Weather Forecasts Research Articles

The added value and potential of long-term radio occultation data for climatological wind field monitoring

Abstract. Global long-term stable 3D wind fields provide valuable information for climate-oriented analyses of the dynamics of the atmosphere. Their monitoring remains a challenging task given the shortcomings of available observations. One promising option for progress is the use of radio occultation (RO) satellite data, which enable deriving dynamics based on thermodynamic data. In this study we focus on three main goals, explored through the fifth version of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) and RO datasets, using monthly-mean January and July data over 2007–2020. Our focus is on a 2.5° × 2.5° spatial synoptic scale over the free troposphere to the mid-stratosphere (i.e. 800–10 hPa). First, by comparing ERA5-derived geostrophic and gradient wind speeds to the original ERA5 ones, we examine the regions of validity of the studied approximations at a given synoptic-scale resolution. Second, to assess the possible added value of the RO-derived climatic winds in terms of their long-term stability, we test their consistency with the corresponding ERA5-derived winds. Third, by comparing the RO climatic winds to the original ERA5 winds, we evaluate the potential benefit of RO as an additional dataset for wind analyses and climate monitoring. With this three-step analysis, we decompose the total wind speed bias into the contributions from the approximation and the systematic difference between the RO and ERA5 datasets. We find that the geostrophic approximation is a valid method to estimate winds in the free troposphere, while the gradient wind approximation works better in the lower stratosphere. Both approximations generally work well over the mentioned altitudes, within an accuracy of 2 m s−1 for the latitudes 5–82.5°. Exceptions are found in winter in the monsoonal area and above larger mountain ranges in the free troposphere, as well as above the northern polar regions in the mid-stratosphere. RO- and ERA5-derived geostrophic winds mostly showed good agreement (within 2 m s−1). However, temporal change in the systematic difference higher than 0.5 m s−1 per decade was found. This points to a possible impact of changes in the source of the assimilated data in ERA5. The overall high accuracy of the monthly-mean wind fields, backed by the long-term stability and fine vertical resolution of the underlying RO data, highlights the added value and potential benefit of RO-derived climatic winds for climate monitoring and analyses.

The Weather On-Demand Framework

This paper describes the Weather On-Demand (WOD) forecasting framework which is a software stack used to run operational and on-demand weather forecasts. The WOD framework is a distributed system for the following: (1) running the Weather Research and Forecast (WRF) model for data assimilation and forecasts by triggering either scheduled or on-demand jobs; (2) gathering upstream weather forecasts and observations from a wide variety of sources; (3) reducing output data file sizes for permanent storage; (4) making results available through Application Programming Interfaces (APIs); (5) making data files available to custom post-processors. Much effort is put into starting processing as soon as the required data become available, and in parallel where possible. In addition to being able to create short- to medium-range weather forecasts for any location on the globe, users are granted access to a plethora of both global and regional weather forecasts and observations, as well as seasonal outlooks from the National Oceanic and Atmospheric Administration (NOAA) in the USA through WOD integrated-APIs. All this information can be integrated with third-party software solutions via WOD APIs. The software is maintained in the Git distributed version control system and can be installed on suitable hardware, bringing the full flexibility and power of the WRF modelling system to the user in a matter of hours.

Impact of high-resolution land use data on numerical simulations of Colombo’s thermal environment during heatwaves

Land Use and Land Cover (LULC) play a crucial role in the regional climate models, as they influence various physical processes interconnected with energy, mass, and atmospheric interactions. This study has examined the impact of high-resolution LULC data on the accuracy of the Weather Research and Forecasting (WRF) model with the Urban Canopy Model in capturing the thermal environment of the Colombo Metropolitan Region (CMR) during heatwaves (HWs). In the performed simulations we used four LULC datasets: USGS, MODIS30, MODIS15, and Esri (Sentinel2). Our investigation is the first application of the WRF model to study the thermal environment in a tropical city such as Colombo. This is also the first research investigation of HWs in the Sri Lanka context from the mesoscale viewpoint. Additionally, this is the first time high-resolution Esri land-use data have been used in a numerical model to examine the thermal environment. By evaluating the three HW cases, we compared the simulation outcomes with observational data from three meteorological stations in CMR. The results from numerical simulations revealed that the LU4 dataset provided the closest agreement with observed Land Surface Temperatures (LST). Daytime LSTs in urban areas peaked above 45 °C, while nighttime temperatures reached 30 °C in commercial zones. Coastal regions consistently exhibited lower temperatures, with daytime LSTs ranging from 40 to 45 °C and nighttime LSTs from 25 to 30 °C, compared to the urban cores. LU4 showed the closest alignment with Himawari satellite data, ensuring superior accuracy. Temperature anomaly analysis indicated that LU4 reduced daytime temperature anomalies by up to 2.5 °C and nighttime anomalies by 1.0 °C compared to other datasets. Statistical evaluations (R = 0.94 in Colombo) further highlight the importance of high-resolution LULC data for enhancing the accuracy of the WRF model in representing urban thermal environments.

