Mesoscale Numerical Weather Prediction Model Research Articles

Can reanalysis products outperform mesoscale numerical weather prediction models in modeling the wind resource in simple terrain?

Abstract. Mesoscale numerical weather prediction (NWP) models are generally considered more accurate than reanalysis products in characterizing the wind resource at heights of interest for wind energy, given their finer spatial resolution and more comprehensive physics. However, advancements in the latest ERA-5 reanalysis product motivate an assessment on whether ERA-5 can model wind speeds as well as a state-of-the-art NWP model – the Weather Research and Forecasting (WRF) Model. We consider this research question for both simple terrain and offshore applications. Specifically, we compare wind profiles from ERA-5 and the preliminary WRF runs of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED) to those observed by lidars at a site in Oklahoma, United States, and in a United States Atlantic offshore wind energy area. We find that ERA-5 shows a significant negative bias (∼-1ms-1) at both locations, with a larger bias at the land-based site. WTK-LED-predicted wind speed profiles show a limited negative bias (∼-0.5ms-1) offshore and a slight positive bias (∼+0.5ms-1) at the land-based site. On the other hand, we find that ERA-5 outperforms WTK-LED in terms of the centered root-mean-square error (cRMSE) and correlation coefficient, for both the land-based and offshore cases, in all atmospheric stability conditions. We find that WTK-LED's higher cRMSE is caused by its tendency to overpredict the amplitude of the wind speed diurnal cycle. At the land-based site, this is partially caused by wind plant wake effects not being accurately captured by WTK-LED.

A multi-scale model for day-ahead wind speed forecasting: A case study of the Houhoku wind farm, Japan

Accurate day-ahead wind prediction from numerical weather prediction (NWP) models is crucial for integrating wind power into existing grids and wind farm arrangements. Most wind farms in Japan are scattered in mountainous areas susceptible to strong winds and wind patterns that are highly fluctuating and cannot be captured well with mesoscale NWP models. Aiming to provide reliable day-ahead forecasts in rugged zones with complex topography, we introduce a multi-scale model based on coupling the Weather Research Forecasting (WRF) model and the Open Source Field Operation and Manipulation (OpenFOAM) Computational Fluid Dynamics model. The WRF’s predictions are used as initial/boundary conditions for a microscale simulation using OpenFOAM. This coupled model can overcome the shortcomings when employing each component independently. Numerical simulations and validations are carried out at a wind farm in Houhoku, Japan, during summer and winter. The multi-scale model results show a considerable reduction in the bias, mean absolute error, and root mean square error compared to the WRF-alone forecasts. It also has a superior skill against most post-processing algorithms or artificial intelligence techniques to reduce random forecasting errors. In addition, analysis of computational cost demonstrates the promising capability of this multi-scale model being a routine tool.

An Algorithm for Detecting the Chesapeake Bay Breeze from Mesoscale NWP Model Output

Abstract A novel algorithm is developed for detecting and classifying the Chesapeake Bay breeze and similar water-body breezes in output from mesoscale numerical weather prediction (NWP) models. To assess the generality of the new model-based detection algorithm (MBDA), it is tested on simulations from the Weather Research and Forecasting (WRF) Model and on analyses and forecasts from the High-Resolution Rapid Refresh (HRRR) model. The MBDA outperforms three observation-based detection algorithms (OBDAs) when applied to the same model output. In addition, by defining the onshore wind directions on the basis of model land-use data and not on the actual geography of the region of interest, performance of the OBDAs with model output can be improved. Although simulations by the WRF Model were used to develop the new MBDA, it performed best when applied to HRRR analyses. The generality of the MBDA is promising, and additional tuning of its parameters might improve it further.

