases in WBGT are occurring during DJF, potentially lengthening the warm season in the future. Heat wave definitions based on maximum, minimum, and mean WBGT are used to calculate heat wave characteristics and trends with the largest number of heat waves occurring in the southern USGP. Further, the number of heat waves is generally increasing across the domain. This study shows that heat wave days based on minimum WBGT have increased significantly which could have important impacts on human heat stress recovery.

Haoxing Liu, Renjie Xie, Haoshen Qin et al.

With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve the early warning capability of flight dangerous weather and ensure the safe flight of aircraft is the primary task of aviation meteorological services. In this work, support vector machine (SVM) models are used to predict hazardous flight weather, especially for meteorological conditions with high uncertainty such as storms and turbulence. SVM is a supervised learning method that distinguishes between different classes of data by finding optimal decision boundaries in a high-dimensional space. In order to meet the needs of this study, we chose the radial basis function (RBF) as the kernel function, which helps to deal with nonlinear problems and enables the model to better capture complex meteorological data structures. During the model training phase, we used historical meteorological observations from multiple weather stations, including temperature, humidity, wind speed, wind direction, and other meteorological indicators closely related to flight safety. From this data, the SVM model learns how to distinguish between normal and dangerous flight weather conditions.

Satoru Okajima, Hisashi Nakamura, Akira Kuwano-Yoshida et al.

The frequency of extratropical cyclones in East Asia, including those traveling along the Kuroshio off the south coast of Japan, maximizes climatologically in spring in harmony with local enhancement of precipitation. The springtime cyclone activity is of great socioeconomic importance for East Asian countries. However, mechanisms for the spring peak in the East Asian cyclone activity have been poorly understood. This study aims to unravel the mechanisms, focusing particularly on favorable conditions for relevant cyclogenesis. Through a composite analysis based on atmospheric reanalysis data, we show that cyclogenesis enhanced around the East China Sea under anomalously strengthened cyclonic wind shear and temperature gradient, in addition to enhanced moisture flux from the south, is important for the spring peak in the cyclone activity in East Asia. In spring, climatologically strengthened cyclonic shear north of the low-level jet axis and associated frequent atmospheric frontogenesis in southern China and the East China Sea serve as favorable background conditions for low-level cyclogenesis. We also demonstrate that climatologically enhanced diabatic heating around East Asia is pivotal in strengthening of the low-level jet through a set of linear baroclinic model experiments. Our findings suggest the importance of the seasonal evolution of diabatic heating in East Asia for that of the climate system around East Asia from winter to spring, encompassing the spring peak in the cyclone activity and climatological precipitation.

Sándor Baran, Julio C. Marín, Omar Cuevas et al.

By the end of 2023, renewable sources cover 63.4% of the total electric power demand of Chile, and in line with the global trend, photovoltaic (PV) power shows the most dynamic increase. Although Chile's Atacama Desert is considered the sunniest place on Earth, PV power production, even in this area, can be highly volatile. Successful integration of PV energy into the country's power grid requires accurate short-term PV power forecasts, which can be obtained from predictions of solar irradiance and related weather quantities. Nowadays, in weather forecasting, the state-of-the-art approach is the use of ensemble forecasts based on multiple runs of numerical weather prediction models. However, ensemble forecasts still tend to be uncalibrated or biased, thus requiring some form of post-processing. The present work investigates probabilistic forecasts of solar irradiance for Regions III and IV in Chile. For this reason, 8-member short-term ensemble forecasts of solar irradiance for calendar year 2021 are generated using the Weather Research and Forecasting (WRF) model, which are then calibrated using the benchmark ensemble model output statistics (EMOS) method based on a censored Gaussian law, and its machine learning-based distributional regression network (DRN) counterpart. Furthermore, we also propose a neural network-based post-processing method resulting in improved 8-member ensemble predictions. All forecasts are evaluated against station observations for 30 locations, and the skill of post-processed predictions is compared to the raw WRF ensemble. Our case study confirms that all studied post-processing methods substantially improve both the calibration of probabilistic- and the accuracy of point forecasts. Among the methods tested, the corrected ensemble exhibits the best overall performance. Additionally, the DRN model generally outperforms the corresponding EMOS approach.

Denny M. Oliveira, Robert C. Allen, Livia R. Alves et al.

Abstract Interplanetary (IP) shocks are perturbations observed in the solar wind. IP shocks correlate well with solar activity, being more numerous during times of high sunspot numbers. Earth‐bound IP shocks cause many space weather effects that are promptly observed in geospace and on the ground. Such effects can pose considerable threats to human assets in space and on the ground, including satellites in the upper atmosphere and power infrastructure. Thus, it is of great interest to the space weather community to (a) keep an accurate catalog of shocks observed near Earth, and (b) be able to forecast shock occurrence as a function of the solar cycle (SC). In this work, we use a supervised machine learning regression model to predict the number of shocks expected in SC25 using three previously published sunspot predictions for the same cycle. We predict shock counts to be around 275 ± 10, which is ∼47% higher than the shock occurrence in SC24 (187 ± 8), but still smaller than the shock occurrence in SC23 (343 ± 12). With the perspective of having more IP shocks on the horizon for SC25, we briefly discuss many opportunities in space weather research for the remainder years of SC25. The next decade or so will bring unprecedented opportunities for research and forecasting effects in the solar wind, magnetosphere, ionosphere, and on the ground. As a result, we predict SC25 will offer excellent opportunities for shock occurrences and data availability for conducting space weather research and forecasting.

