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Rokko‐oroshi is a northerly local wind blowing in the mega‐city Kobe, Japan. This wind blows from the Rokko Mountains. This study analyzed the three‐dimensional structure of Rokko‐oroshi observed with a near‐surface anemometer and Doppler lidar on January 16, 2023. Furthermore, numerical simulations using the Weather Research and Forecasting (WRF) model revealed the factors responsible for the strong winds. The results showed that Rokko‐oroshi on January 16, 2023 was a bora‐type downslope windstorm. The Doppler lidar observed the strong winds of Rokko‐oroshi and a stagnant layer immediately above them. Numerical simulation results indicated the stagnant layer was formed by mountain‐wave breaking. Under this stagnant layer, the airflow transitioned from subcritical to supercritical, resulting in the strong winds of Rokko‐oroshi. This Rokko‐oroshi was accompanied by a hydraulic jump. The occurrence of the Rokko‐oroshi was supported by an upper‐level critical layer and a lower‐level strong stable layer on the windward side of the Rokko Mountains. This study revealed that the local winds known as “Rokko‐oroshi” in the megacity of Kobe, Japan, is a bora‐type downslope windstorm. Numerical simulation results showed the presence of a strong stable layer and a critical layer at slightly higher altitudes than the mountaintop on the windward side during the downslope windstorm event. Additionally, the results of numerical simulation and a Doppler lidar observation indicated the weak wind region above the downslope windstorm.

This study uses the Weather Research and Forecasting (WRF) model with five different cumulus parameterization schemes (CPSs) at a resolution of 30 km to simulate the summer (June, July, and August) extreme precipitation event in the Yellow River Basin (YRB) during 2018. The goal of this study is to investigate the sensitivity of extreme precipitation simulation in the YRB during the summer of 2018 to CPSs in the WRF model. The results show that all five CPSs were capable of approximately simulating the direction of the rain bands in the YRB during the summer of 2018, but the simulation results of all CPSs tended to overestimate the value of precipitation amount. Upon further evaluation using seven different methods, it was found that the Betts–Miller–Janjic scheme provided the best simulation of this event. The complex orography of the YRB has a significant influence on moisture transport. The WRF model may have overestimated the moisture flux, which could have contributed to the overestimation of precipitation. The summer extreme precipitation event in the YRB during 2018 may have been influenced by an influx of excessive moisture from the western boundary.

The present study simulates the devastating floods in Kashmir that caused widespread damage in the valley from September 2-6 2014. The study used NCEP-NCAR FNL data for the initialization and simulation of the WRF ARW model. Statistical analysis of temperature over four places namely Anantnag, Srinagar, Pulwama, and Baramulla taking RMSE and MBIAS at 24, 48, 72, and 96 hours was also done against observed ECMWF-ERA5 temperature data. Further analysis of RMSE and MBIAS showed a minimum value at 48, 72, and 96 h indicating the improvement of prediction after 6 hours. Rainfall amount was under-predicted by the model with a time lag of 4 h while temperature time series over four districts were significantly closer to observation. Furthermore, the Model was able to capture the strong vertical velocities along with sufficient moisture content up to 600 hPa at the time of observed rainfall.

We present a high-resolution atmospheric inversion system combining a Lagrangian Particle Dispersion Model (LPDM) and the Weather Research and Forecasting model (WRF), and test the impact of assimilating meteorological observation on transport accuracy. A Four Dimensional Data Assimilation (FDDA) technique continuously assimilates meteorological observations from various observing systems into the transport modeling system, and is coupled to the high resolution CO2 emission product Hestia to simulate the atmospheric mole fractions of CO2. For the Indianapolis Flux Experiment (INFLUX) project, we evaluated the impact of assimilating different meteorological observation systems on the linearized adjoint solutions and the CO2 inverse fluxes estimated using observed CO2 mole fractions from 11 out of 12 communications towers over Indianapolis for the Sep.-Nov. 2013 period. While assimilating WMO surface measurements improved the simulated wind speed and direction, their impact on the planetary boundary layer (PBL) was limited. Simulated PBL wind statistics improved significantly when assimilating upper-air observations from the commercial airline program Aircraft Communications Addressing and Reporting System (ACARS) and continuous ground-based Doppler lidar wind observations. Wind direction mean absolute error (MAE) decreased from 26 to 14 degrees and the wind speed MAE decreased from 2.0 to 1.2 m s–1, while the bias remains small in all configurations (< 6 degrees and 0.2 m s–1). Wind speed MAE and ME are larger in daytime than in nighttime. PBL depth MAE is reduced by ~10%, with little bias reduction. The inverse results indicate that the spatial distribution of CO2 inverse fluxes were affected by the model performance while the overall flux estimates changed little across WRF simulations when aggregated over the entire domain. Our results show that PBL wind observations are a potent tool for increasing the precision of urban meteorological reanalyses, but that the impact on inverse flux estimates is dependent on the specific urban environment.

The incidence of heatstroke has been increasing in Japan, and future climate change is likely to increase heatstroke risk. We therefore developed a method to quantify the spatial distribution of outdoor heatstroke risk and predicted future changes in this risk considering the predicted climate change in Sendai, Japan. Heatstroke risk was quantified by assessing hazard, vulnerability and exposure. Daily maximum wet-bulb globe temperature (WBGT) was selected as the hazard index. The distribution of WBGT was predicted by mesoscale meteorological simulations using the Weather Research and Forecasting (WRF) model. The relationship between daily maximum WBGT and the daily incidence rate was approximated by analyzing emergency transport data. This relationship was selected as the vulnerability index. Using the hazard and vulnerability indices, a spatial distribution of the monthly incidence rate was obtained. Finally, the total number of heatstroke patients per month was estimated by multiplying the monthly incidence rate by the population density. The outdoor heatstroke risk for August was then estimated for current (2000s) and near-future (2030s) climatic conditions in Sendai. WBGT at coastal areas in the 2030s increased owing to increases in humidity, while WBGT at inland areas increased owing to increases in air temperature. This increase in WBGT drove increases in heatstroke risk.

