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Characterized by sudden changes in strength, complex influencing factors, and significant impacts, the wind speed in the circum-Bohai Sea area is relatively challenging to forecast. On the western side of Bohai Bay, as the economic center of the circum-Bohai Sea, Tianjin exhibits a high demand for accurate wind forecasting. In this study, three machine learning algorithms were employed and compared as post-processing methods to correct wind speed forecasts by the Weather Research and Forecast (WRF) model for Tianjin. The results showed that the random forest (RF) achieved better performance in improving the forecasts because it substantially reduced the model bias at a lower computing cost, while the support vector machine (SVM) performed slightly worse (especially for stronger winds), but it required an approximately 15 times longer computing time. The back propagation (BP) neural network produced an average forecast significantly closer to the observed forecast but insufficiently reduced the RMSE. In regard to wind speed frequency forecasting, the RF method commendably corrected the forecasts of the frequency of moderate (force 3) wind speeds, while the BP method showed a desirable capability for correcting the forecasts of stronger (force > 6) winds. In addition, the 10-m u and v components of wind ( u 10 and v 10 ), 2-m relative humidity (RH 2 ) and temperature ( T 2 ), 925-hPa u ( u 925 ), sea level pressure (SLP), and 500-hPa temperature ( T 500 ) were identified as the main factors leading to bias in wind speed forecasting by the WRF model in Tianjin, indicating the importance of local dynamical/thermodynamic processes in regulating the wind speed. This study demonstrates that the combination of numerical models and machine learning techniques has important implications for refined local wind forecasting.

Wildland fires exert substantial impacts on tundra ecosystems of the high northern latitudes (HNL), ranging from biogeochemical impact on climate system to habitat suitability for various species. Cloud-to-ground (CG) lightning is the primary ignition source of wildfires. It is critical to understand mechanisms and factors driving lightning strikes in this cold, treeless environment to support operational modeling and forecasting of fire activity. Existing studies on lightning strikes primarily focus on Alaskan and Canadian boreal forests where land-atmospheric interactions are different and, thus, not likely to represent tundra conditions. In this study, we designed an empirical-dynamical method integrating Weather Research and Forecast (WRF) simulation and machine learning algorithm to model the probability of lightning strikes across Alaskan tundra between 2001 and 2017. We recommended using Thompson 2-moment and Mellor-Yamada-Janjic schemes as microphysics and planetary boundary layer parameterizations for WRF simulations in the tundra. Our modeling and forecasting test results have shown a strong capability to predict CG lightning probability in Alaskan tundra, with the values of area under the receiver operator characteristics curves above 0.9. We found that parcel lifted index and vertical profiles of atmospheric variables, including geopotential height, dew point temperature, relative humidity, and velocity speed, important in predicting lightning occurrence, suggesting the key role of convection in lightning formation in the tundra. Our method can be applied to data-scarce regions and support future studies of fire potential in the HNL.

This work aims to better understand how small scale disturbances that are generated at the air-sea interface propagate into the surrounding atmosphere under realistic environmental conditions. To that end, a one-way coupled atmosphere-ocean model is presented, in which predictions of sea surface currents and sea surface temperatures from a microscale ocean model are used as constant boundary conditions in a larger atmospheric model. The coupled model consists of an ocean component implemented while using the open source CFD software OpenFOAM, an atmospheric component solved using the Weather Research and Forecast (WRF) model, and a Python-based utility foamToWRF, which is responsible for mapping field data between the ocean and atmospheric domains. The results are presented for two demonstration cases, which indicate that the proposed coupled model is able to capture the propagation of small scale sea surface disturbances in the atmosphere, although a more thorough study is required in order to properly validate the model.

On 21 September 2012 the entire sky was covered by contrails over the Gulf of Lyon (NW of the Mediterranean basin). These clouds were well recorded by ground observers as well as by Meteosat imagery. The atmospheric characteristics at the levels where these anthropic clouds formed are analyzed by performing a WRF simulation in the area where Meteosat recorded contrail clouds. According to the vertical profiles of temperature and the relative humidity respect to the ice (RHI), the environmental condition favors that the water vapor exhaust emitted by the aircraft engines reaches the deposition point and form crystal clouds, which spread out because the temperature remained below 230 K and the RHI was higher than 70% during the whole episode.

Land use and land cover (LULC) in the Beijing–Tianjin–Hebei region has changed significantly since 1990. To comprehensively understand its impact on meteorology, this paper uses an LULC transfer matrix to discuss changes 10 years before the millennium (BM) and 10 years after the millennium (AM). The Weather Research and Forecasting (WRF) model is used to simulate air temperature, precipitation, and wind speed in January and July using 1990, 2000, and 2010 LULC data. The temperature increase in the AM period (0.118 °C) was greater than that in the BM period (0.042 °C) and increased by 0.36 °C in response to the transformation from croplands to urban and built-up lands (UBL). The change in precipitation showed an obvious seasonal difference, where the trend decreased in winter and increased in summer. There was a small band of precipitation change that extended from Beijing to Shijiazhuang. The wind speed at 10 m decreased by 0.04 m/s in winter and 0.017 m/s in summer. Simultaneously, the impact on meteorology from different classification transformations was quantified. The region where land was transformed into UBL had the largest reduction in wind speed, and was later transformed into woodlands. A comprehensive discussion on the impact of LULC changes on climate should support the future development of more environmentally compatible and sustainable land planning.

