Weather Research And Forecasting Research Articles

Numerical simulations of the heavy rain event in the Democratic People's Republic of Korea during 9–10 August 2020

Accurate forecasting of heavy rainfalls and understanding of their dynamics are important to minimize the damage caused by them in the Democratic People's Republic of Korea (DPR Korea). This study is conducted on a heavy rainfall event (452 mm) on 9–10 August 2020 over Pankyo region located on the midlands of the Korean peninsula. To verify the proper configuration of convection-permitting simulations, sensitivity experiments were performed with five microphysical schemes (Lin, Goddard, Thompson, Morrison and WDM6) of the Weather Research and Forecasting (WRF) model. The results suggested that all high-resolution simulations reflect the main characteristics of observed precipitation pattern well, but the location and intensity of maximum precipitation from scheme to scheme. Among the considered all the microphysics, the Lin scheme showed the best agreement with observed precipitation. Results also showed that the Lin scheme reproduced the vertical distribution and time variation of several hydrometeors, as well as dynamic and thermodynamic parameters associated with heavy rainfall well. These outcomes suggest that the suitable selection of microphysics schemes with WRF model is important to predict and understand heavy rainfall events over the DPR Korea.

Characteristics of Atmospheric Rivers and the Impact of Urban Roof Roughness on Precipitation during the “23.7” Extreme Rainstorm against the Background of Climate Warming

In July 2023, Baoding in Hebei Province experienced unprecedented torrential rainfall, breaking historical records and causing severe flooding. However, our understanding of the multi-scale circulation systems and physical mechanisms driving this extreme precipitation event remains incomplete. This study utilizes multi-source observational data and the Weather Research and Forecasting (WRF) numerical model to conduct a weather diagnosis and numerical simulation of this extreme rainfall event, focusing on the impact of atmospheric rivers (ARS) and urban rooftop roughness on the precipitation process against the background of climate warming. The study found that this extremely heavy rainstorm occurred in the circulation background formed by the factors of subtropical high ectopics, typhoon residual vortex retention, double typhoon water-vapor transmission, and stable high-level divergence. The ARS provided abundant moisture, with its vapor pathway significantly altered following the landfall of Typhoon Doksuri. The interaction between the ARS and the Taihang Mountains was crucial in triggering and intensifying the rainstorm in the foothills. Urbanization significantly affected the distribution of precipitation, with moderate urban roughness enhancing rainfall in and around the city, whereas excessive roughness suppressed it. These results contribute to a deeper understanding of the mechanisms behind extreme precipitation under climate change and provide a scientific basis for improving the forecasting and mitigation of such events.

Numerical modeling of microclimate of re-waterlogged lands of Belarusian Polesie

Based on the mesoscale hydrodynamic model WRF (Weather Research and Forecasting), the balance model of atmospheric moisture and the remote sensing data, we obtained the estimates of microclimate changes as a result of reswamping of lands in Belarusian Polesie. The calculations were performed on the example of the Khoiniki district of the Gomel region of Belarus. Numerical experiments considered the driest summer periods of the last two decades. Based on the modeling results, the maps of changes in mean daily temperature, amplitude of daily temperature variations, evapotranspiration and precipitation were constructed, which can be used to predict the consequences of land reclamation in different scenarios of adaptation to climate changes.

Aerosol impacts on summer precipitation forecast over the North China Plain by using Thompson aerosol aware scheme in WRF: Process analysis and response sensitivity to aerosol concentrations during a Heavy Rainfall Event in Beijing

Aerosol-Cloud-Interactions (ACIs) are possibly important factors affecting precipitation while are usually not considered in numerical weather prediction systems (NWP) due to the complexities and uncertainties involved by considering aerosol information. This research focused on the use of the Thompson aerosol-aware scheme in WRF (Weather Research and Forecasting model). This paper summarized the process analysis and response sensitivity to aerosol concentrations during a heavy rainfall event in Beijing. The July 16, 2018 rainfall event -a typical precipitation case involving both warm and cold-phase clouds was thoroughly analyzed, including cloud fraction, downward shortwave radiation, water vapor consumption, cloud microphysical processes, net latent heat etc., which verified that the aerosol-cloud interaction paths were accurately and adequately considered in Thompson aerosol-ware scheme. By tracking key parameter changes, it is confirmed that the CCN/IN activation and relative roles in changing precipitation at different stages are reasonable in Thompson aerosol-aerosol scheme. According to the rainfall characteristics, the precipitation was divided into two phases. And the difference in the aerosol effect on precipitation in the two precipitation phases is mainly due to the complexity of cold rain process and hypothesis. Sensitivity simulations also revealed that the current concentrations of background aerosol in Beijing are actually high enough and further ten-times of aerosol concentration increase didn't bring significant additional precipitation response.