Hu Tao, Guanglu Hu, Yalun Fan et al.

In response to increasing ecological and environmental challenges in arid areas, it is of great significance to investigate the ecosystem service value (ESV), accompanying the changes in ecological sensitivity for the protection of ecologically vulnerable areas. Our analysis seeks to elucidate the ESV and ecological sensitivity changes in the middle and lower reaches of the Shiyang River to determine the trends and influencing factors of ESV under changing land use patterns. The key findings include: (1) From 1995 to 2020, the ESV in the study area witnessed fluctuations, culminating in an overall decline of 1.249 × 108 yuan. (2) In 2020, sensitivity coefficients (CSs) for ESV were as follows: 0.4335 for grassland, 0.2586 for farmland, and 0.1170 for unused land within the study area. Furthermore, coefficients of improved cross-sensitivity (CICSs) for the reciprocal transformation of farmland, grassland, and unused land were 1.10, 1.18, and 1.54, respectively, indicating the pivotal role of the three land types in driving ESV fluctuations.

Min-Seok Kim, Youn-Hee Lim, Jongmin Oh et al.

Ozone concentrations have increased in recent decades, and several studies have reported that long-term exposure to ozone increases the mortality risk induced by respiratory conditions. However, research on cause-specific mortality related to ozone exposure and respiratory diseases remains scarce. We constructed a retrospective cohort of 5,360,032 adults aged ≥ 65 years from the National Health Insurance Service of Republic of Korea, and death certificates were obtained from Statistics Republic of Korea to determine the cause of death between 2010 and 2019. The daily maximum 8 h average levels of ozone during the warm season annually (May–September) and other air pollutants were determined for the residential district. We analyzed the data using a time-varying Cox proportional hazards model with individual- and district-level covariates, incorporating a competing risk framework to address deaths from causes other than chronic obstructive pulmonary disease (COPD) and asthma. In our single-pollutant model with a 3-year moving average, a 1 ppb increase in ozone exposure was associated with a hazard ratio (HR) of 1.011 (95% confidence interval [CI]: 1.008–1.013) for COPD mortality and an HR of 1.016 (95% CI: 1.011–1.022) for asthma mortality. In our model adjusted for the presence of underlying diseases and district-level variables, the HRs were 1.009 (95% CI: 1.008–1.014) for COPD and 1.017 (95% CI: 1.011–1.023) for asthma, respectively. These associations remained robust in our two-pollutant model, except for NO₂ and COPD. A linear concentration–response relationship was identified between ozone concentration, COPD, and asthma mortality. In this large nationwide cohort study, long-term exposure to ozone was associated with an increased risk of death from COPD and asthma in older Korean adults.

Gregor von Laszewski, Ruochen Gu

Cloud masking is a crucial task that is well-motivated for meteorology and its applications in environmental and atmospheric sciences. Its goal is, given satellite images, to accurately generate cloud masks that identify each pixel in image to contain either cloud or clear sky. In this paper, we summarize some of the ongoing research activities in cloud masking, with a focus on the research and benchmark currently conducted in MLCommons Science Working Group. This overview is produced with the hope that others will have an easier time getting started and collaborate on the activities related to MLCommons Cloud Mask Benchmark.

Aleksander Grochowicz, Koen van Greevenbroek, Hannah C. Bloomfield

In highly renewable power systems the increased weather dependence can result in new resilience challenges, such as renewable energy droughts, or a lack of sufficient renewable generation at times of high demand. The weather conditions responsible for these challenges have been well-studied in the literature. However, in reality multi-day resilience challenges are triggered by complex interactions between high demand, low renewable availability, electricity transmission constraints and storage dynamics. We show these challenges cannot be rigorously understood from an exclusively power systems, or meteorological, perspective. We propose a new method that uses electricity shadow prices - obtained by a European power system model based on 40 years of reanalysis data - to identify the most difficult periods driving system investments. Such difficult periods are driven by large-scale weather conditions such as low wind and cold temperature periods of various lengths associated with stationary high pressure over Europe. However, purely meteorological approaches fail to identify which events lead to the largest system stress over the multi-decadal study period due to the influence of subtle transmission bottlenecks and storage issues across multiple regions. These extreme events also do not relate strongly to traditional weather patterns (such as Euro-Atlantic weather regimes or the North Atlantic Oscillation index). We therefore compile a new set of weather patterns to define energy system stress events which include the impacts of electricity storage and large-scale interconnection. Without interdisciplinary studies combining state-of-the-art energy meteorology and modelling, further strive for adequate renewable power systems will be hampered.

