Weather Research And Forecasting Single-Moment Research Articles

Assimilation of Radar Reflectivity via a Full-Hydrometeor Assimilation Scheme Based on the WSM6 Microphysics Scheme in WRF 4D-Var

Abstract The microphysical parameterization scheme employed in four-dimensional variational data assimilation (4D-Var) plays an important role in the assimilation of humidity and cloud-sensitive observations. In this study, a newly developed full-hydrometeor assimilation scheme, integrating warm-rain and cold-cloud processes, has been implemented in the Weather Research and Forecasting (WRF) 4D-Var system. This scheme is based on the WRF single-moment 6-class microphysics scheme (WSM6). Its primary objective is to directly assimilate radar reflectivity observations, with the goal of evaluating its effects on model initialization and subsequent forecasting performance. Four assimilation experiments were conducted to assess the performance of the full-hydrometeor assimilation scheme against the warm-rain assimilation scheme. These experiments also investigated reflectivity assimilation using both indirect and direct methods. We found that the nonlinearity of the radar operator in the two direct reflectivity assimilation experiments requires more iterations for cost function reduction than in the indirect assimilation method. The hydrometeor fields were reasonably analyzed using the full-hydrometeor assimilation scheme, particularly improving the simulation of ice-phase hydrometeors and reflectivity above the melting layer. The assimilation of radar reflectivity led to improvements in short-term (0–3 h) precipitation forecasting with the full-hydrometeor assimilation scheme. Assimilation experiments across multiple case studies reaffirmed that assimilating radar reflectivity observations with the full-hydrometeor assimilation scheme accelerated model spinup and yielded enhancements in 0–3-h total accumulate precipitation forecasts for a range of precipitation thresholds.

The impacts of hail microphysics on maximum potential intensity of idealized tropical cyclone

Maximum potential intensity (MPI), which a TC may reach in certain environment conditions, can be affected by microphysical processes. Latent heat released in the process of TC development plays a significant role in it. However, the impacts of hail added both to single-moment and double-moment microphysics parameterization scheme on the MPI remain unclear. In this study, high-resolution sensitivity experiments are conducted in the Weather Research and Forecasting (WRF) model by using four bulk microphysics schemes belonging to a family, namely, WRF Single-Moment 6-Class (WSM6) scheme, WRF Double-Moment 6-Class (WDM6) scheme, WRF Single-Moment 7-Class (WSM7) scheme, WRF Double-Moment 7-Class (WDM7) scheme. Results show that SM schemes simulate the greater MPI than DM schemes. Adding hail in SM scheme increases the MPI while in DM scheme makes less difference. There is a close relationship between the MPI and the radial peak location and intensity of latent heat. The closer the latent heat peak is to the TC center and the greater the peak intensity is, the greater the MPI can be achieved. Though the presence of hail plays a cooling effect thermally, it may affect the TC structures due to the larger sedimentation speed. WSM7 scheme including hail microphysics simulates the TC with smaller size and eye wall inclination, and thus the latent heating efficiency in the eye wall is higher, which is more conducive to TC intensification. However, the larger content of hail resulting from the accretion of liquid water in WDM7 scheme brings a stronger cooling effect and probably offsets the dynamic advantage.

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

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

Simulating Heavy Rainfall Associated with Tropical Cyclones and Atmospheric Disturbances in Thailand Using the Coupled WRF-ROMS Model—Sensitivity Analysis of Microphysics and Cumulus Parameterization Schemes

