Weather Research And Forecasting Simulations Research Articles

A Machine Learning Approach to Model Over-Ocean Tropical Cyclone Precipitation

Abstract Extreme rainfall found in tropical cyclones (TCs) is a risk for human life and property in many low- to midlatitude regions. Probabilistic modeling of TC rainfall in risk assessment and forecasting can be computationally expensive, and existing models are largely unable to model key rainfall asymmetries such as rainbands and extratropical transition. Here, a machine learning–based framework is developed to model overwater TC rainfall for the North Atlantic basin. First, a catalog of high-resolution TC precipitation simulations for 26 historical events is assembled for the North Atlantic basin using the Weather Research and Forecasting (WRF) Model. The simulated spatial distribution of rainfall for these historical events are then decomposed via principal component analysis (PCA), and quantile regression forest (QRF) models are trained to predict the conditional distributions of the first five principal component (PC) weights. Conditional distributions of rain-rate levels are estimated separately using historical satellite data and a QRF model. With these models, probabilistic predictions of rainfall maps can be made given a set of storm characteristics and local environmental conditions. The model is able to capture storm total rainfall compared to satellite observations with a correlation coefficient of 0.96 and r2 value of 0.93. Additionally, the model shows good accuracy in modeling hourly total rainfall compared to satellite observations. Rain-rate maps predicted by the model are also compared to historical satellite observations and to the WRF simulations during cross validation, and the spatial distribution of estimates captures rainfall variability consistent with TC rainbands, wavenumber asymmetries, and possibly extratropical transition.

The Importance of Wind Simulations over Dried Lake Beds for Dust Emissions in the Middle East

Dust storms are one of the major environmental hazards affecting the Middle East countries, and largely originate in vast deserts and narrow dried lake beds. This study analyzes the inter-annual variation in dust weather conditions from 2000 to 2020 using data obtained from ten meteorological stations located around dried (completely or partly) lakes in Northwest (Urmia Lake) and South (Bakhtegan Lake) Iran. Since the wind regime is one of the most important factors controlling dust emissions in the dust source areas, wind speed simulations from the Weather Research and Forecasting (WRF) Model for 134,113 grid points covering the Middle East area, with a resolution of 5 km, were analyzed and compared with wind measurements at the stations around Urmia and Bakhtegan Lakes from 2005 to 2015. The analysis shows that the annual number of dust days was highly variable, presenting a significant increase at the stations around Urmia Lake during 2008–2011 and at the stations around Bakhtegan Lake in 2007–2012. Eleven years of WRF simulations of the mean diurnal wind patterns revealed that the highest 10 m wind speed occurred mostly around the local noon (12 to 15 UTC), generally coinciding with the majority of the reported dust codes within this time frame, as a result of the association between wind speed and dust emissions (dust weather conditions) around these lake basins. Consequently, accurate wind simulation has high importance for unbiased numerical prediction and forecasting of dust conditions. The comparison between the measured mean monthly 10 m wind speed and WRF-simulated 10 m wind speed revealed that the model overestimated wind data in all the stations around the Bakhtegan Lake but performed better at reconstructing the wind speeds at stations around Urmia Lake. Furthermore, notable differences were observed between measured and simulated wind directions, thus leading to uncertainties in the simulations of the dust-plume transport.

Assessment of physical schemes for WRF model in convection-permitting mode over southern Iberian Peninsula

