scholarly journals Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin

2018 ◽  
Vol 22 (2) ◽  
pp. 1095-1117 ◽  
Author(s):  
Ila Chawla ◽  
Krishna K. Osuri ◽  
Pradeep P. Mujumdar ◽  
Dev Niyogi
Keyword(s):  
Land Surface ◽  
Heavy Rainfall ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Weather Research And Forecasting ◽  
Grid Spacing ◽  
Ganga Basin ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Heavy Rainfall Events

Abstract. Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods. Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis. Therefore, regional mesoscale models such as the advanced research version of the Weather Research and Forecasting (WRF) model are often used to provide rainfall estimates at fine grid spacing. Modelling heavy rainfall events is an enduring challenge, as such events depend on multi-scale interactions, and the model configurations such as grid spacing, physical parameterization and initialization. With this background, the WRF model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15–18 June 2013 over the Ganga Basin in India, which is located at the foothills of the Himalayas. This event is simulated with ensembles involving four different microphysics (MP), two cumulus (CU) parameterizations, two planetary boundary layers (PBLs) and two land surface physics options, as well as different resolutions (grid spacing) within the WRF model. The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42RT version 7 data. From the analysis, it should be noted that the choice of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influences the magnitude of rainfall in the model simulations. Further, the WRF run with Goddard MP, Mellor–Yamada–Janjic PBL and Betts–Miller–Janjic CU scheme is found to perform best in simulating this heavy rain event. The selected configuration is evaluated for several heavy to extremely heavy rainfall events that occurred across different months of the monsoon season in the region. The model performance improved through incorporation of detailed land surface processes involving prognostic soil moisture evolution in Noah scheme compared to the simple Slab model. To analyse the effect of model grid spacing, two sets of downscaling ratios – (i) 1 : 3, global to regional (G2R) scale and (ii) 1 : 9, global to convection-permitting scale (G2C) – are employed. Results indicate that a higher downscaling ratio (G2C) causes higher variability and consequently large errors in the simulations. Therefore, G2R is adopted as a suitable choice for simulating heavy rainfall event in the present case study. Further, the WRF-simulated rainfall is found to exhibit less bias when compared with the NCEP FiNaL (FNL) reanalysis data.

scholarly journals Assessment of the Weather Research and Forecasting (WRF) Model for Extreme Rainfall Event Simulations in the Upper Ganga Basin

2017 ◽  
Author(s):  
Ila Chawla ◽  
Krishna K. Osuri ◽  
Pradeep P. Mujumdar ◽  
Dev Niyogi
Keyword(s):  
Land Surface ◽  
Heavy Rainfall ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Simulated Rainfall ◽  
Grid Spacing ◽  
Rainfall Events ◽  
Arw Model

Abstract. Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods. Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis. Therefore, regional mesoscale models such as the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model, are often used to provide rainfall estimates at fine grid spacing. Modelling heavy rainfall events is an enduring challenge, as such events depend on multiscale interactions, and the model configurations such as grid spacing, physical parameterization and initialization. With this background, the WRF-ARW model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15–18 June 2013 over the Ganges basin in India, which is located at the foothills of the Himalayas. This event is simulated with ensembles involving four different microphysics (MP), two cumulus (CU) parameterizations, two planetary boundary layer (PBL), and two land surface physics options; and different resolutions (grid spacing) within the WRF model. The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42RT version 7 data. From the analysis, it is noted that the selection of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influence the magnitude of rainfall in the model simulations. Further, WRF run with Goddard MP, Mellor–Yamada–Janjic PBL and Betts–Miller–Janjic' CU scheme is found to perform best in simulating this heavy rain event. The model performance improved through incorporation of detailed land surface processes involving prognostic soil moisture evolution in Noah scheme as compared to the simple Slab model. To analyze the effect of model grid spacing, two sets of downscaling ratios – (i) 1 : 3, Global to Regional (G2R) scale; and (ii) 1 : 9, Global to Convection-permitting scale (G2C) are employed. Results indicate that higher downscaling ratio (G2C) causes higher variability and consequently, large errors in the simulations. Therefore, G2R is opted as a suitable choice for simulating heavy rainfall event in the present case study. Further, the WRF simulated rainfall is found to exhibit least bias when compared with that of the Coordinated Regional Climate Downscaling Experiment (CORDEX) data and the NCEP FiNaL (FNL) reanalysis data.

