scholarly journals Verification of heavy rainfall in NWP models : A case study

MAUSAM ◽  
2021 ◽  
Vol 68 (4) ◽  
pp. 699-712
Author(s):  
KULDEEP SHARMA ◽  
RAGHAVENDRA ASHRIT ◽  
R. BHATLA ◽  
R. RAKHI ◽  
G. R. IYENGAR ◽  
...  

Forecasting of heavy rainfall events is still a challenge even for the most advanced state-of-art high resolution NWP modelling systems. Very often the models fail to accurately predict the track and movement of the low pressure systems leading to large spatial errors in the predicted rain. Quantification of errors in forecast rainfall location and amounts is important for forecasters (to choose a forecast and interpret) and modelers for monitoring the impact of changes and improvements in model physics and dynamics configurations. This study aims to quantify and summarize errors in rainfall forecast for heavy rains associated with a Bay of Bengal (BOB) low pressure systems. The verification analysis is based on three heavy rain events during June to September (JJAS) 2015. The performance of the three deterministic models, NCMRWF’s Global Forecast Systems (NGFS), NCMRWF’s Unified Model (NCUM) and Australian Community Climate and Earth-System Simulator – Global (ACCESS-G) in predicting these heavy rainfall events has been analysed. In addition to standard verification metrics like RMSE, ETS, POD and HK Score, this paper also uses new family of scores like EDS (Extreme Dependency Score), EDI (Extremal Dependence Index) and Symmetric EDI with special emphasis on verification of extreme rainfall to bring out the relative performance of the models for these three rainfall events. The results indicate that Unified modeling framework in NCUM and ACCESS-G by and large performs better than NGFS in rainfall forecasts over India specially at higher lead times. Relatively improved skill in NCUM forecasts can be attributed to (i) improved resolution (~17 km) and (ii) END Game dynamics of NCUM.

MAUSAM ◽  
2021 ◽  
Vol 64 (2) ◽  
pp. 265-280
Author(s):  
MEHFOOZ ALI MEHFOOZALI ◽  
U.P. SINGH ◽  
D. JOARDAR ◽  
NIZAMUDDIN NIZAMUDDIN