Characterization of winter PM2.5 source contributions and impacts of meteorological conditions and anthropogenic emission changes in the Sichuan Basin, 2002–2020

In this study, the Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality–Integrated Source Apportionment Method (CMAQ–ISAM) were utilized, which were integrated with the Multiresolution Emission Inventory for China (MEIC) emission inventory, to simulate winter PM2.5 concentrations, regional transport, and changes in emission source contributions in the Sichuan basin (SCB) from 2002 to 2020, considering variations in meteorological conditions and anthropogenic emissions. The results indicated a gradual decrease in the basin's winter average PM2.5 concentration from 300 μg/m3 to 120 μg/m3, with the most significant decrease occurring after 2014, reflecting the actual impact of China's air pollution control measures. Spatially, the main pollution area shifted from Chongqing to Chengdu and the western basin. The sources of PM2.5 at the eastern and western margins of the basin have remained stable and have been dominated by local emissions for many years, while the sources of PM2.5 in the central part of the basin have evolved from a multiregional co-influenced source during the early period to a high proportion of local emissions; except for boundary condition sources, residential sources were the main PM2.5 sources in the basin (approximately 29.70 %), followed by industrial sources (approximately 14.11 %). Industrial sources exhibited higher contributions in Chengdu and Chongqing and gradually stabilized with residential sources over the years, while residential sources dominated in the eastern and western parts of the basin and exhibited a declining trend. Meteorological conditions exacerbated pollution in the whole basin from 2008 to 2014, especially in the west (21–40 μg/m3). The eastern basin and Chongqing exhibited more years with alleviated meteorological pollution, including a 40+ μg/m3 decrease in Chongqing from 2002 to 2005. Reduced anthropogenic emissions alleviated annual pollution levels, with a greater reduction (> −20 μg/m3) after 2011 due to pollution control measures.

Application of the NCAR FastEddy® Microscale Model to a Lake Breeze Front

This study investigates how urban environments influence boundary layer processes during the passage of a Great Salt Lake breeze using a multi-scale modeling system, NCAR’s WRF-Coupled GPU-accelerated FastEddy® (FE) model. Motivated by the need for sub-10 m scale decision support tools for uncrewed aerial systems (UAS), the FE model was used to simulate turbulent flows around urban structures at 5 m horizontal resolution with a 9 km × 9 km domain centered on the Salt Lake City International Airport. FE was one-way nested within a 1 km resolution Weather Research and Forecasting (WRF) domain spanning 400 × 400 km. Focused on the late morning lake breeze on 3 June 2022, an FE simulation was compared with WRF outputs and validated using surface and radar observations. The FE simulation revealed low sensible heat flux and cool near-surface temperatures, attributed to a relatively low specification of thermal roughness suitable for previously tested FE applications. Lake breeze characteristics were minimally affected, as FE effectively resolved interactions between the lake breeze and urban-induced turbulent eddies, providing insights into fine-scale boundary layer processes. FE’s GPU acceleration ensured efficient simulations, underscoring its potential for aiding decision support in UAS operations in complex urban environments.

Enhancing irrigation water productivity using short-range ensemble weather forecasts at basin scale: A novel framework for regions with high hydro-climatic variability