Convection-permitting models (CPMs) enable the representation of meteorological variables at horizontal high-resolution spatial scales (≤ 4 km), where convection plays a significant role. In this regard, physical schemes need to be evaluated considering factors in the studied region such as orography and climate variability. This study investigates the sensitivity of the Weather Research and Forecasting (WRF) model as CPM to the use of different physics schemes on Andalusia, a complex orography region in the southern part of the Iberian Peninsula (IP). To do that, a set of 1-year WRF simulations was completed based on two “one-way” nested domains: the parent domain (d01) spanning the entire IP with 5 km spatial resolution and the nested domain (d02) for the region of Andalusia at 1 km of spatial resolution. 12 physic schemes were examined from combinations of microphysics (MP) schemes including THOMPSON, WRF single moment 6-class (WSM6), and WRF single moment 7-class (WSM7), and different options for the convection in d01, the Grell 3D (G3), Grell-Freitas (GF), Kain-Fritsch (KF), and deactivated cumulus parameterization (OFF). The simulated precipitation and 2-m temperature for the year 2018, characterized to be a very wet year, were compared with observational datasets from different sources to determine the optimal WRF configuration, including point-to-point and station-point comparisons at different time aggregations (from annual to hourly). In general, greater differences were shown when comparing the results of convection schemes in d01. Simulations completed with GF or OFF presented better performance compared to the reference datasets. Concerning the MP, although THOMPSON showed a better fit in high mountain areas, it generally presents a worse agreement with the reference datasets. In terms of temperature, the results were very similar and, therefore, the selection of the “best” configuration was based mainly on the precipitation results with the WSM7-GF scheme being suitable for Andalusia region.

Evaluation of three radiation schemes of the WRF-Solar model for global surface solar radiation forecast: A case study in Egypt

This study aims to assess the performance of three different radiation schemes in Weather Research and Forecasting (WRF) mesoscale model against ECMWF fifth generation (ERA5) Reanalysis data over Egypt. Dudhia, rrtmg, and Goddard schemes were used to simulate Global Horizontal Irradiance (SR) during 2017. Sixteen stations were selected to represent the different climate conditions in Egypt. The observed SR data is only available at five stations and used to evaluate the accuracy of ERA5 and WRF output. SR data at the other eleven stations were extracted from ERA5 to evaluate the corresponding values from WRF simulation. Coefficient of determination (R2), mean bias error (MBE), root mean square error (RMSE), monthly standard deviation (STDV) and mean absolute percentage error (MAPE%) are the implemented evaluation statistical parameters in the study. The results showed that ERA5 and WRF Dudhia scheme have reliable results as compared to the observations at the five stations. Also, the WRF Dudhia scheme simulates the SR at the other eleven stations better than rrtmg, and Goddard schemes as compared to ERA5 dataset. Where, the three WRF radiation schemes overestimate the SR along the eleven stations and underestimate at Alexandria station by −0.15 KWh/m2, which may due to the weather conditions over these sites. But the Dudhia scheme over the eleven stations has the lowest MBE (<0.34 KWh/m2), RMSE (<0.58 KWh/m2), and MAPE (<−2%), with R2 more than 0.985 at most stations.

Knee Point‐Based Multiobjective Optimization for the Numerical Weather Prediction Model in the Greater Beijing Area

AbstractDetermination of the optimal parameter values in numerical weather prediction (NWP) models has a significant impact on predictions. Here, we propose a knee point‐based multiobjective optimization (KMO) method to find an optimal solution of the NWP model parameters. We apply it to optimize the Weather Research and Forecasting (WRF) model's summer precipitation and temperature simulations for the Greater Beijing area. The results showed that it required fewer than 125 samples (i.e., 25 times the number of dimensions of the parameter space) to obtain the WRF model's optimal parameter values. The optimal parameters determined by KMO outperform the default parameters in WRF simulations for summer precipitation and temperature prediction in the Greater Beijing area, across all periods (calibration, validation, and testing). Additionally, clear physical interpretations are provided to explain why the optimal parameters lead to improved precipitation and temperature forecasting. Overall, the proposed method is effective and efficient to improve NWP.

