Weather Research And Forecasting Single-Moment 6-class Scheme Research Articles

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.

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.

Applying the WRF Double-Moment Six-Class Microphysics Scheme in the GRAPES_Meso Model: A Case Study

This study incorporated the Weather Research and Forecasting (WRF) model double-moment 6-class (WDM6) microphysics scheme into the mesoscale version of the Global/Regional Assimilation and PrEdiction System (GRAPES_Meso). A rainfall event that occurred during 3–5 June 2015 around Beijing was simulated by using the WDM6, the WRF single-moment 6-class scheme (WSM6), and the NCEP 5-class scheme, respectively. The results show that both the distribution and magnitude of the rainfall simulated with WDM6 were more consistent with the observation. Compared with WDM6, WSM6 simulated larger cloud liquid water content, which provided more water vapor for graupel growth, leading to increased precipitation in the cold-rain processes. For areas with the warm-rain processes, the sensitivity experiments using WDM6 showed that an increase in cloud condensation nuclei (CCN) number concentration led to enhanced CCN activation ratio and larger cloud droplet number concentration (Nc) but decreased cloud droplet effective diameter. The formation of more small-size cloud droplets resulted in a decrease in raindrop number concentration (Nr), inhibiting the warm-rain processes, thus gradually decreasing the amount of precipitation. For areas mainly with the cold-rain processes, the overall amount of precipitation increased; however, it gradually decreased when the CCN number concentration reached a certain magnitude. Hence, the effect of CCN number concentration on precipitation exhibits significant differences in different rainfall areas of the same precipitation event.

Analysis of Extreme Rainfall Event with Different Microphysics and Parameterization Schemes in WRF Model

In the present study, the imitation of heavy rainfall event which occurred over Jharkhand during 18 August 2016 was taken as a case study. Weather Research and Forecasting (WRF) model has been utilized for this study. National Centers for Environmental Prediction (NCEP) analysis data is compared with GSMaP data with different combination of physical parameterization scheme like microphysics (MP) and cumulus parameterization (CP). In the present study, three MP schemes: Kessler scheme, Lin et al. scheme and WRF Single-moment 6-class scheme with combination of three CP schemes: Betts-Miller-Janjic scheme, Multi-scale Kain-Fritsch scheme and New simplified Arakawa-Schubert scheme have been used. The model predicted humidity, temperature and precipitation were compared with the GSMaP product. The model nicely depicted the cloud pattern and recognized the rain event spatially. The obtained result shows that the model overestimates the precipitation for all the schemes.

WRF 모형의 집중호우사상 시공간적 패턴 재현 능력 평가

본 연구는 대기 프로세스의 비정상성의 효과를 반영할 수 있는 물리적 기반의 지역 기후 모델을 이용하는 새로운 방법을 제시하기 위한 기반연구이다. 이를 위해 WRF를 이용하여 과거 호우사상을 재현하였고 모의능력을 평가하였다. 모형의 초기조건 및 경계조건을 설정하기 위해서 NCEP의 재분석 자료와 해수면 온도 자료, 지상관측 자료가 사용되었다. 미세물리 과정과 적운 모수화 과정을 변경해 가며 총 9개의 수치 실험이 실시되었다. 수치모의 결과는 강수량이 초기조건 및 경계조건과 적운 모수화에 영향을 받는 것으로 나타났다. 물리 옵션 중에서 WRF single-Moment 6-class 방안과 New Kain-Fritsch 방안은 강우의 공간적인 분포는 합리적으로 재현하였지만, 총 발생강수량이 과대 산정되었으며, 시간적인 분포는 재현하지 못했다. 이는 방재분야에 WRF를 적용하기 위해서는 새로운 방법론이 필요하다는 것을 나타낸다. This paper presented a base study for new methodology that uses a physical based regional climate model that can also incorporate the effect of the nonstationarity of atmospheric process. To this end, we examined the capability of the WRF in reproducing the selected historical heavy rainfall events over Korea. WRF used the NCEP FNL reanalysis data, RTG Sea Surface Temperature data and ADP Global Surface Observational Weather data for its initial and boundary conditions. Nine experiments are carried out by changing the physics option (microphysics processes and cumulus parameterization process). Numerical simulation results showed a big variation in rainfall according to initial and boundary condition and cumulus parameterization. In physics option, WRF single-Moment 6-class scheme and New Kain-Fritsch scheme were reasonably reproduced in spatial distribution of rainfall, but the amount was overestimated and dose not reproduce temporal distribution of rainfall. This indicates that a new methodology necessary to WRF for hazard mitigation.