Predicting heavy rainfall events associated with Tropical Cyclones (TCs) and atmospheric disturbances in Thailand remains challenging. This study introduces a novel approach to enhance forecasting precision by utilizing the coupled Weather Research and Forecasting (WRF) and Regional Oceanic Model (ROMS), known as WRF-ROMS. We aim to identify the optimal combination of microphysics (MP) and cumulus (CU) parameterization schemes. Three CU schemes, namely, Betts-Miller-Janjic (BMJ), Grell 3D Ensemble (G3), and Kain-Fritsch (KF), along with three MP schemes, namely, Eta (ETA), Purdue Lin (LIN), and WRF Single-moment 3-class (WSM3), are selected for the sensitivity analysis. Seven instances of heavy (35.1–90.0 mm) to violent (>90.1 mm) rainfall in Thailand, occurring in 2020 and associated with tropical storms and atmospheric disturbances, are simulated using all possible combinations of the chosen physics schemes. The simulated rain intensities are compared against observations from the National Hydroinformatics Data Center. Performance was assessed using the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) metrics. While the models performed well for light (0.1–10.0 mm) to moderate (10.1–35.0 mm) rainfall, forecasting heavy rainfall remained challenging. Certain parameter combinations showed promise, like BMJ and KF with LIN microphysics, but challenges persisted. Analyzing density distribution of daily rainfall, we found effective parameterizations for different sub-regions. Our findings emphasize the importance of tailored parameterizations for accurate rainfall prediction in Thailand. This customization can benefit water resource management, flood control, and disaster preparedness. Further research should expand datasets, focusing on significant heavy rainfall events and considering climate factors, for example, the Madden-Julian Oscillation (MJO) for extended-range forecasts, potentially contributing to sub-seasonal and seasonal (S2S) predictions.

Evaluation of meteorological microphysical schemas based on the WRF model for simulation of rainfall in the northeastern region of Iran

Study regionThe Khorasan Razavi Province represents a variety of climatic types in the northeastern region of Iran. It lies between 56° 19′ and 61° 16′ east longitude and 33° 52′–37° 42′ north latitude, which is one of the largest and most densely populated provinces in Iran. Study focusThe aim of this study is using the Weather Research and Forecasting (WRF) model to evaluate and simulate rainfall in the northeastern region of Iran based on the climatic conditions in order to rainfall forecasting, also warn about and control probable floods. New hydrological insights for the regionFollowing the climatic classification of the study area, the six WRF model schemas were employed to implement the model in this area: Purdue-Lin (Lin), WRF Single-Moment class 3, 5, 6 (WSM3, WSM5, WSM6), and WRF Double-Moment class 5, 6 (WDM5, WDM6). The results revealed that the Threat Score (TS) coefficients for the WDM5 and Lin schemas in the desert climate are 1 and 0.97, respectively; the TS coefficient for each one of the Lin, WSM3, and WDM6 have the value of 0.60 in the semi-desert climate. The Lin scheme performs the best in all climates of the study area. The results revealed that the WRF model accurately simulates rainfall in the climatic conditions of the region. Hydrologists and planners will be able to use this meteorological model as an input to hydrological models based on the output of the WRF model, which can provide early flood warnings within the next 24 h based on these findings.

Turbulence analysis on the flight of Etihad airways in Bangka Island using the WRF case study May 4, 2016

<p>Accurate weather forecasts should support the increase in safety of aviation operations in Indonesia. This weather forecast is needed, especially in detecting turbulence, considering that geographically Indonesia has effective solar radiation resulting in convective cloud formation. Convective clouds can trigger turbulence then produce disruption and even accidents on flights. This research uses a case study on the Etihad Airways flight on Bangka Island on May 4, 2016. At the time of the incident, there was turbulence at 39,000 feet altitude, and the aircraft did not enter the cloudy area. The Weather Research and Forecasting (WRF) model is used to simulate the turbulence in this study, which is downscaled up to 3 km with a microphysics parameterization of WRF Single Moment 6 Class (WSM6). The results were then verified using correlation and linear regression for temperature, wind direction, wind speed, and pattern resemblance between cloud fraction and the convective nuclei distribution. The turbulence is analyzed from the south-north and west-east vertical airflow. The turbulence spotted at 06.40 UTC when there is a quite strong updraft which can cause turbulence. The turbulence parameters used, such as the eddy dissipation rate (EDR) parameter, which has a value of 0.05 , Richardson number with a value of less than 0.25, and turbulence index (TI 1) with a maximum value of 4 x 10<sup>-7</sup> s<sup>-2</sup> found that turbulence was in a strong category. The turbulence that occurs in this study is identified as near cloud turbulence (NCT) event due to cloud formation observed in the west of the turbulence and intense updraft activity at the location of turbulence.</p>

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.