Estimation of Crops Future Irrigation Water Needs in a Mediterranean Plain

Agriculture is a vulnerable sector to climate change due to its sensitivity to weather conditions. Changes in climatic parameters such as temperature and precipitation significantly affect productivity as well as the consumption of natural resources like water to meet irrigation water needs. There has been a large amount of research on regional climate change. However, this study placed specific crops at first place and considered their irrigation water needs that will arise due to evapotranspiration increase. The aim of this study was to estimate the future irrigation water needs of wheat, cotton, and alfalfa in the east part of Thessaly Plain in central Greece, where Lake Karla, a recently restored lake, is located. The Weather Research and Forecasting (WRF) model was applied as a high-resolution regional climate model to simulate temperature and precipitation for two 5-year periods, namely 2046–2050 (future period) and 2006–2010 (reference period). Simulations refer to the RCP8.5 emission scenario (worst-case). A methodology proposed by the Food and Agriculture Organization (FAO) of the United Nations was followed to estimate the reference crop evapotranspiration, the crop evapotranspiration based on each crop factor, which was determined for each crop, the effective rainfall, and finally, the irrigation water needs for each crop, for the two 5-year periods. Based on WRF simulations, temperature was projected to be 1.1 °C higher in the future period compared to the reference period, while precipitation and effective precipitation were projected to decrease by 32% and 45%, respectively. Based on the WRF projections, by 2025, the irrigation water needs of wheat and alfalfa are expected to increase by more than 16% and more than 11%, respectively, while irrigation water needs of cotton are expected to increase by 7%. An extension of wheat’s irrigation period for one month (i.e., December) was also identified. Good practices that could be applied in the frame of precision agriculture principles in order to save irrigation water were suggested. The results of this study could be exploited by water resources and land use managers when planning short and long-term strategies to adapt to climate change impacts.

Wind field simulation using WRF model in complex terrain: A sensitivity study with orthogonal design

Accurately simulating wind speed is of utmost importance for wind power assessments. The objective of this study is to investigate the performance of wind speed simulation in complex terrain using different parameterization schemes from the Weather Research and Forecasting (WRF) model, employing an orthogonal design methodology. Specifically, nine WRF simulations are conducted based on the orthogonal test, considering various configurations of the planetary boundary layer (PBL), microphysics (MP), and land surface (LS) options. The numerical results are then compared to actual wind data obtained from a measuring station at two different heights (50 m and 80 m) during summer and winter periods. Furthermore, range and variance analyses are employed to rank the three schemes and identify the optimal combination. Moreover, we examine the impact of each parameterization scheme on the accuracy of wind speed predictions based on the results obtained from the orthogonal simulations. Additionally, we discuss the influence of using different evaluation indices within the orthogonal test on the outcomes and analyze the WRF simulated results under the optimal scheme combination. Lastly, we conduct an uncertainty analysis of results from the optimal scheme combination. The findings reveal that both the PBL and MP schemes exhibit highly significant effects on the accuracy of wind speed predictions (significance: **), followed by the LS scheme (significance: *). The order of importance for these three options is ranked as follows: PBL > MP > LS, which is independent of the seasons. The optimal configurations vary from summer and winter periods. Specifically, the optimal scheme combination is determined to be PBL-ACM2, MP-Kessler, and LS-Noah MP in summer, while PBL-BouLac, MP-Lin, and LS-Noah MP in winter. The simulating accuracy of the wind speeds is satisfactory under this optimal combination when considering the uncertainty of on-site measurements during these two periods. These results provide valuable insights for selecting appropriate PBL, MP, and LS options (from the WRF model) for wind speed estimates and wind power development in the studied region.

Evaluation of HY-2C and CFOSAT Satellite Retrieval Offshore Wind Energy Using Weather Research and Forecasting (WRF) Simulations