Generating long-term sub-hourly wind speed time series by coupling mesoscale models with full-scale spectra

Time series-based wind studies are gaining more and more interest and importance within the wind resource community. Benefits of such time series-based analysis being, among others, more accurate production and loss estimations, better representation of ramp-up and extreme events, and improvements in the dimensioning of energy storage systems. While mesoscale Numerical Weather Prediction (NWP) models can simulate long-term winds that capture non-stationary weather patterns, it is known that they are not able to properly resolve sub-scale processes leading to a smoothing effect. Recent studies presented spectral models that describe the full atmospheric spectrum of boundary layer winds which connect the microscale turbulent movements to the large, mesoscale fluctuations. In this work, mesoscale simulations from the Weather Research and Forecasting (WRF) model are coupled with stochastic turbulence simulations using state-of-the-art full-scale boundary-layer spectra to efficiently bridge the spectral gap between the mesoscale and the turbulence fluctuations, without requiring any local measurements nor expensive CFD simulations. The study provides a practical step-by-step approach to generate long wind speed time series at high sampling rate at any desired location and height.

Urban-Scale Computational Fluid Dynamics Simulations with Boundary Conditions from Similarity Theory and a Mesoscale Model

Mesoscale numerical weather prediction models usually provide information regarding environmental parameters near urban areas at a spatial resolution of the order of thousands or hundreds of meters, at best. If detailed information is required at the building scale, an urban-scale model is necessary. Proper definition of the boundary conditions for the urban-scale simulation is very demanding in terms of its compatibility with environmental conditions and numerical modeling. Here, steady-state computational fluid dynamics (CFD) microscale simulations of the wind and thermal environment are performed over an urban area of Kozani, Greece, using both the k-ε and k-ω SST turbulence models. For the boundary conditions, instead of interpolating vertical profiles from the mesoscale solution, which is obtained with the atmospheric pollution model (TAPM), a novel approach is proposed, relying on previously developed analytic expressions, based on the Monin Obuhkov similarity theory, and one-way coupling with minimal information from mesoscale indices (Vy = 10 m, Ty = 100 m, L*). The extra computational cost is negligible compared to direct interpolation from mesoscale data, and the methodology provides design phase flexibility, allowing for the representation of discrete urban-scale atmospheric conditions, as defined by the mesoscale indices. The results compared favorably with the common interpolation practice and with the following measurements obtained for the current study: SODAR for vertical profiles of wind speed and a meteorological temperature profiler for temperature. The significance of including the effects of diverse atmospheric conditions is manifested in the microscale simulations, through significant variations (~30%) in the critical building-related design parameters, such as the surface pressure distributions and local wind patterns.

A nested multi-scale model for assessing urban wind conditions : Comparison of Large Eddy Simulation versus RANS turbulence models when operating at the finest scale of the nesting.

Good understanding of micro-scale urban-wind phenomena is needed for optimizing power generation capabilities of building-integrated wind turbines and for safety of futuristic urban transport involving drones. The current work involves development of a multiscale methodology for obtaining wind fields in an urban landscape. The multi-scale methodology involves coupling three models operating on different scales namely an operational meso-scale numerical weather prediction model HARMONIE, a micro-scale model that captures terrain- induced wind influence and a super-micro scale Computational Fluid Dynamics model using large eddy simulation and RANS model to capture the building-induced wind flow. Here, we present a comparison of the wind velocity predicted by two different turbulence models (LES and RANS) that are operating at the finest scale with the measured experiment data for a realistic case of flow in vicinity of the Oslo university hospital. The reasons behind the observed better prediction of LES model are outlined, and use of such models is advocated to improve accuracy.LES, multi-scale, wind, urban climate, drones, wind energy

Comparison of rainfall microphysics characteristics derived by numerical weather prediction modelling and dual‐frequency precipitation radar