Simulation of Severe Thunderstorms in Conjugation of Westerly and Easterly on 31 March 2019 in Bangladesh

The diagnostic and prognostic studies of thunderstorms/squalls are very important to save live and loss of properties. The present study aims at diagnose the different tropospheric parameters, instability and synoptic conditions associated the severe thunderstorms with squalls, which occurred at different places in Bangladesh on 31 March 2019. For prognostic purposes, the severe thunderstorms occurred on 31 March 2019 have been numerically simulated. In this regard, the Weather Research and Forecasting (WRF) model is used to predict different atmospheric conditions associated with the severe storms. The study domain is selected for 9 km horizontal resolution, which almost covers the south Asian region. Numerical experiments have been conducted with the combination of WRF single-moment 6 class (WSM6) microphysics scheme with Yonsei University (YSU) PBL scheme in simulation of the squall events. Model simulated results are compared with the available observations. The observed values of CAPE at Kolkata both at 0000 and 1200 UTC were 2680.4 and 3039.9 J kg-1 respectively on 31 March 2019 and are found to be comparable with the simulated values. The area averaged actual rainfall for 24 hrs is found is 22.4 mm, which complies with the simulated rainfall of 20-25 mm for 24 hrs. Journal of Engineering Science 12(3), 2021, 29-43

Atmospheric–hydrological modeling for Beijing's sub-center based on WRF and SWMM

The combination of intense climate change and rapid urbanization means that urban storms and floods capable of causing substantial economic losses are becoming one of the principal disasters that threaten sustainable urban development. Therefore, atmospheric–hydrological modeling, which could effectively extend the forecast period and support related research in regions lacking precipitation data, has received increasing attention in relation to urban hydrology. In this study, considering more comprehensive optimization of the physical parameterization schemes of the Weather Research and Forecasting (WRF) model, an atmospheric–hydrological modeling system based on the WRF model and Storm Water Management Model (SWMM) was proposed and applied to Beijing's sub-center. Results showed that the cumulus parameterization scheme has greatest impact on simulation of heavy rainfall. Overall, the combination of the WRF Single-Moment 5-class scheme, Grell–Freitas scheme, Yonsei University scheme, the newer version of the Rapid Radiative Transfer Model, Revised MM5 Monin–Obukhov scheme, Noah land surface model, and the Urban canopy model produced the best performance. Application of the proposed system demonstrated the feasibility of simulating urban storms and floods via WRF–SWMM system, which can represent the entire process from the rainfall produced by atmospheric movements to the formation of the urban floods.

Simulation of Intensity, Track and Landfall of Tropical Cyclonic Storm Roanu over the Bay of Bengal Using WRF-ARW Model

Almost every year, tropical cyclone forms over the Bay of Bengal in pre-monsoon and post-monsoon which strikes Bangladesh coast and the east coast of India. As the full thermodynamic features of a cyclone is not solved yet, an attempt has been made to simulate the track and landfall of cyclonic disturbances over the Bay of Bengal by using Weather Research and Forecasting (WRF) model. The WRF model (version 3.8) was run in a single domain of 20 km horizontal resolution. The model was run using WRF Single-Moment 3- class microphysics scheme, Kain- Fritsch (new Eta) cumulus physics scheme, Yonsei University planetary boundary layer scheme, revised MM5 surface layer physics scheme, Rapid Radiative Transfer Model (RRTM) for long-wave and Dudhia scheme for short-wave scheme. The model was run for 24-h, 48-h, 72-h and 96-h using the National Centre for Environmental Prediction (NCEP) high-resolution Global Final (FNL) Analysis 6-hourly data using initial and lateral boundary conditions. The model simulated landfall position errors are found 53 km, 129 km, 119km and 23 km and time errors are found 02 E, 06 D, 02 E and 00 for 96-h, 72-h, 48-h and 24-h model run respectively (E indicates Earlier and D indicates Delay). The minimum time and position error is found in 24-hrs simulation. The spatial distribution is captured by the model is almost appropriate but the computational station rainfall is found less than that of observed rainfall. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 10(1), 2021, P 33-45

