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The Four-Dimensional Variational (4DVar) data assimilation system of the Advanced Research Weather Research and Forecasting (WRF) model, developed by the international community dedicated to data assimilation research and operations, is customised to simulate the super cyclonic storm \"Amphan\" formed over the Bay of Bengal during May 16, 2020, to May 21, 2020. Five simulations are conducted using five different microphysics schemes namely, Kessler, Lin et al., WRF Single Moment 3-class (WSM3), WSM5, and WSM6 at a horizontal resolution of 18 km, keeping the Kain–Fritsch cumulus and the Yonsei University planetary boundary-layer scheme fixed. The model simulated features of \"Amphan\" are compared with observational data from the India Meteorological Department (IMD), the Global Precipitation Measurement mission (GPM), and the 5th generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA-5) over the specified region. Among all the schemes, Lin et al. scheme shows track remarkably close to the observed track. Lin et al. (WSM5) scheme shows least along track (AT) error of 7.47 km at 24-h forecast length. Lin et al. shows least AT error of 5.8 km (28.12 km) for 48-h (72-h) forecast length. All schemes except Kessler and WSM3 show the spatial distribution of maximum sustained wind (MSW) surrounding the eye of the cyclone which is similar with ERA5 data. All the schemes underestimate the 10m-MSW during the entire life of the storm. However, the Kessler scheme simulates higher 10m-MSW during 00 UTC 18 May to 12 UTC 19 May in comparison to other schemes and further the simulated MSW matches with IMD observation up to 06 UTC 20 May. The Kessler scheme overestimates the MSLP for the intensity level ESCS-VSCS-SCS-CS valid 09 UTC on 19 May to 00 UTC on 21 May and other schemes underestimate during this period. The analysis carried out with the Method for Object-Based Diagnostic Evaluation tool reveals that the Lin et al. (WSM6) scheme indicates enhanced forecast proficiency for accumulation valid 00 UTC 20 May (21 May) 2020. The analysis of vertically integrated moisture transport (VIMT) and vertically integrated moisture divergence (VIMD) suggests that the greater moisture transport is quite evident in Lin et al. scheme during the SuCS intensity level. Kessler scheme is efficient in simulating warm-rain process and high intensity storm.

China has highly emphasized the research and operational application of numerical weather prediction. This paper determines the objective function parameters, such as CAPE and SRH, to apply an ensemble numerical prediction model in weather forecasting. Preprocessing and evaluating rainfall data is necessary to construct the WRF-ARW numerical weather prediction model. The WRF-ARW model is applied to simulate the weather forecasts in Henan Province, and the difficulties and challenges faced in the efficient implementation of the parameterized scheme are outlined. The WRFARW model’s prediction errors for the maximum rainfall and total rainfall in Henan Province range from 1.78%-13.51% and 0.16%-3.78%, respectively, which are significantly less than 15%, and the model is more predictive than the others. The raw data test set’s credibility ranges from 0.957 to 0.997, which is close to 1, indicating that the raw data collected in this paper are highly credible. The WRF-ARW model’s qualification rates for forecasting maximum rainfall and total rainfall are 86.7% and 93.3%, respectively, and its overall accuracy is grade B and grade A, respectively. The pass rates for the peak occurrence time of maximum rainfall and total rainfall were 93.3% and 86.7%, respectively, and the overall prediction accuracy was Grade A and Grade B, respectively. The WRF-ARW model is effective in weather forecasting throughout Henan Province. In summary, the WRF-ARW model is very effective in improving the efficiency of ensemble numerical weather prediction and parameterization schemes in Henan Province.

The convection and planetary boundary layer (PBL) processes play significant role in the genesis and intensification of tropical cyclones (TCs). Several convection and PBL parameterization schemes incorporate these processes in the numerical weather prediction models. Therefore, a systematic intercomparison of performance of parameterization schemes is essential to customize a model. In this context, six combinations of physical parameterization schemes (2 PBL Schemes, YSU and MYJ, and 3 convection schemes, KF, BM, and GD) of WRF-ARW model are employed to obtain the optimum combination for the prediction of TCs over North Indian Ocean. Five cyclones are studied for sensitivity experiments and the out-coming combination is tested on real-time prediction of TCs during 2008. The tracks are also compared with those provided by the operational centers like NCEP, ECMWF, UKMO, NCMRWF, and IMD. It is found that the combination of YSU PBL scheme with KF convection scheme (YKF) provides a better prediction of intensity, track, and rainfall consistently. The average RMSE of intensity (13 hPa in CSLP and 11 m s −1 in 10-m wind), mean track, and landfall errors is found to be least with YKF combination. The equitable threat score (ETS) of YKF combination is more than 0.2 for the prediction of 24-h accumulated rainfall up to 125 mm. The vertical structural characteristics of cyclone inner core also recommend the YKF combination for Indian seas cyclones. In the real-time prediction of 2008 TCs, the 72-, 48-, and 24-h mean track errors are 172, 129, and 155 km and the mean landfall errors are 125, 73, and 66 km, respectively. Compared with the track of leading operational agencies, the WRF model is competing in 24 h (116 km error) and 72 h (166 km) but superior in 48-h (119 km) track forecast.