Identifying, Tracking, and Evaluating Mechanisms of North American Cold Air Outbreaks (CAOs) Using a Feature Tracking Approach

Abstract North American Cold Air Outbreaks (CAOs) are large-scale temperature extremes that typically originate in the high latitudes and impact the midlatitudes in winter. As they transit southward, they can have significant socioeconomic consequences. CAOs from winter (DJF) 1979 to 2020 were identified in the European Center for Medium-Range Weather Forecasts reanalysis dataset (ERA5) using an automated feature tracking approach (TempestExtremesV2.1). This allowed for the systematic identification of a large number of cases without using pre-determined, Eulerian regions. Another important advantage of this approach was the ability to compute a feature tracked thermodynamic energy budget in a non-fixed domain for every identified CAO event. As an example, the thermodynamic energy budget analysis was used to quantify important processes for the 18th–23rd January, 1985 CAO. The dominant mechanisms of cooling and warming as well as lysis locations (i.e. eastern or western) were then used to generalize detected CAO events into subcategories. The associated statistics, spatial footprints, and composites of 500-hPa height, sea level pressure, and temperature and winds at 850-hPa were analyzed for three subcategories that contained the majority of events. This analysis revealed that CAO events that form and dissipate through different mechanisms occur in different regions, have different intensities, and are associated with different large-scale circulation patterns. Finally, analysis of associated North Atlantic Oscillation (NAO) and Pacific-North American Pattern (PNA) teleconnections revealed that the PNA is typically in a positive phase for eastern CAO events and negative phase for western events resulting primarily from horizontal advection, whereas the NAO did not have any significant relationship.

On the Museum Monitoring at the Time of COVID-19. Methodological Approach and Application to Palazzo Altemps in Rome

ABSTRACT This article presents a multiparametric and multiscale methodological approach devised during the COVID-19 pandemic event for the environmental monitoring of artworks housed in a museum. The pandemic event provided the opportunity to analyze the museum environment in the absence of visitors, thus enabling the evaluation of indoor conditions solely influenced by environmental, meteorological, and microclimatic factors. The experimental analysis was conducted in the Altemps Palace in Rome. Microclimatic variables, such as temperature, relative humidity, and air quality (PM10 and PM2.5) were monitored using a network of MeMS sensors, which were made interoperable through Long Range transmission technology. To establish a comparative reference, indoor were compared with outdoor measurements, along with ground measurements (provided by the Regional Prevention and Environment Agency, and weather forecasts based on the COSMO model. At the same time, analyses were conducted on the conservation status of the exhibited artworks. The analysis of the collected data revealed critical issues regarding the indoor microclimatic conditions, particularly related to the significant variability of the relative humidity. Additionally, numerous episodes exceeding the maximum threshold for PM10 and PM2.5 were logged, emphasizing pollution concerns. The research findings have practical implications for the conservation and protection of the artworks housed in the museum.

Multivariate time series weather forecasting model using integrated secondary decomposition and Self-Attentive spatio-temporal learning network

<p>Weather forecasting is an essential but complex task because of its substantial influence on human life and advanced atmospheric dynamics. In recent years, deep learning-based techniques have gain high attention for weather forecasting. Nevertheless, current forecasting models ignore the interdependent relationship between variables across regions and primarily examine temporal patterns of weather data. In this work, a new Multivariate time series weather forecasting model is proposed using integrated secondary decomposition and Self-Attentive spatio-temporal learning network (SASTLNet). Particularly, the weather data is filtered using a Singular Spectrum Decomposition with Fuzzy Entropy (SSD-FE) for removing the irrelevant components and screening the component containing vital information. Moreover, the spatiotemporal fluctuations in the meteorological variables are investigated using an enhanced empirical mode decomposition (E3MD) method and an SASTLNet model. The self-attention block of the SASTLNet model is discovered using ladder format to lower the processing costs while representing the temporal and spatial features through single memory cell. The simulation results prove that the suggested model can outperforms the baseline models in terms of efficacy and accuracy.</p>