scholarly journals Heavy Rainfall Simulation over Sinai Peninsula Using the Weather Research and Forecasting Model

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Gamal El Afandi ◽  
Mostafa Morsy ◽  
Fathy El Hussieny
Keyword(s):  
Heavy Rainfall ◽  
Flash Flood ◽  
Wrf Model ◽  
Rain Gauge ◽  
Rainfall Simulation ◽  
Weather Research And Forecasting ◽  
Sinai Peninsula ◽  
Rainfall Events ◽  
Heavy Rainfall Events ◽  
Weather Research

Heavy rainfall is one of major severe weather over Sinai Peninsula and causes many flash floods over the region. The good forecasting of rainfall is very much necessary for providing early warning before the flash flood events to avoid or minimize disasters. In the present study using the Weather Research and Forecasting (WRF) Model, heavy rainfall events that occurred over Sinai Peninsula and caused flash flood have been investigated. The flash flood that occurred on January 18, 2010, over different parts of Sinai Peninsula has been predicted and analyzed using the Advanced Weather Research and Forecast (WRF-ARW) Model. The predicted rainfall in four dimensions (space and time) has been calibrated with the measurements recorded at rain gauge stations. The results show that the WRF model was able to capture the heavy rainfall events over different regions of Sinai. It is also observed that WRF model was able to predict rainfall in a significant consistency with real measurements. In this study, several synoptic characteristics of the depressions that developed during the course of study have been investigated. Also, several dynamic characteristics during the evolution of the depressions were studied: relative vorticity, thermal advection, and geopotential height.

scholarly journals Association of geopotential height patterns with heavy rainfall events in Cyprus

2010 ◽  
Vol 23 ◽  
pp. 73-78 ◽  
Author(s):  
F. Tymvios ◽  
K. Savvidou ◽  
S. C. Michaelides
Keyword(s):  
Geopotential Height ◽  
Heavy Rainfall ◽  
Eastern Mediterranean ◽  
Statistical Processing ◽  
Extreme Rainfall ◽  
Classification Performance ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
The Relationship ◽  
Heavy Rainfall Events

Abstract. Dynamically induced rainfall is strongly connected with synoptic atmospheric circulation patterns at the upper levels. This study investigates the relationship between days of high precipitation volume events in the eastern Mediterranean and the associated geopotential height patterns at 500 hPa. To reduce the number of different patterns and to simplify the statistical processing, the input days were classified into clusters of synoptic cases having similar characteristics, by utilizing Kohonen Self Organizing Maps (SOM) architecture. Using this architecture, synoptic patterns were grouped into 9, 18, 27 and 36 clusters which were subsequently used in the analysis. The classification performance was tested by applying the method to extreme rainfall events in the eastern Mediterranean. The relationship of the synoptic upper air patterns (500 hPa height) and surface features (heavy rainfall events) was established, while the 36 member classification proved to be the most efficient.

Use of Normalized Anomaly Fields to Anticipate Extreme Rainfall in the Mountains of Northern California

2008 ◽  
Vol 23 (3) ◽  
pp. 336-356 ◽  
Author(s):  
Norman W. Junker ◽  
Richard H. Grumm ◽  
Robert Hart ◽  
Lance F. Bosart ◽  
Katherine M. Bell ◽  
...  
Keyword(s):  
Geopotential Height ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
The United States ◽  
Height Anomaly ◽  
Geopotential Height Anomaly ◽  
Northern California ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Heavy Rainfall Events

Abstract Extreme rainfall events contribute a large portion of wintertime precipitation to northern California. The motivations of this paper were to study the observed differences in the patterns between extreme and more commonly occurring lighter rainfall events, and to study whether anomaly fields might be used to discriminate between them. Daily (1200–1200 UTC) precipitation amounts were binned into three progressively heavier categories (12.5–50.0 mm, light; 50–100 mm, moderate; and >100 mm, heavy) in order to help identify the physical processes responsible for extreme precipitation in the Sierra Nevada range between 37.5° and 41.0°N. The composite fields revealed marked differences between the synoptic patterns associated with the three different groups. The heavy composites showed a much stronger, larger-scale, and slower-moving negative geopotential height anomaly off the Pacific coast of Oregon and Washington than was revealed in either of the other two composites. The heavy rainfall events were also typically associated with an atmospheric river with anomalously high precipitable water (PW) and 850-hPa moisture flux (MF) within it. The standardized PW and MF anomalies associated with the heavy grouping were higher and were slower moving than in either of the lighter bins. Three multiday heavy rainfall events were closely examined in order to ascertain whether anomaly patterns could provide forecast utility. Each of the multiday extreme rainfall events investigated was associated with atmospheric rivers that contained highly anomalous 850-hPa MF and PW within it. Each case was also associated with an unusually intense negative geopotential height anomaly that was similarly located off of the west coast of the United States. The similarities in the anomaly pattern among the three multiday extreme events suggest that standardized anomalies might be useful in predicting extreme multiday rainfall events in the northern Sierra range.