AbstractThe understanding of large‐scale rainfall microphysical characteristics plays a significant role in meteorology, hydrology and natural hazards managements. Traditional instruments for estimating raindrop size distribution (DSD), including disdrometers and ground dual‐polarization radars, are available only in limited areas. However, the development of space‐based radars and mesoscale numerical weather prediction models would allow for DSD estimation on a large scale. This study investigated the performance of the weather research and forecasting (WRF) model and the global precipitation measurement mission (GPM) dual‐frequency precipitation radar for DSD retrieval under different conditions. The DSD parameters (Dm and Nw), rain rate (R), rainfall kinetic energy (KE) and radar reflectivity (Z) were estimated in Chilbolton, United Kingdom, by using long‐term disdrometer observations for validation. The rainfall kinetic energy–rain rate (KE–R) and radar reflectivity–rain rate (Z–R) relationships were explored using a disdrometer, the WRF model and GPM. It was found that the DSD parameter distribution trends of the three approaches are similar although the WRF model has larger Dm and smaller Nw values. In terms of the rainfall microphysical relationship, GPM performs better when both Ku‐ and Ka‐band precipitation radars (KuPR and KaPR) observe precipitation simultaneously (R > 0.5 mm h−1), while the WRF model shows high accuracy in light rain (R < 0.5 mm h−1). The fusion of GPM and WRF model is recommended for the improved understanding of rainfall microphysical characteristics in ungauged areas.

Persistent Increases in Nighttime Heat Stress From Urban Expansion Despite Heat Island Mitigation

AbstractUrban areas generally have higher near‐surface air temperature and lower air humidity than rural areas. Little is known about how heat stress, the combined effect of high air temperature and high humidity on human physiology, will be affected by future urban land expansion. Here we use a mesoscale numerical weather prediction model to examine the effects of urban land expansion from 2000 to 2050 on heat stress (measured as wet‐bulb globe temperature, WBGT) in the urban areas of China, India, and Nigeria, which are projected to account for one‐third of global urban population growth through 2050. Our results show that urban expansion slightly reduces heat stress during the day (∼0.2°C) but substantially intensifies it at night, by ∼1°C on average and by up to 2–3°C in five mega‐urban regions (MURs). These effects exist with or without climate change induced by rising concentrations of greenhouse gases (GHGs). Installing cool roofs—an urban heat island mitigation measures—can reduce the daytime WBGT by 0.5–1°C, partially offsetting the heat stress conditions caused by GHG‐induced climate change. However, even with cool roofs, the nighttime WBGTs are higher by 0.3–0.9°C over the whole countries studied, and by 1–2°C in the MURs under the urban expansion scenario, compared to the situation in which urban areas remain unchanged. These results show that future urban expansion and heat island mitigation can result in potential daytime benefits but also persistent nighttime risks.

New consideration of lateral boundary treatment for meso- and micro-scale nested PBL simulations over complex terrain

A detailed planetary boundary layer (PBL) flow over complex terrain can be simulated by nesting mesoscale numerical weather prediction (NWP) models and microscale computational fluid dynamics (CFD). When fully prescribed lateral boundaries are encountered, the over-specified treatment of CFD or limited-area models may result in ill-posedness and numerical instabilities. Such behaviors are due to inconsistent governing equations, turbulent viscosity models, and underlying surfaces between meso- and micro-scale formulations. In this study, by applying the relaxation treatment in marginal zones of CFD, its wind components are forced toward the externally specified NWP values. The relaxation schemes, deduced from simple advective transport equations, are optimized using genetic algorithms. Their numerical abilities in absorbing wave reflections on shallow-water systems are more efficient than the published approach. Then, key questions concerning the selection of relaxation strength, turbulence model constants, and prognostic variables specified on NWP–CFD nested boundaries are systemically investigated in the neutrally stratified Askervein Hill case. The relaxation scheme from simpler systems also performs effectively in CFD and decreases the adaption distance of wind velocities near over-specified outlet boundaries. A full set approach to obtain well-posed PBL solutions from the microscale domains sustained with inflow boundaries from NWP is proposed and validated.