Evaluation of the Sensitivity of the Weather Research and Forecasting Model to Changes in Physical Parameterizations During a Torrential Precipitation Event of the El Niño Costero 2017 in Peru

This study evaluates the sensitivity of the Weather Research and Forecasting (WRF-ARW) model in its version 4.3.3 during different experiments on a torrential precipitation event associated with the 2017 El Niño Costero in Peru. The results are compared with two reference datasets: precipitation estimations from CHIRPS satellite data and SENAMHI meteorological station values. The event, which had significant economic and social impacts, is simulated using two nested domains with resolutions of 9 km (d01) and 3 km (d02). A total of 22 experiments are conducted, resulting from the combination of two planetary boundary layer (PBL) schemes: Yonsei University (YSU) and Mellor–Yamada–Janjic (MYJ), with five cumulus parameterization schemes: Betts–Miller–Janjic (BMJ), Grell–Devenyi (GD), Grell–Freitas (GF), Kain–Fritsch (KF), and New Tiedtke (NT). Additionally, the effect of turning off cumulus parameterization in the inner domain (d02) or in both (d01 and d02) is explored. The results show that the YSU scheme generally provides better results than the MYJ scheme in detecting the precipitation patterns observed during the event. Furthermore, it is concluded that turning off cumulus parameterization in both domains produces satisfactory results for certain regions when it is combined with the YSU PBL scheme. However, the KF cumulus parameterization is considered the most effective for intense precipitation events in this region, although it tends to overestimate precipitation in high mountain areas. In contrast, for lighter rains, combinations of the YSU PBL scheme with the GD or NT parameterization show a superior performance. It is worth nothing that for all experiments here used, there is a clear underestimation in terms of precipitation, except in high mountain regions, where the model tends to overestimate rainfall.

Super Typhoons Simulation: A Comparison of WRF and Empirical Parameterized Models for High Wind Speeds

As extreme forms of tropical cyclones (TCs), typhoons pose significant threats to both human society and the natural environment. To better understand and predict their behavior, scientists have relied on numerical simulations. Current typhoon modeling primarily falls into two categories: (1) complex simulations based on fluid dynamics and thermodynamics, and (2) empirical parameterized models. Most comparative studies on these models have focused on wind speed below 50 m/s, with fewer studies addressing high wind speed (above 50 m/s). In this study, we design and compare four different simulation approaches to model two super typhoons: Typhoon Surigae (2102) and Typhoon Nepartak (1601). These approaches include: (1) The Weather Research and Forecasting (WRF) model simulation driven by NCEP Final Operational Global Analysis data (FNL), (2) WRF simulation driven by the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), (3) the empirical parameterized Holland model, and (4) the empirical parameterized Jelesnianski model. The simulated wind fields were compared with the measured wind data from The Soil Moisture Active Passive (SMAP) platform, and the resulting wind fields were then used as inputs for the Simulating WAves Nearshore (SWAN) model to simulate typhoon-induced waves. Our findings are as follows: (1) for high wind speeds, the performance of the empirical models surpasses that of the WRF simulations; (2) using more accurate driving wind data improves the WRF model’s performance in simulating typhoon wind speeds, and WRF simulations excel in representing wind fields in the outer regions of the typhoon; (3) careful adjustment of the maximum wind speed radius parameter is essential for improving the accuracy of the empirical models.

