Customization of WRF-ARW model with physical parameterization schemes for the simulation of tropical cyclones over North Indian Ocean

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.     

Impact of physical parameterization schemes on numerical simulation of super cyclone Gonu

The objective of this study is to investigate in detail the sensitivity of cumulus, planetary boundary layer and explicit cloud microphysics parameterization schemes on intensity and track forecast of super cyclone Gonu (2007) using the Pennsylvania State University-National Center for Atmospheric Research Fifth-Generation Mesoscale Model (MM5). Three sets of sensitivity experiments (totally 11 experiments) are conducted to examine the impact of each of the aforementioned parameterization schemes on the storm’s track and intensity forecast. Convective parameterization schemes (CPS) include Grell (Gr), Betts–Miller (BM) and updated Kain–Fritsch (KF2); planetary boundary layer (PBL) schemes include Burk–Thompson (BT), Eta Mellor–Yamada (MY) and the Medium-Range Forecast (MRF); and cloud microphysics parameterization schemes (MPS) comprise Warm Rain (WR), Simple Ice (SI), Mixed Phase (MP), Goddard Graupel (GG), Reisner Graupel (RG) and Schultz (Sc). The model configuration for CPS and PBL experiments includes two nested domains (90- and 30-km resolution), and for MPS experiments includes three nested domains (90-, 30- and 10-km grid resolution). It is found that the forecast track and intensity of the cyclone are most sensitive to CPS compared to other physical parameterization schemes (i.e., PBL and MPS). The simulated cyclone with Gr scheme has the least forecast track error, and KF2 scheme has highest intensity. From the results, influence of cumulus convection on steering flow of the cyclone is evident. It appears that combined effect of midlatitude trough interaction, strength of the anticyclone and intensity of the storm in each of these model forecasts are responsible for the differences in respective track forecast of the cyclone. The PBL group of experiments has less influence on the track forecast of the cyclone compared to CPS. However, we do note a considerable variation in intensity forecast due to variations in PBL schemes. The MY scheme produced reasonably better forecast within the group with a sustained warm core and better surface wind fields. Finally, results from MPS set of experiments demonstrate that explicit moisture schemes have profound impact on cyclone intensity and moderate impact on cyclone track forecast. The storm produced from WR scheme is the most intensive in the group and closer to the observed strength. The possible reason attributed for this intensification is the combined effect of reduction in cooling tendencies within the storm core due to the absence of melting process and reduction of water loading in the model due to absence of frozen hydrometeors in the WR scheme. We also note a good correlation between evolution of frozen condensate and storm intensification rate among these experiments. It appears that the Sc scheme has some systematic bias and because of that we note a substantial reduction in the rain water formation in the simulated storm when compared to others within the group. In general, it is noted that all the sensitivity experiments have a tendency to unrealistically intensify the storm at the later part of the integration phase.     

Numerical simulation of tropical cyclone thane: role of boundary layer and surface drag parameterization schemes

In the recent times, several advanced numerical models are utilized for the prediction of the intensity, track and landfall time of a cyclone. Still there are number of issues concerning their prediction and the limitation of numerical models in addressing those issues. The most pertinent question is how intensive a cyclone can become before it makes a landfall and where the cyclone moves under the ambient large-scale flow. In this paper, detailed study has been carried out using Weather Research Forecast model with two boundary schemes to address the above question by considering a recent tropical cyclone in Bay of Bengal region of North Indian Ocean. In addition, the impact of the surface drag effect on the low-level winds and the intensity of the cyclone are also studied. The result reveals that large differences are noted in the ocean surface fluxes between YSU and MYJ with MYJ producing relatively higher fluxes than YSU. It is found that the YSU scheme produced a better simulation for the THANE cyclone in terms of winds, pressure distribution and cloud fractions. Comparison with available observations indicated the characteristics of horizontal divergence, vorticity and vector track positions produced by YSU experiment are more realistic than with MYJ and other experiments. However, when the drag coefficient is changed as 0.5 or 2.0 from the default values, appreciable changes in the surface fluxes are not noticed. A maximum precipitation is reported in YSU as compared to the MYJ PBL scheme for the tropical cyclone THANE.     