A Simulated Radar Reflectivity Calculation Method in Numerical Weather Prediction Models

AbstractThe simulated radar reflectivity used by current mesoscale numerical weather prediction models can reflect the grid precipitation but cannot reflect the subgrid precipitation generated by a cumulus parameterization scheme. To solve this problem, this study developed a new simulated radar reflectivity calculation method to obtain the new radar reflectivity corresponding to the subgrid-scale and grid-scale precipitation based on the mesoscale Global/Regional Assimilation and Prediction System (GRAPES) model of the China Meteorological Administration. Based on this new method, two 15-day forecast experiments were carried out for two different time periods (11–25 April 2019 and 1–15 August 2019), and the radar reflectivity products obtained by the new method and previous method were compared. The results show that the radar reflectivity obtained by the new simulated radar reflectivity calculation method gives a clear indication of the subgrid-scale precipitation in the model. Verification results show that the threat scores of the improved experiments are better than those of the control experiments in general and that the reliability of the simulated radar reflectivity for the indication of precipitation is improved. It is concluded that the new simulated radar reflectivity calculation method is effective and significantly improves the reflectivity products. This method has good prospects for providing more information about forecasting precipitation and convective activity in operational models.

Studying the influence of aerosol types on solar radiation prediction

The influence of aerosol types on Solar radiation prediction was studied in this paper. According to Chinese population density, China administration districts were divided into areas with urban aerosol type and rural aerosol type based on Arcgis. The Solar radiation all over China was simulated by the Mesoscale numerical Weather Prediction Model (WRF). The forecasting was compared with the data of “Clouds and the Earth’s Radiant Energy System” released by NASA. The results showed that there was a big improvement when the different aerosol types in different areas were taken into consideration.

The use of satellite and surface observations for initializing clouds in the HARMONIE NWP model

AbstractThe study tests and further develops a previously presented method for adjusting initial clouds in the HARMONIE mesoscale numerical weather prediction (NWP) model. The method uses satellite‐observed cloud properties from the Nowcasting and very short range forecasting Satellite Application Facility (NWCSAF) together with ground‐based synoptic stations (SYNOP) observations, adjusting the humidity profile of the model based on the available cloud information with the overall aim to improve the forecast, in particular as regards clouds and solar radiation. A reference set‐up of the HARMONIE model is used as a baseline against which the cloud initialization experiments are compared. All HARMONIE experiments are run for July 2016, with a model run starting at 06Z each day. The HARMONIE outputs are compared with cloud fraction and solar radiation observations from ground‐based stations, while satellite cloud observations are also used for inspecting the behaviour of the forecasts. The performance of other generally used verification parameters is also studied. The results as regards clouds are encouraging. Symmetrical extremal dependence index (SEDI) skill scores show improvement in initial cloud conditions in 84% and 74% of the cases, when evaluated for cloud‐free (0–1 octas) and cloudy conditions (7–8 octas), respectively. The improvement lasts 2–4 hr into the forecast. Other parameters, however, show somewhat degraded skill as compared with the reference model run, while for solar radiation, the cloud initialization scheme exhibits somewhat ambiguous results. The distribution of solar radiation values is improved for relatively sunny conditions, while at the other end of the distribution, the cloud initialization scheme produces too many cases with relatively strongly attenuated radiation.

Modeling Seasonal Variation in Urban Weather in Sub-Tropical Region of Delhi

Complexity and heterogeneity of urban areas lead to difficulty in urban weather simulations and climate modeling. Diversity and size of urban areas necessitate to downscale global climate models to urban scale (~ hundreds of meters) and to enhance urban parameterization in the models to realistically simulate urban weather conditions. Hence, in this study, a methodology has been developed to generate multi-class urban land use land cover (LULC) by employing Resourcesat-2 LISS IV data. Weather Research and Forecast (WRF) model which is also a mesoscale numerical weather prediction and regional climate model was utilized to downscale the meteorological parameters up to 0.5 km grid resolution. Multi-class urban LULC prepared with improved urban parameters and updated Land Surface Parameters (LSPs) was ingested in model for Delhi to evaluate the model performance in three dominant seasons, i.e., summer, monsoon and winter. Evaluation of model performance with ground observation data revealed that multi-class urban LULC along with updated LSPs provided improved RMSE values of 2.31° C, 1.79 m/s and 0.94 mbar as compared to ingestion of multi-class urban LULC only (RMSE values of 3.42° C, 3.72 m/s and 1.58 mbar) for temperature at 2 m, wind speed and surface pressure, respectively. Temperature is found to be highest in summer season (38.58° C) and lowest in winter season while relative humidity is highest in monsoon season (~ 88%) and lowest in summer season (~ 30%). The study highlights the importance of ingestion of updated LSPs along with updated multi-class urban LULC for enhanced model performance.

