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Simulated 6-hourly cumulative rainfall (cm) valid at 0600 UTC July 26, 2005 to 0000 UTC July 27, 2005 from EXP25GD and EXP26GD (Contour levels 1, 2, 4, 8, 16, 32, and 48).
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The simulation of a severe weather phenomenon, in this case the unprecedented heavy rainfall over Mumbai in India on July 26, 2005, was selected for this study. The mesoscale numerical weather prediction model used here utilized the Advanced Research Weather Research Forecast model (version 3.0.1), as developed at the National Center for Atmospheri...
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Assimilating satellite-retrieved cloud properties into storm-scale models has received limited attention despite its potential to provide a wide array of information to a model analysis. Available retrievals include cloud water path (CWP), which represents the amount of cloud water and cloud ice present in an integrated column, and cloud-top and cl...
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... Many studies have used the WRF model with physical parameterization schemes in different parts of the world, including India at regional and basin levels (Pithani et al., 2019;Jeworrek et al., 2021), like flood assessment and modeling using the WRF model (Chawla et al., 2018). Kirtsaeng et al. (2010) examined that, many global models with time location underestimate and contain errors in predicting heavy rainfall events. However, increasing the resolution of NWP models enhances the ability to capture fine-scale atmospheric features, leading to more accurate forecasts (Collins et al., 2013;Allahyari et al., 2020;Kumar et al., 2022). ...
A reliability-based assessment of heavy rainfall events can provide insights for developing appropriate measures and predictions to control flood risk. One of the contemporary challenges is to model heavy rainfall events, which depend on different synoptic conditions and model configurations. In this study, the regional mesoscale Weather Research and Forecasting (WRF) model was used to evaluate the applicability of different parameterization schemes for simulating rainfall events over the Mahi River basin between 22 and 26 August 2020 with the maximum rainfall occurring on 23 August 2020. A high-resolution triple-nested (27, 9, and 3 km) WRF model was configured, and data for initial boundary conditions were obtained from NCEP/NCAR FNL with a resolution of 1 • × 1 • (six-hourly). The model results were evaluated using gridded rainfall of ECMWF Reanalysis v5 (ERA5) and India Meteorological Department (IMD) at a spatial resolution of 0.25 • × 0.25 •. Further to verify the model output, GPM-IMERG was used to compute the skill score (bias, threat score, probability of detection, and accuracy) metrics at 24 hourly and 3 hourly scales. Eight sensitivity tests were conducted to identify the optimal sets of microphysics (WSM6 and Goddard), cumulus (Kain and BMJ), and planetary boundary layer (YSU and ACM2) parameterization schemes for rainfall simulation of the selected events. The Kain-Goddard-YSU and Kain-Goddard-ACM2 parameterization schemes demonstrated the optimal performance for simulating the rainfall event. Relative humidity and reflectivity were used for analyzing moisture and cloud brightness temperature, respectively. The model score revealed that the Kain-Goddard scheme outperformed other schemes in case of heavy precipitation, with a threshold of 60 mm/day over the Mahi basin. The findings of this study showed that the simulation of heavy rainfall events with a set of selected combinations of parameterization schemes can reproduce the structure of convective organization as well as prominent synoptic features associated with heavy rainfall events over the Mahi basin. Moreover, this information may help to better understand the future forecast trend of precipitation and explore solutions for sustainable water management to support decision-makers and operational water managers.
... A cold surge occurs in the northeast monsoon. It has crossed the South China Sea and flows to the cyclonic circulation over east southern Thailand and northern Malay Peninsula, this reason might be a main factor occurrence heavy rainfall in this area [2][3][4] Sometime, several extreme event or heavy rainfall event was occurred suddenly over southern Thailand [2][3][4]. For example, an extreme rainfall event occurred on March 23-30, 2011 caused severe big flooding over east of southern Thailand. ...
... A cold surge occurs in the northeast monsoon. It has crossed the South China Sea and flows to the cyclonic circulation over east southern Thailand and northern Malay Peninsula, this reason might be a main factor occurrence heavy rainfall in this area [2][3][4] Sometime, several extreme event or heavy rainfall event was occurred suddenly over southern Thailand [2][3][4]. For example, an extreme rainfall event occurred on March 23-30, 2011 caused severe big flooding over east of southern Thailand. ...
... in/pages/nwp_main.php?adta=wrf&adtb=rainfall. Many studies have used the model to successfully simulate and diagnose the dynamics of extreme rainfall events in India such as the extreme Mumbai rainfall during the summer of 2005 (Jenamani et al. 2006;Kumar et al. 2008;Kirtsaeng et al. 2010;Bhaskar Rao and Ratna 2010;Hariprasad et al. 2011; Srinivas et al. 2013;Cruz and Narisma 2016), and prediction of tropical cyclone tracks in the north Indian ...