Integrating short-term weather forecasts with crop growth models has emerged as a valuable decision-support tool for enhancing irrigation water productivity in water-intensive crops. Our primary focus lies in formulating a simplified Quasi-Farmer Behavior Routine to utilize ensemble weather forecasts to provide irrigation guidance. The study proposes the concept of Rainfall Confidence Quotient (RCQ) that is optimized based on the forecast uncertainty levels for rainfall categories that affect the irrigation decision. We utilize two publicly available ensemble weather forecasts from NCEP-GEFS and ECM and a regionally fine-tuned high-resolution Weather Research and Forecasting (WRF) ensemble forecast system with a 3-day lead time. We have refined our methodology by conducting 800 simulation experiments across diverse scenarios, each reflecting distinct climate regimes, soil types, cropping seasons, and management practices. The proposed method consists of a sequential 3-checkpoint algorithm incorporating ensemble rainfall forecasts, antecedent soil moisture, and evapotranspiration. During the wet crop season of 2015–16, the enhanced methodology yielded a substantial increase in irrigation water productivity (IWP), averaging around 20–30 % increase across spatial locations, in contrast to the conventional irrigation practices that do not consider weather forecasts. However, during 2016–17, a dry year, the improvement in IWP only ranged between 2–10 %. On average, water savings (at field scale) of 200–500 mm were recorded in the wet year crop season of 2015–16, while 100–300 mm of water savings were achieved during the dry year crop season of 2016–17, without any considerable reductions in the crop yield. The robustness and adaptability of the developed approach have been established through comprehensive evaluation with field observation and remote sensing techniques, suggesting its potential scalability to similar hydro-climatic typologies across the world.

Subtropical Foehn Winds, Southeast Queensland, Australia

AbstractFoehn winds have been a focus of research in mid‐latitude mountainous regions for more than 150 years, where their onset is typically associated with warm, dry, and gusty winds. This research has now extended into high latitude regions, yet research of foehn winds in subtropical and tropical regions remains scarce. Here we present results from the first investigation of foehn winds in the subtropics of Southeast Queensland (SEQ), Australia. Analysis of meteorological records found that foehn winds occur throughout the year with peak frequency and duration in late winter (August) associated with the passage of shortwave troughs over southern Australia. Modeling of wind fields and atmospheric boundary layer conditions for three case studies was conducted using the Weather Research and Forecasting (WRF) model. Results showed foehn events in SEQ can be associated with mountain waves and hydraulic jump features in the lee of topographic barriers. Over lee slopes, acceleration of wind speeds and topographic channeling of foehn winds was found to occur, along with substantial increases in air temperature, and decreases in relative humidity. Warming of the foehn airstream is believed to occur primarily through isentropic drawdown with a likely contribution from surface sensible heat flux. Recommendations for future research are made in light of the importance of foehn winds to wildfire management and mitigation in SEQ.

Storm Gust Prediction with the Integration of Machine Learning Algorithms and WRF Model Variables for the Northeast United States

Abstract Wind gusts are often associated with severe hazards and can cause structural and environmental damages, making gust prediction a crucial element of weather forecasting services. In this study, we explored the utilization of machine learning (ML) algorithms integrated with numerical weather prediction outputs from the Weather Research and Forecasting (WRF) Model, to align the estimation of wind gust potential with observed gusts. We have used two ML algorithms, namely, random forest (RF) and extreme gradient boosting (XGB), along with two statistical techniques: generalized linear model with identity link function (GLM-Identity) and generalized linear model with log link function (GLM-Log), to predict storm wind gusts for the northeast (NE) United States. We used 61 simulated extratropical and tropical storms that occurred between 2005 and 2020 to develop and validate the ML and statistical models. To assess the ML model performance, we compared our results with postprocessed gust potential from WRF. Our findings showed that ML models, especially XGB, performed significantly better than statistical models and Unified Post Processor for the WRF (WRF-UPP) Model and were able to better align predicted with observed gusts across all storms. The ML models faced challenges capturing the upper tail of the gust distribution, and the learning curves suggested that XGB was more effective than RF in generating better predictions with fewer storms.

Testing the Sensitivity of a WRF-Based Great Lakes Regional Climate Model to Cumulus Parameterization and Spectral Nudging

Abstract The Weather Research and Forecasting (WRF) Model is used to dynamically downscale ERA-Interim global reanalysis data to test its performance as a regional climate model (RCM) for the Great Lakes region (GLR). Four cumulus parameterizations and three spectral nudging techniques applied to moisture are evaluated based on 2-m temperature and precipitation accumulation in the Great Lakes drainage basin (GLDB). Results are compared to a control simulation without spectral nudging, and additional analysis is presented showing the contribution of each nudged variable to temperature, moisture, and precipitation. All but one of the RCM test simulations have a dry precipitation bias in the warm months, and the only simulation with a wet bias also has the least precipitation error. It is found that the inclusion of spectral nudging of temperature dramatically improves a cold-season cold bias, and while the nudging of moisture improves simulated annual and diurnal temperature ranges, its impact on precipitation is complicated. Significance Statement Global climate models are vital to understanding our changing climate. While many include a coarse representation of the Great Lakes, they lack the resolution to represent effects like lake effect precipitation, lake breeze, and surface air temperature modification. Therefore, using a regional climate model to downscale global data is imperative to correctly simulate the land–lake–atmosphere feedbacks that contribute to regional climate. Modeling precipitation is particularly important because it plays a direct role in the Great Lakes’ water cycle. The purpose of this study is to identify the configuration of the Weather Research and Forecasting Model that best simulates precipitation and temperature in the Great Lakes region by testing cumulus parameterizations and methods of nudging the regional model toward the global model.

