Weather Research And Forecasting Research Articles

Cold-Frontal Rainbands over the United Kingdom: Sensitivity of Precipitation Processes to Microphysics Schemes in WRF Simulations

Abstract A narrow cold-frontal rainband (NCFR) and wide cold-frontal rainband (WCFR) over the United Kingdom on 24 January 2018 are selected to evaluate the sensitivity of cold-season precipitation to three bulk cloud microphysics schemes in the Weather Research and Forecasting (WRF) Model. The three simulations tend to overpredict the intensity and the areal coverage of NCFRs but underpredict those of WCFRs against the Met Office radar imagery. Nonetheless, the WRF double-moment 6-class (WDM6) simulation produces the most realistic NCFR and WCFR. More specifically, the intensity of the NCFR in the WDM6 simulation is overpredicted in the developing stage and underpredicted in the dissipating stage when compared to the Thompson and Morrison simulations. This phenomenon is mainly attributed to the rain mixing ratio at 1 km. The more intense and larger area of the WCFR in the WDM6 simulation is associated with more low-level snow, graupel, and rain than the other two simulations. In the WDM6 simulation, the larger low-level snow mixing ratios are due to the higher rates of aggregation between cloud ice and snow, whereas the larger rain mixing ratio is due to higher melting rates. Thus, differences in low-level ice-phase processes between microphysics schemes are large, strongly influencing the intensity of WCFRs, but the differences in warm-rain processes are smaller. Additionally, the small size of raindrops in the WDM6 simulation leads to wider and more intense WCFR as these raindrops are preferentially sent rearward relative to the cold front. Significance Statement Models are useful tools to predict weather but have errors in predicting different weather types. In the United Kingdom, precipitation is often predicted to be too strong at cold fronts and too weak behind the fronts compared to radar observations. Choosing different options for how precipitation is represented in one model produced different precipitation forecasts, with WDM6 being the best. One reason why WDM6 was the best was because it produced more and smaller raindrops, which would be transported behind the front, and it had higher melting rates at low levels, resulting in more precipitation behind the fronts than other options. Our results help identify why different options produce different forecasts, leading to possible future improvements in these options.

An Assessment of the Weather Research and Forecasting Model for Solar Irradiance Forecasting under the Influence of Cold Fronts in a Desert in Northwestern Mexico

Northwestern Mexico has a desert climate with high solar resources. Clear skies and low humidity during most of the year favor their use. In winter, the arrival of cold air masses from the polar latitudes cause instability and abrupt changes in atmospheric variables, increasing the error of short-term forecasts. This work focuses on the evaluation of the Weather Research and Forecasting (WRF) model for predicting the global horizontal irradiance (GHI), considering different parameterizations of shortwave and longwave solar radiation during the influence of five cold fronts that affected the desert region of northwestern Mexico. The simulation was carried out under four main shortwave configurations and the results were evaluated with surface measurements and compared with climate information from NASA-POWER. The GHI predicted with the Dudhia parameterization showed an overestimation of the WRF model during most of the analyzed events; the most accurate predictions obtained correlation values between 0.85 and 0.91 and a mean absolute error between 15 and 45 W m−2. In periods where intermittent clouds prevailed, the mean error increased by almost 20%. An evaluation of the different proposed configurations shows advantages with the shortwave Dudhia and longwave RRTM parameterizations, providing a useful meteorological tool for predicting short-range variations in the GHI to improve the operability of solar power generation systems.

Adjoint‐based observation impact on meteorological forecast errors in the Arctic

AbstractAccurate weather forecasting using numerical weather prediction in the Arctic remains a challenge owing to the complex atmospheric dynamics and relatively sparse observations of the region. Nevertheless, the impact of observations used for data assimilation (DA) on forecast errors (observation impacts, OIs) in the Arctic has not been fully explored. This study evaluated the OIs on 24 hr forecast errors in the Arctic by the adjoint‐based forecast sensitivity OI (FSOI) method using the Polar Weather Research and Forecasting (WRF) model, three‐dimensional variational DA, and adjoint model of WRFPLUS. The time‐averaged FSOI during August 2018 was analyzed in terms of the type, variable, satellite channel, altitude, and horizontal location of the observations. The results showed that the total FSOI was greatest for aircraft observations (AIRCRAFT), followed by sonde observations, scatterometer sea‐surface winds, advanced microwave sounding unit‐A radiance data from each satellite, and surface synoptic observations. The impact of AIRCRAFT in reducing forecast errors was greater in the Arctic than in other regions of the globe. The total FSOI of the advanced microwave sounding unit‐A radiance data from all four satellites was the greatest among the total FSOI of the individual observation types. For both AIRCRAFT and sonde observations, the FSOI per observation increased at higher latitudes, indicating that forecast errors in the Arctic could be reduced when upper atmospheric in‐situ observations at higher latitudes are used for DA. The beneficial ratio of the Global Positioning System precipitable water in the Arctic was greater than that in the midlatitudes with relatively moist atmospheres that cause greater uncertainties in converting zenith wet delay. The results of this study can contribute to configuring an optimal observing system in the Arctic by presenting the relative importance of observations for reducing forecast errors.