Influence of cloud microphysical parameterization on the characteristics of monsoon depressions over the Indian region

AbstractIndian summer monsoon (ISM) low‐pressure systems are considered as the lifeline for seasonal monsoon rainfall over the Indian region. However, the current models have limitations in predicting their characteristics and rainfall. This study assesses the influence of five cloud microphysical parameterization Schemes (MP) on simulations of 14 monsoon deep depressions (DD) using the weather research and forecasting (WRF) model. The simulations are carried out with a lead time up to 96 hr in a nested domain resolution of 27, 9, and 3 km. The five cloud microphysical parameterizations, that is, WRF Double moment (WDM6), WRF single moment (WSM6), Milbrandt (MIL), Thompson (THOM), and Aerosol Aware Thompson (AAT), are considered in this study, leading to a total of 70 simulations. The results are validated through composite analysis at a radial distance of 300 km from the respective storm centres. The choice of MP significantly impacts the key characteristics of the monsoon DDs such as rainfall, wind, temperature, moisture, humidity, hydrometeors, and associated convective processes. In terms of rainfall, it is found that WDM6 (AAT) has the best (worst) performance with a lead time up to day‐4. Besides, WDM6 has simulated the best result in terms of temperature and specific humidity. Examining the hydrometeors distribution around the storm intense convective region (i.e., 300 km), it is noted that frozen hydrometeors (i.e., ice, snow, and graupel) are mainly modulating the rainfall. There is a general tendency to overestimate snow and graupel among MP except WDM6. Further, ice hydrometeors are well represented in WDM6 compared with others leading to better rainfall forecast. The moisture flux convergence and absolute vorticity are two major mechanisms determining the convection within the storm's core zone, and WDM6 stands out the best among these schemes. The findings of this study are relevant and have direct consequences to the operational applications.

Weather Reasearch and Forecasting Model Simulation of a Snowfall Event in Southern Brazil

This study evaluates the ability of the Weather Research and Forecasting (WRF) Model to simulate a snowfall event in the southern Brazil. The event in August 2013 was considered one of the most intense in recent years in the region with the highest topographic elevations between the states of Rio Grande do Sul (RS) and Santa Catarina (SC). The Snowfall in the mountain region of RS and SC was associated with the configuration involving a polar anticyclone and the intensification of an extratropical cyclone over the Atlantic Ocean. The WRF simulation results demonstrated the model's viability to predict the event, but without the magnitude representation of the phenomenon. The simulations represented few snow accumulations, with a maximum value of 1.2 mm, and occurred in only two meteorological monitoring stations among the seven evaluated, differently from what was reported in the media. These results may be linked to the simulation of the warmer air temperature near to the surface in relation to the observations, and consequently to the increase in the simulation of precipitation in liquid form. These results were attributed to the choice of WRF Single–moment 6–class (WSM6) microphysics and in the Noah Land Surface Model scheme. Despite these limitations, WRF has proved to be an important tool for predicting the spatial and temporal distribution of snowfall and precipitation in the higher regions of southern Brazil.