Parameter Sensitivity Analysis of the WRF-Hydro Modeling System for Streamflow Simulation: a Case Study in Semi-Humid and Semi-Arid Catchments of Northern China

The WRF model is nowadays the most widely applied mesoscale numerical weather prediction model. Its land-surface hydrological modeling module, WRF-Hydro, which is designed to facilitate the land-surface modeling being coupled with WRF, draws more and more attentions from both the meteorological and the hydrological community. In this study, four sensitive and principle parameters of WRF-Hydro are tested in semi-humid and semi-arid areas of northern China. These parameters include the runoff infiltration parameter (REFKDT), the surface retention depth (RETDEPRT) controlled by a scaling parameter named RETDEPRTFAC, the channel Manning roughness parameter (MannN), and the overland flow roughness parameter (OVROUGHRT) controlled by the scaling parameter OVROUGHRTFAC. WRF-Hydro is designed with a 100-m horizontal grid spacing in two catchments of northern China. The performance of WRF-Hydro with different parameterisation combination schemes is tested for simulating a typical 24-h storm events with uniform rainfall evenness in space and time. The Nash-Sutcliffe efficiency and the root mean squared error of the simulated streamflow, together with the cumulative amount of the simulated rainfall is chosen as the evaluation statistics. It is found that REFKDT and MannN are the most sensitive parameter among the four parameters, and the case is especially evident with unsaturated soil conditions. In order to obtain the most reasonable value range, REFKDT and MannN are further verified by another three 24-h storm events with different spatial and temporal evenness. The range of REFKDT from 2.0 to 3.0, and the MannN scale factor from 1.5 to 1.8 is found to give the best results. The findings of this study can be used as references for calibration of the WRF-Hydro modeling system in semi-humid and semi-arid regions with similar rainfall-runoff response characteristics. The methodologies to design and test the combination schemes of parameterisations can also be regarded as a reference for evaluation of the WRF/WRF-Hydro coupled system for land-surface process modeling.

Evaluating the performance of a novel WUDAPT averaging technique to define urban morphology with mesoscale models

The definition of accurate input datasets, appropriately representing urban morphology characteristics, has been identified as a crucial point for improving the simulation of urban boundary layer (UBL) dynamics by means of mesoscale numerical weather prediction (NWP) models. However, the scarcity of suitable data to adequately describe urban morphology is in many cases a significant obstacle to overcome. For this purpose, the World Urban Database Portal Tool (WUDAPT) framework was developed in order to obtain a standard classification of urban morphology, even in the absence of ad-hoc data for any city.In the present work, a modified WUDAPT method to define urban morphology is proposed and compared with two state-of-the-art methods, i.e. the standard WUDAPT method and a urban morphology parameters obtained from LIDAR data. In particular, here morphological features of Local Climate Zones (LCZs), resulting from a 30-m resolution WUDAPT classification, are interpolated to NWP cells, providing averaged features of the urban morphology. In this way, the method produces a unique value of the different urban morphology parameters for each model cell. This technique is tested by means of simulations with the Weather Research and Forecasting (WRF) model at 500 m resolution for the city of Bologna (Italy), located in the Po Plain. Simulation output is compared with measurements from weather stations. Results show that simulations using the modified WUDAPT method reproduce better atmospheric dynamics with respect to those implementing the standard WUDAPT method, and are comparable with simulations using the urban morphology defined with LIDAR data, considered as reference. In particular, the most relevant improvements are found in areas where LCZs are more heterogeneous and where the boundary between rural and urban areas is not clearly defined. It follows that the use of this method improves the classic WUDAPT method for small and scattered cities with respect to large and clustered urban areas, and can be particularly useful for urban areas where detailed LIDAR data are not available.