Investigation of the characteristics of low-level jets over North America in a convection-permitting Weather Research and Forecasting simulation

Abstract. In this study, we utilized a high-resolution (4 km) convection-permitting Weather Research and Forecasting (WRF) simulation spanning a 13-year period (2000–2013) to investigate the climatological features of low-level jets (LLJs) over North America. The 4 km simulation enabled us to represent the effects of orography and the underlying surface on the boundary layer winds better. Focusing on the continental US and the adjacent border regions of Canada and Mexico, this study not only identified several well-known large-scale LLJs, such as the southerly Great Plains LLJ and the summer northerly California coastal LLJ, but also the winter Quebec northerly LLJ which received less focus before. All these LLJs reach their peak in the nighttime in the diurnal cycle. Thus, the different thermal and dynamic mechanisms forming these three significant LLJs are investigated in this paper. Inertial oscillation theory dominates in the Great Plain LLJ, and the California coastal LLJ is formed by the baroclinic theory, whereas the Quebec LLJ is associated with both theories. Moreover, the high-resolution simulation revealed climatic characteristics of weaker and smaller-scale LLJs or low-level wind maxima in regions with complex terrains, such as the northerly LLJs in the foothill regions of the Rocky Mountains and the Appalachians during the winter. This study provides valuable insights into the climatological features of LLJs in North America, and the high-resolution simulation offers a more detailed understanding of LLJ behavior near complex terrains and other smaller-scale features.

A Novel Approach for Predicting the Mid–Long-Term Radiation Dose in the Case of a Hypothetical STSBO Nuclear Accident for an Operating Nuclear Power Plant

Four severe nuclear accident scenarios have been identified for operating nuclear power plants (ONPPs). However, there is a research gap in predicting the mid–long-term radiation doses for these scenarios. This study aims to address this gap by proposing a novel approach for predicting the mid–long-term radiation dose in the case of a hypothetical short-term station blackout (STSBO) scenario, one of the aforementioned scenarios. Firstly, the Weather Research and Forecasting (WRF) model was coupled with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) (WRF-HYSPLIT) model to establish an atmospheric transport and diffusion model for airborne radionuclides, and the regularity of the atmospheric transport and diffusion for the airborne radionuclides was determined. Subsequently, the Residual Radioactive Material Guidelines (RESRAD) OFFSITE (RESRAD-OFFSITE) code was utilized to establish a radiation dose model for predicting the mid–long-term radiation dose resulting from the airborne radionuclides, and the evolution of the mid–long-term radiation dose was analyzed. Finally, the proposed approach was applied to an ONPP, and the results were used to predict the mid–long-term public radiation dose. The results indicated that the total radiation dose would be lower than the dose limit recommended by the International Commission on Radiological Protection (1 mSv/yr) from the second month to the 100th year after the hypothetical STSBO nuclear accident, and the total radiation dose would decrease slowly over time. Recommendations are made for offsite emergency response measures. These research findings can assist ONPPs in analyzing their environmental impacts in the event of an STSBO scenario.