Southeast India experienced a heavy rainfall during 30 Nov - 2 Dec 2015. Particularly, the Chennai city, the fourth major metropolitan city in India with a population of 5 million, experienced extreme flooding and causalities. Using various observed/reanalysed datasets, we find that the concurrent southern Bay of Bengal (BoB) sea surface temperatures (SST) were anomalously warm. Our analysis shows that BoB sea surface temperature anomalies (SSTA) are indeed positively, and significantly, correlated with the northeastern Indian monsoonal rainfall during this season. Our sensitivity experiments carried out with the Weather Research and Forecasting (WRF) model at 25 km resolution suggest that, while the strong concurrent El Niño conditions contributed to about 18.5% of the intensity of the extreme Chennai rainfall through its signals in the local SST mentioned above, the warming trend in BoB SST also contributed equally to the extremity of the event. Further the El Niño southern oscillation (ENSO) impacts on the intensity of the synoptic events in the BoB during the northeast monsoon are manifested largely through the local SST in the BoB as compared through its signature in the atmospheric circulations over the BoB.
... Prediksi hujan menggunakan model numerik cuaca telah banyak digunakan di berbagai negara, misalnya Taiwan [1], India [2], dan Indonesia [3]. Salah satu model numerik cuaca yang digunakan adalah model ...
Model numerik Weather Research and Forecasting Environmental Modelling System (WRF-EMS) digunakan pada studi ini dengan tujuan untuk mengkaji hasil keluaran prediksi hujan model WRF-EMS 1 dan 2 hari ke depan di 6 stasiun pengamatan cuaca terhadap data sinoptik dan TRMM, 8 initial and boundary conditions, serta mengkaji hasil keluaran prediksi hujan secara spasial model WRF-EMS. Konfigurasi skema standar dari model WRF-EMS dipertahankan dan hanya mengubah skema kumulus saja, yaitu menggunakan skema kumulus Betts-Miller-Janjic. Akurasi model ditunjukkan oleh rentang nilai threat score (TS) hasil prediksi hujan 1 dan 2 hari kedepan. Hasil prediksi hujan menunjukkan bahwa model WRF-EMS cukup baik untuk mensimulasikan kejadian tidak-hujan di Pulau Sumatera dan Kalimantan. Nilai TS di stasiun pengamatan cuaca Citeko dari model dengan resolusi horizontal 27 km untuk prediksi 1 dan 2 hari kedepan masing-masing berkisar antara 0 – 0.33 dan 0 – 0.30 sedangkan nilai TS dari model dengan resolusi horizontal 3 km untuk prediksi hujan 1 dan 2 hari kedepan masing-masing berkisar antara 0,17 – 0,75 dan 0.38 – 0.63. Peningkatan resolusi horizontal dari 27 km menjadi 3 km dapat meningkatkan nilai TS. Hal ini mengindikasikan bahwa model dengan resolusi horizontal 3 km lebih akurat untuk memprediksi hujan dibandingkan dengan model resolusi horizontal 27 km. Model WRF-EMS menghasilkan prediksi hujan yang over estimated secara spasial dan model ini lebih sensitif memprediksi hujan di Stasiun Citeko.
WRF-EMS numerical models use to examine the output prediction of rain on 1 or two days ahead using the WRF-EMS model at 6 weather observations stations and the TRMM data, 8 initials and boundary conditions, and to examine the output prediction of rain spatially using the WRF-EMS. In this study, the configuration of the standard scheme using the WRF-EMS model is maintained; however, the cumulus scheme is the Betts-Miller-Janjic cumulus scheme. The accuracy shown by threat values score (TS) of rain predicted on the 1 and 2 days later. The result shows that the model predictions of rain using WRF-EMS is good enough to simulate a no-rain events on the island of Sumatra and Kalimantan. The TS value at Citeko, horizontal resolution of 27 km, for the prediction of 1 and 2 days in advance of each ranged from 0 – 0.33, and 0 – 0.30, while the TS value of the model with a horizontal resolution of 3 km for rainfall predictions 1 and 2 days in advance each ranged from 0.17 to 0.75 and 0.38 - 0.63. The increased of horizontal resolution from 27 km to 3 km can also increase the value of TS. This indicates that the model with a horizontal resolution of 3 km is more accurate for predicting rain compared to the model horizontal resolution of 27 km. The rainfall prediction was spatially over estimated when using the WRF-EMS Model, and this model is more sensitive in predicting rain at Citeko Station.
... The lower part has extensive irrigation networks and hence intensive rice paddy cultivation. The study about estimation and prediction of rainfall are absolutely necessary for an effective water management in agriculture area [12][13][14][15][16][17].The geography of central region is shown in Figure 2. ...
