Mesoscale Weather Forecasting Research Articles

Scaling effects of fixed-wing ground-generation airborne wind energy systems

Abstract. While some airborne wind energy system (AWES) companies aim at small, temporary or remote off-grid markets, others aim at utility-scale, multi-megawatt integration into the electricity grid. This study investigates the scaling effects of single-wing, ground-generation AWESs from small- to utility-scale systems, subject to realistic 10 min, onshore and offshore wind conditions derived from a numerical mesoscale Weather Research And Forecasting (WRF) model. To reduce computational cost, vertical wind velocity profiles are grouped into 10 clusters using k-means clustering. Three representative profiles from each cluster are implemented into a nonlinear AWES optimal control model to determine power-optimal trajectories. We compare the effects of three different aircraft masses and two sets of nonlinear aerodynamic coefficients for aircraft with wing areas ranging from 10 to 150 m2 on operating parameters and flight trajectories. We predict size- and mass-dependent AWES power curves, annual energy production (AEP) and capacity factors (cf) and compare them to a quasi-steady-state reference model. Instantaneous force, tether-reeling speed and power fluctuations as well as power losses associated with tether drag and system mass are quantified.

Mesoscale impact of the sea surface on the performance of offshore wind farms

The impact of the ocean on the performance of offshore wind farms is a challenging study in the wind energy field. In the present study, by coupling a mesoscale numerical weather prediction model, Weather Research and Forecasting (WRF), with a sea surface model and a refined wind farm parameterization, a systematic atmosphere-sea surface-wind farm simulation method is established. The question of how the ocean-atmosphere interaction changes the wake effect and power output of wind farms based on the method is explored. The results show that the momentum deficit due to the operating wind turbines is remarkable, and the wind speed deficit has a strong horizontal diffusion on a sea surface. Wake from the offshore wind farm is prone to mix and extends with a shorter length than that on the flat land. The wake mixing enhances the interference from upwind to downwind turbines, resulting in the power outputs of downwind turbines being decreased by 10%. Besides, the increasing wind shear due to the ocean waves decreases the wind power by around 3.5%. Accordingly, it is significant for applying such a coupled air-sea surface-wind farm simulation method when assessing the offshore wind farm performance and the impact of the wind farms on the marine atmospheric boundary layer.

Development and Evaluation of a New Urban Parameterization in the Weather Research and Forecasting (WRF) Model

AbstractIn mesoscale Weather Research and Forecasting (WRF) model, several urban‐modeling options exist, such as Single‐layer Urban Canopy Model, Building Effect Parameterization, and Building Energy Model. However, these models have limitations in terms of the choice of land surface models (LSMs) and planetary boundary layer (PBL) schemes, the associated computational expenses, and other constraints. Recently, Dy et al. (2019, https://doi.org/10.1029/2018JD029333) included the urban‐momentum drag effect for wind‐speed modeling by developing a new multilayer model based on a modified nonlocal Asymmetric Convective Model version‐2 (ACM2) PBL scheme. This urban‐based ACM2 (UACM) has demonstrated a significantly improved ability over the base‐ACM2 (BACM) to predict wind speed near the urban ground surface and shown an inflection point at roof level in the vertical profile of horizontal wind. In this study, urban thermal and moisture components are introduced into the Pleim‐Xiu (PX) LSM and coupled with UACM. The diurnal variation in street, walls, and roof‐surface temperatures is modeled using the two‐layer force‐restore algorithm. Simple radiation treatment is considered to account for shadowing on streets based on the solar zenith angle and building morphology. Heat and moisture flux evolution are considered explicitly on all urban surfaces. The advantages of this novel UACM are simple formulation, more efficient execution, and its requirement for only a few fundamental urban morphological parameters. The idealized and real urban data case WRF simulations are performed over the Pearl River Delta region in southern China to test the new UACM. The computationally efficient UACM is expected to perform faster for operational forecasting runs.