Relationship between Synoptic Weather Patterns and Topography on Snowfall in the Idaho Mountainous Regions during the SNOWIE Project

Abstract Snowpack melting is a crucial water resource for local ecosystems, agriculture, and hydropower in the Intermountain West of the United States. Glaciogenic seeding, a method widely used in mountain regions to enhance precipitation, has been subject to numerous field studies aiming to understand and validate this mechanism. However, investigating precipitation distribution and amounts in mountainous areas is complicated due to the intricate interplay of synoptic circulation patterns and local complex topography. These interactions significantly influence microphysical processes, ultimately affecting the amount and distribution of surface precipitation. To address these challenges, this study leverages Weather Research and Forecasting (WRF) Model simulations, providing high-resolution (900 m), hourly data, spanning the Payette region of Idaho from January to March 2017. We applied the self-organizing map approach to categorize the most representative synoptic circulation patterns and conducted a multiscale analysis to explore their associated environmental conditions and microphysical processes, aiming to assess the cloud seeding potential. The analysis identified four primary synoptic patterns: cold zonal flow (CZF), cold southwesterly flow (CSWF), warm zonal flow (WZF), and warm southwesterly flow (WSWF), constituting 21.3%, 23.1%, 30.0%, and 25.5%, respectively. CSWF and WSWF demonstrated efficiency in generating natural precipitation. These patterns were characterized by abundant supercooled liquid water (SLW) and ice particles, facilitating cloud droplet growth through seeder–feeder processes. On the other hand, CZF exhibited the least SLW and limited potential for cloud seeding, while WZF displayed a lower ice water content but substantial SLW in the diffusion/dendritic growth layer, suggesting a favorable scenario for cloud seeding. Significance Statement Understanding snowfall amounts and distribution in the mountains and how it is linked to topography, synoptic flow, and microphysical processes will help in the development of effective strategies for cloud seeding operations, managing runoff, reservoir, and mitigating flood risks, garnering substantial interest from stakeholders and the government agencies.

Deep Learning of a 200-Member Ensemble with a Limited Historical Training to Improve the Prediction of Extreme Precipitation Events

Abstract This study introduces a deep learning (DL) scheme to generate reliable and skillful probabilistic quantitative precipitation forecasts (PQPFs) in a postprocessing framework. Enhanced machine learning model architecture and training mechanisms are proposed to improve the reliability and skill of PQPFs while permitting computationally efficient model fitting using a short training dataset. The methodology is applied to postprocessing of 24-h accumulated PQPFs from an ensemble forecast system recently introduced by the Center for Western Weather and Water Extremes (CW3E) and for lead times from 1 to 6 days. The ensemble system was designed based on a high-resolution version of the Weather Research and Forecasting (WRF) Model, named West-WRF, to produce a 200-member ensemble in near–real time (NRT) over the western United States during the boreal cool seasons to support Forecast-Informdayed Reservoir Operations (FIRO) and studies of prediction of heavy-to-extreme events. Postprocessed PQPFs are compared with those from the raw West-WRF ensemble, the operational Global Ensemble Forecast System version 12 (GEFSv12), and the ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF). As an additional baseline, we provide PQPF verification metrics from a recently developed neural network postprocessing scheme. The results demonstrate that the skill of postprocessed forecasts significantly outperforms PQPFs and deterministic forecasts from raw ensembles and the recently developed algorithm. The resulting PQPFs broadly improve upon the reliability and skill of baselines in predicting heavy-to-extreme precipitation (e.g., >75 mm) across all lead times while maintaining the spatial structure of the high-resolution raw ensemble.