Karolin S. Ferner, Marita Boettcher, K. Heinke Schlünzen

Building induced winds change the falling of rain, leading to heterogeneous patterns of rain on ground and on building surfaces. These rain heterogeneities also occur in small urban scales like an urban neighbourhood, which covers an area of a few km2. For the investigation of rain heterogeneities within an urban neighbourhood the micro-scale, obstacle-resolving model MITRAS is used, which employs a microphysics parameterisation for cloud and rain processes. MITRAS has been extended by boundary conditions for cloud and rain water at building surfaces. An initialisation with radar data is implemented and the model output is successfully compared with in‑situ precipitation data. Simulations for an urban area are performed using different initial wind speeds, rain amounts, wind directions, and domain configurations. For the rain heterogeneity within this urban neighbourhood, the processes between buildings are found to be of small influence for the rain already falling. However, exchange processes from the canopy to the air above are found to influence the above-canopy rain pattern. The influence of the meteorological situation and the city's geometry on the wind field within and above the buildings are relevant to realistically represent a rain event and to create high-resolution precipitation data.

Ramit Debnath, Felix Creutzig, Benjamin K. Sovacool et al.

Abstract The ongoing global race for bigger and better artificial intelligence (AI) systems is expected to have a profound societal and environmental impact by altering job markets, disrupting business models, and enabling new governance and societal welfare structures that can affect global consensus for climate action pathways. However, the current AI systems are trained on biased datasets that could destabilize political agencies impacting climate change mitigation and adaptation decisions and compromise social stability, potentially leading to societal tipping events. Thus, the appropriate design of a less biased AI system that reflects both direct and indirect effects on societies and planetary challenges is a question of paramount importance. In this paper, we tackle the question of data-centric knowledge generation for climate action in ways that minimize biased AI. We argue for the need to co-align a less biased AI with an epistemic web on planetary health challenges for more trustworthy decision-making. A human-in-the-loop AI can be designed to align with three goals. First, it can contribute to a planetary epistemic web that supports climate action. Second, it can directly enable mitigation and adaptation interventions through knowledge of social tipping elements. Finally, it can reduce the data injustices associated with AI pretraining datasets.

Inmaculada Leyva, Johann Martinez, Cristina Masoller et al.

In 2002, in a seminal article, Christoph Bandt and Bernd Pompe proposed a new methodology for the analysis of complex time series, now known as Ordinal Analysis. The ordinal methodology is based on the computation of symbols (known as ordinal patterns) which are defined in terms of the temporal ordering of data points in a time series, and whose probabilities are known as ordinal probabilities. With the ordinal probabilities, the Shannon entropy can be calculated, which is the permutation entropy. Since it was proposed, the ordinal method has found applications in fields as diverse as biomedicine and climatology. However, some properties of ordinal probabilities are still not fully understood, and how to combine the ordinal approach of feature extraction with machine learning techniques for model identification, time series classification or forecasting remains a challenge. The objective of this perspective article is to present some recent advances and to discuss some open problems.

Ying Wang, Ru-Jin Huang, Wei Xu et al.

Abstract Staggered-peak production (SP)—a measure to halt industrial production in the heating season—has been implemented in North China Plain to alleviate air pollution. We compared the variations of PM1 composition in Beijing during the SP period in the 2016 heating season (SPhs) with those in the normal production (NP) periods during the 2015 heating season (NPhs) and 2016 non-heating season (NPnhs) to investigate the effectiveness of SP. The PM1 mass concentration decreased from 70.0 ± 54.4 μg m−3 in NPhs to 53.0 ± 56.4 μg m−3 in SPhs, with prominent reductions in primary emissions. However, the fraction of nitrate during SPhs (20.2%) was roughly twice that during NPhs (12.7%) despite a large decrease of NOx, suggesting an efficient transformation of NOx to nitrate during the SP period. This is consistent with the increase of oxygenated organic aerosol (OOA), which almost doubled from NPhs (22.5%) to SPhs (43.0%) in the total organic aerosol (OA) fraction, highlighting efficient secondary formation during SP. The PM1 loading was similar between SPhs (53.0 ± 56.4 μg m−3) and NPnhs (50.7 ± 49.4 μg m−3), indicating a smaller difference in PM pollution between heating and non-heating seasons after the implementation of the SP measure. In addition, a machine learning technique was used to decouple the impact of meteorology on air pollutants. The deweathered results were comparable with the observed results, indicating that meteorological conditions did not have a large impact on the comparison results. Our study indicates that the SP policy is effective in reducing primary emissions but promotes the formation of secondary species.

Mingjia Shangguan, Jiawei Qiu, Jinlong Yuan et al.

Doppler wind lidar (DWL) uses the optical Doppler effect to measure atmospheric wind speed with high spatial-temporal resolution and long detection range and has been widely applied in scientific research and engineering applications. With the development of related technology, especially laser and detector technology, the performance of the DWL has significantly improved for the past few decades. DWL utilizes different principles and different tracers to sense the wind speed from the ground to the mesosphere, which leads to the difference in choosing the laser working wavelength. This article will review the working wavelength consideration of DWL, and typical DWLs will present from ultraviolet to near-infrared, after which three typical applications will be shown.