This study simulated the spatial distribution of wind speeds and wind energy density by using the WRF model, and we used the WRF-simulated results to evaluate the sea surface wind speeds retrieved from the HY-2C and CFOSAT satellite-borne microwave scatterometers over the Yellow Sea region. The main conclusions were as follows: (1) The combination of the MRF boundary layer parameterization scheme, the MM5 near-surface parameterization scheme, and the Global Data Assimilation System (GDAS) initial field demonstrated the best performance in simulating the 10 m wind speed in the Yellow Sea region, with a root-mean-square error (RMSE) of 1.57, bias of 1.24 m/s, and mean absolute percentage error (MAPE) of 17%. (2) The MAPE of the HY-2C inversion data was 9%, while the CFOSAT inversion data had an MAPE of 6%. The sea surface wind speeds derived from the HY-2C and CFOSAT satellite scatterometer inversions demonstrated high accuracy and applicability in this region. (3) The wind speed was found to increase with altitude over the Yellow Sea, with higher wind speeds observed in the southern region compared to the northern region. The wind power density increased with altitude, and the wind power density in the southern area of the Yellow Sea was higher than in the northern region. (4) The CFOSAT satellite inversion products were in good agreement with the WRF simulation results under low wind speed conditions. In contrast, the HY-2C satellite inversion products showed better agreement under moderate wind speed conditions. Under high wind speed conditions, both satellite inversion products exhibited minor deviations, but the HY-2C product had an overall overestimation, while the CFOSAT product remained within the range of −1 to 1 m/s. (6) The wind power density increased with the satellite-inverted 10 m wind speed. When the 10 m wind speed was less than 9 m/s, the wind power density exhibited a roughly cubic trend of increase. However, when the 10 m wind speed exceeded 9 m/s, the wind power density no longer increased with the rise in 10 m wind speed. These findings provide valuable insights into wind energy resources in the Yellow Sea region and demonstrate the effectiveness of satellite scatterometer inversions for wind speed estimation. The results have implications for renewable energy planning and management in the area.

The Influence of Mesoscale Atmospheric Convection on Local Infrasound Propagation

AbstractInfrasound—that is, acoustic waves with frequencies below the threshold of human hearing—has historically been used to detect and locate distant explosive events over global ranges (≥1,000 km). Simulations over these ranges have traditionally relied on large‐scale, synoptic meteorological information. However, infrasound propagation over shorter, local ranges (0–100 km) may be affected by smaller, mesoscale meteorological features. To identify the effects of these mesoscale meteorological features on local infrasound propagation, simulations were conducted using the Weather Research and Forecasting (WRF) meteorological model to approximate the meteorological conditions associated with a series of historical, small‐scale explosive test events that occurred at the Big Black Test Site in Bovina, Mississippi. These meteorological conditions were then incorporated into a full‐wave acoustic model to generate meteorology‐informed predictions of infrasound propagation. A series of WRF simulations was conducted with varying degrees of horizontal resolution—1, 3, and 15 km—to investigate the spatial sensitivity of these infrasound predictions. The results illustrate that convective precipitation events demonstrate potentially observable effects on local infrasound propagation due to strong, heterogeneous gradients in temperature and wind associated with the convective events themselves. Therefore, to accurately predict infrasound propagation on local scales, it may be necessary to use convection‐permitting meteorological models with a horizontal resolution ≤4 km at locations and times that support mesoscale convective activity.

Assessing hydrological sensitivity to future climate change over the Canadian southern boreal forest