Identification and Characterization of Persistent Cold Pool Events from Temperature and Wind Profilers in the Columbia River Basin

AbstractCold pool events occur when deep layers of stable, cold air remain trapped in a valley or basin for multiple days, without mixing out from daytime heating. With large impacts on air quality, freezing events, and especially on wind energy production, they are often poorly forecast by modern mesoscale numerical weather prediction (NWP) models. Understanding the characteristics of cold pools is, therefore, important to provide more accurate forecasts. This study analyzes cold pool characteristics with data collected during the Second Wind Forecast Improvement Project (WFIP2), which took place in the Columbia River basin and Gorge of Oregon and Washington from fall 2015 until spring 2017. A subset of the instrumentation included three microwave radiometer profilers, six radar wind profilers with radio acoustic sounding systems, and seven sodars, which together provided seven sites with collocated vertical profiles of temperature, humidity, wind speed, and wind direction. Using these collocated observations, we developed a set of criteria to determine if a cold pool was present based on stability, wind speed, direction, and temporal continuity, and then developed an automated algorithm based on these criteria to identify all cold pool events over the 18 months of the field project. Characteristics of these events are described, including statistics of the wind speed distributions and profiles, stability conditions, cold pool depths, and descent rates of the cold pool top. The goal of this study is a better understanding of these characteristics and their processes to ultimately lead to improved physical parameterizations in NWP models, and consequently improve forecasts of cold pool events in the study region as well at other locations that experiences similar events.

A Novel Hybrid Model of WRF and Clearness Index-Based Kalman Filter for Day-Ahead Solar Radiation Forecasting

Day-ahead forecasting of solar radiation is essential for grid balancing, real-time unit dispatching, scheduling and trading in the solar energy utilization system. In order to provide reliable forecasts of solar radiation, a novel hybrid model is proposed in this study. The hybrid model consists of two modules: a mesoscale numerical weather prediction model (WRF: Weather Research and Forecasting) and Kalman filter. However, the Kalman filter is less likely to predict sudden changes in the forecasting errors. To address this shortcoming, we develop a new framework to implement a Kalman filter based on the clearness index. The performance of this hybrid model is evaluated using a one-year dataset of solar radiation taken from a photovoltaic plant located at Maizuru, Japan and Qinghai, China, respectively. The numerical results reveal that the proposed hybrid model performs much better in comparison with the WRF-alone forecasts under different sky conditions. In particular, in the case of clear sky conditions, the hybrid model can improve the forecasting accuracy by 95.7% and 90.9% in mean bias error (MBE), and 42.2% and 26.8% in root mean square error (RMSE) for Maizuru and Qinghai sites, respectively.

A New Horizontal Length Scale for a Three-Dimensional Turbulence Parameterization in Mesoscale Atmospheric Modeling over Highly Complex Terrain

AbstractThe correct simulation of the atmospheric boundary layer (ABL) in highly complex terrain is a challenge for mesoscale numerical weather prediction models. An improvement in model performance is possible if horizontal contributions to turbulence kinetic energy (TKE) production, such as horizontal shear production, are implemented in the model’s turbulence parameterization. However, 3D turbulence parameterizations often only have a constant horizontal length scale that depends on the horizontal grid spacing. This is unphysical for mesoscale applications, because such parameterizations were initially developed for much smaller model grid spacings (e.g., for large-eddy simulations). In this study, we develop a new physically based horizontal length scale for the high-resolution mesoscale model COSMO. We analyze days dominated by thermally driven circulations (valley wind days) in the Inn Valley, Austria. Results show that the new horizontal length scale improves TKE simulations in the valley, when horizontal shear processes contribute to the overall TKE budget. Vertical profiles of TKE and transects across the valley indicate that the model simulates the ABL in a more realistic way than standard turbulence schemes, because the new scheme is able to account for terrain inhomogeneities. A model validation with 88 stations in Austria for four case study days indicates no change in the mean surface fields of temperature, relative humidity, and wind speed by the new turbulence parameterization.