Projected Thermally Driven Elderly Mortality for Beijing Under Greenhouse Gas and Stratospheric Aerosol Geoengineering Scenarios

AbstractBeijing is undergoing multiple challenges including urbanization, warming and aging. The Beijing megalopolis of 20 million people now suffers more cold‐related than heat‐related deaths. Stratospheric aerosol injection (SAI) geoengineering is designed to lower surface temperatures, so if SAI were ever done, it may reduce future heat‐related mortality, while also increasing cold‐related mortality. Here we use four Earth System Models (ESM) downscaled to 10 km resolution with the Weather Research and Forecasting (WRF) system to capture urban temperature, humidity and wind speeds. Temperature‐related mortality risk were calculated using a distributed lag nonlinear model (DLNM) of the elderly (over 65s) under the dynamically downscaled moderate (RCP4.5) and extreme (RCP8.5) greenhouse gas, and the G4 SAI scenarios. We used population demographics for all five shared socioeconomic pathways (SSP) and various adaptation measures. Heat‐related excess deaths under G4 are 630∼3,160 per year fewer than RCP4.5, while cold‐related deaths are 370∼1,990 more than RCP4.5 during 2060–2069, with a marginally significant net reduction. G4 significantly reduces the excess deaths relative to RCP8.5. Both heat‐related and cold‐related mortality will increase by 240∼490% when the aging population is accounted for, and decrease by 11%, 23% and 44% under low, medium and high adaptation relative to a no adaptation scenario. Dynamical downscaling produces better quality climate simulations than commonly used statistical approaches, and in the case of Beijing, significantly fewer heat‐related deaths. The marginal health benefits of modest future SAI in Beijing may be representative of the population impacts in the extra‐tropics where deaths due to cold are more than those caused by heat.

Improving the Modeled Variability Estimates of Offshore Winds in Northern Europe by Nudging ASCAT-Derived Winds

Abstract The paper aims to demonstrate how to enhance the accuracy of offshore wind resource estimation, specifically by incorporating near-surface satellite-derived wind observations into mesoscale models. We utilized the Weather Research and Forecasting (WRF) Model and applied observational nudging by integrating ASCAT data over offshore areas to achieve this. We then evaluated the accuracy of the nudged WRF Model simulations by comparing them with data from ocean oil platforms, tall masts, and a wind lidar mounted on a commercial ferry crossing the southern Baltic Sea. Our findings indicate that including satellite-derived ASCAT wind speeds through nudging enhances the correlation and reduces the error of the mesoscale simulations across all validation platforms. Moreover, it consistently outperforms the control and previously published WRF-based wind atlases. Using satellite-derived winds directly in the model simulations also solves the issue of lifting near-surface winds to wind turbine heights, which has been challenging in estimating wind resources at such heights. The comparison of the 1-yr-long simulations with and without nudging reveals intriguing differences in the sign and magnitude between the Baltic and North Seas, which vary seasonally. The pattern highlights a distinct regional pattern attributed to regional dynamics, sea surface temperature, atmospheric stability, and the number of available ASCAT samples. Significance Statement We aim to showcase a method for improving the precision of hub-height estimation of wind resources offshore. This involves integrating wind observations obtained from near-surface satellites into the model simulations. To assess the accuracy of the simulations, we compare the simulated winds to data gathered from multiple offshore sources, including oil platforms, tall masts, and a wind lidar installed on a commercial ferry.

A new, high‐resolution atmospheric dataset for southern New Zealand, 2005–2020

AbstractThe regional climate of New Zealand's South Island is shaped by the interaction of the Southern Hemisphere westerlies with the complex orography of the Southern Alps. Due to its isolated geographical setting in the south‐west Pacific, the influence of the surrounding oceans on the atmospheric circulation is strong. Therefore, variations in sea surface temperature (SST) impact various spatial and temporal scales and are statistically detectable down to temperature anomalies and glacier mass changes in the high mountains of the Southern Alps. To enable future studies on the processes that govern the link between large‐scale SST and local‐scale high‐mountain climate, we utilized dynamical downscaling with the Weather Research and Forecasting (WRF) model to produce a regional atmospheric modelling dataset for the South Island of New Zealand over a 16‐year period between 2005 and 2020. The 2 km horizontal resolution ensures realistic representation of high‐mountain topography and glaciers, as well as explicit simulation of convection. The dataset is extensively evaluated against observations, including weather station and satellite data, on both regional (in the inner domain) and local (on Brewster Glacier in the Southern Alps) scales. Variability in both atmospheric water content and near‐surface meteorological conditions is well captured, with minor seasonal and spatial biases. The local high‐mountain climate at Brewster Glacier, where land use and topographic model settings have been optimized, yields remarkable accuracy on both monthly and daily time scales. The data provide a valuable resource to researchers from various disciplines studying the local and regional impacts of climate variability on society, economies and ecosystems in New Zealand. The model output from the highest resolution model domain is available for download in daily temporal resolution from a public repository at the German Climate Computation Center (DKRZ) in Hamburg, Germany (Kropač et al., 2023; 16‐year WRF simulation for the Southern Alps of New Zealand, World Data Center for Climate (WDCC) at DKRZ [data set]. https://doi.org/10.26050/WDCC/NZ‐PROXY_16yrWRF).