vR;f/kd o"kkZ gksus ds dkj.k HkwL[kyu gksrk gS vDlekr ck<+ vk tkrh gS vkSj Qly dks {kfr igq¡prh gSA lekt] vFkZO;oLFkk vkSj i;kZoj.k ij bldk cgqr nq"izHkko iM+rk gSA i;kZoj.kh; vkSj flukWfIVd fLFkfr;ksa ds mRiUu gksus ls  vR;f/kd vFkok cgqr Hkkjh o"kkZ gksus ds dkj.k Hkkjr esa nf{k.k if’peh ekulwu _rq ds nkSjku vf/kdk¡’kr% ck<+ vkrh gSA bl 'kks/k i= esa izeq[k flukWfIVd dkj.kksa dk irk yxkus dk iz;kl fd;k x;k gS tks y?kq vof/k iwokZuqeku ds {ks= esa fodflr iwokZuqeku rduhd vkSj vk/kqfud izs{k.kkRed izkS|ksfxdh ij vk/kkfjr o"kZ 1998&2010 dh vof/k dh bl o"kkZ  vkSj ok;qeaMyh; iz.kkfy;ksa ds e/; laca/kksa ds fo’ys"k.k ds ek/;e ls ;equk ds fupys tyxzg.k {ks= ¼,y-okbZ-lh-½ esa vR;f/kd Hkkjh o"kkZ dh ?kVukvksa ds fy, mRrjnk;h gSA bl v/;;u ls ;g irk pyk gS fd  bl {ks= esa caxky dh [kkM+h esa fuEu nkc iz.kkfy;ksa dk cuuk izeq[k dkjd gS fuLlansg ;fn LFkkuh; fLFkfr;k¡ izHkkoh gks tSlsa fd xehZ dk c<+uk rks ogk¡ ij Hkkjh o"kkZ gksrh gSA lkekU;r% caxky dh [kkM+h esa fuEu vcnkc iz.kkfy;k¡ ¼pØokr] vonkc] fuEu vonkc {ks= vkfn tSls ¼,y-ih-,l-½ fodflr gqbZ tks if’pe ls mRrjh  if’peh fn’kk dh vksj c<+h rFkk ;equk ds fupys tyxzg.k ¼,y-okbZ-lh-½ {ks= esa igq¡phA ,slh ?kVukvksa ds fy, mRrjnk;h mifjru  ok;q pØokrh ifjlapj.k ¼lkblj½ ds izHkko ls ogha ij ,y- ih- ,l- Hkh cu ldrk gSA ,slh iz.kkyh ls bDds&nwDds LFkkuksa ij vR;f/kd Hkkjh o"kkZ dh ?kVuk,¡ ¼lkekU;r% iz.kkyh ds nf{k.k if’pe {ks= esa½ vkSj dqN LFkkuksa ij Hkkjh ls cgqr Hkkjh o"kkZ gqbZ ftlds dkj.k ck<+ vkbZA ;fn ;equk ds fupys tyxzg.k ¼,y-okbZ-lh-½ {ks= esa ,y-ih-,l- fuf"Ø; ;k /khek iM+ tkrk gS rks bl izdkj dh o"kkZ dh ?kVukvksa dh laHkkouk c<+ ldrh gSA ,y-ih-,l- ds vkxs c<+us dk lgh iwokZuqeku nsus ds fy, vkj-,l-,e-lh- ¼Hkkjr ekSle foKku foHkkx½ ubZ fnYYkh ds iwoZuqeku :i js[kk ds ,u-MCY;w-ih- mRikn@72] 48 vkSj 24 ?kaVksa ds iou pkVZ lgh lk/ku ik, x, gSaA vR;f/kd o"kkZ dh ?kVukvksa ds iwokZuqeku esa bl izdkj dh lwpuk nsus ls iwokZuqekudrkvksa dks fuf’pr :i ls lgh iwokZuqeku feysxk rkfd ftyk izkf/kdkjh le; jgrs vkink dh rS;kjh ds fy, vko’;d ewyHkwr lqfo/kk,¡ miyC/k djk ldsaA  Extreme rainfall results in landslides, flash flood and crop damage that have major impact on society, the economy and the environment. During southwest monsoon season, flood mostly occurs in India due to extremely or very heavy rain that originates from environmental and   synoptic conditions. An attempt has been made to identify the main synoptic reasons, which are responsible for extremely heavy rainfall events over Lower Yamuna catchment (LYC) through the analysis of the relationship between this rainfall and atmospheric systems for the period 1998-2010 based on modern observational technology and developed forecasting technique in the field of short range prediction. The finding of this study show that the major factor have is the arrival of Bay of Bengal low pressure systems in this region, of course if the ascent local conditions such as heat occur, causing the heaviest rains there. The low pressure systems (LPS like, Cyclone, depression, low pressure area etc.) developed generally over Bay of Bengal moved in west to north-westwards direction and reached over the LYC region. Also LPS may be formed in situ under the influence of upper air cyclonic circulation (cycir) responsible for such events. Such system yield extremely heavy rainfall events (generally in the south-west sector of the system) at isolated places and heavy to very heavy rainfall at a few places and there by caused flood situation. The possibility of occurrence of such type of rainfall would be higher if the LPS is either stagnate or slow over LYC region. The NWP products of RSMC (IMD) New Delhi forecast contours / wind charts for 72, 48 & 24 hrs were found good tool for accurate forecast position of the movement of the LPS. Such information certainly facilitate to forecaster in prediction of extreme rainfall events more accurately so that district authorities may set up necessary infrastructures for disaster preparedness in time.


2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Jeff Callaghan

An extensive search has been carried out to find all major flood and very heavy rainfall events in Victoria since 1876 when Southern Oscillation (SOI) data became available. The synoptic weather patterns were analysed and of the 319 events studied,121 events were found to be East Coast Lows (ECLs) and 82 were other types of low-pressure systems. Tropical influences also played a large role with 105 events being associated with tropical air advecting down to Victoria into weather systems. Examples are presented of all the major synoptic patterns identified. The SOI was found to be an important climate driver with positive SOIs being associated with many events over the 144 years studied. The 1976 Climate Shift and its influence on significant Victorian rainfall events is studied and negative SOI monthly values were shown to dominate following the Shift.However,one of the most active periods in 144 years of Victorian heavy rain occurred after the shift with a sustained period of positive SOI events from 2007 to 2014. Therefore, it is critical for forecasting future Victorian heavy rainfall is to understand if sequences of these positive SOI events continue like those preceding the Shift. Possible relationships between the Shift and Global Temperature rises are also explored. Upper wind data available from some of the heaviest rainfall events showed the presence of anticyclonic turning of the winds between 850hPa and 500hPa levels which has been found to be linked with extreme rainfall around the Globe. 