Under global warming, extreme rainfall events have increased, causing annual economic losses of billions. Previous studies often treated high temperature and heavy rainfall as opposing phenomena, rarely considering their joint effects. However, emerging evidence suggests a direct link between antecedent heat conditions and subsequent precipitation extremes. As part of a typical extreme rainfall process, the high temperatures on July 26–27, 2023, drove the intensity and distribution of subsequent rainfall in the Beijing–Tianjin–Hebei region from July 29 to August 1. Numerical simulations and sensitivity experiments reveal that the July 26–27 heat event significantly intensified moisture transport and convective activity over the Taihang Mountains. Specifically, net moisture influx increased by 19.5% ± 24%, while convective intensity rose by 15% ± 13%. These heat‐induced enhancements collectively resulted in a 22.2% ± 7% increase in total rainfall. This mechanism provides a new explanation for future extreme rainfall events.

In the tropical Andes, the identification of the present synoptic mechanisms associated with the diurnal cycle of precipitation and its interaction with orography is a key step to understand how the atmospheric circulation influences the patterns of precipitation variability on longer time-scales. In particular we aim to better understand the combination of the local and regional mechanisms controlling the diurnal cycle of summertime (DJF) precipitation in the Northern Central Andes (NCA) region of Southern Peru. A climatology of the diurnal cycle is obtained from 15 wet seasons (2000–2014) of 3-hourly TRMM-3B42 data (0.25° × 0.25°) and swath data from the TRMM-2A25 precipitation radar product (5 km × 5 km). The main findings are: (1) in the NCA region, the diurnal cycle shows a maximum precipitation occurring during the day (night) in the western (eastern) side of the Andes highlands, (2) in the valleys of the Cuzco region and in the Amazon slope of the Andes the maximum (minimum) precipitation occurs during the night (day). The WRF (Weather Research and Forecasting) regional atmospheric model is used to simulate the mean diurnal cycle in the NCA region for the same period at 27 km and 9 km horizontal grid spacing and 3-hourly output, and at 3 km only for the month of January 2010 in the Cuzco valleys. Sensitivity experiments were also performed to investigate the effect of the topography on the observed rainfall patterns. The model reproduces the main diurnal precipitation features. The main atmospheric processes identified are: (1) the presence of a regional-scale cyclonic circulation strengthening during the afternoon, (2) diurnal thermally driven circulations at local scale, including upslope (downslope) wind and moisture transport during the day (night), (3) channelization of the upslope moisture transport from the Amazon along the Apurimac valleys toward the western part of the cordillera.

Urbanization modifies surface energy and water budgets, and has significant impacts on local and regional hydroclimate. In recent decades, a number of urban canopy models have been developed and implemented into the Weather Research and Forecasting (WRF) model to capture urban land-surface processes. Most of these models are inadequate due to the lack of realistic representation of urban hydrological processes. Here, we implement physically-based parametrizations of urban hydrological processes into the single layer urban canopy model in the WRF model. The new single-layer urban canopy model features the integration of, (1) anthropogenic latent heat, (2) urban irrigation, (3) evaporation from paved surfaces, and (4) the urban oasis effect. The new WRF–urban modelling system is evaluated against field measurements for four different cities; results show that the model performance is substantially improved as compared to the current schemes, especially for latent heat flux. In particular, to evaluate the performance of green roofs as an urban heat island mitigation strategy, we integrate in the urban canopy model a multilayer green roof system, enabled by the physical urban hydrological schemes. Simulations show that green roofs are capable of reducing surface temperature and sensible heat flux as well as enhancing building energy efficiency.

The mean climatological distribution of convective environmental parameters from the ERA5 reanalysis and WRF regional climate simulations is evaluated using radiosonde observations. The investigation area covers parts of Central and Eastern Europe. Severe weather proxies are calculated from daily 1200 UTC sounding measurements and collocated ERA5 and WRF pseudo-profiles in the 1985–2010 period. The pressure level and the native ERA5 reanalysis, and two WRF runs with grid spacings of 50 and 10 km are verified. ERA5 represents convective parameters remarkably well with correlation coefficients higher than 0.9 for multiple variables and mean errors close to zero for precipitable water and mid-tropospheric lapse rate. Monthly mean mixed-layer CAPE biases are reduced in the full hybrid-sigma ERA5 dataset by 20–30 J/kg compared to its pressure level version. The WRF model can reproduce the annual cycle of thunderstorm predictors but with considerably lower correlations and higher errors than ERA5. Surface elevation differences between the stations and the corresponding grid points in the 50-km WRF run lead to biases and false error compensations in the convective indices. The 10-km grid spacing is sufficient to avoid such discrepancies. The evaluation of convection-related parameters contributes to a better understanding of regional climate model behavior. For example, a strong suppression of convective activity might explain precipitation underestimation in summer. A decreasing correlation of WRF-derived wind shear away from the western domain boundaries indicates a deterioration of the large-scale circulation as the constraining effect of the driving reanalysis weakens.