Simulated Influence of Mountainous Wind Farms Operations on Local Climate

Renewable energy sources, especially wind power, were believed to be able to slow down global warming; however, evidence in recent years shows that wind farms may also induce climate change. With the rapid development of wind power industry, the number of wind farms installed in mountains has gradually increased. Therefore, it is necessary to study the impact of wind farms in mountainous areas on local climate. The Suizhou and Dawu wind farms in northern Hubei Province were chosen for the present study on the impact of wind farm operations on the local climate in mountainous areas. The mesoscale meteorological numerical model Weather Research and Forecasting Model (WRF) and the Fitch model, together with turbulence correction factor, were used to simulate wind farm operations and study their effects on local climate. The results showed the characteristics of wind speed attenuation in mountainous wind farms: the amplitude and range of wind speed attenuation were stronger in the nighttime than in the daytime, and stronger in summer than in winter. The surface temperature increased and became more significant in summer. However, a cooling variation was observed above the surface warming center. The height of this center was higher in the daytime than it was in the nighttime. The latent heat flux in the wind farms decreased at night, accompanied by an increase in sensible heat flux. However, these changes were not significant. Some differences were observed between the impact of wind farms on the climate in the plains and the mountains. Such differences are more likely to be related to complex terrain conditions, climate conditions, and the density of wind turbines. The present study may provide support for the development and construction of wind farms in mountainous areas.

Hydrometeorological analysis of a flash flood event in an ungauged Mediterranean watershed under an operational forecasting and monitoring context

AbstractThe current study presents the first attempt to investigate the November 2019 catastrophic flash flood in Olympiada (North Greece) under an operational forecasting and monitoring context, based on the mesoscale weather and research forecasting (WRF) model and the integrated multi‐satellite retrievals for global precipitation measurement (GPM‐IMERG) algorithm. When evaluated against ground‐based rainfall measurements, WRF showed an adequate predictive capability concerning the severity of the observed rainfall, even though the model's performance suffered from phase and spatial displacement errors. The GPM‐IMERG algorithm yielded the best performance in capturing the timing of the observed excessive rainfall. Concerning the hydrological outcome using the Hydrologic Engineering Center‐Hydrologic Modelling System (HEC‐HMS), a strong indication of the forthcoming flash flood could have been provided at least 2 days in advance based on the WRF‐based HEC‐HMS‐simulated flood peak (139.3 m3 s−1), as it was close to the drainage capacity of a constructed bridge in the plain stream bed, and to the 100‐year return period flood discharge.

Development of a micro-in-meso-scale framework for simulating pollutant dispersion and wind environment in building groups

Simulation of pollutant dispersion and wind environment is of significant importance to the safety and health of pedestrians and residents. However, the high threshold of data collection and model configuration has hindered its application in the building industry. To address the problem, this paper proposes a Micro-in-Meso-scale (MiM) simulation framework for automatic and foolproof pollutant dispersion and wind environment simulation. The meso-scale Weather Research and Forecast (WRF) model is first carried out to obtain city-level meteorology data with sufficient resolution. The micro-scale Computational Fluid Dynamics (CFD) model is then initialized to simulate pollutant dispersion and wind environment in building groups. The two models are coupled where the numerical results of WRF are provided to CFD for the configuration of boundary conditions. Fluid dynamics knowledge and spatial characteristics of building groups are also embedded in the model to lower the threshold of performing atmospheric simulations. A Geographic Information System (GIS)-based data management module is established simultaneously to achieve automatic collection, processing and management of raw data for simulation. The conversion algorithm of GIS data is further implemented to achieve the automatic generation of the CFD domain. Finally, the framework is developed to embed the data module and simulation module, and provide an easy-to-operate environment for building industry personnel. The framework is validated by data of actual building groups in four major cities in China. The test results illustrate the efficiency and feasibility of the platform. The study is expected to enhance the application of wind environment simulation in building industries.

Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, China

The mesoscale Weather Research and Forecasting (WRF) model has been widely employed to forecast day-ahead rainfalls. However, the deterministic predictions from the WRF model incorporate relatively large errors due to numerical discretization, inaccuracies in initial/boundary conditions and parameterizations, etc. Among them, the uncertainties in parameterization schemes have a huge impact on the forecasting skill of rainfalls, especially over the Sichuan Basin which is located east of the Tibetan Plateau in southwestern China. To figure out the impact of various parameterization schemes and their interactions on rainfall predictions over the Sichuan Basin, the Global Forecast System data are chosen as the initial/boundary conditions for the WRF model and 48 ensemble tests have been conducted based on different combinations of four microphysical (MP) schemes, four planetary boundary layer (PBL) schemes, and three cumulus (CU) schemes, for four rainfall cases in summer. Compared to the observations obtained from the Chinese ground-based and encrypted stations, it is found that the Goddard MP scheme together with the asymmetric convective model version 2 PBL scheme outperforms other combinations. Next, as the first step to explore further improvement of the WRF physical schemes, the polynomial chaos expansion (PCE) approach is then adopted to quantify the impacts of several empirical parameters with uncertainties in the WRF Single Moment 6-class (WSM6) MP scheme, the Yonsei University (YSU) PBL scheme and the Kain-Fritsch CU scheme on WRF rainfall predictions. The PCE statistics show that the uncertainty of the scaling factor applied to ice fall velocity in the WSM6 scheme and the profile shape exponent in the YSU scheme affects more dominantly the rainfall predictions in comparison with other parameters, which sheds a light on the importance of these schemes for the rainfall predictions over the Sichuan Basin and suggests the next step to further improve the physical schemes.

The Effect of Anthropogenic Heat and Moisture on Local Weather at Industrial Heat Islands: A Numerical Experiment

The current study addresses the role of heat and moisture emitted from anthropogenic sources on the local weather with the aid of numerical weather prediction (NWP). The heat and moisture emitted by industries to the atmosphere are considered main sources in this study. In order to understand the effect of heat and moisture on local weather, the study is conducted to capture the impact of heat with no moisture change. The results are compared against a control run case without perturbation and also against the case where both heat and moisture are perturbed with temperature as a single parameter. The Angul district in Odisha that houses over 4000 industries is considered our study region. The numerical simulations are performed using the mesoscale Weather Research and Forecasting (WRF) model for two rain events, namely a light rain case and a heavy rain case, with different physics options available in the WRF model. The WRF simulated maximum rainfall rate using various microphysics schemes are compared with the Tropical rainfall measuring mission (TRMM) observations for validation purposes. Our study shows that the WDM6 double moment microphysics scheme is better in capturing rain events. The TRMM-validated WRF simulation is considered a reference state of the atmosphere against which comparisons for the perturbed case are made. The surface temperature is perturbed by increasing it by 10 K near the industrial site and exponentially decreasing it with height up to the atmospheric boundary layer. A numerical experiment represents heating without addition of moisture by recalculating the relative humidity (RH) corresponding to the perturbed temperature. The perturbed temperature affects sensible heat (SH) and latent heat (LH) parameters in the numerical experiment. From the results of the numerical investigation, it is found that the near-surface rainfall rate increases locally in a reasonable manner with the addition of sensible heat to the atmosphere. A comparison against the case where moisture is added shows that enhanced rainfall is more sensitive to sensible and latent heat than sensible heat alone.

High-resolution regional modeling of urban moisture island: mechanisms and implications on thermal comfort