2018 ◽  
Author(s):  
Sungmin O ◽  
Ulrich Foelsche

Abstract. Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at sub-pixel scales and over short periods of time. Just a few studies have applied a similar approach, employing dense gauge networks, to local-scale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, we assess spatial uncertainty in observed heavy rainfall events. The network is located in southeastern Austria over an area of 20 km × 15 km with no significant orography. First, the spatial variability of rainfall in the region was characterised using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between wet and dry seasons are apparent and we selected heavy rainfall events, the upper 10 % of wettest days during the wet season, for further analyses because of their high potential for causing hazard risk. Secondly, we investigated uncertainty in estimating mean areal rainfall arising from a limited gauge density. The number of gauges required to obtain areal rainfall with > 20 % accuracy tends to increase roughly following a power law as time scale decreases. Lastly, the impact of spatial aggregation on extreme rainfall was examined using gridded rainfall data with horizontal grid spacings from 0.1° to 0.01°. The spatial scale dependence was clearly observed at high intensity thresholds and high temporal resolutions. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the rational use of the data in relevant applications. Our findings are generalisable to other plain regions in mid-latitudes, however the degree of uncertainty could be affected by regional variations, like rainfall type or topography.


2018 ◽  
Vol 10 (9) ◽  
pp. 1380 ◽  
Author(s):  
Yanhui Xie ◽  
Jiancheng Shi ◽  
Shuiyong Fan ◽  
Min Chen ◽  
Youjun Dou ◽  
...  

Herein, a case study on the impact of assimilating satellite radiance observation data into the rapid-refresh multi-scale analysis and prediction system (RMAPS) is presented. This case study targeted the 48 h period from 19–20 July 2016, which was characterized by the passage of a low pressure system that produced heavy rainfall over North China. Two experiments were performed and 24 h forecasts were produced every 3 h. The results indicated that the forecast prior to the satellite radiance data assimilation could not accurately predict heavy rainfall events over Beijing and the surrounding area. The assimilation of satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) improved the skills of the quantitative precipitation forecast to a certain extent. In comparison with the control experiment that only assimilated conventional observations, the experiment with the integrated satellite radiance data improved the rainfall forecast accuracy for 6 h accumulated precipitation after about 6 h, especially for rainfall amounts that were greater than 25 mm. The average rainfall score was improved by 14.2% for the 25 mm threshold and by 35.8% for 50 mm of rainfall. The results also indicated a positive impact of assimilating satellite radiances, which was primarily reflected by the improved performance of quantitative precipitation forecasting and higher spatial correlation in the forecast range of 6–12 h. Satellite radiance observations provided certain valuable information that was related to the temperature profile, which increased the scope of the prediction of heavy rainfall and led to an improvement in the rainfall scoring in the RMAPS. The inclusion of satellite radiance observations was found to have a small but beneficial impact on the prediction of heavy rainfall events as it relates to our case study conditions. These findings suggest that the assimilation of satellite radiance data in the RMAPS can provide an overall improvement in heavy rainfall forecasting.


2021 ◽  
Author(s):  
Dang Nguyen Dong Phuong ◽  
Nguyen Thi Huyen ◽  
Nguyen Duy Liem ◽  
Nguyen Thi Hong ◽  
Dang Kien Cuong ◽  
...  

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.


2008 ◽  
Vol 3 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Masaomi Nakamura ◽  
◽  
Sachie Kaneda ◽  
Yasutaka Wakazuki ◽  
Chiashi Muroi ◽  
...  

Under the Kyosei-4 Project, unprecedented high resolution global and regional climate models were developed on the Earth Simulator to investigate the effect of global warming on tropical cyclones, baiu frontal rainfall systems, and heavy rainfall events that could not be resolved using conventional climate models.For the regional climate model, a nonhydrostatic model (NHM) with a horizontal resolution of 5 km was developed to be used in the simulation of heavy rainfall during the baiu season in Japan. Simulations in June and July were executed for 10 years in present and future global warming climates. It was found that, due to global warming, mean rainfall is projected to increase except in eastern and northern Japan, the frequency of heavy rainfall events would increase and its increment rate become higher for heavier rainfall, and return values for extreme rainfall would grow.Experiments using an NHM with a horizontal resolution of 1 km were conducted to study the effects of resolution. Compared to 5 km resolution, it expresses the organization of rainfall systems causing heavy rainfall and the appearance-frequency distribution of rainfall for variable intensities more realistically.