分辨率和陆面方案对长江流域短期气候预测影响

利用区域气候模式RegCM4.5,分别选取不同陆面参数化方案和空间分辨率,对5个长江流域降水异常年份进行短期气候回报试验,分析对气温和降水预测效果的影响及其最优组合。结果表明:空间分辨率的提高可以改善流域降水和气温的预测性能;而不同陆面方案引起的地表净辐射能量分布不同及其地表蒸散差异,最终导致流域内气温和降水预测效果不一致。RegCM（CLM4.5+30 km）对流域内小雨预测结果最好,而RegCM（BATS+30 km）预测流域内大雨和暴雨效果最优;RegCM（CLM3.5+30 km）对流域内气温预测能力最好。     

Impact of variational assimilation technique on simulation of a heavy rainfall event over Pune,India

Prediction of heavy rainfall events due to severe convective storms in terms of their spatial and temporal scales is a challenging task for an operational forecaster. The present study is about a record-breaking heavy rainfall event observed in Pune (18°31′N, 73°55′E) on October 4, 2010. The day witnessed highest 24-h accumulated precipitation of 181.3 mm and caused flash floods in the city. The WRF model-based real-time weather system, operating daily at Centre for Development of Advanced Computing using PARAM Yuva supercomputer showed the signature of this convective event 4-h before, but failed to capture the actual peak rainfall and its location with reference to the city’s observational network. To investigate further, five numerical experiments were conducted to check the impact of assimilation of observations in the WRF model forecast. First, a control experiment was conducted with initialization using National Centre for Environmental Prediction (NCEP)’s Global Forecast System 0.5° data, while surface observational data from NCEP Prepbufr system were assimilated in the second experiment (VARSFC). In the third experiment (VARAMV), NCEP Prepbufr atmospheric motion vectors were assimilated. Fourth experiment (VARPRO) was assimilated with conventional soundings data, and all the available NCEP Prepbufr observations were assimilated in the fifth experiment (VARALL). Model runs were compared with observations from automated weather stations (AWS), synoptic charts of Indian Meteorological Department (IMD). Comparison of 24-h accumulated rainfall with IMD AWS 24-h gridded data showed that the fifth experiment (VARALL) produced better picture of heavy rainfall, maximum up to 251 mm/day toward the southern side, 31 km away from Pune’s IMD observatory. It was noticed that the effect of soundings observations experiment (VARPRO) caused heavy precipitation of 210 mm toward the southern side 49 km away from Pune. The wind analysis at 850 and 200 hPa indicated that the surface and atmospheric motion vector observations (VARAMV) helped in shifting its peak rainfall toward Pune, IMD observatory by 18 km, though VARALL over-predicted rainfall by 60 mm than the observed.     

The hazard risk assessment of regional heavy rainfall over Sichuan Basin of China

Natural Hazards - In this study, predictors for regional disastrous rainfall events over Sichuan Basin and a conceptual model for assessing their associated hazard risk levels are developed using...     

卫星资料在暴雨数值模拟中的应用研究

分析研究了2001年5月15日～8月15日3个月GMS卫星资料在湖南资水流域实时数值预报中的应用以及将TRMM(Tropical Rainfall Measuring Mission)卫星上的TMI(Microwave Imager)雨水资料适时融入数值模式改变当时模式中雨水分布场,数值模拟还研究了发生在淮河流域的10次暴雨过程。结果表明:资水流域3个月的实时预报效果良好,准确预报出其中出现的3次致洪暴雨和1次特大暴雨;对淮河流域暴雨,由于TMI资料空间分辨率较高,能够很好地反映中小尺度系统的空间结构,加入模式后使得模拟出来的降雨强度,雨量中心时空分布更接近实际情况,10次暴雨过程的TS评分较不使用TMI资料更好。     

Numerical simulation of the heavy rainfall caused by a convection band over Korea: a case study on the comparison of WRF and CReSS

Natural Hazards - This study investigates the capability of two numerical models, namely the Weather Research and Forecasting (WRF) and Cloud Resolving Storm Simulator (CReSS), to simulate the...     

Some characteristics of very heavy rainfall over Orissa during summer monsoon season