The urban moisture island (UMI) can aggravate the thermal stress due to the urban heat island (UHI) in subtropical and tropical cities. In this study, we investigated the spatiotemporal variation patterns of UMI in Hong Kong, a subtropical coastal city, using the fine-resolution mesoscale Weather Research and Forecasting (WRF) model by integrating local climate zone (LCZ) maps based on the World Urban Database and Access Portal Tools (WUDAPT). Our results show that at regional scale, the UMI phenomenon tends to occur in coastal areas, possibly owing to rich moisture sources from sea breeze and inhibited moisture penetration due to barrier effects of mountains. Specifically, an all-day UMI effect was found in coastal low-density low-rise areas (LCZ5&8&10), while a nocturnal UMI effect and a daytime urban dry island (UDI) effect were found in coastal high-density high-rise areas (LCZ1&2). The UDI effect at daytime can be attributed to strong vertical moisture convection associated with intensive surface sensible heat fluxes in a strongly mixed urban boundary layer (UBL). The UMI effect at night can be attributed to blocked ventilation aisle, inhibited dewfall due to UHI, and weakened upward motion in a stable UBL. On the other hand, UMI can increase regional heat risks with additional 37.5% neighbourhoods in Extreme caution level and additional 6.1% neighbourhoods in Danger level. In addition, the impact of UMI on human thermal stress was found to be dominant at daytime in coastal low-density low-rise areas (LCZ5&8&10) and at nighttime in coastal high-density high-rise areas (LCZ1&2).

Impacts of Projected Urban Expansion on Rainfall and Temperature during Rainy Season in the Middle-Eastern Region in Tanzania

Future changes of land use and land cover (LULC) due to urbanization can cause variations in the frequency and severity of extreme weather events, affecting local climate and potentially worsening impact of such events. This work examines the local climatic impacts associated with projected urban expansion through simulations of rainfall and temperature over the rapidly growing city of the middle-eastern region in Tanzania. Simulations were conducted using a mesoscale Weather Research and Forecasting (WRF) model for a period of 10 days during the rainfall season in April 2018. The Global Forecasting System data of 0.25° resolution was used to simulate the WRF model in two-way nested domains at resolutions of 12 km and 4 km correspondingly. Urban and built-up areas under the current state, low urbanization (30%), and high urbanization (99%) scenarios were taken into account as LULC categories. As the urbanized area increased, daily mean, maximum and minimum air temperatures, as well as precipitation increased. Local circulation affected the spatial irregularities of air temperature and precipitation. Results imply that urbanization can amplify the impacts of future climate changes dramatically. These results can be applicable to the city planning to minimize the adverse effect of urbanization on temperature and precipitation.

Performance evaluation of the WRF model in a tropical region: wind speed analysis at different sites

In this study, the performance of the mesoscale Weather Research and Forecasting (WRF) model is evaluated using combinations of three Planetary Boundary Layer (PBL) and three Land Surface Model (LSM) schemes, in order to identify the optimal parameters for the determination of wind speed in a tropical region. The state of Bahia in Brazil is selected as the location for the case study and simulations are performed over a period of eight months between 2015 and 2016. This is done to ensure that the dry and rainy seasons at the three different experimental sites—Esplanada, Mucuri, and Mucugê—are well separated from each other. The results of the simulations are compared with the observational data obtained from three towers equipped with anemometers at heights of 80, 100, 120 and 150 m, strategically placed at each site. Overestimation of wind speed is observed in the simulations, despite similarities between the simulated and observed wind directions. In addition, the accuracies of simulations corresponding to sites that are closer to the ocean are observed to be lower—the most accurate wind speed estimates are obtained corresponding to Mucugê, which is located farthest from the ocean. Finally, analysis of the results obtained from each tower accounting for periods with higher and lower precipitation reveals that the combination of the PBL-YSU scheme with the LSM-RUC scheme yields the best results.

Impact of Assimilating FY-3D MWTS-2 Upper Air Sounding Data on Forecasting Typhoon Lekima (2019)