Author(s):  
J.M. Senciales-González ◽  
J.D. Ruiz-Sinoga

Heavy rainfall events in the Mediterranean can be of high intensity, commonly exceeding 100 mm day-1, and have irregular spatio-temporal distribution. Such events can have significant impacts both on soils and human structures. The aim of this paper is to highlight a systematic comparison of synoptic conditions with heavy rainfall events in Mediterranean Southern Spain, assessing the weather types responsible for meteorological risk in specific locations of this mountainous region. To do this, we analyzed the maximum intensity of rainfall in observational periods ranging from 10 min to 24 h using a database from 132 rain gauge stations across the study area since 1943; then, the heavy rain has been associated with the weather type which triggers it. This analysis identified a pattern of heavy rainfall which differs from that previously reported in the Mediterranean area. Thus, in this research, the maximum number of heavy rainfall events uses to come from a dominant pattern of low pressures associated to front systems and East-Northeast winds; but the maximum volumes use to be associated to Cold Drops and the same winds; in addition, there are differences throughout the territory, showing several patterns and seasonal incidence when analyzing sub-zones, which may be related with different erosive conditions according to its position with respect to Atlantic or Mediterranean sea, and the entity of its relief.


2019 ◽  
Vol 23 (7) ◽  
pp. 2863-2875 ◽  
Author(s):  
Sungmin O ◽  
Ulrich Foelsche

Abstract. Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at small spatial scales (e.g., within remote-sensing subpixel areas) and over short periods of time. Just a few studies have applied a similar approach, i.e., employing dense gauge networks to catchment-scale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, WegenerNet (WEGN), we assess the spatial uncertainty in observed heavy rainfall events. The WEGN network is located in southeastern Austria over an area of 20 km × 15 km with moderate orography. First, the spatial variability in rainfall in the region was characterized using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between warm and cold seasons are apparent, and we selected heavy rainfall events, the upper 10 % of wettest days during the warm season, for further analyses because of their high potential for causing hazards. Secondly, we investigated the uncertainty in estimating mean areal rainfall arising from a limited gauge density. The average number of gauges required to obtain areal rainfall with errors less than a certain threshold (≤20 % normalized root-mean-square error – RMSE – is considered here) tends to increase, roughly following a power law as the timescale decreases, while the errors can be significantly reduced by establishing regularly distributed gauges. Lastly, the impact of spatial aggregation on extreme rainfall was examined, using gridded rainfall data with various horizontal grid spacings. The spatial-scale dependence was clearly observed at high intensity thresholds and high temporal resolutions; e.g., the 5 min extreme intensity increases by 44 % for the 99.9th and by 25 % for the 99th percentile, with increasing horizontal resolution from 0.1 to 0.01∘. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the sensible use of the data in relevant applications. Our findings could be transferred to midlatitude regions with moderate topography, but only to a limited extent, given that regional factors that can affect rainfall type and process are not explicitly considered in the study.


Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 205 ◽  
Author(s):  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Jen-Her Chen ◽  
Liping Deng

During May and June (the Meiyu season) of 2017, Taiwan was affected by three heavy frontal rainfall events, which led to large economic losses. Using satellite observations and reanalysis data, this study investigates the impact of boreal summer intra-seasonal oscillations (BSISOs, including a 30–60 day ISO mode named BSISO1 and a 10–30 day ISO mode named BSISO2) on the heavy rainfall events in Taiwan during the 2017 Meiyu season. Our examinations show that BSISO2 is more important than BSISO1 in determining the formation of heavy rainfall events in Taiwan during the 2017 Meiyu season. The heavy rainfall events generally formed in Taiwan at phases 4–6 of BSISO2, when the enhanced southwesterly wind and moisture flux convergence center propagate northward into the Taiwan area. In addition, we examined the forecast rainfall data (at lead times of one day to 16 days) obtained from the National Centers for Environmental Prediction Global Forecast System (NCEPgfs) and the Taiwan Central Weather Bureau Global Forecast System (CWBgfs). Our results show that the better the model’s capability in forecasting the BSISO2 index is, the better the model’s capability in forecasting the timing of rainfall formation in Taiwan during the 2017 Meiyu season is. These findings highlight the importance of BSISO2 in affecting the rainfall characteristics in East Asia during the Meiyu season.


2018 ◽  
Vol 22 (2) ◽  
pp. 1095-1117 ◽  
Author(s):  
Ila Chawla ◽  
Krishna K. Osuri ◽  
Pradeep P. Mujumdar ◽  
Dev Niyogi

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.


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