Climate projection of tropical cyclone lifetime in the western North Pacific basin

Abstract In this study, the potential changes of tropical cyclone (TC) lifetime in the western North Pacific basin are examined for different future climates. Using homogeneous 9 km-resolution dynamical downscaling with the Weather Research and Forecasting (WRF) model, we show that TC averaged lifetime displays insignificant change under both low and high greenhouse-gas concentration scenarios. However, more noticeable changes in the tails of TC lifetime statistics are captured in our downscaling simulations, with more frequent long-lived TCs (lifetime of 8-11 days) and less short-lived TCs (lifetime of 3-5 days). Unlike present-day simulations, it is found that the correlation between TC lifetime and the Niño index is relatively weak and insignificant in all future downscaling simulations, thus offering little explanation for these changes in TC lifetime statistics based on the El Niño-Southern Oscillation. More detailed analyses of TC track distribution in the western North Pacific basin reveal, nevertheless, a noticeable shift of TC track patterns towards the end of the 21st century. Such a change in TC track climatology results in an overall longer duration of TCs over the open ocean, which is consistent across future scenarios and periods examined in this study. This shift in the TC track pattern is ultimately linked to changes in the Western North Pacific Subtropical High, which retreats to the south during July and to the east during August-September. The results obtained in this study provide new insights into how large-scale circulations can affect TC lifetime in the western North Pacific basin in warmer climates.

Evaluation of WRF model performance with different microphysics schemes for extreme rainfall prediction in Lagos, Nigeria: Implications for urban flood risk management

In Nigeria, particularly in urban areas like Lagos, flooding is a frequent natural hazard. In 2011, Lagos experienced one of its worst floods resulting in significant economic losses and displacement of people. In recent years, Lagos has continued to grapple with flooding challenges, with an equally significant flood episode occurring in 2021. This study focuses on predicting floods and forecasting extremely heavy rainfall in West Africa's equatorial zone using the Weather Research and Forecasting (WRF) model, particularly in humid tropical environments like Lagos. The study discusses the need to review existing flood models and adopt alternative flood models to address the limitations of flood prediction. As potential causes of these rainfall episodes, the interconnections between synoptic systems such as tropical easterly waves, southwesterly winds related to the West African Monsoon, and local topography and oceanic conditions are investigated. Three key metrics: root mean square error (RMSE), mean bias (MB), and mean absolute error (MAE) are used to assess the effectiveness of the computational model. Results indicate that the WRF model, specifically when using the Thompson parameterisation, can estimate the amount of rainfall accumulated over a 24-h period. This suggests that the model can predict the size of daily precipitation during intense rain events. The Thompson scheme shows better performance compared to the WSM6 scheme while evaluating the stations and episodes. During the rainfall episode on July 10, 2011, Thompson's spatial rainfall predictions were better than WSM6, resulting in a decrease in root mean square error (RMSE) of 15–31% depending on the area. Simulations of the July 2021 episode also show better performance, with a decrease in RMSE of 11–25% when comparing Thompson to WSM6 scheme. The Thompson scheme’s improved ability is directly linked to a more accurate depiction of the microphysical mechanisms that control the rainfall formation. By explicitly simulating the dynamics of ice crystals and graupel, it is possible to accurately replicate the processes of orographic lifting and moist convection that are responsible for driving intense monsoon precipitation. In addition, Thompson scheme shows a reduced degree of systemic bias in comparison to WSM6, with a 75% reduction in the average bias in rainfall accumulation over the research area. The combination of the advanced Thompson microphysics method and WRF's atmospheric dynamics shows a high level of accuracy in predicting intense rainfall and the risk of floods in this area with diverse tropical topography.

Climatology of Orographic Precipitation Gradients Over High Mountain Asia Derived From Dynamical Downscaling

AbstractWithin High Mountain Asia (HMA), the annual melting of glaciers and snowpack provides vital freshwater to populations living downstream. Precipitation over HMA can directly affect the freshwater availability in this region by altering the mass balance of glaciers and snowpack. However, available reanalyses and downscaling simulations lack the resolution required to understand important glacier‐scale variations in precipitation. This study aimed to determine the current characteristics of orographic precipitation gradients (OPG) by curve‐fitting daily precipitation as a function of elevation from a 15‐year, 4‐km grid spaced Weather Research and Forecasting (WRF) model simulation focused on the Himalayan, Karakoram, and Hindu‐Kush mountain ranges. To facilitate precipitation curve‐fitting, the WRF model grid points were separated into regions of similar orientation, referred to as facets. Akaike Information Criterion‐corrected values and an F‐test p‐value identified the need for a curvature term to account for a varying OPG with elevation. Regions with similar seasonal variability were found using ‐means clustering of the monthly mean OPG coefficients. The central Himalayan slope's intra‐seasonal variability of OPG depended on synoptic scale conditions, in which cyclonically‐forced heavy‐precipitation events produced strong sublinear increases in precipitation with elevation. Initial testing of precipitation estimates using monthly coefficients showed promising results in downscaling daily WRF precipitation; the daily mean absolute error at each grid point had a lower magnitude than the daily mean precipitation total, on average. Results provide a physically‐based context for machine learning algorithms being developed to predict OPG and downscale precipitation output from global climate models over HMA.

Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG

Abstract. Atmospheric transport models are often used to simulate the distribution of greenhouse gases (GHGs). This can be in the context of forward modeling of tracer transport using surface–atmosphere fluxes or flux estimation through inverse modeling, whereby atmospheric tracer measurements are used in combination with simulated transport. In both of these contexts, transport errors can bias the results and should therefore be minimized. Here, we analyze transport uncertainties in the commonly used Weather Research and Forecasting (WRF) model coupled with the greenhouse gas module (WRF-GHG), enabling passive tracer transport simulation of CO2 and CH4. As a mesoscale numerical weather prediction model, WRF's transport is constrained by global meteorological fields via initialization and at the lateral boundaries of the domain of interest. These global fields were generated by assimilating various meteorological data to increase the accuracy of modeled fields. However, in limited-domain models like WRF, the winds in the center of the domain can deviate considerably from these driving fields. As the accuracy of the wind speed and direction is critical to the prediction of tracer transport, maintaining a close link to the observations across the simulation domain is desired. On the other hand, a link that is too close to the global meteorological fields can degrade performance at smaller spatial scales that are better represented by the mesoscale model. In this work, we evaluated the performance of strategies for keeping WRF's meteorology compatible with meteorological observations. To avoid the complexity of assimilating meteorological observations directly, two main strategies of coupling WRF-GHG with ERA5 meteorological reanalysis data were tested over a 2-month-long simulation over the European domain: (a) restarting the model daily with fresh initial conditions (ICs) from ERA5 and (b) nudging the atmospheric winds, temperatures, and moisture to those of ERA5 continuously throughout the simulation period, using WRF's built-in four-dimensional data assimilation (FDDA) in grid-nudging mode. Meteorological variables and simulated mole fractions of CO2 and CH4 were compared against observations to assess the performance of the different strategies. We also compared planetary boundary layer height (PBLH) with radiosonde-derived estimates. Either nudging or daily restarts similarly improved the meteorology and GHG transport in our simulations, with a small advantage of using both methods in combination. However, notable differences in soil moisture were found that accumulated over the course of the simulation when not using frequent restarts. The soil moisture drift had an impact on the simulated PBLH, presumably via changing the Bowen ratio. This is partially mitigated through nudging without requiring daily restarts, although not entirely alleviated. Soil moisture drift did not have a noticeable impact on GHG performance in our case, likely because it was dominated by other errors. However, since the PBLH is critical for accurately simulating GHG transport, we recommend transport model setups that tie soil moisture to observations. Our method of frequently re-initializing simulations with meteorological reanalysis fields proved suitable for this purpose.

Nonlinear changes in urban heat island intensity, urban breeze intensity, and urban air pollutant concentration with roof albedo

This study systematically examines how the urban heat island (UHI) and urban breeze circulation (UBC) respond to an increase in roof albedo (αr) and its influence on urban air pollutant dispersion. For this, idealized ensemble simulations are performed using the Weather Research and Forecasting (WRF) model. The increase in αr from 0.20 to 0.65 decreases the UHI intensity, UBC intensity, and urban planetary boundary layer (PBL) height in the daytime (from 1200 to 1700 LST) by 47%, 36%, and 6%, respectively. As both UBC intensity and urban PBL height decrease, the daytime urban near-surface passive tracer concentration increases by 115%. The daytime UHI intensity, UBC intensity, and urban tracer concentration nonlinearly change with αr: For 0.10 ≤ αr < 0.80, the rates of changes in the UHI intensity, UBC intensity, and urban tracer concentration with αr overall increase as αr increases. For αr ≥ 0.80, the daytime roof surface temperature is notably lower than the daytime urban near-surface air temperature, the UHI intensity, UBC intensity, and urban tracer concentration very slightly changing with αr. This study provides insights into the associations between changes in roof surface temperature and roof surface energy fluxes with αr and those in UHI intensity.