In this study, the Fengyun-3D (FY-3D) clear-sky microwave temperature sounder-2 (MWTS-2) radiances were directly assimilated in the regional mesoscale Weather Research and Forecasting (WRF) model using the Gridpoint Statistical Interpolation (GSI) data assimilation system. The assimilation experiments were conducted to compare the track errors of typhoon Lekima from uses of the Advanced Microwave Sounding Unit-A (AMSU-A) radiances (EXP_AD) with those from FY-3D MWTS-2 upper-air sounding data at channels 5–7 (EXP_AMD). The clear-sky mean bias-corrected observation-minus-background (O-B) values of FY-3D MWTS-2 channels 5, 6, and 7 are 0.27, 0.10 and 0.57 K, respectively, which are smaller than those without bias corrections. Compared with the control experiment, which was the forecast of the WRF model without use of satellite data, the assimilation of satellite radiances can improve the forecast performance and reduce the mean track error by 8.7% (~18.4 km) and 30% (~58.6 km) beyond 36 h through the EXP_AD and EXP_AMD, respectively. The direction of simulated steering flow changed from southwest in the EXP_AD to southeast in the EXP_AMD, which can be pivotal to forecasting the landfall of typhoon Lekima (2019) three days in advance. Assimilation of MWTS-2 upper-troposphere channels 5–7 has great potential to improve the track forecasts for typhoon Lekima.

Overhead Line Ampacity Forecasting With a Focus on Safety

Predictions of the ampacity of overhead lines can be framed in a general context that aims to make electric grids highly efficient and reliable. In this paper, a methodology is presented that provides ampacity forecasts, which are valid for both the very short term, such as a few minutes or hours, and longer terms, up to 24 hours ahead. The former can be useful for grid operations, while the latter may be valuable in electricity markets. A time series methodology and mesoscale weather forecasts have been combined in machine learning algorithms for producing reliable ampacity forecasts for a span located in complex terrain. In a prior step, the developed algorithms made point forecasts, but finally, a computationally inexpensive algorithm produces probabilistic forecasts. These probabilistic forecasts are nonparametric, as they are not based on predefined probability distributions, and they demonstrate how a low risk in overhead lines is closely related to the reliability of ampacity forecasts.

Numerical Simulation of Near-Surface Wind during a Severe Wind Event in a Complex Terrain by Multisource Data Assimilation and Dynamic Downscaling

Accurate forecast and simulation of near-surface wind is a great challenge for numerical weather prediction models due to the significant transient and intermittent nature of near-surface wind. Based on the analyses of the impact of assimilating in situ and Advanced Tiros Operational Vertical Sounder (ATOVS) satellite radiance data on the simulation of near-surface wind during a severe wind event, using the new generation mesoscale Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system, the dynamic downscaling of near-surface wind is further investigated by coupling the microscale California Meteorological (CALMET) model with the WRF and its 3DVAR system. Results indicate that assimilating in situ and ATOVS radiance observations strengthens the airflow across the Alataw valley and triggers the downward transport of momentum from the upper atmosphere in the downstream area of the valley in the initial conditions, thus improving near-surface wind simulations. Further investigations indicate that the CALMET model provides more refined microtopographic structures than the WRF model in the vicinity of the wind towers. Although using the CALMET model achieves the best simulation of near-surface wind through dynamic downscaling of the output from the WRF and its 3DVAR assimilation, the simulation improvements of near-surface wind speed are mainly within 1 m s−1. Specifically, the mean improvement proportions of near-surface wind speed are 64.8% for the whole simulation period, 58.7% for the severe wind period, 68.3% for the severe wind decay period, and 75.4% for the weak wind period. The observed near-surface wind directions in the weak wind conditions are better simulated in the coupled model with CALMET downscaling than in the WRF and its 3DVAR system. It is concluded that the simulation improvements of CALMET downscaling are distinct when near-surface winds are weak, and the downscaling effects are mainly manifested in the simulation of near-surface wind directions.