In this work the very severe cyclonic storm Thane which formed over the Bay of Bengal during 25–31 December 2011 and struck the East coast of India was simulated using the Weather Research and Forecasting (WRF)-Advanced Research WRF (WRF-ARW) mesoscale model. Normally, very severe cyclones rarely form in this late season. The moisture transport, intensity, track and structure of the cyclone is analyzed through vertically integrated moisture flux convergence and planetary boundary layer physics of the Yonsei University (YSU), Mellor–Yamada–Janjic (MYJ) and Asymmetrical Convective Model version 2 (ACM2) schemes. Cyclonic circulation and moisture convergence are seen 6 days ahead of the development of the cyclone and strengthened by the transport of moisture advected from the South China Sea. From the three planetary boundary layer (PBL) schemes, the YSU scheme gives better results both qualitatively and quantitatively for the moisture flux convergence. The MYJ scheme produced the least errors for cyclone intensity from genesis to the landfall stage, while the ACM2 scheme gave better results after landfall. The track of the cyclone with the YSU scheme produced the least errors throughout the life cycle which gives the least landfall error. The structure of the cyclone in terms of tangential winds, the spatial distribution of cloud bands, vertical cross section of temperature anomaly, relative humidity and vertical winds was well simulated by the ACM2 scheme.

Investigating the trends in the major climatic variables over the Upper Indus Basin (UIB) region is difficult for many reasons, including highly complex terrain with heterogeneous spatial precipitation patterns and a scarcity of gauge stations. The Weather Research and Forecasting (WRF) model was applied to simulate the spatiotemporal variability of precipitation and temperature over the Indus Basin from 2000 through 2015 with boundary conditions derived from the Climate Forecast System Reanalysis (CFSR) data. The WRF model was configured with three nested domains (d01–d03) with horizontal resolutions increasing inward from 36 km to 12 km to 4 km horizontal resolution, respectively. These simulations were a continuous run with a spin-up year (i.e., 2000) to equilibrate the soil moisture, snow cover, and temperature at the beginning of the simulation. The simulations were then compared with TRMM and station data for the same time period using root mean squared error (RMSE), percentage bias (PBIAS), mean bias error (MBE), and the Pearson correlation coefficient. The results showed that the precipitation and temperature simulations were largely improved from d01 to d03. However, WRF tended to overestimate precipitation and underestimate temperature in all domains. This study presents high-resolution climatological datasets, which could be useful for the study of climate change and hydrological processes in this data-sparse region.

The paper considers possibilities of taking into account data from lightning networks in the procedure for lightning data assimilation in numerical models of atmospheric dynamics. A universal procedure is suggested and implemented as a code within the WRF-ARW model. According to the data from lightning detection networks, cells of a computational grid are defined, where lightnings have been recorded. Then moisture is iteratively added in these cells until the occurrence of thermodynamic instability and, hence, convection. The effect of using this procedure on the forecast of precipitation, temperature, and humidity is studied, and the suggested procedure is compared with other lightning assimilation methods. The use of data from lightning detectors makes it possible to locally improve the forecast of heavy precipitation and temperature in areas where thunderstorms were observed. The Peirce–Obukhov coefficient increases from 0.26 to 0.40 when this procedure is used for forecasting heavy precipitation.