Improving a WRF-Based High-Impact Weather Forecast System for a Northern California Power Utility

We describe enhancements to an operational forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&amp;E) to forecast conditions in Northern and Central California for critical decision-making such as proactively de-energizing selected circuits within the power grid. WRF forecasts are routinely produced on a 2 km grid, and the results are used as input to wildfire fuel moisture, fire probability, wildfire spread, and outage probability models. This forecast system produces skillful real-time forecasts while achieving an optimal blend of model resolution and ensemble size appropriate for today’s computational resources afforded to utilities. Numerous experiments were performed with different model settings, grid spacing, and ensemble configuration to develop an operational forecast system optimized for skill and cost. Dry biases were reduced by leveraging a new irrigation scheme, while wind skill was improved through a novel approach involving the selection of Global Ensemble Forecast System (GEFS) members used to drive WRF. We hope that findings in this study can help other utilities (especially those with similar weather impacts) improve their own forecast system.

Highly inhomogeneous interactions between background climate and urban warming across typical local climate zones in heatwave and non-heatwave days

Abstract Urban heat island (UHI) in conjunction with heatwave (HW) leads to exacerbation of thermal stress in urban areas. Prior research on UHI and HW has predominantly concentrated on examining the thermal conditions at the surface and near-surface, with few investigations extending to the radiative and dynamical interactions of UHI and HW, particularly with a focus on the inhomogeneities across local climate zones (LCZs). Here, we analyse the temperature disparity between HW and non-HW conditions across LCZs in the Sydney area by quantifying the contributions of individual radiative and dynamical processes using the coupled surface-atmosphere climate feedback-response analysis method (CFRAM). Three moist HW events in 2017, 2019, and 2020 are simulated using the Weather Research and Forecasting (WRF) model coupled with the single-layer urban canopy model (SLUCM). It is found that the maximum surface and 900 hPa temperature difference between HW and non-HW days may reach up to 10 K, with the increased net solar radiation during HWs being comparable to the typical level of anthropogenic heat flux in urban areas. It is also found that the reduction of clouds, the presence of vapour, and the increase of sensible heat contribute to the warming effect to various degrees, with the contribution of clouds being the most dominant. Conversely, the generation of dry convection and the increase of latent heat flux lead to cooling effects, with the latter being more dominant and capable of causing up to 10 K surface temperature difference between LCZ1 (compact high-rise) and LCZ9 (sparsely built). The differences in the contributions of climate feedback processes across different LCZs become more evident during more severe and humid HWs. These findings underscore the necessity of implementing LCZ-tailored heat mitigation strategies.

The added value of using convective-permitting regional climate model simulations to represent cloud band events over South America

AbstractClimate science has long explored whether higher resolution regional climate models (RCMs) provide improved simulation of regional climates over global climate models (GCMs). The advent of convective-permitting RCMs (CPRCMs), where sufficiently fine-scale grids allow explicitly resolving rather than parametrising convection, has created a clear distinction between RCM and GCM formulations. This study investigates the simulation of tropical-extratropical (TE) cloud bands in a suite of pan-South America convective-permitting Met Office Unified Model (UM) and Weather Research and Forecasting (WRF) climate simulations. All simulations produce annual cycles in TE cloud band frequency within 10–30% of observed climatology. However, too few cloud band days are simulated during the early summer (Nov–Dec) and too many during the core summer (Jan–Feb). Compared with their parent forcing, CPRCMs simulate more dry days but systematically higher daily rainfall rates, keeping the total rain biases low. During cloud band systems, the CPRCMs correctly reproduced the observed changes in tropical rain rates and their importance to climatology. Circulation analysis suggests that simulated lower subtropical rain rates during cloud bands systems, in contrast to the higher rates in the tropics, are associated with weaker northwesterly moisture flux from the Amazon towards southeast South America, more evident in the CPRCMs. Taken together, the results suggest that CPRCMs tend to be more effective at producing heavy daily rainfall rates than parametrised simulations for a given level of near-surface moist energy. The extent to which this improves or degrades biases present in the parent simulations is strongly region-dependent.