Simulations suggest offshore wind farms modify low-level jets

Abstract. Offshore wind farms are scheduled to be constructed along the East Coast of the US in the coming years. Low-level jets (LLJs) – layers of relatively fast winds at low altitudes – also occur frequently in this region. Because LLJs provide considerable wind resources, it is important to understand how LLJs might change with turbine construction. LLJs also influence moisture and pollution transport; thus, the effects of wind farms on LLJs could also affect the region’s meteorology. In the absence of observations or significant wind farm construction as yet, we compare 1 year of simulations from the Weather Research and Forecasting (WRF) model with and without wind farms incorporated, focusing on locations chosen by their proximity to future wind development areas. We develop and present an algorithm to detect LLJs at each hour of the year at each of these locations. We validate the algorithm to the extent possible by comparing LLJs identified by lidar, constrained to the lowest 200 m, to WRF simulations of these very low LLJs (vLLJs). In the NOW-WAKES simulation data set, we find offshore LLJs in this region occur about 25 % of the time, most frequently at night, in the spring and summer months, in stably stratified conditions, and when a southwesterly wind is blowing. LLJ wind speed maxima range from 10 m s−1 to over 40 m s−1. The altitude of maximum wind speed, or the jet “nose”, is typically 300 m above the surface, above the height of most profiling lidars, although several hours of vLLJs occur in each month in the data set. The diurnal cycle for vLLJs is less pronounced than for all LLJs. Wind farms erode LLJs, as LLJs occur less frequently (19 %–20 % of hours) in the wind farm simulations than in the no-wind-farm (NWF) simulation (25 % of hours). When LLJs do occur in the simulation with wind farms, their noses are higher than in the NWF simulation: the LLJ nose has a mean altitude near 300 m for the NWF jets, but that nose height moves higher in the presence of wind farms, to a mean altitude near 400 m. Rotor region (30–250 m) wind veer is reduced across almost all months of the year in the wind farm simulations, while rotor region wind shear is similar in both simulations.

Skillful subseasonal ensemble predictions of heat wave onsets through better representation of land surface uncertainties

Uncertainties in land surface processes notably limit subseasonal heat wave (HW) onset predictions. A better representation of the uncertainties in land surface processes using ensemble prediction methods may be an important way to improve HW onset predictions. However, generating ensemble members that adequately represent land surface process uncertainties, particularly those related to land surface parameters, remains challenging. In this study, a conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach was employed to generate ensemble members for representing the uncertainties in land surface processes resulting from parameters. Via six strong and long-lasting HW events over the middle and lower reaches of the Yangtze River (MLYR), HW onset ensemble forecast experiments were conducted with the Weather Research and Forecasting (WRF) model. The performance of the CNOP-P approach and the traditional random parameter perturbation ensemble prediction method was evaluated. The results demonstrate that the deterministic and probabilistic skills of HW onset predictions show greater excellence using the CNOP-P approach, leading to much better predictions of extreme air temperatures than those using the traditional method. This occurred because the ensemble members generated by the CNOP-P method better represented the uncertainties in important land physical processes determining HW onsets over the MLYR, notably vegetation process uncertainties, whereas the ensemble members generated by the random parameter perturbation method could not. This finding suggests that the CNOP-P method is suitable for producing ensemble members that more appropriately represent model uncertainties through more reasonable parameter error characterization.

Are Farmers Willing to Invest on Weather Forecast Information? Empirical Evidence from Tigray, Ethiopia

Are Farmers Willing to Invest on Weather Forecast Information? Empirical Evidence from Tigray, Ethiopia

The Performance of a High-Resolution WRF Modelling System in the Simulation of Severe Tropical Cyclones over the Bay of Bengal Using the IMDAA Regional Reanalysis Dataset

Extremely severe cyclonic storms over the North Indian Ocean increased by approximately 10% during the past 30 years. The climatological characteristics of tropical cyclones for 38 years were assessed over the Bay of Bengal (BoB). A total of 24 ESCSs formed over the BoB, having their genesis in the southeast BoB, and the intensity and duration of these storms have increased in recent times. The Advanced Research version of the Weather Research and Forecasting (ARW) model is utilized to simulate the five extremely severe cyclonic storms (ESCSs) over the BoB during the past two decades using the Indian Monsoon Data Assimilation and Analysis (IMDAA) data. The initial and lateral boundary conditions are derived from the IMDAA datasets with a horizontal resolution of 0.12° × 0.12°. Five ESCSs from the past two decades were considered: Sidr 2007, Phailin 2013, Hudhud 2014, Fani 2019, and Amphan 2020. The model was integrated up to 96 h using double-nested domains of 12 km and 4 km. Model performance was evaluated using the 4 km results, compared with the available observational datasets, including the best-fit data from the India Meteorological Department (IMD), the Tropical Rainfall Measuring Mission (TRMM) satellite, and the Doppler Weather Radar (DWR). The results indicated that IMDAA provided accurate forecasts for Fani, Hudhud, and Phailin regarding the track, intensity, and mean sea level pressure, aligning well with the IMD observational datasets. Statistical evaluation was performed to estimate the model skills using Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), the Probability of Detection (POD), the Brier Score, and the Critical Successive Index (CSI). The calculated mean absolute maximum sustained wind speed errors ranged from 8.4 m/s to 10.6 m/s from day 1 to day 4, while mean track errors ranged from 100 km to 496 km for a day. The results highlighted the prediction of rainfall, maximum reflectivity, and the associated structure of the storms. The predicted 24 h accumulated rainfall is well captured by the model with a high POD (96% for the range of 35.6–64.4 mm/day) and a good correlation (65–97%) for the majority of storms. Similarly, the Brier Score showed a value of 0.01, indicating the high performance of the model forecast for maximum surface winds. The Critical Successive Index was 0.6, indicating the moderate model performance in the prediction of tracks. It is evident from the statistical analysis that the performance of the model is good in forecasting storm structure, intensity and rainfall. However, the IMDAA data have certain limitations in predicting the tracks due to inadequate representation of the large-scale circulations, necessitating improvement.