This study develops a physically based hydrological process model using the Cold Region Hydrological Modelling (CRHM) platform to simulate the water budget storages and fluxes over the Canadian southern boreal forest (SBF). Evaluation of the CRHM-based model in a well-gauged SBF basin, White Gull Creek (WGC), Saskatchewan, Canada, indicated quite good performance in reproducing historical observations of streamflow, snow water equivalent (SWE), evapotranspiration (ET) and soil moisture without parameter calibration from streamflow. The entire SBF was then evenly divided into 2243 virtual basins, each of which was structured and parameterized with the same land cover, soil and hydrological parameters as the WGC basin, but with local latitude and topography in order to examine the sensitivity of governing hydrological processes to future climate variability and perturbation. Hydrological sensitivity in the virtual basins was assessed by examining the differences between hydrological simulations driven by 4-km gridded convection-permitting Weather Research and Forecasting (WRF) outputs in the current period (ctrl, 2001–2013) and a Pseudo Global Warming period (pgw, 2087–2099). The WRF simulation in the pgw period was forced by a perturbation of the same boundary conditions from ERA reanalysis data as for the ctrl period, and the perturbation was based on the ensemble-mean of projected changes from the CMIP5 RCP 8.5 emission scenario. Results showed that temperature would increase by 4.5℃ to 7℃ over the SBF but increases in annual precipitation of 15–24% would more than compensate for the effects of warming on runoff generation and result in greater streamflow volumes. Annual streamflow volumes would increase by 64 mm (35%) and 95 mm (16%) in the west and east, and by 48 mm (17%) in the central SBF. Annual snowfall and maximum SWE would decrease by 89–109 mm (∼29%) in the east, 3–8 mm (∼6%) in the west, and 31–50 mm (∼20%) in the central SBF. Annual mean soil moisture storage would decrease by 54–56 mm (27%) in the west and central, and by only 37 mm (14%) in the east SBF. Decreases in soil moisture would be caused by reduced soil freezing and enhanced thawing under future warming which would increase soil water loss from ET, subsurface runoff and percolation into groundwater storage. The larger sensitivity of streamflow and snow processes in the east SBF is partly due to the wetter climate and the larger increase in annual precipitation, the later also buffered the sensitivity of soil moisture to warming. These results show that the SBF would switch to a higher water yield region, dominated by rainfall-runoff fed streamflow over a longer snow-free season, and provide first-order guidance for sustainable water management of the SBF in the future.

Efficient Super‐Resolution of Near‐Surface Climate Modeling Using the Fourier Neural Operator

AbstractDownscaling methods are critical in efficiently generating high‐resolution atmospheric data. However, state‐of‐the‐art statistical or dynamical downscaling techniques either suffer from the high computational cost of running a physical model or require high‐resolution data to develop a downscaling tool. Here, we demonstrate a recently proposed zero‐shot super‐resolution method, the Fourier neural operator (FNO), to efficiently perform downscaling without the need for high‐resolution data. Because the FNO learns dynamics in Fourier space, FNO is a resolution‐invariant emulator; it can be trained at a coarse resolution and produces emulation at any high resolution. We applied FNO to downscale a 4‐km resolution Weather Research and Forecasting (WRF) Model simulation of near‐surface heat‐related variables over the Great Lakes region. The FNO is driven by the atmospheric forcings and topographic features used in the WRF model at the same resolution. We incorporated a physics‐constrained loss in FNO by using the Clausius–Clapeyron relation to better constrain the relations among the emulated states. Trained on merely 600 WRF snapshots at 4‐km resolution, the FNO shows comparable performance with a widely‐used convolutional network, U‐Net, achieving averaged modified Kling–Gupta Efficiency of 0.88 and 0.94 on the test data set for temperature and pressure, respectively. We then employed the FNO to produce 1‐km emulations to reproduce the fine climate features. Further, by taking the WRF simulation as ground truth, we show consistent performances at the two resolutions, suggesting the reliability of FNO in producing high‐resolution dynamics. Our study demonstrates the potential of using FNO for zero‐shot super‐resolution in generating first‐order estimation on atmospheric modeling.

Evaluation of snow depth from multiple observation-based, reanalysis, and regional climate model datasets over a low-altitude Central European region

This study evaluates snow depth (SD) from several data sources: a combined satellite-based and in situ snow water equivalent product from the Copernicus Global Land Service (CGLS), a dataset constructed from temperature, precipitation, and relative humidity using a snow model (CARPATCLIM), two state-of-the-art reanalyses by ECMWF (ERA5 and ERA5-Land), and Weather Research and Forecasting (WRF) regional climate simulations at grid spacings of 50 km and 10 km. SD observations from weather stations are used as a reference for the pointwise comparison. The study area covers the Pannonian Basin region (part of Central and Eastern Europe). Results are presented for the 2006–2010 and 1985–2010 periods. All datasets adequately reproduce the average day-to-day variation of SD but with different error magnitudes. The ERA5 reanalysis and the CGLS product represent SD remarkably well, with correlation coefficients above 0.9 and mean errors close to zero. On the other hand, ERA5-Land and CARPATCLIM overestimate daily mean SD by 2–3 cm for some stations and display lower correlations (0.7–0.9) during the 26-year time span. The WRF simulations significantly overestimate SD in the melting period (February–March). Reduction of the grid spacing from 50 to 10 km does not improve the results. The excessive snow cover might negatively impact land–atmosphere interactions in the model and lead to biases like temperature underestimation found in previous regional climate model evaluation studies. The results indicate that even in regions where snow is not a major climatic factor, SD errors can be substantial and should be considered in model evaluation and adaptation. Over the Carpathian Mountain ranges, SD from the different data sources diverges to the extent that the sign of the monthly mean model bias changes depending on the choice of the reference dataset.