Improved Orographic Gravity Wave Drag Parameterization Accounting for the Nonhydrostatic Effect in the Weather Research and Forecasting Model: Tests for Short-Range Forecast of Northeast China Cold Vortices

Abstract The parameterization of orographic gravity wave drag (OGWD) is essential for accurate numerical weather prediction in regions of complex terrain. Current OGWD schemes assume hydrostatic OGWs but the parameterized OGWs in fine-resolution models with narrow subgrid-scale orography can be significantly affected by the nonhydrostatic effects (NHE). In our recent work, the OGWD scheme in the Model for Prediction Across Scales (MPAS) was revised by accounting for the NHE on the surface wave momentum flux of upward-propagating OGWs. Herein, the revised OGWD scheme is implemented in the Weather Research and Forecasting (WRF) model to evaluate its performance in short-range weather forecast. Two sets of 36-hr WRF simulations are conducted for nine Northeast China cold vortices (NECVs) that occurred in the warm season of 2011 using the original and revised OGWD schemes. Results show that the WRF model tends to underestimate the intensity of the NECVs, producing too high geopotential height. When accounting for the NHE in the OGWD scheme, the NECV intensity biases are significantly reduced. Analyses reveal that the NHE act to weaken the lower-tropospheric OGWD by decreasing the surface wave momentum flux, which strengthens the NECV in the lower troposphere. Consequently, the strengthened low-level cyclonic circulation increases the post-trough cold advection to the southwest of the NECV which in turn enhances the NECV in the mid-upper troposphere with reduced geopotential height. The NHE are found to increase as the model horizontal resolution increases, suggesting greater importance of the NHE in the OGWD parameterization of high-resolution numerical models.

Insights into the Australian mid-Holocene climate using downscaled climate models

Abstract. The mid-Holocene climate of Australia and the equatorial tropics of the Indonesian–Australian monsoon region is investigated using the Community Earth System Model (CESM) and the Weather Research and Forecasting (WRF) model. Each model is used to simulate the pre-industrial (1850) and the mid-Holocene (6000 years before 1950) climate. The results of these four simulations are compared to existing bioclimatic modelling of temperature and precipitation. The finer-resolution WRF simulations reduce the bias between the model and bioclimatic data results for three of the four variables available in the proxy data set. The model results show that temperatures over southern Australia at the mid-Holocene and in the pre-industrial period were similar, and temperatures were slightly warmer during the mid-Holocene over northern Australia and into the tropics compared to the pre-industrial. During the mid-Holocene, precipitation was generally reduced over northern Australia and in the Indonesian–Australian monsoon region, particularly during summertime. The results highlight the improved value of using finer-resolution models such as WRF to simulate the palaeoclimate.

Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models

This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability.

Simulating lake ice phenology using a coupled atmosphere–lake model at Nam Co, a typical deep alpine lake on the Tibetan Plateau

Abstract. Simulating the ice phenology of deep alpine lakes is important and challenging in coupled atmosphere–lake models. In this study, the Weather Research and Forecasting (WRF) model, coupled with two lake models, the freshwater lake (WRF–FLake) model and the default lake (WRF–CLake) model, was applied to Nam Co, a typical deep alpine lake located in the centre of the Tibetan Plateau, to simulate its lake ice phenology. Due to the large errors in simulating lake ice phenology, related key parameters and parameterizations were improved in the coupled model based on observations and physics-based schemes. By improving the momentum, hydraulic, and thermal roughness length parameterizations, both the WRF–FLake model and the WRF–CLake model reasonably simulated the lake freeze-up date. By improving the key parameters associated with shortwave radiation transfer processes when lake ice exists, both models generally simulated the lake break-up date well. Compared with WRF–CLake without improvements, the coupled model with both revised lake models significantly improved the simulation of lake ice phenology. However, there were still considerable errors in simulating the spatial patterns of freeze-up and break-up dates, implying that significant challenges in simulating the lake ice phenology still exist in representing some important model physics, including lake physics such as grid-scale water circulation and atmospheric processes such as snowfall and surface snow dynamics. Therefore, this work can provide valuable new implications for advancing lake ice phenology simulations in coupled models, and the improved model also has practical application prospects in weather and climate forecasts.