Impacts of Moisture Advection Scheme on Precipitation in the Steep Topography Region between the Tibetan Plateau and the Sichuan Basin

Abstract Water vapor transport is a crucial process in modeling and can contribute to errors in precipitation forecasts. To investigate the sensitivity of precipitation to the moisture advection scheme, this study introduced the two-step shape-preserving advection scheme (TSPAS), which has been proven to improve precipitation simulation over steep topography at lower resolutions, into the Southwest Center Weather Research and Forecast (WRF)-based Intelligent Numerical Grid Forecast System (SWC-WINGS) at a convection-permitting resolution. According to experiments conducted throughout the summer of 2021, the precipitation over the eastern slope of the Tibetan Plateau (TP) is highly sensitive to the moisture advection scheme. TSPAS successfully improved precipitation over the eastern slope of the TP, especially for torrential rainfall. The fractions skill score (FSS) is improved by 0.075 (27.78%) for daily precipitation with a threshold of 100 mm. Compared with the experiment with the original WRF advection scheme, the TSPAS reduced the overestimation of precipitation in the topographic region and excessive water vapor transport in a low-level atmosphere. To understand the precipitation improvement contributed by the advection scheme, additional experiments were conducted for a particular precipitation process from two approaches: switching advection schemes during the rainfall evolution and updating the variables related to moisture advection individually. Results demonstrate that the precipitation improvement is mainly contributed by the moisture advection scheme before the precipitation. Among the different variables, the combination of wind and water vapor was the most influential factor causing the precipitation improvement under the TSPAS.

Analyzing the Improvement Effect of the k-Distribution Method on the Radiation Parameterization for WRF Model

To address the need for the accurate parameterization of radiative absorption by gasses (for predicting atmospheric warming), Chou et al. developed a new k-distribution method. In this study, we compared the improved k-distribution method (hereinafter referred to as the NEW method) with the New Goddard radiation schemes (hereinafter referred to as the OLD method) for the WRF (the weather research and forecasting) model. The results of radiative flux calculations by the NEW and OLD methods of k-distribution using the New Goddard Radiation Scheme were compared with the results of the line-by-line (LBL) method, and the results showed that the radiative flux calculated by the NEW was accurate to within 1.00 Wm−2 with respect to the LBL, while the OLD showed large differences at altitudes above the upper troposphere and near the surface. Therefore, in this study, we selected clear-sky and cloudy-day conditions and compared the weather elements prediction results of WRF using the NEW and OLD methods. For the clear-sky days, the downward shortwave radiation at the surface and the temperature at 2 m above the surface (hereinafter referred to as T2) over land and ocean were reversed in sign due to the highly sensitive absorption coefficients of gasses. For cloudy days, the absorption effect by gasses harmonized with the scattering effect induced by cloud droplets; the differences in the shortwave and longwave radiations and radiative heating rate between the NEW and OLD methods were obvious. Thus, it was analyzed that the proposed NEW method could lead to significant improvements in forecasting weather elements.