scholarly journals Sensitivity of Simulations of Zambian Heavy Rainfall Events to the Atmospheric Boundary Layer Schemes

Climate ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 38
Author(s):  
Mary-Jane M. Bopape ◽  
David Waitolo ◽  
Robert S. Plant ◽  
Elelwani Phaduli ◽  
Edson Nkonde ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Heavy Rainfall ◽  
Large Scale ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Maximum Temperature ◽  
Rainfall Events ◽  
Heavy Rainfall Events

Weather forecasting relies on the use of numerical weather prediction (NWP) models, whose resolution is informed by the available computational resources. The models resolve large scale processes, while subgrid processes are parametrized. One of the processes that is parametrized is turbulence which is represented in planetary boundary layer (PBL) schemes. In this study, we evaluate the sensitivity of heavy rainfall events over Zambia to four different PBL schemes in the Weather Research and Forecasting (WRF) model using a parent domain with a 9 km grid length and a 3 km grid spacing child domain. The four PBL schemes are the Yonsei University (YSU), nonlocal first-order medium-range forecasting (MRF), University of Washington (UW) and Mellor–Yamada–Nakanishi–Niino (MYNN) schemes. Simulations were done for three case studies of extreme rainfall on 17 December 2016, 21 January 2017 and 17 April 2019. The use of YSU produced the highest rainfall peaks across all three cases; however, it produced performance statistics similar to UW that are higher than those of the two other schemes. These statistics are not maintained when adjusted for random hits, indicating that the extra events are mainly random rather than being skillfully placed. UW simulated the lowest PBL height, while MRF produced the highest PBL height, but this was not matched by the temperature simulation. The YSU and MYNN PBL heights were intermediate at the time of the peak; however, MYNN is associated with a slower decay and higher PBL heights at night. WRF underestimated the maximum temperature during all cases and for all PBL schemes, with a larger bias in the MYNN scheme. We support further use of the YSU scheme, which is the scheme selected for the tropical suite in WRF. The different simulations were in some respects more similar to one another than to the available observations. Satellite rainfall estimates and the ERA5 reanalysis showed different rainfall distributions, which indicates a need for more ground observations to assist with studies like this one.

scholarly journals Brief communication "Climatic covariates for the frequency analysis of heavy rainfall in the Mediterranean region"

2011 ◽  
Vol 11 (9) ◽  
pp. 2463-2468 ◽  
Author(s):  
Y. Tramblay ◽  
L. Neppel ◽  
J. Carreau
Keyword(s):  
Extreme Events ◽  
Heavy Rainfall ◽  
Climate Models ◽  
Extreme Event ◽  
Generalized Pareto Distribution ◽  
Climatic Conditions ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
The Future ◽  
Heavy Rainfall Events

Abstract. In Mediterranean regions, climate studies indicate for the future a possible increase in the extreme rainfall events occurrence and intensity. To evaluate the future changes in the extreme event distribution, there is a need to provide non-stationary models taking into account the non-stationarity of climate. In this study, several climatic covariates are tested in a non-stationary peaks-over-threshold modeling approach for heavy rainfall events in Southern France. Results indicate that the introduction of climatic covariates could improve the statistical modeling of extreme events. In the case study, the frequency of southern synoptic circulation patterns is found to improve the occurrence process of extreme events modeled via a Poisson distribution, whereas for the magnitude of the events, the air temperature and sea level pressure appear as valid covariates for the Generalized Pareto distribution scale parameter. Covariates describing the humidity fluxes at monthly and seasonal time scales also provide significant model improvements for the occurrence and the magnitude of heavy rainfall events. With such models including climatic covariates, it becomes possible to asses the risk of extreme events given certain climatic conditions at monthly or seasonal timescales. The future changes in the heavy rainfall distribution can also be evaluated using covariates computed by climate models.