Orissa is one of the most flood prone states of India. The floods in Orissa mostly occur during monsoon season due to very heavy rainfall caused by synoptic scale monsoon disturbances. Hence a study is undertaken to find out the characteristic features of very heavy rainfall (24 hours rainfall ≥125 mm) over Orissa during summer monsoon season (June–September) by analysing 20 years (1980–1999) daily rainfall data of different stations in Orissa. The principal objective of this study is to find out the role of synoptic scale monsoon disturbances in spatial and temporal variability of very heavy rainfall over Orissa. Most of the very heavy rainfall events occur in July and August. The region, extending from central part of coastal Orissa in the southeast towards Sambalpur district in the northwest, experiences higher frequency and higher intensity of very heavy rainfall with less interannual variability. It is due to the fact that most of the causative synoptic disturbances like low pressure systems (LPS) develop over northwest (NW) Bay of Bengal with minimum interannual variation and the monsoon trough extends in west-northwesterly direction from the centre of the system. The very heavy rainfall occurs more frequently with less interannual variability on the western side of Eastern Ghat during all the months and the season except September. It occurs more frequently with less interannual variability on the eastern side of Eastern Ghat during September. The NW Bay followed by Gangetic West Bengal/Orissa is the most favourable region of LPS to cause very heavy rainfall over different parts of Orissa except eastern side of Eastern Ghat. The NW Bay and west central (WC) Bay are equally favourable regions of LPS to cause very heavy rainfall over eastern side of Eastern Ghat. The frequency of very heavy rainfall does not show any significant trend in recent years over Orissa except some places in north-east Orissa which exhibit significant rising trend in all the monsoon months and the season as a whole.     

Analysis of the 1 November 2015 heavy rainfall episode in Algarve by using weather radar and rain gauge data

The trigger for the study presented in this paper was the extreme rain event of 1 November 2015 in Algarve region. The main objective was the analysis and improvement of the precipitation field using a radar–rain gauge merging method. Ordinary kriging with radar-based error correction has been applied to hourly values of precipitation from both sensors. The merging technique allowed keeping the better radar spatial pattern, being the respective estimates corrected by the rain gauges observations. The procedure led to a reduction in the errors of the precipitation estimates, evaluated by cross-validation, when compared to univariate interpolation of rain gauge observation or radar rain product. Finally, some discussion is also added on the problematic of flooding in urban areas, especially those with absent or deficient urban planning.

     

Analysis of the 1 November 2015 heavy rainfall episode in Algarve by using weather radar and rain gauge data

The trigger for the study presented in this paper was the extreme rain event of 1 November 2015 in Algarve region. The main objective was the analysis and improvement of the precipitation field using a radar–rain gauge merging method. Ordinary kriging with radar-based error correction has been applied to hourly values of precipitation from both sensors. The merging technique allowed keeping the better radar spatial pattern, being the respective estimates corrected by the rain gauges observations. The procedure led to a reduction in the errors of the precipitation estimates, evaluated by cross-validation, when compared to univariate interpolation of rain gauge observation or radar rain product. Finally, some discussion is also added on the problematic of flooding in urban areas, especially those with absent or deficient urban planning.     

Impact of satellite soundings on the simulation of heavy rainfall associated with tropical depressions

The present study is carried out to examine the impact of temperature and humidity profiles from moderate resolution imaging spectroradiometer (MODIS) or/and atmospheric infrared sounder (AIRS) on the numerical simulation of heavy rainfall events over the India. The Pennsylvania State University–National Centre for Atmospheric Research fifth-generation mesoscale model (MM5) and its three-dimensional variational (3D-Var) assimilation technique is used for the numerical simulations. The heavy rainfall events occurred during October 26–29, 2005, and October 27–30, 2006, were chosen for the numerical simulations. The results showed that there were large differences observed in the initial meteorological fields from control experiment (CNT; without satellite data) and assimilation experiments (MODIS (assimilating MODIS data), AIRS; (assimilating AIRS data); BOTH (assimilating MODIS and AIRS data together)). The assimilation of satellite data (MODIS, AIRS, and BOTH) improved the predicted thermal and moisture structure of the atmosphere when compared to CNT. Among the experiments, the predicted track of tropical depressions from MODIS was closer to the observed track. Assimilation of MODIS data also showed positive impact on the spatial distribution and intensity of predicted rainfall associated with the depressions. The statistical skill scores obtained for different experiments showed that assimilation of satellite data (MODIS, AIRS, and BOTH) improved the rainfall prediction skill when compared to CNT. Root mean square error in quantitative rainfall prediction is less in the experiment which assimilated MODIS data when compared to other experiments.     

Dominant impact of South Asian low heat on summer monsoon rainfall over Central India

Although previous literature have considered Southern Oscillation Index (SOI), Indian Dipole, and SST as the major teleconnection patterns to explain the variability of summer monsoon rainfall over India. South Asia low pressure and Indian Ocean high are the centers of action that dominates atmospheric circulations in Indian continent. This paper examines the possible impact of South Asian low pressure distribution on the variability of summer monsoon rainfall of India using centers of action approach. Our analysis demonstrates that the explanation of summer monsoon rainfall variability over Central India is improved significantly if the SOI is replaced by South Asian low heat. This contribution also explains the physical mechanisms to establish the relationships between the South Asian low heat and regional climate by examining composite maps of large-scale circulation fields using NCEP/NCAR Reanalysis data.     