Predicting the reduction in heatstroke and heart disease-related mortality under urban modification scenarios using machine learning

Abstract This study proposes a novel approach combining machine learning (ML) techniques with meteorological model simulations to evaluate the heat-related mortality reduction potential of a climate change adaptation measure, namely, the installation of energy-saving or temperature-decreasing modifications in an urban area (e.g., greening, high-albedo paints, and photovoltaics). These methods have been used separately to assess the future urban health. The Weather Research and Forecasting–Canopy-Building Energy Model (WRF–CMBEM) was used to simulate spatiotemporal urban meteorological conditions, and ML was applied to predict daily heat-related deaths in the 23 wards of Tokyo during the extremely hot summer of 2018. The urban energy-saving and heat island mitigation scenarios evaluated in this study were ground surface greening, no anthropogenic heat from buildings to the atmosphere, rooftop photovoltaics, and cool roofs. ML accurately predicted heatstroke- and ischemic heart disease (IHD)-related daily deaths using important meteorological factors. After meteorological changes from the control case to four urban modification scenarios were predicted using the WRF–CMBEM, potential reductions in heat stress-related deaths were estimated using previously successful ML-trained models. The results showed that in July–August 2018, the ground surface greening case effectively decreased the outdoor surface air temperature by 0.28 °C (50-percentile), 0.37 °C (90-percentile), and 0.56 °C (Max) in all grids resolved at 1 km. Temperature changes reduced heatstroke deaths by 43% and IHD deaths by 18% during the peak period of deaths in summer 2018. Cool roofs resulted in temperature decreases of 0.23 °C (50-percentile), 0.31 °C (90-percentile), and 0.36 °C (Max) and 14% and 13% reductions in heatstroke and IHD deaths, respectively. The results suggest that the implementation of urban modifications can effectively reduce heat-related deaths, especially during heatwaves and extremely hot summers.

Establishment and Evaluation of Atmospheric Water Vapor Inversion Model Without Meteorological Parameters Based on Machine Learning

Precipitable water vapor (PWV) is an important indicator to characterize the spatial and temporal variability of water vapor. A high spatial and temporal resolution of atmospheric precipitable water can be obtained using ground-based GNSS, but its inversion accuracy is usually limited by the weighted mean temperature, Tm. For this reason, based on the data of 17 ground-based GNSS stations and water vapor reanalysis products over 2 years in the Hong Kong region, a new model for water vapor inversion without the Tm parameter is established by deep learning in this paper, the research results showed that, compared with the PWV information calculated by the traditional model using Tm parameter, the accuracy of the PWV retrieved by the new model proposed in this paper is higher, and its accuracy index parameters BIAS, MAE, and RMSE are improved by 38% on average. At the same time, the PWV was inverted by radiosonde data in the study area as a reference to verify the water vapor inversion results of the new model, and it was found that the BIAS of the new model is only 0.8 mm, which has high accuracy. Further, compared with the LSTM model, the new model is more universal when the accuracy is comparable. In addition, in order to evaluate the spatial and temporal variation characteristics of the atmospheric water vapor retrieved by the new model, based on the rainstorm event caused by typhoon in Hong Kong of September 2023, the ERA5 GSMaP rainfall products and inverted PWV information were comprehensively used for analysis. The results show that the PWV increased sharply with the arrival of the typhoon and the occurrence of a rainstorm event. After the rain stopped, the PWV gradually decreased and tended to be stable. The spatial and temporal variation in the PWV have a strong correlation with the occurrence of extreme rainstorm events. This shows that the PWV inverted by the new model can respond well to extreme rainstorm events, which proves the feasibility and reliability of the new model and provides a reference method for meteorological monitoring and weather forecasting.