The influence of moisture on precipitation patterns across the Western Tibetan Plateau and its response to sea surface temperature warming

Abstract The distribution of water resources in sub-basins across the Western Tibetan Plateau (WTP) is of critical importance due to not only ecological vulnerability resulting from the extremely arid climatology but also the political sensitivities surrounding the international rivers. In this study, we utilize an advanced water vapor tracer (WVT) embedded in the widely used regional climate model – Weather and Research Forecast (WRF), to quantify moisture contributions from four main sources towards precipitation over the WTP region. We also analyze influences on other sub-basins in the TP for comparison purposes. We examine how changes in sea surface temperature (SST) during 2010s compared to 1980s have influenced precipitation patterns and moisture contributions over recent decades. Our findings indicate that terrestrial moisture sources contribute more than oceanic sources towards the endorheic TP region. Recycling processes originating from highlands area are revealed to play a greater role in contributing moisture over WTP compared to those from lowlands areas. Furthermore, our results demonstrate stronger agreements between wetting distribution patterns and distributions of liquid/solid hydrometeors rather than water vapor distribution itself, highlighting condensation/freezing as critical factors. Notably, we observe different responses within Amu Dayra basin compared to the main WTP when subjected to SST changes. This study focuses on delineating distinct roles of terrestrial and oceanic moisture sources in driving precipitation changes over WTP, while specifically emphasizing condensation process’ contribution to inner TP’s precipitation and highlighting moisture transport form oceans’ influence on precipitation patterns over Amu Dayra basin.

Enhancing Weather Forecast Accuracy Through the Integration of WRF and BP Neural Networks: A Novel Approach

AbstractIn the past century, scholars from both domestic and international communities have delved into the study of numerical weather prediction models to promptly understand meteorological factors and mitigate the impacts of extreme weather events on humanity. Effective and precise prediction models enable the forecasting of meteorological conditions in the upcoming days, empowering individuals to implement proactive measures to minimize the adverse effects of extreme weather (Liang et al., 2021). The WRF (Weather Research and Forecasting) modeling system is commonly used for forecasting meteorological elements. However, uncertainties terribly hamper the correctness of the forecasting results. To this end, the present study was conducted to build a secondary model on the basis of the WRF forecast model. The WRF‐BPNN model was proposed for verification after constructing the network, the temperature vertical profile and the mixing ratio vertical profile were predicted, and the results on the validation set were tested. The results showed that the WRF‐BPNN model could effectively predict the temperature profile and mixing ratio profile, presenting better performance than the traditional WRF model.

Enhancing Surface Wind Speed and Temperature Prediction Using Surface-Layer Emulator and Transfer Learning

Abstract Accurate prediction of surface wind speed and temperature is crucial for many sectors. Physical schemes in numerical weather prediction (NWP) and data-driven correction approaches have limitations due to uncertainties in parameterization and lack of robustness, respectively. This study introduces a physics-informed data model, PhyCorNet, which combines a deep learning–based physics emulator (PhyNet) and a subsequent correction network (CorNet). PhyNet imitates the revised surface-layer parameterization scheme [default option of the Weather Research and Forecasting (WRF) Model]. CorNet refines predictions by mitigating the difference between PhyNet and observations. PhyCorNet enables gridded forecasts despite the training data being point based. Therefore, PhyCorNet can be regarded as a rediagnostic scheme for surface wind speed at 10 m (WS10) and temperature at 2 m (T2). Compared to WRF, it reduced diagnostic root-mean-square errors in the 24-h forecasts for WS10 and T2 by 40% and 36%, respectively, across China, with unbiased forecasts at almost all sites. PhyCorNet addresses the oversmoothing prediction issue of other deep learning models by providing the ability to represent fine-scale features and perform well in statistically extreme samples. In grid cells without observations for training, PhyCorNet performed much better than WRF, demonstrating the zero-shot learning capability. This study implies that the emulator plus bias correction provided by PhyCorNet could be used as a simple but effective approach to improve the performance of other diagnostic quantities in NWP. Significance Statement We developed a new model called PhyCorNet to improve the surface wind speed and temperature forecasts. Existing weather forecast models have limitations in accurately predicting these variables. PhyCorNet first uses a neural network (PhyNet) to mimic an existing physics-based wind and temperature calculation method. Another neural network [correction network (CorNet)] improves the PhyNet forecasts by comparing them with observations. Across China, PhyCorNet reduced 24-h forecast errors for wind speed by 40% and temperature by 36% compared to a conventional model. It performed well under diverse conditions and overcame the oversmoothing issues faced by other machine learning models. Importantly, PhyCorNet outperformed traditional models in areas without historical observations. We suggest that this new framework can also be used to enhance the forecasts of other atmospheric variables.