To develop a progressive multimetric configuration optimisation method for WRF simulations of extreme rainfall events over Egypt

The Weather Research and Forecasting (WRF) model can help to improve our understanding of analysing and forecasting hydrometeorological disasters. Especially for some regions like the Nile Delta, which faces growing climate hazards but has inadequate in situ rainfall observations. However, identifying an optimal configuration to run the WRF model is often a challenge. In this study, the WRF model was used to simulate extreme rainfall events at high spatial and temporal resolutions centered around Alexandria, in northern Egypt. In particular, a progressive multimetric configuration optimisation (PMCO) method is proposed to identify the possible optimal configurations of WRF in the aspect of domain size, numbers of vertical levels, nesting ratio, spin-up times, and physical parameterization schemes (microphysics, planetary boundary layer, and cumulus), based on 48 specifically designed experiments. The simulation performances are quantified and sorted by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). All WRF simulations use the ERA5 reanalysis dataset as boundary conditions and the WRF results are verified against the Integrated Multi-satellitE Retrievals for GPM (IMERG). The results show that the rainfall distribution and magnitude are most sensitive to the spin-up time and physical parameterization schemes. It is also observed that the improvement of WRF's reproducibility of rainfall intensity is usually accompanied by a decrease in the reproducibility of rainfall distribution. The best model configuration for the study area comprises of three-level nesting (D01 80x80; D02 112x112; D03 88X88), 58 vertical levels, 1:3:3 downscaling ratio, 48h spin-up time, WRF Single-Moment 6-class microphysics scheme, Mellor-Yamada-Janjic planetary boundary layer scheme, and Grell-Freitas cumulus. The stability of this configuration is also verified with the other three extreme rainfall events over Egypt. The results show that there exists a common WRF configuration set in Egypt that produces the relatively good simulations for extreme rainfall events.

Evaluating the performances of cloud microphysical parameterizations in WRF for the heavy rainfall event of Kerala (2018)

This study evaluates the performances of four different cloud microphysical parameterization (CMP) schemes of the Weather Research and Forecasting (WRF) model at 3 km horizontal resolution (lead time up to 96 h) for the Heavy Rainfall Event (HRE) over Kerala in August 2018. The goal is to identify the major drivers for rain making mechanism and evaluate the ability of CMPs to accurately simulate the event with special emphasis on rainfall. It is found that the choice of CMP has a considerable impact on the rainfall forecast characteristics and associated convection. Results are validated against the India Meteorological Department (IMD) station data and Global Precipitation Measurement (GPM) observations and found that, among the four CMP schemes, viz., Milbrandt (MIL), Thompson Aerosol Aware (TAA), WRF double-moment 6-class scheme (WDM6) and WRF single-moment 6-class scheme (WSM6); WDM6 is the best performing scheme in terms of rainfall. It is noted that mixed phase processes are dominant in this scenario and the inability (ability) of MIL and TAA (WDM6 and WSM6) to predict the frozen hydrometeors, and thus simulating the cold rain processes realistically led to large (small) errors in the rainfall forecast. The moisture convergence was prominent in the foothills of the Western Ghats and highly influential in facilitating orography driven lifting of moisture. The moisture budget results suggest that horizontal moisture flux convergence (MFC) was the major driver of convection with WDM6 predicting the peaks of MFC most consistently with the observed Tropical Rainfall Measuring Mission (TRMM) rainfall product. Additionally, from Contiguous Rain Area analysis it is also found that the WDM6 has the least volumetric error. This is to highlight that hydrometeor distributions are strongly modulated by MFC, which further impacts the latent heat generation and rainfall over the region. Overall results infer the substantial influence of CMPs on the forecast of the Heavy Rainfall Event. The findings of this study will be highly useful for operational forecasting agencies and disaster management authorities for mitigation of damages caused by this kind of severe HREs in the future.

Comparison of Urban Heat Island Intensity Estimation Methods Using Urbanized WRF in Berlin, Germany