Evaluation of Nine Planetary Boundary Layer Turbulence Parameterization Schemes of the Weather Research and Forecasting Model Applied to Simulate Planetary Boundary Layer Surface Properties in the Metropolitan Region of São Paulo Megacity, Brazil

This study evaluates nine Planetary Boundary Layer (PBL) turbulence parameterization schemes from the Weather Research and Forecasting (WRF) mesoscale meteorological model, comparing hourly values of meteorological variables observed and simulated at the surface of the Metropolitan Region of São Paulo (MRSP). The numerical results were objectively compared with high-quality observations carried out on three micrometeorological platforms representing typical urban, suburban, and rural land use areas of the MRSP, during the 2013 summer and winter field campaigns as part of the MCITY BRAZIL project. The main objective is to identify which PBL scheme best represents the diurnal evolution of conventional meteorological variables (temperature, relative and specific humidity, wind speed, and direction) and unconventional (sensible and latent heat fluxes, net radiation, and incoming downward solar radiation) on the surface. During the summer field campaign and over the suburban area of the MRSP, most PBL scheme simulations exhibited a cold and dry bias and overestimated wind speed. They also overestimated sensible heat flux, with high agreement index and correlation values. In general, the PBL scheme simulations performed well for latent heat flux, displaying low mean bias error and root square mean error values. Both incoming downward solar radiation and net radiation were also accurately simulated by most of them. The comparison of the nine PBL schemes indicated the local Mellor-Yamada-Janjic (MYJ) scheme performed best during the summer period, particularly for conventional meteorological variables for the land use suburban in the MRSP. During the winter field campaign, simulation outcomes varied significantly based on the site’s land use and the meteorological variable analyzed. The MYJ, Bougeault-Lacarrère (BouLac), and Mellor-Yamada Nakanishi-Niino (MYNN) schemes effectively simulated temperature and humidity, especially in the urban land use area. The MYNN scheme also simulated net radiation accurately. There was a tendency to overestimate sensible and latent heat fluxes, except for the rural land use area where they were consistently underestimated. However, the rural area exhibited superior correlations compared to the urban area. Overall, the MYJ scheme was deemed the most suitable for representing the convectional and nonconventional meteorological variables on the surface in all urban, suburban, and rural land use areas of the MRSP.

Onset of flash drought based on the WRF in the Poyang Lake Basin of China

The occurrence of flash droughts rapidly triggers soil water deficits, easily causing significant impacts on agricultural production, water resources, and ecosystems. As a critical phase in the formation and widespread outbreaks of flash drought, the rapid onset increases the difficulty of monitoring and forecasting. However, the evolutionary mechanisms of flash drought have not been given sufficient attention. In this study, we identified flash drought based on soil moisture from 1993 to 2022 to deepen the understanding of flash drought and presented simulations of three typical flash drought events (occurring in 2019, 2021, and 2022) to explore drought processes by the Weather Research and Forecasting (WRF) model over the largest flesh lake basin of China, Poyang Lake Basin (PLB). The driving factors and short-term atmospheric characteristics of flash drought were used to illustrate the rapid onset. The results show that (1) the PLB experienced increasements in flash drought. The frequency of flash droughts was approximately 4–10 times per decade, with more flash droughts occurring in the northern part of the PLB than in the south. The average duration of flash drought onset was between 9 and 13 days. The types of flash drought onset with two pentads had the highest ratio (70%). (2) The meteorological drivers (precipitation, temperature, evapotranspiration) had strong impacts on the soil evolution in the central and northern parts of the PLB. Precipitation always appeared before or at the very beginning of onset and almost disappeared during the onset of flash drought. (3) The atmosphere over the PLB was relatively dry during the onset. Different atmospheric conditions were observed to follow flash drought onset, which were likely to result in different recovery periods.

Role of assimilation of microwave humidity sounder (MHS) satellite radiance in forecast of structure and intensity of VSCS Vardah 2016

The present study evaluated the performance of the Weather Research and Forecasting (WRF) model in the forecast of a very severe cyclonic storm (VSCS) Vardah that developed over the Bay of Bengal (BoB) and make landfall over the Tamil Nadu coast on 12 December 2016. The study examined the impact of microwave humidity sounder (MHS) satellite radiance data assimilation to forecast of structure, intensity and rainfall of Vardah cyclone near to the coast using two different land surface model (LSM; Noah and Noah-MP). The mean track error is about 33 and 38 km with data assimilation using Noah and Noah-MP LSM respectively and this error is about 41 km in without data assimilation using Noah and Noah-MP LSM. The predicted intensity of the storm in terms of the maximum surface wind is also slightly better predicted in data assimilation experiment with Noah LSM, this is due to the improved initial condition. Accumulated rainfall and the maximum reflectivity in terms of spatial distribution and magnitude of the VSCS Vardah is well simulated in all the experiments but slightly better in data assimilation experiments. Hovmoller diagram is also presented to see the shifting pattern of accumulated rainfall across the region during simulation period.