Effect of urbanization on gust wind speed in summer over an urban area in Eastern China

Previous studies have extensively examined effects of urbanization on mean wind speed, but few studies were aimed at gust wind speed, while large wind gust could cause safety and economic hazard to a variety of activities. In this study, the effect of urbanization on the gust wind speed in Nanjing, China is assessed using the Weather Research and Forecasting (WRF) model with a parameterization of the gust wind speed. The WRF simulations are run for the summer period of 2013 with the underlying surface before and after the urbanization. The results indicate that although the mean wind speed is reduced, the gust wind speed in the urban areas is increased significantly due to the enhanced friction velocity and less atmospheric stability induced by the urbanization, while the contribution of deep convection is relatively small. The gust wind speed increases more in the nighttime (0.6–0.9 m s−1) than in the daytime (less than 0.3 m s−1), since the turbulence is enhanced more in the nighttime than in the daytime after the urbanization. The probability distribution shows that the increase of gust wind speed is mainly between 0.0–0.5 m s−1 in the urban areas. In different urban land categories, the increase of the gust wind speed is larger in the commercial or industrial areas than in high-intensity and low-intensity residential urban areas. Averagely, the gust wind speed in the entire city after the urbanization increases by 0.02, 0.36 and 0.19 m s−1 for the daytime, nighttime and daily mean, respectively.

Comparison of Meteorological Drivers of Two Large Coastal Slope-Land Wildfire Events in Croatia and South-East Australia

Understanding the relationship between fire behavior and the driving weather conditions is critical for fire management and long-term fire risk assessment. In this study, we focus on two wildfire events: the Split wildfire in Croatia and the Forcett–Dunalley wildfire in Tasmania, Australia. The antecedent weather in both events included extremely dry conditions and higher-than-average air temperatures in the months prior to the events. The synoptic patterns in both events consisted of a large surface pressure gradient, which generated strong wind, driving the fire’s spread. The Weather Research and Forecasting (WRF) model was utilized to simulate fire weather conditions during the development of the two events. In the innermost domain of WRF, resolution is 500 m with explicit moisture calculation only, and there are 66 vertical levels, with about 20 of them to resolve the boundary layer. The WRF simulations are well verified by station observations, including upper-level wind speeds. The convergence line pattern in the Tasmanian event, which was conducive to intense plume development, has been well simulated. Only a slight discrepancy was identified in the simulation of the coastal change in wind direction in the Croatian event. It is identified that in the Split case, bura wind was highly coupled with an upper-level trough, which induced subsidence of the upper-level dry and cold air to the surface, causing rapid drying of the fuel. During the Forcett–Dunalley fire, the atmosphere was unstable, which enabled deep pyrocumulonimbus development. In general, the development from ignition to the timing of the most extreme fire intensity in both events was largely determined by the evolution of the surface to upper-level meteorological drivers. While these extreme meteorological conditions would impact fire-fighting strategies such as aircraft operations, a model-based estimate of the high-risk areas is critical. Our findings would also benefit an estimate of the climatology of fire events with similar behavior and thus a long-term fire risk assessment.