Weather radar is an important tool that integrates meteorology and hydrology. It plays an important part in the hydrology and it is also associated with the quantitative use of radar measurements that find the relationship between radar reflectivity (Z) and rain intensity (R) such as Z-R relationships. To obtain better rainfall estimation for daily rainmaking purposes over the central region of Thailand, the calibration of the Z-R relationship were estimated using the series of Z-R pair data which derived from weather radar and rain gauge network of DRRAA and TMD. The a and b parameters of Z-R relationship will be calibrated by minimizing the Root Mean Square Error (RMSE) and linear regression method between radar and corresponding rain gauge rainfall with the constant of b parameter of 1.5. The 98 convective rainfall events during rainy season in May to September 2013 over the middle part of Thailand are chosen then these selected rainfall events were divided into two groups. The first group of 35 events with rain intensity from DRRAA rain gauges was used to Z-R calibration and derived parameters. The second group of 63 events with rainfalls from MET were considered as validated data. The Z-R relationships were derived using RMSE as Z = 144R^1.5 and a regression method as Z = 206R^1.5. These rainfall events were used to test an accuracy of the proposed radar rainfall estimation based on different Z-R relationship including Z-R relationships obtained from the calibration. It was evident that using of proposed Z-R relationship in rainy season over central Thailand; Z=144R^1.5, gave the smallest RMSE compared to using Z=206R^1.5, Z = 200R^1.6 and Z = 300R^1.4.
... Many researchers have been trying to estimate the amount of precipitation to be used in various activities regarding the research that defined its use [4][5][6][7][8][9]. As the precipitation is the key factor that caused flooding; therefore, the problem we faced in the research is the insufficiency of input data with spatial resolution [10][11][12]. ...
The south of Thailand is the wettest region of the country. It has two sides of the peninsula: east and west coast. The two sub regions have different period of wet season. In eastern South coast, the rains lasts from November to February while the western South, Andaman Sea coast, is forecast outbreaks of showers in May to October. This nature, along with massive water in the catchments, can trigger flash floods, overbank flows, and landslides down each sub region if torrential rain occurs. Satellite-based rainfall, an indirect measure with limitation though, plays a key role in approaching data that cover remote and terrain areas. With this technique, if the rainfall data are proved true, it benefits the notice of possible downpour in the case study. In the validation process, the Global Satellite Mapping of Precipitation (GSMaP) product and rainfall data of the Thai Meteorological Department (TMD) from 2010-2011 are used to correct equation for the monsoon and
the off-monsoon period. The data from 2012-2013 are validated for TMD’s point rain gauges. Likewise, the root mean square error (RMSE) and the mean absolute error (MAE) are applied. The bias correcting satellite-based rainfall estimation provides better effective primary data set regarding spatial and temporal coverage. In progress, the probability of detection (POD) and the false alarm ratio (FAR) are applied to achieve heavy rainfall warning system. As a result, POD’s score is extremely acceptable. Therefore, the alerting system, without point rain gauge, can be applied for heavy rainfall in isolated area.
... The river also touches one of the largest slum colonies in the world, Dharavi(Wikipedia, Dharavi, 2014) resulting in slum encroachment. The studies on the 2005 event(Kirtsaeng, 2010 andHallegatte, 2010) brought to light the above reasons for the widespread damage, destruction and loss of hundreds of lives when the Mithi River overflowed and flooded a large part of the hugely populated central Mumbai. Looking at the timing of the previous four mega rain events over a one hundred year period ...
Mumbai is at a cross-road with big economic growth, increasing wealth on the one hand and big risks associated with large weather events such as flooding, major health issues for the population, and changes in the topology with dwindling land mass, and property loss on the other. Mumbai thus shows a complex inter-play of topology, socio-economic dynamics and weather. In a 2007 study Mumbai was named among the top 20 cities for “exposed population under future climate change and socio-economic change” (Nicholls, 2007). Similarly Sherbinin et al (Sherbinin, 2007) has noted similar vulnerability for Mumbai to climate change. Mumbai’s land use affected by urban sprawl particularly in flood prone areas, reduction of green spaces, impermeability of built up areas, lack of a good drainage and impact of poor waste management on the already strained drainage and storm systems, development of informal settlements (slums) outside of the protection of flood defenses, high levels of population density and congestion have become contributing factors to the excessive discharge of water during heavy rains. (Jha, 2014) becoming precarious during a large weather event. This paper studies these factors affecting Mumbai from a climate change perspective, and explores mitigation, and adaptation approaches.