City-Scale Building Anthropogenic Heating during Heat Waves

More frequent and longer duration heat waves have been observed worldwide and are recognized as a serious threat to human health and the stability of electrical grids. Past studies have identified a positive feedback between heat waves and urban heat island effects. Anthropogenic heat emissions from buildings have a crucial impact on the urban environment, and hence it is critical to understand the interactive effects of urban microclimate and building heat emissions in terms of the urban energy balance. Here we developed a coupled-simulation approach to quantify these effects, mapping urban environmental data generated by the mesoscale Weather Research and Forecasting (WRF) coupled to Urban Canopy Model (UCM) to urban building energy models (UBEM). We conducted a case study in the city of Los Angeles, California, during a five-day heat wave event in September 2009. We analyzed the surge in city-scale building heat emission and energy use during the extreme heat event. We first simulated the urban microclimate at a high resolution (500 m by 500 m) using WRF-UCM. We then generated grid-level building heat emission profiles and aggregated them using prototype building energy models informed by spatially disaggregated urban land use and urban building density data. The spatial patterns of anthropogenic heat discharge from the building sector were analyzed, and the quantitative relationship with weather conditions and urban land-use dynamics were assessed at the grid level. The simulation results indicate that the dispersion of anthropogenic heat from urban buildings to the urban environment increases by up to 20% on average and varies significantly, both in time and space, during the heat wave event. The heat dispersion from the air-conditioning heat rejection contributes most (86.5%) of the total waste heat from the buildings to the urban environment. We also found that the waste heat discharge in inland, dense urban districts is more sensitive to extreme events than it is in coastal or suburban areas. The generated anthropogenic heat profiles can be used in urban microclimate models to provide a more accurate estimation of urban air temperature rises during heat waves.

A dataset of microclimate and radiation and energy fluxes from the Lake Taihu eddy flux network

Abstract. Eddy covariance data are widely used for the investigation of surface–air interactions. Although numerous datasets exist in public depositories for land ecosystems, few research groups have released eddy covariance data collected over lakes. In this paper, we describe a dataset from the Lake Taihu eddy flux network, a network consisting of seven lake sites and one land site. Lake Taihu is the third-largest freshwater lake (area of 2400 km2) in China, under the influence of subtropical climate. The dataset spans the period from June 2010 to December 2018. Data variables are saved as half-hourly averages and include micrometeorology (air temperature, humidity, wind speed, wind direction, rainfall, and water or soil temperature profile), the four components of surface radiation balance, friction velocity, and sensible and latent heat fluxes. Except for rainfall and wind direction, all other variables are gap-filled, with each data point marked by a quality flag. Several areas of research can potentially benefit from the publication of this dataset, including evaluation of mesoscale weather forecast models, development of lake–air flux parameterizations, investigation of climatic controls on lake evaporation, validation of remote-sensing surface data products and global synthesis on lake–air interactions. The dataset is publicly available at https://yncenter.sites.yale.edu/data-access (last access: 24 October 2020) and from the Harvard Dataverse (https://doi.org/10.7910/DVN/HEWCWM; Zhang et al., 2020).

Statistics and analysis of high-altitude wind above the western Tibetan Plateau

ABSTRACT This article aims at studying the characteristics of high-altitude wind at the Ali Observatory on the western Tibetan Plateau, as the high-altitude wind has been put forward as a critical parameter for site evaluation, especially for adaptive optics. We have run a meso-scale numerical weather research and forecasting (WRF) model in three nested domains with different horizontal resolutions, centred at the Ali Observatory; the model results with the highest horizontal resolution of 1 km and temporal resolution of 0.5 h are presented, and also statistical analyseis of vertical wind profiles and 200 hPa wind speed are performed. Moreover, comparisons of wind speeds obtained from model and radiosoundings are presented; as the vertical resolution has been proved to be key to the estimation of optical turbulence with meso-scale models, the vertical resolutions are both set to 50 m for wind profiles, which reveals a high level of agreement and provides a useful tool for site assessment. The results prove the good character of the high-altitude wind over the Ali region, especially in summer half year, with a yearly median 200 hPa wind speed of 33.6 m s−1 in 2016, and also provide proof of the potential advantage of the Ali Observatory for adaptive optics. Furthermore, we certify that meso-scale models can offer dependable estimation of high-altitude wind over the Tibetan Plateau; the wind simulations provided by the WRF model will be of great benefit for adaptive optics, which also provides the vertical distributions of CN2 and τ0 above astronomical observatories.