Wind Simulations over Western Patagonia Using the Weather Research and Forecasting model and Reanalysis

The Chilean Western Patagonia has the highest wind potential resources in South America. Its complex terrain deserves a special attention for wind modeling and assessments. In this work, we have performed a comprehensive meso-scale climate simulation on Weather Research and Forecasting (WRF) in order to provide new insights into the wind climatology in Western Patagonia. Simulations were carried out from 1989 to 2020, and we considered a previous sensitivity analysis for their configuration. In situ data from a wind mast, meteorological information and data from eddy flux stations were used to evaluate the results. Reanalysis data from ERA-5, MERRA-2 and RECON80-17 were also used to perform a comparison of the obtained results with the WRF simulation. The results show that the WRF simulation using ERA-5 presented in this work is slightly different to a mathematical reconstruction using MERRA-2 (RECON80-17), which is widely accepted in Chile for wind resource assessments, presenting a statistical difference of about EMD = 0.8 [m s−1] and RMSE = 0.5. Non-significative differences were found between the WRF simulation and MERRA-2 reanalysis, while ERA-5 with MERRA-2 presented a remarkable statistical difference of about EMD = 1.64 [m s−1] and RMSE = 1.8. In relation to flux comparison, reanalysis and WRF in contrast with in situ observations presented a good performance during the summer season, although a spatial resolution bias was noticed. These results can be used as an input for further research related to WRF simulations in Western Patagonia to provide reliable information on wind energy exploration and extreme climatological phenomena such as heat waves.

Comparison of Bias Correction Methods for Summertime Daily Rainfall in South Korea Using Quantile Mapping and Machine Learning Model

This study compares the bias correction techniques of empirical quantile mapping (QM) and the Long Short-Term Memory (LSTM) machine learning model for summertime daily rainfall simulation focusing on precipitation-dependent bias and temporal variation. Numerical experiments using Weather Research and Forecasting (WRF) were conducted over South Korea with lateral boundary conditions of ERA5 reanalysis data. For the spatial distribution of mean summertime rainfall, the bias-uncorrected WRF simulation (WRF_RAW) showed dry bias for most of the region of South Korea. The WRF results corrected by QM and LSTM (WRF_QM and WRF_LSTM, respectively) were improved for the mean summer rainfall simulation with the root mean square error values of 0.17 and 0.69, respectively, which were smaller than those of the WRF_RAW (1.10). Although the WRF_QM performed better than the WRF_LSTM in terms of the summertime mean and monthly precipitation, the WRF_LSTM presented a closer interannual rainfall variation to the observation than the WRF_QM. The coefficient of determination for calendar-day mean rainfall was the highest in the following order: the WRF_LSTM (0.451), WRF_QM (0.230), and WRF_RAW (0.201). However, the WRF_LSTM had a limitation in reproducing extreme rainfall exceeding 50 mm/day due to the few cases of extreme precipitation in training data. Nevertheless, the WRF_LSTM better simulated the observed light-to-moderate precipitation (10–50 mm/day) than the others.

Historical simulation of photovoltaic potential over China within the CORDEX-EA-II framework

This study evaluates historical simulation of solar photovoltaic potential (PVpot) during 1989–2008 over China against the ERA5 reanalysis, using the Weather Research and Forecasting (WRF) model and Regional Climate Model version 4 (RegCM4) within the framework of the Coordinated Regional Downscaling Experiment-East Asia second phase (CORDEX-EA-II). The impacts of spectral nudging technique and land surface model on the simulated PVpot are investigated as well. Results indicate that the observed PVpot is abundant over western China, which can reach up to 26% in summer particularly. The WRF simulations significantly overestimate the PVpot over most areas of China, with the bias about 9% over southeastern China, while the utilization of spectral nudging method and CLM4 land surface model can greatly reduce the deviation. The RegCM4 simulations generate underestimation of PVpot over Northwest China and overestimation over Southeast China. As for the interannual variation, the observed PVpot features an increase of 0.9%/decade over southeastern China. The WRF and RegCM4 simulations can reproduce the rising trend of PVpot, while the magnitude is much lower than ERA5. The WRF simulations can properly portray the characteristics of seasonal cycle of PVpot, with the peaks in May over entire China and most subregions, while RegCM4 exhibits poor skill in reproducing the intra-annual variation. Moreover, the simulated bias in the clear-sky solar radiation (RSDSCS), low-level cloud fraction, and light rain during the daylight may contribute to the deficiency of PVpot.