... Few studies (Gallus 1999;Dudhia et al. 2003;Melissa and Mullen 2005) over Central United States have demonstrated that differences in cumulus parameterizations can have substantial impact on simulated storm convection and precipitation. Over the Indian region, sensitivity to physics parameterization and cumulus schemes for heavy rainfall events (Rama Rao et al. 2007;Vaidya and Kulkarni 2007;Kumar et al. 2008;Deb et al. 2008Deb et al. , 2010Sukrit et al. 2010) and cyclones (Patra et al. 2000;Mandal et al. 2003Mandal et al. , 2004Mandal and Mohanty 2006;Trivedi et al. 2006;Rao and Prasad 2006;Srinivas et al. 2007;Ramalingeswara Rao et al. 2009;Deshpande et al. 2010;Mukhopadhyay et al. 2011;Osuri et al. 2012) has been reported adequately. The assessment of cumulus parameterization schemes in the short-range prediction of rainfall during the onset phase of the Indian Southwest Monsoon using MM5 Model has been reported by Dodla et al. (2012). ...
Abstract
Numerical simulation of a typical tropical thunder storm event at Pune (18.53 N, 73.85 E), India, has been performed using the three nested domain configuration of Weather Research and Forecasting-Advanced Research Weather Model (version 3.2). The model simulations have been compared with observations. Sensitivity to cumulus parameterization schemes, namely Betts–Miller (BM), Grell–Devenyi (GD), and Kain–
Fritsch (KF), for simulation of vertical structure and time evolution of weather parameters has been evaluated using observations from automatic weather station and global positioning system radiosonde ascents. Comparison of spatial distribution of 24-h accumulated rain with Tropical Rainfall Measuring Mission data shows that BM scheme could simulate better rain than GD and KF schemes. The BM scheme could well simulate the development of storm and heavy rain as it could generate sufficiently humid and deep layer in the lower and middle atmosphere, along with co-existence of updrafts and downdrafts and frozen hydrometeors at the middle level and rain water near the surface.
... This event of unusual heavy rainfall severely affected the socio economic activities of the people; created water logging and flood situation; affected on agriculture, energy, fisheries, transportation and more over at least 14 people were died. Kirtsaeng et al. (2010) simulated very heavy rainfall event over Mumbai, using the Weather Research and Forecasting (WRF) Model. Bohra, et al. (2006) has done a qualitative assessment of guidance from NCMRWF operational global and regional Numerical Weather Prediction (NWP) systems in the episode of unprecedented rainfall over Mumbai". ...
Continuous observations of wind speed and turbulence parameters are being carried over Manora peak (29.40N, 79.50E, 1951 m amsl) using an ultrasonic anemometer since May 2012. The north and northeast of Manora peak has sharply undulating topography of Himalayan mountain ranges, where as very low elevation and densely populated plain land merging into the Ganga basin to the southwest. Various parameters such as three components of wind, direction, friction velocity, Monin-Obukhov stability parameter, drag coefficient, turbulence intensity (all the three components), and vertical heat flux are being generated by the instrument every 5 seconds. Diurnal and seasonal analysis of wind, its components and momentum flux during pre monsoon, monsoon and post monsoon is presented and discussed in this paper. This study will help in understanding the surface dynamics and local meteorology of a high altitude station in the central Himalayas.
... As results, this experiment gave better estimate than the original-GPI in both rain position and its amount [3]. Chayatham and Kirtsaeng have studied TRMM products to develop for validation of model [4][5]. Wei et al. (2006) has collected data from the buckets and satellite for forecasting floods by GOES satellite and the RNN (Recurrent Neural Network) for Wu-Tu Basin in Taiwan. ...
Rain is a meteorological constitution that is important for all creatures. Knowing rainfall amount and its intensity is useful for the survival of the living things and for planning activities. However, current rainfall data are still limited because we can get them only from where monitoring tools have been installed. For this reason, not having these data covering remote areas, mountains and seas makes possible incorrect analysis of isohyets. To eliminate the above weak point, a meteorological satellite of Geostationary has been developed for supporting the forecast of rainfall. The Geo-data are the spatial information with a high temporal resolution suitable for monitoring real-time rainfalls. The study on “Application of Artificial Neuron Networks (ANNs) and Satellite Data for Rainfall Estimation in North of Thailand” aimed to determine the relationship between brightness temperatures and rainfall intensity by predicting its amount in northern Thailand with the satellite and then comparing with actual rainfall. FY-2E data were used in this study due to their location adjacent to Thailand compared with other meteorological satellites. ANN method was used to determine the relationship between the brightness temperatures and in situ data. As a result, the correlation for estimating rainfall gave r-square of 0.72. When comparing this value with the actual rainfall measurements, the root-mean-square error equaled 63.11. The research concluded that the rainfall estimate from FY-2E satellite was useful for favoring agriculture, water management, disaster warning, and planning activities concerning.