Evaluation of a simple analytical model for offshore wind farm wake recovery by in situ data and Weather Research and Forecasting simulations

AbstractThe recovery of offshore wind farm wakes in the German Bight was analyzed by a unique in situ data set, measured on‐board the research aircraft Dornier Do‐128 during the WIPAFF project in 2016 and 2017. These observations were used to validate a simple analytical wake recovery model in five case studies. The observed recovery rates were compared with the results of the mesoscale Weather Research and Forecasting (WRF) model. The airborne data show that the wake recovery can be described by an exponential function as expected by the analytical model and strengthens the hypothesis that the vertical downward moment flux has an important influence on the wake recovery. However, the predicted wake recovery rates (by the analytical model) do not always fully agree with the observations. Although, as a first‐order approximation, the model seems to perform well, further optimization has to be implemented to account for wind park layout, turbine induced turbulence, and horizontal momentum flux. WRF simulations reveal an exponential recovery, although the mesoscale model does not reproduce the correct atmospheric conditions for most of the cases. Therefore, the wake recovery rates estimated by WRF disagree with the measured data in most of the studied cases.

Evaluation of Orographic Cloud Seeding Using a Bin Microphysics Scheme: Three-Dimensional Simulation of Real Cases

AbstractThe University of Pécs and NCAR Bin (UPNB) microphysical scheme was implemented into the mesoscale Weather Research and Forecast (WRF) Model that was used to study the impact of silver iodide (AgI) seeding on precipitation formation in winter orographic clouds. Four different experimental units were chosen from the Wyoming Weather Modification Pilot Project to simulate the seeding effect. The results of the numerical experiments show the following: (i) Comparisons with the soundings, snow gauges, and microwave radiometer data indicate that the three-dimensional simulations with detailed microphysics reasonably represent both the dynamics and the microphysics of real clouds. (ii) The dispersion of the AgI particles from the simulated ground-based seeding was effective because of turbulent mixing. (iii) In the investigated cases (surface temperature is less than 0°C), surface precipitation and precipitation efficiency show low susceptibility to the concentrations of cloud condensation nuclei and natural ice nucleating particles. (iv) If the available liquid water content promotes the enhancement of the number of snowflakes by diffusional growth, the surface precipitation can be increased by more than 5%. A novel parameter relevant to orographic clouds, horizontally integrated liquid water path (LWP), was evaluated to find the relation between seeding efficiency and liquid water content. The impact of seeding is negligible if the horizontal LWP is less than 0.1 mm and is apparent if the horizontal LWP is larger than 1 mm, as based on the cases investigated in this study.

Atmospheric Simulations of Total Column CO2 Mole Fractions from Global to Mesoscale within the Carbon Monitoring System Flux Inversion Framework

Quantifying the uncertainty of inversion-derived CO2 surface fluxes and attributing the uncertainty to errors in either flux or atmospheric transport simulations continue to be challenges in the characterization of surface sources and sinks of carbon dioxide (CO2). Despite recent studies inferring fluxes while using higher-resolution modeling systems, the utility of regional-scale models remains unclear when compared to existing coarse-resolution global systems. Here, we present an off-line coupling of the mesoscale Weather Research and Forecasting (WRF) model to optimized biogenic CO2 fluxes and mole fractions from the global Carbon Monitoring System inversion system (CMS-Flux). The coupling framework consists of methods to constrain the mass of CO2 introduced into WRF, effectively nesting our regional domain covering most of North America (except the northern half of Canada) within the CMS global model. We test the coupling by simulating Greenhouse gases Observing SATellite (GOSAT) column-averaged dry-air mole fractions (XCO2) over North America for 2010. We find mean model-model differences in summer of ∼0.12 ppm, significantly lower than the original coupling scheme (from 0.5 to 1.5 ppm, depending on the boundary). While 85% of the XCO2 values are due to long-range transport from outside our North American domain, most of the model-model differences appear to be due to transport differences in the fraction of the troposphere below 850 hPa. Satellite data from GOSAT and tower and aircraft data are used to show that vertical transport above the Planetary Boundary Layer is responsible for significant model-model differences in the horizontal distribution of column XCO2 across North America.