Thermal Environmental Impact of Urban Development Scenarios from a Low Carbon Perspective: A Case Study of Wuhan

Amidst the increasingly escalating global concern regarding climate change, adopting a low-carbon approach has become crucial for charting the future developmental trajectory of urban areas. It also offers a novel angle for cities to avoid high-temperature risks. This paper estimates carbon emissions in Wuhan City from both direct and indirect aspects. Then, the ANN (artificial neural network)–CA (Cellular Automata) model is employed to establish three distinct development scenarios (Ecological Priority, Tight Growth, and Natural Growth) to predict future urban expansion. Additionally, the WRF (Weather Research and Forecasting Model)—UCM (Urban Canopy Model) model is used to investigate the thermal environmental impacts of varying urban development scenarios. This study uses a low-carbon perspective to explore how cities can develop scientifically sound urban strategies to meet climate change challenges and achieve sustainable development goals. The conclusions are as follows: (1) The net carbon emission for Wuhan in 2022 is estimated to be approximately 20.8353 million tonnes. Should the city maintain an average annual emission reduction rate of 10%, the carbon sink capacity of Wuhan would need to be enhanced by 382,200 tonnes by 2060. (2) In the absence of anthropogenic influence, there is a propensity for the urban construction zone of Wuhan to expand primarily towards the southeast and western sectors. (3) The Ecological Priority (EP) and Tight Growth (TG) scenarios are effective in alleviating the urban thermal environment, achieving a reduction of 0.88% and 2.48%, respectively, in the urban heat island index during afternoon hours. In contrast, the Natural Growth (NG) scenario results in a degradation of the urban thermal environment, with a significant increase of over 4% in the urban heat island index during the morning and evening periods. (4) An overabundance of urban green spaces and water bodies could exacerbate the urban heat island effect during the early morning and at night. The findings of this study enhance the comprehension of the climatic implications associated with various urban development paradigms and are instrumental in delineating future trajectories for low-carbon sustainable urban development models.

Aerosol transport and associated boundary layer thermodynamics under contrasting synoptic conditions over a semiarid site.

Understanding the kinematics of aerosol horizontal transport and vertical mixing near the surface, within the atmospheric boundary layer (ABL), and in the overlying free troposphere (FT) is critical for various applications, including air quality and weather forecasting, aviation, road safety, and dispersion modeling. Empirical evidence of aerosol mixing processes within the ABL during synoptic-scale events over arid and semiarid regions (i.e., drylands) remains sparse. We explored how synoptic-scale weather systems impact aerosol mixing processes within the daytime ABL over a site located in a dryland. We used ground-based Doppler lidar measurements collected during three events: a cold-front passage, a fair-weather day, and a dryline passage over Lubbock, Texas. The measurements of backscatter and vertical velocity fields were obtained with temporal and vertical resolutions of 1s and 60m, respectively. Here, we documented observations of aerosol transport and mixing within the ABL and found that frontal passages are crucial for understanding ABL features and aerosol mixing processes. For example, our findings suggest that during a dryline passage yielding a water vapor mixing ratio drop of 10g kg-1, the boundary layer characteristics transition from being shallow and stratified throughout a stable, pre-dryline ABL aerosol regime (300m deep) to a deep and well-mixed structure within the post-dryline ABL (2200m deep) confirming a higher ABL depth growth rate (∼300mh-1) than under quiescent conditions (∼125mh-1). The results for the frontal case reported aerosol mixing via frontal lifting to an altitude of 1250m from the ground due to strong updrafts (>7ms-1). Additionally, Doppler lidar measurements helped to characterize the aerosol mixing and transport processes in dry regions under different weather conditions which yielded close correspondence with the observed variability in near-surface particulate matter (i.e., PM2.5) concentrations (e.g., increase in PM2.5 from 9μgm-3 to 27μgm-3 due to a cold front passage). The aerosol transport, along with the derived properties of the mean up- and downdraft observations and variance-based (both vertical velocity and aerosol backscatter) turbulence profiling helped explain how frontal airmass exchanges impact aerosol loading near the surface. The results obtained emphasize the need to consider the impact of synoptic-scale events over drylands in both observational and atmospheric modeling studies.