The present study explores the cloud microphysics (MPs) impact on the simulation of two convective rainfall events (CREs) over the complex topography of Andes mountains, using the Weather Research and Forecasting- Advanced Research (WRF-ARW) model. The events occurred on December 29 2015 (CRE1) and January 7 2016 (CRE2). Six microphysical parameterizations (MPPs) (Thompson, WSM6, Morrison, Goddard, Milbrandt and Lin) were tested, which had been previously applied in complex orography areas. The one-way nesting technique was applied to four domains, with horizontal resolutions of 18, 6, and 3 km for the outer ones, in which cumulus and MP parameterizations were applied, while for the innermost domain, with a resolution of 0.75 km, only MP parameterization was used. It was integrated for 36 h with National Centers for Environmental Prediction (NCEP Final Operational Global Analysis (NFL) initial conditions at 00:00 UTC (Coordinated Universal Time). The simulations were verified using Geostationary Operational Environmental Satellites (GOES) brightness temperature, Ka band cloud radar, and surface meteorology variables observed at the Huancayo Observatory. All the MPPs detected the surface temperature signature of the CREs, but for CRE2, it was underestimated during its lifetime in its vicinity, matching well after the simulated event. For CRE1, all the schemes gave good estimations of 24 h precipitation, but for CRE2, Goddard and Milbrandt underestimated the 24 h precipitation in the inner domain. The Morrison and Lin configurations reproduced the general dynamics of the development of cloud systems for the two case studies. The vertical profiles of the hydrometeors simulated by different schemes showed significant differences. The best performance of the Morrison scheme for both case studies may be related to its ability to simulate the role of graupel in precipitation formation. The analysis of the maximum reflectivity field, cloud top distribution, and vertical structure of the simulated cloud field also shows that the Morrison parameterization reproduced the convective systems consistently with observations.

Numerical weather modelling has piqued the attention of the hydrological community because precise predictions from the models might lessen the extreme hydrological repercussions. Despite the paucity of existing studies, significant tropical storms frequently affect the Asian island of Sri Lanka. This research investigates the Weather Research Forecast (WRF-ARW) model's cumulus parameterization condition and physical parameterization schemes for a 2019 northeast monsoon event over the Badulu Oya Basin, Sri Lanka. Three cumulus schemes (Kain–Fritsch (KF), Betts–Miller–Janjic (BMJ) and Multi-scale Kain–Fritsch (MKF)) and four microphysics schemes (WRF single-moment 5-class (WSM5), WRF single-moment 6-class (WSM6), Kessler (KSL) and WRF double moment 6-class (WDM6)) were evaluated for their impact on modelled rainfall. The model performances were assessed using 24-hr accumulated model rainfall and observed rainfall with various model configurations at a horizontal grid resolution of 3 km using categorical and two quantitative comparison techniques. The study concluded that the activated KF scheme with a finer domain resolution (3 km) would be preferred for cumulus parameterization in the study region. The KF-WSM5 combination was the best since it produced the highest statistics: ETS is 0.38, B is 0.95, r is 0.76, NSD is 1.06, NRMSE is 0.72, and CCPA is 75%. Research highlights We simulated an extreme northeast monsoon precipitation event over the Badulu Oya catchment, Sri Lanka. Simulations were performed using the Weather Research and Forecasting model (WRF-ARW). Sensitivity studied to cumulus parameterization condition and microphysics schemes (CPSs- KF, BMJ, MKF and MPSs-WSM5, WSM6, KSL and WDM6). The activated Kain–Fritsch cumulus scheme at 3 km resolution was found to be the most accurate. Combination of KF-WSM5 with activated cumulus scheme in the finer domain could be preferable option for heavy rainfall simulations over the study area.

In the present study, the water budget of the Bay of Bengal tropical cyclones at varying intensities is analyzed. Results show that rainfall is not directly related to the intensities of tropical cyclones. A secondary peak in precipitation after landfall causes huge damage through floods and mud slides. The analysis of the water budget shows that the moisture flux convergence was the dominant term before landfall and contributes to 61% of the rainfall, while the remaining 39% is contributed by evaporation. After landfall, evaporation contributed 63% of the rainfall and 37% of rainfall was contributed by moisture flux convergence. The contribution of evaporation changed little with time in all the 12 case studies. Out of the 12 cyclones of varying intensities, seven cyclones either showed a secondary peak in precipitation or maintained a high rainfall over land. For the high rainfall over land, after the landfall, soil moisture was found to be important both in the observation and simulations of the Weather Research and Forecasting model. The predicted cyclone track errors are reduced in the model experiment with soil moisture, while the predicted cyclone intensity errors are less in the experiment without soil moisture. Accurate soil-moisture data are required for better prediction of cyclone track and their intensities.

In this work, the optimal parametrization of a mesoscale meteorological model is sought based on a comparative analysis of model forecasts and measurement results on temperature inversions in the planetary boundary layer of the atmosphere of the Moscow megapolis. The WRF–ARW model was tested with several different combinations of physical parameterizations to assess the prediction quality for temperature inversion parameter over Moscow. The dynamic and statistical characteristics of temperature inversions have been calculated and analyzed in selecting criteria for the comparisons. The terms of temperature inversion destruction are estimated depending on the inversion type. The measurement results on temperature profiles in the layer of up to 1 km obtained by an MTP-5 passive microwave profiler from 2018 to 2021 served as the data source. One MTP-5 in the north of Moscow was used to tune the model parameters, and another one on the east of Moscow was used for validation. The comparison results show that the model can be optimally tuned using a set of several parameterization variants.