强降雨条件下碎屑岩滑坡远程运动模拟分析——以牛儿湾滑坡为例

中国西南砂泥岩地层山区在强降雨条件下频发远程滑坡灾害,是防灾减灾领域亟待解决的关键问题。以2020年7月13日重庆武隆牛儿湾滑坡为例,通过无人机航飞、野外调查和地质条件分析等手段,运用PFC^3D模拟,对中国西南砂泥岩地层山区强降雨条件下流化滑坡远程运动成灾模式开展研究。研究结果显示：独特的地层结构(上部为第四系残坡积土,下部为砂泥岩)是导致滑坡顺层失稳,并远程流化运动的根本原因;强降雨条件是导致滑坡深层失稳、整体下滑,同时使表层残破积土层饱水流化远程运动的关键影响因素;顺层滑坡远程流化成灾模式主要表现出下层整体滑移、中层粗细颗粒混合和上层饱水流化的特征,流化过程可分为整体高位失稳—混合加速—运动流化堆积三个阶段。基于以上研究,认为砂泥岩地层山区的远程流化滑坡风险调查与预测过程应当充分基于滑体远程流化运动的成灾特点进行调查与评价,以此为防灾减灾提供定量化科学依据。     

Estimation of heavy rainfall during cyclonic storms from microwave observations using nonlinear approach over Indian Ocean

In this study, an effort has been made to study heavy rainfall events during cyclonic storms over Indian Ocean. This estimate is based on microwave observations from tropical rainfall measuring mission (TRMM) Microwave Imager (TMI). Regional scattering index (SI) developed for Indian region based on measurements at 19-, 21- and 85-GHz brightness temperature and polarization corrected temperature (PCT) at 85?GHz have been utilized in this study. These PCT and SI are collocated against Precipitation Radar (PR) onboard TRMM to establish a relationship between rainfall rate, PCT and SI. The retrieval technique using both linear and nonlinear regressions has been developed utilizing SI, PCT and the combination of SI and PCT. The results have been compared with the observations from PR. It was found that a nonlinear algorithm using combination of SI and PCT is more accurate than linear algorithm or nonlinear algorithm using either SI or PCT. Statistical comparison with PR exhibits the correlation coefficients (CC) of 0.68, 0.66 and 0.70, and root mean square error (RMSE) of 1.78, 1.96 and 1.68?mm/h from the observations of SI, PCT and combination of SI and PCT respectively using linear regressions. When nonlinear regression is used, the CC of 0.73, 0.71, 0.79 and RMSE of 1.64, 1.95, 1.54?mm/h are observed from the observations of SI, PCT and combination of SI and PCT, respectively. The error statistics for high rain events (above 10?mm/h) shows the CC of 0.58, 0.59, 0.60 and RMSE of 5.07, 5.47, 5.03?mm/h from the observations of SI, PCT and combination of SI and PCT, respectively, using linear regression, and on the other hand, use of nonlinear regression yields the CC of 0.66, 0.64, 0.71 and RMSE of 4.68, 5.78 and 4.02?mm/h from the observations of SI, PCT and combined SI and PCT, respectively.     

The WRF model performance for the simulation of heavy precipitating events over Ahmedabad during August 2006

The summer monsoon season of the year 2006 was highlighted by an unprecedented number of monsoon lows over the central and the western parts of India, particularly giving widespread rainfall over Gujarat and Rajasthan. Ahmedabad had received 540.2mm of rainfall in the month of August 2006 against the climatological mean of 219.8mm. The two spells of very heavy rainfall of 108.4mm and 97.7mm were recorded on 8 and 12 August 2006 respectively. Due to meteorological complexities involved in replicating the rainfall occurrences over a region, the Weather Research and Forecast (WRF-ARW version) modeling system with two different cumulus schemes in a nested configuration is chosen for simulating these events. The spatial distributions of large-scale circulation and moisture fields have been simulated reasonably well in this model, though there are some spatial biases in the simulated rainfall pattern. The rainfall amount over Ahmedabad has been underestimated by both the cumulus parameterization schemes. The quantitative validation of the simulated rainfall is done by calculating the categorical skill scores like frequency bias, threat scores (TS) and equitable threat scores (ETS). In this case the KF scheme has outperformed the GD scheme for the low precipitation threshold.