In this study, we present a meso-scale simulation of the urban microclimate in Berlin, Germany, using the Weather Research and Forecasting (WRF) numerical weather prediction platform. The objective of the study is to derive an accurate estimate of the near-surface urban heat island (UHI) intensity. The simulation is conducted over a two-week summer period. We compare different physical schemes, different urban canopy schemes and different methods for estimating the UHI intensity. The urban fraction of each urban category is derived using the Copernicus Impervious Density data and the Corine Land Cover data. High-resolution City Geography Markup Language (CityGML) data is used to estimate the building height densities required by the multi-layer urban canopy model (UCM). Within the single-layer UCM, we implement an anthropogenic heat profile based on the large scale urban consumption of energy (LUCY) model. The optimal model configuration combines the WRF Single Moment Five-Class (WSM5) microphysics scheme, the Bougeault–Lacarrère planetary boundary layer scheme, the eta similarity (Mellor–Yamada–Janjic) surface layer scheme, the Noah Multi-Parameterization land surface model, the Dudhia and Rapid Radiative Transfer Model (RRTM) radiation schemes, and the multi-layer UCM (including the building energy model). Our simulated UHI intensity results agree well with measurements with a root mean squared error of 0.86K and a mean bias error of 0.20K. After model validation, we proceed to compare several UHI intensity calculation methods, including the ‘ring rural reference’ (RRR) method and the ‘virtual rural reference’ (VRR) method. The VRR mthod is also known as the ‘urban increment’ method. We suggest and argument that the VRR approach is superior.

Satellite Observation for Evaluating Cloud Properties of the Microphysical Schemes in Weather Research and Forecasting Simulation: A Case Study of the Mei-Yu Front Precipitation System

Radiative transfer model can be used to convert the geophysical variables (e.g., atmospheric thermodynamic state) to the radiation field. In this study, the Community Radiative Transfer Model (CRTM) is used to connect regional Weather Research and Forecasting (WRF) model outputs and satellite observations. A heavy rainfall event caused by the Mei-Yu front on the June 1, 2017, in the vicinity of Taiwan, was chosen as a case study. The simulated cloud performance of WRF with four microphysics schemes (i.e., Goddard (GCE), WRF single-moment 6 class (WSM), WRF double-moment 6 class (WDM), and Morrison (MOR) schemes) was investigated objectively using multichannel observed satellite radiances from a Japanese geostationary satellite Himawari-8. The results over the East Asia domain (9 km) illustrate that all four microphysics schemes overestimate cloudy pixels, in particular, the high cloud of simulation with MOR when comparing with satellite data. Sensitivity tests reveal that the excess condensation of ice at ≥14 km with MOR might be associated with the overestimated high cloud cover. However, GCE displayed an improved performance on water vapor channel in clear skies. When focusing on Taiwan using a higher (3 km) model resolution, each scheme displayed a decent performance on cloudy pixels. In the grid-by-grid skill score analysis, the distribution of high clouds was the most accurate among the three cloud types. The results also suggested that all schemes required a longer simulation time to describe the low cloud horizontal extend.

Evaluation of Simulated Winter Precipitation Using WRF-ARW during the ICE-POP 2018 Field Campaign

AbstractThis study evaluates the performance of several cloud microphysics parameterizations in simulating surface precipitation for two snowstorm cases during the International Collaborative Experiment held at the PyeongChang 2018 Olympics and Winter Paralympic Games (ICE-POP 2018) field campaign. We compared four different schemes in the Weather Research and Forecasting (WRF) Model, namely the double-moment 6-class (WDM6), the WRF single-moment 6-class (WSM6), and Thompson and Morrison parameterizations. Both WSM6 and WDM6 overestimated the precipitation amount for the shallow precipitation system because of the substantial amount of cloud ice, mostly generated by the deposition process. The simulated precipitation amount and distribution for the deep precipitation system showed no noticeable differences in the different cloud microphysics parameterizations. However, the simulated hydrometeor type at the surface using WSM6 and WDM6 showed good agreement with observations for all cases. The accuracy of the mean mass-weighted terminal velocity of cloud ice applied in WSM6 and WDM6 is ±20%. The number concentration of cloud ice and the ice microphysics processes are newly retrieved with 1.2 times increased . For the shallow snowstorm, the precipitation amount was reduced by approximately 8% because of the inefficient deposition and its effects on the subsequent ice microphysical processes, such as the accretion of cloud ice by snow and the conversion from cloud ice to snow.