Simulating urban climate at sub‐kilometre scale for representing the intra‐urban variability of Zurich, Switzerland

AbstractIn the face of an increasing number of urban climate modelling studies performed at sub‐kilometre resolution, systematic investigations of the performance of high‐resolution urban climate simulations and their dependency on spatial resolution are still very sparse. This study investigates the impact of the scale of representation of the urban area on the urban climate simulation with a multi‐layer urban canopy model (UCM) integrated in a mesoscale numerical weather prediction model for different sub‐kilometre resolutions. The potential of using such a model system for representing the intra‐urban climate variability is explored. The weather and climate model COSMO in Climate Mode (CCLM), coupled with the multi‐layer UCM Double‐Canyon effect parameterization (CCLM‐DCEP), was used at increasing resolution from 1 km to 250 m grid spacing to simulate the pronounced heat wave event of June–July 2015 over the city of Zurich, Switzerland. Air temperature and wind speed measurements from a network of urban stations as well as surface temperatures (STs) from Landsat 7 imagery have been used to evaluate the model results. CCLM‐DCEP showed good performance against observed air temperature, ST, and wind speed in the urban area. The model performance did not change significantly with model resolution and a performance improvement with model resolution was not found. Small‐scale features such as urban parks and large railway areas started to be resolved at sub‐kilometre grid spacing. At the finest model resolution (250 m), a spatial variability in air temperature of up to 2 K and wind speed of up to 1.5 m/s was found within the grid cell of the coarsest resolution grid (1 km). CCLM‐DCEP showed the potential to represent the urban climate at the neighbourhood scale when used at high (sub‐kilometre) resolution, which is needed to support applications such as urban planning, building energy use and urban air quality.

Sensitivity analysis of raindrop size distribution parameterizations in WRF rainfall simulation

Numerical weather models such as weather research and forecasting (WRF) are increasingly used in studies on water resources. However, they have suffered from relatively poor performance in rainfall estimation. Among the various influential factors, a critical parameter in the WRF model rainfall retrieval is raindrop size distribution (DSD), which has not been fully explored. The analysis of sensitivity and uncertainty of the DSD model accuracy is significant for rainfall forecasts based on mesoscale numerical weather prediction (NWP) models. A WRF-disdrometer integrated error assessment framework is developed to analyze the accuracy and sensitivity of DSD parameterizations of gamma distribution in WRF rainfall simulation. This study adopts three different microphysics parameterizations (Morrison, WDM6, and Thompson aerosol-aware) to simulate the DSD of approximately 100 rainfall events in Chilbolton, UK that are categorized into 12 scenarios based on the season, rainfall evenness, and rainfall rate. The Thompson aerosol-aware microphysics scheme shows the best performance among the three. In comparisons of WRF rainfall simulations across different scenarios of evenness and rainfall rate, a higher accuracy is obtained with more even rain and a higher rainfall rate. The sensitivity results of different DSD parameterizations indicate that the sensitivity to the intercept parameter N0 is pronouncedly higher than those to the shape parameter μ and slope parameter λ for all studied schemes. The overall WRF rainfall shows a trend of slight underestimation followed by overestimation as μ increases; further, the rainfall is overestimated when log10N0 or λ decreases and is underestimated when it increases and then remains constant. Comparisons of different scenarios reveal that variations of DSD parameters of even rain have a relatively high impact on rainfall recognizability, and the DSD parameterizations show a higher sensitivity for rainfall with a low rate. Moreover, the sensitivity discrimination is not clear among the rainfall of different seasons. The uncertainty assessment of the WRF rainfall retrieval caused by the shape parameter suggests that a gamma DSD model with a variable shape parameter should be developed according to the evenness, rainfall rate, and microphysics parameterizations by using the WRF model. Some modified algorithms of the WRF gamma DSD model for achieving better accuracy in WRF rainfall retrievals will be explored in future studies with various climatic regimes by adjusting the DSD parameterization based on the assimilation of measured data.