Does internet-based climate information improve non-fishing employment opportunities? Insights from smallholder fishermen in Indonesia

ABSTRACT In the face of climate change, smallholder fishermen increasingly turn to non-fishing employment to sustain their livelihoods. Diversifying sources of income becomes paramount to mitigate the impact of unpredictable weather patterns on fish populations and, hence, household livelihoods. However, fishermen need convenient climate information access to navigate this transition effectively. With information about weather trends and forecasts, they can make informed decisions about when to fish and when to focus on alternative sources of income. This study offers novelty by investigating the impact of Internet-based climate information on non-fishing employment among smallholder fishermen in Indonesia. The study employs cross-sectional data collected from 503 smallholder fishermen in Indonesia, analyzed using a conditional mixed process (CMP) approach to tackle the endogeneity issue that may arise in the estimation. The results highlight that adopting Internet-based climate information among smallholder fishermen was positively and significantly associated with education, distance to fishing ports, social activity, fishing location, adoption of multiple gears, and access to the Internet among family members and relatives. However, the age of fishermen shows otherwise. Furthermore, our empirical findings confirm that Internet-based climate information significantly increases opportunities for non-fishing employment among fishermen. Disaggregate estimation by fishing boat size category strengthens this finding by showing a positive and significant coefficient in all categories. Therefore, this finding implies a need to improve and develop Internet-based climate information platforms in fisheries.

Short- to Medium-Term Weather Forecast Skill of the AI-Based Pangu-Weather Model Using Automatic Weather Stations in China

Short- to Medium-Term Weather Forecast Skill of the AI-Based Pangu-Weather Model Using Automatic Weather Stations in China

To what extent is the description of streets important in estimating local air quality: a case study over Paris

Abstract. Modeling atmospheric composition at street level is challenging because pollutant concentrations within street canyons depend on both local emissions and the transport of polluted air masses from remote areas. Therefore, regional-scale modeling and local applications must be combined to provide accurate simulations of the atmospheric composition at street locations. In our study, we compare two strategies: (i) a subgrid-scale approach embedded in the chemistry–transport model (denoted Subgrid) and (ii) the street-network model MUNICH (Model of Urban Network of Intersecting Canyons and Highways). In both cases, the regional-scale chemistry–transport model CHIMERE provides the urban background concentrations, and the meteorological model Weather Research and Forecasting (WRF), coupled with CHIMERE, is used to provide meteorological fields. Simulation results for NOx, NO2, and PM2.5 concentrations over the city of Paris from both modeling approaches are compared with in situ measurements from traffic air quality stations. At stations located in downtown areas, with low traffic emissions, the street-network model MUNICH exhibits superior performance compared to the Subgrid approach for NOx concentrations, while comparable results are obtained for NO2. However, significant discrepancies between the two methods are observed for all analyzed pollutants at stations heavily influenced by road traffic. These stations are typically located near highways, where the difference between the two approaches can reach 58 %. The ability of the Subgrid approach to estimate accurate emission data is limited, leading to potential underestimation or overestimation of gas and fine-particle concentrations based on the emission heterogeneity it handles. The performance of MUNICH appears to be highly sensitive to the friction velocity, a parameter influenced by the anthropogenic heat flux used in the WRF model. Street dimensions do contribute to the performance disparities observed between the two approaches, yet emissions remain the predominant factor.

Long-term variability of extreme precipitation with WRF model at a complex terrain River Basin

Global warming has profound effects on precipitation patterns, leading to more frequent and extreme precipitation events over the world. These changes pose significant challenges to the sustainable development of socio-economic and ecological environments. This study evaluated the performance of the new generation of the mesoscale Weather Research and Forecasting (WRF) model in simulating long-term extreme precipitation events over the Minjiang River Basin (MRB) of China from 1981 to 2020. We calculated 12 extreme precipitation indices from the WRF simulations and compared them with observations. The spatio-temporal variations of extreme precipitation were further analyzed in terms of intensity, frequency, and duration. The results indicated that the WRF model can appropriately reproduce the spatial distribution of extreme precipitation indices with acceptable biases. The performance is significantly better for intensity and frequency indices compared to duration indices. Except for PRCPTOT and R10mm, WRF accurately captures the interannual variations of extreme precipitation. Meanwhile, the results of the pre-whitening Mann-Kendall (PWMK) test suggested that WRF can identify significant increasing trends in extreme precipitation, particularly for R95p, R99p, and R50mm. This study provides valuable insights for extreme precipitation forecasting and warning in other mountainous regions.