Performance of the WRF model in simulating intense precipitation events over the Hanjiang River Basin, China – A multi-physics ensemble approach

Intense precipitation events frequently occur in the Hanjiang River Basin (HRB) in China, causing major economic losses and affecting human security. Accurate precipitation simulations are sensitive to the choice of physical schemes in numerical weather prediction models. Therefore, in this study the performance of the different combinations of physical schemes in the Weather Research and Forecasting (WRF) model was investigated for the HRB. Five intense precipitation events in summer during the period 2008–2011 were selected. For simulating each intense precipitation event, an 18-member multi-physics ensemble was designed by using three cumulus (CU) schemes and four microphysics (MP) schemes. The temporal dynamics and spatial patterns of the simulated precipitation were evaluated systematically by a comparison to the China precipitation analysis reference data. A quantitative analysis using the Euclidean distance metric to synthesize the evaluation results was performed. The performed uncertainty analysis suggests that the skill of precipitation simulation is more sensitive to the choice of the CU scheme and less sensitive to the choice of the MP scheme. Our analysis shows that a suitable WRF physical configuration consists of Kain-Fritsch and WRF Single-Moment 6-class, which reaches a performance similar to that of the ensemble mean method. To our knowledge, this multi-physics ensemble study is the first effort to comprehensively evaluate the applicability of the WRF model to intense precipitation simulations over the HRB. Therefore, this study potentially contributes to the improvement of climate simulations for central China.

Sensitivity of physical parameterization schemes in WRF model for dynamic downscaling of climatic variables over the MRB

Abstract The Weather Research and Forecasting (WRF) model was tested through 18 different combinations of physics parameters to simulate the regional climate over the Mackenzie River Basin (MRB). The objective was to investigate the response to the physics parameters for dynamic downscaling of climatic variables. The rainfall, temperature, albedo, and surface pressure from the 18 different WRF setups were compared with the reference data and were found sensitive to land surface physics and microphysics and to the radiation physics. The combination of Noah Land Surface Physics with the WRF Single-moment 6-class microphysics and CAM shortwave and longwave schemes produced comparable results for summer 2009. This WRF setup was further tested for summers 1979–1991 and it was found that WRF could simulate air temperature more accurately than the rainfall, since the rainfall over the mountainous regions was over-simulated. Then the selected combinations of WRF parameterizations were used to downscale the CanESM2 historical temperature and rainfall for summers 1979–2005, which showed good agreement with the reference data. The suggested WRF parameters from this study could be utilized for regional climate modeling of MRB.

Effects of single- and double-moment microphysics schemes on the intensity of super typhoon Sarika (2016)

Abstract The Weather Research and Forecasting (WRF) Double Moment (DM) 6-class (WDM6)/WRF DM 5-class (WDM5) and WRF Single Moment (SM) 6-class (WSM6)/ WRF SM 5-class (WSM5) microphysics schemes were selected to conduct four simulation experiments for super tropical cyclone (TC) Sarika (2016) using the WRF model. Both the SM and DM schemes had little effect on TC track, but there were significantly difference in TC intensity simulations. TC simulated by the SM schemes are stronger than that by the DM ones and are also closer to the observed. The SM and DM schemes affect TC intensity through its dynamic and thermal structures. TC simulated by the SM schemes has a smaller eye, a smaller radius of maximum wind (RMW), stronger tangential wind, closer latent heating area to TC center, higher positive temperature deviation in about TC center, and thus stronger TC. The latent heating rate of condensation in the SM schemes is higher than those of DM ones. The higher latent heating areas of condensation in the SM schemes are in the inner side of the eyewall and are significantly closer to the TC center and strengthen TC. On the contrary, the higher latent heating areas of condensation in the DM schemes exist within in the outer side of the eyewall and TC weakened. For the DM schemes, since equations predicting cloud water and rain water number concentrations are added, the production rates for autoconversion and accretion are also changed. The DM schemes have distinctly different cloud water and rain water distribution characteristics when compared with the SM ones.