Parallel GPU architecture framework for the WRF Single Moment 6-class microphysics scheme

An Earth-observing remote sensing instrument is used to collect information about the physical environment within its instantaneous-field-of-view and is often placed aboard a suborbital or satellite platform for maximal spatial coverage. Remote sensing inversion techniques can extract valuable meteorological parameters that are subsequently passed through weather models for research and forecasting. One of the several microphysics packages for clouds and precipitation in weather models is WRF Single Moment 6-class (WSM6) scheme, and it is now widely used. With the advancement in Graphics Processing Units (GPUs), implementation of a fast and parallel WSM6 scheme is achievable. This paper describes a massively parallel GPU design of the WSM6 scheme. The performance is compared to a CPU implementation running on Intel Xeon E5-2603 at 1.8GHz. Our implementation shows a speedup of 216× using a single NVIDIA K40 GPU as compared to its CPU counterpart running on one CPU core, whereas the speedup for one CPU socket (4 cores) with respect to one CPU core is only 3.7×.

Impact of precipitating ice on the simulation of a heavy rainfall event with advanced research WRF using two bulk microphysical schemes

In this study, the Weather Research and Forecasting (WRF) model version 3.2 is used to examine the impact of precipitating ice and especially snow-graupel partitioning in the simulation of a heavy rainfall event over Chalkidiki peninsula in Northern Greece. This major precipitation event, associated with a case of cyclogenesis over the Aegean Sea, occurred on the 8th of October 2006 causing severe flooding and damage. Two widely used microphysical parameterizations, the Purdue Lin (PLIN) and WRF Single-Moment 6-class scheme (WSM6) are compared with available raingauge measurements over the complex topography of Chalkidiki. To further investigate the importance of snow and graupel relative mass content and the treatment of precipitating ice sedimentation velocity, two older versions of the WSM6 scheme were compiled and run with the current model. The verification results indicate that all simulations were found to match raingauge data more closely over the eastern mountainous Chalkidiki peninsula where maximum accumulations were observed. In other stations all schemes overestimate 24h accumulated rainfall except a station situated at the western part of the peninsula, where none of the simulations was able to reproduce observed rainfall. Graupel dominance in PLIN generates rapid precipitation fallout at the point of maximum predicted 24h accumulation. Similar behavior is shown in WSM6 from WRF version 2, but with significant less rainfall. Increasing snow amounts aloft, due to the unified treatment of precipitating ice in WSM6 from WRF version 3, modifies rain dynamics which decrease rainfall rates, but increases 24h accumulations. A sensitivity experiment where PLIN is used with snow accretion by graupel turned off, indicated that this process seems to be the most important factor controlling the differences in surface precipitation between PLIN and WSM6 from WRF version 3, determining the spatial and temporal distribution of this heavy precipitation event. The results also revealed that snow overestimation can lead to high rainfall accumulations, even though rain is more evenly distributed over the 24h period, deteriorating precipitation forecast.

Development of an Effective Double-Moment Cloud Microphysics Scheme with Prognostic Cloud Condensation Nuclei (CCN) for Weather and Climate Models

Abstract A new double-moment bulk cloud microphysics scheme, the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) Microphysics scheme, which is based on the WRF Single-Moment 6-class (WSM6) Microphysics scheme, has been developed. In addition to the prediction for the mixing ratios of six water species (water vapor, cloud droplets, cloud ice, snow, rain, and graupel) in the WSM6 scheme, the number concentrations for cloud and rainwater are also predicted in the WDM6 scheme, together with a prognostic variable of cloud condensation nuclei (CCN) number concentration. The new scheme was evaluated on an idealized 2D thunderstorm test bed. Compared to the simulations from the WSM6 scheme, there are greater differences in the droplet concentration between the convective core and stratiform region in WDM6. The reduction of light precipitation and the increase of moderate precipitation accompanying a marked radar bright band near the freezing level from the WDM6 simulation tend to alleviate existing systematic biases in the case of the WSM6 scheme. The strength of this new microphysics scheme is its ability to allow flexibility in variable raindrop size distribution by predicting the number concentrations of clouds and rain, coupled with the explicit CCN distribution, at a reasonable computational cost.