scholarly journals On the Use of an Innovative Trend Analysis Methodology for Temporal Trend Identification in Extreme Rainfall Indices Over the Central Highlands, Vietnam

2021 ◽  
Author(s):  
Dang Nguyen Dong Phuong ◽  
Nguyen Thi Huyen ◽  
Nguyen Duy Liem ◽  
Nguyen Thi Hong ◽  
Dang Kien Cuong ◽  
...  
Keyword(s):  
Trend Analysis ◽  
Heavy Rainfall ◽  
Precipitation Amount ◽  
Extreme Rainfall ◽  
Rainfall Events ◽  
Monotonic Trend ◽  
Innovative Trend Analysis ◽  
Central Highlands ◽  
Warming World ◽  
Heavy Rainfall Events

Abstract Understanding past changes in the characteristics of climate extremes (such as frequency, intensity, and duration) forms an essential part of viable countermeasures to cope with climate-induced risks under a rapidly warming world. Thus, this paper endeavored to explore possible non-monotonic trend components in heavy rainfall events over the Central Highlands of Vietnam by employing the Şen’s innovative trend analysis (ITA) method in conjunction with the well-defined extreme rainfall indices developed by the Joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). The outcomes show that the overall trends in most extreme rainfall indices exhibited significant increases at several stations. Moreover, the high-value subgroups of most analyzed indices (such as maximum 5-day precipitation amount (Rx5day), simple daily intensity index (SDII), very wet days (R95p), extremely wet days (R99p), number of extremely heavy precipitation days (R50mm), and consecutive dry days (CDD)) were characterized mainly by significant increasing trends, thereby implying that heavy rainfall events have become more frequent and intense over recent decades. Some stations also exposed significant increasing trend behaviors in a given extreme index within all low-, medium-, and high-value subgroups. In general, it is expected that these findings yield more insightful knowledge on rainfall extremes to local decision-makers and other stakeholders.

scholarly journals ­Trends and Zonal Variability of Extreme Rainfall Events Over East Africa During 1960-2017

2021 ◽  
Author(s):  
Moses.A Ojara ◽  
Yunsheng Lou ◽  
Hasssen Babaousmail ◽  
Peter Wasswa
Keyword(s):  
Extreme Events ◽  
Heavy Rainfall ◽  
Rainwater Harvesting ◽  
Southern Oscillation ◽  
Extreme Rainfall ◽  
Mitigation Measures ◽  
Fluctuation Analysis ◽  
Positive Trend ◽  
Extreme Rainfall Events ◽  
Rainfall Events

Abstract East African countries (Uganda, Kenya, Tanzania, Rwanda, and Burundi) are prone to weather extreme events. In this regard; the past occurrence of extreme rainfall events is analyzed for 25 stations following the Expert Team on Climate Change Detection and Indices (ETCCDI) regression method. Detrended Fluctuation Analysis (DFA) is used to show the future development of extreme events. Pearson’s correlation analysis is performed to show the relationship of extreme events between different rainfall zones and their association with El Niño -Southern Oscillation (ENSO and Indian Ocean dipole (IOD) IOD-DMI indices. Results revealed that the consecutive wet day's index (CWD) was decreasing trend in 72% of the stations analyzed, moreover consecutive dry days (CDD) index also indicated a positive trend in 44% of the stations analyzed. Heavy rainfall days index (R10mm) showed a positive trend at 52% of the stations and was statistically significant at a few stations. In light of the extremely heavy rainfall days (R25mm) index, 56% of the stations revealed a decreasing trend for the index and statistically significant trend at some stations. Further, a low correlation coefficient of extreme rainfall events in the regions; and between rainfall extreme indices with the atmospheric teleconnection indices (Dipole Mode Index-DMI and Nino 3.4) (r = -0.1 to r = 0.35). Most rainfall zones showed a positive correlation between the R95p index and DMI, while 5/8 of the rainfall zones experienced a negative correlation between Nino 3.4 index and the R95p. In light of the highly variable trends of extremes events, we recommend planning adaptation and mitigation measures that consider the occurrence of such high variability. Measures such as rainwater harvesting, stored and used during needs, planned settlement, and improved drainage systems management supported by accurate climate and weather forecasts is highly advised.

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