During the dry season, from November to April, agricultural biomass burning and forest fires especially from March to late April in mainland Southeast Asian countries of Myanmar, Thailand, Laos and Vietnam frequently cause severe particulate pollution not only in the local areas but also across the whole region and beyond due to the prevailing meteorological conditions. Recently, the BASE-ASIA (Biomass-burning Aerosols in South East Asia: Smoke Impact Assessment) and 7-SEAS (7-South-East Asian Studies) studies have provided detailed analysis and important understandings of the transport of pollutants, in particular, the aerosols and their characteristics across the region due to biomass burning in Southeast Asia (SEA). Following these studies, in this paper, we study the transport of particulate air pollution across the peninsular region of SEA and beyond during the March 2014 burning period using meteorological modelling approach and available ground-based and satellite measurements to ascertain the extent of the aerosol pollution and transport in the region of this particular event. The results show that the air pollutants from SEA biomass burning in March 2014 were transported at high altitude to southern China, Hong Kong, Taiwan and beyond as has been highlighted in the BASE-ASIA and 7-SEAS studies. There are strong evidences that the biomass burning in SEA especially in mid-March 2014 has not only caused widespread high particle pollution in Thailand (especially the northern region where most of the fires occurred) but also impacted on the air quality in Hong Kong as measured at the ground-based stations and in LulinC (Taiwan) where a remote background monitoring station is located.

A constant value of air density based on its annual average value at a given location is commonly used for the computation of the annual energy production in wind industry. Thus, the correction required in the estimation of daily, monthly or seasonal wind energy production, due to the use of air density, is ordinarily omitted in existing literature. The general method, based on the implementation of the wind speed’s Weibull distribution over the power curve of the turbine, omits it if the power curve is not corrected according to the air density of the site. In this study, the seasonal variation of air density was shown to be highly relevant for the computation of offshore wind energy potential around the Iberian Peninsula. If the temperature, pressure, and moisture are taken into account, the wind power density and turbine capacity factor corrections derived from these variations are also significant. In order to demonstrate this, the advanced Weather Research and Forecasting mesoscale Model (WRF) using data assimilation was executed in the study area to obtain a spatial representation of these corrections. According to the results, the wind power density, estimated by taking into account the air density correction, exhibits a difference of 8% between summer and winter, compared with that estimated without the density correction. This implies that seasonal capacity factor estimation corrections of up to 1% in percentage points are necessary for wind turbines mainly for summer and winter, due to air density changes.

The North Atlantic Oscillation (NAO) is considered to be the main atmospheric factor explaining the winter climate and snow evolution over much of the Northern Hemisphere. However, the absence of long-term snow data in mountain regions has prevented full assessment of the impact of the NAO at the regional scales, where data are limited. In this study, we assessed the relationship between the NAO of the winter months (DJFM-NAO) and the snowpack of the Iberian Peninsula. We simulated temperature, precipitation, and snow data for the period 1979–2014 by dynamic downscaling of ERA-Interim reanalysis data, and correlated this with the DJFM-NAO for the five main mountain ranges of the Iberian Peninsula (Cantabrian Range, Central Range, Iberian Range, the Pyrenees, and the Sierra Nevada). The results confirmed that negative DJFM-NAO values generally occur during wet and mild conditions over most of the Iberian Peninsula. Due to the direction of the wet air masses, the NAO has a large influence on snow duration and the annual peak snow water equivalent (peak SWE) in most of the mountain ranges in the study, mostly on the slopes south of the main axis of the ranges. In contrast, the impact of NAO variability is limited on north-facing slopes. Negative (positive) DJFM-NAO values were associated with longer (shorter) duration and higher (lower) peak SWEs in all mountains analyzed in the study. We found marked variability in correlations of the DJFM-NAO with snow indices within each mountain range, even when only the south-facing slopes were considered. The correlations were stronger for higher elevations in the mountain ranges, but geographical longitude also explained the intra-range variability in the majority of the studied mountains.

Weather radar plays a key role in natural disaster mitigation just as surveillance radar does in detecting objects that threaten homeland security. Both together comprise an instrumental part of radar observation. Therefore, quality control of the data gathered through radar detection is extremely important. However, radar waves propagate in the atmosphere, and an anomalous echo can occur if there are significant discontinuities in temperature and humidity in the lower boundary layer. The refractive curvature of the earth makes some errors in observation inevitable. On the night of July 3, 2003, Next Generation Radar (NEXRAD) weather radar detected an abnormal echo. The Weather Research and Forecast (WRF) model was utilized to simulate the atmospheric conditions. Radar propagation was simulated using the Advanced Refractivity Engineering Prediction System (AREPS) as well as the GIS. The results show the feasibility of establishing an abnormal propagation early-warning system and extending the application of the GIS in serving as the foundation of a Common Operation Picture (COP). Furthermore, the parameters of the boundary layer near the sea's surface in the numerical weather forecasting model need remodification.

Regional weather forecasting models like the Weather Research and Forecasting (WRF) model allow for nested domains to save computational effort and provide detailed results for mesoscale weather phenomena. The sudden resolution change by nesting may cause artefacts in the model results. On the contrary, the novel global Model for Prediction Across Scales (MPAS) runs on Voronoi meshes that allow for smooth resolution transition towards the desired high resolution in the region of interest. This minimises the resolution-related artefacts, while still saving computational effort. We evaluate the MPAS model over Europe focussing on three mesoscale weather events: a synoptic gale over the North Sea, a föhn effect in Switzerland, and a case of organised convection with hail over the Netherlands. We use four different MPAS meshes (60 km global refined to-3 km (60– 3 km), analogous 30–3 km, 15–3 km, global 3 km) and compare their results to routine observations and a WRF setup with a single domain of 3 km grid spacing. We also discuss the computational requirements for the different MPAS meshes and the operational WRF setup. In general, the MPAS 3 km and WRF model results correspond to the observations. However, a global model at 3 km resolution as a replacement for WRF is not feasible for operational use. More importantly, all variable-resolution meshes employed in this study show comparable skills in short-term forecasting within the high-resolution area at considerably lower computational costs.