Examining the Effectiveness of Doppler Lidar-Based Observation Nudging in WRF Simulation for Wind Field: A Case Study over Osaka, Japan

This study attempts to improve the accuracy of wind field simulations in the Weather Research and Forecasting (WRF) model by incorporating Doppler lidar-based wind observations over the Osaka region of Japan. To achieve this, a Doppler lidar was deployed in Osaka city, and multi-layer wind measurements were obtained for one month (August 2022). These measurements were then assimilated into the WRF model using the observation nudging technique. Two simulations were conducted: one with nudging disabled, and the other with nudging enabled with data assimilation, while keeping all other configurations constant. The results were evaluated by comparing the simulations with the lidar observation at the lidar location where the wind data were nudged during the simulation, as well as with the AMeDAS station observations at other locations far from the lidar. The results indicated that not only the wind field, but other weather variables such as temperature, were better captured in the simulation using lidar-based nudging compared to the simulation without nudging.

Evaluation of the Ice Particle Simulation of Microphysics Schemes with Aircraft Measurements of a Stratiform Cloud in North China

Abstract Airborne microphysical measurements of a frontal precipitation event in North China were used to evaluate five microphysics schemes for predicting the bulk properties of ice particles. They are the Morrison and Thompson schemes, which use predetermined categories, the 1-ice- and 2-ice-category configurations of the Predicted Particle Properties (P3) scheme and the Ice-Spheroids Habit Model with Aspect-Ratio Evolution (ISHMAEL) scheme, which model the evolution of particle properties, and the spectral bin fast version (SBM_fast) microphysics scheme within the Weather Research and Forecasting (WRF) Model. WRF simulations with these schemes successfully reproduced the observed temperature and the liquid and total water content profiles at corresponding times and locations, allowing for a credible comparison of the predictions of particle properties with the aircraft measurements. The simulated results with the 1-ice-category P3 scheme are in good agreement with the observations for all the particle properties we examined. The 2-ice-category P3 scheme overestimates the spectrum width and underestimates the number concentration, which can be alleviated by reducing the ice collection efficiency. The simulation with the SBM_fast scheme deviates from the observed ice particle size distributions since the mass–diameter relationship of snow-sized particles adopted in this scheme may not be applicable to this stratiform cloud case.

Satellite-Based Estimation of Roughness Length over Vegetated Surfaces and Its Utilization in WRF Simulations

Based on morphological methods, MODIS satellite remote sensing data were used to establish a dataset of the local roughness length (Z0) of vegetation-covered surfaces in Guangdong Province. The local Z0 was used to update the mesoscale Weather Research and Forecasting (WRF) model in order to quantitatively evaluate its impact on the thermodynamic environment of vegetation-covered surfaces. The specific results are as follows: evergreen broad-leaved forests showed the largest average Z0 values at 1.27 m (spring), 1.15 m (summer), 1.03 m (autumn), and 1.15 m (winter); the average Z0 values of mixed forests ranged from 0.90 to 1.20 m; and those for cropland-covered surfaces ranged from 0.17 to 0.20 m. The Z0 values of individual vegetation coverage types all exhibited relatively high values in spring and low values in autumn, and the default Z0 corresponding to specific vegetation-covered surfaces was significantly underestimated in the WRF model. Modifying the default Z0 of surfaces underlying evergreen broad-leaved forests, mixed forests, and croplands in the model induced only relatively small changes (&lt;1%) in their 2 m temperature, relative humidity, skin surface temperature, and the planetary boundary layer height. However, the average daily wind speed of surfaces covered by evergreen broad-leaved forests, mixed forests, and croplands was reduced by 0.48 m/s, 0.43 m/s, and 0.26 m/s, respectively, accounting for changes of 12.0%, 11.1%, and 6.5%, respectively.